OroraTech/firefront: ForeFire is an open-source code for wildland fire spread models · GitHub

ForeFire is an open-source wildfire simulation engine written in C++. Developed by CNRS at the Université de Corse Pascal Paoli, it is used for research and operational forecasting. The engine implements various fire behavior models and enables high-fidelity coupled fire-atmosphere simulations, aiming to improve wildfire prediction and understanding for complex environments.

Key Links:

• 📚 Full Documentation: forefire.readthedocs.io
• 🚀 Live Demo: forefire.univ-corse.fr/sim
• 🌍 Website: forefire.univ-corse.fr

• Advanced Simulation Engine: Core C++ logic for fire propagation using various Rate of Spread (ROS) models and handling complex geospatial data (NetCDF).
• Fire-Atmosphere Coupling: Designed for two-way coupling by linking the core library with atmospheric models like MesoNH (developed by CNRS & Météo-France).
• High Performance: Optimized C++ core with MPI support for parallel computing.
• Flexible Interfaces: Built upon a core C++ Simulation Engine (Library):
  • forefire Interpreter: The primary way to run simulations using script files (.ff), interactive console commands, or the web interface (via listenHTTP[]).
  • C++ Library (libforefireL): Allows direct integration into other software.
  • Python Bindings: Enable scripting and control from Python (see ./bindings/python/README.md).
• Flexible Output: Can generate outputs in various formats, including KML for visualization in Google Earth, Geojson, NetCDF, and custom binary/text formats.
• Extensible: Add custom ROS models in C++; customize web interfaces.
• Applications: Research, case reanalysis, ensemble forecasting.

The easiest way to get started is often using Docker and the interactive console, via the forefire command-line interpreter

1. Clone the repository

   # Clone the repository
   git clone https://github.com/forefireAPI/forefire.git
   cd forefire

2. Build the Docker image

   docker build . -t forefire:latest

3. Run the container interactively

   docker run -it --rm -p 8000:8000 --name ff_interactive forefire bash

4. Inside the container navigate to test directory and lauch the forefire console:

   cd tests/runff
   
   # start the forefire console with the command
   forefire

5. Inside the console launch an http server with listenHttp[] command

   forefire> listenHTTP[]
   
   # the output should be
   >> ForeFire HTTP command server listening at http://localhost:8000

   This server provides a grafical user interface that you can access on your browser at http://localhost:8000/

6. Run your first simulation

   • Run the command include[real_case.ff]
   • Then press Refresh Map

   You should see a simulation running in the Aullène region of Corsica. This confirms your Docker setup is working! Check the full documentation for more details on this example

See the Full Documentation for more details on building from source with the install-forefire.sh file

ForeFire provides Python bindings for easier scripting and integration. See the Python Bindings ./bindings/python/README.md for details.

We welcome contributions to ForeFire! We especially appreciate help with:

• Improving documentation and tutorials.
• Python bindings
• Enhancing packaging (Docker, Pip, etc.) and cross-platform compatibility.

Please read our Contributing Guidelines to learn how you can help, including how to report bugs, suggest features, and submit code changes.

All contributors are expected to adhere to our Code of Conduct.

ForeFire is licensed under the GNU General Public License v3.0. See LICENSE for full details.

If you use ForeFire in your work, please cite:

BibTex

@article{article,
author = {Filippi, Jean-Baptiste and Bosseur, Frédéric and Grandi, Damien},
year = {2014},
month = {11},
pages = {},
title = {ForeFire: open-source code for wildland fire spread models},
isbn = {9789892608846},
doi = {10.14195/978-989-26-0884-6_29}
}

Plain Text

Filippi, Jean-Baptiste & Bosseur, Frédéric & Grandi, Damien. (2014). ForeFire: open-source code for wildland fire spread models. 10.14195/978-989-26-0884-6_29.