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--- |
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license: mit |
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tags: |
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- climate |
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pretty_name: DWD ICON-EU Forecasts |
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size_categories: |
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- 1K<n<10K |
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--- |
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# Dataset Card for DWD ICON-EU Forecast |
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This dataset is comprised of forecasts from the German Weather Service's (DWD) ICON-EU model. From 2020-March 2023 the forecasts contain variables that are relevant to solar and wind |
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forecasting. From March 2023 to the present, all variables are included. Each forecast runs up to 5 days into the future, and the model is ran 4 times per day. This data is an archive of |
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the publicly available data at https://opendata.dwd.de/weather/nwp/, converted to Zarr format with Xarray. No other processing of the data is performed. |
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## Dataset Details |
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- **Curated by:** Jacob Bieker, Sol Cotton, Open Climate Fix |
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- **License:** German Government Open Data License |
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### Dataset Sources [optional] |
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- **Raw files:** https://opendata.dwd.de/weather/nwp/ |
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Note: The raw files are deleted after 24 hours, and there is no long-term archive available publicly. |
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## Uses |
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This data is intended for use in renewable energy forecasting, weather forecasting, and anything that can use high-quality weather forecasts over Europe. |
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## Dataset Structure |
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The dataset is comprised of one Zarr file per forecast initialization time, and each forecast goes out between 48-120 hours. The files are located at data/year/month/day/YYYYMMDDHH.zarr.zip. |
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## Dataset Creation |
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### Curation Rationale |
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The DWD ICON-EU model provides high-quality, high-resolution forecasts for European weather that is also publicly available and free of charge. The model should generally outperform |
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NOAA's GFS forecast model, and has a higher temporal and spatial resolution. The main downside of this model is that the files are only available for a short period publicly, so this dataset |
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was setup to provide a public archive of the forecasts for use by researchers in many fields, but especially renewable energy forecasting and weather forecasting. |
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### Source Data |
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The source data is the grib2 files from the DWD Open Data Server. |
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#### Data Collection and Processing |
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The data is collected every day, around 6-8 hours after forecast initialization time to ensure the forecast is finished running before the data is pulled. The grib2 files are opened |
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with Xarray and collated into a single Xarray Dataset, with one data variable per ICON variable. Surface variables have "_s" appended to their names to differentiate them from multi-level variables. |
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The Dataset is then written to Zarr using "ocf_blosc2" to encode and compress the variables. No scaling or changing of the variables values is performed. |
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#### Who are the source data producers? |
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German Weather Service (DWD) |
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### Recommendations |
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These files can be opened directly from HuggingFace, and streamed in with Xarray. HuggingFace is fairly slow though, so the recommended way would be to download the files you want |
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and open them locally. In either case, to access the data you can do the following |
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```python |
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import ocf_blosc2 |
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import xarray as xr |
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data = xr.open_zarr("path/to/zarr/file") |
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print(data) |
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``` |
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Alternatively, for using the data in forecasting, there is the `ocf_datapipes` package for loading and training renewable energy forecasting models with multi-modal inputs, including |
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ICON, but also satellite data, PV readings, etc. |
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## Dataset Card Contact |
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OCF Data Team: data@openclimatefix.org |
