DOI for Scientific and Technical Data
3-monthly anomalies and quality assessment of seasonal climate predictions for Germany (EPISODES) version 2022.01
3-monthly anomalies and quality assessment of seasonal climate predictions (GCFS2.1) downscaled over Germany using the empirical-statistical downscaling method DWD-EPISODES version 2022
Pasternack, Alexander; Hoff, Amelie; Wehring, Sabrina; Fröhlich, Kristina; Lorenz, Philip; Paxian, Andreas; Kreienkamp, Frank; Früh, Barbara (2023):
3-monthly anomalies and quality assessment of seasonal climate predictions for Germany (EPISODES) version 2022.01
https://doi.org/10.5676/DWD/SEASONAL-EPI-DE_V2022.01_QA
Pasternack, Alexander; Hoff, Amelie; Wehring, Sabrina; Fröhlich, Kristina; Lorenz, Philip; Paxian, Andreas; Kreienkamp, Frank; Früh, Barbara
Deutscher Wetterdienst (DWD, http://www.dwd.de/EN/)
2023
The 3-monthly anomalies (for months 1-3, 2-4, 3-5 and 4-6), the mean squared errors (mse) and the Pearson correlation coefficients (corr_pea) are calculated for the seasonal climate predictions of the German Climate Forecast System GCFS2.1 which are downscaled over Germany using the empirical-statistical downscaling method DWD-EPISODES version 2022. The 3-monthly anomalies and the corresponding quality measures are available on a Germany-wide grid of about 10 km x 10 km (regular 0.15° x 0.1° grid). The anomalies of the following variables are included in the data set: air temperature 2 m (daily mean: tasAnom, daily maximum: tasmaxAnom, daily minimum: tasminAnom), precipitation (prAnom), relative humidity 2 m (hursAnom), global radiation (rsdsAnom), sea level pressure (pslAnom), and wind speed 10 m (sfcWindAnom). The 3-monthly anomalies are calculated in comparison to the climate time period from 1991 to 2020.
The German Climate Forecast System is described by: Fröhlich, K., Dobrynin, M., Isensee, K. et al. (2021). The german climate forecast system: GCFS. Journal of Advances in Modeling Earth Systems, 13. DOI: 10.1029/2020MS002101. https://agupubs.onlinelibrary.wiley.com/doi/10.1029/2020MS002101.
The empirical-statistical downscaling method EPISODES version 2018 is described by: Kreienkamp, F., Paxian, A., Früh, B. et al. (2019). Evaluation of the empirical-statistical downscaling method EPISODES. Clim Dyn, 52, 991-1026. DOI: 10.1007/s00382-018-4276-2. https://link.springer.com/article/10.1007/s00382-018-4276-2. Kreienkamp, F., Lorenz, P., Geiger, T. (2020). Statistically Downscaled CMIP6 Projections Show Stronger Warming for Germany. Atmosphere 11, 1245. DOI: 10.3390/atmos11111245. https://www.mdpi.com/2073-4433/11/11/1245. The latest EPISODES version 2022 contains minor bug fixes and technical updates for climate predictions.
Along with the 3-monthly aggregated model forecast, the associated quality measures mse and corr_pea are also provided. These measures were calculated grid point-wise for the verification period from 1991 to 2020 with an analogous 3-monthly temporal aggregation using data based on observations or reanalysis. The variables pr, tas, tasmin, tasmax, hurs and rsds were verified using the HYRAS operational observation (https://www.dwd.de/DE/leistungen/hyras/hyras.html). Regarding the verification of the variable sfcWind ERA5-LAND (https://confluence.ecmwf.int/display/CKB/ERA5-Land%3A+data+documentation) was used and for the remaining variable psl ERA5 (https://confluence.ecmwf.int/display/CKB/ERA5%3A+data+documentation) was used. For further technical information regarding the provided seasonal climate predictions please visit https://esgf.dwd.de/projects/climatepredictionsde/seasonal-epi-de-v2022-01.
Climate predictions should only be used considering the respective climate prediction skills and the recommended time aggregations. Please note that climate prediction skill generally increases if aggregated over time and space, and that the data only partly consider urban heat island effects. Please find figures of the climate prediction skills on a regular grid with 0.3° x 0.2° for Germany and further background information on climate predictions (e.g. on the recommended time aggregations) on https://www.dwd.de/climatepredictions.
V2022.01
September 2022 to present
3-monthly
Monthly
DE-015x01 (Germany on a regular 0.15° x 0.1° grid, approx. 10 km x 10 km)
NetCDF4
approx. 400 kB per netcdf file
The Deutscher Wetterdienst (DWD) is the producer of the data. The General Terms and Conditions of Business and Delivery apply for services provided by DWD
http://www.dwd.de/EN/service/terms/terms.html
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