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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)

   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.
2. 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
3. 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.
4. Do we need weather prediction models to account for local weather modifications by wildland fires?

   https://doi.org/10.14195/978-989-26-16-506_108  

   Kochanski, Adam ; Mallia, Derek V. ; Fearon, Matthew G. ; Brown, Tim ; Mandel, Jan ; Vaughan, Joseph K. ( August 2018 , Advances in Forest Fire Research 2018)

   Viegas, Domingos Xavier (Ed.)

   During the summer of 2015, a number of wildfires fires burned across northern California, which produced significant smoke across the region. Smoke from these wildfires hindered fire-fighting efforts by delaying helicopter operations and exposed communities to high concentrations of atmospheric pollutants. Nighttime inversions are common across the western U.S. and usually mix out during the early afternoon as a result of convective mixing from daytime heating. However, atmospheric conditions in valleys adjacent to the aforementioned wildfires remained stable throughout the afternoon. It is hypothesized that the smoke from nearby wildfires enhanced atmospheric stability due to surface cooling caused by reduced incoming solar radiation, and possibly by warming aloft due to absorption of the incoming solar radiation in the smoke layer. At the same time, mid-level heating from the wildfire could have increased atmospheric stability and extended the duration of the inversion. In this study, we utilize the WRF-SFIRE-CHEM modeling framework, which couples an atmospheric, chemical, and fire spread model in an effort the model the impacts of smoke on local inversions and to improve the physical understanding behind these smoke-induced inversion episodes. This modeling framework was used to simulate the Route and South Complex fires between August 10 – Augustmore »26th, 2015. Preliminary results indicate that wildfire smoke may have significantly reduced incoming solar radiation, leading to local surface cooling by up to 2-3 degrees. Direct heating from the fire itself does not significantly enhance atmospheric stability. However, mid-level warming was observed in the smoke layer suggesting that absorption in this layer may have enhanced the inversion. This study suggests the including the fire-smoke- atmosphere feedbacks in a coupled modeling framework such as WRF-SFIRE-CHEM may help in capturing the impacts of wildfire smoke on near-surface stability and local inversions.« less
5. Evaluation and intercomparison of wildfire smoke forecasts from multiple modeling systems for the 2019 Williams Flats fire

   https://doi.org/10.5194/acp-21-14427-2021  

   Ye, Xinxin ; Arab, Pargoal ; Ahmadov, Ravan ; James, Eric ; Grell, Georg A. ; Pierce, Bradley ; Kumar, Aditya ; Makar, Paul ; Chen, Jack ; Davignon, Didier ; et al ( January 2021 , Atmospheric Chemistry and Physics)

   Abstract. Wildfire smoke is one of the most significant concerns ofhuman and environmental health, associated with its substantial impacts onair quality, weather, and climate. However, biomass burning emissions andsmoke remain among the largest sources of uncertainties in air qualityforecasts. In this study, we evaluate the smoke emissions and plumeforecasts from 12 state-of-the-art air quality forecasting systemsduring the Williams Flats fire in Washington State, US, August 2019, whichwas intensively observed during the Fire Influence on Regional to GlobalEnvironments and Air Quality (FIREX-AQ) field campaign. Model forecasts withlead times within 1 d are intercompared under the same framework basedon observations from multiple platforms to reveal their performanceregarding fire emissions, aerosol optical depth (AOD), surface PM2.5,plume injection, and surface PM2.5 to AOD ratio. The comparison ofsmoke organic carbon (OC) emissions suggests a large range of daily totalsamong the models, with a factor of 20 to 50. Limited representations of thediurnal patterns and day-to-day variations of emissions highlight the needto incorporate new methodologies to predict the temporal evolution andreduce uncertainty of smoke emission estimates. The evaluation of smoke AOD(sAOD) forecasts suggests overall underpredictions in both the magnitude andsmoke plume area for nearly all models, although the high-resolution modelshave a better representation of the fine-scale structures of smoke plumes.Themore »models driven by fire radiativepower (FRP)-based fire emissions or assimilating satellite AODdata generally outperform the others. Additionally, limitations of thepersistence assumption used when predicting smoke emissions are revealed bysubstantial underpredictions of sAOD on 8 August 2019, mainly over thetransported smoke plumes, owing to the underestimated emissions on7 August. In contrast, the surface smoke PM2.5 (sPM2.5) forecastsshow both positive and negative overall biases for these models, with mostmembers presenting more considerable diurnal variations of sPM2.5.Overpredictions of sPM2.5 are found for the models driven by FRP-basedemissions during nighttime, suggesting the necessity to improve verticalemission allocation within and above the planetary boundary layer (PBL).Smoke injection heights are further evaluated using the NASA LangleyResearch Center's Differential Absorption High Spectral Resolution Lidar(DIAL-HSRL) data collected during the flight observations. As the firebecame stronger over 3–8 August, the plume height became deeper, with aday-to-day range of about 2–9 km a.g.l. However, narrower ranges arefound for all models, with a tendency of overpredicting the plume heights forthe shallower injection transects and underpredicting for the days showingdeeper injections. The misrepresented plume injection heights lead toinaccurate vertical plume allocations along the transects corresponding totransported smoke that is 1 d old. Discrepancies in model performance forsurface PM2.5 and AOD are further suggested by the evaluation of theirratio, which cannot be compensated for by solely adjusting the smoke emissionsbut are more attributable to model representations of plume injections,besides other possible factors including the evolution of PBL depths andaerosol optical property assumptions. By consolidating multiple forecastsystems, these results provide strategic insight on pathways to improvesmoke forecasts.« less