The Advanced Scatterometer (ASCAT) is a C-band scatterometer designed to be less sensitive to rain contamination than other higher frequency scatterometers. However, the radar backscatter is still affected by rain which increases error during wind estimation. The error can be reduced in rainy conditions by combining a rain backscatter model with the existing wind only (WO) backscatter model to perform simultaneous wind and rain (SWR) estimation. I derive and test several 2.5 km resolution rain backscatter models for ASCAT data which are used with the WO model to estimate the near surface winds. Various rain models optimal for different purposes are discussed. The best rain model for estimating wind speed lowers the root mean square error (RMSE) in the presence of rain by 13.6% when compared to using the WO model alone. The rain model which best predicts rain rates has a RMSE of 7.9 mm/h. A neural network (NN) is designed to discriminate the presence of rain using ASCAT's backscatter measurements. Such a NN enables the SWR algorithm to be used only on rainy samples and thus improves estimation. By removing all samples identified by the NN as rain, the WO algorithm's speed estimate improved by 2.83%.

Ocean surface wind vectors over the ocean present vital information for scientists and forecasters in their attempt to understand the Earth's global weather and climate. As the demand for global wind velocity information has increased, the number of satellite missions that carry wind-measuring sensors has also increased; however, there are still not sufficient numbers of instruments in orbit today to fulfill the need for operational meteorological and scientific wind vector data. Over the last three decades operational measurements of global ocean wind speeds have been obtained from passive microwave radiometers. Also, vector ocean surface wind data were primarily obtained from several scatterometry missions that have flown since the early 1990's. However, other than SeaSat-A in 1978, there has not been combined active and passive wind measurements on the same satellite until the launch of the second Advanced Earth Observing Satellite (ADEOS-II) in 2002. This mission has provided a unique data set of coincident measurements between the SeaWinds scatterometer and the Advanced Microwave Scanning Radiometer (AMSR). AMSR observes the vertical and horizontal brightness temperature (TB) at six frequency bands between 6.9 GHz and 89.0 GHz. Although these measurements contain some wind direction information, the overlying atmospheric influence can easily obscure this signal and make wind direction retrieval from passive microwave measurements very difficult. However, at radiometer frequencies between 10 and 37 GHz, a certain linear combination of vertical and horizontal brightness temperatures causes the atmospheric dependence to be nearly cancelled and surface parameters such as wind speed, wind direction and sea surface temperature to dominate the resulting signal. This brightness temperature combination may be expressed as ATBV-TBH, where A is a constant to be determined and the TBV and TBH are the brightness temperatures for the vertical and horizontal polarization respectively. In this dissertation, an empirical relationship between the AMSR's ATBV-TBH and SeaWinds' surface wind vector retrievals was established for three microwave frequencies: 10, 18 and 37 GHz. This newly developed model function for a passive microwave radiometer could provide the basis for wind vector retrievals either separately or in combination with scatterometer measurements.