Performance of the operational <scp>ECMWF</scp> ensemble in predicting heavy rain associated with the Emilia‐Romagna flood in May 2023

Abstract A devastating flood occurred in May 2023 in the Emilia‐Romagna region in northern Italy following a 2‐day period with widespread accumulated precipitation exceeding 150mm. The event was associated with a quasi‐stationary upper‐level cutoff cyclone. This study investigates the performance of the operational ECMWF ensemble prediction system in capturing the cutoff formation and the occurrence of intense precipitation. The key results are that forecasts with a 1‐day lead time were highly accurate and that a predictability jump occurred for lead times larger than 4 days, when the ensemble failed in representing the formation of the cutoff.

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Publication Details

Journal
Weather
Published
2025-12-18
DOI
https://doi.org/10.1002/wea.70017
Primary Topic
Meteorological Phenomena and Simulations
Type
article
Field-Weighted Citation Impact
0.00

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article

Performance of the operational ECMWF ensemble in predicting heavy rain associated with the Emilia‐Romagna flood in May 2023

Han-Qing Wu, Katharina Hartmuth, Heini Wernli
Weather
Meteorological Phenomena and Simulations
article

Performance of the operational ECMWF ensemble in predicting heavy rain associated with the Emilia‐Romagna flood in May 2023

Han-Qing Wu, Katharina Hartmuth, Heini Wernli
article en

Abstract

Abstract A devastating flood occurred in May 2023 in the Emilia‐Romagna region in northern Italy following a 2‐day period with widespread accumulated precipitation exceeding 150mm. The event was associated with a quasi‐stationary upper‐level cutoff cyclone. This study investigates the performance of the operational ECMWF ensemble prediction system in capturing the cutoff formation and the occurrence of intense precipitation. The key results are that forecasts with a 1‐day lead time were highly accurate and that a predictability jump occurred for lead times larger than 4 days, when the ensemble failed in representing the formation of the cutoff.

Weather
ETH Zurich (CH)
National Science Foundation, Schweizerischer Nationalfonds zur Förderung der Wissenschaftlichen Forschung
Openalex Percentile: Top 55%
Meteorological Phenomena and Simulations
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