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

Abstract

The correct representation of wind speeds at hub height (e.g., 100m 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 the help of a diagnostic wind model, which reduces the horizontal grid point spacing by a factor of 8 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 a few measurements by masts or lidars are available at hub height, an improvement of the wind speed in the root-mean-squared error of almost 30% can be achieved. A final comparison with radiosonde observations confirms the added value of combining the physical and statistical approaches in post-processing the wind speed.