Progress on an AERNET Aerosol Optophysical Typology: Refining Aerosol Subtypes Using Analytic Functions Based upon Density Distribution Functions and Polarimetric Criteria Analysis

We 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 aerosol observation as one of these aerosol types is determined by its five-dimensional Mahalanobis distance (MD) to each reference cluster (itself a 5-D hyperellipsoid). To retain a greater number of AERONET sites in the study (190+), we kept the variable space to 5-D. To generate reference clusters we only keep 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. 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 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.] 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, and Angstrom exponents of specific aerosol types. 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 variables, and employing mathematical strategies to compare the composed polarization functions 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 validating radiative forcing calculations in atmospheric models.