Coupled Weather-Fire Modeling

Decision Support For Managing Wildland Fires

To fight wildland fires, decision makers need reliable, accurate, frequently updated, readily accessible, geo-referenced information about current and predicted weather and fire behavior. With this information, decision makers can better determine how a fire is behaving now and might behave in the future. Reliable information about the potential for a fire to spread rapidly and behave erratically is essential for saving life and property.

Currently, operational systems that predict how wildland fires move and behave are not coupled to numerical weather prediction (NWP) models. These systems often rely on wind fields that lack critical details in space and time. Those are details essential to accurately predicting fire spread when winds change rapidly due to storm outflows, density currents, frontal passages, complex terrain, and other factors. Furthermore, large wildfires generate their own powerful updrafts and intense local winds, which drive flames quickly across the landscape. Large wildfires also generate vast, thick smoke plumes that can affect radiative transfer, while lofted particles and moisture can form pyrocumulus clouds. All of these phenomena can be predicted only by coupled models.

To fill this gap, scientists and engineers in RAL extended the functionality of the Weather Research and Forecasting (WRF) NWP model, based on the Coupled Atmosphere Wildland Fire Environment (CAWFE) model. New developments aim to improve the fire-spread model, investigate alternative fuel models and fuel-moisture data, allowing users to fine-tune fuel moisture in simulations, and predict where new spot-fires are likely to ignite. These developments are being included in the community WRF-Fire model.  This modeling system is being extensively evaluated and improved, based on what we're learning from fires observed in Colorado and other parts of the United States.


Scientists and engineers recently convened to develop a new computational platform to predict wildfire risks within days to weeks before a blaze occurs. This will inform wildfire managers, emergency responders, and utility companies to better anticipate where and when fires may ignite so they can plan and mobilize resources in advance.

The project is led by the University of Nevada, Reno, and includes researchers from the Desert Research Institute; the University of California, Los Angeles; and the University at Buffalo. It is funded by the National Science Foundation, through the Leading Engineering for America's Prosperity, Health, and Infrastructure (LEAP-HI) Program.

The goal is to develop a unique system for centralizing detailed assessments of wildland fire risk, alerting residents and firefighters days to weeks in advance of the potential for a major fire, RAL's assignment in this effort is combining satellite imagery of land surfaces with highly detailed weather forecasts. These data will be fed into WRF-Fire, which will help identify areas most at risk from blazes.

For more about the LEAP-HI project, see our feature article.

Search through all publications in NCAR's OpenSky Library.

Jimenez, P. A., D. Muñoz-Esparza, and B. Kosović, 2018: A high resolution coupled fire-atmosphere forecasting system to minimize the impacts of wildland fires: Applications to the Chimney Tops II wildland event. Atmosphere, 9, 197.

Muñoz-Esparza, D., B. Kosović, P. A. Jiménez, and J. L. Coen, 2018: An accurate fire-spread algorithm in the Weather Research and Forecasting model using the level-set method. Journal of Advances in Modeling Earth Systems, 10, 908-926. doi: 10.1002/2017MS001108.

Coupled Weather-Fire Modeling