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Grid- versus station-based postprocessing of ensemble temperature forecasts

Statistical postprocessing aims to improve ensemble model output by delivering calibrated predictive distributions. To train and assess these methods, it is crucial to choose appropriate verification data. Reanalyses cover the entire globe on the same spatiotemporal scale as the forecasting model, w...

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Hauptverfasser: Feldmann, Kira (VerfasserIn) , Richardson, David (VerfasserIn) , Gneiting, Tilmann (VerfasserIn)
Dokumenttyp: Article (Journal)
Sprache:Englisch
Veröffentlicht: 4 JUL 2019
In: Geophysical research letters
Year: 2019, Jahrgang: 46, Heft: 13, Pages: 7744-7751
ISSN:1944-8007
DOI:10.1029/2019GL083189
Online-Zugang:Verlag, Volltext: https://doi.org/10.1029/2019GL083189
Verlag, Volltext: https://agupubs.onlinelibrary.wiley.com/doi/abs/10.1029/2019GL083189
Volltext
Verfasserangaben:Kira Feldmann, David S. Richardson, and Tilmann Gneiting

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520 |a Statistical postprocessing aims to improve ensemble model output by delivering calibrated predictive distributions. To train and assess these methods, it is crucial to choose appropriate verification data. Reanalyses cover the entire globe on the same spatiotemporal scale as the forecasting model, while observation stations are scattered across planet Earth. Here we compare the benefits of postprocessing with gridded analyses against postprocessing at observation sites. In a case study, we apply local Ensemble Model Output Statistics to 2-m temperature forecasts by the European Centre for Medium-Range Weather Forecasts ensemble system. Our evaluation period ranges from November 2016 to December 2017. Postprocessing yields improvements over the raw ensemble at all lead times. The relative improvement achieved by postprocessing is greater when trained and verified against station observations. 
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