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Extreme wave heights in the North Atlantic from Altimeter Data

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  • Wimmer, Werenfrid
  • Challenor, Peter
  • Retzler, Chris

Abstract

Extreme waves are an important ocean feature. We estimate return values of significant wave height from measurements by satellite altimeters over the North Atlantic. The data were divided into 2° latitude by 2° longitude grid squares and the median along the satellite track was taken in each. Return values were estimated by fitting a Generalised Pareto Distribution to all values above a threshold, which was allowed to vary spatially. This method is objective, more statistically robust and thus theoretically preferable to fitting a distribution to all the data. The novel method gave return values that were up to 37% smaller than those estimated by fitting a Fisher-Tippet 1 distribution to all the data.

Suggested Citation

  • Wimmer, Werenfrid & Challenor, Peter & Retzler, Chris, 2006. "Extreme wave heights in the North Atlantic from Altimeter Data," Renewable Energy, Elsevier, vol. 31(2), pages 241-248.
    • Handle: RePEc:eee:renene:v:31:y:2006:i:2:p:241-248
    DOI: 10.1016/j.renene.2005.08.019
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    References listed on IDEAS

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    1. M. E. Robinson & J. A. Tawn, 2000. "Extremal analysis of processes sampled at different frequencies," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 62(1), pages 117-135.
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    Cited by:

    1. Agarwal, Atul & Venugopal, Vengatesan & Harrison, Gareth P., 2013. "The assessment of extreme wave analysis methods applied to potential marine energy sites using numerical model data," Renewable and Sustainable Energy Reviews, Elsevier, vol. 27(C), pages 244-257.
    2. Larsén, Xiaoli Guo & Kalogeri, Christina & Galanis, George & Kallos, George, 2015. "A statistical methodology for the estimation of extreme wave conditions for offshore renewable applications," Renewable Energy, Elsevier, vol. 80(C), pages 205-218.
    3. Cornejo-Bueno, L. & Nieto-Borge, J.C. & García-Díaz, P. & Rodríguez, G. & Salcedo-Sanz, S., 2016. "Significant wave height and energy flux prediction for marine energy applications: A grouping genetic algorithm – Extreme Learning Machine approach," Renewable Energy, Elsevier, vol. 97(C), pages 380-389.

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