Correcting Atmospheric Temperature and Vapor Density Profiles of Ground-Based Microwave Radiometer in Diverse Skies by Regression Model and Artificial Neural Network Methods

A ground-based microwave radiometer (MWR) can retrieve temperature and vapor density profiles with a temporal resolution at the minute level, which is significant for studying atmospheric thermodynamic stratification and its evolution. Improving MWR retrieval accuracy is crucial for MWR application research. Based on 9-year observations of MWR and radiosonde in Wuhan, China, this study adopts regression model and artificial neural network (ANN) methods to correct MWR temperature and vapor density deviations against radiosondes in diverse skies. Due to the impacts of solar heating and raindrops, MWR temperature presents a cold bias from radiosondes in clear and cloudy skies, but a warm bias in rainy skies, while the MWR vapor density is generally wetter than radiosondes, especially in rainy skies. The validation results show that both regression and ANN models can reduce the biases of MWR temperature and vapor density against radiosondes to around zero in diverse skies, and the MWR vapor density RMSE in rainy skies shows a marked decrease. After correcting using the regression model, the RMSE of MWR temperature (vapor density) declines by 14% (7%), 7% (4%), and 12% (29%) in clear, cloudy, and rainy skies, respectively, and the correction effect of the ANN model is slightly better than the regression model, with corresponding decreases of 19% (8%), 10% (8%), and 12% (30%), respectively. However, the consistency of MWR retrievals with radiosondes is rarely improved after the corrections of regression and ANN models. These results indicate that the regression and ANN models have a reasonable ability to correct MWR retrieval deviation in diverse skies, and there is remaining room for further improvement in MWR retrieval accuracy.