<table><caption>**Table 9**
Payload of the DRAGON Mobile Units for FIREX-AQ</caption><thead><tr><th>Parameter measured</th><th>Technique</th><th>Frequency (Hz)</th><th>Investigator</th><th>Institution</th><th>References</th></tr></thead><tbody><tr><td>Spectral Irradiance retrieval of AOD and VSD</td><td>CIMEL CE318T</td><td>1</td><td>Brent Holben</td><td>NASA GSFC</td><td>cimel.fr</td></tr><tr><td>Simultaneous AOD and VSD</td><td>PLASMA</td><td>0.1</td><td>Philippe Goloub</td><td>Laboratoire d'Optique Atmospherique/CIMEL</td><td>Karol et al. (2013)</td></tr><tr><td>Aerosol optical thickness</td><td>Calitoo</td><td>1</td><td></td><td></td><td>cimel.fr</td></tr><tr><td>aerosols and clouds vertical distribution</td><td>CIMEL Lidar (CE370 and CE376)</td><td>0.1</td><td></td><td></td><td>calitoo.fr</td></tr><tr><td>Aerosol optical thickness</td><td>Microtops II</td><td>1</td><td>David Giles</td><td>NASA</td><td>solarlight.com</td></tr></tbody></table>

The EFs of the measured pollutants were quantified as the wildfires evolved. An example is shown for CH₄ during the Springs Fire and the Kincade Fire measurements (Figure 12b) with the following observations: (a) The EF of CH₄ (EF<sub>CH4</sub>) decreased with increasing MCE for both the Springs Fire and the Kincade Fire emissions, which is consistent with previous studies (Guerette et al., 2018); (b) The EF values are comparable with those reported in the literature (Akagi et al., 2011); and (c) The different slopes of EF<sub>CH4</sub> versus MCE for the two fires reflected the influence of vegetation type on the EF.

## 2.5. Ground Sites

### 2.5.1. McCall, ID

When not driving, the AML measured and operated out of McCall, ID, where additional instruments were deployed to a ground site called the Activity Barn Ground Site. The location is also shown on the map in Figure 2 and Figure S9 in Supporting Information S1. The additional instrument list is shown in Table 11, and the two fires that brought smoke to the site were the Nethker Fire and the prescribed fire listed in Table 2. The Comprehensive Thermal desorption Aerosol Gas chromatograph (C-TAG) with high-resolution time-of-flight mass spectrometer

<figure><img src="image_14.png" /><figcaption>**Figure 11.** Cimel CE370 lidar range corrected signal (top left) measured during the 204 Cow Fire in Oregon on 26 August 2019 by DRAGON Mobile Unit 2. Coincident measurements from the PLASMA instrument AOD (bottom left) are interpolated to lidar wavelength at 532 nm and Angstrom Exponent from 440 to 870 nm. Aqua MODIS true color imagery (bottom right).</figcaption></figure>
<table><caption>**Table 9**
Payload of the DRAGON Mobile Units for FIREX-AQ</caption><thead><tr><th>Parameter measured</th><th>Technique</th><th>Frequency (Hz)</th><th>Investigator</th><th>Institution</th><th>References</th></tr></thead><tbody><tr><td>Spectral Irradiance retrieval of AOD and VSD</td><td>CIMEL CE318T</td><td>1</td><td>Brent Holben</td><td>NASA GSFC</td><td>cimel.fr</td></tr><tr><td>Simultaneous AOD and VSD</td><td>PLASMA</td><td>0.1</td><td>Philippe Goloub</td><td>Laboratoire d'Optique Atmospherique/CIMEL</td><td>Karol et al. (2013)</td></tr><tr><td>Aerosol optical thickness</td><td>Calitoo</td><td>1</td><td></td><td></td><td>cimel.fr</td></tr><tr><td>aerosols and clouds vertical distribution</td><td>CIMEL Lidar (CE370 and CE376)</td><td>0.1</td><td></td><td></td><td>calitoo.fr</td></tr><tr><td>Aerosol optical thickness</td><td>Microtops II</td><td>1</td><td>David Giles</td><td>NASA</td><td>solarlight.com</td></tr></tbody></table>

The EFs of the measured pollutants were quantified as the wildfires evolved. An example is shown for CH₄ during the Springs Fire and the Kincade Fire measurements (Figure 12b) with the following observations: (a) The EF of CH₄ (EF<sub>CH4</sub>) decreased with increasing MCE for both the Springs Fire and the Kincade Fire emissions, which is consistent with previous studies (Guerette et al., 2018); (b) The EF values are comparable with those reported in the literature (Akagi et al., 2011); and (c) The different slopes of EF<sub>CH4</sub> versus MCE for the two fires reflected the influence of vegetation type on the EF.

## 2.5. Ground Sites

### 2.5.1. McCall, ID

When not driving, the AML measured and operated out of McCall, ID, where additional instruments were deployed to a ground site called the Activity Barn Ground Site. The location is also shown on the map in Figure 2 and Figure S9 in Supporting Information S1. The additional instrument list is shown in Table 11, and the two fires that brought smoke to the site were the Nethker Fire and the prescribed fire listed in Table 2. The Comprehensive Thermal desorption Aerosol Gas chromatograph (C-TAG) with high-resolution time-of-flight mass spectrometer

<figure><img src="image_14.png" /><figcaption>**Figure 11.** Cimel CE370 lidar range corrected signal (top left) measured during the 204 Cow Fire in Oregon on 26 August 2019 by DRAGON Mobile Unit 2. Coincident measurements from the PLASMA instrument AOD (bottom left) are interpolated to lidar wavelength at 532 nm and Angstrom Exponent from 440 to 870 nm. Aqua MODIS true color imagery (bottom right).</figcaption></figure>
<table><caption>**Table 10**
Payload of the CARB Mobile Laboratory (MMP) for FIREX-AQ in 2019</caption><thead><tr><th>Species measured</th><th>Technique</th><th>Frequency (Hz)</th><th>Investigator</th><th>Institution</th><th>References</th></tr></thead><tbody><tr><td>NO, NO<sub>2</sub>, NO<sub>x</sub></td><td>2B Model 405 NO<sub>x</sub> Monitor</td><td>1</td><td>Shang Liu</td><td>CARB</td><td>Birks et al. (2018)</td></tr><tr><td>O<sub>3</sub></td><td>2B Model 205 O<sub>3</sub> Monitor</td><td>1</td><td></td><td></td><td>Andersen et al. (2010)</td></tr><tr><td>CH<sub>4</sub>, CO<sub>2</sub>, CO, H<sub>2</sub>O</td><td>Picarro G2401</td><td>0.2</td><td></td><td></td><td>Crosson (2008)</td></tr><tr><td>BC/7-λ aerosol absorption</td><td>Aethalometer AE33</td><td>1</td><td></td><td></td><td>Drinovec et al. (2015)</td></tr><tr><td>T, RH, WS, WD</td><td>Airmar Weather Station 200WX</td><td>1</td><td></td><td></td><td>Airmar</td></tr><tr><td>OC, EC, Speciated components in PM<sub>2.5</sub></td><td>Filter samples with GCxGC-ToF-MS and Sunset OC/EC analyzer</td><td>30-60 min</td><td>Allen Goldstein</td><td>UC Berkeley</td><td>Jen et al. (2019), Liang et al. (2022)</td></tr><tr><td>Speciated VOCs</td><td>Sorbent tube samples with GC × GC TOF-MS</td><td>30-60 min</td><td>Kelley Barsanti</td><td>UC Riverside</td><td>Pankow et al. (2012)</td></tr></tbody></table>

(HR-TOF-MS) was deployed to this site together with a gas chromatograph electron impact high-resolution time of flight mass spectrometer (GC-EI-ToF). Weekly passive sorbent tubes analyzed by thermal desorption-gas chromatography-mass spectrometry (TD-GC-MS) were also collected at the site.

In 2019, the smoke impact on the site was unusually small, and overall, only modest fire influence was detected at McCall during FIREX-AQ. With the exception of the day where the prescribed burn near McCall ID was observed (Figure 13), the ground site experienced smoke impact mostly from residential burning. Time series are shown for BC from the SP-AMS, furfural from the GC-EI-TOF, ACSM-measured organic aerosol mass loading, and an ACSM estimate of BB organic aerosol (BBOA) concentration based on the intensity of the signal measured at m/z 60, a tracer signal for the presence of levoglucosan in the aerosol (Zhang et al., 2015). These instruments all show a plume arriving around 5:45 a.m. UTC (11:45 p.m. local time), likely due to smoke from the prescribed burn settling in the valley. During this event, the GC-EI-TOF saw elevations of VOCs in the gas phase that are considered tracers of BB like furfural. Throughout the campaign, furfural did not demonstrate a strong diurnal pattern (from background of the local Nethker fire and residential burning). The strong spike in concentration on the evening of the prescribed burn indicates that furfural may be a good tracer of fresh, non-residential BB emissions.

### 2.5.2. Lewis-Clark State College, ID

Researchers from the Lewis-Clark State College (LCSC) Air Chemistry Research Group deployed instrumentation for ground sampling during FIREX-AQ in June–September 2019 with the main goal of determining human exposure to a variety of air toxics from wildfire smoke. Daily to weekly averaged passive (diffusive) sorbent tube air sampling with subsequent thermal desorption/gas chromatography/mass spectrometry (TD/GC/MS) analysis of VOCs (up to 27 hydrocarbons and halogenates) was accomplished at stationary locations including the campaign ground sites at Missoula, MT, McCall, ID, Boise, ID, Lewiston, ID, Moscow, ID, and Spokane, WA (Figure 2 and Figure S9 in Supporting Information S1) (Miller et al., 2022). Mobile sorbent tube grab sampling was also utilized at active fire events, including Williams Flats (eastern WA) and Nethker (central ID) fires, often coinciding with the AML mobile labs and the AERONET sites. This resulted in the analysis of 97 VOC species, including hydrocarbons, halogenates, oxygenates, and sulfur species. In addition to VOC measurements, ambient sulfur dioxide levels were monitored at the Lewiston, ID stationary site, using a Teledyne T102 sulfur fluorescence analyzer.

The measured VOC concentrations were applied to EPA risk assessment techniques to determine human exposure to a variety of air toxics before and during the 2019 wildfire season in the Northwest and nearby populated areas during FIREX-AQ. Although the stationary sites did not have significant wildfire smoke exposures in 2019, they served as background to the wildfire sites mobile sampled. For example, the health risk due to benzene was calculated in the Nethker fire and Williams Flats fire, using the proximal cities of McCall, ID and Spokane, WA as background. The exposure concentration was equated to the mean observed concentration multiplied by an exposure weighting (the product of the exposure time, frequency, and duration divided by the averaging time). The cancer risk was then calculated from the exposure concentration multiplied by the inhalation unit risk (compound dependent). In the Nethker fire samples, elevated benzene (a known carcinogen and air toxic) concentrations were seen over 100 times elevated compared to those at
<table><caption>**Table 10**
Payload of the CARB Mobile Laboratory (MMP) for FIREX-AQ in 2019</caption><thead><tr><th>Species measured</th><th>Technique</th><th>Frequency (Hz)</th><th>Investigator</th><th>Institution</th><th>References</th></tr></thead><tbody><tr><td>NO, NO<sub>2</sub>, NO<sub>x</sub></td><td>2B Model 405 NO<sub>x</sub> Monitor</td><td>1</td><td>Shang Liu</td><td>CARB</td><td>Birks et al. (2018)</td></tr><tr><td>O<sub>3</sub></td><td>2B Model 205 O<sub>3</sub> Monitor</td><td>1</td><td></td><td></td><td>Andersen et al. (2010)</td></tr><tr><td>CH<sub>4</sub>, CO<sub>2</sub>, CO, H<sub>2</sub>O</td><td>Picarro G2401</td><td>0.2</td><td></td><td></td><td>Crosson (2008)</td></tr><tr><td>BC/7-λ aerosol absorption</td><td>Aethalometer AE33</td><td>1</td><td></td><td></td><td>Drinovec et al. (2015)</td></tr><tr><td>T, RH, WS, WD</td><td>Airmar Weather Station 200WX</td><td>1</td><td></td><td></td><td>Airmar</td></tr><tr><td>OC, EC, Speciated components in PM<sub>2.5</sub></td><td>Filter samples with GCxGC-ToF-MS and Sunset OC/EC analyzer</td><td>30-60 min</td><td>Allen Goldstein</td><td>UC Berkeley</td><td>Jen et al. (2019), Liang et al. (2022)</td></tr><tr><td>Speciated VOCs</td><td>Sorbent tube samples with GC × GC TOF-MS</td><td>30-60 min</td><td>Kelley Barsanti</td><td>UC Riverside</td><td>Pankow et al. (2012)</td></tr></tbody></table>

(HR-TOF-MS) was deployed to this site together with a gas chromatograph electron impact high-resolution time of flight mass spectrometer (GC-EI-ToF). Weekly passive sorbent tubes analyzed by thermal desorption-gas chromatography-mass spectrometry (TD-GC-MS) were also collected at the site.

In 2019, the smoke impact on the site was unusually small, and overall, only modest fire influence was detected at McCall during FIREX-AQ. With the exception of the day where the prescribed burn near McCall ID was observed (Figure 13), the ground site experienced smoke impact mostly from residential burning. Time series are shown for BC from the SP-AMS, furfural from the GC-EI-TOF, ACSM-measured organic aerosol mass loading, and an ACSM estimate of BB organic aerosol (BBOA) concentration based on the intensity of the signal measured at m/z 60, a tracer signal for the presence of levoglucosan in the aerosol (Zhang et al., 2015). These instruments all show a plume arriving around 5:45 a.m. UTC (11:45 p.m. local time), likely due to smoke from the prescribed burn settling in the valley. During this event, the GC-EI-TOF saw elevations of VOCs in the gas phase that are considered tracers of BB like furfural. Throughout the campaign, furfural did not demonstrate a strong diurnal pattern (from background of the local Nethker fire and residential burning). The strong spike in concentration on the evening of the prescribed burn indicates that furfural may be a good tracer of fresh, non-residential BB emissions.

### 2.5.2. Lewis-Clark State College, ID

Researchers from the Lewis-Clark State College (LCSC) Air Chemistry Research Group deployed instrumentation for ground sampling during FIREX-AQ in June–September 2019 with the main goal of determining human exposure to a variety of air toxics from wildfire smoke. Daily to weekly averaged passive (diffusive) sorbent tube air sampling with subsequent thermal desorption/gas chromatography/mass spectrometry (TD/GC/MS) analysis of VOCs (up to 27 hydrocarbons and halogenates) was accomplished at stationary locations including the campaign ground sites at Missoula, MT, McCall, ID, Boise, ID, Lewiston, ID, Moscow, ID, and Spokane, WA (Figure 2 and Figure S9 in Supporting Information S1) (Miller et al., 2022). Mobile sorbent tube grab sampling was also utilized at active fire events, including Williams Flats (eastern WA) and Nethker (central ID) fires, often coinciding with the AML mobile labs and the AERONET sites. This resulted in the analysis of 97 VOC species, including hydrocarbons, halogenates, oxygenates, and sulfur species. In addition to VOC measurements, ambient sulfur dioxide levels were monitored at the Lewiston, ID stationary site, using a Teledyne T102 sulfur fluorescence analyzer.

The measured VOC concentrations were applied to EPA risk assessment techniques to determine human exposure to a variety of air toxics before and during the 2019 wildfire season in the Northwest and nearby populated areas during FIREX-AQ. Although the stationary sites did not have significant wildfire smoke exposures in 2019, they served as background to the wildfire sites mobile sampled. For example, the health risk due to benzene was calculated in the Nethker fire and Williams Flats fire, using the proximal cities of McCall, ID and Spokane, WA as background. The exposure concentration was equated to the mean observed concentration multiplied by an exposure weighting (the product of the exposure time, frequency, and duration divided by the averaging time). The cancer risk was then calculated from the exposure concentration multiplied by the inhalation unit risk (compound dependent). In the Nethker fire samples, elevated benzene (a known carcinogen and air toxic) concentrations were seen over 100 times elevated compared to those at
<figure><img src="image_15.png" /><figcaption>**Figure 12.** (a) The time series of CO, CO₂, MCE, and wind direction during the Springs Fire measurement on 30 August 2019. (b) The emission factor of CH₄ as a function of MCE.</figcaption></figure>

McCall, ID which lies 30 miles south. Considering a 30-day wildfire smoke exposure scenario such as this each year, for 26 years of one's lifetime (average residence in a locale), the risk of cancer goes up by a factor of 19 compared to background (Dickinson et al., 2022). This results in potentially serious human health implications for sub-chronic to chronic exposure to wildfire smoke, especially with more active and longer duration wildfire seasons in the Northwest. The use of sorbent tube air sampling was successful in determining the effects of many wildfire smoke VOCs on air quality of surrounding regions and the associated health risks to nearby communities.
<figure><img src="image_15.png" /><figcaption>**Figure 12.** (a) The time series of CO, CO₂, MCE, and wind direction during the Springs Fire measurement on 30 August 2019. (b) The emission factor of CH₄ as a function of MCE.</figcaption></figure>

McCall, ID which lies 30 miles south. Considering a 30-day wildfire smoke exposure scenario such as this each year, for 26 years of one's lifetime (average residence in a locale), the risk of cancer goes up by a factor of 19 compared to background (Dickinson et al., 2022). This results in potentially serious human health implications for sub-chronic to chronic exposure to wildfire smoke, especially with more active and longer duration wildfire seasons in the Northwest. The use of sorbent tube air sampling was successful in determining the effects of many wildfire smoke VOCs on air quality of surrounding regions and the associated health risks to nearby communities.
<table><caption>**Table 11**
Instrument List for the McCall Ground Site for FIREX-AQ in 2019</caption><thead><tr><th>Parameters measured</th><th>Technique</th><th>Freq.</th><th>Investigator</th><th>Institution</th><th>References</th></tr></thead><tbody><tr><td>Speciated VOCs</td><td>GC-EI-TOF</td><td>Every 20 min</td><td>Megan Claflin</td><td>Aerodyne Research, Inc.</td><td>Claflin et al. (2021)</td></tr><tr><td>Aerosol size and composition</td><td>Aerosol Chemical Speciation Monitor (ACSM)</td><td>1 min</td><td>Phil Croteau</td><td></td><td>Ng et al. (2011)</td></tr><tr><td>NO<sub>x</sub>, CO, PM</td><td>ARIsense</td><td>10s</td><td>Eben Cross</td><td></td><td>Cross et al. (2017)</td></tr><tr><td>OC, EC, levoglucosan, dehydroabietic acid</td><td>Filter samples with GCxGC-ToF-MS (cTAG) and Sunset OC/EC analyzer</td><td>30–60 min</td><td>Allen Goldstein</td><td>UC Berkeley</td><td>Jen et al. (2019), Liang et al. (2022)</td></tr><tr><td>Met data (T, RH, P)</td><td>Airmar Weather Station 200WX</td><td>1 s</td><td>Tara Yacovitch</td><td>Aerodyne Research, Inc.</td><td></td></tr><tr><td>Cloud and aerosol profiles</td><td>Micro pulse Lidara</td><td>1 min</td><td>E.J. Welton</td><td>NASA GSFC</td><td>Campbell et al. (2002), Welton et al. (2000)</td></tr></tbody></table>

### 2.5.3. Other Ground Sites

Other sites operating during FIREX-AQ and shown in Figure 3 include long-term observations at Missoula, MT and Mt. Bachelor, as well as a temporary site in Boise, ID. Due to the relatively mild fire conditions in 2019, there was no direct coordination between the FIREX-AQ aircraft observations and these sites. These ground sites documented thousands of hours of fresh to aged smoke impacts on urban areas with a focus on $O_3$, downwind gas-particle partitioning, evolving aerosol optical properties, etc. and therefore provide valuable context regarding the episodic impact of fires over a longer period than the campaign. Additional material on these sites can be found in the SI (Sections 4.1–4.3).

<figure><img src="image_16.png" /><figcaption>**Figure 13.** 2019 prescribed burn impact at the McCall, ID ground site. Time series of Organic aerosol (ACSM), BBOA Tracer (ACSM), Furfural (GC-EI-ToF), and Black Carbon aerosol (SP-AMS AML) all measured at the McCall Activity Barn ground site (Analysis and figures by Megan Claflin).</figcaption></figure>

<footnote>ªData are available at: https://mplnet.gsfc.nasa.gov/data?k=McCall&v=V3.</footnote>
<table><caption>**Table 11**
Instrument List for the McCall Ground Site for FIREX-AQ in 2019</caption><thead><tr><th>Parameters measured</th><th>Technique</th><th>Freq.</th><th>Investigator</th><th>Institution</th><th>References</th></tr></thead><tbody><tr><td>Speciated VOCs</td><td>GC-EI-TOF</td><td>Every 20 min</td><td>Megan Claflin</td><td>Aerodyne Research, Inc.</td><td>Claflin et al. (2021)</td></tr><tr><td>Aerosol size and composition</td><td>Aerosol Chemical Speciation Monitor (ACSM)</td><td>1 min</td><td>Phil Croteau</td><td></td><td>Ng et al. (2011)</td></tr><tr><td>NO<sub>x</sub>, CO, PM</td><td>ARIsense</td><td>10s</td><td>Eben Cross</td><td></td><td>Cross et al. (2017)</td></tr><tr><td>OC, EC, levoglucosan, dehydroabietic acid</td><td>Filter samples with GCxGC-ToF-MS (cTAG) and Sunset OC/EC analyzer</td><td>30–60 min</td><td>Allen Goldstein</td><td>UC Berkeley</td><td>Jen et al. (2019), Liang et al. (2022)</td></tr><tr><td>Met data (T, RH, P)</td><td>Airmar Weather Station 200WX</td><td>1 s</td><td>Tara Yacovitch</td><td>Aerodyne Research, Inc.</td><td></td></tr><tr><td>Cloud and aerosol profiles</td><td>Micro pulse Lidara</td><td>1 min</td><td>E.J. Welton</td><td>NASA GSFC</td><td>Campbell et al. (2002), Welton et al. (2000)</td></tr></tbody></table>

### 2.5.3. Other Ground Sites

Other sites operating during FIREX-AQ and shown in Figure 3 include long-term observations at Missoula, MT and Mt. Bachelor, as well as a temporary site in Boise, ID. Due to the relatively mild fire conditions in 2019, there was no direct coordination between the FIREX-AQ aircraft observations and these sites. These ground sites documented thousands of hours of fresh to aged smoke impacts on urban areas with a focus on $O_3$, downwind gas-particle partitioning, evolving aerosol optical properties, etc. and therefore provide valuable context regarding the episodic impact of fires over a longer period than the campaign. Additional material on these sites can be found in the SI (Sections 4.1–4.3).

<figure><img src="image_16.png" /><figcaption>**Figure 13.** 2019 prescribed burn impact at the McCall, ID ground site. Time series of Organic aerosol (ACSM), BBOA Tracer (ACSM), Furfural (GC-EI-ToF), and Black Carbon aerosol (SP-AMS AML) all measured at the McCall Activity Barn ground site (Analysis and figures by Megan Claflin).</figcaption></figure>

<footnote>ªData are available at: https://mplnet.gsfc.nasa.gov/data?k=McCall&v=V3.</footnote>
## 2.6. Forecasting and Nowcasting Support

### 2.6.1. Meteorology

For the western portion of the campaign, a team from the Naval Research Laboratory provided daily briefings on large scale circulation and regional forecasts in areas where fires were burning. Briefings contained meteorological forecast of the potential for new and sustained fires over the next days and the potential for pyro-Cb (pyro-cumulonimbus) development. Several members of this team were in Boise and then one member provided large-scale support during the Salina portion of the campaign. Briefings were customized for all the mobile platforms listed above except the CARB MMP, and these mobile briefings continued after the DC-8 was based in Salina. In Salina, the team from Florida State University provided the daily meteorological briefings. Included were experimental forecasts of prescribed fire activity based on forecast weather conditions.

### 2.6.2. Fire Weather and Fire Behavior

The Fuel2Fire team combined data from multiple sources to prepare daily reports to provide information to guide the FIREX-AQ campaign on the timing and selection of fires to target. For the western fires, the Fuel2Fire team assessed the previous day's fire activity, overnight fire activity, and the forecasted fire weather to determine which fires and regions showed the most potential to burn. Data analyzed to determine fire potential and behavior included: National Interagency Coordination Center Incident Management Situation Report, USFS Wildland Fire Assessment System (WFAS), Fire Weather and Fire Behavior maps, and overnight and 24-hr remotely-derived fire activity (see Section 3.4 for more detail). The Fuel2Fire team provided the fires and regions that were most likely to burn to Stan Kubota (Stan's Consulting Service), to the meteorological and smoke forecasting teams, and to the entire FIREX-AQ team. The forecasting teams needed the target fires to model well in advance of the flights. Kubota provided insights from the Incident Commanders (IC) that were leading fire actions on the ground and aircraft attacks. A representation from the National Interagency Fire Center (NIFC) attended the daily briefings and provided information to support the campaign. This information was essential to coordinate sampling by the FIREX-AQ aircraft and mobile units and included continually updated IC reports on fire behavior and planned suppression efforts.

In the southeast, the fires were largely smaller and often burned on private lands. Prescription fires are intentional ignitions, where the fires are planned to meet management or cultural objectives, when fire weather is minimal. Several prescription fires were planned and/or coordinated by the Fuel2Fire team, and others were identified through relationships with regional extensions, state agencies, and conservation organizations that were established in advance of the campaign. Cropland and pile fires burn rapidly, so near-real-time 5- to 15-min Geostationary Operational Environmental Satellites (GOES 16 and 17) were critical to identifying small fires and guiding the DC-8 aircraft. Additionally, the Fuel2Fire team provided background on the typical cropland fire timing by hosting a Fuel2Fire van that identified potential fire activity.

### 2.6.3. Smoke Forecasting

Forecasts of smoke, aerosol, and trace gas concentrations were provided by several groups using 16 different models as described in Table 12. These forecasts ranged from operational to highly experimental. The differences among models include: fire emissions, plume injection parameterization, assimilation of satellite AOD retrievals, complexity of chemistry, dynamic core and meteorology, time of initialization, and domain extent and resolution. Representatives of most modeling teams deployed to Boise and Salina and contributed to flight planning activities; teams in the field included GEOSFP, RAQMS and WISC WRF-Chem, NAAPS, WACCM and NCAR WRF-Chem, UIOWA WRF-Chem, NAQFC Experimental, HRRR-Smoke, GEFS-Aerosols, FLEX-PART, and UCLA WRF-Chem. CU-Boulder team was also in the field and contributed to flight planning. The model PIs are listed in the SI. High resolution (2.5 km) experimental forecasts for western North America (ARQI FireWork experimental) specially implemented for FIREX-AQ were provided by campaign partner Environment and Climate Change Canada. Other forecasting systems listed in Table 12 that run operationally were obtained through collaborations.

Model output from the majority of these systems was downloaded in near-real time from the providers and was plotted using the NASA GMAO Framework for Live User-Invoked Data (FLUID) system (https://fluid.nccs.nasa.gov/about/), which enabled the science team to have all forecasts plotted in the same domain and color-scale. Spatial maps at pressure levels and total column, as well as vertical cross-sections at fixed locations were included
## 2.6. Forecasting and Nowcasting Support

### 2.6.1. Meteorology

For the western portion of the campaign, a team from the Naval Research Laboratory provided daily briefings on large scale circulation and regional forecasts in areas where fires were burning. Briefings contained meteorological forecast of the potential for new and sustained fires over the next days and the potential for pyro-Cb (pyro-cumulonimbus) development. Several members of this team were in Boise and then one member provided large-scale support during the Salina portion of the campaign. Briefings were customized for all the mobile platforms listed above except the CARB MMP, and these mobile briefings continued after the DC-8 was based in Salina. In Salina, the team from Florida State University provided the daily meteorological briefings. Included were experimental forecasts of prescribed fire activity based on forecast weather conditions.

### 2.6.2. Fire Weather and Fire Behavior

The Fuel2Fire team combined data from multiple sources to prepare daily reports to provide information to guide the FIREX-AQ campaign on the timing and selection of fires to target. For the western fires, the Fuel2Fire team assessed the previous day's fire activity, overnight fire activity, and the forecasted fire weather to determine which fires and regions showed the most potential to burn. Data analyzed to determine fire potential and behavior included: National Interagency Coordination Center Incident Management Situation Report, USFS Wildland Fire Assessment System (WFAS), Fire Weather and Fire Behavior maps, and overnight and 24-hr remotely-derived fire activity (see Section 3.4 for more detail). The Fuel2Fire team provided the fires and regions that were most likely to burn to Stan Kubota (Stan's Consulting Service), to the meteorological and smoke forecasting teams, and to the entire FIREX-AQ team. The forecasting teams needed the target fires to model well in advance of the flights. Kubota provided insights from the Incident Commanders (IC) that were leading fire actions on the ground and aircraft attacks. A representation from the National Interagency Fire Center (NIFC) attended the daily briefings and provided information to support the campaign. This information was essential to coordinate sampling by the FIREX-AQ aircraft and mobile units and included continually updated IC reports on fire behavior and planned suppression efforts.

In the southeast, the fires were largely smaller and often burned on private lands. Prescription fires are intentional ignitions, where the fires are planned to meet management or cultural objectives, when fire weather is minimal. Several prescription fires were planned and/or coordinated by the Fuel2Fire team, and others were identified through relationships with regional extensions, state agencies, and conservation organizations that were established in advance of the campaign. Cropland and pile fires burn rapidly, so near-real-time 5- to 15-min Geostationary Operational Environmental Satellites (GOES 16 and 17) were critical to identifying small fires and guiding the DC-8 aircraft. Additionally, the Fuel2Fire team provided background on the typical cropland fire timing by hosting a Fuel2Fire van that identified potential fire activity.

### 2.6.3. Smoke Forecasting

Forecasts of smoke, aerosol, and trace gas concentrations were provided by several groups using 16 different models as described in Table 12. These forecasts ranged from operational to highly experimental. The differences among models include: fire emissions, plume injection parameterization, assimilation of satellite AOD retrievals, complexity of chemistry, dynamic core and meteorology, time of initialization, and domain extent and resolution. Representatives of most modeling teams deployed to Boise and Salina and contributed to flight planning activities; teams in the field included GEOSFP, RAQMS and WISC WRF-Chem, NAAPS, WACCM and NCAR WRF-Chem, UIOWA WRF-Chem, NAQFC Experimental, HRRR-Smoke, GEFS-Aerosols, FLEX-PART, and UCLA WRF-Chem. CU-Boulder team was also in the field and contributed to flight planning. The model PIs are listed in the SI. High resolution (2.5 km) experimental forecasts for western North America (ARQI FireWork experimental) specially implemented for FIREX-AQ were provided by campaign partner Environment and Climate Change Canada. Other forecasting systems listed in Table 12 that run operationally were obtained through collaborations.

Model output from the majority of these systems was downloaded in near-real time from the providers and was plotted using the NASA GMAO Framework for Live User-Invoked Data (FLUID) system (https://fluid.nccs.nasa.gov/about/), which enabled the science team to have all forecasts plotted in the same domain and color-scale. Spatial maps at pressure levels and total column, as well as vertical cross-sections at fixed locations were included
<table><caption>**Table 12**

Sixteen Different Forecast Models Predicting Smoke Used During FIREX-AQ in 2019</caption><thead><tr><th>Model</th><th>Forecast domain</th><th>Institution</th><th>Grid resolution</th><th>Initial time (UTC)</th><th>Chemical mechanism</th><th>Fire emission key parameter (inventory)</th><th>Plume injection</th><th>Assim. satellite data</th></tr></thead><tbody><tr><td>GEOSFP</td><td>Global</td><td>NASA GMAO</td><td>5/16 × 1/4 deg</td><td>00</td><td>Simplified</td><td>FRP (QFED)</td><td>No</td><td>Yes</td></tr><tr><td>CAMS</td><td>Global</td><td>ECMWF</td><td>0.4 deg</td><td>00</td><td>Full</td><td>FRP (GFASv1.2)</td><td>Yes</td><td>Yes</td></tr><tr><td>RAQMS</td><td>Global</td><td>University of Wisconsin</td><td>1.0 deg</td><td>12</td><td>Full</td><td>hotspots (RAQMS)</td><td>No</td><td>Yes</td></tr><tr><td>NAAPS</td><td>Global</td><td>NRL</td><td>30 km</td><td>00</td><td>Simplified</td><td>hotspots (FLAMBE)</td><td>No</td><td>Yes</td></tr><tr><td>WACCM</td><td>Global</td><td>NCAR</td><td>1.0 deg</td><td>00</td><td>Full</td><td>hotspots (FINN)</td><td>No</td><td>No</td></tr><tr><td>GEFS-Aerosols</td><td>Global</td><td>NOAA CSL</td><td>25 km</td><td>00</td><td>Simplified</td><td>blended (GBBEPx)</td><td>Yes</td><td>No</td></tr><tr><td>ARQI FireWork experimental</td><td>NW US and SW Canada</td><td>ECCC</td><td>2.5 km</td><td>12</td><td>Full</td><td>hotspots (CFFEPSv4.0)</td><td>Yes</td><td>No</td></tr><tr><td>HRRR-Smoke</td><td>CONUS</td><td>NOAA GSL</td><td>3 km</td><td>00</td><td>Smoke tracer</td><td>FRP</td><td>Yes</td><td>No</td></tr><tr><td>AIRPACT</td><td>NW US</td><td>Washington State University</td><td>4 km</td><td>08</td><td>Full</td><td>hotspots (SMARTFIRE/ Bluesky)</td><td>Yes</td><td>No</td></tr><tr><td>UCLA WRF-Chem</td><td>W US</td><td>UCLA</td><td>4 km</td><td>00</td><td>Simplified</td><td>FRP (QFED, with inversion)</td><td>Yes</td><td>Yes</td></tr><tr><td>UIWOA WRF-Chem</td><td>W US</td><td>University of Iowa</td><td>8 km</td><td>12</td><td>Full</td><td>FRP (QFED)</td><td>Yes</td><td>No</td></tr><tr><td>WISC WRF-Chem</td><td>CONUS</td><td>University of Wisconsin</td><td>8 km</td><td>12</td><td>Full</td><td>hotspots (PREP-CHEM)</td><td>Yes</td><td>Yes</td></tr><tr><td>FireWork</td><td>North America</td><td>ECCC</td><td>10 km</td><td>12</td><td>Full</td><td>hotspots (CFFEPSv2.1)</td><td>Yes</td><td>No</td></tr><tr><td>NAQFC</td><td>CONUS</td><td>NOAA NCEP</td><td>12 km</td><td>12</td><td>Full</td><td>hotspots (HMS/Bluesky)</td><td>Yes</td><td>No</td></tr><tr><td>NAQFC Experimental</td><td>CONUS</td><td>NOAA ARL</td><td>12 km</td><td>12</td><td>Full</td><td>hotspots (HMS/Bluesky)</td><td>Yes</td><td>Yes</td></tr><tr><td>NCAR WRF-Chem</td><td>CONUS</td><td>NCAR ACOM</td><td>12 km</td><td>00</td><td>Full</td><td>hotspots (FINN)</td><td>Yes</td><td>No</td></tr><tr><td>FLEXPART</td><td>CONUS</td><td>NCAR and Ludwig-Maximilians Uni.-Munich</td><td>25 km</td><td>00</td><td>Smoke tracers</td><td>Hotspots (GFAS)</td><td>Yes</td><td>No</td></tr></tbody></table>

*Note.* See additional details in Ye et al. (2021).

in these plots which enabled a better comparison of the guidance provided by the different models. Individual forecasting teams contributed more specialized products from their forecasting systems which included vertical cross-section along user defined locations, tracers tracking emissions from different sectors and from different BB emission inventories, and plume age estimates.

### 2.6.4. Nowcasting

During all flights by any of the FIREX-AQ aircraft, representatives of the meteorology, fire weather, and chemical forecasting teams provided updates on the behavior of all fires in the region of operations. These were based on near-real-time satellite imagery (primarily GOES 16 and 17) and reports from IC overseeing firefighting actions. During the Salina phase the nowcasting team was essential to direct the DC-8 to regions where small fires were burning, as these were nearly always very short-lived and could not be targeted more than 10–15 min in advance. Cropland, pile, and silviculture fire nowcasting relied heavily on thermal anomalies in the shortwave infrared window (3.9 µm band) from GOES-16 (e.g., active fire detections). Key information was saved during each flight to support data interpretation after the campaign.

## 3. Execution of the FIREX-AQ 2019 Intensive Campaign

### 3.1. Fire Season 2019

NIFC publishes the annual fire activity on their website (www.nifc.gov/fireInfo/fireInfo_statistics.html) and the data can be used to relate the 2019 fire season of the FIREX-AQ field experiments to previous years including
<table><caption>**Table 12**

Sixteen Different Forecast Models Predicting Smoke Used During FIREX-AQ in 2019</caption><thead><tr><th>Model</th><th>Forecast domain</th><th>Institution</th><th>Grid resolution</th><th>Initial time (UTC)</th><th>Chemical mechanism</th><th>Fire emission key parameter (inventory)</th><th>Plume injection</th><th>Assim. satellite data</th></tr></thead><tbody><tr><td>GEOSFP</td><td>Global</td><td>NASA GMAO</td><td>5/16 × 1/4 deg</td><td>00</td><td>Simplified</td><td>FRP (QFED)</td><td>No</td><td>Yes</td></tr><tr><td>CAMS</td><td>Global</td><td>ECMWF</td><td>0.4 deg</td><td>00</td><td>Full</td><td>FRP (GFASv1.2)</td><td>Yes</td><td>Yes</td></tr><tr><td>RAQMS</td><td>Global</td><td>University of Wisconsin</td><td>1.0 deg</td><td>12</td><td>Full</td><td>hotspots (RAQMS)</td><td>No</td><td>Yes</td></tr><tr><td>NAAPS</td><td>Global</td><td>NRL</td><td>30 km</td><td>00</td><td>Simplified</td><td>hotspots (FLAMBE)</td><td>No</td><td>Yes</td></tr><tr><td>WACCM</td><td>Global</td><td>NCAR</td><td>1.0 deg</td><td>00</td><td>Full</td><td>hotspots (FINN)</td><td>No</td><td>No</td></tr><tr><td>GEFS-Aerosols</td><td>Global</td><td>NOAA CSL</td><td>25 km</td><td>00</td><td>Simplified</td><td>blended (GBBEPx)</td><td>Yes</td><td>No</td></tr><tr><td>ARQI FireWork experimental</td><td>NW US and SW Canada</td><td>ECCC</td><td>2.5 km</td><td>12</td><td>Full</td><td>hotspots (CFFEPSv4.0)</td><td>Yes</td><td>No</td></tr><tr><td>HRRR-Smoke</td><td>CONUS</td><td>NOAA GSL</td><td>3 km</td><td>00</td><td>Smoke tracer</td><td>FRP</td><td>Yes</td><td>No</td></tr><tr><td>AIRPACT</td><td>NW US</td><td>Washington State University</td><td>4 km</td><td>08</td><td>Full</td><td>hotspots (SMARTFIRE/ Bluesky)</td><td>Yes</td><td>No</td></tr><tr><td>UCLA WRF-Chem</td><td>W US</td><td>UCLA</td><td>4 km</td><td>00</td><td>Simplified</td><td>FRP (QFED, with inversion)</td><td>Yes</td><td>Yes</td></tr><tr><td>UIWOA WRF-Chem</td><td>W US</td><td>University of Iowa</td><td>8 km</td><td>12</td><td>Full</td><td>FRP (QFED)</td><td>Yes</td><td>No</td></tr><tr><td>WISC WRF-Chem</td><td>CONUS</td><td>University of Wisconsin</td><td>8 km</td><td>12</td><td>Full</td><td>hotspots (PREP-CHEM)</td><td>Yes</td><td>Yes</td></tr><tr><td>FireWork</td><td>North America</td><td>ECCC</td><td>10 km</td><td>12</td><td>Full</td><td>hotspots (CFFEPSv2.1)</td><td>Yes</td><td>No</td></tr><tr><td>NAQFC</td><td>CONUS</td><td>NOAA NCEP</td><td>12 km</td><td>12</td><td>Full</td><td>hotspots (HMS/Bluesky)</td><td>Yes</td><td>No</td></tr><tr><td>NAQFC Experimental</td><td>CONUS</td><td>NOAA ARL</td><td>12 km</td><td>12</td><td>Full</td><td>hotspots (HMS/Bluesky)</td><td>Yes</td><td>Yes</td></tr><tr><td>NCAR WRF-Chem</td><td>CONUS</td><td>NCAR ACOM</td><td>12 km</td><td>00</td><td>Full</td><td>hotspots (FINN)</td><td>Yes</td><td>No</td></tr><tr><td>FLEXPART</td><td>CONUS</td><td>NCAR and Ludwig-Maximilians Uni.-Munich</td><td>25 km</td><td>00</td><td>Smoke tracers</td><td>Hotspots (GFAS)</td><td>Yes</td><td>No</td></tr></tbody></table>

Note. See additional details in Ye et al. (2021).

in these plots which enabled a better comparison of the guidance provided by the different models. Individual forecasting teams contributed more specialized products from their forecasting systems which included vertical cross-section along user defined locations, tracers tracking emissions from different sectors and from different BB emission inventories, and plume age estimates.

### 2.6.4. Nowcasting

During all flights by any of the FIREX-AQ aircraft, representatives of the meteorology, fire weather, and chemical forecasting teams provided updates on the behavior of all fires in the region of operations. These were based on near-real-time satellite imagery (primarily GOES 16 and 17) and reports from IC overseeing firefighting actions. During the Salina phase the nowcasting team was essential to direct the DC-8 to regions where small fires were burning, as these were nearly always very short-lived and could not be targeted more than 10–15 min in advance. Cropland, pile, and silviculture fire nowcasting relied heavily on thermal anomalies in the shortwave infrared window (3.9 µm band) from GOES-16 (e.g., active fire detections). Key information was saved during each flight to support data interpretation after the campaign.

## 3. Execution of the FIREX-AQ 2019 Intensive Campaign

### 3.1. Fire Season 2019

NIFC publishes the annual fire activity on their website (www.nifc.gov/fireInfo/fireInfo_statistics.html) and the data can be used to relate the 2019 fire season of the FIREX-AQ field experiments to previous years including
<figure><img src="image_17.png" /><figcaption>**Figure 14.** Cumulative area burned in the western US (California, Great Basin, Northern Rockies, and Northwest) for 2017–2021. (Data source: NIFC: https://www predictiveservices.nifc.gov/intelligence/archive/archive2021.html).</figcaption></figure>

2018 for the WE-CAN experiment. The wildfire seasonality for wildfires in the Northwest is shown in Figure 14 and typically peaks between August and September as was the case for 2017–2021. The western U.S., targeted from Boise, ID, had about a quarter of the 10-year average area burned (0.97 million acres). In 2019 there were only eight wildfires over 40,000 acres in the mainland U.S. and the DC-8 aircraft investigated two of those fires: The Sheep Fire (112,106 acres, a ~1 day grass fire) and the Williams Flats fire (44,446 acres, which burned over 1 week). The U.S. trend in 2019 does not change long-term patterns and likely resulted from anomalies such as heavy spring and mid-season precipitation that left forests and grasslands wetter than normal (Frank, 2020).

In contrast, the 2018 fire season that WE-CAN experienced was more normal and had 5.4 million acres burned in the western U.S. and 45 significant fires above 40,000 acres. The low 2019 fire season provided the FIREX-AQ aircraft opportunities to investigate individual isolated fire plumes well, while the C-130 during WE-CAN had the additional opportunity to study complex mixtures of multiple fire plumes.

## 3.2. Weather Forecast and Meteorological Conditions in Summer 2019

Meteorological conditions in 2019 were a significant driver for the low fire year. While individual fires can be influenced by a variety of small-scale features, larger-scale (synoptic) weather patterns are the primary driver of regional fire activity, fire danger, and fire potential. During the months prior to FIREX-AQ, the western U.S. was affected by a persistent and anomalously deep trough of low pressure (negative Arctic Oscillation pattern), resulting in relatively cool and moist weather. Measurable rainfall occurred unusually late in California, when the dry season is typically setting in. Fuel moisture therefore remained elevated as the fire season began, likely explaining the lack of widespread, large wildfires during FIREX-AQ. This synoptic weather pattern also resulted in a relatively wet and stormy spring and early summer in the central U.S.

The Boise deployment can be separated into three distinct phases based on synoptic meteorology (Figures 15a–15c): (a) persistent anticyclone, (b) ridge breakdown, and (c) northern trough. The first phase (22 July–05 August) featured a relatively strong, and persistent anticyclone (high pressure), centered over the Four Corners region (Figure 15a). This anticyclone is the primary feature controlling the transport of monsoonal moisture from the tropical Eastern Pacific, Gulf of California, and/or Gulf of Mexico to the western U.S. (e.g., Higgins et al., 1997). At the start of FIREX-AQ, moisture transport persisted in the mid-troposphere (4–7 km) over portions of Arizona, Nevada, and Utah, resulting in widespread cloud cover and thunderstorm development during the local afternoon and evening hours. Surges of moisture intermittently reached portions of the interior Pacific Northwest, facilitating thunderstorm development above a very dry, near-surface mixed layer. Precipitation generally evaporated before reaching the ground, allowing lightning strikes to ignite several fires. Airborne sampling was generally focused temporally between moisture surges to the north and west of the primary moisture pathway, including the Shady, Tucker, North Hills, and Williams Flats fires.

The ridge axis associated with the anticyclone amplified and extended over the Pacific Northwest during the second phase of the Boise deployment (06–08 August; Figure 15b). A low-pressure trough concurrently approached from the west. This “ridge breakdown” weather pattern is often associated with periods of extreme fire and smoke plume behavior (e.g., Westphal & Toon, 1991), including the development of wildfire-driven, smoke-infused thunderstorms, known as pyrocumulonimbus or pyroCb (Peterson, Fromm, et al., 2017; Peterson, Hyer, et al., 2017; Peterson et al., 2018, 2021). Approaching weather disturbances also coincide with persistence of intense burning well into the nighttime hours (Peterson et al., 2015; Saide et al., 2015). The Williams Flats fire in Washington State was significantly affected by this unique meteorology. Fire spread and smoke release gradually increased as the approaching low-pressure trough gradually enhanced surface wind speeds in the preceding hot and dry air mass. Smoke plume altitude also increased each day, coinciding with enhanced moisture transport and decreased stability in the mid-troposphere. Both the DC-8 and ER-2 obtained measurements on all 3 days of this short sampling phase, providing a comprehensive data set over a period of extreme
<figure><img src="image_17.png" /><figcaption>**Figure 14.** Cumulative area burned in the western US (California, Great Basin, Northern Rockies, and Northwest) for 2017–2021. (Data source: NIFC: https://www predictiveservices.nifc.gov/intelligence/archive/archive2021.html).</figcaption></figure>

2018 for the WE-CAN experiment. The wildfire seasonality for wildfires in the Northwest is shown in Figure 14 and typically peaks between August and September as was the case for 2017–2021. The western U.S., targeted from Boise, ID, had about a quarter of the 10-year average area burned (0.97 million acres). In 2019 there were only eight wildfires over 40,000 acres in the mainland U.S. and the DC-8 aircraft investigated two of those fires: The Sheep Fire (112,106 acres, a ~1 day grass fire) and the Williams Flats fire (44,446 acres, which burned over 1 week). The U.S. trend in 2019 does not change long-term patterns and likely resulted from anomalies such as heavy spring and mid-season precipitation that left forests and grasslands wetter than normal (Frank, 2020).

In contrast, the 2018 fire season that WE-CAN experienced was more normal and had 5.4 million acres burned in the western U.S. and 45 significant fires above 40,000 acres. The low 2019 fire season provided the FIREX-AQ aircraft opportunities to investigate individual isolated fire plumes well, while the C-130 during WE-CAN had the additional opportunity to study complex mixtures of multiple fire plumes.

## 3.2. Weather Forecast and Meteorological Conditions in Summer 2019

Meteorological conditions in 2019 were a significant driver for the low fire year. While individual fires can be influenced by a variety of small-scale features, larger-scale (synoptic) weather patterns are the primary driver of regional fire activity, fire danger, and fire potential. During the months prior to FIREX-AQ, the western U.S. was affected by a persistent and anomalously deep trough of low pressure (negative Arctic Oscillation pattern), resulting in relatively cool and moist weather. Measurable rainfall occurred unusually late in California, when the dry season is typically setting in. Fuel moisture therefore remained elevated as the fire season began, likely explaining the lack of widespread, large wildfires during FIREX-AQ. This synoptic weather pattern also resulted in a relatively wet and stormy spring and early summer in the central U.S.

The Boise deployment can be separated into three distinct phases based on synoptic meteorology (Figures 15a–15c): (a) persistent anticyclone, (b) ridge breakdown, and (c) northern trough. The first phase (22 July–05 August) featured a relatively strong, and persistent anticyclone (high pressure), centered over the Four Corners region (Figure 15a). This anticyclone is the primary feature controlling the transport of monsoonal moisture from the tropical Eastern Pacific, Gulf of California, and/or Gulf of Mexico to the western U.S. (e.g., Higgins et al., 1997). At the start of FIREX-AQ, moisture transport persisted in the mid-troposphere (4–7 km) over portions of Arizona, Nevada, and Utah, resulting in widespread cloud cover and thunderstorm development during the local afternoon and evening hours. Surges of moisture intermittently reached portions of the interior Pacific Northwest, facilitating thunderstorm development above a very dry, near-surface mixed layer. Precipitation generally evaporated before reaching the ground, allowing lightning strikes to ignite several fires. Airborne sampling was generally focused temporally between moisture surges to the north and west of the primary moisture pathway, including the Shady, Tucker, North Hills, and Williams Flats fires.

The ridge axis associated with the anticyclone amplified and extended over the Pacific Northwest during the second phase of the Boise deployment (06–08 August; Figure 15b). A low-pressure trough concurrently approached from the west. This “ridge breakdown” weather pattern is often associated with periods of extreme fire and smoke plume behavior (e.g., Westphal & Toon, 1991), including the development of wildfire-driven, smoke-infused thunderstorms, known as pyrocumulonimbus or pyroCb (Peterson, Fromm, et al., 2017; Peterson, Hyer, et al., 2017; Peterson et al., 2018, 2021). Approaching weather disturbances also coincide with persistence of intense burning well into the nighttime hours (Peterson et al., 2015; Saide et al., 2015). The Williams Flats fire in Washington State was significantly affected by this unique meteorology. Fire spread and smoke release gradually increased as the approaching low-pressure trough gradually enhanced surface wind speeds in the preceding hot and dry air mass. Smoke plume altitude also increased each day, coinciding with enhanced moisture transport and decreased stability in the mid-troposphere. Both the DC-8 and ER-2 obtained measurements on all 3 days of this short sampling phase, providing a comprehensive data set over a period of extreme
<figure><img src="image_18.png" /><figcaption>**Figure 15.** The large-scale weather patterns influencing fire patterns during FIREX-AQ in 2019.</figcaption></figure>

fire behavior. This culminated on 08 August, when the DC-8 obtained the first detailed measurements from inside the high-altitude outflow region of a pyroCb (Peterson et al., 2022) (https://earthobservatory.nasa.gov/images/145446/flying-through-a-fire-cloud).

The third phase of the Boise deployment (9–18 August; Figure 15c) occurred after the change in synoptic meteorology described above. The low-pressure trough moved ashore, producing relatively cool, moist, and cloudy conditions over the northern portion of the study region. Fire activity decreased and the Williams Flats fire was effectively extinguished by rainfall. To the south, the anticyclone shifted into Texas, deflecting monsoonal moisture to the east of the primary study region. This resulted in significant drying and clearing over much of the southwestern U.S. Fire activity rapidly increased, especially in Arizona and Utah. FIREX-AQ sampling therefore shifted to this region, with an emphasis on the Springs, Castle, Trumbull, Sheridan, Ikes, and Boulin fires.

Synoptic meteorology for the final portion of FIREX-AQ (19 August–05 September) is summarized in Figure 15d. This period featured an amplified synoptic pattern over North America, with anomalous ridging (high pressure) over the western U.S. and a deep trough (low pressure) in the east. The high-pressure ridge over the western US promoted a return to warm and dry conditions. Fires that survived the preceding period of cool and damp weather in the Northwest were reinvigorated. New fire ignitions also occurred, providing a variety of sampling
<figure><img src="image_18.png" /><figcaption>**Figure 15.** The large-scale weather patterns influencing fire patterns during FIREX-AQ in 2019.</figcaption></figure>

fire behavior. This culminated on 08 August, when the DC-8 obtained the first detailed measurements from inside the high-altitude outflow region of a pyroCb (Peterson et al., 2022) (https://earthobservatory.nasa.gov/images/145446/flying-through-a-fire-cloud).

The third phase of the Boise deployment (9–18 August; Figure 15c) occurred after the change in synoptic meteorology described above. The low-pressure trough moved ashore, producing relatively cool, moist, and cloudy conditions over the northern portion of the study region. Fire activity decreased and the Williams Flats fire was effectively extinguished by rainfall. To the south, the anticyclone shifted into Texas, deflecting monsoonal moisture to the east of the primary study region. This resulted in significant drying and clearing over much of the southwestern U.S. Fire activity rapidly increased, especially in Arizona and Utah. FIREX-AQ sampling therefore shifted to this region, with an emphasis on the Springs, Castle, Trumbull, Sheridan, Ikes, and Boulin fires.

Synoptic meteorology for the final portion of FIREX-AQ (19 August–05 September) is summarized in Figure 15d. This period featured an amplified synoptic pattern over North America, with anomalous ridging (high pressure) over the western U.S. and a deep trough (low pressure) in the east. The high-pressure ridge over the western US promoted a return to warm and dry conditions. Fires that survived the preceding period of cool and damp weather in the Northwest were reinvigorated. New fire ignitions also occurred, providing a variety of sampling
<figure><img src="image_19.png" /><figcaption>**Figure 16.** Left: Smoke trajectory ensemble forecast for the Ridgetop fire in Montana for multiple potential smoke injection altitudes in which low-level trajectories depict extensive vertical plume spreading to the north and northwest (blue, orange, and red lines) while high-level trajectories show horizontal plume confinement to the east (gray lines). (Above Ground Level (AGL), Mean Sea Level (MSL)). Right: Satellite image of smoke distribution at the time of aircraft sampling (gray line), showing diffuse low-level smoke transported to the north and high-elevation smoke outflow from pyroconvection transported to the east.</figcaption></figure>

opportunities for the FIREX-AQ assets that remained in that region after DC-8 moved to Salina. Relatively cool and moist conditions prevailed over much of the Midwest. Intermittent frontal boundaries traversed the central U.S. and portions of the Southeast, occasionally reaching the Gulf Coast. These fronts served as a forcing mechanism for convective activity (thunderstorms) and rainfall. Sampling of fires with the DC-8 out of Salina was generally limited to periods between frontal passages in the greater MRV area, Texas, Oklahoma, Nebraska, and Kansas.

### 3.3. Forecast Models Predicting Smoke

Smoke trajectory forecasts were used frequently for flight planning. Fire locations, size, containment, and suppression plans were often known with good accuracy from fire incident reports (e.g., InciWeb, https://inci-web.nwcg.gov). After several fires were identified by the Fuel2Fire team (Section 2.6.2) for potential sampling on future days, HYSPLIT forward trajectory ensembles were run with high-resolution meteorological forecasts (NAM CONUS Nest 3 km) for all of these fires by the Florida State University team. Trajectories were initialized at multiple altitudes from the surface to 4 km altitude to encompass a range of possible smoke injection heights. The resulting trajectory maps provided guidance on interpreting satellite images of active smoke plumes with a quick visualization of plume development or stagnation, plume transport direction at multiple potential altitudes, height of the smoke above terrain downwind, and wind shifts over the course of the day, all of which were used for determining the suitability of a smoke plume to be sampled by the aircraft. Figure 16 shows a sample smoke trajectory ensemble forecast for the Ridgetop fire, which had a lot of diversity on the trajectory direction for the different altitudes. The Ridgetop Fire was sampled on 2 August 2019 by the DC-8 and trajectories are compared to satellite imagery of the sampled plume.

Chemical and smoke forecasts served multiple purposes for flight planning. Global models and regional models covering the whole of North America were important to provide guidance on the influence of long-range transport during the campaign. There were two major smoke events that were tracked during the campaign. The first was smoke from Canadian fires located in northern Alberta that stayed out of reach from Boise but ended up producing surface PM₂.₅ enhancements in the eastern US between 24 and 26 July. Observed surface PM levels were raised to Moderate AQI (12–35 µg/m³) over extended regions of the central and eastern US, with most forecasts showing similar PM enhancements. HRRR-Smoke showed similar patterns of enhancements, and thus this confirms the source is smoke (as this model only tracks smoke). The second was smoke from Siberia during the first half of August that affected the sampling region occasionally but remained mostly lofted. This smoke layer was sampled by the DC-8 during the 3 August 2019 flight.
<figure><img src="image_19.png" /><figcaption>**Figure 16.** Left: Smoke trajectory ensemble forecast for the Ridgetop fire in Montana for multiple potential smoke injection altitudes in which low-level trajectories depict extensive vertical plume spreading to the north and northwest (blue, orange, and red lines) while high-level trajectories show horizontal plume confinement to the east (gray lines). (Above Ground Level (AGL), Mean Sea Level (MSL)). Right: Satellite image of smoke distribution at the time of aircraft sampling (gray line), showing diffuse low-level smoke transported to the north and high-elevation smoke outflow from pyroconvection transported to the east.</figcaption></figure>

opportunities for the FIREX-AQ assets that remained in that region after DC-8 moved to Salina. Relatively cool and moist conditions prevailed over much of the Midwest. Intermittent frontal boundaries traversed the central U.S. and portions of the Southeast, occasionally reaching the Gulf Coast. These fronts served as a forcing mechanism for convective activity (thunderstorms) and rainfall. Sampling of fires with the DC-8 out of Salina was generally limited to periods between frontal passages in the greater MRV area, Texas, Oklahoma, Nebraska, and Kansas.

### 3.3. Forecast Models Predicting Smoke

Smoke trajectory forecasts were used frequently for flight planning. Fire locations, size, containment, and suppression plans were often known with good accuracy from fire incident reports (e.g., InciWeb, https://inci-web.nwcg.gov). After several fires were identified by the Fuel2Fire team (Section 2.6.2) for potential sampling on future days, HYSPLIT forward trajectory ensembles were run with high-resolution meteorological forecasts (NAM CONUS Nest 3 km) for all of these fires by the Florida State University team. Trajectories were initialized at multiple altitudes from the surface to 4 km altitude to encompass a range of possible smoke injection heights. The resulting trajectory maps provided guidance on interpreting satellite images of active smoke plumes with a quick visualization of plume development or stagnation, plume transport direction at multiple potential altitudes, height of the smoke above terrain downwind, and wind shifts over the course of the day, all of which were used for determining the suitability of a smoke plume to be sampled by the aircraft. Figure 16 shows a sample smoke trajectory ensemble forecast for the Ridgetop fire, which had a lot of diversity on the trajectory direction for the different altitudes. The Ridgetop Fire was sampled on 2 August 2019 by the DC-8 and trajectories are compared to satellite imagery of the sampled plume.

Chemical and smoke forecasts served multiple purposes for flight planning. Global models and regional models covering the whole of North America were important to provide guidance on the influence of long-range transport during the campaign. There were two major smoke events that were tracked during the campaign. The first was smoke from Canadian fires located in northern Alberta that stayed out of reach from Boise but ended up producing surface PM₂.₅ enhancements in the eastern US between 24 and 26 July. Observed surface PM levels were raised to Moderate AQI (12–35 µg/m³) over extended regions of the central and eastern US, with most forecasts showing similar PM enhancements. HRRR-Smoke showed similar patterns of enhancements, and thus this confirms the source is smoke (as this model only tracks smoke). The second was smoke from Siberia during the first half of August that affected the sampling region occasionally but remained mostly lofted. This smoke layer was sampled by the DC-8 during the 3 August 2019 flight.
<figure><img src="image_20.png" /><figcaption>**Figure 17.** Forecasts of aerosol concentrations (OC and PM<sub>2.5</sub>) at 700 hPa valid at 6 p.m. on 7 August 2019 showing enhancements due to 1-day old Williams Flats fire smoke (red ovals) from multiple forecasting systems. Open circles represent locations of interest (blue: cities, red: AERONET sites). Source: FIREX-AQ Chemical forecasts on 6 August 2019.</figcaption></figure>

These forecasts were rarely used for flight planning for fresh smoke plumes, because multiple limitations were encountered. Model resolution from global models was generally too coarse to provide this type of guidance. Higher resolution models had other limitations. One of them was the latency on which emissions are updated, which created issues of fires not showing up when they had recently started or when clouds obscured the fire during a satellite overpass. At other times fires remained in the forecasts even when they had been mostly contained in reality. Assumptions related to smoke emissions and injection heights also created large diversity between models. Guidance on 1-day old smoke was generally more useful and helped interpret the satellite images as the effect of some of these limitations was reduced. An example of this guidance is shown in Figure 17 for the DC-8 flight on 7 August 2019 that sampled 1-day old smoke from the Williams Flats fire over Montana.

### 3.4. Active Fire Information for Flight Planning

As part of the flight planning and preparation, the Fuel2Fire team provided active fire information during the mission for decisions on which fires to target. The objective of this active-fire support was to identify active-fire regions and specific fires that were expected to burn. During the western campaign, these data were provided daily to model and meteorological forecasting teams for flight planning preparations (Section 2.6.2). The data and information presented included: basic fire information such as fire name, location, and size; fire weather and likelihood of continued fire growth, satellite data (fire detection, FRP, and smoke); ecosystem types and a fuels narrative; status of fuel lidar data collection; fire behavior; percent contained and estimated date of containment; actioned status (personnel, crews, engines, helicopters); daily 209 and NIFC reports; and Temporary Flight Restrictions.

Also available for flight planning were: (a) fire weather information, which provides the potential likelihood of continued fire growth, (b) National Weather Service 7-day fire potential, WFAS observed and forecasted Fire Danger ratings, severe fire weather, and fire behavior potential maps, (c) Satellite data of visible and active-fire detection data (MODIS and Visible Infrared Imaging Radiometer Suite (VIIRS)), (d) National Infrared Operations (NIROPS) data showing the active overnight firelines, (e) smoke extent using visible imagery and the NOAA Hazard Mapping System smoke product, (f) coincident satellite overpass potential (Aqua, Terra, Met-Op-B, Sentinel-5, CALIPSO), and (g) ecosystem types. Daily briefings are available on the FIREX-AQ data archive (https://www-air.larc.nasa.gov/missions/firex-aq/).
<figure><img src="image_20.png" /><figcaption>**Figure 17.** Forecasts of aerosol concentrations (OC and PM<sub>2.5</sub>) at 700 hPa valid at 6 p.m. on 7 August 2019 showing enhancements due to 1-day old Williams Flats fire smoke (red ovals) from multiple forecasting systems. Open circles represent locations of interest (blue: cities, red: AERONET sites). Source: FIREX-AQ Chemical forecasts on 6 August 2019.</figcaption></figure>

These forecasts were rarely used for flight planning for fresh smoke plumes, because multiple limitations were encountered. Model resolution from global models was generally too coarse to provide this type of guidance. Higher resolution models had other limitations. One of them was the latency on which emissions are updated, which created issues of fires not showing up when they had recently started or when clouds obscured the fire during a satellite overpass. At other times fires remained in the forecasts even when they had been mostly contained in reality. Assumptions related to smoke emissions and injection heights also created large diversity between models. Guidance on 1-day old smoke was generally more useful and helped interpret the satellite images as the effect of some of these limitations was reduced. An example of this guidance is shown in Figure 17 for the DC-8 flight on 7 August 2019 that sampled 1-day old smoke from the Williams Flats fire over Montana.

### 3.4. Active Fire Information for Flight Planning

As part of the flight planning and preparation, the Fuel2Fire team provided active fire information during the mission for decisions on which fires to target. The objective of this active-fire support was to identify active-fire regions and specific fires that were expected to burn. During the western campaign, these data were provided daily to model and meteorological forecasting teams for flight planning preparations (Section 2.6.2). The data and information presented included: basic fire information such as fire name, location, and size; fire weather and likelihood of continued fire growth, satellite data (fire detection, FRP, and smoke); ecosystem types and a fuels narrative; status of fuel lidar data collection; fire behavior; percent contained and estimated date of containment; actioned status (personnel, crews, engines, helicopters); daily 209 and NIFC reports; and Temporary Flight Restrictions.

Also available for flight planning were: (a) fire weather information, which provides the potential likelihood of continued fire growth, (b) National Weather Service 7-day fire potential, WFAS observed and forecasted Fire Danger ratings, severe fire weather, and fire behavior potential maps, (c) Satellite data of visible and active-fire detection data (MODIS and Visible Infrared Imaging Radiometer Suite (VIIRS)), (d) National Infrared Operations (NIROPS) data showing the active overnight firelines, (e) smoke extent using visible imagery and the NOAA Hazard Mapping System smoke product, (f) coincident satellite overpass potential (Aqua, Terra, Met-Op-B, Sentinel-5, CALIPSO), and (g) ecosystem types. Daily briefings are available on the FIREX-AQ data archive (https://www-air.larc.nasa.gov/missions/firex-aq/).
For the eastern campaign, fire support was reliant on information from the fire science exchanges (https://south-ernfireexchange.org/), states, parks, and conservation networks (see Section 3.6 for more detail). GOES data were crucial for locating small cropland and pile burning fires, due to the instruments capacity to provide 5-min data. Even though GOES is a moderate-to-low resolution instrument, it has a demonstrated ability to detect smaller fires due the instruments high-temporal resolution (Soja et al., 2009). The Fuel2Fire team planned several prescribed fires in advance of the campaign that were targeted by the DC-8 and the Fuel2Fire ground crew during the campaign.

### 3.5. Sampling of Western Wildfires

The sampling strategy of western wildfires is discussed using the DC-8 as an example and further explained by describing the coordinated sampling of the Williams Flats fire by multiple FIREX-AQ platforms.

#### 3.5.1. DC-8

Flight planning for the Western wildfires considered fire weather, forecast models, and firefighting/monitoring operations input. Additional factors considered in flight planning included: satellite overpass locations and timing, fire fuel type, fire size and intensity, firefighting and flight restrictions, smoke age and fresh smoke accessibility, resampling of smoke measured the previous day, coordination with other platforms, and potential smoke impact on urban areas.

Flights were designed to measure the emissions as fresh as possible, determine the chemical transformation in the smoke during transport, and understand the variability in emissions and chemistry over time. An example of a flight for western wildfires is shown in Figure 18 for the Williams Flats fire on 7 August 2019. The flight track color-coded by carbon monoxide (CO) is shown on the top in the figure. This flight used the typical sampling strategy for the fresh smoke, but also included aged smoke, which was sampled after take-off in Boise, ID.

On most DC-8 flights, the fresh fire plume was initially approached with a longitudinal (i.e., over the length of the plume determined by local winds) overflight to map the vertical structure of the plume in aerosol backscatter using the DIAL-HSRL, and to provide a level flight leg for the MASTER instrument to record the active fire and the burn scar. Visible and IR images of the Williams Flats fire from MASTER are shown in the insert in Figure 18. Ideally the plume was first overflown from the aged smoke towards the fire location as was done on the Williams Flats fire in Figure 18. With this approach, the DC-8 was able to immediately start sampling smoke as young/close as possible at the fire itself with minimal time lost to aircraft positioning requirements. During some flights, the transit from Boise came from a direction such that two remote sensing overflights had to be flown to measure the plume structure, first away from fire along the smoke plume followed by a return flight to the fire and freshest smoke.

The HSRL-DIAL backscatter data (also shown in Figure 18) were available in real-time on the aircraft and were used to determine the ideal in situ sampling altitude of the plume, which was generally the center of the plume. In situ plume sampling patterns started with an upwind leg in clear air followed by a series of plume crossings at increasing distance downwind of the fire. Near the fire, the legs were spaced more closely than further downwind as seen in Figure 8. On some flights, select legs were repeated at different altitudes to investigate the difference in chemistry in the center of the plume, where light is strongly attenuated, compared to the top edge of the plume with higher photolysis values. The in-situ backscatter data were also used to adjust the aircraft altitude during plume sampling further away from the fire to account for changes in plume height as can be seen in Figure 18.

This pattern was repeated two or three times on some fires to investigate changes in emissions and chemistry. Fire emissions can change rapidly due to changes in local meteorology or the fire moving to areas with different slopes that impact fire spread rates or have different fuels or fuel loadings. The MCE, also shown in Figure 18, for the two repeat patterns of the Williams Flats changed from 0.904 to 0.908 on 07 August 2019, while the MCE from the Williams Flats flight on 03 August 2019 changed more substantially from 0.919 to 0.908.

The aged smoke that was targeted on the first part of the flight on 7 August 2019 was predicted by most forecast models and was observed in GOES 16 and 17 images, shown in Figure 19. Using the HSRL the vertical distribution of the smoke was determined with a longitudinal overflight at 25,000 ft (pressure altitude) of the one-day old smoke plume starting from fresher towards more aged smoke. The same flight track in reverse direction was flown at 12,000 ft inside the smoke plume. Aged smoke was only clearly observed from the Williams Flats fire
For the eastern campaign, fire support was reliant on information from the fire science exchanges (https://south-
ernfireexchange.org/), states, parks, and conservation networks (see Section 3.6 for more detail). GOES data
were crucial for locating small cropland and pile burning fires, due to the instruments capacity to provide 5-min
data. Even though GOES is a moderate-to-low resolution instrument, it has a demonstrated ability to detect
smaller fires due the instruments high-temporal resolution (Soja et al., 2009). The Fuel2Fire team planned several
prescribed fires in advance of the campaign that were targeted by the DC-8 and the Fuel2Fire ground crew during
the campaign.

**3.5. Sampling of Western Wildfires**

The sampling strategy of western wildfires is discussed using the DC-8 as an example and further explained by describing the coordinated sampling of the Williams Flats fire by multiple FIREX-AQ platforms.

3.5.1. DC-8

Flight planning for the Western wildfires considered fire weather, forecast models, and firefighting/monitor-
ing operations input. Additional factors considered in flight planning included: satellite overpass locations and
timing, fire fuel type, fire size and intensity, firefighting and flight restrictions, smoke age and fresh smoke
accessibility, resampling of smoke measured the previous day, coordination with other platforms, and potential
smoke impact on urban areas.

Flights were designed to measure the emissions as fresh as possible, determine the chemical transformation in the smoke during transport, and understand the variability in emissions and chemistry over time. An example of a flight for western wildfires is shown in Figure 18 for the Williams Flats fire on 7 August 2019. The flight track color-coded by carbon monoxide (CO) is shown on the top in the figure. This flight used the typical sampling strategy for the fresh smoke, but also included aged smoke, which was sampled after take-off in Boise, ID.

On most DC-8 flights, the fresh fire plume was initially approached with a longitudinal (i.e., over the length of the plume determined by local winds) overflight to map the vertical structure of the plume in aerosol backscatter using the DIAL-HSRL, and to provide a level flight leg for the MASTER instrument to record the active fire and the burn scar. Visible and IR images of the Williams Flats fire from MASTER are shown in the insert in Figure 18. Ideally the plume was first overflown from the aged smoke towards the fire location as was done on the Williams Flats fire in Figure 18. With this approach, the DC-8 was able to immediately start sampling smoke as young/close as possible at the fire itself with minimal time lost to aircraft positioning requirements. During some flights, the transit from Boise came from a direction such that two remote sensing overflights had to be flown to measure the plume structure, first away from fire along the smoke plume followed by a return flight to the fire and freshest smoke.

The HSRL-DIAL backscatter data (also shown in Figure 18) were available in real-time on the aircraft and were
used to determine the ideal in situ sampling altitude of the plume, which was generally the center of the plume.
In situ plume sampling patterns started with an upwind leg in clear air followed by a series of plume crossings at
increasing distance downwind of the fire. Near the fire, the legs were spaced more closely than further downwind
as seen in Figure 8. On some flights, select legs were repeated at different altitudes to investigate the difference
in chemistry in the center of the plume, where light is strongly attenuated, compared to the top edge of the plume
with higher photolysis values. The in-situ backscatter data were also used to adjust the aircraft altitude during
plume sampling further away from the fire to account for changes in plume height as can be seen in Figure 18.

This pattern was repeated two or three times on some fires to investigate changes in emissions and chemistry.
Fire emissions can change rapidly due to changes in local meteorology or the fire moving to areas with different
slopes that impact fire spread rates or have different fuels or fuel loadings. The MCE, also shown in Figure 18,
for the two repeat patterns of the Williams Flats changed from 0.904 to 0.908 on 07 August 2019, while the MCE
from the Williams Flats flight on 03 August 2019 changed more substantially from 0.919 to 0.908.

The aged smoke that was targeted on the first part of the flight on 7 August 2019 was predicted by most forecast
models and was observed in GOES 16 and 17 images, shown in Figure 19. Using the HSRL the vertical distri-
bution of the smoke was determined with a longitudinal overflight at 25,000 ft (pressure altitude) of the one-day
old smoke plume starting from fresher towards more aged smoke. The same flight track in reverse direction was
flown at 12,000 ft inside the smoke plume. Aged smoke was only clearly observed from the Williams Flats fire
<figure><img src="image_21.png" /><figcaption>**Figure 18.** (a) Flight track of the NASA DC-8 FIREX-AQ flight on 07 August 2019 to the Williams Flats fire in Washington color coded with CO. (b) The carbon flux for several plume crossings computed from the carbon to aerosol backscatter measured by the HSRL LIDAR as calculated by Stockwell et al. (2022). (c) Regional overview of the flight plan including sampling aged smoke. The insert shows data from the MASTER instrument of the fire in the visible and IR. (d) The time series of CO and ozone together with the flight altitude.</figcaption></figure>
<figure><img src="image_21.png" /><figcaption>**Figure 18.** (a) Flight track of the NASA DC-8 FIREX-AQ flight on 07 August 2019 to the Williams Flats fire in Washington color coded with CO. (b) The carbon flux for several plume crossings computed from the carbon to aerosol backscatter measured by the HSRL LIDAR as calculated by Stockwell et al. (2022). (c) Regional overview of the flight plan including sampling aged smoke. The insert shows data from the MASTER instrument of the fire in the visible and IR. (d) The time series of CO and ozone together with the flight altitude.</figcaption></figure>
<figure><img src="image_22.png" /><figcaption>**Figure 19.** A typical forecast product available for the fresh and aged smoke from the Williams Flats fire on 07 August 2019 along with the nowcast satellite.</figcaption></figure>

on two flights and only one other flight sampled aged smoke, where the emission location and time was well understood (FIREX-AQ flight on 3 July 2019).

The results of the smoke sampling strategy are demonstrated by the time series of CO and ozone in Figure 18. The first part of the flight sampled the aged smoke and a clear enhancement of CO (and ozone) is observed that is significantly smaller than in the fresh smoke. The first sample pattern in the fresh smoke showed significant CO decreases with plume age due to dilution, but also potentially due to decreases in emissions during the extended plume sampling with the aircraft (Wiggins et al., 2020), while the second pattern had a more constant mixing ratios during transport. The ozone time series indicates very active photochemistry. In the closest transects to the fire ozone is titrated. Ozone enhancements occur downwind first at the edges of the plume, where more light is available. In the furthest downwind transects ozone is enhanced in the whole plume.

### 3.5.2. Coordinated Sampling of Western Wildfires

One of the primary objectives of FIREX-AQ was to combine near and far-field observations to understand emissions, chemical evolution and transport to evaluate downwind impacts of wildfires. To look at fires holistically and achieve this objective, input from multiple research disciplines, platforms, models, and satellite observations had to be coordinated. The FIREX-AQ campaign brought an unprecedented opportunity to coordinate differently focused research platforms.

The NASA DC-8 sampled wildfire plumes from near the point of emission to downwind impacts on a regional scale. These efforts provided data to understand the influence of fire emissions on atmospheric composition with continuity from initial emissions to evolved impacts several hours downwind. With its ability to explore an extremely wide range of smoke ages, the NASA DC-8 coordinated with the NASA ER-2 and the two NOAA Twin Otter aircraft with complementary payloads and goals. For example, the Chem Twin Otter sampled fire plumes at night to investigate the nighttime chemical evolution of fire plumes. The mobile laboratories were used to examine smoke in locations and at times inaccessible to any of the aircraft, such as smoke from smoldering parts of the fire that did not get lofted high enough to be measurable by aircraft, which can cause smoke filled valleys at night and early morning (Selimovic et al., 2019), leading to some of the highest exposures for local residents.

The NASA ER-2 flew 11 science flights out of Palmdale, CA with ~70 science flight hours between 01 and 21 August 2019: flight tracks are shown in Figure 2. The ER-2 team, working with the DC-8 leads and the FIREX-AQ forecasting team, coordinated NASA ER-2 and NASA DC-8 aircraft over the Williams Flats fire, reachable by both aircraft, on 6, 7, and 8 August 2019. NASA ER-2 instruments captured the smoke development of the Williams Flats fire over the 3 days, and provided a variety of remote sensing observations of meteorological conditions and smoke heights prior to pyro-cumulonimbus clouds (pyroCbs) development captured by the NASA
<figure><img src="image_22.png" /><figcaption>**Figure 19.** A typical forecast product available for the fresh and aged smoke from the Williams Flats fire on 07 August 2019 along with the nowcast satellite.</figcaption></figure>

on two flights and only one other flight sampled aged smoke, where the emission location and time was well understood (FIREX-AQ flight on 3 July 2019).

The results of the smoke sampling strategy are demonstrated by the time series of CO and ozone in Figure 18. The first part of the flight sampled the aged smoke and a clear enhancement of CO (and ozone) is observed that is significantly smaller than in the fresh smoke. The first sample pattern in the fresh smoke showed significant CO decreases with plume age due to dilution, but also potentially due to decreases in emissions during the extended plume sampling with the aircraft (Wiggins et al., 2020), while the second pattern had a more constant mixing ratios during transport. The ozone time series indicates very active photochemistry. In the closest transects to the fire ozone is titrated. Ozone enhancements occur downwind first at the edges of the plume, where more light is available. In the furthest downwind transects ozone is enhanced in the whole plume.

### 3.5.2. Coordinated Sampling of Western Wildfires

One of the primary objectives of FIREX-AQ was to combine near and far-field observations to understand emissions, chemical evolution and transport to evaluate downwind impacts of wildfires. To look at fires holistically and achieve this objective, input from multiple research disciplines, platforms, models, and satellite observations had to be coordinated. The FIREX-AQ campaign brought an unprecedented opportunity to coordinate differently focused research platforms.

The NASA DC-8 sampled wildfire plumes from near the point of emission to downwind impacts on a regional scale. These efforts provided data to understand the influence of fire emissions on atmospheric composition with continuity from initial emissions to evolved impacts several hours downwind. With its ability to explore an extremely wide range of smoke ages, the NASA DC-8 coordinated with the NASA ER-2 and the two NOAA Twin Otter aircraft with complementary payloads and goals. For example, the Chem Twin Otter sampled fire plumes at night to investigate the nighttime chemical evolution of fire plumes. The mobile laboratories were used to examine smoke in locations and at times inaccessible to any of the aircraft, such as smoke from smoldering parts of the fire that did not get lofted high enough to be measurable by aircraft, which can cause smoke filled valleys at night and early morning (Selimovic et al., 2019), leading to some of the highest exposures for local residents.

The NASA ER-2 flew 11 science flights out of Palmdale, CA with ~70 science flight hours between 01 and 21 August 2019: flight tracks are shown in Figure 2. The ER-2 team, working with the DC-8 leads and the FIREX-AQ forecasting team, coordinated NASA ER-2 and NASA DC-8 aircraft over the Williams Flats fire, reachable by both aircraft, on 6, 7, and 8 August 2019. NASA ER-2 instruments captured the smoke development of the Williams Flats fire over the 3 days, and provided a variety of remote sensing observations of meteorological conditions and smoke heights prior to pyro-cumulonimbus clouds (pyroCbs) development captured by the NASA
<figure><img src="image_23.png" /><figcaption>**Figure 20.** Coordination of multiple platforms at the Williams Flats fire on 6 August 2019 during FIREX-AQ.</figcaption></figure>

DC-8 on 8 August 2019. NASA ER-2 and NASA DC-8 aircraft were coordinated under satellites on 6 August 2019, addressing the main NASA ER-2 goal to bridge in-situ and satellite observations. As fire activity moved to the Southwest, the NASA ER-2 shifted flight patterns to Arizona and Utah, coordinating again with NASA DC-8 aircraft over the Sheridan fire on 15 August 2019. The ER-2 team was also closely working with NOAA aircraft leads, and coordinated with the NOAA Chem Twin Otter aircraft over Little Bear and Ikes fires on August 20 and 21, 2019. NASA ER-2 flights were coordinated with satellites in a variety of smoke conditions to evaluate how well satellite retrievals can handle small-scale sub-pixel variabilities. The satellite coordination included NASA ER-2 flight legs on and parallel to satellite tracks to evaluate viewing angle uncertainties in satellite retrievals.

Figure 20 shows results from the most detailed coordination by several platforms during FIREX-AQ: DC-8, ER-2, LARGE mobile lab, mobile AERONET, and several satellite overpasses (NOAA20, GOES 16 and 17, VIIRS, MISR, CALIPSO, and METOP-B) on the Williams Flats fire on 06 August 2019. The top left shows that smoke was expected from the hourly AQI forecasting on that day and the routine air quality monitoring sites detected smoke around Spokane, WA. The fire was clearly detected by satellites as is demonstrated by the VIIRS fire detections shown in the left bottom of Figure 20. The flight and drive tracks of the DC-8, ER-2, MACH-2, mobile AERONET are shown in the center plot. The NASA DC-8 flew the typical pattern with a downward looking remote sensing overflight followed by several plume crossings. Images from the Williams Flats fire by the MASTER instrument on the DC-8 in the visible and IR are also shown in Figure 20. At the same time, the NASA ER-2 flew high above along the length of the plume six times and the DRAGONs and MACH-2 drove under the plume and aircraft for upward looking remote sensing and in-situ measurements. In addition to all the measurements during the fire, the fuels on the ground were sampled after the fire season by the Fuel2Fire team.

The comparison between the MACH-2 and the DC-8 during the 6 August 2019 sampling of the Williams Flats fire is shown in Figure 21. While the NASA DC-8 and ER-2 were completing longitudinal runs along the length of the smoke plume and cross-sectional transects across the width of the smoke plume, the MACH-2 and mobile AERONET platform drove from the southwest corner to the northeast corner of the dashed box as denoted by the blue and magenta traces in Figure 20. Similar aerosol optical measurements on the DC-8 and the MACH-2 allow for direct comparisons between remotely-sensed, vertically-resolved smoke backscatter and in situ extinction coefficients. For example, airborne HSRL curtains of calibrated aerosol backscatter coefficient that show the smoke plume being injected up to an altitude of roughly 2–3 km Above Ground Level (AGL) and then being subsequently mixed down to the surface (Figure 21a). The surface elevation in Figure 21a is given by the white trace. Meanwhile, the uncalibrated, attenuated backscatter curtain from MACH-2 zenith-point ceilometer in Figure 21c shows similar structure in the boundary layer height also extending up to about 2 km AGL with
<figure><img src="image_23.png" /><figcaption>**Figure 20.** Coordination of multiple platforms at the Williams Flats fire on 6 August 2019 during FIREX-AQ.</figcaption></figure>

DC-8 on 8 August 2019. NASA ER-2 and NASA DC-8 aircraft were coordinated under satellites on 6 August 2019, addressing the main NASA ER-2 goal to bridge in-situ and satellite observations. As fire activity moved to the Southwest, the NASA ER-2 shifted flight patterns to Arizona and Utah, coordinating again with NASA DC-8 aircraft over the Sheridan fire on 15 August 2019. The ER-2 team was also closely working with NOAA aircraft leads, and coordinated with the NOAA Chem Twin Otter aircraft over Little Bear and Ikes fires on August 20 and 21, 2019. NASA ER-2 flights were coordinated with satellites in a variety of smoke conditions to evaluate how well satellite retrievals can handle small-scale sub-pixel variabilities. The satellite coordination included NASA ER-2 flight legs on and parallel to satellite tracks to evaluate viewing angle uncertainties in satellite retrievals.

Figure 20 shows results from the most detailed coordination by several platforms during FIREX-AQ: DC-8, ER-2, LARGE mobile lab, mobile AERONET, and several satellite overpasses (NOAA20, GOES 16 and 17, VIIRS, MISR, CALIPSO, and METOP-B) on the Williams Flats fire on 06 August 2019. The top left shows that smoke was expected from the hourly AQI forecasting on that day and the routine air quality monitoring sites detected smoke around Spokane, WA. The fire was clearly detected by satellites as is demonstrated by the VIIRS fire detections shown in the left bottom of Figure 20. The flight and drive tracks of the DC-8, ER-2, MACH-2, mobile AERONET are shown in the center plot. The NASA DC-8 flew the typical pattern with a downward looking remote sensing overflight followed by several plume crossings. Images from the Williams Flats fire by the MASTER instrument on the DC-8 in the visible and IR are also shown in Figure 20. At the same time, the NASA ER-2 flew high above along the length of the plume six times and the DRAGONs and MACH-2 drove under the plume and aircraft for upward looking remote sensing and in-situ measurements. In addition to all the measurements during the fire, the fuels on the ground were sampled after the fire season by the Fuel2Fire team.

The comparison between the MACH-2 and the DC-8 during the 6 August 2019 sampling of the Williams Flats fire is shown in Figure 21. While the NASA DC-8 and ER-2 were completing longitudinal runs along the length of the smoke plume and cross-sectional transects across the width of the smoke plume, the MACH-2 and mobile AERONET platform drove from the southwest corner to the northeast corner of the dashed box as denoted by the blue and magenta traces in Figure 20. Similar aerosol optical measurements on the DC-8 and the MACH-2 allow for direct comparisons between remotely-sensed, vertically-resolved smoke backscatter and in situ extinction coefficients. For example, airborne HSRL curtains of calibrated aerosol backscatter coefficient that show the smoke plume being injected up to an altitude of roughly 2–3 km Above Ground Level (AGL) and then being subsequently mixed down to the surface (Figure 21a). The surface elevation in Figure 21a is given by the white trace. Meanwhile, the uncalibrated, attenuated backscatter curtain from MACH-2 zenith-point ceilometer in Figure 21c shows similar structure in the boundary layer height also extending up to about 2 km AGL with
<figure><img src="image_24.png" /><figcaption>**Figure 21.** Comparison of the NASA MACH-2 with the DC-8 during the Williams Flats fire measurements on 6 August 2019. (a) DC-8 HSRL longitudinal (top) and cross-section (bottom) flight curtains. (b) MACH-2 ceilometer curtains for the day (top) and collocation times (bottom). (c) Comparison of in situ aerosol SSA and extinction measurements with DC-8 and MACH-2 within the white dashed box in Figure 20.</figcaption></figure>

periods of elevated plumes and periods where the smoke mixed down to the surface. It should be noted here that the data in Figure 21 from the DC-8 is covering a large distance over a short time and the data from MACH-2 is covering a short distance over a long time. A direct comparison of select aerosol intensive and extensive aerosol optical properties is shown in Figure 21b for the complementary DC-8 and MACH-2 measurements. While the relatively slow-moving MACH-2 and fast-moving DC-8 are not spatially coordinated in time, the elevated background measurements are consistent with each other, as well as the aerosol single scattering albedo of approximately 0.9–0.95.

These early analyses of the combined airborne and ground-based mobile laboratory aerosol measurements demonstrate the power of lidar-based remote sensing for placing the measurement results in proper context. While the detailed information obtained from the HSRL is invaluable, it is worth noting that the layer height
<figure><img src="image_24.png" /><figcaption>**Figure 21.** Comparison of the NASA MACH-2 with the DC-8 during the Williams Flats fire measurements on 6 August 2019. (a) DC-8 HSRL longitudinal (top) and cross-section (bottom) flight curtains. (b) MACH-2 ceilometer curtains for the day (top) and collocation times (bottom). (c) Comparison of in situ aerosol SSA and extinction measurements with DC-8 and MACH-2 within the white dashed box in Figure 20.</figcaption></figure>

periods of elevated plumes and periods where the smoke mixed down to the surface. It should be noted here that the data in Figure 21 from the DC-8 is covering a large distance over a short time and the data from MACH-2 is covering a short distance over a long time. A direct comparison of select aerosol intensive and extensive aerosol optical properties is shown in Figure 21b for the complementary DC-8 and MACH-2 measurements. While the relatively slow-moving MACH-2 and fast-moving DC-8 are not spatially coordinated in time, the elevated background measurements are consistent with each other, as well as the aerosol single scattering albedo of approximately 0.9–0.95.

These early analyses of the combined airborne and ground-based mobile laboratory aerosol measurements demonstrate the power of lidar-based remote sensing for placing the measurement results in proper context. While the detailed information obtained from the HSRL is invaluable, it is worth noting that the layer height
information and un-calibrated attenuated aerosol backscatter structure from the ceilometer clearly show when the plume mixed down to the MACH-2 sampling level and when it did not.

### 3.6. Flight Planning and Sampling Strategies for Smaller Eastern Fires

Liu et al. (2016) analyzed 15 crop fires in the MRV sampled from the NASA DC-8 during SEAC⁴RS in 2013 and emissions from those fires showed some statistically significant differences from previously published emissions estimates, indicating that crop residue fires are a diverse and globally understudied source. This motivated the second main objective for FIREX-AQ, which was to sample a large number of small mostly prescribed fires to build statistics on EFs and fuels, plume rise, satellite detectability, and integrated impacts for these types of sources.

Small fire activity occurred within the reach of the DC-8 every day during the Salina, KS deployment 19 August–3 September 2019. Flight planning for the small fire sampling drew on multiple sources of information including data on burning permits issued by state and local organizations, fire spotting by members of the Fuel2Fire team collecting fuels in the region, and weather forecasting specialized for prescribed fire conditions. With the important exceptions of several large prescribed burns that were coordinated in advance between Fuel2Fire and the responsible agencies, all of these planning tools could only indicate general regions where small fires were expected to occur. Near-real-time fire detections from GOES-16 provided essential guidance enabling the DC-8 to effectively sample as many fires as possible on each flight. The nowcasting team on the ground would inform the aircraft of current fire detections, which often happened in regional clusters, and the aircraft would re-direct to that location quickly, before the short-lived small agricultural fires had run their course.

The flight tracks of the NASA DC-8 for the southeastern small fires are shown in Figure 22 and the fires that were investigated are listed in Table 13 with more detail. The DC-8 conducted seven flights out of Salina, KS and investigated 87 different small fires in 12 states. The fires investigated included prescribed fires in prairies and forests, agricultural burning of corn, soybeans, and rice fields, slash and pile burning. The area around Kansas, Nebraska and Northern Texas has mostly rangeland and cropland, the MRV mostly cropland, and Louisiana, Georgia, Northern Florida mostly silviculture, cropland and wildland fires. In coordination with the respective land management agency, several larger prescribed fires were investigated in the southeast. The Blackwater River prescribed fire in the Florida Panhandle was in an oak and mature-longleaf pine forest with shrub-grass and litter understory. Several grass prairie prescribed fires were sampled during the growing season in Nebraska and Kansas in the Tallgrass Prairie National Reserve.

A photo of a typical fire is shown in Figure 22 taken just before the aircraft penetrated the plume at about 1,000 ft altitude above ground right over the fire. Most fire plumes were crossed at least two times in two different directions. For some fires the plume was crossed at different altitudes as well. The time series of CO in Figure 22 for a section of the flight on 31 August 2019 shows that very high mixing ratios of trace gases and aerosol (up to 20 ppm of CO) were observed in the plumes, which were only a few seconds wide. The high frequency measurements on the aircraft as shown in Table 3 were essential for the precise determination of emission ratios and factors in such short plume encounters.

The MCE from the small fires, shown in Figure 23, varied greatly between the various southeastern burns due to the different fire stages and also fuel type, but in general corn fields burn at a higher MCE than land clearing, grassland fires or pile burning. The timeseries also shows the high variability in MCE and resulting CO, CO₂, and NO emissions. Shown are 13 s of data that included two separate plume crossings from two adjacent corn fields, which had very different CO/CO₂ and CO/NO ratios.

## 4. Data From FIREX-AQ 2019

### 4.1. Plume Identification and Smoke Age Calculation

A detailed description of each smoke transect is available on the FIREX-AQ data archive. CO and BC enhancements above the background were used to identify and flag all of the sampled smoke plume transects. The start and end times of the plume, the fire, fuel, ignition, and transect type were determined together with the fire name or source. For each of these transects, the MCE was also calculated and provided.
information and un-calibrated attenuated aerosol backscatter structure from the ceilometer clearly show when the plume mixed down to the MACH-2 sampling level and when it did not.

### 3.6. Flight Planning and Sampling Strategies for Smaller Eastern Fires

Liu et al. (2016) analyzed 15 crop fires in the MRV sampled from the NASA DC-8 during SEAC⁴RS in 2013 and emissions from those fires showed some statistically significant differences from previously published emissions estimates, indicating that crop residue fires are a diverse and globally understudied source. This motivated the second main objective for FIREX-AQ, which was to sample a large number of small mostly prescribed fires to build statistics on EFs and fuels, plume rise, satellite detectability, and integrated impacts for these types of sources.

Small fire activity occurred within the reach of the DC-8 every day during the Salina, KS deployment 19 August–3 September 2019. Flight planning for the small fire sampling drew on multiple sources of information including data on burning permits issued by state and local organizations, fire spotting by members of the Fuel2Fire team collecting fuels in the region, and weather forecasting specialized for prescribed fire conditions. With the important exceptions of several large prescribed burns that were coordinated in advance between Fuel2Fire and the responsible agencies, all of these planning tools could only indicate general regions where small fires were expected to occur. Near-real-time fire detections from GOES-16 provided essential guidance enabling the DC-8 to effectively sample as many fires as possible on each flight. The nowcasting team on the ground would inform the aircraft of current fire detections, which often happened in regional clusters, and the aircraft would re-direct to that location quickly, before the short-lived small agricultural fires had run their course.

The flight tracks of the NASA DC-8 for the southeastern small fires are shown in Figure 22 and the fires that were investigated are listed in Table 13 with more detail. The DC-8 conducted seven flights out of Salina, KS and investigated 87 different small fires in 12 states. The fires investigated included prescribed fires in prairies and forests, agricultural burning of corn, soybeans, and rice fields, slash and pile burning. The area around Kansas, Nebraska and Northern Texas has mostly rangeland and cropland, the MRV mostly cropland, and Louisiana, Georgia, Northern Florida mostly silviculture, cropland and wildland fires. In coordination with the respective land management agency, several larger prescribed fires were investigated in the southeast. The Blackwater River prescribed fire in the Florida Panhandle was in an oak and mature-longleaf pine forest with shrub-grass and litter understory. Several grass prairie prescribed fires were sampled during the growing season in Nebraska and Kansas in the Tallgrass Prairie National Reserve.

A photo of a typical fire is shown in Figure 22 taken just before the aircraft penetrated the plume at about 1,000 ft altitude above ground right over the fire. Most fire plumes were crossed at least two times in two different directions. For some fires the plume was crossed at different altitudes as well. The time series of CO in Figure 22 for a section of the flight on 31 August 2019 shows that very high mixing ratios of trace gases and aerosol (up to 20 ppm of CO) were observed in the plumes, which were only a few seconds wide. The high frequency measurements on the aircraft as shown in Table 3 were essential for the precise determination of emission ratios and factors in such short plume encounters.

The MCE from the small fires, shown in Figure 23, varied greatly between the various southeastern burns due to the different fire stages and also fuel type, but in general corn fields burn at a higher MCE than land clearing, grassland fires or pile burning. The timeseries also shows the high variability in MCE and resulting CO, CO₂, and NO emissions. Shown are 13 s of data that included two separate plume crossings from two adjacent corn fields, which had very different CO/CO₂ and CO/NO ratios.

## 4. Data From FIREX-AQ 2019

### 4.1. Plume Identification and Smoke Age Calculation

A detailed description of each smoke transect is available on the FIREX-AQ data archive. CO and BC enhancements above the background were used to identify and flag all of the sampled smoke plume transects. The start and end times of the plume, the fire, fuel, ignition, and transect type were determined together with the fire name or source. For each of these transects, the MCE was also calculated and provided.
<figure><img src="image_25.png" /><figcaption>**Figure 22.** (Top) Flight tracks of the NASA DC-8 color and size coded by CO for the FIREX-AQ eastern fire portion flown out of Salina, KS. (middle) The time series of CO for a section of the flight from 31 August 2019 that investigated 15 small agricultural fires from mostly corn fields. (bottom) A photo of a typical fire.</figcaption></figure>
<figure><img src="image_25.png" /><figcaption>**Figure 22.** (Top) Flight tracks of the NASA DC-8 color and size coded by CO for the FIREX-AQ eastern fire portion flown out of Salina, KS. (middle) The time series of CO for a section of the flight from 31 August 2019 that investigated 15 small agricultural fires from mostly corn fields. (bottom) A photo of a typical fire.</figcaption></figure>
<caption>Table 13</caption>

Small Fires Investigated During FIREX-AQ by the NASA DC-8 From Salina, KS

<table><thead><tr><th>State</th><th>Latitude</th><th>Longitude</th><th>Major fuel</th><th>Detailed fuel type</th></tr></thead><tbody><tr><td colspan="5"><strong>08/21/2019</strong></td></tr><tr><td>TX</td><td>34.0575</td><td>-99.8753</td><td>Forest</td><td>Mesquite savanna</td></tr><tr><td>TX</td><td>34.1602</td><td>-99.7702</td><td>Savanna</td><td>Post-blackjack oak forest/Mesquite savanna</td></tr><tr><td>TX</td><td>32.5380</td><td>-94.1157</td><td>Slash</td><td>Silviculture: conifer slash (minimal piles), post harvest</td></tr><tr><td>TX</td><td>32.6473</td><td>-94.3526</td><td>Pile</td><td>Land clearing of grass, shrub, young trees mix</td></tr><tr><td>LA</td><td>32.7412</td><td>-91.3834</td><td>Cropland</td><td>Corn</td></tr><tr><td>LA</td><td>32.6653</td><td>-91.2677</td><td>Cropland</td><td>Corn</td></tr><tr><td>LA</td><td>32.5442</td><td>-91.4962</td><td>Urban/Barren</td><td>House</td></tr><tr><td>LA</td><td>32.6006</td><td>-91.5481</td><td>Cropland</td><td>Corn</td></tr><tr><td>LA</td><td>32.6247</td><td>-91.7975</td><td>Cropland</td><td>Corn</td></tr><tr><td>MS</td><td>32.8147</td><td>-90.9172</td><td>Cropland</td><td>Corn</td></tr><tr><td>MS</td><td>32.7792</td><td>-90.9529</td><td>Cropland</td><td>Corn</td></tr><tr><td>MS</td><td>33.0429</td><td>-90.8815</td><td>Cropland</td><td>Corn</td></tr><tr><td>MS</td><td>33.1205</td><td>-90.8801</td><td>Cropland</td><td>Corn</td></tr><tr><td colspan="5"><strong>08/23/2019</strong></td></tr><tr><td>MS</td><td>33.6811</td><td>-90.3483</td><td>Cropland</td><td>Corn</td></tr><tr><td>MS</td><td>33.5939</td><td>-90.3648</td><td>Cropland</td><td>Corn</td></tr><tr><td>MS</td><td>33.1014</td><td>-90.8664</td><td>Cropland</td><td>Corn</td></tr><tr><td>MS</td><td>33.1293</td><td>-90.8964</td><td>Pile</td><td>Silviculture: Pile (mixed deciduous/coniferous), post harvest</td></tr><tr><td>MS</td><td>32.9036</td><td>-91.0292</td><td>Cropland</td><td>Corn</td></tr><tr><td>LA</td><td>32.7511</td><td>-91.3865</td><td>Cropland</td><td>Soybean</td></tr><tr><td>LA</td><td>32.8191</td><td>-91.4972</td><td>Shrubland</td><td>Uncertain - found mixed shrub and rice near</td></tr><tr><td>LA</td><td>32.6100</td><td>-91.5408</td><td>Cropland</td><td>Corn</td></tr><tr><td>LA</td><td>32.6219</td><td>-91.7284</td><td>Cropland</td><td>Corn</td></tr><tr><td>LA</td><td>32.5856</td><td>-91.8398</td><td>Cropland</td><td>Corn</td></tr><tr><td>AR</td><td>34.4652</td><td>-90.8945</td><td>Cropland</td><td>Corn</td></tr><tr><td>MS</td><td>33.8409</td><td>-90.6277</td><td>Cropland</td><td>Rice</td></tr><tr><td>MS</td><td>33.7486</td><td>-90.3361</td><td>Cropland</td><td>Corn</td></tr><tr><td colspan="5"><strong>08/26/2019</strong></td></tr><tr><td>OK</td><td>34.1538</td><td>-95.3132</td><td>Slash</td><td>Silviculture: slash, piles, mixed (deciduous/coniferous), post harvest</td></tr><tr><td>AR</td><td>34.0675</td><td>-94.0568</td><td>Slash</td><td>Silviculture: slash, piles, mixed (deciduous/coniferous), post harvest</td></tr><tr><td>TX</td><td>33.34108</td><td>-95.48532</td><td>Cropland</td><td>Corn</td></tr><tr><td>LA</td><td>32.4413</td><td>-91.7402</td><td>Cropland</td><td>Corn</td></tr><tr><td>LA</td><td>32.4490</td><td>-91.8393</td><td>Cropland</td><td>Corn</td></tr><tr><td>LA</td><td>32.5623</td><td>-91.8286</td><td>Cropland</td><td>Corn</td></tr><tr><td>LA</td><td>32.6317</td><td>-91.7834</td><td>Cropland</td><td>Corn</td></tr><tr><td>LA</td><td>32.7603</td><td>-91.3526</td><td>Cropland</td><td>Corn</td></tr><tr><td>OK</td><td>34.2237</td><td>-95.451</td><td>Forest</td><td>Prescribed severe and understory, mature not harvested and young coniferous pine timber w/deciduous - some accidental</td></tr><tr><td>OK</td><td>34.1538</td><td>-95.3132</td><td>Slash</td><td>Silviculture: slash, piles, and understory; coniferous/deciduous mix</td></tr><tr><td colspan="5"><strong>08/29/2019</strong></td></tr><tr><td>NE</td><td>40.3142</td><td>-96.3607</td><td>Grassland</td><td>Green growing season grass (not dry)</td></tr><tr><td>KS</td><td>38.4107</td><td>-96.5549</td><td>Grassland</td><td>Tallgrass Prairie, Green growing season grass (not dry)</td></tr></tbody></table>
<caption>Table 13</caption>

Small Fires Investigated During FIREX-AQ by the NASA DC-8 From Salina, KS

<table><thead><tr><th>State</th><th>Latitude</th><th>Longitude</th><th>Major fuel</th><th>Detailed fuel type</th></tr></thead><tbody><tr><td colspan="5">08/21/2019</td></tr><tr><td>TX</td><td>34.0575</td><td>-99.8753</td><td>Forest</td><td>Mesquite savanna</td></tr><tr><td>TX</td><td>34.1602</td><td>-99.7702</td><td>Savanna</td><td>Post-blackjack oak forest/Mesquite savanna</td></tr><tr><td>TX</td><td>32.5380</td><td>-94.1157</td><td>Slash</td><td>Silviculture: conifer slash (minimal piles), post harvest</td></tr><tr><td>TX</td><td>32.6473</td><td>-94.3526</td><td>Pile</td><td>Land clearing of grass, shrub, young trees mix</td></tr><tr><td>LA</td><td>32.7412</td><td>-91.3834</td><td>Cropland</td><td>Corn</td></tr><tr><td>LA</td><td>32.6653</td><td>-91.2677</td><td>Cropland</td><td>Corn</td></tr><tr><td>LA</td><td>32.5442</td><td>-91.4962</td><td>Urban/Barren</td><td>House</td></tr><tr><td>LA</td><td>32.6006</td><td>-91.5481</td><td>Cropland</td><td>Corn</td></tr><tr><td>LA</td><td>32.6247</td><td>-91.7975</td><td>Cropland</td><td>Corn</td></tr><tr><td>MS</td><td>32.8147</td><td>-90.9172</td><td>Cropland</td><td>Corn</td></tr><tr><td>MS</td><td>32.7792</td><td>-90.9529</td><td>Cropland</td><td>Corn</td></tr><tr><td>MS</td><td>33.0429</td><td>-90.8815</td><td>Cropland</td><td>Corn</td></tr><tr><td>MS</td><td>33.1205</td><td>-90.8801</td><td>Cropland</td><td>Corn</td></tr><tr><td colspan="5">08/23/2019</td></tr><tr><td>MS</td><td>33.6811</td><td>-90.3483</td><td>Cropland</td><td>Corn</td></tr><tr><td>MS</td><td>33.5939</td><td>-90.3648</td><td>Cropland</td><td>Corn</td></tr><tr><td>MS</td><td>33.1014</td><td>-90.8664</td><td>Cropland</td><td>Corn</td></tr><tr><td>MS</td><td>33.1293</td><td>-90.8964</td><td>Pile</td><td>Silviculture: Pile (mixed deciduous/coniferous), post harvest</td></tr><tr><td>MS</td><td>32.9036</td><td>-91.0292</td><td>Cropland</td><td>Corn</td></tr><tr><td>LA</td><td>32.7511</td><td>-91.3865</td><td>Cropland</td><td>Soybean</td></tr><tr><td>LA</td><td>32.8191</td><td>-91.4972</td><td>Shrubland</td><td>Uncertain - found mixed shrub and rice near</td></tr><tr><td>LA</td><td>32.6100</td><td>-91.5408</td><td>Cropland</td><td>Corn</td></tr><tr><td>LA</td><td>32.6219</td><td>-91.7284</td><td>Cropland</td><td>Corn</td></tr><tr><td>LA</td><td>32.5856</td><td>-91.8398</td><td>Cropland</td><td>Corn</td></tr><tr><td>AR</td><td>34.4652</td><td>-90.8945</td><td>Cropland</td><td>Corn</td></tr><tr><td>MS</td><td>33.8409</td><td>-90.6277</td><td>Cropland</td><td>Rice</td></tr><tr><td>MS</td><td>33.7486</td><td>-90.3361</td><td>Cropland</td><td>Corn</td></tr><tr><td colspan="5">08/26/2019</td></tr><tr><td>OK</td><td>34.1538</td><td>-95.3132</td><td>Slash</td><td>Silviculture: slash, piles, mixed (deciduous/coniferous), post harvest</td></tr><tr><td>AR</td><td>34.0675</td><td>-94.0568</td><td>Slash</td><td>Silviculture: slash, piles, mixed (deciduous/coniferous), post harvest</td></tr><tr><td>TX</td><td>33.34108</td><td>-95.48532</td><td>Cropland</td><td>Corn</td></tr><tr><td>LA</td><td>32.4413</td><td>-91.7402</td><td>Cropland</td><td>Corn</td></tr><tr><td>LA</td><td>32.4490</td><td>-91.8393</td><td>Cropland</td><td>Corn</td></tr><tr><td>LA</td><td>32.5623</td><td>-91.8286</td><td>Cropland</td><td>Corn</td></tr><tr><td>LA</td><td>32.6317</td><td>-91.7834</td><td>Cropland</td><td>Corn</td></tr><tr><td>LA</td><td>32.7603</td><td>-91.3526</td><td>Cropland</td><td>Corn</td></tr><tr><td>OK</td><td>34.2237</td><td>-95.451</td><td>Forest</td><td>Prescribed severe and understory, mature not harvested and young coniferous pine timber w/deciduous - some accidental</td></tr><tr><td>OK</td><td>34.1538</td><td>-95.3132</td><td>Slash</td><td>Silviculture: slash, piles, and understory; coniferous/deciduous mix</td></tr><tr><td colspan="5">08/29/2019</td></tr><tr><td>NE</td><td>40.3142</td><td>-96.3607</td><td>Grassland</td><td>Green growing season grass (not dry)</td></tr><tr><td>KS</td><td>38.4107</td><td>-96.5549</td><td>Grassland</td><td>Tallgrass Prairie, Green growing season grass (not dry)</td></tr></tbody></table>
<caption>Table 13</caption>

*Continued*

<table><thead><tr><th>State</th><th>Latitude</th><th>Longitude</th><th>Major fuel</th><th>Detailed fuel type</th></tr></thead><tbody><tr><td>KS</td><td>38.8749</td><td>-97.4115</td><td>Pile</td><td>Vegetation, trash, and/or BBQ</td></tr><tr><td>KS</td><td>38.8453</td><td>-97.4501</td><td>Cropland</td><td>Rice</td></tr><tr><td>KS</td><td>38.8685</td><td>-97.3258</td><td>Cropland</td><td>Fallow winter wheat</td></tr><tr><td>OK</td><td>36.7578</td><td>-96.373</td><td>Grassland</td><td>Pasture</td></tr><tr><td>OK</td><td>36.8521</td><td>-96.319</td><td>Grassland</td><td>Pasture, shrub, timber litter</td></tr><tr><td>KS</td><td>37.0902</td><td>-96.2498</td><td>Grassland</td><td>Pasture</td></tr><tr><td colspan="5">08/30/2019</td></tr><tr><td>FL</td><td>30.9779</td><td>-86.9288</td><td>Understory</td><td>Blackwater River Prescription: primarily shrubs, grasses and litter from loblolly-longleaf-slash pine, willow-laurel-turkey-water oak, and magnolia forest</td></tr><tr><td>GA</td><td>32.0596</td><td>-83.1305</td><td>Shrubland</td><td>Land clearing, shrub, grass, coniferous</td></tr><tr><td>GA</td><td>31.9658</td><td>-83.189</td><td>Pile</td><td>Longleaf pine w/tree stump</td></tr><tr><td>GA</td><td>31.8816</td><td>-83.5098</td><td>Slash</td><td>Near mixed coniferous, deciduous silviculture</td></tr><tr><td>GA</td><td>31.5406</td><td>-83.8855</td><td>Slash</td><td>Land clearing, mix deciduous coniferous</td></tr><tr><td>GA</td><td>31.712</td><td>-84.086</td><td>Cropland</td><td>Corn</td></tr><tr><td>GA</td><td>31.7416</td><td>-84.0305</td><td>Cropland</td><td>Corn</td></tr><tr><td>GA</td><td>31.4995</td><td>-84.3223</td><td>Pile</td><td>Piles next to pecan fields and slash mixed forest, post cleared</td></tr><tr><td>GA</td><td>31.7416</td><td>-84.0305</td><td>Cropland</td><td>Corn</td></tr><tr><td>GA</td><td>32.5132</td><td>-84.2492</td><td>Cropland</td><td>Corn</td></tr><tr><td>AL</td><td>32.6060</td><td>-85.2553</td><td>Slash</td><td>Land clearing of mixed deciduous/coniferous forest, slash with piles, post harvest</td></tr><tr><td>AL</td><td>32.3715</td><td>-85.5115</td><td>Slash</td><td>Silviculture: slash, piles, mixed (deciduous/coniferous), post harvest</td></tr><tr><td>AL</td><td>33.3762</td><td>-86.2203</td><td>Slash</td><td>Silviculture: conifer slash (minimal piles), post harvest</td></tr><tr><td colspan="5">8/31/2019</td></tr><tr><td>MS</td><td>33.1391</td><td>-90.9207</td><td>Cropland</td><td>Rice</td></tr><tr><td>MS</td><td>33.0937</td><td>-90.8297</td><td>Cropland</td><td>Corn</td></tr><tr><td>MS</td><td>33.3493</td><td>-90.3351</td><td>Cropland</td><td>Corn</td></tr><tr><td>MS</td><td>33.3016</td><td>-90.5021</td><td>Cropland</td><td>Soybean and grass</td></tr><tr><td>MS</td><td>33.6099</td><td>-91.0219</td><td>Cropland</td><td>Corn</td></tr><tr><td>AR</td><td>33.9333</td><td>-91.4202</td><td>Cropland</td><td>Corn and soybean</td></tr><tr><td>AR</td><td>33.8983</td><td>-91.4059</td><td>Cropland</td><td>Soybean</td></tr><tr><td>AR</td><td>34.5803</td><td>-90.7037</td><td>Cropland</td><td>Corn</td></tr><tr><td>AR</td><td>35.0924</td><td>-90.8554</td><td>Cropland</td><td>Soybean</td></tr><tr><td>MS</td><td>33.8936</td><td>-90.6083</td><td>Cropland</td><td>Corn</td></tr><tr><td>MS</td><td>34.0614</td><td>-90.5754</td><td>Cropland</td><td>Rice</td></tr><tr><td>MS</td><td>33.1535</td><td>-91.025</td><td>Cropland</td><td>Rice</td></tr><tr><td>MS</td><td>33.1501</td><td>-90.8568</td><td>Cropland</td><td>Corn</td></tr><tr><td>MS</td><td>33.0777</td><td>-90.8157</td><td>Cropland</td><td>Corn</td></tr><tr><td>LA</td><td>31.3626</td><td>-92.9486</td><td>Slash</td><td>Silviculture: conifer slash (minimal piles), post harvest</td></tr><tr><td colspan="5">9/3/2019</td></tr><tr><td>MO</td><td>36.8085</td><td>-90.0508</td><td>Cropland</td><td>Corn</td></tr><tr><td>MO</td><td>36.9949</td><td>-90.0102</td><td>Shrubland</td><td>Shrubs, grass</td></tr><tr><td>IL</td><td>37.4367</td><td>-88.6022</td><td>Cropland</td><td>Rice</td></tr><tr><td>AR</td><td>35.9493</td><td>-90.4444</td><td>Cropland</td><td>Corn</td></tr></tbody></table>
<table><caption>Table 13</caption><thead><tr><th>State</th><th>Latitude</th><th>Longitude</th><th>Major fuel</th><th>Detailed fuel type</th></tr></thead><tbody><tr><td>KS</td><td>38.8749</td><td>-97.4115</td><td>Pile</td><td>Vegetation, trash, and/or BBQ</td></tr><tr><td>KS</td><td>38.8453</td><td>-97.4501</td><td>Cropland</td><td>Rice</td></tr><tr><td>KS</td><td>38.8685</td><td>-97.3258</td><td>Cropland</td><td>Fallow winter wheat</td></tr><tr><td>OK</td><td>36.7578</td><td>-96.373</td><td>Grassland</td><td>Pasture</td></tr><tr><td>OK</td><td>36.8521</td><td>-96.319</td><td>Grassland</td><td>Pasture, shrub, timber litter</td></tr><tr><td>KS</td><td>37.0902</td><td>-96.2498</td><td>Grassland</td><td>Pasture</td></tr><tr><td colspan="5">08/30/2019</td></tr><tr><td>FL</td><td>30.9779</td><td>-86.9288</td><td>Understory</td><td>Blackwater River Prescription: primarily shrubs, grasses and litter from loblolly-longleaf-slash pine, willow-laurel-turkey-water oak, and magnolia forest</td></tr><tr><td>GA</td><td>32.0596</td><td>-83.1305</td><td>Shrubland</td><td>Land clearing, shrub, grass, coniferous</td></tr><tr><td>GA</td><td>31.9658</td><td>-83.189</td><td>Pile</td><td>Longleaf pine w/tree stump</td></tr><tr><td>GA</td><td>31.8816</td><td>-83.5098</td><td>Slash</td><td>Near mixed coniferous, deciduous silviculture</td></tr><tr><td>GA</td><td>31.5406</td><td>-83.8855</td><td>Slash</td><td>Land clearing, mix deciduous coniferous</td></tr><tr><td>GA</td><td>31.712</td><td>-84.086</td><td>Cropland</td><td>Corn</td></tr><tr><td>GA</td><td>31.7416</td><td>-84.0305</td><td>Cropland</td><td>Corn</td></tr><tr><td>GA</td><td>31.4995</td><td>-84.3223</td><td>Pile</td><td>Piles next to pecan fields and slash mixed forest, post cleared</td></tr><tr><td>GA</td><td>31.7416</td><td>-84.0305</td><td>Cropland</td><td>Corn</td></tr><tr><td>GA</td><td>32.5132</td><td>-84.2492</td><td>Cropland</td><td>Corn</td></tr><tr><td>AL</td><td>32.6060</td><td>-85.2553</td><td>Slash</td><td>Land clearing of mixed deciduous/coniferous forest, slash with piles, post harvest</td></tr><tr><td>AL</td><td>32.3715</td><td>-85.5115</td><td>Slash</td><td>Silviculture: slash, piles, mixed (deciduous/coniferous), post harvest</td></tr><tr><td>AL</td><td>33.3762</td><td>-86.2203</td><td>Slash</td><td>Silviculture: conifer slash (minimal piles), post harvest</td></tr><tr><td colspan="5">8/31/2019</td></tr><tr><td>MS</td><td>33.1391</td><td>-90.9207</td><td>Cropland</td><td>Rice</td></tr><tr><td>MS</td><td>33.0937</td><td>-90.8297</td><td>Cropland</td><td>Corn</td></tr><tr><td>MS</td><td>33.3493</td><td>-90.3351</td><td>Cropland</td><td>Corn</td></tr><tr><td>MS</td><td>33.3016</td><td>-90.5021</td><td>Cropland</td><td>Soybean and grass</td></tr><tr><td>MS</td><td>33.6099</td><td>-91.0219</td><td>Cropland</td><td>Corn</td></tr><tr><td>AR</td><td>33.9333</td><td>-91.4202</td><td>Cropland</td><td>Corn and soybean</td></tr><tr><td>AR</td><td>33.8983</td><td>-91.4059</td><td>Cropland</td><td>Soybean</td></tr><tr><td>AR</td><td>34.5803</td><td>-90.7037</td><td>Cropland</td><td>Corn</td></tr><tr><td>AR</td><td>35.0924</td><td>-90.8554</td><td>Cropland</td><td>Soybean</td></tr><tr><td>MS</td><td>33.8936</td><td>-90.6083</td><td>Cropland</td><td>Corn</td></tr><tr><td>MS</td><td>34.0614</td><td>-90.5754</td><td>Cropland</td><td>Rice</td></tr><tr><td>MS</td><td>33.1535</td><td>-91.025</td><td>Cropland</td><td>Rice</td></tr><tr><td>MS</td><td>33.1501</td><td>-90.8568</td><td>Cropland</td><td>Corn</td></tr><tr><td>MS</td><td>33.0777</td><td>-90.8157</td><td>Cropland</td><td>Corn</td></tr><tr><td>LA</td><td>31.3626</td><td>-92.9486</td><td>Slash</td><td>Silviculture: conifer slash (minimal piles), post harvest</td></tr><tr><td colspan="5">9/3/2019</td></tr><tr><td>MO</td><td>36.8085</td><td>-90.0508</td><td>Cropland</td><td>Corn</td></tr><tr><td>MO</td><td>36.9949</td><td>-90.0102</td><td>Shrubland</td><td>Shrubs, grass</td></tr><tr><td>IL</td><td>37.4367</td><td>-88.6022</td><td>Cropland</td><td>Rice</td></tr><tr><td>AR</td><td>35.9493</td><td>-90.4444</td><td>Cropland</td><td>Corn</td></tr></tbody></table>
<table><caption>Table 13</caption><thead><tr><th>State</th><th>Latitude</th><th>Longitude</th><th>Major fuel</th><th>Detailed fuel type</th></tr></thead><tbody><tr><td>AR</td><td>35.7377</td><td>-90.0215</td><td>Cropland</td><td>Rice</td></tr><tr><td>AR</td><td>35.5140</td><td>-90.0742</td><td>Cropland</td><td>Rice</td></tr><tr><td>AR</td><td>35.5194</td><td>-90.6233</td><td>Cropland</td><td>Rice</td></tr><tr><td>AR</td><td>35.526</td><td>-90.7043</td><td>Pile</td><td>Land clearing, mix deciduous coniferous</td></tr><tr><td>AR</td><td>35.0903</td><td>-91.0314</td><td>Pile</td><td>Shrubs, grass, crop residue</td></tr><tr><td>AR</td><td>34.8254</td><td>-91.686</td><td>Cropland</td><td>Corn</td></tr><tr><td>AR</td><td>34.4610</td><td>-90.7637</td><td>Cropland</td><td>Corn</td></tr><tr><td>AR</td><td>34.4006</td><td>-90.6913</td><td>Cropland</td><td>Rice</td></tr><tr><td>MS</td><td>33.8584</td><td>-90.7738</td><td>Cropland</td><td>Rice</td></tr><tr><td>MS</td><td>33.7019</td><td>-90.777</td><td>Cropland</td><td>Corn</td></tr><tr><td>MS</td><td>33.6976</td><td>-90.803</td><td>Cropland</td><td>Corn</td></tr><tr><td>MS</td><td>33.69036</td><td>-90.8265</td><td>Cropland</td><td>Corn w/rice</td></tr><tr><td>MS</td><td>33.5931</td><td>-90.7076</td><td>Cropland</td><td>Corn</td></tr></tbody></table>

*Continued*

The physical age of smoke, meaning the time elapsed since the smoke was produced in a fire, is a critical parameter for understanding the chemical aging of fire emissions. For each transect as described above, two methods of estimating smoke physical age at the time of sampling by aircraft were used during FIREX-AQ. The first method used the mean horizontal wind speed, as measured on the aircraft in the vicinity of the fire on each flight day. Using this wind speed, the smoke age was inferred from the distance between the fire and the center point of a smoke plume transect. The second method used the HYSPLIT model (Stein et al., 2015) to compute air parcel back trajectories from the aircraft to the upwind source fire. Smoke ages were determined with trajectories from multiple high-resolution meteorological datasets (HRRR 3 km, NAM CONUS Nest 3 km, and GFS 0.25°), which were then averaged, plus time for buoyant plume rise (at 7 m s⁻¹ mean vertical velocity (Clark et al., 1996; Lareau & Clements, 2017)). Uncertainties in the smoke ages are assessed from the range of ages between meteorological datasets, mismatch between measured and modeled winds, and trajectory spatial error in missing the known fire source.

While the first method for estimating smoke age benefits from accurate wind measurements, it assumes that winds are homogeneous in space and time during the plume transport, which can systematically bias the age estimates. In FIREX-AQ these systematic biases are most commonly due to wind changes during the day-to-night transition. The second method accounts for spatial and temporal variations in winds, but is susceptible to errors in the meteorological model. Through careful comparison of the simulated trajectories to smoke transport observed from geostationary satellites, however, periods with large meteorological errors can be identified and removed from calculations to improve the smoke age estimates. The two methods of estimating smoke age (trajectory-based and mean wind-based) typically differ by 27% (median absolute difference) for smoke plume ages up to several hours. The ensemble statistics of trajectories with different meteorological fields can also identify periods when the smoke age uncertainties are likely greater. Figure 24 shows the flight track color coded with physical plume age on top of a GOES17 smoke image of the Williams Flats fire.

## 4.2. Fuels and Fuel Consumption

The Fuel2Fire team provided detailed fire and fuels information post-mission such as FRP, burned area, fuels, total daily and 1-s carbon emissions, diurnal fire cycles for each western fire, and other related data products for both the western wildland fires and the southeastern prescribed, silviculture, and cropland fires. The fuel type for forest and shrublands was determined using the FCCS database, and cropland fires were classified using the Cropland Data Layer and videos from the DC-8 overflights. A subset of the fire and fuel information is shown in Tables 2 and 13 for fires investigated by each platform in this manuscript. The goal was to connect the fuels and fire burning on the ground to the smoke composition measured by the DC-8 to improve modeling of impacts on chemistry, air quality, and health.
<table><caption>Table 13</caption><thead><tr><th>State</th><th>Latitude</th><th>Longitude</th><th>Major fuel</th><th>Detailed fuel type</th></tr></thead><tbody><tr><td>AR</td><td>35.7377</td><td>-90.0215</td><td>Cropland</td><td>Rice</td></tr><tr><td>AR</td><td>35.5140</td><td>-90.0742</td><td>Cropland</td><td>Rice</td></tr><tr><td>AR</td><td>35.5194</td><td>-90.6233</td><td>Cropland</td><td>Rice</td></tr><tr><td>AR</td><td>35.526</td><td>-90.7043</td><td>Pile</td><td>Land clearing, mix deciduous coniferous</td></tr><tr><td>AR</td><td>35.0903</td><td>-91.0314</td><td>Pile</td><td>Shrubs, grass, crop residue</td></tr><tr><td>AR</td><td>34.8254</td><td>-91.686</td><td>Cropland</td><td>Corn</td></tr><tr><td>AR</td><td>34.4610</td><td>-90.7637</td><td>Cropland</td><td>Corn</td></tr><tr><td>AR</td><td>34.4006</td><td>-90.6913</td><td>Cropland</td><td>Rice</td></tr><tr><td>MS</td><td>33.8584</td><td>-90.7738</td><td>Cropland</td><td>Rice</td></tr><tr><td>MS</td><td>33.7019</td><td>-90.777</td><td>Cropland</td><td>Corn</td></tr><tr><td>MS</td><td>33.6976</td><td>-90.803</td><td>Cropland</td><td>Corn</td></tr><tr><td>MS</td><td>33.69036</td><td>-90.8265</td><td>Cropland</td><td>Corn w/rice</td></tr><tr><td>MS</td><td>33.5931</td><td>-90.7076</td><td>Cropland</td><td>Corn</td></tr></tbody></table>

*Continued*

The physical age of smoke, meaning the time elapsed since the smoke was produced in a fire, is a critical parameter for understanding the chemical aging of fire emissions. For each transect as described above, two methods of estimating smoke physical age at the time of sampling by aircraft were used during FIREX-AQ. The first method used the mean horizontal wind speed, as measured on the aircraft in the vicinity of the fire on each flight day. Using this wind speed, the smoke age was inferred from the distance between the fire and the center point of a smoke plume transect. The second method used the HYSPLIT model (Stein et al., 2015) to compute air parcel back trajectories from the aircraft to the upwind source fire. Smoke ages were determined with trajectories from multiple high-resolution meteorological datasets (HRRR 3 km, NAM CONUS Nest 3 km, and GFS 0.25°), which were then averaged, plus time for buoyant plume rise (at 7 m s⁻¹ mean vertical velocity (Clark et al., 1996; Lareau & Clements, 2017)). Uncertainties in the smoke ages are assessed from the range of ages between meteorological datasets, mismatch between measured and modeled winds, and trajectory spatial error in missing the known fire source.

While the first method for estimating smoke age benefits from accurate wind measurements, it assumes that winds are homogeneous in space and time during the plume transport, which can systematically bias the age estimates. In FIREX-AQ these systematic biases are most commonly due to wind changes during the day-to-night transition. The second method accounts for spatial and temporal variations in winds, but is susceptible to errors in the meteorological model. Through careful comparison of the simulated trajectories to smoke transport observed from geostationary satellites, however, periods with large meteorological errors can be identified and removed from calculations to improve the smoke age estimates. The two methods of estimating smoke age (trajectory-based and mean wind-based) typically differ by 27% (median absolute difference) for smoke plume ages up to several hours. The ensemble statistics of trajectories with different meteorological fields can also identify periods when the smoke age uncertainties are likely greater. Figure 24 shows the flight track color coded with physical plume age on top of a GOES17 smoke image of the Williams Flats fire.

## 4.2. Fuels and Fuel Consumption

The Fuel2Fire team provided detailed fire and fuels information post-mission such as FRP, burned area, fuels, total daily and 1-s carbon emissions, diurnal fire cycles for each western fire, and other related data products for both the western wildland fires and the southeastern prescribed, silviculture, and cropland fires. The fuel type for forest and shrublands was determined using the FCCS database, and cropland fires were classified using the Cropland Data Layer and videos from the DC-8 overflights. A subset of the fire and fuel information is shown in Tables 2 and 13 for fires investigated by each platform in this manuscript. The goal was to connect the fuels and fire burning on the ground to the smoke composition measured by the DC-8 to improve modeling of impacts on chemistry, air quality, and health.
<figure><img src="image_26.png" /><figcaption>**Figure 23.** The modified combustion efficiency (MCE) of all the southeastern small fires. Highest MCE values shown in red are dominated by crop fires, while lowest MCE values shown in blue are dominated by land clearing, green grassland fires, and pile burning. An example timeseries of CO, CO₂, and NO of two smoke plumes from adjacent fields demonstrates the varying chemical signatures with changes in MCE.</figcaption></figure>
<figure><img src="image_26.png" /><figcaption>**Figure 23.** The modified combustion efficiency (MCE) of all the southeastern small fires. Highest MCE values shown in red are dominated by crop fires, while lowest MCE values shown in blue are dominated by land clearing, green grassland fires, and pile burning. An example timeseries of CO, CO₂, and NO of two smoke plumes from adjacent fields demonstrates the varying chemical signatures with changes in MCE.</figcaption></figure>
<figure><img src="image_27.png" /><figcaption>**Figure 24.** The flight track of the NASA DC-8 on 3 August 2019 color coded with the physical plume age calculated with the trajectory method.</figcaption></figure>

For the western campaign, Fuel2Fire provided daily post-fire data for every fire flown on the fuels and burned area, fire behavior, and daily 1-s carbon emissions. Fuel types can change as fires burn across a landscape (Figure 25), and the amount of fuel burned is dependent on what is available or dry enough to burn, under the control of cumulative fire weather (temperature, wind speed, precipitation and RH). Using the Williams Flats fire as an example, burned area is greater on 3 August 2019 (fire weather High), but the amount of fuel consumed is greater on 6 August 2019 (fire weather Very High). The diurnal cycle and relative energy released by a fire is also under the control of fire weather conditions. Typically fire fuel consumption rates peak in the late afternoon when temperatures are highest and RH is the lowest (Figure 25).

Post-fire analysis for the eastern campaign included fire location identification using Google Earth and the DC-8 flight videos, as well as ecosystem-specific data. The diversity of fires sampled during this 7-day period includes: 5 crop types, slash and pile burning, silviculture, grassland, understory, pasture, and several prescribed fires (e.g., Tallgrass prairies) (Table 13). The Blackwater River State Forest prescribed understory fire was sampled in detail before and after the fire to gather detailed fuels data to connect to satellite data and chemistry.

### 4.3. Satellite Data

While satellite data are publicly available, a subset of satellite products are archived to facilitate analysis of the FIREX-AQ observations. This includes active fire detections from both polar orbiting and geostationary satellites provided through the Wildfire Automated Biomass Burning Algorithm (WF-ABBA) processing system. Polar orbiting data available from the VIIRS sensor onboard the Suomi National Polar-orbiting Partnership and NOAA-20 Joint Polar Satellite System (JPSS-1) satellites. Geostationary data are available from the Advanced Baseline
<figure><img src="image_27.png" /><figcaption>**Figure 24.** The flight track of the NASA DC-8 on 3 August 2019 color coded with the physical plume age calculated with the trajectory method.</figcaption></figure>

For the western campaign, Fuel2Fire provided daily post-fire data for every fire flown on the fuels and burned area, fire behavior, and daily 1-s carbon emissions. Fuel types can change as fires burn across a landscape (Figure 25), and the amount of fuel burned is dependent on what is available or dry enough to burn, under the control of cumulative fire weather (temperature, wind speed, precipitation and RH). Using the Williams Flats fire as an example, burned area is greater on 3 August 2019 (fire weather High), but the amount of fuel consumed is greater on 6 August 2019 (fire weather Very High). The diurnal cycle and relative energy released by a fire is also under the control of fire weather conditions. Typically fire fuel consumption rates peak in the late afternoon when temperatures are highest and RH is the lowest (Figure 25).

Post-fire analysis for the eastern campaign included fire location identification using Google Earth and the DC-8 flight videos, as well as ecosystem-specific data. The diversity of fires sampled during this 7-day period includes: 5 crop types, slash and pile burning, silviculture, grassland, understory, pasture, and several prescribed fires (e.g., Tallgrass prairies) (Table 13). The Blackwater River State Forest prescribed understory fire was sampled in detail before and after the fire to gather detailed fuels data to connect to satellite data and chemistry.

### 4.3. Satellite Data

While satellite data are publicly available, a subset of satellite products are archived to facilitate analysis of the FIREX-AQ observations. This includes active fire detections from both polar orbiting and geostationary satellites provided through the Wildfire Automated Biomass Burning Algorithm (WF-ABBA) processing system. Polar orbiting data available from the VIIRS sensor onboard the Suomi National Polar-orbiting Partnership and NOAA-20 Joint Polar Satellite System (JPSS-1) satellites. Geostationary data are available from the Advanced Baseline
<figure><img src="image_28.png" /><figcaption>**Figure 25.** Ecosystem types for the Williams Flats fire. Thirty eight different fuel types are mapped at 30-m resolution for the Williams Flats fire using the FCCS database. All the GeoMAC perimeter maps are shown with active fires overlaid on 3 and 6 August, highlighting the unique fuels combusted on different days as the fire burns across the landscape. On 3 August, the primary fuels burned were in grasslands (beige colors), and in contrast, the primary fuels burned on the 6th are forests (green colors).</figcaption></figure>

Imager (ABI) sensors onboard GOES-16 (East) and GOES-17 (West). Time-resolved FRP from these assets relevant to FIREX-AQ in-situ and remotely sensed fires is also included in the data archive.

Satellite visible imagery is also of great value given that the flight paths alone do not convey the dynamic nature of fire plume evolution. True color imagery from GOES-16 and GOES-17 are available on the FIREX-AQ archive as still images and movies for all the sampled western wildfires and a few selected eastern fires. Each image is overlaid with the aircraft locations and flight paths until that point in time.

# 5. Emerging Results From FIREX-AQ

## 5.1. Emissions

A major science goal of FIREX-AQ was to improve estimates of BB gas and aerosol emissions and their representation in models. To enable a detailed assessment of emissions based on the FIREX-AQ in situ observations, a key element was daily total carbon emissions estimates provided by the Fuel2Fire team for the specific fires sampled by the DC-8 aircraft. These emissions estimates were grounded in observed and estimated fuel consumption and temporally distributed with a diurnal variation proportional to the FRP observed by the GOES satellites. Along with the plume age calculations, these emissions could then be related to specific cross-plume sampling transects downwind of the fires.

As a first step, Wiggins et al. (2020) were able to show that between consecutive transects the relative trend in FRP was correlated to the relative trend in conserved smoke tracers (CO₂, CO, and BC aerosol). Thus, when continuously available, FRP measurements can be used to account for changes in the amount of emissions and
<figure><img src="image_28.png" /><figcaption>**Figure 25.** Ecosystem types for the Williams Flats fire. Thirty eight different fuel types are mapped at 30-m resolution for the Williams Flats fire using the FCCS database. All the GeoMAC perimeter maps are shown with active fires overlaid on 3 and 6 August, highlighting the unique fuels combusted on different days as the fire burns across the landscape. On 3 August, the primary fuels burned were in grasslands (beige colors), and in contrast, the primary fuels burned on the 6th are forests (green colors).</figcaption></figure>

Imager (ABI) sensors onboard GOES-16 (East) and GOES-17 (West). Time-resolved FRP from these assets relevant to FIREX-AQ in-situ and remotely sensed fires is also included in the data archive.

Satellite visible imagery is also of great value given that the flight paths alone do not convey the dynamic nature of fire plume evolution. True color imagery from GOES-16 and GOES-17 are available on the FIREX-AQ archive as still images and movies for all the sampled western wildfires and a few selected eastern fires. Each image is overlaid with the aircraft locations and flight paths until that point in time.

# 5. Emerging Results From FIREX-AQ

## 5.1. Emissions

A major science goal of FIREX-AQ was to improve estimates of BB gas and aerosol emissions and their representation in models. To enable a detailed assessment of emissions based on the FIREX-AQ in situ observations, a key element was daily total carbon emissions estimates provided by the Fuel2Fire team for the specific fires sampled by the DC-8 aircraft. These emissions estimates were grounded in observed and estimated fuel consumption and temporally distributed with a diurnal variation proportional to the FRP observed by the GOES satellites. Along with the plume age calculations, these emissions could then be related to specific cross-plume sampling transects downwind of the fires.

As a first step, Wiggins et al. (2020) were able to show that between consecutive transects the relative trend in FRP was correlated to the relative trend in conserved smoke tracers (CO₂, CO, and BC aerosol). Thus, when continuously available, FRP measurements can be used to account for changes in the amount of emissions and
these changes have to be considered together with dilution and chemistry in the transported fire plumes. This work only looked at the relative rate of change, but did not directly estimate the magnitude of emissions.

To more fully evaluate Fuel2Fire estimated emissions versus emission fluxes based on airborne observations, HSRL lidar data were necessary to extrapolate in situ observations across the full cross section of the plume at each downwind distance in combination with transport speeds based on wind observations. Two independent emissions assessments based on DC-8 observations were conducted by Wiggins et al. (2021) and Stockwell et al. (2022). While each assessment took a slightly different approach, both found reasonable agreement across all western wildfires between Fuel2Fire emissions estimates and their own based on airborne observations with regression slopes of 1.15 (r² = 0.83) and 1.33 (r² = 0.92), respectively.

Stockwell et al. (2022) focused on total carbon emissions from in-situ observations and HSRL lidar data, which provide the most direct comparison with the Fuel2Fire inventory, and also compared in situ estimated emissions broadly to existing model inventories of total carbon. The FIREX-AQ in situ and Fuel2Fire emissions estimates compared the best to the top-down FRP-based inventories (GFAS, QFED, and FEER), while bottom-up inventories (FINN, GFED, and 3BEM) tended to underestimate emissions. Two other experimental inventories (GBBEP and HRRR) exhibited strong overestimates.

Using the continuous, time-resolved GOES FRP product from the Fuel2Fire team, Stockwell et al. (2022) also provide a comprehensive set of smoke emission coefficients for 88 trace gas and aerosol components of smoke from western wildfires. While smoke coefficients have been reported in the literature for bulk aerosol, the FIREX-AQ data provide the first such information for many of the trace gas species and specific aerosol components.

Wiggins et al. (2021) focused on aerosol emissions, assessing uncertainty in bottom-up emissions through comparison to the GFED4.1s and in top-down emissions to the FEERv1.0 inventory. For GFED, the average aerosol EF calculated from the FIREX-AQ in situ observations agreed well with GFED. For FEER, the smoke emission coefficient was estimated by regressing estimated emissions versus GOES FRP for the in situ estimated emissions, Fuel2Fire emissions, and a lidar-based estimate using lidar AOD. The estimates from these high-resolution inventories were all lower than the FEER value, but still within the large statistical uncertainties. Two potential causes for this difference were the use of MODIS FRP instead of temporally resolved GOES FRP and the used constant mass extinction efficiency (MEE) instead of one that is increasing with smoke age as observed on the DC-8. This result raised the question of whether top-down methods are more vulnerable to uncertainties in MEE than previously thought.

FIREX-AQ data are contributing a very comprehensive set of EFs for use in models. FIREX-AQ consistently sampled a large number of fires with a larger suite of instruments than previous experiments. Emission factors for the western wildfires (Gkatzelis et al., in prep) largely corroborate results from FireLab and recent compilations from SEAC⁴RS (Liu et al., 2017), WE-CAN (Permar et al., 2021), and the literature update of Andreae (2019) for temperate forest EFs. For the smaller eastern fires Travis et al., (in prep), is providing valuable information on the diversity of fuels (e.g., corn, rice, soybeans, grass, land-clearing) and how these fuels differ in expected MCE and resulting EFs. Method advancements allowed the AMS to report EFs for submicron potassium and organic sulfur with high time resolution and estimate their EFs, both for wildfires and agricultural fires (Guo et al., 2021).

Ground measurements from the AML enabled an assessment of VOC emissions from near-field sampling of seven western wildfires (Majluf et al., 2022). Emissions ratios for 240 mass spectral signals were calculated and evaluated using PMF to identify two factors relating to high and low temperature pyrolysis. These factors corroborated the temperature dependent factors obtained by Sekimoto et al. (2018) from the FireLab. These factors indicate processes that cannot be discriminated using MCE.

## 5.2. Plume Chemistry

Fire emissions undergo rapid changes in the near field and continue to evolve as smoke ages and is transported downwind. These changes are particularly important for understanding impacts on air quality, climate, ozone production, and the evolution of aerosol abundance, composition, and optical properties. FIREX-AQ provided an opportunity to look at these impacts through both observational constraints and model evaluation of observed changes.
these changes have to be considered together with dilution and chemistry in the transported fire plumes. This work only looked at the relative rate of change, but did not directly estimate the magnitude of emissions.

To more fully evaluate Fuel2Fire estimated emissions versus emission fluxes based on airborne observations, HSRL lidar data were necessary to extrapolate in situ observations across the full cross section of the plume at each downwind distance in combination with transport speeds based on wind observations. Two independent emissions assessments based on DC-8 observations were conducted by Wiggins et al. (2021) and Stockwell et al. (2022). While each assessment took a slightly different approach, both found reasonable agreement across all western wildfires between Fuel2Fire emissions estimates and their own based on airborne observations with regression slopes of 1.15 (r² = 0.83) and 1.33 (r² = 0.92), respectively.

Stockwell et al. (2022) focused on total carbon emissions from in-situ observations and HSRL lidar data, which provide the most direct comparison with the Fuel2Fire inventory, and also compared in situ estimated emissions broadly to existing model inventories of total carbon. The FIREX-AQ in situ and Fuel2Fire emissions estimates compared the best to the top-down FRP-based inventories (GFAS, QFED, and FEER), while bottom-up inventories (FINN, GFED, and 3BEM) tended to underestimate emissions. Two other experimental inventories (GBBEP and HRRR) exhibited strong overestimates.

Using the continuous, time-resolved GOES FRP product from the Fuel2Fire team, Stockwell et al. (2022) also provide a comprehensive set of smoke emission coefficients for 88 trace gas and aerosol components of smoke from western wildfires. While smoke coefficients have been reported in the literature for bulk aerosol, the FIREX-AQ data provide the first such information for many of the trace gas species and specific aerosol components.

Wiggins et al. (2021) focused on aerosol emissions, assessing uncertainty in bottom-up emissions through comparison to the GFED4.1s and in top-down emissions to the FEERv1.0 inventory. For GFED, the average aerosol EF calculated from the FIREX-AQ in situ observations agreed well with GFED. For FEER, the smoke emission coefficient was estimated by regressing estimated emissions versus GOES FRP for the in situ estimated emissions, Fuel2Fire emissions, and a lidar-based estimate using lidar AOD. The estimates from these high-resolution inventories were all lower than the FEER value, but still within the large statistical uncertainties. Two potential causes for this difference were the use of MODIS FRP instead of temporally resolved GOES FRP and the used constant mass extinction efficiency (MEE) instead of one that is increasing with smoke age as observed on the DC-8. This result raised the question of whether top-down methods are more vulnerable to uncertainties in MEE than previously thought.

FIREX-AQ data are contributing a very comprehensive set of EFs for use in models. FIREX-AQ consistently sampled a large number of fires with a larger suite of instruments than previous experiments. Emission factors for the western wildfires (Gkatzelis et al., in prep) largely corroborate results from FireLab and recent compilations from SEAC⁴RS (Liu et al., 2017), WE-CAN (Permar et al., 2021), and the literature update of Andreae (2019) for temperate forest EFs. For the smaller eastern fires Travis et al., (in prep), is providing valuable information on the diversity of fuels (e.g., corn, rice, soybeans, grass, land-clearing) and how these fuels differ in expected MCE and resulting EFs. Method advancements allowed the AMS to report EFs for submicron potassium and organic sulfur with high time resolution and estimate their EFs, both for wildfires and agricultural fires (Guo et al., 2021).

Ground measurements from the AML enabled an assessment of VOC emissions from near-field sampling of seven western wildfires (Majluf et al., 2022). Emissions ratios for 240 mass spectral signals were calculated and evaluated using PMF to identify two factors relating to high and low temperature pyrolysis. These factors corroborated the temperature dependent factors obtained by Sekimoto et al. (2018) from the FireLab. These factors indicate processes that cannot be discriminated using MCE.

## 5.2. Plume Chemistry

Fire emissions undergo rapid changes in the near field and continue to evolve as smoke ages and is transported downwind. These changes are particularly important for understanding impacts on air quality, climate, ozone production, and the evolution of aerosol abundance, composition, and optical properties. FIREX-AQ provided an opportunity to look at these impacts through both observational constraints and model evaluation of observed changes.
### 5.2.1. Role of HONO

The importance of HONO from fires has been well established and several FIREX-AQ publications address the dominant role of HONO in the rapid chemical changes occurring in the first few hours after emission. FIREX-AQ observations suggest that subsequent chemical production of HONO is also important (Chai et al., 2021). Using stable isotopes of oxygen and nitrogen in HONO from the NASA MACH-2, Chai et al. (2021) provided evidence for significant secondary production in near-ground smoke samples in both daytime and nighttime smoke. Additional analysis of the NASA MACH-2 observations of HONO by Kaspari et al. (2021) showed that HONO was not depleted in far-field smoke compared to near-field smoke sampled at ground level. While ground-surface chemistry is expected to play a significant role in these observations, conditions aloft are also expected to contribute to HONO production when dense smoke with sufficient particle surface area is present. Secondary production of HONO in fresh smoke plumes is corroborated by large eddy simulations of near-field smoke evolution by Wang et al. (2021). Using FIREX-AQ observations for comparison, the model captures the strong variability in HONO across the plume due to the variation in photolysis rates between the well-lit plume edges and the darker conditions in the center and lower parts of dense plumes. To capture the observed downwind gradient in total HONO, the model requires a secondary heterogeneous source constrained by aerosol uptake of NO₂. This secondary chemistry is able to best reproduce the downwind decay in total HONO as compared to simply increasing expected emissions. Even when increasing HONO emissions by a factor of five to match observed HONO concentrations in the smoke sampling transects closest to the fire, the decay is still more rapid than that observed across subsequent downwind transects (Wang et al., 2021).

### 5.2.2. Ozone Production

Several FIREX-AQ studies advanced understanding of ozone production in fire plumes. As shown by Akagi et al. (2013) a large range of ozone formation rates and total ozone formed were observed in various fire plumes during several field experiments. Prior to FIREX-AQ, the reasons for the large variation in ozone formation rates were not well understood, but clearly depended on fuel specific emissions, VOC/NOₓ ratios, meteorology, and plume thickness influencing the light availability (Akagi et al., 2012; de Gouw et al., 2006; Jaffe & Wigder, 2012; Yokelson et al., 2009).

Using the detailed observations from FIREX-AQ, Xu et al. (2021) provided the first observationally constrained analysis of near-field daytime ozone production in fire plumes by recognizing that individual crosswind smoke sampling transects provide a continuum between fresher (plume center, slower photochemistry) and more aged (plume edge, faster photochemistry) air. The similarity in physical age across each perpendicular transect allows the spectrum of chemical age to be interpreted without the need to consider changes in fire state and emissions that complicate a comparison between consecutive downwind transects. Twenty-five “Single Transect Analyses” were identified with adequate physical conditions and chemical gradients to estimate ozone production. These analyses used observationally estimated OH exposure, VOC reactivity, and peroxy radical fate (based on ratios of organic peroxides to hydroxynitrates) to calculate Oₓ chemical closure (Oₓ = O₃ + NO₂ + HNO₃ + peroxyacylnitrates + particulate nitrate). The resulting agreement between observationally estimated Oₓ production and observed changes in Oₓ enabled a deeper evaluation of near-field photochemistry resulting in a parameterization of O₃ + NO₂ production for use in models. The parameterization accounts for both the initial NO₂ emissions (dependent on MCE) and the subsequent photochemical production of O₃ (dependent on OH exposure) (Xu et al., 2021). This parameterization is intended to enable Chemical Transport Models to incorporate near-field ozone production that primarily occurs at subgrid scales.

An important aspect of fire plume chemistry is the potential for extreme variability in actinic flux throughout the plume. To look deeper into the range of chemical conditions in fire plumes, Decker et al. (2021) examined evidence for “dark chemistry” both during the day and night for plumes sampled by the Twin Otter and DC-8. They found evidence for NO₃ chemistry at all times of day due to the reactivity of NO₃ radicals in fire plumes that is orders of magnitude larger than urban or forested environments. In terms of BB VOC oxidation, NO₃ was found to be important mainly for phenolic compounds and was competitive with OH and O₃ even during the day. Thus, NO₃ chemistry was found to be an important element to understanding SOA and BrC formation in fire plumes.

Recognizing that fire emissions often peak in the late afternoon and extend into the evening, Robinson et al. (2021) investigated the time-of-day dependence of plume chemistry. Using a 0-D box model approach, key differences in the rate and NOₓ-VOC sensitivity of O₃ production were identified. In typical afternoon conditions, O₃ production
### 5.2.1. Role of HONO

The importance of HONO from fires has been well established and several FIREX-AQ publications address the dominant role of HONO in the rapid chemical changes occurring in the first few hours after emission. FIREX-AQ observations suggest that subsequent chemical production of HONO is also important (Chai et al., 2021). Using stable isotopes of oxygen and nitrogen in HONO from the NASA MACH-2, Chai et al. (2021) provided evidence for significant secondary production in near-ground smoke samples in both daytime and nighttime smoke. Additional analysis of the NASA MACH-2 observations of HONO by Kaspari et al. (2021) showed that HONO was not depleted in far-field smoke compared to near-field smoke sampled at ground level. While ground-surface chemistry is expected to play a significant role in these observations, conditions aloft are also expected to contribute to HONO production when dense smoke with sufficient particle surface area is present. Secondary production of HONO in fresh smoke plumes is corroborated by large eddy simulations of near-field smoke evolution by Wang et al. (2021). Using FIREX-AQ observations for comparison, the model captures the strong variability in HONO across the plume due to the variation in photolysis rates between the well-lit plume edges and the darker conditions in the center and lower parts of dense plumes. To capture the observed downwind gradient in total HONO, the model requires a secondary heterogeneous source constrained by aerosol uptake of NO₂. This secondary chemistry is able to best reproduce the downwind decay in total HONO as compared to simply increasing expected emissions. Even when increasing HONO emissions by a factor of five to match observed HONO concentrations in the smoke sampling transects closest to the fire, the decay is still more rapid than that observed across subsequent downwind transects (Wang et al., 2021).

### 5.2.2. Ozone Production

Several FIREX-AQ studies advanced understanding of ozone production in fire plumes. As shown by Akagi et al. (2013) a large range of ozone formation rates and total ozone formed were observed in various fire plumes during several field experiments. Prior to FIREX-AQ, the reasons for the large variation in ozone formation rates were not well understood, but clearly depended on fuel specific emissions, VOC/NOₓ ratios, meteorology, and plume thickness influencing the light availability (Akagi et al., 2012; de Gouw et al., 2006; Jaffe & Wigder, 2012; Yokelson et al., 2009).

Using the detailed observations from FIREX-AQ, Xu et al. (2021) provided the first observationally constrained analysis of near-field daytime ozone production in fire plumes by recognizing that individual crosswind smoke sampling transects provide a continuum between fresher (plume center, slower photochemistry) and more aged (plume edge, faster photochemistry) air. The similarity in physical age across each perpendicular transect allows the spectrum of chemical age to be interpreted without the need to consider changes in fire state and emissions that complicate a comparison between consecutive downwind transects. Twenty-five “Single Transect Analyses” were identified with adequate physical conditions and chemical gradients to estimate ozone production. These analyses used observationally estimated OH exposure, VOC reactivity, and peroxy radical fate (based on ratios of organic peroxides to hydroxynitrates) to calculate Oₓ chemical closure (Oₓ = O₃ + NO₂ + HNO₃ + peroxyacylnitrates + particulate nitrate). The resulting agreement between observationally estimated Oₓ production and observed changes in Oₓ enabled a deeper evaluation of near-field photochemistry resulting in a parameterization of O₃ + NO₂ production for use in models. The parameterization accounts for both the initial NO₂ emissions (dependent on MCE) and the subsequent photochemical production of O₃ (dependent on OH exposure) (Xu et al., 2021). This parameterization is intended to enable Chemical Transport Models to incorporate near-field ozone production that primarily occurs at subgrid scales.

An important aspect of fire plume chemistry is the potential for extreme variability in actinic flux throughout the plume. To look deeper into the range of chemical conditions in fire plumes, Decker et al. (2021) examined evidence for “dark chemistry” both during the day and night for plumes sampled by the Twin Otter and DC-8. They found evidence for NO₃ chemistry at all times of day due to the reactivity of NO₃ radicals in fire plumes that is orders of magnitude larger than urban or forested environments. In terms of BB VOC oxidation, NO₃ was found to be important mainly for phenolic compounds and was competitive with OH and O₃ even during the day. Thus, NO₃ chemistry was found to be an important element to understanding SOA and BrC formation in fire plumes.

Recognizing that fire emissions often peak in the late afternoon and extend into the evening, Robinson et al. (2021) investigated the time-of-day dependence of plume chemistry. Using a 0-D box model approach, key differences in the rate and NOₓ-VOC sensitivity of O₃ production were identified. In typical afternoon conditions, O₃ production
and NOₓ depletion occurred extremely rapidly following emission (~20 min) with HONO and formaldehyde photolysis being the dominant radical sources. The near-field transition from VOC-sensitive to NOₓ-sensitive conditions for ozone production was similar for afternoon versus evening plumes, although on average the NOₓ depletion occurred more rapidly for afternoon plumes. The O₃ to CO ratio subsequent to the most rapid phase of the O₃ production varied strongly with time of day and was larger on average for afternoon than evening plumes.

### 5.2.3. Aerosol Chemistry and Physics

The complexity of the near-field environment for aerosol chemistry was investigated from a number of perspectives, including emissions, trends in BrC absorption, and organic aerosol volatility. Considering that secondary production of BrC in smoke plumes is competing with multiple loss processes (evaporation, chemical processing, and photobleaching) it is perhaps not surprising that both the DC-8 and Chem Otter observed wildly variable trends in normalized BrC concentrations, with increasing, relatively constant, and decreasing trends in dilution-corrected BrC observed during the first few hours of downwind transport and evolution (Washenfelder et al., 2022; Zeng et al., 2022).

The need to better understand the components of BrC and their chemistry is highlighted in a case study from the Sheridan fire which provided the opportunity to sample a descending smoke plume with an associated downwind temperature increase of 15° (287–302K). Across this temperature gradient, evaporation was shown to decrease 4-nitrocatechol by two-thirds while total BrC absorption was unchanged (Zeng et al., 2022). Despite the clear decrease in normalized OA mass as temperature increased during this plume transect, the normalized absorption by BrC measured with a photoacoustic spectrometer was essentially constant. Zeng et al. (2022) note that 4-nitrocatechol is a major contributor to BrC, which means there must have been substantial production of additional chromophores to maintain nearly constant absorption. Washenfelder et al. (2022) found that 4-nitrocatechol accounted for a much smaller fraction of BrC absorption in selected smoke plumes sampled from the Chem Twin Otter during FIREX-AQ than was found from an analysis of WE-CAN plumes discussed by Palm et al. (2020). Washenfelder et al. (2022) suggested that BrC arises from a much larger number of chromophores with a complex time evolution that includes both primary and secondary sources, a change from the previous view of BrC as having a well-defined lifetime from limited prior aircraft work (Forrister et al., 2015).

In spite of the near-field complexity in aerosol chemistry, clear evidence for decreasing absorption in smoke sampled over longer aging further downwind was lacking. Figure S11 in Supporting Information S1 shows the initial aerosol Angstrom exponent (AAE) in FireLab smoke that was about 5 s old (Selimovic et al., 2018), where AAE is expected to scale with BrC fractional absorption contributions. Figure S11 in Supporting Information S1 also shows the trend in AAE values for four precisely aged and isolated smoke plumes that impacted Missoula in 2017–2019. The youngest smoke was from the Rice Ridge Fire in 2017 and other plumes were wildfire season prescribed fires, one in 2018 and two in 2019. Note that in contrast to brief airborne plume transects, the Missoula measurements benefit from hours of signal averaging at a reasonably narrow range of physical age. The data indicate a decrease in AAE with a half-life of about 10 hr. Airborne data are more scattered and sometimes in thicker smoke but are not inconsistent with the UM ground-based trend (Forrister et al., 2015; Selimovic et al., 2020).

Diversity in near-field observations during FIREX-AQ imply that both the initial AAE and subsequent evolution in the first few hours substantially broaden the range of AAE values that can be expected in downwind observations. With only three opportunities during FIREX-AQ to sample smoke 1 day after emission, it is difficult to determine whether the complexity in downwind behavior is entirely due to differences in near-field conditions.

The complexity in BrC absorption is further examined by Jordan et al. (2022) based on in situ hyperspectral absorption (300–700 nm) measurements taken from the LARGE mobile lab. Results show that the spectral curvature in absorption as a function of wavelength is better represented by a second order polynomial than the linear AE fit commonly used for observations at a few discrete wavelengths. Even when second order polynomials are applied, remaining deviations from the fit can differ substantially from fire to fire. These deviations also occur across the full wavelength range, not just the shorter visible and near-UV wavelengths. While the exact nature of these spectral details remains to be understood, they do provide a potential path for understanding the observed diversity in BrC absorption behavior.

A more comprehensive assessment of OA volatility was made possible by measurements using a thermal denuder coupled to an AMS and Extractive Electrospray Ionization Time-of-Flight Spectrometer (EESI-MS) (Pagonis et al., 2021). These measurements allow for the relative roles of temperature and dilution to be tested along with
and NOₓ depletion occurred extremely rapidly following emission (~20 min) with HONO and formaldehyde photolysis being the dominant radical sources. The near-field transition from VOC-sensitive to NOₓ-sensitive conditions for ozone production was similar for afternoon versus evening plumes, although on average the NOₓ depletion occurred more rapidly for afternoon plumes. The O₃ to CO ratio subsequent to the most rapid phase of the O₃ production varied strongly with time of day and was larger on average for afternoon than evening plumes.

### 5.2.3. Aerosol Chemistry and Physics

The complexity of the near-field environment for aerosol chemistry was investigated from a number of perspectives, including emissions, trends in BrC absorption, and organic aerosol volatility. Considering that secondary production of BrC in smoke plumes is competing with multiple loss processes (evaporation, chemical processing, and photobleaching) it is perhaps not surprising that both the DC-8 and Chem Otter observed wildly variable trends in normalized BrC concentrations, with increasing, relatively constant, and decreasing trends in dilution-corrected BrC observed during the first few hours of downwind transport and evolution (Washenfelder et al., 2022; Zeng et al., 2022).

The need to better understand the components of BrC and their chemistry is highlighted in a case study from the Sheridan fire which provided the opportunity to sample a descending smoke plume with an associated downwind temperature increase of 15° (287–302K). Across this temperature gradient, evaporation was shown to decrease 4-nitrocatechol by two-thirds while total BrC absorption was unchanged (Zeng et al., 2022). Despite the clear decrease in normalized OA mass as temperature increased during this plume transect, the normalized absorption by BrC measured with a photoacoustic spectrometer was essentially constant. Zeng et al. (2022) note that 4-nitrocatechol is a major contributor to BrC, which means there must have been substantial production of additional chromophores to maintain nearly constant absorption. Washenfelder et al. (2022) found that 4-nitrocatechol accounted for a much smaller fraction of BrC absorption in selected smoke plumes sampled from the Chem Twin Otter during FIREX-AQ than was found from an analysis of WE-CAN plumes discussed by Palm et al. (2020). Washenfelder et al. (2022) suggested that BrC arises from a much larger number of chromophores with a complex time evolution that includes both primary and secondary sources, a change from the previous view of BrC as having a well-defined lifetime from limited prior aircraft work (Forrister et al., 2015).

In spite of the near-field complexity in aerosol chemistry, clear evidence for decreasing absorption in smoke sampled over longer aging further downwind was lacking. Figure S11 in Supporting Information S1 shows the initial aerosol Angstrom exponent (AAE) in FireLab smoke that was about 5 s old (Selimovic et al., 2018), where AAE is expected to scale with BrC fractional absorption contributions. Figure S11 in Supporting Information S1 also shows the trend in AAE values for four precisely aged and isolated smoke plumes that impacted Missoula in 2017–2019. The youngest smoke was from the Rice Ridge Fire in 2017 and other plumes were wildfire season prescribed fires, one in 2018 and two in 2019. Note that in contrast to brief airborne plume transects, the Missoula measurements benefit from hours of signal averaging at a reasonably narrow range of physical age. The data indicate a decrease in AAE with a half-life of about 10 hr. Airborne data are more scattered and sometimes in thicker smoke but are not inconsistent with the UM ground-based trend (Forrister et al., 2015; Selimovic et al., 2020).

Diversity in near-field observations during FIREX-AQ imply that both the initial AAE and subsequent evolution in the first few hours substantially broaden the range of AAE values that can be expected in downwind observations. With only three opportunities during FIREX-AQ to sample smoke 1 day after emission, it is difficult to determine whether the complexity in downwind behavior is entirely due to differences in near-field conditions.

The complexity in BrC absorption is further examined by Jordan et al. (2022) based on in situ hyperspectral absorption (300–700 nm) measurements taken from the LARGE mobile lab. Results show that the spectral curvature in absorption as a function of wavelength is better represented by a second order polynomial than the linear AE fit commonly used for observations at a few discrete wavelengths. Even when second order polynomials are applied, remaining deviations from the fit can differ substantially from fire to fire. These deviations also occur across the full wavelength range, not just the shorter visible and near-UV wavelengths. While the exact nature of these spectral details remains to be understood, they do provide a potential path for understanding the observed diversity in BrC absorption behavior.

A more comprehensive assessment of OA volatility was made possible by measurements using a thermal denuder coupled to an AMS and Extractive Electrospray Ionization Time-of-Flight Spectrometer (EESI-MS) (Pagonis et al., 2021). These measurements allow for the relative roles of temperature and dilution to be tested along with
changes in aerosol composition in the ambient atmosphere for comparison with expectations from laboratory and ground studies.

The efficiency of smoke aerosol mass at producing light extinction and scattering (mass extinction efficiency and mass scattering efficiency) was studied by Saide et al. (2022). It was found that mid-visible smoke MEE can change from 2.5 to 3.2 m²/g for fresh smoke (<2 hr old) to 6–7 m²/g for one-day-old smoke emitted on the day the Williams Flats fire was showing its most extreme behavior. While increases in aerosol size partially explained this trend, changes in the real part of the aerosol refractive index (real(n)) were necessary to provide full closure assuming Mie theory. Real(n) estimates from multiple days were found to be positively correlated with organic aerosol oxidation state and negatively correlated with smoke volatility. Future studies should focus on better understanding and parameterizing these relationships to fully represent smoke aging.

Adding further to understanding of the complexity of aerosol composition in fire plumes, Adachi et al. (2022) identified fine ash-bearing particles, which represent a small, but significant, component of aerosol mass and number. This identification was made possible through analysis of filter samples with both transmission electron microscopy and ion chromatography. While ash is often recognized as a contributor to coarse particle emissions, these fine ash particles composed mainly of Ca and Mg carbonates are carried much further downwind, with implications for BB aerosol indirect effects for example, as ice nuclei. Overall, fine ash was found to account for ~8% and 5% of aerosol number and mass in smoke plumes. The fine ash component was observed in both the western wildfires and eastern agricultural burning plumes.

### 5.3. Improving Models and the Importance of Satellite Observations

One of the stated goals for FIREX-AQ was to evaluate and improve the ability of models to predict fire impacts on local to regional scales. A first step toward this goal was the comparison of 12 forecast models performed by Ye et al. (2021). The comparison focused on the multi-day sampling of the Williams Flats fire to evaluate model differences in fire emissions, plume injection height, diurnal cycle of emissions, initialization time, chemical mechanisms, satellite AOD assimilation, treatment of aerosol processes, and boundary conditions. Given the diversity in the models, the resulting recommendations were generally applicable rather than specific to any particular model. In terms of emissions, a major limitation relates to the use of fixed diurnal emission cycles. The adoption of satellite FRP-based emissions and diurnal variation were highlighted as an important path for improvement. Assimilation of satellite AOD was also shown to benefit model forecasts, leading to better predictions of smoke AOD. Smoke AOD was not a good indicator of model performance for surface PM₂.₅ concentration, raising the importance of representing plume rise and the subsequent vertical distribution of smoke. The use of persistence for emissions is a problem across all models, especially for days with drastic fire growth. A potential way to reduce the impact of the persistence assumption would be to more frequently integrate satellite-based updates to emissions, but such solutions are not trivial to implement.

In a specific case study, better WRF-Chem (v3.5.1) performance was demonstrated by Kumar et al. (2022) after adopting FRP-based fire emissions and plume rise estimation (adopted from HRRR-Smoke (Ahmadov et al., 2017)) as well as diurnal emissions profiles based on mean FRP profiles for three longitude bands across North America. Incorporation of FRP-based emissions was shown to have the greatest impact, raising emissions by a few orders of magnitude above the severely underestimated values provided by 3BEM (Brazilian Burning Emission Model), which is the burned area, bottom-up approach normally used in WRF-Chem (Freitas et al., 2011). Also important was the improved diurnal cycle of emissions, which was better suited for western wildfires rather than the Amazonian fire cycle used in the base model. The impact of FRP-based plume rise was more subtle without clear evidence for improved performance.

Thapa et al. (2022) took a closer look at the commonly used plume rise parameterization in WRF-Chem and HRRR-Smoke from Freitas et al. (2007). Using Active Fire Area and FRP measured by the MASTER instrument during overflights by the DC-8, Fire Radiative Energy (FRE) was calculated and compared to the model estimates. Modeled FRE exhibited a much wider range across different fuels than was seen in the observations. Observed values fell within the range of model values for savannah and grassland, despite many of the western wildfires including forest for which model estimates were significantly high. This contributed to an overprediction of smoke injection height into the free troposphere. HSRL data revealed that smoke lofted into the free troposphere for 37% of fire plumes, while the model predicted this condition for 80%–95% of the plumes between
changes in aerosol composition in the ambient atmosphere for comparison with expectations from laboratory and ground studies.

The efficiency of smoke aerosol mass at producing light extinction and scattering (mass extinction efficiency and mass scattering efficiency) was studied by Saide et al. (2022). It was found that mid-visible smoke MEE can change from 2.5 to 3.2 m²/g for fresh smoke (<2 hr old) to 6–7 m²/g for one-day-old smoke emitted on the day the Williams Flats fire was showing its most extreme behavior. While increases in aerosol size partially explained this trend, changes in the real part of the aerosol refractive index (real(n)) were necessary to provide full closure assuming Mie theory. Real(n) estimates from multiple days were found to be positively correlated with organic aerosol oxidation state and negatively correlated with smoke volatility. Future studies should focus on better understanding and parameterizing these relationships to fully represent smoke aging.

Adding further to understanding of the complexity of aerosol composition in fire plumes, Adachi et al. (2022) identified fine ash-bearing particles, which represent a small, but significant, component of aerosol mass and number. This identification was made possible through analysis of filter samples with both transmission electron microscopy and ion chromatography. While ash is often recognized as a contributor to coarse particle emissions, these fine ash particles composed mainly of Ca and Mg carbonates are carried much further downwind, with implications for BB aerosol indirect effects for example, as ice nuclei. Overall, fine ash was found to account for ~8% and 5% of aerosol number and mass in smoke plumes. The fine ash component was observed in both the western wildfires and eastern agricultural burning plumes.

### 5.3. Improving Models and the Importance of Satellite Observations

One of the stated goals for FIREX-AQ was to evaluate and improve the ability of models to predict fire impacts on local to regional scales. A first step toward this goal was the comparison of 12 forecast models performed by Ye et al. (2021). The comparison focused on the multi-day sampling of the Williams Flats fire to evaluate model differences in fire emissions, plume injection height, diurnal cycle of emissions, initialization time, chemical mechanisms, satellite AOD assimilation, treatment of aerosol processes, and boundary conditions. Given the diversity in the models, the resulting recommendations were generally applicable rather than specific to any particular model. In terms of emissions, a major limitation relates to the use of fixed diurnal emission cycles. The adoption of satellite FRP-based emissions and diurnal variation were highlighted as an important path for improvement. Assimilation of satellite AOD was also shown to benefit model forecasts, leading to better predictions of smoke AOD. Smoke AOD was not a good indicator of model performance for surface PM₂.₅ concentration, raising the importance of representing plume rise and the subsequent vertical distribution of smoke. The use of persistence for emissions is a problem across all models, especially for days with drastic fire growth. A potential way to reduce the impact of the persistence assumption would be to more frequently integrate satellite-based updates to emissions, but such solutions are not trivial to implement.

In a specific case study, better WRF-Chem (v3.5.1) performance was demonstrated by Kumar et al. (2022) after adopting FRP-based fire emissions and plume rise estimation (adopted from HRRR-Smoke (Ahmadov et al., 2017)) as well as diurnal emissions profiles based on mean FRP profiles for three longitude bands across North America. Incorporation of FRP-based emissions was shown to have the greatest impact, raising emissions by a few orders of magnitude above the severely underestimated values provided by 3BEM (Brazilian Burning Emission Model), which is the burned area, bottom-up approach normally used in WRF-Chem (Freitas et al., 2011). Also important was the improved diurnal cycle of emissions, which was better suited for western wildfires rather than the Amazonian fire cycle used in the base model. The impact of FRP-based plume rise was more subtle without clear evidence for improved performance.

Thapa et al. (2022) took a closer look at the commonly used plume rise parameterization in WRF-Chem and HRRR-Smoke from Freitas et al. (2007). Using Active Fire Area and FRP measured by the MASTER instrument during overflights by the DC-8, Fire Radiative Energy (FRE) was calculated and compared to the model estimates. Modeled FRE exhibited a much wider range across different fuels than was seen in the observations. Observed values fell within the range of model values for savannah and grassland, despite many of the western wildfires including forest for which model estimates were significantly high. This contributed to an overprediction of smoke injection height into the free troposphere. HSRL data revealed that smoke lofted into the free troposphere for 37% of fire plumes, while the model predicted this condition for 80%–95% of the plumes between
<figure><img src="image_29.png" /><figcaption>**Figure 26.** Two flight tracks from the eastern fires sampled on 21 and 26 August 2019, highlight the improved capability of satellites to detect small fires. The fuel type and the number of satellite detects from GOES, MODIS, and VIIRS is indicated for each sampled fire.</figcaption></figure>

the two models. Adjusting model calculations based on observed FRE resulted in fewer injections to the free troposphere but did not entirely eliminate the bias.

Ye et al. (2022) further addressed model plume rise by examining the vertical distribution of smoke in WRF-Chem. Taking four observed cases of smoke reaching into the free troposphere identified by Thapa et al. (2022), the vertical distribution of fire emissions was constrained with an inversion system used to assimilate HSRL observations. These constrained runs were compared to base runs using the Freitas et al. (2007) parameterization. While the base runs apportioned the smoke roughly equally between the boundary layer and free troposphere, the constrained runs showed that more than 80% of the smoke was reaching the free troposphere in each of the four cases. Despite an evaluation of several model parameters related to fire and atmospheric conditions, no clear relationships were found to enable an empirical prediction of the fraction of smoke reaching the free troposphere. Thus, there remains a need to improve both the frequency with which models inject smoke into the free troposphere and the fraction of smoke injected when this occurs. This is of particular importance to smoke transport and impacts downwind.

Tang et al. (2022) showed that the MUSICAv0 (Multi-Scale Infrastructure for Chemistry and Aerosols Version 0) model during the FIREX-AQ was improved using plume rise parameterizations and diurnal cycle of fire emissions, where the impact of plume rise is the larger effect.

For representation of small fire activity in models, detection continues to be a major challenge. GOES-16 and 17 FRP has greatly improved prospects for small fire detection. The DC-8 flight paths on August 21 and 26, 2019 are shown in Figure 26. Over these two days, 2 wildland fires, 4 silviculture burns, 2 pile burns, 13 corn field burns, 1 prescribed fire, and 1 structural fire were sampled. Most of the cropland fires were burning more than one corn residue field (~mean 3 fields), and most of these were detectable from space, with only one exception. GOES was able to detect 83% of the fires sampled; VIIRS detected 74%; and MODIS identified 30% of the fires. Also, note the large number of fire detections by GOES for the slash (126) and silviculture (40) fires. This demonstrates that detected fire duration from GOES is a potential discriminator between crop residue and land clearing fires that would not be distinguishable from polar orbiting satellites.

In addition to the value of satellite FRP and AOD observations, remote sensing of trace gas concentrations from fires (e.g., CO, HCHO, NO₂, HONO) are also improving. Stockwell et al. (2022) compared satellite derived CO emissions from TROPOMI with estimates derived from airborne in situ measurements of smoke emitted within
<figure><img src="image_29.png" /><figcaption>**Figure 26.** Two flight tracks from the eastern fires sampled on 21 and 26 August 2019, highlight the improved capability of satellites to detect small fires. The fuel type and the number of satellite detects from GOES, MODIS, and VIIRS is indicated for each sampled fire.</figcaption></figure>

the two models. Adjusting model calculations based on observed FRE resulted in fewer injections to the free troposphere but did not entirely eliminate the bias.

Ye et al. (2022) further addressed model plume rise by examining the vertical distribution of smoke in WRF-Chem. Taking four observed cases of smoke reaching into the free troposphere identified by Thapa et al. (2022), the vertical distribution of fire emissions was constrained with an inversion system used to assimilate HSRL observations. These constrained runs were compared to base runs using the Freitas et al. (2007) parameterization. While the base runs apportioned the smoke roughly equally between the boundary layer and free troposphere, the constrained runs showed that more than 80% of the smoke was reaching the free troposphere in each of the four cases. Despite an evaluation of several model parameters related to fire and atmospheric conditions, no clear relationships were found to enable an empirical prediction of the fraction of smoke reaching the free troposphere. Thus, there remains a need to improve both the frequency with which models inject smoke into the free troposphere and the fraction of smoke injected when this occurs. This is of particular importance to smoke transport and impacts downwind.

Tang et al. (2022) showed that the MUSICAv0 (Multi-Scale Infrastructure for Chemistry and Aerosols Version 0) model during the FIREX-AQ was improved using plume rise parameterizations and diurnal cycle of fire emissions, where the impact of plume rise is the larger effect.

For representation of small fire activity in models, detection continues to be a major challenge. GOES-16 and 17 FRP has greatly improved prospects for small fire detection. The DC-8 flight paths on August 21 and 26, 2019 are shown in Figure 26. Over these two days, 2 wildland fires, 4 silviculture burns, 2 pile burns, 13 corn field burns, 1 prescribed fire, and 1 structural fire were sampled. Most of the cropland fires were burning more than one corn residue field (~mean 3 fields), and most of these were detectable from space, with only one exception. GOES was able to detect 83% of the fires sampled; VIIRS detected 74%; and MODIS identified 30% of the fires. Also, note the large number of fire detections by GOES for the slash (126) and silviculture (40) fires. This demonstrates that detected fire duration from GOES is a potential discriminator between crop residue and land clearing fires that would not be distinguishable from polar orbiting satellites.

In addition to the value of satellite FRP and AOD observations, remote sensing of trace gas concentrations from fires (e.g., CO, HCHO, NO₂, HONO) are also improving. Stockwell et al. (2022) compared satellite derived CO emissions from TROPOMI with estimates derived from airborne in situ measurements of smoke emitted within
90 min of the satellite overpass. For the five fires satisfying this criteria, satellite and airborne estimates were in excellent agreement.

Griffin et al. (2021) focused on satellite-derived NOₓ emissions using the high-spatial-resolution TROPOMI NO₂ data set. They demonstrated that emissions can be accurately estimated from single TROPOMI overpasses, but an explicit aerosol correction had to be applied to improve the agreement with aircraft observations (by about 10%–25%). Based on chemistry transport model simulations and aircraft observations, the net emissions of NOₓ are 1.3–1.5 times greater than the satellite-derived NO₂ emissions and a correction factor needs to be used to infer net NOₓ from satellite NO₂.

Liao et al. (2021) analyzed western and eastern fires for secondary HCHO production. Secondary HCHO was calculated by subtracting initially emitted HCHO from the measured HCHO. The primary HCHO contribution time profile was calculated from HCHO observed close to the fire and estimated loss from OH oxidation. In 9 of 12 fires, this production more than offset the photochemical loss of the initially emitted HCHO. A comparison of the plumes revealed that OH abundance (determined from decay of VOC pairs of different reactivity) rather than the dilution-normalized reactivity of the VOC mixture was the source of variability in the strength of HCHO production. From this result, it was suggested that satellite observations of HCHO and CO might be used to estimate oxidant levels in smoke plumes. This would also depend on the need to accurately represent photolysis rates in the plume, where the complex radiative environment challenges satellite retrievals as well.

## 6. Summary

The FIREX-AQ experiment was a large coordinated multi-agency, multi-platform research effort to investigate US fires in 2019. Various platforms for in situ and remote sensing measurements, several models, and expansive satellite research were coordinated with fuels and fuel consumption data to investigate the atmospheric science of fires in the most complete way to date. An overview of the FIREX-AQ effort is presented here that includes the study motivation, design, science goals, and outcomes. Detailed descriptions of all the measurements and analyses conducted to date are provided.

Even though 2019 was a below average fire year, FIREX-AQ was able to collect a large and unique new data set of western wildfires and smaller eastern fires and a summary of emerging and novel results on emissions, plume chemistry, model improvements, and important satellite observations are presented here and represent major advances in our understanding; for example, linking fire emissions to satellite FRP data and parametrizing ozone formation in fire plumes. Many more publications are expected from FIREX-AQ results in the future.

While FIREX-AQ was a milestone in fire research, many questions remain unanswered; for example, the emissions and reasons for the observed trends of BrC in aging fire plumes remains unclear. With extreme fire seasons expected to continue into the future, fire research needs are at an all-time high and need to be continued after FIREX-AQ.

## Appendix A

https://espo.nasa.gov/firex-aq/content/FIREX-AQ_Participants.

## Data Availability Statement

FIREX-AQ was primarily funded by the public through support of NOAA and NASA research and the full quality-assured data set acquired from all aspects of the mission and value-added products described below are publicly available. The data can be found on the FIREX-AQ website (https://www-air.larc.nasa.gov/missions/firex-aq/) and the data archive (https://doi.org/10.5067/SUBORBITAL/FIREXAQ2019/DATA001) (FIREX-AQ science team, 2019). A custom merging tool is available that can generate the full data set or just a subset.
90 min of the satellite overpass. For the five fires satisfying this criteria, satellite and airborne estimates were in excellent agreement.

Griffin et al. (2021) focused on satellite-derived NOₓ emissions using the high-spatial-resolution TROPOMI NO₂ data set. They demonstrated that emissions can be accurately estimated from single TROPOMI overpasses, but an explicit aerosol correction had to be applied to improve the agreement with aircraft observations (by about 10%–25%). Based on chemistry transport model simulations and aircraft observations, the net emissions of NOₓ are 1.3–1.5 times greater than the satellite-derived NO₂ emissions and a correction factor needs to be used to infer net NOₓ from satellite NO₂.

Liao et al. (2021) analyzed western and eastern fires for secondary HCHO production. Secondary HCHO was calculated by subtracting initially emitted HCHO from the measured HCHO. The primary HCHO contribution time profile was calculated from HCHO observed close to the fire and estimated loss from OH oxidation. In 9 of 12 fires, this production more than offset the photochemical loss of the initially emitted HCHO. A comparison of the plumes revealed that OH abundance (determined from decay of VOC pairs of different reactivity) rather than the dilution-normalized reactivity of the VOC mixture was the source of variability in the strength of HCHO production. From this result, it was suggested that satellite observations of HCHO and CO might be used to estimate oxidant levels in smoke plumes. This would also depend on the need to accurately represent photolysis rates in the plume, where the complex radiative environment challenges satellite retrievals as well.

## 6. Summary

The FIREX-AQ experiment was a large coordinated multi-agency, multi-platform research effort to investigate US fires in 2019. Various platforms for in situ and remote sensing measurements, several models, and expansive satellite research were coordinated with fuels and fuel consumption data to investigate the atmospheric science of fires in the most complete way to date. An overview of the FIREX-AQ effort is presented here that includes the study motivation, design, science goals, and outcomes. Detailed descriptions of all the measurements and analyses conducted to date are provided.

Even though 2019 was a below average fire year, FIREX-AQ was able to collect a large and unique new data set of western wildfires and smaller eastern fires and a summary of emerging and novel results on emissions, plume chemistry, model improvements, and important satellite observations are presented here and represent major advances in our understanding; for example, linking fire emissions to satellite FRP data and parametrizing ozone formation in fire plumes. Many more publications are expected from FIREX-AQ results in the future.

While FIREX-AQ was a milestone in fire research, many questions remain unanswered; for example, the emissions and reasons for the observed trends of BrC in aging fire plumes remains unclear. With extreme fire seasons expected to continue into the future, fire research needs are at an all-time high and need to be continued after FIREX-AQ.

## Appendix A

https://espo.nasa.gov/firex-aq/content/FIREX-AQ_Participants.

## Data Availability Statement

FIREX-AQ was primarily funded by the public through support of NOAA and NASA research and the full quality-assured data set acquired from all aspects of the mission and value-added products described below are publicly available. The data can be found on the FIREX-AQ website (https://www-air.larc.nasa.gov/missions/firex-aq/) and the data archive (https://doi.org/10.5067/SUBORBITAL/FIREXAQ2019/DATA001) (FIREX-AQ science team, 2019). A custom merging tool is available that can generate the full data set or just a subset.