Modeling seasonal tropical cyclone activity in the Fiji region as a binary classification problem
- Authors: Chand, Savin , Walsh, Kevin
- Date: 2012
- Type: Text , Journal article
- Relation: Journal of Climate Vol. 25, no. 14 (2012), p. 5057-5071
- Full Text: false
- Reviewed:
- Description: 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.
Forecasting tropical cyclone formation in the Fiji region: A probit regression approach using bayesian fitting
- Authors: Chand, Savin , Walsh, Kevin
- Date: 2011
- Type: Text , Journal article
- Relation: Weather and Forecasting Vol. 26, no. 2 (2011), p. 150-165
- Full Text: false
- Reviewed:
- Description: 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.