Spatial Predictability of Heavy Rainfall Events in East China and the Application of Spatial-Based Methods of Probabilistic Forecasting

Abstract

One of the major issues in developing convective-scale ensemble forecasts is what is widely known as under-dispersion. This can be addressed through the consideration of spatial uncertainties via post-processing, motivating the development of various techniques to represent the spatial uncertainties of ensembles. In this study, a recently developed fraction-based approach (the ensemble agreement scale, EAS) is applied to characterize the spatial predictability and spread–skill performances of precipitation forecasts using a WRF-EnKF convective-scale ensemble forecast system over the Yangtze and Huai river valleys, China. Fourteen heavy rainfall events during the Meiyu season of 2013 and 2014 were classified into two categories—strong forcing (SF) and weak forcing (WF)—using the convective adjustment timescale. The results show that the spatial predictability and spread–skill relationship are highly regime-dependent and that both exhibit lower values under WF. Furthermore, a new object-based probabilistic approach (OBJ_NEP) is proposed as a supplement to traditional neighborhood ensemble probability (NEP) and a recently proposed fraction-based approach (EAS_NEP). The results of the application of OBJ_NEP are evaluated, and a comparison is made between NEP and EAS_NEP for the analysis of experiments involving both idealized and ‘real’ events by using objective verification methods. The results imply that OBJ_NEP can be effectively employed under different large-scale forcings. Consequently, these results can aid the understanding of spatial-based approaches to probabilistic forecasting, which has been widely applied to post-processing processes of convective-scale ensemble forecast systems (CSEFs) in recent years.