Results on SSH neural network forecasting in the Mediterranean Sea

Nowadays, satellites are the only monitoring systems that cover almost continuously all possible ocean areas and are now an essential part of operational oceanography. A novel approach based on artificial intelligence (AI) concepts, exploits pasts time series of satellite images to infer near future ocean conditions at the surface by neural networks and genetic algorithms. The size of the AI problem is drastically reduced by splitting the spatio-temporal variability contained in the remote sensing data by using empirical orthogonal function (EOF) decomposition. The problem of forecasting the dynamics of a 2D surface field can thus be reduced by selecting the most relevant empirical modes, and non-linear time series predictors are then applied on the amplitudes only. In the present case study, we use altimetric maps of the Mediterranean Sea, combining TOPEX-POSEIDON and ERS-1/2 data for the period 1992 to 1997. The learning procedure is applied to each mode individually. The final forecast is then reconstructed form the EOFs and the forecasted amplitudes and compared to the real observed field for validation of the method.