Validation of space-born Moderate Resolution Imaging Spectroradiometer remote sensors aerosol products using application of ground-based Multi-wavelength Radiometer

Available online at www.sciencedirect.com

Advances in Space Research 50 (2012) 1391–1404 www.elsevier.com/locate/asr

Validation of space-born Moderate Resolution Imaging Spectroradiometer remote sensors aerosol products using application of ground-based Multi-wavelength Radiometer Raj Paul Guleria a, Jagdish Chandra Kuniyal a,⇑, Pitamber Prasad Dhyani b a

G.B. Pant Institute of Himalayan Environment and Development, Himachal Unit, Mohal-Kullu 175126, India b G.B. Pant Institute of Himalayan Environment and Development, Kosi-Katarmal, Almora 263643, India Received 22 August 2010; received in revised form 24 June 2012; accepted 2 July 2012 Available online 11 July 2012

Abstract The measurements of aerosol optical properties were carried out during April 2006 to March 2011 over Mohal (31.9°N, 77.12°E) in the northwestern Indian Himalaya, using the application of ground-based Multi-wavelength Radiometer (MWR) and space-born Moderate Resolution Imaging Spectroradiometer (MODIS) remote sensors. The average (±standard deviation) values of aerosol optical ˚ ngstro¨m exponent and turbidity coefficient during the entire measurement period were 0.25 ± 0.09, depth (AOD) at 500 nm, A 1.15 ± 0.42 and 0.12 ± 0.06 respectively. About 86% AOD values retrieved from MODIS remote sensor were found within an uncertainty limit (Dspk = ±0.05 ± 0.15spk). In general, the MWR derived AOD values were higher than that of MODIS retrieval with absolute difference 0.02. During the entire period of measurement space-born MODIS remote sensor and ground-based MWR observation showed good correspondence with significant correlation coefficient 0.78 and root mean square difference 0.06. For daily observations the relative difference between these two estimates stood less than 9%. However, satellite-based and ground-based observation showed good correspondence, but further efforts still needed to eliminate systematic errors in the existing MODIS algorithm. Ó 2012 Published by Elsevier Ltd. on behalf of COSPAR. ˚ ngstro¨m parameters; MODIS; Single scattering albedo Keywords: Aerosol optical depth; A

1. Introduction The climate change is one of the most burning issues over the globe and the aerosols have great potential to bring out the changes in climatic conditions at regional and global scale (IPCC, 2007; Jayaraman et al., 2010). Aerosols affect the solar radiation budget of earthatmosphere system by scattering and absorption processes (Charlson et al., 1992), by altering atmospheric thermodynamics and cloud microphysics (Twomey, 1977; Ramanathan et al., 2001). Badarinath et al. (2008) also ⇑ Corresponding author. Tel.: +91 9418154941/1902 225329; fax: +91 1902 226347. E-mail addresses: [email protected] (R.P. Guleria), jckuniyal@rediffmail.com (J.C. Kuniyal), [email protected] (P.P. Dhyani).

0273-1177/$36.00 Ó 2012 Published by Elsevier Ltd. on behalf of COSPAR. http://dx.doi.org/10.1016/j.asr.2012.07.002

studied the influence of aerosols on reducing the ultra-violet (UV) radiation and UV-Index. Aerosol optical depth (AOD) is considered to be one of the most important optical properties of aerosols, which is directly related to the magnitude of attenuation of direct solar radiation by scattering and absorption processes in the atmosphere (Ranjan et al., 2007). The other parameters of interest responsible to influence the solar radiation budget of the earth-atmo˚ ngstro¨m exponent (a) and turbidity coeffisphere are the A ˚ ngstro¨m exponent provides some basic cient (b). A information on the aerosol size distribution and the turbidity coefficient is a measure of the total aerosol load in the vertical and equal to AOD at 1 lm wavelength. The ˚ ngstro¨m exponent is widely being used in the study of A ˚ ngstro¨m, aerosol research since its early publications (A 1929, 1930). In later publications this parameter was

1392

R.P. Guleria et al. / Advances in Space Research 50 (2012) 1391–1404

mainly applied to the description of the spectral behavior ˚ ngstro¨m, of the atmospheric extinction and transmission (A 1961, 1964), it is now also being applied to a variety of similar optical properties of aerosols such as atmospheric scat˚ ngstro¨m tering or backscattering coefficients. The use of A exponent has become most popular in atmospheric aerosol research due to simpler form of the respective equation, because it enables to interpolate or to extrapolate aerosol ˚ ngstro¨m exponent optical properties. Typically values of A range from greater than 2.0 for fresh biomass burning smoke particles, which are dominated by accumulation mode aerosols (Kaufman et al., 1992) to nearly zero for desert aerosols dominated by coarse mode aerosols (Holben et al., 1991). Recent studies have shown useful ˚ ngstro¨m exponent measurements for applications of A characterization of aerosol physical and radiative properties. The a computed from the spectral AOD values have been used to characterize biomass burning aerosols in South America and Africa (Reid et al., 1999), urban aerosols in North America (Eck et al., 1999), Europe (Dubovik et al., 2002) and Southeastern Asia (Kim et al., 2004; Latha and Badarinath, 2005), maritime aerosol component in islands (Smirnov et al., 2002) and desert dust aerosols in the Sahara and East Asia (Eck et al., 1999, 2005). The char˚ ngstro¨m acterization of aerosol using application of A exponent on regional as well as global scale is important in number of ways as it provides general information about source emission strength based on the aerosol size distribution, it is also important to improve the accuracy of satellite retrieval algorithms, which rely on assumptions about the optical properties of different aerosol types (Kaufman et al., 1997; King et al., 1999). Extensive analysis on the ˚ ngstro¨m exponent and its spectral variations have been A made by Kaskaoutis et al. (2007) while Jacovides et al. (2005) have analyzed both ‘a’ and ‘b’, even in different spectral bands. Aerosols generated at one place are transported over long distances by the action of wind and produce consequent effects at locations far away from the source (Prospero et al., 1983; Hoppel et al., 1990). In this regard, air trajectories play an important role in transporting natural and anthropogenic aerosols (Moorthy et al., 1999; Satheesh et al., 2001; Kaskaoutis et al., 2010; Sharma et al., 2010). The transport pathways of the air pollutants and aerosol over India showed the dependence of the trajectory on the air-mass altitude (Badarinath et al., 2007, 2009). In recent, the characterization of urban aerosol become most popular among the scientists of the world, due to the rapid growth of both population and economy activities, with associated increases in fossil fuel combustion, and the possible regional and global climatic impacts from source emissions. In addition, with growing activities in the form of forest fires and agriculture crop residue burning, emitting heavy loadings of biomass burning smoke. The scientific interest in the impact of biomass burning on aerosol science research grew when it became

evident that it is an important source of atmospheric pollution and its products could affect large areas of the world as a consequence of long-range transport (Fearnside et al., 2005). In tropical continents, biomass burning aerosols is a major source of atmospheric pollution (Crutzen and Andreae, 1990). Both urban and biomass burning aerosol significantly alter the atmospheric thermodynamics which leads to regional climatic implications (IPCC, 2007). Over Indian subcontinent, heavy loadings of biomass burning aerosols is of considerable importance due to significant reduction in surface reaching solar radiation (Badarinath et al., 2009). In recent years, there has been growing concern that aerosols from natural and anthropogenic sources might be one of the most important factors causing regional disturbances in existing weather and climatic conditions (Ramanathan et al., 2005; Lau et al., 2006; IPCC, 2007; Gautam et al., 2009). Thus, to understand their impacts on existing weather and climatic conditions, the accurate quantification of aerosol distribution on regional as well as global scale must be characterized. The accurate information of aerosol distribution from different parts of the globe is very rare. In this direction Moderate Resolution Imaging Spectroradiometer (MODIS) remote sensors onboard the Earth Observing System’s (EOS) Terra and Aqua polar-orbiting satellites have provided a powerful means for assessing the distribution of aerosols from different parts of the globe (Kaufman et al., 2002; King et al., 1999). Recently, Remer et al. (2005) have made comprehensive validation exercise over the land on a global scale using broadly distributed Aerosol Robotic Network (AERONET). But due to a few AERONET stations, the global validation is not a representative measure of the applicability of MODIS aerosol products, this is due to different aerosol types, wide variety of ecosystems and complex surface conditions. However, due to lack of large-scale and longterm data, the accuracy of the MODIS aerosol products is even more uncertain over the globe. Therefore, regional characterization of aerosols on a global scale can provide a basic data to understand their impacts on existing weather and climatic conditions. In India, the systematic studies on aerosols were initiated under Indian Space Research Organization-Geosphere Biosphere Programme (ISROGBP) using broadly distributed Multi-wavelength Radiometer (MWR) network. Under the aegis of ISRO-GBP there are nationwide nearly 30 MWR network stations representing different ecological and geographic regions. With these efforts, the preliminary results on aerosols were published by some of the scholars (Moorthy et al., 1998; Narasimhamurthy et al., 1998; Parameswaran et al., 1999; Beegum et al., 2008; Kaskaoutis et al., 2009; Kuniyal et al., 2009). In order to improve upon the accuracy of the MODIS data set and to build a long-term database for climatology studies, detailed validation of MODIS AOD by using the ground-based MWR and MICROTOPS, especially the sunphotometers from the AERONET have been

R.P. Guleria et al. / Advances in Space Research 50 (2012) 1391–1404

performed by many authors over Indian sub-continent (Remer et al., 2005; Tripathi et al., 2005; Prasad et al., 2007; Aloysius et al., 2008; Vinoj et al., 2008; Misra et al., 2010; Pathak et al., 2010; Sharma et al., 2010). Validation of MODIS AOD product has significantly been improved but still has a room for further improvement according to recent ground-based measurements. The present experimental site Mohal (31.9°N, 77.12°E, 1154 m amsl) is located in the Kullu valley of northwestern part of the Indian Himalaya. It is a semi-urban experimental site which falls under the ISRO-GBP network in India. This part of the Indian Himalaya has attracted a significant scientific interest due to transport of ever increasing anthropogenic aerosols from the Indo-Gangetic Plain (IGP) and natural aerosols from the Thar Desert to the Himalaya and their resultant adverse impact on glaciers (Gautam et al., 2009; Prasad et al., 2009). In this context, the present study on aerosols from the northern Indian Himalaya stands one of the most important studies among others. This part of the Indian Himalaya is topographically very fragile and ecologically very delicate. Due to a variety of ecosystems, surface conditions, and aerosol properties, in-situ observations of aerosols in this part of the Himalaya have been so far very rare. The validation of satellite products with the ground-based observations has therefore become important among the aerosol and climate change studies. By analyzing the same, the present analysis focus on the impact of aerosol types upon MODIS AOD retrieval uncertainties. However, short-term (March to May 2006) information on aerosol optical parameters using ground-based observations over Mohal were also presented and discussed in the earlier paper (Kuniyal et al., 2009). This is the first time that the long-term data (April 2006 to March 2011) from MODIS Level-2 product over Mohal are being reported. The reported results from present experimental site Mohal in integration with the results from different parts of the globe may promote the scientific community to advance their research and further improvement in MODIS remote sensor algorithm. It is also hoped that the results from present study will constitute the base for further aerosol research over the area using groundbased measurements.

1393

different wavelengths as a function of solar zenith angle (SZA). The radiation passes through a field of view (FOV) limited to 2° using lens pin-hole detector optics, so that the effect of diffuse radiation entering into FOV on the retrieved optical depths may be minimized. However, the light scattered from dust particles contributes even to this small angle thereby reducing AOD. Estimates have shown that the error arises due to diffused scatter radiation is always less than 0.02 (Moorthy et al., 1989, 1991; Gogoi et al., 2008). The instrument is semi-automatic in nature. Before to track the sun, the optical unit is mechanically adjusted on equatorial mount and allowed to move at every 12s around the orthogonal axis with an angular speed equal to 0.05° in 12s in order to keep the MWR always aligned towards the sun. The raw data are obtained through MWR when no visible clouds appear around solar disc and are analyzed following the Langley technique to deduce the total columnar optical depth (Shaw et al., 1973; Moorthy et al., 1989, 1991; Gogoi et al., 2008). The attenuation of solar radiation along the slant path depends on the scattering and absorption of radiation as described by the Lambert-Beer law, Ek ¼ Eok expðmsÞ;

ð1Þ

where Ek is the ground reaching solar radiation, Eok is the solar radiation incident at the top of the atmosphere, m is the relative air-mass as a function of SZA 6 70° and s is the total columnar optical depth that stands for different atmospheric extinction processes, i.e. scattering by air molecule (srk), aerosols (spk), and due to gaseous absorption (sak) (Moorthy et al., 1989, 1991), which is explained as under, s ¼ srk þ sak þ spk ;

ð2Þ

In logarithm scale Eq. (1) yields a straight line equation, ln Ek ¼ ln Eok  ms;

ð3Þ

Therefore, a plot of lnEk versus m, i.e. Langley plot, yield a straight line of slope s (Moorthy et al., 1999). The spectral aerosol optical depth spk estimated for each observation day using Eq. (2) (Moorthy et al., 1996), spk ¼ s  ðsrk þ sak Þ;

ð4Þ

2. Instrumentation, data and analysis

The errors and uncertainties in the retrievals of AOD at any wavelength for the most unfavorable case (i.e., AOD = 1) are given by,

2.1. Ground-based measurements

r2spk ¼ r2s þ r2srk þ r2sak ;

The ground-based AOD measurements are carried out during April 2006 to March 2011 under cloud free days using MWR designed and developed at Space Physics Laboratory, Thiruvananthapuram following the principle of filter wheel radiometer (Shaw et al., 1973; Moorthy et al., 1999). The MWR measures spectral extinction of ground reaching direct solar flux at ten wavelengths, focused at 380, 400, 450, 500, 600, 650,750, 850, 935 and 1025 nm with full width half maximum band at the range of 6 to 10 nm at

where term r stands for atmospheric extinction crosssection. The error in rs remains always < 0.02 which arises due to 1-sec resolution in time for air-mass calculation, statistical regression analysis and variation in the Langley intercept. The error arises in rsrk may vary by 1% and values remain <0.01. Ozone models also vary which may contribute uncertainty of 0.003 at wavelengths between 500 to 650 nm. rspk may therefore have a maximum uncertainty of 0.03. For the detailed analysis of MWR data and the

ð5Þ

1394

R.P. Guleria et al. / Advances in Space Research 50 (2012) 1391–1404

error involved in it, see also Moorthy et al., 1999; Sagar et al., 2004; Saha et al., 2005 and Gogoi et al., 2008. Spectral aerosol optical depth (spk) contains information pertaining to aerosol size distribution. In describing the general information about aerosol size distribution ˚ ngstro¨m proposed an empirical formula (A ˚ ngstro¨m, A 1929), spk ¼ bka ;

ð6Þ

where k is expressed in lm. Logarithm scale of Eq. (6) yields a straight line equation, ln spk ¼ ln b  a ln k;

ð7Þ

a and b are computed in the wavelength interval 3801025 nm using Eq. (7) by evolving a linear least square fit in lnspk versus lnk relationship. Therefore, lnspk versus lnk, yield a straight line of slope a and intercept lnb. The optical depth at wavelength 935 nm was not included in the a and b calculations via the least square method due to strong water vapor absorption band; about 75% to 90% of measured optical depth at this wavelength is due to atmospheric water vapor (Moorthy et al., 1991). 2.2. Satellite-based measurements 2.2.1. MODIS data MODIS is a remote sensor onboard the EOS Terra and Aqua polar-orbiting satellites. Both satellites operate at an altitude of 705 km, with Terra on a descending orbit (southward) over the equator about 10:30 local sun time, and Aqua on an ascending orbit (northward) over the equator about 13:30 local sun time (Remer et al., 2005). Terra and Aqua satellites are providing an opportunity to study aerosol from space with high accuracy nearly at a global scale (Yu et al., 2004; Remer et al., 2005). MODIS acquires daily global data at 36 spectral bands from visible to thermal infrared spectral bands. The spatial resolution of these spectral bands (pixel size at nadir) is 1000 m, 500 m, and 250 m with 29, 5, and 2 spectral bands respectively. These satellites are providing data since February, 2000 and July, 2002 respectively. In the present study, MODIS Level-2 daily AOD data at 550 nm obtained from MOD04_L2 (data collected from Terra platform) and MYD04_L2 (data collected from Aqua platform) products are used. MODIS have at 550 nm 500 m resolution. This results in a 2020 pixel boxes for 1010 km resolution. The geolocation of each output pixel is computed from aggregation of 1010 boxes of Level-1B 1-km input, taking the average of the four central (column, row) pairs such as: (5,5), (5,6), (6,5), (6,6) 1km Level-1B input pixels. The AOD derived from MWR separately two different times of a day (within ±1 h of Terra and Aqua satellite overpass); usually forenoon (corresponding to Terra satellite observations) and afternoon (corresponding to Aqua satellite observations) hours are averaged to be considered as a single data set. The data collected from Terra and Aqua satellites as a representative of

forenoon and afternoon parts of the day, respectively are also averaged to be considered a single data set. The similar techniques to mix data set are also adopted by Aloysius et al. (2008) and Kedia and Ramachandran (2008) to obtain daily mean AOD. Remer et al. (2005) confirmed that the AOD values retrieved from MODIS are accurate within an uncertainty limit of Dspk = ±0.05 ± 0.15spk over the land, where spk is the ground-based AOD value. While comparing AOD from ground-based and satellite observations, the error occurred with a difference in wavelength dependence is recovered from linear interpolation technique to calculate the AOD by MWR at 550 nm using Power Law (Prasad et al., 2007) as follows,  a 550 AOD550nm ¼ AOD500nm ; ð8Þ 500 Where a is obtained in visible spectrum 400–750 nm. The root mean square difference (RMSD), mean absolute bias difference (MABD), relative difference (RD) and expected error for number of data pairs (n) of AOD between MWR and MODIS are calculated using following equations, sffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi 1X 2 RMSD ¼ ðAODMWR  AODMODIS Þ ; ð9Þ n n MABD ¼

1X jAODMWR  AODMODIS j; n

ð10Þ

and %RD ¼

jAODMWR  AODMODIS j  100; AODMWR

Expected error ¼

jAODMWR  AODMODIS j ; 0:05  0:15 AODMWR

ð11Þ ð12Þ

3. Results and discussion The measurements of aerosol optical properties are carried out over Mohal during April 2006 to March 2011 under cloud free days. The average (±standard deviation) value of AOD at 500 nm during the entire measurement period observed to be 0.25 ± 0.09 and found to be in the range 0.10–0.55. Relating to local seasonal basis namely: summer (April–July), monsoon (August–September), autumn (October–November) and winter (December– March), combined for entire period of measurement, the seasonal highest AOD at 500 nm noticed during summer (0.32 ± 0.09). The summer is followed by monsoon (0.25 ± 0.07), autumn (0.23 ± 0.07) and winter (0.22 ± 0.08). For retrieval of aerosol size distribution and total aerosol loading in a columnar environment, a and b are computed in the wavelength band 380–1025 nm. The annual daily averaged value of a and b equated over the entire period of observations are obtained as 1.15 ± 0.42 and 0.12 ± 0.06 respectively. The seasonal variations

R.P. Guleria et al. / Advances in Space Research 50 (2012) 1391–1404

˚ ngstro¨m parameters, a and b during April Fig. 1. Seasonal variation in A 2006 to March 2011. The vertical error bars represent the standard deviation from mean of the observations.

˚ ngstro¨m parameters are shown in Fig. 1. When a among A values increase b values decrease and vice-versa. This feature examines after establishing an inverse relationship between a and b. The correlation (r = 0.72) between these parameters stood to be more than 35 times the value of probable error, i.e., 0.02. This indicates a considerable neg˚ ngstro¨m parameative correlation between a and b. The A ters are found to show strong seasonal variability. The a values combined for entire data period of measurement (April 2006 to March 2011) are highest in autumn (1.45 ± 0.31) and winter (1.26 ± 0.40), and lowest in summer (0.80 ± 0.33) and monsoon (0.98 ± 0.34). Extensive measurements over different geographical environments have shown that the values of a close to zero indicate size distributions dominated by coarse mode aerosols having radii P0.5 lm, that are typically associated with desert aerosols (Eck et al., 1999; Holben et al., 1991; Schuster et al., 2006). The high values of a > 2 indicate size distributions dominated by fine mode aerosols having radii 6 0.5 lm, that are typically associated with anthropogenic aerosols or/fresh biomass burning smoke particles (Kaufman et al., 1992; Behnert et al., 2007). It can be seen ˚ ngstro¨m that (see Fig. 1) during summer the values of A exponent are very low which indicates that the size distri-

˚ ngstro¨m exponent during April 2006 Fig. 2. Frequency distribution of A to March 2011.

1395

bution is dominated by coarse mode aerosols. During ˚ ngstro¨m exponent are autumn and winter, the values of A higher this indicating that the size distribution is dominated by fine mode aerosol. To examine the distribution of a over the seasons combined for the entire period of observations, the frequency of occurrence of this parameter is estimated and shown in Fig. 2. This shows that during summer season about in 78% cases the values of a are <1.0; with a median of 0.64, indicates the dominant influence of a coarse mode aerosol type, which becomes rather weak during autumn and winter seasons. During autumn about in 62% cases the values of a are in the range 1 6 a < 1.5; with a median of 1.3, further in 32% cases are P1.5; with a median of 1.8, which shows strong dominance of fine mode aerosols. However, during winter the dominance of fine mode aerosols becomes rather weak as compared to autumn. As per usual practice during autumn and winter seasons, the degree of fuel wood burning in the valley reached at its maximum level and here remained the main source of fine aerosols. Thereafter, with the decrease in anthropogenic activities like burning fuel wood, coal etc., there is a sharp reduction in fine size aerosols up to April. Air back-trajectory analysis in conjunction with MODIS and Cloud-Aerosol Lidar with Orthogonal Polarization (CALIOP) data reveals that the frequency of dust storm (Gautam et al., 2009; Kuniyal et al., 2009) and forest fire events (Vadrevu et al., 2012) increase after March in the areas located in the foothills of the Indian Himalaya. The smoke aerosol released as a result of forest fire events get mixed with desert dust and thereby transport towards the present experimental site. A typical example of polluted dust transport taken on 8 May 2006 and 8 October 2010 is shown in Fig. 3. Red green and blue (RGB) “true color” images created from the Level-1B data for the events, examined in this study to document the existence and transport of air pollution, specifically smoke, haze and dust. This is further illustrated with the Level-2 image of the AOD. The transport of large plumes of smoke mixing with dust towards the present experimental site from over the plains of Punjab state of India and IGP can be easily noticed (see Fig. 3a). This is the smooth slightly yellowish-white plume over the northwestern part of India. The color band scale shown in Fig. 3b indicates that on 8 May 2006 this polluted dust aerosol transport has increased AOD in the areas located in the foothills of the Indian Himalaya. The signature of biomass burning released smoke particles transport from the plains of Punjab state of India and IGP towards Mohal can also be distinguished from Fig. 3c. This study reveals that on 8 October 2010, the AOD in the foothills of the northwestern Indian Himalaya is mainly attributed to smoke particles (Fig. 3d). The studies of polluted dust aerosol transportation from the northwestern desert part of Pakistan and the Arabian Peninsula towards the Himalayan Gangetic region also have been reported by other scholars (Middleton, 1986; Prospero et al., 2002; Singh et al., 2004). The long-range transport causes the sharp

1396

R.P. Guleria et al. / Advances in Space Research 50 (2012) 1391–1404

Fig. 3. A typical examples of polluted dust transport investigated using MODIS images taken on: (a–b) 8 May 2006 and (c–d) 8 October 2010. The red green blue (RGB) ‘‘true color’’ images created from the MODIS Level-1B data are on the left panel and Level-2 AOD images are on right panel. (for interpretation of the references to colour in this figure legend, the reader is referred to the web version of this article.)

increase in coarse size particles which is noticed after March and these events of polluted dust transportation continued up to August. On the other hand, there was continuous increase in a after April. The major reason for this is considered to be high degree of human interference in the form of unregulated tourism and biomass burning which peak around summer season (Vadrevu et al., 2012). After September, the influence of human interferences again starts to increase due to International Kullu Dussehra festival which every year almost falls in October. After getting over Kullu Dussehra, the festival influence minimizes but due to onset of winter, fuel wood burning for cooking and heating purposes increases, as a result fine size aerosols increased and/or remained constant from October to December. Turbidity coefficient (b) remains low (<0.2) for most of the seasons. The highest aerosol loading occurs during summer in comparison to other seasons. The detail results of MODIS AOD product validation over Mohal for the entire period of measurement and different seasons combined for entire data period of measurement are shown in Table 1. The slopes, intercepts and

correlation coefficients of linear regressions are used for analyzing accuracy and error sources, with consideration of factors probably affecting the exactness of MODIS derived AOD. Figs. 4 and 5 represent the scatter plot between AOD (at k = 550 nm) derived from MODIS and MWR for the entire period of measurement and seasons combined for entire period of measurement respectively. The MODIS retrieved data were sorted according to MWR AOD. The dashed line is the 1:1 line and the error bars denote the expected uncertainty calculated from prelaunch analysis over land (Dspk = ±0.05 ± 0.15spk) (Remer et al., 2005). Figs. 4 and 5 showed overall good agreement for entire as well as seasonal observations between MODIS derived and MWR observed AOD at common wavelength (k = 550 nm). The linear regression equation between MODIS derived and MWR observed AOD at 550 nm for daily observations during entire data period of measurement expressed as, AODMODIS ¼ 0:79AODMWR þ 0:03

R.P. Guleria et al. / Advances in Space Research 50 (2012) 1391–1404

1397

Table 1 Comparison between AOD (at k = 550 nm) derived from MODIS and MWR. The seasons are combined for entire period (April 2006 to March 2011) of measurement. Case

n

AODMWR

AODMODIS

RMSD

MABD

% RD

% Retrieval

Slope

Intercept

For entire period of measurement (April 2006 to March 2011) Summer Monsoon Autumn Winter

262

0.23 ± 0.08

0.21 ± 0.09

0.06

0.05

9

86

0.79

54 41 61 106

0.30 ± 0.08 0.23 ± 0.07 0.20 ± 0.07 0.20 ± 0.07

0.29 ± 0.09 0.21 ± 0.10 0.16 ± 0.06 0.18 ± 0.06

0.06 0.07 0.08 0.06

0.05 0.04 0.06 0.04

4 8 18 9

88 80 80 91

0.83 0.98 0.51 0.57

r

Exp error (%)

0.03

0.78

24

0.04 0.01 0.06 0.07

0.72 0.72 0.66 0.70

11 18 46 25

Note: n = number of data pairs; AODMWR = MWR AOD; AODMODIS = MODIS AOD; RMSD = root mean square difference; MABD = mean absolute bias difference;% RD = percentage relative difference;% retrieval = percentage retrieval; r = correlation coefficient; Exp error = expected error.

In comparison with global validation results, reported by Levy et al. (2007), AODMODIS ¼ 1:01AODAERONET þ 0:02 MODIS AOD retrieval has higher errors over Mohal. For daily observations, Remer et al. (2005) reported the expected accuracy of MODIS AOD in respect to AERONET derived AOD over the Indian subcontinent around 90%. This study found that for daily observations in Mohal region about 86% of the AOD values retrieved from MODIS are found within the expected uncertainty (Dspk = ±0.05 ± 0.15spk), whereas monthly mean and seasonal mean values fall within the expected error. The time series of percentage difference in MWR derived and MODIS retrieved AOD is depicted in Fig. 6. The data point lies on or around zero line indicate a perfect MODIS AOD retrieval. The positive value of the percentage rela-

Fig. 4. Comparison of MODIS and MWR derived AOD at common wavelength (k = 550 nm), encompassing 262 points during the entire measurement period (April 2006 to March 2011). The dark solid line represent the slope of linear regression, the error bars indicate the range of MODIS expected accuracy (Dspk = ±0.05 ± 0.15spk) from the 1:1 line.

tive difference implies an underestimation by MWR derived AOD. The percentage relative difference between MODIS retrieval and MWR observations at 550 nm is 9%, showing a systematic bias in which MODIS is underestimated by MWR AOD. It is observed that during autumn and winter period the large number of values are positive, this indicates that during these period the MWR AOD values most of the days were higher as compared to MODIS AOD. The overestimation strengthens the positive offset under low to moderate values of AOD (see Figs. 4 and 5). It is obvious from the Figs. 4 and 5 that the MODIS AOD retrieval has good correlation with MWR measurements. For daily observations, the RMSD, MABD, correlation coefficient and intercept are 0.06, 0.05, 0.78 and 0.03 respectively. However, MODIS seems to underestimate its AOD compared to MWR; the regressed slope is 0.79 for daily observations. The scatter plots (see Figs. 4 and 5) of ground-based AOD measurement with the corresponding values are derived from MODIS which showed good agreement (see Table 1) for daily as well as seasonal plotted observations. These agreements enabled us to validate the MODIS AOD data. However, MODIS seems to underestimate its AOD compared to MWR, the regressed slopes came below 1.0 for daily and seasonal observations. The deviation of the slopes from unity of correction line 1:1 indicates a systematic bias, which could be caused due to error in instrument calibration and/or inappropriate choice of the aerosol model in the MODIS retrieval algorithm, and some other factors (Chu et al., 2002; Zhao et al., 2002; Remer et al., 2005). The intercepts less than 0.1 are always negative, implying a small amount of over correction for the surface reflectance in MODIS channel at 550 nm (Misra et al., 2008). Relatively, positive offset of MODIS AOD over Mohal, with intercept 0.03 (April 2006 to March 2011), indicate poor estimates in surface reflectance, especially in autumn (intercept  0.06) and winter season (intercept  0.07). In terms of slope, small deviations from unity of correction line 1:1 indicate a systematic bias between MODIS and MWR observations over Mohal. The assumed single scattering albedo (SSA) in MODIS algorithm may differ slightly from the actual SSA in areas, resulting in a systematic bias between MODIS and ground-based observations (Ichoku et al., 2003). The similar systematic deviation

1398

R.P. Guleria et al. / Advances in Space Research 50 (2012) 1391–1404

Fig. 5. Comparison of MODIS and MWR derived AOD at common wavelength (k = 550 nm), encompassing 54, 41, 61, and 106 points during summer, monsoon, autumn, and winter seasons respectively. The seasons are combined for entire period (April 2006 to March 2011) of measurement. The dark solid line represent the slope of linear regression, the error bars indicate the range of MODIS expected accuracy (Dspk = ±0.05 ± 0.15spk) from the 1:1 line.

between satellite and ground-based observations are also reported by numerous authors from different parts of the globe indicating deviation from unity of the slope and representing systematic biases; they were mainly due to the aerosol model assumptions in MODIS algorithm (e.g. Tripathi et al., 2005; Gogoi et al., 2008; Misra et al., 2008; Yang et al., 2010; Sharma et al., 2012). The MODIS AOD has been validated by AERONET sunphotometer measurements around the world with an estimate of 25% error (Chu et al., 2002). Here, we have mainly focused on the results reported by numerous authors from the Asian regions. Sanya (18.23°N, 109.52°E) is a tropical coastal site in China, where Yang et al. (2010) have made attempt to

validate MODIS retrieved AOD from ground-based MICROTOPS II observations during July 2005 to June 2006. The slope, intercept and correlation coefficient of linear regressions were as, AODMODIS ¼ 0:80AODMICROTOPS þ 0:005;

r ¼ 0:91

Yang et al. (2010) reported that the MODIS AOD retrievals at 550 nm have good correlation with the measurements from the MICROTOPS II sunphotometer. Dibrugarh (27.3°N, 94.6°E) is located in the northeastern part of the Indian Himalayas. From where Gogoi et al. (2008) have reported good correspondence between AOD

R.P. Guleria et al. / Advances in Space Research 50 (2012) 1391–1404

1399

Fig. 6. Time series of percentage relative difference between AOD (at k = 550 nm) derived from MODIS and MWR during April 2006 to March 2011.

derived from MODIS and MWR observations during 2003 to 2005 with RMSD and MABD 0.07 and 0.027 respectively. The estimates show a linear relationship as, AODMODIS ¼ 0:79AODMWR þ 0:07;

r ¼ 0:84

Kanpur (26°N, 80°E) is an industrial city lying in the Ganga Basin in the northern part of India and is an important experimental site under the AERONET network (Tripathi et al., 2005). Tripathi et al. (2005) establish the relationship between MODIS and AERONET derived AOD during 2004 with absolute difference 0.4 as, AODMODIS ¼ 0:69AODAERONET þ 0:12;

r ¼ 0:84

Patiala (30.32°N, 76.4°E) is located in the centre of the Agrarian region of the northwestern India, where Sharma et al. (2012) conducted the study of aerosol characteristics during April-June 2010 using ground-based as well as satellite-based remote sensors. Here, the results presented through the linear regression equation as, AODMODIS ¼ 1:0AODMICROTOPS þ 0:005;

r ¼ 0:92

MODIS derived AOD was also compared with the ground-based observations from MICROTOPS from over Ahmedabad (23.03°N, 72.53°E), a semiarid location in the western India. The regression results evaluated during the observation period 2004–2005 presented as (Misra et al., 2008), AODMODIS ¼ 0:69AODMICROTOPS þ 0:03;

r ¼ 0:78

It is worthwhile to mention here that the validation efforts from different parts (except global, efforts for example, Ichoku et al., 2002; Levy et al., 2005; Remer et al., 2005) of the globe have been made by numerous scientists. The slopes, intercepts and correlation coefficients of linear

regressions are used as major techniques for analyzing accuracy and error sources adopting the similar procedure as followed by Ichoku et al. (2002). Fig. 7(a) and (b) show the monthly mean and seasonal mean AOD values on daily basis at 550 nm retrieved from MODIS and MWR over Mohal during April 2006 to March 2011. It can be seen from Fig. 7(a) and (b) that for each month and season, MODIS AOD values are mostly matched with corresponding MWR values. But the MODIS AOD values are relatively less than the MWR values except a couple of months. The monthly mean and seasonal mean AOD showed very similar pattern. The absolute difference between the MODIS AOD and the MWR AOD for monthly and seasonal observations are obtained as 0.02. The absolute difference between MODIS and MWR AOD is found to be low during summer (0.00–0.03) and monsoon (0.01–0.03) seasons and much higher during autumn (0.02–0.07) and winter (0.06) seasons. During autumn and winter seasons, the MWR AOD values observed are much higher as compared to MODIS AOD whereas during summer and monsoon seasons, MODIS and MWR observations approximately are matching. During summer and monsoon seasons Mohal is mainly influenced by dust aerosol which are transport from the western desert (Kuniyal et al., 2009). Further, in summer and monsoon the values of a are very low (<1.0) suggesting that the coarse mode aerosols are of dominating nature. As the international Kullu Dussehra festival approaches, the anthropogenic activity such as vehicles influx, burning of fuel wood activities start to increase and reaches its peak in October, whose impacts continue for a month or more. After getting over Kullu Dussehra, the festival influence minimizes, but due to onset of winter the emission of fine size aerosols from fuel wood burning in the valley reached at its maximum level. The agricultural waste burning in the IGP and Punjab is a significant source

1400

R.P. Guleria et al. / Advances in Space Research 50 (2012) 1391–1404

Fig. 7. Variations in monthly and seasonal averaged AOD retrieved from MODIS and MWR at common wavelength 550 nm from April 2006 to March 2011, the error bars represent the standard deviation of AOD.

of smoke aerosols during October-November (Badarinath et al., 2009; Sharma et al., 2010). Therefore, in addition to local sources the transport of smoke aerosol from these regions also contribute to the existing columnar AOD over Mohal. The SSA provides important information about the nature of the particles. Its values range from 0 (for purely absorbing particles) to 1 (for purely scattering particles). In the present study, the SSA is estimated using Optical Properties of Aerosol and Clouds version (OPAC) database by adopting the external mixing approach (Hess et al., 1998). The zero-order approximation is performed by adopting polluted continental aerosol types (autumn and winter months) and desert aerosol types (summer and monsoon months) in the OPAC database. The seasonal variation in SSA is shown in Fig. 8. The values of SSA are found very low during autumn and winter season as compared to other seasons. This indicates that during these seasons the concentration of absorbing nature of particles such as soot, smoke, polluted dust etc., are significant in the atmosphere. This study suggests that during autumn and winter seasons in Mohal region, the aerosols are in the form of smoke produced from biomass burning. Through

the MODIS airborne-simulator data acquired during Smoke Carbon Aerosol and Radiation-Brazil (SCAR-B), it was found that over smoke aerosol regions the value of SSA is very sensitive to the retrieved AOD (Chu et al., 1998). Ichoku et al. (2003) conducted ground-based observations on aerosol properties during the Southern African

Fig. 8. Seasonal variation in SSA during April 2006 to March 2011. The vertical error bars represent the standard deviation from mean of the observations.

R.P. Guleria et al. / Advances in Space Research 50 (2012) 1391–1404

Regional Science Initiative (SAFARI-2000) experiment. The investigations were mainly focused on the behavior of MODIS aerosol product especially in regard to the smoke aerosol produced due to biomass burning. Their study observed that during the biomass burning period, the AOD values predicted by MODIS were lower than the ground-based observations. This was attributed to the SSA. During summer and monsoon period, the Mohal site showed good comparison between MODIS and MWR observations. During this period, Mohal remained mainly influenced by dust aerosols. So the MODIS algorithm considers the aerosol type to be as dust. The main difference in the retrieval of AOD is observed during autumn and winter period. In the light of the above discussion, this difference in the retrieval of MODIS AOD is attributed to the presence of smoke aerosol produced by biomass burning. The SSA for black carbon aerosols due to biomass burning is significantly lower than that of dust aerosols. This in turn reduces the surface albedo. Myhre et al. (2005) reported significant reduction in surface albedo in fire prone areas over the African continent typically in the ranges of 5–8%. As a result, the surface reflectance reduced very significantly by a factor of around 2. The intercept estimate over Mohal is less than 0.1 which implies a small amount of over correction for the surface reflectance in MODIS channel at 550 nm. In light of the above results, this study further reveals that the

1401

surface reflectance in the MODIS aerosol model is over estimated which results in underestimation of MODIS AOD. Hence, to recover systematic bias, the existing retrieval algorithm needs to be modified in view of the changing aerosol optical properties especially during the biomass burning period (autumn and winter seasons). The frequency distribution of AOD derived from MWR and MODIS observations during April 2006 to March 2011 is shown in Fig. 9(a). The frequencies of MODIS AOD 6 0.10 are considerably higher than those of MWR AOD, and the frequency difference recorded as 7%. Corresponding to MODIS AOD 6 0.10 the relative absolute errors are high (Fig. 9(b)). This indicates large discrepancies in retrieval of MODIS AOD corresponding to smaller AOD values. However, in majority of cases the AOD derived from MWR and MODIS are in the range 0.10 < AOD 6 0.20 where the probability distribution difference is around 5%. While in other AOD ranges, the frequencies of MODIS AOD are relatively lower than those of MWR AOD. The frequency distribution patterns shown in Fig. 9(a) supported the underestimation of MODIS AOD in the range 0.10 < AOD 6 0.30 with respect to the MWR. The absolute errors in retrieval of MODIS AOD show a linear relationship with its AOD and found to be, Relative absolute error ¼ 0:48 AODMODIS þ 0:30 However, the correlation between MODIS AOD and relative absolute error is weak. During the measurement period (April 2006–March 2011) in the retrieval of MODIS AOD, the overall error in intercept and slope is found around 14%. In general, autumn and winter seasons the absolute difference between MODIS and MWR observations is found to be quite high with the absolute error of MODIS AOD around 22% and 15%, respectively of the absolute MODIS AOD values. During summer and monsoon season, the absolute error of MODIS AOD decreases and reaches around 5% and 6% respectively of the absolute MODIS AOD values. During autumn and winter seasons, the overestimation of the surface reflectance, due to the difference in the MODIS SSA model and actual value of SSA, is likely to be the main reason for the observed discrepancy. Comparison between AOD derived from MODIS and MWR over the Mohal shows good comparison during the observation period April 2006 to March 2011. However, further efforts to eliminate systematic errors in the existing MODIS algorithm are needed. 4. Conclusion

Fig. 9. (a) Frequency distribution of AOD derived from MODIS and MWR and (b) a scatter plot between the relative absolute error in MODIS AOD retrieval and MODIS AOD at 550 nm. The figures are representative of the entire period of measurement (April 2006 to March 2012).

The validation of space-born MODIS remote sensors aerosol products have performed over Mohal using ground-based Multi-wavelength Radiometer under cloud free days during April 2006 to March 2011. The summary of the results show that during the entire observation period the average (±standard deviation) values of AOD at ˚ ngstro¨m exponent and turbidity coefficient stood 500 nm, A to be 0.25 ± 0.09, 1.15 ± 0.42 and 0.12 ± 0.06 respectively.

1402

R.P. Guleria et al. / Advances in Space Research 50 (2012) 1391–1404

A comparative study of AOD values through MWR and MODIS depicts that for daily observations in Mohal region about 86% of the AOD values retrieved from MODIS are found within the expected uncertainty (Dspk = ±0.05 ± 0.15spk), whereas expected uncertainty in respect to AERONET derived AOD over the Indian subcontinent was 90%. MODIS and MWR derived AOD showed good agreement for daily observations with 0.06, 0.05 and 0.78 RMSD, MABD and correlation coefficient respectively. The absolute difference in AOD is observed as 0.02. In general the MWR derived AOD values were higher than that of MODIS retrieval. This is mainly attributed to assumed surface reflectance in MODIS algorithm. This study reveals that the surface reflectance in the MODIS aerosol model was over estimated which results in underestimation of MODIS AOD. The present study therefore suggests that the MODIS AOD retrievals are able to effectively characterize AOD distribution over Mohal. However, further efforts to eliminate systematic errors in the existing retrieval algorithm needs to be modified in view of the changing aerosol optical properties especially during the biomass burning period (autumn and winter seasons). Acknowledgements The authors thank to the Director, G.B. Pant Institute of Himalayan Environment and Development, KosiKatarmal, Almora, Uttarakhand for providing necessary facilities. The authors are grateful to ISRO, Bangalore for providing financial assistance to this ISRO-GBP project under ARFI programme through Space Physics Laboratory, VSSC, Thiruvanthpuram, Kerala, India. The authors would like to thank the NASA MODIS aerosol team for providing MODIS satellite data which we have used in this publication. We are highly obliged to the worthy anonymous reviewers who have spared their invaluable time to go through our manuscript and to suggest very important constructive comments, which helped further to improve the quality of paper. References Aloysius, M., Mohan, M., Parameswaran, K., George, S.K., Nair, P.R. Aerosol transport over the Gangetic basin during ISRO-GBP land campaign-II. Ann. Geophys. 26, 431–440, 2008. ˚ ngstro¨m, A. On the atmospheric transmission of sun radiation and on A dust in the air. Geografiska Annaler 11, 156–166, 1929. ˚ ngstro¨m, A. On the atmospheric transmission of sun radiation II, A Geografiska Annaler 12, 130–159, 1930. ˚ ngstro¨m, A. Techniques of determining the turbidity of the atmosphere. A Tellus 13, 214–223, 1961. ˚ ngstro¨m, A. The parameters of atmospheric turbidity. Tellus 16, 64–75, A 1964. Badarinath, K.V.S., Kharol, S.K., Kambezidis, H.D. Case study of a dust storm over Hyderabad area, India: its impact on solar radiation using satellite data and ground measurements. Sci. Tot. Environ. 384, 316– 332, 2007. Badarinath, K.V.S., Kharol, S.K., Prasad, V.K., Sharma, A.R., Reddi, E.U.B., Kambezidis, H.D., Kaskaoutis, D.G. Influence of natural and anthropogenic activities on UV Index variations: a study over tropical

urban region using ground-based observations and satellite data. Atmos. Chem. Phys. 59, 219–236, 2008. Badarinath, K.V.S., Kharol, S.K., Sharma, A.R. Long-range transport of aerosols from agriculture crop residue burning in Indo-Gangetic Plains-A study using LIDAR, ground measurements and satellite data. J. Atmos. Sol. Terr. Phys. 71, 112–120, 2009. Beegum, S.N., Moorthy, K.K., Nair, V.S., et al. Characteristics of spectral aerosol optical depths over India during ICARB. J. Earth Syst. Sci. 117 (S1), 303–313, 2008. Behnert, I., Matthias, V., Doerffer, R. Aerosol climatology from groundbased measurements for the southern North Sea. Atmos. Res. 84, 201– 220, 2007. Charlson, R.J., Schwartz, S.E., Hales, J.M., Cess, R.D., Coakley, J.A., Hansen, J.E., Hoffman, D.J. Climate forcing by anthropogenic aerosols. Science 255, 423–430, 1992. Chu, D.A., Kaufman, Y.J., Ichoku, C., Remer, L.A., Tanre´, D., Holben, B.N. Validation of MODIS aerosol optical depth retrieval over land. Geophys. Res. Lett. 29, 8007, doi:10.1029/2001GL013205, 2002. Chu, D.A., Kaufman, Y.J., Remer, L.A., Holben, B.N. Remote sensing of smoke from MODIS airborne simulator during the SCAR-B experiment. J. Geophys. Res. 103, 31,979–31,987, 1998. Crutzen, P.J., Andreae, M.O. Biomass burning in the tropics: impact on atmospheric chemistry and biogeochemical cycles. Science 250, 16691678, 1990. Dubovik, O., Holben, B.N, Eck, T.F., Smirnov, A., Kaufman, Y.J., King, M.D., Tanr‘e, D., Slutsker, I. Variability of absorption and optical properties of key aerosol types observed in worldwide locations. J. Atmos. Sci. 59, 590–608, 2002. Eck, T.F., Holben, B.N., Dubovic, O., Smirnov, A., Goloub, P., Chen, H.B., Chatenet, B., Gomes, L., Zhang, X.Y., Tsay, S.C., Ji, Q., Giles, D., Slutsker, I. Columnar aerosol optical properties at AERONET sites in central eastern Asia and aerosol transport to the tropical midPacific. J. Geophys. Res. 110, D06202, doi:10.1029/2004JD005274, 2005. Eck, T.F., Holben, B.N., Reid, J.S., Dubovik, O., Smirnov, A., O’Neill, N.T., Slutsker, I., Kinne, S. Wavelength dependence of the optical depth of biomass burning, urban, and desert dust aerosols. J. Geophys. Res. 104, 31,333–31,349, 1999. Fearnside, P.M., Laurance, W.F., Albernaz, A.K.M., Vasconcelos, H.L., Ferreira, L.V. Adelicate balance in Amazonia – Response. Science 307 (5712), 1045–1045, 2005. Gautam, R., Hsu, N.C., Lau, K.-M., Kafatos, M. Aerosol and rainfall variability over the Indian monsoon region: distributions, trends and coupling. Ann. Geophys. 27, 3691–3703, 2009. Gogoi, M.M., Bhuyan, P.K., Moorthy, K.K. Estimation of the effect of long-range transport on seasonal variation of aerosols over northeastern India. Ann. Geophys. 26, 1365–1377, 2008. Hess, M., Koepke, P., Schult, I. Optical properties of aerosols and clouds: the software package OPAC. B. Am. Meteorol. Soc. 79, 831–844, 1998. Holben, B.N., Eck, T.F., Fraser, R.S. Temporal and spatial variability of aerosol optical depth in the Sahel region in relation to vegetation remote sensing. Int. J. Remote Sens. 12, 1147-1163, 1991. Hoppel, W.A., Fitzgerald, J.W., Frick, G.M., Larson, R.E., Mack, E.J. Aerosol size distributions and optical properties found in the marine boundary layer over the Atlantic Ocean. J. Geophys. Res. 95, 3659– 3686, 1990. Ichoku, C., Chu, D.A., Mattoo, S., Kaufman, Y.J., Remer, L.A., Tanre’, D., Slutsker, I., Holben, B. A spatio-temporal approach for global validation and analysis of MODIS aerosol products. Geophys. Res. Lett. 29 (12), 8006, doi:10.1029/2001GL013206, 2002. Ichoku, C., Remer, L.A., Kaufman, Y.J., Levy, R., Chu, D.A., Tanre’, D., Holben, B. MODIS observation of aerosols and estimation of aerosol radiative forcing over southern Africa during SAFARI 2000. J. Geophys. Res. 108 (D13), 8499, doi:10.1029/2002JD002366, 2003. IPCC Climate change 2007: the physical science basis. Cambridge University Press, Cambridge, 2007. Jacovides, C.P., Kaltsounides, N.A., Asimakopoulos, D.N., Kaskaoutis, ˚ ngstro¨m’s parameters in D.G. Spectral aerosol optical depth and the A

R.P. Guleria et al. / Advances in Space Research 50 (2012) 1391–1404 the polluted Athenes atmosphere. Theor. Appl. Climatolo. 81, 161– 167, 2005. Jayaraman, A., Ratnam, M.V., Patra, A.K., Rao, T.N., Sridharan, S., Rajeevan, M., Gadhavi, H., Kesarkar, A.P., Srinivasulu, P., Raghunath, K. Study of atmospheric forcing and responses (SAFAR) campaign: overview. Ann. Geophys. 28, 89–101, 2010. Kaskaoutis, D.G., Badarinath, K.V.S., Kharol, S.K., Sharma, A.R., Kambezidis, H.D. Variations in the aerosol optical properties and types over the tropical urban site of Hyderabad, India. J. Geophys. Res. 114, D22204, doi:10.1029/2009JD012423, 2009. Kaskaoutis, D.G., Kambezidis, H.D., Hatzianastassiou, N., Kosmopoulos, P.G., Badarinath, K.V.S. Aerosol climatology: dependence of the wavelength exponent on wavelength over four AERONET sites. Atmos. Chem. Phys. Discuss. 7, 7347–7397, 2007. Kaskaoutis, D.G., Kosmopoulos, P.G., Kambezidis, H.D., Nastos, P.T. Identification of the aerosol types over Athens, Greece: the influence of air-mass transport. Adv. Meteorol. 2010, 168346, doi:10.1155/2010/ 168346, 2010. Kaufman, Y.J., Setzer, A., Ward, D., Tanr‘e, D., Holben, B.N., Menzel, P., Pereira, M.C., Ramussen, R. Biomass burning airborne and spaceborne experiment in the Amazonas (BASE-A). J. Geophys. Res. 97, 14581–14599, 1992. Kaufman, Y.J., Tanr‘e, D., Remer, L.A., Vermote, E.F., Chu, A., Holben, B.N. Operational remote sensing of tropospheric aerosol over land from EOS moderate resolution imaging spectroradiometer. J. Geophys. Res. 102, 17,051–17,067, 1997. Kaufman, Y.J., Tanre, D., Boucher, O. A satellite view of aerosols in the climate system. Nature 419, 215–223, 2002. Kedia, S., Ramachandran, S. Latitudinal and longitudinal variation in aerosol characteristics from Sunphotometer and MODIS over the Bay of Bengal and Arabian Sea during ICARB. J. Earth Syst. Sci. 117, 375–387, 2008. Kim, D.H., Sohn, B.J., Nakajima, T., Takamura, T., Choi, B.C., Yoon, S.C. Aerosol optical properties over eastern Asia determined from ground-based sky radiation measurements. J. Geophys. Res. 109, D02209, doi:101029/2003JD003387, 2004. King, M.D., Kaufman, Y.J., Tanre´, D., Nakajima, T. Remote sensing of tropospheric aerosols from space: past, present, and future. Bull. Am. Meteorol. Soc. 80 (11), 2229–2259, 1999. Kuniyal, J.C., Thakur, A., Thakur, H.K., Sharma, S., Pant, P., Rawat, P.S., Moorthy, K.K. Aerosol optical depths at Mohal-Kullu in the northwestern Indian Himalayan high altitude station during ICARB. J. Earth Syst. Sci. 118 (1), 41–48, 2009. Latha, M.K., Badarinath, K.V.S. Seasonal variations of black carbon aerosols and total aerosol mass concentrations over urban environment in India. Atmos. Environ. 39, 4129–4141, 2005. Lau, K.M., Ki, M.K., Kim, K.M. Asian summer monsoon anomalies induced by aerosol direct forcing: the role of the Tibetan Plateau. Clim. Dyn. 26, 855–864, 2006. Levy, R.C., Remer, L.A., Dubovik, O. Global aerosol optical properties and application to Moderate Resolution Imaging Spectroradiometer aerosol retrieval over land. J. Geophys. Res. 112, D13210, doi:10.1029/ 2006JD007815, 2007. Levy, R.C., Remer, L.A., Martins, J.V., Kaufman, Y.J., Plana-Fattori, A., Redemann, J., Wenny, B. Evaluation of the MODIS aerosol retrievals over ocean and land during CLAMS. J. Atmos. Sci. 62 (4), 974–992, 2005. Middleton, N.J. A geography of dust storms in southwest Asia. Int. J. Climatolo. 6, 183–196, 1986. Misra, A., Jayaraman, A., Ganguly, D. A comparative study of MODIS derived aerosol optical depth from two consecutive versions. IASTA 19, 212, 2010. Misra, A., Jayaraman, A., Ganguly, D. Validation of MODIS derived aerosol optical depth over western India. J. Geophys. Res. 113, D04203, doi:10.1029/2007JD009075, 2008. Moorthy, K.K., Nair, P.R., Murthy, B.V.K. Multi-wavelength solar radiometer network and features of aerosol spectral optical depth at Trivandrum. Indian J. Radio Space Phys. 18, 194–201, 1989.

1403

Moorthy, K.K., Nair, P.R., Murthy, B.V.K. Size distribution of coastal aerosols: effects of local sources and sinks. J. Appl. Meteorol. 30, 844– 852, 1991. Moorthy, K.K., Nair, P.R., Murthy, B.V.K., Satheesh, S.K. Time evolution of the optical effects and aerosol characteristics of Mt. Pinatubo origin from ground-based observations. J. Atmos. Sol. Terr. Phys. 58 (10), 1101–1116, 1996. Moorthy, K.K., Niranjan, K., Narasimhamurthy, B., Agashe, V.V., Murthy, B.V.K. Aerosol climatology over India. ISRO-GBP MWR network and data base. Scientific report, SR-03-99, Indian Space Research Organisation, Bangalore, India, 1999. Moorthy, K.K., Satheesh, S.K., Murthy, B.V.K. Characteristics of spectral optical depths and size distributions of aerosols over tropical oceanic regions. J. Atmos. Sol. Terr. Phys. 60(10), 981–992, 1998. Myhre, G., Govaerts, Y., Haywood, J.M., Berntsen, T.K., Lattanzio, A. Radiative effect of surface albedo change from biomass burning. Geophys. Res. Lett. 32, L20812, doi:10.1029/2005GL022897, 2005. Narasimhamurthy, B., Raju, N.V., Tukarama, M., Prasad, B.S.N., Moorthy, K.K. Studies of the temporal and spectral features of atmospheric aerosols over Mysore (12.3.-N), global change studies, ISRO: GBP: GSC-02-98, 77–92, 1998. Parameswaran, K., Nair, P.R., Rajan, R., Ramana, M.V. Aerosol loading in coastal and marine environments in the Indian Ocean region during winter season. Curr. Sci. 76(7), 947–955, 1999. Pathak, B., Bhuyan, P.K., Gogoi, M.M., Kalita, G., Bhuyan, K. Validation of satellite derived AOD by ground-based observation over Dibrugarh. IASTA 19, 219–222, 2010. Prasad, A.K., Singh, S., Chauhan, S.S., Srivastava, M.K., Singh, R.P., Singh, R. Aerosol radiative forcing over the Indo-Gangetic plains during major dust storms. Atmos. Environ. 41, 6289–6301, 2007. Prasad, A.K., Yang, K.-H.S., El-Askary, H.M., Kafatos, M. Melting of major Glaciers in the western Himalayas: evidence of climate changes from long term MSU derived tropospheric temperature trend (1979– 2008). Ann. Geophys. 27, 4505–4519, 2009. Prospero, J.M., Charlson, R.J., Mohnen, V., Jaenicke, R., Delany, A.C., Moyers, J., Zoller, W., Rahn, K. Atmospheric aerosol system: an overview. Rev. Geophys. Space Phys. 21, 1607–1630, 1983. Prospero, J.M., Ginoux, P., Torres, O., Nicholson, S.E., Gill, T.E. Environmental characterization of global sources of atmospheric soil dust identified with the Nimbus 7 Total Ozone Mapping Spectrometer (TOMS) absorbing aerosol product. Rev. Geophys. 40, 1002, doi:10.1029/2000RG000095, 2002. Ramanathan, V., Chung, C., Kim, D., Bettge, T., Buja, L., Kiehl, J.T., Washington, W.M., Fu, Q., Sikka, D.R., Wild, M. Atmospheric brown clouds: impacts on South Asian climate and hydrological cycle. Proc. Natl. Acad. Sci. USA 102, 5326–5333, 2005. Ramanathan, V., Crutzen, P.J., Kiehl, J.T., Rosenfeld, D. Aerosols, climate, and the hydrological cycle. Science 294, 2119–2124, 2001. Ranjan, R.R., Joshi, H.P., Iyer, K.N. Spectral variation of total column aerosol optical depth over Rajkot: a tropical semi-arid Indian station. Aerosol Air. Qual. Res. 7 (1), 33–45, 2007. Reid, J.S., Eck, T.F., Christopher, S.A., Hobbs, P.V., Holben, B.N. Use of ˚ ngstro¨m exponent to estimate the variability of optical and the A physical properties of aging smoke particles in Brazil. J. Geophys. Res. 104, 27,473– 27,489, 1999. Remer, L., Kaufman, Y.J., Tanre´, D., et al. The MODIS aerosol algorithm, products, and validation. J. Atmos. Sci. 62 (4), 947–973, 2005. Sagar, R., Kumar, B., Dumca, U.C., Moorthy, K.K., Pant, P. Characteristics of aerosol optical depths over Manora peak: a high altitude station in the central Himalaya. J. Geophys. Res. 109, 006207, doi:10.1029/2003JD003954, 2004. Saha, A., Moorthy, K.K., Niranjan, K. Inter-annual variations of aerosol optical depth over coastal India: relation to synoptic meteorology. J. Appl. Meteorol. 44, 1066–1077, 2005. Satheesh, S.K., Moorthy, K.K., Das, I. Aerosol spectral optical depths over the Bay of Bengal, Arabian Sea and Indian Ocean. Curr. Sci. 81 (12), 1617–1625, 2001.

1404

R.P. Guleria et al. / Advances in Space Research 50 (2012) 1391–1404

˚ ngstro¨m exponent and Schuster, G.L., Dubovik, O., Holben, B.N. A bimodal aerosol size distributions. J. Geophys. Res. 111, D07207, doi:101029/2005JD006328, 2006. Sharma, A.R., Kharol, S.K., Badarinath, K.V.S., Singh, D. Impact of agriculture crop residue burning on atmospheric aerosol loading - a study over Punjab State, India. Ann. Geophys. 28, 367–379, 2010. Sharma, D., Singh, D., Kaskaoutis, D.G. Impact of two intense dust storms on aerosol characteristics and radiative forcing over Patiala, northwestern India. Adv. Meteorol. 2012, 956814, doi:10.1155/2012/ 956814, 2012. Shaw, G.E., Reagan, J.A., Herman, B.M. Investigations of atmospheric extinction using direct solar radiation measurements made with a multiple wavelength radiometer. J. Appl. Meteorol. 12, 374–380, 1973. Singh, R.P., Dey, S.S., Tripathi, N., Tare, V., Holben, B. Variability of aerosol parameters over Kanpur, northern India. J. Geophys. Res. 109, D23206, doi:10.1029/2004JD004966, 2004. Smirnov, A., Holben, B.N., Kaufman, Y.J., Dubovic, O., Eck, T.F., Slutsker, I., Pietras, C., Halthore, R.N. Optical properties of atmospheric aerosol in maritime environments. J. Atmos. Sci. 59, 501–523, 2002. Tripathi, S.N., Dey, S., Chandel, A., Srivastava, S., Singh, R.P., Holben, B.N. Comparison of MODIS and AERONET derived aerosol optical depth over the Ganga Basin, India. Ann. Geophys. 23, 1093–1101, 2005.

Twomey, S. The influence of pollution on the shortwave albedo of clouds. J. Atmos. Sci. 34, 1149–1152, 1977. Vadrevu, K.P., Ellicott, E., Giglio, L., Badarinath, K.V.S., Vermote, E., Justice, C., Lau, W.K.M. Vegetation fires in the Himalayan region – Aerosol load, black carbon emissions and smoke plume heights. Atmos. Environ. 47, 241–251, 2012. Vinoj, V., Satheesh, S.K., Moorthy, K.K. Aerosol characteristics at a remote island: Minicoy in southern Arabian Sea. J. Earth Syst. Sci. 117, 389–397, 2008. Yang, J.-M., Qiu, J.-H., Zhao, Y.-L. Validation of aerosol optical depth from Terra and Aqua MODIS retrievals over a tropical coastal site in China. Atmos. Oceanic Sci. Lett. 3, 3639, 2010. Yu, H., Dickinson, R.E., Chin, M., Kaufman, Y.J., Zhou, L., Tian, Y., Dubovik, O., Holben, B.N. Direct radiative effect of aerosols as determined from a combination of MODIS retrievals and GOCART simulations. J. Geophys. Res. 109, D03206, doi:10.1029/ 2003JD003914, 2004. Zhao, T.X.-P., Stowe, L.L., Smirnov, A., Crosby, D., Sapper, J., McClain, C.R. Development of a global validation package for satellite oceanic aerosol optical thickness retrieval based on AERONET observations and its application to NOAA/NESDIS operational aerosol retrievals. J. Atmos. Sci. 59, 294–312, 2002.