Representativeness of wind measurements in fire experiments: Lessons learned from large-eddy simulations in a homogeneous forest

Abstract

Experimental fires often aim to relate fire behavior to fuel and weather conditions, such as wind speeds. These experiments are typically limited to short durations (∼300s) and small lateral extents (∼100m). Although most studies include measurements of wind velocities, such measurements are often taken at some distance from the fire experiments, and may not represent conditions at the fire location. This disparity may potentially introduce errors of unknown magnitude in empirical models based upon the data collected. At present, little guidance is available regarding how well remote anemometry measurements are actually representative of wind velocities at the fire front. A number of factors may affect this representativeness, including the fire itself (size, spread rate and duration), the reference height for fire wind measurement, the sensors (number and location), the vegetation, and weather conditions (wind speed and atmospheric stability).In the present study, we use large-eddy simulations of wind flows to compute fire-front wind (at a virtual moving fire line) and measured wind (at anemometer locations) corresponding to hypothetical fire experiments. Replicates of these hypothetical experiments were used to quantify wind measurement representativeness, by computing the errors resulting from the estimation of the fire-front wind by remote anemometers. We then examine the sensitivity of these errors to the factors mentioned above.We found that the main factors were the size of the experiment, the reference height for wind measurement, the ratio of ambient wind speed to expected spread rate, and the number of sensors. Convective instability and distance between anemometers and fire plots played a minor role in most cases. We propose a simple model to characterize this error as it is influenced by the main factors.The simple model reproduces and generalizes outcomes reported by an earlier field study and shows a clear picture of the respective role of the factors cited above. It can be used to estimate errors in wind measurement in completed experiments. Practical guidelines are provided to apply this model to the design of future experiments.