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The automated multiwavelength Raman polarization and water-vapor lidar PollyXT: The neXT generation
2016, Engelmann, Ronny, Kanitz, Thomas, Baars, Holger, Heese, Birgit, Althausen, Dietrich, Skupin, Annett, Wandinger, Ulla, Komppula, Mika, Stachlewska, Iwona S., Amiridis, Vassilis, Marinou, Eleni, Mattis, Ina, Linné, Holger, Ansmann, Albert
The atmospheric science community demands autonomous and quality-assured vertically resolved measurements of aerosol and cloud properties. For this purpose, a portable lidar called Polly was developed at TROPOS in 2003. The lidar system was continuously improved with gained experience from the EARLINET community, involvement in worldwide field campaigns, and international institute collaborations within the last 10 years. Here we present recent changes of the setup of the portable multiwavelength Raman and polarization lidar PollyXT and discuss the improved capabilities of the system by means of a case study. The latest system developments include an additional near-range receiver unit for Raman measurements of the backscatter and extinction coefficient down to 120 m above ground, a water-vapor channel, and channels for simultaneous measurements of the particle linear depolarization ratio at 355 and 532 nm. Quality improvements were achieved by systematically following the EARLINET guidelines and the international PollyNET quality assurance developments. A modified ship radar ensures measurements in agreement with air-traffic safety regulations and allows for 24∕7 monitoring of the atmospheric state with PollyXT.
An overview of the first decade of PollyNET: An emerging network of automated Raman-polarization lidars for continuous aerosol profiling
2016, Baars, Holger, Kanitz, Thomas, Engelmann, Ronny, Althausen, Dietrich, Heese, Birgit, Komppula, Mika, Preißler, Jana, Tesche, Matthias, Ansmann, Albert, Wandinger, Ulla, Lim, Jae-Hyun, Ahn, Joon Young, Stachlewska, Iwona S., Amiridis, Vassilis, Marinou, Eleni, Seifert, Patric, Hofer, Julian, Skupin, Annett, Schneider, Florian, Bohlmann, Stephanie, Foth, Andreas, Bley, Sebastian, Pfüller, Anne, Giannakaki, Eleni, Lihavainen, Heikki, Viisanen, Yrjö, Hooda, Rakesh Kumar, Pereira, Sérgio Nepomuceno, Bortol, Daniele, Wagner, Frank, Mattis, Ina, Janicka, Lucja, Markowicz, Krzysztof M., Achtert, Peggy, Artaxo, Paulo, Pauliquevis, Theotonio, Souza, Rodrigo A.F., Sharma, Ved Prakesh, van Zyl, Pieter Gideon, Beukes, Johan Paul, Sun, Junying, Rohwer, Erich G., Deng, Ruru, Mamouri, Rodanthi-Elisavet, Zamorano, Felix
A global vertically resolved aerosol data set covering more than 10 years of observations at more than 20 measurement sites distributed from 63° N to 52° S and 72° W to 124° E has been achieved within the Raman and polarization lidar network PollyNET. This network consists of portable, remote-controlled multiwavelength-polarization-Raman lidars (Polly) for automated and continuous 24/7 observations of clouds and aerosols. PollyNET is an independent, voluntary, and scientific network. All Polly lidars feature a standardized instrument design with different capabilities ranging from single wavelength to multiwavelength systems, and now apply unified calibration, quality control, and data analysis. The observations are processed in near-real time without manual intervention, and are presented online at http://polly.tropos.de/. The paper gives an overview of the observations on four continents and two research vessels obtained with eight Polly systems. The specific aerosol types at these locations (mineral dust, smoke, dust-smoke and other dusty mixtures, urban haze, and volcanic ash) are identified by their Ångström exponent, lidar ratio, and depolarization ratio. The vertical aerosol distribution at the PollyNET locations is discussed on the basis of more than 55 000 automatically retrieved 30 min particle backscatter coefficient profiles at 532 nm as this operating wavelength is available for all Polly lidar systems. A seasonal analysis of measurements at selected sites revealed typical and extraordinary aerosol conditions as well as seasonal differences. These studies show the potential of PollyNET to support the establishment of a global aerosol climatology that covers the entire troposphere.
GARRLiC and LIRIC: Strengths and limitations for the characterization of dust and marine particles along with their mixtures
2017, Tsekeri, Alexandra, Lopatin, Anton, Amiridis, Vassilis, Marinou, Eleni, Igloffstein, Julia, Siomos, Nikolaos, Solomos, Stavros, Kokkalis, Panagiotis, Engelmann, Ronny, Baars, Holger, Gratsea, Myrto, Raptis, Panagiotis I., Binietoglou, Ioannis, Mihalopoulos, Nikolaos, Kalivitis, Nikolaos, Kouvarakis, Giorgos, Bartsotas, Nikolaos, Kallos, George, Basart, Sara, Schuettemeyer, Dirk, Wandinger, Ulla, Ansmann, Albert, Chaikovsky, Anatoli P., Dubovik, Oleg
The Generalized Aerosol Retrieval from Radiometer and Lidar Combined data algorithm (GARRLiC) and the LIdar-Radiometer Inversion Code (LIRIC) provide the opportunity to study the aerosol vertical distribution by combining ground-based lidar and sun-photometric measurements. Here, we utilize the capabilities of both algorithms for the characterization of Saharan dust and marine particles, along with their mixtures, in the south-eastern Mediterranean during the CHARacterization of Aerosol mixtures of Dust and Marine origin Experiment (CHARADMExp). Three case studies are presented, focusing on dust-dominated, marinedominated and dust-marine mixing conditions. GARRLiC and LIRIC achieve a satisfactory characterization for the dust-dominated case in terms of particle microphysical properties and concentration profiles. The marine-dominated and the mixture cases are more challenging for both algorithms, although GARRLiC manages to provide more detailed microphysical retrievals compared to AERONET, while LIRIC effectively discriminates dust and marine particles in its concentration profile retrievals. The results are also compared with modelled dust and marine concentration profiles and surface in situ measurements.
Application of the Garrlic algorithm for the characterization of dust and marine particles utilizing the lidar-sunphotometer synergy
2016, Tsekeri, Alexandra, Amiridis, Vassilis, Lopatin, Anton, Marinou, Eleni, Kokkalis, Panos, Solomos, Stavros, Engelmann, Ronny, Baars, Holger, Wandinger, Ulla, Ansmann, Albert, Schüttemeyer, Dirk, Dubovik, Oleg
The importance of studying the vertical distribution of aerosol plumes is prominent in regional and climate studies. The new Generalized Aerosol Retrieval from Radiometer and Lidar Combined data algorithm (GARRLiC) provides this opportunity combining active and passive ground-based remote sensing from lidar and sunphotometer measurements. Here, we utilize GARRLiC capabilities for the characterization of Saharan dust and marine particles at the Eastern Mediterranean region during the Characterization of Aerosol mixtures of Dust And Marine origin Experiment (CHARADMExp). Two different case studies are presented, a dust-dominated case which we managed to characterize successfully in terms of the particle microphysical properties and their vertical distribution and a case of two separate layers of marine and dust particles for which the characterization proved to be more challenging.
EARLINET evaluation of the CATS Level 2 aerosol backscatter coefficient product
2019, Proestakis, Emmanouil, Amiridis, Vassilis, Marinou, Eleni, Binietoglou, Ioannis, Ansmann, Albert, Wandinger, Ulla, Hofer, Julian, Yorks, John, Nowottnick, Edward, Makhmudov, Abduvosit, Papayannis, Alexandros, Pietruczuk, Aleksander, Gialitaki, Anna, Apituley, Arnoud, Szkop, Artur, Muñoz Porcar, Constantino, Bortoli, Daniele, Dionisi, Davide, Althausen, Dietrich, Mamali, Dimitra, Balis, Dimitris, Nicolae, Doina, Tetoni, Eleni, Liberti, Gian Luigi, Baars, Holger, Mattis, Ina, Stachlewska, Iwona Sylwia, Voudouri, Kalliopi Artemis, Mona, Lucia, Mylonaki, Maria, Perrone, Maria Rita, Costa, Maria João, Sicard, Michael, Papagiannopoulos, Nikolaos, Siomos, Nikolaos, Burlizzi, Pasquale, Pauly, Rebecca, Engelmann, Ronny, Abdullaev, Sabur, Pappalardo, Gelsomina
We present the evaluation activity of the European Aerosol Research Lidar Network (EARLINET) for the quantitative assessment of the Level 2 aerosol backscatter coefficient product derived by the Cloud-Aerosol Transport System (CATS) aboard the International Space Station (ISS; Rodier et al., 2015). The study employs correlative CATS and EARLINET backscatter measurements within a 50km distance between the ground station and the ISS overpass and as close in time as possible, typically with the starting time or stopping time of the EARLINET performed measurement time window within 90min of the ISS overpass, for the period from February 2015 to September 2016. The results demonstrate the good agreement of the CATS Level 2 backscatter coefficient and EARLINET. Three ISS overpasses close to the EARLINET stations of Leipzig, Germany; Évora, Portugal; and Dushanbe, Tajikistan, are analyzed here to demonstrate the performance of the CATS lidar system under different conditions. The results show that under cloud-free, relative homogeneous aerosol conditions, CATS is in good agreement with EARLINET, independent of daytime and nighttime conditions. CATS low negative biases are observed, partially attributed to the deficiency of lidar systems to detect tenuous aerosol layers of backscatter signal below the minimum detection thresholds; these are biases which may lead to systematic deviations and slight underestimations of the total aerosol optical depth (AOD) in climate studies. In addition, CATS misclassification of aerosol layers as clouds, and vice versa, in cases of coexistent and/or adjacent aerosol and cloud features, occasionally leads to non-representative, unrealistic, and cloud-contaminated aerosol profiles. Regarding solar illumination conditions, low negative biases in CATS backscatter coefficient profiles, of the order of 6.1%, indicate the good nighttime performance of CATS. During daytime, a reduced signal-to-noise ratio by solar background illumination prevents retrievals of weakly scattering atmospheric layers that would otherwise be detectable during nighttime, leading to higher negative biases, of the order of 22.3%. © Author(s) 2019.