NASA’s “Wildfire Digital Twin” project will equip firefighters and wildfire managers with a superior tool for monitoring wildfires and predicting harmful air pollution events and help researchers observe global wildfire trends more precisely. The tool, developed with funding from NASA’s Earth Science Technology Office and NASA’s FireSense Program, will use artificial intelligence and machine learning to forecast potential burn paths in real time, merging data from in situ, airborne, and spaceborne sensors to produce global models with high precision.
Whereas current global models describing the spread of wildfires and smoke have a spatial resolution of about 10 kilometers per pixel, the Wildfire Digital Twin would produce regional ensemble models with a spatial resolution of 10-to-30 meters per pixel, an improvement of two orders of magnitude. These models could be generated in a span of mere minutes. By comparison, current global models can take hours to produce. Models with such high spatial resolution produced at this speed would be immensely valuable to first-responders and wildfire managers trying to observe and contain dynamic burns.
Milton Halem, a Professor of Computer Science and Electrical Engineering at the University of Maryland, Baltimore County, leads the Wildfire Digital Twin project, which includes a team of more than 20 researchers from six universities.
“We want to be able to provide firefighters with useful, timely information,” said Halem, adding that in the field, “there is generally no internet, and no access to big supercomputers, but with our API version of the model, they could run the digital twin not just on a laptop, but even a tablet,” he said. NASA’s FireSense project is focused on leveraging the agency’s unique Earth science and technological capabilities to achieve improved wildfire management across the United States.
Halem’s team is working on new satellite data assimilation techniques that will blend information from space-based remote sensors into their Wildfire Digital Twin, enabling improved global data forecasts that will be useful for emergencies and science missions alike. In October, Halem’s team participated in the first FireSense field campaign in collaboration with the National Forest Service’s Fire and Smoke Model Evaluation Experiment (FASMEE) to observe smoke as it traveled more than 10 miles during a controlled burn in Utah, using a ceilometer. Now the team is feeding that data into their modeling software to help it track plumes more accurately.
