Abstract: Abundant mesoscale hydrometeor information is included in dual polarimetric radar data, which plays an essential role in the monitoring of convective storms. To assimilate dual polarimetric radar data more effectively, a direct dual polarimetric radar data assimilation scheme is developed. The tangent linear and adjoint examination for observation operators and its adjoint, single observation tests, and cycling data assimilation and forecasting experiments in a real-data case are conducted. The results in tangent linear and adjoint examination indicate that this dual polarimetric radar data direct assimilation scheme satisfies the requirement of precision, and the operator is reasonably constructed. It can be seen from the results of single observation tests that the observation information from dual polarimetric radars can be transferred to relative variables such as hydrometeors via hydrometeor background error covariance, and the analysis becomes more cooperated. The results from cycling assimilation and forecasting experiments in a real-data case show that dynamic, thermodynamic and microphysical structures can be optimized by dual polarimetric radar data assimilation, and the capability of rainfall prediction is improved. It can be concluded that the direct dual polarimetric radar data assimilation scheme under variational framework can make the dual polarimetric radar observation information to be assimilated more reasonably, and the quality of 6-hour prediction can be improved. It has more computing efficiency compared with ensemble Kalman filter, and is more convenient for operational application.