Search Results
Earlinet database: New design and new products for a wider use of aerosol lidar data
2018, Mona, Lucia, D’Amico, Giuseppe, Amato, Francesco, Linné, Holger, Baars, Holger, Wandinger, Ulla, 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 EARLINET database is facing a complete reshaping to meet the wide request for more intuitive products and to face the even wider request related to the new initiatives such as Copernicus, the European Earth observation programme. The new design has been carried out in continuity with the past, to take advantage from long-term database. In particular, the new structure will provide information suitable for synergy with other instruments, near real time (NRT) applications, validation and process studies and climate applications.
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.
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.