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Statistical postprocessing for precipitation forecasts during the West African Monsoon

Vogel, Peter; Gneiting, T.; Knippertz, P. ORCID iD icon; Fink, A.; Schlüter, Andreas

Abstract (englisch):

Statistical postprocessing for ensemble forecasts has undergone many improvements recently. Commonly used methods are Bayesian Model Averaging (BMA) and Ensemble Model Output Statistics (EMOS), but have predominantly been applied over the midlatitudes (e.g. North America or Europe). The prediction of precipitation events during the wet period of the West African Monsoon (WAM) is highly challenging and ensemble forecasts for precipitation in West Africa during this period have low skill. The present contribution investigates for the first time how statistical postprocessing methods can improve precipitation forecasts to obtain calibrated and sharp predictive distributions. Perhaps surprisingly, the ECMWF ensemble is unable to outperform climatological forecasts. However, BMA and EMOS postprocessed forecasts can cope with the poor quality of the raw ensemble forecasts and yield predictive distributions that are as calibrated as, but sharper than, climatology.

Zugehörige Institution(en) am KIT Institut für Meteorologie und Klimaforschung Troposphärenforschung (IMKTRO)
Institut für Stochastik (STOCH)
KIT-Zentrum Klima und Umwelt (ZKU)
Publikationstyp Vortrag
Publikationsjahr 2016
Sprache Englisch
Identifikator KITopen-ID: 1000064929
HGF-Programm 12.01.02 (POF III, LK 01) Proc.res.f.multisc.predictab.of weather
Veranstaltung EGU General Assembly, Vienna, Austria, 17–22 April 2016
Bemerkung zur Veröffentlichung EGU2016-17028
KIT – Die Forschungsuniversität in der Helmholtz-Gemeinschaft
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