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

   https://doi.org/10.3390/atmos11080832  

   Mallia, Derek V. ; Kochanski, Adam K. ; Urbanski, Shawn P. ; Mandel, Jan ; Farguell, Angel ; Krueger, Steven K. ( August 2020 , Atmosphere)

   null (Ed.)

   Forecasting fire growth, plume rise and smoke impacts on air quality remains a challenging task. Wildland fires dynamically interact with the atmosphere, which can impact fire behavior, plume rises, and smoke dispersion. For understory fires, the fire propagation is driven by winds attenuated by the forest canopy. However, most numerical weather prediction models providing meteorological forcing for fire models are unable to resolve canopy winds. In this study, an improved canopy model parameterization was implemented within a coupled fire-atmosphere model (WRF-SFIRE) to simulate a prescribed burn within a forested plot. Simulations with and without a canopy wind model were generated to determine the sensitivity of fire growth, plume rise, and smoke dispersion to canopy effects on near-surface wind flow. Results presented here found strong linkages between the simulated fire rate of spread, heat release and smoke plume evolution. The standard WRF-SFIRE configuration, which uses a logarithmic interpolation to estimate sub-canopy winds, overestimated wind speeds (by a factor 2), fire growth rates and plume rise heights. WRF-SFIRE simulations that implemented a canopy model based on a non-dimensional wind profile, saw significant improvements in sub-canopy winds, fire growth rates and smoke dispersion when evaluated with observations. 

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2. Assessing Vertical Allocation of Wildfire Smoke Emissions Using Observational Constraints From Airborne Lidar in the Western U.S.

   https://doi.org/10.1029/2022JD036808  

   Ye, Xinxin ; Saide, Pablo E. ; Hair, Johnathan ; Fenn, Marta ; Shingler, Taylor ; Soja, Amber ; Gargulinski, Emily ; Wiggins, Elizabeth ( November 2022 , Journal of Geophysical Research: Atmospheres)

   Wildfire emissions are a key contributor of carbonaceous aerosols and trace gases to the atmosphere. Induced by buoyant lifting, smoke plumes can be injected into the free troposphere and lower stratosphere, which by consequence significantly affects the magnitude and distance of their influences on air quality and radiation budget. However, the vertical allocation of emissions when smoke escapes the planetary boundary layer (PBL) and the mechanism modulating it remain unclear. We present an inverse modeling framework to estimate the wildfire emissions, with their temporal and vertical evolution being constrained by assimilating aerosol extinction profiles observed from the airborne Differential Absorption Lidar‐High Spectral Resolution Lidar during the Fire Influence on Regional to Global Environments and Air Quality field campaign. Three fire events in the western U.S., which exhibit free‐tropospheric injections are examined. The constrained smoke emissions indicate considerably larger fractions of smoke injected above the PBL (f_>PBL, 80%–94%) versus the column total, compared to those estimated by the WRF‐Chem model using the default plume rise option (12%–52%). The updated emission profiles yield improvements for the simulated vertical structures of the downwind transported smoke, but limited refinement of regional smoke aerosol optical depth distributions due to the spatiotemporal coverage of flight observations. These results highlight the significance of improving vertical allocation of fire emissions on advancing the modeling and forecasting of the environmental impacts of smoke.

    

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3. 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

   https://doi.org/10.1029/2020JD032712  

   Mallia, Derek V. ; Kochanski, Adam K. ; Kelly, Kerry E. ; Whitaker, Ross ; Xing, Wei ; Mitchell, Logan E. ; Jacques, Alex ; Farguell, Angel ; Mandel, Jan ; Gaillardon, Pierre‐Emmanuel ; et al ( October 2020 , Journal of Geophysical Research: Atmospheres)

   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_2.5observations from publicly accessible fixed sites and a semicontinuously running mobile platform. Calibrated measurements of PM_2.5made 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.

    

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4. An Interactive Data-Driven HPC System for Forecasting Weather, Wildland Fire, and Smoke

   https://doi.org/10.1109/UrgentHPC49580.2019.00010  

   Mandel, Jan ; Vejmelka, Martin ; Kochanski, Adam ; Farguell, Angel ; Haley, James ; Mallia, Derek ; Hilburn, Kyle ( November 2019 , 2019 IEEE/ACM HPC for Urgent Decision Making (UrgentHPC))

   We present an interactive HPC framework for coupled fire and weather simulations. The system is suitable for urgent simulations and forecast of wildfire propagation and smoke. It does not require expert knowledge to set up and run the forecasts. The core of the system is a coupled weather, wildland fire, fuel moisture, and smoke model, running in an interactive workflow and data management system. The system automates job setup, data acquisition, preprocessing, and simulation on an HPC cluster. It provides animated visualization of the results on a dedicated mapping portal in the cloud, and as GIS files or Google Earth KML files. The system also serves as an extensible framework for further research, including data assimilation and applications of machine learning to initialize the simulations from satellite data. Index Terms—WRF-SFIRE, coupled atmosphere-fire model, MODIS, VIIRS, satellite data, fire arrival time, data assimilation, machine learning 

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5. Heat flux assumptions contribute to overestimation of wildfire smoke injection into the free troposphere

   https://doi.org/10.1038/s43247-022-00563-x  

   Thapa, Laura H. ; Ye, Xinxin ; Hair, Johnathan W. ; Fenn, Marta A. ; Shingler, Taylor ; Kondragunta, Shobha ; Ichoku, Charles ; Dominguez, RoseAnne ; Ellison, Luke ; Soja, Amber J. ; et al ( October 2022 , Communications Earth & Environment)

   Injections of wildfire smoke plumes into the free troposphere impact air quality, yet model forecasts of injections are poor. Here, we use aircraft observations obtained during the 2019 western US wildfires (FIREX-AQ) to evaluate a commonly used smoke plume rise parameterization in two atmospheric chemistry-transport models (WRF-Chem and HRRR-Smoke). Observations show that smoke injections into the free troposphere occur in 35% of plumes, whereas the models forecast 59–95% indicating false injections in the simulations. False injections were associated with both models overestimating fire heat flux and terrain height, and with WRF-Chem underestimating planetary boundary layer height. We estimate that the radiant fraction of heat flux is 0.5 to 25 times larger in models than in observations, depending on fuel type. Model performance was substantially improved by using observed heat flux and boundary layer heights, confirming that models need accurate heat fluxes and boundary layer heights to correctly forecast plume injections.

    

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