Search Results
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.
An automatic aerosol classification for earlinet: Application and results
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.
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.
Aerosol absorption profiling from the synergy of lidar and sun-photometry: The ACTRIS-2 campaigns in Germany, Greece and Cyprus
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.
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.