Combined sun-photometer-lidar inversion: lessons learned during the EARLINET/ACTRIS COVID-19 campaign

• Main Authors: Tsekeri, A., Gialitaki, A., Di Paolantonio, M., Dionisi, D., Liberti, G. L., Fernandes, A., Szkop, A., Pietruczuk, A., Pã©rez-Ramã­rez, D., Muã±oz, M. J. G., Guerrero-Rascado, J. L., Alados-Arboledas, L., Pantaleã³n, D. B., Bravo-Aranda, J. A., Kampouri, A., Marinou, E., Amiridis, V., Sicard, M., Comerã³n, A., Muã±oz-Porcar, C., Rodrã­guez-Gã³mez, A., Romano, S., Perrone, M. R., Shang, X. X., Komppula, M., Mamouri, R. E., Nisantzi, A., Hadjimitsis, D., Navas-Guzmã¡n, F., Haefele, A., Szczepanik, D., Tomczak, A., Stachlewska, I. S., Belegante, L., Nicolae, D., Voudouri, K. A., Balis, D., Floutsi, A. A., Baars, H., Miladi, L., Pascal, N., Doubovik, Oleg, Lopatin, A.
• Other Authors: Université de Lille, CNRS, Institute for Astronomy, Astrophysics, Space Applications and Remote Sensing Penteli IAASARS, Università degli studi della Basilicata = University of Basilicata UNIBAS, Istituto di Science Marine ISMAR, Institute of Geophysics Warsaw, Universidad de Granada = University of Granada UGR, Laboratoire de l'Atmosphère et des Cyclones LACy, Department of Signal Theory and Communications Barcelona TSC, University of Salento Lecce, Finnish Meteorological Institute FMI, National Technical University of Athens Athens NTUA, Cyprus University of Technology, Federal Office of Meteorology and Climatology MeteoSwiss, University of Warsaw UW, National Institute of Research and Development for Optoelectronics INOE, Aristotle University of Thessaloniki, Leibniz Institute for Tropospheric Research TROPOS, interaction Clouds Aerosols Radiations - ICARE/AERIS Data and Services Center - UMS 2877 ICARE, Laboratoire d'Optique Atmosphérique (LOA) - UMR 8518
• Format: Article in Journal/Newspaper
• Language: English
• Published: 2024
• Subjects: Algorithm;meteorological;GRASP;GARRLIC;AOD;AERONET;lidar;radiometer;aerosol;particles;atmospheric;photometer;uncertainties;inversion algorithm;combined data;properties;characteristics; Aerosol Robotic Network
• Online Access: https://hdl.handle.net/20.500.12210/114153

The European Aerosol Research Lidar Network (EARLINET), part of the Aerosols, Clouds and Trace gases Research Infrastructure (ACTRIS), organized an intensive observational campaign in May 2020, with the objective of monitoring the atmospheric state over Europe during the COVID-19 lockdown and relaxation period. Besides the standard operational processing of the lidar data in EARLINET, for seven EARLINET sites having collocated sun-photometric observations in the Aerosol Robotic Network (AERONET), a network exercise was held in order to derive profiles of the concentration and effective column size distributions of the aerosols in the atmosphere, by applying the GRASP/GARRLiC (from Generalized Aerosol Retrieval from Radiometer and Lidar Combined data – GARRLiC – part of the Generalized Retrieval of Atmosphere and Surface Properties – GRASP) inversion algorithm. The objective of this network exercise was to explore the possibility of identifying the anthropogenic component and of monitoring its spatial and temporal characteristics in the COVID-19 lockdown and relaxation period. While the number of cases is far from being statistically significant so as to provide a conclusive description of the atmospheric aerosols over Europe during this period, this network exercise was fundamental to deriving a common methodology for applying GRASP/GARRLiC to a network of instruments with different characteristics. The limits of the approach are discussed, in particular the missing information close to the ground in the lidar measurements due to the instrument geometry and the sensitivity of the GRASP/GARRLiC retrieval to the settings used, especially for cases with low aerosol optical depth (AOD) like the ones we show here. We found that this sensitivity is well-characterized in the GRASP/GARRLiC products, since it is included in their retrieval uncertainties. 16;