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A Bayesian Forecast Model of Australian Region Tropical Cyclone Formation
Citation
Werner, A and Holbrook, NJ, A Bayesian Forecast Model of Australian Region Tropical Cyclone Formation, Journal of Climate, 24, (23) pp. 6114-6131. ISSN 0894-8755 (2011) [Refereed Article]
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Copyright Statement
© Copyright 2011 AMS
DOI: doi:10.1175/2011JCLI4231.1
Abstract
A new and potentially skillful seasonal forecast model of tropical cyclone formation [tropical cyclogenesis
(TCG)] is developed for the Australian region. The model is based on Poisson regression using the Bayesian
approach. Predictor combinations are chosen using a step-by-step predictor selection. The three-predictor
model based on derived indices of June–August average convective available potential energy, May–July
average meridional winds at 850 hPa (V850), and July–September geopotential height at 500 hPa produces the
smallest standard error (se 5 0.36) and root-mean-squared error (RMSE 5 5.20) for the leave-one-out crossvalidated
TCG hindcasts over the 40-yr record between 1968/89–2007/08. The corresponding correlation
coefficient between observed annual TCG totals and cross-validated model hindcasts is r 5 0.73. Using
fourfold cross-validation, model hindcast skill is robust with 85% of the observed seasonal TCG totals
hindcast within the model standard deviations. Seasonal TCG totals during ENSO events are typically well
captured with RMSE 5 5.14 during El Nin˜ o, and RMSE 5 6.04 during La Nin˜ a years. The model is shown
to be valuable in hindcasting seasonal TCG totals in the eastern Australian subregion (r 5 0.73) and also
provides some skill for the western Australian region (r 5 42), while it not useful for the northern region. In
summary, the authors find that the three-predictor Bayesian model provides substantial improvement over
existing statistical TCG forecast models, with remarkably skillful hindcasts (forecasts)of Australian region
AU1 and subregional seasonal TCG totals provided one month ahead of the TC season.
Item Details
Item Type: | Refereed Article |
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Keywords: | Tropical cyclone forecast, Bayesian models |
Research Division: | Earth Sciences |
Research Group: | Atmospheric sciences |
Research Field: | Atmospheric dynamics |
Objective Division: | Environmental Management |
Objective Group: | Air quality, atmosphere and weather |
Objective Field: | Atmospheric processes and dynamics |
UTAS Author: | Holbrook, NJ (Professor Neil Holbrook) |
ID Code: | 73298 |
Year Published: | 2011 |
Web of Science® Times Cited: | 20 |
Deposited By: | Geography and Environmental Studies |
Deposited On: | 2011-09-26 |
Last Modified: | 2017-11-01 |
Downloads: | 0 |
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