Improvements in Ku-band scatterometer wind ambiguity removal using ASCAT-derived empirical background error correlations

Wind scatterometry is an established technique for accurately measuring wind vectors over the global oceans. Inversion of the Geophysical Model function (GMF) in general leads to a number of ambiguous solutions, and an ambiguity removal procedure for selecting the best solution is required. In this study Two-Dimensional Variational Ambiguity Removal (2DVAR) is considered. 2DVAR makes an analysis of the ambiguous scatterometer wind solutions (observations, O) and collocated ECMWF forecasts (background, B), and then selects the ambiguity closest to the analysis. The properties of 2DVAR are determined by the observation and background error variances and by the background error correlations (BECs). Empirical background error correlations (EBECs) can be obtained from direct integration of O-B correlations, and their application in 2DVAR has a beneficial effect on ASCAT wind quality, notably in situations where there is a mismatch between the measured and forecasted position of mesoscale structures. In this study EBECs will be applied to wind retrieval from Ku-band scatterometer systems, in particular RapidScat on the International Space Station and OSCAT on OceanSat-2. The difference of such systems with ASCAT is that they have poor direction skill in the nadir part of the swath. This can be improved by letting 2DVAR take the full wind vector probability density function into account. It will be shown that for Ku-band systems 2DVAR with EBECs yields a wind product that compares better to buoys and has less quality control flagging than the default product. This is due to more detail in the 2DVAR analysis. This is paradoxical, since EBECs are much broader than the default Gaussian BECs used in 2DVAR. However, BECs are defined in the wind potential and stream function domain, whereas their effect on the analysis in the spatial wind domain is determined by their second derivatives, which appear much narrower.