Search Results
Vertical profiles of aerosol mass concentration derived by unmanned airborne in situ and remote sensing instruments during dust events
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, George
In 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.
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, Comerón, Adolfo, Dionisi, Davide, Falconieri, Alfredo, Fréville, Patrick, Kampouri, Anna, Mattis, Ina, Mijić, Zoran, Molero, Francisco, Papayannis, Alex, Pappalardo, Gelsomina, RodrÃguez-Gómez, 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).
CALIPSO climatological products: Evaluation and suggestions from EARLINET
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.
Earlinet single calculus chain: New products overview
2018, D’Amico, Giuseppe, Mattis, Ina, Binietoglou, Ioannis, Baars, Holger, Mona, Lucia, Amato, Francesco, Kokkalis, Panos, RodrÃguez-Gómez, Alejandro, Soupiona, Ourania, Kalliopi-Artemis, Voudouri, 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.
The Single Calculus Chain (SCC) is an automatic and flexible tool to analyze raw lidar data using EARLINET quality assured retrieval algorithms. It has been already demonstrated the SCC can retrieve reliable aerosol backscatter and extinction coefficient profiles for different lidar systems. In this paper we provide an overview of new SCC products like particle linear depolarization ratio, cloud masking, aerosol layering allowing relevant improvements in the atmospheric aerosol characterization.
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.
Lidar Ice nuclei estimates and how they relate with airborne in-situ measurements
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, Jean
By 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.
An automatic observation-based aerosol typing method for EARLINET
2018, Papagiannopoulos, Nikolaos, Mona, Lucia, Amodeo, Aldo, D'Amico, Giuseppe, Gumà Claramunt, Pilar, Pappalardo, Gelsomina, Alados-Arboledas, Lucas, Guerrero-Rascado, Juan LuÃs, Amiridis, Vassilis, Kokkalis, Panagiotis, Apituley, Arnoud, Baars, Holger, Schwarz, Anja, Wandinger, Ulla, Binietoglou, Ioannis, Nicolae, Doina, Bortoli, Daniele, Comerón, Adolfo, RodrÃguez-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.
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.
A methodology for cloud masking uncalibrated lidar signals
2018, Binietoglou, Ioannis, D’Amico, Giuseppe, Baars, Holger, Belegante, Livio, Marinou, Eleni, 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.
Most lidar processing algorithms, such as those included in EARLINET's Single Calculus Chain, can be applied only to cloud-free atmospheric scenes. In this paper, we present a methodology for masking clouds in uncalibrated lidar signals. First, we construct a reference dataset based on manual inspection and then train a classifier to separate clouds and cloud-free regions. Here we present details of this approach together with an example cloud masks from an EARLINET station.
Earlinet validation of CATS L2 product
2018, Proestakis, Emmanouil, Amiridis, Vassilis, Kottas, Michael, Marinou, Eleni, Binietoglou, Ioannis, Ansmann, Albert, Wandinger, Ulla, Yorks, John, Nowottnick, Edward, Makhmudov, Abduvosit, Papayannis, Alexandros, Pietruczuk, Aleksander, Gialitaki, Anna, Apituley, Arnoud, Muñoz-Porcar, Constantino, Bortoli, Daniele, Dionisi, Davide, Althausen, Dietrich, Mamali, Dimitra, Balis, Dimitris, Nicolae, Doina, Tetoni, Eleni, Luigi Liberti, Gian, Baars, Holger, Stachlewska, Iwona S., Voudouri, Kalliopi-Artemis, Mona, Lucia, Mylonaki, Maria, Rita Perrone, Maria, João Costa, Maria, Sicard, Michael, Papagiannopoulos, Nikolaos, Siomos, Nikolaos, Burlizzi, Pasquale, Engelmann, Ronny, Abdullaev, Sabur F., Hofer, Julian, Pappalardo, Gelsomina, 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.
The Cloud-Aerosol Transport System (CATS) onboard the International Space Station (ISS), is a lidar system providing vertically resolved aerosol and cloud profiles since February 2015. In this study, the CATS aerosol product is validated against the aerosol profiles provided by the European Aerosol Research Lidar Network (EARLINET). This validation activity is based on collocated CATS-EARLINET measurements and the comparison of the particle backscatter coefficient at 1064nm.