LAUSR

It is shown that the a priori data on the atmospheric conditions, in addition to the climate’s characteristics, can enhance the efficiency of the algorithms for reconstructing the atmospheric profiles using satellite microwave radiometric observations. Such additional data are sought and their efficiency in remote sensing is estimated. The possibility of expanding the statistical approach by including new types of a priori data on the temperature and humidity of atmospheric conditions is discussed. It is demonstrated that the developed technique can be used to estimate the efficiency of using the following types of additional a priori data in the statistical regularization method: (i) the covariance matrix of the full vector of temperature and humidity variations within a vertical atmospheric column, (ii) the covariance matrix of the temperature and humidity variations within horizontal atmospheric strata, (iii) physical limits of the humidity variation amplitude, and (iv) statistically average model representations of the microwave radiation transfer parameters in the cloud layer.