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EARLINET Single Calculus Chain – technical – Part 1: Pre-processing of raw lidar data
2016, D'Amico, Giuseppe, Amodeo, Aldo, Mattis, Ina, Freudenthaler, Volker, Pappalardo, Gelsomina
In this paper we describe an automatic tool for the pre-processing of aerosol lidar data called ELPP (EARLINET Lidar Pre-Processor). It is one of two calculus modules of the EARLINET Single Calculus Chain (SCC), the automatic tool for the analysis of EARLINET data. ELPP is an open source module that executes instrumental corrections and data handling of the raw lidar signals, making the lidar data ready to be processed by the optical retrieval algorithms. According to the specific lidar configuration, ELPP automatically performs dead-time correction, atmospheric and electronic background subtraction, gluing of lidar signals, and trigger-delay correction. Moreover, the signal-to-noise ratio of the pre-processed signals can be improved by means of configurable time integration of the raw signals and/or spatial smoothing. ELPP delivers the statistical uncertainties of the final products by means of error propagation or Monte Carlo simulations. During the development of ELPP, particular attention has been payed to make the tool flexible enough to handle all lidar configurations currently used within the EARLINET community. Moreover, it has been designed in a modular way to allow an easy extension to lidar configurations not yet implemented. The primary goal of ELPP is to enable the application of quality-assured procedures in the lidar data analysis starting from the raw lidar data. This provides the added value of full traceability of each delivered lidar product. Several tests have been performed to check the proper functioning of ELPP. The whole SCC has been tested with the same synthetic data sets, which were used for the EARLINET algorithm inter-comparison exercise. ELPP has been successfully employed for the automatic near-real-time pre-processing of the raw lidar data measured during several EARLINET inter-comparison campaigns as well as during intense field campaigns.
EARLINET instrument intercomparison campaigns: Overview on strategy and results
2016, Wandinger, Ulla, Freudenthaler, Volker, Baars, Holger, Amodeo, Aldo, Engelmann, Ronny, Mattis, Ina, GroĂŸ, Silke, Pappalardo, Gelsomina, Giunta, Aldo, D'Amico, Giuseppe, Chaikovsky, Anatoli, Osipenko, Fiodor, Slesar, Alexander, Nicolae, Doina, Belegante, Livio, Talianu, Camelia, Serikov, Ilya, LinnĂ©, Holger, Jansen, Friedhelm, Apituley, Arnoud, Wilson, Keith M., de Graaf, Martin, Trickl, Thomas, Giehl, Helmut, Adam, Mariana, ComerĂ³n, Adolfo, Muñoz-Porcar, Constantino, Rocadenbosch, Francesc, Sicard, MichaĂ«l, TomĂ¡s, Sergio, Lange, Diego, Kumar, Dhiraj, Pujadas, Manuel, Molero, Francisco, FernĂ¡ndez, Alfonso J., Alados-Arboledas, Lucas, Bravo-Aranda, Juan Antonio, Navas-GuzmĂ¡n, Francisco, Guerrero-Rascado, Juan Luis, Granados-Muñoz, MarĂa JosĂ©, PreiĂŸler, Jana, Wagner, Frank, Gausa, Michael, Grigorov, Ivan, Stoyanov, Dimitar, Iarlori, Marco, Rizi, Vincenco, Spinelli, Nicola, Boselli, Antonella, Wang, Xuan, Feudo, Teresa Lo, Perrone, Maria Rita, De Tomas, Ferdinando, Burlizzi, Pasquale
This paper introduces the recent European Aerosol Research Lidar Network (EARLINET) quality-assurance efforts at instrument level. Within two dedicated campaigns and five single-site intercomparison activities, 21 EARLINET systems from 18 EARLINET stations were intercompared between 2009 and 2013. A comprehensive strategy for campaign setup and data evaluation has been established. Eleven systems from nine EARLINET stations participated in the EARLINET Lidar Intercomparison 2009 (EARLI09). In this campaign, three reference systems were qualified which served as traveling standards thereafter. EARLINET systems from nine other stations have been compared against these reference systems since 2009. We present and discuss comparisons at signal and at product level from all campaigns for more than 100 individual measurement channels at the wavelengths of 355, 387, 532, and 607 nm. It is shown that in most cases, a very good agreement of the compared systems with the respective reference is obtained. Mean signal deviations in predefined height ranges are typically below ±2 %. Particle backscatter and extinction coefficients agree within ±2â€¯â€‰Ă—â€‰â€¯10−4 km−1 sr−1 and ± 0.01 km−1, respectively, in most cases. For systems or channels that showed larger discrepancies, an in-depth analysis of deficiencies was performed and technical solutions and upgrades were proposed and realized. The intercomparisons have reinforced confidence in the EARLINET data quality and allowed us to draw conclusions on necessary system improvements for some instruments and to identify major challenges that need to be tackled in the future.
An EARLINET early warning system for atmospheric aerosol aviation hazards
2020, Papagiannopoulos, Nikolaos, D’Amico, Giuseppe, Gialitaki, Anna, Ajtai, Nicolae, Alados-Arboledas, Lucas, Amodeo, Aldo, Amiridis, Vassilis, Baars, Holger, Balis, Dimitris, Binietoglou, Ioannis, ComerĂ³n, Adolfo, Dionisi, Davide, Falconieri, Alfredo, FrĂ©ville, Patrick, Kampouri, Anna, Mattis, Ina, Mijić, Zoran, Molero, Francisco, Papayannis, Alex, Pappalardo, Gelsomina, RodrĂguez-GĂ³mez, Alejandro, Solomos, Stavros, Mona, Lucia
A 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).