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Although originally designed solely for wind retrieval, the QuikSCAT scatterometer has proved to be a useful tool for rain estimation as well. Resolution enhancement algorithms designed for QuikSCAT allow for ultra-high-resolution (UHR) (2.5 km) simultaneous wind and rain (SWR) retrieval. The principle advantage of UHR SWR estimation is that compared to conventional resolution, the higher resolution allows for identification of much smaller rain events and their effects on the wind field. To enable SWR retrieval, we adjust the geophysical model function to account for rain effects such as attenuation and increased backscatter due to increased surface roughness. Two possible rain models are proposed, a phenomenological rain model and an effective rain model. Both models are compared by evaluating data fit and rain estimation performance. Comparisons of a co-located data set show that QuikSCAT UHR SWR integrated rain rates are comparable to those from tropical rain measuring mission precipitation radar (TRMM PR) but have higher variance. Buoy comparisons reveal improved wind estimates in the presence of rain. The theoretic estimator bounds are compared to both the simulated estimator variance and the actual estimator variance. The estimator bounds indicate that despite high-noise levels, wind and rain information is still retrievable at UHR, although certain directions have degraded estimator bounds. Both rain models are compared to truth data and are shown to have comparable performance for most rain rates. Comparison with buoy measurements shows that in the presence of rain, QuikSCAT UHR SWR wind estimates have less bias and variability than wind-only estimates. Although QuikSCAT UHR SWR rain estimates are noisier than TRMM PR rain rates, they provide a useful rain flag for QuikSCAT winds.