2017, Solomos, Stavros, Ansmann, Albert, Mamouri, Rodanthi-Elisavet, Binietoglou, Ioannis, Patlakas, Platon, Marinou, Eleni, Amiridis, Vassilis

The extreme dust storm that affected the Middle East and the eastern Mediterranean in September 2015 resulted in record-breaking dust loads over Cyprus with aerosol optical depth exceeding 5.0 at 550ĝ€nm. We analyse this event using profiles from the European Aerosol Research Lidar Network (EARLINET) and the Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observation (CALIPSO), geostationary observations from the Meteosat Second Generation (MSG) Spinning Enhanced Visible and Infrared Imager (SEVIRI), and high-resolution simulations from the Regional Atmospheric Modeling System (RAMS). The analysis of modelling and remote sensing data reveals the main mechanisms that resulted in the generation and persistence of the dust cloud over the Middle East and Cyprus. A combination of meteorological and surface processes is found, including (a) the development of a thermal low in the area of Syria that results in unstable atmospheric conditions and dust mobilization in this area, (b) the convective activity over northern Iraq that triggers the formation of westward-moving haboobs that merge with the previously elevated dust layer, and (c) the changes in land use due to war in the areas of northern Iraq and Syria that enhance dust erodibility.

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.