IASTA 2010 Proceedings
+ Invited Talks
+ SESSION A - AEROSOL CHARACTERIZATION
A-O-1 Aerosol Effect on Precipitation...
A-O-2 Sunshine Duration Trend...
A-O-3 A Model Study of the...
A-O-4 Small Ion Concentration...
A-O-5 Vertical Distribution of...
A-O-6 Aerosol transport and...
A-O-7 Aerosol and Cloud...
A-O-8 Effects of Rain Drop-...
A-O-9 On the Association...
A-O-10 Evaluation of the Seasonal...
A-P-1 A Study on Distribution...
A-P-2 A Study on Optical...
A-P-3 The Relationship between...
A-P-4 Influence of Tropical...
A-P-5 Vertical Aerosol Profiles...
A-P-6 Dissimilarities in Maximum...
A-P-7 Measurement of the Atmospheric...
A-P-8 Assessment of Solid and...
A-P-9 Seasonal Variations in PM10...
A-P-10 Variation of Aerosol Optical...
A-P-11 Effect of Dust and Rain...
A-P-12 Aerosol Properties of the...
A-P-13 A Study of the Aerosol...
A-P-14 Retrieval of Background...
A-P-15 Influence of Charged Dust...
A-P-16 Number Density Characteristics...
A-P-17 Temporal and Spectral...
A-P-18 Numerical Estimation of the...
A-P-19 Lidar Measurements of Vertical...
A-P-20 Aircraft Observations of Cloud...
A-P-21 Aerosol Characteristics at High...
A-P-22 Seasonal Variability in Aerosol...
A-P-23 Spatial and Temporal Variability...
A-P-24 Airborne Measurements of Micron-...
A-P-25 Wintertime Vertical Profiles of...
A-P-26 Relationship between Pre-monsoon...
A-P-27 Seasonal Variation of Aerosol...
A-P-28 Influence of Aerosols on near...
A-P-29 Association between Stratosphere...
A-P-30 Response of Surface Ozone...
A-P-31 Aerosol Source Characteristics...
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+ SESSION B - AEROSOL REMOTE SENSING-I
+ SESSION C- RADIOACTIVE AEROSOL, HEALTH EFFECTS
+ SESSION D AEROSOL CAMPAIGNS / SPECIAL EVENTS
+ SESSION E AEROSOL REMOTE SENSING
+ SESSION F CARBONACEOUS AEROSOLS
+ SESSION G NANO PARTICLES SYNTHESIS
+ SESSION H AEROSOL CHARACTERIZATION II
+ SESSIONS I INDOOR AEROSOLS
IASTA-2

SESSION B

Aerosol Remote Sensing-I

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B–O–1

Aerosol Indirect Effect over Indian Region during Four Successive Contrasting Monsoon Seasons

A. S. Panicker, G. Pandithurai and S. Dipu

Indian Institute of Tropical Meteorology, Pune, India

Introduction

Atmospheric aerosols influence the climate indirectly by modifying the cloud microphysical properties, known as aerosol indirect effect. The intergovernmental panel on climate change fourth assessment report (IPCC, 2007) estimate for the global annual radiative forcing of the first indirect effect is -0.7 Wm-2 with an uncertainty range of -1.8 to -0.3 Wm-2. While different types of AIE have been proposed (Twomey, 1977; Albrecht, 1989; Kaufman and Fraser, 1997), the most widely accepted one is the first indirect effect or Twomey effect, which describes the reduction in cloud effective radius with increase in aerosol loading for fixed liquid water path. Opposite to this effect i.e. increase in cloud droplet size with increase in aerosol load also were reported over some parts of the world (Yuan et al., 2008). Many studies qualitatively suggest that aerosols and AIE has a key role in modulating and in determining breaks and floods of Indian Summer Monsoon Rainfall (ISMR) (Ravi Kiran et al., 2009; Patra et al., 2005). Hence it is necessary to have a quantitative estimate of AIE to find the modulation of ISMR by aerosol-cloud interactions. In this study we quantitatively estimate the AIE values in four successive contrasting monsoon years viz. 2001 (normal year), 2002 (drought year) and 2003 (excess monsoon year), 2004 (below normal rainfall year) and hence show that AIE play a key role in modulating the ISMR.

Data and Methodology

Since rainfall during core monsoon month (July) determines the main features of ISMR, we analysed July data sets during successive contrasting monsoon years, 2001 (normal year), 2002 (drought year) and 2003 (excess monsoon year), 2004 (below normal rainfall year) obtained from Moderate resolution imaging spectroradiometer (MODIS) for this study. Also we used September data sets to see whether any significant changes are there in observed AIE features during September. MODIS is a 36 band scanning radiometer onboard NASA’s Terra and Aqua satellites. We have used Terra MODIS Level-3 daily data products of Aerosol Optical Depth (AOD), Fine mode fraction (FMF) and cloud parameters such as cloud liquid water path (CLWP),cloud ice path (CIP), cloud ice radius (CIR) and cloud water radius (CWR) for the analysis. It has been reported that absorbing aerosols like black carbon is a major component in altering cloud microphysics and hence inducing AIE (Conant et al., 2002). Also it is found that absorbing aerosols play a key role in modulating ISMR (Lau and Kim. 2006). So, in this scenario, we selected six regions over India (Figure 1), which has been classified according to different dominant types of absorbing aerosols and its sources by Habib et al. (2006) for this study. The selected

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regions include Northeast India (R1), East India (R2), Indo-Gangetic plains (R3), South India (R4), West (R5) and Northwest India (R6). The main absorbing aerosol source for different regions is described in Habib et al. (2006).
Each of the selected regions was further sub-divided in to grids of 1º × 1º and analyses were performed for each grid. The MODIS 1º × 1º derived daily CLWP and CIP has been divided in to 14 different bins which were mainly available during the season, ranging between 1 and 350 gm -2 at an interval of 25 gm -2. The indirect effect for each bin for the season has been calculated using the formula
(1)
Where ta is aerosol optical depth and re is the cloud effective radius (re-cloud water radius for fixed CLWP and cloud ice radius for fixed CIP).
Results and conclusions
The variation of aerosol optical depth (AOD), Fine mode fraction (FMF), Aerosol Index (AI), cloud droplet effective radius for ice phase (CIR) and droplet effective radius for water phase (CWR) during July and September of 2001 to 2004 over all six regions were analyzed. The AOD values indicating the aerosol loading over all regions found to be highest during July 2002 (Except over R6) as compared to all other months. Lower AOD values were found over all regions (Except over R4) during July 2003. To understand the nature of prevailing aerosols, we have used the TOMS Aerosol Index (AI) and was seen that AI values are positive over all the six regions in all months, indicating the strong presence of absorbing aerosols. The AI value found to be highest over region R6, obviously due to more absorbing desert
dust. The AI values found to be higher over all regions during July 2002 (except over R4) compared to all other years. To understand the contribution of fine mode aerosols, which are having maximum probability of acting as cloud condensation nuclei (CCN), we have analyzed the FMF values. FMF values found to be higher during September in all years, indicating more fine mode aerosols. FMF value found to be lowest during July 2002, indicating the reduction of fine mode aerosols. The CIR and CWR values found to be lowest during July 2002.
This may be due to the fact that
increase in AOD for fixed liquid Figure 1. Six Indian regions selected for the study based on dominant aerosol sources (Habib et al., 2006)
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water availability inhibits the droplet growth and hence droplet radius as explained by Twomey (1977), prevailed during July 2002 might have been the reason for decrease in CIR and CWR. The CWR and CIR values found to be highest during July 2003 and September 2004 respectively, which may be associated with the lower AOD values during these months.

In order to estimate the actual reduction / increase in cloud droplet radius in both water and ice phases (CWR and CIR) with respect to AOD, AIE has been estimated as explained in Section 2 for different available CLWP and CIP bins. It was seen that in 2001 (normal monsoon year) and in 2003 (Excess monsoon year), majority of the bins showing negative indirect effect in both the months, i.e. increase in cloud radius with increase in AOD. In July 2001 over R1 region, majority of the AOD and CIR bins were confined in the major 9 bins viz. 26-50, 51-75, 101-125, 126-150, 151-175, 176-200, 201-225, 226-250, 251-275 gm-2. It was seen that out of 9 bins, 6 bins (i.e. 66%) are showing negative indirect effect (increase in cloud ice radius with AOD). The average AIE value for all the bins found to be -0.035. The percentage of fixed CIP bins showing negative AIE during 2001 July found to be 84%, 80%, 71%, 60% and 75% over R2, R3, R4, R5 and R6 regions. The negative AIE was found to be more pronounced during 2003. Around 80-100% of the CLWP bins were found to be showing negative AIE over different regions with average AIE values ranging from -0.007 to -0.22. No considerable variation of % of bins showing negative AIE or average AIE values could be observed during September 2001 and 2003 compared to July 2001 and 2003 months for CIP and CLWP bins. The percentage of bins showing negative AIE and average AIE values averaged for all available bins are depicted in Table 1. In contrast to 2001 and 2003, AIE values for majority of the bins were found to be positive for both CIP and CLWP during 2002 and 2004 indicating the reduction of CIR and CWR with increase in AOD. During July 2002, over R1 region, 90% of the CLWP bins were showing positive AIE with an average AIE value of 0.25. The AIE impact was higher over all other regions with 100% of the fixed CLWP bins showing positive AIE. CIP bins during July 2002 also found to be showing 89 to 100% positive AIE over different regions (Table 1). Also, during July 2004, 66-100% of CIP and CLWP bins were found to be showing positive AIE over different regions (Table 1).

The main reason for positive and negative AIEs in contrasting monsoon years are associated with changes in circulation patterns, and associated air mass origins consisting aerosols of different sources. It is shown that main aerosol source over India during summer from 2000 to 2003 was aerosols transported from Middle-East and north Africa (Patra et al., 2005). Also it is shown that the anomalous wind pattern due to prevailing monsoon dynamics during July 2002 brought more absorbing aerosols from middle east (biomass burning byproducts) and north African region (desert dust) to the Indian subcontinent (Patra et al., 2005), most of which may be of hydrophobic nature, and hence inhibiting cloud droplet growth. It can be seen from Figure 2 that positive AI values, indicating the absorbing aerosol loading was highest during July 2002 compared to all other seasons. Similarly AI values were higher in July 2004 also. Correspondingly, FMF values also were lower in these months indicating less fine mode particles, and hence lower CCN. Also, it could be seen that the aerosol loading, indicated by AOD also were higher during these two months, viz. July 2002 and 2004, which decreases the water availability of individual aerosols and hence growth of cloud water / ice droplets and hence inducing Twomey effect. It is also seen that there was a considerable reduction in

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Table 1. The average values of AIE during for July and September (2001 to 2004) for fixed CIP and CLWP bins. Also the % of cases showing positive (negative) effects are given in parentheses

Month R1 R2 R3 R4 R5 R6
             
Jul2001_AIE@CIP -0.035 (60%) -0.085(84%) -0.117 (80%) -0.11(71%) -0.18(60%) -0.057(75%)
Jul2001_AIE@CLWP -0.12 (68%) -0.03 (71%) -0.07 (60%) -0.11(71%) -0.15(71%) -0.03(75%)
Sep 2001_AIE@CIP -0.06(75%) -0.06(63%) -0.07(66%) - 0.02(66%) -0.07(71%) -0.06(80%)
Sep2001_AIE@CLWP -0.097(63%) -0.05(57%) -0.02(71%) -0.04(75%) -0.05(50%) -0.03(60%)
Jul 2002_AIE@CIP 0.19(89%) 0.17(100%) 0.15(100%) 0.15(100%) 0.14(100%) 0.199(100%)
Jul2002_AIE@CLWP 0.25(90%) 0.14(100%) 0.21(100%) 0.22(100%) 0.21(100%) 0.37(100%)
Sep2002_AIE@CIP 0.01(50%) 0.003 (66%) 0.005(57%) 0.03(71%) 0.05(60%) 0.03(80%)
Sep2002_AIE@CLWP 0.13(80%) 0.07(70%) 0.09(75%) 0.09(70%) 0.03(60%) 0.07(75%)
Jul 2003_AIE@CIP -0.11(81%) -0.02(83%) -0.11(83%) -0.04(80%) -0.18(75%) -0.007(60%)
Jul2003_AIE@CLWP -0.13(89%) -0.13(89%) -0.16(86%) -0.148(84%) -0.22(100%) -0.1(80%)
Sep2003_AIE@CIP -0.13(90%) -0.09(86%) -0.11(83%) -0.12(80%) -0.14(75%) -0.1(80%)
Sep2003_AIE@CLWP -0.11(90%) -0.11(83%) -0.13(86%) -0.14(83%) -0.19(85%) -0.02(60%)
Jul 2004_AIE@CIP 0.125(78%) 0.11(80%) 0.09(71%) 0.12(66%) 0.08(80%) 0.075(75%)
Jul2004_AIE@CLWP 0.09(71%) 0.009(66%) 0.05(66%) 0.03(66%) 0.09(100%) 0.11(100%)
Sep2004_AIE@CIP 0.071(75%) 0.06(80%) 0.1(100%) 0.06(66%) 0.06(77%) 0.03(50%)
Sep2004_AIE@CLWP 0.08(50%) 0.06(57%) 0.02(57%) 0.11(88%) 0.085(67%) 0.065(100%)
             

Twomey effect during September 2002, which obviously associated with lower AOD, AI and higher FMF compared to July 2002. So our study suggests that higher AOD, AI and lower FMF values over the regions during July 2002 and July 2004, which inhibits the cloud droplet growth and hence precipitation through positive AIE (Twomey effect) lead to less ISMR (less monsoon rainfall) and the opposite conditions during July 2001 and 2003, which induced negative AIE (Anti Twomey effect) lead to normal and good monsoon.

Acknowledgements

The Authors would like to thank B. N. Goswami, Director, IITM and P.C.S. Devara, Head, PM & A Division for their encouragement. Data from MODIS and TOMS are also acknowledged with thanks. A. S. Panicker and S. Dipu gratefully acknowledge CSIR, for research fellowship.

References

Albrecht, B. A., 1989. Aerosols, cloud microphysics, and fractional cloudiness, Science., 245, 1227-1230.

Conant, W. C., Nenes, A., Seinfeld, J. H., 2002. Black carbon radiative heating effects on cloud microphysics and implications for aerosol indirect forcing, 1. Extended Kohler theory, J. Geophys. Res.,107, (D21) 4604,doi:10.1029/2002JD002094.

Habib, G., Venkataraman, C., Chiapello, I., Ramachandran, S., Boucher, O.,Shekar Reddy, M., 2006. Seasonal and interannual variability in absorbing aerosols over India derived from TOMS: Relationship to regional meteorology and emissions, Atmos Environment, 40, 1909-1921.

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Intergovernmental panel on climate change (IPCC)., 2007. Summary for policymakers, in Climate change 2007: The scientific Basis Contribution of working group I to the fourth assessment Report of the Intergovernmental panel on climate change, edited by S. Solomon et al., pp1-18, Cambridge Univ. Press, New York.

Kaufman, Y. J., Fraser, R. S.,1997. The effect of smoke particles in clouds and climate forcing, Science, 277, 1636-1639.

Lau, K. M., Kim, K. M., 2006., Observational relationships between aerosol and asian monsoon rainfall, and circulation, Geophys. Res. Lett, 33, L21810, doi: 10.1029/2006GL027546.

Patra, P. K., Behera, S.K., Herman, J. R., Maksyutov, S., Akimoto, H., Yamagata.T., 2005, The Indian summer monsoon rainfall: Interplay of coupled dynamics, radiation and cloud microphysics, Atmos. Chem. Phys., 5, 2181-2188.

Ravi Kiran, V., Rajeevan, M., Vijaya Bhaskara Rao, S., Prabhakara Rao, N., 2009, Analysis of variations of cloud and aerosol properties associated with active and break spells of Indian summer monsoon using MODIS data, Geophys. Res. Lett., 36, L09706, doi: 10.1029/2008GL037135.

Yuan, T.,. Li, Z., Zhang, R., Fan, J., 2008, Increase of cloud droplet size with aerosol optical depth: An observation and modelling study, J. Geophys. Res., 113, D04201, doi: 10.1029/2007JD008632.

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B–O–2

The Role of Aerosol Types in the Modification of Solar Spectral Radiation

D.G. Kaskaoutis1, Shailesh Kumar Kharol2, Anu Rani Sharma2 and

K.V.S. Badarinath2

1Atmospheric Research Team, Institute for Environmental Research and Sustainable Development, National Observatory of Athens, Lofos Nymphon, P.O.Box 20048, GR-11810 Athens, Greece

2Atmospheric Science Section, National Remote Sensing Centre, Dept. of Space-Govt. of India, Balanagar, Hyderabad – 500 625, India.

E-mail - dimitriskask@hotmail.com

Introduction

Information about solar irradiance at the earth’s surface is necessary for various applications, e.g. climate change or solar energy utilization. With the increasing use of spectrally selective devices, such as photovoltaic cells and coated glazings, the scientific interest is not limited to determining the total or “broadband” solar irradiance incident at ground level, but now also encompasses its spectral distribution. The presence of aerosols in the atmosphere has a significant impact on both the overall transmission of solar radiation (dominated by scattering in the ultraviolet and visible; Gueymard and Kambezidis, 2004; Kambezidis et al., 2005) and the global atmospheric heating process (dominated by absorption in the near-infrared). The spectral distribution of solar radiation critically depends on the aerosol optical properties under cloud-free skies. The spectral Diffuse-to-Direct- normal Ratio (DDR) is directly affected by the aerosol optical depth (AOD) and has strong dependence on wavelength and solar zenith angle (SZA). DDR is also affected by three other aerosol-related variables (Ångström wavelength exponent [a], single-scattering albedo [SSA] and asymmetry factor [g]), and by the surface’s spectral reflectance (which conditions the backscattering processes). This study investigates the modification of the clear-sky spectral DDR as a function of the SZA, AOD and other aerosol properties. The solar spectrum under various atmospheric conditions is predicted here with the SMARTS radiative transfer model, using ideal urban and continental aerosol properties.

Methodology

The results of this study are based on the predictions of the SMARTS model (Gueymard, 1995). SMARTS model has been proved to simulate the solar spectral radiation with great accuracy in case the aerosol properties (e.g. AOD, Angstrom exponent, single scattering albedo, asymmetry factor) are accurately measured or estimated. However, some limitations in the solar radiation simulation occurred for large SZAs and under turbid conditions (Kaskaoutis and Kambezidis, 2008). In order to generate the data set, SMARTS has been run several times for two SZAs, 20o and 60o. In this study two different aerosol models are used throughout, namely the urban (UR) and the continental (CT) types, both part of the options offered by the Standard Radiation Atmosphere.

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The spectral variation of the AOD is characterized by the wavelength exponent a. SMARTS uses two values of a, a1 and a2, which are representative of wavelengths below and above 500 nm, respectively. The average values of a1, obtained by linearly fitting the spectral AOD of the UR and CT aerosol models, are respectively 0.940 and 1.047, while those of a2 are 1.335 and 1.472, respectively. These two coefficients are part of the inputs required by SMARTS. The SSA in these aerosol models varies with wavelength, and is 0.65 and 0.89 at 500 nm. The asymmetry factor, g, is 0.6 and 0.64, respectively for the UR and CT aerosol models. The radiative transfer model was run to prepare look-up tables for the two aerosol types, expressing DDR as a function of AOD for the two SZAs.

Results and Discussions

(a) General characteristics of the spectral DDR distribution

DDR always exhibits the same spectral pattern, with higher values at the shorter wavelengths and lower values in the near infrared (NIR). In all cases the spectral distribution of DDR can be described by a power law (R2=0.96–0.99):

DDR = a -b. (1)

The spectral distribution of DDR is shown in Fig. 1a for 20o SZA in a log-log plot. The spectral DDR variation is investigated for the two aerosol models under different atmospheric conditions, characterized by widely different values of AOD500: (a) for clean conditions (AOD500 = 0.05) and (b) turbid conditions (AOD500 = 0.5). DDR increases with AOD and SZA at all wavelengths. For both aerosol models the increase in spectral DDR with AOD appears to be wavelength dependent. This increase is more noticeable at the shorter wavelengths, being related to the more intense scattering process in this spectral band. Figure 1a further reveals that DDR is dependent on the aerosol model, which is particularly evident at the longer wavelengths when using a log-log scale. As AOD decreases, so does the difference in DDR from the two aerosol models.

(b) The AOD effect

The DDR dependence on AOD varies significantly with wavelength, and for this reason two distinct wavelengths (440 and 870 nm) are selected for a more in-depth analysis. It is obvious from Fig. 1b that a quasi-linear relationship exists between DDR and AOD500, at least for SZA = 20°. At both wavelengths, the predicted DDR is larger for CT than for UR, while the difference is larger at shorter wavelengths and for high AODs. The noted differences can be attributed to the different aerosol properties for each model, like SSA, a and g. The CT aerosol model is characterized by low absorption, hence a higher SSA value. Consequently, the scattering process is more intense with CT than with UR, especially at shorter wavelengths. Fig. 1b also shows the results from an experimental study that was performed in Athens and involved spectral measurements at 440 and 870 nm (Kaskaoutis et al., 2007). Comparison between the experimental data and modeled results, as shown in Fig. 1d, indicates a good overall agreement, and suggests that urban aerosol conditions prevailed during the measurement period because the majority of the points are closer to the calculated results for the UR model. This is especially true at 870 nm, judging by the limited scatter.

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(c) Effect of the single scattering albedo

The variation of DDR as a function of SSA is shown in Fig. 1c for SZA=20o and three values of AOD500. It is clear that the SSA effect tends to be most significant at the shorter wavelengths and for large AODs, where a small variation in SSA can cause large changes in DDR. In contrast, at the longer wavelengths and especially for low AODs, variations in SSA do not seem to affect DDR.

(d) Effect of the Ångström exponent

For the results reported in this section, the variable inputs to the SMARTS model are limited to the value of a for <500 nm (a1) and two SSA. A fixed SZA of 60o is used, as higher SZAs yield large changes in DDR. The relationship between DDR and a is shown in Fig. 1d as a function of SSA. In general, higher a values produce larger DDRs for a given AOD. Nevertheless, the absorption efficiency of the aerosol particles seems to have a

Figure 1. (a) Spectral distribution of ln(DDR) versus ln for SZA=20o. The spectral curves correspond to two aerosol models, urban (UR) and continental (CT) under clean (AOD500 =0.05) and turbid (AOD500 =0.5) atmospheric

conditions. (b) Correlation between DDR and AOD500 at 440 and 870 nm via the predictions of the UR and CT aerosol models for SZA=20o. The SZA for the experimental data ranges between 35o and 66o. (c) Correlation

between DDR and SSA at 440 and 870 nm (AOD500=0.05, 0.3, 0.7 and SZA=20o). (d) Correlation between DDR and a1 for two SSA values according to the SMARTS model (SZA=60o, AOD500=0.3). The experimental a values

are determined in the 350-500 nm spectral region

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more intense effect on DDR than its size distribution. Therefore, for SSA=0.6, an increase in a1 from 0.2 to 2.6 causes an increase in DDR of about 0.11, while for SSA=0.9 this increase is almost twice, 0.21. In Fig. 1d, the scatter in the experimental data points (Kaskaoutis et al., 2007) is noticeable, and can be explained by the experimental uncertainty in a and the wide range of SZA (35o-66o). In addition, the model calculations used a fixed AOD500 value of 0.3, while the measured AOD500 values range from ~0.1 to 0.6 (Kaskaoutis et al., 2007). Nevertheless, the increasing trend of the observed DDR with a confirms the model simulations.

Conclusions

Using the SMARTS radiative transfer model (version 2.9.2), the dependence of the DDR upon the SZA, AOD500, SSA and a has been investigated. The two selected aerosol models, urban and continental, predicted different DDR values especially at the shorter wavelengths, under large AODs and high SZAs. Additionally, DDR strongly depends on SSA and a, especially at the shorter wavelengths, under fixed AODs and SZAs. It is worth noticing that the SMARTS results are in accordance with those derived from experimental measurements in Athens. The DDR is particularly sensitive to changes in SZA. For small SZA the ratio is smallest, while for large SZA the ratio increases, especially at shorter wavelengths. The sensitivity of the DDR to changes in aerosol load is also significant. The effect of SZA on DDR is more pronounced at the shorter wavelengths, while at mid-VIS and NIR wavelengths DDR is only affected by AOD. Both SSA and a1 present a strong effect on DDR. Less absorbing and smaller aerosol particles enhance the diffuse irradiance especially at the shorter wavelengths, yielding higher DDR under identical AOD values. The effect of SSA on DDR is more intense at the shorter wavelengths and under high AODs. Therefore, a small variation in SSA can cause large changes in DDR in polluted atmospheres.

Acknowledgements

The first author expresses his thanks to Dr. Chris Gueymard and Dr. Harry Kambezidis for his valuable help in many parts of this study.

References

Gueymard, C., 1995. SMARTS, a simple model of the atmospheric radiative transfer of sunshine. Algorithms and performance assessment. FSEC-PF-270-93. Florida Solar Energy Center, Cocoa, FL; available from http://www.fsec.ucf.edu/en/publications/pdf/FSEC-PF-270-95.pdf.

Kambezidis, H.D., Adamopoulos, A.D., Zevgolis, D., 2005. Spectral aerosol transmittance in the ultraviolet and visible spectrum in Athens, Greece. Pure Appl. Geophys. 162, 625-647.

Kaskaoutis, D.G., Kambezidis, H.D., Toth, Z., 2007. Investigation about the dependence of spectral diffuse-to- direct-beam irradiance ratio on atmospheric turbidity and solar zenith angle. Theor. Appl. Clim. 89, 245-256.

Kaskaoutis, D.G. and Kambezidis, H.D., 2008. The role of aerosol models of the SMARTS code in predicting the spectral direct-beam irradiance in an urban area. Renewable Energy 33, 1532-1543.

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B–O–3

Study of Columnar Aerosol Optical Depth using Prede Sun Photometer over Gadanki

M. N. Sai Suman, H. Gadhavi and A. Jayaraman

National Atmospheric Research Laboratory, Gadanki 517112,AP, India

Introduction

Atmospheric aerosols play important role both in regional and global climate by interacting with incoming solar radiation directly as well as indirectly by modifying the cloud properties. They are also found to affect the precipitation formation and distribution (Menon et al., 2002; Ramanathan et al., 2005). Aerosol optical depth (AOD) is a measure of transmission loss of direct solar radiation in the atmosphere by aerosol. It depends on columnar aerosol amount, type and size distribution (Gupta et al., 2003). AOD can be directly related to aerosol radiative forcing for a given region (Jayaraman, 2001).

Observations of AOD are carried out over Gadanki (13.450 N, 79.180 E), a rural location in southern peninsular India between April 2008 and June 2009 using an automated skyradiometer (Prede Co. Ltd., Japan) at five wavelengths. In this paper we present the results of seasonal variation of AOD from Feb 09 to June 09.

Methodology

A skyradiometer (Prede Co. Ltd., Japan) is used for deriving the AOD. It is an automated instrument, which measures both direct solar radiation and the scattered sky radiation at fixed angles at seven wavelengths viz., 310 nm, 400 nm, 500 nm, 675 nm, 870 nm, 940 nm and 1020 nm with a spectral band width of 10 nm full width half maximum (FWHM).. Two of these wavelengths namely 310 nm and 940 nm are not used for AOD derivation owing to high absorption by ozone and water vapour at these wavelengths. The instrument makes the direct radiation measurement at every one minute and scattered radiation measurements at every ten minutes. The calculations of AOD are carried out using a software package SKYRAD based on Nakajima et al. (1996). Though software package takes care of cloud contamination, all the data are manually checked for cloud contamination before using it for further analysis.

Results

The monthly box and whisker plots of AOD at 500 nm are shown in Figure 1. The median AOD for entire period is found to be 0.50. The AOD values are low during winter and monsoon seasons and they are high during pre-monsoon. Though the median AOD values during June and February are comparable, the spread in the values is significantly high during June. The low values during February is due to lower boundary layer height which supports less amount of particles per unit column, whereas low value during monsoon is mainly due to rain washout of aerosols.

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Figure 1. Measured monthly variation of AOD at 500 nm over Gadanki

Figure 2. Monthly variation of Angstrom exponent derived from the spectral variation of AOD

References

Spectral variation of AOD depends on the aerosol size distribution. Empirically the logarithm of AOD can be linearly related to logarithm of wavelength, where the negative of slope known as Angstrom exponent alpha (a) is function of size distribution; smaller the particles, higher the alpha and vice-verse. In the Figure 2, we show box and whisker plots of monthly Angstrom exponent alpha (a).The values of a gradually decreases, starting from February till June. This indicates that the overall aerosol size increases from February to June. During same period, relative humidity and wind speed also increases. The higher wind speed is favorable for lifting soil dust particles which are coarse mode particles as well as in generation of coarse sea-salt aerosols which can be advected to the observation site. Increasing relative humidity also help the hygroscopic aerosols to grow and hence effectively increasing the mean size of the particles. The transition from smaller size aerosols to bigger size aerosols takes place during April when median value of a is close to 1.

Gupta, P., H. Gadhavi and A. Jayaraman (2003), Aerosol Optical Depth Variation Observed using Sun- Photometer Over Indore, Indian Journal of Radio Space Physics, 32 ,229-237

Jayaraman, A. (2001), Aerosol radiation cloud interactions over the tropical Indian Ocean prior to the onset of the summer monsoon, Current Science, 81 (11) ,1437-1445

Menon, S., J. Hansen, L. Nazarenko and Y. Luo (2002), Climate Effects of Black Carbon Aerosols in China and India, Science, 297 (5590) ,2250-2253 ,doi:10.1126/science.1075159

Nakajima, T., G. Tonna, R. Rao, P. Boi, Y. Kaufman and B. Holben (1996), Use of sky brightness measurements from ground for remote sensing of particulate polydispersions, Appl. Opt., OSA, 35 (15) ,2672-2686 ,doi:10.1364/AO.35.002672

Ramanathan, V., C. Chung, D. Kim, T. Bettge, L. Buja, J. T. Kiehl, W. M. Washington, Q. Fu, D. R. Sikka, and M. Wild (2005), Atmospheric brown clouds: impacts on South Asian climate and hydrological cycle., Proc Natl Acad Sci U S A, 102 (15) ,5326-5333 ,doi:10.1073/pnas.0500656102

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B–O–4

A Lidar Study of Boundary-Layer Clouds and their Relationship with Meteorological Parameters during Monsoon 2009

M.G. Manoj, P.C.S. Devara and Y. Jaya Rao

Indian Institute of Tropical Meteorology, Dr. Homi Bhabha Road, Pune, India

Introduction

Atmospheric boundary layer (ABL) has the potential to alter aerosols and clouds depending on the background meteorological conditions and associated turbulence. Since major contribution towards the total aerosol content in the atmosphere comes from the ABL, it is interesting to explore the influence of the meteorological parameters first on aerosol production and further on their interaction with low-level stratiform clouds. Aerosols and clouds are vital components of the Earth-atmosphere radiation balance as well as the hydrological cycle of the Earth system (Ramanathan et al., 2001). Due to their large spatial and temporal variabilities, and heterogeneous physical and chemical characteristics, the interplay between these parameters under different atmospheric conditions is complex but really a challenging topic for the atmospheric scientists. Lidars play an important role in these studies because of their capability to make very precise continuous measurements of different aerosol and cloud parameters (e.g., McCormick et al., 1993).

The importance of background meteorological conditions to the growth, sustenance and dissipation of low-level clouds in a monsoon environment is investigated in the present paper. Observation of stratiform clouds using a polarized lidar at the Indian Institute of Tropical Meteorology (IITM) has aided the study of interaction of vertical profiles of meteorological parameters with that of lidar backscatter due to clouds and aerosols. Moreover, the stability of the atmosphere explained via vertical profile of the Richardson number indicates the favourable conditions for the development of stratus clouds which align themselves with the location of the monsoon Low-Level Jet (LLJ).

Description of Experimental Site

The study has been carried out at the IITM campus in Pune (18032' N, 73051' E, 559 m AMSL), India, which is on the eastern side of the Arabian Sea coast and also on the leeward side of the Western Ghats. The summer monsoon generally arrives in Pune around second week of June and the withdrawal starts in late September. Clouds present during this period are low-level thick stratus/stratocumulus. The experimental site is surrounded by hillocks of about 150 m high to its three sides (Devara and Raj, 1991) and this particular topography often favors strong shear of winds and thereby well mixing of boundary layer aerosols. In the monsoon time, the southwesterly wind passes over this station bringing moisture-rich marine air-mass modified by the local environment.

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Data and Method of Analysis

The present study is undertaken with an autonomous Dual Polarization Micro Pulse Lidar (Foretech Model DPMPL 0.3C) at IITM, Pune. It operates at 532 nm wavelength and has the capability of acquiring vertical profiles of aerosol backscatter intensity at fine spatial resolution of 30 cm and temporal resolution of about a minute in real-time mode. A detailed description and working principle of the lidar including the data analysis procedure have been published elsewhere (Devara et al., 2008). The lidar observations were performed on some selected days during the summer monsoon months of 2009. The backscatter profiles were utilized to derive cloud parameters; depolarization ratio (Sassen, 1991) of clouds (phase) and aerosols (sphericity). From the return backscatter intensity, the vertical profiles of the aerosol extinction coefficients were derived following standard methods (Kulkarni et al., 2008 and references therein). The meteorological fields (temperature, wind and relative humidity) for the observational days were obtained from the operational Year of Tropical Convection (YOTC) data available in the website of European Centre for Medium-Range Weather Forecasts (ECMWF).

Discussions of Results

Lidar experiments were performed on 17 July, 29 August and 23 September in 2009. The Height-Time-Intensity (HTI) plot for each of these days is shown in Fig. 1. The very

Figure 1. HTI plots of lidar backsatter intensity observed on 17 July, 29 August and 23 September, 2009. The evolution of the macrophysial structure of low-level monsoon clouds may be noted

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bottom part of each frame shows high intensity of backscatter signal strength indicating the well mixed boundary layer. Above the mixed layer the fragmented structure seen is the low-level clouds that formed at the altitude range of 300-800 m above ground level on all the three observational days. It is interesting to note that there are signatures of upward transport of boundary layer aerosols at certain epochs of time, for example at around 20:05 hrs on 17 July and at 20:10, 20:15, 20:30 and 20:40 hrs on 29 August 2009. Under favorable meteo-rological conditions, these aerosols could act as condensation nuclei for cloud development. The fragmented cloud structures are considered to be due to the wind-shear induced turbulence. The LDR plots are shown in Fig. 2. The LDR value of around 0.5 in the mixed layer normally implies that aerosols are of asymmetric in shape. Also the LDR for the cloud cells indicate the presence of liquid water in the sensing region. Similar values of LDR have been reported recently by Dubey et al. (2009) for Delhi.

Figure 2. Plots of Linear Depolarization Ratio for (A) 17 July and (B) 29 August, 2009

Figure 3 displays the time evolution of the extinction coefficient averaged for every 10 minutes interval for all the above days. Evident in each figure is the presence of clouds around the altitude range of 300-800 m. The direction of the arrows indicates the upward / downward motion of the cloud cells. The broken-cloud structures may have been caused by the shear of both horizontal and vertical winds. Figure 4 depicts the association of the occurrence of clouds with the prevailing horizontal as well as vertical wind and the Ri profile for 17 July (Fig. 4A) and 29 August (Fig. 4B) at 20:30 hrs. Similar results were obtained for 23 September also (Figure not shown). It may be noted that both the wind components started peaking at the altitude where clouds started forming. The increased updraft due to high vertical velocity plus the amplified horizontal advection of marine air mass could support the development and sustenance of cloud at that level. The synoptic- wind pattern over the experimental site during this time was southwesterly from the Arabian Sea (Figure not shown). If the turbulence induced by the wind shear is strong enough, the moisture in the lower level can be efficiently transported upward to form clouds (Zhu et al., 2001). Further effect due to strong wind shear is that it can cause structural changes in the cloud macroscopic properties such as thickness and shape. The Richardson number profile shows a minimum value at the lower levels where clouds formed, and started shooting up at upper levels. The low values of Ri indicates higher values of wind shear near the ground and thus unstable condition, and higher values of Ri at upper level is an indication of greater

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Figure 3. Time evolution of the vertical profiles of extinction coefficient averaged for every ten minutes interval for 17 July, 29 August and 23 September, 2009. The arrows in each figure show the ascending/ descending motion of the cloud cells

stability achieved by the air parcel. The combined effect of the transport of aerosols plus the prevailing synoptic conditions could be responsible for the sustenance and structure of the observed clouds.

Figure 4. Comparison of vertical profiles of lidar extinction coefficient (black solid line) with those of horizontal wind (left panel), vertical wind (middle panel) and Richardson number (right panel) for (A) 17 July and (B) 29 August, 2009

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Acknowledgements

The authors are thankful to Director, IITM for encouragement. One of the authors (MGM) is thankful to the Council of Scientific and Industrial Research, India, for awarding research fellowship.

References

Devara P.C.S., and P.E. Raj (1991): Study of atmospheric aerosols in a terrain-induced nocturnal boundary layer using bi-static lidar. Atmos. Environment 25A, 655-660.

Devara, P.C.S., and Co-authors (2008): Mobile lidar profiling of tropical aerosols and clouds, J. Atmos. Ocean. Tech., 25, 1288-1295.

Dubey, P.K., and Co-authors (2009): Depolarization ratio measurement using single photomultiplier tube in micro-pulse lidar, Rev. Sci. Instrum., 80, 053111(1-5).

Kulkarni, P., and Co-authors (2008): Features of upper troposphere and lower stratosphere aerosols observed by lidar over Gadanki, a tropical Indian station, J. Geophys. Res., 113(D17207), 1-11.

McCormick, M. P., and Coauthors (1993): Scientific investigations planned for the Lidar In-space Technology Experiment (LITE). Bull. Amer. Meteorol. Soc., 74, 205-214.

Ramanathan, V., and Co-authors (2001): Aerosols, climate and the hydrological cycle, Science, 294, 2119-2124.

Sassen, K. (1991): The polarization lidar technique for cloud research: A review and current assessment. Bull. Amer. Meteorol. Soc., 72, 1848-1866.

Zhu, P., and Co-authors (2001): Formation and development of nocturnal boundary layer clouds over the Southern Great Plains, J. Atmos. Sci., 58, 1409-1426.

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B–O–5

Lidar Observations of Contrasting Aerosol Layers Over Trivandrum (8.5ºN, 77ºE) During Pre-monsoon and Summer Monsoon Seasons

Manoj Kumar Mishra*, Bijoy V. Thampi, Anish Kumar M. Nair, K. Rajeev and K. Parameswaran

Space Physics Laboratory,Vikram Sarabhai Space Centre,Thiruvananthapuram 695 022

*Corresponding Author: E-Mail: manoj_mishra@vssc.gov.in

Introduction

Vertical distribution of aerosols has a profound influence on the radiative heating of the atmosphere and the long-range transport of particulates away from their source regions. During the pre-monsoon period (March-May), the transport of aerosols from the adjoining continents paves way for a significant aerosol plume over the east Arabian Sea. During the Asian summer monsoon season, the large-scale transport of mineral dust from the Arabian region results in an intense aerosol plume over the Arabian Sea, particularly at regions north of 10°N, with the aerosol optical depth (AOD) exceeding 0.7 over a vast area. Trivandrum (8.5°N, 77°E), a tropical coastal station in the southwest coast of Peninsular India, is covered by the East Arabian Sea Plume (EASP) during the pre- monsoon period while it is very close to the southern fringe of the dust plume during the Asian summer monsoon season. Systematic observations of the altitude profiles of aerosol backscattering coefficient and the linear depolarization ratio (LDR) over Trivandrum using a dual polarization Micropulse Lidar (MPL) are used to investigate the particulate properties associated with the above aerosol plumes and identify the pathways of aerosol transport at different altitude regions during the pre-monsoon and summer monsoon seasons.

Experimental Methods and Data Analysis

The dual polarization Micropulse Lidar system consists of a diode-pumped frequency- doubled solid state Nd:YAG laser emitting laser pulses at the wavelength of 532 nm having a pulse width of 7 ns at 2500 Hz PRF with a maximum pulse energy of 8 J. A Maksutov-Cassegrain type telescope with 178 mm diameter is used for transmitting the laser beam as well as receiving the backscattered signal. Observations are made with a range resolution of 30 m and time integration of 60 seconds. The lidar system alternates between the two states of polarization (co-polarized and cross polarized) at an interval of 60 seconds. The raw data are subsequently corrected for the detector noise, dead-time, and geometrical correction factor. Lidar data with clear sky condition prevailing at least up to an altitude of 6 km and for a minimum period of 30 minutes are used to derive the altitude profile of aerosol backscatter coefficient ( a) using Fernald’s method (Fernald, 1984), assuming an aerosol extinction-to-backscatter ratio (S1) of 40 Sr-1. In order to

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further reduce the errors due to the assumed parameters used in the lidar inversion, lidar- derived profiles of aerosol extinction and backscatter coefficients are further weighed by the AOD observed using sunphotometer (Microtop-II, Solar Light Co.).

Results and Discussions

(1) Pre-monsoon Season

Highly polluted conditions, with mean AOD of 0.52 prevailed during 1-16 May 2008, while relatively clean (21-23 May) or moderately polluted conditions (27-30 May) prevailed during the rest of the period in May. The mean altitude profiles of a and LDR during these periods are depicted in Fig.1, which shows a rather weak altitude variation of a up to 4 km followed by a sharp decrease above during the polluted conditions. Altitude profiles of LDR clearly show the presence of two distinct layers with relatively large value of LDR (~0.09) between 2-4 km. Though the value of LDR below 2 km is comparable to that of the elevated aerosol layer, it is significantly larger than that during the low and moderately polluted days. High value of LDR shows the presence of significant amount of non-spherical aerosols. In contrast to the significantly large non-sphericity of aerosols observed between 0-4 km altitude during the highly polluted period, aerosols below 2 km altitude are nearly spherical during the less and moderately polluted periods. Radiosonde observations over Trivandrum shows that the atmosphere below 2 km is highly humid (RH>80%) while dry conditions (RH<60%) prevail in the altitude region between 2-4 km. Effect of long-range transport in modulating the altitude profiles of a and the total AOD are investigated based on the 7-day air back-trajectory analysis obtained using the NOAA- ARL-HYSPLIT transport dispersion model. Contribution of the elevated aerosol layer to the total column integrated backscatter coefficient (IBC) is largest (25%) during the highly polluted period. This corresponds to an AOD (t532) of 0.13 for the layer at 2-4 km, which is almost four times larger than the corresponding value (0.03) during the less polluted period. During the pre-monsoon season, the elevated layer is prominent when the advection of dry air occurs from the northern parts of the Indian subcontinent and north Arabian Sea.

Figure 1. The mean altitude profiles of a (left panel) and linear depolarization ratio (right panel) for different conditions of AOD during May 2008 The horizontal lines show standard errors

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Summer-monsoon Season

Figure 2 shows the mean altitude profiles of a and LDR representing three different periods of contrasting aerosol properties observed during June 2008. It reveals a prominent layer of highly non-spherical aerosols occurring in the altitude band of 1 to 4 km during 11-17 June 2008. The LDR (~0.15) associated with this layer is considerably larger than the mean LDR (<0.05) associated with the aerosols below 1 km. Note that this value of LDR is considerably larger than that of the polluted aerosol layer at 2-4 km observed during the pre-monsoon season. The seven-day back-trajectories reaching Trivandrum below 1 km shows that the transport of airmass mainly occurs from the south Arabian Sea and Indian Ocean, while a mixture of airmass originating from the western Arabian Sea and Arabia reaches around 1-4 km. This, along with the high values of LDR and satellite observations of high AOD over the Arabian Sea unambiguously shows that the layer at 1-4 km should be associated with the dust transported from the Arabian region. When the dust layer is prominent, the rate of decrease of a with altitude is rather small up to about 4 km. During such events, about 50% of the column integrated aerosol backscatter is contributed by the aerosols in the dust layer between 1 - 4 km. Whenever the dust layer is weak or absent, the contribution to the integrated backscatter from aerosols above 1 km decreases to <35%.

Figure 2. The mean altitude profiles of a (left panel) and linear depolarization ratio (right panel) for different conditions of AOD during June 2008 The horizontal lines show standard errors

Conclusions

Micropulse lidar (MPL) observations of the altitude profiles of a and LDR over Trivandrum (8.5ºN, 77ºE) show prominent aerosol layers with contrasting LDR values during the pre-monsoon and the Asian summer monsoon seasons. Two distinct aerosol layers persist in the lower troposphere during both these seasons: around 0-2 km and 2-4 km during the pre-monsoon season and 0-1 km and 1-4 km during the summer monsoon season. The lower layer has larger integrated extinction coefficient and consists of highly spherical aerosols (LDR<0.05) while the elevated layer contains significant amount of non-spherical aerosols. Though the optical depth of this layer is significantly larger during the pre-monsoon season (~0.13) compared to the summer monsoon period (~0.08), the non-sphericity of aerosols is more significant during the latter (LDR ~ 0.15) compared to

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the former period (LDR ~ 0.10). During the pre-monsoon season, the elevated layer is prominent when the advection of dry air occurs from the northern parts of the Indian subcontinent and north Arabian Sea and is absent when the transported air-mass originates from the southern parts of Indian Ocean or Bay of Bengal. During both the seasons, the lower layer is associated with the airmass transport from the Arabian Sea (northeast Arabian Sea during pre-monsoon season and southwest Arabian Sea during the summer monsoon period). High relative humidity (80-97%) and low values of LDR associated with this layer suggest that the particles in this layer might have undergone significant hygroscopic growth resulting in the highly spherical shape. During the pre-monsoon season, aerosols tend to increase the atmospheric heating rate by about 0.8 K/day at 500 m and 0.3 K/day at 3 km, which are around ~80% and 30% of the heating rate due to the background aerosol-free atmosphere.

References

Fernald, F. G., 1984, Analysis of atmospheric lidar observations: some comments, Appl. Opt., 23, 652-653.

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B–O–6

Multi-year Investigations of Near-surface and Columnar Aerosols over Dibrugarh, Northeastern Location of India

Mukunda M Gogoi1, K Krishna Moorthy1, Pradip K Bhuyan2, Kalyan Bhuyan2, Binita Pathak2, Gayatri Kalita2 and S Suresh Babu1

1Space Physics Laboratory,Vikram Sarabhai Space Centre,Trivandrum-695022, India

2Department of Physics,Dibrugarh University, Dibrugarh-786004, India

Introduction

Despite the very significant role of near surface aerosols on columnar properties, characterization of their microphysical properties with extensive data are sparse over south Asian region and India in general, and over the northeastern part of India in particular. This region is unique owing to its distinct topography, vast water bodies, dense vegetations and the low level of industrial development and energy utilization, a scenario conducive for the dominance of natural aerosols. As such, we investigate the total and size segregated near-surface aerosol mass concentrations obtained using a Quartz Crystal Microbalance (QCM) impactor over Dibrugarh (27.3oN, 94.6oE, 111 masl), in the extreme northeastern part of India, during the period May 2007 to April 2009 with concurrent measurements of columnar spectral aerosol optical depths (AOD) to infer on the seasonal changes in the general homogeneity in aerosol vertical distribution.

Results

Near surface aerosol mass concentrations

From the two-year data of the QCM, the monthly mean values of total (Mt) and submicron (Ma, particular diameter dpi < 1 μm) aerosol mass concentrations were estimated from the individual measurements. The annual variations of these are shown in Fig.1, where the points are the monthly means and the vertical bars through them are the standard errors. As, in general, the accumulation mode (sub-micron) particles are believed to be mostly of anthropogenic origin, we estimated the accumulation mode mass fraction Af (= Ma/Mt) and its annual variation is also shown in Fig.1. The figure reveals the following:

1.Both the Mt and Ma depict very similar annual variations with a broad annual peak from December till February and low during June-July; with peak to trough ratio of ~ 3.

2.The annual high of Mt was 75.6 ± 17.7 μg m-3 in February and low 24.8 ± 6.5 μg m-3 in June.

3.Accumulation mode aerosols contributed more than 50% to the total aerosol mass

concentration throughout the year; being highest during Dec-Feb (mean A-f ~ 0.87

± 0.03) and lowest (~ 0.54 ± 0.01) in July.

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This annual variation is attributed mainly to the contrasting prevailing synoptic meteorological conditions at Dibrugarh.

Figure 1. Annual variation of (a) total (Mt) and submicron (Ma) aerosol mass concentrations and (b) Accumulation aerosol mass fraction (Af)

Columnar aerosol optical depth

Columnar AODs were estimated using a Multi wavelength solar radiometer (MWR), which provided continuous measurements of directly transmitted, ground reaching solar flux at ten narrow wavelength bands, centered at 380, 400, 450, 500, 600, 650, 750, 850, 935 and 1025 nm. The annual variation of AOD at 500 nm is examined in Fig.2, along with the Ångström wavelength exponent a. High values of a (> 1) generally indicate the dominance of accumulation mode particles (radii < 1 μm), while low values are representations of the dominance of coarse mode particles (radii > 1 μm).

As seen from the figure, the AOD (at 500 nm) peaks in the month of March (~ 0.57 ± 0.04), decreases subsequently gradually to the lowest value in October (~ 0.19 ± 0.11) with a weak secondary peak in September. a decreases rapidly from the high value in January (~ 1.32 ± 0.07) to lowest value (~ 0.76 ± 0.09) in May and increases again to a higher value in July (~ 1.38 ± 0.40). During September to December, the values are moderate (~ 1.1) and nearly stable. The annual variation of a thus shows high accumulation mode

Figure 2. Annual variation of (a) columnar aerosol optical depth (AOD) at 500 nm and (b) Ångström wavelength exponent (a)

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domination (a >1.0) throughout the year, except during the pre-monsoon months (March, April and May), during which the values of a < 1.0 indicate the coarse mode domination in the column.

Role of near surface aerosols on columnar optical properties

With a view to examining the role of near surface aerosols on columnar AOD, we have estimated the surface extinction coefficients at = 500 nm using the daily mean number size distributions n(r) derived from the QCM measured mass size distributions and assuming a well mixed layer of vertical extent Z = 1 km, following the equation

(1)

where, Qext is the Mie extinction efficiency parameter, which is a function of particle size (r) in relation to the wavelength ( ) of radiation and its complex refractive index (m), ra and rb are respectively the lower and upper cutoff radii of the particles. The extinction coefficients estimated for the different months are shown in Fig.3 for their annual features.

Fig. 3. Annual variation of surface extinction coefficients and climatological AOD at 500 nm

The near surface extinction coefficient turns out to be maximum (~ 0.3 ± 0.13) in February associated with the highest mass concentration during this month, which decrease to ~ 0.05 ± 0.01 in July, when the suspended particles are mostly washed out by heavy monsoon rains. The values increase again till December (~ 0.19 ± 0.0.6) with the continuous increase of total and accumulation mode aerosol concentrations. It is interesting to note that the annual pattern of extinction coefficients in general follows that of AOD (Fig.2). There are some noticeable differences; while the peak in surface extinction occurred in February, highest in AOD occurred in March. Similarly, both fall off gradually to the lowest value in July. The annual features of near surface and columnar aerosols are thus indicative of their varying associations at different seasons. With a view to this, we estimated the percentage contribution of near surface extinction coefficients to columnar AOD for each day of the MWR observations and their monthly mean and standard deviations are calculated, irrespective of years. The analysis showed that the near surface aerosols contributed ~35% to 70% to columnar extinction during ret-monsoon and winter seasons, while it was lower (< 26%) during pre-monsoon and monsoon season (except that in the monsoon month September).

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B–O–7

Comparison of Aerosol Properties by Collocated CIMEL and PREDE Sun-Sky Radiometers over Pune, India

P.C.S. Devara, K. Sumit, G. Pandithurai and S. Dipu

Indian Institute of Tropical Meteorology, Dr. Homi Bhabha Road, Pune 411 008, India

Introduction

It is well known that the aerosol distributions (both in space and time) are complex due to the increasing emissions of sulfuric, nitric, carbonaceous, and other aerosols associated with economic growth. Aerosols involving soil dust, generated mainly due to land- cover/ use changing patterns, is also significant. To comprehend the characteristics of highly uncertain aerosols, presently two types of ground-based instruments, namely, CIMEL and PREDE Sun/Sky photometers/radiometers are available in the world. The basic data from these radiometers are synthesized and the final products are archived at AERONET and SKYNET monitoring networks, respectively. These radiometers provide very important optical, microphysical and radiative parameters of aerosols such as aerosol optical depth (AOD), Angstrom exponent (AE), aerosol size distribution (ASD), single scattering albedo (SSA), asymmetry parameter (AP) and refractive index (RI) based on standard processing systems involving inversion methods applied to the data obtained from the CIMEL ((Holben et al., 1998, 2001) and PREDE (Nakajima et al., 2003). In the present study, we compare the performance of the CIMEL and PREDE radiometers that have been in regular operation concurrently since the last more than 5 years, at the Indian Institute of Tropical Meteorology (IITM), Pune, India. The salient features of aerosols including similarities and deviations observed by the above two radiometers are documented and discussed. Such exercises would be of immense help in multi-dimensional mapping of aerosol characteristics over the globe by integrating the products from both AERONET and SKYNET monitoring networks.

Performance Characteristics of CIMEL and PREDE Sky Radiometers

Sun-sky radiometer is a research tool for analysis of AOD and particle size distribution, both major factors in the climatology of planet Earth. It can measure direct and diffuse (sky) solar radiation in different spectral bands across the sky. It consists of a tracking system, a spectral sensing radiometer, a rain detector, a sun sensor and a control unit. These radiometers are world-class instruments providing a wealth of information to satellite data validation and Earth’s radiation budget studies. Presented in the Table 1 are the chief specifications of both CIMEL and PREDE radiometers being applied for aerosol characterization experiments at the IITM, Pune, which is one of the fast growing urban locations in India. It is evident from the table that compared to PREDE, CIMEL photometers have certain advantages like, auto-power (though solar panel) to the instrument, cloud-screening algorithm, robot parking at home when the instrument is

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not in use in order to avoid entry of dust and insects into the telescope collimators. Over and above, CIMEL is highly portable and light weight, hence can be more suitable of continuous outdoor operation, particularly over remote stations. Both CIMEL and PREDE radiometers have been in operation at IITM, Pune.

Table 1. Comparison between the characteristics of CIMEL and PREDE sky radiometers

Characteristic PREDE CIMEL
     
Filter wavelengths 315, 400, 500, 675, 870, 940 and 1020 nm 340, 380, 440, 500, 670, 870,
    940, and 1020 nm
Bandwidth of filters 3 nm for 315 nm and 15 nm for all other 10 nm for all wavelengths
  wavelengths  
Detector Single detector Two detectors
  Silicon Photodiode 2 Silicon sensors for Sun and
    Sky radiance
Sun positioning sensor 4 element photodiode Four quad silicon cells
Tracking Automatic Automatic
Scan angles ± 170 azimuth ± 180 azimuth
  -20 to +150 zenith -6 to +140 zenith
Scan accuracy 0.1 degrees 0.1 degrees
Method of calibration Solid view angle. Disk scan calibrated Two meter integrating sphere
  by the manufacturer. Spectral V0 from for sky channels. Spectral V0
  modified Langley plots from Mauna Loa Langley plots
Method of recalibration On-site measurement combined with With NASA GSFC reference
  later processing, both for solid state instruments
  opening angle and for absolute sensitivity  
Scan accuracy 0.1 degrees 0.1 degrees
Stability Effects of temperature and the effects of  
  possible drifting of more than one  
  detector are eliminated  
     

Results and Discussion

Since the two radiometers have only three common wavelengths (675, 870 and 1020 nm), the optical depth at 440 nm wavelength for PREDE radiometer was calculated using Angstrom exponent

(a400-500 nm). Figure 1 shows the plots of AOD at each wavelength derived from

the solar direct irradiance between the two instruments. High correlation was found with a significant coefficient larger than 0.92 at each band. In another study, Che et al. (2008) had found better correlation coefficient (~0.995) at each band. The difference (defined as “mean

SKYNET – mean AERONET / mean AERONET %”)

Figure 1. Comparison of AOD obtained from CIMEL and PREDE radiometers at four wavelengths

195

Figure 2. Size distribution of aerosol derived from CIMEL and PREDE data over Pune.
Figure 3. Single scattering albedo from PREDE (400, 675, 870, 1020 nm) and CIMEL (440, 675, 870 and 1020nm)

AEROSOLS & CLOUDS : CLIMATE CHANGE PERSPECTIVES

between the two instruments at 440, 675, 870 and 1020 nm is -3.27%, -5.44%, -4.95% and -2.0%, respectively. The AOD obtained from two radiometers shows good agreement at all the wavelengths. These results suggest good coincidence in direct-sunlight observations by the two radiometers.

Both size distribution functions are bi-modal, and agree well in case of fine- mode but it is difficult to say in their agreement, in case of coarse-mode. In general, the coarse-mode values retrieved by PREDE are larger than those by CIMEL radiometer for all the months, as is also observed by Che et al. (2008). Che et al. (2008) have also observed that fine-mode volume concentration of SKYNET is less than that of AERONET. Single scattering albedo (SSA) retrieved from the PREDE and CIMEL radiometers are compared in Figure 3. The SSA values retrieved from PREDE radiometer are consistently higher than those by the CIMEL radiometer. Also, the difference among month-to-month SSA at each wavelength for PREDE radiometer is negligible. The Table 2 shows the difference between PREDE and CIMEL radiometer-retrieved SSA values at each wavelength. The difference seems to be

reducing as we approach monsoon season. The variability in SSA seems to be happening in CIMEL retrieved values, which projects to be quiet consistent with the seasonal change in values. Since, meteorological conditions over Pune changes from northeasterly to southwesterly from winter to monsoon seasons.

Table 2. Difference among SSA values for four wavelengths from February-2009 to June-2009

Wavelength Feb Mar May June
(nm)        
         
440 0.13 0.10 0.05 0.03
675 0.20 0.17 0.09 0.05
870 0.22 0.19 0.10 0.05
1020 0.23 0.20 0.09 0.04
         

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References

Che, H., and Co-authors, 2008: Inter-comparison between aerosol optical properties by a PREDE sky radiometer and CIMEL sunphotometer over Beijing, China, Atmos. Chem. Phys., 8, 3199–3214.

Holben, B.N., and Co-authors, 2001: An emerging ground-based aerosol climatology: Aerosol Optical Depth from AERONET, J. Geophys. Res., 106, 12 067-12 097.

Holben B.N., and Co-authors, 1998: AERONET - A federated instrument network and data archive for aerosol characterization, Rem. Sens. Environ., 66, 1-16.

Nakajima, T., and Co-authors, 2003: Significance of direct and indirect radiative forcings of aerosols in the East China Sea region, J. Geophys. Res., 108 (D23), 8658, doi:10.1029/2002JD003261.

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B–O–8

Aerosols and Their Radiative Forcing over Mohal-Kullu in the northwestern Indian Himalaya

Raj Paul Guleriaa, Jagdish C. Kuniyala, Pan Singh Rawatb, Nand Lal Sharmaa and Ajay Kumar Thakura

aG.B. Pant Institute of Himalayan Environment and Development, Himachal Unit, Mohal-Kullu, Himachal Pradesh, India

bDepartment of Physics,D.S.B.Campus, Kumaun University, Nainital, Uttarakhand, India

E-mails: kuniyaljc@yahoo.com, jckuniyal@rediffmail.com

Introduction

The aerosols have been an indicator to represent the health status of ambient air environment in climate change processes. These aerosols influence earth radiation budget and leads to climate forcing through scattering and absorption processes (Charlson et al., 1992). Any change in climatic system which alters the radiation balance is known as radiative forcing (Russell et al., 1999). The presence of aerosols in the atmosphere controls the cooling/heating effect of the earth surface and in turn cooling/heating process of the atmosphere governs the atmospheric process. Aerosols having scattering efficiency increase the planetary albedo, leading to a negative surface forcing and produce cooling effect. The presence of radiation observing aerosols plays a major role to enhance radiative forcing by dimming the surface and heating the atmosphere. Consequently, this effect alters normal climate (Ramanathan et al., 2007), which is likely to lead global warming and the melting of worldwide glaciers and sea-ice.

Methodology

The present study reports status of aerosol radiative forcing under full clear days and its relation with aerosols made over April 2006 to December 2008. The sun light extinction integrated over the total atmospheric column was directly measured by Multi wavelength Radiometer (MWR) over Mohal (31.9°N, 77.12°E, 1154 m amsl) located in the Kullu valley of the northwestern Indian Himalaya. The raw data obtained from MWR was analyzed using Langley techniques to obtain the total columnar aerosol optical depth (AOD) (Shaw et al., 1973). The obtained AOD was used to estimate aerosol radiative forcing (F) in Fu- Liou model as:

(F)S,TOA = (FNA)S,TOA - (FA)S,TOA

where FNA and FA, the short wave fluxes without and with aerosols respectively, and the subscript S and TOA refer to the earth’s surface and top of the atmosphere respectively. Atmospheric forcing evaluated as:

(F)Atm = (F)TOA - (F)S

The net aerosol absorption forcing efficiency within the atmosphere is calculated as:

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(ÄFAtm) = ( F /t) – ( F /t)

where F /t and F / t is forcing efficiency at the surface and TOA leaving the

atmosphere to the change in columnar aerosol amount (t). The amount of flux, which was observed in the atmosphere, gets transformed into heat assessed by Liou (2002).

T/ t = (g/Cp) [FAtm/P]

where T/ t is the heating rate, g is the acceleration due to gravity, Cp the specific heat capacity of air at constant pressure and P is atmospheric pressure.

Results and Discussions

Figure 1 indicates the variation of AOD at 500 nm. During 2006, 2007 and 2008, the daily averaged value of AOD was 0.27±0.02, 0.22±0.01 and 0.23±0.01 respectively. While over the entire observation period, it stood to be 0.24±01. The highest ever AOD was 0.48 on May 8, 2006 whereas lowest remained 0.10 on December 27, 2006.

Figure 1. Variation in AOD at 500 nm Figure 2. Seasonal variation in Radiative forcing

The (F)TOA, (F)S, and (F)ATM due to aerosols vary in a range of -3.2 to -21.5 Wm-2, -13 to -58.9 Wm-2 and 8.3 to 48.7 Wm-2 respectively. The corresponding mean values were -

8.9±0.28 Wm-2, -31.7±0.76 Wm-2 and 22.7±0.68 Wm-2. The F /t was -113.4 Wm-2

AOD-1, whereas corresponding F /t and ( FAtm) were -8.9 Wm-2 and 104.4 Wm-2 respectively. The computed radiative forcing for different seasons is shown in Figure 2.

Table 1. Forcing efficiency and atmospheric heating rate for different seasons

Season (2006-2008) Forcing efficiency in Wm-2AOD-1   Heating rate
  TOA Surface Atmosphere   (K day-1)
Winter (Dec, Jan, Feb, Mar) -49.9 -139 89 0.51
Summer (Apr, May, Jun, Jul) -19.8 -124.5 104.7 0.95
Monsoon (Aug, Sep) -18.8 -120.2 101.3 0.71
Autumn (Oct, Nov) -54.6 -142.5 87.8 0.52
           

The diurnally averaged (F)S was as low as -28.3 Wm-2 in winter and increase in magnitude to reach -40.5 Wm-2 by summer, and gradually decreases in monsoon and autumn seasons. The (F)Atm was highest in summer (+33.9 Wm-2) and lowest in winter (+18 Wm-2). The forcing efficiency estimated for different seasons are given in Table 1.

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The atmospheric absorption (+22.7 Wm-2), translates into atmospheric heating of 0.64 K day-1. The heating rate of the atmosphere during winter was 0.51K day-1 and reach to its maximum value of 0.95 K day-1 during summer. A comparison of the aerosol radiative forcing was estimated over Mohal with other similar estimates elsewhere as is listed in Table 2. All the major locations have atmospheric forcing values > +30 Wm-2 and imply the effect of pollution which is maximum over these sites in comparison to present site. The atmospheric forcing during winter over Mohal was 57 % of the metropolitan city— Delhi.

Table 2. Comparison of the aerosol radiative forcing (in Wm-2) in the present study site at Mohal and other different locations in the country

Location Period TOA Surface Atmosphere Reference
           
Mohal Dec-Mar -10.3 -28.3 18 Present study
Kanpur Dec-Feb -9.1 -40.4 31.3 Dey and Tripathi, 2007
Delhi Dec-Feb -8.1 -39.6 31.5 Dey and Tripathi, 2007
Allahabad Dec-Feb -9.5 -44.3 34.8 Dey and Tripathi, 2007
Kolkata Dec-Feb -9.6 -46.2 36.6 Dey and Tripathi, 2007
Nainital Dec 0.7 -4.2 4.9 Pant et al., 2006
Indian Ocean Feb-Mar -10 -29 19 Satheesh et al., 2002
           

Acknowledgement

The authors are thankful to the Director, G.B. Pant Institute of Himalayan Environment and Development, Kosi-Katarmal, Almora (Uttarakhand) for providing facilities.

References

Charlson, R.J., Schwartz, S.E., Hales, J.M., Cess, R.D., Coakley, J.A., Hansen, J.E. and Hoffman, D.J., 1992. Climate forcing by anthropogenic aerosols. Science 225, 423-430.

Dey, S. and Tripathi, S.N., 2007. Estimation of aerosol optical properties and radiative effects in the Ganga basin, northern India, during the wintertime. J. Geophys. Res. 112 D03203, doi:10.1029/2006JD007267.

Liou, K.N., 2002. An introduction to atmospheric radiation, 583pp., Elsevier, new York.

Pant, P., Hegde, P., Dumka, U.C., Sagar, R., Satheesh, S.K., Moorthy, K.K., Saha, A. and Srivastava, M.K., 2006. Aerosol characteristics at a high altitude location in central Himalayas: Optical properties and radiative forcing. J. Geophys. Res. 111 D17206, doi:10.1029/2005JD006768.

Ramanathan, V., Li, F., Ramana, M.V., Praveen, P.S., Kim, D., Corrigan, C.E., Nguyen, H., Stone, E.A., Schauer, J.J., Carmichael, G.R., Adhikary, B. and Yoon, S.C., 2007. Atmospheric brown clouds: hemispherical and regional variations in long range transport, absorption and radiative forcing. J. Geophys. Res. 112 D22S21, doi:10.1029/2006JD008124.

Russell, P.B., Hobbs, P.V and Stowe, L.L., 1999. Aerosol properties and radiative effects in the United States East Coast haze plume: An overview of the tropospheric aerosol radiative forcing observational experiment (TARFOX). J. Geophys. Res. 104 (D2), 2213-2222.

Satheesh, S.K., Ramanathan, V., Holben, B.N., Moorthy, K.K., Loeb, N.G., Maring, H., Prospero, J.M. and Savoie, D., 2002. Chemical, microphysical, and radiative effects of Indian Ocean aerosols. J. Geophys. Res. 107 D234725, doi:10.1029/2002JD002463.

Shaw, G.E., Reagan, J.A., Herman, B.M., 1973. Investigations of atmospheric extinction using direct solar radiation measurements made with a multiple wavelength radiometer. J. Applied Meteorol. 12, 374-380.

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B–P–1

Diurnal Variation in Aerosol Optical Depth during North-East Monsoon over Darjeeling, a high Altitude Observation Site at North-Eastern Himalayas, India

A. Chatterjee*, A. Adak, S. K. Ghosh, A. Mitra, S. Raha and A. K. Singh

Bose Institute, Kolkata

* Presently at National Atmospheric Research Laboratory, Gadanki,India – 517 112

Introduction

The diurnal variation in atmospheric conditions and several atmospheric processes like humidification, nucleation, coagulation, and precipitation result into the large temporal variation in optical properties of atmospheric aerosol. Thus the information on the diurnal variability of aerosol optical properties is important for various applications including the validation of satellite aerosol retrievals, radiative forcing estimates and study of aerosol- cloud interactions (Pandithurai et al., 2007). This study presents diurnal variability of AOD over a high altitude observation site, Darjeeling at north-eastern Himalayas during the winter months of 2008.

Experiment

In order to measure AOD at different wavelengths, a hand-held multi-band sunphotometer MICROTOPS II developed by Solar Light Company, USA (2002) was used. The AOD were measured using the optical filters transmitting the radiation centered at five wavelengths; 380, 500, 675, 936 and 1020 nm. Aerosol number concentration of different size modes were measured using Environmental Dust Monitor (GRIMM) running continuously during the entire study period.

Measurements were made on days with clear skies and no clouds during premonsoon (April-May) season in 2008. Data were collected during 09:00-16:00 hrs (IST) at 15-30 min interval with enough care to avoid the strong seasonal effects such as strong wind, cloudy sky, drizzle etc.

Results

Fig 1 shows the diurnal variation of average AOD for the winter months during 2008 at five wavelengths and at one hour interval. It is observed that starting from a high value in the early hours, AOD decreases gradually as the day advances in all the cases. The higher AOD at shorter wavelengths in early hours is found to be synchronous with the higher concentration of submicron aerosols (less than 1 μm). The diurnal variations of number concentration of aerosols of different size regions are shown in fig 2 during the study period.

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Occurrence of morning haze in the surface layers due to the nocturnal capping inversion during the early morning hours and its confinement till late forenoon could result into the higher AODs. The breaking up of radiative inversion with the advancement of the day is found to influence the variation of aerosol loading in the atmosphere (mainly the submicron particles) resulting into the variation in AOD.

The micro meteorological parameters mainly temperature and humidity governed the AOT variations between forenoon and afternoon. The AOT at all wavelengths were found to gradually decrease with time whereas AOT at 1020 nm shows a slow and steady variation during afternoon. The variation in AOD at shorter wavelengths is synchronous with the variation in submicron particles.

In order to better understand the differences in AOT between forenoon and afternoon we determined the “asymmetry factor” which was ~ 50 % for all the wavelengths except 1020 nm which shows least asymmetry (33 %) between forenoon and afternoon.

References

Figure 1. Diurnal variation of AOD at five wavelengths

Figure 2. Diurnal variation of aerosol number concentration at four size modes

G. Pandithurai, R. T. Pinker, P. C. S. Devara, T. Takamura, and K. K. Dani (2007). Seasonal asymmetry in diurnal variation of aerosol optical characteristics over Pune, western India. Journal of Geophysical Research, 112, D08208, doi:10.1029/2006JD007803

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B–P–2

Spectral Dependence of Angstrom Wavelength Exponent on Dust and Anthropogenic Aerosol over Darjeeling, a High Altitude Observation Site at North-Eastern Himalayas, India

A. Chatterjee*, A. Adak, S. K. Ghosh, A. Mitra, S. Raha and A. K. Singh

Bose Institute, Kolkata

(* Presently at National Atmospheric Research Laboratory, Gadanki, India – 517 112)

Introduction

The interaction of aerosol with solar and terrestrial radiation influences the energy balance of the Earth-atmosphere climate system. In a clear atmosphere without clouds, aerosols govern absorption, scattering and reflection of solar radiation. The aerosols in urban, semi urban and industrialized environments mostly containing anthropogenic species and the natural aerosols like sea-salt, soil dust advected from distant source regions by the synoptic scale airmass movements produce consequent signature on columnar aerosol optical depth, AOD (Saha et al., 2005; Kaskaoutis et al., 2006).

This study is focused on the relative influence of dust and anthropogenic aerosol on AOD in two different season; premonsoon and postmonsoon over a high altitude observation site, Darjeeling, at North-Eastern Himalaya.

Experiment

In order to measure AOD at different wavelengths, a hand-held multi-band sunphotometer MICROTOPS II developed by Solar Light Company, USA (2002) was used. The AOD were measured using the optical filters transmitting the radiation centered at five wavelengths; 380, 500, 675, 936 and 1020 nm.

Measurements were made on days with clear skies and no clouds during two different seasons; premonsoon (April-May) and postmonsoon (Oct-Nov) in 2008. Data were collected during 09:00-16:00 hrs (IST) at 15-30 mins. interval with enough care to avoid the strong seasonal effects such as strong wind, cloudy sky, drizzle etc.

Aerosols were collected with the help of high-volume sampler whereas an Aethelometer continuously measured the black carbon concentrations at an interval of 5 mins. during the entire study period. Water soluble ionic species were analysed by Ion Chromatograph and trace metals were analysed by AAS.

Results

The mean AOD at five wavelengths in two different seasons, premonsoon and postmonsoon, are shown in figure 1. The basic shapes of AOD spectra show sharply contrasting features between the seasons. The AOD at shorter wavelengths are higher

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Figure 1. Variation in AOD in different seasons as a function of wavelength
Figure 2. Angstrom exponent and its derivatives indicating different types of aerosol loading in different seasons

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during postmonsoon whereas the AOD at longer wavelengths are higher during premonsoon season.

Angstrom wavelength exponent, a, was determined from the slope of the linear plot of AOD (ln AOD) and wavelength (ln ) using Angstrom

formula: , where t is columnar

aerosol optical depth at wavelength and a is the Angstrom turbidity parameter. As a is fairly accurate over the short range of wavelength, so another parameter, a2, the derivative of Angstrom wavelength exponent was computed (according to Lee et al., 1993) in AOD domain and studied in order to better understand the influence of fine and coarse mode aerosol on columnar AOD. a2 is fairly applicable for the wider range of wavelengths. The higher value of a (>2) corresponds to coarse mode aerosol, lower value (<1) corresponds to fine mode aerosol and the moderate value (>1<2) corresponds to both fine and coarse mode aerosol contribution. On the other hand, the positive value of a2 indicates the fine mode aerosol loading whereas the negative value indicates the coarse mode aerosol loading.

Here in this study we observed that within a short range of a, ashows large

variations between the seasons indicating the loading of different types of aerosol (fig 2).

Chemical analysis for Fe, Al, Si, non-sea-SO42-, black carbon in aerosol in different seasons show the high dust loading over Darjeeling by long range transportation resulted higher AOD at longer wavelengths with negative a2 during April, May and October. In contrast, the loading of anthropogenic fine mode aerosol during November results higher AOD at shorter wavelength with high positive a2 (1.5).

References

Kaskaoutis, D.G., Kambezidis,H.D., Adamopoulos, A.D., Kssomenos, P.A., 2006.On the characterization of aerosols using the Angstrom exponent in Athens area. Journal of Atmospheric and Solar Terrestrial Physics, 68, 2147–2163.

Saha,A., Moorthy,K.K., Niranjan,K., 2005. Interannual variations of aerosol optical depth over coastal India: relation to synoptic meteorology. Journal of Applied Meteorology, 44,1066–1077.

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B–P–3

Optical and Radiative Properties of Aerosols over the Indo- Gangetic Plains: A Sky Radiometer Study

A. K. Srivastava1, S. Tiwari1, P. C. S. Devara2 and D. S. Bisht1

1Indian Institute of Tropical Meteorology (Branch), Prof Ramnath Vij Marg,

New Delhi

2Indian Institute of Tropical Meteorology, Dr. Homi Bhabha Marg, Pashan, Pune

Introduction

The Indo-Gangetic Basin (IGB) is located in the northern part of India and surrounded by the unique topography with the Himalaya to the north and hills to the south. The entire IGB is one of the highly polluted regions in India but less explored from an aerosol perspective. By using emission inventories, Reddy and Venketaraman [2002a, 2002b] have reported much higher aerosol emissions from different sources in the IGB region. Moreover, recent measurements indicate that the IGB is one of the major source regions for various natural and anthropogenic aerosols [Tare et al., 2006; Tripathi et al., 2006; Rengarajan et al., 2007]. During summer, IGB receives large number of natural dust particles, transported from the neighboring Desert regions, that modify the IGB aerosol properties after mixing with various anthropogenic species, and affect the regional climate system significantly

[Dey et al., 2004; Pandithurai et al., 2008].

Experimental set-up and site description

In view of the immense importance of aerosols over the IGB region, we have synthesized the CIMEL sun/sky radiometer data, archived at Indian Institute of Tropical Meteorology, New Delhi Branch (28.6oN, 77.2oE), Indian Institute of Technology, Kanpur (26.4oN, 80.4oE) and Gandhi College, Ballia (25.8oN, 84.2oE) as a part of the AERONET/TIGERZ Program of NASA, USA. The data products are available in three categories: the cloud contaminated (Level 1.0), cloud screen (Level 1.5) and quality assured (Level 2.0), of which we have used cloud screened level 1.5 data in the present study.

The data sets contain direct Sun measurements that are made at eight spectral channels (0.34, 0.38, 0.44, 0.5, 0.67, 0.87, 0.94, and 1.02 m) with triplet observations at each wavelength and sky radiance measurements at four spectral channels (0.44, 0.67, 0.87 and 1.02 m). Water vapor content in the atmosphere was estimated from the differential absorption measured between the 0.94 and 1.02 m channels. The aerosol optical depth (AOD) data were determined from irradiance measurements made at seven channels. Finer products, such as size distribution, single scattering albedo (SSA) and refractive indices were retrieved by utilizing two basic sky radiance observation modes, namely, almucantar and principle plane as reported by Holben et al. [1998].

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Results and discussions

From the daily means, monthly mean values of AOD at 0.5 m and Angstrom exponent (AE) at the wavelength pair of 0.44 and 0.87 m, observed over New Delhi, Kanpur and Ballia during April-August 2009 are shown plotted in Figure 1a and 1b, respectively. The vertical bars at each histogram indicate one sigma error. It can be seen from the figure that AOD values vary from 0.15 to 1.41 (average: 0.64±0.24), 0.15 to 1.65 (average: 0.66±0.28) and 0.13 to 1.70 (average: 0.64±0.33) with the corresponding mean AE varying from 0.14 to 1.50 (average: 0.63±0.32), 0.07 to 1.52 (average: 0.63±0.33) and 0.01 to 1.58 (average: 0.79±0.34), respectively at New Delhi, Kanpur and Ballia. AOD was found to be higher at New Delhi as compared to the other two stations during all the months except June (Figure 1a), which is noticed to be highly associated with smaller values of AE implying coarser dust particles over New Delhi as compared to Kanpur and Ballia. In contrast, significantly larger values of AE at Ballia during almost all the months clearly indicate the influence of fine aerosol particles generated through anthropogenic activities at that site.

Figure 1. Monthly mean (a) AOD (at 0.5 m) and (b) AE (0.44-0.87 m) at New Delhi, Kanpur and Ballia during April to August 2009

The fine- and coarse-mode fractions of aerosol particles have been studied at the above three stations. The results exhibit significant contributions of coarse-mode over New Delhi, fine-mode over Ballia and accumulation-mode over Kanpur during these periods. Aerosol volume size spectra also show bi-modal distributions at all the three stations with significantly different volume concentrations, and effective radius in fine- and coarse- modes. Further, to substantiate the source origin of these aerosols, air mass back-trajectories, employing the Hybrid Single Particle Lagrangian Integrated Trajectory (HYSPLIT) model, were also examined at all the three stations.

The other aerosol parameters, namely, single scattering albedo (SSA), refractive index and asymmetry parameter at these stations were also analyzed (not shown here). The optical properties derived from sun/sky radiometer made during the study period were used in a radiative-transfer model to estimate short-wave (SW) aerosol radiative forcing at the bottom-of-the atmosphere (BOA) and at the top-of-the atmosphere (TOA). The resultant SW atmospheric forcing due to aerosols was observed to be positive at all the stations

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(Figure 2), which implies net warming effect. The monthly variations in radiative forcing values at all the stations were found to be highly influenced by the variations in AOD, SSA and asymmetry parameter during different months.

Figure 2. Short-wave atmospheric forcing (Wm-2) at New Delhi, Kanpur and Ballia during April to August 2009

Acknowledgements

We are thankful to Brent Holben and his Group of AERONET, NASA; and PHOTONS French Service d’Observation from LOA, University of Lille 1 /CNRS for sun/sky radiometer data (http://aeronet.gsfc.nasa.gov/). Thanks are also due to Director IITM, Pune for his support and encouragement.

References

Dey, S., S. N. Tripathi, R. P. Singh, and B. N. Holben (2004), Influence of dust storms on the aerosol optical properties over the Indo-Gangetic basin, J. Geophys. Res., 109, D20211, doi:10.1029/2004JD004924.

Holben, B., et al. (1998), AERONET- A federated instrument network and data archive for aerosol characterization, Remote Sens. Environ., 66, 1-16.

Pandithurai, G., S. Dipu, K. K. Dani, S. Tiwari, D. S. Bisht, P. C. S. Devara, and R. T. Pinker (2008), Aerosol radiative forcing during dust events over New Delhi, India, J. Geophys. Res., 113, D13209, doi:10.1029/ 2008JD009804.

Reddy, M. S., and C. Venketaraman (2002a), Inventories of aerosols and sulphur dioxide emissions from India: I. Fossil fuel combustion, Atmos. Environ., 36, 677-697.

Reddy, M. S., and C. Venketaraman (2002b), Inventories of aerosols and sulphur dioxide emissions from India: II. Biomass combustion, Atmos. Environ., 36, 699-712.

Rengarajan, R., M. M. Sarin, and A. K. Sudheer (2007), Carbonaceous and inorganic species in atmospheric aerosols during wintertime over urban and high-altitude sites in North India, J. Geophys. Res., 112, D21307, doi:10.1029/2006JD008150.

Tare, V., et al. (2006), Measurements of atmospheric parameters during Indian Space Research Organization Geosphere Biosphere Program Land Campaign II at a typical location in the Ganga Basin: 2. Chemical properties, J. Geophys. Res., 111, D23210, doi:10.1029/2006JD007279.

Tripathi, S. N., et al. (2006), Measurements of atmospheric parameters during Indian Space Research Organization Geosphere Biosphere Programme Land Campaign II at a typical location in the Ganga Basin: 1. Physical and optical properties, J. Geophys. Res., 111, D23209, doi:10.1029/2006JD007278.

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