2018, Papagiannopoulos, Nikolaos, Mona, Lucia, Amodeo, Aldo, D'Amico, Giuseppe, Gumà Claramunt, Pilar, Pappalardo, Gelsomina, Alados-Arboledas, Lucas, Guerrero-Rascado, Juan Luís, Amiridis, Vassilis, Kokkalis, Panagiotis, Apituley, Arnoud, Baars, Holger, Schwarz, Anja, Wandinger, Ulla, Binietoglou, Ioannis, Nicolae, Doina, Bortoli, Daniele, Comerón, Adolfo, Rodríguez-Gómez, Alejandro, Sicard, Michaël, Papayannis, Alex, Wiegner, Matthias

We present an automatic aerosol classification method based solely on the European Aerosol Research Lidar Network (EARLINET) intensive optical parameters with the aim of building a network-wide classification tool that could provide near-real-time aerosol typing information. The presented method depends on a supervised learning technique and makes use of the Mahalanobis distance function that relates each unclassified measurement to a predefined aerosol type. As a first step (training phase), a reference dataset is set up consisting of already classified EARLINET data. Using this dataset, we defined 8 aerosol classes: clean continental, polluted continental, dust, mixed dust, polluted dust, mixed marine, smoke, and volcanic ash. The effect of the number of aerosol classes has been explored, as well as the optimal set of intensive parameters to separate different aerosol types. Furthermore, the algorithm is trained with literature particle linear depolarization ratio values. As a second step (testing phase), we apply the method to an already classified EARLINET dataset and analyze the results of the comparison to this classified dataset. The predictive accuracy of the automatic classification varies between 59% (minimum) and 90% (maximum) from 8 to 4 aerosol classes, respectively, when evaluated against pre-classified EARLINET lidar. This indicates the potential use of the automatic classification to all network lidar data. Furthermore, the training of the algorithm with particle linear depolarization values found in the literature further improves the accuracy with values for all the aerosol classes around 80%. Additionally, the algorithm has proven to be highly versatile as it adapts to changes in the size of the training dataset and the number of aerosol classes and classifying parameters. Finally, the low computational time and demand for resources make the algorithm extremely suitable for the implementation within the single calculus chain (SCC), the EARLINET centralized processing suite.

2018, Belegante, Livio, Bravo-Aranda, Juan Antonio, Freudenthaler, Volker, Nicolae, Doina, Nemuc, Anca, Ene, Dragos, Alados-Arboledas, Lucas, Amodeo, Aldo, Pappalardo, Gelsomina, D'Amico, Giuseppe, Amato, Francesco, Engelmann, Ronny, Baars, Holger, Wandinger, Ulla, Papayannis, Alexandros, Kokkalis, Panos, Pereira, Sérgio N.

Particle depolarization ratio retrieved from lidar measurements are commonly used for aerosol-typing studies, microphysical inversion, or mass concentration retrievals. The particle depolarization ratio is one of the primary parameters that can differentiate several major aerosol components but only if the measurements are accurate enough. The accuracy related to the retrieval of particle depolarization ratios is the driving factor for assessing and improving the uncertainties of the depolarization products. This paper presents different depolarization calibration procedures used to improve the quality of the depolarization data. The results illustrate a significant improvement of the depolarization lidar products for all the selected lidar stations that have implemented depolarization calibration procedures. The calibrated volume and particle depolarization profiles at 532-nm show values that fall within a range that is generally accepted in the literature.

2018, Papagiannopoulos, Nikolaos, Mona, Lucia, Amiridis, Vassilis, Binietoglou, Ioannis, D’Amico, Giuseppe, Guma-Claramunt, P., Schwarz, Anja, Alados-Arboledas, Lucas, Amodeo, Aldo, Apituley, Arnoud, Baars, Holger, Bortoli, Daniele, Comeron, Adolfo, Guerrero-Rascado, Juan Luis, Kokkalis, Panos, Nicolae, Doina, Papayannis, Alex, Pappalardo, Gelsomina, Wandinger, Ulla, Wiegner, Matthias, Nicolae, D., Makoto, A., Vassilis, A., Balis, D., Behrendt, A., Comeron, A., Gibert, F., Landulfo, E., McCormick, M.P., Senff, C., Veselovskii, I., Wandinger, U.

Aerosol typing is essential for understanding the impact of the different aerosol sources on climate, weather system and air quality. An aerosol classification method for EARLINET (European Aerosol Research Lidar Network) measurements is introduced which makes use the Mahalanobis distance classifier. The performance of the automatic classification is tested against manually classified EARLINET data. Results of the application of the method to an extensive aerosol dataset will be presented.