Smoke Plume Research Articles (Page 1)

Abstract Forecasting the impacts of wildfires has taken on more importance in recent years due to their increased frequency and significant human impacts. Wildfires often generate large smoke plumes which can loft aerosols deep into the atmosphere, which are then transported far downstream by the prevailing environment. Convection-allowing models such as the HRRR-Smoke currently forecast the evolution of smoke plumes, but many uncertainties in these forecasts remain. A smoke enabled version of the Warn-on-Forecast System (WoFS) has been developed to combine the existing smoke injection algorithms into a short-term (0–6 h) probabilistic forecast system. WoFS cycles at 15-min intervals, allowing for continuous updates of rapidly changing wildfire characteristics derived from geostationary satellite data. However, neither system assimilates any observations associated with the actual smoke plume. Few real-time, high-density observations of smoke aerosol concentrations are currently available. Precipitation radars, such as the WSR-88D, can detect biomass burning debris (BBD) lofted by wildfires. Smoke aerosols themselves are too small to be readily detectable from these radars, but the lofted debris is larger. Returned radar reflectivity and radial velocity provide an estimate on the spatial distribution of smoke and its movement within the environment. Radial velocity observations can be assimilated with minimal modifications to the existing system. This work explores the impact of assimilating radial velocity observations of BBD for three large wildfires in 2024. Assimilating these data has a large impact on the forecast smoke plume characteristics. Comparisons of forecast smoke with visible satellite imagery and radar reflectivity show that the forecasted spatial extent of wildfire smoke is improved. Significance Statement This research utilizes measurements of lofted smoke and burned debris from wildfires by Doppler radars to improve smoke forecasts in numerical weather prediction models. These data act to adjust the model wind field with actual measurements where no observations are currently assimilated into the system. Applying these data to three case studies showed that smoke forecasts are improved using these data, and this work represents the first of its kind utilizing Doppler radial velocity measurements for smoke forecasting applications.

Between June 6 and 8, 2023, wildfires in Quebec, Canada generated massive smoke plumes that traveled long distances and deteriorated air quality across the Northeastern United States (US). Surface daily PM2.5 observations exceeded 100 µg m-3, affecting major cities such as New York City and Philadelphia, while many areas lacked PM2.5 monitors, making it difficult to assess local air quality conditions. To address this gap, we developed a WRF-CMAQ-BenMAP modeling system to provide rapid, spatially continuous estimates of wildfire-attributable PM2.5 concentrations and associated health impacts, particularly benefiting regions lacking air quality monitoring. CMAQ simulations driven by two wildfire emissions datasets and two meteorological drivers showed good agreement with PM2.5 observations, with linear regression results of R2 ∼0.6 and slope ∼0.9. We further quantified uncertainties introduced by varying emissions and meteorological drivers and found the choice of wildfire emissions dataset alone can alter PM2.5 simulations by up to 40 µg m-3 (∼40%). Short-term health impacts were evaluated using the BenMAP model. Validation against asthma-associated emergency department (ED) visits in New York State confirmed the framework's ability to replicate real-world outcomes, with ED visits increased up to ∼40%. The modeling results identified counties most severely affected by wildfire plumes, the majority of which lack regulatory air quality monitors. Our approach highlights the value of integrated modeling for identifying vulnerable populations and delivering timely health burden estimates, regardless of local monitoring availability.

Aerosols influence forest ecosystems through changes in radiation and climate affecting plant physiology and structure. Conversely, forests also contribute to aerosol formation. They emit primary aerosol particles and volatile organic compounds, which promote secondary organic aerosol formation in the atmosphere. This forest-aerosol coupling is highly dynamic, influenced by temperature, radiation, humidity, and trace gases. Wildfires add further complexity via smoke plumes altering radiation and ecosystem functioning, tropospheric ozone levels and stratospheric chemistry. Aerosols modify the quantity, directionality, and composition of solar radiation. The type of diffuse light produced by aerosol particles is however strongly dissimilar to the one produced under clouds, and the relevance of the traditional diffuse/direct binary paradigm is discussed. Therefore, potential benefits from increased diffuse radiation are contingent on aerosol load, canopy structure, and prevailing environmental conditions. Beyond photosynthetic responses, aerosols alter forest water-use efficiency and microclimate, yet their long-term effects on plant development, architecture, and community composition remain poorly understood. This review highlights significant knowledge gaps and recent advances in understanding aerosol-forest interactions across temporal and spatial scales. We underline the urgent need for improved experiments with realistic diffuse shading, extensive in situ observations, mechanistic model intercomparison, and global validation to guide future research and policy.

Evaluation of modeled smoke plumes of wildfires in the Argentinian Patagonia, against satellite observations.

Abstract Urban aerosol pollution is evolving rapidly with global change and poses significant risks to public health. Measurements and machine learning-enabled chemical analysis of aerosol from a suburb of New York City in 2023 reveal emerging sources and drivers in a modern megacity. Regional wildfire smoke averaged 25% of organic aerosol (OA) mass and drove variability via enhancements of biogenic OA formation within smoke plumes. This biogenic OA contributed 40% of aerosol mass. Urban heatwaves enhanced both biogenic and anthropogenic sources, with ~20% of OA mass exhibiting significant heatwave sensitivity. For the first time, volatile chemical product (VCP) compounds were directly observed, speciated, and characterized in urban aerosol. Contributions to total OA averaged 15%, double the contribution from traffic. Together, this work identifies wildfire smoke, biogenic emissions, heat, and emerging anthropogenic emissions as critical global change vulnerabilities for North American urban aerosol pollution that pose unique challenges for control strategies.

ABSTRACT SmokePath Explorer is a web-based decision-support tool for California, U.S.A. that quantifies smoke transport probability and population exposure risk across the state, enabling data-driven strategies to minimize impacts while advancing fire management effectiveness. SmokePath integrates the California and Nevada Smoke and Air Committee (CANSAC) high-resolution (2-km) 20-year reanalysis climatology with HYSPLIT trajectory modeling. A total of 1.3 billion transport trajectories were precomputed with initializations four times per day at four distinct height levels capturing diurnal variations and injection height influences across various prescribed fire scenarios to support probabilistic smoke projections. Within the SmokePath online dashboard, users can input fire parameters and select specific months or weeks to assess smoke transport risk. The tool generates risk-level smoke transport contours from precomputed data and summarizes key meteorological variables. The system also provides population exposure estimates, including the total affected population, the number of smoke-sensitive facilities (i.e. educational and healthcare), and impacted USPS ZIP codes. User feedback has been key to developing SmokePath, enhancing usability, data integration, and decision-making for prescribed fire planning. To assess SmokePath’s efficacy, we conducted case studies across diverse regions and validated results against independent observation datasets. These use cases assessed the accuracy of the WRF-CANSAC reanalysis dataset, which serves as the meteorological input for fire weather climatology and trajectory modeling. The fire case studies focused on identifying optimal burn windows and quantifying smoke transport patterns from (i) large multi-day prescribed fires, (ii) short duration (single-day) pile burns, and (iii) burns across diverse regions with complex topographic features. In most cases, modeled transport probability aligned with satellite-observed smoke plumes, capturing predominant dispersion patterns. Stakeholder feedback further supported the tool’s practical utility — 85% indicated they would use SmokePath for prescribed fire planning, and 62% found it useful during wildfires. Implications: With catastrophic wildfires on the rise, California is expanding fuel treatments, including prescribed fire. While essential for mitigation, prescribed burns release smoke that can harm public health if unmanaged. SmokePath addresses this challenge by providing evidence-based insights on plume behavior to guide short- and long-term planning. The tool helps land managers schedule burns to minimize community impact, especially for vulnerable populations, and supports wildfire response with rapid smoke risk information. By improving public communication and enabling protective actions, SmokePath advances both health protection and the strategic use of prescribed fire.

Abstract Wildfires are considered a natural factor which leaves detrimental effects on the environment. In this study, the occurrence of wildfire smoke coincided with the occurrence of clouds, and this underscored the need to separate the wildfire smoke from the clouds. The sigmoid activation function, coupled with momentum gradient optimizer (MGD) optimizer, was applied to spectrally reconfigure selected Sentinel-2 bands to smoke plumes. Bartlett’s k-comparison of equal variance statistical was applied to determine spectral radiance properties of smoke plumes and clouds across selected Sentinel-2 bands. The Relative Operation Characteristics (ROC) was used to evaluate the performance of the performance of the sigmoid activation function with MGD in characterizing smoke plumes. Bartlett’s test revealed variations in the radiance properties of smoke and clouds across the selected spectral bands of Sentinel-2 sensor, with the p-value of < 0.001 for both smoke and clouds. The mean radiance values for smoke plume were noted to be lower than that of the clouds across all the selected spectral channels besides the shortwave infrared (SWIR) cirrus channel for both original and calibrated image, where smoke and clouds had similar radiance properties. The relative operation characteristics (ROC) results confirmed the calibrated blue and green spectral bands to be effective in detecting smoke plume, with area under curve (AUC) value of 0.81 and 0.73 respectively. This research emphasized the significance of integrating machine learning and multispectral remote sensing in mitigating wildfire disaster. Because wildfire is an unpredictable incident, the findings of this study were not validated with ground-based data.

This paper presents a unified compressible flow map framework designed to accommodate diverse compressible flow systems, including high-Mach-number flows (e.g., shock waves and supersonic aircraft), weakly compressible systems (e.g., smoke plumes and ink diffusion), and incompressible systems evolving through compressible acoustic quantities (e.g., free-surface shallow water). At the core of our approach is a theoretical foundation for compressible flow maps based on Lagrangian path integrals, a novel advection scheme for the conservative transport of density and energy, and a unified numerical framework for solving compressible flows with varying pressure treatments. We validate our method across three representative compressible flow systems, characterized by varying fluid morphologies, governing equations, and compressibility levels, demonstrating its ability to preserve and evolve spatiotemporal features such as vortical structures and wave interactions governed by different flow physics. Our results highlight a wide range of novel phenomena, from ink torus breakup to delta wing tail vortices and vortex shedding on free surfaces, significantly expanding the range of fluid systems that flow-map methods can handle.

This study describes a quite special and interesting atmospheric event characterized by the simultaneous presence of fresh and aged smoke layers. These peculiar conditions occurred on 16 July 2024 at the CNR-IMAA atmospheric observatory (CIAO) in Potenza (Italy), and represent an ideal case for the evaluation of the impact of aging and transport mechanisms on both the optical and microphysical properties of biomass burning aerosol. The fresh smoke was originated by a local wildfire about 2 km from the measurement site and observed about one hour after its ignition. The other smoke layer was due to a wide wildfire occurring in Canada that, according to backward trajectory analysis, traveled for about 5–6 days before reaching the observatory. Synergetic use of lidar, ceilometer, radar, and microwave radiometer measurements revealed that particles from the local wildfire, located at about 3 km a.s.l., acted as condensation nuclei for cloud formation as a result of high humidity concentrations at this altitude range. Optical characterization of the fresh smoke layer based on Raman lidar measurements provided lidar ratio (LR) values of 46 ± 4 sr and 34 ± 3 sr, at 355 and 532 nm, respectively. The particle linear depolarization ratio (PLDR) at 532 nm was 0.067 ± 0.002, while backscatter-related Ångström exponent (AEβ) values were 1.21 ± 0.03, 1.23 ± 0.03, and 1.22 ± 0.04 in the spectral ranges of 355–532 nm, 355–1064 nm and 532–1064 nm, respectively. Microphysical inversion caused by these intensive optical parameters indicates a low contribution of black carbon (BC) and, despite their small size, particles remained outside the ultrafine range. Moreover, a combined use of CIAO remote sensing and in situ instrumentation shows that the particle properties are affected by humidity variations, thus suggesting a marked particle hygroscopic behavior. In contrast, the smoke plume from the Canadian wildfire traveled at altitudes between 6 and 8 km a.s.l., remaining unaffected by local humidity. Absorption in this case was higher, and, as observed in other aged wildfires, the LR at 532 nm was larger than that at 355 nm. Specifically, the LR at 355 nm was 55 ± 2 sr, while at 532 nm it was 82 ± 3 sr. The AEβ values were 1.77 ± 0.13 and 1.41 ± 0.07 at 355–532 nm and 532–1064 nm, respectively and the PLDR at 532 nm was 0.040 ± 0.003. Microphysical analysis suggests the presence of larger, yet much more absorbent particles. This analysis indicates that both optical and microphysical properties of smoke can vary significantly depending on its origin, persistence, and transport in the atmosphere. These factors that must be carefully incorporated into future climate models, especially considering the frequent occurrences of fire events worldwide.

Biomass burning processes affect many semi-rural areas in the Mediterranean, but there is a lack of long-term datasets focusing on their classification, obtained by monitoring carbonaceous particle concentrations and optical properties variations. To address this issue, a campaign to measure equivalent black carbon (eBC) and particle number size distributions (0.3–10 μm) was carried out from August 2019 to November 2020 at a coastal semi-rural site in the Basilicata region of Southern Italy. Long-term datasets were useful for aerosol characterization, helping to clearly identify traffic as a constant eBC source. For a shorter period, PM2.5 mass concentrations were also measured, allowing the estimation of elemental and organic carbon (EC and OC), and chemical and SEM (scanning electron microscope) analysis of aerosols collected on filters. This multi-instrumental approach enabled the discrimination among different biomass burning (BB) processes, and the analysis of three case studies related to domestic heating, regional smoke plume transport, and a local smoldering process. The AAE (Ångström absorption exponent) daily pattern was characterized as having a peak late in the morning and mean hourly values that were always higher than 1.3.

Contrary to what is represented in geospatial databases, places are dynamic and shaped by events. Point clustering analysis commonly assumes events occur in an empty space and therefore ignores geospatial features where events take place. This research introduces relational density, a novel concept redefining density as relative to the spatial structure of geospatial features rather than an absolute measure. Building on this, we developed Space-Time Plume, a new algorithm for detecting and tracking evolving event clusters as smoke plumes in space and time, representing dynamic places. Unlike conventional density-based methods, Space-Time Plume dynamically adapts spatial reachability based on the underlying spatial structure and other zone-based parameters across multiple temporal intervals to capture hierarchical plume dynamics. The algorithm tracks plume progression, identifies spatiotemporal relationships, and reveals the emergence, evolution, and disappearance of event-driven places. A case study of crime events in Dallas, Texas, USA, demonstrates the algorithm’s performance and its capacity to represent and compute criminogenic places. We further enhance metaball rendering with Perlin noise to visualize plume structures and their spatiotemporal evolution. A comparative analysis with ST-DBSCAN shows Space-Time Plume’s competitive computational efficiency and ability to represent dynamic places with richer geographic insights.

Abstract Many fire weather index products have been developed to assist land managers, weather forecasters, and firefighters with anticipating weather conditions that may impact existing or potential new wildland fires in coming days. Most of these indices are designed to provide a single value for an entire 24-h period. Extreme wildfire activity in the western US in recent years, including the impact of mesoscale and microscale phenomena such as thunderstorm gust frontal passages, radiative shading by dense smoke plumes, and pyrocumulonimbus development and collapse, as well as the advent of operational convection-allowing model forecasts, has highlighted the need for a more frequently updated index. In this study, we present a proof of concept for an hourly fire weather index developed specifically for application within a rapidly-updating convection-allowing model. The index, termed the Hourly Wildfire Potential (HWP), is developed based on observations of fire radiative power (FRP) from polar-orbiting satellites during large western US wildfires in 2018 and 2020, and is evaluated against a merged dataset of FRP from polar-orbiting and geostationary satellites. The index is computed based on meteorological output from the NOAA operational High-Resolution Rapid Refresh (HRRR) model. The HWP index exhibits an improved representation of hourly FRP compared to a climatological approach, and also shows promise for distinguishing between conditions associated with flaming and smoldering combustion. This work paves the way for improved prediction of wildfire smoke emissions in the coming hours and days.

Efficient segmentation of smoke plumes is crucial for environmental monitoring and industrial safety. Existing models often face high computational demands and limited adaptability to diverse smoke appearances. To address these issues, we propose SmokeNet, a deep learning architecture integrating multiscale convolutions, multiview linear attention, and layer-specific loss functions. Specifically, multiscale convolutions capture diverse smoke shapes by employing varying kernel sizes optimized for different plume orientations. Subsequently, multiview linear attention emphasizes spatial and channel-wise features relevant to smoke segmentation tasks. Additionally, layer-specific loss functions promote consistent feature refinement across network layers, facilitating accurate and robust segmentation. SmokeNet achieves a segmentation accuracy of 72.74% mean Intersection over Union (mIoU) on our newly introduced quarry blast smoke dataset and maintains comparable performance on three benchmark smoke datasets, reaching up to 76.45% mIoU on the Smoke100k dataset. With a computational complexity of only 0.34 M parameters and 0.07 Giga Floating Point Operations (GFLOPs), SmokeNet is suitable for real-time applications. Evaluations conducted across these datasets demonstrate SmokeNet’s effectiveness and versatility in handling complex real-world scenarios.

To determine the long-term effect of Australian bushfires on the upper tropospheric composition in the South Pacific, we investigated the variation in CO and hydrocarbon species in the South Pacific according to the extent of Australian bushfires (2004–2020). We conducted analyses using satellite data on hydrocarbon and CO from the Atmospheric Chemistry Experiment Fourier Transform Spectrometer (ACE-FTS), and on fire (fire count, burned area, and fire radiative power) from the Moderate Resolution Imaging Spectroradiometer (MODIS). Additionally, we compared the effects of bushfires between Northern and Southeastern Australia (N_Aus and SE_Aus, respectively). Our analyses show that Australian bushfires in austral spring (September to November) result in the largest increase in CO and hydrocarbon species in the South Pacific and even in the west of South America, indicating the trans-Pacific transport of smoke plumes. In addition to HCN (a well-known wildfire indicator), CO and other hydrocarbon species (C2H2, C2H6, CH3OH, HCOOH) are also considerably increased by Australian bushfires. A unique finding in this study is that the hydrocarbon increase in the South Pacific mostly relates to the bushfires in N_Aus, implying that we need to be more vigilant of bushfires in N_Aus, although the severe Australian bushfire in 2019–2020 occurred in SE_Aus. Due to the surface conditions in springtime, bushfires on grassland in N_Aus during this time account for most Australian bushfires. All results show that satellite data enables us to assess the long-term effect of bushfires on the air composition over remote areas not having surface monitoring platforms.

Abstract. This study quantifies wildfire contributions to O3 pollution in Arizona, relative to local and regional emissions. Using WRF-Chem with O3 and CO tags, we analyzed emissions during June 2021, a period of drought, extreme heat, and wildfires. Our results show that background O3 accounted for ∼50 % of total O3, while local anthropogenic emissions contributed 24 %–40 %, consistent with recent estimates for Phoenix. During peak smoke conditions, fire-related O3 ranged from 5 to 23 ppb (5 %–21 % of total O3), averaging 15 ppb (15 %). These estimates were compared with model sensitivity tests excluding fire emissions, which confirmed the spatiotemporal pattern of fire-driven O3, though the model underestimated the magnitude by a factor of 1.4. The results further demonstrate that wildfires exacerbate O3 exceedances over urban areas. Our analysis reveals key differences in O3 sources: Phoenix's O3 was mainly driven by local emissions, while Yuma's was heavily influenced by transboundary transport from California and Mexico. Wildfires not only boosted O3 formation but also altered winds and atmospheric chemistry in Phoenix and downwind areas. O3 increases along the smoke plume resulted from NOX and volatile organic compound (VOC) interactions, with fire-driven O3 forming in NOX-limited zones near the urban interface. Downwind, O3 chemistry shifted, shaped by higher NOX in central Phoenix and more VOCs in suburban and rural areas. Winds weakened and turned westerly near fire-affected areas. This study highlights the value of high-resolution modeling with tagging to disentangle wildfire and regional O3 sources, particularly in arid regions, where extreme heat intensifies O3 pollution, making accurate source attribution essential.

ABSTRACTDuring the fire season of 2023, extreme continuous wildfires in Canada exported smoke to distant areas. On June 6–8, record‐breaking smoke concentrations impacted human health and the environment in New York City (NYC) and its surroundings. In this work, for the first time, we incorporate Lagrangian airmass trajectories with Copernicus Atmospheric Monitoring Service (CAMS) forecasts to trace back the origin of the smoke in NYC and identify the weather systems governing its transport. We locate the main smoke plume which originated from fires in Quebec. The smoke traveled at a height of about 500 hPa southward and descended slantwise to NYC behind a deep cyclone over the east coast. A second peak in smoke concentration in NYC emerged by air that circulated around the cyclone back to the city, collecting smoke again from the fires in Quebec. Smoke from the major fires in western Canada did not contribute significantly to the NYC event but was transported at tropopause level toward Europe. The findings highlight the critical role of synoptic‐scale systems in the transport of wildfire smoke.

Abstract Climate change is simultaneously worsening wildfire and extreme heat events in California increasing the likelihood of exposure to compound hazards (CH). This study examines the exposure distribution of compound wildfire smoke and extreme heat in California, 2011–2020, and characterizes disproportionate population vulnerabilities. We obtained fine resolution temperature data (4‐km) from GridMET and wildfire‐influenced fine particulate matter (PM2.5) estimates (3‐km) from a combined metric of geostatistical modeled total PM2.5 and satellite‐detected wildfire smoke plumes. Estimates were aggregated to the ZIP‐Code Tabulation Area (ZCTA) level and population weighted. Exposure days to CH and single hazards were defined using a 2‐day exposure lag window with binary indicators for wildfire smoke and extreme heat. Daily exposure counts were summed by year and over the 10 years for descriptive mapping. Ten‐year exposures were characterized by community factors and differences were tested using ANOVA. Exposures to compound wildfire smoke and extreme heat varied temporally and geographically, primarily driven by wildfire smoke. On average, ZCTAs experienced 3–4 CH days annually, peaking in 2020 (9.85 days). From the early (2011–2015) to later period (2016–2020), ZCTAs experienced 2.77 more annual CH days (95% CI: 2.62, 2.92; p < 0.0001). The number of ZCTAs exposed annually also increased. ZCTAs with persistently higher CH days had significantly higher proportions of minority populations, lower median incomes, and more urban characteristics. Our results show increasing and unequal exposure to compound wildfire smoke and extreme heat. These risks should be considered in mitigation strategies for climate‐vulnerable populations.

3D characterization of smoke plume dispersion using multi-view drone swarm.

Abstract As wildfire pervasiveness increases with a changing climate, there is a need to develop new techniques with emerging technologies to understand the interaction between wildfires and the surrounding atmosphere at a high spatiotemporal resolution. The Fire Influence on Regional to Global Environments and Air Quality (FIREX‐AQ) experiment conducted in 2019 focused on wildfires using several measurement platforms aboard aircraft. In this study, we interpret wind measurements across a smoke plume transported vertically and advected along the top of the boundary layer (BL) from an airborne Doppler lidar (DL). Flight transects parallel and perpendicular to the orientation of the plume enabled the characterization of key features such as a fire‐induced spanwise vortex, large downdrafts that modified the smoke plume, and the evolving velocity structure at different distances downwind of the fire‐induced convergence zone (FICZ) at an unprecedented horizontal and vertical resolution of 10s of meters.

The 2020 wildfire season in the Western U.S. was historic in its intensity and impact on the land and atmosphere. This study aims to characterize satellite retrievals of carbon monoxide (CO), a tracer of combustion and signature of those fires, from two key satellite instruments: the Cross-track Infrared Sounder (CrIS) and the Tropospheric Monitoring Instrument (TROPOMI). We evaluate them during this event and assess their synergies. These two retrievals are matched temporally, as the host satellites are in tandem orbit and spatially by aggregating TROPOMI to the CrIS resolution. Both instruments show that the Western U.S. displayed significantly higher daily average CO columns compared to the Central and Eastern U.S. during the wildfires. TROPOMI showed up to a factor of two larger daily averages than CrIS during the most intense fire period, likely due to differences in the vertical sensitivity of the two instruments and representative of near-surface CO abundance near the fires. On the other hand, there was excellent agreement between the instruments in downwind free tropospheric plumes (scatter plot slopes of 0.96–0.99), consistent with their vertical sensitivities and indicative of mostly lofted smoke. Temporally, TROPOMI CO column peaks were delayed relative to the Fire Radiative Power (FRP), and CrIS peaks were delayed with respect to TROPOMI, particularly during the intense initial weeks of September, suggesting boundary layer buildup and ventilation. Satellite retrievals were evaluated using ground-based CO column estimates from the Network for the Detection of Atmospheric Composition Change (NDACC) and the Total Carbon Column Observing Network (TCCON), showing Normalized Mean Errors (NMEs) for CrIS and TROPOMI below 32% and 24%, respectively, when compared to all stations studied. While Normalized Mean Bias (NMB) was typically low (absolute value below 15%), there were larger negative biases at Pasadena, likely associated with sharp spatial gradients due to topography and proximity to a large city, which is consistent with previous research. In situ CO profiles from AirCore showed an elevated smoke plume for 15 September 2020, highlighted consistency between TROPOMI and CrIS CO columns for lofted plumes. This study demonstrates that both CrIS and TROPOMI provide complementary information on CO distribution. CrIS’s sensitivity in the middle and lower free troposphere, coupled with TROPOMI’s effectiveness at capturing total columns, offers a more comprehensive view of CO distribution during the wildfires than either retrieval alone. By combining data from both satellites as a ratio, more detailed information about the vertical location of the plumes can potentially be extracted. This approach can enhance air quality models, improve vertical estimation accuracy, and establish a new method for assessing lower tropospheric CO concentrations during significant wildfire events.