ACPAtmospheric Chemistry and PhysicsACPAtmos. Chem. Phys.1680-7324Copernicus PublicationsGöttingen, Germany10.5194/acp-16-907-2016A review of approaches to estimate wildfire plume injection height within large-scale atmospheric chemical transport modelsPaugamR.ronan.paugam@kcl.ac.ukhttps://orcid.org/0000-0001-6429-6910WoosterM.FreitasS.https://orcid.org/0000-0002-9879-646XVal MartinM.Kings College London, London, UKCenter for Weather Forecasting and Climate Studies, INPE, Cachoeira Paulista, BrazilAtmospheric Science Department, Colorado State University, Fort Collins, CO, USAChemical and Biological Engineering Department, The University of Sheffield, Sheffield, UKR. Paugam (ronan.paugam@kcl.ac.uk)26January201616290792520January201531March201530September201516October2015This work is licensed under a Creative Commons Attribution 3.0 Unported License. To view a copy of this license, visit http://creativecommons.org/licenses/by/3.0/This article is available from https://acp.copernicus.org/articles/16/907/2016/acp-16-907-2016.htmlThe full text article is available as a PDF file from https://acp.copernicus.org/articles/16/907/2016/acp-16-907-2016.pdf
Landscape fires produce smoke containing a very wide variety of chemical
species, both gases and aerosols. For larger, more intense fires that produce
the greatest amounts of emissions per unit time, the smoke tends initially to
be transported vertically or semi-vertically close by the source region,
driven by the intense heat and convective energy released by the burning
vegetation. The column of hot smoke rapidly entrains cooler ambient air,
forming a rising plume within which the fire emissions are transported. The
characteristics of this plume, and in particular the height to which it rises
before releasing the majority of the smoke burden into the wider atmosphere,
are important in terms of how the fire emissions are ultimately transported,
since for example winds at different altitudes may be quite different. This
difference in atmospheric transport then may also affect the longevity,
chemical conversion, and fate of the plumes chemical constituents, with for
example very high plume injection heights being associated with extreme
long-range atmospheric transport. Here we review how such landscape-scale
fire smoke plume injection heights are represented in larger-scale
atmospheric transport models aiming to represent the impacts of wildfire
emissions on component of the Earth system. In particular we detail (i)
satellite Earth observation data sets capable of being used to remotely assess
wildfire plume height distributions and (ii) the driving characteristics of
the causal fires. We also discuss both the physical mechanisms and dynamics
taking place in fire plumes and investigate the efficiency and limitations
of currently available injection height parameterizations. Finally, we
conclude by suggesting some future parameterization developments and ideas on
Earth observation data selection that may be relevant to the instigation of enhanced
methodologies aimed at injection height representation.
Introduction
Biomass burning is a major dynamic of the Earth system
responsible for the emission of massive quantities of trace gases and
aerosols to the atmosphere
e.g.. To understand and
quantify the effects of these biomass burning emissions on atmospheric
composition, air quality, weather, and climate, many fire emission
inventories have been developed at scales such as individual areas,
countries or regions e.g., continents
e.g., or the entire globe (e.g. FLAMBE
(Naval Research Laboratory), GFED (G. van der Wref, VU University Amsterdam),
FINN (National Center for Atmospheric Research, NCAR), GFAS (European Center
for Medium-range Weather Forecast, ECMWF))
respectively.
The use of satellite Earth observation (EO) data is generally considered to
be critical to providing the temporal coverage, spatial sampling frequency,
and directly observable parameters necessary for creating these inventories,
particularly so since landscape fires are highly variable emissions sources,
and the exact amounts of material released by the combustion process is
highly variable in both space and time .
When a landscape fire occurs, a rising plume created from the intense heat
and convection produced by the energy released by burning vegetation
interacts with the ambient atmosphere and transports the smoke emissions,
affecting their longevity, chemical conversion, and fate .
This makes the manner in which the fire emissions are injected into the
atmosphere highly variable and sensitive to the smoke plume dynamics. To
follow the terminology commonly used in the literature
e.g., when not specified, the term “fire emission”
refers to the gaseous and aerosols emissions only and not the heat fluxes
(e.g. radiation) emitted by the fire.
Figure shows EO satellite views of the evolution of the
smoke plume generated by the “county fire”, which occurred in Ocala
National Forest (Florida) in 2012. The fire was active from 5 to
13 April 2012 and burned across nearly 14 000 hectares (140 km2) of
land. The apparent intensity and direction of travel of the smoke plume
changes every day, and such variability is most likely related to both
changes in the fire activity (for the former) and the local ambient
atmospheric conditions (for the later). Together the fire and ambient
atmospheric characteristics are the main drivers of the plume dynamics and
therefore ultimately of the smoke emissions transport.
True colour composite of daytime observations of the county fire
(USA), made from the Moderate Resolution Imaging Spectroradiometer (MODIS)
satellite EO sensor. The fire occurred in Ocala National Forest (Florida)
between 5 and 13 April 2012. MODIS data from all available Terra and Aqua
satellite overpasses are shown, with the local time indicated. Overlain on
the colour composite imagery are red vectors that outline pixels detected as
containing active fires by the MODIS MOD14/MYD14 Active Fire and Thermal
Anomaly Products . The regularly
changing nature of the fire and the smoke transport apparent from this time
series, as well as the presence on some days of bifurcated plumes, is very
apparent.
In addition to the use of in situ measurements e.g. and
satellite Earth observation e.g., the wide-ranging
controls on and impacts of landscape-scale fire emissions can be investigated
using atmospheric chemistry transport models (CTMs)
e.g.. Such models require
information on the quantity and timing of the fire emissions, as well as
their chemical makeup, and these generally come from the aforementioned
emissions inventories. However, for a more complete representation of the
source fires, many CTMs can also make use of information on the altitude at
which the bulk of the emitted species is injected into the wider atmosphere,
where they can fully interact with ambient atmospheric circulation. In a
recent study on fire emission transport use the GEOS-Chem
CTM with a horizontal resolution of 2∘×2.5∘
and 47σ levels forming a vertical stretched mesh with a resolution of
150–200 m near the planetary boundary layer (PBL). Since at these
resolutions we cannot resolve the plume dynamics ≲100 m, parameterizations are therefore required to
represent these “smoke plume injection heights” (InjH). The aim of this paper
is to review the different approaches required for providing these
parameterizations. The paper is structured as follows. First,
Sect. provides the background detail on fire plume
observations and modelling in large-scale CTMs. The main physical mechanisms
responsible for the fire plume dynamics are discussed in
Sect. . The primary satellite EO data used currently to
study plume injection height properties are detailed in Sect. .
Then, the currently available injection height models and their
implementations are discussed in Sect. . Finally, a
summary and suggestions for further developments in this area are provided in
Sect. .
Introduction to landscape fire plume observations and modelling
Fire emissions are a particular case of emissions to the atmosphere, since
they can be injected into the atmosphere far above the PBL and can thus
potentially spread over a long distance according to local atmospheric
circulation patterns. Only emissions from aircraft traffic
and volcanoes , which are also coupled
with intense dynamical mechanisms, offer a similar capability.
The question of the impact of fire emission injection in the atmosphere was
first introduced by and was later extensively reported
in EO data. For example, injections of gases and aerosols emitted from
vegetation fires have been observed at various heights in troposphere and
occasionally even the lower stratosphere . Smoke remnants
from certain tropical fires have been observed at 15 km altitude
, and plumes from individual Canadian stand-replacing
forest fires can also reportedly approach such heights .
For the largest events, observations from show that a
single fire was able to induce a significant average surface temperature
decrease at the hemispherical scale. The emissions from such large fire
events are capable of spreading extremely rapidly, and
show that the transport of emissions from an Australian fire in 2006 spread
around the globe in only 12 days.
Using EO data from the Multi-angle Imaging SpectroRadiometer (MISR)
instrument onboard the Terra satellite we estimate that 5 to
18 % of 664 plumes observed from boreal forest fires over Alaska and the
Canadian Yukon in 2004 reached the free troposphere (FT). Using
AI peak observation from TOMS, backward trajectories to identify location of
the causal fire, and then GOES and/or American and Canadian fire report data
bases for confirmation, identify a total of 17 plumes that
reached an altitude of at least 10 km for the year 2002. Fires whose
smoke columns reach these elevations are also likely to be those that emit
large quantity of gases and aerosols; therefore, even though such large
and intensely burning fires are relatively less common than smaller, less
intense events, their impacts are likely to be much greater than the
“average fire” . Other evidence shows that fires from
agricultural or grassland vegetation type (usually less intense than those
from boreal forest) can also generate plumes reaching the FT.
show that half of the agricultural fires they observed
over eastern Europe for the period 2006–2008 reach heights above the
PBL. show that 26 % of the 27 Australian grassland
fires they studied with various stereo-height retrieval algorithms rose above
the PBL. In summary, the height to which biomass burning plumes rise, and the
distance over which the emissions are therefore transported, is highly
variable. Possibly even more variable than fire behaviour, since the same
fire burning under different ambient atmospheric conditions will probably
result in different plume behaviours. It is important to note however that
certain atmospheric conditions are more favourable to fire occurrence than
others, such as high pressure and/or low moisture (dry
season) conditions .
Fully modelling the impacts of biomass burning emissions at large scales
requires an understanding of plume dynamics, including their InjH. Some InjH inventories are already available, for example derived from
satellite EO data of aerosols or CO. For example,
screened aerosol index (AI) measurements extracted from data
collected by the Ozone Monitoring Instrument (OMI) and the Total Ozone
Mapping Spectrometer (TOMS) to map high aerosol clouds (>5 km) related to
wildfires over the period 1978–2009;
and use an inverse modelling method based on the GEOS-Chem
model and EO-derived vertical measurements of CO concentration in
the free troposphere and lower stratosphere (from the Tropospheric Emission
Spectrometer (TES) and the Microwave Limb Sounder (MLS) sensors). The
approach was able to retrieve an estimate of both the emitted CO magnitude
and the injection height profile.
Although the above EO-based approaches are useful to understand and quantify
the occurrence of wildfire plumes at different heights, they have limited
sensitivity to the potential variability of InjH. Both inventories are
therefore quite difficult to couple to fire emissions inventories and cannot
be easily linked to particular fires and therefore to actual emission totals.
Capturing the high variability of plume dynamics, estimating InjH, and
implementing this within a CTM therefore remains a current topic of very
active research
, and the task
of this paper is to review the different approaches currently available.
Physics of landscape fire plumes
The injection height of a smoke plume is controlled by the plume dynamics,
which are driven by both the energy released by the fire and the ambient
atmospheric conditions (both stability and humidity)
. In the time period between the emissions
being first released by the combustion process (which happens at the flame
scale of ∼ mm), and their later release into the wider atmosphere
(which operates on a metre to kilometre scale), the smoke emissions are trapped in the
plume (see Fig. ). Here the dynamics are dominated by
Schematic view of the physical processes involved in fire plume
dynamics. Red and yellow colours stand for atmospheric or fire-induced
mechanisms respectively.
the buoyancy flux induced by the convective heat flux (CHF) generated by
the fire itself;
the size of the combustion zone, which controls the surface area of the
plume interacting with the atmosphere ;
the
ambient atmospheric stratification which acts on the buoyancy of the initial
updraft and also on the later level of the detrained smoke
as smoke injected above the PBL tends to accumulate in layers of relative
stability ;
the degree
of turbulent mixing occurring at the edge of the plume, which affects the
entrainment and detrainment of ambient air into the plume and which slows
down the initial updraft and control the release of the smoke into the wider
atmosphere ;
the wind shear, which also affects
horizontal mixing and therefore the ent-/detrainment mechanism in the plume;
the latent heat released from the condensation of water vapour
entrained into the plume from the combustion zone (water is a primary
combustion product) and/or from the ambient fresh air
.
In some scenarios, the combination of these processes initially triggered by
the heat released from the vegetation combustion is capable to producing deep
convection in places where natural convection would not normally be possible;
the so-called pyroconvection phenomena .
show that in the case of large events like the Chisholm
fire documented by, the energy budget of the plume is
essentially driven by the latent heat released from the condensation of the
entrained water vapour.
Depending on the quantity of water vapour condensed during the plumes development, three types of vegetation fire plume can be identified
.
Dry smoke plumes containing water vapour rather than liquid droplets. These are typically
created by smaller, weakly burning and low intensity fires and usually stay trapped in the
PBL.
Pyrocumulus (PyroCu), which are formed from cloud droplets. Water vapour here condenses
in the plume after it has reached the altitude of the lifted condensation level (LCL).
Depending of the stratification and ambient humidity of the atmosphere, these plumes may be trapped in the PBL or reach the FT.
Pyrocumulonimbus (PyroCb) which contain ice particles present in an anvil
shape capped over the plume. Such plumes can reach the stratosphere, aided by
the extra heat released from the ice formation. They are not frequent but
rather extreme scenarios that can be compared in nature to plumes from
explosive volcanic eruptions. For example, reported 17
events in North America for the year 2002, while for the same time period 73 457 fires were reported only for the USA (source: National Interagency
Fire Center). PyroCb are usually triggered by very large, intensely burning
fires occurring in favourable atmospheric conditions for the phenomena. The
exact conditions are still a matter of debate; however several studies have
demonstrated the influence of fire size , unstable lower
atmosphere , the ambient mid-level moisture
, and/or the presence of an approaching cold front
. For examples of
PyroCb see the website http://pyrocb.ssec.wisc.edu, which has been reporting
PyroCb events since May 2013.
Since the initial trigger of plume rise is the heat released by the casual fire,
InjH are strongly influenced by fire diurnal cycles .
This leads to lower nocturnal InjH which are amplified by the combination of
nighttime stable atmosphere and lower PBL . However some
meteorological conditions can intensify fire activity over night, as for
example the Santa Ana foehn wind , and keep them
running. Few observations of nocturnal plumes triggered by those intense
fires are available , and to our knowledge only
tackle the issue of modelling nocturnal InjH. Their
approach relies on a simulated diurnal cycle based on the high temporal
resolution (∼15 min) fire radiative power (FRP) product of the geostationary orbiting
satellite SEVIRI and the parameterization of
(further discussed in Sect. ).
Despite the low resolution of SEVIRI (>3 km), their empirical diurnal
cycle captures the expected fire intensity increase at night, but no effects
were found on InjH. Their resulting modelled InjH shows a strong diurnal
pattern with low nocturnal InjH (e.g. maximum monthly mean nocturnal InjH
lower than 2.5 km).
Of course, a full understanding of the complex coupled mechanisms inherent in
fire plume dynamics is extremely challenging, and many points remain unclear: for example, the role of soot and aerosol in the heat transfer within the
plume column and the effect of the number of initial
cloud condensation nuclei on the triggering of pyroconvection
.
Earth observation data used to support wildfire injection height estimation
Sensors and imagers onboard EO satellites can provide various information on
wildfire plumes, including their trace gas ratios e.g., aerosol burden e.g., and
their height, including on occasion the vertical distribution of material
within them e.g.. provide a recent
review of this topic. EO data also provide information on the characteristics
of the causal fires themselves, including “active fire” (AF) products that
detail the location, timing, and FRP of the landscape-scale fires occurring within the EO satellite pixels
.
FRP is a fire characteristic that has been shown to relate quite directly to
the total heat produced by the combustion process and
also to the rate of fuel consumption , trace gas
, and aerosol e.g. emission. Such
active fire products are usually derived from thermal wavelength Earth
observations
.
No satellite product is yet able to derive information on plume heights at a
spatial and temporal resolution than matches those of sensors used for active
fire detection and smoke emission estimation, such as e.g. the Moderate
Resolution Imaging Spectroradiometer (MODIS), Meteosat SEVIRI, or the
Geostationary Orbiting Environmental Satellite (GOES)
. Therefore,
determination of the injection heights at spatiotemporal scales and levels
of completeness approximately matching these type of active fire observations
is more likely to rely on InjH parameterizations.
Direct measures of smoke plume height
Smoke plume height can be evaluated from spaceborne platform using either
Lidar technology (Sect. ) or stereo-matching algorithm
based on passing imaging system (Sect. ).
Spaceborne lidar
The primary spaceborne lidar used for estimating smoke plume heights is the
Cloud-Aerosol Lidar with Orthogonal Polarization (CALIOP), operated onboard
the CALIPSO satellite. CALIOP provides a backscatter signal at 562 and
1064 nm over a 70 m wide ground track. Measures in the two wavebands
are used to derive a Level-2 product that classifies aerosol layers into
dust, smoke, or marine classes, as well as providing height profiles (see
Fig. ).
Example of profiles for Level-1 CALIOP 532 nm total attenuated
backscatter data product (top) and the matching Level-2 product of aerosol
layers (bottom) for the 28 August 2006 over the Klamath Mountains in
California and Oregon. The presence of aerosols classified as biomass burning
smoke can be seen. Image from .
CALIPSO is part of the A-train satellite constellation and flies 75 s
behind the Aqua satellite. The main advantage provided by CALIOP is its high
vertical resolution of 120 m, and its main limitations are (i) noise
effects created by sunlight that impact the results from daytime overpasses
and (ii) the narrow ground track that limits the number
of observed plumes that can be linked to their causal fires
.
While the CALIOP Level-2 product is able to directly sense the altitude and
thickness of the plume layer detrained in the atmosphere (see
Fig. for a particular case where the plume axis is capture
by the CALIOP track), most studies only refer to the top plume height, which
in most cases is used to determine the InjH measure
e.g..
Using CALIOP data, examined plume heights from fires
occurring in a number of countries and regions worldwide. Only in South
Africa and Australia were definitive conclusions drawn, as in eastern
Europe, Portugal, Indonesia, and the western United States cloud cover was too
complete and/or CALIPSO overpasses were not well timed with regard to regions
affected by fires. Whilst did not examine collocated
CALIOP and active fire product data, they did examine the bulk effect of fire
emissions in South Africa and parts of Australia, where fire activity is
mostly controlled by smaller, highly numerous savannah fires. They found that
for most of the CALIOP ground track, the aerosol layer was trapped within the
PBL. Their conclusion that most fires inject material into the PBL may be true
for this type of fire activity but may not be the case for other regions
such as forests where more intense fires can occur . In
another study based on CALIOP data covering eastern Europe,
focused on agricultural fire emissions over
2006–2008. They found that 50 % of the 163 fires examined were
above the PBL, with injection heights ranging from 1677 to 5940 m.
collocated the CALIOP overpasses with MODIS active fire
data from the Aqua satellite and used FRP measures derived from the MODIS
observations as a proxy for the strength of the fire activity. They concluded
that the aerosols seen to be located above the PBL were a direct result of
fire emissions and were not related to large-scale atmospheric transport.
Furthermore, they demonstrated that in the presence of an unstable
atmospheric layer in the troposphere, a linear relationship holds between FRP (from MODIS) and plume-top height (from CALIOP).
This is a similar result as that shown by with respect to MISR-derived plume heights (see below).
Stereo-imagers
Cloud-top heights have long been derived from stereo imaging, and the same
methodology can be used to derive heights of smoke plumes
. The primary instrument used for this purpose is the
MISR, operated aboard the NASA Terra
satellite. This satellite is not part of the A-train but rather has a daytime
Equator crossing of around 3 h before Aqua (at 10:30 a.m.).
Example of smoke plume height derivation using data from the
Multiangle Imaging SpectroRadiometer (MISR) that operates on the Terra
satellite. This example is extracted from . The fire took
place on the 12 July 2013 in New Mexico and was observed by MISR at
18:09 UTC. The MISR nadir RGB image showing a smoke plume in grey and
a PyroCb in bright white is reported in (a), when the plume
stereo-heights derived from the MINX software are shown in
(b). MINX height retrieval profiles are shown in (c). Note
the dramatic difference in the heights which reach 11–12 km a.s.l. in
the PyroCb and stay trapped in a layer around 6–8 km a.s.l. in the
vicinity.
MISR can retrieve (i) total column aerosol optical thickness and
(ii) the altitude of the atmospheric cloud or aerosol layer over cloud-free
land and water surfaces. The altitude retrieval is based on a stereo-matching
algorithm that uses the nine MISR collocated images available for each
location wherever clouds or aerosol plumes have discernible spatial contrast,
with about 500 m vertical accuracy at a 1.1 km horizontal resolution
. The 380 km swath of MISR is centred within the
2330 km swath of the MODIS sensor present on the Terra satellite. Up to
now, the specific derivation of smoke plume height from raw MISR data has
been made using the MINX software tool .
Figure shows an example of MINX output for a wildfire smoke
plume, and provide full details of the use of MINX for
this purpose. One constraint of MINX is the manual nature of the process
required to digitize the smoke plume contour, used by the algorithm to
compute the wind vector during the plume height retrieval. This wind vector
is required to correct for the displacement occurring between the times of
the nine collocated but differently angled MISR views. Post-processed plume
heights for more than 25 000 plumes worldwide are accessible through the
MISR Plume Height Project
Because of its relatively high degree of spatial coverage
estimate that MISR is at a minimum 40 times more likely to observe a plume
that can be linked to a causal fire than CALIOP is. explain
that MISR and CALIOP are, however, highly complementary since (i) they have
different overpass local time as they are on differently orbiting satellites
and (ii) CALIOP is able to detect optically thin older plumes, while
MISR is essentially sensitive to only young plumes exhibiting high contrast
with the background. One major drawback of MISR is, however, its relatively
early daytime overpass, which limits its ability to observe mature PyroCu as
they typically reach their maturity in the late afternoon around
18:00 local time;. Therefore, MISR-derived plume heights
are biased toward lower altitude plumes . The relative
lack of highly elevated plume observations from MISR was also reported by
. For some of the fire events encountered in their study,
pointed out that the subsequent transport of CO and
black carbon were better captured by a crude model of homogeneously spread
emissions up to the top of the troposphere than by an emission profile based
on MISR-derived plume heights.
Statistical analyses of MISR-derived plume height data are available in
, , ,
, , and . These studies
confirm that the majority of the detected plumes are trapped within the PBL,
though geographical location and land cover type have an influence. For
example, show in their study on fires located in Alaska and
the Yukon regions that 5 to 18 % of the fires they observed for the
summer 2004 reach the free troposphere, while showed that the quasi-
totality of fires observed in Borneo and Sumatra (areas impacted strongly by peat fires) from 2001 to 2009 (317 fires)
were trapped in the PBL. conducted a detailed analysis of the
MISR-derived plume height data for fires in North America over a 5-year
time period (2002, 2004–2007), finding no clear rules governing the
capability of plumes to reach the FT, even when the fires were split per
biome. However, show that the percentage of plumes
reaching the FT in forest fires (more intense) was larger than crop/grassland
fires (less intense).
The along-track scanning radiometer series of sensors have provided a 512 km wide swath
stereo-viewing capability since 1991, and recently
developed an automated stereo-height retrieval algorithm
(M6) working with data from the advanced along-track scanning radiometer
(AATSR). Unlike MINX, M6 is not able to correct for the plume displacement
induced by the ambient wind. However, it was estimated that such correction
would lie in the error of the M6 algorithm (D. Fisher, personal
communication, 2015). M6 was applied to AATSR data of Eurasian boreal forests
for the April–September period of 4 years between 2008 and 2011 and
showed successful comparisons with collocated observations of smoke layer
height derived from CALIOP lidar collections and MINX-derived stereo-heights
from MISR. Unfortunately, AATSR also has a bias towards low injection heights
since the overpass time is similar to MISR. A wider swath instrument
following on from AATSR, the Sea and Land Surface Temperature Radiometer
(SLSTR), will operate from 2015 . However, this
will still not provide daily stereo-data worldwide, and with a limited number
of stereo-observations the continuous, direct measurement of smoke plume
heights at the global scale appears to be a difficult task.
Measure of buoyancy flux and fire size
Among the processes inherent to the plume dynamics and listed in Sect. , the buoyancy flux and the fire size are the two sets of
information needed to characterize the fire. The buoyancy flux generated by
the combustion heat release is the primary source of energy responsible of
the plume rise. The latent heat, which provides energy to the plume is a
secondary source, can only be trigger if the plume reaches its LCL altitude.
This LCL altitude can be different from the atmospheric LCL as water content
and temperature profiles in plume usually differ from the ambient conditions.
To understand the behaviour of the plume dynamics and explain variation in
InjH, quantitative information on both the buoyant flux and the fire size is
therefore needed. The vertical buoyant flux F is defined as
F=g(ρ-ρ0)ρw=gRcppoQc,
where g is the gravity constant, R is the ideal gas constant, ρ is
the density of the plume, w is the vertical velocity of the plume, ρo
and po are the ambient density and pressure, cp is the heat capacity at
constant pressure, and Qc is the convective heat flux. In large-scale models (>100 m) wildfires are usually represented with a constant
partition of convective and radiative energy emission
with a ratio β of convection to
radiation ranging from 1 to 5, so that Qc is related to the
radiative heat flux Qr: Qc=βQr. The
values of β are essentially based on experimental studies performed at
small scale , and their applications to large
scale remain uncertain. In a model sensitivity study of the Chisholm fire run
with the high-resolution three-dimensional plume model ATHAM,
show that a ratio β greater than unity is crucial
in their case to trigger the mechanism of pyroconvection. With value of
β lower than unity, not enough latent heat is able to reach the
condensation level.
A bi-spectral algorithm based on middle infrared (MIR) and thermal infrared bands was proposed by to estimate the kinetic
temperature Tf and the AF area Af of the black body that
would emit the same radiances as the observed fire. According to the
Stefan–Boltzmann equation, Qr=σTf4, where σ is the
Boltzmann constant. This makes the buoyancy flux F a direct function of
Tf. The Dozier algorithm is therefore able to provide all information
necessary to characterize the fire (i.e. F=f(Tf) and fire size) as AF
area can be used as a proxy for the fire size.
Several implementations of this algorithm have been developed and used with
sensor of different resolution: e.g. the BIRD Hot spot Recognition Sensor
185 m,, MODIS 1 km,, or
GOES 3 km,. The algorithm is found to be highly
sensitive to the determination of the long-wave brightness temperature
background and to a lesser extent to the atmospheric
transmittance . As a result it is not converging for
≈10 % of the case. However, this method represents the best
available option to estimate buoyancy flux and fire size.
Current representation of wildfire emissions injection height in CTMs
A number of studies have determined the very serious implications that
incorrect InjH estimates have on the ability of CTMs to represent emissions
transport e.g.. Consequently it may also
effect (i) “top-down” emission estimates based on the inversion of
observed atmospheric concentrations of biomass burning species
and (ii) radiative forcing studies .
This section aims to review the different parameterizations that are
currently available to tackle the issue of InjH. They are based either on
empirical, deterministic, or statistical models.
Because of the complexity of fire plume dynamics, in the early endeavour of
biomass burning impact on the atmosphere, CTMs often assume a single fixed altitude for
all biomass burning emissions usually presuming that all pollutants are
contained solely within the PBL e.g.. However,
such assumption cannot represent the observed variability of injection height
described in Sect. . To improve the representation of
fire emission at large scale, some studies used a prescribed fixed profile
either build on (i) simple hypothetical ratio between boundary layer and
tropospheric emission e.g.
or (ii) average local observations . In the latter work, the
authors use the GEOS-Chem model with different vertical and temporal
emissions distribution to simulate CO and aerosol transport over North
America during the fire season 2004. Comparing their simulation results
with satellite-, aircraft-, and ground-based measurements, they show that the
use of finer temporal distribution enhances long-term transport, while
changes due to different InjH implementation are small. However, as already
mentioned in Sect. , they also point out that the
finer vertical modelled profile emission they implemented is probably
affected by MISR observation bias. Most of these early studies do not provide
grounded solutions to the problem of fire emission injection role in the
atmospheric circulation but rather emphasize the challenge of developing InjH
models.
Deterministic modelsInjH models description
Several studies develop deterministic models capable of being host in CTMs.
They are usually based either on physical or dimensional analysis.
review the different type of existing plume rise models.
In particular, they discuss the use of plume rise models in the framework of
the Blue Sky project, which aims to derive smoke emission for air quality
models such as the Community Multiscale Air Quality (CMAQ) modelling system.
Here, we limit our review to plume rise models originally built to handle
fire plume dynamics (see list of physical processes in
Sect. ). Models like Daysmoke or the
Briggs equation , which are both available in the CMAQ
system, are more suitable for small fires like control burns
to forecast or prevent emission dispersion and air
pollution (i.e. local PM2.5 concentration). When used with
wildfires, they generally fail to predict large fire impact, certainly
because of their weak representation of microphysical processes
which affect the simulation of PyroCu and PyroCb
plumes. For example, using the Briggs equation and the CMAQ model to simulate
fires emission in the USA between 2006 and 2008, show
that most of their plumes where below the level expected from remote-sensing
measurement.
At present, three parameterizations of plume rise model stand out of the
literature, namely , , and
. A brief description of each models is reported below.
develop a one-dimensional cloud-resolving
model (hereafter named Plume Rise Model version 0, PRMv0) based on the
original plume model of , in which equations for vertical
momentum, first thermodynamic law, and continuity of water phases are solved
explicitly. The model is solved offline and the final injection height is
then used in the host CTM. In their approach the fire is modelled as an
homogeneous circle defined with (i) a size derived from the active fire area
of the WF-ABBA GOES product Wild Fire Automated Biomass Burning
Algorithm; (ii) and a buoyant flux/CHF calculated as a constant
fraction of the total heat. The total heat is set as a
prescribed value depending of the vegetation type. The cloud physics is based
on a simple microphysical module counting three hydrometeors (cloud, rain, ice).
Additionally, the horizontal momentum is parameterized through two entrainment
coefficients modelling the effect of (i) the turbulence at the edge of the
stack ∝|w|R; and (ii)
the drag caused by the ambient wind shear ∝(ue-u)R;. In previous formula, R is the radius
of the plume, and u, ue, and w are the horizontal plume, horizontal
ambient, and vertical plume velocities respectively. R, u, and w are prognostic
variables of the model.
implement in the LMDZ model a parameterization based on an
eddy diffusivity/mass flux (EDMF) scheme originally developed to model
similarly shallow convection and dry convection. In comparison with the
implementation of , this adaptation of EDMF for
pyroconvection (pyro-EDMF hereafter) is not based on prognostic equation
solved offline but rather evaluates turbulent fluxes produced by the
temperature anomaly created by the fire at a sub-grid level and directly adds
the source term to the transport equations of the conservative variables of
the host CTM. The fire is considered a sub-grid effect and its CHF is
modelled as a fraction of the surface sensible heat flux averaged over the
host model grid cell. The interest of this approach is that the dynamics of
the plume is coupled with the ambient atmosphere, so that for example change
in the stability of the atmosphere induced by the fire can impact the later
development of the plume. In their approach, apply this extra
turbulent flux to the total water, the liquid potential temperature, and the
CO2 concentration, so that the effect of latent heat can be handle in the
CTM, simplifying the formulation of the parameterization. The mass flux
formulation of pyro-EDMF relies on the definition of two entrainment and
detrainment fluxes which are set differently in the PBL and above. Therefore,
the mass transfer between the plume and the ambient atmosphere is solved all
along the plume. One limitation of the current version of pyro-EDMF is that
ambient shear at sub-grid level is not represented. This certainly
overpredicts injection height of small fires which are more sensitive to
wind drag.
use energy balance in the up-draft and some dimensional
analysis to develop an equation for the prediction of plume top height based
on input of the FRP, the Brunt–Väisälä frequency, and the PBL height. The
equation parameters are fitted using a learning data set of plume height
measurement randomly selected in the MISR data set. This formulation does not
take explicitly into account effects from either entrainment, cloud
formation, or ambient wind shear. Another limitation of the equation of
is inherent to the selection of the fires used to fit the
equation parameters. All events from the learning (and the control) data set
used in this study are lower than 4 km. This implies that few PyroCu and
certainly no PyroCb are present in the fit of the model.
InjH model validation: fire per fire comparison
Although validation on a fire per fire basis appears to be the best way to
ensure the correct functioning of plume rise parameterization, because when
implemented in the host model it is highly coupled with the large-scale
circulation, few validation exist and generally show poor agreement. In their
original presentation, PRM and pyro-EDMF have been compared with documented
fire events as for example the three-dimensional LES simulation of the Chisholm fire
, but those tests are far
from being a systematic validation ranging over different fire and atmosphere
configuration. Example of those comparisons are reported in
Figs. and for PRMv0 and
pyro-EDMF respectively.
Results from the one-dimensional plume rise model (PRM) of
for a fire burning in (a, c) calm
and (b, d) windy atmosphere scenario, as studied by
. The fire has an active fire area (AF area) of 10 ha.
The quantities shown are vertical velocity (W, ms-1 ), vertical mass
distribution (VMD, %), entrainment acceleration (Ea, 10-1 ms-2),
buoyancy acceleration (Ba, 10-1 m s-2), and total condensate water (CW, g kg-1).
Model results considering the environmental wind drag are shown in red, whilst those in black depicts the results from
simulations disregarding this effect. Grey rectangles indicate the main injection height simulated
by the three-dimensional ATHAM model for the same fire scenario. Figure from .
Smoke plume characteristics for the Chisholm fire, as simulated by
pyro-EDMF: virtual potential temperature (K), vertical velocity excess
(m s-1), and cloud liquid water (g kg-1) are shown. Figure from
.
propose the first evaluation of the PRMv0 model. They run
a comparison against ∼600 fires events captured by MISR that occur in
Alaska in spring 2008 during the 10 days of the NASA Arctic Research of
the Composition of the Troposphere from Aircraft and Satellites (ARCTAS)
campaign. They implement two fire initialization schemes, both based on
WF-ABBA and MODIS data for fire detection but using different temporal
representation of the fire size based on either the diurnal cycle estimated
in the FLAMBE inventory or kept constant as in the preprocessing of WRF-Chem.
They found the best comparison PRMv0-MISR for the FLAMBE-based initialization
with a one-to-one correlation of 0.45. They infer the bad response of PRMv0
partly to the quality of their atmospheric profile, emphasize the importance
of correct atmospheric profile as already mentioned by or
.
More recently, compare a subset of the MISR data set
for North America with prediction from an improved version of PRMv0. Their
model (PRMv1 hereafter) keeps the same model core but uses a new
initialization module where CHF and fire size information are derived for
each fire from MODIS observation. Despite a selection of several method to
estimate PRMv1 input data, show that over the large
range of conditions encountered, PRMv1 is not able to reproduce the plume
heights observed by MISR or to even locate the fire correctly above or below
the PBL. Their comparison is based on a total of 584 plumes selected from
the MISR data where the following constraint apply: the plume height is
computed immediately above the fire (not from the whole plume as in the
original MISR data), the plume is formed of at least five stereo-height
retrievals, the clustered MODIS fire pixels are located within 2 km of the
plume origin, and the terrain height of the input atmospheric profile do not
differ from the terrain elevation used in the MINX software by more than
250 m. Despite this data quality screening, the best one-to-one
correlation they obtain is about 0.3.
In their approach, use the whole MISR data set (counting
2000 fires at that time) without any filtering. Because of its derivation
based on an optimization procedure, their model compares relatively well to
the selected MISR data. However, when compared with the current full data set
for North America, results are not as good, showing a constant
underestimation of plume height, in particular for high plumes.
Figure shows together a comparison of our implementation of the
Sofiev model against (i) the original version of the model (ii) and against
3206 “good” quality flag fires of the North American subset of the MISR
data set. Even if our implementation of the model exhibits a slight positive
bias certainly due to a different estimation of the PBL height which
we read from the diagnostic products of the forecast run of,
our comparison with the MISR data shows a strong negative bias of the model.
Similar behaviour was also shown for PRMv1 in the study of
. When compared with the PRMv1 sensitivity study of
(Fig. b and Fig. 2 of
, show the same metrics), the Sofiev model does not
perform better, showing a regression line slope of 0.4 for the Sofiev model
against 0.8 for the best set-up of PRMv1. Note however that here we are
using a larger extent of the MISR data set than in .
Comparison of our implementation of the plume rise parameterization
of to (a) the original results from for
the same fires and (b) plume stereo-height retrievals extracted from the
North American subset of the (MISR) plume height project data
, derived using the MINX tool as shown in Fig. . Our implementation of the model differs
from the original in its definition of the PBL height, which in our approach
is extracted from the diagnostic product of ECMWF forecast runs
. See Figs. and
for a statistical overview of the North American MISR data set. Note that
the model did not retrieve simulated plume heights for all
the 3320 selected fires of that data set. For 114 fires, either the
Brunt–Väisälä frequency could not be retrieved or the FRP of the most
powerful pixel listed in the MISR product was unavailable seefor
details in the initialization of the model. Panel (b)
shows the same metrics as Fig. 2 of , i.e. two-sided
regression line (grey), box plots of the distributions of model heights and
500 m resolution MISR heights for central 67 % (box) and central 90 %
(cap), median distribution regression line (magenta), and 1:1 relationship
(dashed black).
InjH models implementation
Simulations performed using the pyro-EDMF plume rise model of
for sub-Saharan Africa between 10 and 30 July 2006. In the
upper panel, (a) shows the maximal injection height of CO2
emissions simulated with the LMDZ model and pyro-EDMF between 5
and 20∘ S over the 20 days of the simulation. (b)
reports the maximal injection height (green), mean injection height of
emissions injected above the boundary layer height (red), and mean boundary
layer height (black) averaged between 5 and 20∘ S altitude and
over the 20 days of the simulation. (c) shows the percentage of
cases for which the injection height passes the boundary layer height. In the
lower panel, (e) shows the averaged vertical distribution of
CO2 mixing ratio (ppmv) for the same reference simulation and
(d) for simulations without pyro-EDMF and (f) with
pyro-EDMF set up with a lower value of the ratio β=entrainmentdetrainment=0.1 (right). The reference
simulation in (e) uses a value of β=0.4. Figure from
.
Despite the lack of conclusive fire per fire validation (see previous
section), plume rise parameterizations have been implemented in several
regional and large-scale models. PRMv0 has been coupled with the Weather
Research and Forecasting (WRF) Model
and the Coupled Aerosol and Tracer
Transport model to the Brazilian developments on the Regional Atmospheric
Modelling System CATT-BRAMS;. Additionally, pyro-EDMF
is present in the mesoscale non-hydrostatic model
MesoNH; and the general circulation model LMDZ
. See Table 1 of for a more complete
list of atmospheric models with plume rise parameterization.
Several studies highlight the need to inject fire emission at high altitude
, and recent in situ
and remote-sensing observations show the frequent
occurrence of large PyroCb. However, the role of plume rise parameterization
in transport of fire emission at a large scale in CTM simulation is still a
matter of debate. A list of different conclusion from recent studies is
reported below.
who are using PRMv0 embedded in WRF-Chem, simulate 10
days of the Spring 2008 ARCTAS campaign. As for their fire per fire
comparison (see previous section), they show that among their two
initialization schemes, the use of the FLAMBE-based initialization gives the
best emission transport when compared with the Atmospheric InfraRed Sounder
(AIRS) total columns CO and CALIOP aerosol profiles. Also a
comparison with coarser injection schemes (distributing all fire emissions in
the PBL or between altitude levels of 3 and 5 km) shows that the use of
PRMv0 is improving the simulation.
run simulations of the LMDZ model over the month of July
2006 for Africa on a strip located in the tropics between 5 and 20∘ south. Fires locations and emissions are estimated from the burnt area
product L3JRC while fire activity is idealized with a constant fire area of
2 km2 and a Gaussian diurnal cycle peaking at 15:45 LTC.
Figure show results from their simulations for
different values of their parameter β which defines the ratio between
the entrainment (ϵ) and detrainment (δ) coefficients for the
levels located above the PBL. Both ϵ and δ are set constant
(no altitude dependence) and inversely proportional to the base of the plume
radius. Their results show that pyro-EDMF is sensitive to the value of the
parameter β as the detrainment altitude control the final spread of the
smoke emission. also show that LMDZ was able to predict the
daily tropospheric emission (DTE) of CO2 (daily variation of CO2 in the
troposphere) observed by . However, their simulated
amplitude of DTE for southern Africa is much lower than the observed value.
focus only on tropical fire in Africa. In the tropics,
natural convection is more active than in higher latitude and fire-generated
heat and vertical water transport could be a trigger to initiate natural
convection (private communication Ben Johnson). Testing pyro-EDMF on a boreal
forest fire scenario would be interesting.
run the
WRF model coupled with PRMv0 initialized with fire size input data estimated
from in situ measurement. Running WRF at cloud-resolving scale over Alaska
for 2 days for summer 2004, they show that the use of PRMv0 improves the
results when compared to radio sounding.
run
WRF-Chem coupled with PRMv0 to examine CO budget in California over 1 month
of the summer of 2008, coinciding with the ARCTAS campaign. WRF-Chem was also
coupled with the global Model for OZone and Related Chemical Tracers
(MOZART) which is used to provide boundary conditions. Such a system allows the
estimation of the relative importance of local sources versus pollution
inflow on the distribution of CO at the surface and in the free troposphere.
Fire emissions are based on the FINN inventory which
in their case study shows a clear underestimation of CO emission over
California. Model results are compared against airborne and ground
measurement of CO as well as CO total column from MOPITT. In
the perspective of InjH modelling, show that (i) in their
case study PRMv0 injects half of the fire in the FT and captures the
timing and location of fire plume well when compared to airborne
CO measurements (ii) and that their comparison with surface measurement is
impacted by a large underestimation of CO fire emission in the FINN
inventory.
The conclusions of these studies emphasize the fact that the evaluation of
plume rise effects on large-scale atmospheric transport simulation is a
challenging task. As emission transport is dependent of both quantity and
the geographical location of the injection, both emission inventory and local condition (i.e. atmospheric
profile) need to be correctly input to allow the evaluation of InjH
estimation.
Fire locations contained within the Multi-angle Imaging SpectroRadiometer (MISR) plume height data set of over North
America for the time period 2001–2008 (black dots). White dots indicate
the locations of the 22 fires plumes classed as having a plume height in
excess of 4.5 km. The map in the background shows the land cover used
within the GFEDv3 biomass burning emissions inventory of
where SA, AG, TF, PEAT, and EF stand for savannah,
agriculture, tropical forest, peat land, and extra tropical forest
respectively.
Distribution of FRP (a), active fire area (b), top
plume height (c), and local time observation (d) for the
3320 fires of the current North American subset of the Multi-angle Imaging
SpectroRadiometer (MISR) plume height project data set of
derived using the MINX tool shown in Fig. .
Overview of the highest fire plumes present in the current North
American subset of the MISR plume height data set of ,
derived using the MINX tool shown in Fig. . The reference of
this fire in the MISR data set is O45791-B41-P1, and it was observed in the
Northwest Territories (Canada) on 27 July 2008. It shows the nadir image
recorded by MISR, together with the plume contour set by the operator of the
MINX software (a), the estimated wind direction (yellow arrow in
a), and the stereo-height retrieval (b). Part of the image
is black as the fire was located on the edge of the MISR swath. Images are
taken from the MISR plume height project website (see footnote 1).
Images from the wider-swath Moderate Resolution Imaging
Spectroradiometer (MODIS) for the same fire as in Fig. at
the same time. MODIS is mounted on the same Terra satellite as MISR (see
Sect. ). (a) is the false colour composite
image of the area observed. (b) is the Middle Infra-Red brightness
temperature. (c) is the optical cloud phase properties of the
version 6 of the MODIS cloud product . Cross markers in
(a) and (c) (red and green respectively) denote the
location of MODIS pixels detected as containing active fire in the MODIS
MOD14 active fire product of .
Statistical models
As an alternative to the unreliable prediction of the PRM model, a
statistically based approach using 584 plume height measurements of the MISR
data set was presented by . Classifying observed fires
between low (<1 km), medium (<2.5 km), and high (>2.5 km) plumes,
they derive per biome the mean and standard deviation of FRP
(MW) and
atmospheric stable layer strength (K km-1) for each plume height class
See Table 4 of. Although this approach is
attractive because of its inexpensive computational cost, its implementation
appears to be difficult as most of the standard deviation for FRP and the
stable layer strength are extremely high, yielding crossover between the
characterization of FRP and stable layer strength ranges of the different
plume categories and therefore large uncertainty on the InjH estimation.
More recently propose the idea of a model predicting the
probability of injection above the PBL. Using an implementation of the
algorithm based on MODIS input data, and 1028 boreal
fire plumes extracted from the Northern American subset of the MISR data set,
they show that the presence of plume in the troposphere can be independently
related to the value of the classical FRP , fire size, FRP
derived from the Dozier algorithm (FRPf), or the FRPf flux. By only
showing a trend between fire characteristic variation and probability of
injection in the troposphere, no real model is formulated and their
conclusion highlights the potential importance of atmospheric stability in the
plume rise (which they do not take into account).
To our knowledge, no statistically based models has already been implemented
in CTMs. However, as their CPU cost would remain relatively low compared to any
deterministic models, they show a great potential for implementation in large-scale model, in particular in climate model. However, their derivation is
entirely relying on the good quality of their learning data set.
Summary and conclusions
Weakly burning landscape-scale fires appear to release their smoke mainly
into the planetary boundary layer, but larger and/or more intensely burning
wildfires produce smoke columns that can rise rapidly and semi-vertically
above the source region, driven by the intense heat and convective energy
released by the burning vegetation. These columns of hot smoke entrains
cooler ambient air, developing into a rising plume within which the trace
gases and aerosols are transported to potentially quite high altitudes, in
the most extreme cases into the stratosphere. The characteristics of these
rising plumes, and in particular the height that they reach before releasing
the majority of the smoke burden, are now acknowledged as an important control
on the atmospheric transport of emissions from certain of these larger fire
events . However, results
comparing model-based estimates of smoke plume rise parameter to actual plume
height observations made from satellite EO instruments
e.g. do not yet provide a strong quantitative
agreement . Furthermore, the degree of improvement
given by actually including plume rise parameterizations in atmospheric
chemistry transport models can be difficult to interpret due to the complex
interactions with other atmospheric processes .
Apart from simulations based on single fire events, where plume injection
height is carefully prescribed or where
highly detailed simulations are run at very high resolution
, the impact of fire-induced up-draft on wildfire plume
lofting appears, in general, to remain rather poorly understood and often
weakly represented in current large-scale atmospheric modelling efforts. The
impact of possibly coupled effects on ambient atmospheric processes, such as
the convection induced by the nearby presence of a cold front, is also not
well determined. At the scale of global CTMs, wildfire plume rise is
generally represented by some form of parameterized model
. The ideal parameterization should
account for the main physical processes responsible for the plume dynamics,
using inputs regarding the fire characteristics that are available from EO
satellites in near real time and with concurrent measurements of fire
activity and plume height from single fire events available to validate the
resulting system (and reduce any impact from larger-scale transport effects
that influence comparisons of downwind plume characteristics).
Despite a demonstrated diurnal bias of MISR-derived plume heights towards
lower plumes , the current MISR data set for North
America counts 22 fires with plume top higher that 4.5 km (see
Figs. and for an overview of the
current MISR data over North America).
However, those high plumes might not be fully representative of standard fire
behaviour as show that PyroCb plume maturity peaks around
18:00, and no fires are observed around that time with MISR (see local
time observation distribution in Fig. d). Therefore, any
PyroCb contained within the MISR-derived plume height data set are certainly
few in number, which leads to questions regarding the full representativeness
of a random selection of fire events selected from this sample
. In their approach, apply several
selection criteria when taking a subsample of fires extracted from the MISR
plume height data set for use in evaluation of their parameterized plume rise
model, which is an adaptation of the widely used model of
. However, even with this carefully selected evaluation
data set, the validation of this PRM model fails to show very convincing
results. Nevertheless, in future, such validation (or optimization) of plume
rise models should continue to pay attention to the quality of the evaluation
data sets, including the following questions.
Are the fire activity (FRP) and the plume dynamics (plume top height) linked? A time
delay is necessary for the plume to dynamically adjust to change in the forcing induced by the energy release by the
fire. For example, during the simulation of PyroCb of the Chisholm fire by the ATHAM model, it takes 40 min for
the plume to reach its stationary altitude with a constant forcing . As the smoke plumes observed
by MISR are more likely in a relatively early
stage of development due to the morning overpass of
the Terra satellite (see Sect. 4.1.2), the effect of this time
lag might be even more important than if fires were randomly observed at any time of their development.
Is the radiation of the fire affecting by absorption from the plume? In low ambient wind conditions,
the fire plume is often located just above the fire and in case of large fires this might mask some of the fire-emitted
radiation due to the thick aerosol layer causing significant scattering and/or absorption of the radiant energy, possibly
causing underestimation of FRP and unreliable CHF and fire size retrievals using the Dozier algorithm. As an example, we note that the
fire from the Northern American MISR plume height data set observed with the highest plume height of 12 km
(see Fig. c) is reported to have a relatively low total FRP of 6 GW, when compare with the FRP distribution of the whole
MISR data set (see Fig. a). The FRP is here determined as in : it is
the FRP of the strongest cluster in the vicinity of the plume, in this case the top cluster in Fig. a.
Figure b shows the optical cloud phase properties of the MODIS cloud product for
this same fire. A large part of the plume is formed of ice, which lets us assume that we are in the presence of a PyroCb
event. This means that the plume is formed of liquid water and ice particles that could be absorbing part of the MIR signal
emitted by the fire. A close inspection of the MODIS MIR band (Fig. c) shows that in this particular event
all high radiance pixels are outside the plume and that the fire detection algorithm of the MOD14 product misses a part of the fire front.
This underestimation is even further accentuated in the official MISR data set as the plume contour set by the MINX operator
includes only a part of the detected fire pixels (see Fig. ). An even more extreme scenario is
shown in Fig. 14 of , where no fire pixels were found for a high plume (marked P2 in their figure) which occurred
in Québec on 6 July 2002. In these particularly extreme fire cases, it seems that fire pixels attached to the plume could be located underneath it and remain undetected by the MODIS active fire product.
Despite these difficulties, the range of relevant data provided on actively
burning fires and their smoke plumes by EO satellites continues to grow
e.g.. For example, GOES-R and
Himawari-8 will provide capabilities similar to MODIS,
with a temporal frequency potentially as high as 30 s, while Suomi NPP
carrying VIIRS and TET-1/BIRDS
will provide thermal bands with resolution up to 375 m. This will allow
for detailed observations of pyroconvection during peak burning hours. These
improving capabilities, together with continuing advances in the extent to
which plume rise models can be parameterized and incorporated into
large-scale atmospheric CTMs , can be
expected to continue to advance the accuracy of smoke plume injection
estimates and the resulting impact on long-range atmospheric transport of
these globally important emissions.
Acknowledgements
This study was supported by the NERC grant NE/E016863/1, by the NERC National
Centre for Earth Observation (NCEO), and by the EU in the FP7 and H2020
projects MACC-II and MACC-III (contracts 283576 and 633080). The authors want
also to thank M. Sofiev for sharing results from the implementation of his
plume rise parameterization.Edited by:
R. Engelen
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