In this paper, aiming at the quality problem of the retrieved data of ground-based microwave radiometer, on the basis of traditional quality control methods, a new quality control scheme was designed by adding double weight check and deviation correction methods by using the temperature and humidity data retrieved by RPG-HATPRO microwave radiometer at 7 stations in Beijing area. Then, the assimilation and application of temperature and humidity profile after quality control were studied. Results show that: only individual stations have large inversion temperature error, and the maximum Root Mean Square Error (RMSE) of stations with large inversion temperature error is 8 ℃ before quality control, but after quality control, the RMSE drops to less than 4 ℃, and the error drops by more than 50％. However, for sites with small inversion temperature error, quality control only improves the high-level data. Further analysis shows that the relative humidity inversion error of all stations is relatively large, and the RMSE of relative humidity inversion before quality control is the largest or even reaches 50％, but the RMSE of relative humidity inversion of all stations after quality control is reduced to less than 20％, and the error is significantly reduced. After quality control, the Average Deviation (AD) of inversion temperature and humidity data is reduced to about zero, and the error distribution meets Gaussian distribution, which can meet the requirements before assimilation. This quality control scheme provides technical support for the effective use of temperature and humidity profile data of ground-based microwave radiometer in numerical model. Assimilation test shows that the inversion of temperature and humidity profile data by ground-based microwave radiometer after assimilation quality control can effectively improve the model's 6-hour cumulative precipitation forecast for all orders of magnitude, which makes the model's precipitation forecast closer to the reality. Assimilation test also shows that assimilation has great influence on the temperature and humidity field, which makes the model's temperature and humidity field more reasonable and can effectively improve the model's forecast accuracy.