On the Estimation of Low-Altitude Water Vapor Profiles from Ground-Based Infrared Measurements

Abstract

Information on atmospheric constituents is contained in the remotely measured spectral radiances. Two iteration methods, linear and nonlinear, are presented to demonstrate the possibility of inferring the water vapor profile from ground-based measurements. The linear inversion method which linearizes the radiative transfer equation is found to have a narrow range of convergence. A study of the vertical resolution of the inferred profile through the linear inversion technique indicates that fine-scale detailed structure of the profile cannot be reconstructed. The nonlinear iteration procedure, which minimizes the root-mean-squares residual of the random noise along the direction of ?steepest? descent, is found capable of inferring a reasonably stable solution with wide range of convergence and is proven in numerical stability superior to the linear technique. The effects of the errors both in radiance measurements and in temperature profile on the inferred profile are also presented.