  In weather forecasting, nonhomogeneous regression is used to statistically
postprocess forecast ensembles in order to obtain calibrated predictive
distributions. For wind speed forecasts, the regression model is given by a
truncated normal distribution where location and spread are derived from the
ensemble. This paper proposes two alternative approaches which utilize the
generalized extreme value (GEV) distribution. A direct alternative to the
truncated normal regression is to apply a predictive distribution from the GEV
family, while a regime switching approach based on the median of the forecast
ensemble incorporates both distributions. In a case study on daily maximum wind
speed over Germany with the forecast ensemble from the European Centre for
Medium-Range Weather Forecasts, all three approaches provide calibrated and
sharp predictive distributions with the regime switching approach showing the
highest skill in the upper tail.
