Retrieval of Aerosol Optical Properties via an All-Sky Imager and Machine Learning: Uncertainty in Direct Normal Irradiance Estimations
Logothetis, Stavros-Andreas; Giannaklis, Christos-Panagiotis; Salamalikis, Vasileios; Tzoumanikas, Panagiotis; Raptis, Panagiotis-Ioannis; Amiridis, Vassilis; Eleftheratos, Kostas; Kazantzidis, Andreas
Journal article
Published version
Date
2023Metadata
Show full item recordCollections
- Publikasjoner fra Cristin - NILU [1363]
- Vitenskapelige publikasjoner [1098]
Original version
Environmental Sciences Proceedings. 2023, 26, 133. 10.3390/environsciproc2023026133Abstract
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. Retrieval of Aerosol Optical Properties via an All-Sky Imager and Machine Learning: Uncertainty in Direct Normal Irradiance Estimations