Comparison of non-homogeneous regression models for probabilistic wind speed forecasting

In weather forecasting, non-homogeneous regression (NR) is used to statistically post-process forecast
ensembles in order to obtain calibrated predictive distributions. For wind speed forecasts, the regression model
is given by a truncated normal (TN) distribution, where location and spread derive from the ensemble. This
article proposes two alternative approaches which utilise the generalised extreme value (GEV) distribution. A
direct alternative to the TN 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 (ECMWF), all three approaches significantly improve the calibration
as well as the overall skill of the raw ensemble with the regime-switching approach showing the highest skill in
the upper tail.