Non-linear wave data assimilation with an ANN-type wind-wave model and Ensemble Kalman Filter (EnKF)

摘要

Non-linear data assimilation for a wind-wave dynamical surrogate model in a reduced space is presented. A dynamic artificial neural network is used for surrogate modeling. It provides a fast emulation of a wind-wave model which is used for the evaluation of the system states during a small period of time. The system state consists of wave height and wave direction in the reduced space which is affected by the reduced space wind field. The projection from the full space to the reduced one is performed by a principal component analysis. Ensemble methods require the evaluation of dynamics for a large number of statistical ensembles, so coupling this surrogate (instead of a full model) with an Ensemble Kalman Filter (EnKF) leads to computational efficiency. Application of the procedure is demonstrated through 6 month hindcast study of wind waves over the Caspian Sea using the third-generation wave model and the analysis of the ECMWF wind field. The trained network is embedded into the stochastic environment. Then, the EnKF is used to find estimate of the system states. Experiments show that the proposed data assimilation technique can correct the prediction of the wind-waves requiring just a modest execution time.