Prospects for Improving the Operational Seasonal Prediction of Tropical Cyclone Activity in the Southern Hemisphere


Tropical cyclones (TCs) are the most destructive weather phenomena to impact on tropical regions, and reliable predicttion of TC seasonal activity is important for preparedness of coastal communities in the tropics. In investigating prospects for improving the skill of TC seasonal prediction in the South Indian and South Pacific Oceans, including the Australian Region, we used linear regression to model the relationship between the annual number of cyclones and three indices (SOI, NI?O3.4 and 5VAR) describing the strength of the El Ni?o-Southern Oscillation (ENSO). The correlation between the number of Australian Region (90?E - 160?E) TCs and the indices was strong (3-month 5VAR ?0.65, NI?O3.4 ?0.62 and SOI +0.64), and a cross-validation assessment demonstrated that the models which used July-August-September indices and the temporal trend as the predictors performed well. The predicted number of TCs in the Australian Region for 2010/2011 and 2011/2012 seasons was 14 (11 recorded) and 12, respectively. We also found that the correlation between the numbers of TCs in the western South Indian region (30?E to 90?E) and the eastern South Pacific region (east of 170?E) and the indices was weak, and it is therefore not sensible to build linear regression forecast models for these regions. We conclude that for the Australian Region, the new statistical model provides prospects for improvement in forecasting skill compared to the statistical model currently employed at the National Climate Centre, Australian Bureau of Meteorology. The next step towards improving the skill of TC seasonal prediction in the various regions of the Southern Hemisphere will be undertaken through analysis of outputs from the dynamical climate model POAMA (Predictive Ocean-Atmosphere Model for Australia).

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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, 2012, pp. 298-306. doi: 10.4236/acs.2012.23027.

Conflicts of Interest

The authors declare no conflicts of interest.


[1] W. M. Gray, “Hurricanes: Their Formation, Structure and likely Role in the Tropical Circulation,” In: D. B. Shaw, Ed., Supplement to Meteorology Over the Tropical Oceans, James Glaisher House, Bracknell, 1979, pp. 155- 218.
[2] Y. Kuleshov, R. Fawcett, L. Qi, B. Trewin, D. Jones, J. McBride and H. Ramsay, “Trends in Tropical Cyclones in the South Indian Ocean and the South Pacific Ocean,” Journal of Geophysical Research, Vol. 115, 2010, 9 pages. doi:10.1029/2009JD012372
[3] M. Broomhall, I. Grant, L. Majewski, M. Willmott, D. Jones and Y. Kuleshov, “Improving the Australian Tropical Cyclone Database: Extension of GMS Satellite Image Archive,” In: Y. Charabi, Ed., Indian Ocean Tropical Cyclones and Climate Change, Springer, New York, 2010, pp. 199-206. doi:10.1007/978-90-481-3109-9_24
[4] G. J. Holland, “On the Climatology and Structure of Tropical Cyclones in the Australian/Southwest Pacific Region: I. Data and T. Storms,” Australian Meteorological Magazine, Vol. 32, No. 1, 1984, pp. 1-15.
[5] 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, 2008, 5 pages. doi:10.1029/2007GL032983
[6] N. Nicholls, “A Possible Method for Predicting Seasonal Tropical Cyclone Activity in the Australian Region,” Monthly Weather Review, Vol. 107, No. 9, 1979, pp. 1221- 1224. doi:10.1175/1520-0493(1979)107<1221:APMFPS>2.0.CO;2
[7] N. Nicholls, “Recent Performance of a Method for Forecasting Australian Seasonal Tropical Cyclone Activity,” Australian Meteorological Magazine, Vol. 40, No. 2, 1992, pp. 105-110.
[8] Y. Kuleshov, L. Qi, R. Fawcett and D. Jones, “Improving Preparedness to Natural Hazards: Tropical Cyclone Prediction for the Southern Hemisphere,” In: J. Gan, Ed., Advances in Geosciences, World Scientific Publishing, Singapore City, 2009, pp. 127-143.
[9] K. S. Liu and J. C. L. Chan, “Interannual Variation of Southern Hemisphere Tropical Cyclone Activity and Seasonal Forecast of Tropical Cyclone Number in the Australian Region,” International Journal of Climatology, Vol. 32, No. 2, 2010, pp. 190-202. doi:10.1002/joc.2259
[10] NCC, “2010/11 Australian Tropical Cyclone Seasonal Outlook,” 2010.
[11] GCACIC, 2010. Retrieved 2011-11-11
[12] H. A. Ramsay, L. M. Leslie, P. J. Lamb, M. B. Rickman and M. Leplastrier, “Interannual Variability of Tropical Cyclones in the Australian Region: Role of Large-Scale Environment,” Journal of Climate, Vol. 21, No. 5, 2008, pp. 1083-1103. doi:10.1175/2007JCLI1970.1
[13] D. A. Belsley, K. Edwin and E. W. Roy, “Regression Diagnostics: Identifying Influential Data and Sources of Collinearity,” Wiley Series in Probability and Mathematical Statistics, John Wiley & Sons Ltd., New York, 1980.
[14] N. Nicholls, C. W. Landsea and J. Gill, “Recent Trends in Australian Region Tropical Cyclone Activity,” Meteorology and Atmospheric Physics, Vol. 65, No. 3-4, 1998, pp. 197-205. doi:10.1007/BF01030788
[15] K. S. Liu and J. C. L. Chan, “Interdecadal Variability of Western North Pacific Tropical Cyclone Tracks,” Journal of Climate, Vol. 21, No. 17, 2008, pp. 4464-4476. doi:10.1175/2008JCLI2207.1
[16] G. Wang, O. Alves, D. Hudson, H. Hendon, G. Liu and F. Tseitkin, “SST Skill Assessment from the New POAMA- 1.5 System,” BMRC Research Letter, No. 8, 2008.
[17] H. H. Hendon, E. Lim, G. Wang, O. Alves and D. Hudson, “Prospects for Predicting Two Flavors of El Nino,” Geophysical Research Letters, Vol. 36, 2009, 6 pages. doi:10.1029/2009GL040100

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