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

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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.

Opis fizyczny
  • 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
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