A multiparameter aerosol classification method and its application to retrievals from spaceborne polarimetry

Abstract

Classifying observed aerosols into types (e.g., urban-industrial, biomass burning, mineral dust, maritime) helps to understand aerosol sources, transformations, effects, and feedback mechanisms; to improve accuracy of satellite retrievals; and to quantify aerosol radiative impacts on climate. The number of aerosol parameters retrieved from spaceborne sensors has been growing, from the initial aerosol optical depth (AOD) at one or a few wavelengths to a list that now includes AOD, complex refractive index, single scattering albedo (SSA), and depolarization of backscatter, each at several wavelengths, plus several particle size and shape parameters. Making optimal use of these varied data products requires objective, multidimensional analysis methods. We describe such a method, which makes explicit use of uncertainties in input parameters. It treats an N-parameter retrieved data point and its N-dimensional uncertainty as an extended data point, E. It then uses a modified Mahalanobis distance, D EC , to assign an observation to the class (cluster) C that has minimum D EC from the point. We use parameters retrieved from the Aerosol Robotic Network (AERONET) to define seven prespecified clusters (pure dust, polluted dust, urban-industrial/developed economy, urban-industrial/developing economy, dark biomass smoke, light biomass smoke, and pure marine), and we demonstrate application of the method to a 5 year record of retrievals from the spaceborne Polarization and Directionality of the Earth's Reflectances 3 (POLDER 3) polarimeter over the island of Crete, Greece. Results show changes of aerosol type at this location in the eastern Mediterranean Sea, which is influenced by a wide variety of aerosol sources.

Remote sensing of soot carbon – Part 2: Understanding the absorption Ångström exponent

“…The spectral dependence of absorption (i.e., AAE) depends upon particle size and the imaginary refractive index, and excellent overviews of the relationship between AAE and aerosol microphysics are provided by Moosmüller et al (2009Moosmüller et al ( , 2011. Generally, AAE is obtained from photoacoustic measurements (Lewis et al, 2008;Chow et al, 2009;Gyawali et al, 2009;Flowers et al, 2010;Lack et al, 2012b;Chakrabarty et al, 2013;Utry et al, 2014), particle soot absorption photometer measurements (Roden et al, 2006), aethalometer measurements (Ganguly et al, 2005), the solar-flux-AOD technique (Russell et al, 2010), integrating plate methods (Schnaiter et al, 2003), differencing extinction and scattering coefficients , or aerosol robotic network (AERONET) retrievals (Bahadur et al, 2012;Chung et al, 2012b;Giles et al, 2012;Cazorla et al, 2013;Xu et al, 2013;Russell et al, 2014).…”

HETEAC: The Aerosol Classification Model for EarthCARE

“…concentration-independent, optical properties but also on aerosol load, geographic location or altitude of occurrence. Such schemes have been developed in the context of the CALIPSO mission [3,4], derived from dedicated lidar field studies [5,6] or retrieved from passive remote-sensing observations [7].…”

Classifying aerosol type using in situ surface spectral aerosol optical properties

“…Additionally, knowledge of aerosol type helps identify the aerosol source, which can be useful in implementing controls or policies to reduce aerosols that negatively influence air quality and public health and also to better understand atmospheric dynamics and long-range transport. Constraining aerosol type is also needed for improving aerosol satellite retrieval algorithms and for validating climate models (Russell et al, 2014).…”

“…The literature on classifying aerosols has been largely dominated by the analysis of ground-based remote sensing or satellite data (Cazorla et al, 2013;Russell et al, 2010Russell et al, , 2014Omar et al, 2005;Giles et al, 2012;Bergstrom et al, 2007Bergstrom et al, , 2010Bahadur et al, 2012;, with fewer analyses done using surface in situ aerosol optical property measurements (Cappa et al, 2016;Costabile et al, 2013;Yang et al, 2009;Lee et al, 2012). The analyses in this paper utilize ground-based in situ spectral optical data that afford a unique insight into long-term, quality-assured point observations.…”