International Telemetering Conference Proceedings / October 30-November 02, 1995 / Riviera Hotel, Las Vegas, Nevada / A radar scatterometer transmits a series of RF pulses and measures the total-power (energy) of the backscattered signal. Measurements of the backscattered energy from the ocean's surface can be used to infer the near-surface wind vector [7]. Accurate backscatter energy measurements are required to insure accurate wind estimates. Unfortunately, the signal measurement is noisy so a separate measurement of the noise-only total-power is subtracted from the signal measurement to estimate the echo signal energy. A common metric for evaluating the accuracy of the scatterometer energy measurement is the normalized signal variance, termed K(p). In designing a scatterometer tradeoffs in design parameters are made to minimize K(p). Spaceborne scatterometers have traditionally been based on fan-beam antennas and CW modulation for which expressions for K(p) exist. Advanced pencil-beam scatterometers, such as SeaWinds currently being developed by NASA use modulated Signals so that new K(p) expressions are required. This paper outlines the derivation of the generalized K(p) expression. While very complicated in its exact form, with a simplified geometry the K(p) expression can be related to the radar ambiguity function. The resulting analysis yields insights into the tradeoffs inherent in a scatterometer design and permits analytic tradeoffs in system performance.

Satellite scatterometers have recently gained popularity due to their unique ability to measure global geophysical data on a daily basis. Increased interest in scatterometry mandates improved design and calibration of these instruments. This dissertation presents new techniques for scatterometer calibration and addresses issues related to the design of future instruments and applications. First, the use of a calibration ground station is considered. A new methodology is established for calibration of SeaWinds, NASA's current scatterometer, using a receive-only ground station. Principles of the methodology are implemented, new analysis techniques developed, and important results obtained for instrument timing, frequency, power, position, and pointing. Second, an investigation into methods for calibration of measurement surface location is conducted. Two new approaches are proposed and results of both approaches using SeaWinds data are provided. Third, measurement correlation, a critical issue related to new scatterometer designs, particularly those which significantly oversample the surface is considered. General statistical expressions for measurement correlation are derived and analysis of the effects on data variance is presented. Finally, a new data simulation model is developed to support instrument and application development. New applications require sophisticated models which are general, yet accurate, enabling them to rapidly and easily simulate data from multiple instruments. The model generates data which is statistically equivalent (in a mean and variance sense) to actual scatterometer measurements by separately accounting for the two main forms of variation present in scatterometer data, multiplicative fading and additive noise, and also accounting for correlation between measurements. The model is valuable for a variety of data applications including image generation and high resolution wind retrieval.

Knowledge of iceberg location and size is important for safety reasons as well as for understanding many geophysical and biological processes. This dissertation analyzes large tabular icebergs in the Southern Ocean using the SeaWinds scatterometer. SeaWinds is a spaceborne radar designed to measure the microwave backscatter from the Earth's surface. Using resolution-enhancement techniques, backscatter measurements are processed into backscatter images in which icebergs can be observed. An iceberg detection methodology is formalized using daily scatterometer images. Radar profiles from common Antarctic scatterers are quantified and an iceberg detection methodology is formalized using daily scatterometer images. Iceberg positions are determined in real-time and a time-series of iceberg positions is maintained in an Antarctic iceberg database. Using the Antarctic iceberg database, characteristic iceberg motion trends are identified. Iceberg detection and tracking is demonstrated through real-time operational support of the 2005, 2008, and 2009 National Science Foundation Antarctic cruises. To supplement iceberg position reports, I develop multiple algorithms to estimate iceberg size and rotational orientation from backscatter images and from raw backscatter measurements. Estimates derived from SeaWinds images are found to be more accurate. Using iceberg size parameters in conjunction with Newton's equations of motion and forcing profiles (e.g., ocean and air currents), I also develop an iceberg motion model to predict the translational and rotational motion of large tabular icebergs. To improve model results, a Kalman filter is used to incorporate actual iceberg measurements into the motion model, and statistics from the Kalman filter are used to evaluate model performance. Simulated iceberg motion is found to best coincide with observed iceberg motion in regions where slower iceberg drift speeds are observed. The model is less accurate at high speeds. The iceberg motion model is inverted to produce estimates of ocean currents given observations of iceberg size and motion. Multiple ocean current estimates are combined using reconstruction techniques and compared with numerically-derived ocean currents from the Ocean Circulation and Climate Advanced Modeling (OCCAM) project. It is found that reconstructed ocean currents coincide with OCCAM currents in regions where observed iceberg motion is not extreme. Also, reconstructed ocean currents coincide more with OCCAM currents that have been averaged over multiple years than with monthly-reported values.

Field-wise wind estimation (also known as model-based wind estimation) is a sophisticated technique to derive wind estimates from radar backscatter measurements. In contrast to the more traditional method known as point-wise wind retrieval, field-wise techniques estimate wind field model parameters. In this way, neighboring wind vectors are jointly estimated, ensuring consistency. This work presents and implementation for field-wise wind retrieval for the SeaWinds scatterometer on the QuikSCAT satellite. Due to its sophistication, field-wise wind retrieval adds computational complexity and intensity. The tradeoffs necessary for practical implementations are examined and quantified. The Levenberg-Marquardt algorithm for minimizing the field-wise objective function is presented. As the objective function has several near-global local minima, several wind fields represent ambiguous wind field estimates. A deterministic method is proposed to ensure sufficient ambiguities are obtained. An improved method for selecting between ambiguous wind field estimates is also proposed. With a large set of Sea-Winds measurements and estimates available, the σ° measurement statistics are examined. The traditional noise model is evaluated for accuracy. A data-driven parameterization is proposed and shown to effectively estimate measurement bias and variance. The parameterized measurement model is used to generate Cramer-Rao bounds on estimator performance. Using the Cramer-Rao bound, field-wise and point-wise performances are compared.

Spaceborne scatterometer instruments are important tools for the remote sensing of the Earth's environment. In addition to the primary goal of measuring ocean winds, data from scatterometers have proven useful in the study of a variety of land and cryopshere processes as well. Several satellites carrying scatterometers have flown in the last two decades. These previous systems have been "fan-beam" scatterometers, where multiple antennas placed in fixed positions are used. The fan-beam scatterometer approach, however, has disadvantages which limit its utility for future missions. An alternate approach, the conically-scanning "pencil-beam" scatterometer technique, alleviates many of the problems encountered with earlier systems and provides additional measurement capability. Due to these advantages, the pencil-beam approach has been selected by NASA as the basis for future scatterometer missions. Whereas the fan-beam approach is mature and well understood, there is need for a fundamental study of the unique aspects of the pencil-beam technique. In this dissertation, a comprehensive treatment of the design issues associated with pencil-beam scatterometers is presented. A new methodology is established for evaluating and optimizing the performance of conically-scanning radar systems. Employing this methodology, key results are developed and used in the design of the SeaWinds instrument - NASA's first pencil-beam scatterometer. Further, the theoretical framework presented in this study is used to propose new scatterometer techniques which will significantly improve the spatial resolution and measurement accuracy of future instruments.

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.

The SeaWinds Calibration Ground Station (CGS) is a passive ground station used to receive and sample transmissions from the SeaWinds scatterometer. During post processing, the received transmissions are characterized in order to verify proper instrument operation and to eliminate error in satellite telemetry and in data products generated from processing SeaWinds data. Sources of instrument error include uncertainties in transmitted power, pulse timing, and carrier frequency drift. Identifying these errors prevents their propagation to data products. A key aspect of this analysis involves accurately estimating the parameters of the SeaWinds transmissions. As better parameter estimates are researched and developed, the scatterometer can be more finely calibrated and better characterized, allowing improved accuracy of environmental measurements. This work explores several methods to estimate SeaWinds frequency parameters by parametrically modeling the signal as a series of linear FM pulses. Improved frequency estimates are obtained by transforming the signal into appropriate signal spaces. These methods are compared and their tradeoffs revealed. SNR regions are assigned to each method to mark appropriate performance bounds, and improvements over previous SeaWinds data analysis methods are shown. Finally, recent estimates of SeaWinds parameters are disclosed. This analysis helps to advance the level to which future scatterometer instruments may be calibrated, providing the potential for more accurate scatterometer data products.

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.

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.