An improved adjoint-based ocean wave reconstruction and prediction method

Jie Wu, Xuanting Hao, Lian Shen

Research output: Contribution to journalArticlepeer-review

1 Scopus citations

Abstract

We propose an improved adjoint-based method for the reconstruction and prediction of the nonlinear wave field from coarse-resolution measurement data. We adopt the data assimilation framework using an adjoint equation to search for the optimal initial wave field to match the wave field simulation result at later times with the given measurement data. Compared with the conventional approach where the optimised initial surface elevation and velocity potential are independent of each other, our method features an additional constraint to dynamically connect these two control variables based on the dispersion relation of waves. The performance of our new method and the conventional method is assessed with the nonlinear wave data generated from phase-resolved nonlinear wave simulations using the high-order spectral method. We consider a variety of wave steepness and noise levels for the nonlinear irregular waves. It is found that the conventional method tends to overestimate the surface elevation in the high-frequency region and underestimate the velocity potential. In comparison, our new method shows significantly improved performance in the reconstruction and prediction of instantaneous surface elevation, surface velocity potential and high-order wave statistics, including the skewness and kurtosis.

Original languageEnglish (US)
Article numberE2
JournalFlow
Volume2
DOIs
StatePublished - Jan 24 2022

Bibliographical note

Funding Information:
The work is supported by the Office of Naval Research and University of Minnesota Sea Grant.

Publisher Copyright:
©

Keywords

  • Data assimilation
  • Surface gravity waves
  • Wave forecasting

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