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- ItemAn EARLINET early warning system for atmospheric aerosol aviation hazards(Katlenburg-Lindau : EGU, 2020) Papagiannopoulos, Nikolaos; D’Amico, Giuseppe; Gialitaki, Anna; Ajtai, Nicolae; Alados-Arboledas, Lucas; Amodeo, Aldo; Amiridis, Vassilis; Baars, Holger; Balis, Dimitris; Binietoglou, Ioannis; Comerón, Adolfo; Dionisi, Davide; Falconieri, Alfredo; Fréville, Patrick; Kampouri, Anna; Mattis, Ina; Mijić, Zoran; Molero, Francisco; Papayannis, Alex; Pappalardo, Gelsomina; Rodríguez-Gómez, Alejandro; Solomos, Stavros; Mona, LuciaA stand-alone lidar-based method for detecting airborne hazards for aviation in near real time (NRT) is presented. A polarization lidar allows for the identification of irregular-shaped particles such as volcanic dust and desert dust. The Single Calculus Chain (SCC) of the European Aerosol Research Lidar Network (EARLINET) delivers high-resolution preprocessed data: the calibrated total attenuated backscatter and the calibrated volume linear depolarization ratio time series. From these calibrated lidar signals, the particle backscatter coefficient and the particle depolarization ratio can be derived in temporally high resolution and thus provide the basis of the NRT early warning system (EWS). In particular, an iterative method for the retrieval of the particle backscatter is implemented. This improved capability was designed as a pilot that will produce alerts for imminent threats for aviation. The method is applied to data during two diverse aerosol scenarios: first, a record breaking desert dust intrusion in March 2018 over Finokalia, Greece, and, second, an intrusion of volcanic particles originating from Mount Etna, Italy, in June 2019 over Antikythera, Greece. Additionally, a devoted observational period including several EARLINET lidar systems demonstrates the network's preparedness to offer insight into natural hazards that affect the aviation sector. © 2020 Author(s).
- ItemAn automatic observation-based aerosol typing method for EARLINET(Katlenburg-Lindau : EGU, 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, MatthiasWe 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.
- ItemThe unprecedented 2017–2018 stratospheric smoke event: decay phase and aerosol properties observed with the EARLINET(Katlenburg-Lindau : EGU, 2019) Baars, Holger; Ansmann, Albert; Ohneiser, Kevin; Haarig, Moritz; Engelmann, Ronny; Althausen, Dietrich; Hanssen, Ingrid; Gausa, Michael; Pietruczuk, Aleksander; Szkop, Artur; Stachlewska, Iwona S.; Wang, Dongxiang; Reichardt, Jens; Skupin, Annett; Mattis, Ina; Trickl, Thomas; Vogelmann, Hannes; Navas-Guzmán, Francisco; Haefele, Alexander; Acheson, Karen; Ruth, Albert A.; Tatarov, Boyan; Müller, Detlef; Hu, Qiaoyun; Podvin, Thierry; Goloub, Philippe; Veselovskii, Igor; Pietras, Christophe; Haeffelin, Martial; Fréville, Patrick; Sicard, Michaël; Comerón, Adolfo; García, Alfonso Javier Fernández; Molero Menéndez, Francisco; Córdoba-Jabonero, Carmen; Guerrero-Rascado, Juan Luis; Alados-Arboledas, Lucas; Bortoli, Daniele; Costa, Maria João; Dionisi, Davide; Liberti, Gian Luigi; Wang, Xuan; Sannino, Alessia; Papagiannopoulos, Nikolaos; Boselli, Antonella; Mona, Lucia; D’Amico, Guiseppe; Romano, Salvatore; Perrone, Maria Rita; Belegante, Livio; Nicolae, Doina; Grigorov, Ivan; Gialitaki, Anna; Amiridis, Vassilis; Soupiona, Ourania; Papayannis, Alexandros; Mamouri, Rodanthi-Elisaveth; Nisantzi, Argyro; Heese, Birgit; Hofer, Julian; Schechner, Yoav Y.; Wandinger, Ulla; Pappalardo, GelsominaSix months of stratospheric aerosol observations with the European Aerosol Research Lidar Network (EARLINET) from August 2017 to January 2018 are presented. The decay phase of an unprecedented, record-breaking stratospheric perturbation caused by wildfire smoke is reported and discussed in terms of geometrical, optical, and microphysical aerosol properties. Enormous amounts of smoke were injected into the upper troposphere and lower stratosphere over fire areas in western Canada on 12 August 2017 during strong thunderstorm–pyrocumulonimbus activity. The stratospheric fire plumes spread over the entire Northern Hemisphere in the following weeks and months. Twenty-eight European lidar stations from northern Norway to southern Portugal and the eastern Mediterranean monitored the strong stratospheric perturbation on a continental scale. The main smoke layer (over central, western, southern, and eastern Europe) was found at heights between 15 and 20 km since September 2017 (about 2 weeks after entering the stratosphere). Thin layers of smoke were detected at heights of up to 22–23 km. The stratospheric aerosol optical thickness at 532 nm decreased from values > 0.25 on 21–23 August 2017 to 0.005–0.03 until 5–10 September and was mainly 0.003–0.004 from October to December 2017 and thus was still significantly above the stratospheric background (0.001–0.002). Stratospheric particle extinction coefficients (532 nm) were as high as 50–200 Mm−1 until the beginning of September and on the order of 1 Mm−1 (0.5–5 Mm−1) from October 2017 until the end of January 2018. The corresponding layer mean particle mass concentration was on the order of 0.05–0.5 µg m−3 over these months. Soot particles (light-absorbing carbonaceous particles) are efficient ice-nucleating particles (INPs) at upper tropospheric (cirrus) temperatures and available to influence cirrus formation when entering the tropopause from above. We estimated INP concentrations of 50–500 L−1 until the first days in September and afterwards 5–50 L−1 until the end of the year 2017 in the lower stratosphere for typical cirrus formation temperatures of −55 ∘C and an ice supersaturation level of 1.15. The measured profiles of the particle linear depolarization ratio indicated a predominance of nonspherical smoke particles. The 532 nm depolarization ratio decreased slowly with time in the main smoke layer from values of 0.15–0.25 (August–September) to values of 0.05–0.10 (October–November) and < 0.05 (December–January). The decrease of the depolarization ratio is consistent with aging of the smoke particles, growing of a coating around the solid black carbon core (aggregates), and thus change of the shape towards a spherical form. We found ascending aerosol layer features over the most southern European stations, especially over the eastern Mediterranean at 32–35∘ N, that ascended from heights of about 18–19 to 22–23 km from the beginning of October to the beginning of December 2017 (about 2 km per month). We discuss several transport and lifting mechanisms that may have had an impact on the found aerosol layering structures.