Characterization of the optical properties of biomass burning aerosols in Zambia during the 1997 ZIBBEE field campaign

Eck, T. F.; Holben, B. N.; Ward, Darold E.; Dubovik, Оleg; Reid, Jeffrey S.; Smirnov, A.; Mukelabai, M. M.; Hsu, N. Christina; O’Neill, N. T.; Slutsker, I.

doi:10.1029/2000jd900555

Cited by 237 publications

(259 citation statements)

References 45 publications

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“…In fact, both Alta Floresta and Mongu (the other biomassburning location) are sites with cloud contamination well hidden in the data, therefore difficult to remove even by wavelength dependent cloud screening (Kaufman et al, 2006). Overall, the two AdA plots of Mongu and Alta Floresta show smaller R f values at Mongu and also show greater δα values at Mongu that result in part from greater aerosol absorption (higher black carbon fraction) at this location (Eck et al, 2001).…”

Section: Application To Aeronet Datamentioning

confidence: 91%

“…The δα and α values are then evaluated for each of these combinations and reported on the plot to make a reference grid. After verifying that δα values of the coarse modes do not vary significantly in the 440, 870 nm range (e.g., O'Neill et al, 2001), the four δα, α pairs (each pair corresponding to one of the four R c values) have been averaged to provide a single (η, R f ) combination. Therefore, each (η, R f ) grid point plotted in our figures represents the average of the relevant coarse modes results.…”

Section: Aerosol Classificationmentioning

confidence: 99%

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Classification of aerosol properties derived from AERONET direct sun data

Gobbi

Kaufman²,

Koren³

et al. 2007

Atmos. Chem. Phys.

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219

218

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Abstract. Aerosol spectral measurements by sunphotometers can be characterized by three independent pieces of information: 1) the optical thickness (AOT), a measure of the column aerosol concentration, 2) the optical thickness average spectral dependence, given by the Angstrom exponent (α), and 3) the spectral curvature of α (δα). We propose a simple graphical method to visually convert (α, δα) to the contribution of fine aerosol to the AOT and the size of the fine aerosols. This information can be used to track mixtures of pollution aerosol with dust, to distinguish aerosol growth from cloud contamination and to observe aerosol humidification. The graphical method is applied to the analysis of yearly records at 8 sites in 3 continents, characterized by different levels of pollution, biomass burning and mineral dust concentrations. Results depict the dominance of fine mode aerosols in driving the AOT at polluted sites. In stable meteorological conditions, we see an increase in the size of the fine aerosol as the pollution stagnates and increases in optical thickness. Coexistence of coarse and fine particles is evidenced at the polluted sites downwind of arid regions.

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Section: Application To Aeronet Datamentioning

confidence: 91%

Section: Aerosol Classificationmentioning

confidence: 99%

Classification of aerosol properties derived from AERONET direct sun data

Gobbi

Kaufman²,

Koren³

et al. 2007

Atmos. Chem. Phys.

Self Cite

219

218

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“…4.3). The increase of r f at nighttime might be associated with aerosol aging (e.g., Reid et al, 1998Reid et al, , 1999Dubovik et al, 2002;Eck et al, 2001Eck et al, , 2003a.…”

Section: Temporal Evolution Of Columnar Aerosol Opticalmentioning

confidence: 99%

Columnar aerosol properties from sun-and-star photometry: statistical comparisons and day-to-night dynamic

et al. 2012

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Abstract. This work presents the first analysis of longterm correlative day-to-night columnar aerosol optical properties. The aim is to better understand columnar aerosol dynamic from ground-based observations, which are poorly studied until now. To this end we have used a combination of sun-and-star photometry measurements acquired in the city of Granada (37.16 • N, 3.60 • W, 680 m a.s.l.; South-East of Spain) from 2007 to 2010. For the whole study period, mean aerosol optical depth (AOD) around 440 nm (±standard deviation) is 0.18 ± 0.10 and 0.19 ± 0.11 for daytime and nighttime, respectively, while the mean Angström exponent (α) is 1.0 ± 0.4 and 0.9 ± 0.4 for daytime and nighttime. The ANOVA statistical tests reveal that there are no significant differences between AOD and α obtained at daytime and those at nighttime. Additionally, the mean daytime values of AOD and α obtained during this study period are coherent with the values obtained in the surrounding AERONET stations. On the other hand, AOD around 440 nm present evident seasonal patterns characterised by large values in summer (mean value of 0.20 ± 0.10 both at daytime and nighttime) and low values in winter (mean value of 0.15 ± 0.09 at daytime and 0.17 ± 0.10 at nighttime). The Angström exponents also present seasonal patterns, but with low values in summer (mean values of 0.8 ± 0.4 and 0.9 ± 0.4 at dayand night-time) and relatively large values in winter (mean values of 1.2 ± 0.4 and 1.0 ± 0.3 at daytime and nighttime). These seasonal patterns are explained by the differences in the meteorological conditions and by the differences in the strength of the aerosol sources. To take more insight about the changes in aerosol particles between day and night, the spectral differences of the Angström exponent as function of the Angström exponent are also studied. These analyses reveal increases of the fine mode radius and of the fine mode contribution to AOD during nighttime, being more remarkable in the summer seasons. These variations are explained by the changes of the local aerosol sources and by the meteorological conditions between daytime and nighttime, as well as aerosol aging processes. Case studies during summer and winter for different aerosol loads and types are also presented to clearly illustrate these findings.

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“…We used a linear regression method on log l-log w plane (where l is the wavelength and w stands for t; o; g) to determine the aerosol parameters at 19 wavelength-bands. Since the wavelength dependency of the aerosol parameters is relatively simple within the short wavelength region (Smirnov et al 1995;Russell et al 1999;Quijano et al 2000;Eck et al 2001), the linear regression fitting method can be used to determine the aerosol properties in the short wave region from the AERONET data. This obviates the need to assume the aerosols properties in advance, and utilizes real time measurement, and estimate the ADRF case by case with fewer assumptions about the aerosol, because we use measured aerosol properties as input to the CRM.…”

Section: Radiative Transfer Modelmentioning

confidence: 99%

Estimation of Direct Radiative Forcing of Asian Dust Aerosols with Sun/Sky Radiometer and Lidar Measurements at Gosan, Korea

Won

Yoon

Kim

et al. 2004

Journal of the Meteorological Society of Japan

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In this study the aerosol direct radiative forcing (ADRF) of Asian dust is evaluated by model simulation at Gosan, Jeju using the data from a sun/sky radiometer, a Micro-Pulse Lidar (MPL), and column radiometer measurements of solar downwelling irradiance in April, 2001. We suggest a method of determining aerosol parameters for the radiative transfer model from the Aerosol Robotic Network (AERONET) data set. Since the AERONET measurements provide the refractive indices at only four wavelengths, and the aerosol parameters can be calculated at these wavelengths with a Mie code, we use a linear regression method for extending these measurements to the full wavelength spectrum of the radiative transfer model.The aerosol forcing by the Asian dust aerosols is estimated and compared to the aerosol forcing of non-dust aerosols. On the Asian dust event day, April 13, the daily average ADRF was estimated as À58.1 W/m 2 at the surface and À25.7 W/m 2 at the top of the atmosphere (TOA). On April 15, a nondust day slightly influenced by anthropogenic aerosols, the ADRF was À29.0 W/m 2 at the surface and À11.6 W/m 2 at the TOA. Although the Asian dust aerosols show larger forcing, its forcing efficiency (forcing per unit optical thickness) is smaller than that of non-dust aerosols; À41.0 W/m 2 /t 670 at the TOA and À94.9 W/m 2 /t 670 at the surface on the dust day for dust aerosols, as opposed to À50.0 W/m 2 /t 670 at the TOA and À129.3 W/m 2 /t 670 at the surface on the non-dust day for non-dust aerosols. We believe that this is due to the larger single scattering albedo of dust aerosols, which causes smaller absorption, and the larger asymmetry factor which causes more forward scattering or less reflection, compared to anthropogenic aerosols.The model results were validated with the surface irradiance measurement data and the comparison showed a good agreement. The radiative transfer calculation underestimates the solar irradiance of 2@3% on average. The aerosol profiles measured by lidar are used to estimate the influence of the vertical distribution of Asian dust aerosols on the ADRF. Using the vertical aerosol profiles, we found an Corresponding author: Jae-Gwang Won, School of Earth and Environmental Sciences, Seoul National University, Seoul 151-747, Korea. E-mail: wonjg@air.snu.ac.kr ( 2004, Meteorological Society of Japan instantaneous short wave radiative heating larger than 2 K/day. We believe the enhanced heating rate by the aerosol layers contributes to the increase in static stability within the dust layer. This fact is verified by the temperature profile measured by the sonde, and may explain the longevity and consequently long-range transport of Asian dust.

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Characterization of the optical properties of biomass burning aerosols in Zambia during the 1997 ZIBBEE field campaign

Cited by 237 publications

References 45 publications

Classification of aerosol properties derived from AERONET direct sun data

Classification of aerosol properties derived from AERONET direct sun data

Columnar aerosol properties from sun-and-star photometry: statistical comparisons and day-to-night dynamic

Estimation of Direct Radiative Forcing of Asian Dust Aerosols with Sun/Sky Radiometer and Lidar Measurements at Gosan, Korea

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