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Advances in Meteorology
Volume 2014, Article ID 838746, 8 pages
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.


Tropical cyclones (TCs) can have a major impact on the coastal communities of Australia and Pacific Island countries. Preparedness is one of the key factors to limit TC impacts and the Australian Bureau of Meteorology issues an outlook of TC seasonal activity ahead of TC season for the Australian Region (AR; 5°S to 40°S, 90°E to 160°E) and the South Pacific Ocean (SPO; 5°S to 40°S, 142.5°E to 120°W). This paper investigates the use of support vector regression models and new explanatory variables to improve the accuracy of seasonal TC predictions. Correlation analysis and subsequent cross-validation of the generated models showed that the Dipole Mode Index (DMI) performs well as an explanatory variable for TC prediction in both AR and SPO, Niño4 SST anomalies—in AR and Niño1+2 SST anomalies—in SPO. For both AR and SPO, the developed model which utilised the combination of Niño1+2 SST anomalies, Niño4 SST anomalies, and DMI had the best forecasting performance. The support vector regression models outperform the current models based on linear discriminant analysis approach for both regions, improving the standard deviation of errors in cross-validation from 2.87 to 2.27 for AR and from 4.91 to 3.92 for SPO.