• Subscribe

Cutting-edge simulations and satellite data combine to sharpen wildfire forecasting

Portraying the interaction of weather and fire behavior, a new technique combines cutting-edge simulations with newly available satellite observations of active wildfires. This approach offers the promise of continually updated daylong predictions of wildfire growth - throughout the lifetime of long-lived blazes. Devised by scientists at the US National Center for Atmospheric Research (NCAR) and the University of Maryland, in College Park, US, the computer model predicts critical details such as the extent of the blaze and changes in its behavior.

Satellites provide different levels of wildfire details. The image at left, produced from data generated by the MODIS instrument aboard NASA's Aqua satellite, uses 1-kilometer pixels (a bit over half a mile across) to approximate a fire burning in Brazil from March 26 to 30, 2013. The image at right, produced with data from the new VIIRS instrument, shows the same fire in far greater detail with 375-meter pixels (a bit over 1,200 feet across). Image courtesy Wilfrid Schroeder, University of Maryland. Cover image courtesy Arizona Department of Health Services.

The breakthrough is described in a study appearing in an online issue of Geophysical Research Letters. "With this technique it's possible to continually issue good forecasts throughout a fire's lifetime, even if it burns for weeks or months," says NCAR scientist Janice Coen, the lead 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."

Over the last decade, Coen developed the Coupled Atmosphere-Wildland Fire Environment (CAWFE) computer model, which connects how weather drives fires and, in turn, how fires create their own weather. However, because the accuracy of all fine-scale weather simulations declines significantly after a day or two, CAWFE cannot reliably produce a longer-term prediction of an ongoing fire. An accurate forecast would also have to include updates on the effects of firefighting and processes like spotting, in which the fire plume lofts embers that then drop ahead of a fire, igniting new flames.

Until now, the kind of real-time data that would be needed to regularly update the model has not been available. Firefighters currently use tools that can estimate the speed of a fire's leading edge but are too simple to capture crucial effects caused by the interaction of fire and weather. Satellite instruments offer only coarse observations of fires, providing images in which each pixel represents an area a little more than a half-mile across (1 kilometer by 1 kilometer). These images might show several places burning, but they cannot distinguish boundaries between burning and non-burning areas, except within the largest wildfires.

To solve the problem, 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), which is jointly operated by NASA and the National Oceanic and Atmospheric Administration (NOAA). Launched in 2011, this new tool provides coverage of the entire globe at intervals of 12 hours or less, with pixels about 1,200 feet across (375 meters). The higher resolution enables the two researchers to outline the active fire perimeter in much greater detail.

The scientists fed the VIIRS fire observations into the CAWFE model, retrospectively testing the new technique on the 2012 Little Bear Fire in New Mexico, US, which burned for almost three weeks and destroyed more buildings than any other wildfire in the state's history. By restarting the model every 12 hours with the latest observations of the fire's extent, they could accurately predict the course of the Little Bear fire in 12- to 24-hour increments during five days of the historic blaze. Continuing this cycle would make it possible to simulate the entire lifetime of even a very long-lived fire, from ignition to extinction.

"The transformative event has been the arrival of this new satellite data," says Schroeder, a professor of geographical sciences who is also a visiting scientist with NOAA. "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."

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 Arizona firefighters perished in the Yarnell Hill fire in June 2013. "Lives and homes are at stake, depending on some of these decisions, and the interaction of fuels, terrain, and changing weather is so complicated that even seasoned managers can't always anticipate rapidly changing conditions," Coen says. "Many people have resigned themselves to believing that wildfires are unpredictable. We're showing that's not true."

The research is funded by NASA, the Federal Emergency Management Agency, and the National Science Foundation.

Join the conversation

Do you have story ideas or something to contribute? Let us know!

Copyright © 2021 Science Node ™  |  Privacy Notice  |  Sitemap

Disclaimer: While Science Node ™ does its best to provide complete and up-to-date information, it does not warrant that the information is error-free and disclaims all liability with respect to results from the use of the information.

Republish

We encourage you to republish this article online and in print, it’s free under our creative commons attribution license, but please follow some simple guidelines:
  1. You have to credit our authors.
  2. You have to credit ScienceNode.org — where possible include our logo with a link back to the original article.
  3. You can simply run the first few lines of the article and then add: “Read the full article on ScienceNode.org” containing a link back to the original article.
  4. The easiest way to get the article on your site is to embed the code below.