Coupled fire-atmosphere behavior observations from a grassland prescribed burn in a Northern California valley

Prescribed burns, primarily aimed at preempting uncontrolled fires, present a valuable opportunity for obtaining field measurements on fire and smoke-plume behavior at micro- and sub-microscales. However, this potential remains underutilized for comprehensive data collection with broad spatio-temporal coverage across the burn unit, in part due to underexplored instrumentation strategies and a lack of synchronous, multidisciplinary observations. During a grassland prescribed burn experiment in a valley region, situated in Trinity County, California, the diverse and extensive instrumentation deployed around the 10-acre burn unit enabled the integration of fire-induced wind patterns with fireline evolution history, air-quality measurements, and fuel characteristics. Small uncrewed aircraft system (sUAS)–based infrared imagery tracked fireline progression and spread rate, together with sUAS-based RGB video that additionally helped quantify flame height via computer-vision techniques. Moreover, high-resolution (cm-scale), sUAS-based measurements of pre- and post-burn multispectral imagery and LiDAR point cloud helped quantify burn severity and post-fire residual fuels in combination with ground-based sampling of fuel characteristics (load, height, moisture). In addition, an autonomous, nano-sized, WeatherHive sUAS swarm sampled high‑resolution temperature, relative humidity, and wind data inside the smoke plumes along “lawnmower” trajectories. An Optical Particle Sizer and a DustTrak II measured high-frequency particle size distributions and mass concentrations near the surface, and were collocated with eddy-covariance (EC) instruments along the burn-unit edges, which measured in-situ turbulence and energy flux statistics. Strong fire-induced horizontal wind convergence at the burn-unit edges was captured by the EC sensors amid variable ambient winds. Within the plume, the WeatherHive swarm recorded temperature excursions up to 8°C with upward redirection of near-surface horizontal flow into strong buoyant updrafts. The dynamic local wind direction and fireline proximity strongly modulated the observed near-surface aerosol mass and number concentrations, which were dominated by fine particulate matter (PM_2.5), with background conditions recovered about 2.5 hours post-burn. Additionally, data were leveraged to evaluate a physics-based computational module utilizing the popular Reynold-Averaged Navier Stokes or RANS turbulence model. These integrated datasets provide deeper insight into coupled fire-behavior processes, while also illuminating improved measurement strategies for future experiments, including prolonged pre-burn deployment to characterize terrain-induced ambient flow and calculated sensor placement to capture the burn area flux footprint more effectively. Thus, they contribute to a growing observational database useful in advancing predictive models describing fire and smoke behavior, thereby increasing the reliance on prescribed burns for fire management.