| Autor: |
Stavros-Andreas Logothetis, Christos-Panagiotis Giannaklis, Vasileios Salamalikis, Panagiotis Tzoumanikas, Panagiotis-Ioannis Raptis, Vassilis Amiridis, Kostas Eleftheratos, Andreas Kazantzidis |
| Jazyk: |
angličtina |
| Rok vydání: |
2023 |
| Předmět: |
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| Zdroj: |
Environmental Sciences Proceedings, Vol 26, Iss 1, p 133 (2023) |
| Druh dokumentu: |
article |
| ISSN: |
2673-4931 |
| DOI: |
10.3390/environsciproc2023026133 |
| Popis: |
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. |
| Databáze: |
Directory of Open Access Journals |
| Externí odkaz: |
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