In scatterometry, the wind vector retrieval problem is ambiguous, i.e., the inversion procedure does not result in a unique wind solution. To remove such ambiguity, a spatial filter is applied over the ambiguous wind field. Such filtering methods succeed in most of the cases. However, as the resolution increases both the noise and the direction ambiguity in retrieved winds increases, leading to arbitrary local minima wind solutions. Exploiting the full wind vector probability density function of the wind inversion, and adopting spatial meteorological balance constraints in a 2D-Var ambiguity removal (AR) alleviates the problem of arbitrary minima and noise, and provides a spatially consistent scatterometer wind field at high resolution. In other words, the method has the advanced filtering properties needed for maintaining small-scale meteorological information in scatterometers, while reducing noise. The method can be adopted in the context of 3D- or 4D-Var data assimilation systems. Moreover, these findings will be used to develop a high resolution (12.5-km sampled) coastal wind product from the new ASCAT scatterometer.
M Portabella, A Stoffelen, J Vogelzang, A Verhoef, J Verspeek. Towards a high-resolution ASCAT scatterometer wind product
2007, 0, IEEE