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Estimating the parameters of ocean wave spectra

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Estimating the parameters of ocean wave spectra. / Grainger, Jake; Sykulski, Adam; Jonathan, Philip; Ewans, Kevin.

In: Ocean Engineering, Vol. 229, 108934, 01.06.2021.

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@article{4118ead746ef499da3581365bbf2a4f3,
title = "Estimating the parameters of ocean wave spectra",
abstract = "Wind-generated waves are often treated as stochastic processes. There is particular interest in their spectral density functions, which are often expressed in some parametric form. Such spectral density functions are used as inputs when modelling structural response or other engineering concerns. Therefore, accurate and precise recovery of the parameters of such a form, from observed wave records, is important. Current techniques are known to struggle with recovering certain parameters, especially the peak enhancement factor and spectral tail decay. We introduce an approach from the statistical literature, known as the de-biased Whittle likelihood, and address some practical concerns regarding its implementation in the context of wind-generated waves. We demonstrate, through numerical simulation, that the de-biased Whittle likelihood outperforms current techniques, such as least squares fitting, both in terms of accuracy and precision of the recovered parameters. We also provide a method for estimating the uncertainty of parameter estimates. We perform an example analysis on a data-set recorded off the coast of New Zealand, to illustrate some of the extra practical concerns that arise when estimating the parameters of spectra from observed data.",
keywords = "Wave spectrum, Parameter estimation, JONSWAP, Spectral-likelihood, De-biased Whittle likelihood, Parameter uncertainty",
author = "Jake Grainger and Adam Sykulski and Philip Jonathan and Kevin Ewans",
year = "2021",
month = jun,
day = "1",
doi = "10.1016/j.oceaneng.2021.108934",
language = "English",
volume = "229",
journal = "Ocean Engineering",
issn = "0029-8018",
publisher = "Elsevier Ltd",

}

RIS

TY - JOUR

T1 - Estimating the parameters of ocean wave spectra

AU - Grainger, Jake

AU - Sykulski, Adam

AU - Jonathan, Philip

AU - Ewans, Kevin

PY - 2021/6/1

Y1 - 2021/6/1

N2 - Wind-generated waves are often treated as stochastic processes. There is particular interest in their spectral density functions, which are often expressed in some parametric form. Such spectral density functions are used as inputs when modelling structural response or other engineering concerns. Therefore, accurate and precise recovery of the parameters of such a form, from observed wave records, is important. Current techniques are known to struggle with recovering certain parameters, especially the peak enhancement factor and spectral tail decay. We introduce an approach from the statistical literature, known as the de-biased Whittle likelihood, and address some practical concerns regarding its implementation in the context of wind-generated waves. We demonstrate, through numerical simulation, that the de-biased Whittle likelihood outperforms current techniques, such as least squares fitting, both in terms of accuracy and precision of the recovered parameters. We also provide a method for estimating the uncertainty of parameter estimates. We perform an example analysis on a data-set recorded off the coast of New Zealand, to illustrate some of the extra practical concerns that arise when estimating the parameters of spectra from observed data.

AB - Wind-generated waves are often treated as stochastic processes. There is particular interest in their spectral density functions, which are often expressed in some parametric form. Such spectral density functions are used as inputs when modelling structural response or other engineering concerns. Therefore, accurate and precise recovery of the parameters of such a form, from observed wave records, is important. Current techniques are known to struggle with recovering certain parameters, especially the peak enhancement factor and spectral tail decay. We introduce an approach from the statistical literature, known as the de-biased Whittle likelihood, and address some practical concerns regarding its implementation in the context of wind-generated waves. We demonstrate, through numerical simulation, that the de-biased Whittle likelihood outperforms current techniques, such as least squares fitting, both in terms of accuracy and precision of the recovered parameters. We also provide a method for estimating the uncertainty of parameter estimates. We perform an example analysis on a data-set recorded off the coast of New Zealand, to illustrate some of the extra practical concerns that arise when estimating the parameters of spectra from observed data.

KW - Wave spectrum

KW - Parameter estimation

KW - JONSWAP

KW - Spectral-likelihood

KW - De-biased Whittle likelihood

KW - Parameter uncertainty

U2 - 10.1016/j.oceaneng.2021.108934

DO - 10.1016/j.oceaneng.2021.108934

M3 - Journal article

VL - 229

JO - Ocean Engineering

JF - Ocean Engineering

SN - 0029-8018

M1 - 108934

ER -