Evaluating Wildfire Smoke Transport Within a Coupled Fire‐Atmosphere Model Using a High‐Density Observation Network for an Episodic Smoke Event Along Utah's Wasatch Front

Abstract

One of the primary challenges associated with evaluating smoke models is the availability of observations. The limited density of traditional air quality monitoring networks makes evaluating wildfire smoke transport challenging, particularly over regions where smoke plumes exhibit significant spatiotemporal variability. In this study, we analyzed smoke dispersion for the 2018 Pole Creek and Bald Mountain Fires, which were located in central Utah. Smoke simulations were generated using a coupled fire-atmosphere model, which simultaneously renders fire growth, fire emissions, plume rise, smoke dispersion, and fire-atmosphere interactions. Smoke simulations were evaluated using PM 2.5 observations from publicly accessible fixed sites and a semicontinuously running mobile platform. Calibrated measurements of PM 2.5 made by low-cost sensors from the Air Quality and yoU (AQ&U) network were within 10% of values reported at nearby air quality sites that used Federal Equivalent Methods. Furthermore, results from this study show that low-cost sensor networks and mobile measurements are useful for characterizing smoke plumes while also serving as an invaluable data set for evaluating smoke transport models. Finally, coupled fire-atmosphere model simulations were able to capture the spatiotemporal variability of wildfire smoke in complex terrain for an isolated smoke event caused by local fires. Results here suggest that resolving local drainage flow could be critical for simulating smoke transport in regions of significant topographic relief. Plain Language Summary Smoke forecasts for wildfires in central Utah were evaluated using low-cost air quality sensors and measurements from an instrument attached to a public transit train car. Preliminary results from this study suggest that calibrated low-cost sensors can measure pollutant concentrations during wildfire smoke events within 10% of values measured by traditional air quality stations. A unique benefit of low-cost sensor and mobile measurement networks is that they can delineate the edges of smoke plumes and are useful for identifying small-scale processes that effect smoke plume dispersion. Smoke forecasts from a weather prediction model were able to capture the timing of a smoke plume, which inundated the Salt Lake Valley during the morning of 15 September 2018. However, local observations indicated that forecasted smoke was overpredicted by a factor of 2. Smoke forecast errors could potentially be attributed to fire growth errors in the fire spread model used within the weather prediction model.

Integration of a Coupled Fire-Atmosphere Model Into a Regional Air Quality Forecasting System for Wildfire Events

“…Despite each fire being located near several AQ stations, we suspect that the relative scarcity of AQ observations limited our ability to definitively evaluate the model performance for the Pioneer Fire. This issue is discussed more in-depth in Mallia et al (2020a). Another issue with this analysis is that due to many concurrent wildfire events, it is difficult to ascertain whether smoke from other regional wildfires were contributing to model errors observed here.…”

“…WRF-SFIRE has been successfully applied for selected wildfire events (Kochanski et al, 2016(Kochanski et al, , 2019Mallia et al, 2020b) where simulated plume heights compared favorably with plume heights derived from the multi-angle imaging spectroradiometer (MISR). In another study, WRF-SFIRE was used to forecast smoke from a large wildfire near Salt Lake City, UT during the fall of 2018 (Mallia et al, 2020a). Smoke simulations in this study were evaluated with a high-density AQ network with over 300 sensors.…”

“…Smoke simulations in this study were evaluated with a high-density AQ network with over 300 sensors. Mallia et al (2020a) found that WRF-SFIRE was able to resolve inter-basin air exchanges and larger-scale canyon flows, while also capturing the smoke plume orientation and horizontal extent, along with the duration and timing of the smoke episode.…”

Fire-weather interaction fed the 2020 western USA gigafire

“…S6C), the humidity in the fire region would definitely be extremely sensitive to changes in the wind field, particularly zonal winds. The enhanced easterly winds can also influence humidity and air temperature through the foehn wind process, which has been extensively acknowledged as a critical factor triggering wildfire in the western USA (23,32,35).…”

Performance Evaluation of an Operational Rapid Response Fire Spread Forecasting System in the Southeast Mediterranean (Greece)

“…While adapted to a real-time response, coupled fire-atmosphere models have been proven to be able to reproduce wildfire spread [22][23][24][25][26][27][28][29][30] and smoke dispersion [31][32][33], and even anticipate several dynamic, transient phenomena, such as convective plumes, fire-induced winds, and horizontal roll vortices [34][35][36][37]. Nevertheless, such models are still not widely used in wildfire incident management.…”