Skip to main content
SpringerLink
Log in

Data Complexity in Tropical Cyclone Positioning and Classification

  • Chapter
Data Complexity in Pattern Recognition

Part of the book series: Advanced Information and Knowledge Processing ((AI&KP))

Summary

Tropical cyclones (TCs), life-threatening and destructive, warrant analysis and forecast by meteorologists so that early warnings can be issued. To do that, the position of a TC should be located and its intensity classified. In this chapter, we briefly introduce the problem of TC positioning and classification, discuss its associated data complexity issues, and suggest future research directions in the field.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 129.00
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 169.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info
Hardcover Book
USD 169.99
Price excludes VAT (USA)
  • Durable hardcover edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. Coupled Ocean/Atmosphere Mesoscale Prediction System (COAMPS). http://meted.ucar.edu/nwp/pcu2/coamps/index.htm.

    Google Scholar 

  2. European Centre for Medium-Range Weather Forecast (ECMWF) global atmospheric model. http://www.ecmwf.int/products/forecasts/guide/.

    Google Scholar 

  3. The fifth-generation NCAR/Penn State Mesoscale Model (MM5). http://www.mmm.ucar.edu/mm5/overview.html.

    Google Scholar 

  4. Hong Kong Observatory. http://www.hko.gov.hk/.

    Google Scholar 

  5. Japan Meteorological Agency. http://www.jma.go.jp/.

    Google Scholar 

  6. National Oceanic and Atmospheric Administration (NOAA). http://www.noaa.gov/.

    Google Scholar 

  7. J.M. Moran, M.D. Morgan, P.M. Pauley. Meteorology: The Atmosphere and the Science of Weather, Englewood Cliffs, NJ: Prentice-Hall, 5th ed., 1997.

    Google Scholar 

  8. Japan Meteorological Agency. Outline of the operational numerical weather prediction at the Japan Meteorological Agency. In WMO Numerical Weather Prediction Progress Report, chapter appendix. March 2002. http://www.jma.go.jp/JMA HP/jma/jma-eng/jma-center/nwp/.

    Google Scholar 

  9. M.A. Bramer. Knowledge Discovery and Data Mining. Institution of Electrical Engineers, 1999.

    Google Scholar 

  10. T. Corpetti, É. Mémin, P. Pérez. Dense motion analysis in fluid imagery. In Proceedings of the 7th European Conference on Computer Vision (ECCV-2002), volume 2350 of LNCS, pages 676–691, Copenhagen, New York: Springer-Verlag, 2000.

    Google Scholar 

  11. T. Corpetti, É. Mémin, P. Pérez. Extraction of singular points from dense motion fields: an analytic approach. Journal of Mathematical Imaging and Vision, 19, 175–198, 2003.

    Article  MathSciNet  MATH  Google Scholar 

  12. F. Dell’Acqua, P. Gamba. A novel technique for hurricane detection in Meteosat images by means of contour analysis. In Proceedings of the International Symposium on Geoscience and Remote Sensing, Sydney, Australia, volume 3, pages 1512–1514, July 2001.

    Google Scholar 

  13. V.F. Dvorak. A technique for the analysis and forecasting of tropical cyclone intensities from satellite pictures. Technical Report NESS 45, National Oceanic and Atmospheric Administration (NOAA), US Department of Commerce, 1973. 19 pages.

    Google Scholar 

  14. V.F. Dvorak. Tropical cyclone intensity analysis and forecasting from satellite imagery. Monthly Weather Review, 103, 420–430, 1975.

    Article  Google Scholar 

  15. V.F. Dvorak. Tropical cyclone intensity analysis using satelite data. Technical Report NESDIS 11, National Oceanic and Atmospheric Administration (NOAA), US Department of Commerce, 1984. 47 pages.

    Google Scholar 

  16. J. Han, M. Kamber. Data mining: concepts and techniques. San Francisco, CA: Morgan Kaufmann, 2001.

    Google Scholar 

  17. A.F. Hasler, K. Palaniappan, C. Kambhammetu, P. Black, E. Uhlhorn, D. Chesters. High resolution wind fields within the inner-core and eye of a mature tropical cyclone from GOES one-minute images. Bulletin of the American Meteorological Society, 79(11), 2483–2496, 1998.

    Article  Google Scholar 

  18. G.J. Holland, ed. Global Guide to Tropical Cyclone Forecasting. NumberWMO/TC-No. 560, Report No. TCP-31. Bureau of Meteorology Research Centre, Melbourne, Victoria, Australia. http://www.bom.gov.au/bmrc/pubs/tcguide/.

    Google Scholar 

  19. R. Honda, S. Wang, T. Kikuchi, O. Konishi. Mining of moving objects from time-series images and its application to satellite weather imagery. Journal of Intelligent Information Systems, 19(1), 79–93, 2002.

    Article  Google Scholar 

  20. Hong Kong Observatory. Operational Regional Spectral Model (ORSM). Web page. http://www.hko.gov.hk/wservice/tsheet/nwp.htm.

    Google Scholar 

  21. A. Kitamoto. The development of typhoon image database with content-based search. In Proceedings of the 1st International Symposium on Advanced Informatics, Chiyodaku, Tokyo, pages 163–170, March 9–10, 2000.

    Google Scholar 

  22. A. Kitamoto. Data mining for typhoon image collection. In Proceedings of the 2nd International Workshop on Multimedia Data Mining, San Francisco, CA, pages 68–77, August 26–29, 2001.

    Google Scholar 

  23. E.S.T. Lai. TREC application in tropical cyclone observation. In ESCAP/WMO Typhoon Committee Annual Review, pages 135–139, 1998.

    Google Scholar 

  24. C.Y. Lam. Operational tropical cyclone forecasting from the perspective of a small weather service. In Proceedings of ICSU/WMO International Symposium on Tropical Cyclone Disasters, Beijing, pages 530–541, October 1992.

    Google Scholar 

  25. C. Landsea, S. Goldenberg. Tropical Cyclone FAQ. Web page, 13_August 2004. Version 4.0, http://www.aoml.noaa.gov/hrd/tcfaq/A1.html.

    Google Scholar 

  26. C. Landsea, S. Goldenberg. Tropical Cyclone FAQ. Web page, 13_August 2004. Version 4.0, http://www.aoml.noaa.gov/hrd/tcfaq/E10.html.

    Google Scholar 

  27. R.S.T. Lee, J.N.K. Liu. An elastic graph dynamic link model for tropical cyclone pattern recognition. In Proceedings of the 6th International Conference on Neural Information Processing (ICONIP-1999), Perth, Australia, volume 1, pages 177–182, November 16–20, 1999.

    Google Scholar 

  28. R.S.T. Lee, J.N.K. Liu. Tropical cyclone identification and tracking system using integrated neural oscillatory elastic graph matching and hybrid RBF network track mining. IEEE Transactions on Neural Networks, 11(3), 680–689, 2000.

    Article  MathSciNet  Google Scholar 

  29. R.S.T. Lee, J.N.K. Liu. An elastic contour matching model for tropical cyclone pattern recognition. IEEE Transactions on Systems, Man and Cybernetics — Part B: Cybernetics, 31(3), 413–417, 2001.

    Article  MathSciNet  Google Scholar 

  30. T.C. Lee, M. Leung. Performance of multiple-model ensemble techniques in tropical cyclone track prediction. The 35th session of the Typhoon Committee, November 2002.

    Google Scholar 

  31. T.C. Lee, M.S. Wong. The use of multiple-model ensemble techniques for tropical cyclone track forecast at the Hong Kong Observatory. In Proceedings of the WMO Commission for Basic Systems Technical Conference on Data Processing and Forecasting Systems, Cairns, Australia, December 2–3, 2002.

    Google Scholar 

  32. P.W. Li, S.T. Lai. Short range quantitative precipitation forecasting in Hong Kong. Journal of Hydrology, 288, 189–209, 2004.

    Article  Google Scholar 

  33. P.W. Li, W.K. Wong, K.Y. Chan, E.S.T. Lai. SWIRLS-an evolving nowcasting system. Technical Report 100, Hong Kong Observatory, 1999.

    Google Scholar 

  34. P.S. Maybeck. Stochastic models, estimation, and control, volume 1 of Mathematics in Science and Engineering. New York: Academic Press, 1979.

    Google Scholar 

  35. The Hong Kong Observatory. Nature and structure of tropical cyclones. Web page, 13 May 2003. http://www.hko.gov.hk/informtc/nature.htm.

    Google Scholar 

  36. A. Ottenbacher, M. Tomassini, K. Holmlund, J. Schmetz. Low-level cloud motion winds from Meteosat high resolution visible imagery. Weather and Forecasting, 12, 175–184, 1997.

    Article  Google Scholar 

  37. K. Palaniappan, C. Faisal, M. Kambhamettu, A.F. and Hasler. Implementation of an automatic semi-fluid motion analysis algorithm on a massively parallel computer. In Proceedings of the 10th International Parallel Processing Symposium, Honolulu, Hawaii, pages 864–872, April 15–19, 1996.

    Google Scholar 

  38. K. Palaniappan, C. Kambhamettu, A.F. Hasler, D.B. Goldgof. Structure and semi-fluid motion analysis of stereoscopic satellite images for cloud tracking. In Proceedings of the 5th International Conference on Computer Vision (ICCV-1995), Boston, MA, pages 659–665, June 20–23, 1995.

    Google Scholar 

  39. G.S. Pankiewicz. Pattern recognition techniques for the identification of cloud and cloud systems. Meteorological Applications, 2, 257–271, 1995.

    Article  Google Scholar 

  40. J.C.H. Poon, C.P. Chau, M. Ghadiali. Using fuzzy mathematical morphology for locating tropical cyclones in satellite imagery. In Proceedings of the International Circuits and Systems Symposium, Hong Kong, pages 1353–1356, June 1997.

    Google Scholar 

  41. R. Ramachandran, J. Rushing, H. Conover, S. Graves, K. Keiser. Flexible framework for mining meteorological data. In Proceedings of the 19th Conference on Interactive Information and Processing Systems for Meteorology, Oceanography, and Hydrology, February 2003.

    Google Scholar 

  42. R.E. Rinehart. Internal storm motions from a single non-Doppler weather radar. Technical Report TN-146+STR, National Center for Atmospheric Research, 1979. 262 pp.

    Google Scholar 

  43. J. Schemetz, K. Holmlund, J. Hoffman, B. Strauss, B. Mason, V. Gaertner, A. Koch, L. Van De Berg. Operational cloud-motion winds from Meteosat infrared images. Journal of Applied Meteorology, 32, 1206–1225, 1993.

    Article  Google Scholar 

  44. H.V. Senn, H.W. Hiser. On the origin of hurricane spiral rain bands. Journal of Meteorology, 16, 419–426, 1959.

    Google Scholar 

  45. C.F. Shu, R.C. Jain. Vector field analysis for oriented patterns. IEEE Transactions on Pattern Analysis and Machine Intelligence, 16(9), 946–950, 1994.

    Article  Google Scholar 

  46. M.V. Sivaramakrishnan, M. Selvam. On the use of the spiral overlay technique for estimating the center positions of tropical cyclones from satellite photographs taken over the Indian region. In Proceedings of the 12th Conference on Radar Meteorology, Norman, OK, pages 440–446, October 17–20, 1966.

    Google Scholar 

  47. T.B. Trafalis, M.B. Richman, A. White, B. Santosa. Data mining techniques for improved WSR-88D rainfall estimation. Computers and Industrial Engineering, 43(4), 775–786, 2002.

    Article  Google Scholar 

  48. K.S. Tsui. Tropical cyclone tracking and forecasting, parts I and II. TheWMO Regional Training Seminar for National Meteorological Instructors of RAs II & V, November 1982.

    Google Scholar 

  49. J. Tuttle, R. Gall. A single-radar technique for estimating the winds in tropical cyclones. Bulletin of the American Meteorological Society, 80(4), 653–668, 1999.

    Article  Google Scholar 

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

    Article  Google Scholar 

  51. Y. Wang, H. Wang, H. Chen, W-C. Sun. Tropical cyclone center location with digital image process. In Proceedings of the International Conferences on Info-tech and Infonet, volume 3, pages 563–567, Beijing, November 2001.

    Google Scholar 

  52. G. Welch, G. Bishop. An introduction to the Kalman filter. In Proceedings of the ACM Computer Graphics Conference (SIGGRAPH-2001), Los Angeles, CA, August, 2001.

    Google Scholar 

  53. A. Wimmers, C. Velden. Satellite-based center-fixing of tropical cyclones: New automated approaches. In Proceedings of the 26th Conference on Hurricanes and Tropical Meteorology, May 3–7, Miami, FL, 2004.

    Google Scholar 

  54. I.H. Witten, E. Frank. Data Mining: practical machine learning tools and techniques with Java implementations. San Francisco, CA: Morgan Kaufmann, 2000.

    Google Scholar 

  55. K.Y. Wong, C.L. Yip, P.W. Li, W.W. Tsang. Automatic template matching method for tropical cyclone eye fix. In Proceedings of the 17th International Conference on Pattern Recognition (ICPR-2004), volume 3, pages 650–653, Cambridge, UK, August 2004.

    Google Scholar 

  56. V.T. Wood. A technique for detecting a tropical cyclone center using a Doppler radar. Journal of Atmospheric and Oceanic Technology, 11, 1207–1216, October 1994.

    Article  Google Scholar 

  57. C.L Yip, K.Y. Wong. Efficient and effective tropical cyclone eye fix using genetic algorithms. In Proceedings of the 8th International Conference on Knowledge-Based Intelligent Information and Engineering Systems (KES-2004), Lecture Notes in Artificial Intelligence LNAI, pages 654–660, Wellington, New Zealand, New York: Springer-Verlag, 2004.

    Google Scholar 

  58. C.L. Yip, K.Y. Wong. Tropical cyclone eye fix using genetic algorithm. Technical Report TR-2004-06, Department of Computer Science, The University of Hong Kong, 2004.

    Google Scholar 

  59. R. Zehr. Improving objective satellite estimates of tropical cyclone intensity. In Proceedings of the 18th Conference on Hurricanes and Tropical Meteorology, San Diego, CA, pages J25–J28, May 16–19,1989.

    Google Scholar 

  60. L. Zhou, C. Kambhamettu, D.B. Goldgof. Extracting nonrigid motion and 3D structure of hurricanes from satellite image sequences without correspondences. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR-1999), Fort Collins, USA, June 1999.

    Google Scholar 

  61. L. Zhou, C. Kambhamettu, D.B. Goldgof. Fluid structure and motion analysis from multi-spectrum 2D cloud image sequences. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR-2000), Los Alamitos, June 2000.

    Google Scholar 

  62. L. Zhou, C. Kambhamettu, D.B. Goldgof, K. Palaniappan, A.F. Hasler. Tracking nonrigid motion and 3D structure of hurricanes from satellite image sequences without correspondences. IEEE Transactions on Pattern Analysis and Machine Intelligence, 23(11), 1330–1336, 2001.

    Article  Google Scholar 

  63. D.G. Zill. Differential Equations with Computer Lab Experiments. 2nd ed. Monterey, CA: Brooks/Cole, 1998.

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Department of Computer Science, The University of Hong Kong, Pokfulam, Hong Kong

    Chi Lap Yip & Ka Yan Wong

  2. Hong Kong Observatory, 134A, Nathan Road, Kowloon, Hong Kong

    Ping Wah Li

Authors
  1. Chi Lap Yip
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Ka Yan Wong
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Ping Wah Li
    View author publications

    You can also search for this author in PubMed Google Scholar

Editor information

Editors and Affiliations

  1. Electrical Engineering Department, City College, City University of New York, USA

    Mitra Basu PhD

  2. Bell Laboratories, Lucent Technologies, New Jersey, USA

    Tin Kam Ho BBA, MS, PhD

Rights and permissions

Reprints and permissions

Copyright information

© 2006 Springer Verlag London Limited

About this chapter

Cite this chapter

Yip, C.L., Wong, K.Y., Li, P.W. (2006). Data Complexity in Tropical Cyclone Positioning and Classification. In: Basu, M., Ho, T.K. (eds) Data Complexity in Pattern Recognition. Advanced Information and Knowledge Processing. Springer, London. https://doi.org/10.1007/978-1-84628-172-3_13

Download citation

  • DOI: https://doi.org/10.1007/978-1-84628-172-3_13

  • Publisher Name: Springer, London

  • Print ISBN: 978-1-84628-171-6

  • Online ISBN: 978-1-84628-172-3

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation