Effect of Sampling Variability on Hindcast and Measured Wave Heights

Forristall, George Z.; Heideman, John C.; Leggett, Ian M.; Roskam, Bram; Vanderschuren, Luc

doi:10.1061/(asce)0733-950x(1996)122:5(216)

Cited by 22 publications

(13 citation statements)

References 6 publications

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“…Awareness of the presence of sampling variability in wave observations existed for decades, but analysis of its effect on wave characteristics for many years was mostly limited to temporal data (e.g., References [3,[10][11][12][13][14]). Focus on the importance of applying spatial statistics in wave description started to increase in the last decade, particularly due to the introduction of the Piterbarg theorem [15] to oceanography in 2004 by Krogstad et al [16], showing that single-point temporal measurements may greatly underestimate the actual maximum wave displacements that can occur on sea surface areas, especially in short-crested seas.…”

Section: Introductionmentioning

confidence: 99%

Challenges in Description of Nonlinear Waves Due to Sampling Variability

Bitner‐Gregersen

Gramstad

Magnusson

et al. 2020

JMSE

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Wave description is affected by several uncertainties, with sampling variability due to limited number of observations being one of them. Ideally, temporal/spatial wave registrations should be as large as possible to eliminate this uncertainty. This is difficult to reach in nature, where stationarity of sea states is an issue, but it can in principle be obtained in laboratory tests and numerical simulations, where initial wave conditions can be kept constant and intrinsic variability can be accounted for by changing random seeds for each run. Using linear, second-order, and third-order unidirectional numerical simulations, we compare temporal and spatial statistics of selected wave parameters and show how sampling variability affects their estimators. The JONSWAP spectrum with gamma peakedness parameters γ = 1, 3.3, and 6 is used in the analysis. The third-order wave data are simulated by a numerical solver based on the higher-order spectral method which includes the leading-order nonlinear dynamical effects. Field data support the analysis. We demonstrate that the nonlinear wave field including dynamical effects is more sensitive to sampling variability than the second-order and linear ones. Furthermore, we show that the mean values of temporal and spatial wave parameters can be equal if the number of simulations is sufficiently large. Consequences for design work are discussed.

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Section: Introductionmentioning

confidence: 99%

Challenges in Description of Nonlinear Waves Due to Sampling Variability

Bitner‐Gregersen

Gramstad

Magnusson

et al. 2020

JMSE

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“…Consequently the sampling variance of the H m0 and T e time-series were calculated from the spectral moments (assuming the no correlation between the locations) [7,19]. Forristall [20] notes that for practical purposes the sampling distribution of H m0 can be approximated as normal; for the purposes of this work it is assumed that the same applies to the sampling distribution of T e . In the MCS, each H m0 and T e record in the time-series is perturbed randomly and independently based on the sampling variance.…”

Section: Sampling Uncertainty In the Historic Met-ocean Datamentioning

confidence: 99%

Wave Energy Converter Annual Energy Production Uncertainty Using Simulations

Hiles¹,

Beatty²,

Andrés

2016

JMSE

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Critical to evaluating the economic viability of a wave energy project is: (1) a robust estimate of the electricity production throughout the project lifetime and (2) an understanding of the uncertainty associated with said estimate. Standardization efforts have established mean annual energy production (MAEP) as the metric for quantification of wave energy converter (WEC) electricity production and the performance matrix approach as the appropriate method for calculation. General acceptance of a method for calculating the MAEP uncertainty has not yet been achieved. Several authors have proposed methods based on the standard engineering approach to error propagation, however, a lack of available WEC deployment data has restricted testing of these methods. In this work the magnitude and sensitivity of MAEP uncertainty is investigated. The analysis is driven by data from simulated deployments of 2 WECs of different operating principle at 4 different locations. A Monte Carlo simulation approach is proposed for calculating the variability of MAEP estimates and is used to explore the sensitivity of the calculation. The uncertainty of MAEP ranged from 2%-20% of the mean value. Of the contributing uncertainties studied, the variability in the wave climate was found responsible for most of the uncertainty in MAEP. Uncertainty in MAEP differs considerably between WEC types and between deployment locations and is sensitive to the length of the input data-sets. This implies that if a certain maximum level of uncertainty in MAEP is targeted, the minimum required lengths of the input data-sets will be different for every WEC-location combination.

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“…Specifically, in the current study, cluster maxima at a distance of less than 48 hours apart were treated as belonging to the same cluster (storm). Following, a filter was applied to compensate for over-and underestimation due to sampling variability (Forristall et al [12]). …”

Section: Initial Extreme Value Analysis 41 Data Collection For Pot Amentioning

confidence: 99%

Extreme offshore wave statistics in the North Sea

Roscoe

Caires

Diermanse

et al. 2010

WIT Transactions on Ecology and the Environment

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As part of the flood risk assessment in The Netherlands, extreme hydraulic boundary conditions (HBC) at coastal water defenses are calculated to assess the safety and sufficiency of the defenses. For this purpose, extreme offshore values of wave height and wave period with a frequency of occurrence of 1/10,000 years need to be estimated. The estimation is carried out using measurements at nine offshore locations in the North Sea, with a record length of 30 years.To estimate extreme wave heights and wave periods, the Generalized Pareto Distribution (GPD), which follows from extreme value theory, was fitted to peaks-over-threshold (POT) at each of the nine measurement stations. The fitted GPD parameters and consequently the extrapolated extreme values are particularly sensitive to the choice of threshold. A reproducible method was employed for threshold selection based on the anticipated behavior of the GPD shape parameter as the threshold approaches the domain of attraction. A modified regional frequency analysis (MRFA) was subsequently carried out to spatially smooth the GPD shape parameters. Final GPD fits were made using the shape-parameter output of the MRFA to produce final extrapolated extreme value estimates. The extreme values were compared with those resulting from an operationally-used distribution function in The Netherlands known as the conditional Weibull distribution.

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Effect of Sampling Variability on Hindcast and Measured Wave Heights

Cited by 22 publications

References 6 publications

Challenges in Description of Nonlinear Waves Due to Sampling Variability

Challenges in Description of Nonlinear Waves Due to Sampling Variability

Wave Energy Converter Annual Energy Production Uncertainty Using Simulations

Extreme offshore wave statistics in the North Sea

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