Real-world rogue wave probabilities

Abstract Rogue waves are dangerous ocean waves at least twice as high as the surrounding waves. Despite an abundance of studies conducting simulations or wave tank experiments, there is so far no reliable forecast for them. In this study, we use data mining and interpretable machine learning to anal...

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Autores principales:	Dion Häfner, Johannes Gemmrich, Markus Jochum
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2021
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/b5d0812f84834e4ebcbd7eb156c92a3c
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spelling	oai:doaj.org-article:b5d0812f84834e4ebcbd7eb156c92a3c2021-12-02T16:50:17ZReal-world rogue wave probabilities10.1038/s41598-021-89359-12045-2322https://doaj.org/article/b5d0812f84834e4ebcbd7eb156c92a3c2021-05-01T00:00:00Zhttps://doi.org/10.1038/s41598-021-89359-1https://doaj.org/toc/2045-2322Abstract Rogue waves are dangerous ocean waves at least twice as high as the surrounding waves. Despite an abundance of studies conducting simulations or wave tank experiments, there is so far no reliable forecast for them. In this study, we use data mining and interpretable machine learning to analyze large amounts of observational data instead (more than 1 billion waves). This reveals how rogue wave occurrence depends on the sea state. We find that traditionally favored parameters such as surface elevation kurtosis, steepness, and Benjamin–Feir index are weak predictors for real-world rogue wave risk. In the studied regime, kurtosis is only informative within a single wave group, and is not useful for forecasting. Instead, crest-trough correlation is the dominating parameter in all studied conditions, water depths, and locations, explaining about a factor of 10 in rogue wave risk variation. For rogue crests, where bandwidth effects are unimportant, we find that skewness, steepness, and Ursell number are the strongest predictors, in line with second-order theory. Our results suggest that linear superposition in bandwidth-limited seas is the main pathway to “everyday” rogue waves, with nonlinear contributions providing a minor correction. This casts some doubt whether the common rogue wave definition as any wave exceeding a certain height threshold is meaningful in practice.Dion HäfnerJohannes GemmrichMarkus JochumNature PortfolioarticleMedicineRScienceQENScientific Reports, Vol 11, Iss 1, Pp 1-11 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Dion Häfner Johannes Gemmrich Markus Jochum Real-world rogue wave probabilities
description	Abstract Rogue waves are dangerous ocean waves at least twice as high as the surrounding waves. Despite an abundance of studies conducting simulations or wave tank experiments, there is so far no reliable forecast for them. In this study, we use data mining and interpretable machine learning to analyze large amounts of observational data instead (more than 1 billion waves). This reveals how rogue wave occurrence depends on the sea state. We find that traditionally favored parameters such as surface elevation kurtosis, steepness, and Benjamin–Feir index are weak predictors for real-world rogue wave risk. In the studied regime, kurtosis is only informative within a single wave group, and is not useful for forecasting. Instead, crest-trough correlation is the dominating parameter in all studied conditions, water depths, and locations, explaining about a factor of 10 in rogue wave risk variation. For rogue crests, where bandwidth effects are unimportant, we find that skewness, steepness, and Ursell number are the strongest predictors, in line with second-order theory. Our results suggest that linear superposition in bandwidth-limited seas is the main pathway to “everyday” rogue waves, with nonlinear contributions providing a minor correction. This casts some doubt whether the common rogue wave definition as any wave exceeding a certain height threshold is meaningful in practice.
format	article
author	Dion Häfner Johannes Gemmrich Markus Jochum
author_facet	Dion Häfner Johannes Gemmrich Markus Jochum
author_sort	Dion Häfner
title	Real-world rogue wave probabilities
title_short	Real-world rogue wave probabilities
title_full	Real-world rogue wave probabilities
title_fullStr	Real-world rogue wave probabilities
title_full_unstemmed	Real-world rogue wave probabilities
title_sort	real-world rogue wave probabilities
publisher	Nature Portfolio
publishDate	2021
url	https://doaj.org/article/b5d0812f84834e4ebcbd7eb156c92a3c
work_keys_str_mv	AT dionhafner realworldroguewaveprobabilities AT johannesgemmrich realworldroguewaveprobabilities AT markusjochum realworldroguewaveprobabilities
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Real-world rogue wave probabilities

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