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 f...

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Hauptverfasser: Lerch, Sebastian (VerfasserIn) , Thorarinsdottir, Thordis (VerfasserIn)
Dokumenttyp: Article (Journal)
Sprache:Englisch
Veröffentlicht: 14 Nov 2013
In: Tellus. Series A, Dynamic meteorology and oceanography
Year: 2013, Jahrgang: 65, Heft: 1, Pages: 1-13
ISSN:1600-0870
DOI:10.3402/tellusa.v65i0.21206
Online-Zugang:Verlag, lizenzpflichtig, Volltext: https://doi.org/10.3402/tellusa.v65i0.21206
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Verfasserangaben:by Sebastian Lerch and Thordis L. Thorarinsdottir

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520 |a 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. 
650 4 |a ensemble post-processing 
650 4 |a non-homogeneous regression 
650 4 |a predictive distribution 
650 4 |a probabilistic forecasting 
650 4 |a weather forecasting 
650 4 |a wind speed 
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