Short-term Nonlinear Wave Field Prediction Based on Ensemble Kalman Filter
When using nonlinear wave models for wave field prediction,the initial data errors may lead to a gradual decrease in prediction accuracy.In this paper,the Ensemble Kalman Filter(EnKF)method is combined with the Pseudo-spectral Fourier Legendre(PFL)wave model to conduct numerical simulations for two dimensional nonlinear wave fields,which effectively improves the predictive performance of the model under initial data disturbance.Firstly,nonlinear wave field data is generated based on wave theory and typical wave spectrum.Then,random perturbations which conform to a certain spatial distribution correlation are added to these data for initialization.Next,comparing the prediction error iteration of the method combining EnKF and PFL models with the method using PFL models alone to verify the effectiveness of the proposed method.Finally,the influence of various assimilation parameters on the assimilation effect is explored through a variable-controlling approach.The results show that the EnKF can effectively improve the short-term prediction of nonlinear waves.
Nonlinear wave predictionEnsemble Kalman FilterData assimilationSelection of assimilation parameters
万方数据
维普
