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

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Title: Combined sun-photometer–lidar inversion: lessons learned during the EARLINET/ACTRIS COVID-19 campaign
Authors: A. Tsekeri, A. Gialitaki, M. Di Paolantonio, D. Dionisi, G. L. Liberti, A. Fernandes, A. Szkop, A. Pietruczuk, D. Pérez-Ramírez, M. J. Granados Muñoz, J. L. Guerrero-Rascado, L. Alados-Arboledas, D. Bermejo Pantaleón, J. A. Bravo-Aranda, A. Kampouri, E. Marinou, V. Amiridis, M. Sicard, A. Comerón, C. Muñoz-Porcar, A. Rodríguez-Gómez, S. Romano, M. R. Perrone, X. Shang, M. Komppula, R.-E. Mamouri, A. Nisantzi, D. Hadjimitsis, F. Navas-Guzmán, A. Haefele, D. Szczepanik, A. Tomczak, I. S. Stachlewska, L. Belegante, D. Nicolae, K. A. Voudouri, D. Balis, A. A. Floutsi, H. Baars, L. Miladi, N. Pascal, O. Dubovik, A. Lopatin
Contributors: Doubovik, Oleg, Bonnet, Camille, Universitat Politècnica de Catalunya. Departament de Teoria del Senyal i Comunicacions, Universitat Politècnica de Catalunya. CommSensLab-UPC - Centre Específic de Recerca en Comunicació i Detecció UPC
Source: UPCommons. Portal del coneixement obert de la UPC
Universitat Politècnica de Catalunya (UPC)
Atmospheric Measurement Techniques, Vol 16, Pp 6025-6050 (2023)
Atmospheric Measurement Techniques
Publisher Information: Copernicus GmbH, 2023.
Publication Year: 2023
Subject Terms: [SDE] Environmental Sciences, aerosol, characteristics, Environmental engineering, AOD, atmospheric, Air Quality, combined data, [SDU] Sciences of the Universe [physics], Earthwork. Foundations, Sun-photometer–lidar inversion, GRASP, photometer, Àrees temàtiques de la UPC::Enginyeria de la telecomunicació, lidar, particles, Aerosols, GARRLIC, [SDU.OCEAN] Sciences of the Universe [physics]/Ocean, Atmosphere, TA715-787, meteorological, COVID-19, TA170-171, radiometer, uncertainties, 3. Good health, Algorithm, inversion algorithm, 13. Climate action, properties, AERONET
Description: 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.
Document Type: Article
Other literature type
File Description: application/pdf
Language: English
ISSN: 1867-8548
DOI: 10.5194/amt-16-6025-2023
DOI: 10.5281/zenodo.12796572
DOI: 10.5281/zenodo.12796571
Access URL: https://amt.copernicus.org/articles/16/6025/2023/
https://doaj.org/article/df1dc22ba5cb491cb4b2fe6ef473c5c0
https://hal.science/hal-04763279v1
https://doi.org/10.5194/amt-16-6025-2023
Rights: CC BY
CC BY NC ND
Accession Number: edsair.doi.dedup.....fa06dba857fd1a5dbcb7b43d683b82d2
Database: OpenAIRE
Description
Abstract: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.
ISSN:18678548
DOI:10.5194/amt-16-6025-2023