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2015 | 1 | 1 |
Article title

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

Content
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EN
Abstracts
EN
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.
Publisher

Year
Volume
1
Issue
1
Physical description
Dates
received
12 - 6 - 2015
accepted
17 - 8 - 2015
online
29 - 9 - 2015
Contributors
author
  • School of Mathematics and Statistics, The University of Melbourne, Parkville VIC, Australia
author
  • School of Mathematics and Statistics, The University of Melbourne, Parkville VIC, Australia
author
  • JBA Consulting, Skipton, United Kingdom
  • Australian Bureau of Meteorology, Docklands VIC, Australia
author
  • School of Energy and Environment, City University of Hong Kong, PRC
author
  • 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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Document Type
Publication order reference
Identifiers
YADDA identifier
bwmeta1.element.-psjd-doi-10_1515_mcwf-2015-0002
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