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Volume 17, issue 19
Atmos. Chem. Phys., 17, 12097–12120, 2017
© Author(s) 2017. This work is distributed under
the Creative Commons Attribution 3.0 License.
Atmos. Chem. Phys., 17, 12097–12120, 2017
© Author(s) 2017. This work is distributed under
the Creative Commons Attribution 3.0 License.

Research article 12 Oct 2017

Research article | 12 Oct 2017

Classifying aerosol type using in situ surface spectral aerosol optical properties

Lauren Schmeisser1,2,a, Elisabeth Andrews1,2, John A. Ogren1, Patrick Sheridan1, Anne Jefferson1,2, Sangeeta Sharma3, Jeong Eun Kim4, James P. Sherman5, Mar Sorribas6, Ivo Kalapov7, Todor Arsov7, Christo Angelov7, Olga L. Mayol-Bracero8, Casper Labuschagne9,10, Sang-Woo Kim11, András Hoffer12, Neng-Huei Lin13, Hao-Ping Chia13, Michael Bergin14, Junying Sun15, Peng Liu16, and Hao Wu16 Lauren Schmeisser et al.
  • 1National Oceanic and Atmospheric Administration, Earth Systems Research Laboratory, Boulder, CO, USA
  • 2University of Colorado at Boulder, Cooperative Institute for Research in Environmental Sciences (CIRES), Boulder, CO, USA
  • 3Environment and Climate Change Canada, Science and Technology Branch, Ontario, Canada
  • 4Environmental Meteorology Research Division, National Institute of Meteorological Sciences, Seoul, Korea
  • 5Department of Physics and Astronomy, Appalachian State University, Boone, NC, USA
  • 6Atmospheric Sounding Station, El Arenosillo, Atmospheric Research and Instrumentation Branch, INTA, 21130, Mazagón, Huelva, Spain
  • 7Institute for Nuclear Research and Nuclear Energy of the Bulgarian Academy of Sciences, Sofia, Bulgaria
  • 8University of Puerto Rico, Department of Environmental Science, San Juan, PR, USA
  • 9South African Weather Service, Stellenbosch, South Africa
  • 10Unit for Environmental Sciences and Management, North-West University, Potchefstroom Campus, South Africa
  • 11School of Earth and Environmental Sciences, Seoul National University, Seoul 08826, Korea
  • 12MTA-PE Air Chemistry Research Group, Veszprém, P.O. Box 158, 8201, Hungary
  • 13National Central University, Department of Atmospheric Sciences, Chung-LI, Taoyuan City, Taiwan
  • 14Duke University, Department of Civil & Environmental Engineering, Durham, NC, USA
  • 15State Key Laboratory of Severe Weather & Key Laboratory of Atmospheric Chemistry of CMA, Chinese Academy of Meteorological Sciences, Beijing 100081, China
  • 16China GAW Baseline Observatory, Qinghai Meteorological Bureau, Xining 810001, China
  • anow at: University of Washington, Department of Atmospheric Sciences, Seattle, WA, USA

Abstract. Knowledge of aerosol size and composition is important for determining radiative forcing effects of aerosols, identifying aerosol sources and improving aerosol satellite retrieval algorithms. The ability to extrapolate aerosol size and composition, or type, from intensive aerosol optical properties can help expand the current knowledge of spatiotemporal variability in aerosol type globally, particularly where chemical composition measurements do not exist concurrently with optical property measurements. This study uses medians of the scattering Ångström exponent (SAE), absorption Ångström exponent (AAE) and single scattering albedo (SSA) from 24 stations within the NOAA/ESRL Federated Aerosol Monitoring Network to infer aerosol type using previously published aerosol classification schemes.

Three methods are implemented to obtain a best estimate of dominant aerosol type at each station using aerosol optical properties. The first method plots station medians into an AAE vs. SAE plot space, so that a unique combination of intensive properties corresponds with an aerosol type. The second typing method expands on the first by introducing a multivariate cluster analysis, which aims to group stations with similar optical characteristics and thus similar dominant aerosol type. The third and final classification method pairs 3-day backward air mass trajectories with median aerosol optical properties to explore the relationship between trajectory origin (proxy for likely aerosol type) and aerosol intensive parameters, while allowing for multiple dominant aerosol types at each station.

The three aerosol classification methods have some common, and thus robust, results. In general, estimating dominant aerosol type using optical properties is best suited for site locations with a stable and homogenous aerosol population, particularly continental polluted (carbonaceous aerosol), marine polluted (carbonaceous aerosol mixed with sea salt) and continental dust/biomass sites (dust and carbonaceous aerosol); however, current classification schemes perform poorly when predicting dominant aerosol type at remote marine and Arctic sites and at stations with more complex locations and topography where variable aerosol populations are not well represented by median optical properties. Although the aerosol classification methods presented here provide new ways to reduce ambiguity in typing schemes, there is more work needed to find aerosol typing methods that are useful for a larger range of geographic locations and aerosol populations.

Publications Copernicus
Short summary
Three methods are used to classify aerosol type from aerosol optical properties measured in situ at 24 surface sites. Classification methods work best at sites with stable, homogenous aerosol at particularly polluted and dust-prone continental and marine sites. Classification methods are poor at remote marine and Arctic sites. Using these methods to extrapolate aerosol type from optical properties can help determine aerosol radiative forcing and improve aerosol satellite retrieval algorithms.
Three methods are used to classify aerosol type from aerosol optical properties measured in situ...
Final-revised paper