Articles | Volume 12, issue 7
https://doi.org/10.5194/amt-12-3789-2019
© Author(s) 2019. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/amt-12-3789-2019
© Author(s) 2019. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Aerosol-type classification based on AERONET version 3 inversion products
Sung-Kyun Shin
School of Physics, Astronomy and Mathematics, University of Hertfordshire, Hatfield, UK
now at: Department of Atmospheric Particulate Matter Research, Seoul Institute of Technology, Seoul, Republic of Korea
Matthias Tesche
School of Physics, Astronomy and Mathematics, University of Hertfordshire, Hatfield, UK
now at: Leipzig Institute for Meteorology, Leipzig University, Leipzig, Germany
Youngmin Noh
CORRESPONDING AUTHOR
Department of Environmental Engineering, Pukyong National University, Busan, Republic of Korea
Detlef Müller
School of Physics, Astronomy and Mathematics, University of Hertfordshire, Hatfield, UK
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- A Bayesian Framework to Quantify Uncertainty in Aerosol Optical Model Selection Applied to TROPOMI Measurements A. Kauppi et al. 10.3390/rs16111945
- The contribution of different aerosol types to direct radiative forcing over distinct environments of Pakistan inferred from the AERONET data R. Khan et al. 10.1088/1748-9326/aba2a6
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Latest update: 23 Nov 2024
Short summary
This study proposes an aerosol-type classification based on parameters from the AErosol RObotic NETwork (AERONET) version 3 level 2.0 inversion product that describe light depolarization and absorption properties of atmospheric particles.
We compare our classification with an earlier method and find that the new approach allows for a refined classification of mineral dust that occurs as a mixture with other absorbing aerosols.
This study proposes an aerosol-type classification based on parameters from the AErosol RObotic...