Editorial Type:
Article
Article Type:
Research Article

Estimation of the Upper-Layer Rotation and Maximum Wind Speed of Tropical Cyclones via Satellite Imagery

Chun-Chieh Chao Institute of Atmospheric Physics, National Central University, Tao-Yuan, Taiwan

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Gin-Rong Liu Center for Space and Remote Sensing Research, and Institute of Atmospheric Physics, National Central University, Tao-Yuan, Taiwan

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Chung-Chih Liu Minghsin University of Science and Technology, Hsin-Chu, Taiwan

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Print Publication:
01 Mar 2011
DOI:
https://doi.org/10.1175/2010JAMC2519.1
Page(s):
750–766
Restricted access

Abstract

The movement of convective rainbands embedded in a tropical cyclone (TC) is usually derived from satellite images via the atmospheric motion vector (AMV) method or through the calculation of a radar’s echo track. In estimating the rotation speed of a TC rainband, however, the land-based radar can only detect approaching tropical cyclones within the vicinity. The AMV method is unable to fully account for the TC eyewall movement, thus making it difficult to estimate the TC intensity. The widely used method in estimating the TC maximum wind speed is the Dvorak technique in which the cloud pattern is extracted from only one image. In this study, the rainband rotation speeds are computed via satellite imagery and further applied in estimating the TC maximum wind speed. In contrast to previous research, this study adopts an innovative method by using two subsequent geostationary satellite images. The TC spin rates observed by weather satellites could often be seen to be positively related to the TC intensity. Analyses of the relationship between the typhoon wind intensity and estimated rotation speed at the 130–260-km ring via infrared channels are conducted for major typhoon cases that occurred during 2000–05 in the northwestern Pacific Ocean. Results show that the correlation between the wind intensity and estimated rotation speeds is strong for most of the cases. The highest R2 value from the individual cases could reach 0.93, and on an annual basis it could attain a value of 0.67. The mean R2 value for the 2000–05 dataset was roughly 0.53. The correlation between the wind intensity and estimated rotation speeds is further improved by factoring in the previous 6-h average rotation speeds. A regression equation is derived from the chosen typhoon cases between 2000 and 2005, which is utilized in verifying the major typhoon occurrences during 2006–08. The mean absolute error (MAE) of the hourly and 6-h average intensity estimates during 2000–08 was 20 and 18.7 kt, respectively (1 kt ≃ 0.5 m s−1). The best verification result occurred during 2008, for which the R2 value and MAE could reach 0.7 and 15.6, respectively. These research results demonstrate the suitability of using geostationary satellite image data in estimating the maximum wind speed. Nevertheless, the drawback of this study is that sometimes the rotation speeds will become slower when tropical cyclones mature because of the strong outflow of the secondary circulation. It is assumed that the relationship between the estimated rotation speeds and wind intensity can be further improved if the outflow speed of the tropical cyclones is also considered.

Corresponding author address: Dr. Gin-Rong Liu, Center for Space and Remote Sensing Research, No. 300, Jhongda Rd., Jhongli City, Taoyuan County 32001, Taiwan. Email: grliu@csrsr.ncu.edu.tw

Abstract

The movement of convective rainbands embedded in a tropical cyclone (TC) is usually derived from satellite images via the atmospheric motion vector (AMV) method or through the calculation of a radar’s echo track. In estimating the rotation speed of a TC rainband, however, the land-based radar can only detect approaching tropical cyclones within the vicinity. The AMV method is unable to fully account for the TC eyewall movement, thus making it difficult to estimate the TC intensity. The widely used method in estimating the TC maximum wind speed is the Dvorak technique in which the cloud pattern is extracted from only one image. In this study, the rainband rotation speeds are computed via satellite imagery and further applied in estimating the TC maximum wind speed. In contrast to previous research, this study adopts an innovative method by using two subsequent geostationary satellite images. The TC spin rates observed by weather satellites could often be seen to be positively related to the TC intensity. Analyses of the relationship between the typhoon wind intensity and estimated rotation speed at the 130–260-km ring via infrared channels are conducted for major typhoon cases that occurred during 2000–05 in the northwestern Pacific Ocean. Results show that the correlation between the wind intensity and estimated rotation speeds is strong for most of the cases. The highest R2 value from the individual cases could reach 0.93, and on an annual basis it could attain a value of 0.67. The mean R2 value for the 2000–05 dataset was roughly 0.53. The correlation between the wind intensity and estimated rotation speeds is further improved by factoring in the previous 6-h average rotation speeds. A regression equation is derived from the chosen typhoon cases between 2000 and 2005, which is utilized in verifying the major typhoon occurrences during 2006–08. The mean absolute error (MAE) of the hourly and 6-h average intensity estimates during 2000–08 was 20 and 18.7 kt, respectively (1 kt ≃ 0.5 m s−1). The best verification result occurred during 2008, for which the R2 value and MAE could reach 0.7 and 15.6, respectively. These research results demonstrate the suitability of using geostationary satellite image data in estimating the maximum wind speed. Nevertheless, the drawback of this study is that sometimes the rotation speeds will become slower when tropical cyclones mature because of the strong outflow of the secondary circulation. It is assumed that the relationship between the estimated rotation speeds and wind intensity can be further improved if the outflow speed of the tropical cyclones is also considered.

Corresponding author address: Dr. Gin-Rong Liu, Center for Space and Remote Sensing Research, No. 300, Jhongda Rd., Jhongli City, Taoyuan County 32001, Taiwan. Email: grliu@csrsr.ncu.edu.tw

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  • Bankert, R. L., and P. M. Tag, 2002: An automated method to estimate tropical cyclone intensity using SSM/I imagery. J. Appl. Meteor., 41 , 461–472.

    • Search Google Scholar
    • Export Citation

  • Bessho, K., M. DeMaria, and J. A. Knaff, 2006: Tropical cyclone wind retrievals from the Advanced Microwave Sounder Unit (AMSU): Application to surface wind analysis. J. Appl. Meteor. Climatol., 45 , 399–415.

    • Search Google Scholar
    • Export Citation

  • Brown, D. P., and J. L. Franklin, 2002: Accuracy of pressure–wind relationships and Dvorak satellite intensity estimates for tropical cyclones determined from recent reconnaissance-based “best track” data. Preprints, 25th Conf. on Hurricanes and Tropical Meteorology, San Diego, CA, Amer. Meteor. Soc., 458–459.

    • Search Google Scholar
    • Export Citation

  • Dvorak, V. F., 1975: Tropical cyclone intensity analysis and forecasting from imagery. Mon. Wea. Rev., 103 , 420–430.

  • Emanuel, A., 2005: Increasing destructiveness of tropical cyclones over the past 30 years. Nature, 436 , 686–688.

  • Herndon, D. C., C. Veldon, T. Wimmers, T. Olander, and J. Hawkins, 2010: Evaluation of the Tropical Cyclone Satellite Intensity Consensus (SATCON). Preprints. 29th Conf. on Hurricanes and Tropical Meteorology, Tucson, AZ, Amer. Meteor. Soc., 4D.4. [Available online at http://ams.confex.com/ams/pdfpapers/167959.pdf].

    • Search Google Scholar
    • Export Citation

  • Hoshino, S., and T. Nakazawa, 2007: Estimation of tropical cyclone’s intensity using TRMM/TMI brightness temperature data. J. Meteor. Soc. Japan, 85 , 437–454.

    • Search Google Scholar
    • Export Citation

  • Kidder, S. Q., J. A. Knaff, S. J. Kusselson, M. Turk, R. R. Ferraro, and R. J. Kuligowski, 2005: The tropical rainfall potential (TRaP) technique. Part I: Description and examples. Wea. Forecasting, 20 , 456–464.

    • Search Google Scholar
    • Export Citation

  • Kossin, J. P., 2002: Daily hurricane variability inferred from GOES infrared imagery. Mon. Wea. Rev., 130 , 2260–2270.

  • Kossin, J. P., J. A. Knaff, H. I. Berger, D. C. Herndon, T. A. Cram, C. S. Velden, R. J. Murnane, and J. D. Hawkins, 2007: Estimating hurricane wind structure in the absence of aircraft reconnaissance. Wea. Forecasting, 22 , 89–101.

    • Search Google Scholar
    • Export Citation

  • Liu, G.-R., C.-C. Chao, and C.-Y. Ho, 2008: Applying satellite-estimated storm rotation speed to improve typhoon rainfall potential technique. Wea. Forecasting, 23 , 259–269.

    • Search Google Scholar
    • Export Citation

  • Mueller, K. J., M. DeMaria, J. A. Knaff, J. P. Kossin, and T. H. Vonder Haar, 2006: Objective estimation of tropical cyclone wind structure from infrared satellite data. Wea. Forecasting, 21 , 907–922.

    • Search Google Scholar
    • Export Citation

  • Nieman, J. S., W. P. Menzel, C. M. Hayden, D. Gray, S. T. Wanzong, C. S. Velden, and J. Daniels, 1997: Fully automated cloud-drift winds in NESDIS operations. Bull. Amer. Meteor. Soc., 78 , 1121–1133.

    • Search Google Scholar
    • Export Citation

  • Olander, T. L., and C. S. Velden, 2007: The advanced Dvorak technique: Continued development of an objective scheme to estimate tropical cyclone intensity using geostationary infrared satellite imagery. Wea. Forecasting, 22 , 287–298.

    • Search Google Scholar
    • Export Citation

  • Olander, T. L., and C. S. Velden, 2009: Tropical cyclone convection and intensity analysis using differenced infrared and water vapor imagery. Wea. Forecasting, 24 , 1558–1572.

    • Search Google Scholar
    • Export Citation

  • Tuttle, J., and R. Gall, 1999: A single-radar technique for estimating the winds in tropical cyclones. Bull. Amer. Meteor. Soc., 80 , 653–668.

    • Search Google Scholar
    • Export Citation

  • Velden, C. S., T. L. Olander, and R. M. Zehr, 1998: Development of an objective scheme to estimate tropical cyclone intensity from digital geostationary satellite infrared imagery. Wea. Forecasting, 13 , 172–186.

    • Search Google Scholar
    • Export Citation

  • Velden, C. S., and Coauthors, 2005: Recent innovations in deriving tropospheric winds from meteorological satellites. Bull. Amer. Meteor. Soc., 86 , 205–223.

    • Search Google Scholar
    • Export Citation

  • Wimmers, A. J., and C. S. Velden, 2007: MIMIC: A new approach to visualizing satellite microwave imagery of tropical cyclones. Bull. Amer. Meteor. Soc., 88 , 1187–1196.

    • Search Google Scholar
    • Export Citation

  • Zhang, D.-L., Y. Liu, and M. K. Yau, 2001: A multiscale numerical study of Hurricane Andrew (1992). Part IV: Unbalanced flows. Mon. Wea. Rev., 129 , 92–107.

    • Search Google Scholar
    • Export Citation

  • Zhong, W., D.-L. Zhang, and H. C. Lu, 2009: A theory for mixed vortex Rossby–gravity waves in tropical cyclones. J. Atmos. Sci., 66 , 3366–3381.

    • Search Google Scholar
    • Export Citation
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