Two-dimensional variational ambiguity removal in wind scatterometry

Wind scatterometry is an important technique for retrieving high-resolution nearsurface wind information in ocean areas where other techniques provide sparse coverage. Scatterometer winds are principally ambiguous in wind direction. In this paper a variational method for ambiguity removal, named 2DVAR, is presented. 2DVAR performs an incremental analysis based on the ambiguous scatterometer wind vector solutions and a model forecast, and selects the ambiguity closest to the analysis as solution. The correctness of the 2DVAR implementation is demonstrated to machine precision. The merits of 2DVAR are shown in a number of case studies and in a more general statistical comparison. SeaWinds observations at 25 km resolution are known to be noisy, especially in the nadir part of the swath, due to the observation geometry. It is shown that the noise is effectively suppressed by application of 2DVAR in combination with the Multi Solution Scheme (MSS). MSS retains the local wind vector probability density function after inversion, rather than only a limited number of ambiguous solutions. The effect of MSS is to increase the influence of the background. This can be decreased again by adjusting the parameters in the observation wind error model. A case study on an extratropical hurricane observed with SeaWinds shows that reliable wind estimates can be obtained wind speeds more than 40 m/s. ASCAT has a better observation geometry than SeaWinds and therefore the MSS is not needed, but a case study on a tropical cyclone shows that the 2DVAR error model should be tuned right to reproduce the correct circulation patterns.