NWP-Initialized Satellite Temperature Retrievals Using Statistical Regularization and Singular Value Decomposition Methods

Abstract

Several research groups have announced plans to merge satellite profile retrieval methods and numerical weather prediction methods into ?interactive? satellite retrieval approaches for both weather and climate-scale endeavors. Satellite profile retrievals, produced from algorithms that depend on hydrodynamic weather prediction models for first-guess and conditioning data, may be expected to contain some influence of the numerical weather prediction (NWP) model quite distinct from any influence of the satellite measurements. Research is described in this paper in which possible adverse impacts of NWP-produced first-guess information on temperature profile retrievals appear to signal danger for interactive methodologies. Deep-layer, synoptically correlated NWP forecast errors influence satellite retrieval errors in such a way that systematic distortions of the hydrostatic and baroclinic character of the resulting fields could lead to degradations of a subsequent forecast cycle rather than improvements. Two related temperature retrieval algorithms are examined and compared using initializing and conditioning data derived from NMC T80 spectral model forecasts. The algorithms are the well-known statistical regularization method, also called the ?minimum variance method,? and a method derived from a singular value decomposition (SVD) of the radiative transfer operator with regularization accomplished by truncation rather than a priori statistics. The two algorithms allow for a rational distinction between the effects of ?statistics? and ?physics? on the results. The SVD method provides an opportunity to explicitly examine the adverse effects of retrieval matrix instability and to infer how that may he influencing the statistical regularization algorithm for which matrix instability is an implicit property of both the physics and statistics incorporated into that algorithm. Finally, the effect of linearization of the retrieval problem on retrieval errors is examined. For systematic first-guess error fields such as those encountered in this study, the contribution to retrieval error attributable to linearization is substantial. The retrieval algorithm based on SVD can he unambiguously iterated to reduce this source of error.