IDEAS home Printed from https://ideas.repec.org/a/eee/renene/v208y2023icp130-140.html
   My bibliography  Save this article

Global atlas of extreme significant wave heights and relative risk ratios

Author

Listed:
  • Neary, Vincent S.
  • Ahn, Seongho

Abstract

Interest in offshore wind, wave, and tidal energy power generation to supply electricity to the grid nearshore or power off-grid applications far offshore has motivated numerous studies of the ocean wave climate, including extreme wave conditions that characterize project risk and wave loads for design. The present study uses the 30′ resolution 31-year Global WAVEWATCH III® model hindcast, the 4′ resolution 31-year coastal US model hindcast, and the US NOAA buoy network to estimate and map the global distribution of the mean, 50-, 5- and 1-year significant wave heights and the relative-risk-ratios computed by non-dimensionalizing these n-year extreme wave heights with their mean values. A two-step linear correction method is applied to correct systematic underbias of model-derived extreme wave heights relative to the observation-derived values, resulting in a 20% or more increase in model-derived values compared to uncorrected ones. The corrected 30’ resolution extreme wave height and relative risk ratio atlas generated herein provides important metrics that support resource characterization and international standards development for the marine energy industry, including resource and site assessment, and the establishment of upper design limits for device type classification and certification to streamline product line development.

Suggested Citation

  • Neary, Vincent S. & Ahn, Seongho, 2023. "Global atlas of extreme significant wave heights and relative risk ratios," Renewable Energy, Elsevier, vol. 208(C), pages 130-140.
  • Handle: RePEc:eee:renene:v:208:y:2023:i:c:p:130-140
    DOI: 10.1016/j.renene.2023.03.079
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0960148123003749
    Download Restriction: Full text for ScienceDirect subscribers only

    File URL: https://libkey.io/10.1016/j.renene.2023.03.079?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Ahn, Seongho & Haas, Kevin A. & Neary, Vincent S., 2019. "Wave energy resource classification system for US coastal waters," Renewable and Sustainable Energy Reviews, Elsevier, vol. 104(C), pages 54-68.
    2. Reguero, B.G. & Losada, I.J. & Méndez, F.J., 2015. "A global wave power resource and its seasonal, interannual and long-term variability," Applied Energy, Elsevier, vol. 148(C), pages 366-380.
    3. Fairley, Iain & Lewis, Matthew & Robertson, Bryson & Hemer, Mark & Masters, Ian & Horrillo-Caraballo, Jose & Karunarathna, Harshinie & Reeve, Dominic E., 2020. "A classification system for global wave energy resources based on multivariate clustering," Applied Energy, Elsevier, vol. 262(C).
    4. Niebuhr, C.M. & van Dijk, M. & Neary, V.S. & Bhagwan, J.N., 2019. "A review of hydrokinetic turbines and enhancement techniques for canal installations: Technology, applicability and potential," Renewable and Sustainable Energy Reviews, Elsevier, vol. 113(C), pages 1-1.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Coe, Ryan G. & Ahn, Seongho & Neary, Vincent S. & Kobos, Peter H. & Bacelli, Giorgio, 2021. "Maybe less is more: Considering capacity factor, saturation, variability, and filtering effects of wave energy devices," Applied Energy, Elsevier, vol. 291(C).
    2. Ahn, Seongho & Neary, Vincent S. & Allahdadi, Mohammad Nabi & He, Ruoying, 2021. "Nearshore wave energy resource characterization along the East Coast of the United States," Renewable Energy, Elsevier, vol. 172(C), pages 1212-1224.
    3. Ahn, Seongho & Neary, Vincent S. & Haas, Kevin A., 2022. "Global wave energy resource classification system for regional energy planning and project development," Renewable and Sustainable Energy Reviews, Elsevier, vol. 162(C).
    4. Ribeiro, A.S. & deCastro, M. & Costoya, X. & Rusu, Liliana & Dias, J.M. & Gomez-Gesteira, M., 2021. "A Delphi method to classify wave energy resource for the 21st century: Application to the NW Iberian Peninsula," Energy, Elsevier, vol. 235(C).
    5. Tunde Aderinto & Hua Li, 2020. "Effect of Spatial and Temporal Resolution Data on Design and Power Capture of a Heaving Point Absorber," Sustainability, MDPI, vol. 12(22), pages 1-17, November.
    6. Seongho Ahn & Kevin A. Haas & Vincent S. Neary, 2020. "Dominant Wave Energy Systems and Conditional Wave Resource Characterization for Coastal Waters of the United States," Energies, MDPI, vol. 13(12), pages 1-26, June.
    7. Shi, Xueli & Liang, Bingchen & Li, Shaowu & Zhao, Jianchun & Wang, Junhui & Wang, Zhenlu, 2024. "Wave energy resource classification system for the China East Adjacent Seas based on multivariate clustering," Energy, Elsevier, vol. 299(C).
    8. Simon Krüner & Christoph M. Hackl, 2022. "Nonlinear Modelling and Control of a Power Smoothing System for a Novel Wave Energy Converter Prototype," Sustainability, MDPI, vol. 14(21), pages 1-17, October.
    9. Masoud, Alaa A., 2022. "On the Nile Fan's wave power potential and controlling factors integrating spectral and geostatistical techniques," Renewable Energy, Elsevier, vol. 196(C), pages 921-945.
    10. Guillou, Nicolas, 2020. "Estimating wave energy flux from significant wave height and peak period," Renewable Energy, Elsevier, vol. 155(C), pages 1383-1393.
    11. Shih-Chun Hsiao & Chao-Tzuen Cheng & Tzu-Yin Chang & Wei-Bo Chen & Han-Lun Wu & Jiun-Huei Jang & Lee-Yaw Lin, 2021. "Assessment of Offshore Wave Energy Resources in Taiwan Using Long-Term Dynamically Downscaled Winds from a Third-Generation Reanalysis Product," Energies, MDPI, vol. 14(3), pages 1-25, January.
    12. Ahn, Seongho & Neary, Vincent S. & Ha, Taemin, 2023. "A practical method for modeling temporally-averaged ocean wave frequency-directional spectra for characterizing wave energy climates," Renewable Energy, Elsevier, vol. 207(C), pages 499-511.
    13. Martinez, A. & Iglesias, G., 2020. "Wave exploitability index and wave resource classification," Renewable and Sustainable Energy Reviews, Elsevier, vol. 134(C).
    14. Lavidas, George, 2020. "Selection index for Wave Energy Deployments (SIWED): A near-deterministic index for wave energy converters," Energy, Elsevier, vol. 196(C).
    15. Choupin, Ophelie & Del Río-Gamero, B. & Schallenberg-Rodríguez, Julieta & Yánez-Rosales, Pablo, 2022. "Integration of assessment-methods for wave renewable energy: Resource and installation feasibility," Renewable Energy, Elsevier, vol. 185(C), pages 455-482.
    16. Fairley, Iain & Lewis, Matthew & Robertson, Bryson & Hemer, Mark & Masters, Ian & Horrillo-Caraballo, Jose & Karunarathna, Harshinie & Reeve, Dominic E., 2020. "A classification system for global wave energy resources based on multivariate clustering," Applied Energy, Elsevier, vol. 262(C).
    17. Chen, Wei-Bo, 2024. "Analysing seven decades of global wave power trends: The impact of prolonged ocean warming," Applied Energy, Elsevier, vol. 356(C).
    18. Orszaghova, J. & Lemoine, S. & Santo, H. & Taylor, P.H. & Kurniawan, A. & McGrath, N. & Zhao, W. & Cuttler, M.V.W., 2022. "Variability of wave power production of the M4 machine at two energetic open ocean locations: Off Albany, Western Australia and at EMEC, Orkney, UK," Renewable Energy, Elsevier, vol. 197(C), pages 417-431.
    19. Penalba, Markel & Aizpurua, Jose Ignacio & Martinez-Perurena, Ander & Iglesias, Gregorio, 2022. "A data-driven long-term metocean data forecasting approach for the design of marine renewable energy systems," Renewable and Sustainable Energy Reviews, Elsevier, vol. 167(C).
    20. Zheng, Chong-wei, 2021. "Dynamic self-adjusting classification for global wave energy resources under different requirements," Energy, Elsevier, vol. 236(C).

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:eee:renene:v:208:y:2023:i:c:p:130-140. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Catherine Liu (email available below). General contact details of provider: http://www.journals.elsevier.com/renewable-energy .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.