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Development and validation of a high-resolution regional wave hindcast model for U.S. West Coast wave resource characterization

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  • Wu, Wei-Cheng
  • Wang, Taiping
  • Yang, Zhaoqing
  • García-Medina, Gabriel

Abstract

Wave resource characterization is an essential step for wave energy converter development in the ocean. However, accurate and detailed resource characterization at a regional scale poses a great challenge because of the requirements for high model grid resolution, extensive model validation, and a high-performance-computing resource. This study presents a multi-scale, multi-resolution approach using the WaveWatchIII and Simulating WAve Nearshore (SWAN) wave models to provide accurate long-term wave hindcasts with a spatial resolution of approximate 300 m in the nearshore region on the U.S. West Coast. Extensive model validation for the six wave resource parameters recommended by the International Electrotechnical Commission, bivariate histograms, and frequency-directional spectra distributions were conducted using a set of model performance metrics and measurements from 28 wave buoys along the West Coast. Model skills in simulating large waves under extreme storm events were also evaluated. Model results showed that the high-resolution SWAN model is able to accurately simulate the wave climate on the West Coast, especially in the nearshore region. This study also demonstrates that the multi-scale and multi-resolution modeling framework is an efficient approach for generating accurate long-term, high-resolution wave hindcasts for wave resource characterization at the regional scale.

Suggested Citation

  • Wu, Wei-Cheng & Wang, Taiping & Yang, Zhaoqing & García-Medina, Gabriel, 2020. "Development and validation of a high-resolution regional wave hindcast model for U.S. West Coast wave resource characterization," Renewable Energy, Elsevier, vol. 152(C), pages 736-753.
    • Handle: RePEc:eee:renene:v:152:y:2020:i:c:p:736-753
    DOI: 10.1016/j.renene.2020.01.077
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    4. Yang, Zhaoqing & García Medina, Gabriel & Neary, Vincent S. & Ahn, Seongho & Kilcher, Levi & Bharath, Aidan, 2023. "Multi-decade high-resolution regional hindcasts for wave energy resource characterization in U.S. coastal waters," Renewable Energy, Elsevier, vol. 212(C), pages 803-817.
    5. García Medina, Gabriel & Yang, Zhaoqing & Li, Ning & Cheung, Kwok Fai & Lutu-McMoore, Elinor, 2023. "Wave climate and energy resources in American Samoa from a 42-year high-resolution hindcast," Renewable Energy, Elsevier, vol. 210(C), pages 604-617.
    6. Zhang, Na & Li, Shuai & Wu, Yongsheng & Wang, Keh-Han & Zhang, Qinghe & You, Zai-Jin & Wang, Jin, 2020. "Effects of sea ice on wave energy flux distribution in the Bohai Sea," Renewable Energy, Elsevier, vol. 162(C), pages 2330-2343.
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    8. Li, Ning & García-Medina, Gabriel & Cheung, Kwok Fai & Yang, Zhaoqing, 2021. "Wave energy resources assessment for the multi-modal sea state of Hawaii," Renewable Energy, Elsevier, vol. 174(C), pages 1036-1055.
    9. Beya, Ignacio & Buckham, Bradley & Robertson, Bryson, 2021. "Impact of tidal currents and model fidelity on wave energy resource assessments," Renewable Energy, Elsevier, vol. 176(C), pages 50-66.
    10. 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).
    11. Ahn, Seongho & Haas, Kevin A. & Neary, Vincent S., 2020. "Wave energy resource characterization and assessment for coastal waters of the United States," Applied Energy, Elsevier, vol. 267(C).
    12. Delpey, Matthias & Lastiri, Ximun & Abadie, Stéphane & Roeber, Volker & Maron, Philippe & Liria, Pedro & Mader, Julien, 2021. "Characterization of the wave resource variability in the French Basque coastal area based on a high-resolution hindcast," Renewable Energy, Elsevier, vol. 178(C), pages 79-95.
    13. 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.
    14. Ruth Branch & Gabriel García-Medina & Zhaoqing Yang & Taiping Wang & Fadia Ticona Rollano & Lucia Hosekova, 2021. "Modeling Sea Ice Effects for Wave Energy Resource Assessments," Energies, MDPI, vol. 14(12), pages 1-15, June.

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