- Title
- Forecasting tropical cyclone formation in the Fiji region: A probit regression approach using bayesian fitting
- Creator
- Chand, Savin; Walsh, Kevin
- Date
- 2011
- Type
- Text; Journal article
- Identifier
- http://researchonline.federation.edu.au/vital/access/HandleResolver/1959.17/103136
- Identifier
- vital:10855
- Identifier
-
https://doi.org/10.1175/2010WAF2222452.1
- Identifier
- ISSN:08828156
- Abstract
- An objective methodology for forecasting the probability of tropical cyclone (TC) formation in the Fiji, Samoa, and Tonga regions (collectively the FST region) using antecedent large-scale environmental conditions is investigated. Three separate probabilistic forecast schemes are developed using a probit regression approach where model parameters are determined via Bayesian fitting. These schemes provide forecasts of TC formation from an existing system (i) within the next 24 h (W24h), (ii) within the next 48 h (W48h), and (iii) within the next 72 h (W72h). To assess the performance of the three forecast schemes in practice, verification methods such as the posterior expected error, Brier skill scores, and relative operating characteristic skill scores are applied. Results suggest that the W24h scheme, which is formulated using large-scale environmental parameters, on average, performs better than that formulated using climatology and persistence (CLIPER) variables. In contrast, the W48h (W72h) scheme formulated using large-scale environmental parameters performs similar to (poorer than) that formulated using CLIPER variables. Therefore, large-scale environmental parameters (CLIPER variables) are preferred as predictors when forecasting TC formation in the FST region within 24 h (at least 48 h) using models formulated in the present investigation. © 2011 American Meteorological Society.
- Relation
- Weather and Forecasting Vol. 26, no. 2 (2011), p. 150-165
- Rights
- Copyright American Meteorological Society
- Rights
- This metadata is freely available under a CCO license
- Subject
- Bayesian methods; Forecasting; Regression analysis; Tropical cyclones; Bayesian fitting; Environmental conditions; Environmental parameter; Existing systems; Model parameters; Probabilistic forecasts; Relative operating characteristics; Skill Score; Tropical cyclone; Verification method; Bayesian networks; Climatology; Hurricanes; Tropics; Bayesian analysis; Probability; Weather forecasting; Fiji; Samoa; Tonga; 0401 Atmospheric Sciences
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