The authors compare the performance of seven wind-estimation algorithms, including the weighted least squares in the log domain, maximum-likelihood (ML), least squares, weighted least squares, adjustable weighted least squares, L1 norm, and least wind speed squares algorithms, for wind retrieval. For each algorithm, they present performance simulation results for the NASA scatterometer system planned to be launched in the 1990s. A relative performance merit based on the root-mean-square value of wind vector error is devised. It is found that performances of all algorithms are quite comparable. However, the results do indicate that the ML algorithm performs best for the 50-km wind resolution cell case and the L1 norm algorithm performs best for the 25-km wind resolution cell case.
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