A benchmarking exercise for environmental contours

Haselsteiner, Andreas F.; Coe, Ryan G.; Manuel, Lance; Chai, Wei; Leira, Bernt J.; Clarindo, G.; Soares, C. Guedes; Hannesdóttir, Ásta; Dimitrov, Nikolay Krasimirov; Sander, Aljoscha; Ohlendorf, Jan-Hendrik; Thoben, Klaus‐Dieter; Hauteclocque, Guillaume de; Mackay, Ed; Jonathan, Philip; Qiao, Chi; Myers, Andrew C.; Rode, Anna; Hildebrandt, Arndt; Schmidt, Boso; Vanem, Erik; Huseby, Arne Bang

doi:10.1016/j.oceaneng.2021.109504

“…The factors that are relevant for all marine structures have already been analyzed to a great extent in the literature. The uncertainty of choosing a joint model has been analyzed in a recent benchmarking study on environmental contours [56,57]. Different definitions for contour exceedance have been analyzed [6,58e60].…”

Section: Events Considered Independentmentioning

confidence: 99%

“…4). This 50-year dataset was also used in a recent benchmarking exercise on environmental contours [56].…”

Section: Environmental Conditionsmentioning

confidence: 99%

Long-term extreme response of an offshore turbine: How accurate are contour-based estimates?

Haselsteiner

¹

,

Frieling

²

,

Mackay

³

et al. 2022

Self Cite

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“…Indeed, a second benchmark exercise was announced at OMAE 2021 calling for estimates of extreme environmental conditions based on specific datasets of significant wave height that were made available [9]. This is a follow-up of a previous benchmarking exercise that, inter alia, highlighted the effect of serial correlation on extreme value estimates [10], as elaborated further in [11].…”

Section: Introductionmentioning

confidence: 97%

Analyzing Extreme Sea State Conditions by Time-Series Simulation Accounting for Seasonality

Vanem

¹

2023

Journal of Offshore Mechanics and Arctic Engineering

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This paper presents an extreme value analysis on data of significant wave height based on time-series simulation. A method to simulate time series with given marginal distribution and preserving the autocorrelation structure in the data is applied to significant wave height data. Then, extreme value analysis is performed by simulating from the fitted time-series model that preserves both the marginal probability distribution and the autocorrelation. In this way, the effect of serial correlation on the extreme values can be taken into account, without subsampling and de-clustering of the data. The effect of serial correlation on estimating extreme wave conditions have previously been highlighted, and failure to account for this effect will typically lead to an overestimation of extreme conditions. This is demonstrated by this study, that compares extreme value estimates from the simulated times-series model with estimates obtained directly from the marginal distribution assuming that 3-hourly significant wave heights are independent and identically distributed. A dataset of significant wave height provided as part of a second benchmark exercise on environmental extremes that was presented at OMAE 2021, has been analysed. This paper is an extension of a study presented at OMAE 2022 (OMAE2022-78795), and includes additional pre-processing of the data to account for seasonality and new results.

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“…Indeed, a second benchmark exercise was announced at OMAE 2021 calling for estimates of extreme environmental conditions based on specific datasets of significant wave height that were made available [9]. This is a follow-up of a previous benchmarking exercise that, inter alia, highlighted the effect of serial correlation on extreme value estimates [10], as elaborated further in [11]. This paper presents extreme value estimates of significant wave height from the datasets presented in the second benchmark exercise, based on a time-series model that preserves the marginal distribution and the autocorrelation of the data.…”

Section: Introductionmentioning

confidence: 99%

Analyzing Extreme Sea State Conditions by Time-Series Simulation

Vanem

¹

2022

Volume 2: Structures, Safety, and Reliability

View full text Add to dashboard Cite

This paper presents an extreme value analysis on data of significant wave height based on time-series simulation. A method to simulate time series with given marginal distribution and preserving the autocorrelation structure in the data is applied to significant wave height data. Then, extreme value analysis is performed by simulating from the fitted time-series model that preserves both the marginal probability distribution and the autocorrelation. In this way, the effect of serial correlation on the extreme values can be taken into account, without subsampling and de-clustering of the data. The effect of serial correlation on estimating extreme wave conditions have previously been highlighted, and failure to account for this effect will typically lead to an overestimation of extreme conditions. This is demonstrated by this study, that compares extreme value estimates from the simulated times-series model with estimates obtained directly from the marginal distribution assuming that 3-hourly significant wave heights are independent and identically distributed. A dataset of significant wave height provided as part of a second benchmark exercise on environmental extremes that was presented at OMAE 2021, has been analysed.

show abstract

A benchmarking exercise for environmental contours

Cited by 46 publications

References 62 publications

Long-term extreme response of an offshore turbine: How accurate are contour-based estimates?

Long-term extreme response of an offshore turbine: How accurate are contour-based estimates?

Analyzing Extreme Sea State Conditions by Time-Series Simulation Accounting for Seasonality

Analyzing Extreme Sea State Conditions by Time-Series Simulation

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