Wildland Fire Weather-Behavior Support
Megafires have become the new normal for the American west. Attempting to manage them is like trying to manage a hurricane or tornado. These fires have devastated communities and risked the lives of first responders more profoundly than ever. Fire behavior is complex, shaped by interactions among heat output, humidity changes, wind, updrafts and downdrafts, fuel, and terrain, naming only a few. The intensity and aberrant behavior of the largest and most aggressive fires pose life-threatening challenges to fire-management efforts. Decision-makers need weather- and wildfire-prediction tools to develop more effective strategies to protect property and lives.
Basic weather prediction is not enough to effectively inform fire-management strategies. To fill this gap, RAL scientists and engineers extended the Weather Research and Forecasting (WRF) numerical weather prediction model to simulate how a wildfire behaves in response to weather, fuel conditions, and terrain. In turn, the wildfire’s effect on the local weather is also simulated. The model-generated fire and atmosphere continually co-evolve, predicting the fire’s extent and rate of spread, flame length, heat, and smoke, thereby alerting firefighting personnel and local agencies to respond accordingly.
The LEAP-HI project convenes scientists and engineers to develop a new computational platform to predict wildfire risks from days to weeks before a blaze ignites. Our effort in this collaborative project focuses on combining satellite imagery of land surfaces with highly detailed weather forecasts. These data will be fed into the WRF-Fire computer model, which will identify areas most at risk.
We develop tools that predict the behavior of wildfires to help firefighters, local authorities, and resource managers direct their efforts more effectively. Tactically placing firefighting personnel, equipment, and promptly notifying the public saves property, lives, and livelihoods.