Tropical Cyclone Intensity Estimation Using RVM and DADI Based on Infrared Brightness Temperature

Chang-Jiang Zhang College of Mathematics, Physics and Information Engineering, Zhejiang Normal University, Jinhua, China

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Jin-Fang Qian College of Mathematics, Physics and Information Engineering, Zhejiang Normal University, Jinhua, China

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Lei-Ming Ma Shanghai Typhoon Institute, Shanghai, China

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Xiao-Qin Lu Shanghai Typhoon Institute, Shanghai, China

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Abstract

An objective technique is presented to estimate tropical cyclone intensity using the relevance vector machine (RVM) and deviation angle distribution inhomogeneity (DADI) based on infrared satellite images of the northwest Pacific Ocean basin from China’s FY-2C geostationary satellite. Using this technique, structures of a deviation-angle gradient co-occurrence matrix, which include 15 statistical parameters nonlinearly related to tropical cyclone intensity, were derived from infrared satellite imagery. RVM was then used to relate these statistical parameters to tropical cyclone intensity. Twenty-two tropical cyclones occurred in the northwest Pacific during 2005–09 and were selected to verify the performance of the proposed technique. The results show that, in comparison with the traditional linear regression method, the proposed technique can significantly improve the accuracy of tropical cyclone intensity estimation. The average absolute error of intensity estimation using the linear regression method is between 15 and 29 m s−1. Compared to the linear regression method, the average absolute error of the intensity estimation using RVM is between 3 and 10 m s−1.

Corresponding author address: Chang-Jiang Zhang, College of Mathematics, Physics and Information Engineering, Zhejiang Normal University, 688 Yingbin Avenue, Jinhua 321004, China. E-mail: zcj74922@zjnu.edu.cn

Abstract

An objective technique is presented to estimate tropical cyclone intensity using the relevance vector machine (RVM) and deviation angle distribution inhomogeneity (DADI) based on infrared satellite images of the northwest Pacific Ocean basin from China’s FY-2C geostationary satellite. Using this technique, structures of a deviation-angle gradient co-occurrence matrix, which include 15 statistical parameters nonlinearly related to tropical cyclone intensity, were derived from infrared satellite imagery. RVM was then used to relate these statistical parameters to tropical cyclone intensity. Twenty-two tropical cyclones occurred in the northwest Pacific during 2005–09 and were selected to verify the performance of the proposed technique. The results show that, in comparison with the traditional linear regression method, the proposed technique can significantly improve the accuracy of tropical cyclone intensity estimation. The average absolute error of intensity estimation using the linear regression method is between 15 and 29 m s−1. Compared to the linear regression method, the average absolute error of the intensity estimation using RVM is between 3 and 10 m s−1.

Corresponding author address: Chang-Jiang Zhang, College of Mathematics, Physics and Information Engineering, Zhejiang Normal University, 688 Yingbin Avenue, Jinhua 321004, China. E-mail: zcj74922@zjnu.edu.cn
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  • CMA, 2007: Yearbook of Tropical Cyclone 2005. China Meteorological Press, 206 pp.

  • CMA, 2008: Yearbook of Tropical Cyclone 2006. China Meteorological Press, 228 pp.

  • CMA, 2009: Yearbook of Tropical Cyclone 2007. China Meteorological Press, 228 pp.

  • CMA, 2010: Yearbook of Tropical Cyclone 2008. China Meteorological Press, 218 pp.

  • CMA, 2011: Yearbook of Tropical Cyclone 2009. China Meteorological Press, 208 pp.

  • DeMaria, M., 1996: The effect of vertical shear on tropical cyclone intensity change. J. Atmos. Sci., 53, 20762087, doi:10.1175/1520-0469(1996)053<2076:TEOVSO>2.0.CO;2.

    • Search Google Scholar
    • Export Citation
  • Dvorak, C. A., 1972: A technique for the analysis and forecasting of tropical cyclone intensities from satellite pictures. NOAA Tech. Memo. NES 36, 15 pp.

  • Dvorak, C. A., 1975: Tropical cyclone intensity analysis and forecasting from satellite imagery. Mon. Wea. Rev., 103, 420430, doi:10.1175/1520-0493(1975)103<0420:TCIAAF>2.0.CO;2.

    • Search Google Scholar
    • Export Citation
  • Dvorak, C. A., 1984: Tropical cyclone intensity analysis using satellite data. NOAA Tech. Rep. 11, 45 pp.

  • Dvorak, C. A., and Smigielski F. , 1995: A Workbook on Tropical Clouds and Cloud Systems Observed in Satellite Imagery. Vol. 2, NOAA/NESDIS, 359 pp.

  • Erickson, C. O., 1967: Some aspects of the development of Hurricane Dorothy. Mon. Wea. Rev., 95, 121130, doi:10.1175/1520-0493(1967)095<0121:SAOTDO>2.3.CO;2.

    • Search Google Scholar
    • Export Citation
  • Fetanat, G., and Homaifar A. , 2013: Objective tropical cyclone intensity estimation using analogs of spatial features in satellite data. Wea. Forecasting, 28, 14461459, doi:10.1175/WAF-D-13-00006.1.

    • Search Google Scholar
    • Export Citation
  • Fett, R. W., 1964: Aspects of hurricane structure: New model considerations suggested by TIROS and Project Mercury observations. Mon. Wea. Rev., 92, 4360, doi:10.1175/1520-0493(1964)092<0043:AOHSNM>2.3.CO;2.

    • Search Google Scholar
    • Export Citation
  • Fritz, S., Hubert L. F. , and Timchalk A. , 1966: Some inferences from satellite pictures of tropical disturbances. Mon. Wea. Rev., 94, 231236, doi:10.1175/1520-0493(1966)094<0231:SIFSPO>2.3.CO;2.

    • Search Google Scholar
    • Export Citation
  • Hong, J. X., 1984: Gray level-gradient co-occurrence matrix texture analysis method. Acta Autom. Sin., 10, 2225.

  • Jiang, H. Y., 2012: The relationship between tropical cyclone intensity change and the strength of inner-core convection. Mon. Wea. Rev., 140, 11641176, doi:10.1175/MWR-D-11-00134.1.

    • Search Google Scholar
    • Export Citation
  • Kossin, J. P., and Velden C. S. , 2004: A pronounced bias in tropical cyclone minimum sea level pressure estimation based on the Dvorak technique. Mon. Wea. Rev., 132, 165173, doi:10.1175/1520-0493(2004)132<0165:APBITC>2.0.CO;2.

    • Search Google Scholar
    • Export Citation
  • Kossin, J. P., Knaff J. A. , Berger H. I. , Herndon D. C. , Cram T. A. , Velden C. S. , Murnane R. J. , and Hawkins J. D. , 2007: Estimating hurricane wind structure in the absence of aircraft reconnaissance. Wea. Forecasting, 22, 89101, doi:10.1175/WAF985.1.

    • Search Google Scholar
    • Export Citation
  • McCulloch, W. S., and Pitts W. , 1943: A logical calculus of the ideas immanent in nervous activity. J. Bull. Math. Biophys., 5, 115133, doi:10.1007/BF02478259.

    • Search Google Scholar
    • Export Citation
  • Moody, J., and Darken C. J. , 1989: Fast learning in networks of locally-tuned processing units. J. Neural Comput., 1, 281294, doi:10.1162/neco.1989.1.2.281.

    • Search Google Scholar
    • Export Citation
  • Olander, T. L., and Velden C. S. , 2007: The advanced Dvorak technique: Continued development of an objective scheme to estimate tropical cyclone intensity using geostationary infrared satellite imagery. Wea. Forecasting, 22, 287298, doi:10.1175/WAF975.1.

    • Search Google Scholar
    • Export Citation
  • Olander, T. L., and Velden C. S. , 2009: Tropical cyclone convection and intensity analysis using differenced infrared and water vapor imagery. Wea. Forecasting, 24, 15581572, doi:10.1175/2009WAF2222284.1.

    • Search Google Scholar
    • Export Citation
  • Olander, T. L., Velden C. S. , and Turk M. A. , 2002: Development of the advanced objective Dvorak technique (AODT)—Current progress and future directions. Preprints, 25th Conf. on Hurricane and Tropical Meteorology, San Diego, CA, Amer. Meteor. Soc., 15A.4. [Available online at https://ams.confex.com/ams/pdfpapers/35977.pdf.]

  • Olander, T. L., Velden C. S. , and Kossin J. P. , 2004: The advanced objective Dvorak technique (AODT)—Latest upgrades and future directions. Preprints, 26th Conf. on Hurricane and Tropical Meteorology, Miami, FL, Amer. Meteor. Soc., P1.19. [Available online at https://ams.confex.com/ams/pdfpapers/75417.pdf.]

  • Piñeros, M. F., Ritchie E. A. , and Tyo J. S. , 2008: Objective measures of tropical cyclone structure and intensity change from remotely sensed infrared image data. IEEE Trans. Geosci. Remote Sens., 46, 35743580, doi:10.1109/TGRS.2008.2000819.

    • Search Google Scholar
    • Export Citation
  • Piñeros, M. F., Ritchie E. A. , and Tyo J. S. , 2011: Estimating tropical cyclone intensity from infrared image data. Wea. Forecasting, 26, 690698, doi:10.1175/WAF-D-10-05062.1.

    • Search Google Scholar
    • Export Citation
  • Ritchie, E. A., Valliere-Kelley G. , Piñeros M. F. , and Tyo J. S. , 2012: Tropical cyclone intensity estimation in the North Atlantic basin using an improved deviation angle variance technique. Wea. Forecasting, 27, 12641277, doi:10.1175/WAF-D-11-00156.1.

    • Search Google Scholar
    • Export Citation
  • Ritchie, E. A., Wood K. M. , Rodriguez-Herrera O. G. , Piñeros M. F. , and Tyo J. S. , 2014: Satellite-derived tropical cyclone intensity in the North Pacific Ocean using the deviation-angle variance technique. Wea. Forecasting, 29, 505516, doi:10.1175/WAF-D-13-00133.1.

    • Search Google Scholar
    • Export Citation
  • Sadler, J. C., 1964: Tropical cyclones of the eastern North Pacific as revealed by TIROS observations. J. Appl. Meteor., 3, 347366, doi:10.1175/1520-0450(1964)003<0347:TCOTEN>2.0.CO;2.

    • Search Google Scholar
    • Export Citation
  • Sanabia, E. R., Barrett B. S. , and Fine C. M. , 2014: Relationships between tropical cyclone intensity and eyewall structure as determined by radial profiles of inner-core infrared brightness temperature. Mon. Wea. Rev., 142, 45814599, doi:10.1175/MWR-D-13-00336.1.

    • Search Google Scholar
    • Export Citation
  • Tipping, M. E., 2001: Sparse Bayesian learning and relevance vector machine. J. Mach. Learn. Res., 1, 211244.

  • Vapnik, V., Golowich S. E. , and Smola A. , 1997: Support vector method for function approximation, regression estimation, and signal processing. Adv. Neural Inf. Process. Syst., 9, 281287.

    • Search Google Scholar
    • Export Citation
  • Velden, C. S., Olander T. L. , and Zehr R. M. , 1998: Development of an objective scheme to estimate tropical cyclone intensity from digital geostationary satellite infrared imagery. Wea. Forecasting, 13, 172186, doi:10.1175/1520-0434(1998)013<0172:DOAOST>2.0.CO;2.

    • Search Google Scholar
    • Export Citation
  • Velden, C. S., and Coauthors, 2006: The Dvorak tropical cyclone intensity estimation technique: A satellite-based method that has endured for over 30 years. Bull. Amer. Meteor. Soc., 87, 11951210, doi:10.1175/BAMS-87-9-1195.

    • Search Google Scholar
    • Export Citation
  • Zehr, R. M., 2003: Environmental vertical wind shear with Hurricane Bertha (1996). Wea. Forecasting, 18, 345356, doi:10.1175/1520-0434(2003)018<0345:EVWSWH>2.0.CO;2.

    • Search Google Scholar
    • Export Citation
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