Predictive anisotropy is a phenomenon referring to unequal predictability of surface wind components in different directions. This study addresses the question of whether predictive anisotropy resulting from statistical prediction is influenced by physical factors or by types of regression methods (linear vs nonlinear) used to construct the statistical prediction. A systematic study of statistical predictability of surface wind components at 2109 land stations across the globe is carried out. The results show that predictive anisotropy is a common characteristic for both linear and nonlinear statistical prediction, which suggests that the type of regression method is not a major influential factor. Both strong predictive anisotropy and poor predictability are more likely to be associated with wind components characterized by relatively weak and non-Gaussian variability and in areas characterized by surface heterogeneity. An idealized mathematical model is developed separating predictive signal and noise between large-scale (predictable) and local (unpredictable) contributions to the variability of surface wind, such that small signal-to-noise ratio (SNR) corresponds to low and anisotropic predictability associated with non-Gaussian local variability. The comparison of observed and simulated statistical predictability by Regional Climate models (RCM) and reanalysis in the Northern Hemisphere indicates that small-scale processes that cannot be captured well by RCMs contribute to poor predictability and strong predictive anisotropy in observations. A second idealized mathematical model shows that spatial variability in specifically the minimum directional predictability, resulting from local processes, is the major contributor to predictive anisotropy. / Graduate

The Imaging Wind and RAin Profiler (IWRAP) is an airborne scatterometer system built and operated by University of Massachusetts Amherst's Microwave Remote Sensing Laboratory (MIRSL). The radar is seasonally deployed aboard one of the two National Oceanic and Atmospheric Administration (NOAA) WP-3D Orion ``Hurricane Hunter'' aircraft based out of MacDill AFB in Tampa, Florida. IWRAP is a dual-frequency, Ku- and C-band, scatterometer that uses two conically scanning antennas to estimate the ocean surface wind vectors as well as intervening rain profiles. Data that is gathered with IWRAP is used to improve current Geophysical Model Functions (GMF) or to help derive new GMFs for other undocumented incidence angles. This thesis outlines the improvements and changes made to the IWRAP system from 2009-2011. Chapter Two describes the IWRAP instrument including a description of the instrument status as of Fall 2009, and a summary of instrument operations in 2010 and 2011. Chapter Three describes hardware and software modifications to support dual-polarization. It also describes hardware-based and flight-based attempts to observe at large incidence angles. Chapter Four is an analysis of the stability of the internal calibration both during flights and over a season. System documentation is consolidated into a single technical manual in Appendix A.

FundaÃÃo Cearense de Apoio ao Desenvolvimento Cientifico e TecnolÃgico / Esta tese introduz uma nova metodologia para determinar a direÃÃo do vento sobre a superfÃcie dos oceanos utilizando tÃcnicas de processamento das imagens de Radar de Abertura SintÃtica (SAR, do inglÃs Synthetic Aperture Radar). A literatura relacionada demonstra um crescente interesse no processamento dessas imagens para detecÃÃo de alvos, classificaÃÃo de regiÃes, extraÃÃo de campos de ventos, monitoramento de derrames de Ãleo, aplicaÃÃes geofÃsicas e meteorolÃgicas. A extraÃÃo de campos de ventos em imagens SAR à uma tarefa desafiadora devido à contaminaÃÃo das mesmas por um ruÃdo oriundo do sistema de aquisiÃÃo, denominado speckle, que dificulta tarefas de processamento e interpretaÃÃo das mesmas. Portanto, esta tese propÃe metodologias de extraÃÃo da direÃÃo do vento por transformada de Fourier, transformadas wavelets e mÃtodos baseados em textura. As transformadas wavelets utilizadas para esta tarefa sÃo Gabor, ChapÃu Mexicano e o algoritmo à trous. Com relaÃÃo à anÃlise de textura utilizada, esta se baseia na informaÃÃo espacial da matriz de co-ocorrÃncia dos nÃveis de cinza para estimar a direÃÃo de padrÃes lineares em imagens contaminadas com speckle. Os experimentos foram realizados em imagens de textura sintÃticas, imagens do Ãlbum de Brodatz e imagens SAR sintÃticas e reais. Foi observado que os mÃtodos propostos foram capazes de estimar direÃÃes de padrÃes lineares e extrair campos de streaks de vento visÃveis em imagens SAR reais. As principais contribuiÃÃes desta tese sÃo: o mÃtodo proposto para estimaÃÃo de direÃÃo de ventos na superfÃcie do oceano e a extensÃo de tÃcnica jà existente na literatura, possibilitando assim a estimaÃÃo da velocidade dos ventos na faixa de 4 a 10 m/s. Os melhores resultados obtidos nesta tese foram alcanÃados utilizando o mÃtodo proposto que combina transformada wavelet e anÃlise de textura. / This thesis introduces a new methodology to determine the wind direction over the ocean surface using image processing techniques on SAR (Synthetic Aperture Radar) images. Related literature demonstrates a growing interest in processing these images for target detection, region classification, wind field extraction, oil spill monitoring, geophysical and meteorological applications. Wind field extraction in SAR images is a challenging task due to contamination acquisition system by speckle noise, which makes difficult processing and interpretation tasks. Thus, this thesis proposes methods for wind direction estimation by applying image transforms, such as Fourier and wavelets and furthermore texture-based methods. The wavelet transforms used for this task are Gabor, Mexican Hat and the à trous algorithm. Concerning the texture approach, it is based on the co-occurrence matrix to estimate direction of linear patterns in speckled images. The experiments were performed on synthetic texture, Brodatz album, synthetic and real SAR images. It was observed that the proposed methods were able to estimate directions of linear patterns and extract wind fields from visible wind-induced streaks on SAR images. The main contributions of this thesis are: to propose methods for wind direction estimation on the ocean surface and to extend existing techniques in the literature in order to provide wind vector estimation in the range of 4 to 10 m/s. The best results of this tese were achieved with the proposed method that combines wavelet transform and texture analysis.