Forecasting severe convective storms with WRF-based RTFDDA radar data assimilation in Guangdong, China

Abstract

Radar data assimilation is an important method for short-term convection forecasting or nowcasting. To improve the short-term (mainly 0–3 h) precipitation forecasts for severe convective storms, an analysis nudging (Newtonian relaxation) based hydrometeor and latent heat nudging (HLHN) technique was developed to effectively assimilate radar reflectivity data in a Weather Research and Forecasting (WRF)-based real time four-dimensional data assimilation and short-term forecasting system (RTFDDA). The purpose of this study is to investigate the performance of the RTFDDA system with radar data assimilation (RTFDDA-RDA) with rapid-cycling forecasting applications for Shenzhen, a subtropical coastal metropolis in southern China. The RTFDDA-RDA system was run to produce hindcasts for ten severe convective storm events occurred in Guangdong region during the 2017 rainy season. Results show that, through nudging cloud hydrometeors retrieved from radar reflectivity and the associated latent heat release, RTFDDA-RDA is able to produce the meso- and convective-scale features of the convective storms in a good accuracy and improve the short-term precipitation forecasting of the convective storms. Subjective and statistical evaluation results demonstrate that RTFDDA-RDA presents a reasonable capability for forecasting convective systems with improving the initial conditions and resulting in significant improvements of precipitation forecasting skills, especially for the 0–3-h nowcasting range. The sensitivity experiments on different latent heating schemes show that, the convective-stratiform separated heating scheme has the best performance of forecasts. Finally, intercomparison of different radar data assimilation approaches will be conducted in future.