The AERONET Version 3 aerosol retrieval algorithm, associated uncertainties and comparisons to Version 2

Abstract

Abstract. The Aerosol Robotic Network (AERONET) version 3 (V3) aerosol retrieval algorithm is described, which is based on the version 2 (V2) algorithm with numerous updates. Comparisons of V3 aerosol retrievals to those of V2 are presented, along with a new approach to estimate uncertainties in many of the retrieved aerosol parameters. Changes in V3 aerosol retrieval algorithm include: 1) a new polarized radiative transfer code (RTC), which replaced the scalar RTC of V2, 2) detailed characterization of gas absorption by adding NO2 and H2O to specify total gas absorption in the atmospheric column, specification of vertical profiles of all the atmospheric species, 3) new Bidirectional Reflectance Distribution Function (BRDF) parameters for land sites adopted from the MODIS BRDF/Albedo product, 4) a new version of the extraterrestrial solar flux spectrum, and 5) new temperature correction procedure of both direct sun and sky radiance measurements. The potential effect of each change in V3 on single scattering albedo (SSA) retrievals was analyzed. The operational almucantar retrievals of V2 versus V3 were compared for four AERONET sites: GSFC, Mezaira, Mongu, and Kanpur. Analysis showed very good agreement in retrieved parameters of the size distributions. Comparisons of SSA retrievals for dust aerosols (Mezaira) showed a good agreement in 440 nm SSA while for longer wavelengths V3 SSAs are systematically higher than those of V2 with the largest mean difference at 675 nm due to cumulative effects of both extraterrestrial solar flux and BRDF changes. For non-dust aerosols, the largest SSA deviation is at 675 nm due to differences in extraterrestrial solar flux spectrums used in each version. Further, the SSA 675 nm mean differences are very different for weakly (GSFC) and strongly (Mongu) absorbing aerosols which is explained by the lower sensitivity to a bias in aerosol scattering optical depth by less absorbing aerosols. A new hybrid (HYB) sky radiance measurements scan is introduced and discussed. The HYB combines features of scans in two different planes to maximize the range of scattering angles and achieve scan symmetry, thereby allowing for cloud screening and spatial averaging which is an advantage over the principal plane scan that lacks robust symmetry. We show that due to extended range of scattering angles HYB SSA retrievals for dust aerosols exhibit smaller variability with SZA than those of almucantar (ALM) which allows extending HYB SSA retrievals to solar zenith angles (SZA) less than 50° to as small as 25°. The comparison of SSA retrievals from closely time matched HYB and ALM scans in the 50° to 75° SZA range showed good agreement with the differences below ~0.005. We also present an approach to estimate retrieval uncertainties which utilizes the variability in retrieved parameters generated by perturbing both measurements and auxiliary input parameters as a proxy for retrievals uncertainty. The perturbations in measurements and auxiliary inputs are assumed as estimated biases in aerosol optical depth (AOD), radiometric calibration of sky radiances combined with solar spectral irradiance, and surface reflectance. For each set of Level 2 Sun/sky radiometer observations, 27 inputs corresponding to 27 combinations of biases were produced and separately inverted and to generate the following statistics of the inversion results: average, standard deviation, minimum and maximum values. From these statistics standard deviation (labeled as U27) is used as a proxy for estimated uncertainty and a lookup table (LUT) approach was implemented to reduce the computational time. The U27 climatological LUT was generated from the entire AERONET almucantar (1993–2018) and hybrid (2014–2018) scan database by binning U27s in AOD (440 nm), Angstrom Exponent (AE, 440–870nm), and SSA (440, 675, 870, 1020 nm). Using this LUT approach, the uncertainty estimates U27 for each individual V3 Level 2 retrieval can be obtained by interpolation using the corresponding measured and inverted combination of AOD, AE, and SSA.

Surprising Changes in Aerosol Loading over India amid COVID-19 Lockdown

“…Linear regression of AOD as a function of RH and WS shows that an increase (decrease) in RH (WS) of 20 RH unit (1 ms -1 ) can increase AOD by 0.10 (0.04). The increase in AOD with RH is in concurrence with previous studies (Bar-Or et al, 2012;Brock et al, 2016;Eck et al, 2020;Li et al, 2020;Yoon and Kim, 2006;Zang et al, 2019). The degree of change obtained using regression equation is similar to a previous study done by Yoon and Kim, 2006, an increase in RH values from 50 to 70 % (in RH unit) leads to an increase in AOD at 550nm by a factor 1.24, which in the present study is 1.20.…”

“…The retrieval algorithm has gone through substantial improvement in version 3, as compared to version 2. It includes a new polarized radiative transfer code and fully automated near-real-time quality assurance (Giles et al, 2019;Sinyuk et al, 2020). AOD…”

“…1 reported in the SO2 concentration over major Indian cities (Navinya et al, 2020b;. Also, the presence of clouds in the vicinity of the aerosol layer may enhance AOD due to the hygroscopic growth, which remains valid for all aerosol types and size ranges (Eck et al, 2014(Eck et al, , 2020. Cloud can also illuminate the nearby aerosol particles, which falsely translate into AOD without an increase in actual aerosol amount.…”

The Determination of Snow Albedo from Satellite Measurements Using Fast Atmospheric Correction Technique

“…Figure A3. The asymmetry parameter climatology (2007-2017) for five considered sites in Greenland (version 3 [22,23]) data, Level 1.5 retrievals with the residual error < 5%, and AOT (440 nm) < 0.20). Total number of retrievals is 5316 divided in 12 groups with 443 asymmetry parameters in each group.…”

Automated Aerosol Classification from Spectral UV Measurements Using Machine Learning Clustering

“…The AOD and the fine mode AOD values at all available wavelengths, as well as the SSA at 440 nm are provided directly from the standard AERONET algorithms [40,41]. The uncertainties of the SSA and FMF products are discussed in [41,42], respectively. In our analysis, we used Version 3 (see, [43]) Level 2.0 products and also Version 3 Level 1.5 products whenever the Level 2.0 Inversion algorithms successfully produced the aerosol size distribution.…”