17 results
Search Results
Now showing 1 - 10 of 17
- ItemThe automated multiwavelength Raman polarization and water-vapor lidar PollyXT: The neXT generation(München : European Geopyhsical Union, 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, AlbertThe 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.
- ItemVertical profiles of aerosol mass concentration derived by unmanned airborne in situ and remote sensing instruments during dust events(Katlenburg-Lindau : Copernicus, 2018) Mamali, Dimitra; Marinou, Eleni; Sciare, Jean; Pikridas, Michael; Kokkalis, Panagiotis; Kottas, Michael; Binietoglou, Ioannis; Tsekeri, Alexandra; Keleshis, Christos; Engelmann, Ronny; Baars, Holger; Ansmann, Albert; Amiridis, Vassilis; Russchenberg, Herman; Biskos, GeorgeIn situ measurements using unmanned aerial vehicles (UAVs) and remote sensing observations can independently provide dense vertically resolved measurements of atmospheric aerosols, information which is strongly required in climate models. In both cases, inverting the recorded signals to useful information requires assumptions and constraints, and this can make the comparison of the results difficult. Here we compare, for the first time, vertical profiles of the aerosol mass concentration derived from light detection and ranging (lidar) observations and in situ measurements using an optical particle counter on board a UAV during moderate and weak Saharan dust episodes. Agreement between the two measurement methods was within experimental uncertainty for the coarse mode (i.e. particles having radii > 0.5 μm), where the properties of dust particles can be assumed with good accuracy. This result proves that the two techniques can be used interchangeably for determining the vertical profiles of aerosol concentrations, bringing them a step closer towards their systematic exploitation in climate models.
- ItemGARRLiC and LIRIC: Strengths and limitations for the characterization of dust and marine particles along with their mixtures(Katlenburg-Lindau : Copernicus, 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, OlegThe 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.
- ItemRetrieval of ice-nucleating particle concentrations from lidar observations and comparison with UAV in situ measurements(Katlenburg-Lindau : EGU, 2019) Marinou, Eleni; Tesche, Matthias; Nenes, Athanasios; Ansmann, Albert; Schrod, Jann; Mamali, Dimitra; Tsekeri, Alexandra; Pikridas, Michael; Baars, Holger; Engelmann, Ronny; Voudouri, Kalliopi-Artemis; Solomos, Stavros; Sciare, Jean; Groß, Silke; Ewald, Florian; Amiridis, VassilisAerosols that are efficient ice-nucleating particles (INPs) are crucial for the formation of cloud ice via heterogeneous nucleation in the atmosphere. The distribution of INPs on a large spatial scale and as a function of height determines their impact on clouds and climate. However, in situ measurements of INPs provide sparse coverage over space and time. A promising approach to address this gap is to retrieve INP concentration profiles by combining particle concentration profiles derived by lidar measurements with INP efficiency parameterizations for different freezing mechanisms (immersion freezing, deposition nucleation). Here, we assess the feasibility of this new method for both ground-based and spaceborne lidar measurements, using in situ observations collected with unmanned aerial vehicles (UAVs) and subsequently analyzed with the FRIDGE (FRankfurt Ice nucleation Deposition freezinG Experiment) INP counter from an experimental campaign at Cyprus in April 2016. Analyzing five case studies we calculated the cloud-relevant particle number concentrations using lidar measurements (n250,dry with an uncertainty of 20 % to 40 % and Sdry with an uncertainty of 30 % to 50 %), and we assessed the suitability of the different INP parameterizations with respect to the temperature range and the type of particles considered. Specifically, our analysis suggests that our calculations using the parameterization of Ullrich et al. (2017) (applicable for the temperature range −50 to −33 ∘C) agree within 1 order of magnitude with the in situ observations of nINP; thus, the parameterization of Ullrich et al. (2017) can efficiently address the deposition nucleation pathway in dust-dominated environments. Additionally, our calculations using the combination of the parameterizations of DeMott et al. (2015, 2010) (applicable for the temperature range −35 to −9 ∘C) agree within 2 orders of magnitude with the in situ observations of INP concentrations (nINP) and can thus efficiently address the immersion/condensation pathway of dust and nondust particles. The same conclusion is derived from the compilation of the parameterizations of DeMott et al. (2015) for dust and Ullrich et al. (2017) for soot.
- ItemEARLINET evaluation of the CATS Level 2 aerosol backscatter coefficient product(Katlenburg-Lindau : EGU, 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, GelsominaWe 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.
- ItemObservation of Arabian and Saharan dust in Cyprus with a new generation of the smart Raman lidar Polly(Les Ulis : EDP Sciences, 2016) Engelmann, Ronny; Ansmann, Albert; Bühl, Johannes; Heese, Birgit; Baars, Holger; Althausen, Dietrich; Marinou, Eleni; Amiridis, Vassilis; Mamouri, Rodanthi-Elisavet; Vrekoussis, MihalisThe atmospheric science community demands for autonomous and quality-assured vertically resolved measurements of aerosol and cloud properties. Aiming this goal, TROPOS developed the fully automated multiwavelength polarization Raman lidar Polly since over 10 years [1, 2]. In cooperation with different partner research institutes the system was improved continuously. Our latest lidar developments include aside the “3+2” measurements also a near-range receiver to measure aerosol extinction and backscatter down to 120 m above the lidar, a water-vapor channel, and measurements of the linear depolarization at two wavelengths. The latest system was built in cooperation with the National Observatory of Athens (NOA). Its first campaign however was performed at the Cyprus Institute of Nicosia from March to April 2015, aiming specifically at the observation of ice nuclei with in-situ and lidar remote sensing techniques in the framework of BACCHUS [3, 4].
- ItemLidar Ice nuclei estimates and how they relate with airborne in-situ measurements(Les Ulis : EDP Sciences, 2018) Marinou, Eleni; Amiridis, Vassilis; Ansmann, Albert; Nenes, Athanasios; Balis, Dimitris; Schrod, Jann; Binietoglou, Ioannis; Solomos, Stavros; Mamali, Dimitra; Engelmann, Ronny; Baars, Holger; Kottas, Michael; Tsekeri, Alexandra; Proestakis, Emmanouil; Kokkalis, Panagiotis; Goloub, Philippe; Cvetkovic, Bojan; Nichovic, Slobodan; Mamouri, Rodanthi; Pikridas, Michael; Stavroulas, Iasonas; Keleshis, Christos; Sciare, JeanBy means of available ice nucleating particle (INP) parameterization schemes we compute profiles of dust INP number concentration utilizing Polly-XT and CALIPSO lidar observations during the INUIT-BACCHUS-ACTRIS 2016 campaign. The polarization-lidar photometer networking (POLIPHON) method is used to separate dust and non-dust aerosol backscatter, extinction, mass concentration, particle number concentration (for particles with radius > 250 nm) and surface area concentration. The INP final products are compared with aerosol samples collected from unmanned aircraft systems (UAS) and analyzed using the ice nucleus counter FRIDGE.
- ItemAn overview of the first decade of PollyNET: An emerging network of automated Raman-polarization lidars for continuous aerosol profiling(München : European Geopyhsical Union, 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, FelixA 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.
- ItemCALIPSO climatological products: Evaluation and suggestions from EARLINET(München : European Geopyhsical Union, 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, GelsominaThe 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.
- ItemAn automatic observation-based aerosol typing method for EARLINET(Katlenburg-Lindau : EGU, 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, MatthiasWe 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.