AUTHOR=Gao Li , Zhao Zuosen , Qin Jun , Chen Quanliang , Cai Hongke TITLE=Stepwise correction of ECMWF ensemble forecasts of severe rainfall in China based on segmented hierarchical clustering JOURNAL=Frontiers in Earth Science VOLUME=10 YEAR=2023 URL=https://www.frontiersin.org/journals/earth-science/articles/10.3389/feart.2022.1079225 DOI=10.3389/feart.2022.1079225 ISSN=2296-6463 ABSTRACT=
Ensemble forecast plays a vital role in numerical weather prediction. Hence, effectively extracting useful information from ensemble members to improve precipitation forecasting skills has always been an important issue. Using the ensemble forecast data on precipitation from the ECMWF-GEPS (Global Ensemble Prediction System), we propose a stepwise correction method, based on segmented hierarchical clustering (SHC), for forecast of daily precipitation. This method employs a segmented correction scheme, thereby generating more probabilistic forecast information and improving forecasts. Validations of the SHC method have been performed by comparison with two other methods, namely the ensemble-mean (EM) method and the direct hierarchical clustering (HC) method. Our results showed that deterministic forecast