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An IMERG-Based Optimal Extended Probabilistic Climatology (EPC) as a Benchmark Ensemble Forecast for Precipitation in the Tropics and Subtropics

Walz, Eva-Maria 1; Maranan, Marlon 2; Linden, Roderick van der 2; Fink, Andreas H. 2; Knippertz, Peter ORCID iD icon 2
1 Institut für Stochastik (STOCH), Karlsruher Institut für Technologie (KIT)
2 Institut für Meteorologie und Klimaforschung (IMK), Karlsruher Institut für Technologie (KIT)


Current numerical weather prediction models show limited skill in predicting low-latitude precipitation. To aid future improvements, be it with better dynamical or statistical models, we propose a well-defined benchmark forecast. We use the arguably best available high-resolution, gauge-calibrated, gridded precipitation product, the Integrated Multisatellite Retrievals for GPM (IMERG) “final run” in a ±15-day window around the date of interest to build an empirical climatological ensemble forecast. This window size is an optimal compromise between statistical robustness and flexibility to represent seasonal changes. We refer to this benchmark as extended probabilistic climatology (EPC) and compute it on a 0.1° × 0.1° grid for 40°S–40°N and the period 2001–19. To reduce and standardize information, a mixed Bernoulli–Gamma distribution is fitted to the empirical EPC, which hardly affects predictive performance. The EPC is then compared to 1-day ensemble predictions from the European Centre for Medium-Range Weather Forecasts (ECMWF) using standard verification scores. With respect to rainfall amount, ECMWF performs only slightly better than EPS over most of the low latitudes and worse over high-mountain and dry oceanic areas as well as over tropical Africa, where the lack of skill is also evident in independent station data. ... mehr

Verlagsausgabe §
DOI: 10.5445/IR/1000134084
Veröffentlicht am 14.02.2022
DOI: 10.1175/WAF-D-20-0233.1
Zitationen: 8
Cover der Publikation
Zugehörige Institution(en) am KIT Institut für Meteorologie und Klimaforschung Troposphärenforschung (IMKTRO)
Publikationstyp Zeitschriftenaufsatz
Publikationsjahr 2021
Sprache Englisch
Identifikator ISSN: 0882-8156, 1520-0434
KITopen-ID: 1000134084
HGF-Programm 12.11.34 (POF IV, LK 01) Improved predictions from weather to climate scales
Erschienen in Weather and forecasting
Verlag American Meteorological Society
Band 36
Heft 4
Seiten 1561-1573
Vorab online veröffentlicht am 13.08.2021
Schlagwörter Africa, Subtropics, Tropics, Precipitation, Satellite observations, Ensembles, Forecasting
Nachgewiesen in Web of Science
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