- Title
- Modeling seasonal tropical cyclone activity in the Fiji region as a binary classification problem
- Creator
- Chand, Savin; Walsh, Kevin
- Date
- 2012
- Type
- Text; Journal article
- Identifier
- http://researchonline.federation.edu.au/vital/access/HandleResolver/1959.17/101904
- Identifier
- vital:10707
- Identifier
- ISSN:08948755
- 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 probabilitymodel for describing binary response data, is developed to determine at least a fewmonths 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 Nin{ogonek} o-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. © 2012 American Meteorological Society.
- Relation
- Journal of Climate Vol. 25, no. 14 (2012), p. 5057-5071
- Rights
- Copyright AMS
- Rights
- This metadata is freely available under a CCO license
- Subject
- Bayesian methods; Climate variability; El Nino; ENSO; Forecast verification/skill; Forecasting techniques; Hindcasts; Probability forecasts/models/distribution; Seasonal forecasting; Statistical forecasting; Statistical techniques; Forecast verifications; Probability forecasts; Atmospheric pressure; Bayesian networks; Climatology; Hurricanes; Nickel compounds; Regression analysis; Storms; Forecasting; Bayesian analysis; Climate modeling; Climate prediction; El Nino-Southern Oscillation; Hindcasting; Numerical model; Probability; Seasonal variation; Tropical cyclone; Weather forecasting; Fiji
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