Aerosol Features during Drought and Normal Monsoon Years: A Study Undertaken with Multi-Platform Measurements over a Tropical Urban Site

This paper reports the results of aerosol optical characteristics over a tropical urban station at Pune, India, during drought and normal monsoon years, based on ground-based and satellite observations for the period 2008–2010. Ground-based data from MICROTOPS-II and AERONET, and satellite data products from Moderate Resolution Imaging Spectroradiometer (MODIS) and Ozone Monitoring Instrument (OMI) sensors, were utilized in the study. Detailed analysis of this data revealed that the maximum values of aerosol optical depth (AOD) and minimum values of precipitable water content (PWC) were observed during a drought year (2009) compared to normal monsoon years (2008 and 2010). In order to characterize aerosols further, the Ångström parameters α and β were evaluated. Using the least squares method, α is calculated in the spectral interval of 380–1020 nm, along with the coefficients a1 and a2 of the second-order polynomial fitted to the plotted logarithm of AOD versus the logarithm of wavelength. Meteorological parameters, such as temperature and relative humidity, have been measured during the course of the study, as well as the variations in monthly rainfall over the experimental site. The high surface temperatures and low rainfall amounts during the pre-monsoon and monsoon seasons of 2009 lead to an increase in AOD as compared to those in 2008 and 2010. The ground-based observations from MICROTOPS-II reveal good correlation with satellite observations of the MODIS and OMI sensors, and also correlate with the AERONET observations, corroborating the results. Discrimination of aerosol types from radiometric measurements and the tropospheric circulation obtained from the NCEP/NCAR (National Centers for Environmental Prediction/National Center for Atmospheric Research) reanalysis were also discussed, and found to be consistent.


INTRODUCTION
Atmospheric aerosols from natural and anthropogenic sources have great importance because they scatter and absorb solar and terrestrial radiation, involving in the formation of clouds and precipitation as cloud condensation and ice nuclei, and change the microphysical structure and possibly the lifetime of clouds within the atmosphere (Haywood and Boucher, 2000;Bellouin et al., 2005;Rosenfeld et al., 2006).To understand the effects of aerosols on our geo-biosphere systems, it is essential to characterize their optical, physical, and chemical properties at different locations because of the regional heterogeneity of their properties and their short lifetime (Satheesh et al., 2002).The life time of aerosols is mainly determined by the size range of particles.It also depends on external factors such as wind and rain.Optical properties of aerosols can vary greatly depending on environmental conditions as well as on local sources.Moreover, these properties depend on the aerosol size distribution as well as on the refractive index and total atmospheric loading.This results in attenuation or extinction of solar radiation reaching the earth`s surface (Ranjan et al., 2007).
The distribution of atmospheric aerosol optical depth (AOD) is influenced by both surface and atmospheric conditions.Asia is the most prominent monsoon region (Wang, 2006); the monsoon circulation as a large dynamical system occurs over southern and eastern Asia.The monsoon circulation may influence the spatial and temporal variation patterns of aerosol loading.For example, Bao et al. (2008) using rotated principal component analysis showed that the spatial and temporal characteristics of the AOD over eastern Asia were closely associated with wind fields.Statistical analyses on the Moderate Resolution Imaging Spectroradiometer (MODIS) data proved that wind direction is responsible for the observed negative and positive AOD anomalies, corroborating the negative and positive rainfall anomalies observed over the Arabian Sea in July (Rahul et al., 2008).Li and Ramanathan (2002) used 5 years of satellite-derived AOD data to document the large seasonal variations in the AOD modulated by the monsoons.Badarinath et al. (2007a) have studied on multiyear groundbased and satellite observations of aerosol properties over a tropical urban area in India during 2003-2005.It is found that AOD concentrations at shorter wavelengths were high during drought year 2004 compared to normal years 2003 and 2005, suggesting increased loading of fine-mode particles over a tropical urban area in India and higher incidence of fires during 2004 over the Indian region compared to other years.This is reflected in higher AOD values observed during 2004.Recently, Bhawar and Devara (2010) worked extensively on successive contrasting monsoons 2001-2002 in terms of aerosol variability over Pune, India and pointed out that the higher AODs and less precipitation was observed in drought year 2002 compared to active monsoon year 2001.Rahul et al. (2011) have also reported the interface between absorbing aerosols and droughts.
A wide variety of aerosol, ozone and water vapor products have been routinely measured using a ground-based, MICROTOPS II (covering from UV to NIR wavelength region) at the Indian Institute of Tropical Meteorology (IITM), Pune (Lat.18°32'N, Long.73°51'E, Altitude 559 m), India (Dani et al., 2010).These products include columnintegrated aerosol optical depth (AOD), ozone (TCO) and precipitable water content (PWC).In this communication, we present the results of the synthesis of this extensive database, focusing on the spatial and temporal variations in aerosol properties and association with synchronous satellite measurements during 2008-2010.Temporal variation of Ångström exponent and turbidity coefficient and also discrimination of aerosol types are studied.NCEP/NCAR reanalysis winds are discussed.

EXPERIMENTAL STATION AND METEOROLOGY
The experimental station of Pune is situated on the leeside of the Western Ghats and is about 100 km inland from the west coast of India.Observations were carried out on the terrace of the building at about 10 m above the surface, at the Indian Institute of Tropical Meteorology, Pune.The environment in the immediate vicinity of the station is urban, with several small industries nearby, and the possible aerosol type present over the station is a mixture of watersoluble, dust-like and soot-like aerosols (Khemani, 1989).Soil dust is the major source of aerosols present over the experimental station (Khemani et al., 1985;Praveen, 2008).Formation of aerosols in the accumulation mode is considered to be due to gas-to-particle conversion processes, whereas coarse aerosols are attributed mainly to windblown dust (Khemani et al., 1982).The site is surrounded on all sides by hillocks of variable heights (up to 200 m), forming a valley like appearance.Brick kilns are situated at a distance of about 1 km to the west of observational site.
The weather at the experimental site during the premonsoon season (March-May) is very hot with mostly gusty surface winds and the daytime maximum temperature reaching around 40°C.During this season the dust content in the atmosphere is at a maximum, and cumulonimbustype cloud development takes place around late afternoon to evening (Khemani et al., 1982;Khemani, 1989).Development of low pressure system due to increased heating over land starts over India in the pre-monsoon, when all over India has the same pressure distribution.The air flow in the lower troposphere is predominantly westerly during the south-west (SW) monsoon season (June-September), which brings a large influx of moist air from the Arabian Sea.The region receives light, continuous or intermittent rain, and the atmosphere is relatively free from dust during this season.The westerly flow sets in during the post-monsoon season (October-November).The continental air masses, rich in nuclei of continental origin, pass over the region during this season.The daily minimum temperature falls very rapidly by the end of October.Fair-weather conditions, with clear skies and very low relative humidity, exist during the winter (December-February) season.Lowlevel inversions during the morning and evening hours, and dust haze during the morning hours, occur during this season.

MICROTOPS-II
The MICROprocessor-controlled Total Ozone Portable Spectrometer (MICROTOPS-II) was used to measure the aerosol optical depth (AOD) at the wavelengths 380, 440, 500, 675, 870 and 1020 nm (Morys et al., 2001) as well as the total column ozone (TCO) and precipitable water content (PWC), which were operated every 10-30 min from sunrise to sunset period.The sun photometer works on the principle of measuring the solar radiation intensity at some specified wavelengths and converts it to optical depth by knowing the corresponding intensities at the top of the atmosphere (TOA).
The TOA irradiance at each wavelength was calculated via the well-known Langley method.For this, the expression given by Kasten and Young (1989) for the air mass computation was used.The instrument measures the irradiance signals at different wavelengths in mV, from which the absolute irradiance in Wm -2 is obtained by multiplying the signal with calibration factor (Wm -2 /mV).The calibration relies on a high-performance voltage reference with the temperature coefficient ≤ 0.001% per degree Celsius and long-term stability of ~0.005% per year.The full width at half maximum bandwidth at each of these wavelength channels is 2.4 ± 0.4 nm, and the accuracy of the sun-targeting angle is better than 0.1°.In order to avoid any errors in sun-targeting angle, the MICROTOPS-II was mounted on a tripod stand throughout the experimental period.The whole procedure for the AOD derivation can be found in the literature (for example, Devara et al., 2001;Badarinath et al., 2007b).

CIMEL Sun-Sky Radiometer
The Sun-sky radiometer measurements reported in this paper were made with a CIMEL Electronique CE-318 instrument as a part of the AERONET global network.This instrument is described in detail in the work of Holben et al. (1998Holben et al. ( , 2001)); however, a brief description highlighting the essential features is given here.The automatic-tracking Sun-sky scanning radiometers make direct Sun measurements with a 1.2° full field of view at least every 15 min interval at 440, 675, 870, 940, and 1020 nm (nominal wavelengths), in addition to three polarized channels at 870 nm.The direct Sun measurements take 8 sec to scan all eight wavelengths, with a motor-driven filter wheel positioning each filter in front of the detector.A sequence of three measurements (termed a triplet) taken 30 s apart are made resulting in three measurements at each wavelength within a 1 min period.These solar extinction measurements are then used to compute aerosol optical depth at each wavelength except for the 940 nm channel, which is used to retrieve precipitable water vapor in centimeters.The spectral AOD data have been screened for clouds following the methodology of Smirnov et al. (2000), which relies on the greater temporal variance of cloud optical depth versus aerosol optical depth.

Ångström Coefficients α and β
The spectral dependence of the AOD was used in this work to compute the Ångström's exponent "α".A spectrallyaveraged value of this exponent, which contains information about the size of the particles or the volume fraction of the fine versus coarse-mode particles (Schuster et al., 2006), can be obtained by fitting the Ångström's formula (Ångström et al., 1964): where AOD λ is the estimated AOD at the wavelength λ, β is the Ångström's turbidity coefficient which equals AOD at λ = 1 μm, and the wavelength exponent α is a good indicator of the fraction of accumulation mode particles (r < 1 μm) to coarse-mode particles (r >1 μm).Although in definition α is assumed independent to the wavelength, it is well known that α depends on λ (Kaskaoutis et al., 2009).The Ångström formula is a special case of a more complicated equation valid for a limited range of particle diameters and a limited interval of wavelengths.The validity of this theory presupposes that the Junge powerlaw is valid for the particle-radius range, where significant extinction takes place and that the spectral variation of the refractive index does not impose significant variations on the Mie extinction factor (Kaskaoutis et al., 2006).Taking the logarithms of both sides of Eq. (1) obtains: A more precise empirical relationship between aerosol extinction and wavelength is obtained with a second-order polynomial fit (Eck et al., 1999(Eck et al., , 2001a;;Pedros et al., 2003;Kaskaoutis and Kambezidis, 2006) as where the coefficient "a 2 " accounts for a curvature often observed in sun-photometry measurements.This curvature can be an indicator of the aerosol-particle size, with negative curvature indicating aerosol-size distributions dominated by fine-mode and positive curvature indicating size distributions with significant contribution by the coarse-mode aerosols (Schuster et al., 2006).
The spectral variation of Ångström exponent is commonly referred to as curvature; and the spectral curvature provides additional information on aerosol size distribution.The negative curvatures favor high fine volume fractions and positive curvatures favor low fine volume fractions at the intermediate values of alpha.Thus, the presence of curvature can be used to improve the assessment of aerosol size distributions at intermediate Ångström exponents (alpha values between 1 and 2).Multi-wavelength Ångström exponents, covering broader wavelength region, provide more accurate results under low turbidity conditions.
In the present study, the values of α were computed in the wavelength interval 380-1020 nm, applying the least squares method to Eq. ( 2).The linear fit to the logarithmic function of Eq. ( 2) is the most precise method, although the results may also depend on the spectral interval considered (Pedros et al., 2003;Kaskaoutis and Kambezidis, 2008).The second-order polynomial fit (Eq.( 3)) was also applied to the AOD values at six wavelengths (380, 440, 500, 675, 870 and 1020 nm), although three of them are sufficient for this computation as indicated by Eck et al. (2001a).Although the polynomial fit to Eq. 3 is more precise than the linear fit to Eq. ( 2), large errors can appear especially under low turbidity conditions.These errors were found to be lower when the spectral interval 380-1020 nm is used instead of 380-870 nm.For this reason, the spectral range 380-1020 nm was used for the computation of α, a 1 and a 2 .

MODIS Satellite Retrievals
The Moderate Resolution Imaging Spectroradiometer (MODIS) is a key instrument aboard the Terra (EOS AM) and Aqua (EOS PM) satellites.The MODIS sensor is onboard the polar orbiting NASA-EOS Terra and Aqua spacecrafts with equator crossing times of 10:30 and 13:30 Local Solar Time (LST), respectively (Levy et al., 2007).Terra's orbit around the Earth is timed so that it passes from north to south across the equator in the morning, while Aqua passes south to north over the equator in the afternoon.Because of the difference in direction, mid-latitude time differences between Terra and Aqua are approximately 1.5 hours in the Northern Hemisphere and 4.5 hours in the Southern Hemisphere (Kaufman et al., 2005).MODIS acquires daily global data in 36 spectral bands from visible to thermal infrared (29 spectral bands with 1-km, 5 spectral bands with 500-m and 2 spectral bands with 250-m, nadir pixel dimensions) ranging from 0.4 to 14.4 μm.Numerous parameters describing various properties of physical features on land and ocean surfaces as well as in the atmosphere are retrieved operationally from MODIS data at different spatial and temporal resolutions.Several aerosol parameters are retrieved at 10-km spatial resolution from MODIS daytime data.The MODIS aerosol algorithm has two components, one adapted for retrieval over land (Kaufman et al., 1997), and the other for retrieval over ocean (Tanré et al., 1997) surfaces, because of the inherent differences in the solar spectral radiance interaction with these surfaces.The parameters retrieved include: aerosol optical depth (AOD) at several wavelengths (λ) and Ångström exponent (α) over land and ocean; and effective radius (r eff ), and the proportion of AOD contributed by the small mode aerosols over ocean.To assess the quality of these parameters, a substantial part of the Terra-MODIS aerosol products acquired in 2000 have been validated globally and regionally, as reported by Chu et al. (2002), Ichoku et al. (2002) and Remer et al. (2005).In this study, the Terra and Aqua MODIS Level 3 (spatial resolution 1° × 1°) products are obtained from Giovanni website (http://gdata1.sci.gsfc.nasa.gov/daac-bin/G3/gui.cgi?instance_id=MODIS_DAILY_L3) during 2008-2010.

NCEP/NCAR Reanalysis Data
The NCEP/NCAR (National Centers for Environmental Prediction/National Center for Atmospheric Research) reanalysis wind field indicates that the atmospheric circulation, especially the horizontal wind convergence is the major factor that guides the formation and dynamics of the cluster.The surface flow patterns obtained from NCEP/ NCAR reanalysis (http://www.cdc.noaa.gov)(Kalnay et al., 1996) have been used to ascertain the synoptic meteorological conditions and source variability to the observation site during 2008-2010.The NCEP reanalysis provides long time series of gridded atmospheric and surface fields.The most reliable fields such as pressure heights, tropospheric temperature and specific humidity are obtained by merging short-term atmospheric forecasts with observations.NCEP fields are available from 1948, but since 1979, corresponding to the advent of new generation satellite data streams, the fields are of higher quality (Rawlins et al., 2009).In addition, the meteorological data such as air temperature, relative humidity and rainfall datasets were collected from the India Meteorological Department (IMD), Pune and analyzed.

Monthly Variation in Aerosol Optical Depth (AOD)
Fig. 1 shows spectral variations of monthly mean AOD during 2008-2010, measured using Microtops-II Sunphotometer over the study area.The gaps in the datasets are due to unfavorable sky conditions during the south-west monsoon months.It can be seen from Fig. 1 that AOD values are higher during 2009 compared to other years, suggesting an additional loading of aerosols over the region.Higher concentrations of aerosol loading during the summer period could be attributed to the increased concentration of continental aerosol loading due to high temperatures in the study region, as well as other sources such as long-range transport of dust from west of the Arabian Sea (Devara et al., 2002;Sumit et al., 2011).In winter, strong nocturnal inversions are frequent, and whatever aerosols due to various human activities (domestic cooking, vehicular emissions, industrial emissions, etc.) are let out into the surface layer get trapped in the lower atmosphere due to less ventilation.Also, due to calm wind conditions, aerosols of soil-dust type are less during winter.Monthly mean AODs during the transition period of October-November (post-monsoon) are slightly less in magnitude at all wavelengths compared to that in the pre-monsoon and winter periods.The spectral variations of AOD during different seasons of 2008, 2009 and 2010 are presented in Table 1.Most of the aerosol load that gets into the atmosphere during the pre-monsoon is removed by rain-washout and cloud-scavenging processes during the summer monsoon (rainy) season.

Monthly Variation in Precipitable Water Content (PWC)
Monthly variations in PWC during 2008-2010 are shown in Fig. 2. PWC is high in summer and low in winter, as expected.Comparison between Figs. 1 and 2 reveals a correspondence between AOD and PWC, which suggests the growth of aerosol particles associated with higher PWC values.Such an association has already been reported by Gupta et al. (2003) and Pandey et al. (2004).Raj et al. (2008) studied temporal variations in sun photometer measured PWC in near IR band and its comparison with model estimates at a tropical Indian station and found the PWC during May 1998-May 2006 was about 1.32 cm, which was consistent with the present value of about 1.57 cm.Recently, Sumit et al. (2011)

OMI and MICROTOPS Total Column Ozone (TCO) Variations
Ozone is an important trace gases that is being studied widely all over the world.In order to investigate the compatibility between the ozone variations obtained from ground-based and satellite techniques, MICROTOPS determined TCO values are compared with OMI derived values in Fig. 3.Both measurements show distinct annual oscillation with broad maximum during monsoon and minimum during winter months over this observing station which may allow the UV radiation to reach the surface.Thus, the agreement between these two techniques appears to be fairly good within the experimental limitations.An interesting feature is that both OMI and MICROTOPS exhibited lower ozone values during winter and premonsoon months and higher values during monsoon and post-monsoon seasons of 2009 compared to 2008 and 2010.The ground-based TCO values overestimate the OMI derived ozone.Such differences in TCO between MICROTOPS and OMI are considered to be due to large variability in the surface-level ozone concentration at the experimental location close to the urban site.Similar results have been reported for this experimental site by Raj et al. (2004) and Kalapureddy et al. (2008).Possible reasons for the discrepancy between satellite and ground-based measurements may be related to the instrument calibration and differences in the spatial resolution.In addition, temporal differences can also occur because of difference in timing as well as changes in atmospheric conditions.Further, MICROTOPS provides point observations as compared to about 125 × 100 km grid average values of OMI.
Albeit the summer peak does not show significant yearto-year change, the significant inter-annual variation during the winter-time minimum could be due to the influence of quasi-biennial oscillation in total ozone variations.A highdegree of year-to-year variability and largest absolute changes during the dynamically active winter and summer periods have been reported (WMO, 2003).Moreover, the height of the boundary layer or the mixing depth can significantly influence the dispersal of pollutants and ozone pre-cursor gases in the lower troposphere.Thus the lower mixing depth during monsoon and post-monsoon months and higher mixing depths during winter and premonsoon months could be responsible for the observed features.Raj et al. (2009) have studied the changes observed in sun photometer derived total column ozone and possible implications on surface-reaching UV radiation over Pune during May 1998-May 2003 and found the average value was 254 DU, which is consistent with that during 2008-2010 (286 DU) (286 DU).These changes are considered to be due to large-scale circulation-induced currents during monsoon over the experimental region.
The overall ozone concentration is found to be maximum in May.It decreases during monsoon and post-monsoon and reaches minimum in December.Thereafter, it gradually increases throughout the winter and reaches peak value in summer.The primary cause of large values of ozone in May month may be due to the high solar flux acting upon a pool of accumulated NO x and hydrocarbons built up during winter resulting in local photochemical production.Ozone is produced by photo-oxidation of pollutants (carbon monooxide and hydrocarbons) in the presence of adequate amount of nitrogen oxides at lower altitudes.Once ozone is produced, it gets transported along with the wind, resulting in a high concentration in summer.During monsoon the pristine air from the Arabian Sea blows over Pune, resulting in a decrease in ozone concentration due to wash-out effects and transport of monsoon air masses.

Temporal Variations in Ångström Exponent and Turbidity Coefficient
The Ångström wavelength exponent (α), which represents the particle size distribution and the turbidity coefficient (β), which represents the aerosol loading have been derived and shown plotted in Fig. 4. It is evident from this figure that the values of alpha and beta are almost opposite to each other; implying higher values of beta are associated with smaller values of alpha, a fact that agrees with earlier studies by Bhawar (2008) and Dani et al. (2010).
The overall mean value of α at this urban location is 0.94, and the mean β is 0.23.It is also interesting to note that winter season mean α (1.11 ± 0.14) is higher than the pre-monsoon mean (0.78 ± 0.02).As a general rule, larger values of α indicate abundance of smaller-size particles, while smaller values indicate abundance of larger/coarse size particles.It can be seen from Fig. 4 that the average values of α are higher during October to February and lower during March to June.The maximum and minimum values of α vary between 1.45 ± 0.10 (December 2009, January 2010) and 0.47 ± 0.27 (April 2010).The maximum and minimum values of β vary between 0.33 ± 0.07 (June 2009) and 0.13 ± 0.01 (January 2010) at the observational site.Higher values of β are seen in the months of March, April, May and June indicate that the total aerosol mass loading is the highest during the pre-monsoon months.The relatively smaller values of α during winter and larger values of β during winter as compared to the pre-monsoon months in certain years are attributed partly due to intense haze formation due to elevated temperature inversions and associated trapping of local anthropogenic aerosols.As explained in the previous sections, over this station, fine-mode particles are more in the winter and coarse-mode particles of soildust origin are abundant during the pre-monsoon season.Added, the higher temperature associated with large-scale circulation features may have intensified this feature during 2009 as compared to that during 2008 and 2010.Thus, the results show the presence of more fine-mode particles in 2008 and 2010 than in 2009 suggesting greater loading of coarse-mode aerosols in 2009 as compared to that in 2008 and 2010 during pre-monsoon season.

Discrimination of Aerosol Types
In order to characterize the aerosol properties, data of both AOD and Ångström exponent values have to be used (Holben et al., 2001) since they both strongly depend on wavelength.The AOD-α patterns have been utilized to describe different aerosol types (e.g., biomass smoke, anthropogenic aerosols, desert dust) at several locations (Eck et al., 1999(Eck et al., , 2001a, b;, b;Masmoudi et al., 2003;Kim et al., 2004).Fig. 5 depicts the density maps of AOD 500 nm versus Ångström exponent (380-1020 nm) over the Pune region.These contour maps were constructed using 0.1 steps for both AOD 500 nm and α 380-1020 nm values.In these maps, the rectangle areas denote urban/industrial (UI), clean maritime (CM), desert dust (DD) and mixed type (MT) aerosols, and boundaries of these areas correspond to the selected threshold values of AOD 500 nm and α 380-1020 nm .This method has been used in a number of studies (for example, Kalapureddy et al., 2008 and references therein) and is based on the sensitivity of the two parameters to different, somewhat independent, microphysical aerosol properties; the Ångström exponent depends on particle size distribution (aerosol type), while the AOD 500 nm depends mainly on the aerosol column density.Therefore, the AOD 500 nm versus α 380-1020 nm plot qualitatively indicates the amount and dimension of the observed aerosols.
In the present study, during 2008-2010, the AOD 500 nm ranges from 0.13 to 0.77 and the α 380-1020 nm spans between 0.32 and 1.72.Therefore, the threshold values must be slightly changed from those utilized by Pace et al. (2006) Jan  Finally, the remaining gaps reveal where the aerosols are difficult to be discriminated and they are considered as MT, bearing in mind the different effects of various aerosol-mixing processes in the atmosphere (e.g., coagulation, condensation, humidification, gas-to-particle conversion).
It is clear from Fig. 5 that, during 2008, the maximum density area is observed for the pair (AOD 500 nm , α 380-1020 nm ) = (0.5, 1.1) indicative of UI aerosols.This clearly indicates the influence of the anthropogenic pollution from the industrialized areas of the Indian west coast, which was found to have a spatial offshore extent of < 100 km at the coast (Moorthy et al., 2008).But in 2009, two maximum density areas are observed.The primary density area is observed for the pair (AOD 500 nm , α 380-1020 nm ) = (0.4,1.5) and secondary maximum density area is found for AOD 500 nm between 0.35 and 0.41; and 1.19 < α 380-1020 nm < 1.12, both indicative of the presence of UI aerosol type.In 2010, the maximum density area is depicted for (AOD 500 nm , α 380-1020 nm ) = (0.39 to 0.42, 1.29 to 1.32), which also corresponds to UI aerosols.
The values of α depend strongly on the spectral range used for their determination.Hence the information contained in the AOD 500 nm versus α 380-1020 nm scatter plot becomes more difficult to interpret, but the detailed spectral information given by the determination of α in different spectral band (Fig. 5) helps in determining and discriminating the aerosol types.

Variations in Spectral Curvature
The coefficients a 1 and a 2 were obtained through the application of the second-order polynomial fit in the spectral region 380-1020 nm.In general, large negative a 1 and a 2 values correspond to fine-mode aerosols, while positive a 2 and near zero or positive a 1 values are indicative of coarsemode particles (Schuster et al., 2006;Kaskaoutis et al., 2007).The coefficients a 1 and a 2 (curvature in the polynomial fit) are plotted against AOD 500 nm for the period 2008-2010 in Figs.6(a) and (b), respectively.As expected via the assertion of Schuster et al. (2006) and Kaskaoutis et al. (2007) the a 1 values are mostly negative, since they are similar to -α in cases of negligible curvature.For all the data, the positive a 1 values appear under low turbidity conditions.According to Schuster et al. (2006), positive a 1 values accompanied by positive curvature can be theoretically derived for very low fine fraction (0.3 or even less).According to Fig. 6(a), the range of a 1 is larger for lower AOD 500 nm .The plot between a 2 and AOD 500 nm provides information on the atmospheric conditions under which α is independent from wavelength, so the spectral variation of AOD 500 nm can be accurately described by the simple Ångström formula.The data lying on or near the a 2 = 0 line belong to the Junge power-law size distribution.Based on these results it can be stated that in general, the error in using the simple linear fit becomes smaller with increasing atmospheric turbidity, especially for desert aerosol environments.The negative a 2 values are characterized by aerosol-size distributions dominated by fine-mode, while the positive curvatures indicate coarse-mode particles.Higher positive a 2 values (coarse-mode aerosols) are seen for the 2009 period as compared to 2008 and 2010.The low AOD 500 nm is found to associate with wide variability in a 2 values (both positive and negative), implying large curvature in Eq. (2).In contrast, for high AOD 500 nm a 2 tends to zero, indicating low curvature and insignificant wavelength dependence of α.
The affinity between the Ångström exponent α and the difference of a 2 and a 1 is shown in Fig. 7.According to Schuster et al. (2006), α is equal to the difference of a 2 -a 1 to a first approximation.An attempt to verify the validity of this statement is given in Fig. 7.When the curvature is negligible (a 2 0), then α = -a 1 .These two parameters are strongly correlated, as indicated by the correlation coefficient of 0.99.Thus the present findings are in good agreement with those of Schuster et al. (2006).

Comparison between Ground-based and Satellite Observations
The correlation between the ground-based MICROTOPS-II derived AOD, PWC and TCO and those derived from MODIS and OMI satellite observations for the latitude/ longitude similar to that of Pune location is depicted as a scatter plot in Fig. 8.The deviations observed in the above comparison could be due to the large variability in the column AOD, PWC and TCO at a location close to urban station.Moreover, differences in filter centre wavelengths and other optical characteristics of the instruments also lead to degrade the correlation (Bhawar and Devara, 2010).The main difficulty in satellite and ground-based measurements lies in the data sampling which leads to differences in their data structures.During its overpass, the satellite crosses a particular site instantaneously whereas the ground-based radiometer makes point measurement several times in a day.Further, the highly variable tropical climate, weather patterns, cloud conditions and local sources may play a pivotal role in the observed low correlations or the differences.

Comparison between MICROTOPS and AERONET Observations
Fig. 9 shows the comparison between Microtops and Aeronet observations at common wavelengths.The intercomparison shows close agreement between the two measurements as indicated by the R values and the linear fit between the retrieved AOD and PWC, thus providing confidence in the operation and quality of the handheld measurements.This type of comparison helps in understanding the application of network measurements to trend studies and the ability to develop a single global climatology from multiple networks without introducing network bias.

Variations in Meteorological Parameters
Pune is about 100 km inland from the west coast of India and is located on the lee-side of the Western Ghats.The air flow in the lower troposphere is predominantly westerly during the south-west (SW) monsoon season when there is a large influx of moisture from the Arabian Sea, and an easterly flow sets in from October, and continental air masses, rich in nuclei of continental origin, pass over the station.Fig. 10 depicts monthly mean variations in temperature, relative humidity and rainfall during 2008-2010.Though the trends of variations in these parameters are more or less similar, considerable differences, particularly in the case of rainfall, are seen between the SW monsoon seasons of 2008, 2009 and 2010.The above figure shows higher temperature (less relative humidity) in 2009 as compared to 2008 and 2010.This will have an impact on formation and growth of aerosols over the experimental site during drought year 2009.Shaw et al. (1988) and Sharma et al. (2003) suggest that the size distribution of aerosols varies significantly with changes in temperature  and relative humidity (RH).Parameswaran and Vijaykumar (1994) found that the RH does not affect significantly the aerosol concentration and size distribution up to a limit of 95%.Devara and Raj (1997) have observed that the higher RH and lower temperature during monsoon period at Pune, India, enhances the growth of cloud droplets which results in higher rainfall.Apart from the atmospheric circulation systems that favor/suppress the monsoon activity, the higher RH (lower temperature) observed during SW monsoon season of 2008 and 2010 might have assisted the cloud condensation nuclei to grow and form more cloud droplets which results in higher rainfall in those years.In 2009, during July, rainfall activity over the country as a whole was near normal.The interaction between monsoon flow and mid-latitude westerlies resulted in good rainfall activity over west and south peninsula and central parts of the country while northwestern and northeastern regions received deficient rainfall.Thus 2009 monsoon failed almost in all months, except in July.The report by the India Meteorological Department (IMD) monsoon report in 2009 pointed out that southwest monsoon season (June to September) rainfall over the whole country was deficient [(78% of its long period average (LPA)] which is the third deficient monsoon year of the decade after 2002 and 2004 (Tyagi, 2009).It also reports (Tyagi, 2008(Tyagi, , 2010) ) that the rainfall for the season (June to September) for the whole country was near normal (98% of its long period average (LPA)) in 2008 and 102% of LPA in 2010.The mean intensity of rainfall does not seem to change between normal and drought years, but the variability seems to be slightly higher during a normal year.The mean seasonal rainfall, heavy/extreme rainfall, one-day maximum rainfall and rainy days associated with heavy rainfall events are significantly low during the drought year, and the seasonal rainy days

Inferences from TRMM Observations
Fig. 11 shows monsoon rain rate over the Indian region during 2008-2010 derived from TRMM.These figures clearly show that, less amount of rainfall was observed over the Indian region during 2009.In 2009, an increase of aerosol optical depth (AOD) causes increase in shallow cloud cover, decrease in cloud droplet size, and reduction/ delay of the rainfall formation.This is consistent with the results reported by Kaufman and Nakajima (1993).Enhancement in AOD, larger values of alpha and increase in temperature indicate more contribution of fine (soil-dust) particles to the cloud formation during pre-monsoon season of 2009.These particles (act as CCN under favorable meteorological conditions) eventually help in the formation of clouds dominated by small droplets but with low coalescence efficiencies (Rosenfeld et al., 2001).Such situation suppresses rainfall because rain formation/ development needs the presence of both smaller and larger droplets.
One of the most prominent features of the tropical oceans is the existence of warm pools (region with temperature greater than 28°C).They exhibit important implications to atmospheric processes, particularly during the onset phase of Indian summer monsoon and associated precipitation characteristics.Tropical warm pools appear as the primary mode in the distribution of tropical sea surface temperature (SST).It has been shown that the minimum SST required for active convection is 28°C (Gadgil et al., 1984;Graham and Barnet, 1987).It has also been found that regions with annual mean SST above 28°C are prone to tropical cyclones (Gray, 1975).Fig. 12 shows the annual mean of SST over the Indian region during 2008-2010.These figures clearly indicate higher SST during 2009 compared to 2008 and 2010.The extent and intensity of warm pool depend on the type of monsoon.Maximum core temperature and wide coverage of warm pool occur during the excess monsoon years compared to normal and deficient monsoon years (Deepa et al., 2010;Neema et al., 2011).
The location and regional extent of the warm pool in the Arabian Sea were distinct during the period of study.In 2009, the warm pool core was located in the equatorial region, whereas in 2010, it spreads to a wide region of the  SST is also important because the properties of the surface and mixed layer air are closely linked to the SST, due to continuous interactions of the upper layer of the ocean and the atmosphere immediately above (Bhat et al., 1996).When the atmosphere is free from clouds, the ocean surface receives net heat, SST increases, enhancing the energy of the surface air as well.Gadgil et al. (1984) concluded that over the Indian Ocean, 28°C is the threshold for deep convection.When deep convection occurs, the clouds block solar radiation and latent heat flux increases due to increased winds.Hence the ocean cools.This can lead to reduced convection.Thus, whereas atmospheric convection depends upon the SST, the SST changes in response to atmospheric convection and the system is coupled.Therefore, understanding of the annual variation of SST over the Indian region is important in the studies related to monsoon onset over Kerala and maintenance of the West coast monsoon.

Fig. 9 .
Fig. 9.The retrieved MICROTOPS AODs and PWC compared with AERONET for the wavelength of 440 nm, 675 nm, 870 nm, 1020 nm and PWC during 2008-2010.Solid and dashed lines indicate regression fit to data and 1:1 correspondence, respectively.

Fig. 10 .
Fig. 10.Monthly mean surface-level temperature, relative humidity and rainfall during 2008-2010.Vertical bars indicate standard deviation from mean of the data for the year 2009.

Fig. 11 .
Fig. 11.Rain rate over the Indian region during monsoon months (June-September) from 2008 to 2010, derived from TRMM.


On the basis of the sunphotometer data, AOD values at all wavelengths are found to be high during 2009 compared to 2008 and 2010, suggesting an additional loading of aerosols over the region. The column PWC is observed to be smaller during 2009 in contrast to that during 2008 and 2010. Both OMI and MICROTOPS-II exhibit lower ozone values during winter and pre-monsoon seasons and higher values during monsoon and post-monsoon seasons of 2008-2010. The case of negative curvature (a 2 < 0) is characteristic of aerosol size distribution dominated by fine-mode particles while the case of positive curvature (a 2 > 0) denotes size distribution dominated by coarse-mode aerosols.The latter dominates during 2009 compared to 2008 and 2010. The ground-based observations from MICROTOPS-II reveal good correlation with satellite and AERONET observations, thus providing confidence in the operation and quality of the handheld measurements. The discrimination of aerosol type over Pune reveals maximum UI aerosols in 2009 and minimum in 2008 and 2010. Higher temperatures observed in 2009, as compared to 2008 and 2010; suppress the formation and growth of aerosols over the experimental site during bad monsoon year 2009.In contrast, higher RH and lower temperature during active monsoon years 2008 and 2010 are found to favor the growth of cloud droplets, resulted in higher rainfall.
have studied the annual mean PWC over the Pune region using the CIMEL sun-sky radiometer.Their results show a mild increasing trend during 2004- Fig. 1.Variation of aerosol optical depth (AOD) at different wavelengths during 2008-2010.Data gaps are due to unfavorable sky conditions.2009.The columnar PWC is observed to be smaller during 2009 in contrast to that during 2008 and 2010.