期刊
COMPUTER PHYSICS COMMUNICATIONS
卷 148, 期 2, 页码 214-226出版社
ELSEVIER
DOI: 10.1016/S0010-4655(02)00555-6
关键词
inverse problems; nonlinear least squares; regularization; atmospheric spectroscopy; remote sensing
In this paper we present an inversion algorithm for nonlinear ill-posed problems arising in atmospheric remote sensing. The proposed method is the iteratively regularized Gauss-Newton method. The dependence of the performance and behaviour of the algorithm on the choice of the regularization matrices and sequences of regularization parameters is studied by means of simulations. A method for improving the accuracy of the solution when the identity matrix is used as regularization matrix is also discussed. Results are presented for atmospheric temperature retrievals from a far infrared spectrum observed by an airborne uplooking heterodyne instrument. (C) 2002 Elsevier Science B.V. All rights reserved.
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