DOI: 10.5061/dryad.0vt4b8h0d
title: A historic global ground-based monthly seasonal aerosol climatology based in AERONET data: a database 1993-2013

description:   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 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 &lt; 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 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. 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. For example, optical
 discrimination into specific sub-types of Biomass Burning aerosol may
 provide insight into sources exhibiting spectrally distinct smoke
 properties. 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. Necessary
 Reference Material: [1] Giordano, M. E., On Interactions of Matter and
 Energy: Light and Particles in a Terrestrial Atmosphere Progress on
 Opto-Physical Recognition and Classification of Aerosols: A PhD
 dissertation, University of Nevada, copyright M.E. Giordano, 294 pages,
 December 2019. URI: http://hdl.handle.net/11714/6686 
 https://scholarworks.unr.edu/handle/11714/6686?show=full [2] 
 Giordano, M.E., Ward, C.S., and Hamill, P.: A Compendium of Aerosol Types
 Based on Mahalanobis Distances and AERONET data. [An internally
 hyperlinked compendium of seasonal aerosol and local aerosol compositions]
 Atmospheric Environment, 140, 213-233,2016. 
 https://doi.org/10.1016/j.atmosenv.2016.06.002  
 https://ars.els-cdn.com/content/image/1-s2.0-S1352231016304265-mmc1.pdf
 [3]  Hamill, P. J., Giordano, M. E., Ward, C.S., Giles, D.,
 Holben, B.: An AERONET - based aerosol classification using the
 Mahalanobis distance, Atmospheric Environment, Volume 140, September, pgs
 213 -233,
 2016. http://dx.doi.org/10.1016/j.atmosenv.2016.06.002and also at
 https://ars.els-cdn.com/content/image/1-s2.0-S1352231016304265-mmc1.pdf
 [4]  Hamill, Patrick, Piedra, Patricio G., Giordano, Marco, E.,
 2020: Simulated Polarization as a Signature of Aerosol Type. Atmospheric
 Environment, Volume 224, 117348 article ATMENVD- 19-01763,
 2020. https://doi.org/10.1016/j.atmosenv.2020.117348  
description: Building upon our previous
 endeavor, we make use of our Mahalanobis distance (MD) aerosol typological
 model and the resulting data base of global seasonal aerosol types
 [Giordano ibid]. An example of the results of which can be demonstrated as
 site specific historic aerosol composition, and seasonal aerosol
 climatology as presented below in Figure 1. This product is a tool for
 modelers and researchers to obtain the seasonal aerosol climatology and
 aerosol composition of every data qualifying AERONET site location.  A
 pair of sites is presented below as composite <b>Figure 1</b>
 (which are Crete actual pg. 74 and Rome, actual page 176 of the original
 compendium). The site’s historical aerosol composition is rendered as a
 pie chart of aerosol types by relative percent of occurrence; the
 historical seasonal aerosol climatology is shown as a histogram of the
 number of times the aerosol is classified by each aerosol type each month
 throughout the historical year. <b>Figure 1:
 </b><b>Aerosol Composition (pie) and Seasonal Climatology
 (bar) : Crete and Rome AERONET sites</b> <b>Figure 1.</b> <b>Panel A)</b> (left) This instrument is on the north side of the island of Crete, approximately 500 meters from the Sea of Crete. (south of the Aegean Sea). It is 12 km west of Heraklion, the main city of Crete, with a population of 180,000. The instrument is operated by Institute of Oceanography at the Hellenic Centre for Marine Research sponsored by the Foundation for Research and Technology – Hellas (FORTH). In the AERONET webpage this is listed as FORTH_CRETE. <b>Panel B)</b> (right) This instrument is located on a roof of the CNR institute near the University of Rome, Tor Vergata campus. This lies in the southern suburbs of Rome about 15 km from the city center, and 30 km inland from the Mediterranean. Rome has a metropolitan population of nearly 4 million. It is an important industrial, political and cultural center. This site is listed in the AERONET website as Rome_Tor_Vergata. Applying simple statistical averaging, to the bar chart histograms of numbers of AERONET retrievals by aerosol type classified,  and determining the most frequent classified aerosol type at each AERONET site for each historic month, we declare the most frequently retrieved aerosol type to be equivalent to the <em>Historic Monthly Dominant Aerosol Type.</em> Applying the results of our global seasonal aerosol typology onto the global AERONET site list using an open-access GIS tool, results in map representations of any specific historic seasonal aerosol climatology by historic month. An example of how this is employed is presented below for <em>historic January</em> between 1996 and January 2013 as Figure 2.  The map color scheme is chosen to be as compatible with those presented in the compendium document [Giordano 2016]. <b>Figure 2:</b> The mapping of 5  BCA aerosol types - depicting global January distributions.  Similar maps were produced for each month from the data base.     <strong>Figure 2:</strong> The mapping of 5  BCA aerosol types - depicting global January distributions.  Similar maps were produced for each month from the data base.  Despite the increasing AERONET growth, the coverage spatially could be enhanced by addressing the updating of our typology model and the ensuing data output. N.B. erratum in title, date range should reflect 1993-2016. mg. 2021 Applying the results of our global seasonal aerosol typology onto the global AERONET site map, results in representations of any specific historic seasonal aerosol climatology by historic month. An example of how this is employed is presented for <em>historic January</em> between 1996 and January 2013 as Figure 2 above.  It is the database of <em>Historic Dominant Monthly Aerosol Type</em> we have reposited in this data archive.
description: The first version of this data set is uploaded as a CSV UTF-8
 formatted text file [July2021].   It is anticipated that the file will be
 properly dimensioned and converted to a NETCDF4 file type in the ensuing
 months.   Expanded versions of the database file incorporating a greater
 number of AERONET sites meeting the criteria as specified in our original
 typlogy model codes [Hamil, et al. 2016], and a deeper time history to
 date, and to incorporate the AERONET V3 inversion data products are
 forthcoming. Data is presented in the csv file as to be
 converted directly into a matrix of 19 columns by the number of AERONET
 sites rendered as each of one row as a record. For each
 record, the columns are as follows: 1.<strong>
 Aeronet_Site_name</strong> (text string); 2.
 <strong>Longitude</strong> (numeric in decimal degree format);
 3. <strong>Latitude</strong> (numeric in decimal degree
 format); 4. <strong>Elevation</strong> (numeric in meters); 5.
 <strong>Data_Time_Start</strong> (text string in the format of
 ddMMyyyy); 6. <strong>Data_Time_End</strong> (text string in
 the format of ddMMyyyy); 7.
 <strong>Data_Time_Range_type</strong>(text string either
 "exact" or "nominal" date ranges; columns 8-19)
 <strong>Historic_Monthly_Dominant_Aerosol_type-January</strong>(text string) etc. for each month. The Columns 2,3,4 will be converted to numeric cordinates with elevations in NETCDF4 format. The Columns 5 and 6 will convert to date strings in NETCDF4 as mmddyyyy format.  The purpose of the dates as listed are merely to provide the end user a date span corresponding to the AERONET retrievals included in the typology and ensuing results.  Most are categorized as "nominal" representing the number of years of data.  The database in not searchable by any specific month, as the data represent reported dominant monthly types integrated over the AERONET site time range. The remaining columns 7-19 will remain as string variables. The dominant aerosol types correlate to the 5-aerosol typology of Hamill et al 2016, and are not yet modified to the 8-aerosol type classification scheme of Giordano 2019. The aerosol types are "<strong>Dust</strong>", "<strong>Mixed</strong>", "<strong>Maritime</strong>", "<strong>Biomass</strong>" (biomass burning) and "<strong>UI</strong>" (urban industrial).  The are textual labels for a specific set of aerosol optical properties and their distribution of values about a centroid value, for each referenced aerosol type as used in the typology model code. This set of 5-dimensional values for each optical property can be summarized in the following tables extracted from Hamill et al. 2016 and Giordano 2019 respectively.  <strong>Left</strong> - for 5 -D optical Space with 5 aerosol types [Hamill et al. 2016]. <strong>Right</strong> - for 5-D optical space classifying 8 aerosol types [Giordano 2019].   The readers should directly refer to the cited literature for detailed explanations and methods of mathematical determination of the tabled values. <strong>NOTA BENE: the null record for months without an aerosol type, either due to a lack of AERONET invertible data</strong> (such as due to <em>cloud screening</em> or the instrument off-line),<strong> or a statistically insufficient amount of data classified by the typology algorithm, are reported as nulls.  Hence, they appear as consecutive commas in the csv version of the database file.</strong> Database users could make use of the AERONET site associated locations, and the optical property values associated with the reported <em>Historic Dominant Monthly Aerosol Type </em>to forward scatter through a <em>radiative transfer</em> scheme, to arrive at the radiative forcing due to the Bulk Columnar Aerosol without the uncertainty of inferring aerosol types based upon non-intensive aerosol properties.