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Extending XBAER Algorithm to Aerosol and Cloud ConditionThe retrieval of cloud optical properties for aerosol contaminated water cloud is challenging because of the complexity of physical processes in such situations. Conventionally, cloud optical data products are typically derived, ignoring the aerosol impacts on radiative transfer in the retrieval process. This is potentially a significant source of error. In this paper, the eXtensible Bremen Aerosol Retrieval (XBAER) algorithm has been optimized for the retrieval of aerosol/cloud properties for the aerosol contaminated cloud (ACC) scenarios. This version of XBAER delivers cloud optical thickness (COT) and cloud effective radius (CER) for ACCs and simultaneously retrieves aerosol optical thickness (AOT). The surface parameterization and aerosol types used in the standard XBAER algorithm have been adapted in this retrieval to account for the ACC conditions. Aerosol types in XBAER for the retrieval of ACC scenarios have been parameterized to comprise weak and strong absorptions. The comparisons of COT, CER, and AOT retrieved using this adapted XBAER algorithm and two new NASA algorithms show good agreements, especially for biomass burning aerosol. The correlation coefficients are >0.9 for AOT, ~0.8 for CER, and ~0.7 for COT. There is also a good agreement for dust plume contaminated cloud scene. The XBAER derived AOT values for dust aerosols are systematically smaller than the NASA retrieval, but both products have the same spatial distribution patterns. The comparison of COT retrieved using the adapted XBAER algorithm and that retrieved from ground-based microwave radiometer (MWR) measurements shows much better agreement for ACC conditions with high AOT.
Document ID
20190029201
Acquisition Source
Goddard Space Flight Center
Document Type
Accepted Manuscript (Version with final changes)
Authors
Linlu Mei ORCID
(University of Bremen Bremen, Germany)
Vladimir Rozanov
(University of Bremen Bremen, Germany)
Hiren Jethva
(Universities Space Research Association Columbia, Maryland, United States)
Kerry G Meyer
(Goddard Space Flight Center Greenbelt, Maryland, United States)
Luca Lelli ORCID
(University of Bremen Bremen, Germany)
Marco Vountas ORCID
(University of Bremen Bremen, Germany)
John Philip Burrows ORCID
(University of Bremen Bremen, Germany)
Date Acquired
August 20, 2019
Publication Date
June 24, 2019
Publication Information
Publication: IEEE Transactions on Geoscience and Remote Sensing
Publisher: Institute of Electrical and Electronics Engineers
Volume: 7
Issue: 10
Issue Publication Date: October 1, 2019
ISSN: 0196-2892
e-ISSN: 1558-0644
URL: https://ieeexplore.ieee.org/abstract/document/8744484
Subject Category
Earth Resources And Remote Sensing
Report/Patent Number
GSFC-E-DAA-TN71551
Funding Number(s)
CONTRACT_GRANT: NNG11HP16A
CONTRACT_GRANT: GLOS FKZ: 50EE1260
Distribution Limits
Public
Copyright
Other
Technical Review
NASA Peer Committee
Keywords
satellite
cloud
retrieval
Aerosol
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