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.

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.

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, Gelsomina

Six 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.

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, Granados-Muñoz, María José, Navas-Guzmán, Francisco, Guerrero-Rascado, Juan Luis, Bravo-Aranda, Juan Antonio, Pereira, Sergio Nepomuceno, Basart, Sara, Baldasano, José María, Belegante, Livio, Chaikovsky, Anatoli, Comerón, Adolfo, D'Amico, Giuseppe, Dubovik, Oleg, Ilic, Luka, Kokkalis, Panos, Muñoz-Porcar, Constantino, Nickovic, Slobodan, Nicolae, Doina, Facchini, Maria Cristina, Olmo, Francisco José, Papayannis, Alexander, Pappalardo, Gelsomina, Rodríguez, Alejandro, Schepanski, Kerstin, Sicard, Michaël, Vukovic, Ana, Wandinger, Ulla, Dulac, François, Alados-Arboledas, Lucas

The simultaneous analysis of aerosol microphysical properties profiles at different European stations is made in the framework of the ChArMEx/EMEP 2012 field campaign (9–11 July 2012). During and in support of this campaign, five lidar ground-based stations (Athens, Barcelona, Bucharest, Évora, and Granada) performed 72 h of continuous lidar measurements and collocated and coincident sun-photometer measurements. Therefore it was possible to retrieve volume concentration profiles with the Lidar Radiometer Inversion Code (LIRIC). Results indicated the presence of a mineral dust plume affecting the western Mediterranean region (mainly the Granada station), whereas a different aerosol plume was observed over the Balkans area. LIRIC profiles showed a predominance of coarse spheroid particles above Granada, as expected for mineral dust, and an aerosol plume composed mainly of fine and coarse spherical particles above Athens and Bucharest. Due to the exceptional characteristics of the ChArMEx database, the analysis of the microphysical properties profiles' temporal evolution was also possible. An in-depth analysis was performed mainly at the Granada station because of the availability of continuous lidar measurements and frequent AERONET inversion retrievals. The analysis at Granada was of special interest since the station was affected by mineral dust during the complete analyzed period. LIRIC was found to be a very useful tool for performing continuous monitoring of mineral dust, allowing for the analysis of the dynamics of the dust event in the vertical and temporal coordinates. Results obtained here illustrate the importance of having collocated and simultaneous advanced lidar and sun-photometer measurements in order to characterize the aerosol microphysical properties in both the vertical and temporal coordinates at a regional scale. In addition, this study revealed that the use of the depolarization information as input in LIRIC in the stations of Bucharest, Évora, and Granada was crucial for the characterization of the aerosol types and their distribution in the vertical column, whereas in stations lacking depolarization lidar channels, ancillary information was needed. Results obtained were also used for the validation of different mineral dust models. In general, the models better forecast the vertical distribution of the mineral dust than the column-integrated mass concentration, which was underestimated in most of the cases.

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.

2016, Papagiannopoulos, Nikolaos, Mona, Lucia, Alados-Arboledas, Lucas, Amiridis, Vassilis, Baars, Holger, Binietoglou, Ioannis, Bortoli, Daniele, D'Amico, Giuseppe, Giunta, Aldo, Guerrero-Rascado, Juan Luis, Schwarz, Anja, Pereira, Sergio, Spinelli, Nicola, Wandinger, Ulla, Wang, Xuan, Pappalardo, Gelsomina

The CALIPSO Level 3 (CL3) product is the most recent data set produced by the observations of the Cloud–Aerosol Lidar with Orthogonal Polarization (CALIOP) instrument onboard the Cloud–Aerosol Lidar and Pathfinder Satellite Observations (CALIPSO) space platform. The European Aerosol Research Lidar Network (EARLINET), based mainly on multi-wavelength Raman lidar systems, is the most appropriate ground-based reference for CALIPSO calibration/validation studies on a continental scale. In this work, CALIPSO data are compared against EARLINET monthly averaged profiles obtained by measurements performed during CALIPSO overpasses. In order to mitigate uncertainties due to spatial and temporal differences, we reproduce a modified version of CL3 data starting from CALIPSO Level 2 (CL2) data. The spatial resolution is finer and nearly 2°  ×  2° (latitude  ×  longitude) and only simultaneous measurements are used for ease of comparison. The CALIPSO monthly mean profiles following this approach are called CALIPSO Level 3*, CL3*. We find good agreement on the aerosol extinction coefficient, yet in most of the cases a small CALIPSO underestimation is observed with an average bias of 0.02 km−1 up to 4 km and 0.003 km−1 higher above. In contrast to CL3 standard product, the CL3* data set offers the possibility to assess the CALIPSO performance also in terms of the particle backscatter coefficient keeping the same quality assurance criteria applied to extinction profiles. The mean relative difference in the comparison improved from 25 % for extinction to 18 % for backscatter, showing better performances of CALIPSO backscatter retrievals. Additionally, the aerosol typing comparison yielded a robust identification of dust and polluted dust. Moreover, the CALIPSO aerosol-type-dependent lidar ratio selection is assessed by means of EARLINET observations, so as to investigate the performance of the extinction retrievals. The aerosol types of dust, polluted dust, and clean continental showed noticeable discrepancy. Finally, the potential improvements of the lidar ratio assignment have been examined by adjusting it according to EARLINET-derived values.

2018, Belegante, Livio, Bravo-Aranda, Juan Antonio, Freudenthaler, Volker, Nicolae, Doina, Nemuc, Anca, Ene, Dragos, Alados-Arboledas, Lucas, Amodeo, Aldo, Pappalardo, Gelsomina, D'Amico, Giuseppe, Amato, Francesco, Engelmann, Ronny, Baars, Holger, Wandinger, Ulla, Papayannis, Alexandros, Kokkalis, Panos, Pereira, Sérgio N.

Particle depolarization ratio retrieved from lidar measurements are commonly used for aerosol-typing studies, microphysical inversion, or mass concentration retrievals. The particle depolarization ratio is one of the primary parameters that can differentiate several major aerosol components but only if the measurements are accurate enough. The accuracy related to the retrieval of particle depolarization ratios is the driving factor for assessing and improving the uncertainties of the depolarization products. This paper presents different depolarization calibration procedures used to improve the quality of the depolarization data. The results illustrate a significant improvement of the depolarization lidar products for all the selected lidar stations that have implemented depolarization calibration procedures. The calibrated volume and particle depolarization profiles at 532-nm show values that fall within a range that is generally accepted in the literature.