Filters

20188

More filters

20238
Reset filters

Settings

Author: Baars, Holger × Author: Binietoglou, Ioannis × End date: 2018 × Start date: 2018 ×

Search Results

Now showing 1 - 8 of 8
  • Thumbnail Image
    Item
    An automatic aerosol classification for earlinet: Application and results
    (Les Ulis : EDP Sciences, 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.
  • Thumbnail Image
    Item
    Earlinet single calculus chain: New products overview
    (Les Ulis : EDP Sciences, 2018) D’Amico, Giuseppe; Mattis, Ina; Binietoglou, Ioannis; Baars, Holger; Mona, Lucia; Amato, Francesco; Kokkalis, Panos; Rodríguez-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.
  • Thumbnail Image
    Item
    Earlinet validation of CATS L2 product
    (Les Ulis : EDP Sciences, 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.
  • Thumbnail Image
    Item
    Vertical 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, 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.
  • Thumbnail Image
    Item
    Aerosol absorption profiling from the synergy of lidar and sun-photometry: The ACTRIS-2 campaigns in Germany, Greece and Cyprus
    (Les Ulis : EDP Sciences, 2018) Tsekeri, Alexandra; Amiridis, Vassilis; Lopatin, Anton; Marinou, Eleni; Giannakaki, Eleni; Pikridas, Michael; Sciare, Jean; Liakakou, Eleni; Gerasopoulos, Evangelos; Duesing, Sebastian; Corbin, Joel C.; Gysel, Martin; Bukowiecki, Nicolas; Baars, Holger; Engelmann, Ronny; Wehner, Birgit; Kottas, Michael; Mamali, Dimitra; Kokkalis, Panagiotis; Raptis, Panagiotis I.; Stavroulas, Iasonas; Keleshis, Christos; Müller, Detlef; Solomos, Stavros; Binietoglou, Ioannis; Mihalopoulos, Nikolaos; Papayannis, Alexandros; Stachlewska, Iwona S.; Igloffstein, Julia; Wandinger, Ulla; Ansmann, Albert; Dubovik, Oleg; Goloub, Philippe; 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 absorption profiling is crucial for radiative transfer calculations and climate modelling. Here, we utilize the synergy of lidar with sun-photometer measurements to derive the absorption coefficient and single scattering albedo profiles during the ACTRIS-2 campaigns held in Germany, Greece and Cyprus. The remote sensing techniques are compared with in situ measurements in order to harmonize and validate the different methodologies and reduce the absorption profiling uncertainties.
  • Thumbnail Image
    Item
    Lidar 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, 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.
  • Thumbnail Image
    Item
    A methodology for cloud masking uncalibrated lidar signals
    (Les Ulis : EDP Sciences, 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.
  • Thumbnail Image
    Item
    An 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, 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.