https://doi.org/10.1051/epjconf/202023708003
Aerosol Typing Based on Multiwavelength Lidar Observations and Meteorological Model Data
1 Laser Remote Sensing Unit, Physics Department, School of Applied Mathematics and Physical Sciences, National Technical University of Athens, 15780 Zografou, Greece
2 Department of Environmental Physics and Meteorology, Faculty of Physics, National and Kapodistrian University of Athens, 15780 Zografou, Greece
3 Finnish Meteorological Institute, P.O.Box 1627, 70211 Kuopio, Finland
* Email: mylonaki.mari@gmail.com
Published online: 7 July 2020
Three different aerosol classification methods have been used to characterize lidar observations: Mahalanobis distance automatic aerosol type classification, Neural Network Aerosol Typing Algorithm (NATALI) and Source and Analysis (SCAN) aerosol classification. The data selection has been made through the EARLINET database depending on the 3b+2a+1δ optical property availability. One hundred aerosol layers from four EARLINET stations (Bucharest, Kuopio, Leipzig and Potenza) have been classified. We present a typical case study of aerosol characterization observed by the MUSA system over Potenza on the 11th of April 2016 (20:30-21:30 UTC).
© The Authors, published by EDP Sciences, 2020
This is an Open Access article distributed under the terms of the Creative Commons Attribution License 4.0, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
