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NASA Contributions to Interagency Fire Science Collaboration

Fire Impacts on Regional Emissions and Chemistry (FIREChem)

Introduction

Vegetation fires ignited through natural processes or by human action occur in various

ecosystems around the globe, consuming massive amounts of biomass and releasing a variety of

particulate and gaseous emissions into the atmosphere, where they can be circulated locally,

regionally, or globally. While fire is important for many ecosystems, it also poses costly risks to

human health and property. These risks have increased in recent decades due to population

growth and climate change (Westerling et al. 2006). Extreme fire seasons are garnering

mounting attention due to the increasing number of costly wildfires that include these events: the

2016 fires that burned across 8 states in the southeast (48,158 ha); the 2016 Anderson Creek

prairie fire that was the largest in Kansas history (161,874 ha); the 2016 Fort McMurray fire,

which is the costliest fire in Canadian history ($2.7 B, 589,552 ha, 2400 structures destroyed);

the 2004 Alaskan fire season (2.74 M ha), the largest in almost 80 years of Alaskan fire history

and the extreme 2015 unusually-early-season Alaskan fires (2.07 M ha); the 2010 peatland fires

near Moscow that resulted in the premature deaths of 56,000 ($15 B, 300,000 ha, during record

drought and high temperatures); and the 2012 Russian fire season, reported to have burned

greater than 25M ha according to the Sukachev Institute of Forest (reported hottest summer in

170 years in Siberia). Since 1960, total burned area has exceeded 3.6 M ha in the United States

in only 4 years, and all of these extreme years occurred in last decade. Along with direct threats

to life and property, clear evidence of the detrimental health effects from wildland-fire induced

poor air quality has been demonstrated, which includes aggravated asthma, chronic bronchitis,

decreased lung function, congestive heart failure, and premature death (Rappold et al., 2011;

Thelen et al., 2013).

Fire emissions are an important but dauntingly complex source affecting atmospheric

composition. The inefficiency of fire combustion leads to a diverse mixture of trace gas and

aerosol emissions with a large component of short-lived, chemically reactive constituents.

Emissions are affected by a wide variety of factors including fuel conditions (type, structure,

quantity, and moisture content), fire intensity, and fire weather (cumulative temperature, relative

humidity, wind speed and precipitation), which in turn can be rapidly and heterogeneously

modified by fires as they burn. Over the life cycle of a fire, different combinations of flaming

and smoldering combustion lead to time varying emissions. These variables also influence plume

rise and the subsequent transport and chemical evolution of fire emissions. Fire initiation can be

natural (by lightning) or by human intervention (e.g., land clearing and agriculture); these two

types of fire initiation pose very different problems for modeling and prediction. Fire growth is

driven by weather conditions and is subject to the limitations of weather-based prediction. While

fire activity can be predicted on a broad seasonal scale, climatologies are inadequate to provide

the information needed to represent fire impacts due to their specific location, timing, and

intensity. This is especially true for impacts related to air quality which depend on the

intersection of fire emissions and populations and the changes in chemistry that can result when

emissions from fires and anthropogenic sources combine. To understand the integrated impact of

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fires, a need exists for both near-fire and broad-scale monitoring of fire processes across local,

regional, and global scales. NASA (and other) observational capabilities across this wide range

of scales are critical for upscaling fire emissions estimates and ultimately for improving

atmospheric composition modeling and predictive capability.

The ubiquity of fire emissions in the atmosphere has been evident in previous NASA airborne

field campaigns. Fires had a major influence on observations collected during NASA’s earliest

airborne field campaigns to the Amazon (ABLE-2A/2B) and the Arctic (ABLE-3A/3B) in the

1980s (e.g., Andreae et al., 1988; Blake et al., 1994). The TRACE-A and SAFARI campaigns

focused heavily on the impacts of southern African fires to the atmosphere, with SAFARI

providing increased connection to ground conditions and some of the first work specifically on

fire emissions (e.g., Andreae et al., 1994; Lindesay et al., 1996). PEM-Tropics A provided the

first documentation of the hemispheric-scale impact of fire emissions on the southern hemisphere

based on observations of long-range transport of biomass burning plumes to the central South

Pacific (e.g., Blake et al., 1999; Singh et al., 2000). Even when focusing squarely on

anthropogenic emissions, the presence of smoke from fires substantially impacted the Asian

Pacific Rim during TRACE-P (Woo et al., 2003), across the eastern U.S during INTEX-A (Singh

et al., 2006), and broadly across the southern U.S., Gulf of Mexico, and Mexico during the

MILAGRO campaign which was focused on the impact of megacity emissions (e.g., Molina et

al., 2010; Crounse et al., 2009; Yokelson et al., 2011). Fire emissions have even emerged under

unexpected circumstances such as the springtime Arctic during ARCTAS, when flights

conducted north of Fairbanks, Alaska to examine arctic haze were dominated by transport of fire

plumes from unexpectedly large early-season Siberian fires. These data complemented fire

emissions sampled in California during ARCTAS-CARB and in Canada during the summer

phase of ARCTAS (e.g., Hecobian et al., 2011; Singh et al., 2012). DC3 offered the opportunity

to examine a case of fire emissions ingested into a thunderstorm (Apel et al., 2015) as well as

broad influence of convection on ventilation of fire emissions (Huntreiser et al., 2016). Most

recently during the SEAC4RS campaign, fire sampling opportunities included emissions from the

Rim Fire, a large wildfire in California (Peterson et al. 2015), and a collection of 15 small

agricultural fires in the Mississippi River Valley (Liu et al., 2016). Despite this wealth of fire

sampling over the years, NASA has never dedicated a full campaign to the sampling and

characterization of fires and their emissions as the sole objective.

In 2019, an opportunity has arisen for NASA to contribute to an interagency collaboration

focused specifically on characterizing North American fires in terms of their emissions,

chemistry, and impacts. This collaboration includes NOAA’s Fire Influence on Regional and

Global Environments Experiment (FIREX; https://www.esrl.noaa.gov/csd/projects/firex/), the

Joint Fire Science Program’s Fire and Smoke Model Evaluation Experiment (FASMEE;

https://www.fasmee.net/), and NSF’s Western wildfire Experiment for Cloud chemistry, Aerosol

absorption and Nitrogen (WE-CAN). This white paper describes plans for NASA’s contribution

to this collaboration and how it complements these partner activities. NASA’s plans are centered

on airborne sampling from NASA’s DC-8 flying laboratory over a period extending from late

July to mid-September.

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A statistically challenged problem

The long list of factors influencing fire emissions creates a difficult statistical challenge. In an

intercomparison of four unique approaches to estimating fire emissions in the United States, Al-

Saadi et al (2008) found that even though patterns were similar, monthly estimates of CO could

vary by a factor of 10. Similar difficulties were encountered by Zhang et al. (2014) for estimates

of smoke emissions over sub-Saharan Africa.

One demonstration of this challenge is provided by considering the observations that form the

basis for recommended emission factors used in models. Akagi et al. (2011) provide an extensive

review of these observations, which include airborne, ground-based, and laboratory sampling.

Screening is applied to ensure that observations are fresh, meaning sampled emissions have

cooled to ambient temperature but have undergone minimal photochemical processing. When

assessing the contributions of airborne field observations, the resulting data are rather limited.

This is due to the relatively small number of airborne samples focused on near-field fire emission

characterization. More traditionally, airborne field campaigns have documented fire impacts

through diagnostic tracers (HCN, CH3CN, K+) and plume interceptions at various distances

downwind, often without detailed knowledge of the source. As a result, only eight airborne

campaigns contribute to the Akagi et al. (2011) assessment of emission factors. For biomes

relevant to North American wildfires, contributions are further limited to samples taken from 39

specific fire events (13-boreal forest, 18-temperate forest, 8-chaparral). Airborne sampling of

crop burning adds another 12 fires.

While there is a modest amount of ground-based sampling and laboratory observations available

to augment airborne data, there is a compelling need for more ambient observations near active

fires. This need becomes more evident when looking at the suite of measurements collected

during each field study included in the Akagi et al. assessment. The overlap in measurements is

limited to a handful of constituents (CO2, CO, CH4, NO and/or NO2, C2H2). Measurements of

other reactive hydrocarbons, tracers, and particulate emissions vary considerably between

studies, leaving even fewer observations for estimating emission factors for many trace gas and

particulate components. It is important to note that measurements of acetonitrile (CH3CN) are

largely absent from these previous studies. CH3CN has emerged as the most reliable conservative

tracer of biomass combustion, being widely measured in more recent airborne field studies to

diagnose fire influence (de Gouw et al., 2003).

A good example of the current uncertainty in emission factors can be found in the recent work of

Liu et al. (2016) comparing statistics from the recent SEAC4RS study with previous

observations. Table 1 is reproduced from Liu et al. and leverages information collected by the

NASA DC-8 from fifteen crop fires in the Mississippi River Valley in 2013. The disparity in the

suite of observations stands out with the SEAC4RS observations offering the broadest field

characterization of trace gas and particulate emissions. Numbers in Table 1 have a red font if the

average values from both SEAC4RS and the compared study differ by more than their standard

deviations or an orange font if only one of the averages falls outside the standard deviation of the

other. Almost 60% of the compared data fall under these conditions. It is important to note that

these differences do not bring the measurements into question. Rather, the differences indicate

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that the distribution of possible emission factors has not been fully explored. This example

applies only to crop burning, but other fuels suffer a similar paucity of observations.

Table 1. Liu et al. [2016] Comparison of EFs (g/kg) Measured in Field and Lab for Crop Residue Fuels1

Source SEAC4RS Liu et al. [2016]

Stockwell et al. [2015]

Akagi et al. [2011]

Kudo et al. [2014]

Hayashi et al. [2014]

Crop type SE U.S.

rice straw

Asian rice

straw

Unidentified

crop residue in

Mexico

Chinese

wheat

Japanese rice straw

Approach Airborne study Lab study Airborne study Ground study Lab studies

Moisture content (%) - - - - 10.6 20.0 MCE 0.930 0.930 0.925 0.930 0.949 0.910

CO2 1339 (26) - 1664 (66) 1598 (5) 803 (65) 946 (49)

CO 64.46 (16.57) - 85.6 (34) 77.2 (6.9) 27.2 (1.7) 59.4 (0.7)

NO 0.251 (0.211) 1.86 (0.28) 2.06 (0.79) - - -

NO2 2.02 (0.80) 1.70 (0.25) 3.48 (2.11) - - -

NOx as NO 1.58 (0.63) 2.97 (0.32) 3.64 (1.13) - - -

NH3 - 1.12 (0.77) 1.76 (1.35) - 0.059 (0.045) 0.025 (0.020)

HCl 0.0181 (0.0144) 0.458 (0.308) - - 0.062 (0.003) 0.022 (0.006)

SO2 0.795 (0.377) 1.22 (0.34) - - - -

HCN 0.610 (0.479) 0.399 (0.160) 0.16 (0.30) - - -

HCHO 2.63 (1.05) 1.29 (0.61) 1.85 (0.92) 1.07 - -

CH3OH 1.41 (1.38) 1.48 (1.56) 2.67 (1.58) 2.94 - -

Hydroxyacetone 2.06 (0.89) 1.33 (1.47) - - - -

CH3CN 0.169 (0.123) 0.230 (0.092) - 0.20 (0.03) - -

Acetaldehyde 1.37 (0.80) 2.09 (1.46) - 1.02 - -

Acetone 0.638 (0.417) 0.989 (0.532) - 0.83 - -

MVK + MACR 0.449 (0.305) 0.489 (0.398) - 0.43 (0.02) - -

Isoprene 0.411 (0.282) 0.203 (0.104) - 0.52 (0.01) - -

Furan - 0.325 (0.496) - - - -

HPALDs 0.406 (0.229) - - - - -

Benzene 0.275 (0.139) 0.302 (0.123) - 0.53 (0.07) - -

Monoterpenes 0.258 (0.164) - - - - -

Toluene 0.167 (0.091) 0.271 (0.138) - 0.32 - -

Ammonium 0.424 (0.261) - - - 0.083 (0.020) 0.245 (0.092)

Nitrate 0.436 (0.337) - - - 0.006 (0.002) 0.008 (0.000)

Chloride 1.07 (0.89) - - - 0.30 (0.02) 0.69 (0.14)

Sulfate 0.160 (0.115) - - - 0.027 (0.000) 0.063 (0.003)

OA 12.9 (6.3) - 3.67 - 1.6 7.4

BC 0.163 (0.141) - 0.75 - - - 1This table has been adapted from Liu et al. [2016]. The original table provides additional footnotes and clarifications and caveats

regarding the compared studies and measurement specifics.

In addition to emission factors, the extent of burning is another statistically challenged aspect of

assessing the impact of fires. This relates primarily to the information available to detect and

characterize fire activity. Satellite observations are at the center of these tasks, with the extent of

fire activity being estimated from information on burned area and active fire detections in

combination with information on ecosystem type. (Friedl et al., 2010; Giglio et al., 2003; Giglio

et al., 2006a). In the Al-Saadi et al (2008) intercomparison, burned area estimates, which were

derived from a variety of satellite data products (GOES and MODIS), varied by a factor of 3.

Geostationary satellites were shown to capture more small fires, even though the instrument

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resolution was low, simply due to the fact that the instruments were overhead when small (brief)

fires were burning (Al-Saadi et al., 2008; Soja et al., 2009).

Burned area products have the advantage of being able to detect the impact of fires after they

have occurred by collecting observations over a period of time that includes multiple satellite

overpasses, thus increasing the chance to observe locations free of cloud cover (Giglio et al.,

2006b; Giglio et al., 2009 Roy et al., 2005; Roy et al., 2008). For instance, the MODIS burned

area product is diagnosed from the 8-day surface reflectance product, providing information at

500m resolution. Even this relatively fine spatial resolution is inadequate for detecting many

small fires (McCarty et al., 2009) which are often associated with agriculture. McCarty et al.

note that the burned area must be at least 20 ha (80% of a 500 m pixel) to enable a detection

while average field sizes in the eastern U.S. are only 16 ha, suggesting that many burned areas

would fall under the threshold of detection.

Active fire detections offer complementary information capable of detecting such small fires, but

at coarser resolution (1 km) and only when fires are actively burning and unobscured by clouds

at the time the satellite is overhead. In an analysis of fire detection products over the U.S.,

McCarty concluded that 65% of active fire detections in croplands were not accompanied by a

burned area detection. This number rose to 70% in the southeast. While these detections are

valuable for detecting small cropland fires, they were found to add only 4% to total burned area

estimated from the MODIS burned area product. Focusing more generally on small fires,

Randerson et al. (2012) came to a different conclusion based on a global analysis of the same

MODIS 500 m burned area products and 1 km active fire detections. Using a set of scaling

factors developed to assign a burned area estimate to active fire detections lacking coincident

burned area detections, it was estimated that small fires increase global burned area by 26%.

More importantly, for the continental U.S. (not including Alaska) the increase in burned area was

estimated to be 44%. An airborne survey of more than 50 fires in Mexico conducted by

Yokelson et al. (2011) demonstrates the general difficulty in detecting small fires by either

method as none of the fires were detected as burned area and only 20% were detected as

hotspots. As with emissions factors, the disparity in these results places emphasis on the need to

validate information on the contribution of small fires to overall emissions. It is important to note

that fire products from other satellites (e.g., GOES-16, VIIRS, Landsat8/OLI) are now available,

offering higher spatial resolution, more frequent coverage, and greater spectral information than

was available for these previous studies.

There are yet additional factors that deserve attention in understanding the impact of large versus

small fires. For instance, along with burned area, the amount of fuel burned must also be

considered. The amount of fuel contained in unique ecosystems can vary by 2 orders of

magnitude, and the amount of fuel available (dry enough) to burn in each ecosystem varies with

fire weather or fire danger conditions. McRae et al. (2006) demonstrated that even in the same

ecosystem, the amount of fuel consumed by a fire can vary by an order of magnitude, dependent

on the fuel conditions and fire severity. This adds credence to the value of accumulating statistics

and connecting the ground to the atmosphere by connecting multiple-agency field campaigns.

More recently, Fire Radiative Power (FRP) and Fire Radiative Energy (FRE) have been related

to fuel consumption (Wooster et al., 2005; Ichoku et al., 2008), and FRE has been suggested as a

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methodology to estimate fire emissions from the top down (Ichoku and Kaufman, 2005; Ichoku

and Ellison, 2014). MODIS FRP has also been shown to have promise in capturing much of the

variability in NOx emissions as diagnosed from OMI NO2 (Mebust et al., 2011; Mebust and

Cohen, 2013, 2014). Maturing these applications, however, is hampered by a lack of quantitative

validation data for fire energetics (see Appendix A for a detailed discussion).

Specific fuel conditions (fuel amount, structure, mass consumed) influence the radiant energy of

a fire and the ventilation of emissions (i.e., plume rise) which is a critical variable for

understanding the transport, chemical evolution, and downwind impacts of fire emissions. If

smoke plume injection height and detrainment in the atmosphere is modeled or parameterized

incorrectly, then the simulated transport, microphysical and chemical processing of these

emissions will be incorrect. Nevertheless, even if plume height can be correctly predicted, there

are challenges to models such as surface smoothing in areas of complex terrain that can affect

transport (Herron-Thorpe et al., 2014). Currently there are two satellite instruments that are

capable of capturing plume injection height, the Multi-angle Imaging SpectroRadiometer

(MISR) and Cloud-Aerosol LIdar with Orthogonal Polarization (CALIOP). Both of these

instruments provide essential and unique information. MISR has a larger swath width, thus a

greater ability to estimate near-fire plumes, and the MISR plume database is mature (Kahn et al.,

2007; Val Martin, 2012; Nelson et al., 2013). However, MISR is on Terra with a morning

overpass while smoke plumes peak in the late afternoon. MISR also requires distinct smoke

edges to estimate plume height and large smoke plumes can fill the MISR field of regard such

that a distinct smoke boundary is not observed. CALIOP (active lidar, 30 m vertical resolution),

paired with a back trajectory model, can enhance the MISR morning database, by characterizing

afternoon plumes (Omar et al., 2009; Soja et al., 2012). While useful for historical context,

CALIOP will not be available in 2019; however, the Cloud-Aerosol Transport System (CATS)

onboard the International Space Station is another lidar that may prove useful.

Finally, fire influence from local scales to downwind regions can be further diagnosed using

satellite observations of smoke which is important to the assessment of radiative impacts and

exposure to poor air quality across urban and rural populations. These issues require additional

information on aerosol optical properties and composition and the vertical distribution of smoke.

These considerations motivate the FIREChem objectives which emphasize a plan to explore the

factors influencing the composition of fire emissions, the detection and quantification of fires,

and understanding of the injection height, transport, evolution, and impact of fire emissions.

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FIREChem Objectives

To maximize the impact of the combined interagency effort. NASA will contribute airborne

observations using the DC-8 flying laboratory to the following three major objectives.

1 – Sampling of wildfires in coordination with interagency partners to combine near and far-

field observations to understand chemical evolution and transport and evaluate downwind

impacts.

This objective recognizes the combined power of multiple instrumented aircraft to sample the

influence of fire emissions on atmospheric composition with continuity from initial emissions to

downwind impacts far from the source. The major emphasis of the airborne component of

NOAA’s FIREX effort will be to sample western wildfires from the point of emission to the

regional scale. It is expected that the NOAA P-3 and NASA DC-8 will have complementary

payloads allowing the DC-8 to take advantage of its extended range to follow emissions far

downwind of fires being sampled in the near-field by NOAA. This objective will take priority

when fires are large enough to generate visible smoke plumes extending for hundreds of

kilometers. Priority will also be given to fires that are expected to intersect with population

centers, affecting local air quality. Taking advice from partners with the Joint Fire Science

Program (JFSP), consideration will also be given to knowledge of ground conditions and best

opportunities for fuel and fire characterization measurements when selecting targets for airborne

sampling. Further integration with NSF C-130 sampling and other potential assets (e.g., mobile

labs, ground sites, etc.) make daily communication among interagency partners critical to ensure

that there is consensus on the chosen targets and deployment of observational assets.

2 – Sampling of small fires to build statistics on emission factors and fuels, plume rise,

satellite detectability, and integrated impacts.

Small fire activity is expected to be somewhere within reach of the DC-8 every day during the

deployment period. Thus, this objective will take priority when large wildfires warranting

coordinated sampling with partners are not occurring. Small fires will also generally be closer to

population centers, potentially having an air quality impact more significant than their

contribution to total emissions. Confidence that the DC-8 can adequately accomplish this

objective comes from the fifteen small fires sampled during the recent SEAC4RS campaign.

Given the modest amount of flight time dedicated to small fire sampling during SEAC4RS, it is

expected that many more fires will be sampled during FIREChem. This objective provides the

best opportunity to build on the needed statistics for the variables described above and

complements emission factor work of the FireLab study recently conducted by NOAA in

advance of the joint field study in 2019. The DC-8 payload described later in this document will

provide the opportunity to measure trace gas and aerosol emissions in great detail as well as

assessing plume rise and fire intensity for each fire. For best results, this objective requires

liaison with state and local authorities to anticipate when and where to expect burning as well as

to obtain ground-level data to the extent possible for understanding the fuel and conditions under

which burning occurred. JFSP partners will be consulted for such advice, but funding for

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participation by an expert to assist in monitoring small fire activity and gathering information on

ground conditions at sampled locations is also possible. The information gathered from small

fires will also provide an opportunity to assess current satellite detection capabilities and reduce

uncertainty in the contribution of small fires to total emissions.

3 – Sampling of prescribed burns in coordination with FASMEE.

The Fire and Smoke Model Evaluation Experiment (FASMEE) offers the opportunity to sample

one or more large prescribed fires planned and executed by JFSP partners. This objective will

take priority when burns are announced. Planned and controlled burned are of special interest,

because maximum information will be available on the variables affecting fire behavior,

development, dynamics, and emissions. Along with the information gathered before, during, and

after the fire, airborne observations gathered during FASMEE provide the best chance for

bridging laboratory and ambient fires. Fuels from several planned FASMEE burn sites were

burned during the pre-campaign Fire Lab experiment at Missoula in 2016.

FIREChem Science Questions

The following list of science questions overlaps heavily with those of interagency partners. Each

is followed by a brief discussion of the necessary observations and strategies needed to

adequately address them.

1) What are the emissions of gases, aerosols, aerosol precursors, and greenhouse gases from

North American fires? How variable are these emissions due to fuel and fire conditions?

As noted in earlier discussion, current knowledge of emissions comes from a limited data set

given the myriad factors that come into play. Thus, sampling as much of the range space of fire

types and conditions as possible is desired. Recent advances offer the opportunity to measure

emissions more completely than ever before. As an example, Müller et al. (2016) demonstrated

the first field use of PTR-ToF-MS in a small fire plume to document total non-methane organic

gases (NMOG) as well as HONO and NH3. In concert with other measurements, these results for

a single fire highlight the need for breadth in the number of measured trace gases as well as the

value of high frequency sampling of small fire plumes on scales of only a few hundred meters.

Relating fire emissions to other variables will be particularly important. This calls for lidar

remote sensing of aerosols and ozone directly over and downwind of fires as well as thermal

imaging of fires to obtain FRP observations. Additionally, information on ground conditions (fire

weather, fuel characterization, and fire behavior) needs to be collected to the greatest extent

possible to understand the factors contributing to the diversity in fire emissions. This information

also provides an opportunity to explore potential benefits to satellite-based estimates by

assessing whether emissions can be related to satellite fire products, e.g., FRP, land cover

classification, space-based lidar, burned area reconciliation, and active fire detections.

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2) How does the composition of fire plumes change as primary species are converted to

secondary gas and aerosol tracers?

For both large and small fire plumes, important gradients exist over very small temporal scales

(hours) and spatial scales (meters) that need to be better documented and understood. This

includes rapid conversion of short-lived reactive trace gases, production of ozone and secondary

organic aerosol, substantial changes in reservoir species such as PAN, and evolution of aerosol

optical and physicochemical properties. At typical DC-8 flight speeds, several hours of plume

transport and chemistry can be sampled over a matter of minutes. Differences in chemistry are

also expected at different altitudes in the plume as smoke impacts on actinic flux can affect

photochemical oxidation rates and differences in temperature can affect the chemistry of

important reservoir species (e.g., PAN). Another aspect to consider is the time varying nature of

fire emissions. Thus, flying downwind to examine plume evolution, gradients along the plume

can be associated with both the changes in fire emissions as well as chemical processing. It is

unrealistic to expect that these two effects can be fully separated. However, having one aircraft

near the fire for an extended period to examine changes in near-field emissions and another

sampling downwind can help put some constraints on the contributions from these two factors.

3) How is local air quality impacted by fires in the continental United States? How well do air

quality models capture such impacts?

While fires in the continental U.S. contribute very little to the global carbon budget, local and

downwind impacts on air quality are of particular concern. Emissions from large wildfires in the

western U.S. can be transported over long distances and impact populations hundreds to

thousands of kilometers downwind. Such large fires attract significant attention and can be

tracked by satellite images of smoke as well as satellite retrievals of CO. Several factors come

into play that complicate predicting impacts of large fire emissions on downwind locations. For

instance, long-range transport requires lofting of emissions into the lower free troposphere.

Predicting when and where these emissions may be mixed down to the surface is challenging for

air quality models to quantitatively predict. Estimates for ozone production are also difficult

since initial reactive nitrogen is controlled by fuel composition and the abundance of reactive

organic gases leads to rapid formation of organic nitrate reservoirs that can delay ozone

production. Observation of ozone production from fires is mixed, but the potential for substantial

enhancements to local pollution levels have been documented in the LA Basin and rural areas

during ARCTAS CARB (Singh et al., 2012) and other studies (e.g., Akagi et al., 2012, 2013;

Jolleys et al., 2015).

Also of interest is the integrated impact of smaller fires. These fires are often associated with

agricultural burning or land management. While each fire is small individually, there are usually

many small fires ignited intentionally in a short period of time throughout a local area since

burning regulations and optimal conditions require favorable meteorology and may specify times

that burning may occur. Long-range transport of these emissions is less often a concern, but

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these types of fires typically occur in areas closer to populations than wildfires. They may also

combine to create regional impacts on air quality.

4) What are the regional impacts of North American fires?

The large-scale impacts of fires depend on several factors: 1) the fraction of emissions remaining

in the boundary layer versus reaching the free troposphere, 2) the residence time, altitude, and

transport distance of emissions in the free troposphere, 3) when and where fire emissions mix

down to the surface and 4) whether they mix with anthropogenic pollution. Examining these

factors for large fires will require sampling in concert with partner aircraft. Ideally, the emissions

from a single fire will influence an area large enough to observe chemical processing in the

background atmosphere and in the presence of urban pollution, but these differences can also be

assessed across a number of fires intersecting with a range of urban to rural conditions. Impacts

of small fires are expected to be minor for individual fires, but smoke from multiple fires

integrated together may have substantial impact on local to regional scales. This will require

sampling of background conditions upwind and downwind of regions of substantial small fire

activity.

5) What are the climate-relevant properties of biomass burning aerosols? What role do brown

carbon and coatings on black carbon particles play in the optical properties? What is the

composition of PM2.5?

Fires are a dominant source of carbonaceous aerosols to the global atmosphere, but uncertainties

in their emissions and properties are large. Both primary emission of particles and secondary

aerosol formation must be considered with a range of chemical effects that include evaporation

of semivolatiles from primary particles (May et al., 2015), condensation of oxidized organics to

form secondary particles, the formation of organic coatings that alter aerosol optical properties

and absorption, and potential changes to size distributions affecting scattering properties. Recent

literature offers examples that span a large range of possibilities. Organic aerosol has been

observed to increase, decrease, and remain fairly constant during downwind transport and aging

of different smoke plumes (e.g., Cubison et al., 2011; Hennigan et al., 2011; Jolleys et al., 2012,

2015). Changes in scattering have been observed to change both in correspondence to and

independent of changes in aerosol abundance (Akagi et al., 2012). During the BBOP study,

Collier et al. (2016) found that MCE and organic aerosol emissions were robustly related and

conserved over transport times of up to two days.

While total aerosol emissions and their downwind evolution can vary considerably, all of the

aforementioned studies agree that organic aerosol undergo substantial chemical processing,

especially in the first few hours after emissions. Thus, observing this early period is critical for

understanding how aerosol optical properties change in relation to composition. Spectral changes

in optical properties are particularly important to understanding the relative contributions of BC

and BrC absorption (Corr et al., 2012; Pokhrel et al., 2016) which can change dramatically due to

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the potential for large losses of BrC within a single day (Forrister et al., 2015) and the effect of

coatings on absorption as the aerosol is chemically processed.

6) How can satellite measurements help with #1-5? And how can we obtain better satellite

estimates of plume height, fire intensity, and fire radiative power?

The global nature of fire impacts and the changing landscape of fire activity are well suited to

satellite observations. These satellites, however, need to be complemented by information from

other perspectives to fully exploit their potential for constraining the magnitude and impact of

fires. The graphic below represents how FIREChem and its interagency partners will implement

an integrated observing strategy for this purpose.

All airborne and ground-based observations collected during the study will be valuable for the

purpose of validating current satellite products. At the most basic level, simply documenting the

locations of all fires sampled provides a framework for evaluating the methods of satellite fire

detection through active-fire counts and burned-area products (e.g., see Table 1 of Yokelson et

al., 2011). From the air, lidar profiling of the smoke plume provides further information on

plume rise, valuable for comparing with modeled plume rise and satellite estimates when

available. In situ and lidar observations of aerosol optical properties are also critical for

comparison with model-reconstructed AOD and satellite products. Thermal imaging of fires and

documentation of field size for small fires will be useful for validating satellite active-fire count

and burned-area products as well as FRP. In situ and remote sensing of trace gases will be useful

to validate satellite observations of trace gases near and downwind of fires. In particular,

12

information on the influence of aerosol/smoke on retrievals needs to be further characterized

(Bousserez et al., 2014). Interesting connections between MODIS FRP and OMI NO2 also

warrant further exploration (Mebust et al., 2011; Mebust and Cohen, 2013; Mebust and Cohen,

2014).

In addition to validation, measurements will also help to influence the interpretation of satellite

measurements. For instance, further exploring the relationship between FRP and emissions based

on in situ measurements will also provide much needed statistics on whether remote sensing of

fire intensity can be effectively used to adjust estimates of fire emissions of trace gases and

smoke.

Lidar observations of depolarization will also provide important information useful for

determining aerosol type and potentially for evaluating the age of smoke. Burton et al. (2015)

highlight three examples showing how dust and smoke both depolarize with different spectral

dependences. The depolarizing characteristics of smoke have also been observed to vary with

altitude and relative humidity (John Hair, personal communication) implying that the degree of

aerosol (and cloud) processing may play a role. Mischenko et al. (2016) provide potential

particle morphologies to explain depolarization by aged smoke, but in situ observations of smoke

properties across a range of conditions (fuel conditions, fire intensity, etc.) and degree of

processing are needed to identify the relationship between actual particle composition and state

that can explain these polarization signals.

13

Experimental Design

To satisfy the FIREChem objectives, NASA will deploy the DC-8 flying laboratory to a central

location, most likely Salina, KS. From this location, the DC-8 can reach fires across the

continental U.S and into southern Canada if desired (see Figure 1). An example of fire counts

from MODIS Terra and Aqua indicate that this range will be necessary if fires in both the

southeast and northwest are to be adequately targeted. These two regions consistently exhibit the

most activity, but specific targets vary considerably from year to year. In anticipation of larger,

more persistent fires in the western U.S., overnight bases in the northwest and southwest need to

be identified to allow longer loiter times on consecutive flight days when and if fire activity

warrants.

DC-8 Observations in support of FIREChem: The tables presented below provide information

on FIREChem measurement priorities and are intended to provide guidance on the selection of

instruments for the DC-8 payload. Separate tables are provided for remote sensing

measurements, in situ trace gas measurements, and in situ measurements of aerosol physical and

optical properties. Measurement priorities refer to both funding priorities and impacts to the

achievement of science goals. Measurements listed as Priority 1 are considered critical to the

scientific outcome of the campaign. While short-term data loss from these instruments can be

tolerated, it is expected that catastrophic loss of these measurements would constitute a

substantial loss to the science and could lead to cancellation of science flights until problems can

be remedied. Priority 2 measurements will be funded pending availability of funds and are

considered to contribute substantially to FIREChem science goals. Priority 3 measurements are

unlikely to be funded unless they can be obtained in combination with one or more of the higher

priority parameters. An asterisk is used to denote measurements that can be provided by NASA

Figure 1. Left Panel: DC-8 sampling range from Salina, KS based on a 3 hour transit time to the edge of the

yellow range ring, thus enabling a minimum of 2 hours loiter time in an 8 hour flight. Right Panel – Distribution

of fire counts detected by MODIS in August 2016. Locations vary from year-to-year, but activity is consistently

weighted to the northwest and southeast U.S.

14

facility instruments which will not be funded competitively. This does not preclude proposed

instrumentation from providing measurements of asterisked parameters as long as the instrument

also provides measurements of other high priority parameters not provided by facility

instruments.

In situ measurements have both a required and desired resolution. The required resolution values

are equivalent to what was typical during the SEAC4RS flights. During that campaign, plume

penetrations when sampling small fires were typically 5 seconds in duration (Liu et al., 2016).

While that was enough to enable analysis, the value of sampling variability on much smaller

scales was demonstrated by Müller et al. (2016) (see Figure 2). The desired values represent

instrument time responses that should be technically feasible, but may require instrument

improvements. The distance from the first to last inlet on the DC-8 is 76 feet and the width of the

fuselage is 12 feet. For DC-8 flight speeds at low altitudes, the 5 Hz rate is essentially equivalent

to one measurement each time the plane travels the distance from the front to rear inlet positions.

In addition to measurement rate, location of instruments on the aircraft will also be a

consideration. In a small fire plume, the distance between inlets can be a factor due to small-

scale plume gradients. Efforts will be made to put the fastest response trace gas instruments on

the forward left of the aircraft and aerosol instruments forward right to both put related inlets in

line with each other and minimize the distance between them.

While fast measurements are a priority for small fire plumes and near-field observations for larger fires,

much of the airborne observations will be conducted outside sharp plumes and in areas of more dilute fire

impacts. Thus, in cases where the desired resolution cannot be attained, measurements at the slower

required rates are still needed and indicate the instrument performance needed to make an important

contribution to the success of the overall mission.

Figure 2. Left Panel - 1 Hz sampling of a small fire over 5 seconds during SEAC4RS (taken from Liu et al., 2016)

Right Panel – 10 Hz sampling of a small fire over 2 seconds (taken from Müller et al., 2016).

15

DC-8 Measurement Requirements and Priorities

Gas Phase In Situ Priority Detection Limit Required Resolution Desired Resolution

O3 1 1 ppbv 1 s 5 Hz

H2O* 1 10 ppmv 1 s 5 Hz

CO* 1 5 ppbv 1 s 5 Hz

CH4* 1 10 ppbv 1 s 5 Hz

C2H6 1 50 pptv 1 s 5 Hz

CO2* 1 0.1 ppm 1 s 5 Hz

NMHCs 1 <10% 1 min (full suite) 5 Hz (selected species)

NO 1 10 pptv 1 s 5 Hz

NO2 1 20 pptv 1 s 5 Hz

HCHO* 1 50 pptv 1 s 5 Hz

CH3CN 1 10 pptv 1 s 5 Hz

HCN 2 10 pptv 1 s -

NH3 2 30 pptv 1 s -

HONO 2 50 pptv 1 s -

Organic Acids 2 10 pptv 10 s 1 s

H2O2 2 50 pptv 10 s 1 s

ROOH 2 50 pptv 10 s 1 s

NOy 2 50 pptv 1 s 5 Hz

HNO3 2 50 pptv 10 s 1 s

PANs 2 50 pptv 10 s 1 s

RONO2 2 50 pptv 10 s 1 s

SO2 2 10 pptv 1 s -

OH reactivity 2 1 s-1 10 s 1 s

OH, HO2, RO2 2 0.01/0.1/0.1 pptv 30 s 1 s

Halocarbons 3 Variable 1 min -

N2O* 3 1 ppbv 1 s -

*Denotes measurements to be provided by NASA facility instruments

Aerosol In Situ Priority Detection Limit Required Resolution Desired Res.

Particle Number* 1 NA 1 s 5 Hz

Size Distribution (10 nm-5 µm)* 1 NA 10 s 1 s

Volatility* 1 NA 1 s 5 Hz

Scattering* 1 1 Mm-1 1 s -

Scattering Phase Function 1 3 Mm-1 5 s -

Hygroscopicity* 1 NA 10 s 1 s

Absorption* 1 0.2 Mm-1 10 s -

Brown Carbon Absorption 1 1 Mm-1 per plume 30 s

Size-resolved Composition 1 100 ng m-3 1 s -

Organic mass 1 100 ng m-3 10 s 1 s

Black Carbon 1 50 ng m-3 1 s 5 Hz

Bulk Composition 2 50 ng m-3 per plume -

Single particle Composition 2 <4 µm dia. 1 s -

CCN/IN 2 <4 µm dia. 1 s -

Cloud particle size dist.* 2 0.05-1000 µm 1 s -

*Denotes measurements to be provided by NASA facility instruments

16

Remote Sensing, Radiation, and Met Priority Detection Limit Required Resolution

Aerosol profiles of extinction* 1 10 Mm-1 or 10% 300 m

Aerosol profiles of backscatter* 1 3% 30 m

Aerosol profiles of depolarization* 1 3% 30 m

High Resolution Met (T, P, winds)* 1 0.3K, 0.3 mb, 1 ms-1 10 Hz

UV spectral actinic flux (4π sr) 1 80° SZA equivalent 1 s

Surface IR Imaging (FRP)* 2 - -

Ozone lidar (nadir/zenith)* 2 5 ppbv or 10% 300 m

Trace Gas Columns (O3,NO2,CH2O)* 2 Variable Variable

Multi-spectral Optical Depth* 3 0.01 1 s

*Denotes measurements to be provided by NASA facility instruments

Flight Planning and the Role of Satellites, Models, and Other Sources of Information:

The information available for flight planning will vary depending on fire size. Satellite

information will be most useful for large fires, which can persist for days to weeks and create

smoke plumes stretching over long distances. Models can provide valuable information on

predicted transport of emissions and the likelihood of influence on the air quality of urban areas.

For smaller fires, guidance and information from authorities responsible for prescribed fire

regulation and scheduling will be more important. This includes state and local agricultural

extension offices, natural resource agencies with fire management offices, and community

organizations (e.g., Tall Timbers). That said, even for small fires, real-time fire counts from

satellites will be valuable for verification or adjustment of areas targeted for sampling, and

models can provide useful forecasts on where conditions will be conducive to burning.

Satellite Fire and Smoke Products for Flight Planning: The table below provides a list of

resources for obtaining near-real time information that will be useful to guide flight planning.

This list is expected to evolve in the time leading up to the field campaign and will be

periodically updated.

Output System Satellite Resolution Latency Access

Active Fires

NASA LANCE:

FIRMS

MODIS 1 km 1-2 hrs Visualization, Data

VIIRS 375 m 3 hrs (max)

Copernicus SLSTR 1 km 3 hrs (max) Data (account required)

HMS Combined 1 km Data

Smoke /

Aerosols

GASP GOES-West 4 km* 10-25 min Data

ASDTA GOES-West 4-km* daily Data

HMS GOES – twice daily Data

NAQFC HYSPLIT

model – daily

Visualization,

Description,

U.S. Trajectories

Visualization WorldView – – – Visualization

WildlandFire – – – Visualization

RSAC – – – Visualization

* The transition to GOES-R will increase the resolution to 2 km.

17

Flight Scenarios and Sampling Approach: Separate sampling protocols are needed for small

and large fires. The protocols discussed below build upon experience gained during SEAC4RS

while sampling crop fires in the Mississippi River Valley and the extensive smoke plume from

the California Rim Fire. These ideas are intended to guide discussion by the mission science

team and should not be construed as final or complete.

When sampling small fires, the goal is to observe many individual events with a combination of

remote sensing and in situ sampling. Specific locations for small fires will not be known, but the

DC-8 will be directed to general areas of expected burning. Once fire activity is encountered,

satellite active fire count products suggest that the distance between fires will be small enough to

enable visual acquisition of other nearby fires. Thus, fire spotting will be an important role for

the flight scientist in directing the pilots to subsequent targets for sampling. Samples will be

measured in seconds rather than minutes and there is not sufficient time for pilots to respond or

adjust. To effectively sample small fires, pilots need to be free to engage the plume within the

requirements of the protocol. This protocol includes overflight of the fire at two altitudes. The

orientation of these overflights would need to account for wind direction and smoke transport.

The initial overflight would approach the fire from upwind and pass over it at the altitude of the

plume rise to allow for in situ sampling of the smoke plume along the direction of downwind

transport. This would be followed by overflight at a higher altitude with remote sensors. After

emerging from the smoke the plane would execute a turn and ascent to an altitude at least 6500

feet above the altitude of the smoke plume. During the ascent, the DC-8 would pass back upwind

of the fire and execute a turn to pass back over the fire at higher altitude. Depending on the final

configuration of remote sensors, the distance upwind and desired flight conditions (altitude,

speed, pitch, etc.) will require further discussion. To optimize the higher altitude overflight,

specific coordinates for the fire will need to be obtained during the initial low altitude pass.

When sampling a region of small fires, it will also be desirable to fly transects upwind and

downwind of the general area to assess any integrated impacts of their combined emissions. This

could be especially useful for areas where small fire activity has been present for several days

and meteorological conditions are limiting transport of emissions out of the region.

Sampling of large fires has greater precedent (e.g., ARCTAS, SEAC4RS), but a basic philosophy

needs to be shared with partners. Unlike previous campaigns, sampling will often include

multiple aircraft, and thoughts regarding how they work together will continue to be negotiated

through meetings leading up to and during the field deployment. A good rule of thumb for

conditions warranting coordinated sampling is the presence of a smoke plume extending a few

hundred kilometers or more. Interactions will include overflight of partner aircraft to link remote

sensing with in situ observations, intercomparison flights to connect common measurements, and

sampling of the smoke plume in a semi-Lagrangian fashion by multiple aircraft. Depending on

the size of the plume and distance of the smoke transport, the DC-8 may work independently far

downstream of the original sampling, especially if emissions are expected to impact areas of high

population.

18

Due to the size of these fires and their persistence over multiple days, the use of satellite data and

model forecasts will allow for a much more predictable flight path compared to small fire

sampling. However, flight scientists will need to make in-flight requests for adjustment based on

actual conditions and location of the plume.

Sampling of smoke starting early in the morning can answer important questions about chemical

processing of smoke released overnight; while daytime smoke dominates total emissions, stable

boundary layers may mean that nighttime smoke has a disproportionate air quality impact.

Consideration must also be given to the prospect that a pyroconvective event occurs during the

campaign; however, the uncertainty of whether such an event will occur precludes it from being

a critical goal of the study. Depending on the location of such an event and the altitude reached,

the target would be given high priority for sampling with the DC-8. For specific details see

Appendix B.

19

FIREChem Needs

In the execution of FIREChem, there are areas where NASA will need to depend on interagency

partners.

1) Help with airspace coordination, especially for fires under active control. Obtaining

permission, developing a plan for aircraft separation, etc.

When fires targeted for sampling are also engaged by active firefighting, Temporary Flight

Restrictions (TFRs) will limit the times and proximity of approach by research aircraft. In these

cases, it is best that Air Traffic Authorities are only communicating with one organization. We

will defer to NOAA for any necessary flight clearances to support joint flight plans.

2) Prediction of small fire activity. Are there organized plans for burning? When are

fires expected to start? Where are conditions conducive to burning? Can GOES

provide guidance or updates on burning progress?

The FIREChem DC-8 science team will have little to no experience in this area. While small fire

activity is frequent, knowing where to place the aircraft for maximum effect requires knowledge

that may be available from partners. Advice from JFSP and other groups is needed in this regard.

Funding for a science team member to serve as the primary advisor in this area will be

considered.

3) Connection to ground conditions. Gathering information on fuel conditions, fire

behavior, duration, fraction of fuel burned, etc.

While this type of information will be optimized for the FASMEE burns, advice is needed on

how to get as much information as possible on all of the small fires sampled. If the right person

could be identified, funding will be considered for this role in combination with the needs listed

under item 2.

4) Advice on selection of fires to target. Which fire is likely to yield more information?

When multiple targets for sampling exist, NASA will rely on partner expertise to help prioritize

where to sample. Possible considerations include whether a fire is likely to impact populated

areas downwind, the quality of preexisiting knowledge of the area and likely fuel conditions,

how long the fire is anticipated to last, proximity of the fire to ground-based resources capable of

deploying to the fire location, and the probability that emissions will impact other measurement

sites (e.g., Storm Peak, LTER sites, etc.)

20

Appendix A - Airborne verification for satellite observations of fire energetics

Satellite observations have been used to monitor and measure open biomass burning for several

decades (initially using visible signals observed by DMSP (Croft, 1973); and then operationally

using infrared bands starting in 1981 (Flannigan and Vonderhaar, 1986; Matson and Dozier,

1981). The 4 µm channel can be used to derive fire radiative power (FRP), which is often used as

a proxy for fire intensity (Wooster et al., 2005). This quantitative information from active fire

detection has been tied to fire type (Giglio et al., 2006b; Smith and Wooster, 2005). While fire

detection using IR radiances has been steadily refined with successive generations of sensors,

large uncertainties remain that limit application of quantitative fire property retrievals (Giglio

and Kendall, 2001; Giglio and Schroeder, 2014), and most operational applications even today

continue to use satellite fire detections in a binary fire/no-fire paradigm (Schroeder et al., 2008;

Wiedinmyer et al., 2011) . In contrast, various research algorithms have also demonstrated the

potential to use existing satellite sensors to improve our quantitative characterization of fires.

Peterson et al. (2014) demonstrated that by combining multiple single-pixel retrievals and

eliminating low-temperature pixels, two pieces of independent information on active fire size

and temperature can be retrieved using a bispectral method. This information is required for

calculating the flux of FRP over the active fire area, which has been shown to drive variation in

smoke plume injection heights in certain circumstances (Val Martin et al., 2012). In addition,

recent work also suggests that retrieved fire temperatures in combination with a measure of

visible light output can increase the capability to detect small fires and could potentially permit

some constraint on combustion phase (e.g., smoldering vs. flaming) for fires at night (Polivka et

al., 2016).

While fire characterization algorithms have made considerable progress, fundamental

uncertainties in our characterization of fires from space still remain, because of the scale

mismatch between weather satellite observations with the repeat overpasses necessary for fire

monitoring, and the size of the fires themselves. Because the area of active burning is ~100 m2 to

~10,000 m2, while the integrating area of the satellite pixel is from 122,500 m2 (350 m VIIRS I-

band pixel, at nadir) to over 100 km2 (observations of high-latitude fires from older geostationary

weather satellites), the fire energy retrieval is extremely sensitive to 1) precise determination of

the temperature of the portion of the satellite pixel that is not actively burning (Giglio and

Kendall, 2001; Peterson and Wang, 2013); 2) position of the actively burning area within the

satellite footprint, which has a variable sensitivity characterized by a point spread function (PSF)

with a peak at the pixel center (e.g., Calle et al., 2009; Cahoon et al. 2000). Both of these

uncertainties are reduced for larger fires, and diminish as the satellite footprint shrinks in size.

The new VIIRS I-band fire retrieval (Schroeder et al., 2014b) represents a 9-fold improvement in

spatial resolution from polar orbit (350 m VIIRS I-band vs 1 km MODIS), while the impending

GOES-R fire products represent a 4-fold improvement in spatial resolution from geostationary

orbit (2 km GOES-R vs 4 km GOES). These resolution improvements will yield dramatic

improvements in sensitivity to smaller fires, and should also improve quantitative retrieval of fire

energetics for larger fires (though full exploitation of the VIIRS I-bands may be hindered by

saturation issues (Schroeder et al., 2014b)). For nighttime detection, shortwave infrared and

visible bands can increase the sensor’s sensitivity for detecting small fires, because the

21

background radiation in these bands is very low. A multispectral method using several bands

ranging from shortwave IR to thermal IR bands (e.g, through spectral fitting) has shown the

possibility to better retrieve fire temperature and size at the same time, and therefore more

precisely retrieve fire energetics (Elvidge et al., 2013), but the results from this method still need

to be validated.

While algorithms to characterize fire properties and energetics from space continue to be refined,

there is an increasingly urgent need for quantitative validation of these retrievals. Ground-based

verification has been effectively used as proof of concept (Dickinson et al., 2016; Wooster et al.,

2011), but ground-only verification cannot provide the characterization needed for operational

applications. The subset of fires most amenable to retrieval from satellite, and also most

important for many operational applications, are very large and very intense. These fires are

outside the realm of most prescribed burn experiments and too large in size to be effectively

measured from towers or tethered balloons. In order to establish a quantitative chain of

verification from laboratory measurements of fire energy release to satellite observations, an

intermediate step of medium-scale observation is required. This intermediate observation must

have these properties:

1. Gapless coverage of large fires including landscape context (field of view at least 40km

in the minimum dimension);

2. Fine spatial resolution such that the area of active burning is on the order of 1% or more

of the pixel integrating area (~20m or better for US agricultural fires). Precise

characterization of pixel footprint (point spread function) and pixel-to-pixel crosstalk

(“blooming”); (Note: pixel oversampling is acceptable and may even be desirable if

properly characterized)

3. Measurements as close as possible to the 3.9um and 11um channels used for operational

fire detection by weather satellites, and well as in the NIR channels at 2.1um, 1.6um and

visible channels that are used by various research algorithm, especially at night.

Observation of fire characteristics at night will be extremely valuable to quantify the

source of uncertainties in various retrieval algorithms, as shortwave radiation from the

Sun and higher surface temperature at daytime can dampen the fire spectral signal.

4. Linear calibration of response in IR channels up to very high temperatures (for instance,

an instrument with a 20m ground footprint would need linear response to roughly 800K;

this is discussed further below).

Previous flight campaigns were conducted using the NASA Autonomous Modular Sensor for

Wildfire (AMS-Wildfire) system on board the NASA Ikhana UAV (Hyer and Ambrosia, 2010).

These campaigns yielded data used to evaluate some aspects of fire properties retrieval, but

many questions could not be addressed because of saturation issues with the 4um channel of the

AMS-Wildfire instrument (Schroeder et al., 2014a; Peterson et al. 2013). Systematic observation

22

of large fires using calibrated ground observations of fire energetics together with an airborne

system meeting the requirements listed above will directly advance exploitation of satellite fire

data by permitting evaluation of the following questions:

1) Does the fire emissivity meaningfully vary with fire properties, or does the graybody

assumption supported by Johnston et al. (2014) hold for all sizes and phases of open burning?

2) What are the effects of smoke on the effective emissivity of fires as observed from aircraft and

from space?

3) Can the combination of infrared and visible observation of fires at night provide useful

constraint on flaming vs. smoldering combustion or fire life cycle, as suggested by Polivka et al.

(2016)?

4) Can the magnitude of FRP uncertainties associated with the point spread function as described

by Schroeder et al. (2010) be verified? Can the information from adjacent pixels be used to

achieve simultaneous constraint of FRP magnitude and subpixel fire location for isolated fires?

5) Can the implication of Peterson et al. (2014) that aggregation of fire pixels can yield

independent (uncorrelated) retrievals of fire area and temperature, be verified?

23

Appendix B - Observations Required to Constrain Upper-Tropospheric Impacts from

Intense Pyroconvection

Large smoke plumes from active fires

often become capped by cumulus clouds

(pyroCu), which enhance the vertical

lofting of smoke through latent heat

release. Under certain conditions, this

“pyroconvection” mechanism allows

pyroCu to develop into a larger fire-

triggered thunderstorm, known as

pyrocumulonimbus (pyroCb),

http://glossary.ametsoc.org/wiki/Pyrocu

mulonimbus). The deep convective

column provides an efficient method for

lofting smoke particles well into the

upper troposphere, drastically enhancing

downwind smoke transport (Fig. 1).

Intense pyroCb activity can also inject a significant quantity of aerosol mass into the lower

stratosphere, occasionally 7-10 km above the tropopause (Fromm et al., 2005, 2008a,b).

While high-altitude aerosol layers are often linked to volcanic eruptions, pyroCb activity is

observed much more frequently, originating almost exclusively in the mid- and high-latitude

forests of North America, Asia, and Australia. The Naval Research Laboratory (NRL) recently

developed a detailed conceptual model for pyroCb initiation and development (Peterson et al.,

2017b). One of the key components of this model is the presence of a deep, dry, and unstable

lower-tropospheric mixed layer, combined with a moist and unstable layer aloft. Similar

meteorology is linked to the development of traditional high-based convection (dry

thunderstorms) found over elevated terrain in semi-arid regions. This implies that pyroCb, far

from the niche phenomenon they initially appeared to be, are in fact a significant and endemic

feature of the regional summer climate in several highly fire-prone regions of the world.

At least 31 pulses of pyroCb activity reached the upper troposphere and lower stratosphere

(UTLS) over western North America in 2013 alone (Peterson et al., 2017a). Since 2000, several

aerosol layers observed in the UTLS, presumed to be of volcanic origin, have been reclassified

as originating from pyroCb activity (Fromm et al., 2010). Fromm et al. (2008a,b) show that a

single pyroCb may inject more than 5% of the background hemispheric aerosol load in the

UTLS. This suggests that annual pyroCb UTLS mass injection is likely similar to a single

moderate volcanic eruption (~106 tons) each year. Therefore, pyroCb are likely a highly

significant process governing the state of UTLS aerosols and chemistry. To date, however, intra-

seasonal UTLS impacts from pyroCb have practically never been considered, much less

systematically explored.

Fig. 1: Intense pyroCb observed in Colorado during June 2013

Fig. 1: Intense pyroCb observed in Colorado during June 2013.

24

Extreme aerosol loading within a pyroCb will induce a microphysical shift relative to traditional

convection (from indirect aerosol effects) toward smaller cloud droplets and ice particles

(Reutter et al., 2014, Chang et al., 2015). Precipitation development is therefore slow or

nonexistent, allowing a large quantity of smoke particles and small ice crystals to reach high

altitudes without being scavenged. This increases the lifetime of pyroCb anvil clouds (Lindsey

and Fromm, 2008). These unique microphysical properties inherent to pyroCb anvil clouds have

been used to develop a method for near-real-time detection and inventory of non-volcanic, high-

altitude aerosol layers (e.g., Peterson et al., 2017a). However, few (if any) in-situ measurements

of pyroCb anvils and downwind UTLS aerosol layers are available from field experiments.

Detailed airborne sampling, in combination with ground and spaceborne observations, is

therefore essential for an improved understanding of pyroCb impacts on UTLS aerosol

composition, chemistry, and cloud properties. These observations must address the following

research gaps:

1. Constraint of aerosol mass injection rates. Understanding the seasonal impact of

pyroCb on the UTLS compared with the background aerosol system requires mass

injection rates derived from satellite remote sensing techniques. However, the total mass

of smoke particles emitted by wildfires has large uncertainty (e.g., French et al., 2011,

Larkin et al., 2014), even for well-observed fires in the USA. In situ observations are

paramount to constrain particle emissions, and determine how much aerosol mass emitted

by the fire actually reaches the UTLS during a pyroCb.

2. UTLS chemistry. Few (if any) studies have addressed the impacts of pyroCb on UTLS

chemistry, including the chemical composition of pyroCb anvils relative to traditional

convective outflow. One important consideration is the fractional partitioning of organic

and black carbon injected into the UTLS, which determines the radiative impact of

elevated particles and can alter their trajectories due to self-lofting. Composition of

pyroCb outflow may also be relevant to chemical processes in the UTLS at regional,

hemispheric, and global scales.

3. Optical properties of smoke particles. The bulk physical and optical properties of

smoke particles from temperate and boreal forest fires have been extensively studied

(e.g., Reid et al., 2005a,b; Akagi et al.,2011). However, particles lofted by pyroCb are

transported to the UTLS through an environment of ice-phase or possibly mixed-phase

condensation, which will likely alter the effective particle properties significantly from

their ground-level values. Dahlkotter et al. (2014) indicates that particle properties of

smoke with a likely pyroCb origin may differ significantly from properties of similarly-

aged smoke measured lower in the troposphere. The amount of mineral dust lofted within

pyroCb plumes and its impact on optical properties also remains unresolved.

4. Effects on cloud particle microphysics. Sampling of ice crystal size (effective radius),

structure (spherical or non-spherical), and habit (e.g., oblate spheroids or rosettes) in the

pyroCb anvil region relative to traditional convection is required for accurate radiative

transfer modeling. These studies are essential for improved pyroCb detection algorithms

(Peterson et al., 2017a), and understanding the life cycle of these unique clouds.

25

5. Relationship to fire energetics. The conceptual model of Peterson et al. (2017b)

indicates that convective lofting of smoke particles through the mid-troposphere is

primarily controlled by latent heating from ambient moisture sources. However, fire

energetics clearly play a role in lifting smoke above the boundary layer (e.g., Val Martin

et al., 2012; Peterson et al., 2014), suggesting that energy released by intense burning

may also serve as a trigger for pyroCb development. Space-based methods of estimating

fire energy release are not valid in the presence of convective clouds. Therefore,

measurements to understand this process will require systematic observation of large fires

from ground, air, and space.

Recommendations for Observations:

Significant contributions to the research gaps outlined above are possible thanks to the upcoming

FIREX, FASMEE, FIREChem and WE-CAN campaigns. Calibrated ground observations of fire

energetics combined with an airborne platform equipped to measure fire size and intensity,

plume/anvil chemical composition, optical and physical properties of smoke particles, and cloud

particle microphysics (size, structure, and habit) can significantly improve understanding of

pyroCb development, evolution, and subsequent UTLS impacts.

26

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