America Is Burning, But We're Still Flying Blind
Right now, the United States is grappling with a surge of wildfires, particularly across the Western states. Thousands of acres are being consumed in a matter of hours. Hundreds of structures are at risk, entire communities have been evacuated, and fire crews are stretched thin across rugged, remote terrain.
The dangerous reality of hotter summers, drier fuels, and more erratic weather is driving wildfires fueled by just the right explosive conditions. Trees bent, dust swirled, and with it comes the embers—tiny sparks lifted high into the sky and flung miles ahead of the main fire. One lands on a rooftop. Another in a patch of dry brush behind a home. Within minutes, those sparks become flames, and the flames become a wall of fire.
And it’s a story that’s repeating itself, again and again. Yet another summer season where we are not preventing fire from becoming catastrophic.
Despite the proliferation of satellites, drones, and aircraft, we were still making decisions with delayed, fragmented, or incomplete data. Some of the most widely used sources for fire detection have severe limitations in temporal resolution, passing over the same area only a couple times per day, and often missing the most critical moments—when the fire is growing rapidly, shifting directions, or threatening new communities. By the time a hot spot is detected, it may already be outdated. And when decisions are being made in minutes—not hours—a 9-to-12-hour delay can be catastrophic.
Current fire maps tell us where the fire was, not where it’s going. They are typically handed off in static formats and not integrated into predictive models that simulate fire spread based on wind, terrain, fuel conditions, and ember travel. Without modeling and temporal forecasting, responders are left without answers to the most critical questions:
Which lives and homes are most at risk in the next 3 hours?
Where should we place resources next?
How much of our resources do we need?
Accurate Predictive Modeling with WindTL
To solve this problem we built WindTL, a wildfire intelligence platform designed to forecast the very thing that makes these fires so unpredictable: embers. WindTL models how embers travel through the air—identifying not just where the fire is now, but where it’s most likely to go next, helping responders predict and prevent spot fires before they ignite. With WindTL, fire crews, emergency managers, and utility companies can log into the web, and easily and quickly gain a forward-looking view—allowing them to pre-position resources, issue early warnings, and protect critical and vulnerable infrastructure in the path of invisible, airborne threats. Over the past few days, we have put WindTL to work over several fires in Utah and Colorado.
Monroe Canyon Fire
In the morning of July 13, the hot, dry, and high wind conditions were adding up to ignite the Monroe Canyon Fire. As the first satellite hotspot was detected, the WindTL tool started to produce warnings: the high risk pre-fire conditions (left) coupled with the increasing winds (right) were fueling a rapidly spreading fire.
While fire propagation through the ground (shown in blue), offered by many tools out there, indicated slow fire growth, the WindTL tool provided an insight no other is capable of: very quickly the fire will be growing and spreading south, as seen on the yellow, orange, and red dots showing the potential for ember spread:
As the satellites came the next day, the prediction by WindTL materialized. The fire had quickly spread to the south:
Unfortunately, the lack of real-time data precluded WindTL from hour-by-hour validations and updated predictions that are only based on ground truth inputs when a satellite passes over the fire. In other words, this kind of critical piece of intelligence is only available every 12 hours.
Deer Creek Fire
When the Deer Creek Fire ignited on July 10 near La Sal, UT, conditions were primed for rapid spread (below, with a general moderate risk in yellow and areas of high risk for fire growth and ember cast in orange).
