DOI: 10.5061/dryad.sxksn036z
title: Centroid values of aerosol optical properties for 8 sub-types based in AERONET inversion data (1993–2018)

description: In this project, we adapted our previously defined 5 aerosol optical
 typology scheme (Hamill et al. 2016) to result in a more discriminating 8
 aerosol typology scheme (Giordano 2019). Previously we presented an
 aerosol classification based upon AERONET level 2.0 almucantar retrieval
 products from the period 1993 to 2012. In the initial phases 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 were 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 were found to lie within 2 MD from the
 data centroid. Our typology is based on AERONET retrieved quantities,
 which do not include low optical depth values (AOD440nm &lt;
 0.4 as per AERONET criteria for almucantar scan inversion).  The
 classifications obtained are made available to be used in interpreting
 aerosol retrievals from satellite-borne instruments and as input for
 regional climate models. A major result of this aerosol typology 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 climatologies by aerosol 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 (Giordano 2022). 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 original five aerosol types (Hamill et al. 2016, Giordano
 2019) was further discriminated into specific sub-types by this same
 scheme to achieve an 8-aerosol typology (Giordano 2019 Chapter 2). For
 example, optical discrimination into specific sub-types of Biomass Burning
 aerosol may provide insight into sources exhibiting spectrally distinct
 smoke properties. Here we segmented the Biomass Burning Aerosol type into
 the sub-types of Flaming (BMF) and Smoldering (BMS) using the centroid
 separation method and the MD criteria for in-class inclusion was adjusted
 to 1.5 MD. Similarly, we found great confidence in discriminating the
 MIXED aerosol type into two distinct regimes which we simply labeled as
 MIXEDtype1 (MIXED1) and MIXEDtype2 (MIXED2). These can be visually
 verified by examining any one of many possible renditions of 3-D optical
 spaces noting their 5-D centroids are separated by a distance of 3.47-3.85
 MD [Giordano 2019 Chapter 2]. Likewise, the Urban Industrial Aerosol class
 was further discriminated into European Urban Industrial (EURO UI) and
 North American (NA UI), whose 5-D centroids are separated by a distance of
 2.60–3.08 MD. We then used the previously employed mathematical strategies
 to sort the global AERONET data retrievals into the aerosol types
 classified against their reference standards. We believe the strategies
 regarding aerosol differentiation using polarization data (Hamill, Piedra
 and Giordano 2020)  are  an additional
 method 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 8-aerosol optical typology, in a
 5-D basis is useful for applications in aerosol optics, including direct
 forward modeling of radiative transfer to determine the effects of aerosol
 absorption and/or scattering on vertical heating profiles and ground
 received irradiance quantities, for input into more complicated remote
 sensing algorithms, used as calibration/validation values for in-situ and
 laboratory experimental studies, and evaluating radiative forcing
 calculations in atmospheric models. [Work related to an 8-aerosol typology
 in 6-D, 8-D, 9-D and 10-D optical property bases, and their
 files, are to be published subsequently as a different database project in
 2023.]
description: <strong>Methodology of Data
 collection:</strong> We started from a complete
 fresh download of the entire AERONET set of all sites for the V2 Inversion
 product [now in V3 available at [https://aeronet.gsfc.nasa.gov/new_web/download_all_v3_inversions.html], capturing the Level 2.0 Almucantar product that includes all points for each site -- not averages. The original package was obtained as a zip archive named in this fashion: "<em>INV_PFN_Level2_All_Points_V3.tar.gz</em>".  The package was unzipped to make available all records for each AERONET site. <strong>Data Cleaning: </strong> The complete set of files is operated on by a simple C-Shell script to remove all individual retrievals from each site that contained records of either "NAN" or "- 99999999" for any field other than the "aeronet site" name as a text field.  Subsequently, we imposed our <em>minimum number of complete records</em> criteria such that any AERONET station that did not report a minimum of 200 complete retrievals as removed. These generally refer to as <em><strong>qualifying sites</strong></em>. NOTE: This same strategy could be performed manually in an editor on each individual file to arrive at the same end result. An example of a C-shell script that can be modified for BASH or any other environment is included as "<em>cleanup</em>.<em>csh</em>". <strong>Data Reduction:  Finding the minimum number of AERONET inversion retrieval variables that span the greatest variance in the global data set</strong> This optimization problem was done using methods of factor analysis, to result in the final working set of files containing complete records of on which to develop our robust global aerosol typology. The details of the intricacies and history of this part of the project are detailed in Giordano 2019. <strong>Data Processing: Determining the number of unique aerosol types globally identifiable through bulk columnar aerosol (BCA) Sun photometry</strong> This problem of analyzing the global data to determine the individually identifiable unique aerosol "groups" was done by invoking various mathematical unique strategies generally thought of as "cluster analysis."  The main goal of this approach is to achieve separability between the individual "groups" while  preserving a maximal "homogeneity" within any individual group.  The details of the intricacies and history of this part of the project are detailed Giordano 2019 and Hamill et al. 2016. <strong>Regarding Uncertainty measures in the reported values:</strong> Regarding the calculated uncertainty values that accompany these data, the author offers the summary of both the centroid values for each of the 5 optical properties, and the tabulated centroid values for each of the 8 reference aerosol types to be used as standards to classify measurements of the global aerosol.  These are below as Figure1 and Table1 respectively. <strong>Table 1:  uncertainty values for the 5 optical properties in the typological basis</strong> <strong>Optical Property</strong> <strong>EAE</strong> <strong>870-440nm</strong> <strong>AAE</strong> <strong>870-440nm</strong> <strong>SSA</strong> <strong>440nm</strong> <strong>RRI</strong> <strong>675nm</strong> <strong>IRI</strong> <strong>675 nm</strong> uncertainty ± 0.3 - 0.6 ± 0.3 - 0.6 ± 0.03 - 0.05 ± 0.02 - 0.05 ± 30 - 50%
description: <em>The original author/creator of this database [M.E.
 Giordano] holds and retains all rights to its use [as it was a
 copy-protected student work in a Doctoral dissertation], and shares these
 rights via Dryad's Creative Commons License with the new readers of
 this work.  The author only respectfully requests that proper citation of
 the work be given, and derivative works attribute the original author. The
 only code file attached to this work [Cleanup.csh] is an example and may
 be used as such.</em> Only the file named
 "8AerosolOpticalPropertiesTypology5Dbasis_CENTROIDS.xlsx" 
 requires specific software such as MS Excel<sup>TM</sup>,
 LibreOffice<sup>TM</sup>, or
 OpenOffice<sup>TM</sup> to be opened and examined.   The
 remaining files are in comma-separated value CSV (UTF8 comma delimited),
 space-separated values (.txt), and tab-separated values (.dat).
 Some suggested uses: This data is
 likely to be used by those importing the specific aerosol reference
 standard centroid value to be compared with aerosol measurements at a
 specific location during a specific season, to classify them into robust
 global aerosol category. The aerosol reference
 standard centroids can be used for studies involving feeding values of
 specific sets of aerosol optical properties into radiative transfer codes
 to arrive at values for various radiative forcing in seasonal and
 geo-referenced locations. The centroid reference
 values can be used to kernel sets of synthesized aerosol optical
 properties by distributing centroid values statistically in a function
 such as a lognormal distribution, or a Gaussian about that centroid
 value. If one is using a Python
 environment, the following might be useful to manipulate the data in this
 database: Numpy
 SciPy xlrd
 openpyxl PANDAS
 XARRAY