An Inversion Method of Atmospheric Temperature and Water Vapor Density Profile Based on RBF Neural Network
LV Xinshuai
TIAN Bin
LIANG Xiang
TAN Yulin
LIU Shengliang
Abstract：By using the ground-based 16-channel microwave radiometer to analyse the data in Wuhan(including 16 channels of brightness temperature data and surface meteorological information data)and corresponding sounding data(including tempera?ture profile and water vapour density profile)to form the network training samples and testing samples,the observation data in the training samples and the corresponding sounding data are respectively used as input and output data for training the neural net?works,and the observation data in the test samples are used as input data to test the trained neural networks. The sounding data of the test sample is compared as standard to the test output. By comparing the training results and the test results of RBF neural net?work and BP neural network,the prediction accuracy and feasibility of RBF neural network under uncertain conditions are verified. The experimental results show that the RBF neural network has faster calculation speed,more accurate inversion capability and stronger generalization ability than the BP neural network in retrieving the atmospheric temperature profile and the water vapour den?sity profile. The advantages of using RBF neural network to invert atmospheric temperature profile and water vapour density profile are significantly greater than BP neural network inversion method. Applying this method to microwave radiometer is of great signifi?cance for improving the level of inversion technology.
Keywords：RBF neural networkmicrowave radiometeratmospheric profile inversion
Publication Date：2019-01-01
Online Publishing Date：2025-08-15(First online date of this platform, not the publication date of the document)
Pages：6( 29-33,93 )