18 September 1997 Wind field models and classification
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Abstract
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 for many resolution elements, simultaneously, using a wind field model that constrains the spatial variability of the wind. In this paper four wind field models are presented or use in field- wise wind estimation. The models considered include tow standard expansions, a data-driven model, and a model based on geophysical constraints on the pressure field. Model accuracy, as a function of the number of model parameters, is reported for each model. This accuracy is evaluated using NSCAT JPL nudged L2.0 data. Because of the inherent compromise between computational complexity of high-order models and the imprecise fit of low-order models, automated classification schemes are developed to identify a priori whether a region will be well modeled by a simple wind model. Classification is performed through hypothesis testing on raw NSCAT data and point-wise estimates.
© (1997) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Charles G. Brown, Paul E. Johnson, Steve L. Richards, David G. Long, "Wind field models and classification", Proc. SPIE 3117, Earth Observing Systems II, (18 September 1997); doi: 10.1117/12.278917; https://doi.org/10.1117/12.278917
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