EUPreciPBench datasets ====================== The *EUPreciPBench* datasets are available on a small portion of Europe stored in `Zarr `_ format for an easy access allowing for slicing. The forecasts and observations datasets are already paired together, providing analysis-ready data for postprocessing benchmarking purposes. It is a forecasts and observations dataset on a regular latitude-longitude grid, containing *high-resolution* precipitation data, along with some predictors in the column above the surface. It's horizontal resolution is higher (0.025°) than the one of the :ref:`files/EUPPBench_datasets:EUPPBench datasets` (0.25°), but they are conveniently defined on the same domain. .. figure:: ../images/gridded_data_EUPP.jpg :scale: 70% :align: center In blue, the EUPreciPBench dataset domain inside the :ref:`files/base_datasets:Base datasets over Europe's domain`. - The gridded EUPPreciBench postprocessing benchmark dataset contains `COSMO`_ DE and D2 ensemble forecasts over a small domain in Europe, from 46.0° to 53.2° in latitude, and from 2.5° to 10.4° in longitude, and covers the years 2017-2020. - It also contains the corresponding `EURADCLIM`_ radar composite for the purpose of providing observations for the benchmark. - The forecasts provided are the COSMO runs initialized at 03Z. - The COSMO ensemble consists of 20 members. - The gridded data resolution is 0.025° x 0.025° which corresponds roughly to 2.5 kilometers. COSMO DE, D2 and EURADCLIM data have been regridded to this resolution from their native grid. - COSMO DE forecasts (prior to May 2018) only covers part of the EUPPBench domain. COSMO D2 forecasts cover the full EUPPBench domain. - Forecasts are hourly, up to 2 days ahead, but do not include the analysis at 03Z. Datasets description -------------------- There are 3 gridded sub-datasets: 1 - Precipitation Forecasts Data -------------------------------- It consists in the total precipitation variable accumulated in the past hour: +----------------------------------------------+-----------+---------+---------+ | Parameter name | ECMWF key | Units | Remarks | +==============================================+===========+=========+=========+ | `Total | tp | mm | | | precipitation `__ | | | | +----------------------------------------------+-----------+---------+---------+ .. warning:: The units for the total precipitation here are not consistent with the :ref:`files/EUPPBench_datasets:EUPPBench datasets` total precipitation units. As the latter uses meters as units, there is a factor 1000 between the two. **Usage:** The precipitation forecasts can be retrieved by calling .. code:: python import climetlab as cml ds = cml.load_dataset('EUPreciPBench-gridded-precipitation-forecasts') ds.to_xarray() Alternatively, one can use the `Intake catalogue`_ .. code:: python import euppbench_datasets cat = euppbench_datasets.open_catalog() ds = cat.euprecipbench.EUPreciPBench_precipitation_forecasts.to_dask() **Example:** .. jupyter-execute:: import climetlab as cml ds = cml.load_dataset('EUPreciPBench-gridded-precipitation-forecasts') ds.to_xarray() 2 - Predictors Forecasts Data ----------------------------- It consists of several forecasts fields on pressure levels: +-------------------------------------+---------------+-----------+---------+---------+ | Parameter name | Levels | ECMWF key | Units | Remarks | +=====================================+===============+===========+=========+=========+ | `Temperature `__ | | | | | +-------------------------------------+---------------+-----------+---------+---------+ | `U component of | 700, 950 | u | m s^-1 | | | wind `__ | | | | | +-------------------------------------+---------------+-----------+---------+---------+ | `V component of | 700, 950 | v | m s^-1 | | | wind `__ | | | | | +-------------------------------------+---------------+-----------+---------+---------+ | `Relative | 700, 850, 950 | r | % | | | humidity `__ | | | | | +-------------------------------------+---------------+-----------+---------+---------+ These fields can for example be used to compute the `Jefferson instability index`_ and used as predictors for postprocessing the precipitation ensemble forecasts. **Usage:** The predictors forecasts can be retrieved by calling .. code:: python import climetlab as cml ds = cml.load_dataset('EUPreciPBench-gridded-predictors-forecasts') ds.to_xarray() Alternatively, one can use the `Intake catalogue`_ .. code:: python import euppbench_datasets cat = euppbench_datasets.open_catalog() ds = cat.euprecipbench.EUPreciPBench_predictors_forecasts.to_dask() **Example:** .. jupyter-execute:: ds = cml.load_dataset('EUPreciPBench-gridded-predictors-forecasts') ds.to_xarray() 3 - Precipitation Observations Data ----------------------------------- It consists in the total precipitation variable accumulated in the past hour: +----------------------------------------------+-----------+---------+---------+ | Parameter name | ECMWF key | Units | Remarks | +==============================================+===========+=========+=========+ | `Total | tp | mm | | | precipitation `__ | | | | +----------------------------------------------+-----------+---------+---------+ .. warning:: The units for the total precipitation here are not consistent with the :ref:`files/EUPPBench_datasets:EUPPBench datasets` total precipitation units. As the latter uses meters as units, there is a factor 1000 between the two. **Usage:** The precipitation observations can be retrieved by calling .. code:: python import climetlab as cml ds = cml.load_dataset('EUPreciPBench-gridded-precipitation-observations') ds.to_xarray() Alternatively, one can use the `Intake catalogue`_ .. code:: python import euppbench_datasets cat = euppbench_datasets.open_catalog() ds = cat.euprecipbench.EUPreciPBench_EURADCLIM_observations.to_dask() **Example:** .. jupyter-execute:: ds = cml.load_dataset('EUPreciPBench-gridded-precipitation-observations') ds.to_xarray() 4 - Static fields ----------------- Various static fields associated to the forecast grid can be obtained, with the purpose of serving as predictors for the postprocessing. .. note:: For consistency with the rest of the dataset, we use the ECMWF parameters name, terminology and units here. However, please note that - except for the Surface Geopotential - the fields provided are from other non-ECMWF data sources evaluated at grid points. Currently, the main data source being used is the `Copernicus Land Monitoring Service `__. It includes: +---------------------------------------------------------------------------------+-----------+-------------------------------------------------------------------------------------------------------------+ | Parameter name | ECMWF key | Remarks | +=================================================================================+===========+=============================================================================================================+ | `Land use `_ | landu | Extracted from the `CORINE 2018`_ dataset. | | | | Values and associated land type differ from the ECMWF one. | | | | Please look at the “legend” entry in the metadata for more details. | +---------------------------------------------------------------------------------+-----------+-------------------------------------------------------------------------------------------------------------+ | `Model terrain height `_ | mterh | Extracted from the `EU-DEMv1.1 `__ data elevation model | | | | dataset. | +---------------------------------------------------------------------------------+-----------+-------------------------------------------------------------------------------------------------------------+ | `Surface Geopotential `_ | z | The model orography can be obtained by dividing the surface geopotential by g=9.80665 ms :math:`{}^{-2}`. | +---------------------------------------------------------------------------------+-----------+-------------------------------------------------------------------------------------------------------------+ **Usage:** The static fields can be retrieved by calling .. code:: python ds = cml.load_dataset('EUPreciPBench-gridded-static-fields', parameter) ds.to_xarray() where the ``parameter`` argument is a string with one of the ECMWF keys described above. It is only possible to download one static field per call. Alternatively, one can use the `Intake catalogue`_ .. code:: python import euppbench_datasets cat = euppbench_datasets.open_catalog() # Fetching the land usage field ds = cat.euprecipbench.EUPreciPBench_land_usage.to_dask() The other static field are also available in the same way. **Example:** .. jupyter-execute:: ds = cml.load_dataset('EUPreciPBench-gridded-static-fields', 'landu') ds.to_xarray() 5 - Explanation of the metadata ------------------------------- For all data, attributes specifying the sources and the license are always present. Depending on the kind of dataset, dimensions and information are embedded in the data as follow: +-------------------------------------+----------------------------------------------------------------------+ | Metadata | Description | +=====================================+======================================================================+ | **latitude** | Latitude of the grid points. | +-------------------------------------+----------------------------------------------------------------------+ | **longitude** | Longitude of the grid points. | +-------------------------------------+----------------------------------------------------------------------+ | **number** | Number of the ensemble member. | +-------------------------------------+----------------------------------------------------------------------+ | **time** | Forecast initialization date | +-------------------------------------+----------------------------------------------------------------------+ | **step** | Step of the forecast (the lead time). | +-------------------------------------+----------------------------------------------------------------------+ | **surface** | Layer of the variable considered | | | (here there is just one, at the surface). | +-------------------------------------+----------------------------------------------------------------------+ | **isobaricInhPa** | Pressure level in hectopascal (or millibar). | +-------------------------------------+----------------------------------------------------------------------+ | valid_time | Actual time and date of the corresponding forecast data. | +-------------------------------------+----------------------------------------------------------------------+ .. note:: **Bold** metadata denotes dimensions indexing the datasets. Data License ------------ See the `DATA_LICENSE `__ file. The COSMO forecasts were produced and provided by the Deutsche Wetterdienst (DWD). The EURADCLIM were produced and provided by KNMI. See https://dataplatform.knmi.nl/dataset/rad-opera-hourly-rainfall-accumulation-euradclim-2-0 and https://doi.org/10.5194/essd-15-1441-2023 . .. _COSMO: https://www.dwd.de/EN/research/weatherforecasting/num_modelling/01_num_weather_prediction_modells/regional_model_cosmo_de.html;jsessionid=78803A010B98F465DA4E8F26975933C6.live31082?nn=484268 .. _EURADCLIM: https://dataplatform.knmi.nl/dataset/rad-opera-hourly-rainfall-accumulation-euradclim-2-0 .. _Intake catalogue: https://github.com/EUPP-benchmark/intake-eumetnet-postprocessing-benchmark .. _Jefferson instability index: https://adgeo.copernicus.org/articles/7/131/2006/ .. _CORINE 2018: https://land.copernicus.eu/pan-european/corine-land-cover