On defining storm intervals: Extreme wave analysis using extremal index inferencing of the run length parameter. (1st December 2020)

• Record Type: Journal Article
• Authors: Oikonomou, C.L.G.; Gradowski, M.; Kalogeri, C.; Sarmento, A.J.N.A.
• Is Part Of: Ocean engineering. Volume 217(2020)
• Publication Date: 2020-12-01
• Subjects: Extreme wave analysis -- Runs de-clustering -- Extremal index -- Pareto distribution -- Return periods; Ocean engineering -- Periodicals; Ocean engineering; Periodicals; 620.4162
• DOI: 10.1016/j.oceaneng.2020.107988
• Languages: English
• ISSNs: 0029-8018

Abstract

Abstract: Extreme wave analysis is essential for the design and deployment of marine structures. Since extremes in natural phenomena tend to occur in clusters, it is necessary to de-cluster them in order to form a dataset of independent samples. There are several algorithms used to identify independent storms (clusters of significant wave height extremes), most of which have the disadvantage of relying on an arbitrarily selected de-clustering parameter. In this paper, an existing statistical method for systematic cluster size inferencing is used with runs de-clustering, and applied for the first time to extreme wave analysis. The Generalised Pareto Distribution (GPD) is fitted to an extreme wave dataset, and the return periods of significant wave height extremes are calculated using the resulting model function. The methodology proposed in this paper is illustrated using hindcast data for the winter months of two locations: one that is exposed to the long Atlantic swell off the west coast of France, and another in the North Sea that is characterised by short fetch. This work demonstrates how extremal index estimation may be used in conjunction with the well-known runs de-clustering algorithm to predict the return periods of significant wave height extremes. Highlights: A novel method is presented for estimating the return periods of Hs extremes. The method infers an optimal run length for every extreme wave threshold examined. The method showed higher qualitative GPD parameter stability than existing algorithms.