Assessment of wave height distributions using an extensive field database

Karmpadakis, Ioannis; Swan, Chris; Christou, Marios

doi:10.1016/j.coastaleng.2019.103630

Cited by 49 publications

(45 citation statements)

References 39 publications

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“…In recent years, more and more studies approached the problem from a different angle: by inferring the dependence of rogue wave occurrence on the sea state from observed field data 3 , 5 , 11 , 18 . However, no study has so far quantified the probability to encounter a rogue wave depending on the sea state throughout a wide regime of conditions, taking into account more than one parameter at a time, and in a statistically robust fashion.…”

Section: Introductionmentioning

confidence: 99%

Real-world rogue wave probabilities

Häfner

Gemmrich

Jochum

2021

Sci Rep

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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.

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Section: Introductionmentioning

confidence: 99%

Real-world rogue wave probabilities

Häfner

Gemmrich

Jochum

2021

Sci Rep

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“…The identification of the most suitable statistical distribution model for describing the measured deep seawater waves data during storms [1]. Suitability of several deep-water waves heights distribution models, such as the Rayleigh model [2], the Forristall model [3], the Naess model [4], and the Tayfun -Fedele 3rd order model [5], were considered and their ability to fit the empirical data was compared.…”

Section: Introductionmentioning

confidence: 99%

Wave Height Distributions and Rogue Waves in the Eastern Mediterranean

Knobler

Bar

Cohen

et al. 2021

JMSE

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There is a lack of scientific knowledge about the physical sea characteristics of the eastern part of the Mediterranean Sea. The current work offers a comprehensive view of wave fields in southern Israel waters covering a period between January 2017 and June 2018. The analyzed data were collected by a meteorological buoy providing wind and waves parameters. As expected for this area, the strongest storm events occurred throughout October–April. In this paper, we analyze the buoy data following two main objectives—identifying the most appropriate statistical distribution model and examining wave data in search of rogue wave presence. The objectives were accomplished by comparing a number of models suitable for deep seawater waves. The Tayfun—Fedele 3rd order model showed the best agreement with the tail of the empirical wave heights distribution. Examination of different statistical thresholds for the identification of rogue waves resulted in the detection of 99 unique waves, all of relatively low height, except for one wave that reached 12.2 m in height which was detected during a powerful January 2018 storm. Characteristics of the detected rogue waves were examined, revealing the majority of them presenting crest to trough symmetry. This finding calls for a reevaluation of the crest amplitude being equal to or above 1.25 the significant wave height threshold which assumes rogue waves carry most of their energy in the crest.

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“…Consequently, the steepness in eqs. (17)(18)(19)(20)(21)(22) will be corrected by the vertical asymmetry S 0 , which in turn modifies the correction parameter:…”

Section: B Stokes Second-order Statisticsmentioning

confidence: 99%

“…Therefore, the resulting distribution cannot account for the influence of sea state parameters [16][17][18]. For similar reasons, higher-order analytical distributions share this inability [19,20]. Though the standard approach has reached considerable success in explaining the observed directional spectrum and wave properties [21][22][23], according to Donelan et al [24], the need for the ergodicity and stationarity assumptions essentially prevent the use of spectral analysis techniques in unsteady conditions or during isolated events, such as rogue waves.…”

Section: Introductionmentioning

confidence: 99%

Non-stationary analysis of rogue wave probability over a shoal

Mendes¹,

Scotti²,

Brunetti³

et al. 2021

Preprint

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Non-equilibrium evolution of wave fields, as occurring over sudden bathymetry variations, can produce rogue seas with anomalous wave statistics. We handle this process by modifying the Rayleigh distribution through the energetics of second-order theory and a weakly non-stationarity reformulation of the Khintchine theorem. The probability model in unsteady conditions is then probed against well-known unidirectional wave tank experiments. We find good agreement and reproduce the enhanced tail of the probability distribution, describe why the peak of rogue wave probability appears atop the shoal and explain variations in peak intensity for different depths. Furthermore, a novel interpretation is proposed to investigate rogue wave likelihoods in finite depth through the H − σ diagram, allowing a quick prediction of abrupt depth change effects apart from the probability distribution.

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Assessment of wave height distributions using an extensive field database

Cited by 49 publications

References 39 publications

Real-world rogue wave probabilities

Real-world rogue wave probabilities

Wave Height Distributions and Rogue Waves in the Eastern Mediterranean

Non-stationary analysis of rogue wave probability over a shoal

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