The Fire and Smoke Model Evaluation Experiment (FASMEE) is designed to collect integrated observations from large wildland fires and provide evaluation datasets for new models and operational systems. Wildland fire, smoke dispersion, and atmospheric chemistry models have become more sophisticated, and next-generation operational models will require evaluation datasets that are coordinated and comprehensive for their evaluation and advancement. Integrated measurements are required, including ground-based observations of fuels and fire behavior, estimates of fire-emitted heat and emissions fluxes, and observations of near-source micrometeorology, plume properties, smoke dispersion, and atmospheric chemistry. To address these requirements the FASMEE campaign design includes a study plan to guide the suite of required measurements in forested sites representative of many prescribed burning programs in the southeastern United States and increasingly common high-intensity fires in the western United States. Here we provide an overview of the proposed experiment and recommendations for key measurements. The FASMEE study provides a template for additional large-scale experimental campaigns to advance fire science and operational fire and smoke models.

Fire plays an important role in wildland ecosystems, critical to sustaining biodiversity, wildlife habitat and ecosystem health. By area, 70% of US prescribed burns take place in the Southeast, where treatment objectives range widely and accomplishing them depends on finding specific weather conditions for the effective and controlled application of fire. The climatological variation of the preferred weather window is examined here using two weather model reanalyses, with focus on conditions critical to smoke dispersion and erratic fire behaviour. Large spatial gradients were evident in some months (e.g. 3× change across the Appalachian Mountains in winter). Over most of the Southeast, availability of preferred conditions in summer was several (up to 8) times less than in autumn or winter. We offer explanation for this variability in terms of the mean seasonal changes of key weather conditions (especially mixing height and transport wind). We also examine the interannual variability of the preferred weather window for linkage to the tropical Pacific (1979–2010). Associations with the subset of El Niño events identified by outgoing-longwave-radiation suggest skilful seasonal fire weather forecasts are feasible. Together, these findings offer a predictive tool to prioritise allocation of scarce prescribed fire resources and maximise annual area treated across this landscape.

Smoke measurements were made during grass and forest understorey prescribed fires as part of a comprehensive programme to understand fire and smoke behaviour. Instruments deployed on the ground, airplane and tethered aerostat platforms characterised the smoke plumes through measurements of carbon dioxide (CO2), carbon monoxide (CO), methane (CH4) and particulate matter (PM), and measurements of optical properties. Distinctions were observed in aerial and ground-based measurements, with aerial measurements exhibiting smaller particle size distributions and PM emission factors, likely due to particle settling. Black carbon emission factors were similar for both burns and were highest during the initial flaming phase. On average, the particles from the forest fire were less light absorbing than those from the grass fires due to the longer duration of smouldering combustion in the forest biomass. CO and CH4 emission factors were over twice as high for the forest burn than for the grass burn, corresponding with a lower modified combustion efficiency and greater smouldering combustion. This dataset reveals the evolution of smoke emissions from two different commonly burned fuel types and demonstrates the complexity of emission factors.

Land managers rely on prescribed burning and naturally ignited wildfires for ecosystem management, and must balance trade-offs of air quality, carbon storage, and ecosystem health. A current challenge for land managers when using fire for ecosystem management is managing smoke production. Smoke emissions are a potential human health hazard due to the production of fine particulate matter (PM2.5), carbon monoxide (CO), and ozone (O3) precursors. In addition, smoke emissions can impact transportation safety and contribute to regional haze issues. Quantifying wildland fire emissions is a critical step for evaluating the impact of smoke on human health and welfare, and is also required for air quality modeling efforts and greenhouse gas reporting. Smoke emissions modeling is a complex process that requires the combination of multiple sources of data, the application of scientific knowledge from divergent scientific disciplines, and the linking of various scientific models in a logical, progressive sequence. Typically, estimates of fire size, available fuel loading (biomass available to burn), and fuel consumption (biomass consumed) are needed to calculate the quantities of pollutants produced by a fire. Here we examine the 2006 Tripod Fire Complex as a case study for comparing alternative data sets and combinations of scientific models available for calculating fire emissions. Specifically, we use five fire size information sources, seven fuel loading maps, and two consumption models (Consume 4.0 and FOFEM 5.7) that also include sets of emissions factors. We find that the choice of fuel loading is the most critical step in the modeling pathway, with different fuel loading maps varying by 108 %, while fire size and fuel consumption show smaller variations (36 % and 23 %, respectively). Moreover, we find that modeled fuel loading maps likely underestimate the amount of fuel burned during wildfires as field assessments of total woody fuel loading were consistently higher than modeled fuel loadings in all cases. The PM2.5 emissions estimates from Consume and FOFEM vary by 37 %. In addition, comparisons with available observational data demonstrate the value of using local data sets where possible.