JOURNAL OF GEOPHYSICAL RESEARCH, VOL. 115, D15204, doi:10.1029/2009JD013395, 2010
A climatology of aerosol optical and microphysical properties
over the Indian subcontinent from 9 years (2000–2008)
of Multiangle Imaging Spectroradiometer (MISR) data
Sagnik Dey1 and Larry Di Girolamo1
Received 16 October 2009; revised 20 January 2010; accepted 25 January 2010; published 5 August 2010.
[1] We present the first detailed analysis of a 9 year (2000–2008) seasonal climatology
of size‐ and shape‐segregated aerosol optical depth (AOD) and Ångström exponent (AE)
over the Indian subcontinent derived from the Multiangle Imaging Spectroradiometer
(MISR). Our analysis is evaluated against in situ observations to better understand the error
characteristics of and to corroborate much of the space‐time variability found within the
MISR aerosol properties. The space‐time variability is discussed in terms of aerosol sources,
meteorology, and topography. We introduce indices based on aerosol size‐ and shape‐
segregated optical depth and their effect on AE that describe the relative seasonal change in
anthropogenic and natural aerosols from the preceding season. Examples of major new
findings include the following: (1) winter to premonsoon changes in aerosol properties are
not just dominated by an increase in dust, as previously thought, but also by an increase
in anthropogenic components, particularly in regions where biomass combustion is
prevalent; (2) ∼15% of the AOD over the high wintertime pollution in the eastern Indo‐
Gangetic basin is due to large dust particles, resulting in the lowest AE (<0.8) over India
in this season and likely caused by rural activities (e.g., agriculture, etc.) from the densely
populated rural area; (3) while AOD decreases from the Indo‐Gangetic basin up to the
Tibetan Plateau, a large peak in AE and the fraction of AOD due to particle radii <0.7 mm
exists in the foothills of the Himalayas, particularly in the premonsoon season; and (4) the
AOD due to nonspherical particles exhibits a strong ocean‐to‐land gradient over all
seasons because of topographical and meteorological controls.
Citation: Dey, S., and L. Di Girolamo (2010), A climatology of aerosol optical and microphysical properties over the Indian
subcontinent from 9 years (2000–2008) of Multiangle Imaging Spectroradiometer (MISR) data, J. Geophys. Res., 115, D15204,
doi:10.1029/2009JD013395.
1. Introduction
[2] Understanding and quantifying the climatic effects of
aerosols have made significant progress since the International Panel on Climate Change (IPCC) first assessment
report in 1990. The estimates have improved significantly in
the last two decades because of integration of in situ and
satellite observations of aerosol optical and microphysical
properties with model simulations, but still the level of
uncertainty is far less than that of greenhouse gases [Forster
et al., 2007]. Part of the difficulty in further reducing the level
of uncertainty lies in the extreme heterogeneity in aerosol
optical and microphysical properties over a wide range of
spatial and temporal scales, of which we have incomplete
knowledge. Thus it is necessary that we continue to improve
our characterization of aerosols over all regions of the globe
with high spatial and temporal frequency, particularly in
1
Department of Atmospheric Sciences, University of Illinois at Urbana‐
Champaign, Urbana, Illinois, USA.
Copyright 2010 by the American Geophysical Union.
0148‐0227/10/2009JD013395
regions with high population where the impact of aerosols on
human health is also a concern.
[3] The Indian subcontinent is one region requiring
improvements in characterization of aerosol properties
because of an insufficient number of in situ observations and
high aerosol loading. More than one billion people (one
sixth of the world’s population) living on the Indian subcontinent are exposed to enormous pollution. The region’s
economy depends heavily on agriculture, which in turn
depends on rainfall that is mostly confined to the monsoon
season (June–September). High aerosol loading over the
region can affect precipitation [Ramanathan et al., 2001],
the degree to which remains an active area of research that
also requires improved aerosol characterization.
[4] Investigation of aerosols in India started as early as the
1960s, when Mani et al. [1969] studied Ångström turbidity
from solar radiance measurements. In 1985, a multiwavelength radiometer was developed by the Indian Space
Research Organization (ISRO) and deployed successfully
at Trivandrum [Moorthy et al., 1999] to measure spectral
aerosol optical depth (AOD) and, in the same year, aerosol
vertical distribution measurements by ground‐based lidar
was initiated at Pune [Devara et al., 2002]. These events
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Figure 1. Ground‐based long‐term measurement sites shown on a topographic map of the Indian subcontinent. The major geographic regions (names in red) considered for climatological mean (± standard deviation) values shown in Table 2 are delineated by black solid lines.
marked the beginning of systematic ground‐based measurements of aerosol properties in India. The Indian Ocean
Experiment [Ramanathan et al., 2001] first addressed the
issue of climatic effects of aerosols in this region in a
comprehensive manner, which led to enhanced, coordinated
efforts for in situ multi‐instrument and multiplatform measurements by ISRO under the Geosphere Biosphere Program
(GBP) [Moorthy et al., 2005; Tripathi et al., 2006; Nair et al.,
2008]. ISRO has conducted several focused campaigns in
recent years over India and its surrounding oceans to characterize the chemical, microphysical and optical properties of
aerosols. Many ship‐based campaigns have been conducted
in the Arabian Sea [Babu et al., 2008; Satheesh et al., 2006a
and references therein] and the Bay of Bengal [Nair et al.,
2008; Satheesh et al., 2006b and references therein] over
multiple seasons. Two ground‐based observatories were
set up to collect data continuously: Port Blair in the Bay
of Bengal [Moorthy and Babu, 2006] and Minicoy in the
Arabian Sea [Vinoj et al., 2008] (Figure 1). Over the mainland, continuous in situ measurements were initiated as part
of the ISRO‐GBP network (Figure 1), but they are mostly
restricted to measurements of spectral aerosol optical depth
(AOD), that too at very few places. Good information about
aerosol optical and microphysical properties from long‐term
measurements is available in the literature only at ten sites
within the interior of the Indian subcontinent (Figure 1),
namely Trivandrum [Moorthy et al., 2007a and references
therein], Pune [Devara et al., 2008 and references therein],
Kanpur [Dey and Tripathi, 2007, 2008], Nainital [Dumka
et al., 2008 and references therein], Ahmedabad [Ganguly
et al., 2006a], Hyderabad, Anantpur [Badarinath et al.,
2009 and references therein], Bangalore [Satheesh et al.,
2006c], Visakhapatnam [Madhavan et al., 2008 and references therein] and the Atmospheric Brown Cloud (ABC)
observatory at Godavari near Katmandu in Nepal [Ramanathan
and Ramana, 2005; Adhikary et al., 2007, 2008]. Long‐term
in situ observations at ISRO‐GBP sites along with AErosol
RObotic NETwork (AERONET) measurements at Kanpur
and Hanimaadhoo, and the ABC observatory in Nepal
(Figure 1) have provided deep insight into the seasonal variability of aerosol optical properties [Devara et al., 2002;
Singh et al., 2004; Sagar et al., 2004; Ganguly et al., 2006a;
Adhikary et al., 2007; Moorthy et al., 2007a; Dey and
Tripathi, 2008]. For example, long‐range transport of desert
dust during the premonsoon season (March–May) has been
found to affect regional aerosol optical properties [Dey et al.,
2004; Prasad and Singh, 2007], and mixing of dust with
anthropogenic particles [Chandra et al., 2004; Dey et al.,
2008] has made the characterization of the aerosols more
difficult. The presence of absorbing dust at elevated heights
over the Indo‐Gangetic basin (IGB) and Tibetan Plateau has
raised several climatic issues [Lau et al., 2006; Moorthy et al.,
2007b; Satheesh et al., 2008], while detailed radiative studies
[Kahnert et al., 2007; Mishra et al., 2008] have revealed the
importance in considering the nonsphericity of these dust
particles in quantifying their radiative effects. Unfortunately,
observations of the nonsphericity of particles are missing in
the studies discussed above.
[5] Besides these continuous measurements, aerosol
microphysical and optical properties were measured in India
during four major field campaigns in the last ten years. The
first land campaign was carried out in south India during
February–March 2004 [Moorthy et al., 2005] followed by a
second campaign focused in the IGB during the winter season
(December–February) of 2004–2005 [Tripathi et al., 2006;
Nair et al., 2007]. The major scientific objective of these two
campaigns was to quantify aerosol direct radiative effects at
several locations by using simultaneous in situ measurements
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of aerosol microphysical, chemical and optical properties.
The Integrated Campaign on Aerosol and Radiation Budget
(ICARB) was carried out in the premonsoon season of the
year 2006 to measure the physicochemical and radiative
properties of aerosols and trace gases and their vertical distributions [Satheesh et al., 2009]. In the premonsoon and
monsoon seasons of the years 2008 and 2009, aircraft and
ground‐based measurements were carried out over the IGB
and central India as part of Continental Tropical Convergence
Zone campaign to quantify the aerosol indirect effect
[Department of Science and Technology, 2008].
[6] All of these studies have greatly improved our understanding of aerosol microphysical and optical properties and
their variability at several locations across the Indian subcontinent and its surrounding waters; however, a complete
spatial analysis remains hindered because of the spatially
limited nature of these data sets. This is where satellite data
become very useful and can complement the in situ measurements. Satellite retrievals of aerosol properties over land
have only been available in recent years and a few studies
have been done using these data over the Indian subcontinent.
Di Girolamo et al. [2004] were the first to study the spatial
distribution of AOD over India using Multiangle Imaging
Spectroradiometer (MISR) in the winter season during 2001–
2004, where they were able to explain the enormous pollution
observed over the IGB based on meteorology, topography
and potential aerosol emission sources. All subsequent studies using Moderate Resolution Imaging Spectroradiometer
(MODIS) data have confirmed this observation [Jethva et al.,
2005; Prasad et al., 2006; Ramachandran and Cherian,
2008] with additional information on the seasonal variability of AOD and fine mode fraction, to some extent.
[7] Here, in a continued effort to develop an integrated
picture of the spatial and temporal characteristics of aerosol
properties over the Indian subcontinent, we analyze 9 years
(March 2000 to November 2008) of MISR data. In addition to
AOD, MISR is able to provide information on particle optical
and microphysical properties based on its multiangle and
multispectral capabilities, under good but not necessarily
ideal viewing conditions [Kahn et al., 2001, 2007]. Particle
properties include Ångström exponent (AE), optical depths
segregated by particle size and shape (i.e., AOD fractions of
“fine,” “medium” and “large” particles and “spherical” and
“nonspherical” particles) and single scattering albedo (SSA)
in two‐to‐four groupings. The retrieval of many of these
aerosol properties from a space‐based passive sensor is
unique to MISR, thus offering a first opportunity to examine
their spatial and temporal characteristics. In particular, we
seek to address the following questions: (1) How do the
aerosol optical and microphysical properties vary spatially
and seasonally over the Indian subcontinent? (2) Is the
observed space‐time variability segregated by aerosol property (e.g., large and nonspherical versus small and spherical
particles)? (3) Can the observed variability be understood in
terms of the variability in meteorology and aerosol emission?
Section 2 evaluates the quality of the MISR aerosol product
and the analysis performed to derive the seasonal climatology. Section 3 provides an overview of the associated factors
such as topography, meteorology, emission inventories and
demography, which influence the seasonal climatology and
interannual variations of aerosol properties. The seasonal
climatology of aerosol properties is discussed in section 4
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followed by implications of the results and a summary of
major conclusions in section 5.
2. MISR Aerosol Properties
[8] MISR, onboard the NASA Earth Observing System’s
Terra spacecraft, is in a Sun‐synchronous orbit that crosses
the equator at ∼1030/2230 local time. It is a push broom
imaging instrument operating at four spectral bands centered
at 446, 558, 672 and 867 nm, in each of nine separate cameras
oriented along the orbital track with surface viewing zenith
angles ranging from ±70.5° [Diner et al., 2008]. Its ∼400 km
swath provides global equatorial coverage every 9 d and 2 d
coverage at polar latitudes. Aerosol retrievals are performed
on 16 × 16 patches of 1.1 km subregions, yielding an aerosol
product at 17.6 × 17.6 km spatial resolution, referred to as a
“Level 2” product [Martonchik et al., 2002].
[9] Kahn et al. [2009] have provided an excellent overview
and details on the MISR Level 2 aerosol product. In brief,
MISR performs retrievals of AOD, SSA, the fraction of AOD
due to “fine” (particle radii <0.35 mm), “medium” (particle
radii between 0.35 and 0.7 mm) and “large” (particle radii
>0.7 mm) particles as well as the fraction of AOD due to
“spherical” and “nonspherical” particles at the four MISR
spectral bands, and AE based on the retrieved spectral AOD.
Throughout this manuscript, the discussion of the relative size
categories strictly corresponds to the ranges given above.
This may differ from other literature; for example, the term
“fine” mode often refers to all submicron particles [O’Neill
et al., 2001]. In the MISR Version 22 retrieval algorithm,
the measured radiances are matched with simulated radiances
from 74 types of modeled aerosol mixtures. In each individual
mixture type, the aerosol is assumed to be an external mixture
of various proportions of pure components, each pure component having fixed microphysical properties and uniform
composition [Diner et al., 2008]. The particle properties of
the mixture with the lowest chi‐square value out of all successful mixtures passing the threshold test during matching
with measured radiances are reported as “best estimates”
within the product file. The terminologies used in the Level 2
aerosol product to represent various aerosol parameters are
listed in Table 1. The additional information in the Level 2
product about the algorithm type flag, surface type flag,
retrieval success flag and best estimate quality flag (Table 1)
help in understanding the quality of the aerosol properties at a
particular region; hence, they are important to keep track of in
scientific analysis [Kahn et al., 2009]. Here, we have analyzed the latest version (Version 22) of the Level 2 aerosol
product of MISR from March 2000 to November 2008, which
includes AOD at 558 nm, AE, optical depth fractions of fine
( ff), medium ( fm), large ( fl ), spherical ( fsp) and nonspherical
( fnsp) particles, and spectral SSA. Optical depth due to a
particular category has been derived by multiplying the AOD
with the corresponding optical depth fraction reported in the
product file (compare Table 1).
2.1. Quality Assessment
[10] MISR‐derived AOD has been validated globally
against coincident AERONET observations [Kahn et al.,
2005], showing that 63% of MISR‐retrieved midvisible
AOD values fall within 0.05 or 20%, and about 40% fall
within 0.03 or 10% of AERONET AOD. Validating particle
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Table 1. MISR Level 2 Version 22 Aerosol Products at 17.6 × 17.6 km Resolution Used in This Studya
Product
Description
RegBestEstimateSpectralOptDepth
RegBestEstimateÅngströmExponent
RegBestEstimateSpectralSSA
RegBestEstimateSpectralOptDepthFraction
AOD at four wavelengths
AE (derived from retrieved AOD at four wavelengths)
SSA at four wavelengths
Fraction of optical depth due to “small” (r < 0.35 mm), “medium” (0.35 < r <
0.7 mm), “large” (r > 0.7 mm), “spherical” and “nonspherical” particles
Information about the best estimates reported in previous rows (0 = estimates
from one successful mixture, 1 = more than one successful mixture, 2 = one or
more successful mixtures in nearby region, 3 = insufficient information for
estimate)
Quality of the estimated particle property (0 = good quality, 1 = poor quality)
Region type during retrieval (0 = clear region, 1 = solar oblique region,
2 = topographically complex region, 3 = cloudy region, 4 = no valid data in
region)
Surface type during retrieval (0 = dark nonpolar water, 1 = shallow/coastal
nonpolar water, 2 = nonpolar land, 3 = polar dark water, 4 = polar shallow/
coastal water, 5 = polar land)
1 = no success matches with aerosol models, 2 = no potential matches, 3 =
retrieval algorithm failure, 4 = retrieval not attempted, 5 = insufficient data to
perform retrieval, 6 = inadequate scene contrast to perform retrieval, 7 =
successful aerosol retrieval, 8 = unsuccessful aerosol retrieval, default value
reported
Numbers of mixtures passing chi‐square test
RegBestEstimateQA
RegParticlePropertyQA
RegClassInd
RegSurfTypeFlag
AerRetrSuccFlag
NumSuccAerMixture
a
For further details see Kahn et al. [2009] and Diner et al. [2008].
property is an ongoing MISR science team effort [Kahn et al.,
2009]. An up‐to‐date quality statement is available at http://
eosweb.larc.nasa.gov/PRODOCS/misr/Quality_ Summaries/
L2_AS_Products.html. According to the quality statement, the
ongoing validation efforts of the MISR aerosol product using a
combination of AEROENT sunphotometer and field campaign
data suggest that spherical versus nonspherical particle distinction is the most robust property retrieval in the Version 22
product, while particle size and AE retrievals show mixed results. Here, we contribute to this validation effort by examining
the quality of the MISR‐retrieved aerosol properties with
coincident data from the Kanpur AERONET station (Figure 1)
in India, which has been operating for more than 8 years.
Although not all of the MISR‐retrieved aerosol properties can
be directly validated by AERONET retrievals, the comparison
does provide enough information to understand the MISR data
quality that is needed to guide our analysis. Further evaluation
of the quality of the particle properties is done by examining
the logical spatial and temporal behavior of the particle properties with meteorology, topography and emission sources [cf.
Di Girolamo et al., 2004], as well as with published knowledge
gained through in situ measurements at ground‐based sites
(Figure 1). MISR‐retrieved aerosol properties at 17.6 ×
17.6 km spatial scale (i.e., one MISR pixel at Level 2) surrounding the Kanpur AERONET station are compared with
AERONET‐retrieved Level 2 cloud‐screened and quality
assured aerosol properties [Holben et al., 1998] within a 2 h
time window centering the MISR overpass time [after Kahn
et al., 2005] for data collected between February 2001 and
November 2008. Altogether we have 339 d of MISR overpass
over Kanpur during this period, but only 69 d for the comparison because either MISR or AERONET did not have
successful retrievals on the remaining days, most likely
because of cloud or very thick haze.
[11] We find very similar results to other AOD comparison
studies (Figure 2a), where MISR AODs are found to be
biased low relative to AERONET AODs [Di Girolamo et al.,
2004; Kahn et al., 2005, 2007]. This bias increases with
increasing AOD [Kahn et al., 2005], and we can expect
the AOD to be underestimated in the high AOD regions at
midvisible wavelength as discussed by Di Girolamo et al.
[2004]. The bias in MISR‐retrieved AOD compared to
AERONET retrieval (DAODMISR‐AERONET) at Kanpur is
quantified by the relation
DAODMISRAERIONET ¼ 0:13 0:6AODAERONET :
ð1Þ
Higher SSA assumed in MISR aerosol retrieval algorithm
compared to the real particle SSA contributes to the systematic bias in this region, because higher AOD is required to
match the observed top‐of‐atmosphere reflectance as particle
SSA decreases [Kahn et al., 2005, 2009; Chen et al., 2008].
[12] AE, the spectral dependence of AOD, provides a
firsthand qualitative approximation about the dominant size
of the particles, with higher AE implying dominantly small
particles and vice versa [Ångström, 1929]. MISR‐retrieved
AE is found to show good (and significant) correlation (R =
0.64) with AERONET retrievals as shown in Figure 2b, but
the comparison reveals a high bias in MISR‐retrieved AE,
particularly when the AERONET‐retrieved AE is very low
(<0.6). The absence of wavelengths larger than 867 nm in
MISR contributes to this overestimate, along with the limited
selection of medium spherical particles in the MISR Version
22 components and mixtures (see the MISR quality statement). In the presence of coarse dust particles, the increase in
AOD at wavelengths greater than 867 nm is higher than the
increase at shorter wavelengths [Dey et al., 2004]. Hence,
the absence of larger wavelengths in MISR suppresses the
diminished spectral dependence of AOD, causing an overestimation of AE in heavy dusty cases. This can be seen from
Figure 2b, where all the points lie above the 1:1 line for
AERONET‐retrieved AE less than 0.6, an indicator of heavy
dust conditions in Kanpur [Singh et al., 2004].
[13] The fnsp from MISR cannot be validated directly
with AERONET, because AERONET does not retrieve
nonspherical particle fraction. Instead, we have compared
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Figure 2. Scatterplot between (a) MISR‐retrieved AOD558 and AERONET‐retrieved AOD500 (the dashed
lines are 1:1 line and envelope of ±0.05 or 20% × AOD, whichever is larger), (b) MISR‐retrieved and
AERONET‐retrieved AE, (c) MISR‐retrieved nonspherical fraction of midvisible AOD (MISR‐fnsp) and
AERONET‐retrieved AE (AEAERONET), (d) MISR‐retrieved small particle fraction of midvisible AOD
(MISR‐fs) and AERONET‐retrieved fine mode fraction (AERONET‐fmf), and (e) MISR‐retrieved SSA
at 446 nm and AERONET‐retrieved SSA at 440 nm over Kanpur in the Indo‐Gangetic basin for data during
February 2001 to November 2008.
MISR‐retrieved fnsp with AERONET‐retrieved AE, because
AE can be used as a proxy to qualitatively determine the
influence of dust on spectral aerosol optical properties in this
region, as dust particles are dominantly of large size and
nonspherical shape. Previous studies [Singh et al., 2004; Dey
et al., 2004] have shown that an AE of 1 can be used as a
rough divide between dusty and nondusty days over Kanpur.
The MISR retrieval groups fnsp at 0.2 intervals because of the
sensitivity of the MISR algorithm to nonspherical particles.
MISR’s medium‐mode dust model was validated for Saharan
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dust [Kalashnikova and Kahn, 2006]. However, as pointed
out by Kalashnikova and Kahn [2006], there is no satisfactory
coarse‐mode dust optical model, and in addition, there are
hardly any field data available for dust types other than
Saharan dust to directly evaluate MISR’s retrieval. As
shown in Figure 2c, AERONET‐retrieved AE is found to
increase with decreasing fnsp and the relation (R = −0.81) is
significant with a 99% confidence interval. Nonzero values
of fnsp for AE < 1 supports the successful retrieval of MISR
to detect nonspherical dust particles in this region. MISR
science team is working to improve the representation of
coarse mode dust optical model in the aerosol algorithm;
however, even with the current limitation, we are able to
logically explain the spatial distribution of optical depth due
to nonspherical particles (AODnsp) by topography and
meteorology in the premonsoon and monsoon seasons
(sections 4.2 and 4.3).
[14] MISR‐retrieved size‐segregated optical depth fractions also cannot be directly compared with AERONET‐
retrieved optical depth fraction, because AERONET retrieves
“fine mode fraction” from optical measurements of spectral
variation of AOD [O’Neill et al., 2003] that is defined differently than MISR. AERONET retrieval does not consider
any microphysical cutoff particle size to define “fine” and
“coarse” mode, and assumes neutral spectral dependence of
coarse mode optical depth. AERONET fine mode optical
depth (AODfine) and coarse mode optical depth retrievals are
indirect retrievals from the retrieved AOD spectrum. They are
also not validated. In eight cases out of 69 coincident sampling days, AERONET‐retrieved AODfine was found to be
higher than AERONET‐retrieved total optical depth; hence
they are excluded from the comparison (Figure 2d). In
any multimodal particle size distribution, the fine (radii <
0.35 mm) and medium size (radii in the range of 0.35 to
0.7 mm) particles (hereafter grouped together and called small
particle, i.e., fraction of small particles to the AOD, fs = ff + fm)
retrieved by MISR are most sensitive to AERONET‐defined
fine mode, and large particles (>0.7 mm) retrieved by MISR
are most sensitive to coarse mode as defined by AERONET.
As shown in Figure 2d, the weak but statistically significant correlation (R = 0.36) between MISR‐retrieved fs and
AERONET‐retrieved fine mode fraction (fmf = AODfine /
AOD) indicates agreement in a broad sense between MISR
and AERONET in retrieving the contribution of small
particles to AOD. The correlation between MISR‐retrieved
AODnsp and AODl (not shown here) reveals that 65% of the
variation in AODnsp can be explained by the variation in the
AODl alone, and if the “medium” size particles are also
considered nonspherical, it can explain 78% of the variation
in AODnsp. This implies that AODnsp reported here may be
even higher particularly during the dusty days.
[15] Unlike the particle properties discussed so far,
MISR‐retrieved SSA at 446 nm shows no correlation with
AERONET‐retrieved SSA at 440 nm as shown in Figure 2e.
While MISR‐retrieved SSA values are very high (mostly
higher than 0.97) over Kanpur, AERONET retrievals indicate
the presence of absorbing aerosols in this region [Singh et al.,
2004; Dey and Tripathi, 2007, 2008]. The high aerosol
absorption has been attributed to high concentrations of black
carbon as measured from ground and aircraft [Tripathi et al.,
2005a, 2005b; Ganguly et al., 2006b] and the mixing of black
carbon with other aerosol components [Chandra et al., 2004;
Dey et al., 2008; Satheesh et al., 2008]. A review of the
mixtures of aerosols assumed by the MISR Version 22
aerosol retrieval algorithm reveals that it lacks an aerosol
mixture containing both carbonaceous aerosols and dust that
are characteristic of the region. Kahn et al. [2009] discusses
this missing mixture as the most likely explanation for the
poor SSA retrieval, which also contributes to the low bias in
AOD in this region. Future versions of the MISR aerosol
algorithm will address this issue. Until then, SSA from MISR
is not examined further in this study. All other MISR aerosol
properties are used to generate seasonal climatology of aerosol
properties, and where possible, tied to past ground‐based
observations on the seasonality of aerosol properties.
2.2. Analysis Procedure
[16] The Indian subcontinent region, defined here to be
bound within 40° north latitude and the equator and 65° and
99° east longitudes, is subdivided into 5440 grids of size
0.5° × 0.5°. This grid resolution increases the number of
samples for a grid cell beyond a grid at the original resolution
of the aerosol product (17.6 × 17.6 km), while remaining
small enough to capture the detailed spatial variation in terms
of emission inventories, topography and meteorology. All
17.6 × 17.6 km pixels whose central coordinates are within a
single 0.5° × 0.5° grid cell during any particular season are
considered for seasonal climatology for that particular grid.
The values of a given aerosol property of all the successfully
retrieval pixels of each day are averaged to derive the daily
mean climatological value for each 0.5° × 0.5° grid cell. For
example, the mean midvisible AOD for day k in year y
(AODk;y ) in a given season for a given grid cell is derived
as:
P
AODk;y ¼
AOD
;
m
ð2Þ
where, AOD is the AOD at one successfully retrieved
17.6 × 17.6 km pixel, m is the total number of successfully
retrieved pixels within that grid in that particular day, and
the summation is carried over all m pixels. The mean seasonal AOD (AODseason) for that particular grid was derived
as:
n
P
AODseason ¼
k¼1
AODk;y
n
;
ð3Þ
where, n is the total number of days of MISR data during a
given season over all 9 years of data. The seasonal climatology of all other aerosol properties has been derived similarly. Since MISR data are available from March 2000, we
have eight winter (December–February) seasons and nine
premonsoon (March–May), monsoon (June–September) and
postmonsoon (October–November) seasons during the time
period considered in this study. The total number of successfully retrieved 17.6 × 17.6 km pixels for a given season
within each 0.5° × 0.5° grid cell that go into the statistics
(i.e., sample numbers) is shown in Figure 3. Lower sample
density (<100 per grid cell) occurs over regions and times
of more frequent cloud cover (e.g., during the monsoon
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Figure 3. Spatial distribution of total number of samples (in log scale) used to generate the seasonal climatology of aerosol characteristics over the Indian subcontinent during the period March 2000 to November
2008.
season), more frequent snow cover (e.g., winter at higher
elevations), and over topographically complex regions (e.g.,
the Himalayan mountain range), as one would expect based
on algorithm design [Diner et al., 2008].
3. Overview of Factors Influencing Aerosol
Variability
[17] The Indian subcontinent is very diverse in topography,
population distribution, meteorology and emission sources,
which influence the observed spatial and seasonal variability
of aerosol properties. Ramachandran and Cherian [2008]
have provided a detailed description of the geographic, climatic and source diversity of various regions of India. Here,
the climatology of AOD, AE, fs, fl, fsp and fnsp are discussed in
view of eight major geographic regions over land and ocean
(Figure 1). The oceanic region comprises of the Arabian Sea,
Bay of Bengal and north Indian Ocean. Large anthropogenic
fraction in the measured aerosol properties at far distances
over the oceanic region has been observed during October to
May (often called the dry season) because of transportation
of aerosols dominated by anthropogenic particles from the
Indian subcontinent landmass, while the aerosol properties
are dominated by natural particles during June to September
(wet season) [Ramana and Ramanathan, 2006]. The IGB, the
world’s most populated river basin at >700 million population, stretches from Pakistan in the west to Bangladesh in the
east, encompassing most of northern India (Figure 1). Central
India is mostly hard rock terrain with undulating topography.
Large areas of the northwestern part of India and the eastern
part of Pakistan are covered by the Great Indian Desert, while
the west coast of India surrounding the mega city Mumbai is
highly industrialized and heavily populated. Southern India,
the peninsular part of India, is the only region to receive
significant precipitation in the summer monsoon as well as
the winter monsoon (October–December). Precipitation in all
other regions is mostly confined to the summer monsoon
(hereafter “monsoon”) season. The northeastern part of India
is sparsely populated and is the most underdeveloped region
in terms of industrialization. The Tibetan Plateau is bordered
by the Himalayan mountain range to the south and the
Taklamakan and Gobi Deserts to the north. The Tibetan
Plateau and the Himalayas have a profound dynamical and
thermal influence on atmospheric circulation, and recent
studies have pointed out the potential impact of pollution over
this region on the south Asian monsoon rainfall through an
“elevated heat pump” effect [Lau et al., 2006].
[18] The meteorological fields are obtained from National
Centers for Environmental Prediction (NCEP) reanalysis data
(http://www.cdc.noaa.gov) to understand the influence of
synoptic‐scale features on the spatial and temporal distribution of aerosol properties. Figure 4 shows the seasonal climatology of vertical winds at 850 mb, wind speed and wind
direction at the surface and at 850 mb averaged over the same
period of MISR data. In the winter, northerly to northwesterly
wind transports a large anthropogenic component to the
aerosol field over the oceans [Ramanathan et al., 2001]. The
vertical winds show subsidence over the eastern part of IGB,
Tibetan Plateau and oceanic regions close to the east and west
coasts of India. In the premonsoon season, surface wind speed
decreases over the ocean as compared to the winter season.
The westerly to northwesterly winds transport desert dust
from the Great Indian Desert and Arabian Peninsula affecting
the aerosol optical properties over the Indian subcontinent
[Dey et al., 2004; Prasad and Singh, 2007; Satheesh et al.,
2008]. Increase in near surface wind speed in the monsoon season as compared to the premonsoon season allows
enhanced production of maritime aerosols [Satheesh et al.,
2006d] and transportation of them by southwesterly winds
to the coastal regions. Precipitation data taken from Global
Precipitation Climatology Project during the same MISR
data period during the monsoon season show strong spatial
(Figure 5a) and interannual (Figure 5b) heterogeneity. Precipitation in the other seasons (not shown here) is mostly
lower than 200 mm over the land region. The synoptic
meteorology in the postmonsoon season is generally similar
to that of the winter season, but the surface wind speed is
lower over the oceanic region compared to the winter season.
[19] As the particle microphysical and optical properties
depend on their sources, it is important to account for the
spatial distribution of various emission sources beforehand to
understand the climatology of aerosol properties. In the
Indian subcontinent, most of the industrial sectors are associated with large cities (i.e., cities with >500,000 population).
The major sources of aerosols in the urban/industrial areas are
fossil fuel combustion in thermal power plants, industries
(e.g., refineries and petrochemical, fertilizers, food, heavy
engineering, cement, steel, textile, paper, nonferrous metals,
brick making, etc.), domestic purposes and transportation,
which generates mostly spherical and smaller (as compared to
natural sources) particles [Reddy and Venkataraman, 2002a].
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Figure 4. Seasonal climatology of vertical wind at 850 mb (top panel), wind speed (in m/s) and wind direction (shown by arrows) at the surface (middle panel), and at 850 mb (bottom panel) over the Indian subcontinent during the same period of MISR data.
These particles can be moderate to highly absorbing
depending on the mass fraction of carbonaceous aerosols.
Biomass combustion (biofuel combustion and open biomass
burning) and soil dust from rural roads, agricultural and bare
lands and mining activities are the major sources of aerosols in the rural areas [Reddy and Venkataraman, 2002b;
Ramachandran and Cherian, 2008]. From a MISR perspective, in a broad sense, small and spherical particles can
be assumed to have a large anthropogenic component. The
desert dust and maritime aerosols are not only larger in size
than the anthropogenic aerosols, the proportion of nonspherical particles in desert dust [Kahnert et al., 2007] and
even in maritime aerosols under suitable conditions
[Chamaillard et al., 2006] is also higher. It should be noted
that it is not possible to distinguish transported desert dust
from local soil dust based on the nonspherical fraction
retrieved by MISR, nor are there any in situ databases
available that make such distinctions. Hence, we do not
attempt to categorize desert dust from local soil dust, and
instead use the term “dust” to interpret the climatology of
nonspherical particles.
[20] Emissions from fossil fuel and biofuel combustion do
not show much seasonal variability [Reddy and Venkataraman,
2002a]; rather they are responsible for the observed spatial
variability in aerosol characteristics. However, emission from
open biomass burning (i.e., forest burning and crop waste
burning) is seasonal in India [Venkataraman et al., 2006].
Crop wastes are burnt from one month after the start of the
harvest season until the end of the season, which is region
specific in India. The western part of IGB has the highest crop
waste availability followed by central and south India,
whereas central India shows highest forest burning followed
by the eastern part of IGB and the northeastern part of India
[Venkataraman et al., 2006]. Forest burning peaks in the
premonsoon season. Rabi (e.g., wheat, burley, mustard, etc.)
and kharif (e.g., rice, millets, pulses, groundnut, cotton,
sugarcane, etc.) crops are sown during late winter and monsoon seasons, respectively, and the crop wastes are burnt after
the harvest, leading to seasonal peaks in open biomass
burning in May (for rabi crop harvest) as well as in October
(for kharif crop harvest) in the IGB. In central India, crop
waste burning occurs during January–April and October–
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Pakistan (www.statpak.gov.pk) and Bangladesh Bureau of
Statistics (www.bbs.gov.bd), respectively. Rural population
contributes 80% of the total population in the high population
density (>600 km−2) region in the eastern part of IGB, while
urban population is high in south India and is mostly centered
on the large cities with high population density elsewhere.
4. Climatology of Aerosol Properties
Figure 5. Spatial distribution of (a) Global Precipitation
Climatology Project–derived mean accumulated rainfall (in
mm) in the monsoon season during 2000 to 2008 and (b) standard deviation of accumulated rainfall during the same period
of MISR data.
December, while in south India it takes place during February–
March consistent with the January–February harvest season
[Venkataraman et al., 2006]. Regional aerosol optical properties are strongly influenced by these emissions. However,
the emission inventories in India have large uncertainties
[Streets et al., 2003; Bond et al., 2004; Venkataraman et al.,
2005, 2006] and the spatial and temporal distributions of
emission data for all emission sources are not available. We
have used population data as a proxy to link anthropogenic
activities to the observed spatial distribution of particle
properties. Population data of the Indian subcontinent has
been collected from the Socioeconomic Data and Application Center Web site (http://sedac.ciesin.columbia.edu/gpw/
global.jsp#), and the population density for the year 2005
is shown in Figure 6 at a 0.5° × 0.5° spatial resolution.
Regionally, 41 large cities with population >500,000 are
situated all along the IGB from west to east, while 16 large
cities are located in western India, of which 11 are concentrated near the west coast of India, and 7 and 14 large cities are
located in central and south India, respectively. Further categorization of the population data of India, Pakistan and
Bangladesh into rural and urban population (not shown here)
is done based on district data collected from the Census of
India under Ministry of Home Affairs of Government of India
(www.censusindia.net), the Statistics Division of Ministry
of Economic Affairs and the Statistics of Government of
[21] Aerosol optical and microphysical properties averaged
over the Indian subcontinent show strong seasonal variability
as revealed by the normalized frequency distributions plotted
as function of seasons from the 2000 premonsoon season to
the 2008 postmonsoon season in Figure 7. Kalashnikova and
Kahn [2008] and Chen et al. [2008] have demonstrated that
the MISR sensitivity to particle properties is best for AOD >
0.15; over the Indian subcontinent this occurs ∼84% of the
time when there is a successful retrieval. Low AOD (<0.15) is
mostly found over the Tibetan Plateau region (Figure 8). The
seasonal distribution of AOD over the Indian subcontinent
(Figure 7a) shows a median value in the range 0.194 to 0.325
with a strong seasonal cycle. The median value is highest in
the premonsoon season followed by the monsoon season,
when the overall frequency distributions are wider than those
in the postmonsoon and winter seasons as revealed by a larger
difference in first‐ and third‐quartile values. The wide frequency distribution of AOD found in the monsoon season
(Figure 7a) is attributed to highly heterogeneous spatial distribution of AOD (Figure 8). While median values of AOD
tend to peak in the premonsoon season, the peak of the normalized frequency distribution occurs in the winter season.
In winter, the peak of the distribution occurs in the 0.25–
0.35 AOD bin, and the width of the distribution is narrowest
compared to all other seasons.
[22] The seasonal median, first‐ and third‐quartile values
of AE vary in the range 0.72–1.06, 0.58–0.92 and 0.92–1.24,
respectively, as shown in Figure 7b. The median value is
lowest (AE < 0.8) in the monsoon season followed by the
premonsoon season. AE frequency distribution is wider in the
monsoon and postmonsoon seasons than the winter and
premonsoon seasons as revealed by a larger difference in the
first‐ and third‐quartile values (Figure 7b) owing to larger
Figure 6. Spatial distribution of population density (km−2)
over the Indian subcontinent.
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Figure 7. Normalized frequencies of mean seasonal (a) AOD at 0.1 bin width, (b) AE at 0.2 bin width,
(c) fs at 0.1 bin width, and (d) AODnsp at 0.03 bin width, along with the seasonal median (black solid line),
first‐quartile (white solid line), and third‐quartile (olive‐green solid line) values for MISR data during the
2000 premonsoon season to the 2008 postmonsoon season. Monsoon season in the x axis index is abbreviated as “M”.
spatial heterogeneity (Figure 8). The frequency distribution
of fs shown in Figure 7c is similar to the frequency distribution of AE; lower median values in the monsoon and premonsoon seasons than the postmonsoon and winter seasons.
The seasonal median, first‐ and third‐quartile values of fs vary
in the range 0.6–0.72, 0.55–0.63 and 0.67–0.81, respectively.
The reduction of AE and fs in the premonsoon season along
with a rise in AOD are primarily due to an increase in dust
emission as observed in earlier studies [e.g., Dey et al., 2004;
Jethva et al., 2005; Prasad and Singh, 2007], while higher
production of maritime aerosols and dust contribute further to
the reduction of AE and fs during the monsoon season
[Satheesh et al., 2006d]. Unlike the AE frequency distribution, the fs distribution is wider in the postmonsoon and winter
seasons than the other two seasons. The relative influence of
anthropogenic particles is high over the Indian landmass and
the surrounding oceans during the postmonsoon and winter
seasons, while coarse particles still dominate over the Tibetan
Plateau, thus contributing to wider frequency distribution
in fs.
[23] The frequency distribution of AODnsp shown in
Figure 7d is different from the other three aerosol parameters.
The median value of AODnsp is highest in the monsoon season followed by the premonsoon season, and it is lower by a
factor of two in the postmonsoon and winter seasons. The
highest (>0.35) normalized frequencies are observed at
AODnsp bin of 0–0.03 during the winter and postmonsoon
seasons, along with its narrowest distribution (smallest difference in the first‐ and third‐quartile values), indicating that
nonspherical particles are at low levels over most of the
region. The normalized frequency at the largest bin (AODnsp
> 0.18) increases substantially in the monsoon season. This is
attributed to the increase in the nonspherical particles over the
oceans (Figure 8), which also leads to a wider frequency
distribution in this season. The seasonal cycle in nonspherical
particle loading is so strong that the first‐quartile values in the
premonsoon and monsoon seasons are similar or higher than
the median values in the postmonsoon and winter seasons
(Figure 7d). However, thin cirrus frequently observed in the
Indian subcontinent in the monsoon season [Wang et al.,
1996] may influence the retrieval of nonspherical particles
(section 4.3). In summary, MISR captures many known
seasonal variability of the particle properties, further substantiating the quality of the data, but the spatial distribution
reveals finer details of the aerosol characteristics influenced
by natural and anthropogenic emissions, topography and
meteorology. The spatial heterogeneity of the aerosol parameters, as shown in Figure 8, is discussed separately below
for each season.
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DEY AND DI GIROLAMO: CLIMATOLOGY OF AEROSOL PROPERTIES
Figure 8. Spatial distribution of climatological mean midvisible AOD (first panel), standard deviation of
AOD, AODs (second panel), mean AE (third panel), mean fs (fourth panel) and mean AODnsp (fifth panel)
during the winter, premonsoon, monsoon, and postmonsoon seasons over the Indian subcontinent retrieved
by MISR during the period March 2000 to November 2008.
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DEY AND DI GIROLAMO: CLIMATOLOGY OF AEROSOL PROPERTIES
Table 2. Mean Seasonal Climatological Values Averaged
Over Major Geographic Regions of the Indian Subcontinent for
Midvisible AOD, AE, fs, fl, fsp, and fnspa
Parameters
Winter
Premonsoon
Monsoon
Indo‐Gangetic Basin
0.45 ± 0.06
0.45
0.96 ± 0.15
1.03
0.67 ± 0.10
0.70
0.33 ± 0.07
0.30
0.80 ± 0.08
0.84
0.20 ± 0.04
0.16
AOD
AE
fs
fl
fsp
fnsp
0.36
0.94
0.65
0.35
0.89
0.11
±
±
±
±
±
±
0.09
0.11
0.04
0.06
0.05
0.01
AOD
AE
fs
fl
fsp
fnsp
0.24
1.13
0.66
0.34
0.92
0.08
±
±
±
±
±
±
0.04
0.09
0.03
0.05
0.07
0.01
Central India
0.36 ± 0.05
1.00 ± 0.13
0.67 ± 0.05
0.33 ± 0.04
0.81 ± 0.04
0.19 ± 0.06
AOD
AE
fs
fl
fsp
fnsp
0.24
0.99
0.62
0.38
0.92
0.08
±
±
±
±
±
±
0.07
0.18
0.07
0.02
0.12
0.02
AOD
AE
fs
fl
fsp
fnsp
0.22
1.06
0.68
0.32
0.86
0.14
±
±
±
±
±
±
0.05
0.09
0.05
0.03
0.08
0.02
AOD
AE
fs
fl
fsp
fnsp
0.09
0.88
0.51
0.49
0.78
0.22
±
±
±
±
±
±
0.04
0.24
0.09
0.03
0.06
0.02
AOD
AE
fs
fl
fsp
fnsp
0.25
1.08
0.77
0.23
0.76
0.24
±
±
±
±
±
±
0.04
0.07
0.04
0.03
0.07
0.01
Arabian Sea
0.33 ± 0.05
0.80 ± 0.13
0.67 ± 0.04
0.33 ± 0.06
0.61 ± 0.03
0.39 ± 0.10
AOD
AE
fs
fl
fsp
fnsp
0.27
1.07
0.77
0.23
0.78
0.22
±
±
±
±
±
±
0.05
0.10
0.04
0.05
0.09
0.01
Bay of
0.33 ±
1.02 ±
0.71 ±
0.29 ±
0.73 ±
0.27 ±
AOD
AE
fs
fl
fsp
fnsp
0.21
0.98
0.74
0.26
0.76
0.24
±
±
±
±
±
±
0.04
0.10
0.04
0.03
0.06
0.02
Postmonsoon
±
±
±
±
±
±
0.08
0.14
0.06
0.09
0.08
0.07
0.35
1.04
0.70
0.30
0.86
0.14
±
±
±
±
±
±
0.08
0.11
0.04
0.07
0.07
0.06
0.36
1.08
0.71
0.29
0.83
0.17
±
±
±
±
±
±
0.05
0.11
0.04
0.07
0.05
0.06
0.25
1.21
0.71
0.29
0.92
0.08
±
±
±
±
±
±
0.04
0.09
0.04
0.05
0.04
0.01
Western India
0.39 ± 0.06
0.76 ± 0.14
0.56 ± 0.06
0.44 ± 0.06
0.79 ± 0.03
0.21 ± 0.12
0.53
0.83
0.60
0.40
0.85
0.15
±
±
±
±
±
±
0.10
0.14
0.06
0.09
0.09
0.07
0.27
1.00
0.64
0.36
0.92
0.08
±
±
±
±
±
±
0.07
0.18
0.08
0.07
0.09
0.03
South India
0.33 ± 0.06
1.11 ± 0.17
0.70 ± 0.04
0.30 ± 0.08
0.82 ± 0.07
0.18 ± 0.04
0.31
1.01
0.69
0.31
0.77
0.23
±
±
±
±
±
±
0.08
0.22
0.07
0.06
0.07
0.06
0.23
1.12
0.71
0.29
0.77
0.23
±
±
±
±
±
±
0.06
0.12
0.05
0.04
0.05
0.03
Plateau
0.08
0.19
0.16
0.83
0.06
0.61
0.10
0.39
0.09
0.79
0.03
0.21
±
±
±
±
±
±
0.07
0.16
0.07
0.08
0.05
0.02
0.09
0.88
0.49
0.51
0.78
0.22
±
±
±
±
±
±
0.04
0.22
0.08
0.05
0.03
0.02
0.47
0.53
0.59
0.41
0.49
0.51
±
±
±
±
±
±
0.10
0.10
0.03
0.08
0.09
0.07
0.29
1.04
0.77
0.23
0.69
0.31
±
±
±
±
±
±
0.04
0.07
0.03
0.03
0.03
0.07
0.35
0.73
0.62
0.38
0.63
0.37
±
±
±
±
±
±
0.06
0.17
0.05
0.06
0.05
0.11
0.25
0.98
0.76
0.24
0.72
0.28
±
±
±
±
±
±
0.04
0.11
0.04
0.05
0.04
0.08
North Indian Ocean
0.21 ± 0.05
0.22
0.92 ± 0.11
0.66
0.69 ± 0.04
0.61
0.31 ± 0.05
0.39
0.67 ± 0.03
0.50
0.33 ± 0.05
0.50
±
±
±
±
±
±
0.05
0.14
0.04
0.06
0.05
0.07
0.18
0.98
0.74
0.26
0.72
0.28
±
±
±
±
±
±
0.06
0.14
0.06
0.05
0.03
0.10
Tibetan
0.26 ±
0.83 ±
0.57 ±
0.43 ±
0.85 ±
0.15 ±
Bengal
0.05
0.12
0.04
0.05
0.05
0.04
a
Mean is ±standard deviation; geographic regions are delineated in
Figure 1; midvisible measured at 558 nm.
D15204
4.1. Winter Season
[24] The spatial distribution of AOD (Figure 8) in the
winter season reveals high AOD over the IGB and its outflow
to the northern Bay of Bengal because of high anthropogenic
emission sources, as previously observed from satellite
[Di Girolamo et al., 2004; Jethva et al., 2005; Prasad et al.,
2006; Ramachandran and Cherian, 2008] and ground‐based
[Singh et al., 2004; Tripathi et al., 2006; Nair et al., 2007]
measurements. Figure 8 shows that AOD averaged over eight
winter seasons is highest (>0.4) over the eastern part of IGB,
which was referred to as the “Bihar pollution pool” by
Di Girolamo et al. [2004]. Aerosols transported across the
IGB from west to east by northwesterly winds (Figure 4)
encounter a narrowing valley floor (Figure 1) and are trapped
efficiently within the atmospheric column in the eastern part
of the IGB by subsiding air (Figure 4). AOD is also higher
than 0.3 over the industrialized sector in the west coast of
India surrounding the Mumbai metropolitan area. Low (<0.2)
AOD in south India, despite high anthropogenic activities
revealed by high population density (Figure 6), is due to blow
out of the aerosols to adjacent oceans by northeasterly to
easterly winds [Di Girolamo et al., 2004], and as the outflow encounters subsidence over the ocean (Figure 4), it
rises above 0.3 (Figure 8). AOD is lower than 0.2 over the
topographically high Western Ghats, Eastern Ghats and
Vindhyan Mountain ranges (Figure 1) and Myanmar (aka
Burma) and even lower than 0.1 over the Tibetan Plateau in
this season. Regionally, mean (± standard deviation (SD))
AOD over the IGB (0.36 ± 0.09) is >50% higher than any
other region (Table 2), while it is comparable over western
India (0.24 ± 0.07), central India (0.24 ± 0.04) and south India
(0.22 ± 0.05) with small interannual variability as suggested
by low AODs values. However, the interannual variability is
high over the eastern IGB, outflow region in the northern Bay
of Bengal and the Taklamakan Desert (AODs > 0.06). The
strong latitudinal gradient of AOD over the oceans is due to
transport of anthropogenic aerosols from the landmass, consistent with previous findings [Ramanathan et al., 2001;
Satheesh et al., 2006a, 2006b]. Mean (±SD) AOD is slightly
higher over the Bay of Bengal (0.27 ± 0.05) than over the
Arabian Sea (0.25 ± 0.04), in part, because of the larger
outflow of aerosols from the IGB.
[25] AE is mostly greater than 1.0 over the Indian subcontinent, except the high AOD zone in the eastern IGB and
the Tibetan Plateau, implying a high relative influence of
small (high anthropogenic component) particles on spectral
aerosol optical properties. In addition to low AE (<0.8),
slightly higher AODnsp (>0.06) in the eastern IGB than the
other parts, suggests high concentration of coarse dust particles emitted possibly by rural activities from the densely
populated rural population. Mean AE is highest over central
and south India (Table 2) in this season. Although the AE is
lower in the eastern IGB, fs is in the range 0.6–0.7 across the
IGB. ff is higher in the central and western parts of IGB than
the eastern part (not shown here), while fm is higher in the
eastern IGB, thus resulting in almost a similar fs (as fs = ff + fm)
across the IGB, but a lower AE in the eastern IGB because of
higher fm. Mean (±SD) fs (0.65 ± 0.04) and fsp (0.89 ± 0.05)
over the IGB are at lower and higher end of the anthropogenic
fraction derived from in situ measurements during ISRO‐
GBP second land campaign [Dey and Tripathi, 2007] as well
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Table 3. Indices That Characterize the Changes in Seasonal Mean Aerosol Properties Compared to the Preceding Season in Terms of
Anthropogenic and Natural Aerosolsa
Index
DAODs
DAODsp
DAODAnthropogenic
DAODl
1
2
3
4
5
6
7
8
0
White
+
−
−
−
+
+
−
+
+
−
−
−
−
+
−
+
+
−−
−
−
×
+
−
++
+
−
+
+
+
−
−
+
DAODnsp
+
−
+
+
+
−
−
+
All Other
DAODNatural
DAE
DAODsp/DAODnsp
DAODAnthropogenic/DAODNatural
++
−
+
+
+
−
−−
+
−
−
−
×
+
+
+
+
−
−
−
−
−
+
+
+
−
−
−
−
×
+
+
+
×
No data (4% of total)
a
Anthropogenic aerosols are defined as dominantly small and spherical particles; Natural aerosols are defined as dominantly large and nonspherical
particles. Respective increase (decrease) of any given particle property in one season from the previous season is indicated by plus (dash) sign. No
change detected and/or change uncertain for any given particle property are marked as cross sign, and larger increase (decrease) in natural compared to
anthropogenic particles and vice versa is denoted by double plus sign (double dash).
as model‐based estimates [Ramanathan and Ramana, 2005].
The close similarities in the MISR retrieved fs and fsp and
in situ measurements reveal the quality of MISR aerosol
retrievals in assessing relative contributions of anthropogenic and natural sources. For example, the high AOD over
the industrialized greater Mumbai area is clearly attributed
to dominant anthropogenic emissions as suggested by very
high AE (>1.2) and fs (>0.8).
[26] Small particles contribute 60–70% to AOD over most
of the landmass, but contribute 55–65% over the Great Indian
Desert and arid to semiarid lands in western India because
of fewer sources of anthropogenic emissions relative to local
sources of dust emissions. fs is also lower than 0.55 over the
Tibetan Plateau (Table 2), thus highlighting the fact that the
Tibetan Plateau is the only region in the Indian subcontinent
where coarse dust particles, raised locally from dry soil [Xia
et al., 2008] by strong surface winds (Figure 4), are the predominant contributors to AOD in the winter season. Large fs
(>0.8) in the high AOD (>0.3) zones over the northern Bay
of Bengal and southern Arabian Sea is attributed to high
anthropogenic fraction in the outflow from the IGB and
central India and from south India, respectively. Higher fs
(∼0.74) over the north Indian Ocean in the winter season
relative to the other seasons is consistent with the seasonal
cycle observed in previous studies [Ramana and Ramanathan,
2006]. The fraction of spherical particles to AOD over the
IGB, central India, western India and south India is more than
0.85, whereas it is less than 0.8 over the Tibetan Plateau and
the oceanic regions (Table 2). A peak in crop waste burning
during the winter harvest season in central and south India
results in a relatively higher fs and AE relative to the IGB
and western India. AODnsp is slightly higher over the IGB and
the oceanic regions (>0.03) than all other regions (AODnsp <
0.03). AODnsp over the Tibetan Plateau looks similar to the
other parts of the subcontinent, although fs (and hence AE) is
much lower. This may be attributed to low sensitivity of
particle property retrieval in this region because of low AOD
(mean value of 0.09) values; however, fnsp is still highest
(∼0.22, Table 2) over the Tibetan Plateau in this season
among the land regions. Nonspherical particles account for
<20% of the AOD over the landmass elsewhere.
[27] We have developed an index (Table 3) based on a
combination of the optical depth segregated by size and shape
of the particles to infer the relative changes in seasonal mean
aerosol properties from the preceding season in terms of
anthropogenic and natural aerosols. Increments in AODl and
Figure 9. Spatial distribution of the index (for details see Table 3) characterizing the changes in seasonal
mean aerosol properties compared to the preceding season. For example, Indices 6 and 7 in winter and Index
6 in postmonsoon represent increasing anthropogenic particle fraction over the ocean because of transport of
aerosols from the mainland; in premonsoon, Index 1 represents increasing natural particle fraction because
of transport of dust, and Index 8 over the land represents increasing anthropogenic particle fraction because
of seasonal peak in biomass burning; and in monsoon, Index 3 represents increasing natural particle fraction
over the ocean because of persisting influence of dust transport and enhanced production of maritime
aerosols. White represents no data.
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AODnsp in the winter season over the preceding postmonsoon
season are assumed to be due to an increase in dominantly
natural aerosols, whereas increments in AODs and AODsp are
assumed to be due to an increase in dominantly anthropogenic
aerosols and vice versa. Figure 9 summarizes the spatial
distribution of this index for each season, compared to the
previous one. The spectral variation of AOD (i.e., AE)
depends on the relative fractions of small and large particles.
If both the natural and anthropogenic aerosols increase in
the winter season over the preceding postmonsoon season,
DAODl, DAODnsp, DAODs and DAODsp (D represents the
difference between the seasonal mean values in the winter and
postmonsoon seasons) will be positive, but DAE will be
negative [positive] for a larger (Index 1) [smaller (Index 8)]
increase in natural aerosols compared to anthropogenic
aerosols. Similarly, if both anthropogenic and natural aerosols decrease in the winter season relative to the preceding
postmonsoon season (i.e., negative DAODl, DAODnsp,
DAODs and DAODsp), a larger decrease in natural aerosols
over anthropogenic aerosols will lead to positive DAE
(Index 7), whereas a larger decrease in anthropogenic
aerosols over natural aerosols will result in a negative DAE
(Index 2). Index 3 [Index 6] represents the regions where
natural aerosols increase [decrease] and anthropogenic
aerosols decrease [increase] in any particular season relative
to its preceding season, resulting in a larger decrease
[increase] in AE. However, it is possible that a simultaneous
increase in large and nonspherical particles and decrease in
small and spherical particles is not large enough to influence
any detectable seasonal change in spectral optical properties
(Index 4). For regions where AODs, AODl and AODnsp
increase, while AODsp decrease, Index 5 is assigned. Index 0
is assigned to the area where the changes in the optical
properties cannot be categorized based on the criteria discussed above, while ∼4% of the area (shown in white in
Figure 9) has no successful retrieval to perform this test.
[28] For winter, Indices 2 and 0 dominate over India and
Bangladesh, Index 7 dominates mainly over Pakistan and the
Arabian Sea, Index 6 dominates over the Bay of Bengal, and
the Tibetan Plateau and north Indian Ocean show no clear
pattern (Figure 9). A reduction of AOD in the winter season
compared to the preceding postmonsoon season over the
western part of IGB, western and central India is attributed to
reduction of both natural and anthropogenic aerosols, but a
larger decrease of natural aerosols is reflected by Index 7 in
the western part of the subcontinent, while a larger decrease in
anthropogenic aerosols is reflected by Index 2 in the western
IGB, western India and central India. The high AOD zone in
the eastern IGB is characterized by Index 0, because AODl
and AODsp increase and AODnsp decreases, while AODs remains similar in this region. Relative humidity (not shown
here) in this region is higher in the postmonsoon season
compared to the winter season, hence hygroscopic growth
seems unlikely to explain the observed increase in AODl and
AODsp during the winter season; rather drier conditions in the
winter compared to the postmonsoon season, facilitated by
stronger subsidence may lead to larger accumulation of local
soil dust and a reduction in the amount of desert dust from
distant sources, the net effect of which leads to a decrease of fs
and AE. AOD decreases over the Arabian Sea in the winter
season compared to the postmonsoon season, but the decrease
is higher for natural over anthropogenic aerosols (Index 7).
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Larger blow out of aerosols from the IGB by stronger wind in
the winter season compared to the postmonsoon season results in Index 8 over the coastal Bay of Bengal, but as the
aerosols are transported deeper into the Bay of Bengal,
stronger subsidence in the winter season (Figure 4) results in
Index 6. No clear pattern over the Tibetan Plateau is attributed
to low sensitivity in retrieved properties in both the postmonsoon and winter seasons because of low AOD.
4.2. Premonsoon Season
[29] In the premonsoon season, aerosol spectral optical
properties change significantly from the preceding winter
season because of enhancement in dust loading [Dey et al.,
2004; Moorthy et al., 2007b; Prasad and Singh, 2007;
Ramachandran and Cherian, 2008]. In general, AOD increases by >25% over the Indian subcontinent, with the
largest increase (nearly three times) observed over the
Tibetan Plateau (Figure 8). AOD is still lower (in the range
0.2–0.3) over the western Ghats and the Tibetan Plateau than
the rest of the landmass. The gradient in AOD from the coast
to deeper ocean over the Arabian Sea and Bay of Bengal is
strongest among all seasons, as also observed by ship‐based
measurements during ICARB [Satheesh et al., 2009]. AOD is
higher over the northern Bay of Bengal along the east coast of
India than the northern Arabian Sea (Figure 8) because, in
addition to dust, emission from open biomass burning in the
IGB, central India and south India are transported to the Bay
of Bengal by lower tropospheric westerly winds and its dispersal is inhibited by strong subsidence along the east coast
(Figure 4). Aerosol mass concentration near the surface has
been found to be much higher over the Bay of Bengal than the
Arabian Sea during ICARB in the 2006 premonsoon season
[Nair et al., 2008], but the MISR 9 year climatology reveals
similar mean (0.33 ± 0.05) columnar AOD over these two
regions (Table 2). However, the spatial coverage of ICARB in
situ measurements and our analysis are not exactly same, and
ICARB measurements might not represent multiyear average
conditions. Aerosol characteristics are very different in these
two oceanic regions as discussed below.
[30] AE reduces in this season because of an increasing
dominance of large particles. The decrease is highest close to
the dust source regions namely the northern Arabian Sea and
western India. AE over the Mumbai metropolitan region still
remains higher than 1.2 because of a higher relative influence
of small particles from large anthropogenic emissions over
large natural particles. Although mean AOD is similar over
the Arabian Sea and Bay of Bengal, mean (±SD) AE (Table 2)
is much higher over the Bay of Bengal (1.02 ± 0.12) than the
Arabian Sea (0.80 ± 0.13). In the high AOD (>0.4) zone of the
northern Arabian Sea, AE is found to be lower than 0.6. Air
mass back trajectories (not shown here) at 850 mb originating
from these high AOD zones in the Arabian Sea and Bay of
Bengal using the NOAA HYSPLIT model [Draxler and
Hess, 1998] support the Great Indian Desert and the Arabian Peninsula as the dust source regions. However, the relative influence of dust on the aerosol characteristics is
stronger over the Arabian Sea compared to the Bay of Bengal
because of the proximity to the source regions and lower
influence of biomass burning emissions transported to the
ocean, as supported by the lower mean values of AE and fs
(Table 2). Higher relative influence of coarse particles on
spectral AOD over the Arabian Sea compared to the Bay of
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Bengal was also observed by the ship‐based measurements
during ICARB [Kedia and Ramachandran, 2009]. The gradient in AE and fs is also sharper from north to south over the
Arabian Sea compared to the Bay of Bengal. AE does not
show any significant change over the Tibetan Plateau in this
season from the preceding winter season, however, fs and
AODnsp do change. One year (August 2006–July 2007)
measurements of aerosol properties at Nam Co (Figure 1)
AERONET station in the Tibetan Plateau have shown that
mean AE remains lower than 0.5 during February to July
[Cong et al., 2009]. Even in other months, mean AE remains
lower than 1. Mean (±SD) annual AERONET‐retrieved AE
over the Tibetan Plateau of 0.42 (±0.27) suggests that coarse
dust particles are a large component of the aerosol field
throughout the year [Cong et al., 2009]. Mean seasonal variation of AE from MISR over the Tibetan Plateau lie in the
narrow range of 0.83 and 0.88 (Table 2), which is consistent
with that of Cong et al. [2009] given the high bias in MISR‐
retrieved AE in heavy dusty conditions (Figure 2b). fs decreases over most of the Indian subcontinent in this season
because of dominant influence of large dust particles. The
Great Indian Desert has the lowest fs (<0.55). While fl (i.e., 1‐
fs) increases over the western and central parts of the IGB, fs
(and hence AE) increases over the eastern IGB relative to the
winter season. Mean premonsoon AE over the IGB is almost
similar to the mean wintertime AE because of this spatial
contrast. Another striking feature is a high gradient of fs and
AE from the IGB to the foothills of the Himalayas (Figure 8),
suggesting an increasing relative influence of small particles
over the foothills than over the basin. This is further supported
by observations of higher AE (0.82 ± 0.22) derived by sunphotometer measurements over Nainital (see the location in
Figure 1) in the Himalayan foothills during the premonsoon
season of 2002 [Sagar et al., 2004] compared to AE (0.56 ±
0.26) retrieved by AERONET at Kanpur [Singh et al., 2004].
We also examined AERONET measurements at these two
locations in the years 2008 and 2009 that show lower mean
(±SD) AE over Kanpur in both the years (0.65 ± 0.33 in the
year 2008 and 0.68 ± 0.14 in the year 2009) compared to
Nainital (0.94 ± 0.04 in the year 2008 and 0.72 ± 0.02 in the
year 2009). This gradient may be attributed to larger fs
because of higher biogenic emission from denser vegetation
coverage and higher emission from forest burning, supported
by MODIS fire counts, over the Himalayan foothills region
compared to the IGB. Pollutants with large anthropogenic
fraction from the valley lifted upward along the Himalayan
foothills through mesoscale circulation [Dumka et al., 2008]
may also contribute to the observed gradient in AE and fs.
Large emission of small particles from open biomass burning
overcompensates the relative influence of dust on spectral
AOD in the eastern part of the IGB as indicated by an increase
of AE and fs in this season compared to the preceding winter
season. This also leads to an overall increase in AOD in that
region compared to the winter season (Figure 8). South India,
particularly the coastal region, has the least relative influence
of dust on the aerosol optical properties in this season as
suggested by an increase in fs and AE from the preceding
winter season.
[31] Nonspherical particles are found to increase from the
winter season substantially over the IGB, western India,
Taklamakan Desert and the oceanic regions, where AODnsp is
greater than 0.09, contributing >20% (Table 2) to AOD.
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AODnsp shows a spatial gradient from high values in the
western to low values in the eastern part of the IGB owing to
settling of dust along the IGB during its long‐range transport
from Arabian Peninsula and Great Indian Desert. AODnsp
shows an increase in this season from the winter season over
some parts of the Tibetan plateau, where AOD rises over 0.2.
The most striking feature in the spatial distribution of
AODnsp, however, is the very high value (>0.12) over the
Arabian Sea and Bay of Bengal along the coasts of India and a
rapid decrease over land. The relative contribution of the
nonspherical dust particles to AOD (i.e., fnsp) is more than
40% in these coastal water regions. The accumulation of dust
transported from the Arabian Peninsula over the Arabian Sea
along the west coast of India is facilitated by the strong
subsiding air (Figure 4) in the region. However, the dust over
the Bay of Bengal has two different dominant pathways of
transportation. The first one is from the Great Indian Desert
via the IGB settling down in the northern Bay of Bengal in the
presence of subsiding air. In the second pathway, dust from
the Arabian Peninsula transported to the western Ghats along
the west coast of India, coupled with the cyclonic flow centered on south central India (Figure 4), is further transported
around the southern tip of India into the Bay of Bengal where
strong subsidence remains. This also restricts transportation
of dust to south India, where the regional mean AE is the
highest (1.11 ± 0.17) among all other regions in this season
(Table 2 and Figure 8). The strong gradient in nonspherical
particles does not seem to be an algorithm issue, because
AODnsp is low (<0.06) over the north Indian Ocean. It also
does not reduce sharply across the land‐ocean boundary;
rather it remains high over the low‐lying coastal area along
the west coast, dropping off only over the topographically
high regions (Figure 1), where it encounters ascending air
(Figure 4).
[32] It is evident from the previous discussion that the
premonsoon season shows a marked change in the aerosol
spectral characteristics over the entire Indian subcontinent
compared to the preceding winter season. But how much is
the change attributed only to dust? Is the seasonal change
in relative contribution of small and spherical particles to
the overall aerosol characteristics negligible compared to the
influence of dust? Indices 1 and 8 (Table 3) dominate in the
Tibetan Plateau (Figure 9) with no spatial pattern because of
strong interannual variability in aerosol loading from local
dust storms and dust transport from the Taklamakan Desert
[Xia et al., 2008]. On the other hand, a definite spatial pattern
emerges over the Indian landmass south of the Himalayas.
The wedge‐shaped region in the western part of the Indian
subcontinent with a larger increase in natural over anthropogenic aerosols (Index 1) is close to the dust source regions.
However, the increment in AODs over the Arabian Sea and
western India is low (<0.05), and thus Index 1 in this region is
mainly due to an increase in large size spherical particles. A
large fraction of spherical particles in these regions seems to
be natural, because the lower tropospheric wind (Figure 4) is
mostly westerly and thus, we cannot use spherical versus
nonspherical properties to distinguish anthropogenic from
natural aerosols in this region. This is also supported by lower
CO (an atmospheric tracer of anthropogenic emission) mixing ratio in the premonsoon season compared to the winter
season over these regions [Kar et al., 2008]. The area with
Index 8 becomes larger as we move along the IGB from the
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DEY AND DI GIROLAMO: CLIMATOLOGY OF AEROSOL PROPERTIES
west to the east implying reduced relative influence of natural
aerosols on aerosol optical properties. The effect of natural
aerosols is least over the Gangetic West Bengal and Bangladesh, close to the northern Bay of Bengal. A separate pattern
(Index 5) has been observed over the central to eastern IGB
in this season. One plausible explanation for this observation
may be that a large fraction of nonspherical dust particles is
smaller than 0.7 mm (the cutoff size between small and large
particles in the MISR retrieval). Mixing of spherical particles with nonspherical particles across the entire size range
leading to an increase in AODnsp and decrease in AODsp may
be another possible explanation. Mixing of dust with
anthropogenic particles, particularly black carbon, has been
found to be a probable mixing state over Kanpur [Dey et al.,
2008], which is situated in the westernmost part (Figure 1)
of the Index 5 zone. However, there is a lack of in situ data
from the eastern part of the IGB to support this observation.
[33] Earlier in situ [Dey et al., 2004; Ganguly et al., 2006a;
Prasad and Singh, 2007; Satheesh et al., 2008] and satellite
observations [Jethva et al., 2005; Moorthy et al., 2007b;
Ramachandran and Cherian, 2008] have attributed the
changes in premonsoon aerosol characteristics to dust loading
only. But, these results show that anthropogenic aerosols also
increase over a large part of the subcontinent and the increment is large enough to influence the overall aerosol properties over certain regions. These observations are consistent
with the fact that open biomass burning is at its peak in the
premonsoon season in the IGB, central India and south India
[Venkataraman et al., 2006], while other emission sources do
not change significantly. A larger increase of anthropogenic
aerosols compared to natural aerosols (Index 8) in this season
has been found over the Himalayan foothills, northeast India,
Myanmar, east coast of India and Indian landmass south of
15°N latitude. Low‐lying topography, favorable meteorology
(Figure 4) and increased emission of biomass burning facilitate a buildup of aerosols along the narrow fringe of the east
coast in south India, where AE and fs increase in the premonsoon season from the winter season (Figure 8) leading to
Index 6. In contrast to these four indices (Indices 1, 5, 6,
and 8), other indices are mostly found over the oceans.
The oceanic region receives substantial anthropogenic pollution from the landmass in the winter season (see
Ramanathan et al. [2001] and subsequent studies), but the
anthropogenic fraction is reduced by a change in wind
direction in the premonsoon season. The effect is more
pronounced in regions far away from the land. The high
AOD and AODnsp zone over the Arabian Sea and Bay of
Bengal has been characterized by Index 1, but AE and fs
distributions reveal higher relative influence of the anthropogenic aerosols on aerosol properties in the Bay of Bengal
than the Arabian Sea. Index 3 dominates in the north Indian
Ocean because transportation of anthropogenic aerosols
over the oceans from the subcontinent is reduced because of
a reversal in wind direction (Figure 4), and natural aerosols
increase because of dust transport. However, no clear pattern
is observed in the southern Bay of Bengal and eastern Indian
Ocean, which is attributed to larger interannual variation in
aerosol properties as shown by AODs in Figure 8.
4.3. Monsoon Season
[34] During the monsoon season, stronger westerly winds
(Figure 4) transport greater components of dust from the
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Arabian Peninsula to the Indian subcontinent. In general, the
spatial distribution of AOD (Figure 8) in this season is
influenced by monsoon precipitation (Figure 5a). For example, very high AOD (>0.6) is observed over the low‐precipitation zones in western India (mainly over the Great Indian
Desert and neighboring arid regions) and the northern Arabian Sea. However, monsoon precipitation is not spatially and
temporally uniform (Figures 5a and 5b). Suppressed precipitation in the monsoon break phase allows for a rapid build up
of aerosols in the high anthropogenic source regions (e.g.,
IGB, Figure 6), while particles are being washed out by the
precipitation in the active monsoon phase. This also leads to
very high intraseasonal and interannual variability in aerosol
characteristics as seen from the large AODs values (Figure 8)
and wider frequency distributions of particle properties
(Figure 7). AODs is higher than 0.12 over the Arabian Sea,
northern and eastern Bay of Bengal and some parts of the
IGB, where the interannual variation in monsoon precipitation (Figure 5b) is highest (>250 mm), thus further establishing the fact that AOD distribution is strongly influenced
by monsoon rainfall. Further investigation on the influence of
monsoon rainfall on AOD was conducted by Ravi Kiran et al.
[2009], who have reported very high and statistically significant differences in AOD over India during active and break
phases of the monsoon using MODIS data. Even with monsoon precipitation, AOD remains high over many regions.
However, we note that the chance of successful retrievals of
aerosol properties (either by MISR or MODIS) is higher
during the break monsoon phases than the active monsoon
phases because of higher cloud coverage in the active phases.
Hence, the monsoon climatology reported here (and in the
literature) is biased toward the observations during the
monsoon break phases.
[35] Mean (±SD) AOD is highest in the monsoon season
among all the seasons over western India (0.53 ± 0.10),
Arabian Sea (0.47 ± 0.10) and Bay of Bengal (0.35 ± 0.06),
and is similar to the premonsoon mean values over the IGB
(0.45 ± 0.08) and central India (0.36 ± 0.05) (Table 2).
Emission of anthropogenic aerosols continues to be high over
the IGB [Habib et al., 2006] and the west coast industrial
sector surrounding the Mumbai metropolitan area. Anthropogenic fraction is much higher in the Mumbai industrialized
region than IGB as suggested by higher AE and fs. AOD
spatial gradient over the Bay of Bengal is lower in the monsoon season compared to the premonsoon season. The high
AOD (>0.5) over the northern Arabian Sea in the monsoon
season has been detected earlier using MODIS data [Satheesh
et al., 2005]. A large increase in AOD (>30%) in this season
from the premonsoon season over the western part of the
subcontinent is attributed to an increase in mainly small and
spherical particles transported from north and northwest Asia
(Figure 4), as supported by an increase in fs and AE (Figure 8).
This is reflected in the characterization of this region by Index
6 (Figure 9), showing an increase in anthropogenic aerosols
and a decrease in natural aerosols compared to the premonsoon season. Enhanced production of maritime aerosols
by strong surface wind (Figure 4) also contributes to the
observed increase of AOD over the Arabian Sea in this season
along with dust, thus showing low AE (<0.6) compared to
over the land. Ground‐based measurements of spectral AOD
by multiwavelength radiometer at Minicoy (Figure 1) also
show AE of ∼0.4 during this season [Vinoj et al., 2008].
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Unfortunately, data from Port Blair in the Bay of Bengal
(Figure 1) are not available in the literature for comparison in
this season. Mean (±SD) AE over the Bay of Bengal (0.73 ±
0.17) is ∼38% higher than the Arabian Sea (0.53 ± 0.10).
Except for the western to central IGB, Great Indian Desert
and parts of central India, AE is greater than 1.0 and fs is
greater than 0.7 over most of the Indian landmass because of
low relative influence of large dust particles on spectral AOD.
fs is mostly <0.6 over the Arabian Sea and in the range 0.55–
0.65 over the Bay of Bengal and north Indian Ocean except
the coastal northern Bay of Bengal, where higher fs may be
attributed to larger removal of coarse particles than small
particles by high (>1500 mm) precipitation (Figure 5a).
[36] AODnsp is highest (mean value of 0.23) over the
Arabian Sea, where it contributes 51% to the AOD, whereas
it contributes ∼37% to the AOD over the Bay of Bengal.
High AODnsp over the oceanic regions indicates a persistent transportation of dust in the monsoon season. High
(>900 mm) precipitation along the west coast of India contributes to a sharp decrease in AOD (as well as AODnsp) from
the Arabian Sea to the western Ghats, while strong westerly
winds continue to transport natural particles across the Bay of
Bengal. By combining MODIS‐derived AOD and NCEP‐
derived wind data, Satheesh et al. [2006d] have shown that
during the monsoon season, the relative contribution of
maritime aerosols to the AOD over the northern Arabian Sea
is lower than that over the southern Arabian Sea. The air mass
to the northern Arabian Sea has been found to come from west
Asia and Africa [Satheesh et al., 2006d], whereas the air mass
to the southern Arabian Sea comes from the open oceans
[Vinoj et al., 2008]. We have also looked into the spatial
gradient of the absorbing aerosol index (AAI) over the Arabian Sea (not shown here) from Total Ozone Mapping
Spectrometer (TOMS, for data up to the year 2005) and
Ozone Monitoring Instrument (OMI, after the year 2005). We
found that AAI decreases from north to south and becomes
very low (∼0) south of 10°N latitude (up to which the high
AODnsp zone exists). This further supports the dominance of
dust to the AOD (Table 2) in the Arabian Sea during this
season. An additional factor to consider is the potential for
thin cirrus contamination, since the occurrence of thin cirrus
is highest during the monsoon season [e.g., Wang et al.,
1996]. Thin cirrus contamination would produce artificially
large AODnsp, which we would be attributing to dust in our
discussion. However, satellite‐based lidar and solar occultation observations have shown that thin cirrus occurrence in
the monsoon season is much larger over the Bay of Bengal
compared to the Arabian Sea [Wang et al., 1996; Nazaryan
et al., 2008], suggesting that if thin cirrus contamination
were prevalent in the MISR retrievals, then AODnsp would be
largest over the Bay of Bengal. Since this is not observed in
Figure 8, we conclude that thin cirrus contamination is
unlikely to dominate the spatial patterns of AODnsp observed
in Figure 8. The sharp reduction in AODnsp from the Arabian
Sea to the west coast of India may also be affected by abrupt
reduction in sample density (Figure 3) over the west coast of
India because of large cloud cover.
[37] As discussed above, changes in the aerosol properties
over most of the oceanic regions in the monsoon season
compared to the premonsoon season are characterized by
Index 3 (Figure 9). However, the northern Arabian Sea and
the central Bay of Bengal are characterized by Index 0 instead
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of Index 3 observed over most of the oceanic region. Surface
wind speed over these regions is very high (Figure 4), which
may increase emission of spherical maritime particles.
Transportation of anthropogenic aerosols from north and
northwest Asia may also contribute to the observed increase
in AODsp over the northern Arabian Sea, but Index 0 over the
central Bay of Bengal seems to be a local feature. A decrease
in AOD over the northern Bay of Bengal near the coast of
Bangladesh is attributed to a larger decrease in anthropogenic
than natural aerosols, which is characterized by Index 2.
Index 6 over the western IGB and central India is attributed to
a reduction in dust transportation as observed in AODnsp
spatial distribution, which also results in an increase of fs and
AE (Figure 8).
4.4. Postmonsoon Season
[38] Aerosol regional mean climatology in the postmonsoon season is very similar to that for the winter season
(compare the mean values in Table 2 and the frequency distributions in Figure 7), but the spatial distribution differs in
several regions. For example, the wintertime high AOD zone
in the IGB shows a larger spread and higher interannual
variability (AODs > 0.08) across the basin in this season,
owing to a stronger peak in crop waste burning in the western
part of IGB than the eastern part [Venkataraman et al., 2006]
and weaker subsidence in the eastern part of IGB compared to
the winter season (Figure 4). The spread of the aerosol outflow from the IGB to the Bay of Bengal, where AOD > 0.3, is
smaller in this season because of smaller subsidence area
compared to the winter season, while the spread of the outflow to the Arabian Sea is larger compared to the winter
season. A similar spatial gradient in AOD over the Bay of
Bengal was also observed during ship‐based measurements
of spectral AOD and black carbon concentration during the
2003 postmonsoon season by Sumanth et al. [2004]. AOD
decreases below 0.1 over most of the Tibetan Plateau, while it
lies in the range 0.2–0.3 in central India and most of south
India, except for the western and eastern Ghats mountain
regions on the west and east coasts of India, where AOD is
smaller than 0.2.
[39] The spatial distribution of the indices characterizing
the seasonal changes of aerosol properties is very different
from the other seasons (Figure 9). Mean AOD reduces by
>25% over most of the geographic regions compared to the
monsoon season (Table 2), primarily because of a reduction
in emission of maritime and transportation of dust particles.
But AODs and AODsp also decrease over part of the landmass
at a lower rate than natural aerosols and thus these regions are
characterized by Index 7. However, this is not the case in all
years. Some years have shown an increase in anthropogenic
aerosols in this season (in that case the regions would have
been characterized by Index 6) and the other years have
shown a decrease (Index 7) compared to the monsoon season,
but the mean difference in AODs and AODsp between the
postmonsoon and monsoon seasons of all the years leads to
the characterization by Index 7. Anthropogenic aerosols are
transported to the north Indian Ocean as suggested by a rise in
fs above 0.75 and as the natural aerosols reduce compared to
the monsoon season, north Indian Ocean is characterized by
Index 6. Over some parts of the Arabian Sea and Bay of
Bengal, AODs, AODsp and AODl increases and AODnsp decreases compared to the monsoon season, thus it is shown as
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Index 0. Transition in aerosol characteristics during this
season [Ramana and Ramanathan, 2006] makes it difficult to
distinguish natural from anthropogenic particles by our criteria based on MISR’s sensitivity.
[40] A larger decrease in natural compared to anthropogenic aerosols (Index 7) over most parts of the Indian subcontinent as we transition to this season from the monsoon
season leads to an overall increase in AE and fs (Figure 8). AE
is mostly >1.0 over the landmass except some parts of IGB
and the Tibetan Plateau, where AODnsp is >0.06. AE almost
doubles (mean AE = 1.04) in the postmonsoon season from
the preceding monsoon season over the Arabian Sea. The
spatial distribution of AE is fairly uniform over the Arabian
Sea (Figure 8), as aerosols with large anthropogenic fraction,
as suggested by high fs (>0.75), are being transported from the
Indian landmass (Figure 4). AE shows a gradient with higher
value (AE > 1.0) in the northern Bay of Bengal compared to
the southern Bay of Bengal. fs is >0.8 over a large part of the
northern Bay of Bengal because of transportation of aerosols
with high anthropogenic fraction from IGB and east Asia
aided by subsidence (Figure 4). The air mass over the
southern Bay of Bengal has a larger maritime component as
suggested by lower fs (in the range 0.7–0.8). AODnsp
(Figure 8) and fnsp (Table 2) are found to decrease over the
Arabian Sea compared to the premonsoon and monsoon
seasons, but still remain higher compared to the winter season. Although AODnsp is much lower compared to the premonsoon season over the Bay of Bengal, fnsp is similar in
these two seasons (Table 2). The overall decrease of AODnsp
in this season relative to the monsoon season may be influenced by a reduction in cirrus contamination, but in situ observations also support the fact that dust transport reduces in
the postmonsoon season [Dey and Tripathi, 2008; Ganguly
et al., 2006a], as shown by AODnsp distribution.
5. Discussion, Summary, and Conclusions
[41] In view of the ongoing efforts to characterize aerosol
properties over the Indian subcontinent, we have presented
an extensive satellite‐based climatology of aerosol optical
and microphysical properties, namely AOD, fs, fl, fsp, fnsp at
558 nm wavelength and AE, using 9 years (2000–2008) of
data from MISR. No previous study has discussed the climatology of particle properties other than AOD and fine
mode fraction (to some extent) with more than five years of
data. Here, the aerosol climatology has been discussed in
view of influencing parameters, viz. meteorology, emission
inventories and topography, within the known limitations of
the MISR retrieval algorithm. Our evaluation of the MISR
aerosol product against AERONET (section 2) points to the
similar conclusions about the quality of MISR aerosol
retrieval (e.g., low bias for high AOD condition and high bias
for AE in heavy dusty days), that is described and explained
by Kahn et al. [2009]. Furthermore, our ability to explain the
spatial and temporal variability of the MISR aerosol properties based on meteorology, emission sources and topography,
and numerous corroborations of the seasonal and regional
variability of MISR aerosol microphysical and optical properties against available in situ observations support our interpretations of the MISR aerosol data.
[42] The mean climatological values of various particle
properties over the major geographical regions of the Indian
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subcontinent (Figure 1) are summarized in Table 2 for easy
comparison. The seasonal variability of aerosol properties
reveals that AOD peaks during the premonsoon and monsoon
season because of enhanced emission of natural aerosols.
Large and nonspherical dust particles dominate the aerosol
optical properties in the Tibetan Plateau throughout the year,
while they contribute significantly to AOD in the premonsoon and monsoon seasons in other regions. Xia et al.
[2008] have also discussed the seasonal and interannual
variability of AOD over the Tibetan Plateau, which is quite
similar to our results. However, the mean climatological
values reported here are slightly different from their study,
because they used older versions of MISR data for seven
years (February 2000–May 2007) period compared to our use
of 9 years of Version 22 MISR data. The broad regional
differences of AOD depend in part on the emission sources,
but the variation within a particular region depends significantly on meteorology and topography. For example,
wintertime AOD in the IGB is higher than any other region
because of higher emissions of anthropogenic aerosols
[Streets et al., 2003; Bond et al., 2004; Venkataraman et al.,
2005, 2006; Habib et al., 2006]. However, higher wintertime
AOD in the eastern part than the western part of the IGB
can be better explained by meteorology and topography
[Di Girolamo et al., 2004].
[43] Although we have presented the first climatology of
aerosol optical and microphysical properties, specifically
AOD, fs, fl, fsp, fnsp at 558 nm wavelength and AE, in both
spatial and temporal contexts, one component missing in our
analysis is the climatology of aerosol absorption (i.e., SSA).
Unfortunately, the existing satellite sensors are not able to
retrieve SSA accurately. MISR overestimates SSA because of
the absence of mixtures containing carbonaceous and dust
particles in the current algorithm. TOMS/OMI derived AAI
is able to provide some information on the presence of
absorbing aerosols during the premonsoon season [Lau et al.,
2006], but AAI tends to be insensitive to the boundary layer
aerosols [Mahowald and Dufresne, 2004]. As the absorbing
carbonaceous aerosols are mostly confined within the shallow boundary layer in the postmonsoon and winter seasons
over the Indian subcontinent [Tripathi et al., 2005b; Ganguly
et al., 2006b; Nair et al., 2007], it is difficult to use AAI to
characterize absorbing aerosols in these seasons. The high
AOD zone in the eastern IGB has high aerosol absorption as
shown by a recent chemical transport model simulation
[Adhikary et al., 2008]. Recent observations during ICARB
have also suggested that the aerosols (predominantly dust)
above the marine boundary layer in the premonsoon season
are absorbing in nature and contribute to large atmospheric
warming [Satheesh et al., 2008]. The issue of mixing of dust
with anthropogenic components and their radiative effect
because of enhanced absorption becomes increasingly relevant in view of high anthropogenic and dust particles in the
premonsoon and monsoon seasons as revealed by MISR data.
The high aerosol loading over the Indian subcontinent as
observed by MISR, a fraction of which is absorbing black
carbon [Ramanathan and Carmichael, 2008], has several
likely climatic implications of significance, e.g., enhancement in precipitation through an elevated heat pump
[Lau et al., 2006], weakening of the Indian monsoon circulation, solar dimming and the retreat of Himalayan glaciers
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DEY AND DI GIROLAMO: CLIMATOLOGY OF AEROSOL PROPERTIES
[Ramanathan and Carmichael, 2008; Ramanathan and
Feng, 2008].
[44] Nonspherical particle shapes can result in an overestimation of TOA aerosol cooling by >10% and atmospheric
warming by ∼6% as compared to spherical particle with
similar composition [Mishra et al., 2008]. Error in radiative
transfer simulations because of approximating nonspherical
particles as spherical particles is of equal magnitude to the
error stemming from uncertainty in aerosol refractive index
[Kahnert et al., 2007]. High climatological AODnsp and fnsp
values as observed by MISR in the premonsoon and monsoon
seasons, particularly over the IGB, Tibetan Plateau and the
oceanic regions where fnsp is >20%, imply that particle nonsphericity must be accounted for to improve the estimates of
aerosol direct radiative forcing, because so far, available estimates in the Indian subcontinent were based on spherical
particle models only. The effect of nonspherical particle
model on the simulated aerosol optical properties for dust‐
anthropogenic particle mixing also needs future attention.
[45] Long‐term exposure to particles smaller than 2.5 mm
(i.e., PM2.5) causes various respiratory and cardiovascular
diseases. The very high climatological AOD over the densely
populated regions (e.g., IGB, Mumbai metropolitan area)
must be having a tremendous impact on the health of millions
of people. However, there is a lack of a robust data set on
surface aerosol concentration to study the health effect of
outdoor aerosol in the Indian subcontinent (the rate of premature deaths in India because of indoor air pollution was
examined by Smith [2000]). MISR data have been used in
conjunction with global chemical transport model to derive
surface PM2.5 concentrations successfully over the United
States by Liu et al. [2004, 2007]. A global analysis by
van Donkelaar et al. [2006] has already shown that mean
annual surface PM2.5 over the IGB during 2001–2002 is in the
range 40–50 mg m−3, four to five times larger than the World
Health Organization air quality guideline of 10 mg m−3.
Similar efforts should be made for the entire region by taking
advantage of the long‐term data set of size‐segregated MISR
particle properties in conjunction with measurements by the
ground‐based network of the Central Pollution Control Board
of India for validation.
[46] Recent global aerosol models have improved because
of the assimilation of satellite and in situ observations [e.g.,
Stier et al., 2005; Adhikary et al., 2007, 2008; Zhang et al.,
2008; Lee and Adams, 2009], but many continue to fail in
capturing the strong spatial variability of AOD [e.g., Stier
et al., 2005, Figure 7a; Chin et al., 2009, Figure 8], as well
as an overall underprediction of AOD [e.g., Lee and Adams,
2009; Chin et al., 2009] over India as compared to the satellite
observations. MISR’s ability to retrieve particle optical and
microphysical properties, even over bright surfaces, provides
an opportunity to both evaluate global aerosol models in
further detail and to improve the simulations through data
assimilation. The MISR‐observed climatology of fs, fl, fsp
and fnsp presented here and their spectral variations (normally
characterized by AE) may also be used in constraining the
regional estimation of aerosol forcing derived by extrapolating forcing efficiency [Chen et al., 2009], that has been
simulated from optically equivalent aerosol models based on
in situ measurements at a few isolated locations in the Indian
subcontinent [Ramanathan and Ramana, 2005; Dey and
Tripathi, 2007]. However, quantitative interpretation of such
D15204
results should be done cautiously, to account for the known
MISR aerosol property biases.
[47] The strong spatial heterogeneity in the aerosol climatology presented here suggests that the number and
monitoring capabilities of existing ground‐based aerosol
measurement sites are insufficient to derive robust statistics
of aerosol optical and microphysical properties for the entire
Indian subcontinent. For example, the eastern part of IGB,
with its dense rural population, has very few in situ observations despite this region’s high aerosol loading and
temporally varying microphysical properties. Central India
also lacks coordinated in situ observations. Even the oceanic
regions surrounding the subcontinent landmass show strong
spatial and temporal variability in aerosol properties, yet
continuous in situ measurements are available at only three
locations (Figure 1), Port Blair in the Bay of Bengal,
Minicoy in the Arabian Sea, and Hanimaadhoo in the north
Indian Ocean. Ship‐borne measurements of aerosol characteristics are almost negligible during the monsoon season,
when the relative influence of dust on aerosol properties is
highest ( fnsp > 0.35) among all seasons. In recent years,
CIMEL sunphotometers were deployed as part of AERONET
at a few more sites in the IGB and foothills of the Himalaya
(see the AERONET home page, http://aeronet.gsfc.nasa.
gov) and more ground‐based observatories are being set up as
part of ISRO‐GBP and ABC (not shown in Figure 1), which
will add valuable information to the ongoing efforts in
understanding the aerosol direct and indirect effects in the
region. The MISR 9 year aerosol climatology presented in
this paper can help in planning future initiatives for in situ
observations in the Indian subcontinent.
[48] In summary, the major conclusions of this work are as
follows:
[49] 1. The MISR aerosol products, except SSA, provide a
good picture of regional and temporal variability of aerosol
characteristics over the Indian subcontinent, as corroborated
by many in situ studies, and tying logically to meteorology,
topography, and emission source behavior. The Indian subcontinent displays strong seasonal and spatial heterogeneity
in all aerosol properties studied here. AOD is not only
high over the urban/industrialized sectors, but also high over
the densely populated rural areas, thus stressing the importance of considering these rural regions for future in situ
measurements.
[50] 2. In the winter season (December–February), high
AOD (>0.3) is observed over the IGB, the industrialized
region surrounding the mega city Mumbai, and their outflow
to adjacent oceans, in part because of high anthropogenic
emission sources. Favorable meteorological conditions allow
a buildup of aerosols over the eastern part of IGB, where
coarse dust particles add to higher AOD (AOD > 0.4) resulting in a lower AE (<0.8) compared to the rest of the
landmass. Mean AOD over the IGB is >50% higher than any
other region. Coarse dust particles dominate the aerosol
optical and microphysical properties ( fnsp = 0.22) over the
Tibetan Plateau. fs is mostly greater than 0.75 over the surrounding oceanic regions because of the transportation of
aerosol with a large anthropogenic fraction from the subcontinent, consistent with the previous findings.
[51] 3. Enhanced dust activity, as suggested by larger
AODnsp compared to the winter season, results in an increase
in AOD by >25% and a decrease in AE over a major fraction
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of the Indian subcontinent in the premonsoon season (March–
May). The largest increase (nearly threefold) in AOD is
observed over the Tibetan Plateau. However, along the
foothills of the Himalaya, AE (>1.2) and fs (>0.75) are large
relative to the Tibetan Plateau to the north and IGB to the
south. This may be due to higher biogenic emission and
emission from open biomass burning in the Himalayan
foothills relative to the other regions. The AOD spatial gradient from the coast to deeper ocean is highest in this season.
A rapid decrease in AODnsp from the Arabian Sea and Bay of
Bengal to the coastal inland is attributed to a combination of
topographic and meteorological effects. Climatologically, the
fraction of natural aerosols to the AOD is lower over the Bay
of Bengal compared to the Arabian Sea resulting in a much
higher AE (1.02 ± 0.12 compared to 0.80 ± 0.13).
[52] 4. The influence of transported dust on the aerosol
optical properties persists into the monsoon season (June–
September), especially over the IGB, western India and
northern Arabian Sea, where AOD is >0.5. However, small
and spherical particles transported from the north and
northwest Asia contribute significantly to the large increase in
AOD observed over the western part of IGB. The highest
land‐ocean contrast in AE is observed in this season, when
enhanced production of coarse maritime particles by strong
surface winds along with dust results in low AE (<0.8) and fs
(<0.6) over the oceans. The large particle fraction is much
higher over the Arabian Sea compared to the Bay of Bengal
because of proximity to dust source regions. AODnsp is
highest (>0.15) in the monsoon season over the Arabian Sea
and Bay of Bengal contributing >35% to the AOD. The intraseasonal and interannual variability of AOD is highest in
this season as suggested by higher AODs compared to the
other seasons.
[53] 5. The regional mean aerosol climatology in the
postmonsoon season (October–November) is similar to the
wintertime mean aerosol climatology, but notable difference
has been observed in the spatial distribution of the aerosol
optical and microphysical properties. For example, high
AOD (>0.4) is uniformly spread across the entire IGB and AE
is much higher over the eastern IGB compared to the winter
season, probably because of a weaker subsidence in the
eastern IGB and higher crop waste burning in the western
IGB in the postmonsoon season. fs and AE are higher over
central and south India because of higher open biomass
burning compared to the winter season. AE shows a gradient
over the Bay of Bengal because of a larger relative influence
of anthropogenic fraction on the AOD over the northern part
compared to the southern part. AE almost doubles over the
Arabian Sea compared to the preceding monsoon season
because of a transportation of aerosols with a high anthropogenic fraction from the subcontinent landmass by a
reversal of winds. The AODnsp decreases over the Indian
subcontinent from the monsoon season, but remains larger
compared to the winter season.
[54] 6. An index is developed to characterize the changes of
mean seasonal aerosol properties compared to the preceding
season in terms of the relative influence of natural and
anthropogenic aerosols on aerosol optical and microphysical
properties (Table 3 and Figure 9). Both natural and anthropogenic aerosols decrease in large parts of the landmass in the
winter compared to the postmonsoon (Index 2) and in the
postmonsoon compared to the monsoon season (Indices 7 and
D15204
2), but the Bay of Bengal is characterized in the winter season
by a decrease in natural and an increase in anthropogenic
aerosols compared to the postmonsoon season (Index 6). The
high AOD zone in the eastern part of the IGB shows an
increase in large and spherical particles, possibly emitted
locally from rural activities and results in a lower AE
compared to the rest of the IGB, and thus is characterized by
Index 0. The dominance of Indices 1 and 8 in the premonsoon
season suggests an increase in small and spherical particles
simultaneously with an increase in natural aerosols, but a
large fraction of small and spherical particles over the ocean
may be of natural sources. An increase in maritime aerosols
along with dust in the monsoon season over the oceanic
region is reflected by Index 3 over the north Indian Ocean.
[55] Acknowledgments. Partial support from the Jet Propulsion Laboratory of the California Institute of Technology under contract 1260125 is
gratefully acknowledged. The MISR aerosol data are distributed by the
NASA Langley Atmospheric Sciences Data Center. The meteorological data
are obtained from the NCEP/NCAR reanalysis data set. PIs of Kanpur and
Nainital AERONET sites, Brent Holben, S. N. Tripathi, R. P. Singh, and
P. Pant are acknowledged for their efforts in maintaining the operation of
the instruments. We thank the reviewers for their comments, which helped
us to improve the manuscript.
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S. Dey and L. Di Girolamo, Department of Atmospheric Sciences,
University of Illinois at Urbana‐Champaign, 105 S. Gregory St., Urbana,
IL 61801, USA. ([email protected])
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