Neural network wind retrieval from ERS-1 scatterometer data

This paper presents a neural network methodology to retrieve wind vectors from ERS1 scatterometer data. First a neural network (NN-INVERSE) computes the most probable wind vectors. Probabilities for the estimated direction are given. At least 75 % of the most probable wind directions are consistent with ECMWF winds (at +/-20 degrees). Then the remaining ambiguities are solved by an adapted PRESCAT method, which uses the probabilities provided by NN-INVERSE. Several statistical tests are presented to evaluate the skill of the method. Its good performance is mainly due to the use of a spatial context and to the probabilistic approach for estimating the direction. Comparisons with other methods are also presented. The good performance of the neural method suggests that self-consistent wind retrieval is possible.