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Atmospheric Chemistry and Physics An interactive open-access journal of the European Geosciences Union
© Author(s) 2017. This work is distributed under
the Creative Commons Attribution 3.0 License.
Research article
29 May 2017
Review status
This discussion paper is a preprint. A revision of this manuscript was accepted for the journal Atmospheric Chemistry and Physics (ACP) and is expected to appear here in due course.
XBAER derived aerosol optical thickness from OLCI/Sentinel-3 observation
Linlu Mei, Vladimir Rozanov, Marco Vountas, John P. Burrows, and Andreas Richter Institute of Environmental Physics, University of Bremen, Germany
Abstract. A prolonged pollution haze event occurred in the northeast part of China during December 16–21, 2016. To assess the impact of such events, the amounts and distribution of aerosol particles formed in such events need to be quantified. The newly launched Ocean Land Color Instrument (OLCI) onboard Sentinel-3 is the successor of the MEdium Resolution Imaging Spectrometer (MERIS). It provides measurements of the radiance and reflectance at the top of the atmosphere which can be used to retrieve the Aerosol Optical Thickness (AOT) on both synoptic to global scales. In this paper, the recently developed AOT retrieval algorithm – eXtensible Bremen AErosol Retrieval (XBAER) has been applied to data from the OLCI instrument for the first time to inlustrate the feasibility of transferring XBAER to new instrument. The first global retrieval results show similar patterns as MODIS and MISR aerosol products. The AOT retrieved from OLCI is validated by comparison with AERONET observations and a correlation coefficient of 0.819 and bias (root mean square) of 0.115 is obtained. The haze episode is well-captured by the OLCI-derived AOT product. XBAER is shown to retrieve AOT from the observations of MERIS and OLCI.

Citation: Mei, L., Rozanov, V., Vountas, M., Burrows, J. P., and Richter, A.: XBAER derived aerosol optical thickness from OLCI/Sentinel-3 observation, Atmos. Chem. Phys. Discuss.,, in review, 2017.
Linlu Mei et al.
Linlu Mei et al.


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