Wave height prediction at the Caspian Sea using a data-driven model and ensemble-based data assimilation methods
There are successful experiences with the application of ANN and ensemble-based data assimilation methods in the field of flood forecasting and estuary flow. In the present work, the combination of dynamic Artificial Neural Network and Ensemble Kalman Filter (EnKF) is applied on wind-wave data. ANN is used for the time propagation mechanism that governs the time evolution of the system state. The system state consists of the significant wave height that is affected by wind speed and wind direction. The relevant inputs are selected by analysing the Average Mutual Information. By help of the observations, the EnKF will correct the output of the ANN to find the best estimate of the wave height. A combination of ANN with EnKF acts as an output correction scheme. To deal with the time-delayed states, the extended state vector is taken and the dynamic equation of the extended state vector is used in EnKF. Application of the proposed scheme is examined by using five-month hourly buoy measurement at the Caspian Sea and several model runs with different assimilation-forecast cycles. The coefficient of performance and root mean square error are used to access performance of the method.
data assimilation ensemble kalman filter wind-waves assimilation forecast cycle dynamic neural network