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Now showing 1 - 5 of 5
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    Earlinet database: New design and new products for a wider use of aerosol lidar data
    (Les Ulis : EDP Sciences, 2018) Mona, Lucia; D’Amico, Giuseppe; Amato, Francesco; Linné, Holger; Baars, Holger; Wandinger, Ulla; Pappalardo, Gelsomina; 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.
    The EARLINET database is facing a complete reshaping to meet the wide request for more intuitive products and to face the even wider request related to the new initiatives such as Copernicus, the European Earth observation programme. The new design has been carried out in continuity with the past, to take advantage from long-term database. In particular, the new structure will provide information suitable for synergy with other instruments, near real time (NRT) applications, validation and process studies and climate applications.
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    Earlinet single calculus chain: New products overview
    (Les Ulis : EDP Sciences, 2018) D’Amico, Giuseppe; Mattis, Ina; Binietoglou, Ioannis; Baars, Holger; Mona, Lucia; Amato, Francesco; Kokkalis, Panos; Rodríguez-Gómez, Alejandro; Soupiona, Ourania; Kalliopi-Artemis, Voudouri; 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.
    The Single Calculus Chain (SCC) is an automatic and flexible tool to analyze raw lidar data using EARLINET quality assured retrieval algorithms. It has been already demonstrated the SCC can retrieve reliable aerosol backscatter and extinction coefficient profiles for different lidar systems. In this paper we provide an overview of new SCC products like particle linear depolarization ratio, cloud masking, aerosol layering allowing relevant improvements in the atmospheric aerosol characterization.
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    Raman LIDAR for UHECR experiments: an overview of the L'Aquila (Italy) lidar station experience for the retrieval of quality-assured data
    (Les Ulis : EDP Sciences, 2017) Iarlori, Marco; Rizi, Vincenzo; D’Amico, Giuseppe; Freudenthaler, Volker; Wandinger, Ulla; Grillo, Aurelio; Trávníček, P.; Prouza, M.; Gaug, M.; Keilhauer, B.
    L'Aquila (Italy) lidar station is part of the EARLINET (European Aerosol Research Lidar Network) since its beginning in the 2000. In the EARLINET community great efforts are devoted to the quality-assurance of the aerosol optical properties inserted in the database. To this end, each lidar station performed intercomparisons with reference instruments, a series of internal hardware checks in order to assess the quality of their instruments and exercises to test the algorithms used to retrieve the aerosol optical parameters. In this paper we give an overview of our experience within EARLINET qualityassurance (QA) program, which was adopted for the Raman lidar (RL) operated in the AUGER Observatory. This program could be systematically adopted for the lidar systems needed for the current and upcoming UHECR experiments, like CTA (Cherenkov Telescope Array).
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    An automatic aerosol classification for earlinet: Application and results
    (Les Ulis : EDP Sciences, 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.
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    A methodology for cloud masking uncalibrated lidar signals
    (Les Ulis : EDP Sciences, 2018) Binietoglou, Ioannis; D’Amico, Giuseppe; Baars, Holger; Belegante, Livio; Marinou, Eleni; 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.
    Most lidar processing algorithms, such as those included in EARLINET's Single Calculus Chain, can be applied only to cloud-free atmospheric scenes. In this paper, we present a methodology for masking clouds in uncalibrated lidar signals. First, we construct a reference dataset based on manual inspection and then train a classifier to separate clouds and cloud-free regions. Here we present details of this approach together with an example cloud masks from an EARLINET station.