Neural network wind retrieval from ERS‐1 scatterometer data

Abstract

This paper presents a neural network methodology to retrieve wind vectors from ERS‐1 scatterometer data. First, a neural network (NN‐INVERSE) computes the most probable wind vectors. Probabilities for the estimated wind direction are given. At least 75% of the most probable wind directions are consistent with European Centre for Medium‐Range Weather Forecasts winds (at ±20°). Then the remaining ambiguities are resolved by an adapted PRESCAT method that uses the probabilities provided by NN‐INVERSE. Several statistical tests are presented to evaluate the skill of the method. The good performance is mainly due to the use of a spatial context and to the probabilistic approach adopted to estimate the wind direction. Comparisons with other methods are also presented. The good performance of the neural network method suggests that a self‐consistent wind retrieval from ERS‐1 scatterometer is possible.