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Skill of Global Raw and Postprocessed Ensemble Predictions of Rainfall over Northern Tropical Africa

Vogel, Peter; Knippertz, Peter ORCID iD icon; Gneiting, Tilmann; Fink, Andreas H.; Schlueter, Andreas


Accumulated precipitation forecasts are of high socioeconomic importance for agriculturally dominated societies in northern tropical Africa. In this study, we analyze the performance of nine operational global ensemble predic- tion systems (EPSs), which are part of the TIGGE dataset, for three regions in northern tropical Africa. Model predictions are assessed relative to climatology-based forecasts for 1 to 5-day accumulated precipitation during the monsoon seasons 2007–2014. To assess the full potential of raw ensemble forecasts across spatial scales, we apply state-of-the-art statistical postprocessing methods in form of Bayesian Model Averaging (BMA) and Ensem- ble Model Output Statistics (EMOS), and verify against station and spatially aggregated, satellite-based gridded observations. Raw ensemble forecasts are uncalibrated, unreliable and underperform relative to climatology, inde- pendently of region, accumulation time, monsoon season and ensemble. Differences between raw ensemble and climatological forecasts are large, and partly stem from poor prediction for low precipitation amounts. BMA and EMOS postprocessed forecasts are calibrated, reliable and strongly improve on the raw ensembles, but - somewhat disappointingly - typically do not outperform climatology. ... mehr

Zugehörige Institution(en) am KIT Institut für Meteorologie und Klimaforschung Troposphärenforschung (IMKTRO)
KIT-Zentrum Klima und Umwelt (ZKU)
Publikationstyp Poster
Publikationsjahr 2018
Sprache Englisch
Identifikator KITopen-ID: 1000085043
HGF-Programm 12.01.02 (POF III, LK 01) Proc.res.f.multisc.predictab.of weather
Veranstaltung European Geosciences Union General Assembly (EGU 2018), Wien, Österreich, 08.04.2018 – 13.04.2018
Bemerkung zur Veröffentlichung Geophysical Research Abstracts, 20 (2018) EGU2018-6773
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