S2S AI challenge Datasets

This is a climetlab plugin for Sub-seasonal to Seasonal (S2S) Artificial Intelligence Challenge: https://s2s-ai-challenge.github.io/.

In this README is a description of how to get the data for the S2S AI challenge. Here is a more general description of the S2S data. The data used for the S2S AI challenge is a subset of the S2S data library. More detail can be found at https://confluence.ecmwf.int/display/S2S and https://confluence.ecmwf.int/display/S2S/Parameters.

There are several ways to use the datasets. Either by direct download (wget, curl, browser) for GRIB and NetCDF formats; or using the climetlab python package with this addon, for GRIB and NetCDF and zarr formats. zarr is a cloud-friendly experimental data format and supports dowloading only the part of the data that is required. It has been designed to work better than classical format on a cloud environment (experimental).

Installation

pip install -U climetlab climetlab_s2s_ai_challenge

API

import climetlab

Use climetlab.load_dataset('s2s-ai-challenge-{datasetname}') with the following keywords:

datasetname: name of the dataset, see dataset description
parameter: variable, see hindcast input for the different models
origin: name of the model [ecmwf, eccc, ncep] or modelling center [ecmf, cwao, kwbc]. Provide origin only for training/test-input/hindcast/forecast-input.
date: YYYYMMDD is the date of the 2020 forecast for test-input/forecast-input. The same dates are required for the on-the-fly training-input/hindcast-input but returns the multi-year hindcast for the given MMDD date. Please provide int, str, np.datetime or list of the former in format YYYYMMDDD. Providing no date keyword downloads all dates.
format: data format, choose from [netcdf (always available), grb (only for input-*), zarr (experimental)]

Coordinates

Overview of the time-related coordinates.

`name`	CF convention `standard_name`	description	comment
`forecast_time`	forecast_reference_time	The forecast reference time in NWP is the "data time", the time of the analysis from which the forecast was made. It is not the time for which the forecast is valid.
`lead_time`	forecast_period	Forecast period is the time interval between the forecast reference time and the validity time.
`valid_time`	time	time for which the forecast is valid	`forecast_time` + `lead_time`

All datasets are on a global 1.5 degree grid.

Parameter

parameter describes the variable to download. The most important two variables for the s2s-ai-challenge are the two target variables t2m and tp:

parameter	long_name	standard_name	unit	description & aggregation type	week 3-4 aggregation	week 5-6 aggregation	link to source
`t2m`	2m temperature	air_temperature	K	Temperature at 2m height averaged for the date given	average [day 14, day 27]	average [day 28, day 41]	model, observations
`tp`	total precipitation	precipitation_amount	kg m-2	Total precipitation accumulated from `forecast_time` until including `valid_time`, e.g. `lead_time = 1 days` accumulates precipitation_flux `pr` from 6-hourly steps 0,6,12,18 at date `forecast_time`	day 28 minus day 14	day 42 minus day 28	model
`pr`	precipitation flux	precipitation_flux	kg m-2	Precipitation accumulated for the date given	use `tp`	use `tp`	observations

Given the different nature of the parameters, with forecast_time Jan 2nd 2020, tp lead_time=1 days and t2m lead_time=0 days describe both the weather of Jan 2nd 2020, as tp is aggregated pr from Jan 2nd 00:00 to Jan 3rd 00:00 and t2m as the average of Jan 2nd. Furthermore tp is aggregated since forecast_time, i.e. tp lead_time=5 days is pr aggregated from Jan 2nd 00:00 to Jan 7th 00:00.

For the remaining variable descriptions, see ECWMF S2S description.

Datasets description

There are four datasets provided for this challenge. As we are aiming at bringing together the two communities of Machine Learning and Weather Prediction, they have been aliases to use both two points of views:

ML	NWP
`training-input`	`hindcast-input`	Training dataset (input for training the ML models)
`training-output-reference`	`hindcast-like-observations`	Training dataset (output for training the ML models)
`training-output-benchmark`	`hindcast-benchmark`	Benchmark output (on the training dataset)
`test-input`	`forecast-input`	Test dataset (DO NOT use for training)
`test-output-reference`	`forecast-like-observations`	Test dataset (DO NOT use)
`test-output-benchmark`	`forecast-benchmark`	Benchmark output (on the test dataset)
`observations`	`observations`	Observations with `time` dimension

Overfitting is always an potential issue when using ML algorithms. To address this, the data is usually split into three datasets : training, validation and test. This terminology has lead to some confusion in the past. Splitting the hindcast-input (training-input) dataset between training and validation is standard way and should be decided carefully.

The forecast-input (test-input) must not be used as a validation dataset: it must not be used to tune the hyperparameters or make decision about the ML model.

Hindcast input

These data are hindcast data. This is used as the input for training the ML models.

The hindcast-input(training-input) dataset consists of data from three different models/centers:

center name	model name
ecmwf	ecmf
eccc	cwao
ncep	kwbc

Use either origin="ecmf" (model name) or origin="ecmwf" (center name).

This dataset is available as format: grib, netcdf.

ECMWF hindcast data
- forecast_time: from 2000/01/02 to 2019/12/31, corresponding to the weekly Thurdays in 2020.
- lead_time: 0 to 46 days
- valid_time (forecast_time + lead_time): from 2000/01/02 to 2020/02/13
- availables parameters : t2m(2t)/siconc(ci)/gh/lsm/msl/q/rsn/sm100/sm20/sp/sst/st100/st20/t/tcc/tcw/tp/ttr/u/v (differing name in MARS database)
ECCC hindcast data
- forecast_time: from 2000/01/02 to 2019/12/31, corresponding to the weekly Thurdays in 2020.
- lead_time: 1 to 32 days
- valid_time (forecast_time + lead_time): from 2000/01/03 to 2020/02/01
- availables parameters: t2m(2t)/siconc(ci)/gh/lsm/msl/q/rsn/sp/sst/t/tcc/tcw/tp/ttr/u/v (differing name in MARS database)
- parameters not available: sm20/sm100/st20/st100
NCEP hindcast data
- forecast_time : from 1999/01/07 to 2010/12/30, corresponding to the weekly Thurdays in 2010, see Vitart et al. 2017, NCEP hindcast dates differ from ECMWF and ECCC hindcasts
- lead_time : 1 to 44 days
- valid_time (forecast_time + lead_time): from 1999/01/07 to 2011/02/11
- availables parameters: t2m(2t)/siconc(ci)/gh/lsm/msl/q/sm100/sm20/sp/sst/st100/st20/t/tcc/tcw/tp/ttr/u/v (differing name in MARS database)
- parameter not available: rsn

hindcast = climetlab.load_dataset('s2s-ai-challenge-training-input', date=[20200102], origin='ecwmf', parameter='tp', format='netcdf').to_xarray()
hindcast.coords
Coordinates:
  * realization    (realization) int64 0 1 2 3 4 5 6 7 8 9 10
  * forecast_time  (forecast_time) datetime64[ns] 2000-01-02 ... 2019-01-02
  * lead_time      (lead_time) timedelta64[ns] 0 days 1 days ... 45 days 46 days
  * latitude       (latitude) float64 90.0 88.5 87.0 85.5 ... -87.0 -88.5 -90.0
  * longitude      (longitude) float64 0.0 1.5 3.0 4.5 ... 355.5 357.0 358.5
    valid_time     (forecast_time, lead_time) datetime64[ns] 2000-01-02 ... 2...
    
# for ncep hindcast provide 2010 date strings
hindcast_ncep = climetlab.load_dataset('s2s-ai-challenge-training-input', date=[20100107], origin='ncep', parameter='tp', format='netcdf').to_xarray()
hindcast_ncep.coords
Coordinates:
  * realization    (realization) int64 0 1 2 3
  * forecast_time  (forecast_time) datetime64[ns] 1999-01-07 ... 2010-01-07
  * lead_time      (lead_time) timedelta64[ns] 1 days 2 days ... 43 days 44 days
  * latitude       (latitude) float64 90.0 88.5 87.0 85.5 ... -87.0 -88.5 -90.0
  * longitude      (longitude) float64 0.0 1.5 3.0 4.5 ... 355.5 357.0 358.5
    valid_time     (forecast_time, lead_time) datetime64[ns] 1999-01-08 ... 2...

List of files : grib, netcdf, zarr (not available)

Forecast input

The forecast-input (test-input) dataset consists also in data from three different models: ECMWF (ecmf), ECCC (cwao), NCEP (eccc), for different dates. These data are forecast data. This could be used the input for applying the ML models in order to generate the output which is submitted for the challenge.

For all 3 models:
- forecast_time: from 2020/01/02 to 2020/12/31, weekly every Thurday in 2020.
- valid_time (forecast_time + lead_time): from 2020/01/02 to 2021/02/xx
- available parameters (same as for hindcast input (training input)
ECMWF forecast
- lead_time: 0 to 46 days
ECCC forecast
- lead_time: 1 to 32 days
NCEP forecast
- lead_time: 1 to 44 days

forecast = climetlab.load_dataset('s2s-ai-challenge-test-input', date=[20200102], origin='ecmwf', parameter='tp', format='netcdf').to_xarray()
forecast.coords
Coordinates:
  * realization    (realization) int64 0 1 2 3 4 5 6 7 ... 44 45 46 47 48 49 50
  * forecast_time  (forecast_time) datetime64[ns] 2020-01-02
  * lead_time      (lead_time) timedelta64[ns] 0 days 1 days ... 45 days 46 days
  * latitude       (latitude) float64 90.0 88.5 87.0 85.5 ... -87.0 -88.5 -90.0
  * longitude      (longitude) float64 0.0 1.5 3.0 4.5 ... 355.5 357.0 358.5
    valid_time     (forecast_time, lead_time) datetime64[ns] 2020-01-02 ... 2...

List of files : grib, netcdf, zarr (missing)

Observations (Reference Output)

The hindcast-like-observations (training-output-reference) dataset matches the training-input of the ECMWF and ECCC model. The forecast-like-observations (test-output-reference) dataset matches the test-input of all three models.

The observations are the ground truth to compare with the ML model output and evaluate them. It consists in observations from instruments of temperature and accumulated total precipitation. The NOAA CPC datasets were downloaded from IRIDL. We provide observations in the same dimensions as the forecasts/hindcasts to have an easy match of forecasts/hindcast and ground truth. See the script for technical details. These observations are the ground truth and do not correspond to a model. The format is always netcdf.

hindcast_like_obs = climetlab.load_dataset('s2s-ai-challenge-training-output-reference', date=[20200102], parameter='tp').to_xarray()  # origin and format not accepted
hindcast_like_obs.coords
Coordinates:
  * latitude       (latitude) float64 90.0 88.5 87.0 85.5 ... -87.0 -88.5 -90.0
  * longitude      (longitude) float64 0.0 1.5 3.0 4.5 ... 355.5 357.0 358.5
  * forecast_time  (forecast_time) datetime64[ns] 2000-01-02 ... 2019-01-02
  * lead_time      (lead_time) timedelta64[ns] 0 days 1 days ... 45 days 46 days
    valid_time     (lead_time, forecast_time) datetime64[ns] 2000-01-02 ... 2...

forecast_like_obs = climetlab.load_dataset('s2s-ai-challenge-test-output-reference', date=[20200102], parameter='tp').to_xarray()  # origin and format not accepted
forecast_like_obs.coords
Coordinates:
  * latitude       (latitude) float64 90.0 88.5 87.0 85.5 ... -87.0 -88.5 -90.0
  * longitude      (longitude) float64 0.0 1.5 3.0 4.5 ... 355.5 357.0 358.5
  * forecast_time  (forecast_time) datetime64[ns] 2020-01-02
  * lead_time      (lead_time) timedelta64[ns] 0 days 1 days ... 45 days 46 days
    valid_time     (lead_time, forecast_time) datetime64[ns] 2020-01-02 ... 2...

In case you want to train on the NCEP hindcast, which has forecast_times from 1999 to 2010, please use download observations with a time dimension s2s-ai-challenge-observations and use climetlab_s2s_ai_challenge.extra.forecast_like_observations to match observations to the corresponding valid_times of the forecast/hindcast.

obs_time = climetlab.load_dataset('s2s-ai-challenge-observations', parameter=['pr', 't2m']).to_xarray()
# equivalent
obs_lead_time_forecast_time = climetlab.load_dataset('s2s-ai-challenge-observations', parameter=['pr', 't2m']).to_xarray(like=hindcast_ncep)
obs_lead_time_forecast_time = climetlab_s2s_ai_challenge.extra.forecast_like_observations(hindcast_ncep, obs_time)
obs_lead_time_forecast_time.coords
<xarray.Dataset>
Dimensions:        (forecast_time: 12, latitude: 121, lead_time: 44, longitude: 240)
Coordinates:
    valid_time     (forecast_time, lead_time) datetime64[ns] 1999-01-08 ... 2...
  * latitude       (latitude) float64 90.0 88.5 87.0 85.5 ... -87.0 -88.5 -90.0
  * longitude      (longitude) float64 0.0 1.5 3.0 4.5 ... 355.5 357.0 358.5
  * forecast_time  (forecast_time) datetime64[ns] 1999-01-07 ... 2010-01-07
  * lead_time      (lead_time) timedelta64[ns] 1 days 2 days ... 43 days 44 days

This function can be used for all initialized hindcasts and forecasts from the SubX and S2S projects. Beware of the different starting dates of 2020 forecasts when using them for the s2s-ai-challenge. Observations from NOAA CPC are regridded conservatively with xesmf to the S2S 1.5 deg grid. The original 0.5 degree spatial resolution raw observations can be obtained via grid='720x360'.

Generally speaking, only data available when the forecast is issued can be used by the ML models to perform their forecast:

Rule: Observed data beyond the forecast date should not be used for prediction, for instance a forecast starting on 2020/07/01 should not use observed data beyond 2020/07/01).

For all rules, see the challenge website.

See also the general rules of the challenge here.

Dates in the observation dataset are from 2000/01/01 to 2021/02/15.

The observations dataset have been build from real instrument observations.

The hindcast-like-observations (training-output-reference) dataset :
Available from 2000/01/01 to 2019/12/31, weekly every 7 days (every Thurday)
Observation data before 2019/12/31 can be used for training (as the truth to evaluate and optimize the ML models or tweak hyper parameters using train/valid split or cross-validation).
The forecast-like-observations (test-output-reference) dataset.
Available from 2020/01/01 to 2021/02/20 , weekly every 7 days (every Thurday)
The test data must not be used during training. In theory, these data should not be disclosed during the challenge, but the nature of the data make is possible to access it from other sources. That is the reason why the code used for training model must be submitted along with the prediction (as a jupyter notebook) and the top ranked proposition will be reviewed by the organizing board.

During forecast phase (i.e. the evaluation phase using the forecast-input dataset), 2020 observation data is used. Rule 1 still stands: Observed data beyond the forecast start date should not be used for prediction.

Forecast Benchmark (Benchmark output)

The forecast-benchmark (test-output-benchmark) dataset is a probabilistic re-calibrated ECMWF forecast with categories below normal, near normal, above normal. The calibration has been performed by using the tercile boundaries from the model climatology rather than from observations.

The benchmark data is available as follows:

forecast_time: from 2020/01/02 to 2020/12/31, weekly every 7 days (every Thurday).
lead_time: 14 days and 28 days, where this day represents the first day of the biweekly aggregate
valid_time (forecast_time + lead_time): from 2020/01/01 to 2021/01/29
category: 'below normal', 'near normal', 'above normal'

bench = climetlab.load_dataset('s2s-ai-challenge-training-output-benchmark', parameter='tp').to_xarray()  # origin, date and format not accepted
bench.coords
Coordinates:
  * category       (category) object 'below normal' 'near normal' 'above normal'
  * forecast_time  (forecast_time) datetime64[ns] 2000-01-02 ... 2019-12-31
  * lead_time      (lead_time) timedelta64[ns] 14 days 28 days
  * latitude       (latitude) float64 90.0 88.5 87.0 85.5 ... -87.0 -88.5 -90.0
  * longitude      (longitude) float64 0.0 1.5 3.0 4.5 ... 355.5 357.0 358.5
    valid_time     (forecast_time, lead_time) datetime64[ns] 2000-01-16 ... 2...

bench = climetlab.load_dataset('s2s-ai-challenge-test-output-benchmark', parameter='tp').to_xarray()  # origin, date and format not accepted
bench.coords
Coordinates:
  * category       (category) object 'below normal' 'near normal' 'above normal'
  * forecast_time  (forecast_time) datetime64[ns] 2020-01-02 ... 2020-12-31
  * lead_time      (lead_time) timedelta64[ns] 14 days 28 days
  * latitude       (latitude) float64 90.0 88.5 87.0 85.5 ... -87.0 -88.5 -90.0
  * longitude      (longitude) float64 0.0 1.5 3.0 4.5 ... 355.5 357.0 358.5
    valid_time     (forecast_time, lead_time) datetime64[ns] 2020-01-16 ... 2...

Observations

For other initialized forecasts, you can download observations for parameters t2m and pr with a time dimension corresponding to valid_time. These are then used to create observations formatted like initialized forecasts/hindcasts locally. Observations are available from 1999 to 2021. See parameter for description.

climetlab_s2s_ai_challenge.extra.forecast_like_observations matches observations to the corresponding valid_times of the forecast/hindcast.

forecast = climetlab.load_dataset('s2s-ai-challenge-training-input',
        date=20100107, origin='ncep', parameter='tp',
        format='netcdf').to_xarray()

obs_lead_time_forecast_time = climetlab.load_dataset('s2s-ai-challenge-observations', parameter=['pr', 't2m']).to_xarray(like=forecast)
# equivalent
obs_time = climetlab.load_dataset('s2s-ai-challenge-observations', parameter=['pr', 't2m']).to_xarray()
obs_time.coords
Coordinates:
  * time       (time) datetime64[ns] 1999-01-01 1999-01-02 ... 2021-04-29
  * latitude   (latitude) float64 90.0 88.5 87.0 85.5 ... -87.0 -88.5 -90.0
  * longitude  (longitude) float64 0.0 1.5 3.0 4.5 ... 354.0 355.5 357.0 358.5

obs_lead_time_forecast_time = climetlab_s2s_ai_challenge.extra.forecast_like_observations(forecast, obs_time)
obs_lead_time_forecast_time
<xarray.Dataset>
Dimensions:        (forecast_time: 12, latitude: 121, lead_time: 44, longitude: 240)
Coordinates:
    valid_time     (forecast_time, lead_time) datetime64[ns] 1999-01-08 ... 2...
  * latitude       (latitude) float64 90.0 88.5 87.0 85.5 ... -87.0 -88.5 -90.0
  * longitude      (longitude) float64 0.0 1.5 3.0 4.5 ... 355.5 357.0 358.5
  * forecast_time  (forecast_time) datetime64[ns] 1999-01-07 ... 2010-01-07
  * lead_time      (lead_time) timedelta64[ns] 1 days 2 days ... 43 days 44 days
Data variables:
    t2m            (forecast_time, lead_time, latitude, longitude) float32 ...
    tp             (forecast_time, lead_time, latitude, longitude) float32 na...
Attributes:
    script:   climetlab_s2s_ai_challenge.extra.forecast_like_observations

Data download (GRIB or NetCDF)

The URLs to download the data are constructed according to the following patterns:

For input datasets, the pattern is https://object-store.os-api.cci1.ecmwf.int/s2s-ai-challenge/data/{datasetname}/0.3.0/{format}/{origin}-{fctype}-{parameter}-YYYYMMDD.nc

For observations datasets (reference output), the pattern is https://object-store.os-api.cci1.ecmwf.int/s2s-ai-challenge/data/{datasetname}/{parameter}-YYYYMMDD.nc

For benchmark datasets, the pattern will be similar to https://object-store.os-api.cci1.ecmwf.int/s2s-ai-challenge/data/training-output-benchmark/{parameter}.nc

{datasetname} : In the URLs the dataset name must follow the ML naming (training-input or training-output-reference or training-output-benchmark).
{format} is netcdf. Training output is also available as GRIB file, using format='grib' and replacing ".nc" by ".grib"
{fctype}: hindcast for training or forecast for test
{parameter} is t2m for surface temperature at 2m, tp for total precipitation
{origin} : ecmwf or eccc or ncep
{weeks} from ["34", "56", ["34", "56"]] only for benchmark
{grid} from ["240x121" (default), "720x360"] only for observations
YYYYMMDD is the date of the 2020 forecast for test-input/forecast-input. The same dates are required for the on-the-fly training-input/hindcast-input but return the multi-year hindcast for that MMDD.

The list of files for the training-input dataset can be found at

The list of files for the training-output-benchmark dataset can be found at https://object-store.os-api.cci1.ecmwf.int/s2s-ai-challenge/data/training-output-benchmark/index.html (NetCDF only)

The list of files for the training-output-reference dataset can be found at https://object-store.os-api.cci1.ecmwf.int/s2s-ai-challenge/data/training-output-reference/index.html (NetCDF only)

Example to retrieve the file with wget :

wget https://object-store.os-api.cci1.ecmwf.int/s2s-ai-challenge/data/training-input/0.3.0/grib/ncep-hindcast-q-20101014.grib (132.8M )

Zarr format (experimental).

The zarr storage location include all the reference data. The zarr urls are not designed to be open in a browser (see zarr): While accessing the zarr storage without climetlab may be possible, we recommend using climetlab with the appropriate plugin (climetlab-s2s-ai-challenge)

Zarr urls are not available.

Using climetlab to access the data (supports grib, netcdf and zarr)

The climetlab python package allows easy access to the data with a few lines of code such as:

!pip install climetlab climetlab_s2s_ai_challenge
import climetlab as cml
cml.load_dataset("s2s-ai-challenge-training-input",
                         origin='ecmwf',
                         date=[20200102,20200109],
                         # optional : format='grib'
                         parameter='tp').to_xarray()
cml.load_dataset("s2s-ai-challenge-training-output-reference",
                         date=[20200102,20200109],
                         parameter='tp').to_xarray()

See the demo notebooks here: https://github.com/ecmwf-lab/climetlab-s2s-ai-challenge/notebooks.

Accessing the training data :

Netcdf nbviewer colab
Grib nbviewer colab

Getting the observation (reference output) dataset see the demo_observations notebook.

Getting the benchmark dataset see the demo_benchmark notebook.

Name		Name	Last commit message	Last commit date
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ansible		ansible
climetlab_s2s_ai_challenge		climetlab_s2s_ai_challenge
notebooks		notebooks
tests		tests
tools		tools
.gitignore		.gitignore
.pre-commit-config.yaml		.pre-commit-config.yaml
LICENSE		LICENSE
MANIFEST.in		MANIFEST.in
README.md		README.md
data_portal.yaml		data_portal.yaml
pyproject.toml		pyproject.toml
requirements-dev.txt		requirements-dev.txt
requirements.txt		requirements.txt
setup.py		setup.py
tox.ini		tox.ini

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S2S AI challenge Datasets

Installation

API

Coordinates

Parameter

Datasets description

Hindcast input

Forecast input

Observations (Reference Output)

Forecast Benchmark (Benchmark output)

Observations

Data download (GRIB or NetCDF)

Zarr format (experimental).

Using climetlab to access the data (supports grib, netcdf and zarr)

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S2S AI challenge Datasets

Installation

API

Coordinates

Parameter

Datasets description

Hindcast input

Forecast input

Observations (Reference Output)

Forecast Benchmark (Benchmark output)

Observations

Data download (GRIB or NetCDF)

Zarr format (experimental).

Using climetlab to access the data (supports grib, netcdf and zarr)

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