Volume 42 Issue 10
Oct.  2023
Turn off MathJax
Article Contents
Liqun Jia, Shimei Wu, Bo Han, Shuqun Cai, Renhao Wu. Wave hindcast under tropical cyclone conditions in the South China Sea: sensitivity to wind fields[J]. Acta Oceanologica Sinica, 2023, 42(10): 36-53. doi: 10.1007/s13131-023-2227-1
Citation: Liqun Jia, Shimei Wu, Bo Han, Shuqun Cai, Renhao Wu. Wave hindcast under tropical cyclone conditions in the South China Sea: sensitivity to wind fields[J]. Acta Oceanologica Sinica, 2023, 42(10): 36-53. doi: 10.1007/s13131-023-2227-1

Wave hindcast under tropical cyclone conditions in the South China Sea: sensitivity to wind fields

doi: 10.1007/s13131-023-2227-1
Funds:  The Major Projects of the National Natural Science Foundation of China under contract No. U21A6001; the Program of Marine Economy Development Special Fund under Department of Natural Resources of Guangdong Province under contract No. GDNRC [2022]18; the Southern Marine Science and Engineering Guangdong Laboratory (Zhuhai) under contract No. SML2021SP207; the Fund of State Key Laboratory of Tropical Oceanography, South China Sea Institute of Oceanology, Chinese Academy of Sciences under contract No. LTO2001.
More Information
  • Corresponding author: wurenhao@mail.sysu.edu.cn
  • Received Date: 2023-04-08
  • Accepted Date: 2023-06-21
  • Available Online: 2023-08-01
  • Publish Date: 2023-10-01
  • Reliable wave information is critical for marine engineering. Numerical wave models are useful tools to obtain wave information with continuous spatiotemporal distributions. However, the accuracy of model results highly depends on the quality of wind forcing. In this study, we utilize observations from five buoys deployed in the northern South China Sea from August to September 2017. Notably, these buoys successfully recorded wind field and wave information during the passage of five tropical cyclones of different intensities without sustaining any damage. Based on these unique observations, we evaluated the quality of four widely used wind products, namely CFSv2, ERA5, CCMP, and ERAI. Our analysis showed that in the northern South China Sea, ERA5 performed best compared to buoy observations, especially in terms of maximum wind speed values at 10 m height (U10), extreme U10 occurrence time, and overall statistical indicators. CFSv2 tended to overestimate non-extreme U10 values. CCMP showed favorable statistical performance at only three of the five buoys, but underestimated extreme U10 values at all buoys. ERAI had the worst performance under both normal and tropical cyclone conditions. In terms of wave hindcast accuracy, ERA5 outperformed the other reanalysis products, with CFSv2 and CCMP following closely. ERAI showed poor performance especially in the upper significant wave heights. Furthermore, we found that the wave hindcasts did not improve with increasing spatiotemporal resolution, with spatial resolution up to 0.5°. These findings would help in improving wave hindcasts under extreme conditions.
  • loading
  • Atlas R, Hoffman R N, Bloom S C, et al. 1996. A multiyear global surface wind velocity dataset using SSM/I wind observations. Bulletin of the American Meteorological Society, 77(5): 869–882. doi: 10.1175/1520-0477(1996)077<0869:AMGSWV>2.0.CO;2
    Atlas R, Hoffman R N, Ardizzone J, et al. 2011. A cross-calibrated, multiplatform ocean surface wind velocity product for meteorological and oceanographic applications. Bulletin of the American Meteorological Society, 92(2): 157–174. doi: 10.1175/2010BAMS2946.1
    Battjes J A, Janssen J P F M. 1978. Energy loss and set-up due to breaking of random waves. In: Proceedings of the 16th International Conference on Coastal Engineering. Hamburg, Germany: American Society of Civil Engineers, 569–587
    Becerra D, Quezada M, Díaz H. 2022. A deep water and nearshore wave height calibration of the ECOWAVES hindcasting database. Latin American Journal of Aquatic Research, 50(4): 573–595. doi: 10.3856/vol50-issue4-fulltext-2811
    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): 7649–7666. doi: 10.1029/98JC02622
    Bourassa M A, Legler D M, O’Brien J J, et al. 2003. SeaWinds validation with research vessels. Journal of Geophysical Research: Oceans, 108(C2): 3019
    Carvalho D, Rocha A, Gómez-Gesteira M, et al. 2014. Comparison of reanalyzed, analyzed, satellite-retrieved and NWP modelled winds with buoy data along the Iberian Peninsula coast. Remote Sensing of Environment, 152: 480–492. doi: 10.1016/j.rse.2014.07.017
    Cavaleri L, Rizzoli P M. 1981. Wind wave prediction in shallow water: Theory and applications. Journal of Geophysical Research: Oceans, 86(C11): 10961–10973. doi: 10.1029/JC086iC11p10961
    Chalikov D. 2018. Numerical modeling of surface wave development under the action of wind. Ocean Science, 14(3): 453–470. doi: 10.5194/os-14-453-2018
    Chauvin F, Douville H, Ribes A. 2017. Atlantic tropical cyclones water budget in observations and CNRM-CM5 model. Climate Dynamics, 49(11): 4009–4021
    Chelton D B, Freilich M H. 2005. Scatterometer-based assessment of 10-m wind analyses from the operational ECMWF and NCEP numerical weather prediction models. Monthly Weather Review, 133(2): 409–429. doi: 10.1175/MWR-2861.1
    Chen Weibo, Chen Hongey, Hsiao Shihchun, et al. 2019. Wind forcing effect on hindcasting of typhoon-driven extreme waves. Ocean Engineering, 188: 106260. doi: 10.1016/j.oceaneng.2019.106260
    Dee D P, Uppala S M, Simmons A J, et al. 2011. The ERA-Interim reanalysis: Configuration and performance of the data assimilation system. Quarterly Journal of the Royal Meteorological Society, 137(656): 553–597. doi: 10.1002/qj.828
    Gualtieri G. 2021. Reliability of ERA5 reanalysis data for wind resource assessment: A comparison against tall towers. Energies, 14(14): 4169. doi: 10.3390/en14144169
    Hasselmann K, Barnett T, Bouws E, et al. 1973. Measurements of wind-wave growth and swell decay during the Joint North Sea Wave Project (JONSWAP). In: Ergänzungsheft zur Deutschen Hydrographischen Zeitschrift. Hamburg: Deutches Hydrographisches Institut, 8: 1–95
    Hasselmann S, Hasselmann K, Allender J H, et al. 1985. Computations and parameterizations of the nonlinear energy transfer in a gravity-wave spectrum. Part II: Parameterizations of the nonlinear energy transfer for application in wave models. Journal of Physical Oceanography, 15(11): 1378–1391. doi: 10.1175/1520-0485(1985)015<1378:CAPOTN>2.0.CO;2
    Hawkins S, Eager D, Harrison G P. 2011. Characterising the reliability of production from future British offshore wind fleets. In: IET Conference on Renewable Power Generation (RPG 2011). Edinburgh: IET
    Hersbach H, Bell B, Berrisford P, et al. 2020. The ERA5 global reanalysis. Quarterly Journal of the Royal Meteorological Society, 146(730): 1999–2049. doi: 10.1002/qj.3803
    Hoffman R N, Leidner S M, Henderson J M, et al. 2003. A two-dimensional variational analysis method for NSCAT ambiguity removal: Methodology, sensitivity, and tuning. Journal of Atmospheric and Oceanic Technology, 20(5): 585–605. doi: 10.1175/1520-0426(2003)20<585:ATDVAM>2.0.CO;2
    Janssen P A E M. 1989. Wave-induced stress and the drag of air flow over sea waves. Journal of Physical Oceanography, 19(6): 745–754. doi: 10.1175/1520-0485(1989)019<0745:WISATD>2.0.CO;2
    Jun K C, Jeong W M, Choi J Y, et al. 2015. Simulation of the extreme waves generated by typhoon Bolaven (1215) in the East China Sea and Yellow Sea. Acta Oceanologica Sinica, 34(12): 19–28. doi: 10.1007/s13131-015-0779-4
    Kanwal A, Tahir Z R, Asim M, et al. 2022. Evaluation of Reanalysis and Analysis Datasets Against Measured Wind Data for Wind Resource Assessment. Bonn, Germany: World Wind Energy Association
    Kara A B, Wallcraft A J, Bourassa M A. 2008. Air-sea stability effects on the 10 m winds over the global ocean: Evaluations of air-sea flux algorithms. Journal of Geophysical Research: Oceans, 113(C4): C04009
    Komen G J, Hasselmann K, Hasselmann K. 1984. On the existence of a fully developed wind-sea spectrum. Journal of Physical Oceanography, 14(8): 1271–1285. doi: 10.1175/1520-0485(1984)014<1271:OTEOAF>2.0.CO;2
    Lavrenov I V. 2003. Wind-Waves in Oceans: Dynamics and Numerical Simulations. Berlin, Heidelberg: Springer
    Liu W Timothy, Tang Wenqing. 1996. Equivalent Neutral Wind. Washington, DC: National Aeronautics and Space Administration
    Mears C A, Smith D K, Wentz F. 2001. Comparison of Special Sensor Microwave Imager and buoy-measured wind speeds from 1987 to 1997. Journal of Geophysical Research, 106: 11719–11729. doi: 10.1029/1999JC000097
    Monin A S, Obhukov A. 1954. Osnovnye zakonomernosti turbulentnogo peremeshivanija v prizemnon sloe atmosfery (Basic laws of turbulent mixing in the atmosphere near the ground). Trudy Geofizicheskogo Instituta, Akademiya Nauk SSSR, 24: 163–187
    Morim J, Erikson L H, Hemer M, et al. 2022. A global ensemble of ocean wave climate statistics from contemporary wave reanalysis and hindcasts. Scientific Data, 9: 358. doi: 10.1038/s41597-022-01459-3
    Peixoto J P, Oort A H. 1992. Physics of Climate. New York: American Institute of Physics
    Qiao Wenli, Song Jinbao, He Hailun, et al. 2019. Application of different wind field models and wave boundary layer model to typhoon waves numerical simulation in WAVEWATCH III model. Tellus A: Dynamic Meteorology and Oceanography, 71(1): 1657552. doi: 10.1080/16000870.2019.1657552
    Rapizo H, Liu Qingxiang, Babanin A V. 2022. Performance of the observation-based source terms in a high-resolution wave hindcast for the North Sea. In: Proceedings of the 41st International Conference on Ocean, Offshore and Arctic Engineering: Volume 2: Structures, Safety, and Reliability. Hamburg, Germany: ASME
    Ruti P M, Marullo S, D’Ortenzio F, et al. 2008. Comparison of analyzed and measured wind speeds in the perspective of oceanic simulations over the Mediterranean basin: Analyses, QuikSCAT and buoy data. Journal of Marine Systems, 70(1–2): 33–48
    Saha S, Moorthi S, Wu Xingren, et al. 2014. The NCEP Climate Forecast System version 2. Journal of Climate, 27(6): 2185–2208. doi: 10.1175/JCLI-D-12-00823.1
    Snyder R L, Dobson F W, Elliott J A, et al. 1981. Array measurements of atmospheric pressure fluctuations above surface gravity waves. Journal of Fluid Mechanics, 102: 1–59. doi: 10.1017/S0022112081002528
    Stopa J E. 2018. Wind forcing calibration and wave hindcast comparison using multiple reanalysis and merged satellite wind datasets. Ocean Modelling, 127: 55–69. doi: 10.1016/j.ocemod.2018.04.008
    Taylor K E. 2005. Taylor diagram primer. https://pcmdi.llnl.gov/staff/taylor/CV/Taylor_diagram_primer.pdf[2005-01-23/2023-05-28]
    Van Vledder G P, Akpınar A. 2015. Wave model predictions in the Black Sea: Sensitivity to wind fields. Applied Ocean Research, 53: 161–178. doi: 10.1016/j.apor.2015.08.006
    Wang Guo-sen, Wang Xidong, Wang Hui, et al. 2020. Evaluation on monthly sea surface wind speed of four reanalysis data sets over the China seas after 1988. Acta Oceanologica Sinica, 39(1): 83–90. doi: 10.1007/s13131-019-1525-0
    Weintrit A. 2009. Marine Navigation and Safety of Sea Transportation (1st ed. ). London: CRC Press
    Wu Wenfang, Li Pieliang, Zhai Fanggou, et al. 2020. Evaluation of different wind resources in simulating wave height for the Bohai, Yellow, and East China Seas (BYES) with SWAN model. Continental Shelf Research, 207: 104217. doi: 10.1016/j.csr.2020.104217
    Xie Shangping, Chang Chueh-hsin, Xie Qiang, et al. 2007. Intraseasonal variability in the summer South China Sea: Wind jet, cold filament, and recirculations. Journal of Geophysical Research: Oceans, 112(C10): C10008. doi: 10.1029/2007JC004238
  • 加载中


    通讯作者: 陈斌, bchen63@163.com
    • 1. 

      沈阳化工大学材料科学与工程学院 沈阳 110142

    1. 本站搜索
    2. 百度学术搜索
    3. 万方数据库搜索
    4. CNKI搜索

    Figures(14)  / Tables(6)

    Article Metrics

    Article views (473) PDF downloads(29) Cited by()
    Proportional views


    DownLoad:  Full-Size Img  PowerPoint