A probabilistic analysis of wind gusts using extreme value statistics

The spatial variability of wind gusts is probably as large as that of precipitation, but the observational weather station network is much less dense. The lack of an area-wide observational analysis hampers the forecast veri. cation of wind gust warnings. This article develops and compares several approaches to derive a probabilistic analysis of wind gusts for Germany. Such an analysis provides a probability that a wind gust exceeds a certain warning level. To that end we have 5 years of observations of hourly wind maxima at about 140 weather stations of the German weather service at our disposal. The approaches are based on linear statistical modeling using generalized linear models, extreme value theory and quantile regression. Warning level exceedance probabilities are estimated in response to predictor variables such as the observed mean wind or the operational analysis of the wind velocity at a height of 10 m above ground provided by the European Centre for Medium Range Weather Forecasts (ECMWF). The study shows that approaches that apply to the differences between the recorded wind gust and the mean wind perform better in terms of the Brier skill score (which measures the quality of a probability forecast) than those using the gust factor or the wind gusts only. The study points to the benefit from using extreme value theory as the most appropriate and theoretically consistent statistical model. The most informative predictors are the observed mean wind, but also the observed gust velocities recorded at the neighboring stations. Out of the predictors used from the ECMWF analysis, the wind velocity at 10 m above ground is the most informative predictor, whereas the wind shear and the vertical velocity provide no additional skill. For illustration the results for January 2007 and during the winter storm Kyrill are shown.