license: mit
tags:
- climate
pretty_name: DWD ICON-EU Forecasts
size_categories:
- 1K<n<10K
Dataset Card for DWD ICON-EU Forecast
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 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 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.
Dataset Details
- Curated by: Jacob Bieker, Sol Cotton, Open Climate Fix
- License: German Government Open Data License
Dataset Sources [optional]
- Raw files: https://opendata.dwd.de/weather/nwp/
Note: The raw files are deleted after 24 hours, and there is no long-term archive available publicly.
Uses
This data is intended for use in renewable energy forecasting, weather forecasting, and anything that can use high-quality weather forecasts over Europe.
Dataset Structure
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.
Dataset Creation
Curation Rationale
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 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 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.
Source Data
The source data is the grib2 files from the DWD Open Data Server.
Data Collection and Processing
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 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. 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.
Who are the source data producers?
German Weather Service (DWD)
Recommendations
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 and open them locally. In either case, to access the data you can do the following
import ocf_blosc2
import xarray as xr
data = xr.open_zarr("path/to/zarr/file")
print(data)
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
ICON, but also satellite data, PV readings, etc.
Dataset Card Contact
OCF Data Team: data@openclimatefix.org