The selection of directional sectors for the  analysis of extreme wind speed

Folgueras, Pedro; Solari, Sebastián; Losada, Miguel Á.

doi:10.5194/nhess-19-221-2019

Cited by 10 publications

(7 citation statements)

References 31 publications

(36 reference statements)

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“…When dealing with directional variables, it is common to group the data according to different directional sectors (Cook and Miller, 1999;Forristall, 2004), with such an approach being recommended in many regulations as well (API, 2002;ISO, 2005;DNV, 2010, among others). However, the use of directional sectors involves certain drawbacks.…”

Section: Introductionmentioning

confidence: 99%

“…Second, data showing the same direction may be due to different forcing; in the context of wave climate, an example is of waves propagating in shallow waters being affected by refraction and/or diffraction. Finally, the borders of the directional sectors are often subjectively defined, without verifying if the data belonging to each subset are homogeneous and independent with respect to those of the other sectors (see Folgueras et al, 2019, in which they tackled this issue and proposed a methodology to overcome it).…”

Section: Introductionmentioning

confidence: 99%

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Extreme wave analysis based on atmospheric pattern classification: an application along the Italian coast

Leo

Solari

Besio

2020

Nat. Hazards Earth Syst. Sci.

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Abstract. This paper provides a methodology for classifying samples of significant wave-height peaks in homogeneous subsets in terms of the atmospheric circulation patterns behind the observed extreme wave conditions. Then, a methodology is given for the computation of the overall extreme value distribution by starting from the distributions fitted to each single subset. To this end, the k-means clustering technique is used to classify the shape of the wind fields that occurred simultaneously to and prior to the occurrences of the extreme wave events. This results in a small number of characteristic circulation patterns related to as many subsets of extreme wave values. After fitting an extreme value distribution to each subset, bootstrapping is used to reconstruct the omni-circulation pattern's extreme value distribution. The methodology is applied to several locations along the Italian buoy network, and it is concluded from the obtained results that it yields a two-fold advantage: first, it is capable of identifying clearly differentiated subsets driven by homogeneous circulation patterns; second, it allows one to estimate high-return-period quantiles consistent with those resulting from the usual extreme value analysis. In particular, the circulation patterns highlighted are analyzed in the context of the Mediterranean Sea's atmospheric climatology and are shown to be due to well-known cyclonic systems typically crossing the Mediterranean basin.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Extreme wave analysis based on atmospheric pattern classification: an application along the Italian coast

Leo

Solari

Besio

2020

Nat. Hazards Earth Syst. Sci.

Self Cite

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“…Similar approaches for modelling extreme wind speed with a directional dependence structure have been presented by for example, Simiu et al [34] and Solari and Losada [35]. A methodology for the appropriate selection of uncorrelated directional sectors has been proposed by Folgueras et al [36], which reduces also the uncertainty in the estimation of design values of wind speed. Sea currents have been investigated in the work of Robinson and Tawn [37] by means of a parametric model for extreme current data by handling not only directionality but temporal dependence and non-stationarity as well.…”

Section: Introductionmentioning

confidence: 91%

Directional Extreme Value Models in Wave Energy Applications

2020

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A wide range of wave energy applications relies on the accurate estimation of extreme wave conditions, while some of them are frequently affected by directionality. For example, attenuators and terminators are the most common types of wave energy converters whose performance is highly influenced by their orientation with respect to the prevailing wave direction. In this work, four locations in the eastern Mediterranean Sea are selected with relatively high wave energy flux values and extreme wave heights are examined with wave direction as a covariate. The Generalized Pareto distribution is used to model the extreme values of wave height over a pre-defined threshold, with its parameters being expressed as a function of wave direction through Fourier series expansion. In order to be consistent with the analysis obtained from the independent fits for directional sectors, the estimation of parameters is based on a penalized maximum likelihood criterion that ensures a good agreement between the two approaches. The obtained results validate the integration of directionality in extreme value models for the examined locations and design values of significant wave height are provided with respect to direction for the 50- and 100-year return period with bootstrap confidence intervals.

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“…This approach (i) accommodates directional nonstationarity (by conducting independent inferences per directional octant) and (ii) allows extreme quantiles for specific directional sectors to be estimated. Choice of number and widths of directional sectors is an open problem (see, e.g., Folgueras et al, 2019; Ross et al, 2018). Moreover, nonstationary inference provides a number of challenges in practical assessment and interpretation of uncertainty and risk (e.g., Feld et al, 2019; Jonathan & Ewans, 2007; Mackay et al, 2010).…”

Section: Introductionmentioning

confidence: 99%

Modeling nonstationary extremes of storm severity: Comparing parametric and semiparametric inference

2021

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This article compares the modeling of nonstationary extreme events using parametric models with local parametric and semiparametric approaches also motivated by extreme value theory. Specifically, three estimators are compared based on (a) (local) semiparametric moment estimation, (b) (local) maximum likelihood estimation, and (c) spline‐based maximum likelihood estimation. Inference is performed in a sequential manner, highlighting the synergies between the different approaches to estimating extreme quantiles, including the T‐year level and right endpoint when finite. We present a novel heuristic to estimate nonstationary extreme value threshold with exceedances varying on a circular domain, and hypothesis‐testing procedures for identifying max‐domain of attraction in the nonstationary setting. Bootstrapping is used to estimate nonstationary confidence bounds throughout. We provide step‐by‐step guides for estimation, and explore the different inference strategies in application to directional modeling of hindcast storm peak significant wave heights recorded in the North Sea.

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The selection of directional sectors for the analysis of extreme wind speed

Cited by 10 publications

References 31 publications

Extreme wave analysis based on atmospheric pattern classification: an application along the Italian coast

Extreme wave analysis based on atmospheric pattern classification: an application along the Italian coast

Directional Extreme Value Models in Wave Energy Applications

Modeling nonstationary extremes of storm severity: Comparing parametric and semiparametric inference

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