2016, Bravo-Aranda, Juan Antonio, Belegante, Livio, Freudenthaler, Volker, Alados-Arboledas, Lucas, Nicolae, Doina, Granados-Muñoz, María José, Guerrero-Rascado, Juan Luis, Amodeo, Aldo, D'Amico, Giusseppe, Engelmann, Ronny, Pappalardo, Gelsomina, Kokkalis, Panos, Mamouri, Rodanthy, Papayannis, Alex, Navas-Guzmán, Francisco, Olmo, Francisco José, Wandinger, Ulla, Amato, Francesco, Haeffelin, Martial

Lidar depolarization measurements distinguish between spherical and non-spherical aerosol particles based on the change of the polarization state between the emitted and received signal. The particle shape information in combination with other aerosol optical properties allows the characterization of different aerosol types and the retrieval of aerosol particle microphysical properties. Regarding the microphysical inversions, the lidar depolarization technique is becoming a key method since particle shape information can be used by algorithms based on spheres and spheroids, optimizing the retrieval procedure. Thus, the identification of the depolarization error sources and the quantification of their effects are crucial. This work presents a new tool to assess the systematic error of the volume linear depolarization ratio (δ), combining the Stokes–Müller formalism and the complete sampling of the error space using the lidar model presented in Freudenthaler (2016a). This tool is applied to a synthetic lidar system and to several EARLINET lidars with depolarization capabilities at 355 or 532 nm. The lidar systems show relative errors of δ larger than 100 % for δ values around molecular linear depolarization ratios (∼ 0.004 and up to ∼  10 % for δ = 0.45). However, one system shows only relative errors of 25 and 0.22 % for δ = 0.004 and δ = 0.45, respectively, and gives an example of how a proper identification and reduction of the main error sources can drastically reduce the systematic errors of δ. In this regard, we provide some indications of how to reduce the systematic errors.

2016, Groß, Silke, Gasteiger, Josef, Freudenthaler, Volker, Müller, Thomas, Sauer, Daniel, Toledano, Carlos, Ansmann, Albert

Dual-wavelength lidar measurements with the small lidar system POLIS of the Ludwig-Maximilians-Universität München were performed during the SALTRACE experiment at Barbados in June and July 2013. Based on high-accuracy measurements of the linear depolarization ratio down to about 200 m above ground level, the dust volume fraction and the dust mass concentration within the convective marine boundary layer can be derived. Additional information from radiosonde launches at the ground-based measurement site provide independent information on the convective marine boundary layer height and the meteorological situation within the convective marine boundary layer. We investigate the lidar-derived optical properties, the lidar ratio and the particle linear depolarization ratio at 355 and 532 nm and find mean values of 0.04 (SD 0.03) and 0.05 (SD 0.04) at 355 and 532 nm, respectively, for the particle linear depolarization ratio, and (26 ± 5) sr for the lidar ratio at 355 and 532 nm. For the concentration of dust in the convective marine boundary layer we find that most values were between 20 and 50 µgm−3. On most days the dust contribution to total aerosol volume was about 30–40 %. Comparing the dust contribution to the column-integrated sun-photometer measurements we see a correlation between high dust contribution, high total aerosol optical depth and a low Angström exponent, and of low dust contribution with low total aerosol optical depth.

2016, Wandinger, Ulla, Freudenthaler, Volker, Baars, Holger, Amodeo, Aldo, Engelmann, Ronny, Mattis, Ina, Groß, Silke, Pappalardo, Gelsomina, Giunta, Aldo, D'Amico, Giuseppe, Chaikovsky, Anatoli, Osipenko, Fiodor, Slesar, Alexander, Nicolae, Doina, Belegante, Livio, Talianu, Camelia, Serikov, Ilya, Linné, Holger, Jansen, Friedhelm, Apituley, Arnoud, Wilson, Keith M., de Graaf, Martin, Trickl, Thomas, Giehl, Helmut, Adam, Mariana, Comerón, Adolfo, Muñoz-Porcar, Constantino, Rocadenbosch, Francesc, Sicard, Michaël, Tomás, Sergio, Lange, Diego, Kumar, Dhiraj, Pujadas, Manuel, Molero, Francisco, Fernández, Alfonso J., Alados-Arboledas, Lucas, Bravo-Aranda, Juan Antonio, Navas-Guzmán, Francisco, Guerrero-Rascado, Juan Luis, Granados-Muñoz, María José, Preißler, 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 ±2  ×  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.

2016, Chaikovsky, Anatoli, Dubovik, Oleg, Holben, Brent, Bril, Andrey, Goloub, Philippe, Tanré, Didier, Pappalardo, Gelsomina, Wandinger, Ulla, Chaikovskaya, Ludmila, Denisov, Sergey, Grudo, Jan, Lopatin, Anton, Karol, Yana, Lapyonok, Tatsiana, Amiridis, Vassilis, Ansmann, Albert, Apituley, Arnoud, Allados-Arboledas, Lucas, Binietoglou, Ioannis, Boselli, Antonella, D'Amico, Giuseppe, Freudenthaler, Volker, Giles, David, Granados-Muñoz, María José, Kokkalis, Panayotis, Nicolae, Doina, Oshchepkov, Sergey, Papayannis, Alex, Perrone, Maria Rita, Pietruczuk, Alexander, Rocadenbosch, Francesc, Sicard, Michaël, Slutsker, Ilya, Talianu, Camelia, De Tomasi, Ferdinando, Tsekeri, Alexandra, Wagner, Janet, Wang, Xuan

This paper presents a detailed description of LIRIC (LIdar-Radiometer Inversion Code) algorithm for simultaneous processing of coincident lidar and radiometric (sun photometric) observations for the retrieval of the aerosol concentration vertical profiles. As the lidar/radiometric input data we use measurements from European Aerosol Research Lidar Network (EARLINET) lidars and collocated sun-photometers of Aerosol Robotic Network (AERONET). The LIRIC data processing provides sequential inversion of the combined lidar and radiometric data. The algorithm starts with the estimations of column-integrated aerosol parameters from radiometric measurements followed by the retrieval of height dependent concentrations of fine and coarse aerosols from lidar signals using integrated column characteristics of aerosol layer as a priori constraints. The use of polarized lidar observations allows us to discriminate between spherical and non-spherical particles of the coarse aerosol mode. The LIRIC software package was implemented and tested at a number of EARLINET stations. Intercomparison of the LIRIC-based aerosol retrievals was performed for the observations by seven EARLINET lidars in Leipzig, Germany on 25 May 2009. We found close agreement between the aerosol parameters derived from different lidars that supports high robustness of the LIRIC algorithm. The sensitivity of the retrieval results to the possible reduction of the available observation data is also discussed.

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.