Although the third-generation formulation of the ocean wave model describes the wave generation, dissipation and nonlinear interaction processes explicitly, many empirical parameters exist in the model which have to be determined experimentally. With the advance in oceanographic remote-sensing techniques, information on oceanic parameters including significant wave height (SWH) can be obtained daily by satellite altimeters. The assimilation of these data into the wave model provides a way of improving the hindcasting results. However, for wave forecasting, no altimeter data exist during the forecasting period, by definition. To improve the forecasting accuracy of the wave model, Artificial Neural Networks (ANN) are introduced to mimic the errors introduced by the wave model. This is achieved by training the ANN using the wave model output as input, and the results after data assimilation as the targeted output. The trained ANN is then used as a post-processor of the output from the wave model. The proposed method has been applied in wave simulation in the northwestern Pacific Ocean. The statistical interpolation method is used to assimilate the altimeter data into the wave model output and a back-propagation ANN is used to mimic the relation between the wave model outputs with or without data assimilation. The results show that an apparent improvement in the accuracy of forecasting can be obtained.
Incorporation of artificial neural networks and data assimilation techniques into a third-generation wind–wave model for wave forecasting
Zhixu Zhang, Chi-Wai Li, Yok-Sheung Li, Yiquan Qi; Incorporation of artificial neural networks and data assimilation techniques into a third-generation wind–wave model for wave forecasting. Journal of Hydroinformatics 1 January 2006; 8 (1): 65–76. doi: https://doi.org/10.2166/jh.2006.005
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