On Interactions of Matter and Energy: Light and Particles in a Terrestrial Atmosphere - Progress on Opto-Physical Recognition and Classification of Aerosols
AdvisorWilcox, Eric M.
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AbstractWe present an aerosol classification based upon AERONET level 2.0 almucantar retrieval products from the period 1993 to 2012. In the initial phase of this research we opto-physically identified five major types of Bulk Columnar Aerosol (BCA) - based solely upon intensive optical properties of spectral Single Scattering Albedo (SSA), spectral Indices of Refraction (real – RRI and imaginary - IRI), and two Angstrom Exponents (extinction – EAE and absorption - AAE). These BCA we classified as Maritime Aerosol, Dust Aerosol, Urban Industrial Aerosol, Biomass Burning Aerosol, and Mixed Aerosol. The classification of a particular observation as one of these aerosol types is determined by its five-dimensional Mahalanobis distance (MD) to the centroid of each reference cluster (itself a 5-D hyperellipsoid). To retain a greater number of AERONET sites in the study (200+), we kept the variable space to 5-D. To generate reference clusters, we only retained data points that lie within 2 MD from the data centroid. Our typology is based on AERONET retrieved quantities, which do not include low optical depth values (AOD=440nm < 0.4 as per AERONET criteria for almucantar scan inversion). The classifications obtained will be useful in interpreting aerosol retrievals from satellite borne instruments and as input for regional climate models. The result is a dataset describing the types of aerosol particles that are distinct from one another in optical properties, and a geographic distribution of those aerosol types. We used the typology scheme upon the qualifying AERONET data archive, and produced seasonal aerosol distributions by type for each of the AERONET sites included in the study, regional aerosol climatology maps, and a time-integrated global aerosol climatology map based entirely upon ground-based photometric data. An internally hyperlinked compendium of the individual AERONET site aerosol climatologies was produced to contain the results of the first phase of this work [available at https://ars.els-cdn.com/content/image/1-s2.0-S1352231016304265-mmc1.pdf]. Each of these five aerosol types can be further discriminated into specific sub-types by this same scheme. Optical discrimination into sub-types of Biomass Burning aerosol may provide insight into sources exhibiting spectrally distinct smoke properties. In the second phase of this research: to refine the sub-space regions of the classification space, we pursue experiments in developing analytic expressions for the single scattering albedo (SSA), and the Angstrom exponents based upon density distribution functions (DDF) composed from “best-fits” on density histograms of retrieved values. These “fits” can be expressed in analytic form for the single scattering albedo, indices of refraction, and Angstrom exponents of each specific aerosol type. In the third phase of this research, we present experiments regarding aerosol classification (based upon the same set of AERONET Level 2 retrieved data) by calculating polarization quantities in the form of the Stokes components from AERONET data archive input parameters, and employing mathematical strategies to compare the composed polarization functions (simulated degree of linear polarization - SDLP, as a function of scattering angle) for the set of aerosol type reference clusters data. We then use the mathematical strategies to sort the global AERONET data retrievals into the aerosol type classified against the reference standards. We believe these strategies regarding aerosol differentiation using polarization data will be useful for analysis of the newer AERONET version 3 data retrievals, and data collected from the deployment of newer CIMEL sun-photometers (with enhanced polarization measurement capabilities) to the network. The resulting AERONET-based aerosol typology is useful for applications in aerosol optics, including forward modeling or radiative transfer for remote sensing algorithms, or evaluating radiative forcing calculations in atmospheric models.Keywords: Atmospheric aerosols, Aerosol typing, AERONET, Mahalanobis distance, Seasonal aerosol variation, High AOD events, Global aerosol, aerosol compendium, aerosol, classiﬁcation, AERONET, polarization, Frechet, Frechet distance, polarization signature, aerosol polarization• Aerosols characterized as Urban-Industrial, Biomass, Dust, Mixed and Maritime.• Aerosol typing using AERONET retrieved parameters.• Seasonal variation in aerosol type at AERONET sites.• Use of Mahalanobis distance to identify type of individual AERONET measurements.