Comparison of the Ångström parameters retrieval in different spectral ranges with the use of different techniques

Kaskaoutis, Dimitris G.; Kambezidis, H. D.

doi:10.1007/s00703-007-0279-y

Cited by 62 publications

(44 citation statements)

References 42 publications

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“…7 for Avignon (<ÅE> = 1.43) and Ispra (<ÅE> = 1.51) over Western Europe were influenced by industrial, urban, and traffic pollutants, such as ammonium salts of sulphate and nitrate (González et al, 2000;Benkovitz et al, 1996;Kambezidis and Kaskaoutis, 2008;Mazzola et al, 2010). The seasonal variation ofÅE is small as the major source during the year is industrial pollutant.…”

Section: Western Europementioning

confidence: 99%

“…In general, submicron or supermicron aerosols have higher or lowerÅEs accordingly. However,ÅE is not an ideal indicator to show the exact average size of particles as it also is dependent on aerosol absorption and size distribution (Kaskaoutis and Kambezidis, 2008). Another candidate for aerosol classification is utilizing the aerosol optical properties (e.g.…”

Section: Published By Copernicus Publications On Behalf Of the Europementioning

confidence: 99%

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Trend analysis of aerosol optical thickness and Ångström exponent derived from the global AERONET spectral observations

et al. 2012

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Abstract. Regular aerosol observations based on wellcalibrated instruments have led to a better understanding of the aerosol radiative budget on Earth. In recent years, these instruments have played an important role in the determination of the increase of anthropogenic aerosols by means of long-term studies. Only few investigations regarding longterm trends of aerosol optical characteristics (e.g. aerosol optical thickness (AOT) andÅngström exponent (ÅE)) have been derived from ground-based observations. This paper aims to derive and discuss linear trends of AOT (440, 675, 870, and 1020 nm) andÅE (440-870 nm) using AErosol RObotic NETwork (AERONET) level 2.0 spectral observations. Additionally, temporal trends of coarse-and fine-mode dominant AOTs (CdAOT and FdAOT) have been estimated by applying an aerosol classification based on accurateÅE andÅngström exponent difference (ÅED). In order to take into account the fact that cloud disturbance is having a significant influence on the trend analysis of aerosols, we introduce a weighted least squares regression depending on two weights: (1) monthly standard deviation (σ t ) and (2) number of observations per month (n t ).Temporal increase of FdAOTs (440 nm) prevails over newly industrializing countries in East Asia (weighted trends; +6.23 % yr −1 at Beijing) and active agricultural burning regions in South Africa (+1.89 % yr −1 at Mongu). On the other hand, insignificant or negative trends for FdAOTs are detected over Western Europe (+0.25 % yr −1 at Avignon and −2.29 % yr −1 at Ispra) and North America (−0.52 % yr −1 for GSFC and −0.01 % yr −1 at MD Science Center). Over desert regions, both increase and decrease of CdAOTs (+3.37 % yr −1 at Solar Village and −1.18 % yr −1 at Ouagadougou) are observed depending on meteorological conditions.

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Section: Western Europementioning

confidence: 99%

Section: Published By Copernicus Publications On Behalf Of the Europementioning

confidence: 99%

Trend analysis of aerosol optical thickness and Ångström exponent derived from the global AERONET spectral observations

et al. 2012

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“…Although in this definition, α is assumed independent from the wavelength, it is established that the opposite exist. (e.g., Kaskaoutis and Kambezidis, 2008). 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.…”

Section: Methods Of Data Analysismentioning

confidence: 99%

“…Uncertainty in quantifying the climatic impacts of aerosols continues to be greater than that of greenhouse gases (IPCC, 2007) due to variety of their sources, varying trends in aerosol loading and extreme heterogeneity in the spatial and temporal variability of their optical and microphysical properties (Morgan et al, 2006;Kaskaoutis et al, 2010). Therefore, the accurate assessment of the aerosol impact on radiative transfer is a complex task, since various aerosol types cause different effects on the solar radiation spectral distribution (Kaskaoutis and Kambezidis, 2008). Thus, detailed knowledge of the optical properties of the key aerosol types is highly essential.…”

Section: Introductionmentioning

confidence: 99%

Identification and Classification of Different Aerosol Types over a Subtropical Rural Site in Mpumalanga, South Africa: Seasonal Variations as Retrieved from the AERONET Sunphotometer

Kumar

Sivakumar

Reddy

et al. 2014

Aerosol Air Qual. Res.

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This paper reports the observational results of aerosol optical characteristics, modification processes and discrimination of key aerosol types over Skukuza (24.9°S, 31.5°E, and 150 m), a subtropical rural site in South Africa (SA), using CIMEL Sunphotometer data, part of the AErosol RObotic NETwork (AERONET), from December 2005 to November 2006. The results show that a pronounced spectral and temporal variability in the optical properties of aerosols is mainly due to anthropogenic emissions. The discrimination of different aerosol types over Skukuza is also made using the daily mean values of aerosol optical depth at 500 nm (AOD 500 ) and Ångström exponent (α ) by applying the threshold values. The results of the analysis indentified three individual components (biomass burning/urban (BU), desert dust (DD) and clean maritime (CM) aerosol types) of differing origin, composition and optical characteristics, and revealed that the percentage contribution of each of type of aerosol changed significantly from season to season. We also derived the curvature of a 2 in an attempt to obtain information on aerosol-particle size and type, although the results revealed that the curvature alone is not enough to achieve this. In addition,, we analyzed the seasonal changes in aerosol characteristics using the classification scheme introduced in Gobbi et al. (2007) based on the measured scattering properties (α, dα) derived from the Sunphotometer data. The results show that during spring an extremely large fraction of fine-mode aerosols (η > 70%, R f ~0.1 µm) in the turbid atmosphere was mainly caused by local anthropogenic pollution or biomass aerosol transported from forest fires. Whereas in summer, the low AOD (< 0.2) and smaller α (< 1.0) and η < 50% suggest the influence of transported mineral dust (coarse) over the region.

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“…(1) (Kaskaoutis and Kambezidis, 2006) the consistency in the retrievals of Angstrom exponent via different methods gives credit to the accuracy of the spectral AOD retrievals and minimizes the curvature effect (Kaskaoutis and Kambezidis, 2008). Here, we correlate the α values obtained via the least-squares method (LSM, total use of four wavelengths) with those obtained via the Volz method (VM, use of two wavelengths) and those obtained via the polynomial fit method (PFM, a 2 -a 1 = α (according to Schuster et al, 2006) (Fig.…”

Section: Methodology and Error Analysismentioning

confidence: 99%

Seasonal Variability of Atmospheric Aerosol Parameters over Greater Noida Using Ground Sunphotometer Observations

Sharma

Kaskaoutis

Singh

et al. 2014

Aerosol Air Qual. Res.

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Atmospheric aerosols over northern India are subject of significant temporal and spatial variability and many studies have been carried out to investigate their physico-chemical and optical properties. The present work emphasizes on examining the aerosol optical properties and types over Greater Noida, Delhi region, using ground-based sun photometer data during the period 2010-2012. The analysis reveals a relatively high mean aerosol optical depth at 500 nm (AOD 500 = 0.82 ± 0.39), associated with a moderate Angstrom exponent α 440-870 of 0.95 ± 0.37. Both parameters, exhibit significant daily, monthly and seasonal variability with higher values of AOD 500 during post-monsoon (0.98 ± 0.50) and winter (0.87 ± 0.35) seasons associated with high α values (> 1.1) suggesting significant urban and biomass-burning contribution. On monthly basis, the highest AOD is found during July and November and the lowest one in the transition months of March and September. The aerosol-type discrimination via the relationship AOD vs. α shows a clear dominance of urban/industrial and biomass-burning aerosols during post-monsoon and winter in fractions of 74.5% and 72%, respectively, while aerosols of desert-dust characteristics were most frequent in pre-monsoon (41.7%) and monsoon (21%) seasons. In general, the analysis shows a rather well-mixed aerosol type under very turbid atmosphere, which is associated with the long range transport of pollutants through the westerly winds from the Thar desert and biomass burning in the western parts of India.

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Comparison of the Ångström parameters retrieval in different spectral ranges with the use of different techniques

Cited by 62 publications

References 42 publications

Trend analysis of aerosol optical thickness and Ångström exponent derived from the global AERONET spectral observations

Trend analysis of aerosol optical thickness and Ångström exponent derived from the global AERONET spectral observations

Identification and Classification of Different Aerosol Types over a Subtropical Rural Site in Mpumalanga, South Africa: Seasonal Variations as Retrieved from the AERONET Sunphotometer

Seasonal Variability of Atmospheric Aerosol Parameters over Greater Noida Using Ground Sunphotometer Observations

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