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Volume 18, issue 21 | Copyright

Special issue: EARLINET aerosol profiling: contributions to atmospheric and...

Atmos. Chem. Phys., 18, 15879-15901, 2018
© Author(s) 2018. This work is distributed under
the Creative Commons Attribution 4.0 License.

Research article 06 Nov 2018

Research article | 06 Nov 2018

An automatic observation-based aerosol typing method for EARLINET

Nikolaos Papagiannopoulos1,2, Lucia Mona1, Aldo Amodeo1, Giuseppe D'Amico1, Pilar Gumà Claramunt1, Gelsomina Pappalardo1, Lucas Alados-Arboledas3,4, Juan Luís Guerrero-Rascado3,4, Vassilis Amiridis5, Panagiotis Kokkalis5,6, Arnoud Apituley7, Holger Baars8, Anja Schwarz8, Ulla Wandinger8, Ioannis Binietoglou9, Doina Nicolae9, Daniele Bortoli10, Adolfo Comerón2, Alejandro Rodríguez-Gómez2, Michaël Sicard2,11, Alex Papayannis6, and Matthias Wiegner12 Nikolaos Papagiannopoulos et al.
  • 1Consiglio Nazionale delle Ricerche, Istituto di Metodologie per l'Analisi Ambientale (CNR-IMAA), C.da S. Loja, Tito Scalo (PZ), 85050, Italy
  • 2CommSensLab, Dept. of Signal Theory and Communications, Universitat Politècnica de Catalunya, Barcelona, Spain
  • 3Andalusian Institute for Earth System Research (IISTA-CEAMA), 18006, Granada, Spain
  • 4Department of Applied Physics, University of Granada, 18071, Granada, Spain
  • 5IAASARS, National Observatory of Athens, Athens, Greece
  • 6Laser Remote Sensing Unit, Physics Dept., National Technical University of Athens, Athens, Greece
  • 7Royal Netherlands Meteorological Institute KNMI, De Bilt, the Netherlands
  • 8Leibniz Institute for Tropospheric Research (TROPOS), Leipzig, Germany
  • 9National Institute of R&D for Optoelectronics (INOE), Magurele, Romania
  • 10Earth Science Institute-(ICT), Évora, Portugal
  • 11Ciències i Tecnologies de l'Espai – Centre de Recerca de l'Aeronàutica i de l'Espai/Institut d'Estudis Espacials de Catalunya (CTE-CRAE/IEEC), Universitat Politècnica de Catalunya, Barcelona, Spain
  • 12Ludwig-Maximilians-Universität (LMU), Meteorologisches Institut, Theresienstraße 37, 80333 Munich, Germany

Abstract. 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.

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Short summary
A stand-alone automatic method for typing observations of the European Aerosol Research Lidar Network (EARLINET) is presented. The method compares the observations to model distributions that were constructed using EARLINET pre-classified data. The algorithm’s versatility and adaptability makes it suitable for network-wide typing studies.
A stand-alone automatic method for typing observations of the European Aerosol Research Lidar...