An Assessment of the Nasa Scatterometer Ambiguity Removal Technique

The NASA Scatterometer (NSCAT) estimates the wind speed and direction of near-surface ocean wind. Several possible wind vectors are estimated for each resolution element known as a wind vector cell (wvc). Typically, the speeds of the possible wind vectors are nearly the same, but the directions are very di erent. The correct wind must be distinguished in a step called ambiguity removal. Unfortunately, ambiguity removal algorithms are subject to error. In an attempt to evaluate the accuracy of the Jet Propulsion Laboratory (JPL) NSCAT product, I developed a new model-based quality assurance (QA) algorithm which uses only NSCAT data. The algorithm segments the swath into overlapping 12x12 wvc regions and classi es each region according to estimated quality. The nine month NSCAT mission dataset is analyzed. In 82% of the regions, the ambiguity removal is over 99% e ective with the ambiguity rmoval errors correctable using a model-based correction technique. In 5% of the regions, areas of signi cant ambiguity removal errors are found. For remaining regions, all of which have root mean square (rms) wind speeds less than 4 m/s, there is too much uncertainty in the wind eld model or too much noise in the measurements to uniquely evaluate ambiguity removal accuracy with su cient con dence. I thus conservatively conclude that for the set of regions with rms wind speed greater than 4 m/s, NSCAT ambiguity removal is at least 95% e ective, i.e. NSCAT may not be e ective for the 5% of regions with signi cant errors.