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

Brune, Sebastian; Keller, Jan D.

doi:https://doi.org/10.5194/wes-7-1905-2022

Articles | Volume 7, issue 5

https://doi.org/10.5194/wes-7-1905-2022

© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.

https://doi.org/10.5194/wes-7-1905-2022

© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.

Articles | Volume 7, issue 5

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

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.