Efficient nonlinear inversion for atmospheric sounding and other applications

Author: Richard Lynch, Jean-Luc Moncet and Xu Liu
Date: 
March 23, 2009
Type: 
Journal Article
Venue: 
Applied Optics
Citation: 

Lynch, Richard; Moncet, Jean-Luc; Liu, Xu 2009: Efficient nonlinear inversion for atmospheric sounding and other applications. Appl. Optics. vo. 48 issue 10, pp.1790-1796. doi:10.1364/AO.48.001790

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.