Impacts of Doppler Radar Data Assimilation on Precipitation Forecast of a Severe Convective Process in Hainan, China

Abstract

In this study, the Weather Research and Forecasting (WRF) model is used to simulate a severe convective process in Hainan, China on September 16, 2019. For the purpose of improving precipitation simulation and forecast, the WRF model’s three-dimensional Variational (3DVAR) assimilation system is used to assimilate CINRAD/SA Weather radar data. The assimilation results and precipitation forecast are compared and analyzed in order to study the influence of weather radar data assimilation on precipitation prediction. Four experiments of assimilation are carried out: 1) Control experiment: no assimilation of any data; 2) Only radar reflectivity data is assimilated; 3) Assimilating the radar radial velocity data; 4) Simultaneously assimilating the radar reflectivity and radial velocity data. The results show that the assimilation of the reflectivity data from the weather radar can effectively adjust the spatial distribution and magnitude of water vapor and temperature field in the background field. Assimilating the radar radial velocity data mainly improves the wind field of initial field. The assimilation of reflectivity can better adjust the precipitation forecast compared with the assimilation of radial velocity. Assimilating both reflectivity and radial velocity at the same time shows the best positive effect on precipitation forecast.