Advances in Modeling Wildfire Air Quality Events Using Remotely Sensed Data

Event Details

Advances in Modeling Wildfire Air Quality Events using Remotely Sensed Data

Apr 15 2021, 11am - 12pm

2020-2021 Seminar Series

Susan O’Neill, USDA FS

Wildfires in the western US have been particularly impactful in recent years not only in terms of loss of life and property but widespread smoke affecting millions of people. Several new satellites have launched in recent years, dramatically improving our ability to view fire occurrence and visible smoke plumes, and providing the opportunity to improve our smoke modeling and forecasting capabilities. The new geostationary satellites (GOES-16 and GOES-17) return fire detection data every five minutes for the continental US at a 2-km resolution at nadir. Previous GOES suites returned data every 15 minutes at a 4-km resolution at nadir. This greatly improves our ability to view fire progression in near real time for large wildfires and revolutionizes our capability to model smoke production, making it possible to calculate fire emissions in near-real time as the fire moves from pixel to pixel. Two Visible Infrared Imaging Radiometer Suite (VIIRS) instruments have also been launched in recent years on polar orbiting satellites, returning data twice a day at 375 m and 700 m resolutions. This seminar will profile two new approaches to using these data for retrospective analyses 1) useful to Land Managers managing wildfires for resource benefit, and 2) create datasets useful to health researchers analyzing the health impacts from these fires. Ultimately the goal is to apply what we learn in these retrospective analyses to smoke forecasting systems, giving us the ability to account for fire behavior such as the explosive early morning growth of the Camp wildfire in 2018 in Northern California.

Missoula Fire Sciences Laboratory

Rocky Mountain Research Station (RMRS)
Fire, Fuel, and Smoke Science (FFS) Program

Advances in Modeling Wildfire Air Quality Events Using Remotely Sensed Data