Abstract: The mesoscale convective system (MCS) occurred on 5 June 2009 in eastern China is simulated using the Advanced Regional Prediction System (ARPS) model and Doppler Radar data is assimilated with EnSRF. Bayesian inflation method is introduced in this study, which allows the inflation parameter to vary in space and time. The impact of Bayesian inflation method on assimilation of radar data with the ensemble square root filter (EnSRF) is investigated by comparing with the simulation using the multiplicative inflation method. Experimental results show that: the simulated composite reflectivity and cold pool from the Bayesian inflation experiment are stronger than that from the multiplicative inflation experiment; Bayes inflation method improves the performance of EnSRF, which always underestimates convection at the storm center. In the convective region, root mean square innovation of radial velocity and reflectivity in the Bayes inflation experiment are lower than that in the multiplicative inflation experiment. Further analysis indicates that the structure of Bayes inflation parameter corresponds very well to the root mean square innovation of reflectivity, which explains why the performance of EnSRF based on Bayes inflation method is improved. It is found that Bayes inflation method can give more weight to radar observations by increasing background error and provides bigger analysis increment when the root mean square innovation (RMSI) of background is bigger. Simulations of the two analysis fields show that the reflectivity near Hefei is stronger and the convective area of MCS is larger in Bayes inflation experiment than in the multiplicative inflation experiment. The simulated cold pool is colder and the area is bigger from Bayes inflation experiment than from the multiplicative inflation experiment, and corresponds well with observed reflectivity. ETS (Equitable Threat Score) of composite reflectivity from Bayes inflation experiment is higher than that from the multiplicative inflation experiment for various thresold. These resulsts suggest that Bayes inflation method improves the performance of EnSRF in radar data assimilation compared to that based on multiplicative inflation method.