Efficiently Improving Ensemble Forecasts of Warm-Sector Heavy Rainfall over Coastal Southern China: Targeted Assimilation to Reduce the Critical Initial Field Errors  

作　　者：Xinghua BAO Rudi XIA Yali LUO Jian YUE 

机构地区：[1]State Key Laboratory of Severe Weather,Chinese Academy of Meteorological Sciences(CAMS),China Meteorological Administration(CMA),Beijing 100081 [2]Collaborative Innovation Center on Forecast and Evaluation of Meteorological Disasters(CIC-FEMD),Nanjing University of Information Science&Technology,Nanjing 210044 [3]CMA Earth System Modeling and Prediction Centre,China Meteorological Administration,Beijing 100081

出　　处：《Journal of Meteorological Research》2023年第4期486-507,共22页气象学报（英文版）

基　　金：Supported by the National Key Research and Development Program of China (2022YFC3003903);National Natural Science Foundation of China (42030610 and 41774002);Science and Technology Development Fund of CAMS (2019KJ018);Basic Research Fund of CAMS (2023Z008, 2023Z001, and 2023Z020)。

摘　　要：Warm-sector heavy rainfall events over southern China are difficult to accurately forecast, due in part to inaccurate initial fields in numerical weather prediction models. In order to determine an efficient way of reducing the critical initial field errors, this study conducts and compares two sets of 60-member ensemble forecast experiments of a warm-sector heavy rainfall event over coastal southern China without data assimilation(NODA) and with radar radial velocity data assimilation(RadarDA). Yangjiang radar data, which can provide offshore high-resolution wind field information, were assimilated by using a Weather Research and Forecasting(WRF)-based ensemble Kalman filter(EnKF) system. The results show that the speed and direction errors of the southeasterly airflow in the marine boundary layer over the northern South China Sea may primarily be responsible for the forecast errors in rainfall and convection evolution. Targeted assimilation of radial velocity data from the Yangjiang radar can reduce the critical initial field errors of most members, resulting in improvements to the ensemble forecast. Specifically, RadarDA simulations indicate that radial-velocity data assimilation(VrDA) can directly reduce the initial field errors in wind speed and direction, and indirectly and slightly adjust the initial moisture fields in most members, thereby improving the evolution features of moisture transport during the subsequent forecast period. Therefore, these RadarDA members can better capture the initiation and development of convection and have higher forecast skill for the convection evolution and rainfall. The improvement in the deterministic forecasts of most members results in an improved overall ensemble forecast performance. However, VrDA sometimes results in inappropriate adjustment of the initial wind field,so the forecast skill of a few members decreases rather than increases after VrDA. This suggests that a degree of uncertainty remains about the effect of the WRF-based EnKF system. Moreover, the results

关 键 词：ensemble forecast targeted assimilation warm-sector heavy rainfall 

分 类 号：P457.6[天文地球—大气科学及气象学]