A stepwise correction method based on segmented hierarchical clustering for ensemble mean forecasts of severe rainfall in China

Ensemble prediction play a vital role in numerical weather prediction. Hence, how to effectively extract information from ensemble members to improve deterministic precipitation forecasting skills has always been a challenging issue. Based on precipitation data from the CMA-GEPS (China Meteorological Administration Global Ensemble Prediction System), a stepwise correction method based on Segmented Hierarchical Clustering (SHC) for daily precipitation is proposed. To evaluate the effect of the SHC method, two comparative experiments are further verified in this study based on TS score and ETS score. Effects of SHC are compared with that of an Ensemble Mean (EM) forecast method and a directed Hierarchical Clustering (HC) method. Results indicate that the deterministic forecast by the proposed SHC method can improve the predictability of heavy precipitation. Taking more probabilistic forecasting via the segmented correction scheme in correction method, SHC performs better than EM and HC. Meanwhile, SHC shows a better feasibility based on long-term forecast verification during the summertime in 2021; it also has much better effects on extreme precipitation event such as the case of heavy rainfall in Zhengzhou on 20 July 2021. For operational system, the developed SHC method provides a new tool that can further improve the result of ensemble forecasting.