GARRLiC and LIRIC: strengths and limitations for the characterization of dust and marine particles along with their mixtures
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hdl:2117/112433
Document typeArticle
Defense date2017-12-20
PublisherEuropean Geosciences Union (EGU)
Rights accessOpen Access
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ProjectD-TECT - Does dust triboelectrification affect our climate? (EC-H2020-725698)
ECARS - East European Centre for Atmospheric Remote Sensing (EC-H2020-692014)
ACTRIS-2 - Aerosols, Clouds, and Trace gases Research InfraStructure (EC-H2020-654109)
ECARS - East European Centre for Atmospheric Remote Sensing (EC-H2020-692014)
ACTRIS-2 - Aerosols, Clouds, and Trace gases Research InfraStructure (EC-H2020-654109)
Abstract
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, marine-dominated 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.
CitationTsekeri, A. [et al.]. GARRLiC and LIRIC: strengths and limitations for the characterization of dust and marine particles along with their mixtures. "Atmospheric Measurement Techniques", 20 Desembre 2017, vol. 10, p. 4995-5016.
ISSN1867-1381
Publisher versionhttps://www.atmos-meas-tech.net/10/4995/2017/
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