@article{3005327,
    title = "On defining storm intervals: Extreme wave analysis using extremal index inferencing of the run length parameter",
    author = "Oikonomou, C.L.G. and Gradowski, M. and Kalogeri, C. and Sarmento, A.J.N.A.",
    journal = "Ocean Engineering",
    year = "2020",
    volume = "217",
    publisher = "Elsevier Ireland Ltd",
    issn = "0029-8018",
    doi = "10.1016/j.oceaneng.2020.107988",
    keywords = "Ocean currents;  Offshore structures;  Pareto principle;  Storms;  Water waves, Extremal index;  Generalised Pareto distributions;  Independent samples;  Model functions;  Natural phenomena;  Return periods;  Significant wave height;  Winter months, Clustering algorithms, algorithm;  return period;  significant wave height;  swell;  wave-structure interaction, Atlantic Coast [Europe];  Atlantic Coast [France];  Atlantic Ocean;  France;  North Sea",
    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. © 2020 Elsevier Ltd"
}