Skip to Main Content
Wind vectors derived from scatterometer measurements are spatially detailed as compared to global numerical weather prediction (NWP) model fields. Since the Advanced Scatterometer (ASCAT)'s wind vector ambiguities are, in general, well defined, ambiguity removal results in accurate wind fields. The dense and regular spatial sampling of ASCAT winds represents a unique resource to study the NWP model field spatial error structure. The current level 2 ASCAT data processor employs 2-D variational ambiguity removal (2DVAR), in which an analysis is made from the ambiguous wind solutions and a prior NWP wind field using a variational technique, and, subsequently, the ambiguity closest to the analysis is selected as best wind. 2DVAR will yield an optimal analysis when the structure functions (background error correlations in the potential domain) are well specified. In this paper, a new method is presented to calculate structure functions from autocorrelations of observed scatterometer wind components minus NWP model predictions (O-B). It is based on direct integration of the differential equations relating structure functions and observed autocorrelations. Reprocessing ASCAT data at 12.5-km grid size with structure functions obtained this way shows a considerable increase in the spectral density of the analysis for scales from about 800 to about 100 km, with the largest effect at scales of around 250 km. In line with this finding, it is shown in a case study that a more detailed analysis leads to fewer ambiguity removal errors for ASCAT data recorded over a frontal zone with rapidly varying wind direction.