Aerosol Optical Properties and Type Retrieval via Machine Learning and an All-Sky Imager
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Published:2023-08-10
Issue:8
Volume:14
Page:1266
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ISSN:2073-4433
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Container-title:Atmosphere
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language:en
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Short-container-title:Atmosphere
Author:
Logothetis Stavros-Andreas1, Giannaklis Christos-Panagiotis1, Salamalikis Vasileios2, Tzoumanikas Panagiotis1, Raptis Panagiotis-Ioannis3ORCID, Amiridis Vassilis4, Eleftheratos Kostas35ORCID, Kazantzidis Andreas1ORCID
Affiliation:
1. Laboratory of Atmospheric Physics, Physics Department, University of Patras, GR-26500 Patras, Greece 2. NILU—Norwegian Institute for Air Research, P.O. Box 100, 2027 Kjeller, Norway 3. Department of Geology and Geoenvironment, National and Kapodistrian University of Athens, GR-15784 Athens, Greece 4. Institute for Astronomy, Astrophysics, Space Applications and Remote Sensing, National Observatory of Athens, GR-15236 Athens, Greece 5. Center for Environmental Effects on Health, Biomedical Research Foundation of the Academy of Athens, GR-11527 Athens, Greece
Abstract
This study investigates the applicability of using the sky information from an all-sky imager (ASI) to retrieve aerosol optical properties and type. Sky information from the ASI, in terms of Red-Green-Blue (RGB) channels and sun saturation area, are imported into a supervised machine learning algorithm for estimating five different aerosol optical properties related to aerosol burden (aerosol optical depth, AOD at 440, 500 and 675 nm) and size (Ångström Exponent at 440–675 nm, and Fine Mode Fraction at 500 nm). The retrieved aerosol optical properties are compared against reference measurements from the AERONET station, showing adequate agreement (R: 0.89–0.95). The AOD errors increased for higher AOD values, whereas for AE and FMF, the biases increased for coarse particles. Regarding aerosol type classification, the retrieved properties can capture 77.5% of the total aerosol type cases, with excellent results for dust identification (>95% of the cases). The results of this work promote ASI as a valuable tool for aerosol optical properties and type retrieval.
Funder
European Union and Greek national funds through the Operational Program Competitiveness, Entrepreneurship and Innovation
Subject
Atmospheric Science,Environmental Science (miscellaneous)
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