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paper/paper.bib

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@article{lautenberger2013,
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title = {Wildland fire modeling with an Eulerian level set method and automated calibration},
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title = {Wildland fire modeling with an {Eulerian} level set method and automated calibration},
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volume = {62},
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ISSN = {0379-7112},
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url = {https://github.com/lautenberger/elmfire/},
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note = {Available from U.S. Forest Service Fire Sciences Laboratory}
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}
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@article{XIA2025106401,
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title = {PyTorchFire: A GPU-accelerated wildfire simulator with Differentiable Cellular Automata},
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title = {{PyTorchFire}: A {GPU}-accelerated wildfire simulator with Differentiable Cellular Automata},
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journal = {Environmental Modelling & Software},
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volume = {188},
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pages = {106401},
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keywords = {Wildfire simulation, Differentiable Cellular Automata, PyTorch-based software, Parallel computing techniques, GPU-acceleration},
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}
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@book{Scott2005,
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title = {Standard fire behavior fuel models: a comprehensive set for use with Rothermel’s surface fire spread model},
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title = {Standard fire behavior fuel models: a comprehensive set for use with {Rothermel’s} surface fire spread model},
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url = {http://dx.doi.org/10.2737/RMRS-GTR-153},
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DOI = {10.2737/rmrs-gtr-153},
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institution = {U.S. Department of Agriculture, Forest Service, Rocky Mountain Research Station},
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year = {2009}
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}
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@book{andrews2018,
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title = {The Rothermel surface fire spread model and associated developments: A comprehensive explanation},
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title = {The {Rothermel} surface fire spread model and associated developments: A comprehensive explanation},
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url = {http://dx.doi.org/10.2737/RMRS-GTR-371},
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DOI = {10.2737/rmrs-gtr-371},
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institution = {U.S. Department of Agriculture, Forest Service, Rocky Mountain Research Station},
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author = {Andrews, Patricia L.},
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year = {2018}
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}
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@article{garcia2008,
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title = {Smoothing and bootstrapping the PROMETHEUS fire growth model},
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title = {Smoothing and bootstrapping the {PROMETHEUS} fire growth model},
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volume = {19},
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ISSN = {1099-095X},
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url = {http://dx.doi.org/10.1002/env.907},
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pages = {836–848}
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}
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@article{pais2021,
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title = {Cell2Fire: A Cell-Based Forest Fire Growth Model to Support Strategic Landscape Management Planning},
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title = {{Cell2Fire}: A {Cell-Based} Forest Fire Growth Model to Support Strategic Landscape Management Planning},
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volume = {4},
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ISSN = {2624-893X},
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url = {http://dx.doi.org/10.3389/ffgc.2021.692706},
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month = nov
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}
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@article{mandel2011,
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title = {Coupled atmosphere-wildland fire modeling with WRF 3.3 and SFIRE 2011},
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title = {Coupled atmosphere-wildland fire modeling with {WRF} 3.3 and {SFIRE} 2011},
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volume = {4},
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ISSN = {1991-9603},
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url = {http://dx.doi.org/10.5194/gmd-4-591-2011},
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}
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@article{campos2023,
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title = {Modelling pyro-convection phenomenon during a mega-fire event in Portugal},
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title = {Modelling pyro-convection phenomenon during a mega-fire event in {Portugal}},
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volume = {290},
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ISSN = {0169-8095},
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url = {http://dx.doi.org/10.1016/j.atmosres.2023.106776},
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pages = {106776}
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}
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@article{couto2024,
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title = {Numerical investigation of the Pedrógão Grande pyrocumulonimbus using a fire to atmosphere coupled model},
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title = {Numerical investigation of the {Pedrógão Grande} pyrocumulonimbus using a fire to atmosphere coupled model},
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volume = {299},
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ISSN = {0169-8095},
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url = {http://dx.doi.org/10.1016/j.atmosres.2024.107223},
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}
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@Article{filippi2021,
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AUTHOR = {Filippi, Jean-Baptiste and Durand, Jonathan and Tulet, Pierre and Bielli, Soline},
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TITLE = {Multiscale Modeling of Convection and Pollutant Transport Associated with Volcanic Eruption and Lava Flow: Application to the April 2007 Eruption of the Piton de la Fournaise (Reunion Island)},
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TITLE = {Multiscale Modeling of Convection and Pollutant Transport Associated with Volcanic Eruption and Lava Flow: Application to the April 2007 Eruption of the {Piton de la Fournaise} ({Reunion Island})},
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JOURNAL = {Atmosphere},
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VOLUME = {12},
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YEAR = {2021},
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DOI = {10.3390/atmos12040507}
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}
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@article{filippi2013,
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title = {Assessment of FOREFIRE/MESONH for wildland fire/atmosphere coupled simulation of the FireFlux experiment},
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title = {Assessment of {FOREFIRE}/{MESONH} for wildland fire/atmosphere coupled simulation of the {FireFlux} experiment},
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author = {Filippi, Jean-Baptiste and Pialat, Xavier and Clements, Craig},
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abstract = {{Numerical simulations using a coupled approach between Meso-NH (Non-Hydrostatic) LES (Large Eddy Simulation) mesoscale atmospheric model and ForeFire wildland fire area simulator are compared to experimental data to assess the performance of the proposed coupled approach in predicting fine-scale properties of the dynamics of wildland fires. Meso-NH is a non-hydrostatic, large eddy simulation capable, atmospheric research model. ForeFire insures a front tracking of the fire front by means of Lagrangian markers evolving on the earth's surface according to a physical rate-ofspread model. The atmospheric model forces the fire behaviour through the surface wind field, whereas the fire forces the atmosphere simulation through surface boundary conditions of heat and vapour fluxes. The FireFlux experiment, an experimental 32Ha burn of tall grass instrumented with wind profilers and thermocouples, was designed specifically to estimate the atmospheric perturbation introduced by wildland fire. Comparisons of the simulations at different resolutions with the largescale experiment validate the chosen coupling methodology and the choice of a coupled approach with a meso-scale atmospheric model for the prediction of wildland fire propagation. Distinct fire propagation behaviour is simulated between coupled and non-coupled simulation. While the simulations did not reproduce high frequency perturbations, it is shown that the atmospheric model captures well atmospheric perturbations induced by combustion at the ground level in terms of behaviour and amplitude.}},
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keywords = {pub},
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affiliation = {Sciences pour l'environnement - SPE , Department of Meteorology and Climate Science -},
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pages = {2633-2640},
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journal = {PROCEEDINGS OF THE COMBUSTION INSTITUTE},
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journal = {Proceedings of the Combustion Institute},
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volume = {34},
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number = {2 },
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audience = {international },
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month = may,
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pages = {104348}
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}
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paper/paper.md

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## Rapid prototyping of new models
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ForeFire implements several standard fire flux and spread rate models, such as Rothermel [@andrews2018] and Balbi [@balbi2009], and makes it trivial to switch, extend, or add to this base with a single `.cpp` file using any existing model file as a template.
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Internally, data is handled as *layers* that can come from a NumPy array, be read from NetCDF, or be generated on the fly by ForeFire (e.g., slope derived from the elevation layer, fuel loaded as an index map with tabulated fuel — with part of [@Scott2005] fuel table already available).
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Developing a Rate Of Spread wildfire model was the original purpose of this simulation code and helped to iterate versions of the Balbi Rate Of Spread formulation on case studies in [@balbi2009] and [@santoni2011]. It also served to implement various heat and chemical species flux models used for volcanic eruption in [@filippi2021], plume chemistry [@strada2012], or industrial fires in [@baggio2022]. In addition, the code includes a generic `ANNPropagationModel` that implements a feedforward artificial neural network (ANN) and expects a pre-trained graph file.
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Internally, data is handled as *layers* that can come from a NumPy array, be read from NetCDF, or be generated on the fly by ForeFire (e.g., slope derived from the elevation layer, fuel loaded as an index map with tabulated fuel — with part of `@Scott2005` fuel tables already available).
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Developing a Rate Of Spread wildfire model was the original purpose of this simulation code and helped to iterate versions of the Balbi Rate Of Spread formulation on case studies [@balbi2009;@santoni2011]. It also served to implement various heat and chemical species flux models used for volcanic eruption [@filippi2021], plume chemistry [@strada2012], or industrial fires [@baggio2022]. In addition, the code includes a generic `ANNPropagationModel` that implements a feedforward artificial neural network (ANN) and expects a pre-trained graph file.
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## Batch simulations with the ForeFire scripting
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Custom FF language allows users to easily generate multiple scenarios, including fire-fighting strategies, model evaluation [@filippi2014], ensemble forecasts [@allaire2020], or generate a deep learning database [@allaire2021]. A FF script is a set of scheduled instructions that are interpreted in real-time, advancing the simulation clock with a `step[dt=]` or a `goTo[t=]` command.
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Coupled simulations generate gigabytes of 3D data that can be converted to VTK/VTU files using Python helper scripts to visualize in the open-source tool ParaView, as shown in \autoref{fig:coupled}.
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![Coupled simulation of the Pedrogao Grande wildfire [@couto2024] (Paraview). On the ground, the burned area is in orange, while among atmospheric variables, downbursts are highlighted in red and pyro-cumulonimbus clouds in blue.\label{fig:coupled}](coupled.jpg)
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![Coupled simulation of the Pedrogao Grande wildfire [@couto2024] (Paraview rendering). On the ground, the burned area is in orange, while among atmospheric variables, downbursts are highlighted in red and pyro-cumulonimbus clouds in blue.\label{fig:coupled}](coupled.jpg)
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# Acknowledgements
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This work has been supported by the Centre National de la Recherche Scientifique and French National Research Agency under grants **ANR-09-COSI-006-01 (IDEA)** and **ANR-16-CE04-0006 (FIRECASTER)**. The authors thank all contributors and collaborators who have assisted in the development and testing of the ForeFire software.

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