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Title: Estimated desert-dust ice nuclei profiles from polarization lidar: Methodology and case studies
Authors: Mamouri, Rodanthi-Elisavet 
Ansmann, Albert 
Keywords: AERONET;Aerosol property;Dust;Estimation method;Ice;Lidar
Category: Environmental Engineering
Field: Engineering and Technology
Issue Date: 20-Mar-2015
Publisher: Copernicus GmbH
Source: Atmospheric Chemistry and Physics, Volume 15, Issue 6, 20 March 2015, Pages 3463-3477
DOI: 10.5194/acp-15-3463-2015
Project: ACTRIS PPP - Aerosols, Clouds and Trace gases Preparatory Phase Project 
Abstract: A lidar method is presented that permits the estimation of height profiles of ice nuclei concentrations (INC) in desert dust layers. The polarization lidar technique is applied to separate dust and non-dust backscatter and extinction coefficients. The desert dust extinction coefficients σ<inf>d</inf> are then converted to aerosol particle number concentrations APC<inf>280</inf> which consider particles with radius > 280 nm only. By using profiles of APC<inf>280</inf> and ambient temperature T along the laser beam, the profile of INC can be estimated within a factor of 3 by means of APC-T-INC parameterizations from the literature. The observed close relationship between σ<inf>d</inf> at 500 nm and APC<inf>280</inf> is of key importance for a successful INC retrieval. We studied this link by means of AERONET (Aerosol Robotic Network) sun/sky photometer observations at Morocco, Cabo Verde, Barbados, and Cyprus during desert dust outbreaks. The new INC retrieval method is applied to lidar observations of dust layers with the spaceborne lidar CALIOP (Cloud Aerosol Lidar with Orthogonal Polarization) during two overpasses over the EARLINET (European Aerosol Research Lidar Network) lidar site of the Cyprus University of Technology (CUT), Limassol (34.7° N, 33° E), Cyprus. The good agreement between the CALIOP and CUT lidar retrievals of σ<inf>d</inf>, APC<inf>280</inf>, and INC profiles corroborates the potential of CALIOP to provide 3-D global desert dust APC<inf>280</inf> and INC data sets.
ISSN: 16807316
Rights: © Author(s) 2015.
Type: Article
Appears in Collections:Άρθρα/Articles

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