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Modeling Seasonal Tropical Cyclone Activity in the Fiji Region as a Binary Classification Problem

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  • 1 Centre for Australian Weather and Climate Research, Bureau of Meteorology, Melbourne, Victoria, Australia
  • | 2 School of Earth Sciences, University of Melbourne, Melbourne, Victoria, Australia
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Abstract

This study presents a binary classification model for the prediction of tropical cyclone (TC) activity in the Fiji, Samoa, and Tonga regions (the FST region) using the accumulated cyclone energy (ACE) as a proxy of TC activity. A probit regression model, which is a suitable probability model for describing binary response data, is developed to determine at least a few months in advance (by July in this case) the probability that an upcoming TC season may have for high or low TC activity. Years of “high TC activity” are defined as those years when ACE values exceeded the sample climatology (i.e., the 1985–2008 mean value). Model parameters are determined using the Bayesian method. Various combinations of the El Niño–Southern Oscillation (ENSO) indices and large-scale environmental conditions that are known to affect TCs in the FST region are examined as potential predictors. It was found that a set of predictors comprising low-level relative vorticity, upper-level divergence, and midtropspheric relative humidity provided the best skill in terms of minimum hindcast error. Results based on hindcast verification clearly suggest that the model predicts TC activity in the FST region with substantial skill up to the May–July preseason for all years considered in the analysis, in particular for ENSO-neutral years when TC activity is known to show large variations.

Corresponding author address: Savin S. Chand, Centre for Australian Weather and Climate Research, Bureau of Meteorology, GPO Box 1289, Melbourne VIC 3001, Australia. E-mail: schand@bom.gov.au

Abstract

This study presents a binary classification model for the prediction of tropical cyclone (TC) activity in the Fiji, Samoa, and Tonga regions (the FST region) using the accumulated cyclone energy (ACE) as a proxy of TC activity. A probit regression model, which is a suitable probability model for describing binary response data, is developed to determine at least a few months in advance (by July in this case) the probability that an upcoming TC season may have for high or low TC activity. Years of “high TC activity” are defined as those years when ACE values exceeded the sample climatology (i.e., the 1985–2008 mean value). Model parameters are determined using the Bayesian method. Various combinations of the El Niño–Southern Oscillation (ENSO) indices and large-scale environmental conditions that are known to affect TCs in the FST region are examined as potential predictors. It was found that a set of predictors comprising low-level relative vorticity, upper-level divergence, and midtropspheric relative humidity provided the best skill in terms of minimum hindcast error. Results based on hindcast verification clearly suggest that the model predicts TC activity in the FST region with substantial skill up to the May–July preseason for all years considered in the analysis, in particular for ENSO-neutral years when TC activity is known to show large variations.

Corresponding author address: Savin S. Chand, Centre for Australian Weather and Climate Research, Bureau of Meteorology, GPO Box 1289, Melbourne VIC 3001, Australia. E-mail: schand@bom.gov.au
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