The Fire-Flux Experiment

The goal of Fire-Flux project is to collect in-situ observations of atmospheric mean and turbulence data during wildland and prescribed grass fires to improve our understanding of fire-atmosphere interactions. Grass fires, although not as intense as forest fires, present a major threat to life and property during periods of drought in the Great Plains of the United States. Recently, major wildland grass fires in Texas burned nearly 1.6 million acres and destroyed over 730 homes and 1320 other buildings. The fires resulted in the death of 19 people, an estimated loss of 10,000 head of livestock, and more than $628 million in damage making the 2005/2006 fire season the worst season on record for the state of Texas.

Numerical models of coupled fire-atmosphere processes have emerged as viable research tools over the past decade, and grass fires have proven to be a popular choice for initial model testing. While these numerical models make it possible to study the complex interaction of the fire with the ambient atmosphere, the accuracy and uncertainties of these models have not been adequately documented due largely to the lack of appropriate observational data sets, which puts significant limitations to their usage and further improvement.

Fire-Flux is designed to provide observational data sets that can be used to properly test fire behavior and coupled fire-atmosphere models. Two field experiments have been carried out so far. Both experiments took place in Galveston, TX where a 155-acre tall-grass prairie filed was burned. A variety of instrument platforms, including a 143 m tall tower in the center of the burn unit, are used to collect in-situ and remote sensing data within and immediately downwind of the fire.  The data set have been used to determine atmospheric turbulent structures/fluxes associated with intense grass fires and provides a basis to further our understanding of the dynamics of grass fires and their interactions with the atmosphere. The data set is also being used to evaluate and improve coupled fire-atmosphere numerical models.