Atmosphere (Aug 2023)

Aerosol Optical Properties and Type Retrieval via Machine Learning and an All-Sky Imager

  • Stavros-Andreas Logothetis,
  • Christos-Panagiotis Giannaklis,
  • Vasileios Salamalikis,
  • Panagiotis Tzoumanikas,
  • Panagiotis-Ioannis Raptis,
  • Vassilis Amiridis,
  • Kostas Eleftheratos,
  • Andreas Kazantzidis

DOI
https://doi.org/10.3390/atmos14081266
Journal volume & issue
Vol. 14, no. 8
p. 1266

Abstract

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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.

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