Wind reconstruction from ERS-1 scatterometer data using neural network

A dynamic learning neural network is adopted to relate the normalized radar backscattering coefficient sigma degrees to the wind vector. By given the average wind speed and wind direction and their standard deviations, a set of test wind vector fields are simulated. The corresponding sigma degrees values are calculated according to the well known empirical model named CMOD-4. To improve the accuracy of the estimated wind direction, spatial information is considered in the reconstruction process. Thereafter, the neural network is iteratively trained by the input-output pairs generated from the test wind fields. Finally, the wind parameters are reconstructed upon applying sigma degrees values to the well trained neural network. Experimental results indicate that neural network is an effective tool for wind reconstruction.