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While originally designed only for wind measurement, the QuikSCAT scatterometer is capable of making wind and rain estimates over the ocean. Three separate estimators are used, a wind-only estimator, a rain-only estimator, and a simultaneous wind-rain estimator. No one of the estimators is suitable under all wind and rain conditions. We therefore propose a Bayesian estimator selection technique whereby the appropriate estimator can be selected from the estimates themselves. This paper introduces the Bayes estimator selection technique and discusses its application to QuikSCAT wind and rain estimation for conventional (25-km) resolution products. Results indicate that using Bayes estimator selection can improve both the bias and mean-squared error of wind estimates in both raining and nonraining conditions, as well as provide an improved rain flag.