The objective of this project is to develop a prototype data-driven wildland fire simulation capability capable of forecasting the fire spread dynamics.
Sponsored by the UMD Council on the Environment, this project will combine computer-based fire models with air- and spaceborne remote sensing systems using data assimilation.
An NSF-funded end-to-end cyberinfrastructure (CI) for real-time and data-driven simulation, prediction and visualization of wildfire behavior.
A research (FireFly) and operational (FARSITE) fire model are used to simulate fire spread through various configurations.
Data assimilation is used to merge simulation results with detected fires to improve future predictions.
Remotely sensed data is used to improve fire predictions and investigate fire ecology.