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Retrieval of Aerosol Optical Properties via an All-Sky Imager and Machine Learning: Uncertainty in Direct Normal Irradiance Estimations
Quality-assured aerosol optical properties (AOP) with high spatiotemporal resolution are vital for the accurate estimation of direct aerosol radiative forcing and solar irradiance under clear skies. In this study, the sky information from an all-sky imager (ASI) is used with machine learning (ML) synergy to estimate aerosol optical depth (AOD) and the Ångström Exponent (AE). The retrieved AODs (AE) revealed good accuracy, with a dispersion error lower than 0.07 (0.15). The retrieved ML AOPs are used to estimate the DNI by applying radiative transfer modeling. The estimated ML DNI calculations revealed adequate accuracy to reproduce reference measurements with relatively low uncertainties.
PM2.5 Retrieval Using Aerosol Optical Depth, Meteorological Variables, and Artificial Intelligence
Particulate matter (PM) is one of the major air pollutants that has adverse impacts on human health. The aim of this study is to present an alternative approach for retrieving fine PM (particles with an aerodynamic diameter less than 2.5 μm, PM2.5) using artificial intelligence. Ground-based instruments, including a hand-held Microtops II sun photometer (for aerosol optical depth), a PurpleAir sensor (for PM2.5), and Rotronic sensors (for temperature and relative humidity), are used for the machine learning algorithm training. The retrieved PM2.5 reveals an adequate performance with an error of 0.08 μg m−3 and a Pearson correlation coefficient of 0.84.
Simulations of Sky Radiances in Red and Blue Channels at Various Aerosol Conditions Using Radiative Transfer Modeling
We conducted a theoretical analysis of the relationship between red-to-blue (RBR) color intensities and aerosol optical properties. RBR values are obtained by radiative transfer simulations of diffuse sky radiances. Changes in atmospheric aerosol concentration (parametrized by aerosol optical depth, AOD), particle’s size distribution (parametrized by Ångström exponent, AE) and aerosols’ scattering (parametrized by single scattering albedo—SSA) lead to variability in sky radiances and, thus, affect the RBR ratio. RBR is highly sensitive to AOD as high aerosol load in the atmosphere causes high RBR. AE seems to strongly affect the RBR, while SSA effect the RBR, but not to such a great extent.