Seasonal forecasting of tropical cyclone activity in the Australian and the South Pacific Ocean regions
J.S. Wijnands; G. Qian; K.L. Shelton; R.J.B. Fawcett; J.C.L. Chan; Y. Kuleshov
Mathematics of Climate and Weather Forecasting (2015)
- Volume: 1, Issue: 1
- ISSN: 2353-6438
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top- [1] R. E. Basher and X. Zheng. “Tropical cyclones in the southwest Pacific: Spatial patterns and relationships to Southern Oscillation 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 tropical cyclone 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 Bulletin 56.4 (2007), pp. 297–309.
- [6] J. Camp, M. Roberts, C. MacLachlan, E. Wallace, L. Hermanson, A. Brookshaw, A. Arribas, and A. A. Scaife. “Seasonal forecasting of tropical storms using the Met Office GloSea5 seasonal forecast system.” In: Quarterly Journal of the Royal Meteorological 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-neutral and 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 Systems and 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 Computer Science. 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 Pacific Ocean 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 seasonal rainfall. 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] Zbl0622.62039
- [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 cyclone activity 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 in South 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. Zbl1273.62005
- [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 Climate 25.21 (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 of Statistical 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 prediction for 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 the South 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 of tropical 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] Zbl0042.38403
- [28] M. A. Lander. “An exploratory analysis of the relationship between tropical storm formation in the western North Pacific and 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 Science and 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 SIAM International 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: Monthly Weather 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 cyclone activity.” 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: Australian Meteorological 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: Ocean Engineering 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 of ENSO 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: Natural Hazards 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. Zbl0934.62009
- [47] P. J. Webster, G. J. Holland, J. A. Curry, and H.-R. Chang. “Changes in tropical cyclone number, duration, and intensity in a 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 prediction in 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: Proceedings of 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 by support vector regression.” In: Hydrology and Earth System Sciences 15.1 (2011), pp. 65–74. doi: 10.5194/hess-15-65-2011. [Crossref]