A Two-Dimensional Variational Analysis Method for NSCAT Ambiguity Removal: Methodology, Sensitivity, and Tuning

Abstract

In this study, a two-dimensional variational analysis method (2DVAR) is applied to select a wind solution from NASA Scatterometer (NSCAT) ambiguous winds. A 2DVAR method determines a ?best? gridded surface wind analysis by minimizing a cost function. The cost function measures the misfit to the observations, the background, and the filtering and dynamical constraints. The ambiguity closest in direction to the minimizing analysis is selected. The 2DVAR method, sensitivity, and numerical behavior are described. 2DVAR is used with both NSCAT ambiguities and NSCAT backscatter values. Results are roughly comparable. When the background field is poor, 2DVAR ambiguity removal often selects low probability ambiguities. To avoid this behavior, an initial 2DVAR analysis, using only the two most likely ambiguities, provides the first guess for an analysis using all the ambiguities or the backscatter data. 2DVAR and median filter-selected ambiguities usually agree. Both methods require horizontal consistency, so disagreements occur in clumps, or as linear features. In these cases, 2DVAR ambiguities are often more meteorologically reasonable and more consistent with satellite imagery.