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Advances in Meteorology
Volume 2014 (2014), Article ID 838746, 8 pages
http://dx.doi.org/10.1155/2014/838746
Research Article

Improving the Operational Methodology of Tropical Cyclone Seasonal Prediction in the Australian and the South Pacific Ocean Regions

1The University of Melbourne, Parkville, VIC 3010, Australia
2Bureau of Meteorology, Docklands, VIC 3008, Australia

Received 23 July 2013; Revised 20 December 2013; Accepted 6 January 2014; Published 17 March 2014

Academic Editor: Jean-Pierre Barriot

Copyright © 2014 J. S. Wijnands et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Linked References

  1. J. Mottram, Report on Cyclone Tracy, prepared by J. Mottram, Australian Government Publishing Service, Canberra, Australia, 1977.
  2. L. Anderson-Berry, C. Iroi, and A. Rangi, “The environmental and societal impacts of cyclone Zoe and the effectiveness of the tropical cyclone warning systems in Tikopia and Anuta,” James Cook University Centre for Disaster Studies, 2003.
  3. K. L. Shelton and Y. Kuleshov, “Evaluation of statistical predictions of seasonal tropical cyclone activity,” Journal of Climate. In preparation.
  4. M. B. Richman and L. M. Leslie, “Adaptive machine learning approaches to seasonal prediction of tropical cyclones,” Procedia Computer Science, vol. 12, pp. 276–281, 2012. View at Google Scholar
  5. Y. Kuleshov, R. Fawcett, L. Qi et al., “Trends in tropical cyclones in the South Indian Ocean and the South Pacific Ocean,” Journal of Geophysical Research D, vol. 115, no. 1, Article ID D01101, 2010. View at Publisher · View at Google Scholar · View at Scopus
  6. V. F. Dvorak, “Tropical cyclone intensity analysis using satellite data,” NOAA Tech. Report NESDIS 11, 1984. View at Google Scholar
  7. N. Nicholls, L. Landsea, and J. Gill, “Recent trends in Australian region tropical cyclone activity,” Meteorology and Atmospheric Physics, vol. 65, no. 3-4, pp. 197–205, 1998. View at Google Scholar · View at Scopus
  8. Y. Kuleshov, F. C. Ming, L. Qi, I. Chouaibou, C. Hoareau, and F. Roux, “Tropical cyclone genesis in the Southern Hemisphere and its relationship with the ENSO,” Annales Geophysicae, vol. 27, no. 6, pp. 2523–2538, 2009. View at Publisher · View at Google Scholar · View at Scopus
  9. Y. Kuleshov, L. Qi, R. Fawcett, and D. Jones, “Improving preparedness to natural hazards: tropical cyclone prediction for the Southern Hemisphere,” in Advances in Geosciences, J. Gan, Ed., vol. 12 of Ocean Science, pp. 127–143, World Scientific Publishing, Singapore, 2009. View at Google Scholar
  10. Y. Kuleshov, Y. Wang, J. Apajee, R. Fawcett, and D. Jones, “Prospects for improving the operational seasonal prediction of tropical cyclone activity in the southern hemisphere,” Atmospheric and Climate Sciences, vol. 2, no. 3, pp. 298–306, 2012. View at Publisher · View at Google Scholar
  11. Y. Kuleshov, C. Spillman, Y. Wang et al., “Seasonal prediction of climate extremes for the Pacific: tropical cyclones and extreme ocean temperatures,” Journal of Marine Science and Technology, vol. 20, no. 6, pp. 675–683, 2012. View at Publisher · View at Google Scholar
  12. N. Nicholls, “A possible method for predicting seasonal tropical cyclone activity in the Australian region,” Monthly Weather Review, vol. 107, no. 9, pp. 1221–1224, 1979. View at Google Scholar · View at Scopus
  13. N. Nicholls, “Recent performance of a method for forecasting Australian seasonal tropical cyclone activity,” Australian Meteorological Magazine, vol. 40, no. 2, pp. 105–110, 1992. View at Google Scholar · View at Scopus
  14. J. C. L. Chan, “Tropical cyclone activity over the western North Pacific associated with El Nino and La Nina events,” Journal of Climate, vol. 13, no. 16, pp. 2960–2972, 2000. View at Google Scholar · View at Scopus
  15. Y. Kuleshov, L. Qi, R. Fawcett, and D. Jones, “On tropical cyclone activity in the Southern Hemisphere: trends and the ENSO connection,” Geophysical Research Letters, vol. 35, no. 14, Article ID L14S08, 2008. View at Publisher · View at Google Scholar · View at Scopus
  16. K. E. Trenberth, “The definition of el Niño,” Bulletin of the American Meteorological Society, vol. 78, no. 12, pp. 2771–2777, 1997. View at Google Scholar · View at Scopus
  17. K. Ashok, S. K. Behera, S. A. Rao, H. Weng, and T. Yamagata, “El Niño Modoki and its possible teleconnection,” Journal of Geophysical Research C, vol. 112, no. 11, 2007. View at Publisher · View at Google Scholar · View at Scopus
  18. K. Wolter and M. S. Timlin, “El Niño/Southern Oscillation behaviour since 1871 as diagnosed in an extended multivariate ENSO index (MEI.ext),” International Journal of Climatology, vol. 31, no. 7, pp. 1074–1087, 2011. View at Publisher · View at Google Scholar · View at Scopus
  19. G. J. Holland, “On the quality of the Australian tropical cyclone data base,” Australian Meteorological Magazine, vol. 29, pp. 169–181, 1981. View at Google Scholar
  20. M. HalL, E. Frank, G. Holmes, B. Pfahringer, P. Reutemann, and I. H. Witten, “The WEKA data mining software: an update,” ACM SIGKDD Explorations Newsletter, vol. 11, pp. 10–18, 2009. View at Google Scholar
  21. V. Vapnik, The Nature of Statistical Learning Theory, Springer, 2000.
  22. A. J. Smola and B. Schölkopf, “A tutorial on support vector regression,” Statistics and Computing, vol. 14, no. 3, pp. 199–222, 2004. View at Publisher · View at Google Scholar · View at Scopus
  23. H. Ramsay, M. Richman, and L. Leslie, “Exploring variants of ENSO Modoki for the seasonal prediction of Coral Sea tropical cyclone activity,” Submitted to Journal of Climate.
  24. A. Ben-Hur, C. S. Ong, S. Sonnenburg, B. Schölkopf, and G. Rätsch, “Support vector machines and kernels for computational biology,” PLOS Computational Biology, vol. 4, no. 10, Article ID e1000173, 2008. View at Publisher · View at Google Scholar
  25. S. K. Shevade, S. S. Keerthi, C. Bhattacharyya, and K. R. K. Murthy, “Improvements to the SMO algorithm for SVM regression,” IEEE Transactions on Neural Networks, vol. 11, no. 5, pp. 1188–1193, 2000. View at Publisher · View at Google Scholar · View at Scopus
  26. Climate Prediction Center—Noaa, “30 mb zonal wind index—CDAS,” 2013, ftp://ftp.cpc.ncep.noaa.gov/wd52dg/data/indices/qbo.u30.index.
  27. S. J. Camargo and A. H. Sobel, “Revisiting the influence of the quasi-biennial oscillation on tropical cyclone activity,” Journal of Climate, vol. 23, no. 21, pp. 5810–5825, 2010. View at Publisher · View at Google Scholar · View at Scopus
  28. A. J. Dowdy and Y. Kuleshov, “An analysis of tropical cyclone occurrence in the Southern Hemisphere derived from a new satellite-era dataset,” International Journal of Remote Sensing, vol. 23, no. 10, pp. 7382–7397, 2012. View at Publisher · View at Google Scholar
  29. A. Charles, Y. Kuleshov, and D. Jones, “Managing climate risk with seasonal forecasts,” in Book Management—Current Issues and Challenges, chapter 23, pp. 557–584, InTech, 2012. View at Google Scholar
  30. A. Cottrill, H. Hendon, E. -P. Lim et al., “Seasonal forecasting in the Pacific using the coupled model POAMA-2,” Weather and Forecasting, vol. 28, pp. 668–680, 2013. View at Publisher · View at Google Scholar
  31. K. Shelton, A. Charles, H. Hendon, and Y. Kuleshov, “Dynamical seasonal tropical cyclone prediction for the Australian and South Pacific Regions,” Journal of Climate. In preparation.