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1. 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 – August 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. 

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2. Uncertainty in Health Impact Assessments of Smoke From a Wildfire Event

   https://doi.org/10.1029/2021GH000526  

   Johnson, Megan M.; Garcia‐Menendez, Fernando (January 2022, GeoHealth)

   Abstract Wildfires cause elevated air pollution that can be detrimental to human health. However, health impact assessments associated with emissions from wildfire events are subject to uncertainty arising from different sources. Here, we quantify and compare major uncertainties in mortality and morbidity outcomes of exposure to fine particulate matter (PM_2.5) pollution estimated for a series of wildfires in the Southeastern U.S. We present an approach to compare uncertainty in estimated health impacts specifically due to two driving factors, wildfire‐related smoke PM_2.5fields and variability in concentration‐response parameters from epidemiologic studies of ambient and smoke PM_2.5. This analysis, focused on the 2016 Southeastern wildfires, suggests that emissions from these fires had public health consequences in North Carolina. Using several methods based on publicly available monitor data and atmospheric models to represent wildfire‐attributable PM_2.5, we estimate impacts on several health outcomes and quantify associated uncertainty. Multiple concentration‐response parameters derived from studies of ambient and wildfire‐specific PM_2.5are used to assess health‐related uncertainty. Results show large variability and uncertainty in wildfire impact estimates, with comparable uncertainties due to the smoke pollution fields and health response parameters for some outcomes, but substantially larger health‐related uncertainty for several outcomes. Consideration of these uncertainties can support efforts to improve estimates of wildfire impacts and inform fire‐related decision‐making. 

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3. Using Satellite‐Derived Fire Arrival Times for Coupled Wildfire‐Air Quality Simulations at Regional Scales of the 2020 California Wildfire Season

   https://doi.org/10.1029/2022JD037062  

   Lassman, W.; Mirocha, J. D.; Arthur, R. S.; Kochanski, A. K.; Farguell Caus, A.; Bagley, A. M.; Carreras Sospedra, M.; Dabdub, D.; Barbato, M. (March 2023, Journal of Geophysical Research: Atmospheres)

   Abstract Wildfire frequency has increased in the Western US over recent decades, driven by climate change and a legacy of forest management practices. Consequently, human structures, health, and life are increasingly at risk due to wildfires. Furthermore, wildfire smoke presents a growing hazard for regional and national air quality. In response, many scientific tools have been developed to study and forecast wildfire behavior, or test interventions that may mitigate risk. In this study, we present a retrospective analysis of 1 month of the 2020 Northern California wildfire season, when many wildfires with varying environments and behavior impacted regional air quality. We simulated this period using a coupled numerical weather prediction model with online atmospheric chemistry, and compare two approaches to representing smoke emissions: an online fire spread model driven by remotely sensed fire arrival times and a biomass burning emissions inventory. First, we quantify the differences in smoke emissions and timing of fire activity, and characterize the subsequent impact on estimates of smoke emissions. Next, we compare the simulated smoke to surface observations and remotely sensed smoke; we find that despite differences in the simulated smoke surface concentrations, the two models achieve similar levels of accuracy. We present a detailed comparison between the performance and relative strengths of both approaches, and discuss potential refinements that could further improve future simulations of wildfire smoke. Finally, we characterize the interactions between smoke and meteorology during this event, and discuss the implications that increases in regional smoke may have on future meteorological conditions. 

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4. Wildfire Smoke Directly Changes Biogenic Volatile Organic Emissions and Photosynthesis of Ponderosa Pines

   https://doi.org/10.1029/2023GL106667  

   Riches, M.; Berg, T_C; Vermeuel, M_P; Millet, Dylan_B; Farmer, D_K (March 2024, Geophysical Research Letters)

   Abstract Wildfires are increasing across the USA. While smoke events affect human exposure and air quality, wildfire smoke effects on ecosystem‐atmosphere interactions are poorly understood. We investigate smoke effects on biogenic volatile organic compound (VOC) emissions and photosynthesis for ponderosa pines. During several wildfire smoke events, we observed photosynthetic reduction with evidence for stomatal plugging and changes in leaf‐level uptake and emission of both biogenic and wildfire VOCs. During intense smoke events, photosynthesis and VOC emissions were almost entirely suppressed, but increased dramatically upon stomatal opening. We propose four types of VOC responses to this burst in stomatal opening: post‐burst emissions, pulsed emissions, surge emissions, and post‐burst uptake. Our observations suggest that wildfire smoke can affect plant physiology and leaf‐atmosphere gas exchange. 

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5. Firecast Zigzag Convolutional Network for Wildfire Prediction

   https://doi.org/10.1109/BigData62323.2024.10825303  

   Chen, Yuzhou; Castillo, Joel Chacon; Lee, Huikyo; Gel, Yulia R (December 2024, IEEE)

   Each year wildfires result in billions of dollars in property damage. Being one of the major natural hazards, wildfires nowadays are also a global affair whose negative impact is particularly devastating in developing countries. As wildfires are expected to become more frequent and severe, more accurate models to predict wildfires are vital to mitigating risks and developing more informed decision-making. Artificial intelligence (AI) has a potential to enhance wildfire risk analytics on multiple fronts. For example, deep learning (DL) has been successfully used to classify active fires, burned scars, smoke plumes and to track the spread of active wildfires. Since wildfire spread tends to exhibit highly complex spatio-temporal dependencies which often cannot be accurately described with conventional Euclideanbased approaches, we postulate that the tools of topological and geometric deep learning, specifically designed for non-Euclidean objects such as manifolds and graphs, may offer a more competitive solution. We validate the proposed methodology to predict wildfire occurrences in Greece and several regions of Africa. Our results indicate that the Firecast Zigzag Convolutional Network (F-ZCN) outperforms the current baseline methods for wildfire prediction and opens a path for more accurate wildfire risk analytics, even in scenarios of limited and noisy data records. 

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