An algorithm has been developed that enables improved the resolution wind estimates from SeaWinds data. This thesis presents the development of three key portions of the high resolution wind retrieval algorithm: Compositing individual σ-0 measurements and Kp, Retrieved wind bias correction, and ambiguity selection for high resolution winds. The high resolution winds produced by this algorithm are expected to become a useful resource for scientists and engineers studying the ocean winds. The high resolution wind retrieval algorithm allows wind to be retrieved much closer to land than is available from the low resolution winds estimated from the same scatterometer by the Jet Propulsion Laboratory. The high resolution winds allow features such as the eye of hurricanes to be seen with much greater detail than was previously possible.

Scatterometers, such as SeaWinds, can provide wide coverage of ocean surface winds. They estimate near-surface wind vectors by relating measured radar backscatter to a geophysical model function. However, SeaWinds measurements are also sensitive to rain, and conventional wind retrieval degrades in rainy conditions. An algorithm that exploits SeaWinds' sensitivity to both wind and rain has be developed. This algorithm, termed simultaneous wind/rain retrieval, retrieves both wind vectors and rain rates for a given ocean area. Instantaneous results of simultaneous wind/rain retrieval in Hurricane events is analyzed through comparison with the NEXRAD ground-based radar system. This comparison allows validation of retrieved rains. Additionally, conditions that affect the accuracy of SeaWinds wind/rain observations are evaluated. It is shown that, when thresholded, the rains retrieved by SeaWinds give an adequate rain flag. The comparisons of SeaWinds and NEXRAD rain estimates facilitate construction of a model to simulate variability in the SeaWinds rain estimates. The model is used to show that rain estimates are unbiased, though with significant variability. The variability is likely to be primarily driven by the noise inherent to the SeaWinds system.

The QuikSCAT scatterometer provides global daily coverage of oceanic near-surface vector winds. Recently, algorithms have been developed to enhance the spatial resolution of QuikSCAT winds from 25~km to 2.5~km posting. These ultra-high resolution winds are used, in comparison with standard L2B data product winds, to observe and track tropical cyclones. Resolution enhanced winds are found to provide additional storm structure such as inner core size and structure and the presence of multiple eyewalls compared with standard resolution winds. The 2.5~km winds are also able to observe storms nearer to the shore than 25~km winds. An analysis of circulation center locatability with each resolution wind field is performed. Center fixes with enhanced resolution winds are nearer the National Hurricane Center best-track positions than are standard resolution center fixes. A data and image set of every tropical cyclone worldwide observed by Seawinds on QuikSCAT or SeaWinds on ADEOS II from 1999 through 2005 is generated and made available to the scientific community at http://scp.byu.edu.

While the standard wind product (L2B) available operationally in near-real time from SeaWinds on QuikSCAT is only 25 km in resolution, QuikSCAT data can be enhanced to yield a 2.5 km ultra-high resolution (UHR) product. The latter can be used to help estimate Tropical Cyclone (TC) characteristics such as TC eye center and wind radii. Two studies are conducted in this thesis, in which QuikSCAT UHR wind product's effectiveness in estimating these TC characteristics is evaluated. First, a comparison is made between the analyst's choice of eye location based on UHR images and interpolated best-track position. In this analysis, the UHR images are divided into two categories, based on the analyst's confidence level of finding the eye center location. In each category, statistical error quantities are computed. UHR images within the high confidence category can provide, for a given year and basin, mean error distance as small as 19 km with a 10 km standard deviation. Second, a visual comparison of QuikSCAT's performance in estimating wind radii is made. QuikSCAT's performance is gauged against H*wind dataset and the Extended Best-Track (EBT) dataset. Results show that QuikSCAT UHR data yields a correct 34-kt wind radius most of the time regardless of the TC category when compared to both H*wind and EBT, whereas the 50- and 64-kt wind radii visual estimates do not always agree with H*wind and EBT. A more sophisticated method is also implemented to automatically estimate wind radii based on a model fit to QuikSCAT data. Results from this method are compared with EBT wind radii. Wind radii obtained from QuikSCAT model fit are generally highly correlated with EBT estimated wind radii. These two studies show that QuikSCAT UHR wind products are helpful in estimating TC eye location and wind radii, thus improving TC forecasting and analysis.

Long term trends in Arctic sea ice are of particular interest with regard to global temperature, climate change, and industry. This thesis uses microwave scatterometer data from QuikSCAT and radiometer data to analyze intra- and interannual trends in first-year and multi-year Arctic sea ice. It develops a sea ice type classification method. The backscatter of first-year and multi-year sea ice are clearly identifiable and are observed to vary seasonally. Using an average of the annual backscatter trends obtained from QuikSCAT, a classification of multi-year ice is obtained which is dependent on the day of the year (DOY). Validation of the classification method is done using regional ice charts from the Canadian Ice Service. Differences in ice classification are found to be less than 6% during the winters of 06-07, 07-08, and the end of 2008. Anomalies in the distribution of sea ice backscatter from year to year suggest a reduction in multi-year ice cover between 2003 and 2009 and an approximately equivalent increase in first-year ice cover.

The Greenland ice sheet is an area of great interest to the scientific community due to its role as an important bellwether for the global climate. Satellite-borne scatterometers are particularly well-suited to studying temporal changes in the Greenland ice sheet because of their high spatial coverage, frequent sampling, and sensitivity to the presence of liquid water. The dry snow zone is the largest component of the Greenland ice sheet and is identified as the region that experiences negligible annual melt. Due to the lack of melt in the dry snow zone, backscatter was previously assumed to be relatively constant over time in this region. However, this thesis shows that a small seasonal variation in backscatter is present in QuikSCAT data in the dry snow zone. Understanding the cause of this seasonal variability is important to verify the accuracy of QuikSCAT measurements, to better understand the ice sheet conditions, and to improve future scatterometer calibration efforts that may use ice sheets as calibration targets.This thesis provides a study of the temporal behavior of backscatter in the dry snow zone of the Greenland ice sheet focusing on seasonal variation. Spatial averaging of backscatter and the Karhunen-Lo`eve transform are used to identify and study the dominant patterns in annual backscatter behavior. Several QuikSCAT instrumental parameters are tested as possible causes of seasonal variation in backscatter in the dry snow zone to verify the accuracy of QuikSCAT products. None of the tested parameters are found to be related to seasonal variation. Further evidence is given that suggests that the cause of the seasonal variation is geophysical and several geophysical factors are tested. Temperature is found to be highly related to dry snow backscatter and therefore may be driving the seasonal variation in backscatter in the dry snow zone.

Satellite borne radar scatterometers provide frequent estimates of near surface wind vectors over the Earth's oceans. However in the polar oceans, the presence of sea ice in or near the measurement footprint can adversely a ect scatterometer measurements resulting in inaccurate wind estimates. Currently, such ice contamination is mitigated by discarding measurements within 50 km of detected sea ice. This approach is imperfect and causes loss of coverage. This thesis presents a new algorithm which detects ice-contaminated measurements based on a metric called the Ice Contribution Ratio (ICR) which measures the spatial ice contribution for each measurement. The ICR calculation is made for each measurement using a spatial ice probability map which is determined using Bayesian probability theory. Determined by simulation, the ICR processing thresholds the ICR for each measurement depending on local wind, ice backscatter, and cross-track location. ICR processing retrieves winds at a distance of 22.5 km from the ice edge on average, while ensuring wind accuracy. Retrieved wind distributions using ICR processing more closely resembles uncontaminated wind distributions than winds retrieved using previous methods. The algorithm is applied to QuikSCAT in this thesis but could be applied to other scatterometers such as the Oceansat-2 scatterometer.

Over the last three decades, microwave remote sensing has played a significant role in ocean surface wind measurement, and several scatterometer missions have flown in space since early 1990's. Although they have been extremely successful for measuring ocean surface winds with high accuracy for the vast majority of marine weather conditions, unfortunately, the conventional scatterometer cannot measure extreme winds condition such as hurricane. The SeaWinds scatterometer, onboard the QuikSCAT satellite is NASA's only operating scatterometer at present. Like its predecessors, it measures global ocean vector winds; however, for a number of reasons, the quality of the measurements in hurricanes are significantly degraded. The most pressing issues are associated with the presence of precipitation and Ku-band saturation effects, especially in extreme wind speed regime such as tropical cyclones (hurricanes and typhoons). Under this dissertation, an improved hurricane ocean vector wind retrieval approach, named as Q-Winds, was developed using existing SeaWinds scatterometer data. This unique data processing algorithm uses combined SeaWinds active and passive measurements to extend the use of SeaWinds for tropical cyclones up to approximately 50 m/s (Hurricane Category-3). Results show that Q-Winds wind speeds are consistently superior to the standard SeaWinds Project Level 2B wind speeds for hurricane wind speed measurement, and also Q-Winds provides more reliable rain flagging algorithm for quality assurance purposes. By comparing to H*Wind, Q-Winds achieves ~9% of error, while L2B-12.5km exhibits wind speed saturation at ~30 m/s with error of ~31% for high wind speed ( > 40 m/s).

After the launch of NASA's SeaWinds scatterometer in 1999, a radiometer function was implemented in the Science Ground Data Processing Systems to allow the measurement of the earth's microwave brightness temperature. This dissertation presents results of a comprehensive validation to assess the quality of QRad brightness temperature measurements using near-simultaneous ocean Tb comparisons between the SeaWinds on QuikSCAT (QRad) and WindSat polarimetric radiometer on Coriolis. WindSat was selected because it is a well calibrated radiometer that has many suitable collocations with QuikSCAT; and it has a 10.7 GHz channel, which is close to QRad frequency of 13.4 GHz. Brightness temperature normalizations were made for WindSat before comparison to account for expected differences in Tb with QRad because of incidence angle and channel frequency differences. Brightness temperatures for nine months during 2005 and 2006 were spatially collocated for rain-free homogeneous ocean scenes (match-ups) within 1° latitude x longitude boxes and within a ± 60 minute window. To ensure high quality comparison, these collocations were quality controlled and edited to remove non-homogenous ocean scenes and/or transient environmental conditions, including rain contamination. WindSat and QRad Tb's were averaged within 1° boxes and these were used for the radiometric inter-calibration analysis on a monthly basis. Results show that QRad calibrations are stable in the mean within ± 2K over the yearly seasonal cycle.

In this thesis, three improved analysis techniques for scatterometer wind estimation are presented. These techniques build upon previous methods that help validate scatterometer data. This thesis examines the theory connecting the 1D and 2D kinetic energy spectra and uses QuikSCAT data to measure the 2D kinetic energy spectrum of ocean winds. The measured 2D kinetic energy spectrum is compared to the traditional 1D kinetic energy spectrum. The relationship between the 2D kinetic energy spectra and the 1D kinetic energy spectra confirms findings from previous studies that ocean winds modeled in 2D are isotropic and nondivergent. The 1D and 2D kinetic energy spectra also confirm the known conclusion that the zonal and meridional components of ocean winds are uncorrelated. Through simulation, the wind response function (WRF) is calculated for three different QuikSCAT processing algorithms. The WRF quantifies the contribution that the wind at each point of the surface makes to a given wind estimate. The spatial resolution of the different processing algorithms is estimated by their WRFs. The WRFs imply that the spatial resolution of ultrahigh resolution (UHR) processing is finer than the spatial resolution of conventional drop-in-the-bucket (DIB) processing; the spatial resolution of UHR processing is ~5-10 km while the spatial resolution of DIB slice processing is ~12-15 km and the spatial resolution of coarse resolution DIB egg processing is ~30 km. Simulation is used to analyze the effectiveness of various wind retrieval and ambiguity selection algorithms. To assist in the simulation, synthetic wind fields are created through extrapolating the 2D Fourier transform of a numerical weather prediction wind field. These synthetic wind fields are sufficiently realistic to evaluate ambiguity selection algorithms. The simulation employs the synthetic wind fields to compare wind estimation with and without direction interval retrieval (DIR) applied. Both UHR and DIB wind estimation processes are performed in the simulation and UHR winds are shown to resolve finer resolution wind features than DIB winds at the cost of being slightly noisier. DIR added to standard QuikSCAT UHR wind estimation drops the wind direction root-mean-squared error by ~10° to ~24.74° in the swath sweet spot.