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QuikSCAT was designed for ocean wind retrieval. However, its wind estimation performance is limited in rainy conditions. Several estimation techniques have been proposed: wind-only (WO), simultaneous wind and rain (SWR), and rain-only, which are appropriate for different levels of rain contamination. To exploit the strengths of each estimation method at mitigating rain contamination, a Bayes estimator selection (BES) technique has been developed for 25-km wind products to select from among the several estimation techniques for each wind vector cell. This paper adapts the BES concept  to QuikSCAT ultra-high resolution (UHR) 2.5-km, products and extends BES to include prior selection and noise reduction. Prior selection and noise reduction exploit general spatial characteristics of wind and rain fields to improve the accuracy of estimator selections. Together these techniques enable improved estimator selection performance so that the probability of selecting the estimate with minimum squared error approaches optimal levels. Optimal estimator selection reduces variability of wind estimates during rainy conditions and provides rain estimates when possible without using additional sources of information. Overall, UHR wind estimation performance with the new technique has improved bias and root mean-squared error, -0.16 m/s and 2.15 m/s, respectively, which are lower than either of the UHR WO and UHR SWR estimates.