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The latest spaceborne scatterometer, SeaWinds on QuikSCAT, estimates near-surface ocean winds at 25 km resolution over the entire globe. The scatterometer wind retrieval process generates several possible wind vector choices or ambiguities at each resolution cell. Routines for selecting a unique wind vector field are generally ad hoc and error prone. In order to assess SeaWinds ambiguity selection and spatial consistency of retrieved winds, a quality assurance (QA) algorithm is presented based on comparing ambiguity-selected winds to a low-order wind field model fit. Regions exceeding error thresholds are rated according to spatial consistency and flagged as possible ambiguity selection errors. Appropriate error thresholds and additional flagging criteria are set through an analysis of false alarms versus missed detections on a manually-inspected training data set. The QA algorithm correctly identifies 97% of the manually flagged regions with a false alarm rate of less than 2%. Applying the algorithm to 16 months of QuikSCAT wind data, we conclude that SeaWinds ambiguity selection is over 95% effective on regions of rms wind speed greater than 3.5 m/s. The QA algorithm indicates that higher noise occurs at nadir and in areas of low wind speed. additionally, fewer estimated ambiguity selection errors occur at nadir and on the swath edges due to a larger ambiguity set in those regions. The percentage of ambiguity selection errors are found to be highly correlated with the number of cyclonic storms passed by SeaWinds and the percentage of wind vector cells corrupted by rain.
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