Abstract
Tropical cyclones (TCs) are the most destructive weather phenomena to impact on tropical regions. Reliable prediction of seasonal TC activity is important for preparedness of coastal communities of Australia and island nations in the Pacific and Indian Oceans ahead of the coming cyclone season. Over recent decades, statistical model-based methods for prediction of TC activity in the coming season have been developed for a number of regions in various ocean basins, starting with the pioneering work of Gray (1979). Statistical models explore relationships between large-scale environmental drivers which modulate TC activity, for example the El Niño-Southern Oscillation (ENSO) phenomenon, and observed numbers of TCs to derive linear regression equations which can be used for prediction of future cyclone activity. Indices such as the Southern Oscillation Index (SOI) and sea surface temperatures (SSTs) in some oceanic areas are commonly used to build such statistical models. However, there are two major constraints associated with the statistical model-based approach. Firstly, accurate historical cyclone records (ideally records covering a reasonably long period of time) are required.
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Kuleshov, Y., Wang, Y., Apajee, J., Fawcett, R., Jones, D. (2014). Prospects for Improving the Operational Seasonal Prediction of Tropical Cyclone Activity in the Southern Hemisphere. In: Mohanty, U.C., Mohapatra, M., Singh, O.P., Bandyopadhyay, B.K., Rathore, L.S. (eds) Monitoring and Prediction of Tropical Cyclones in the Indian Ocean and Climate Change. Springer, Dordrecht. https://doi.org/10.1007/978-94-007-7720-0_12
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DOI: https://doi.org/10.1007/978-94-007-7720-0_12
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