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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| Main Authors: | , |
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| Format: | Article (Journal) |
| Language: | English |
| Published: |
14 Nov 2013
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| In: |
Tellus. Series A, Dynamic meteorology and oceanography
Year: 2013, Volume: 65, Issue: 1, Pages: 1-13 |
| ISSN: | 1600-0870 |
| DOI: | 10.3402/tellusa.v65i0.21206 |
| Online Access: | Verlag, lizenzpflichtig, Volltext: https://doi.org/10.3402/tellusa.v65i0.21206
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| Author Notes: | by Sebastian Lerch and Thordis L. Thorarinsdottir |
| Summary: | 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. |
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| Item Description: | Gesehen am 26.01.2022 |
| Physical Description: | Online Resource |
| ISSN: | 1600-0870 |
| DOI: | 10.3402/tellusa.v65i0.21206 |