Wave Hight Prediction at Caspian Sea Using Data Driven Model & Ensemble Based Data Assimulation Methods

Abstract :

There are sucessful 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 consist of the significant wave height that is affected by wind speed and wind direction. 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 eqation of rhe extended state vector is used in EnKF. Application of the propossed scheme is examined by using five-month hourly bouy 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.

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Wave Hight Prediction at Caspian Sea Using Data Driven Model & Ensemble Based Data Assimulation Methods