Probabilistic quantitative precipitation forecasting using Ensemble Model Output Statistics
Statistical post-processing of dynamical forecast ensembles is an essential component of weather forecasting. In this article, we present a post-processing method which generates full predictive probability distributions for precipitation accumulations based on ensemble model output statistics (EMOS...
Gespeichert in:
| 1. Verfasser: | |
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| Dokumenttyp: | Article (Journal) |
| Sprache: | Englisch |
| Veröffentlicht: |
2014
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| In: |
Quarterly journal of the Royal Meteorological Society
Year: 2013, Jahrgang: 140, Heft: 680, Pages: 1086-1096 |
| ISSN: | 1477-870X |
| DOI: | 10.1002/qj.2183 |
| Online-Zugang: | Resolving-System, lizenzpflichtig, Volltext: https://doi.org/10.1002/qj.2183 Verlag, lizenzpflichtig, Volltext: https://rmets.onlinelibrary.wiley.com/doi/abs/10.1002/qj.2183 
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| Verfasserangaben: | M. Scheuerer |
MARC
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| 520 | |a Statistical post-processing of dynamical forecast ensembles is an essential component of weather forecasting. In this article, we present a post-processing method which generates full predictive probability distributions for precipitation accumulations based on ensemble model output statistics (EMOS). We model precipitation amounts by a generalized extreme value distribution which is left-censored at zero. | ||
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