SUBSEASONAL-EPI-HYR V2022.01

10.5676/DWD/SUBSEASONAL-EPI-HYR_V2022.01

Subseasonal Climate Predictions (ECMWF IFS-ENS-extended) for Germany and neighboring countries (EPISODES) Version 2022.01 Subseasonal Climate Predictions (ECMWF IFS-ENS-extended) downscaled over Germany and neighboring countries using the empirical-statistical downscaling method EPISODES version 2022 Wehring, Sabrina; Pasternack, Alexander; Hoff, Amelie; Lorenz, Philip; Paxian, Andreas; Kreienkamp, Frank; Früh, Barbara; Walter, Andreas
Subseasonal Climate Predictions (ECMWF IFS-ENS-extended) for Germany and neighboring countries (EPISODES) Version 2022.01 https://doi.org/10.5676/DWD/SUBSEASONAL-EPI-HYR_V2022.01
Wehring, Sabrina; Pasternack, Alexander; Hoff, Amelie; Lorenz, Philip; Paxian, Andreas; Kreienkamp, Frank; Früh, Barbara; Walter, Andreas
Deutscher Wetterdienst (DWD, http://www.dwd.de/EN/Home/home_node.html) 2022 The subseasonal climate predictions for Germany and parts of neighboring countries are performed with the ECMWF (European Centre for Medium-Range Weather Forecasts) Integrated Forecasting System (IFS) ENS/Extended-Range Forecast and downscaled over Germany and neighboring countries using the empirical-statistical downscaling method EPISODES version 2022. The subseasonal climate predictions for Germany are available on a Germany-wide grid of about 5 km x 5 km on the ETRS89-extended LCC Europe map projection (EPSG:3034). The following variables are included in the data set and are aggregated on a daily timescale: air temperature 2 m (daily mean: tas, daily maximum: tasmax, daily minimum: tasmin), precipitation (pr), relative humidity 2 m (hurs), global radiation (rsds), cloud area fraction (clt), sea level pressure (psl), and wind speed 10 m (sfcWind).

The ECMWF Integrated Forecasting System is described by: https://www.ecmwf.int/en/publications/ifs-documentation. Model Configurations for Ensemble Forecasts (ENS) and Extended-Range Forecast is described by https://confluence.ecmwf.int/display/FUG/2.1.2+Model+Configurations.

The empirical-statistical downscaling method EPISODES version 2018 is described by: Kreienkamp, F., Paxian, A., Früh, B. et al. Evaluation of the empirical-statistical downscaling method EPISODES. Clim Dyn 52, 991-1026 (2019). DOI: 10.1007/s00382-018-4276-2. https://link.springer.com/article/10.1007/s00382-018-4276-2. Kreienkamp, F., Lorenz, P., Geiger, T. Statistically Downscaled CMIP6 Projections Show Stronger Warming for Germany. Atmosphere 11, 1245 (2020). 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.

To establish a statistical transfer function between regional and large scales, observations and reanalysis data are required. (For more details please have a look at the publications). The empirical-statistical downscaling method EPISODES version 2022 uses the regional-scale observation data set HYRAS for Germany and neighboring countries (https://www.dwd.de/DE/leistungen/hyras/hyras.html) and the reanalysis COSMO-REA6 for Europe (https://reanalysis.meteo.uni-bonn.de/?COSMO-REA6). The data set HYRAS covers the time period 1951 to 2015 with following versions of variables: HYRAS-TAS v4.0 (for tas), HYRAS-TMAX v4.0 (for tasmax), HYRAS-TMIN v4.0 (for tasmin), HYRAS-PRE v3.0 (for pr), HYRAS-HURS v4.0 (for hurs), HYRAS-RSDS v2.0 (for rsds). The data of COSMO-REA6 cover the time period 1995 to August 2019 with following variables: total cloud cover (for clt), mean sea level pressure (for psl), and wind speed 10 m (for sfcWind). For the analysis of the large-scale circulation the pressure level fields of temperature, geopotential and humidity from the NCEP/NCAR reanlysis for the period from 1948 to the present are used (https://www.psl.noaa.gov/data/gridded/data.ncep.reanalysis.html).

Climate predictions should only be used considering the respective climate prediction skills and the recommended time aggregations. If daily data are used for subsequent modelling the respective output should be aggregated following 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 Daily Weekly HYR-5 (Germany and parts of neighbouring countries on a 5 km x 5 km grid in ETRS89-extended LCC Europe) NetCDF4
approx. 1,5 GB per parameter and prediction (forecast+hindcast) 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 https://www.dwd.de/EN/service/terms/terms.html Zentrales Klimabüro
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