Articles | Volume 21, issue 3
https://doi.org/10.5194/acp-21-2211-2021
© Author(s) 2021. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/acp-21-2211-2021
© Author(s) 2021. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
15 Feb 2021
Research article |
| 15 Feb 2021
| 15 Feb 2021
Aerosol type classification analysis using EARLINET multiwavelength and depolarization lidar observations
Laser Remote Sensing Unit, Department of Physics, National and
Technical University of Athens, Zografou, 15780, Greece
Elina Giannakaki
Department of Environmental Physics and Meteorology, Faculty of
Physics, National and
Kapodistrian University of Athens, Athens, Greece
Finnish Meteorological Institute, P.O. Box 1627, 70211 Kuopio, Finland
Alexandros Papayannis
Laser Remote Sensing Unit, Department of Physics, National and
Technical University of Athens, Zografou, 15780, Greece
Christina-Anna Papanikolaou
Laser Remote Sensing Unit, Department of Physics, National and
Technical University of Athens, Zografou, 15780, Greece
Mika Komppula
Finnish Meteorological Institute, P.O. Box 1627, 70211 Kuopio, Finland
Doina Nicolae
National Institute of R&D for Optoelectronics (INOE), Magurele,
Romania
Nikolaos Papagiannopoulos
Consiglio Nazionale delle Ricerche, Istituto di Metodologie per
l'Analisi Ambientale (CNR-IMAA), C.da S. Loja, Tito Scalo (PZ), 85050, Italy
CommSensLab, Dept. of Signal Theory and Communications, Universitat
Politècnica de Catalunya, Barcelona, Spain
Aldo Amodeo
Consiglio Nazionale delle Ricerche, Istituto di Metodologie per
l'Analisi Ambientale (CNR-IMAA), C.da S. Loja, Tito Scalo (PZ), 85050, Italy
Holger Baars
Leibniz Institute for Tropospheric Research, Leipzig, Germany
Ourania Soupiona
Laser Remote Sensing Unit, Department of Physics, National and
Technical University of Athens, Zografou, 15780, Greece
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- Scattering and absorbing aerosols in the climate system J. Li et al. 10.1038/s43017-022-00296-7
- Retrieval and analysis of the composition of an aerosol mixture through Mie–Raman–fluorescence lidar observations I. Veselovskii et al. 10.5194/amt-17-4137-2024
- Natural Aerosols, Gaseous Precursors and Their Impacts in Greece: A Review from the Remote Sensing Perspective V. Amiridis et al. 10.3390/atmos15070753
- Combined sun-photometer–lidar inversion: lessons learned during the EARLINET/ACTRIS COVID-19 campaign A. Tsekeri et al. 10.5194/amt-16-6025-2023
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- The challenge of identifying dust events in a highly polluted Eastern Mediterranean region I. Rogozovsky et al. 10.1016/j.scitotenv.2024.175920
- Optical and Microphysical Properties of Aged Biomass Burning Aerosols and Mixtures, Based on 9-Year Multiwavelength Raman Lidar Observations in Athens, Greece M. Mylonaki et al. 10.3390/rs13193877
- Automated time–height-resolved air mass source attribution for profiling remote sensing applications M. Radenz et al. 10.5194/acp-21-3015-2021
16 citations as recorded by crossref.
- Vertical Profiling of Fresh Biomass Burning Aerosol Optical Properties over the Greek Urban City of Ioannina, during the PANACEA Winter Campaign C. Papanikolaou et al. 10.3390/atmos13010094
- Aerosol Characterization during the Summer 2017 Huge Fire Event on Mount Vesuvius (Italy) by Remote Sensing and In Situ Observations A. Boselli et al. 10.3390/rs13102001
- Lidar ratio calculations from in situ aerosol optical, microphysical and chemical measurements: Observations at puy de Dôme, France and analysis with CALIOP K. Eswaran et al. 10.1016/j.atmosres.2023.107043
- Combining Mie–Raman and fluorescence observations: a step forward in aerosol classification with lidar technology I. Veselovskii et al. 10.5194/amt-15-4881-2022
- Large-Scale Network-Based Observations of a Saharan Dust Event across the European Continent in Spring 2022 C. Papanikolaou et al. 10.3390/rs16173350
- Diurnal and Seasonal Variability of the Atmospheric Boundary-Layer Height in Marseille (France) for Mistral and Sea/Land Breeze Conditions A. Riandet et al. 10.3390/rs15051185
- Synergy between Short-Range Lidar and In Situ Instruments for Determining the Atmospheric Boundary Layer Lidar Ratio A. Bedoya-Velásquez et al. 10.3390/rs16091583
- Particle Microphysical Parameters and the Complex Refractive Index from 3β + 2α HSRL/Raman Lidar Measurements: Conditions of Accurate Retrieval, Retrieval Uncertainties and Constraints to Suppress the Uncertainties A. Kolgotin et al. 10.3390/atmos14071159
- Scattering and absorbing aerosols in the climate system J. Li et al. 10.1038/s43017-022-00296-7
- Retrieval and analysis of the composition of an aerosol mixture through Mie–Raman–fluorescence lidar observations I. Veselovskii et al. 10.5194/amt-17-4137-2024
- Natural Aerosols, Gaseous Precursors and Their Impacts in Greece: A Review from the Remote Sensing Perspective V. Amiridis et al. 10.3390/atmos15070753
- Combined sun-photometer–lidar inversion: lessons learned during the EARLINET/ACTRIS COVID-19 campaign A. Tsekeri et al. 10.5194/amt-16-6025-2023
- Comparison of scanning aerosol lidar and in situ measurements of aerosol physical properties and boundary layer heights H. Zhang et al. 10.5194/ar-2-135-2024
- Multiwavelength fluorescence lidar observations of smoke plumes I. Veselovskii et al. 10.5194/amt-16-2055-2023
- The challenge of identifying dust events in a highly polluted Eastern Mediterranean region I. Rogozovsky et al. 10.1016/j.scitotenv.2024.175920
- Optical and Microphysical Properties of Aged Biomass Burning Aerosols and Mixtures, Based on 9-Year Multiwavelength Raman Lidar Observations in Athens, Greece M. Mylonaki et al. 10.3390/rs13193877
1 citations as recorded by crossref.
Latest update: 13 Dec 2024
Short summary
We introduce an automated aerosol type classification method, SCAN. The output of SCAN is compared with two aerosol classification methods: (1) the Mahalanobis distance automatic aerosol type classification and (2) a neural network aerosol typing algorithm. A total of 97 free tropospheric aerosol layers from four EARLINET stations in the period 2014–2018 were classified.
We introduce an automated aerosol type classification method, SCAN. The output of SCAN is...
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