Abstract: Based on the WRF (Weather Research Forecast) model and the GSI (Gridpoint Statistical Interpolation) assimilation system, the impact of the assimilating four Doppler weather radars (DWR) reflectivity and radial velocity (Vr) for quantitative precipitation forecasts (QPFs) of the "7.21" Beijing excessive storm have been examined. The GSI directly assimilates Vr, while indirectly assimilates the reflectivity through the cloud analysis. The radar data are assimilated every 30 min from 21:00 UTC 20 Jul to 00:00 UTC 21 Jul 2012. The numerical experiments demonstrate that the DWR data assimilation can improve nowcast and short-term precipitation forecasts, whose ETS scores averagely increase by 0.2. The reflectivity data are used primarily in a cloud analysis that retrieves the amount of hydrometeors and adjusts the in-cloud temperature and moisture which have direct influence on generating precipitation. The assimilating reflectivity makes the root-mean-square error (RMSE) of the geopotential height averaged over between 650 and 250 hPa decreases by 8 gpm. The direct assimilation of DWR Vr in GSI exerts a sure influence in mesoscale wind fields. Through the quantitative verification of the simulation results, the forecast with reflectivity assimilation is better than that with the Vr assimilation.