Inversion of Temperature and Humidity Profile of Microwave Radiometer Based on BP Network

In this paper, the inversion method of atmospheric temperature and humidity profiles via ground-based microwave radiometer is studied. Using the three-layer BP neural network inversion algorithm, four BP neural network models (temperature and humidity models with and without cloud information) are established using L-band radiosonde data obtained from the Atmospheric Exploration base of the China Meteorological Administration from July 2018 to June 2019. Microwave radiometer level 1 data and cloud radar data from July to September 2019 are used to evaluate the model. The four models are compared with the measured sounding data, and the inversion accuracy and the influence of cloud information on the inversion are subsequently analyzed. The results show the following: the average errors of temperature and humidity profiles for the model without cloud information are 1.18 degrees C and 11.7%, while the average errors of temperature and humidity profiles for the model with cloud information are 0.71 degrees C and 6.09%. Compared with the profiles that lack cloud information, the RMSE of most altitudes is reduced to some extent after cloud information is added, which is particularly obvious at layers where cloud is present.