Volume 40 Issue 1
Feb.  2021
Turn off MathJax
Article Contents
Hui Chen, Shuang Li, Hailun He, Jinbao Song, Zheng Ling, Anzhou Cao, Zhongshui Zou, Wenli Qiao. Observational study of super typhoon Meranti (2016) using satellite, surface drifter, Argo float and reanalysis data[J]. Acta Oceanologica Sinica, 2021, 40(1): 70-84. doi: 10.1007/s13131-021-1702-9
Citation: Hui Chen, Shuang Li, Hailun He, Jinbao Song, Zheng Ling, Anzhou Cao, Zhongshui Zou, Wenli Qiao. Observational study of super typhoon Meranti (2016) using satellite, surface drifter, Argo float and reanalysis data[J]. Acta Oceanologica Sinica, 2021, 40(1): 70-84. doi: 10.1007/s13131-021-1702-9

Observational study of super typhoon Meranti (2016) using satellite, surface drifter, Argo float and reanalysis data

doi: 10.1007/s13131-021-1702-9
Funds:  The National Program on Global Change and Air-Sea Interaction under contract No. GASI-IPOVAI-04, the National Natural Science Foundation of China under contract Nos 41830533, 41876003 and 41621064; the China-Sweden (NSFC-STINT) Cooperation and Exchange Project under contract No. 41911530149.
More Information
  • Corresponding author: Email: lshuang@zju.edu.cn
  • Received Date: 2019-09-15
  • Accepted Date: 2020-03-05
  • Available Online: 2021-04-21
  • Publish Date: 2021-01-25
  • The present work describes the basic features of super typhoon Meranti (2016) by multiple data sources. We mainly focus on the upper ocean response to Meranti using multiplatform satellites, in situ surface drifter and Argo floats, and compare the results with the widely used idealized wind vortex model and reanalysis datasets. The pre-existing meso-scale eddy provided a favor underlying surface boundary condition and also modulated the upper ocean response to Meranti. Results show that the maximum sea surface cooling was 2.0°C after Meranti. The satellite surface wind failed to capture the core structure of Meranti as the idealized wind vortex model deduced. According to the observation of sea surface drifters, the near-inertial currents were significantly enhanced during the passage of Meranti. The temperature and salinity profiles from Argo floats revealed both the mixed-layer extension and subsurface upwelling induced by Meranti. The comparison results show that the sea surface temperature and surface wind in the reanalysis datasets differs from those in remote sensing system. Sea surface cooling is similar in both satellite and in situ observation, and sea surface salinity response has a lower correlation with the precipitation rate.
  • loading
  • [1]
    Adler R F, Rodgers E B. 1977. Satellite-observed latent heat release in a tropical cyclone. Monthly Weather Review, 105(8): 956–963. doi: 10.1175/1520-0493(1977)105<0956:SOLHRI>2.0.CO;2
    [2]
    Bao Jianwen. 2016. Physical processes in tropical cyclone models. In: Mohanty U C, Gopalakrishnan S G, eds. Advanced Numerical Modeling and Data Assimilation Techniques for Tropical Cyclone Prediction. Dordrecht: Springer, 107–144
    [3]
    Cao Anzhou, Guo Zheng, Song Jinbao, et al. 2018. Near-inertial waves and their underlying mechanisms based on the South China Sea Internal Wave Experiment (2010–2011). Journal of Geophysical Research: Oceans, 123(7): 5026–5040. doi: 10.1029/2018JC013753
    [4]
    Cassity M M, Colgan S G. 1973. An automated objective technique for constructing tropical cyclone best tracks. Monthly Weather Review, 101(11): 824–829. doi: 10.1175/1520-0493(1973)101<0824:AAOTFC>2.3.CO;2
    [5]
    Chang Y C, Chen G Y, Tseng R S, et al. 2013. Observed near-surface flows under all tropical cyclone intensity levels using drifters in the northwestern Pacific. Journal of Geophysical Research: Oceans, 118(5): 2367–2377. doi: 10.1002/jgrc.20187
    [6]
    Chang L, He X F. 2011. InSAR atmospheric distortions mitigation: GPS observations and NCEP FNL data. Journal of Atmospheric and Solar-Terrestrial Physics, 73(4): 464–471. doi: 10.1016/j.jastp.2010.11.003
    [7]
    Chen S L, Polton J A, Hu J Y, et al. 2015. Local inertial oscillations in the surface ocean generated by time-varying winds. Ocean Dynamics, 65(12): 1633–1641. doi: 10.1007/s10236-015-0899-6
    [8]
    Cione J J. 2015. The relative roles of the ocean and atmosphere as revealed by buoy air-sea observations in hurricanes. Monthly Weather Review, 143(3): 904–913. doi: 10.1175/MWR-D-13-00380.1
    [9]
    Cummings J A. 2005. Operational multivariate ocean data assimilation. Quarterly Journal of the Royal Meteorological Society, 131(613): 3583–3604. doi: 10.1256/qj.05.105
    [10]
    Cummings J A, Smedstad O M. 2013. Variational data assimilation for the global ocean. In: Park S, Xu L, eds. Data Assimilation for Atmospheric, Oceanic and Hydrologic Applications (Vol. II). Berlin, Heidelberg: Springer, 303–343
    [11]
    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
    [12]
    Emanuel K. 2003. Tropical cyclones. Annual Review of Earth and Planetary Sciences, 31: 75–104. doi: 10.1146/annurev.earth.31.100901.141259
    [13]
    Fu L L, Lee T, Liu W T, et al. 2018. 50 years of satellite remote sensing of the ocean. Meteorological Monographs, 59(1): 5.1–5.46. doi: 10.1175/AMSMONOGRAPHS-D-18-0010.1
    [14]
    Guan Shoude, Liu Ze, Song Jinbao, et al. 2017. Upper ocean response to super typhoon Tembin (2012) explored using multiplatform satellites and Argo float observations. International Journal of Remote Sensing, 38(18): 5150–5167. doi: 10.1080/01431161.2017.1335911
    [15]
    Guan Shoude, Zhao Wei, Huthnance J, et al. 2014. Observed upper ocean response to typhoon Megi (2010) in the Northern South China Sea. Journal of Geophysical Research: Oceans, 119(5): 3134–3157. doi: 10.1002/2013JC009661
    [16]
    Hawkins J D, Black P G. 1983. SEASAT scatterometer detection of gale force winds near tropical cyclones. Journal of Geophysical Research: Oceans, 88(C3): 1674–1682. doi: 10.1029/JC088iC03p01674
    [17]
    He Hailun, Wu Qiaoyan, Chen Dake, et al. 2018. Effects of surface waves and sea spray on air-sea fluxes during the passage of Typhoon Hagupit. Acta Oceanologica Sinica, 37(5): 1–7. doi: 10.1007/s13131-018-1208-2
    [18]
    Holland G J. 1980. An analytic model of the wind and pressure profiles in hurricanes. Monthly Weather Review, 108(8): 1212–1218. doi: 10.1175/1520-0493(1980)108<1212:AAMOTW>2.0.CO;2
    [19]
    Hong Xiaodong, Chang S W, Raman S, et al. 2000. The interaction between hurricane Opal (1995) and a warm core ring in the Gulf of Mexico. Monthly Weather Review, 128(5): 1347–1365. doi: 10.1175/1520-0493(2000)128<1347:TIBHOA>2.0.CO;2
    [20]
    Jin Shaohui, Li Xiaofeng, Yang Xiaofeng, et al. 2019. Identification of tropical cyclone centers in SAR Imagery based on template matching and particle swarm optimization algorithms. IEEE Transactions on Geoscience and Remote Sensing, 57(1): 598–608. doi: 10.1109/TGRS.2018.2863259
    [21]
    Jones W L, Cardone V J, Pierson W J, et al. 1999. NSCAT high-resolution surface wind measurements in typhoon Violet. Journal of Geophysical Research: Oceans, 104(C5): 11247–11259. doi: 10.1029/1998JC900107
    [22]
    Kalnay E, Kanamitsu M, Kistler R, et al. 1996. The NCEP/NCAR 40-year reanalysis project. Bulletin of the American Meteorological Society, 77(3): 437–472. doi: 10.1175/1520-0477(1996)077<0437:TNYRP>2.0.CO;2
    [23]
    Kishtawal C M. 2016. Use of satellite observations in tropical cyclone studies. In: Mohanty U C, Gopalakrishnan S G, eds. Advanced Numerical Modeling and Data Assimilation Techniques for Tropical Cyclone Prediction. Dordrecht: Springer, 35–47
    [24]
    Knaff J A, Brown D P, Courtney J, et al. 2010. An evaluation of dvorak techniquebased tropical cyclone intensity estimates. Weather and Forecasting, 25(5): 1362–1379. doi: 10.1175/2010WAF2222375.1
    [25]
    Knapp K R, Kruk M C, Levinson D H, et al. 2010. The international best track archive for climate stewardship (IBTrACS): Unifying tropical cyclone data. Bulletin of the American Meteorological Society, 91(3): 363–376. doi: 10.1175/2009BAMS2755.1
    [26]
    Lee D K, Niiler P P. 1998. The inertial chimney: The near-inertial energy drainage from the ocean surface to the deep layer. Journal of Geophysical Research: Oceans, 103(C4): 7579–7591. doi: 10.1029/97JC03200
    [27]
    Li F N, Song J B, He H L, et al. 2016. Assessment of surface drag coefficient parameterizations based on observations and simulations using the Weather Research and Forecasting model. Atmospheric and Oceanic Science Letters, 9(4): 327–336. doi: 10.1080/16742834.2016.1196105
    [28]
    Moon I J, Ginis I, Hara T, et al. 2007. A physics-based parameterization of air-sea momentum flux at high wind speeds and its impact on hurricane intensity predictions. Monthly Weather Review, 135(8): 2869–2878. doi: 10.1175/MWR3432.1
    [29]
    Nilsson J. 1995. Energy flux from traveling hurricanes to the oceanic internal wave field. Journal of Physical Oceanography, 25(4): 558–573. doi: 10.1175/1520-0485(1995)025<0558:EFFTHT>2.0.CO;2
    [30]
    Price J F. 1981. Upper ocean response to a hurricane. Journal of Physical Oceanography, 11(2): 153–175. doi: 10.1175/1520-0485(1981)011<0153:UORTAH>2.0.CO;2
    [31]
    Pun I F, Chang Y T, Lin I I, et al. 2011. Typhoon-ocean interaction in the Western North Pacific: Part 2. Oceanography, 24(4): 32–41. doi: 10.5670/oceanog.2011.92
    [32]
    Qiu Bo. 1999. Seasonal eddy field modulation of the north pacific subtropical countercurrent: TOPEX/oseidon observations and theory. Journal of Physical Oceanography, 29(10): 2471–2486. doi: 10.1175/1520-0485(1999)029<2471:SEFMOT>2.0.CO;2
    [33]
    Reynolds R W, Smith T M, Liu Chunying, et al. 2007. Daily high-resolution-blended analyses for sea surface temperature. Journal of Climate, 20(22): 5473–5496. doi: 10.1175/2007JCLI1824.1
    [34]
    Shay L K, Goni G J, Black P G. 2000. Effects of a warm oceanic feature on hurricane Opal. Monthly Weather Review, 128(5): 1366–1383. doi: 10.1175/1520-0493(2000)128<1366:EOAWOF>2.0.CO;2
    [35]
    Sun Jia, He Hailun, Hu Xiaomin, et al. 2019. Numerical simulations of typhoon Hagupit (2008) using WRF. Weather and Forecasting, 34(4): 999–1015. doi: 10.1175/WAF-D-18-0150.1
    [36]
    Uppala S M, Kållberg P W, Simmons A J, et al. 2005. The ERA-40 re-analysis. Quarterly Journal of the Royal Meteorological Society, 131(612): 2961–3012. doi: 10.1256/qj.04.176
    [37]
    Velden C, Harper B, Wells F, et al. 2006. The Dvorak tropical cyclone intensity estimation technique: A satellite-based method that has endured for over 30 years. Bulletin of the American Meteorological Society, 87(9): 1195–1210. doi: 10.1175/BAMS-87-9-1195
    [38]
    Wada A, Kanada S, Yamada H. 2018. Effect of air-sea environmental conditions and interfacial processes on extremely intense typhoon Haiyan (2013). Journal of Geophysical Research: Atmospheres, 123(18): 10379–10405
    [39]
    Wada A, Uehara T, Ishizaki S. 2014. Typhoon-induced sea surface cooling during the 2011 and 2012 typhoon seasons: Observational evidence and numerical investigations of the sea surface cooling effect using typhoon simulations. Progress in Earth and Planetary Science, 1: 11. doi: 10.1186/2197-4284-1-11
    [40]
    Webster P J, Holland G J, Curry J A, et al. 2005. Changes in tropical cyclone number, duration, and intensity in a warming environment. Science, 309(5742): 1844–1846. doi: 10.1126/science.1116448
    [41]
    Yablonsky R M, Ginis I. 2009. Limitation of one-dimensional ocean models for coupled hurricane-ocean model forecasts. Monthly Weather Review, 137(12): 4410–4419. doi: 10.1175/2009MWR2863.1
    [42]
    Yue Xinxin, Zhang Biao, Liu Guoqiang, et al. 2018. Upper ocean response to typhoon Kalmaegi and Sarika in the South China Sea from multiple-satellite observations and numerical simulations. Remote Sensing, 10(2): 348
    [43]
    Zhang Qiang, Wu Liguang, Liu Qiufeng. 2009. Tropical cyclone damages in China 1983–2006. Bulletin of the American Meteorological Society, 90(4): 489–496. doi: 10.1175/2008BAMS2631.1
    [44]
    Zhao Biao, Qiao Fangli, Cavaleri L, et al. 2017. Sensitivity of typhoon modeling to surface waves and rainfall. Journal of Geophysical Research: Oceans, 122(3): 1702–1723. doi: 10.1002/2016JC012262
  • 加载中

Catalog

    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(11)

    Article Metrics

    Article views (327) PDF downloads(25) Cited by()
    Proportional views
    Related

    /

    DownLoad:  Full-Size Img  PowerPoint
    Return
    Return