IDEAS home Printed from https://ideas.repec.org/a/eee/renene/v188y2022icp750-764.html
   My bibliography  Save this article

Propagation and attenuation of swell energy in the Pacific Ocean

Author

Listed:
  • Zheng, Chong-wei
  • Wu, Di
  • Wu, Hai-lang
  • Guo, Jing
  • Shen, Chong
  • Tian, Chuan
  • Tian, Xin-long
  • Xiao, Zi-niu
  • Zhou, Wen
  • Li, Chong-yin

Abstract

Understanding the characteristics of swell propagation is practical for swell monitoring, early warning and wave power generation. In this study, the attenuation rate of swell energy during the propagation process is defined. Then, the propagation, attenuation and intraseasonal oscillation characteristics of swell energy in the Pacific Ocean are analyzed based on the 40-year European Centre for Medium-Range Weather Forecasts (ECMWF) reanalysis (ERA-40). Results show that (1) The swells generated from the North Pacific Ocean westerlies (NPOW) and the South Pacific Ocean westerlies (SPOW) usually propagate to the low latitude waters of the Pacific Ocean. (2) Swells in Hawaiian waters, Cologne Islands waters and Iquique waters mainly originate from the winter hemisphere. (3) It takes approximately 8 days for the swell to propagate from the SPOW to Cologne Islands waters. It takes approximately 7.5 days for the swell to travel from the NPOW to Cologne Islands waters. (4) An obvious attenuation of swell energy of 60%–90% occurs during the propagation process. (5) Swell energies in the SPOW, NPOW, Hawaiian waters, Cologne Islands waters and Iquique waters share a common quasi-biweekly oscillation. The attenuation rate of the swell energy has a significant quasi-weekly or quasi-biweekly oscillation.

Suggested Citation

  • Zheng, Chong-wei & Wu, Di & Wu, Hai-lang & Guo, Jing & Shen, Chong & Tian, Chuan & Tian, Xin-long & Xiao, Zi-niu & Zhou, Wen & Li, Chong-yin, 2022. "Propagation and attenuation of swell energy in the Pacific Ocean," Renewable Energy, Elsevier, vol. 188(C), pages 750-764.
    • Handle: RePEc:eee:renene:v:188:y:2022:i:c:p:750-764
    DOI: 10.1016/j.renene.2022.02.071
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0960148122002130
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.renene.2022.02.071?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. Iglesias, G. & Carballo, R., 2011. "Choosing the site for the first wave farm in a region: A case study in the Galician Southwest (Spain)," Energy, Elsevier, vol. 36(9), pages 5525-5531.
    2. 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.
    3. Martinez, A. & Iglesias, G., 2020. "Wave exploitability index and wave resource classification," Renewable and Sustainable Energy Reviews, Elsevier, vol. 134(C).
    4. Liang, Bingchen & Fan, Fei & Yin, Zegao & Shi, Hongda & Lee, Dongyong, 2013. "Numerical modelling of the nearshore wave energy resources of Shandong peninsula, China," Renewable Energy, Elsevier, vol. 57(C), pages 330-338.
    5. Zheng, C.W. & Li, C.Y., 2017. "Propagation characteristic and intraseasonal oscillation of the swell energy of the Indian Ocean," Applied Energy, Elsevier, vol. 197(C), pages 342-353.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Yang, Haoze & Ge, Mingwei & Abkar, Mahdi & Yang, Xiang I.A., 2022. "Large-eddy simulation study of wind turbine array above swell sea," Energy, Elsevier, vol. 256(C).
    2. Fu, Yang & Ying, Feixiang & Huang, Lingling & Liu, Yang, 2023. "Multi-step-ahead significant wave height prediction using a hybrid model based on an innovative two-layer decomposition framework and LSTM," Renewable Energy, Elsevier, vol. 203(C), pages 455-472.

    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. Zheng, Chong-wei, 2021. "Global oceanic wave energy resource dataset—with the Maritime Silk Road as a case study," Renewable Energy, Elsevier, vol. 169(C), pages 843-854.
    2. Wan, Yong & Zheng, Chongwei & Li, Ligang & Dai, Yongshou & Esteban, M. Dolores & López-Gutiérrez, José-Santos & Qu, Xiaojun & Zhang, Xiaoyu, 2020. "Wave energy assessment related to wave energy convertors in the coastal waters of China," Energy, Elsevier, vol. 202(C).
    3. Zheng, Chong-wei, 2021. "Dynamic self-adjusting classification for global wave energy resources under different requirements," Energy, Elsevier, vol. 236(C).
    4. Morim, Joao & Cartwright, Nick & Etemad-Shahidi, Amir & Strauss, Darrell & Hemer, Mark, 2016. "Wave energy resource assessment along the Southeast coast of Australia on the basis of a 31-year hindcast," Applied Energy, Elsevier, vol. 184(C), pages 276-297.
    5. Chongwei Zheng, 2023. "An Overview and Countermeasure of Global Wave Energy Classification," Sustainability, MDPI, vol. 15(12), pages 1-21, June.
    6. Liang, Bingchen & Shao, Zhuxiao & Wu, Guoxiang & Shao, Meng & Sun, Jinwei, 2017. "New equations of wave energy assessment accounting for the water depth," Applied Energy, Elsevier, vol. 188(C), pages 130-139.
    7. Yong Wan & Chenqing Fan & Jie Zhang & Junmin Meng & Yongshou Dai & Ligang Li & Weifeng Sun & Peng Zhou & Jing Wang & Xudong Zhang, 2017. "Wave Energy Resource Assessment off the Coast of China around the Zhoushan Islands," Energies, MDPI, vol. 10(9), pages 1-25, September.
    8. Liang, Bingchen & Fan, Fei & Liu, Fushun & Gao, Shanhong & Zuo, Hongyan, 2014. "22-Year wave energy hindcast for the China East Adjacent Seas," Renewable Energy, Elsevier, vol. 71(C), pages 200-207.
    9. Valentina Vannucchi & Lorenzo Cappietti, 2016. "Wave Energy Assessment and Performance Estimation of State of the Art Wave Energy Converters in Italian Hotspots," Sustainability, MDPI, vol. 8(12), pages 1-21, December.
    10. Kamranzad, Bahareh & Lin, Pengzhi & Iglesias, Gregorio, 2021. "Combining methodologies on the impact of inter and intra-annual variation of wave energy on selection of suitable location and technology," Renewable Energy, Elsevier, vol. 172(C), pages 697-713.
    11. Bingölbali, Bilal & Majidi, Ajab Gul & Akpınar, Adem, 2021. "Inter- and intra-annual wave energy resource assessment in the south-western Black Sea coast," Renewable Energy, Elsevier, vol. 169(C), pages 809-819.
    12. Sierra, J.P. & Mösso, C. & González-Marco, D., 2014. "Wave energy resource assessment in Menorca (Spain)," Renewable Energy, Elsevier, vol. 71(C), pages 51-60.
    13. Sun, Peidong & Xu, Bin & Wang, Jichao, 2022. "Long-term trend analysis and wave energy assessment based on ERA5 wave reanalysis along the Chinese coastline," Applied Energy, Elsevier, vol. 324(C).
    14. Joensen, Bárður & Niclasen, Bárður A. & Bingham, Harry B., 2021. "Wave power assessment in Faroese waters using an oceanic to nearshore scale spectral wave model," Energy, Elsevier, vol. 235(C).
    15. Uihlein, Andreas & Magagna, Davide, 2016. "Wave and tidal current energy – A review of the current state of research beyond technology," Renewable and Sustainable Energy Reviews, Elsevier, vol. 58(C), pages 1070-1081.
    16. Yong Wan & Chenqing Fan & Yongshou Dai & Ligang Li & Weifeng Sun & Peng Zhou & Xiaojun Qu, 2018. "Assessment of the Joint Development Potential of Wave and Wind Energy in the South China Sea," Energies, MDPI, vol. 11(2), pages 1-26, February.
    17. Yaakob, Omar & Hashim, Farah Ellyza & Mohd Omar, Kamaludin & Md Din, Ami Hassan & Koh, Kho King, 2016. "Satellite-based wave data and wave energy resource assessment for South China Sea," Renewable Energy, Elsevier, vol. 88(C), pages 359-371.
    18. Shao, Zhuxiao & Gao, Huijun & Liang, Bingchen & Lee, Dongyoung, 2022. "Potential, trend and economic assessments of global wave power," Renewable Energy, Elsevier, vol. 195(C), pages 1087-1102.
    19. Lin, Yifan & Dong, Sheng & Wang, Zhifeng & Guedes Soares, C., 2019. "Wave energy assessment in the China adjacent seas on the basis of a 20-year SWAN simulation with unstructured grids," Renewable Energy, Elsevier, vol. 136(C), pages 275-295.
    20. Karunarathna, Harshinie & Maduwantha, Pravin & Kamranzad, Bahareh & Rathnasooriya, Harsha & de Silva, Kasun, 2020. "Evaluation of spatio-temporal variability of ocean wave power resource around Sri Lanka," Energy, Elsevier, vol. 200(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:188:y:2022:i:c:p:750-764. 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.