Extensive research on soot in snow and snow grain size has been carried out in the Polar Regions. However, North American prairie snowpacks lack observations of soot in snow on snow albedo which adds uncertainty to the overall global effect that black carbon on snow has on climate. Measurements in freshly fallen prairie snowpacks in Northwestern Iowa and Central Texas were collected from February 25 to March 3, 2007 and April 6, 2007, respectively. Multi-day monitoring locations and a frozen lake were study sites at which snow samples were collected to measure soot in snow concentrations. Ancillary measurements were collected at a subset of the sample sites that included: temperature, density, depth, and grain size. At some locations snow reflectance and snow radiance was collected with an Analytical Spectral Device visible/near infra-red spectroradiometer (350 ? 1500 nm). Snow impurity, consisting of light-absorbing particulate matter, was measured by filtering meltwater through a nucleopore 0.4 micrometer filter. Filters were examined using a photometer to measure mass impurity concentration. Soot observations indicate prairie snowpack concentrations ranging from 1 ng C gm^-1 to 115 ng C gm^-1 with an average of 34.9 ng C gm^-1. These measurements are within range of previously published values and can lower snow albedo. As expected, spectral albedo was found to decrease with increasing impurities. Additionally, as grain size increased impurity concentration increased. Differences in soot concentration were observed between the two Iowa snowfall events. The Texas event had higher soot concentrations than both Iowa snowfalls. Validation of an ADEOS-II snow product algorithm that compares simulated radiances to measured sensor radiances for retrieval of snow grain size and mass fraction of soot in snow was attempted using satellite images acquired by the Moderate Resolution Imaging Spectroradiometer (MODIS). The algorithm was unable to uniquely identify a particular snow grain size and soot concentration that would lead to a converging radiance solution in the two spectral bands measured and compared by the algorithm. The in situ data at the validation site fell within published ranges for freshly fallen snow for both snow grain size and soot concentration; however; the closest algorithm retrievals were considerably higher than in situ measurements for both grain size and impurity concentrations.

The NASA Scatterometer (NSCAT) was launched August 20, 1996 aboard the National Space Development Agency of Japan's Advanced Earth Observing Spacecraft (ADEOS). NSCAT's primary mission was to measure radar backscatter over the world's oceans. These measurements are used to generate estimates of ocean wind speed and direction. Scatterometers must be calibrated before their measurements are scientifically useful. However, the calibration of NSCAT must be done in orbit. A new methodology for selecting land regions for use in extended target spaceborne scatterometer calibration is first developed. Next, a summary of the calibration technique used in this thesis is presented. While the foundation of this technique was previously developed theoretically, the work in this thesis is its first application for calibration/validation of an on-line spaceborne radar system. The technique is extended to estimate simultaneously NSCAT's calibration and the host spacecraft's attitude error. The attitude references reported by the attitude control system on-board ADEOS are deemed erroneous. Results of this expanded technique, applied under varying assumptions, are presented for consideration. A summary and suggestions for future research conclude this work.

The SeaWinds scatterometers aboard the QuikSCAT and ADEOS II satellites were originally designed to measure wind vectors over the ocean by exploiting the relationship between wind-induced surface roughening and the normalized radar backscatter cross-section. Recently, an algorithm for simultaneously retrieving wind and rain (SWR) from scatterometer measurements was developed that enables SeaWinds to correct rain-corrupted wind measurements and retrieve rain rate data. This algorithm is based on co-locating Tropical Rainfall Measuring Mission Precipitation Radar (TRMM PR) and SeaWinds on QuikSCAT data. In this thesis, a new wind and rain radar backscatter model is developed for the SWR algorithm using a global co-located data set with rain data from the Advanced Microwave Scanning Radiometer (AMSR) and backscatter data from the SeaWinds scatterometer aboard the Advanced Earth Observing Satellite 2 (ADEOS II). The model includes the effects of phenomena such as backscatter due to wind stress, atmospheric rain attenuation, and effective rain backscatter. Rain effect parameters of the model vary with integrated rain rate, which is defined as the product of rain height and rain rate. This study accounts for rain height in the model in order to calculate surface rain rate from the integrated rain rate. A simple model for the mean rain height versus latitude and longitude is proposed based on AMSR data and methods of incorporating this model into the SWR retrieval process are developed. The performance of the new SWR algorithm is measured by comparison of wind vectors and rain rates to the previous SWR algorithm, AMSR rain rates, and NCEP numerical weather prediction winds. The new SWR algorithm produces accurate rain estimates and detects rain with a low false alarm rate. The wind correction capabilities of the SWR algorithm are effective at correcting rain-induced inaccuracies. A qualitative comparison of the wind and rain retrieval for Hurricane Isabel demonstrates these capabilities.

Ku-band dual-polarization radar backscatter measurements from the SeaWinds on QuikScat scatterometer and microwave radiometer measurements from the Special Sensor Microwave/Imager (SSM/I) are used to determine periods of surface melt and freeze in the Antarctic ice-shelves. The normalized radar backscatter (sigma-0) and backscatter polarization ratio (PR) are used in the maximum likelihood estimation of the ice-state. This method is used to infer the daily ice-surface conditions for 25 selected study points located on the Ronne, Ross, Larsen, Fimbul, Amery, and Shackleton Ice-shelves. The temporal and spatial variations of the radar response are also observed for various neighborhood sizes surrounding each given point during the study period. Criteria for determining the dates of melt-onset and freeze-up for each Austral summer are also presented. Validation of the ice-state and melt-onset date estimates is performed by analyzing corresponding brightness temperature (Tb) measurements from radiometers. QuikScat sigma-0 measurements from 1999 through 2003 are analyzed and it is shown that Ku-band scatterometers are very useful for determining periods of melt in Antarctic ice-sheets and provide high temporal and spatial resolution ice-state estimates. These estimates can be important for long-term studies of the climatic effects of the seasonal and inter-annual melting of the Antarctic ice-sheets. The SeaWinds on QuikScat (QuikScat) and SeaWinds on ADEOS-2 (SeaWinds) scatterometers are identical radar sensors on different spaceborne platforms traversing similar orbits. QuikSCAT and SeaWinds data are used to infer near-surface wind vectors, polar sea-ice extent, polar-ice melt events, among others. In order to verify the relative calibration of these two sensors a simple cross-calibration method is implemented based on land measurements. A first-order polynomial model for the incidence angle dependence of sigma-0 is used to account for biases in the sigma-0 measurements. This model is applied to selected regions of the Amazon rainforest and the Sahara desert. It is shown that the two sensors are well calibrated. Additionally, evidence of a previously presumed diurnal cycle in the Amazon rainforest backscatter is given.

Ku-band dual-polarization radar backscatter measurements from the SeaWinds on QuikScat (QuikScat) and SeaWinds on ADEOS-2 (SeaWinds) scatterometers are used to classify the melt state and estimate melt severity in Greenland. Backscatter measurements are organized into high temporal and high spatial resolution images created using the Scatterometer Image Reconstruction (SIR) algorithm and a new temporal data segmentation technique. Melt detection is performed using a layered electromagnetic model combined with a Markov chain model. The new melt detection method allows classification of the snow-pack into three states: melt, refreeze, and frozen. Melt severity and refreeze severity indexes are also developed. The melt detection methods developed in this thesis are verified by using a one-dimensional geophysical/electromagnetic model simulation of the snow-pack under melting conditions and by comparison with in situ weather station data at the ETH Camp in western Greenland. The diurnal cycle of backscatter measurements is also analyzed at this location. The melt detection and estimation method is applied to the entire Greenland ice-sheet. The resulting melt classifications and melt severity indexes are used to generate a number of maps outlining the features of the 2003 melt season. Good agreement of the melt severity and a 1978 SASS Greenland ice facies map is observed.

The NASA SeaWinds scatterometer is a radar remote sensor which operates on two satellites; NASA's QuikSCAT launched in June 1999 and on Japan's ADEOS-II satellite launched in December 2002. The purpose of SeaWinds is to provide global measurements of the ocean surface wind vector. On QuikSCAT, a ground data processing algorithm was developed, which allowed the instrument to function as a QuikSCAT Radiometer (QRad) and measure the ocean microwave emissions (brightness temperature, Tb) simultaneously with the backscattered power. When SeaWinds on ADEOS was launched, this same algorithm was applied, but the results were anomalous. The initial SRad brightness temperatures exhibited significant, unexpected, ascending/descending orbit Tb biases. This thesis presents an empirical correction algorithm to correct the anomalous SeaWinds Radiometer (SRad) ocean brightness temperature measurements. I use the Advanced Microwave Scanning Radiometer (AMSR) as a brightness temperature standard to calibrate and then, with independent measurements, validate the corrected SRad Tb measurements. AMSR is a well-calibrated multi-frequency, dual-polarized microwave radiometer that also operates on ADEOS-II. These results demonstrate that, after tuning the Tb algorithm, good quality SRad brightness temperature measurements are obtained over the oceans.

The Ku band microwave remote sensor, SeaWinds, was developed at the National Aeronautics and Space Administration (NASA) Jet Propulsion Laboratory (JPL). Two identical SeaWinds instruments were launched into space. The first was flown onboard NASA QuikSCAT satellite which has been orbiting the Earth since June 1999, and the second instrument flew onboard the Japanese Advanced Earth Observing Satellite II (ADEOS-II) from December 2002 till October 2003 when an irrecoverable solar panel failure caused a premature end to the ADEOS-II satellite mission. SeaWinds operates at a frequency of 13.4 GHz, and was originally designed to measure the speed and direction of the ocean surface wind vector by relating the normalized radar backscatter measurements to the near surface wind vector through a geophysical model function (GMF). In addition to the backscatter measurement capability, SeaWinds simultaneously measures the polarized radiometric emission from the surface and atmosphere, utilizing a ground signal processing algorithm known as the QuikSCAT / SeaWinds Radiometer (QRad / SRad). This dissertation presents the development and validation of a mathematical inversion algorithm that combines the simultaneous active radar backscatter and the passive microwave brightness temperatures observed by the SeaWinds sensor to retrieve the oceanic rainfall. The retrieval algorithm is statistically based, and has been developed using collocated measurements from SeaWinds, the Tropical Rainfall Measuring Mission (TRMM) Microwave Imager (TMI) rain rates, and Numerical Weather Prediction (NWP) wind fields from the National Centers for Environmental Prediction (NCEP). The oceanic rain is retrieved on a spacecraft wind vector cell (WVC) measurement grid that has a spatial resolution of 25 km. To evaluate the accuracy of the retrievals, examples of the passive-only, as well as the combined active / passive rain estimates from SeaWinds are presented, and comparisons are made with the standard TRMM rain data products. Results demonstrate that SeaWinds rain measurements are in good agreement with the independent microwave rain observations obtained from TMI. Further, by applying a threshold on the retrieved rain rates, SeaWinds rain estimates can be utilized as a rain flag. In order to evaluate the performance of the SeaWinds flag, comparisons are made with the Impact based Multidimensional Histogram (IMUDH) rain flag developed by JPL. Results emphasize the powerful rain detection capabilities of the SeaWinds retrieval algorithm. Due to its broad swath coverage, SeaWinds affords additional independent sampling of the oceanic rainfall, which may contribute to the future NASA's Precipitation Measurement Mission (PMM) objectives of improving the global sampling of oceanic rain within 3 hour windows. Also, since SeaWinds is the only sensor onboard QuikSCAT, the SeaWinds rain estimates can be used to improve the flagging of rain-contaminated oceanic wind vector retrievals. The passive-only rainfall retrieval algorithm (QRad / SRad) has been implemented by JPL as part of the level 2B (L2B) science data product, and can be obtained from the Physical Oceanography Distributed Data Archive (PO.DAAC).