Assessing the ocean surface vector wind signal in SSM/I data using neural networks

Minimization techniques have long been used by the remote sensing community to obtain relationships between observed radiances and retrieved geophysical quantities. Originally, special sensor microwave imager (SSM/I) passive microwave wind observations were regressed against coincident wind speed observations from buoys. More recent research has shown that a wind direction signal may also be present in SSM/I observations. This wind direction signal, however, is not very large, meaning that accounting for the error characteristics of this signal is very important, and it is believed that the signal is somewhat nonlinear. Thus, more advanced minimization techniques are required to extract this wind direction signal. The paper discusses neural networks (NN) which are a nonlinear minimization technique and they can be used either in simulation mode, where the net is trained using simulated radiances assumed to be the truth, or in observation mode, where the observed radiances are matched with in situ observations