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Evolving probabilistic modeling for long-term significant wave heights with a focus on extremes

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  • Qin, Jianjun

Abstract

The present paper aims to formulate a novel framework of probabilistic model for long-term significant wave height to facilitate accurate probabilistic analysis and further decision making in engineering practice. It is found that there are several sources of inconsistency through the investigations of the records of significant wave height and correspondingly, three main challenges in the probabilistic modeling together with the relevant characteristics of long-term significant wave height are presented. A cluster based probabilistic model, in which the extremes are highlighted, is derived afterwards by optimization to ensure that it keeps consistent with the information conveyed from the records. Further, the model will also evolve with the recognition of the inconsistency between the records collected from different lengths of period. Finally, a framework for the analysis is formulated and its application is illustrated.

Suggested Citation

  • Qin, Jianjun, 2022. "Evolving probabilistic modeling for long-term significant wave heights with a focus on extremes," Renewable Energy, Elsevier, vol. 187(C), pages 362-370.
    • Handle: RePEc:eee:renene:v:187:y:2022:i:c:p:362-370
    DOI: 10.1016/j.renene.2022.01.069
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    References listed on IDEAS

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    1. Arinaga, Randi A. & Cheung, Kwok Fai, 2012. "Atlas of global wave energy from 10 years of reanalysis and hindcast data," Renewable Energy, Elsevier, vol. 39(1), pages 49-64.
    2. Haselsteiner, Andreas F. & Thoben, Klaus-Dieter, 2020. "Predicting wave heights for marine design by prioritizing extreme events in a global model," Renewable Energy, Elsevier, vol. 156(C), pages 1146-1157.
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