New Methods In Retrieving Atmospheric Temperature And Moisture Profiles From Satellite Observations

This paper aimed at studying the statistical and physical retrieval of atmospheric temperature and moisture vertical profiles from satellite observations. In the aspect of statistical retrieval, observations of FY-3 meteorological satellite were used in training and testing the traditional statistical regression, the back propagation(BP) neural network, and the support vector regression(SVR). Results indicated that compared with the traditional statistical regression, the BPneural network and the SVR method both have a higher precision in terms of temperature and moisture profiles.And compared with the BPneural network, the SVR method is slightly betterin retrieving oftemperature profiles overthe landwitha cloudysky andoverthe sea with a cloudy but not rainy weather. When it comes to the retrieval of moisture profiles, the SVR method has a higher precisionover theland witha clear sky,and a lowerprecisionover the ocean with a not rainy weather, and the precision of these two methods are similar over the land witha cloudysky.In the aspect of physical retrieval, a new method based on the L-curve method and including the concept of entropy was designed to select the regularization parameter in the one-dimensional variational analysis(1DVAR) based sounding retrieval method. In the first iteration, this method uses an empirical regularization parameter derived by minimizing the entropy of variables. During the subsequent iterations, it uses the L-curve method to select the regularization parameter in the vicinity of the regularization parameter selected in the last iteration. The new method was applied to select the regularization parameter in retrieving atmospheric temperature and moisture profiles from Atmospheric Infrared Sounder(AIRS)radiance measurements. Results show that the new method yields better results than those from the original L-curve method and the discrepancy principle method in terms ofretrieval precision.