Improving the Operational Methodology of Tropical Cyclone Seasonal Prediction in the Australian and the South Pacific Ocean Regions
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 Re...
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| Format: | Article |
| Language: | English |
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Wiley
2014-01-01
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| Series: | Advances in Meteorology |
| Online Access: | http://dx.doi.org/10.1155/2014/838746 |
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| author | J. S. Wijnands K. Shelton Y. Kuleshov |
| author_facet | J. S. Wijnands K. Shelton Y. Kuleshov |
| author_sort | J. S. Wijnands |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-857756792bd44370a66f159a0f0dae27 |
| institution | Kabale University |
| issn | 1687-9309 1687-9317 |
| language | English |
| publishDate | 2014-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Advances in Meteorology |
| spelling | doaj-art-857756792bd44370a66f159a0f0dae272025-02-03T01:12:10ZengWileyAdvances in Meteorology1687-93091687-93172014-01-01201410.1155/2014/838746838746Improving the Operational Methodology of Tropical Cyclone Seasonal Prediction in the Australian and the South Pacific Ocean RegionsJ. S. Wijnands0K. Shelton1Y. Kuleshov2The University of Melbourne, Parkville, VIC 3010, AustraliaBureau of Meteorology, Docklands, VIC 3008, AustraliaThe University of Melbourne, Parkville, VIC 3010, AustraliaTropical 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.http://dx.doi.org/10.1155/2014/838746 |
| spellingShingle | J. S. Wijnands K. Shelton Y. Kuleshov Improving the Operational Methodology of Tropical Cyclone Seasonal Prediction in the Australian and the South Pacific Ocean Regions Advances in Meteorology |
| title | Improving the Operational Methodology of Tropical Cyclone Seasonal Prediction in the Australian and the South Pacific Ocean Regions |
| title_full | Improving the Operational Methodology of Tropical Cyclone Seasonal Prediction in the Australian and the South Pacific Ocean Regions |
| title_fullStr | Improving the Operational Methodology of Tropical Cyclone Seasonal Prediction in the Australian and the South Pacific Ocean Regions |
| title_full_unstemmed | Improving the Operational Methodology of Tropical Cyclone Seasonal Prediction in the Australian and the South Pacific Ocean Regions |
| title_short | Improving the Operational Methodology of Tropical Cyclone Seasonal Prediction in the Australian and the South Pacific Ocean Regions |
| title_sort | improving the operational methodology of tropical cyclone seasonal prediction in the australian and the south pacific ocean regions |
| url | http://dx.doi.org/10.1155/2014/838746 |
| work_keys_str_mv | AT jswijnands improvingtheoperationalmethodologyoftropicalcycloneseasonalpredictionintheaustralianandthesouthpacificoceanregions AT kshelton improvingtheoperationalmethodologyoftropicalcycloneseasonalpredictionintheaustralianandthesouthpacificoceanregions AT ykuleshov improvingtheoperationalmethodologyoftropicalcycloneseasonalpredictionintheaustralianandthesouthpacificoceanregions |