A cross validated bayesian retrieval algorithm for nonlinear remote sensing experiments

Abstract A fully automated retrieval algorithm for estimating nonlinearly sensed functions is presented. This algorithm combines a simple Gauss-Newton iteration with an extended form of Generalized Cross Validation. The performance of the algorithm is illustrated in the context of a remote sensing problem arising in satellite meteorology.

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