Abstract

Retrieving atmospheric and/or surface state variables using remote observations typically involves minimizing a nonlinear cost function in measurement space. Many methods exist for minimizing nonlinear functions, but the applicability of one method over another has a large dependence on the degree of nonlinearity of the cost function and the initial guess error. We present a minimization method, called DRAD, that is applicable to highly nonlinear cost functions and to problems where little a priori information about the retrieved variables is available. To illustrate the method, water vapor and temperature profiles are retrieved using simulated Atmospheric Infrared Sounder observations. We focus on the efficiency of DRAD for retrievals generated for a wide range of initial guess errors. For a comparison, we also generate retrievals using the Levenberg–Marquardt method.

© 2009 Optical Society of America

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