Articles | Volume 19, issue 8
https://doi.org/10.5194/gmd-19-3035-2026
© Author(s) 2026. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/gmd-19-3035-2026
© Author(s) 2026. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
The Community Fire Behavior model for coupled fire–atmosphere modeling: implementation in the Unified Forecast System
Pedro A. Jiménez y Muñoz
CORRESPONDING AUTHOR
NSF National Center for Atmospheric Research, 3090 Center Green Drive, Boulder, CO 80301, USA
Maria Frediani
NSF National Center for Atmospheric Research, 3090 Center Green Drive, Boulder, CO 80301, USA
Masih Eghdami
NSF National Center for Atmospheric Research, 3090 Center Green Drive, Boulder, CO 80301, USA
Daniel Rosen
NSF National Center for Atmospheric Research, 3090 Center Green Drive, Boulder, CO 80301, USA
Michael Kavulich
NSF National Center for Atmospheric Research, 3090 Center Green Drive, Boulder, CO 80301, USA
Timothy W. Juliano
NSF National Center for Atmospheric Research, 3090 Center Green Drive, Boulder, CO 80301, USA
AiDASH, 575 High Street, Palo Alto, CA 94301, USA
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Models of wind behavior inform offshore wind farm site investment decisions. Here we compare a newly-developed model to another, historically-used, model based on how these models represent winds and turbulence at two North Sea sites. The best model depends on the site. While the older model performs best at the site above a wind farm, the newer model performs best at the site that is at the same altitude as the wind farm. We support using the new model to represent winds at the turbine level.
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We suggest a model configuration to predict offshore wind speed and wind power density in the Northeast US. We focused on wind droughts, long periods of low wind speed that affect the reliability of wind power generation. We show that wind prediction depends primarily on the initial and boundary conditions, and that it is important to evaluate the connection of wind speed to wind power generation, to select the best model configuration.
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The Mesoscale to Microscale Coupling team, part of the U.S. Department of Energy Atmosphere to Electrons (A2e) initiative, has studied various important challenges related to coupling mesoscale models to microscale models. Lessons learned and discerned best practices are described in the context of the cases studied for the purpose of enabling further deployment of wind energy. It also points to code, assessment tools, and data for testing the methods.
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Numerical weather prediction models are used to predict how wind turbines will interact with the atmosphere. Here, we characterize the uncertainty associated with the choice of turbulence parameterization on modeled wakes. We find that simulated wind speed deficits in turbine wakes can be significantly sensitive to the choice of turbulence parameterization. As such, predictions of future generated power are also sensitive to turbulence parameterization choice.
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Short summary
We present the Community Fire Behavior model (CFBM) a fire behavior model designed to facilitate coupling to atmospheric models. We describe its implementation in the Unified Forecast System (UFS). Simulations of the Cameron Peak fire allowed us to verify our implementation. Our vision is to foster collaborative development in fire behavior modeling with the ultimate goal of increasing our fundamental understanding of fire science and minimizing the adverse impacts of wildland fires.
We present the Community Fire Behavior model (CFBM) a fire behavior model designed to facilitate...