Ambiguity removal and assimilation of scatterometer data

Abstract The ERS‐1 scatterometer has proved to be a source of high quality ocean surface wind data, but a problem remains, namely the dual directional ambiguity of the solutions. an ambiguity removal scheme, called PRESCAT, is described based on our experience (a) that information on wind direction retrieval skill is an important input to ambiguity removal, (b) that wind‐vector filtering is beneficial compared to wind‐direction filtering, and (c) that already meteorological forecast information enables us to remove correctly approximately 95% of all ambiguities. the performance of the ambiguity filter is very good compared with other operational ambiguity removal schemes. Furthermore, a statistical interpolation analysis system called ‘buddy’ check is used effectively to identify and remove the few (approx. 0.1%) wrongly selected solutions. Assimilation of scatterometer winds has a beneficial impact on analyses and short‐range forecasts, probably mainly from improvements on the subsynoptic scales. On the wider temporal and spatial scales, scatterometer winds were also found beneficial, but only in the absence of satellite temperature soundings (SATEMs). In assimilation experiments in which the latter were included, the scatterometer did not provide a beneficial impact on the medium‐range forecast. Moreover, the conventional observations, including SATEMs are shown to have adverse effects on the surface‐wind analysis. We believe that both the redundancy and the adverse effects on the surface‐wind field are explained by the rigid formulation of the 6‐hour‐forecast error structure; the forecast error is assumed to be flow‐independent, and information on the special meteorological conditions in the atmospheric planetary boundary layer is lacking. to make observational systems more useful and complementary for numerical weather prediction the effects of the structure functions have to be investigated more precisely. In an adaptive four‐dimensional variational assimilation scheme the effect of the assumptions on forecast‐error structure will be less. We show that, in a variational framework, scatterometer backscatter measurements are difficult to assimilate directly. Instead, we derive and illustrate an alternative procedure to assimilate retrieved winds rather than backscatter measurements.