Grid- versus station-based postprocessing of ensemble temperature forecasts

• Verfasst von: Feldmann, Kira [VerfasserIn]
• Verfasst von: Richardson, David [VerfasserIn]
• Verfasst von: Gneiting, Tilmann [VerfasserIn]
• Titel: Grid- versus station-based postprocessing of ensemble temperature forecasts
• Verf.angabe: Kira Feldmann, David S. Richardson, and Tilmann Gneiting
• E-Jahr: 2019
• Jahr: 4 JUL 2019
• Umfang: 8 S.
• Fussnoten: Gesehen am 12.11.2019
• Titel Quelle: Enthalten in: Geophysical research letters
• Ort Quelle: Hoboken, NJ : Wiley, 1974
• Jahr Quelle: 2019
• Band/Heft Quelle: 46(2019), 13, Seite 7744-7751
• ISSN Quelle: 1944-8007
• DOI: doi:10.1029/2019GL083189
• Datenträger: Online-Ressource
• Sprache: eng
• Sach-SW: analysis versus observations
• Sach-SW: ensemble forecast
• Sach-SW: model output statistics
• Sach-SW: postprocessing
• Sach-SW: spatio-temporal bootstrap
• K10plus-PPN: 1681686988

Abstract

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.