Binary Classification of Wind Fields through Hypothesis testing on Scatterometer Measurements

University of Utah

Scatterometers are radars specially designed to nearsurface wind over the ocean from space. Traditional scatterometer wind estimation inverts the model function relationship between the wind and backscatter at each resolution element, yielding a set of ambiguities due to the many-to-one mapping of the model function. Field-wise wind estimation dramatically reduces the number of ambiguities by estimating the wind at many resolution elements, simultaneously, using a wind model that constrains the spatial variability of the wind. However, the appropriate choice of the model order needed for a particular wind field is not known a priori. The approximate model order is valuable because of the implicit trade-off between the computational complexity of high-order models and the imprecise model fit of low-order models. In this paper, a simple binary classification of wind fields is proposed which identifies whether or not a region will be well modeled by a low-order wind model. The raw scatterometer measurements provide data about the wind that can be exploited through hypothesis testing to identify the appropriate model order to use in field-wise wind estimation. Improved processing algorithms lead to better use of the data.