BP Neural Network Retrieval for Remote Sensing Atmospheric Profile of Ground-Based Microwave Radiometer

Abstract

Vertical distributions of temperature and humidity are two essential factors for understanding the atmospheric structure, extreme weather events, and regional and global climate. The ground-based microwave radiometer (MWR), which acts as a passive sensor and operates continuously under all weather conditions, has an irreplaceable role in measuring the vertical information of the temperature and water content in the atmosphere. In this letter, we proposed a four-layer back-propagation neural network (BPNN) method to retrieve temperature and relative humidity (RH) profiles from the bright temperature measured by the MWR. In contrast to the traditional BPNN, this method has greater advantages in dealing with the problems of overfitting, gradient disappearance, and gradient explosion in vertical atmospheric retrieval. By adding dropout layers, it can also help to describe the nonlinear relationships for RH profiles. Results showed that the performance of the four-layer BPNN method was better than the quadratic regression (QR, provided by MWR manufacturer) method under both cloud and cloud-free conditions. Compared with measurements of radiosonde data, root-mean-square error of temperature and RH, BPNN achieves 1.88 K and 19.30% under cloud conditions and 2.03 K and 15.10% under cloud-free conditions, respectively, whereas the corresponding values by using the QR method were only 3.07 K and 24.28% under cloud conditions and 4.14 K and 18.96% under cloud-free conditions, respectively. Temperature and RH profiles retrieval with high precision have increased the efficiency of the MWR observations and provided a data foundation for further atmospheric climate research.