A Statistical Forecasting Model for Extremes of the Fire Behaviour Index in Australia
Article
Taylor, Rachel, Marshall, Andrew G., Crimp, Steven, Cary, Geoffrey J. and Harris, Sarah. 2024. "A Statistical Forecasting Model for Extremes of the Fire Behaviour Index in Australia." Atmosphere. 15 (4). https://doi.org/10.3390/atmos15040470
| Article Title | A Statistical Forecasting Model for Extremes of the Fire Behaviour Index in Australia |
|---|---|
| ERA Journal ID | 210170 |
| Article Category | Article |
| Authors | Taylor, Rachel, Marshall, Andrew G., Crimp, Steven, Cary, Geoffrey J. and Harris, Sarah |
| Journal Title | Atmosphere |
| Journal Citation | 15 (4) |
| Article Number | 470 |
| Number of Pages | 22 |
| Year | 2024 |
| Publisher | MDPI AG |
| Place of Publication | Switzerland |
| ISSN | 2073-4433 |
| Digital Object Identifier (DOI) | https://doi.org/10.3390/atmos15040470 |
| Web Address (URL) | https://www.mdpi.com/2073-4433/15/4/470 |
| Abstract | The increasing frequency and duration of severe fire events in Australia further necessitate accurate and timely forecasting to mitigate their consequences. This study evaluated the performance of two distinct approaches to forecasting extreme fire danger at two- to three-week lead times for the period 2003 to 2017: the official Australian climate simulation dynamical model and a statistical model based on climate drivers. We employed linear logistic regression to develop the statistical model, assessing the influence of individual climate drivers using single linear regression. The performance of both models was evaluated through case studies of three significant extreme fire events in Australia: the Canberra (2003), Black Saturday (2009), and Pinery (2015) fires. The results revealed that ACCESS-S2 generally underestimated the spatial extent of all three extreme FBI events, but with accuracy scores ranging from 0.66 to 0.86 across the case studies. Conversely, the statistical model tended to overpredict the area affected by extreme FBI, with high false alarm ratios between 0.44 and 0.66. However, the statistical model demonstrated higher probability of detection scores, ranging from 0.57 to 0.87 compared with 0.03 to 0.57 for the dynamic model. These findings highlight the complementary strengths and limitations of both forecasting approaches. Integrating dynamical and statistical models with transparent communication of their uncertainties could potentially improve accuracy and reduce false alarms. This can be achieved through hybrid forecasting, combined with visual inspection and comparison between the statistical and dynamical forecasts. Hybrid forecasting also has the potential to increase forecast lead times to up to several months, ultimately aiding in decision-making and resource allocation for fire management. |
| Keywords | Australia; extreme fire danger; subseasonal prediction; fire weather; statistical modelling; climate drivers; logistic regression; hybrid forecasting |
| Related Output | |
| Is supplemented by | Correction: Taylor et al. A Statistical Forecasting Model for Extremes of the Fire Behaviour Index in Australia. Atmosphere 2024, 15, 470 |
| Contains Sensitive Content | Does not contain sensitive content |
| ANZSRC Field of Research 2020 | 370105. Atmospheric dynamics |
| Public Notes | This article has been corrected. Please see the Related Output. |
| Byline Affiliations | Australian National University |
| Centre for Applied Climate Sciences | |
| Australian Bureau of Meteorology | |
| Country Fire Authority, Victoria, Australia |
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https://research.usq.edu.au/item/z85qv/a-statistical-forecasting-model-for-extremes-of-the-fire-behaviour-index-in-australia
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