Centroid values of aerosol optical properties for 8 sub-types based in AERONET inversion data (1993–2018)

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 < 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.]

本研究对课题组此前提出的5类气溶胶光学分类方案（Hamill等，2016）进行改进，构建了区分度更高的8类气溶胶光学分类方案（Giordano，2019）。此前，我们基于1993年至2012年间的AERONET（AErosol RObotic NETwork）2.0级圆弧扫描反演产品，构建了一套气溶胶分类方案。本研究初期，我们仅通过光谱单次散射反照率（SSA）、光谱复折射指数（实部RRI与虚部IRI）以及两类安斯特朗指数（消光指数EAE与吸收指数AAE）的密集光学特性，从光学物理层面识别出5类主要的整层柱气溶胶（BCA），分别为海洋气溶胶、沙尘气溶胶、城市工业气溶胶、生物质燃烧气溶胶以及混合气溶胶。将某一观测样本归为某类气溶胶的依据，是该样本与每个参考簇（本身为5维超椭球）质心之间的五维马氏距离（MD）。为纳入更多AERONET观测站点（共200余个），本研究将变量空间保持为5维。在生成参考簇时，我们仅保留距数据质心2倍马氏距离范围内的数据点。本分类方案基于AERONET反演得到的物理量，且剔除了低光学厚度数据（依据AERONET圆弧扫描反演标准，AOD₄₄₀ₙₘ < 0.4）。本研究得到的气溶胶分类结果，可用于解读星载仪器反演的气溶胶数据，亦可作为区域气候模式的输入参数。该气溶胶分类方案的核心成果之一，是一套描述光学特性各异的气溶胶粒子类型及其地理分布的数据集。我们将该分类方案应用于符合筛选标准的AERONET数据存档，针对研究纳入的每个AERONET站点生成了按气溶胶类型划分的季节气溶胶气候态，绘制了区域气溶胶气候态分布图，以及一套完全基于地基光度数据的时间积分全球气溶胶气候态分布图（Giordano，2022）。本研究第一阶段的成果被整理为一份包含各AERONET站点气溶胶气候态的内部超链接汇编文档[可访问https://ars.els-cdn.com/content/image/1-s2.0-S1352231016304265-mmc1.pdf获取]。我们通过同一分类方案，将最初的5类气溶胶（Hamill等，2016；Giordano，2019）进一步细分为特定子类型，最终得到8类气溶胶分类方案（Giordano，2019，第2章）。例如，将生物质燃烧气溶胶进一步光学细分为特定子类型，有助于解析具有光谱特性差异的烟雾排放源。本研究通过质心分离法，将生物质燃烧气溶胶划分为明火燃烧（BMF）与阴燃（BMS）两个子类型，并将类内纳入的马氏距离标准调整为1.5倍MD。类似地，我们可将混合气溶胶划分为两个截然不同的类型，分别记为混合类型1（MIXED1）与混合类型2（MIXED2），分类置信度较高。通过任意三维光学空间的可视化展示可验证这一分类结果：两类混合气溶胶的5维质心间距为3.47~3.85倍MD[Giordano，2019，第2章]。同理，城市工业气溶胶类别可进一步划分为欧洲城市工业气溶胶（EURO UI）与北美城市工业气溶胶（NA UI），二者的5维质心间距为2.60~3.08倍MD。随后我们采用此前的数学方法，将全球AERONET数据反演结果按照参考标准归类为对应的气溶胶类型。我们认为，基于偏振数据的气溶胶分类方法（Hamill、Piedra与Giordano，2020）可作为一种补充手段，用于分析新版AERONET 3.0数据反演结果，以及针对网络中部署的新型CIMEL太阳光度计（具备增强的偏振测量能力）采集的数据开展分析。本研究得到的基于AERONET的5维8类气溶胶光学分类方案，可广泛应用于气溶胶光学研究：包括直接开展辐射传输正演模拟，以解析气溶胶吸收/散射对垂直加热廓线与地面接收辐照度的影响；作为复杂遥感算法的输入参数；用作原位测量与实验室实验研究的校准/验证值；以及用于评估大气模式中的辐射强迫计算结果。基于6维、8维、9维与10维光学特性空间的8类气溶胶分类方案及其相关数据文件的相关研究成果，将于2023年作为独立的数据库项目另行发表。