Abstract: Using the sounding data of Xining Ershilipu meteorological station from 2007 to 2018, the MonoRTM simulated the brightness temperature of 35 channels within 21.985~58.759GHz of the center frequency, and obtained the inversion model through repeated training of BP neural network. The 2019 data set was used as the test sample to respectively invert the temperature, relative humidity and water vapor density, and then compared with the sounding data to discuss the accuracy of the BP inversion algorithm. The results show that under clear sky conditions, BP neural network and microwave radiometer have the best inversion effcet on temperature, followed by water vapor density and relative humidity. The inversion results of BP neural network in winter and spring are better than that of microwave radiometer, and vice versa in summer and autum. Under cloud conditions, the temperature inversion effect of BP neural network is better than that of microwave radiometer in winter, spring and summer. The inversion effect of water vapor density is significantly improved than that of microwave radiometer in four seasons. The inversion effect of relative humidity is better than that of microwave radiometer in winter, spring and summer. Under sunny and cloudy conditions, the average absolute error and standard deviation of BP neural network inversion temperature, water vapor density and relative humidity in different seasons are less than those of microwave radiometer, especially the relative humidity. Under clear sky conditions, the BP neural network inversion temperature profile is the best in spring, summer and autumn. The inversion of water vapor density profile has high accuracy in the middle and low layers, and the inversion of relative humidity profile has poor accuracy, but it is basically consisitent with the trend of sounding data. Under cloud conditions, the BP neural network inversion temperature profile is basically consisitent with the clear sky time, and the accuracy is higher than that of microwave radiometer.The inversion of water vapor density and relative humidity profile is better above 8 km.