Retrieval of Aerosol Optical Properties via an All-Sky Imager and Machine Learning: Uncertainty in Direct Normal Irradiance Estimations

Abstract

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.

Keywords

• all-sky imager
• aerosol optical properties
• machine learning
• aerosol retrieval
• AERONET
• DNI estimations