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Discussion papers
https://doi.org/10.5194/acp-2018-1062
© Author(s) 2018. This work is distributed under
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/acp-2018-1062
© Author(s) 2018. This work is distributed under
the Creative Commons Attribution 4.0 License.

Submitted as: research article 05 Dec 2018

Submitted as: research article | 05 Dec 2018

Review status
This discussion paper is a preprint. A revision of this manuscript was accepted for the journal Atmospheric Chemistry and Physics (ACP) and is expected to appear here in due course.

Comparison of two automated aerosol typing methods and their application on an EARLINET station

Kalliopi Artemis Voudouri1, Nikolaos Siomos1, Konstantinos Michailidis1, Nikolaos Papagiannopoulos2,3, Lucia Mona2, Carmela Cornacchia2, Doina Nicolae4, and Dimitris Balis1 Kalliopi Artemis Voudouri et al.
  • 1Laboratory of atmospheric physics, Physics Department, Aristotle University of Thessaloniki, Greece
  • 2Consiglio Nazionale delle Ricerche, Istituto di Metodologie per l'Analisi Ambientale (CNR-IMAA), Tito Scalo (PZ), Italy
  • 3CommSensLab, Dept. of Signal Theory and Communications, Universitat Politècnica de Catalunya, Barcelona, Spain
  • 4National Institute of R&D for Optoelectronics (INOE2000), Magurele, Romania

Abstract. In this study we compare two automatic algorithms for the characterization of the aerosol layers derived from a Raman lidar and we test their application over a specific environment in continental Thessaloniki. Both automated aerosol typing methods base their typing on aerosol intensive properties. The methodologies are briefly described and the application on two case studies is presented. The results are checked for their consistency with satellite products and model simulations. Further application of the two classification tools was conducted. The evaluated dataset corresponds to ACTRIS/EARLINET (European Aerosol Research Lidar NETwork) Thessaloniki data acquired during the period 2012–2015. 80 layers out of 116 (percentage of 69 %) were successfully typed by both algorithms and assigned to four major types of aerosols: Dust, Maritime, Polluted Smoke and Clean Continental. The analysis showed that the two algorithms are in a very good agreement, when applied to real atmospheric conditions, with an agreement percentage of 88.8 % for Dust, 93.7 % for Polluted Smoke and 70 % for Clean Continental. The Maritime category was the one with the largest spread. These differences are attributed to differences in defining the aerosol types for the two methods. The overall consistency of the aerosol typing between the two automatic procedures despite the different aerosol type definition, allows their applicability to lidar data for characterization purposes. The joint characterization shows the highest degree of confidence in identifying Dust and Polluted Smoke, and emphasizes the need of further investigation for Maritime and Clean Continental type.

Kalliopi Artemis Voudouri et al.
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Kalliopi Artemis Voudouri et al.
Kalliopi Artemis Voudouri et al.
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Short summary
In this study we compare two automatic algorithms for the characterization of the aerosol layers derived from a Raman lidar and we test their application over a specific environment in continental Thessaloniki. The overall consistency of the aerosol typing between the two automatic procedures despite the different aerosol type definition, allows their applicability to lidar data for characterization purposes.
In this study we compare two automatic algorithms for the characterization of the aerosol layers...
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