Journal article

A Bayesian Regression Approach to Seasonal Prediction of Tropical Cyclones Affecting the Fiji Region

Savin S Chand, Kevin JE Walsh, Johnny CL Chan

JOURNAL OF CLIMATE | AMER METEOROLOGICAL SOC | Published : 2010

Abstract

This study presents seasonal prediction schemes for tropical cyclones (TCs) affecting the Fiji, Samoa, and Tonga (FST) region. Two separate Bayesian regression models are developed: (i) for cyclones forming within the FST region (FORM) and (ii) for cyclones entering the FST region (ENT). Predictors examined include various El Niño-Southern Oscillation (ENSO) indices and large-scale environmental parameters. Only those predictors that showed significant correlations with FORM and ENT are retained. Significant preseason correlations are found as early as May-July (approximately three months in advance). Therefore, May-July predictors are used to make initial predictions, and updated prediction..

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University of Melbourne Researchers

Grants

Funding Acknowledgements

The authors are thankful to the Fiji Meteorological Service for providing cyclone data for the Fiji region. The first author also acknowledges the Australian government-sponsored Endeavour Post-graduate Award for funding his doctorate degree at the University of Melbourne. We appreciate the constructive comments by the three reviewers.