JOURNAL OF GEOPHYSICAL RESEARCH, VOL. 110, D10S18, doi:10.1029/2004JD004611, 2005
Total Ozone Mapping Spectrometer measurements of aerosol
absorption from space: Comparison to SAFARI 2000 ground-based
observations
O. Torres,1,2 P. K. Bhartia,3 A. Sinyuk,1,4 E. J. Welton,3 and B. Holben3
Received 6 February 2004; revised 30 July 2004; accepted 3 August 2004; published 24 February 2005.
[1] The capability to detect the presence of absorbing aerosols in the atmosphere using
space-based near-UV observations has been demonstrated in the last few years, as
indicated by the widespread use by the atmospheric sciences community of the Total
Ozone Mapping Spectrometer (TOMS) aerosol index as a qualitative representation of
aerosol absorption. An inversion procedure has been developed to convert the unique
spectral signature generated by the interaction of molecular scattering and particle
absorption into a quantitative measure of aerosol absorption. In this work we evaluate the
accuracy of the near-UV method of aerosol absorption sensing by means of a comparison
of TOMS retrieved aerosol single scattering albedo and extinction optical depth to groundbased measurements of the same parameters by the Aerosol Robotic Network
(AERONET) for a 2-month period during the SAFARI 2000 campaign. The availability of
collocated AERONET observations of aerosol properties, as well as Micropulse Lidar
Network measurements of the aerosol vertical distribution, offered a rare opportunity for
the evaluation of the uncertainty associated with the height of the absorbing aerosol layer
in the TOMS aerosol retrieval algorithm. Results of the comparative analysis indicate that
in the absence of explicit information on the vertical distribution of the aerosols, the
standard TOMS algorithm assumption yields, in most cases, reasonable agreement of
aerosol optical depth (±30%) and single scattering albedo (±0.03) with the AERONET
observations. When information on the aerosol vertical distribution is available, the
accuracy of the retrieved parameters improves significantly in those cases when the actual
aerosol profile is markedly different from the idealized algorithmic assumption.
Citation: Torres, O., P. K. Bhartia, A. Sinyuk, E. J. Welton, and B. Holben (2005), Total Ozone Mapping Spectrometer
measurements of aerosol absorption from space: Comparison to SAFARI 2000 ground-based observations, J. Geophys. Res., 110,
D10S18, doi:10.1029/2004JD004611.
1. Introduction
[2] The role of atmospheric aerosols in the global climate
is one of the largest remaining sources of uncertainty in the
assessment of global climate change. Aerosols directly affect
the energy balance of the Earth-atmosphere system, through
the processes of scattering of solar radiation, which redistributes the incoming solar energy in the atmosphere, and
absorption (of both solar and infrared radiation), which
transforms radiative energy into internal energy of the absorbing particles and heats up the atmosphere. In addition,
aerosols, through their role as cloud condensation nuclei,
1
Joint Center for Earth Systems Technology, University of Maryland,
Baltimore County, Baltimore, Maryland, USA.
2
Also at NASA Goddard Space Flight Center, Greenbelt, Maryland,
USA.
3
NASA Goddard Space Flight Center, Greenbelt, Maryland, USA.
4
Now at Science Systems and Applications Inc., Lanham, Maryland,
USA.
Copyright 2005 by the American Geophysical Union.
0148-0227/05/2004JD004611$09.00
have an indirect effect on climate by affecting the albedo and
lifetime of clouds [Haywood and Boucher, 2000].
[3] The cooling effect of aerosols, associated with the
backscattering to space of a fraction of the incoming solar
energy, is considered to be a very important counteracting
factor of the well known warming effect of the greenhouse
gases. The absorption by aerosol particles of a fraction of
the incident sunlight and, for certain aerosol types, infrared
radiation, results in a heating of the atmosphere. Thus
aerosol absorption reduces the cooling effect commonly
associated with aerosol particles. Although, the impact of
aerosol absorption on climate is still a subject of considerable debate [Penner et al., 2003], recently published theoretical analysis suggest that black carbon may be the second
most important global warming substance (in terms of its
direct radiative forcing effect) after carbon dioxide, and
larger than methane [Jacobson, 2002]. The role of aerosol
absorption effects on climate is, therefore, an issue that
needs to be better understood in order to reduce the
currently large uncertainties of its climatic effect.
[4] In this paper, we present and discuss the results of
the application of the near-UV method to observations by
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the Total Ozone Mapping Spectrometer (TOMS), on board
the Earth Probe (EP) satellite (also known as EP-TOMS),
to retrieve aerosol single scattering albedo during the
Southern African Regional Science Initiative (SAFARI
2000) campaign [Swap et al., 2002]. A brief discussion
of some theoretical aspects associated with the quantification of aerosol absorption is carried out in section 2,
followed by a short review of commonly used remote
sensing approaches to measure aerosol absorption in
section 3. In section 4, the physical basis of the nearUV approach is given, and a brief description of the
TOMS aerosol algorithm is presented. In section 5, the
TOMS retrieved single scattering albedo and optical depth
are compared to collocated retrievals of the same parameters from observations by the Aerosol Robotic Network
(AERONET). The last section includes additional discussion, a summary of results and conclusions of the
analysis.
2. Absorbing Aerosol Components
[5] The fundamental microphysical property driving particle absorption is the imaginary component of the refractive
index, k, of the aerosol components. The absorption coefficient of aerosol particles depends on k, the size of the
particles, and the manner in which the different components
are incorporated into the mixture, i.e., internal or external
mixtures [Sokolik and Toon, 1999]. A commonly used
macroscopic measure of aerosol absorption is the single
scattering albedo, w0, defined as the ratio of the scattering to
extinction (absorption plus scattering) coefficients. For a
detailed discussion of the physical aspects of absorption by
particles, the reader is directed to the review paper by
Horvath [1993].
[6] Atmospheric aerosols absorb radiation over a very
wide region of the solar spectrum, from the ultraviolet (UV)
to the near-infrared (IR). The aerosol component that
contributes the most to particle absorption of solar radiation
is black carbon or soot, found in many aerosol types of
anthropogenic origin such as urban-industrial aerosols and
biomass burning. Black carbon absorption depends only
weakly on wavelength over the near-UV to near-IR spectral
region, where the soot imaginary refractive index is relatively constant [Twitty and Weinman, 1971; Bergstrom et
al., 2002].
[7] Hematite and other iron oxides are the main absorbing components of desert dust aerosols. Unlike soot,
the imaginary refractive index of desert dust is largest in
the ultraviolet and decreases rapidly with wavelength
[Patterson et al., 1977; Alfaro et al., 2004]. Early measurements of the imaginary refractive index of Saharan dust
reported significant absorption from the UV to the visible
[Patterson et al., 1977]. Recent results, however, using both
remote sensing as well as in situ methods, indicate that
Saharan mineral dust absorption in the UV is significantly
less than previously thought [Colarco et al., 2002; Sinyuk et
al., 2003; Alfaro et al., 2004]. In the visible, a combination
of satellite and ground-based remote sensing [Kaufman et
al., 2001], and airborne measurements over the Atlantic
ocean [Haywood et al., 2003] produced single scattering
albedo values of Saharan dust close to unity, significantly
larger than the values associated with the Patterson et al.
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[1977] data on the imaginary component of refractive
index. Airborne in situ measurements in the visible of
mineral aerosol of Asian origin over the North Pacific
[Anderson et al., 2003] also produced single scattering
albedo estimates close to one.
[8] Organic material, a component of some natural
aerosol types, has also been suggested as an important
absorbing species, mainly in the ultraviolet [Jacobson,
1999]. A few measurements of the absorption effects of
organic aerosol types have been conducted [Lyubovtseva,
2002]; however, current knowledge of their optical properties is very poor.
3. Measuring Aerosol Absorption
[9] Aerosol absorption can be directly measured by the
application of a variety of optical techniques both in situ and
in the laboratory. For a detailed description of commonly
used direct methods of measuring aerosol absorption, the
reader is referred to the review papers by Horvath [1993]
and by Heintzenberg et al. [1997].
[10] Inverse ground-based methods to measure columnintegrated single scattering albedo using solar radiation
observations have been applied. Shadow band radiometer
observations of the direct and diffuse components of the
incident solar flux, in conjunction with radiative transfer
calculations have been used to infer w0 [Herman et al.,
1975; King, 1979]. Eck et al. [1998], produced estimates of
the single scattering albedo for biomass burning aerosols in
Amazonia by matching measured to model computed irradiances using as input the observed aerosol optical depth
and particle size distribution. Another method to derive
aerosol absorption using a combination of direct Sun
measurements and sky radiances has been developed as a
part of the Aerosol Robotic Network [Holben et al., 1998].
The technique infers the aerosol particle size distribution
and complex refractive index by fitting measurements of
aerosol optical depth and sky radiances at four wavelengths
(0.44, 0.67, 0.87 and 1.02 mm) to radiative transfer calculations [Dubovik and King, 2000].
[11] The use of satellite observations to measure aerosol
absorption has also been attempted. Kaufman [1987]
developed a method to retrieve aerosol single scattering
albedo, based on the measurement of the radiance change
in the visible between a clear and a hazy day over a
varying surface reflectance. It was shown that for a
certain critical value of the surface reflectance, the net
atmospheric effect due to the competing processes of
scattering and absorption of light is almost independent
of aerosol optical thickness, and is mainly determined by
the aerosol single scattering albedo. Although this method
has been shown to work on a few cases, its practical
applicability is hindered by the limited spatial variability
of the actual surface reflectance, that makes the identification of the critical reflectance value a difficult task.
Recently, another method to measure aerosol absorption
from space in the visible spectral region has been
suggested [Kaufman et al., 2002]. The method proposes
the use of a satellite viewing configuration to measure the
upwelling radiance on and off the Sun glint regions over
the oceans. The off-glint measurements would be used to
characterize the aerosol scattering properties, whereas the
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surfaces facilitates the sensing of aerosols over the oceans
and the continents, including the arid and semiarid
regions of the world.
[14] To illustrate the interaction of the radiative transfer
effects between the molecular atmosphere, the surface, and
the aerosols, as measured by a satellite borne sensor, it is
useful to express the upwelling radiance at the top of the
atmosphere (TOA) in terms of the molecular scattering and
surface reflection components. In the absence of aerosols
and clouds, the TOA ‘‘background’’ radiance, Il, is given
by the combination of the Rayleigh scattering by the
molecular atmosphere I0l, and the reflection by the underlying surface, Isl,
Il ¼ I0l þ Isl :
Figure 1. Model calculations illustrating the spectral
dependence of the upwelling reflectance at the top of the
atmosphere. The Rayleigh scattering contribution is shown
as the solid line. The contribution of different surface types
is also shown: ocean (dotted line), vegetation (dashed line),
and desert (dot-dash line).
strong Sun glint background would be used to quantify
the aerosol absorption effects. This approach would not
measure particle absorption over land where most sources
of anthropogenic aerosols are located.
[12] Aerosol absorption from space can also be measured
using observations of backscattered near-UV radiation
[Torres et al., 1998]. This approach works equally well
over the oceans and the continents, and is the only currently
available technique to globally detect and characterize
aerosol absorption from space. A discussion of results of
aerosol absorption measurements by the near-UV method is
the central theme of this paper.
4. Satellite Measurement of Aerosol Absorption in
the Near-UV
4.1. Physical Basis
[13] The near-UV method of aerosol absorption sensing
from space derives its sensitivity from the interaction of
the large molecular scattering component characteristic of
this spectral range, and absorption by the aerosol particles
of Rayleigh scattered photons. As shown in Figure 1, the
Rayleigh scattered radiation emanating from the scattering
effects of the molecular atmosphere, peaks in the near-UV
spectral region, and decreases rapidly with wavelength. At
340 nm the Rayleigh component is about an order of
magnitude larger than at 600 nm. Also shown is the
calculated spectral dependence of the surface reflected
radiation, for typical surface types. The well-known peak
in the green associated with the reflectance of vegetated
surfaces, does not show up here because of the coarse
spectral resolution of the calculations in that spectral
region (50 nm). Note that in the near-UV, unlike in
the visible and near-IR spectral regions, the surface
contribution is significantly smaller than the Rayleigh
scattering component even over vegetated and desert
surfaces. The low near-UV reflectance of land and water
ð1Þ
When the atmosphere contains aerosol particles, the TOA
radiance is modified by the aerosols scattering and
absorption effects, as shown by the approximation
#
"
m þm
tl m0 m
w0l Pl ðQÞpF0l m0
0
Il 1e
4p
m0 þ m
m þm
tl ð1w0l Þ m0 m
ðps pa ÞI0l
pa
0
þ Isl e
þ I0l þ . . .
þ
ps
ps
ð2Þ
The first term on the right side of equation (2) is the
singly scattered radiance added by the aerosol layer, a
function of the incoming solar flux, pF0, the aerosol
scattering phase function, P(q), single scattering albedo,
w0, and extinction optical depth, t. The parameters m and
m0 represent the cosines of the satellite and solar zenith
angles respectively. The second term represents the
attenuation of the Rayleigh scattered and surface reflected
radiance by the absorption effects of the aerosol layer.
Since the molecular scattering component, depends on
atmospheric pressure, the attenuation of I0l by aerosol
absorption also depends on the height of the aerosol
layer. Therefore the second term of equation (2) includes
only the attenuation of that fraction of molecular
scattering originating from within and underneath the
aerosol layer. The third term accounts for the molecular
scattering above the aerosol layer where the attenuation
effect of the particles is significantly reduced. The terms
pa and ps represent respectively the pressure at the level
of the aerosol layer, and at the surface. (A previously
published version of equation (2) [Torres et al., 2002a],
incorrectly extended the pressure scaling of the aerosol
absorption effect to the surface term Isl).
[15] In this approximation, particle multiple scattering, as
well as other second-order terms (reflected and then scattered radiation and vice versa), have been neglected. Therefore, strictly speaking, this approximation is only valid
when the aerosol optical depth is very small, and particle
multiple scattering can be ignored. We use it here just to
illustrate the Rayleigh-scattering particle absorption interaction that is the backbone of the near-UV method to
characterize aerosol absorption.
[16] The net aerosol induced reflectance change, DIl, is
given by the combined effect of the competing processes of
aerosol scattering and the attenuation by aerosol absorption
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layer lies above an ice/snow covered surface, a cloud deck,
or against the highly reflective background provided by Sun
glitter effects [Kaufman et al., 2002].
Figure 2. Spectral dependence of the aerosol single
scattering contribution (dashed line), the attenuation of the
Rayleigh component by aerosol absorption (dot-dashed
line), and the net aerosol effect (solid line).
effects of the molecular and surface components, as shown
below
#
"
m þm
tl m0 m
w0l Pl ðQÞpF0l m0
0
DIl 1e
4p
m0 þ m
#
"
m þm
tl ð1w0l Þ m0 m
ðps pa ÞI0l
0
þ
þ Isl e
1 :
ps
ð3Þ
Figure 2 shows the spectral dependence of the scattering
and attenuation terms as calculated using equation (3) for an
absorbing aerosol layer at 3 km above the surface. The
aerosol properties used in the calculations are representative
of carbonaceous particles resulting from biomass burning.
For the aerosol model used, the calculated aerosol single
scattering contribution is a weak function of wavelength
resulting from the opposite spectral dependence of the
optical depth and scattering phase function terms (not
shown). The calculated spectral dependence is not a general
result, and may be different for other aerosol models. The
attenuation term is very large in the near-UV and converges
rapidly to near zero as the wavelength increases. The large
near-UV multiple molecular scattering component, increases the length of photon paths through the aerosol,
and provides a ‘‘medium’’ in which aerosol absorption
effects are clearly observed and can be measured. Owing to
the mutual cancellation by the scattering and absorption
processes, the resulting net effect (solid line in Figure 2) is
small in the in the near-UV (it can actually be negative for
strongly absorbing aerosols). The observed spectral contrast
at two near-UV wavelengths, is the quantity commonly
known as the absorbing aerosol index, routinely derived
from TOMS observations [Herman et al., 1997]. At visible
wavelengths, where the attenuation effect is negligible, the
net result is just the single scattering contribution. Note that
for nonabsorbing aerosols (i.e., w0 = 1), the second term of
equation (5) becomes zero and the net aerosol effect is just
the single scattering term. A similar attenuation effect can
be observed, at all wavelengths, for an enhanced contribution of the surface term (Is), as when the absorbing aerosol
4.2. TOMS Algorithm
[17] On the basis of the previously discussed physical
basis, an approach to retrieve aerosol properties using
TOMS measurements in the near-ultraviolet spectral region
has been developed [Torres et al., 1998, 2002a]. In the
TOMS near-UV method, measurements of the backscattered radiance (I) at two wavelengths l1 and l2, (l2 > l1) in
the range 330 – 380 nm are used. Aerosol particles are
characterized by examining the variability of the relationship between the spectral contrast (Il1/Il2) and the radiance
at the longer wavelength (Il2). The inversion procedure uses
a set of precomputed look up tables (LUTs) of radiances
emerging at the top of an aerosol-laden atmosphere. The
LUTs were generated using the University of Arizona
radiative transfer code [Herman et al., 1995] that fully
accounts for multiple scattering and polarization effects.
[18] The TOMS aerosol algorithm uses a combination of
spectral and geographical location considerations to select,
for each pixel, one of four possible aerosol types: desert
dust, carbonaceous, urban-industrial and sulfate aerosols.
Each aerosol type is represented by a set of aerosol models
characterized by an assumed particle size distribution and
fixed real component of the refractive index (spectrally
independent). The relative spectral dependence of the imaginary component of the refractive index in the range l1 – l2
is also prescribed. The imaginary refractive index at the
longer wavelength is allowed to vary in the retrieval
process, to capture the temporal and spatial variability of
the aerosol absorption capacity.
[19] The assumed vertical distribution varies based on the
selected aerosol type. For carbonaceous and desert dust
particles, the aerosol load is assumed to be vertically
distributed following a Gaussian function characterized by
a peak (aerosol layer height) and a half-width (aerosol layer
geometric thickness) values. For sulfate and urban-industrial
aerosols, the aerosol concentration is largest at the surface
and decreases exponentially with height. When the aerosols
are identified as of the carbonaceous type, the aerosol layer is
assumed to be at 3.0 km sea level. A global climatology of
aerosol layer height derived from transport model calculations [Ginoux et al., 2004] is used for desert dust aerosols.
[20] Surface effects are characterized making use of a
climatology of minimum near-UV reflectivity compiled
from Nimbus7/TOMS observations [Herman and Celarier,
1997]. No spectral dependence is assumed in the 330–
380 nm range. A recent analysis using Global Ozone
Monitoring Experiment (GOME) data shows that for most
land surface types, the near-UV reflectivity is only weakly
wavelength-dependent [Koelemeijer et al., 2003]. A weak
spectral dependence in the near-UV prevails over coastal
waters. Over the open oceans, however, a larger spectral
dependence is observed associated with the effects of clear
water absorption in the near-UV [Litjens et al., 1999].
[21] The retrieved parameters are the total optical depth
and the imaginary component of the refractive index at the
longer wavelength. For historical reasons, and to facilitate
validation studies using AERONET data, the retrieved
parameters are reported at 380 nm. The aerosol single-
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scattering albedo associated with the retrieved imaginary
refractive index and the other assumed aerosol properties, is
calculated.
[22] The near-UV algorithm has been applied to the
multiyear-long record of observations by the TOMS
sensor onboard the Nimbus-7 spacecraft (1979 – 1993)
and the Earth Probe platform (1996 to present). The
aerosol optical depth product has been validated using
ground-based Sun photometer measurements [Torres et
al., 2002a, 2002b], and compared to airborne Sun photometer observations during the SAFARI 2000 [Schmid et
al., 2003] and the PRIDE [Livingston et al., 2003] field
campaigns. Comparison of aerosol optical depth to model
calculations [Chin et al., 2002], and to other satellite
retrievals [Myhre et al., 2004] have also been carried out.
The long-term TOMS record on atmospheric aerosol load
has been used to identify aerosol increases in regions of
China and India [Massie et al., 2004]. In this paper
results of the absorption optical depth retrievals are
discussed and compared to independent observations.
5. Evaluation of TOMS Retrievals During
SAFARI 2000
5.1. Biomass Burning Aerosols Observations
[23] The SAFARI 2000 field campaign [Swap et al.,
2002] took place over a vast region of central and southern
Africa covering nine African countries during the period
13 August to 25 September 2000. Several ground-based
[Eck et al., 2003], airborne [Schmid et al., 2003], and
satellite observations were carried out to measure the properties of the dense carbonaceous aerosol layer that during the
dry season covers this region of the world as a result of long
established land clearing agricultural practices. In this section we present results of aerosol absorption measurements
using TOMS observations, and evaluate their accuracy by
comparison with AERONET’s ground-based measurements.
We also examine in detail the sensitivity to the aerosol
vertical distribution, by incorporating into the analysis actual
lidar measured aerosol profiles.
5.1.1. TOMS Retrievals
[24] Aerosol optical depth and single scattering albedo
were obtained by inversion of the TOMS observations at
331 and 360 nm using the algorithm described in section 4.
On the basis of the geographical considerations used by the
algorithm to differentiate between desert dust and biomass
burning particles, the aerosol load was identified as composed of carbonaceous particles over the entire area of the
campaign. The particle size distribution used by the algorithm to characterize this aerosol type is a bimodal function
with mode radii of 0.087 and 0.567, and standard deviations
of 1.537 and 2.203 for the fine and coarse modes respectively. These parameters were compiled from AERONET’s
long-term statistics for biomass burning aerosols [Dubovik
et al., 2002]. The real refractive index is 1.5, and the
imaginary component varies between 0.0 and 0.05. Both
refractive index components are assumed to be spectrally
independent.
[25] Figure 3 shows the aerosol spatial distribution on
5 September, as obtained from TOMS observations. The
dark gray shaded areas correspond to scenes affected by
severe cloud contamination. The data gap observed in the
D10S18
middle of the map (white areas) is due to the lack of coverage
by contiguous orbits. The horizontal extent of the smoke
layer as given by the aerosol index is shown in Figure 3a.
The aerosol index is positive for elevated (about 2 km or
higher) absorbing particles, and negative for low altitude
weakly absorbing aerosols.
[26] The inversion algorithm allows the quantitative characterization of the aerosol load even for those conditions
when the aerosol index is negative. The increased sensitivity
to low altitude and weakly absorbing aerosols in the
retrieval algorithm, is the result of using the single channel
measurement (see section 4.2) in addition to the spectral
ratio (associated with the aerosol index). Thus, for clear
conditions, the results of the inversion procedure allows for
a better characterization of weakly absorbing aerosols, and
aerosol layers at lower altitudes, than possible when only
the aerosol index is used [Torres et al., 1998]. Since cloud
contamination was not a major retrieval obstacle on this day,
the optical depth and single scattering albedo maps in
Figures 3b and 3c, show a larger spatial coverage than the
aerosol index as discussed above. The retrieved aerosol
optical depth (AOD) and single scattering albedo (SSA)
maps show data over the Atlantic Ocean as well as over the
eastern part of the continent where the aerosol index is
negative and fails to positively detect the absorbing aerosol
signal. Optical depth values as high as 3.0, were retrieved in
the densest part of the layer. SSA values as low as 0.82 are
observed; however, the most predominant values are 0.86–
0.90. Figure 3d shows the derived optical depth of absorption. Values as high as 0.5 are clearly observed.
5.1.2. AERONET Measurements
[27] During SAFARI 2000, the AERONET project increased the density of CIMEL Sun photometers to ensure a
good coverage over the entire area of the campaign. The
geographical coordinates and elevation of the sites used in
this analysis are listed in Table 1. AERONET uses directSun irradiance measurements at a 15 min interval to
measure aerosol optical depth at 340, 380, 440, 500, 670,
870 and 1020 nm. In addition to the solar flux observations,
the AERONET instrumentation also measures almucantar
radiances at 440, 670, 870 and 1020 nm, which are used as
input to an inversion algorithm [Dubovik et al., 2000] to
retrieve column averaged particle size distribution, and the
complex refractive index of the tropospheric aerosol load.
The AERONET-retrieved negligible spectral dependence of
the imaginary component of refractive index for carbonaceous aerosols in several different regions of the world,
including the African Savanna [Dubovik et al., 2002], is
consistent with the TOMS algorithmic assumption of wavelength-independent refractive index at 331 and 360 nm.
AERONET reported results indicate no spectral dependence
in the range 440 – 1020 nm, consistent with Twitty and
Weinman [1971], who also show that the lack of spectral
dependence of the imaginary refractive index of soot
extends to the near-UV region.
5.1.3. MPLNET Backscatter Profiles
[28] The NASA Micropulse Lidar Network (MPLNET)
[Welton et al., 2001] consists of micropulse lidar (MPL)
sites colocated with AERONET Sun photometers. During
SAFARI 2000, two MPL systems were deployed to Mongu,
Zambia and Skukuza, South Africa. The MPL system is a
single channel (523 nm), autonomous, eye-safe lidar system
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Figure 3. Geographical distribution of aerosol properties over southern Africa on 5 September 2000 as
derived from TOMS observations. Shown quantities are: (a) aerosol index, (b) extinction optical depth,
(c) single scattering albedo, and (d) the absorption optical depth. The aerosol layer height was assumed to
be 3 km above sea level. Dark gray shading indicates severe cloud contamination. White areas
correspond to missing data, mostly due to lack of interorbital coverage.
that is used to determine the vertical structure of clouds
and aerosols. Raw MPL data were acquired at 1-min time
resolution, and 75 m vertical resolution. The raw data
were converted into uncalibrated lidar signals and used to
infer the altitude of aerosol and cloud heights. Aerosol
extinction profiles were calculated for times coincident
with AERONET aerosol optical depth measurements
using the algorithm described by Welton et al. [2000].
5.2. Validation Analysis
[29] The simultaneous availability of AERONET measurements of aerosol optical depth and single scattering
Table 1. Summary of TOMS-AERONET Comparison of Single Scattering Albedo Retrievals During SAFARI 2000
AERONET
Site
Mongu
Mwinilunga
Ndola
Senanga
Solwezi
Zambezi
Inhaca
Skukuza
Latitude,
Longitude
15,
11,
12,
16,
12,
13,
20,
24,
23
24
28
23
26
23
32
31
Elevation,
m asl
AERONET
Total
TOMS
Total
Coincidence
AERONET
Average
TOMS
Average
Difference
1107
1430
1270
1025
1333
1040
73
150
48
18
22
35
18
24
15
20
44
35
23
43
35
42
10
8
36
13
13
24
12
9
5
8
0.91
0.90
0.87
0.88
0.87
0.87
0.89
0.88
0.90
0.91
0.91
0.90
0.90
0.91
0.92
0.91
0.01
0.01
0.04
0.02
0.03
0.04
0.03
0.03
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Figure 4. Comparison of TOMS retrieved to AERONET
measured aerosol optical depth at 380 nm during SAFARI
2000. Measurements at eight sites were used in the analysis.
The one-to-one correspondence is shown as the solid line.
The dotted lines indicate the TOMS estimated level of
uncertainty: largest of 0.1 or 30% of the ground-based
measurement.
albedo, and the MPLNET information on aerosol vertical
distribution, a source of uncertainty in the near-UV method,
provided an excellent opportunity to evaluate the sensitivity
of the TOMS retrieval products to the assumed aerosol
profile.
5.2.1. Optical Depth Comparison
[30] Figure 4 shows a comparison of the TOMS retrieved
optical depth to AERONET measurements at the eight sites
listed in Table 1 for the standard assumption on the aerosol
vertical distribution, i.e., aerosol layer at 3 km above sea
level. The dotted lines indicate the expected accuracy (the
larger of 0.1 and 30%) according to a previously published
sensitivity analysis [Torres et al., 1998]. Of the total 127
comparison points, 104 (or 82%) are within the expected
accuracy limits. This level of agreement is the same as
reported for different aerosol types in other published
validation analysis [Torres et al., 2002a, 2002b].
5.2.2. Single Scattering Albedo
[31] A comparison of the aerosol single scattering albedo
from AERONET and TOMS observations during SAFARI
2000 was carried out. Because of their reliability, AERONET
sky radiance measurements at large solar zenith angles are
preferentially used in the retrieval algorithm. For this reason,
most single scattering albedo retrievals use either morning or
afternoon measurements and, therefore, an exact collocation
at the time of the satellite overpass (about 1030 local time
(LT)) is not always possible. The evaluation of the space and
ground-based retrievals is carried out by comparing the daily
average single scattering albedo from the AERONET retrieval to the TOMS retrievals averaged over a 1 1 box
centered at the AERONET site. A total of 120 coincident
single scattering albedo observations were compiled (see
Table 1). Because of the lack of a precise time collocation,
the diurnal variability in single scattering albedo and/or
planetary boundary layer height may affect the comparison. The TOMS 380 nm single scattering albedo value is
D10S18
converted to 440 nm, the shortest wavelength for which
AERONET single scattering albedo retrievals are reported.
The adjustment (0.01 reduction) was derived performing
Mie calculations at 440 nm for the particle size distribution
and real refractive index assumed in the look up tables for
carbonaceous aerosols, and the imaginary component retrieved by the TOMS algorithm. The underlying assumption of no spectral dependence of the imaginary refractive
index in the 380– 440 nm range is supported by direct
observations [Twitty and Weinman, 1971] and observations-based estimates [Bergstrom et al., 2002].
[32] Figure 5 shows the difference of AERONET and
TOMS retrieved single scattering albedo as a function of
time, at the eight sites used in this analysis. The differences are reported for three aerosol layer height assumptions (1.5, 3.0 and 6.0 km above sea level) used in the
TOMS algorithm. The thin solid lines at ±0.03 indicate
the expected accuracy level of the AERONET retrieval
[Dubovik et al., 2000]. The 3 km TOMS retrieved single
scattering albedo at Mongu, agrees with AERONET result
within 0.03. The 3 km standard assumption seems to work
reasonably well from the end of August up to about
25 September. A height value increasingly higher than
3 km and approaching 6 km would be required to get
a good match of the two retrievals between the end of
September and mid-October. Again, an altitude between 3
and 6 km would produce a good match toward the end of the
comparison period. The 3 km assumption also works reasonably well at Mwinilunga, yielding retrieval differences
within the AERONET uncertainty. At the Ndola and Senanga sites, an aerosol layer height between 1.5 and 3 km
would yield an excellent match. The standard 3 km assumption, however, still produces agreement with AERONET
within 0.03, although the TOMS retrieval is systematically
larger the AERONET result. At the last two sites (Solwezi
and Zambezi), it is clear than the aerosol layer at 1.5 km does
a much better job than the 3 km assumption. At these sites,
the TOMS-AERONET difference using the 3 km standard
assumption is within 0.05 of the AERONET value. The few
retrievals at the Inhaca and Skukuza sites, show again a
better agreement with the TOMS retrieval at 1.5 km than
when using the standard 3 km assumption.
[33] The observed retrieval differences at Mongu and
Senanga are an intriguing feature of this analysis. In
spite of being the closest two AERONET sites (less than
100 km apart), the comparison suggests 3 km as the most
likely aerosol height at Mongu, whereas a lower aerosol
layer height seems to prevail at Senanga. However, the
AERONET single scattering albedo value at Mongu is
systematically higher, by about 0.03, than the value measured at Senanga, in spite of the proximity of the two
locations. In a recently published analysis of the variability
of AERONET derived optical properties during SAFARI
2000, the Mongu data was excluded from the analysis due
to some uncertainty on the sky radiometer calibration data
[Eck et al., 2003]. Thus there is the possibility that the
apparent difference in inferred aerosol layer height at these
two sites may be a result of a calibration offset of the
AERONET sky radiance measurements at Mongu. It should
be pointed out, however, that the 3 km assumption used in
the TOMS retrieval, also yields excellent agreement in the
TOMS-AERONET optical depth comparison at Mongu, as
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Figure 5. Difference between TOMS retrieved single scattering albedo (using different aerosol layer
height assumptions) and AERONET inferred values at the eight sites used in the analysis. Dotted line,
1.5 km; solid line, 3.0 km; dashed line, 6.0 km.
discussed in the previous section. The AERONET optical
depth observation uses direct Sun measurements and,
therefore, it is not affected by any calibration problem of
the sky radiance measurements.
[34] Figure 6 shows a scatter diagram of the AERONET
and TOMS retrieved single scattering albedo values at the
eight sites, for the TOMS standard retrieval (i.e., aerosol
layer at 3 km). An underestimation of the TOMS retrieved
single scattering albedo value occurs when the actual
aerosol layer altitude is higher than the assumed value.
However, when the actual aerosol height is lower than the
assumption, the satellite derived single scattering albedo is
overestimated. The observed retrieval difference between
the two techniques is also a function of the single scattering
albedo magnitude (as given by the AERONET retrieval).
This is a confirmation of previously published results
[Torres et al., 1998], showing that the sensitivity of the
TOMS single scattering albedo retrieval to aerosol layer
height increases as the aerosol absorption increases. A total
of 63% of the comparison cases produce differences within
the uncertainty of the AERONET retrieval (0.03) whereas
87% fall within 0.05.
[35] A summary of the AERONET-TOMS comparison of
single scattering albedo during SAFARI 2000 for the eight
sites used in the analysis is shown in Table 1. The number
of AERONET and TOMS retrievals available during the
campaign are given in columns 3 and 4 respectively. The
number of coincidences, days when both retrievals were
performed, is given in column 5. The time-averaged value
of the AERONET retrievals is listed in column 6, and
column 7 shows the corresponding TOMS average value.
The TOMS retrievals associated with the standard 3 km
layer height assumption was used in the averaging. The
Figure 6. Comparison of single scattering albedo retrieved
using the near-UV method to ground-based retrievals using
AERONET’s Almucantar measurements. Solid lines indicate TOMS-AERONET differences of ±0.03. Dashed
lines correspond to TOMS-AERONET differences of ±0.05.
See text for details.
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TORRES ET AL.: MEASUREMENTS OF AEROSOL ABSORPTION FROM SPACE
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Figure 7. Normalized MPLNET profiles (solid lines) at Mongu on (a) 2 September, (b) 4 September,
(c) 15 September, and (d) 17 September used in the radiative transfer calculations. The dashed lines
depict the idealized Gaussian vertical distribution used in the TOMS retrieval algorithm. The normalized
profiles are truncated at the terrain elevation above sea level indicated by the horizontal dotted lines.
difference between the AERONET and TOMS average
values is shown in column 8.
5.3. Evaluation of Sensitivity to Aerosol Profile
[36] Backscatter profiles measured by the MPLNET lidar
at Mongu were used to evaluate the performance of the
TOMS retrieval algorithm when an accurate representation
of the aerosol vertical distribution is available. On four
days, measured aerosol profiles were available within
30 min of the satellite overpass time. The normalized actual
profiles, shown in Figure 7, were used in the calculation of
special lookup tables for the retrieval algorithm, instead of
the standard tables using idealized vertical distributions.
Note that, except for the case of 15 September, the
MPLNET measured profiles, show a well-defined single
aerosol layer. On 9 – 15 September, however, the measured
aerosol distribution shows a two-layer distribution, markedly different from the one-layer profile assumed in the
TOMS aerosol algorithm.
[37] A comparison of TOMS and AERONET retrieved
values of single scattering albedo on the four days when the
MPLNET profiles were available is shown in Table 2.
The AERONET measurements are listed in column 2.
Columns 3, 4, and 5 show the absolute AERONET-TOMS
difference for the algorithm assumptions of 1.5, 3.0 and
6.0 km respectively, in aerosol layer height. The AERONET-TOMS difference when the actual profile (as given by
the lidar measurements) was used in the retrieval procedure
is given in column 6. The space-based retrieval results when
the standard 3 km assumption is used, yield values within
0.01 of the ground-based method. The use of the actual
profiles, marginally improves the comparison to an almost
perfect agreement. A graphical depiction of these results is
shown in Figure 8.
[38] Table 3 shows the performance of the algorithm in
the retrieval of the aerosol optical depth using the assumed
and the actually measured aerosol vertical distribution.
Column 2 shows the AERONET observed optical depth.
Table 2. Evaluation of Sensitivity of TOMS Retrieved Single Scattering Albedo to Aerosol Vertical
Distributiona
Date
AERONET
TOMS
Zaer = 1.5 km
TOMS
Zaer = 3.0 km
TOMS
Zaer = 6.0 km
TOMS
Actual Profile
2 Sept. 2003
4 Sept. 2003
15 Sept. 2003
17 Sept. 2003
0.88
0.88
0.90
0.92
0.02
0.04
0.01
0.03
0.01
0.01
0.01
0.01
0.05
0.03
0.03
0.02
0.01
0.00
0.00
0.01
a
Absolute AERONET-TOMS differences for different assumptions on aerosol layer height are shown in columns 3 – 6.
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TORRES ET AL.: MEASUREMENTS OF AEROSOL ABSORPTION FROM SPACE
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Figure 8. Difference in AERONET and TOMS retrievals
of single scattering albedo at Mongu as a function of time
during SAFARI 2000. The solid line shows the difference
for the TOMS standard assumption of an aerosol layer at
3 km. The asterisks indicate the differences when the actual
MPLNET profiles (see Figure 7) are used in the retrieval.
The relative AERONET-TOMS difference for the algorithm
assumptions of 1.5, 3.0 and 6.0 km in aerosol layer height,
are shown in columns 3, 4, and 5 respectively. The obtained
differences when the actual profiles are used, are shown in
column 6. On two days (4 and 15 September), the use of the
actual profile produced an obvious improvement of the
accuracy of the TOMS retrieved aerosol optical depth.
Not surprisingly, the largest improvement (from 45%
to 13%) was observed on 15 September, the day when
the actual profile was very different from the idealized
one-layer distribution used in the standard retrieval. On
4 September, when the actual profile was wider that the
Gaussian assumption, the AERONET-TOMS difference
improved from 13% to 2%. The excellent AERONETTOMS agreement found for the 3 km assumption on 2 and
17 September, when the assumed profiles resembled more
the actual distributions, deteriorated slightly when the
actual profiles were used in the retrieval. This effect could
be the result of the elimination of cancellation of errors
from other sources when the actual profile is used.
6. Summary and Conclusions
[39] The performance of the near-UV approach of measuring aerosol absorption from space has been evaluated
making use of AERONET ground-based observations of
optical depth and single scattering albedo during SAFARI
2000. The availability of lidar profiles during the campaign
D10S18
allowed a detailed analysis of the sensitivity of the retrieval
technique to the aerosol vertical distribution.
[40] The optical depth validation indicates that the TOMS
retrieval result is within 30% of the Sun photometer
measurements for 82% of the 127 comparison cases, when
the aerosol profile is represented as a single layer at 3 km
above the ground. This conclusion is consistent with previous algorithm performance assessments [Torres et al.,
2002a, 2002b]. Sources of uncertainty are the aerosol
vertical distribution, subpixel cloud contamination effects,
and, to a lesser extent, the surface reflectivity.
[41] A total of 120 TOMS-AERONET coincidences at
8 sites were used to evaluate the accuracy of the TOMS
derived single scattering albedo. The evaluation of the
single scattering albedo retrieval with reference to the
AERONET results, shows than in 63% of the cases
the satellite retrieval agrees within 0.03 with the AERONET
results, whereas the agreement is within 0.05 in 87% of the
coincidences used in the analysis. This results indicate that
the near-UV measurements can be used to measure the
aerosol single scattering albedo from space with an accuracy
equivalent to the one reported by the AERONET project of
±0.03 [Dubovik et al., 2002] for aerosol optical depth values
of about 0.4. It should be said that the accuracy of the
AERONET single scattering albedo product improves as the
aerosol layer becomes optically thicker. Thus, at the large
optical depth values (0.6 and higher) encountered during
SAFARI 2000, the accuracy of the AERONET absorption
measurements should be better than 0.03. However, no
estimates of the AERONET accuracy for large aerosol loads
are available in the literature.
[42] On four days during the campaign, MPLNET measurements of the aerosol vertical distribution were available
within 30 min of the satellite overpass. The lidar profiles
were used as input to generate look up tables for the
retrieval in lieu of the standard vertical distribution assumption. When using the actual aerosol profiles the accuracy of
the retrieved aerosol optical depth improved significantly in
the cases when the measured and assumed profiles were
very different. As it would be expected, no significant effect
was observed when the assumed profile resembled the
measured one. The evaluation of the accuracy of the single
scattering albedo showed that even in the one case that the
measured vertical distribution deviated significantly from
the simple one-layer assumption the retrieved ssa using the
standard 3 km profile was still very close (within 0.01) to
the ground-based measurement. The use of the actual
profiles resulted in an additional improvement of the accuracy of the space-based measurements.
[43] The reported results support the use of the 3 km
aerosol layer height for the retrieval of smoke properties in
the absence of accurate vertical distribution information
Table 3. Evaluation of Sensitivity of TOMS Retrieved Optical Depth to Aerosol Vertical Distributiona
Date
AERONET
TOMS
Zaer = 1.5 km
TOMS
Zaer = 3.0 km
TOMS
Zaer = 6.0 km
TOMS
Actual Profile
2 Sept. 2003
4 Sept. 2003
15 Sept. 2003
17 Sept. 2003
1.18
2.11
2.66
2.21
27
78
90
25
5
13
45
2
22
17
13
19
12
2
13
17
a
The AERONET-TOMS differences in percent (relative to AERONET) for different assumptions on aerosol layer height are
shown in columns 3 – 6.
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TORRES ET AL.: MEASUREMENTS OF AEROSOL ABSORPTION FROM SPACE
over the continental areas. As shown by Anderson et al.
[1996], using lidar measurements during TRACE-A, the
aerosol vertical distributions over Africa and South America
are not too different. Elevated aerosol layers, however, may
result downwind of the source areas. As shown in this work,
the use of actual vertical profile information enhances the
accuracy of the near-UV method.
[44] To date the near-UV technique is the only tested and
validated method of measuring aerosol absorption from
space. The multidecade long TOMS record has been used
to produce the only available satellite global data set on
aerosol absorption, from 1979 to present. The Ozone
Monitoring Instrument (OMI) on the EOS-Aura satellite,
launched in July 2004, will continue the record of aerosol
absorption into the future. The Aura mission is one of several
satellites flying in formation in the so-called A-train, that
includes the AQUA and Cloud Aerosol Lidar and Infrared
Pathfinder Satellite Observations (CALIPSO) platforms.
The OMI aerosol algorithm [Torres et al., 2003] takes
advantage of the OMI extended spectral coverage (270 to
500 nm), the smaller-than-TOMS footprint (3 13 km
for aerosol retrieval), and the simultaneous availability of
aerosol related information from other A-train sensors, to
enhance the science value of the aerosol information. For
instance, the nadir aerosol vertical distribution measured
by CALIPSO will undoubtedly benefit the quality of the
OMI aerosol products.
[45] In order to reduce the uncertainty associated with the
absorption effects of aerosols in both climate and air quality
applications, it is necessary to continue and improve current
aerosol absorption sensing capabilities. Because of the weak
wavelength dependence of the imaginary refractive index of
soot (the aerosol component responsible for most of the
absorption effect of anthropogenic aerosols), near-UV absorption retrievals of the imaginary refractive index can be
extrapolated to the visible and near-IR.
[46] The TOMS measurements have played a fundamental role in the development and testing of the near-UV
method, and OMI is certainly an improvement over TOMS
capabilities. The Advanced Earth Orbiting Satellite II
(ADEOS II) Global Imager (GLI) sensor [Nakajima et al.,
1998] was the first satellite instrument to combine near-UV,
visible and near-IR channels at a spatial resolution suitable
for accurate aerosol retrieval. Unfortunately, the satellite
failed just a few months after launch. To take full
advantage of the potential of the near-UV method, future
satellite sensing missions should include measurements in
the near-UV to derive aerosol absorption information, that
combined with measurements in the visible and nearinfrared will significantly contribute to improve the understanding of the radiative transfer effect of aerosols on the
Earth-atmosphere system.
[47] Acknowledgments. We thank David Larko from SSAI for his
assistance with the color graphics. E. J. Welton and MPLNET are funded
by the NASA Earth Observing System and NASA Radiation Sciences
Program.
References
Alfaro, S. C., S. Lafon, J. L. Rajot, P. Formenti, A. Gaudichet, and
M. Maillé (2004), Iron oxides and light absorption by pure desert dust:
An experimental study, J. Geophys. Res., 109, D08208, doi:10.1029/
2003JD004374.
D10S18
Anderson, B. E., et al. (1996), Aerosols from biomass burning over the
tropical South Atlantic region: Distribution and impacts, J. Geophys.
Res., 101, 24,117 – 24,137.
Anderson, T. L., S. J. Masonis, D. S. Covert, N. C. Ahlquist, S. G. Howell,
A. D. Clarke, and C. S. McNaughton (2003), Variability of aerosol optical properties derived from in situ aircraft measurements during ACEAsia, J. Geophys. Res., 108(D23), 8647, doi:10.1029/2002JD003247.
Bergstrom, R. W., P. B. Russell, and P. Hignett (2002), Wavelength dependence of the absorption of black carbon particles: Predictions and results
from the TARFOX experiment and implications for the aerosol single
scattering albedo, J. Atmos. Sci., 59, 567 – 577.
Chin, M., et al. (2002), Tropospheric aerosol optical thickness from the
GOCART model and comparisons with satellite and Sun photometer
measurements, J. Atmos. Sci., 59, 461 – 483.
Colarco, P. R., O. B. Toon, O. Torres, and P. J. Rasch (2002), Determining
the UV imaginary index of refraction of Saharan dust particles from
Total Ozone Mapping Spectrometer data using a three-dimensional
model of dust transport, J. Geophys. Res., 107(D16), 4289, doi:10.1029/
2001JD000903.
Dubovik, O., and M. King (2000), A flexible inversion algorithm for
retrieval of aerosol optical properties from Sun and sky radiance measurements, J. Geophys. Res., 105, 20,673 – 20,696.
Dubovik, O., A. Smirnov, B. N. Holben, M. D. King, Y. J. Kaufman, T. F.
Eck, and I. Slutsker (2000), Accuracy assessments of aerosol optical
properties retrieved from Aerosol Robotic Network (AERONET) Sun
and sky radiance measurements, J. Geophys. Res., 105, 9791 – 9806.
Dubovik, O., B. Holben, T. E. Eck, A. Smirnov, Y. J. Kaufman, M. D.
King, D. Tanre, and I. Slutsker (2002), Variability of absorption and
optical properties of key aerosol types observed in worldwide locations,
J. Atmos. Sci., 59, 590 – 608.
Eck, T. F., B. N. Holben, I. Slutsker, and A. Setzer (1998), Measurements of
irradiance attenuation and estimation of aerosol single scattering albedo
for biomass burning aerosols in Amazonia, J. Geophys. Res., 103,
31,865 – 31,878.
Eck, T. F., et al. (2003), Variability of biomass burning aerosol optical
characteristics in southern Africa during the SAFARI 2000 dry season
campaign and a comparison of single scattering albedo estimates from
radiometric measurements, J. Geophys. Res., 108(D13), 8477,
doi:10.1029/2002JD002321.
Ginoux, P., J. M. Prospero, O. Torres, and M. Chin (2004), Long-term
simulation of dust distribution with the GOCART model: Correlation
with the North Atlantic Oscillation, Environ. Model. Software, 19,
113 – 128.
Haywood, J. M., and O. Boucher (2000), Estimates of the direct and
indirect radiative forcing due to tropospheric aerosols: A review, Rev.
Geophys., 38, 513 – 543.
Haywood, J., P. Francis, S. Osborne, M. Glew, N. Loeb, E. Highwood,
D. Tanré, G. Myhre, P. Formenti, and E. Hirst (2003), Radiative properties and direct radiative effect of Saharan dust measured by the C-130
aircraft during SHADE: 1, Solar spectrum, J. Geophys. Res., 108(D18),
8577, doi:10.1029/2002JD002687.
Heintzenberg, J., R. J. Charlson, A. D. Clarke, C. Liousse, V. Ramaswamy,
K. P. Shine, M. Wendish, and G. Helas (1997), Measurements and
modelling of aerosol single-scattering albedo: Progress, problems and
prospects, Beitr. Phys. Atmos., 70(4), 249 – 263.
Herman, B. M., R. S. Browning, and J. J. De Luisi (1975), Determination of
the effective imaginary term of the complex refractive index of atmospheric dust by remote sensing: The diffuse-direct radiation method,
J. Atmos. Sci., 32, 918 – 925.
Herman, B. M., T. R. Caudill, D. E. Flittner, K. J. Thome, and A. BenDavid (1995), A comparison of the Gauss-Seidel spherical polarized
radiative transfer code with other radiative transfer codes, Appl. Opt.,
34, 4563 – 4572.
Herman, J. R., and E. A. Celarier (1997), Earth surface reflectivity climatology at 340 and 380 nm from TOMS data, J. Geophys. Res., 102,
28,003 – 28,011.
Herman, J. R., P. K. Bhartia, O. Torres, N. C. Hsu, C. J. Seftor, and
E. Celarier (1997), Global distribution of UV-absorbing aerosols from
Nimbus-7/TOMS data, J. Geophys. Res., 102, 16,911 – 16,922.
Holben, B. N., et al. (1998), AERONET—A federated instrument network
and data archive for aerosol characterization, Remote Sens. Environ., 66,
1 – 16.
Horvath, H. (1993), Atmospheric light absorption—A review, Atmos.
Environ. Part A, 27(3), 293 – 317.
Jacobson, M. Z. (1999), Isolating nitrated and aromatic aerosols and
nitrated aromatic gases as sources of ultraviolet light absorption,
J. Geophys. Res., 104, 3527 – 3542.
Jacobson, M. Z. (2002), Control of fossil-fuel particulate black carbon and
organic matter, possibly the most effective method of slowing global
warming, J. Geophys. Res., 107(D19), 4410, doi:10.1029/2001JD001376.
11 of 12
D10S18
TORRES ET AL.: MEASUREMENTS OF AEROSOL ABSORPTION FROM SPACE
Kaufman, J. Y. (1987), Satellite sensing of aerosol absorption, J. Geophys.
Res., 92, 4307 – 4317.
Kaufman, J. Y., D. Tanre, O. Dubovik, A. Karnieli, and L. A. Remer
(2001), Absorption of sunlight by dust as inferred from satellite and
ground-based remote sensing, Geophys. Res. Lett., 28, 1479 – 1482.
Kaufman, Y. J., J. V. Martins, L. A. Remer, M. R. Schoeberl, and M. A.
Yamasoe (2002), Satellite retrieval of aerosol absorption over the
oceans using sunglint, Geophys. Res. Lett., 29(19), 1928, doi:10.1029/
2002GL015403.
King, M. D. (1979), Determination of the ground albedo and the index
of absorption of atmospheric particulates by remote sensing. Part II:
Application, J. Atmos. Sci., 36, 1072 – 1083.
Koelemeijer, R. B. A., J. F. de Haan, and P. Stammes (2003), A database of
spectral surface reflectivity in the range 335 – 772 nm derived from
5.5 years of GOME observations, J. Geophys. Res., 108(D2), 4070,
doi:10.1029/2002JD002429.
Litjens, R. A. J., T. I. Quickenden, and C. G. Freeman (1999), Visible and
near-ultraviolet absorption spectrum of liquid water, Appl. Opt., 38,
1216 – 1223.
Livingston, J. M., et al. (2003), Airborne Sun photometer measurements
of aerosol optical depth and columnar water vapor during the Puerto
Rico Dust Experiment and comparison with land, aircraft, and satellite
measurements, J. Geophys. Res., 108(D19), 8588, doi:10.1029/
2002JD002520.
Lyubovtseva, Y. S. (2002), Particle morphology and light absorption by
atmospheric aerosol in the short-wave spectral region, Izv. Atmos. Oceanic
Phys., 38, 323 – 332.
Massie, S. T., O. Torres, and S. J. Smith (2004), Total Ozone Mapping
Spectrometer (TOMS) observations of increases in Asian aerosol in
winter from 1979 to 2000, J. Geophys. Res., 109, D18211, doi:10.1029/
2004JD004620.
Myhre, G., et al. (2004), Intercomparison of satellite retrieved aerosol optical depth over the ocean, J. Atmos. Sci., 61, 499 – 513.
Nakajima, T. Y., T. Nakajima, M. Nakajima, H. Fukushima, M. Kuji,
A. Uchiyama, and M. Kishino (1998), Optimization of the Advanced
Earth Observing Satellite II global imager channels by use of radiative
transfer calculations, Appl. Opt., 37, 3149 – 3163.
Patterson, E. M., D. A. Gillette, and B. Stockton (1977), Complex index of
refraction between 300 and 700 nm for Saharan aerosols, J. Geophys.
Res., 82, 3153 – 3160.
Penner, J. E., S. Y. Zhang, and C. C. Chuang (2003), Soot and smoke
aerosol may not warm climate, J. Geophys. Res., 108(D21), 4657,
doi:10.1029/2003JD003409.
Schmid, B., et al. (2003), Coordinated airborne, spaceborne, and groundbased measurements of massive thick aerosol layers during the dry
D10S18
season in southern Africa, J. Geophys. Res., 108(D13), 8496,
doi:10.1029/2002JD002297.
Sinyuk, A., O. Torres, and O. Dubovik (2003), Combined use of satellite
and surface observations to infer the imaginary part of refractive index of
Saharan dust, Geophys. Res. Lett., 30(2), 1081, doi:10.1029/
2002GL016189.
Sokolik, I., and B. Toon (1999), Incorporation of mineralogical composition into models of the radiative properties of mineral aerosol from UV to
IR wavelengths, J. Geophys. Res., 104, 9423 – 9444.
Swap, R. J., et al. (2002), The Southern African Regional Science Initiative
(SAFARI 2000), Dry-season field campaign: An overview, S. Afr. J. Sci.,
98, 126 – 130.
Torres, O., P. K. Bhartia, J. R. Herman, and Z. Ahmad (1998), Derivation of
aerosol properties from satellite measurements of backscattered ultraviolet radiation: Theoretical basis, J. Geophys. Res., 103, 17,099 – 17,110.
Torres, O., P. K. Bhartia, J. R. Herman, A. Syniuk, P. Ginoux, and
B. Holben (2002a), A long term record of aerosol optical depth from
TOMS observations and comparison to AERONET measurements,
J. Atmos. Sci., 59, 398 – 413.
Torres, O., J. R. Herman, P. K. Bhartia, and A. Sinyuk (2002b), Aerosol
properties from EP-TOMS near UV observations, Adv. Space Res., 29,
1771 – 1780.
Torres, O., R. Decae, P. Veefkind, and G. de Leeuw (2003), OMI aerosol
retrieval algorithm, in OMI Algorithms Theoretical Basis Document,
vol. 3, Clouds, Aerosols and Surface UV Irradiance, edited by
P. Stammes, pp. 46 – 71, Neth. R. Meteorol. Inst., Utrecht.
Twitty, J. T., and J. A. Weinman (1971), Radiative properties of carbonaceous aerosols, J. Appl. Meteorol., 10, 725 – 731.
Welton, E. J., et al. (2000), Ground-based lidar measurements of aerosols
during ACE-2: Instrument description, results, and comparisons with
other ground-based and airborne measurements, Tellus, Ser. B, 52,
635 – 650.
Welton, E. J., J. R. Campbell, J. D. Spinhirne, and V. S. Scott (2001),
Global monitoring of clouds and aerosols using a network of micro-pulse
lidar systems, Proc. SPIE, 4153, 151 – 158.
P. K. Bhartia and O. Torres, NASA Goddard Space Flight Center, Code
916, Greenbelt, MD 20771, USA. ([email protected])
B. Holben, NASA Goddard Space Flight Center, Code 923, Greenbelt,
MD 20771, USA.
A. Sinyuk, Science Systems and Applications Inc., 10210 Greenbelt
Road, Suite 600, Lanham, MD 20706, USA.
E. J. Welton, NASA Goddard Space Flight Center, Code 912, Greenbelt,
MD 20771, USA.
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