Incorporating a Canopy Parameterization within a Coupled Fire-Atmosphere Model to Improve a Smoke Simulation for a Prescribed Burn

Mallia, Derek V.; Kochanski, Adam K.; Urbanski, S. P.; Mandel, Jan; Farguell, Angel; Krueger, Steven K.

doi:10.3390/atmos11080832

Cited by 17 publications

(8 citation statements)

References 36 publications

(62 reference statements)

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“…The WRF-SFIRE plume rise results were encouraging given that previous work has shown plume rise parameterizations often have a difficult time predicting plume top heights (Val Martin et al, 2012). The WRF-SFIRE plume rise evaluations presented here are consistent with results from Kochanski et al (2016) and Kochanski et al (2019), andMallia et al (2020b).…”

Section: Discussionsupporting

confidence: 80%

“…This approach utilizes the Weather Research and Forecast model (WRF; Skamarock et al, 2008) to simulate meteorology, while a fire spread parameterization combined with a fuel moisture model (SFIRE) is dynamically coupled with local meteorology (WRF-SFIRE; Mandel et al, 2011). 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).…”

Section: Introductionmentioning

confidence: 99%

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

Kochanski

Herron-Thorpe

Mallia

et al. 2021

Front. For. Glob. Change

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The objective of this study was to assess feasibility of integrating a coupled fire-atmosphere model within an air-quality forecast system to create a multiscale air-quality modeling framework designed to simulate wildfire smoke. For this study, a coupled fire-atmosphere model, WRF-SFIRE, was integrated, one-way, with the AIRPACT air-quality modeling system. WRF-SFIRE resolved local meteorology, fire growth, the fire plume rise, and smoke dispersion, and provided AIRPACT with fire inputs. The WRF-SFIRE-forecasted fire area and the explicitly resolved vertical smoke distribution replaced the parameterized BlueSky fire inputs used by AIRPACT. The WRF-SFIRE/AIRPACT integrated framework was successfully tested for two separate wildfire events (2015 Cougar Creek and 2016 Pioneer fires). The execution time for the WRF-SFIRE simulations was <3 h for a 48 h-long forecast, suggesting that integrating coupled fire-atmosphere simulations within the daily AIRPACT cycle is feasible. While the WRF-SFIRE forecasts realistically captured fire growth 2 days in advance, the largest improvements in the air quality simulations were associated with the wildfire plume rise. WRF-SFIRE-estimated plume tops were within 300-m of satellite-estimated plume top heights for both case studies analyzed in this study. Air quality simulations produced by AIRPACT with and without WRF-SFIRE inputs were evaluated with nearby PM2.5 measurement sites to assess the performance of our multiscale smoke modeling framework. The largest improvements when coupling WRF-SFIRE with AIRPACT were observed for the Cougar Creek Fire where model errors were reduced by ∼50%. For the second case (Pioneer fire), the most notable change with WRF-SFIRE coupling was that the probability of detection increased from 16 to 52%.

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Section: Discussionsupporting

confidence: 80%

Section: Introductionmentioning

confidence: 99%

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

Kochanski

Herron-Thorpe

Mallia

et al. 2021

Front. For. Glob. Change

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“…Comparisons of trends [13], with parallels to the development of empirical models, can match macro-scale responses to environmental conditions, such as wind speed, but cannot assess a physics-based model's ability to accurately represent the physical drivers of specific observed behaviors. Comparisons with individual wildfire observations and experiments are more common model applications [14][15][16][17][18][19][20][21][22]. Results from these experiments are complicated to interpret because conditions for observed wildfires are usually not sufficiently characterized to connect specifics of spatially heterogeneous and dynamic winds and heterogeneous fuels with specific burn characteristics of a fire at a given moment in time.…”

Section: Introductionmentioning

confidence: 99%

Modeling Low Intensity Fires: Lessons Learned from 2012 RxCADRE

et al. 2021

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Coupled fire-atmosphere models are increasingly being used to study low-intensity fires, such as those that are used in prescribed fire applications. Thus, the need arises to evaluate these models for their ability to accurately represent fire spread in marginal burning conditions. In this study, wind and fuel data collected during the Prescribed Fire Combustion and Atmospheric Dynamics Research Experiments (RxCADRE) fire campaign were used to generate initial and boundary conditions for coupled fire-atmosphere simulations. We present a novel method to obtain fuels representation at the model grid scale using a combination of imagery, machine learning, and field sampling. Several methods to generate wind input conditions for the model from eight different anemometer measurements are explored. We find a strong sensitivity of fire outcomes to wind inputs. This result highlights the critical need to include variable wind fields as inputs in modeling marginal fire conditions. This work highlights the complexities of comparing physics-based model results against observations, which are more acute in marginal burning conditions, where stronger sensitivities to local variability in wind and fuels drive fire outcomes.

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“…Forecasting smoke dispersion can be particularly challenging as there are a number of fire‐related processes that can be difficult to simulate such as the wildfire plume rise (Val Martin et al, 2012), smoke emissions (Herron‐Thorpe et al, 2014), and fire‐atmosphere feedbacks (Kochanski et al, 2019). Previous work has shown that properly characterizing the vertical smoke plume evolution is critical for forecasting smoke (Christian et al, 2019; Mallia et al, 2018, 2020; Stein et al, 2009; Walter et al, 2016). Unfortunately, regional air quality models often have limited spatial resolution, typically coarser than 3 km, which prevents these models from explicitly resolving wildfire smoke plume dynamics and other smaller‐scale convective processes.…”

Section: Introductionmentioning

confidence: 99%

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

et al. 2020

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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.

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Incorporating a Canopy Parameterization within a Coupled Fire-Atmosphere Model to Improve a Smoke Simulation for a Prescribed Burn

Cited by 17 publications

References 36 publications

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

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

Modeling Low Intensity Fires: Lessons Learned from 2012 RxCADRE

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

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