Spatial estimation of unidirectional wave evolution based on ensemble data assimilation - Zhang, Z; Tang, T; Li, Y (Elsevier, 2024)
With the limitation of the high sensitivity of nonlinear models to initial conditions, the accurate estimation of wave spatial evolution is difficult to perform at a long distance. At this stage, a helpful approach is to improve the accuracy and robustness of the model through data assimilation technique. A robust data assimilation framework is developed by coupling ensemble Kalman filtering (EnKF) with the nonlinear wave model. The spatial evolution is obtained by numerically integrating the viscous modified Nonlinear Schrödinger (MNLS) equation. The performance of the EnKF-MNLS coupled framework is tested using synthetic data and laboratory measurements. The synthetic data is generated by the MNLS simulation superposing the Gaussian noise. In the synthetic cases, the estimated wave envelopes agree well with the clean solution. The results of laboratory experiments indicate that the EnKF-MNLS framework can improve the accuracy of wave forecasts compared to noised MNLS simulations. This study aims to enhance the noise resistance of the nonlinear wave model in spatial evolution and improve the accuracy of the model forecast.