AMTAtmospheric Measurement TechniquesAMTAtmos. Meas. Tech.1867-8548Copernicus PublicationsGöttingen, Germany10.5194/amt-11-5261-2018Quantifying the single-scattering albedo for the January 2017 Chile
wildfires from simulations of the OMI absorbing aerosol indexQuantifying the single-scattering albedo from an absorbing aerosol indexSunJiyuntingjiyunting.sun@knmi.nlhttps://orcid.org/0000-0002-3621-4081VeefkindJ. Pepijnvan VelthovenPeterLeveltPieternel F.Department of R&D Satellite Observations, Royal Netherlands Meteorological Institute, De Bilt, 3731 GA, the NetherlandsDepartment of Geoscience and Remote Sensing (GRS), Civil Engineering and Geosciences, Delft University of Technology, Delft, 2628 CD, the NetherlandsJiyunting Sun (jiyunting.sun@knmi.nl)19September2018119526152775February201812February201810August201827August2018This work is licensed under the Creative Commons Attribution 4.0 International License. To view a copy of this licence, visit https://creativecommons.org/licenses/by/4.0/This article is available from https://amt.copernicus.org/articles/11/5261/2018/amt-11-5261-2018.htmlThe full text article is available as a PDF file from https://amt.copernicus.org/articles/11/5261/2018/amt-11-5261-2018.pdf
The absorbing aerosol index (AAI) is a qualitative parameter
directly calculated from satellite-measured reflectance. Its sensitivity to
absorbing aerosols in combination with a long-term data record since 1978
makes it an important parameter for climate research. In this study, we
attempt to quantify aerosol absorption by retrieving the single-scattering
albedo (ω0) at 550 nm from the satellite-measured AAI. In the first
part of this study, AAI sensitivity studies are presented exclusively for
biomass-burning aerosols. Later on, we employ a radiative transfer model
(DISAMAR) to simulate the AAI measured by the Ozone Monitoring Instrument
(OMI) in order to derive ω0 at 550 nm. Inputs for the radiative
transfer calculations include satellite measurement geometry and surface
conditions from OMI, aerosol optical thickness (τ) from the
Moderate Resolution Imaging Spectroradiometer (MODIS) and aerosol
microphysical parameters from the AErosol RObotic NETwork (AERONET),
respectively. This approach is applied to the Chile wildfires for the period
from 26 to 30 January 2017, when the OMI-observed AAI of this event reached
its peak. The Cloud-Aerosol Lidar with Orthogonal Polarization (CALIOP)
overpasses missed the evolution of the smoke plume over the research region;
therefore the aerosol profile is parameterized. The simulated plume is at an
altitude of 4.5–4.9 km, which is in good agreement with available CALIOP
backscatter coefficient measurements. The data may contain pixels
outside the plume, so an outlier detection criterion is applied. The results
show that the AAI simulated by DISAMAR is consistent with satellite
observations. The correlation coefficients fall into the range between 0.85
and 0.95. The retrieved mean ω0 at 550 nm for the entire plume over
the research period from 26 to 30 January 2017 varies from 0.81 to 0.87, whereas
the nearest AERONET station reported ω0 between 0.89 and 0.92.
The difference in geolocation between the AERONET site and the plume, the
assumption of homogeneous plume properties, the lack of the aerosol profile
information and the uncertainties in the inputs for radiative transfer
calculation are primarily responsible for this discrepancy in ω0.
Introduction
Biomass-burning aerosols are generated from combustion of carbon-containing
fuels, either by natural or anthropogenic processes (Bond et al., 2004; IPCC,
2014). They are of great concern from the climate perspective (Kaufman et al., 2002;
IPCC, 2007, 2014; Koch and Del Genio, 2010; Myhre et al., 2013).
The reported radiative forcing of black carbon (BC) produced by fossil fuel
and biofuel is around 0.4 W m-2 (0.05–0.80 W m-2) (Ramanathan
and Carmichael, 2008; Bond et al., 2013; Huang et al., 2013), but this
estimate is highly uncertain. Accurate measurements of the aerosol single-scattering albedo (ω0) on a global scale can reduce the uncertainty
in aerosol radiative forcing assessments (Hu et al., 2007). ω0 is
defined as the ratio of aerosol scattering over aerosol extinction. Currently
ω0 is mainly measured by ground-based instruments (Dubovik et al.,
1998; Eck et al., 2003; Petters et al., 2003; Kassianov et al., 2005; Corr et
al., 2009; Yin et al., 2015). Satellite sensors, such as the POLarization and
Directionality of the Earth's Reflectance (POLDER), can retrieve
ω0 from a combination of multi-angular, multispectral observations
of the polarized radiation. By measuring the anisotropy of the reflected
radiance for each pixel, POLDER is expected to determine the reflected solar
flux more accurately (Leroy et al., 1997). Unfortunately, there is no
continuous record of ω0 because the first two POLDER missions ended
prematurely due to technical problems, and the third POLDER mission only
covered the period 2004–2014. Other satellite-derived ω0 is
usually retrieved simultaneously with the aerosol optical thickness (τ)
based on the predefined aerosol properties, such as the near-UV aerosol
product (OMAERUV) of the Ozone Monitoring Instrument (OMI) on board Aura
(Torres et al., 2005, 2007). However, this aerosol absorption over near-UV is
highly sensitive to the assumption on aerosol layer height. Satheesh et
al. (2009) therefore used the τ from Moderate Resolution Imaging
Spectroradiometer (MODIS), which is independent of aerosol layer height, to
constrain the OMAERUV retrieval. Their validation showed that compared with
operational OMAERUV algorithm, the retrieved aerosol layer height using the
hybrid method is in a better agreement with air-borne measurements, implying
a potential improvement in aerosol absorption retrieval. This OMI–MODIS joint
retrieval was also evaluated by Gassó and Torres (2016). They found that,
under less absorbing conditions, the hybrid method is sensitive to the
variation in the input τ, which is used to select the retrieved pair of
aerosol layer height and ω0.
Herman et al. (1997) first defined the near
ultra-violet (UV) absorbing aerosol index (AAI), which provides an
alternative methodology to retrieve ω0 from satellite
observations. The near-UV AAI, usually derived from the spectral range
between 340 and 390 nm, is a qualitative measure of absorbing aerosols that
was first provided by the Total Ozone Mapping Spectrometer (TOMS) on board
Nimbus-7 in 1978. Since then several instruments have contributed to the AAI
data record, which now spans over four decades. This long-term record is an
important motivation for us to derive quantitative aerosol absorption
information from the near-UV channel.
The most important advantage of the satellite-retrieved AAI is that it does
not depend on a priori aerosol types, which are major uncertainties in
aerosol parameter retrievals, such as τ. Ginoux et al. (2004)
suggested that comparing model simulations with AAI from TOMS allows
better control of discrepancies because the only error source is the
models. Further advantages of AAI are the low reflectivity of the Earth's
surface and the absence of significant molecular absorption over the near-UV
range. Using this band can ensure that the aerosol absorption is one of the major
contributors to the total signal. Moreover, the near-UV AAI is by
definition highly sensitive to aerosol absorption. Previous studies
have proven the potential of the near-UV AAI of TOMS in absorbing aerosol
properties retrieval. Torres et al. (1998) provided the theoretical basis of
an inversion method to derive τ and ω0 from backscattered
radiation. This method was validated by ground-based observations during the
Southern African Regional Science Initiative (SAFARI) 2000 measurement
campaign. The agreement of τ and ω0 reaches ±30 % and ±0.03, respectively (Torres et al., 2005). Hu et al. (2007)
retrieved global columnar ω0 based on the TOMS AAI with an
average uncertainty of 15 %.
Chile wildfires detected by Terra MODIS on 20 January 2017 (image
source: NASA's Earth Observatory
https://earthobservatory.nasa.gov/images/89496, last access: 11 September 2017).
This study is inspired by previous research to quantify the
aerosol absorption from AAI. We use the near-UV AAI provided by OMI, the
successor of TOMS, to derive the aerosol properties of the central Chile
(Pichilemu 34.39∘ S, 72.00∘ W and Consititución
35.33∘ S, 72.42∘ W) wildfires in January 2017. The
series of fires was triggered by a combination of long-term drought and
high temperature, and this wildfire season was regarded as the worst in
national history (The Guardian, 2017). The fires led to massive losses of
the local forestry industry (pine and eucalyptus forests) (NASA, 2017).
The smoke plume was transported away from the source regions towards the
tropical area of the Pacific Ocean by north-westward winds (Fig. 1). In this
study, we quantitatively retrieve the ω0 of this smoke by
simulating the near-UV AAI of OMI with the radiative transfer model
Determining Instrument Specifications and Analysing Methods for Atmospheric
Retrieval (DISAMAR). The aerosol inputs of DISAMAR include the τ
retrieved from MODIS on board the NASA EOS Aqua satellite, the information
on aerosol microphysical parameters provided by AERONET and parameterized
aerosol profiles. In the next section, we provide a brief introduction to
the near-UV AAI and its sensitivity to various parameters. The methodology
and data sets are described in Sect. 3. In Sect. 4, retrieved results and
uncertainty analysis of Chile 2017 wildfires are discussed, followed by the main
conclusions in Sect. 5.
AAI sensitivity studies based on DISAMAR
In this section, we first introduce the near-UV AAI. In the sensitivity
analysis, we show that the AAI depends not only on aerosol parameters but
also on the surface conditions and the observational geometry. The
sensitivity analysis in this study is only designed for biomass-burning
aerosols.
Near-UV AAI definition
The concept of the near-UV AAI was first conceived to detect UV-absorbing
aerosols from the spectral contrast provided by TOMS observations, known as
the residue method (Herman et al., 1997).
The basic idea of the residue method is that in a pure Rayleigh atmosphere,
the reflectance (or equivalently the radiance, Iλ) decreases
strongly with wavelength. The presence of absorbing aerosols will reduce
this spectral dependency of Iλ. The change in this wavelength
dependency is summarized as the AAI, which is calculated from the
Iλ at the wavelength pair λ1 and λ2 (λ1<λ2):
AAI=-100log10Iλ1Iλ2obs-log10Iλ1Iλ2Ray,
Superscript Obs and Ray refer to Iλ measured by satellite and calculated
using a Rayleigh atmosphere, respectively. The longer wavelength
λ2 is treated as the reference wavelength, where the surface
albedo (as) is determined by fitting the observed radiance, i.e.
Iλ2Ray(as)=Iλ2obs. This is done using an
atmosphere containing only molecular scattering bounded by a Lambertian
surface. The spectral dependency of as is neglected; thus Iλ1Ray is calculated using the same value as λ2. Defining ΔIλ1=Iλ1Ray-Iλ1obs, Eq. (1) can be rewritten as follows:
AAI=100log10ΔIλ1Iλ1obs+1.
It is advantageous to use Eq. (2) because the AAI can be simply interpreted
as the ratio between the simulated and observed radiance at λ1.
In this section, we present results of sensitivity studies performed by the
radiative transfer model DISAMAR. DISAMAR can perform simulations of the
forward Iλ spectrum with a wide spectral coverage (270 nm to
2.4 µm) and model scattering and absorption by gases, aerosols and
clouds as well as reflection by the surface (de Haan, 2011). It uses either
the doubling–adding method or the Layer Based Orders of Scattering (LABOS)
for the radiative transfer calculations. In this study the latter one is
employed, because it is less computationally intensive (de Haan et al., 1987;
de Haan, 2011).
DISAMAR allows several aerosol-scattering approximations to be applied. Here we
assume Mie-scattering aerosols. The parameters used to describe Mie
particles and their corresponding values are listed in Table 1. Considering
the Chile wildfire plumes, which were dominated by biomass-burning aerosols,
these sensitivity studies are specifically performed for parameterized smoke
aerosols, assuming only fine-mode particles and weak linear wavelength
dependency of the complex refractive index (nr and ni). The
default values refer to observations of the daily average of the AERONET
station Santiago_Beauchef (33.46∘ S,
70.66∘ W) on 27 January. We obtain the size distribution function
and complex refractive index at 440, 675, 880 and 1018 nm from AERONET and
apply a linear interpolation/extrapolation to derive the complex
refractive index over the spectrum from 340 to 675 nm, with spectral
resolutions of 2 nm. Then DISAMAR uses the above information to calculate the
aerosol phase function P(Θ) and ω0 over the full
spectrum (340 to 675 nm). The corresponding P(Θ) at 354 nm for the
default case (rg=0.15µm, nr=1.5 and ni=0.06) is presented in Fig. 2. P(Θ) are provided for other cases in
the Appendix A (Figs. A1, A2 and A3). DISAMAR requires τ to be defined
at 550 nm. Surface parameters include a spectrally flat as and a
surface pressure Ps. The aerosol profile is parameterized as a single-layer box shape, with its bottom at zaer-Δz/2 and top at
zaer+Δz/2, where zaer and Δz are the geometric
central height and the geometric thickness of the aerosol layer,
respectively. The whole sensitivity analysis is performed for the cloud-free
condition. The wavelength pair of OMI (354 and 388 nm) is applied to compute
the AAI. To make different sensitivity studies comparable, the AAI
calculated in this section is normalized by the maximum value in each case.
Note that sensitivity studies always use the default settings listed in
Table 1 unless different values are explicitly mentioned.
Aerosol optical properties are determined by microphysics, such as the real
and imaginary parts of the complex refractive index (nr and
ni), and the particle size (rg). Figure 3 shows how
the variation of the AAI, ΔIλ1, Iλ1obs as well as the optical properties (ω0 and the
asymmetry factor g) are associated with the complex refractive index and
the particle size. The asymmetry factor g is the averaged cosine of the
scattering angle Θ, weighted by P(Θ). Figure 3 shows that the
effect of the complex refractive index is dual. As shown in Fig. 3a, an
increase in the real part of the refractive index nr directly
enhances the magnitude of Iλ1obs, whereas ΔIλ1 reduces. This results in low values of the AAI, which
correspond to high ω0 (Fig. 3b). Under the condition that the
measurement angle is Θ=150∘, the declining g implies that
more light is scattered in the line-of-sight of the detector; thus
Iλ1obs is higher. Conversely, the imaginary part of the refractive
index ni, which is directly associated with ω0, has an
opposite influence; see Fig. 3c and d. The particle size distribution has a
more complicated influence on the AAI. As shown in Fig. 3e, the AAI primarily
follows the behaviour of ΔIλ1, which decreases first and
then increases when rg is varying from 0.1 to 0.4 µm,
whereas ω0 is continuously decreasing and g is continuously
increasing.
Phase function P(Θ) at 354 nm of the parameterized Mie-scattering aerosol of default case
(rg=0.15µm, nr=1.5 and ni=0.06) in sensitivity analysis. The markers in the plot correspond to the
value when Θ=60, 90, 120, 150, 180∘.
AAI sensitivity to microphysical parameters
ni(a, b), nr(c, d) and
rg(e, f). The left panels (a, c, e) show the
sensitivity of the normalized AAI (black), the normalized ΔIλ1 (blue) and the normalized Iλ1obs (red). The right
panels (b, d, f) show ω0 (blue) and g (red) at
wavelengths 354 (solid line) and 388 nm (dashed line), respectively.
AAI sensitivity to macrophysical parameters (a)τ
at 550 nm, (b)zaer and (c)Δz.
AAI sensitivity to surface parameters Ps(a)
and as(b). The solid line and dashed line in
panel (b) indicate terrain height at sea level (Ps=1013 hPa) and elevated terrain height (Ps=813 hPa),
respectively.
In addition to the microphysics, the
aerosol amount and distribution also have strong influences on the
AAI. As shown in Fig. 4a, the AAI is positively
correlated with τ. The AAI is highly sensitive to the aerosol vertical
distribution (Herman
et al., 1997; Torres et al., 1998; de Graaf et al., 2005). As the aerosol layer
ascends (Fig. 4b), more molecular scattering beneath the aerosol layer is
shielded, which reduces Iλ1obs while increasing ΔIλ1. The relation between the AAI and zaer is almost linear. Figure 4c shows that, at the same altitude, the AAI slightly increases with
the geometrical thickness of the aerosol layer. The reason could be that a
larger Δz indicates that the sunlight has a higher probability of
being absorbed by aerosols, slightly enhancing the aerosol absorption. Although
the sensitivity exists, the impact is only up to 5 %, which is negligible
for practical purposes.
AAI sensitivity to θ and θ0 at φ=180∘. The black dashed contour in panel (a) indicates
Θ=60, 90, 120, 150∘. The white dashed line in
panel (a) indicates the cross section along viewing angles, with its
corresponding normalized AAI, ΔIλ1 and Iλ1obs presented in panel (b).
The calculated AAI does not only depend on aerosols themselves but also on
ambient conditions such as surface and clouds. Although the near-UV AAI is
capable of distinguishing absorbing and non-absorbing agents (Herman et al., 1997)
and even retrieving aerosol information over clouds (Torres et al., 2012), the uncertainty
triggered by clouds is relatively high and therefore cloudy conditions are
excluded from this study. Surface conditions are parameterized by Ps
and as. It can be seen from Fig. 5a that a decrease in Ps, or
equivalently an elevated terrain height, leads to less Rayleigh scattering
shielded between the surface and the aerosol layer. As a result, the AAI
decreases significantly due to smaller ΔIλ1, in agreement
with previous studies (de Graaf et al., 2005;
Colarco et al., 2017). According to de Graaf et al. (2005), increasing
as has two counteracting effects. On the one hand, it increases the
amount of directly reflected radiation at the top of the atmosphere, namely
a larger Iλ1obs, and on the other hand it enhances the role of
absorption by the aerosol layer rather than the surface, namely a larger
ΔIλ1. The dominant effect of as also depends on
Ps (Fig. 5b). When the aerosol layer is relative to the sea level
(Ps=1013 hPa), the first effect dominates. However, a brighter
surface compensates the loss of molecular scattering shielded by the
aerosols when the terrain height rises (Ps=813 hPa), which makes
the absorbing layer more detectable.
The AAI also depends on the sun-satellite geometry. Here we provide the AAI
as a function of the measurement geometry for the default case with the
relative azimuth angle Δφ=180∘. As
presented in Fig. 6a, the AAI becomes very sensitive to the geometries
with zenith angles is larger than 60∘, which confirms previous
research (Herman et
al., 1997; Torres et al., 1998; de Graaf et al., 2005). This is mainly due
to the significant growth of P(Θ) when Θ becomes smaller
(Fig. 2). Thus, it is suggested that the OMI measurement with θ0
larger than this value should be removed due to large variations in the AAI.
To analyse the radiance behaviour as previously, we plotted the Iλ1obs and ΔIλ1 as a function of Θ along the
cross section, respectively (Fig. 6b). It is noted that Iλ1obs increases when Θ is larger than 90∘, whereas
the P(Θ) decreases at this range Θ (Fig. 2). The reason could
be that the Rayleigh scattering has
an increasing contribution to the total radiance at those measurement angles
(backscattering).
Methodology and data sets
In this section, we first present the data sets and their pre-processing
in this study, followed by the strategy used to retrieve the aerosol ω0 by constraining the simulated near-UV AAI with the observed one.
Data setsOMI and GOME-2 absorbing aerosol index
The TOMS near-UV AAI retrieval has been proven to be a robust algorithm and
applied to successive sensors, such as OMI on board Aura and GOME-2 on board
MetOp-A/B. GOME-2 has higher spectral resolution (0.2–0.4 nm) than TOMS, but
the spatial resolution is rather coarse (80×40 km2). In this
study, GOME-2-measured AAI at wavelength pair 340 and 380 nm
(http://archive.eumetsat.int, last access: 14 July 2017)
is only an independent data set used to assess the potential bias of the OMI measurements.
OMI combines advantages of both TOMS and GOME-2. It covers wavelengths from
264 to 504 nm with a spectral resolution of approximately 0.5 nm and has a
much higher spatial resolution than GOME-2 of 13×24 km2 (Levelt et al., 2006). Note that GOME-2 and OMI have different
equator crossing times (09:30 LT descending node for GOME-2 and 13:45 LT
ascending node for OMI) that may affect the intercomparison of the two
satellite measurements.
Since the launch of OMI in 2004, the AAI retrieved from this instrument has
been widely used in various applications. Kaskaoutis et al. (2010) employed
the OMI-measured AAI for regional research on the aerosol temporal and
spatial distributions in Greece. Torres et al. (2012) utilized the advantage
of near-UV AAI to detect aerosols over clouds. The OMI-observed AAI was even
used to evaluate the impact of surface dust loading on human health
(Deroubaix et al., 2013). Buchard et al. (2015) also validated the NASA MERRA
aerosol reanalysis product with the AAI retrieved from OMI.
In this study, the OMI level 2 product OMAERO (https://disc.gsfc.nasa.gov, last access: 31 December 2017)
is used to provide the AAI retrieved at the wavelength pair of 354 and 388 nm, and the corresponding viewing geometry and the surface condition when
the measurements took place. The samples are included in the radiative
transfer simulation only if θ0 is smaller than 60∘
and if satellite pixels are not contaminated by sun glint, clouds, row
anomalies or other errors of the instrument. The simulation is only applied
to pixels inside the biomass-burning plume, which is defined as AAI values
larger than 1 for both OMI and GOME-2.
MODIS and OMI aerosol optical thickness
MODIS on board Aqua/Terra is a sensor that was specifically designed for
atmosphere and climate research. The combination of two satellites ensures a
daily global coverage. The spatial resolution ranges from 250 m to 1 km and
it has 36 spectral bands from 400 nm to 14.4 µm (Remer et al.,
2005). MODIS employs separated algorithms for aerosol retrieval over oceans
and land (Tanré et al., 1997; Kaufman and Tanré, 1998; Hsu et al., 2004;
Remer et al., 2005). Currently the τ provided by MODIS is one of the
most reliable data sets (Lee et al., 2009), with an estimated uncertainty of
only 3 %–5 % over ocean and 5 %–15 % over land (Remer et
al., 2005). Besides, the MODIS-retrieved τ is free from the uncertainty
triggered by assumed aerosol profile (Satheesh et al., 2009). As mentioned
before, DISAMAR requires τ at 550 nm. This study uses cloud-filtered
τ at 550 nm from the Collection 6 level 2 product MYD04 as the input
for radiative transfer calculation
(https://ladsweb.modaps.eosdis.nasa.gov, last access: 27 February 2017).
In addition, the τ measured by OMI and AERONET are compared with MODIS.
The OMAERO τ retrieval uses multispectral fitting techniques. The
retrieved τ is reported in good accordance with AERONET and is highly
correlated with MODIS (Torres et al., 2007), with a correlation of 0.66 over
land and 0.79 over oceans (Curier et al., 2008), although it suffers from
cloud contamination due to the relatively coarse spatial resolution of OMI.
Considering the wavelength difference, the τ measured by OMI at 442 nm
has to be transferred to 550 nm using the Ångström exponent (ÅE)
440–675 nm taken from AERONET near the time at which OMI flies over the
selected site. The AERONET data set used in this study is introduced in the
next section.
AERONET aerosol properties
AERONET is an aerosol monitoring network of ground-based sun photometers.
With standardized instruments, calibration, processing and distribution,
AERONET provides a long-term global database for aerosol research and
air-borne and space-borne measurement validation. The system takes two basic
measurements. The τ and ÅE are retrieved from the direct solar
irradiance measurements; the rg, P(Θ) (Nakajima
et al., 1983, 1996), ω0 (Dubovik et al., 1998), nr
and ni (Dubovik and King, 2000) are
derived from multi-angular measurements of sky radiance.
The AERONET site nearest to the fire sources of 2017 Chile wildfires is the
Santiago_Beauchef (33.46∘ S, 70.66∘ W)
(https://aeronet.gsfc.nasa.gov, last access: 11 August 2017). The data set in use is the version 2 level 1.5
product. To minimize the influence of temporal difference, the parameters of
AERONET measured closest to the time of the OMI overpasses are used to
simulate the optical properties of Mie-scattering aerosols in DISAMAR. Note
that the AERONET level 1.5 data set is not quality assured. In addition, the
location of this AERONET site is in downtown of Santiago City and close to
major roads, where the presence of scattering urban aerosols may bias the
measurements of the plume.
The AERONET-retrieved τ and ω0 are used to evaluate the
MODIS τ and retrieved ω0, respectively. The AERONET-measured
τ is transferred to 550 nm using the ÅE in the range 440–675 nm, while the ω0 at 550 nm is linearly interpolated by
values at 440 and 675 nm.
The AERONET inversion product needs to be processed into the inputs required
by DISAMAR. Firstly, a conversion from the volume size distribution
V(rv,σv) provided by AERONET to the number size
distribution N(rg,σg) used in DISAMAR is
required:
Nrg,σg=Vrv,σv34πrg3e-4.5σn2,
The following relation between the geometric and volumetric mean radii
(rg and rv) and standard deviations (σg and σv) is assumed:
rg=rve-3σg2,σg=σv,
The fine- and coarse-mode particle sizes are derived by finding the two peaks
of the log-normal distribution function provided by AERONET. The complex
refractive index is assumed the same for both modes. Since bimodal aerosol
is not yet applicable in DISAMAR, we first calculate the optical properties of
two modes individually, then we externally combine the optical properties of
two modes into a bimodal aerosol with a fraction:
wf=Nfrg,f,σg,fNfrg,f,σg,f+Ncrg,c,σg,c,wc=1-wf,
Then the weights for calculating the total ω0 of the mixed
aerosol are as follows:
wσ,f=wfσfwfσf+wcσc,wσ,c=1-wσ,f,
where the σf and σc are the extinction cross
section of the fine and coarse aerosols. The expansion coefficients of the
mixed aerosol are weighed by the ω0 of the fine and coarse
aerosols (ω0,f and ω0,c):
wω0,f=wfσfω0,fwfσfω0,f+wcσcω0,c,wω0,c=1-wω0,f.
The AERONET instrument at this site only covers the visible and infrared
bands (440 to 1018 nm) for sky radiance measurements, i.e. no aerosol
inversion products at the UV band. Due to the absence of observations,
assumptions have to be made on the spectral dependency of aerosol properties
to obtain their values in the near-UV range. The properties of biomass-burning
aerosols depend on the type of fuel, the procedure producing the
smoke, the age of the smoke and also the atmospheric conditions (Reid et
al., 2005). Using measurements to constrain the input aerosol refractive
index may reduce the uncertainties due to a priori knowledge. Our treatment
of the complex refractive index is as follows: (1) take the complex
refractive index at the visible band (440 to 675 nm) from AERONET measurements;
(2) linearly extrapolate the complex refractive index to the near-UV band. The
real part nr for radiative transfer calculation is obtained in this
step. A slight wavelength dependency of nr is found (Fig. 9a); (3) for the imaginary part ni, we multiply it (for the entire wavelength
from UV to visible) with a scaling factor as we set it as a free parameter.
By varying the value of the scaling factor, both the magnitude and the
wavelength dependency of ni can change to meet the requirement of
the retrieval (Fig. 9b).
CALIOP backscattering coefficient
The CALIOP on board CALIPSO, which was launched in 2006, provides
high-resolution profiles of aerosols and clouds. It has three channels with
one measuring the backscattering intensity at 1064 nm and the rest measuring
orthogonally polarized components at 532 nm backscattering intensity (Winker et al., 2009).
Due to the limited spatial coverage, CALIOP did not observe the
Chile wildfires plume for all the cases in which the OMI observations available.
We only use the total attenuated backscatter at 532 nm from level 1B
version 4.10 standard data to evaluate the parameterized aerosol profiles
(https://eosweb.larc.nasa.gov/project/calipso/calipso_table, last access: 12 July 2017).
Methodology
In this study, we employ the radiative transfer model DISAMAR to simulate
the near-UV AAI from OMI and to derive the ω0 for a specific
case, i.e. the Chile wildfires in January 2017. We select the period from 26
to 30 January 2017 (28 January is excluded due to a lack of data) when the
AAI value reached its peak during the wildfire period.
CALIOP backscatter coefficient β at 532 nm. The solid and
dashed lines indicate the retrieved zaer and Δz. The red and black dots indicate clouds, and the orange dots
indicate aerosol layers.
The forward simulation consists of two major steps. First, DISAMAR calculates
the Mie aerosol optical properties with aerosol microphysical information
taken from AERONET measurements (rg, nr and ni). As mentioned
in Sect. 3.1.3, we set the spectral-dependent imaginary refractive index
ni as a free parameter to vary ω0. Then, DISAMAR operates
radiative transfer calculation with the input aerosol properties and
environmental conditions of OMI.
AAI from OMI observations (a–d) and DISAMAR
simulations (e–h) of the Chile wildfires on 26, 27, 29 and
30 January 2017. The black and red cross symbols are the AERONET station and
the main fire sources (Pichilemu 34.39∘ W, 72.00∘ S and
Consititución 35.33∘ S, 72.42∘ W). The
grey dashed line indicates the CALIOP overpasses in the region of interest,
where the overpasses used to validate the plume height are marked by black
dashed lines. The scatter plots (i–l) present the OMI observations
against DISAMAR simulations for only qualified data (red dot) and all data
(blue dot), respectively.
It is noted that the
observed aerosol vertical distribution is limited for the Chile wildfires.
Previous research suggested the AAI cannot be quantitatively used without
τ or zaer information (Gassó and Torres, 2016). Instead, we
implement the same parameterization on the aerosol profile as in the
sensitivity studies. Since the AAI dependency on Δz is minor (Fig. 4c),
and to reduce the computational cost, Δz is set to a constant of
2 km based on the information from the CALIOP measurements of the backscattering
coefficient (β) at 532 nm (Fig. 7). The zaer to which the AAI is
highly sensitive is treated as an unknown variable to be retrieved together
with ω0.
Consequently, with various combinations of zaer and ω0 a look-up table (LUT) of the calculated AAI is constructed
by radiative transfer calculations. It should be noted that for all pixels
in the plume we assume the same aerosol microphysical properties as well as
the same aerosol layer height. Pixels outside the plume (even they have AAI
values larger than 1) may have significantly different properties and this
will affect the results. As shown in Fig. 8, the distribution of OMI
measurements is sparse in space. These geographical outliers may cause the
heterogeneous properties of the plume. Consequently, we apply a data quality
control procedure before retrieving ω0. First, we manually
remove the pixels that are geographically isolated from the main plume.
Furthermore, we remove potential outliers based on statistical tool. We
filter the data set using an outlier detection criterion based on the
interquartile range (IQR) of the AAI difference between DISAMAR simulations
and OMI measurements. According to Tukey's fences (Tukey, 1977), an AAI
difference falling outside the range between Q1 - 1.5 IQR and Q3 + 1.5 IQR may be
regarded as an outlier and removed, where Q1 and Q3 are the first and third
quartiles of the AAI difference, and the IQR is the range between Q1 and Q3.
Only the pixels passing the outlier detection criterion are used to
calculate the cost function (Eq. 12):
RMSE=∑inAAIDSM,iqualified-AAIOMI,i2n.
Here AAIi indicates the AAI for the ith satellite pixel of the selected OMI
data; subscript DSM and OMI indicate the DISAMAR simulation and the OMI
observation. The combination of zaer and ω0
that leads to the minimum AAI residue is used as the retrieval results.
Finally, the simulated AAI is compared with OMI observations. We also employ
the independent data from GOME-2 as a reference for identifying the potential
bias of OMI. Similarly, the τ retrieved from OMI and AERONET serve as
a reference for MODIS. The estimated aerosol layer height and ω0 at 550 nm are evaluated with independent observations from CALIOP
and AERONET, respectively.
Results and discussion
By applying the methodology described in the previous section, we
quantitatively retrieved the aerosol layer height and ω0 at 550 nm of the Chile 2017 wildfires by AAI simulation. The OMI measurements of
the plume are displayed in Fig. 8a–d. The presented satellite pixels
are larger than 1 with AAI values and free of cloud contamination,
sun glint and row anomaly of the instrument. Fortunately, the remaining data
are still able to capture the main plume features. It can be clearly seen
that, from 26 to 30 January, the plume produced by wildfires in the central
Chile was transported by the south-easterly trade wind from the continent
towards the lower-latitude region of the Pacific Ocean. The plume travelled
over a distance of 3000 km during the period.
Summary of retrieved results (after applying IQR outlier
detection). The date format is yyyy-mm-dd.
Date 2017-01-262017-01-272017-01-292017-01-30Number of pixels in the plume 44708275AAIAAI median (OMAERO)2.522.384.052.61AAI median (DISAMAR)2.172.483.812.49Relative difference (%)-13.884.20-5.93-4.60RMSE0.670.510.600.41Aerosol profilezaer (km)4.94.54.74.7Δz (km)2 nini at 354 nm0.03950.03820.03880.0314ni at 388 nm0.03860.03660.03730.0306Relative difference between2.33 %4.37 %4.02 %2.61 %354 and 388 nmω0 at 550 nmω0 (AERONET)0.890.890.920.91ω0 (DISAMAR)0.830.810.870.85Relative difference (%)-6.74-8.99-5.43-6.59
The vertical movement of the plume is given by CALIOP backscattering
coefficient measurements (β) at 532 nm (Fig. 7). The CALIOP overpasses
closest to the plume are marked by a black dashed line in Fig. 7. It is noted
that due to the spatial coverage and the measuring time difference, CALIOP
are not able to represent the entire plume detected by OMI. The aerosol
layer captured by CALIOP is distributed from 2 to 6 km, with an average
height at approximately 4–5 km. The ascent of the plume was driven by the
heat generated by the fires and aerosol sunlight absorption, as well as the
atmospheric vertical motions.
Figure 8e–h show the AAI simulation selected by the data quality control
mentioned in Sect. 3.2. The spatial distribution of the simulated AAI shows
similar patterns to the OMI observations. Some data points that are
geographically isolated from the plume may differ strongly from observations
inside the plume, although their AAI values are larger than 1, e.g. on 26 and
30 January. Including these outliers in the optimization could bias the
retrieved aerosol properties. This can also be seen in Fig. 8i–l, where the
points passing the data quality control described in Sect. 3.2 are
highlighted in red. By removing the outliers, the average spatial correlation
coefficient reaches 0.90.
Table 2 lists the statistics of the qualified AAI data, in terms of the
median, relative difference and RMSE. The median of measured AAI ranges from
2 to 4 during the research period. Except for 26 January, the median of the
simulated AAI in other cases is in good agreement with the measurements,
with relative differences within ±6 %. The RMSE is only
acceptable and reflects that part of the plume cannot be fit with the assumed
homogeneous aerosol properties in the forward simulation. The majority of
the simulated AAI for 26 January is negatively biased, which is reflected by
the small slope without an intercept correction in Fig. 8i. A systematic
bias in the inputs might cause this result. In terms of ω0,
both the AERONET-measured and the AAI-retrieved aerosol absorption become
weaker with time (Table 2). Although the simulated and observed AAI are in
good agreement, the difference in ω0 is significant. The mean
of the retrieved ω0 at 550 nm for the whole period is 0.84,
in contrast to the AERONET measurements with a mean value of 0.90.
There are many sources contributing to the discrepancy in ω0. First
of all, the nearest site Santiago_Beauchef is not exactly in the primary
biomass-burning regions as mentioned in Sect. 3.1.3. The AERONET site is
located in the centre, where reflective urban or industrial aerosols may have
been mixed with the smoke and enhanced the ω0. This would also
affect the spectral dependency of the complex refractive index used to
constrain the radiative transfer calculation. According to Table 2, the
retrieved ni reveals that the difference between 354 and 388 nm
is less than 5 %. This small spectral dependency of ni is
mainly determined by AERONET measurements in the visible band (dashed lines
in Fig. 9b), whereas the effect of the scaling factor is minor in this case.
We thus find a much weaker wavelength dependency than the value in the Jethva
and Torres (2011) study, where a 20 % difference between the two UV
wavelengths was applied to OMAERUV algorithm to achieve the result that
70 % of the retrieved ω0 differ less than ±0.03 from the
ω0 from the AERONET measurements. This 20 % spectral dependency
adopted in their work is associated with findings by Kirchstetter et
al. (2004). According to them, the absorbing Ångström exponent
(AÅE) of urban pollution is a near unit root and that of the
biomass-burning aerosol ranges is approximately 2 between 300 nm and
1 µm. In terms of ni, a 20 % increase at 354 nm
with respect to the value at 388 nm is equivalent to an AÅE value
between 2.5 and 3, depending on the aerosol models of OMAERUV (Jethva and
Torres, 2011). Hoffer et al. (2006) also found similar results. They
conducted in situ measurements on humic-like substances (HULIS) of Amazonia
biomass-burning aerosols and found that around 35 %–50 % of light
absorption occurred at 300 nm, whereas only around 15 % occurred at
400 nm. Bergstrom et al. (2007) also confirmed this conclusion from several
field programs (SAFARI 2000, ACE Asia, PRIDE, TARFOX, INTEX-A). From the
sensitivity study by Jethva and Torres (2011), a stronger spectral dependency
of ni between 354 and 388 nm would allow simulations to reach
the same AAI with ni at a relatively low level. In our study,
this means retrieving a lower ω0 at 550 nm. The presence of
non-absorbing aerosols may enhance the measured ni in the visible
band and the linear extension would weaken the spectral dependency,
particularly in the UV spectral range. Furthermore, the AERONET inversion
product is not error-free. The uncertainty of size distribution retrieval is
minor for biomass-burning aerosols (Dubovik et al., 2000), but under
optically thick circumstances, even when retrievals are quality assured (i.e.
level 2 data), the reported accuracies of the complex refractive index are
0.04 for nr and 30 %–50 % for ni (Dubovik et
al., 2002). It is also reported that AERONET tends to underestimate the
absorption of biomass-burning aerosols compared with in situ measurements
(Dubovik et al., 2002; Reid et al., 2005). The uncertainty of ω0 is
0.03 under high aerosol loading (τ440>0.5) and 0.05–0.07 under low
aerosol loading (Dubovik et al., 2002; Holben et al., 2006). Last but not
least, the spatial representation of the in situ instrument is also a
concern. Santese et al. (2007) showed that the selected AERONET aerosol
parameters can be representative of a 300×300 km2 south-eastern
Italy area. For the Chile wildfires with the most remote pixels over 3000 km
away from the continent, the measurements at AERONET cannot fully represent
the plume detected by the satellite.
Retrieved complex refractive index for each case:
(a)nr and (b)ni. The dashed line
in panel (b) is the wavelength-dependent ni measured by
AERONET.
Apart from AERONET itself, information from other data sets could
also bias the estimation of aerosol absorption. Among all the inputs, the
parameterization of the one-layer box-shaped aerosol profile could be the
largest error source. Although the influence of Δz on the AAI is
limited (Fig. 4c), the AAI calculation highly depends on zaer (Fig. 4b). As shown in Table 2, the estimated plume altitude varies from 4.5 to
4.9 km. As the black solid line indicated in Fig. 7, the retrieved zaer
can approximately capture the measured plume height. The zaer on 26 January seems overestimated because of the temporal and spatial difference.
Concretely, CALIOP sampled the plume near the sources and close to the
surface, while the plume observed by OMI had been already elevated and
transported to the open ocean. The lack of information on the real plume
height makes it challenging to determine whether the plume height is
responsible for the systematic bias in Fig. 8i. Except for 26 January,
zaer in other cases is in good agreement with what CALIOP observed.
Although the retrieved aerosol layer heights are convincing to some extent,
one should keep in mind that CALIOP and OMI observations are not exactly
co-located. Besides, the parameterized aerosol profiles may fail to
represent the spatial variation of the plume. Therefore, the uncertainty
cannot be directly determined due to the lack of validation data.
Among the 4 days for which we retrieved ω0, the value for 27 January is significantly lower than others. For this day the agreement in
terms of zaer with CALIOP is reasonable. We therefore explore the
effect of observational biases of the AAI and τ on the retrieved
ω0. We investigate the potential bias of these two
data sets by plotting the histogram of the AAI measurement difference between
GOME-2 and OMI (Fig. 10a), against the τ measurement difference
between MODIS and OMI (Fig. 10b, both are converted into 550 nm). It is
clear that on 27 January, the AAI from OMI seems to be overestimated
compared to GOME-2. Although the difference in instrumental design and
wavelength pair choice for AAI retrieval, measurement conditions, etc.
could contribute to the AAI discrepancy between GOME-2 and OMI, exploring
the difference between the two data sets is beyond the scope of this study.
In terms of input aerosol concentration, the τ from MODIS could be
potentially underestimated. Fitting a higher AAI with a lower input τ
leads to an overestimation in aerosol absorption. Here, we analytically
quantify the impact of τ for this specific case by systematically
enhancing the τ of MODIS with a constant variation (Δτ)
added to all pixels, with the AAI level and the aerosol layer height
unchanged. Figure 10c presents how the AAI RMSE and the estimated ω0 respond to the enhanced τ. It can be clearly seen that an
increase in overall τ level by 0.07 raises ω0 to 0.84
and optimizes the AAI simulation to a RMSE less than 0.45. If we apply this
τ adaption, the retrieved ω0 of 27 January becomes more
consistent with the other days.
Histogram of (a) the AAI difference between GOME-2 and OMI,
compared to (b) the τ difference at 550 nm between MODIS and OMI
for 27 January. Contour of (c) the AAI RMSE as a function of
variation in τ and ω0 for 27 January. The dashed line is the
best estimation for each pair of Δτ and ω0.
Apart from the observational errors in AERONET, OMI and MODIS data, the
assumption that the plume features are homogeneous could also result in the
discrepancy between the AAI-retrieved and the AERONET-measured ω0. In reality, the plume altitude, the optical properties and even the
chemical compositions could vary from pixel to pixel, while our simulations
cannot consider those effects.
Conclusions
Biomass burning is a major source of absorbing aerosols making a significant
contribution to climate warming. Quantitatively characterizing the
absorption of biomass-burning aerosols is therefore important to reduce the
uncertainty in assessments of global aerosol radiative forcing. Facing the
lack of a long-term ω0 record, this study explores an approach
to retrieve ω0 based on reflectivity in the near-UV channel
measured by OMI. Although AAI is not a geophysical parameter and depends on
many factors, its independency from predefined aerosol types, its high
sensitivity to aerosol absorption and its long-term data record
make it an attractive for aerosol research.
We test the retrieval of ω0 for the wildfires in
central Chile in January 2017. After filtering the data from the outliers, high
spatial correlation coefficients (0.85 to 0.95) are between the simulated
and observed AAI. The retrieved aerosol layer heights indicate that the plume was
elevated to a height of 4.5–4.9 km during the research period. These results
are in agreement with CALIOP measurements. This plume average of the
retrieved ω0 at 550 nm is approximately 0.84, which is 0.06
lower than that of AERONET retrieval. The sources for
discrepancy include the location of the AERONET site that may bias the
measured ω0 and complex refractive index; the simplified
parameterization of the aerosol profile; the insufficient spatial
representativeness of a single AERONET site; the observational errors in the
input aerosol microphysics, τ, as well as AAI; and the assumption of
homogeneous plume properties. We quantitatively analyse the uncertainty of
τ for a specific case (27 January) when the estimated aerosol layer
height is in good agreement with the CALIOP measurements. An improvement in
retrieved ω0 can be seen by adapting the magnitude of the aerosol
concentration.
This study proves the potential of utilizing OMI-measured AAI to
quantitatively characterize aerosol absorption such as ω0.
Currently, it is challenging to retrieve and validate results without
reliable aerosol profile information. In the future, the availability of
daily global aerosol layer height data (e.g. the L2 aerosol layer height
product of TROPOspheric Monitoring Instrument on board Sentinel-5 Precursor, TROPOMI, that is under development; Sanders and de Haan, 2016) are
expected to provide a stronger constraint on the forward calculation and to
reduce the uncertainty in the retrieved aerosol properties. It is also
reliable to retrieve aerosol absorption for each individual pixel with a
constraint on the aerosol layer height information. The problem due to the
poor spatial representativeness of in situ measurements can then be eased by
comparing with the satellite pixels near the ground-based instruments.
Perhaps, more sophisticated assumptions of spectral-dependent aerosol
absorption (e.g. steeper gradient of ni in UV than visible band) have
to be made and evaluated by other observational aerosol properties in UV
spectral range, e.g. measuring AERONET τ in the UV band instead of only
depending on measured refractive index in the visible band.
All the data used in this study can be freely accessed. The
OMAERO can be accessed via https://disc.gsfc.nasa.gov (last access: 31
December 2017, Stammes, P. and Noordhoek, 2002). The GOME-2 data can be
accessed via http://archive.eumetsat.int (last access: 14 July 2017,
Tilstra et al., 2002). The MYD04 can be accessed via
https://ladsweb.modaps.eosdis.nasa.gov (last access: 27 February 2017;
Levy et al., 2013). The Santiago_Beauchef AERONET data can be accessed via
https://aeronet.gsfc.nasa.gov (last access: 11 August 2017, Dubovik and
King, 2000; Dubovik et al., 2000). The CALIOP level 1B data can be accessed
via https://eosweb.larc.nasa.gov/project/calipso/calipso_table (last
access: 17 July 2017, Winker et al., 2009).
The radiative transfer model used in this study (DISAMAR) is proprietary thus
is not shared with public.
All the results created in this study are available with the permission of
authors. If you are interested, please send an email to
jiyunting.sun@knmi.nl.
Phase function P(Θ) at 354 nm of the parameterized
Mie-scattering aerosol in sensitivity studies as a function of rg
(with nr=1.5 and ni=0.06). The markers in the
plot correspond to values when Θ=60, 90, 120, 150, 180∘.
Phase function P(Θ) at 354 nm of the parameterized
Mie-scattering aerosol in sensitivity studies as a function of nr
(with rg=0.15µm and ni=0.06). The
markers in the plot correspond to values when Θ=60, 90, 120, 150,
180∘.
Phase function P(Θ) at 354 nm of the parameterized
Mie-scattering aerosol in sensitivity studies as a function of ni
(with rg=0.15µm and nr=1.5). The
markers in the plot correspond to values when Θ=60, 90, 120, 150,
180∘.
JS, PPV and PvV together conceived the study. PPV and PvV
helped to designed the study. JS performed the experiments with guidance of
PPV and PvV. JS prepared the manuscript with contributions from all authors.
PFL reviewed and edited the manuscript.
The authors declare that they have no conflict of
interest.
Acknowledgements
This work was performed in the framework of the KNMI Multi-Annual Strategic
Research (MSO). The authors thank NASA's GES-DISC, LAADS DAAC and ASDC
for free online access of OMI, MODIS and CALIOP data. The authors also thank
the Centre for Climate Resilience Research (CR)2 at the University of Chile
(CONICYT/FONDAP/15110009) for providing the data from the Santiago_Beauchef AERONET station.
Edited by: Hiren Jethva
Reviewed by: three anonymous referees
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