Modelling significant wave height distributions with quantile functions for estimation of extreme wave heights

Muraleedharan, G.; Lucas, C.; Soares, C. Guedes; Nair, N.b Unnikrishnan; Kurup, P.G.

doi:10.1016/j.oceaneng.2012.07.007

Cited by 18 publications

(11 citation statements)

References 25 publications

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“…Therefore, detailed wave climate information of the site becomes critical to the further development and optimization of the wave energy converting technology. For wave climate analysis, different probabilistic methods have been proposed to describe the long-term wave distribution [8][9][10][11][12][13][14][15]. The Lognormal, Rayleigh and Weibull distribution are the most commonly used models for long-term wave distribution modelling [16,17].…”

Section: It Was Initiatedmentioning

confidence: 99%

Statistical Analysis of Wave Climate Data Using Mixed Distributions and Extreme Wave Prediction

Isberg

Waters

et al. 2016

Energies

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Abstract:The investigation of various aspects of the wave climate at a wave energy test site is essential for the development of reliable and efficient wave energy conversion technology. This paper presents studies of the wave climate based on nine years of wave observations from the 2005-2013 period measured with a wave measurement buoy at the Lysekil wave energy test site located off the west coast of Sweden. A detailed analysis of the wave statistics is investigated to reveal the characteristics of the wave climate at this specific test site. The long-term extreme waves are estimated from applying the Peak over Threshold (POT) method on the measured wave data. The significant wave height and the maximum wave height at the test site for different return periods are also compared. In this study, a new approach using a mixed-distribution model is proposed to describe the long-term behavior of the significant wave height and it shows an impressive goodness of fit to wave data from the test site. The mixed-distribution model is also applied to measured wave data from four other sites and it provides an illustration of the general applicability of the proposed model. The methodologies used in this paper can be applied to general wave climate analysis of wave energy test sites to estimate extreme waves for the survivability assessment of wave energy converters and characterize the long wave climate to forecast the wave energy resource of the test sites and the energy production of the wave energy converters.

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Section: It Was Initiatedmentioning

confidence: 99%

Statistical Analysis of Wave Climate Data Using Mixed Distributions and Extreme Wave Prediction

Isberg

Waters

et al. 2016

Energies

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“…Using hourly values of sea level record for the years 2000 and 2005, Abdullah (2010) studied tide and sea level characteristics at Juaymah on the west coast of the Arabian Gulf. Predictive models on extreme sea level have also been validated at certain regions in the Indian Ocean (Kurup et al, 2007;Muraleedharan et al, 2012).…”

Section: Introductionmentioning

confidence: 99%

Estimation of extremes in the southern Arabian Gulf

Varghese¹,

Nayak²,

Bhat³

2016

J. Oceanogr. Mar. Sci.

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Extreme all-direction wave conditions and joint probabilities of high waves and high water levels are derived at selected locations in the southern Arabian Gulf. The wave conditions are obtained using the spectral wave model SWAN with wind and offshore wave information procured from ocean weather, supplemented with local water level records. Wave transformations from offshore to nearshore and waves generated by local wind conditions are derived using wind speed and direction, offshore wave height, peak period and direction and water level inputs to the SWAN model. The model predicted extreme 3-hourly wind speeds for return periods ranging from 0.06 -200 years and the wave conditions for each storm. The study indicates that future sea level rise to 2058 gives 1:50, 1:100 and 1:250 return period water levels of 2.64, 2.71 and 2.79 mCD respectively at Jebel Ali.

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“…Wadsworth and Tawn [3] presented likelihood-based procedures for threshold diagnostics and uncertainty. Muraleedharan et al [6] and Cai and Reeve [7] modeled significant wave height distributions with quantile func tions for estimation of extreme wave heights. Thompson et al [5] reported Bayesian nonparametric regression using splines.…”

Section: Introductionmentioning

confidence: 99%

Estimation of Storm Peak and Intrastorm Directional–Seasonal Design Conditions in the North Sea

Feld

Randell

et al. 2015

Journal of Offshore Mechanics and Arctic Engineering

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Specification of realistic environmental design conditions for marine structures is of fun damental importance to their reliability over time. Design conditions for extreme waves and storm severities are typically estimated by extreme value analysis of time series of measured or hindcast significant wave height, Hs. This analysis is complicated by two effects. First, Hs exhibits temporal dependence. Second, the characteristics of Hf are nonstationary with respect to multiple covariates, particularly wave direction, and sea son. We develop directional-seasonal design values for storm peak significant wave height (Hf) by estimation of, and simulation under a nonstationary extreme value model for Hf. Design values for significant wave height (Hs) are estimated by simulating storm trajectories of Hs consistent with the simulated storm peak events. Design distributions for individual maximum wave height (H^fi are estimated by marginalization using the known conditional distribution for Hm ax given Hs. Particular attention is paid to the assess ment of model bias and quantification of model parameter and design value uncertainty using bootstrap resampling. We also outline existing work on extension to estimation of maximum crest elevation and total extreme water level.

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Modelling significant wave height distributions with quantile functions for estimation of extreme wave heights

Cited by 18 publications

References 25 publications

Statistical Analysis of Wave Climate Data Using Mixed Distributions and Extreme Wave Prediction

Statistical Analysis of Wave Climate Data Using Mixed Distributions and Extreme Wave Prediction

Estimation of extremes in the southern Arabian Gulf

Estimation of Storm Peak and Intrastorm Directional–Seasonal Design Conditions in the North Sea

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