Inferring black carbon content and specific absorption from Aerosol Robotic Network (AERONET) aerosol retrievals

By Gregory L. Schuster, Oleg Dubovik, Brent N. Holben, Eugene E. Clothiaux. 2005

192 citations recorded by the source corpus.

Subfields: Atmospheric Science, Global and Planetary Change

View Paper: https://www.semanticscholar.org/paper/db6bdeff449164ffbf7b5caae41bf5ae0a560682

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Citations in the corpus

Limitations in the enhancement of visible light absorption due to mixing state (2006, background): “This agrees well with the range estimated by Schuster et al. [2005] from Aerosol Robotic Network data (7.7–12.5 m 2 /g).”
Light Absorption by Carbonaceous Particles: An Investigative Review (2006, background): “Meticulous inversions of remote data have estimated concentrations and MAC that are consistent with measured radiance ( Schuster et al. 2005 ), but even these rely on assumptions about the refractive index and particle mixing.”
Comprehensive Study of Optical, Physical, Chemical, and Radiative Properties of Total Columnar Atmospheric Aerosols over China: An Overview of Sun-Sky Radiometer Observation Network (SONET) Measurements (2017, methodology): “Except for standard AERONET products, the chemical component fractions of columnar aerosol, the additional products of SONET, are derived on the basis of the retrieved optical and physical parameters following recent studies (e.g., Schuster et al. 2005; Ganguly et al. (Li et al. 2013).”
Angstrom exponent and bimodal aerosol size distributions (2006, methodology): “We simulated an internal aerosol mixture for our distributions, tuning the volume fractions of water, ammonium sulfate, and black carbon until we achieved a minimum χ2 fit to a refractive index of m = 1.37 − 0.003i at four wavelengths: 0.44, 0.67, 0.87, and 1.02 µm [Schuster et al., 2005].”
The AERONET Version 3 aerosol retrieval algorithm, associated uncertainties and comparisons to Version 2 (2020, methodology): “Over the years, AERONET aerosol retrievals have been widely used by the aerosol remote sensing community (e.g., Schuster et al., 2005, 2016; Xu et al., 2018; Chen et al., 2019; Wang et al., 2018; Benkhalifa et al., 2017; Lee et al., 2017; Qu et al., 2017; Zhang et al., 2017; Si et al., 2017).”