Advertisement

Journal of Oceanology and Limnology

, Volume 37, Issue 6, pp 1805–1816 | Cite as

Numerical simulation and preliminary analysis of typhoon waves during three typhoons in the Yellow Sea and East China Sea

  • Ning Wang
  • Yijun HouEmail author
  • Shuiqing Li
  • Rui Li
Article

Abstract

In this study, typhoon waves generated during three typhoons (Damrey (1210), Fung-wong (1416), and Chan-hom (1509) in the Yellow Sea and East China Sea were simulated in a simulating waves nearshore (SWAN) model, and the wind forcing was constructed by combining reanalyzed wind data with a Holland typhoon wind model. Various parameters, such as the Holland fitting parameter (B) and the maximum wind radius (R), were investigated in sensitivity experiments in the Holland model that affect the wind field construction. Six different formulations were considered and the parameters determined by comparing the simulated wind results with in-situ wind measurements. The key factors affecting wave growth and dissipation processes from deep to shallow waters were studied, including wind input, whitecapping, and bottom friction. Comparison with in-situ wave measurements suggested that the KOMEN scheme (wind input exponential growth and whitecapping energy dissipation) and the JONSWAP scheme (dissipation of bottom friction) resulted in good reproduction of the significant wave height of typhoon waves. A preliminary analysis of the wave characteristics in terms of wind-sea and swell wave revealed that swell waves dominated with the distance of R to the eye of the typhoon, while wind-sea prevailed in the outer region up to six to eight times the R values despite a clear misalignment between wind and waves. The results support the hypothesis that nonlinear wave-wave interactions may play a key role in the formation of wave characteristics.

Keyword

Holland simulating waves nearshore (SWAN) typhoon waves Yellow Sea East China Sea wind-sea swell 

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

Notes

Acknowledgment

The data set is provided by marine scientific data center, IOCAS, China. The numerical work is supported by the High-Performance Computing Center, IOCAS, China.

References

  1. Booij N, Ris R C, Holthuijsen L H. 1999. A third-generation wave model for coastal regions: 1. Model description and validation. Journal of Geophysical Research: Oceans, 104(C4): 7 649–7 666.CrossRefGoogle Scholar
  2. Deng Z A, Wu K J, Yu T. 2007. The wave transport of the eastern area of the Pacific. Acta Oceanologica Sinica, 29(6): 1–9. (in Chinese with English abstract)Google Scholar
  3. Graham H E. 1959. Meteorological Considerations Pertinent to Standard Project Hurricane, Atlantic and Gulf Coasts of the United States. U.S. Department of Commerce, Weather Bureau, Washington, D.C.Google Scholar
  4. He H L, Song J B, Bai Y F, Xu Y, Wang J J, Bi F. 2018a. Climate and extrema of ocean waves in the East China Sea. Science China Earth Sciences, 61(7): 980–994.CrossRefGoogle Scholar
  5. He H L, Wu Q Y, Chen D K, Sun J, Liang C J, Jin W F, Xu Y. 2018b. Effects of surface waves and sea spray on air-sea fluxes during the passage of Typhoon Hagupit. Acta Oceanologica Sinica, 37(5): 1–7.CrossRefGoogle Scholar
  6. He H L, Xu Y. 2016. Wind-wave hindcast in the Yellow Sea and the Bohai Sea from the year 1988 to 2002. Acta Oceanologica Sinica, 35(3): 46–53.CrossRefGoogle Scholar
  7. He Q Q, Yang J, Wang W Y. 2015. Study on the simulated typhoon waves off Jiangsu coast during Typhoon DAMREY. Marine Science Bulletin, 34(5): 592–599. (in Chinese with English abstract)Google Scholar
  8. Holland G J. 1980. An analytic model of the wind and pressure profiles in hurricanes. Monthly Weather Review, 108(8): 1 212–1 218.CrossRefGoogle Scholar
  9. Hubbert G D, Holland G J, Leslie L M, Manton M J. 1991. A real-time system for forecasting tropical cyclone storm surges. Weather and Forecasting, 6(1): 86–97.CrossRefGoogle Scholar
  10. Jiang Z H, Hua F, Qu P. 2008. A new scheme for adjusting the tropical cyclone parameters. Advances in Marine Science, 26(1): 1–7. (in Chinese with English abstract)Google Scholar
  11. Kato F. 2005. Study on Risk Assessment of Storm Surge Flood. Technical note of National Institute for Land and Infrastructure Management of Japan. National Institute for Land and Infrastructure Management, Tokyo.Google Scholar
  12. Komen G, Cavaleri L, Donelan M, Hasselmann K, Hasselmann S, Janssen P A E M. 1994. Dynamics and Modelling of Ocean Waves. Cambridge University Press, Cambridge.CrossRefGoogle Scholar
  13. Mei C C. 1983. The Applied Dynamics of Ocean Surface Waves. A Wiley-Interscience Publication, New York. 734p.Google Scholar
  14. Padilla-Hernández R, Monbaliu J. 2001. Energy balance of wind waves as a function of the bottom friction formulation. Coastal Engineering, 43(2): 131–148.CrossRefGoogle Scholar
  15. Powell M, Soukup G, Cocke S, Gulati S, Morisseau-Leroy N, Hamid S, Dorst N, Axe L. 2005. State of Florida hurricane loss projection model: atmospheric science component. Journal of Wind Engineering and Industrial Aerodynamics, 93(8): 651–674.CrossRefGoogle Scholar
  16. Qi Q H, Zhu Z X, Wang Z G, Xiong W, Chen Y C, Pang L. 2015. Numerical simulation of storm surge induced by typhoon Dawei in Lianyungang seas. Hydro-Science and Engineering, (5): 60–66. (in Chinese with English abstract)Google Scholar
  17. Ris R C, Holthuijsen L H, Booij N. 1999. A third-generation wave model for coastal regions: 2. Verification. Journal of Geophysical Research: Oceans, 104(C4): 7 667–7 681.CrossRefGoogle Scholar
  18. Shao W, Li X, Hwang P et al. 2017. Bridging the gap between cyclone wind and wave by C-band SAR measurements. Highlights by Journal of Geophysical Research: Oceans, 122(7): 6 714–6 724.Google Scholar
  19. SWAN Team. 2018. SWAN SCIENTIFIC AND TECHNICAL DOCUMENTATION. SWAN Cycle III Version 41.20AB. Delft University of Technology, Technical Documentation, Delft, Netherlands. http://swanmodel.sourceforge.net/.Google Scholar
  20. Tan F, Zhang Q H, Pang Q X, Zhang N, Yang H. 2012. Numerical simulation of WIPHA typhoon waves using WRF-SWAN model. Journal of Waterway and Harbor, 33(1): 14–18. (in Chinese with English abstract)Google Scholar
  21. Vickery P J, Skerlj P F, Steckley A C, Twisdale L A. 2000. Hurricane wind field model for use in hurricane simulations. Journal of Structural Engineering, 126(10): 1 203–1 221.CrossRefGoogle Scholar
  22. Xu Y, He H L, Song J B, Hou Y J, Li F N. 2017. Observations and modeling of typhoon waves in the South China Sea. Journal of Physical Oceanography, 47(6): 1 307–1 324.CrossRefGoogle Scholar
  23. Young I R. 2006. Directional spectra of hurricane wind waves. J. Geophys.Res., 111(C8): 1–14.CrossRefGoogle Scholar

Copyright information

© Chinese Society for Oceanology and Limnology, Science Press and Springer-Verlag GmbH Germany, part of Springer Nature 2019

Authors and Affiliations

  • Ning Wang
    • 1
    • 2
    • 3
  • Yijun Hou
    • 1
    • 2
    • 3
    • 4
    Email author
  • Shuiqing Li
    • 1
    • 2
    • 4
  • Rui Li
    • 5
  1. 1.Key Laboratory of Ocean Circulation and Waves, Institute of OceanologyChinese Academy of SciencesQingdaoChina
  2. 2.Center for Ocean Mega-ScienceChinese Academy of SciencesQingdaoChina
  3. 3.University of Chinese Academy of SciencesBeijingChina
  4. 4.Laboratory for Ocean and Climate DynamicsQingdao National Laboratory for Marine Science and TechnologyQingdaoChina
  5. 5.Numerical Simulation DivisionNorth China Sea Marine Forecasting Center of Ministry of Natural ResourcesQingdaoChina

Personalised recommendations