We present Daysmoke, an empirical-statistical plume rise and dispersion model for simulating smoke from prescribed burns. Prescribed fires are characterized by complex plume structure including multiple-core updrafts which makes modeling with simple plume models difficult. Daysmoke accounts for plume structure in a three-dimensional veering/sheering atmospheric environment, multiple-core updrafts, and detrainment of particulate matter. The number of empirical coefficients appearing in the model theory is reduced through a sensitivity analysis with the Fourier Amplitude Sensitivity Test (FAST). Daysmoke simulations for “bent-over” plumes compare closely with Briggs theory although the two-thirds law is not explicit in Daysmoke. However, the solutions for the “highly-tilted” plume characterized by weak buoyancy, low initial vertical velocity, and large initial plume diameter depart considerably from Briggs theory. Results from a study of weak plumes from prescribed burns at Fort Benning GA showed simulated ground-level PM_2.5 comparing favorably with observations taken within the first eight kilometers of eleven prescribed burns. Daysmoke placed plume tops near the lower end of the range of observed plume tops for six prescribed burns. Daysmoke provides the levels and amounts of smoke injected into regional scale air quality models. Results from CMAQ with and without an adaptive grid are presented.

A cellular automata fire model represents ‘elements’ of fire by autonomous agents. A few simple algebraic expressions substituted for complex physical and meteorological processes and solved iteratively yield simulations for ‘super-diffusive’ fire spread and coupled surface-layer (2-m) fire–atmosphere processes. Pressure anomalies, which are integrals of the thermal properties of the overlying heated plume, drive the surface winds around and through the fire. Five simulations with differing fuel and wind conditions were compared with fire and meteorological data from an experimental grassfire (FireFlux). The fire model accurately simulated bulk patterns of measured time-series of 2-m winds at two towers and observed fire behaviour (spread rate, flaming depth and heat released). Fidelity to spatial windfields in the vicinity of the fire was similar to results from full-physics fire models for other grassfires. Accurate predictions of fire spread depend critically on accurate wind speeds and directions at the location of the fire. Simulated fire–atmosphere coupling using FireFlux data increased wind speeds across the fire line by up to a factor of three. With its computational speed relative to full-physics models, the fire model can inform full-physics modellers regarding problems of interest. Although the fire model is tested for homogeneous fuels on flat terrain, the model is designed for simulating complex distributions of fire within heterogeneous distributions of fuels over complex landscapes.

In ecosystems with frequent surface fires, fire and fuel heterogeneity at relevant scales have been largely ignored. This could be because complete burns give an impression of homogeneity, or due to the difficulty in capturing fine-scale variation in fuel characteristics and fire behaviour. Fire movement between patches of fuel can have implications for modelling fire spread and understanding ecological effects. We collected high resolution (0.8 × 0.8-cm pixels) visual and thermal imaging data during fire passage over 4 × 4-m plots of mixed fuel beds consisting of pine litter and grass during two prescribed burns within the longleaf pine forests of Eglin Air Force Base, FL in February 2011. Fuel types were identified by passing multi-spectral digital images through a colour recognition algorithm in ‘Rabbit Rules,’ an experimental coupled fire-atmosphere fire spread model. Image fuel types were validated against field fuel types. Relationships between fuel characteristics and fire behaviour measurements at multiple resolutions (0.8 × 0.8 cm to 33 × 33 cm) were analysed using a regression tree approach. There were strong relationships between fire behaviour and fuels, especially at the 33 × 33-cm scale (R2 = 0.40–0.69), where image-to-image overlap error was reduced and fuels were well characterised. Distinct signatures were found for individual and coupled fuel types for determining fire behaviour, illustrating the importance of understanding fire-fuel heterogeneity at fine-scales. Simulating fire spread at this fine-scale may be critical for understanding fire effects, such as understorey plant community assembly.

Smoke plume rise is critically dependent on plume updraft structure. Smoke plumes from landscape burns (forest and agricultural burns) are typically structured into “sub-plumes” or multiple-core updrafts with the number of updraft cores depending on characteristics of the landscape, fire, fuels, and weather. The number of updraft cores determines the efficiency of vertical transport of heat and particulate matter and therefore plume rise. Daysmoke, an empirical-stochastic plume rise model designed for simulating wildland fire plumes, requires updraft core number as an input. In this study, updraft core number was gained via a cellular automata fire model applied to an aerial ignition prescribed burn conducted at Eglin AFB on 6 February 2011. Typically four updraft cores were simulated in agreement with a photo-image of the plume showing three/four distinct sub-plumes. Other Daysmoke input variables were calculated including maximum initial updraft core diameter, updraft core vertical velocity, and relative emissions production. Daysmoke simulated a vertical tower that mushroomed 1,000 m above the mixing height. Plume rise was validated by ceilometer. Simulations with two temperature profiles found 89–93 percent of the PM_2.5 released during the flaming phase was transported into the free atmosphere above the mixing layer. The minimal ground-level smoke concentrations were verified by a small network of particulate samplers. Implications of these results for inclusion of wildland fire smoke in air quality models are discussed.

Smoke from wildland burning in association with fog has been implicated as a visibility hazard over roadways in the southern United States. A project began in 2002 to determine whether moisture released during the smoldering phases of southern prescribed burns could contribute to fog formation. Temperature and relative humidity measurements were taken from 27 smoldering ‘smokes’ during 2002 and 2003. These data were converted to a measure of the mass of water vapor present to the mass of dry air containing the vapor (smoke mixing ratio). Some smokes were dry with almost no moisture beyond ambient. Other smokes were moist with moisture excesses as large as 39 g kg–1. Calculations show that ground-level smoke moisture excesses have no impact on ambient relative humidity during the day. However, the impact at night can be large enough to increase the ambient relative humidity to 100%. Therefore smoke moisture may be a contributing factor to the location and timing of fog formation.

Dense fogs, comparable to historical fogs in England, have been implicated in numerous roadway accidents in the southern United States. Many of the fogs have occurred in association with prescribed burning. Direct measurements of superfog (fog reducing visibility to less than 3 m) were taken during burning of forest litter on 22 March 2003. Visibility was measured at 0.1 m implying an extinction coefficient of 39 120 km⁻¹. The number of condensation nuclei required for fog to produce the observed visibility was about 1% of the number of particles released in wood smoke as reported in the literature. A recursive non‐gradient mixing model shows (1) maximum excess liquid water (LWC) released was approximately 7.0 g kg⁻¹, 23 times the LWC in natural fog, (2) superfog can form at the site of combustion then continue to form as it drifts downwind, and (3) superfog can modify air mass stability near the ground and persist for hours until dispersed by changing wind conditions or by solar heating after sunrise. Published in 2008 by John Wiley & Sons Ltd.