Meiyu-front rainstorm is one of the main disastrous weather events in summer in East China. In this study, seven assimilation experiments of multi-type observation data such as wind profile data, microwave radiometer data and radiosonde sounding data are designed to forecast the Meiyu-front rainstorm on 15 June 2020. The results show that the seven experiments can basically simulate the orientation of rain belt. The comprehensive experiment which assimilates all types of observations performs the best in simulating the location of heavy rainstorm and shows good performance in simulating the precipitation above moderate rain. For the comprehensive experiment, the forecast deviation of rainstorm and heavy rainstorm is small, and the equitable threat score has also been greatly improved compared with other experiments. It is found that the convective available potential energy is enhanced after the assimilation of surface observation data. In addition, the wind convergence and water vapor transportation are modified after assimilating wind profile data. Accordingly, the precipitation efficiency is improved in the comprehensive experiment. The profiles of pseudo-equivalent potential temperature, vorticity and divergence show that, the assimilation of new-types observation data from wind profiler radar and microwave radiometer increases the instability of atmospheric stratification and enhances the ascending motion in the heavy precipitation center. The above results show that the introduction of various some new-type data before the numerical simulation can reduce the forecast deviation. In addition, the combined assimilation of microwave radiometer and sounding data presents better performance than single data assimilation, which indicates that data mutual complementation is essential to improving forecast accuracy.

In order to further enhance the numerical application of weather radar radial velocity, this paper proposes a quality control scheme for weather radar radial velocity from the perspective of data assimilation. The proposed scheme is based on the WRFDA (Weather Research and Forecasting Data Assimilation) system and utilizes the biweight algorithm to perform quality control on weather radar radial velocity data. A series of quality control tests conducted over the course of one month demonstrate that the scheme can be seamlessly integrated into the data assimilation process. The scheme is characterized by its simplicity, fast implementation, and ease of maintenance. By determining an appropriate threshold for quality control, the percentage of outliers identified by the scheme remains highly stable over time. Moreover, the mean errors and standard deviations of the O-B (observation-minus-background) values are significantly reduced, improving the overall data quality. The main information and spatial distribution features of the data are preserved effectively. After quality control, the distribution of the O-B Probability Density Function is adjusted in a manner that brings it closer to a Gaussian distribution. This adjustment is beneficial for the subsequent data assimilation process, contributing to more accurate numerical weather predictions. Thus, the proposed quality control scheme provides a valuable tool for improving weather radar data quality and enhancing numerical forecasting performance.