Chinese Journal of Oceanology and Limnology

, Volume 33, Issue 5, pp 1219–1232 | Cite as

Satellite derived upper ocean thermal structure and its application to tropical cyclone intensity forecasting in the Indian Ocean

  • Chunjian Sun (孙春健)
  • Xidong Wang (王喜冬)
  • Xiaojian Cui (崔晓健)
  • Xiaoshuang Zhang (张晓爽)
  • Lianxin Zhang (张连新)
  • Caixia Shao (邵彩霞)
  • Xinrong Wu (吴新荣)
  • Hongli Fu (付红丽)
  • Wei Li (李威)
Remote sensing

Abstract

Upper ocean heat content is a factor critical to the intensity change of tropical cyclones (TCs). Because of the inhomogeneity of in situ observations in the North Indian Ocean, gridded temperature/salinity (T/S) profiles were derived from satellite data for 1993–2012 using a linear regression method. The satellite derived T/S dataset covered the region of 10°S–32°N, 25°–100°E with daily temporal resolution, 0.25°×0.25° spatial resolution, and 26 vertical layers from the sea surface to a depth of 1 000 m at standard layers. Independent Global Temperature Salinity Profile Project data were used to validate the satellite derived T/S fields. The analysis confirmed that the satellite derived temperature field represented the characteristics and vertical structure of the temperature field well. The results demonstrated that the vertically averaged root mean square error of the temperature was 0.83 in the upper 1 000 m and the corresponding correlation coefficient was 0.87, which accounted for 76% of the observed variance. After verification of the satellite derived T/S dataset, the TC heat potential (TCHP) was verified. The results show that the satellite derived values were coherent with observed TCHP data with a correlation coefficient of 0.86 and statistical significance at the 99% confidence level. The intensity change of TC Gonu during a period of rapid intensification was studied using satellite derived TCHP data. A delayed effect of the TCHP was found in relation to the intensity change of Gonu, suggesting a lag feature in the response of the inner core of the TC to the ocean.

Keyword

tropical cyclone intensification tropical cyclone heat potential sea surface temperature sea surface height 

References

  1. Ali M M, Jagadeesh P S V, Jain s. (2007. Effects of eddies on Bay of Bengal cyclone intensity. EOS, Transactions American Geophysical Union, 88 (8): 93–95.CrossRefGoogle Scholar
  2. Bender M A, Ginis I. 2000. Real-case simulations of hurricane–ocean interaction using a high-resolution coupled model: effects on hurricane intensity. Monthly Weather Review, 128 (4): 917–945.CrossRefGoogle Scholar
  3. Cione J J, Kalina E A, Zhang J A et al. 2013. Observations of air-sea interaction and intensity change in hurricanes. Monthly Weather Review, 141 (8): 2368–2382.CrossRefGoogle Scholar
  4. Cione J J, Uhlhorn E W. 2003. Sea surface temperature variability in hurricanes: implications with respect to intensity change. Monthly Weather Review, 131 (8): 1783–1796.CrossRefGoogle Scholar
  5. DeMaria M, Kaplan J. (1994. Sea surface temperature and the maximum intensity of Atlantic tropical cyclones. Journal of Climate, 7 (9): 1324–1334.CrossRefGoogle Scholar
  6. Ducet N, Le Traon P Y, Reverdin G. 2000. Global highresolution mapping of ocean circulation from TOPEX/Poseidon and ERS-1 and-2. Journal of Geophysical Research: Oceans, 105 (C8): 19 477–19 498.CrossRefGoogle Scholar
  7. Emanuel K A. 1999. Thermodynamic control of hurricane intensity. Nature, 401 (6754): 665–669.CrossRefGoogle Scholar
  8. Emanuel K, DesAutels C, Holloway C et al. 2004. Environmental control of tropical cyclone intensity. Journal of the Atmospheric Sciences, 61 (7): 843–858.CrossRefGoogle Scholar
  9. Fox D N, Teague W J, Barron C N et al. 2002. The modular ocean data assimilation system (MODAS). Journal of Atmospheric & Oceanic Technology, 19 (2): 240–252.CrossRefGoogle Scholar
  10. Gallacher P, Rotunno R, Emanuel K A. 1989. Tropical cyclogenesis in a coupled ocean-atmosphere model. 18th Conference on Hurricanes and Tropical Meteorology, San Diego, CA, American Meteorological Society. p.121–122.Google Scholar
  11. Goni G J, Kamholz S, Garzoli S et al. 1996. Dynamics of the Brazil-Malvinas confluence based upon inverted echo sounders and altimetry. Journal of Geophysical Research, 101 (C7): 16 273–16 289.CrossRefGoogle Scholar
  12. Goni G J, Trinanes J A. 2003. Ocean thermal structure monitoring could aid in the intensity forecast of tropical cyclones. EOS, Transactions American Geophysical Union, 84 (51): 573–578.CrossRefGoogle Scholar
  13. Guinehut S, Dhomps A L, Larnicol G et al. 2012. High resolution 3-D temperature and salinity fields derived from in situ and satellite observations. Ocean Science, 8 (5): 845–857.CrossRefGoogle Scholar
  14. Guinehut S, Traon P Y L, Larnicol G et al. 2004. Combining Argo and remote-sensing data to estimate the ocean threedimensional temperature fields—a first approach based on simulated observations. Journal of Marine Systems, 46 (1-4): 85–98.CrossRefGoogle Scholar
  15. Holliday C R, Thompson A H. 1979. Climatological characteristics of rapidly intensifying typhoons. Monthly Weather Review, 107 (8): 1022–1034.CrossRefGoogle Scholar
  16. Hong W, Chang S W, Raman S, Shay L K 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.CrossRefGoogle Scholar
  17. Kaplan J, DeMaria M. 2003. Large-scale characteristics of rapidly intensifying tropical cyclones in the North Atlantic basin. Weather & Forecasting, 18 (6): 1093–1108.CrossRefGoogle Scholar
  18. 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.CrossRefGoogle Scholar
  19. Law K T, Hobgood J s. (2007. A statistical model to forecast short-term Atlantic hurricane intensity. Weather & Forecasting, 22 (5): 967–980.CrossRefGoogle Scholar
  20. Leipper D F, Volgenau D. 1972. Hurricane heat potential of the Gulf of Mexico. Journal of Physical Oceanography, 2 (3): 218–224.CrossRefGoogle Scholar
  21. Levitus s. (2009. World Ocean Database. AGU Fall Meeting Abstracts. 1: 03.Google Scholar
  22. Lin I I, Chen C H, Pun I F et al. 2009a. Warm ocean anomaly, air sea fluxes, and the rapid intensification of tropical cyclone Nargis (2008). Geophysical Research Letters, 36 (3): L03817.CrossRefGoogle Scholar
  23. Lin I I, Goni G J, Knaff J A et al. 2013. Ocean heat content for tropical cyclone intensity forecasting and its impact on storm surge. Natural Hazards, 66 (3): 1481–1500.CrossRefGoogle Scholar
  24. Lin I I, Pun I F, Wu C C. 2009b. Upper-ocean thermal structure and the Western North Pacific category 5 typhoons. Part II: dependence on translation speed. Monthly Weather Review, 137 (11): 3744–3757.CrossRefGoogle Scholar
  25. Lin I I, Wu C C, Emanuel K A et al. 2005. The interaction of Supertyphoon Maemi (2003) with a warm ocean eddy. Monthly Weather Review, 133 (9): 2635–2649.CrossRefGoogle Scholar
  26. Lin I I, Wu C C, Pun I F et al. 2008. Upper-ocean thermal structure and the Western North Pacific category 5 typhoons. Part I: ocean features and the category 5 typhoons intensification. Monthly Weather Review, 136 (9): 3288–3306.CrossRefGoogle Scholar
  27. McPhaden M J, Foltz G R, Lee T et al. 2009. Ocean-atmosphere interactions during cyclone Nargis. EOS, Transactions American Geophysical Union, 90 (7): 53–54.CrossRefGoogle Scholar
  28. Moon I J, Kim S H, Kim M Y et al. 2009. Numerical experiments on typhoon-ocean interaction in the Northwestern Pacific. International Workshop on Tropical Cyclone-Ocean Interaction in the Northwest Pacific, Jeju, Korea, April 2009.Google Scholar
  29. Nagamani P V, Ali M M, Goni G J et al. 2012. Validation of satellite-derived tropical cyclone heat potential with in situ observations in the North Indian Ocean. Remote Sensing Letters, 3 (7): 615–620.CrossRefGoogle Scholar
  30. Olbers D, Gouretski V V, Seiß G et a. 1992. The Hydrographic Atlas of the Southern Ocean. Alfred Wegener Institute for Polar and Marine Research, Bremerhaven, 17:82 plates.Google Scholar
  31. Pun I F, Lin I I, Wu C R et al. 2007. Validation and application of altimetry-derived upper ocean thermal structure in the western North Pacific Ocean for typhoon-intensity forecast. Geoscience and Remote Sensing, 45 (6): 1616–1630.CrossRefGoogle Scholar
  32. Reynolds R W, Smith T M, Liu C et al. 2007. Daily highresolution-blended analyses for sea surface temperature. Journal of Climate, 20 (22): 5473–5496.CrossRefGoogle Scholar
  33. Schade L R, Emanuel K A. 1999. The ocean’s effect on the intensity of tropical cyclones: results from a simple coupled atmosphere-ocean model. Journal of the Atmospheric Sciences, 56 (4): 642–651.CrossRefGoogle Scholar
  34. Scharroo R, Smith W H F, Lillibridge J L. 2005. Satellite altimetry and the intensification of Hurricane Katrina. EOS, Transactions American Geophysical Union, 86 (40): 366–366.CrossRefGoogle Scholar
  35. Shay L K, Brewster J K. 2010. Oceanic heat content variability in the Eastern Pacific Ocean for hurricane intensity forecasting. Monthly Weather Review, 138 (6): 2110–2131.CrossRefGoogle Scholar
  36. 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.CrossRefGoogle Scholar
  37. Sun C. 2012. Global Temperature-Salinity Profile Program. http://www.nodc.noaa.gov/GTSPP/access_data/index.html. Accessed on 2014-04-20.Google Scholar
  38. Wada A, Usui N. 2007. Importance of tropical cyclone heat potential for tropical cyclone intensity and intensification in the western North Pacific. Journal of Oceanography, 63 (3): 427–447.CrossRefGoogle Scholar
  39. Wong A P S, Johnson G C, Owens W B. 2003. Delayed-mode calibration of autonomous CTD profiling float salinity data by θ–S climatology. Journal of Atmospheric & Oceanic Technology, 20 (2): 308–318.CrossRefGoogle Scholar

Copyright information

© Chinese Society for Oceanology and Limnology, Science Press and Springer-Verlag Berlin Heidelberg 2015

Authors and Affiliations

  • Chunjian Sun (孙春健)
    • 1
  • Xidong Wang (王喜冬)
    • 1
  • Xiaojian Cui (崔晓健)
    • 1
  • Xiaoshuang Zhang (张晓爽)
    • 1
  • Lianxin Zhang (张连新)
    • 1
    • 2
  • Caixia Shao (邵彩霞)
    • 1
    • 3
  • Xinrong Wu (吴新荣)
    • 1
  • Hongli Fu (付红丽)
    • 1
  • Wei Li (李威)
    • 1
  1. 1.Key Laboratory of State Oceanic Administration for Marine Environmental Information Technology, National Marine Data and Information ServiceState Oceanic AdministrationTianjinChina
  2. 2.College of Physical and Environmental OceanographyOcean University of ChinaQingdaoChina
  3. 3.National University of Defense TechnologyChangshaChina

Personalised recommendations