Full-text resources of PSJD and other databases are now available in the new Library of Science.
Visit https://bibliotekanauki.pl


Preferences help
enabled [disable] Abstract
Number of results
2015 | 1 | 1 |

Article title

Seasonal forecasting of tropical cyclone
activity in the Australian and the South
Pacific Ocean regions


Title variants

Languages of publication



The Australian Bureau of Meteorology (Bureau) issues operational tropical cyclone (TC) seasonal
forecasts for the Australian region (AR) and the South Pacific Ocean (SPO) and subregions therein. The forecasts
are issued in October, ahead of the Southern Hemisphere TC season (November to April). Improvement
of operational TC seasonal forecasts can lead to more accurate warnings for coastal communities to prepare
for TC hazards. This study investigates the use of support vector regression (SVR) models, exploring new explanatory
variables and non-linear relationships between them, the use of model averaging, and lastly the
integration of forecast intervals based on a bias-corrected and accelerated non-parametric bootstrap. Hindcasting
analyses show that the SVR model outperforms several benchmark methods. Analysis of the generated
models shows that the Dipole Mode Index, 5VAR index and the Southern Oscillation Index are the most
frequently selected as explanatory variables for TC seasonal forecasting in all regions. The usage of ENSOrelated
covariates implies that definitions of regions and subregions may have to be updated to achieve optimal
forecasting performance. Overall, the new SVR methodology is an improvement over the current linear
discriminant analysis models and has the potential to increase accuracy of TC seasonal forecasts in the AR
and SPO.







Physical description


12 - 6 - 2015
17 - 8 - 2015
29 - 9 - 2015


  • School of Mathematics and Statistics, The University of Melbourne, Parkville VIC, Australia
  • School of Mathematics and Statistics, The University of Melbourne, Parkville VIC, Australia
  • JBA Consulting, Skipton, United Kingdom
  • Australian Bureau of Meteorology, Docklands VIC, Australia
  • School of Energy and Environment, City University of Hong Kong, PRC
  • School of Mathematics and Statistics, The University of Melbourne, Parkville VIC, Australia
  • Australian Bureau of Meteorology, Docklands VIC, Australia
  • School of Mathematical and Geospatial Sciences, Royal Melbourne Institute of Technology (RMIT) University, Melbourne
    VIC, Australia
  • Faculty of Sciences, Engineering and Technology, Swinburne University of Technology, Melbourne VIC, Australia


  • [1] R. E. Basher and X. Zheng. “Tropical cyclones in the southwest Pacific: Spatial patterns and relationships to SouthernOscillation and sea surface temperature.” In: Journal of Climate 8.5 (1995), pp. 1249–1260. doi: 10.1175/1520-0442(1995)008<1249:TCITSP>2.0.CO;2.[Crossref]
  • [2] A.I. Belousov, S.A.Verzakov, and J. von Frese. “A flexible classification approach with optimal generalisation performance:support vector machines.” In: Chemometrics and Intelligent Laboratory Systems 64.1 (2002), pp. 15–25. doi: 10.1016/S0169-7439(02)00046-1.[Crossref]
  • [3] S. J. Camargo, K. A. Emanuel, and A. H. Sobel. “Use of a genesis potential index to diagnose ENSO effects on tropicalcyclone genesis.” In: Journal of Climate 20.19 (2007), pp. 4819–4834. doi: 10.1175/JCLI4282.1.[Crossref]
  • [4] S. J. Camargo and A. H. Sobel. “Western North Pacific tropical cyclone intensity and ENSO.” In: Journal of Climate 18.15(2005), pp. 2996–3006. doi: 10.1175/JCLI3457.1.[Crossref]
  • [5] S. J. Camargo, A. G. Barnston, P. J. Klotzbach, and C.W. Landsea. “Seasonal tropical cyclone forecasts.” In: WMO Bulletin56.4 (2007), pp. 297–309.
  • [6] J. Camp, M. Roberts, C. MacLachlan, E. Wallace, L. Hermanson, A. Brookshaw, A. Arribas, and A. A. Scaife. “Seasonalforecasting of tropical storms using the Met Office GloSea5 seasonal forecast system.” In: Quarterly Journal of the RoyalMeteorological Society 141.691 (2015), pp. 2206–2219. doi: 10.1002/qj.2516.[Crossref]
  • [7] S. S. Chand, K. J. Tory, J. L.McBride, M. C. Wheeler, R. A. Dare, and K. J. E.Walsh. “The different impact of positive-neutraland negative-neutral ENSO regimes on Australian tropical cyclones.” In: Journal of Climate 26.20 (2013), pp. 8008–8016.doi: 10.1175/JCLI-D-12-00769.1.[Crossref]
  • [8] C.-C. Chang and C.-J. Lin. “LIBSVM: A library for support vector machines.” In: ACM Transactions on Intelligent Systemsand Technology 2.3 (2011), 27:1–27:27. doi: 10.1145/1961189.1961199.[Crossref]
  • [9] K. Davis, X. Zeng, and E. A. Ritchie. “A new statistical model to predict seasonal North Atlantic hurricane activity.” In:Weather and Forecasting 141 (691 2015), pp. 2206–2219. doi: 10.1175/WAF-D-14-00156.1.[Crossref]
  • [10] T. G. Dietterich. “Ensemble methods in machine learning.” In: Multiple classifier systems. Vol. 1857. Lecture Notes in ComputerScience. Springer Berlin Heidelberg, 2000, pp. 1–15. isbn: 978-3-540-67704-8. doi: 10.1007/3-540-45014-9_1.
  • [11] A. J. Dowdy. “Long-term changes in Australian tropical cyclone numbers.” In: Atmospheric Science Letters 15.4 (2014),pp. 292–298. doi: 10.1002/asl2.502.[Crossref]
  • [12] A. J. Dowdy, L. Qi, D. Jones, H. Ramsay, R. Fawcett, and Y. Kuleshov. “Tropical cyclone climatology of the South PacificOcean and its relationship to El Niño-Southern Oscillation.” In: Journal of Climate 25.18 (2012), pp. 6108–6122. doi: 10.1175/JCLI-D-11-00647.1.[Crossref]
  • [13] W. Drosdowsky and L. E. Chambers. Near global sea surface temperature anomalies as predictors of Australian seasonalrainfall. Tech. rep. 65. Melbourne: Bureau of Meteorology Research Centre, 1998.
  • [14] B. Efron. “Better bootstrap confidence intervals.” In: Journal of the American Statistical Association 82.397 (1987), pp. 171–185. doi: 10.1080/01621459.1987.10478410.[Crossref]
  • [15] K. Emanuel. “Increasing destructiveness of tropical cyclones over the past 30 years.” In: Nature 436.7051 (2005), pp. 686–688. doi: 10.1038/nature03906.[Crossref]
  • [16] J. L. Evans and R. J. Allan. “El Niño/Southern Oscillation modification to the structure of the monsoon and tropical cycloneactivity in the Australasian region.” In: International Journal of Climatology 12.6 (1992), pp. 611–623. doi: 10.1002/joc.3370120607.[Crossref]
  • [17] W. M. Frank and G. S. Young. “The interannual variability of tropical cyclones.” In:MonthlyWeather Review 135.10 (2007),pp. 3587–3598. doi: 10.1175/MWR3435.1.[Crossref]
  • [18] A. Z.-C. Goh and J. C. L. Chan. “An improved statistical scheme for the prediction of tropical cyclones making landfall inSouth China.” In: Weather and Forecasting 25.2 (2010), pp. 587–593. doi: 10.1175/2009WAF2222305.1.[Crossref]
  • [19] T. Hastie, R. Tibshirani, and J. Friedman. The elements of statistical learning. 2nd ed. Springer, 2009. isbn: 978-0-387-84857-0.
  • [20] G. J. Holland. “On the quality of the Australian tropical cyclone data base.” In: Australian Meteorological Magazine 29(1981), pp. 169–181.
  • [21] N.-Y. Kang and J. B. Elsner. “Consensus on climate trends inwestern North Pacific tropical cyclones.” In: Journal of Climate25.21 (2012), pp. 7564–7573. doi: 10.1175/JCLI-D-11-00735.1.[Crossref]
  • [22] A. Karatzoglou, A. Smola, K. Hornik, and A. Zeileis. “kernlab - An S4 package for kernel methods in R.” In: Journal ofStatistical Software 11.9 (2004), pp. 1–20. url: http://www.jstatsoft.org/v11/i09.
  • [23] Y. Kuleshov, L. Qi, R. Fawcett, and D. Jones. “Improving preparedness to natural hazards: Tropical cyclone seasonal predictionfor the Southern Hemisphere.” In: Advances in Geosciences, Volume 12: Ocean Science (OS). Ed. by J. Gan. Vol. 12.Singapore: World Scientific Publishing, 2009, pp. 127–143. doi: 10.1142/9789812836168_0010.
  • [24] Y. Kuleshov, R. Fawcett, L. Qi, B. Trewin, D. Jones, J. McBride, and H. Ramsay. “Trends in tropical cyclones in theSouth Indian Ocean and the South Pacific Ocean.” In: Journal of Geophysical Research 115.D01101 (2010). doi: 10.1029/2009JD012372.[Crossref]
  • [25] Y. Kuleshov, Y.Wang, J. Apajee, R. Fawcett, and D. Jones. “Prospects for improving the operational seasonal prediction oftropical cyclone activity in the Southern Hemisphere.” In: Atmospheric and Climate Sciences 2.3 (2012), pp. 298–306. doi:10.4236/acs.2012.23027.[Crossref]
  • [26] Y. Kuleshov, C. Spillman, Y.Wang, A. Charles, R. deWit, K. Shelton, D. Jones, H. Hendon, C. Ganter, A.Watkins, J. Apajee,and A. Griesser. “Seasonal prediction of climate extremes for the Pacific: Tropical cyclones and extreme ocean temperatures.”In: Journal of Marine Science and Technology 20.6 (2012), pp. 675–683. doi: 10.6119/JMST-012-0628-1.[Crossref]
  • [27] S. Kullback and R. A. Leibler. “On information and sufficiency.” In: The Annals of Mathematical Statistics 22.1 (1951),pp. 79–86. doi: 10.1214/aoms/1177729694.[Crossref]
  • [28] M. A. Lander. “An exploratory analysis of the relationship between tropical storm formation in the western North Pacificand ENSO.” In: Monthly Weather Review 122.4 (1994), pp. 636–651. doi: 10.1175/1520-0493(1994)122<0636:AEAOTR>2.0.CO;2.[Crossref]
  • [29] J.-H. Lee and C.-J. Lin. Automatic model selection for support vector machines. Tech. rep. Department of Computer Scienceand Information Engineering, National Taiwan University, 2000.
  • [30] C.-J. Lin and R. C. Weng. Simple probabilistic predictions for support vector regression. Tech. rep. National Taiwan University,2004.
  • [31] J. Malilay. “Tropical cyclones.” In: The public health consequences of disasters. Ed. by E. K. Noji. Oxford University Press,1996. Chap. 10, pp. 207–227. isbn: 9780199747689.
  • [32] F. Molteni, R. Buizza, T. N. Palmer, and T. Petroliagis. “The ECMWF ensemble prediction system: Methodology and validation.”In: Quarterly Journal of the Royal Meteorological Society 122.529 (1996), pp. 73–119. doi: 10.1002/qj.49712252905.[Crossref]
  • [33] M. Momma and K. P. Bennett. “A pattern search method for model selection of support vector regression.” In: 2002 SIAMInternational Conference on Data Mining. SIAM, 2002, pp. 261–274. doi: 10.1137/1.9781611972726.16.
  • [34] N. Nicholls. “A possible method for predicting seasonal tropical cyclone activity in the Australian region.” In: MonthlyWeather Review 107.9 (1979), pp. 1221–1224. doi: 10.1175/1520-0493(1979)107<1221:APMFPS>2.0.CO;2.[Crossref]
  • [35] N. Nicholls. “The Southern Oscillation, sea-surface-temperature, and interannual fluctuations in Australian tropical cycloneactivity.” In: Journal of Climatology 4.6 (1984), pp. 661–670. doi: 10.1002/joc.3370040609.[Crossref]
  • [36] N. Nicholls. “Recent performance of a method for forecasting Australian seasonal tropical cyclone activity.” In: AustralianMeteorological Magazine 40.2 (1992), pp. 105–110.
  • [37] J. C. Platt. “Fast training of support vector machines using sequential minimal optimization.” In: Advances in KernelMethods.Support Vector Learning. Ed. by B. Schölkopf, C. J. C. Burges, and A. J. Smola. MIT Press, 1999, pp. 185–208. isbn:9780262194167.
  • [38] R Core Team. R: A language and environment for statistical computing. R Foundation for Statistical Computing. Vienna,Austria, 2014. url: http://www.R-project.org/.
  • [39] S. Rajasekaran, S. Gayathri, and T.-L. Lee. “Support vector regression methodology for storm surge predictions.” In: OceanEngineering 35.16 (2008), pp. 1578–1587. doi: 10.1016/j.oceaneng.2008.08.004.[Crossref]
  • [40] H. A. Ramsay, M. B. Richman, and L. M. Leslie. “Seasonal tropical cyclone predictions using optimized combinations ofENSO regions: Application to the Coral Sea basin.” In: Journal of Climate 27.22 (2014), pp. 8527–8542. doi: 10.1175/JCLI-D-14-00017.1.[Crossref]
  • [41] M. B. Richman and L. M. Leslie. “Adaptive machine learning approaches to seasonal prediction of tropical cyclones.” In:Procedia Computer Science 12 (2012), pp. 276–281. doi: 10.1016/j.procs.2012.09.069.[Crossref]
  • [42] K. K. Saha and S. A.Wasimi. “An index to assess the propensity of landfall in Australia of a tropical cyclone.” In: NaturalHazards 72.2 (2014), pp. 1111–1121. doi: 10.1007/s11069-014-1058-y.[Crossref]
  • [43] J. Shao. Mathematical statistics. 2nd ed. Springer, 2003. isbn: 978-0-387-95382-3.
  • [44] K. E. Trenberth. “Signal versus noise in the Southern Oscillation.” In: Monthly Weather Review 112.2 (1984), pp. 326–332.doi: 10.1175/1520-0493(1984)112<0326:SVNITS>2.0.CO;2.[Crossref]
  • [45] K. E. Trenberth. “The definition of El Niño.” In: Bulletin of the AmericanMeteorological Society 78.12 (1997), pp. 2771–2777.doi: 10.1175/1520-0477(1997)078<2771:TDOENO>2.0.CO;2.[Crossref]
  • [46] V. Vapnik. The nature of statistical learning theory. Springer-Verlag New York, 2000. isbn: 978-0-387-98780-4. doi: 10.1007/978-1-4757-3264-1.
  • [47] P. J. Webster, G. J. Holland, J. A. Curry, and H.-R. Chang. “Changes in tropical cyclone number, duration, and intensity ina warming environment.” In: Science 309.5742 (2005), pp. 1844–1846. doi: 10.1126/science.1116448.[Crossref]
  • [48] J. S.Wijnands, K. Shelton, and Y. Kuleshov. “Improving the operational methodology of tropical cyclone seasonal predictionin the Australian and the South Pacific Ocean regions.” In: Advances in Meteorology (2014), pp. 1–8. doi: 10.1155/2014/838746.[Crossref]
  • [49] D. S.Wilks. Statistical methods in the atmospheric sciences. 2nd ed. International Geophysics Series. Elsevier, 2006. isbn:978-0-12-751966-1.
  • [50] K.Wolter and M. S. Timlin. “Monitoring ENSO in COADS with a seasonally adjusted principal component index.” In: Proceedingsof the 17th Annual Climate DiagnosticsWorkshop. Norman, OK: NOAA/NMC/CAC, NSSL, Oklahoma Clim. Survey,CIMMS and the School of Meteor., Univ. of Oklahoma, 1993, pp. 52–57.
  • [51] Z. Zeng,W.W. Hsieh, A. Shabbar, andW. R. Burrows. “Seasonal prediction of winter extreme precipitation over Canada bysupport vector regression.” In: Hydrology and Earth System Sciences 15.1 (2011), pp. 65–74. doi: 10.5194/hess-15-65-2011.[Crossref]

Document Type

Publication order reference


YADDA identifier

JavaScript is turned off in your web browser. Turn it on to take full advantage of this site, then refresh the page.