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

• Main Authors: Sebastian Lerch, Thordis L. Thorarinsdottir
• Format: Article
• Language: English
• Published: Taylor & Francis Group 2013-11-01
• Series: Tellus: Series A, Dynamic Meteorology and Oceanography
• Subjects: ensemble post-processing; non-homogeneous regression; predictive distribution; probabilistic forecasting; weather forecasting; wind speed
• Online Access: http://www.tellusa.net/index.php/tellusa/article/download/21206/pdf_1

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.