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RandomFront 2.3: a physical parameterisation of fire spotting for operational fire spread models - implementation in WRF-SFIRE and response analysis with LSFire

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Kaur,  Inderpreet
Atmospheric Chemistry, Max Planck Institute for Chemistry, Max Planck Society;

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Citation

Trucchia, A., Egorova, V., Butenko, A., Kaur, I., & Pagnini, G. (2019). RandomFront 2.3: a physical parameterisation of fire spotting for operational fire spread models - implementation in WRF-SFIRE and response analysis with LSFire. Geoscientific Model Development, 12(1), 69-87. doi:10.5194/gmd-12-69-2019.


Cite as: http://hdl.handle.net/21.11116/0000-0003-0303-6
Abstract
Fire spotting is often responsible for dangerous flare-ups in wildfires and causes secondary ignitions isolated from the primary fire zone, which lead to perilous situations. The main aim of the present research is to provide a versatile probabilistic model for fire spotting that is suitable for implementation as a post-processing scheme at each time step in any of the existing operational large-scale wildfire propagation models, without calling for any major changes in the original framework. In particular, a complete physical parameterisation of fire spotting is presented and the corresponding updated model RandomFront 2.3 is implemented in a coupled fire–atmosphere model: WRF-SFIRE. A test case is simulated and discussed. Moreover, the results from different simulations with a simple model based on the level set method, namely LSFire+, highlight the response of the parameterisation to varying fire intensities, wind conditions and different firebrand radii. The contribution of the firebrands to increasing the fire perimeter varies according to different concurrent conditions, and the simulations show results in agreement with the physical processes. Among the many rigorous approaches available in the literature to model firebrand transport and distribution, the approach presented here proves to be simple yet versatile for application to operational large-scale fire spread models.