This data is for COVID-19 researchers to explore relationships between COVID-19 and environmental factors.
For more information see our blog post. If you require compute resources to process this data we might be able to help.
Stay up to date with new datasets, corrections, redactions, and other important information by subscribing to this data set's mailing list.
Users are required to acknowledge the Met Office as the source of these data by including the following attribution statement in any resulting products, publications or applications: “Contains Met Office data licensed under the Open Government Licence v3.0”.
This data is made available under the Open Government License version 3.
Global and high-resolution UK numerical weather model output from the UK Met Office. Data is from the very early time steps of the model following data assimilation, as such this data approximates a whole-Earth observation dataset.
The following variables are available:
t1o5m= Air temperature at 1.5m in Ksh= Specific humidity at 1.5m in kg/kg (kg of water vapor in kg of air)sw= Short wave radiation in W/m2 (surrogate for sunshine)precip= Precipitation flux in kg/(m2s) (multiply by 3600 to get mm/hr)rain= Rain flux in kg/(m2s) (multiply by 3600 to get mm/hr)pmsl= Air pressure at mean sea level in Pasnow= Stratiform snowfall flux in kg/(m2s) (multiply by 3600 to get mm/hr)windspeed= Wind speed in m/swindgust= Wind gust in m/scldbase= Cloud base altitude in feetcldfrac= Cloud area fraction assuming maximum random overlap (unitless: 0-1)
Output of the Met Office UK air quality model AQUM is also available. This includes the following variables:
daqi= Daily Air Quality Index, an integer from 1-10no2= Nitrogen dioxide concentration, in µg/m3o3= Ozone concentration, in µg/m3so2= Sulphur dioxide concentration, in µg/m3pm2p5= Concentration of particulate matter less than 2.5 microns diameter, in µg/m3pm10= Concentration of particulate matter less than 10 microns diameter, in µg/m3
This data is made available as NetCDF files.
Global and UK model data updated is available from 01 Jan 2020 onwards. The dataset is updated daily for the previous day.
For detailed information about how this data is generated and the particulars of the parameters please see the technical references:
There are some additional post-processed data aggregations over COVID-19 reporting regions in the UK and USA made available as CSV files. More details below.
A complete Python example of working with thid data is available in the accompanying sample notebook.
This dataset is stored in the East US 2 Azure region, in the following blob container:
https://metdatasa.blob.core.windows.net/covid19-response
Allocating compute resources in East US 2 is recommended for affinity.
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Announcement by the Met Office making this data available in response to the RAMP initiative, asking for assistance in tackling to the COVID-19 pandemic.
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You can browse the data we have made available on Azure here.
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This data is made available under the Open Government License.
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More data is coming! Please subscribe to our Google Groups mailing list to receive updates when that data is available.
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Please contact us at covid19@informaticslab.co.uk if you have any questions or requests for additional data.
The gridded data is updated daily for the previous day.
As of 18/04/20 the dataset totals approximately 352GB, growing by approximately 22GB/week.
We intend tomake this data available as long as we believe it's useful in planning the response to the COVID-19 pandemic.
The data is hosted on Microsoft Azure through their AI for Earth initiative. You can access the data in several ways, such as:
Open the index file in your browser. You will see a list of links to datafiles which you can download by clicking on them in your browser.
There is a range of libraries in a range of languages for working with Azure Blobs. See the Azure Blob documentation for more information.
There are lots of files, so we suggest installing azcopy command line tool, which you can download here. This lets you download whole directories or multiple files using wildcards.
For example...
To download the file global_daily_precip_max_20200101.nc to the current directory:
azcopy cp https://metdatasa.blob.core.windows.net/covid19-response/metoffice_global_daily/precip_max/global_daily_precip_max_20200101.nc .
To download the contents of /metoffice_ukv_daily/snow_mean/ to ukv_daily_snow_mean/:
azcopy cp 'https://metdatasa.blob.core.windows.net/covid19-response/metoffice_ukv_daily/snow_mean/*' ukv_daily_snow_mean/
To download all the US state county-averaged meteorology data which match the pattern us_55*.csv:
azcopy cp --recursive --include-pattern 'us_55*.csv' https://metdatasa.blob.core.windows.net/covid19-response/regional_subset_data/us_data/ .
...contains the Met Office daily global gridded data files. There is a directory for each variable.
Each file is named according to global_daily_{variable}_{statistic}_{YYYYMMDD}.nc, for example:
metoffice_global_daily/precip_max/global_daily_precip_max_20200101.nc
...contains the gridded maximum precipitation data from Jan 1, 2020.
...contains the Met Office hourly global gridded data files.
Each file is named according to global_hourly_{variable}_global_{YYYYMMDD}.nc, for example:
metoffice_global_hourly/precip/global_hourly_precip_20200101.nc
...contains gridded hourly precipitation data from Jan 1, 2020.
...contains the Met Office daily UKV gridded data files.
Each file is named according to ukv_daily_{variable}_{statistic}_{YYYYMMDD}.nc.
...contains the Met Office hourly UKV gridded data files.
Each file is named according to ukv_hourly_{variable}_{YYYYMMDD}.nc.
...contains the Met Office daily AQUM gridded data files.
Each file is named according to aqum_daily_{variable}_{statistic}_{YYYYMMDD}.nc.
...contains the Met Office hourly AQUM gridded data files.
Each file is named according to aqum_hourly_{variable}_{YYYYMMDD}.nc.
...contains processed regional daily values for the UK, the USA, Italy, Brazil, Vietnam, and Uganda as .csv files.
Processed files represent the period from Jan 1 to Apr 19, 2020. Files were processed by subsetting the gridded Met Office global daily files using shapefiles for each region, taking the latitude-longitude mean value for each variable in each region for each date and saving those values as a table in a .csv file.
Each file in this directory (one .csv file per region) is named according to {shapefile_name}/{shapefile_name}_metoffice_global_daily_bbox_{start_date}-{end_date}.csv.
For example, the file:
regional_subset_data/gadm36Uganda2/gadm36Uganda2_metoffice_global_daily_20200101-20200419.csv
...represents processed data for Uganda.
Contains shapefiles for the UK, the USA, Italy, Brazil, Uganda, and Vietnam.
UK/= UK COVID-19 reporting regionsUSA/= USA state countiesItaly/= GADM v3.6 administrative level 2 for ItalyBrazil/= GADM v3.6 administrative level 2 for BrazilUganda/= GADM v3.6 administrative level 2 for UgandaVietnam/= GADM v3.6 administrative level 2 for Vietnam
Where possible, filenames are as described. However, given the short time frames in which this data has been made available, minor variations in filename descriptions may occur. Filenames should still be accurately descriptive of the data. If you find issues with any filenames, or the data itself, please contact us at covid19@informaticslab.co.uk.
For help or additional data requests please contact us at covid19@informaticslab.co.uk.
Microsoft provides this dataset on an "as is" basis. Microsoft makes no warranties (express or implied), guarantees, or conditions with respect to your use of the dataset. To the extent permitted under your local law, Microsoft disclaims all liability for any damages or losses - including direct, consequential, special, indirect, incidental, or punitive - resulting from your use of this dataset. This dataset is provided under the original terms that Microsoft received source data.