The effects of assimilation of multiple Doppler radar data on the simulation of a heavy rainevent

Using Weather Research and Forecasting Model (WRF V3.1) and its three-dimensional variational data assimilation system (WRF-3DVAR), we have directly applied basic reflectivity factor and radial velocity data from CINRAD at home to a mesoscale numerical simulation. We have performed a simulation experiment for a heavy rain event in Meiyu period in Anhui Province. After passing the quality control for the experiment results, we have analyzed the effect of assimilation of data simultaneously from the 6 radars in Anhui Province on the modeling outcomes. The results are as follows. (1) After passing the quality control, assimilating basic reflectivity factor and radial velocity can clearly improve the wind and humidity distribution in the initial field, which is expatiated that assimilating radar data used by the quality control scheme mentioned above is feasible. (2) Assimilating radial velocity from the several Doppler radars can increase the cyclonic of wind field, while assimilating basic reflectivity factor can adjust initial water vapor field and increase the water vapor content in the lower and middle troposphere. (3) Assimilating radial velocity and basic reflectivity factor from the several Doppler radars can better simulate echo structure of severe precipitation in advance, and make the echo structure display clear mesoscale characteristics, as well as the distribution and intensity forecast of rain close to observations. Additionally, the effect of radar data assimilation on precipitation forecast can last for 12 hours, so the assimilation improves the accuracy of 12-hourly precipitation forecast.