ENGINEERING

New supercomputer modeling technique offers promise of continually updated predictions

Scientists have developed a new supercomputer modeling technique that for the first time offers the promise of continually-updated daylong predictions of wildfire growth through the lifetimes of long-lived blazes.

The technique, devised by scientists at the National Center for Atmospheric Research (NCAR) in Boulder, Colo., and the University of Maryland, combines cutting-edge simulations of the interaction of weather and fire with newly available satellite observations of active wildfires.

The breakthrough is described in a paper published today in the online edition of the American Geophysical Union journal Geophysical Research Letters.

The National Science Foundation (NSF), which is NCAR's sponsor, funded the research, along with NASA and the Federal Emergency Management Agency.

"These scientists have developed a unique mechanism that will predict even a long-lived fire's lifecycle, which has the potential to save lives and property from large wildfires in the future," said Gannet Hallar, program director in NSF's Division of Atmospheric and Geospace Sciences, which supported the study.

Updated with new observations every 12 hours, the supercomputer model forecasts critical details such as the extent of a blaze and changes in its behavior.

"With this technique, we believe it's possible to continually issue good forecasts throughout a fire's lifetime, even if it burns for weeks or months," said NCAR scientist Janice Coen, the lead paper author and model developer.

"This model, which combines interactive weather prediction and wildfire behavior, could greatly improve forecasting--particularly for large, intense wildfire events where the current prediction tools are weakest."

Firefighters use tools that can estimate the speed of the leading edge of a fire, but are too simple to capture critical effects caused by the complex interactions of fire and weather.

The researchers successfully tested the new technique by using it retrospectively on the 2012 Little Bear Fire in New Mexico, which burned for almost three weeks and destroyed more buildings than any other wildfire in the state's history.

To generate an accurate forecast of a wildfire, researchers need a computer model that can incorporate current data about the fire and simulate what it will do in the near future.

Over the last decade, Coen has developed a tool, known as the Coupled Atmosphere-Wildland Fire Environment (CAWFE) computer model, that connects how weather drives fires and, in turn, how fires create their own weather.

Using CAWFE, she successfully simulated the details of how large fires grow.

But without the most updated data about a fire's current state, CAWFE could not reliably produce a longer-term prediction of an ongoing fire.

That's because the accuracy of all fine-scale weather simulations declines significantly after a day or two, affecting the simulation of the blaze.

An accurate forecast would also need to include updates on the effects of firefighting and of such processes as spotting, in which embers from a fire are lofted in the fire plume and dropped ahead of a fire, igniting new flames.

Until now, it was not possible to update the model.

Satellite instruments offered only coarse observations of fires, providing images in which each pixel represented an area a little more than a half mile across.

These images might show several places burning, but could not distinguish the boundaries between burning and non-burning areas, except for the largest wildfires.

To solve the problem, Coen's co-author, Wilfrid Schroeder of the University of Maryland, produced higher-resolution fire detection data from a new satellite instrument, the Visible Infrared Imaging Radiometer Suite (VIIRS), jointly operated by NASA and the National Oceanic and Atmospheric Administration. 

The new tool provides coverage of the entire globe at intervals of 12 hours or less, with pixels about 1,200 feet across. The higher resolution enabled the two researchers to outline the active fire perimeter in much greater detail.

Coen and Schroeder then fed the VIIRS fire observations into the CAWFE model. By restarting the model every 12 hours with the latest observations of the fire extent--a process known as cycling--they could accurately predict the course of the Little Bear Fire in 12- to 24-hour increments during five days of the historic blaze.

By continuing that way, it's possible to simulate even a very long-lived fire's entire lifetime, from ignition through extinction.

"The transformative event has been the arrival of this new satellite data," said Schroeder.

"The enhanced capability of the VIIRS data favors detection of newly ignited fires before they erupt into major conflagrations. The satellite data has tremendous potential to supplement fire management and decision support systems, sharpening the local, regional and continental monitoring of wildfires."

The researchers said that forecasts using the new technique could be particularly useful in anticipating sudden blowups and shifts in the direction of the flames, such as what happened when 19 firefighters perished in Arizona last summer.

In addition, they could enable decision makers to look at several newly ignited fires and determine which pose the greatest threat.

"Lives and homes are at stake and depend on these decisions," Coen said. "The interaction of fuels, terrain and changing weather is so complicated that even seasoned managers can't always anticipate rapidly changing conditions.

"Many people have resigned themselves to believing that wildfires are unpredictable. We're showing that's not true."