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Journal of Meteorological Research

, Volume 33, Issue 1, pp 104–114 | Cite as

Detecting Intensity Evolution of the Western North Pacific Super Typhoons in 2016 Using the Deviation Angle Variance Technique with FY Data

  • Meng Yuan
  • Wei ZhongEmail author
Regular Articles
  • 7 Downloads

Abstract

This paper analyzes the complete lifecycle of super typhoons in 2016 in the western North Pacific (WNP) using the deviation angle variance technique (DAV-T). Based on the infrared images from Fengyun (FY) satellites, the DAV-T enables quantification of the axisymmetry of tropical cyclones (TCs) by using the DAV values; and thus, it helps improve the capability of TC intensity estimation. Case analyses of Super Typhoons Lionrock and Meranti were performed to explore the distribution characteristics of the DAV values at the various stages of TC evolution. The results show that the minimum DAV values (i.e., map minimum values: MMVs) gradually decreased and their locations constantly approached the circulation center with enhancement of the TC organization; however, when a ring or disk structure was formed around a TC, significant changes in MMV locations were no longer observed. Nonetheless, when large-scale non-closed deep convective cloud clusters appeared at the early stage or the dissipation stage of the typhoon, the axisymmetry of the TC was poor and the MMV locations tended to lie in the most convective region rather than in the TC circulation center. Overall, the MMVs and their locations, respectively, exhibited a strong correlation with the TC intensity and circulation center, and the correlation increased as the TCs became stronger. Combined with the China Meteorological Administration BestTrack dataset (CMA-BestTrack), statistical analysis of all research samples reveals that the correlation coefficient between the MMVs and maximum surface wind speeds (Vmax) was–0.80; the root mean square error (RMSE) of relative distance between the MMV locations and TC centers was 140.3 km; and especially, when the samples below the tropical depression (TD) intensity were removed, the RMSE of the relative distance decreased dramatically to 95.0 km. The value and location of the MMVs could be used as important indicators for estimating TC intensity and center.

Key words

deviation angle variance technique (DAV-T) axisymmetry super typhoon Fengyun (FY) satellites 

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Notes

Acknowledgments

The FY satellite images were downloaded from the National Satellite Meteorological Center of China Meteorological Administration (https://doi.org/www.nsmc.org.cn/en/NSMC/Home/Index.html). CMA-BestTrack data were obtained from the Shanghai Typhoon Institute of China Meteorological Administration (https://doi.org/www.typhoon.org.cn/). FNL data were obtained from the NCEP of US (https://doi.org/rda.ucar.edu/datasets/ds083.2/#access).

References

  1. Berg, R. J., and L. A. Avila, 2011: Atlantic hurricane season of 2009. Mon. Wea. Rev., 139: 1049–1069, doi: 10.1175/2010MWR3476.1.CrossRefGoogle Scholar
  2. Beven II, J. L., L. A. Avila, E. S. Blake, et al., 2008: Atlantic hurricane season of 2005. Mon. Wea. Rev., 136: 1109–1173, doi: 10.1175/2007MWR2074.1.CrossRefGoogle Scholar
  3. Brueske, K. F., and C. S. Velden, 2003: Satellite-based tropical cyclone intensity estimation using the NOAA-KLM series advanced microwave sounding unit (AMSU). Mon. Wea. Rev., 131: 687–697, doi: 10.1175/1520-0493(2003)131<0687:SB TCIE>2.0.CO;2.CrossRefGoogle Scholar
  4. Dvorak, V. F., 1975: Tropical cyclone intensity analysis and forecasting from satellite imagery. Mon. Wea. Rev., 103: 420–430, doi: 10.1175/1520-0493(1975)103<0420:TCIAAF> 2.0.CO;2.CrossRefGoogle Scholar
  5. Dvorak, V. F., 1984: Tropical Cyclone Intensity Analysis Using Satellite Data. NOAA Tech. Rep., 47 pp.Google Scholar
  6. Fritz, S., 1962: Satellite pictures and the origin of hurricane Anna. Mon. Wea. Rev., 90: 507–513, doi: 10.1175/1520-0493(1962) 090<0507:SPATOO>2.0.CO;2.CrossRefGoogle Scholar
  7. Hubert, L. F., and A. Timchalk, 1969: Estimating hurricane wind speeds from satellite pictures. Mon. Wea. Rev., 97: 382–383, doi: 10.1175/1520-0493(1969)097<0382:EHWSFS>2.3.CO;2.CrossRefGoogle Scholar
  8. Klein, P. M., P. A. Harr, and R. L. Elsberry, 2000: Extratropical transition of western North Pacific tropical cyclones: An overview and conceptual model of the transformation stage. Wea. Forecasting, 15: 373–395, doi: 10.1175/1520-0434 (2000)015<0373:ETOWNP>2.0.CO;2.CrossRefGoogle Scholar
  9. Knaff, J. A., D. P. Brown, J. Courtney, et al., 2010: An evaluation of Dvorak technique–based tropical cyclone intensity estimates. Wea. Forecasting, 25: 1362–1379, doi: 10.1175/2010WAF2222375.1.CrossRefGoogle Scholar
  10. Knaff, J. A., T. A. Cram, A. B. Schumacher, et al., 2008: Objective identification of annular hurricanes. Wea. Forecasting, 23: 17–28, doi: 10.1175/2007WAF2007031.1.CrossRefGoogle Scholar
  11. Kofron, D. E., M. F. Piñeros, E. A. Ritchie, et al., 2009: Defining the lifecycle of the extratropical transition of tropical cyclones using the deviation angle variance technique for remotely-sensed imagery. Proceedings of the 13th Conference on Mesoscale Processes, Amer. Meteor. Soc., Salt Lake City.Google Scholar
  12. Liu, Z., X. Wang, W. B. Li, et al., 2007: Progresses in estimation of tropical cyclone intensity with Dvorak technique. Meteor. Sci. Technol., 35: 453–457, doi: 10.3969/j.issn.1671-6345.2007.04.001. (in Chinese)Google Scholar
  13. 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, doi: 10.1175/WAF975.1.CrossRefGoogle Scholar
  14. Piñeros, M. F., 2009: Objective measures of tropical cyclone intensity and formation from satellite infrared imagery. Ph.D. dissertation, Dept. of Optical Science, University of Arizona, USA, 124 pp.Google Scholar
  15. Piñeros, M. F., E. A. Ritchie, and J. S. Tyo, 2008: Objective measures of tropical cyclone structure and intensity change from remotely sensed infrared image data. IEEE Trans. Geosci. Remote Sens., 46: 3574–3580, doi: 10.1109/TGRS.20 08.2000819.CrossRefGoogle Scholar
  16. Piñeros, M. F., E. A. Ritchie, and J. S. Tyo, 2010: Detecting tropical cyclone genesis from remotely sensed infrared image data. IEEE Geosci. Remote Sens. Lett., 7: 826–830, doi: 10.1109/LGRS.2010.2048694.CrossRefGoogle Scholar
  17. Piñeros, M. F., E. A. Ritchie, and J. S. Tyo, 2011: Estimating tropical cyclone intensity from infrared image data. Wea. Forecasting, 26: 690–698, doi: 10.1175/WAF-D-10-05062.1.CrossRefGoogle Scholar
  18. Ritchie, E. A., K. M. Wood, O. G. Rodríguez-Herrera, et al., 2014: Satellite-derived tropical cyclone intensity in the North Pacific Ocean using the deviation-angle variance technique. Wea. Forecasting, 29: 505–516, doi: 10.1175/WAF-D-13-00133.1.CrossRefGoogle Scholar
  19. Rodríguez-Herrera, O. G., K. M. Wood, K. P. Dolling, et al., 2015: Automatic tracking of pregenesis tropical disturbances within the deviation angle variance system. IEEE Geosci. Remote Sens. Lett., 12: 254–258, doi: 10.1109/LGRS.2014. 2334561.CrossRefGoogle Scholar
  20. Tang, L. L., D. Y. Hu, and X. J. Li, 2012: Spatiotemporal characteristics of tropical cyclone activities in northwestern Pacific from 1951 to 2006. J. Nat. Disast., 21: 31–38, doi: 10. 13577/j.jnd.2012.0105. (in Chinese)Google Scholar
  21. Torn, R. D., and C. Snyder, 2012: Uncertainty of tropical cyclone best-track information. Wea. Forecasting, 27: 715–729, doi: 10.1175/WAF-D-11-00085.1.CrossRefGoogle Scholar
  22. 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, doi: 10.1175/1520-0434(1998) 013<0172:DOAOST>2.0.CO;2.CrossRefGoogle Scholar
  23. Velden, C. S., B. Harper, F. Wells, et al., 2006: The Dvorak tropical cyclone intensity estimation technique: A satellite-based method that has endured for over 30 years. Bull. Amer. Meteor. Soc., 87: 1195–1210, doi: 10.1175/BAMS-87-9-1195.CrossRefGoogle Scholar
  24. Wood, K. M., O. G. Rodríguez-Herrera, E. A. Ritchie, et al., 2015: Tropical cyclogenesis detection in the North Pacific using the deviation angle variance technique. Wea. Forecasting, 30: 1663–1672, doi: 10.1175/WAF-D-14-00113.1.CrossRefGoogle Scholar

Copyright information

© The Chinese Meteorological Society and Springer-Verlag GmbH Germany, part of Springer Nature 2019

Authors and Affiliations

  1. 1.Institute of Meteorology and OceanographyNational University of Defense TechnologyNanjingChina

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