Articles | Volume 19, issue 16
https://doi.org/10.5194/acp-19-10961-2019
© 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/acp-19-10961-2019
© Author(s) 2019. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
29 Aug 2019
Research article |
| 29 Aug 2019
| 29 Aug 2019
Comparison of two automated aerosol typing methods and their application to an EARLINET station
Kalliopi Artemis Voudouri
CORRESPONDING AUTHOR
Laboratory of Atmospheric Physics, Physics Department, Aristotle University of Thessaloniki, Thessaloniki, Greece
Nikolaos Siomos
Laboratory of Atmospheric Physics, Physics Department, Aristotle University of Thessaloniki, Thessaloniki, Greece
Konstantinos Michailidis
Laboratory of Atmospheric Physics, Physics Department, Aristotle University of Thessaloniki, Thessaloniki, Greece
Nikolaos Papagiannopoulos
Consiglio Nazionale delle Ricerche, Istituto di Metodologie per l’Analisi Ambientale (CNR-IMAA), Tito Scalo (PZ), Italy
CommSensLab, Dept. of Signal Theory and Communications, Universitat Politècnica de Catalunya, Barcelona, Spain
Lucia Mona
Consiglio Nazionale delle Ricerche, Istituto di Metodologie per l’Analisi Ambientale (CNR-IMAA), Tito Scalo (PZ), Italy
Carmela Cornacchia
Consiglio Nazionale delle Ricerche, Istituto di Metodologie per l’Analisi Ambientale (CNR-IMAA), Tito Scalo (PZ), Italy
Doina Nicolae
National Institute of R&D for Optoelectronics (INOE2000), Magurele, Romania
Dimitris Balis
Laboratory of Atmospheric Physics, Physics Department, Aristotle University of Thessaloniki, Thessaloniki, Greece
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Cited
12 citations as recorded by crossref.
- Consistency of the Single Calculus Chain Optical Products with Archived Measurements from an EARLINET Lidar Station K. Voudouri et al. 10.3390/rs12233969
- In situ identification of aerosol types in Athens, Greece, based on long-term optical and on online chemical characterization D. Kaskaoutis et al. 10.1016/j.atmosenv.2020.118070
- 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
- Automated Aerosol Classification from Spectral UV Measurements Using Machine Learning Clustering N. Siomos et al. 10.3390/rs12060965
- Optical properties of Central Asian aerosol relevant for spaceborne lidar applications and aerosol typing at 355 and 532 nm J. Hofer et al. 10.5194/acp-20-9265-2020
- Aerosol type classification analysis using EARLINET multiwavelength and depolarization lidar observations M. Mylonaki et al. 10.5194/acp-21-2211-2021
- Aerosol characteristics and types in the marine environments surrounding the East Mediterranean - Middle East (EMME) region during the AQABA campaign D. Kaskaoutis et al. 10.1016/j.atmosenv.2023.119633
- Evaluation of Aerosol Typing with Combination of Remote Sensing Techniques with In Situ Data during the PANACEA Campaigns in Thessaloniki Station, Greece K. Voudouri et al. 10.3390/rs14205076
- Aerosol classification using fuzzy clustering over a tropical rural site A. Krishnaveni et al. 10.1016/j.atmosres.2022.106518
- Atmospheric Dynamics and Numerical Simulations of Six Frontal Dust Storms in the Middle East Region N. Hamzeh et al. 10.3390/atmos12010125
- First validation of GOME-2/MetOp absorbing aerosol height using EARLINET lidar observations K. Michailidis et al. 10.5194/acp-21-3193-2021
- Deriving Aerosol Absorption Properties from Solar Ultraviolet Radiation Spectral Measurements at Thessaloniki, Greece I. Fountoulakis et al. 10.3390/rs11182179
11 citations as recorded by crossref.
- Consistency of the Single Calculus Chain Optical Products with Archived Measurements from an EARLINET Lidar Station K. Voudouri et al. 10.3390/rs12233969
- In situ identification of aerosol types in Athens, Greece, based on long-term optical and on online chemical characterization D. Kaskaoutis et al. 10.1016/j.atmosenv.2020.118070
- 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
- Automated Aerosol Classification from Spectral UV Measurements Using Machine Learning Clustering N. Siomos et al. 10.3390/rs12060965
- Optical properties of Central Asian aerosol relevant for spaceborne lidar applications and aerosol typing at 355 and 532 nm J. Hofer et al. 10.5194/acp-20-9265-2020
- Aerosol type classification analysis using EARLINET multiwavelength and depolarization lidar observations M. Mylonaki et al. 10.5194/acp-21-2211-2021
- Aerosol characteristics and types in the marine environments surrounding the East Mediterranean - Middle East (EMME) region during the AQABA campaign D. Kaskaoutis et al. 10.1016/j.atmosenv.2023.119633
- Evaluation of Aerosol Typing with Combination of Remote Sensing Techniques with In Situ Data during the PANACEA Campaigns in Thessaloniki Station, Greece K. Voudouri et al. 10.3390/rs14205076
- Aerosol classification using fuzzy clustering over a tropical rural site A. Krishnaveni et al. 10.1016/j.atmosres.2022.106518
- Atmospheric Dynamics and Numerical Simulations of Six Frontal Dust Storms in the Middle East Region N. Hamzeh et al. 10.3390/atmos12010125
- First validation of GOME-2/MetOp absorbing aerosol height using EARLINET lidar observations K. Michailidis et al. 10.5194/acp-21-3193-2021
Latest update: 18 Apr 2024
Short summary
In this study, a first attempt at comparing and evaluating two classification tools developed within EARLINET that provide near-real-time aerosol typing information for the lidar profiles of Thessaloniki is presented. Our aim is (i) to check the performance of both supervised learning techniques in their low-resolution mode and (ii) to investigate the reasons for typing agreement and disagreement with respect to the uncertainties and the threshold criteria applied.
In this study, a first attempt at comparing and evaluating two classification tools developed...
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