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

Sebastian Brune, Jan D. Keller

Wind energy science · 16 September 2022 · Deutscher Wetterdienst (DWD)

In one sentence. Neural network and GLM correct COSMO-REA6 reanalysis wind speeds at 100 m after diagnostic wind-model downscaling, reducing RMSE by almost 30% in Central Europe.

Abstract

Abstract. The correct representation of wind speeds at hub height (e.g., 100 m above ground) is becoming more and more important with respect to the expansion of renewable energy. In this study, 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 help of a diagnostic wind model, which reduces the horizontal grid point spacing by a factor of eight compared to COSMO-REA6 and considers different vertical atmospheric stabilities. In the second step, a statistical correction is performed using a neural network as well as a generalized linear model based on different variables of the reanalysis. Although only few measurements by masts or lidars are available at hub height, an improvement of the wind speed in the RMSE of almost 30 % can be achieved. A final comparison with radiosonde observations confirms the added value of combining the physical and statistical approach in post-processing the wind speed.

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