Articles | Volume 7, issue 5
Wind Energ. Sci., 7, 1905–1918, 2022
https://doi.org/10.5194/wes-7-1905-2022
Wind Energ. Sci., 7, 1905–1918, 2022
https://doi.org/10.5194/wes-7-1905-2022
Research article
16 Sep 2022
Research article | 16 Sep 2022

Statistical post-processing of reanalysis wind speeds at hub heights using a diagnostic wind model and neural networks

Sebastian Brune and Jan D. Keller

Data sets

COSMO-REA6 regional reanalysis Deutscher Wetterdienst/Hans-Ertel Centre for Weather Research https://opendata.dwd.de/climate_environment/REA/COSMO_REA6/

High resolution radiosonde data Deutscher Wetterdienst https://opendata.dwd.de/climate_environment/CDC/observations_germany/radiosondes/high_resolution/historical/

FINO-Datenbank Bundesamt für Seeschifffahrt und Hydrographie http://fino.bsh.de

HD(CP)2 long term observations, data of Meteorological tower data (no. 00), by Supersite JOYCE, data version 00, SAMD https://www.cen.uni-hamburg.de/en/icdc/data/atmosphere/samd-ltl-datasets/samd-lt-joyce/sups-joy-mett00-l1-any.html

Hole-filled seamless SRTM data V4 A. Jarvis, H. Reuter, A. Nelson, and E. Guevara https://srtm.csi.cgiar.org

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Short summary
A post-processing of the wind speed of the regional reanalysis COSMO-REA6 in Central Europe is performed based on a combined physical and statistical approach. The physical basis is provided by downscaling wind speeds with the help of a diagnostic wind model, which reduces the horizontal grid point spacing by a factor of 8. The statistical correction using a neural network based on different variables of the reanalysis leads to an improvement of 30 % in RMSE compared to COSMO-REA6.