The forthcoming Indian satellite Oceansat-2 to be launched in 2007 will carry a microwave scatterometer and an ocean colour monitor onboard. The scatterometer, a Ku-band pencil beam sensor similar to that onboard Quikscat satellite, will provide surface vector winds over global oceans with a two days repetivity. An algorithm for retrieving wind vector from scatterometer has been developed with a solution ranking criteria of minimum normalized standard deviation (NSD) of wind speeds derived using backscatter measurements through a geophysical model function (GMF). Using Quikscat observational geometry and QSCAT-1 GMF, simulation based evaluation of algorithm performance under different noise conditions and its comparison with standard algorithm known as Maximum Likelihood Estimator (MLE) algorithm have been performed. Besides having retrieval performance closely comparable with MLE, the present algorithm has quality and rain flagging provisions. Moreover, it is computationally efficient with least subjectivity on various retrieval related parameters. These features are equally desirable for the operational implementation. Results of simulation studies related to retrieval, quality control and rain flagging along with its implementation to limited Quikscat data are presented.
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