Effect of weather forecast errors on fire growth model projections
Trent D. Penman
A C , Dan A. Ababei A , Jane G. Cawson A , Brett A. Cirulis A , Thomas J. Duff A , William Swedosh B and James E. Hilton B
+ Author Affiliations
- Author Affiliations
A Bushfire Behaviour and Management, School of Ecosystem and Forest Sciences, University of Melbourne, Melbourne, Vic. 3010, Australia.
B Data61, Commonwealth Scientific and Industrial Research Organisation, Melbourne, Vic. 3168, Australia.
C Corresponding author. Email: trent.penman@unimelb.edu.au
International Journal of Wildland Fire 29(11) 983-994 https://doi.org/10.1071/WF19199
Submitted: 2 December 2019 Accepted: 2 August 2020 Published: 31 August 2020
Abstract
Fire management agencies use fire behaviour simulation tools to predict the potential spread of a fire in both risk planning and operationally during wildfires. These models are generally based on underlying empirical or quasi-empirical relations and rarely are uncertainties considered. Little attention has been given to the quality of the input data used during operational fire predictions. We examined the extent to which error in weather forecasts can affect fire simulation results. The study was conducted using data representing the State of Victoria in south-eastern Australia, including grassland and forest conditions. Two fire simulator software packages were used to compare fire growth under observed and forecast weather. We found that error in the weather forecast data significantly altered the predicted size and location of fires. Large errors in wind speed and temperature resulted in an overprediction of fire size, whereas large errors in wind direction resulted in an increased spatial error in the fire’s location. As the fire weather intensified, fire predictions using forecast weather under predicted fire size, potentially resulting in greater risks to the community. These results highlight the importance of on-ground intelligence during wildfires and the use of ensembles to improve operational fire predictions.
Additional keywords: Bayesian network, fire prediction, meteorological forecast, sensitivity, simulation.
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