Post-processing of wind gusts from COSMO-REA6 with a spatial Bayesian hierarchical extreme value model

Abstract. The aim of this study is to provide a probabilistic gust analysis for the region of Germany that is calibrated with station observations and with an interpolation to unobserved locations. To this end, we develop a spatial Bayesian hierarchical model (BHM) for the post-processing of surface maximum wind gusts from the COSMO-REA6 reanalysis. Our approach uses a non-stationary extreme value distribution for the gust observations at the top level, with parameters that vary according to a linear model using COSMO-REA6 predictor variables. To capture spatial patterns in surface extreme wind gust behavior, the regression coefficients are modeled as 2-dimensional Gaussian random fields with a constant mean and an isotropic covariance function that depends only on the distance between locations. In addition, we include an elevation offset in the distance metric for the covariance function to account for differences in topography. This allows us to include data from mountaintop stations in the training process and to utilize all available information. The training of the BHM is carried out with an independent data set from which the data at the station to be predicted are excluded. We evaluate the spatial prediction performance at the withheld station using Brier score and quantile score, including their decomposition, and compare the performance of our BHM to climatological forecasts and a non-hierarchical, spatially constant baseline model. This is done for 109 weather stations in Germany. Compared to the spatially constant baseline model, the spatial BHM significantly improves the estimation of local gust parameters. It shows up to 5 % higher skill for prediction quantiles and provides a particularly improved skill for extreme wind gusts. In addition, the BHM improves the prediction of threshold levels at most of the stations. Although a spatially constant approach already provides high skill, our BHM further improves predictions and improves spatial consistency.