RapidScat is a Ku-band scatterometer that was launched September 2014 and is currently operating on the International Space Station. It estimates ocean vector winds through accurate measurement of the normalized radar coefficient (σ0) of the ocean surface. In order to ensure the accuracy of σ0 measurements and consistency with previous Ku-band scatterometers, post-launch calibration and validation is necessary. Calibration and validation is performed using natural land targets, namely the Amazon and Congo rainforests, to complement calibration efforts over the ocean. The σ0 response of the targets is estimated with respect to viewing angle and time of year using previous Ku-band scatterometers. Taking advantage of the ISS orbit, the diurnal response of each target is estimated using RapidScat. Normalizing factors for incidence angle, azimuth angle, local time of day, and time of year are derived from these measured responses. RapidScat σ0 measurements are found to be consistent throughout its mission life with instrumental drift less than 0.3 dB. The effectiveness of slice balancing is evaluated and found to be highly dependent on the pitch of the ISS. Understanding of the diurnal backscatter response and incidence response allow comparison of RapidScat measurements with measurements from the QuikSCAT, NSCAT, and Oceansat-II scatterometers. RapidScat σ0 is found to be biased low compared to QuikSCAT by 0.1--0.3 dB.

The spatial response function (SRF) of the backscatter measurements for a radar scatterometer is often used in reconstruction. It has been found that in many cases the SRF can be approximated as a binary function that is 1 inside the - 6 dB contour of the SRF and 0 outside. This improves the computation speed of reconstruction. Computing the SRF contour can still be a lengthy computation, which can be simplified by precomputing and tabulating key SRF contours. The tabular parameterization for many spinning scatterometers, i.e., QuikSCAT, is straight-forward. For RapidSCAT, this estimation is more involved than other radars due to the irregular orbit of its host platform, the International Space Station (ISS). This thesis presents a process for parameterizing the slice contours for RapidSCAT that are acceptable for reconstruction purposes. This thesis develops a new process for parameterizing slice contours. First, RapidSCAT SRFs are calculated using XfactorRS3, and -6 dB slice contours are found using matplotlib. Then, a suitable filter is found for reducing noise present in slice contours due to quantization error and interpolation inaccuracies. Afterwards, the polygon comparison algorithm is used to determine a set of approximation points. With the approximation points selected, the 3-rd order linear approximation is calculated using parameters available in the L1B data files for RapidSCAT. Finally, analysis of the parameterization is performed. Overall, I developed a process that parameterizes RapidSCAT slice contours with an average root mean square (RMS) error of roughly 1.5 km. This is acceptable for the application of the slice parameterization algorithm and significantly reduces computation compared to fully computing the SRF.

RapidScat is a spaceborne wind scatterometer mounted on the International Space Station (ISS). The RapidScat mission lasted from September 2014 to November 2016. RapidScat enables the measurement of diurnal patterns of sigma-0 measurements. This capability is possible because of the non-sun-synchronous orbit of the ISS, in which the local time of day (LTOD) of sigma-0 measurements gradually shifts over time in any given location. The ISS platform is a relatively unstable platform for wind scatterometers. Because of the varying attitude of the ISS, RapidScat experiences a constant variation of its pointing vector. Variations of the pointing vector cause variations in the incidence angle of the measurement on the ground, which has a direct effect on sigma-0 measurements. In order to mitigate sigma-0 variations caused by incidence angle and LTOD, the dependence of on these parameters is modeled in order to enable a normalization procedure for sigma-0 . These models of sigma-0 dependence are determined in part by comparing RapidScat data with other active Ku-band instruments. The normalization procedure is designed to adjust the mean value of sigma-0 to be constant across the full range of significant parameter values to match the mean of sigma-0 at a particular nominal parameter value. The normalization procedure is tested both in simulation and with real sigma-0 measurements. The simulated normalization procedure is effective at modeling and removing sigma-0 dependence on incidence angle and LTOD over a homogeneous region. The variance in simulated images is reduced by the normalization procedure. The normalization procedure also reduces variance in real backscatter images of the Amazon and an arbitrary region in East Africa.

RapidScat 2.5 km ultrahigh resolution (UHR) wind estimation is introduced and validated it in near-coastal regions. In addition, this thesis applies direction interval retrieval techniques and develops a new wind processing method to enhance the performance of RapidScat UHR wind estimation in the nadir region. The new algorithm is validated with L2B wind estimates, Numerical Weather Prediction (NWP) wind products, and buoy measurements. The wind processing improvements produce more spatially consistent UHR winds that compare well with the wind products mentioned above. Hawaii regional climate model (HRCM), QuikSCAT, and ASCAT wind estimates are compared in the lee of the Big Island with the goal of understanding UHR scatterometer wind retrieval capabilities in this area. UHR wind vectors better resolve fine resolution wind speed features compared to L2B, but still do not resolve the expected wind direction features. A comparison of scatterometer measured σ 0 and HRCM and NWP predicted σ 0 suggests that scatterometers can detect a reverse flow in the lee of the island. Differences between scatterometer measured σ 0 and HRCM predicted σ 0 indicate error in the placement of key reverse flow features by the model. Coarse initialization fields and a large fixed size median filter window are also shown to impede UHR wind retrieval in this area.