Spatial estimation of unidirectional wave evolution based on ensemble data assimilation
type: Item
instance of: scholarly article
title: Spatial estimation of unidirectional wave evolution based on ensemble data assimilation
author: Zitan Zhang
author: Tianning Tang
author: G. Y. Li
published in: European Journal of Mechanics. B. Fluids
publication date: 2024-07-16T00:00:00Z
review text: The manuscript addresses the challenge of accurately predicting the spatial evolution of surface gravity waves. The primary scientific problem explored in this study pertains to the limitations of existing nonlinear wave models, particularly their sensitivity to initial conditions, which complicates the accurate prediction of wave evolution over extended spatial domains. This sensitivity often leads to the accumulation of errors, especially when initial conditions are derived from noisy or imperfect measurements, such as those obtained from buoys or laboratory wave probes. The study proposes to overcome these limitations by integrating data assimilation techniques, specifically the Ensemble Kalman Filter (EnKF), with the viscous modified Nonlinear Schrödinger (MNLS) equation. This integration aims to improve the robustness and accuracy of the wave model, allowing for better prediction of wave evolution by combining model forecasts with observational data.\N\NTo develop their approach, the authors constructed a coupled framework that utilises the EnKF to assimilate data into the MNLS wave model. The framework is designed to integrate noisy observational data with the underlying physical wave model, thereby generating a more reliable estimation of wave dynamics. The MNLS equation, which extends the classical Nonlinear Schrödinger (NLS) equation by incorporating viscosity effects, serves as the basis for simulating wave propagation. The EnKF, functioning within a Bayesian framework, optimises predictions by iteratively adjusting model outputs to better match observational data. This method is particularly advantageous for addressing the phase shifts and nonlinearities inherent in wave systems, which traditional models often struggle to handle effectively. Key techniques, such as adaptive inflation and localisation, were employed to enhance the performance of the data assimilation process, minimising sampling errors and spurious correlations that could otherwise degrade…
reviewed by: Denys Dutykh
Mathematics Subject Classification ID: 76B15
Mathematics Subject Classification ID: 76M35
Mathematics Subject Classification ID: 86A05
zbMATH DE Number: 7881317
zbMATH Keywords: nonlinear wave model
zbMATH Keywords: ensemble Kalman filtering
zbMATH Keywords: viscous nonlinear Schroedinger equation
zbMATH Keywords: Gaussian noise
zbMATH Keywords: Bayesian method
zbMATH Keywords: wave dispersion
MaRDI profile type: Publication
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DOI: 10.1016/J.EUROMECHFLU.2024.02.006
description: scientific article; zbMATH DE number 7881317