Articles | Volume 14, issue 4
https://doi.org/10.5194/amt-14-2787-2021
https://doi.org/10.5194/amt-14-2787-2021
Research article
 | 
12 Apr 2021
Research article |  | 12 Apr 2021

Reducing cloud contamination in aerosol optical depth (AOD) measurements

Verena Schenzinger and Axel Kreuter

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Status: closed
Status: closed
AC: Author comment | RC: Referee comment | SC: Short comment | EC: Editor comment
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AR: Author's response | RR: Referee report | ED: Editor decision
AR by Verena Schenzinger on behalf of the Authors (15 Jan 2021)  Author's response   Manuscript 
ED: Referee Nomination & Report Request started (16 Jan 2021) by Gerd Baumgarten
ED: Publish as is (22 Feb 2021) by Gerd Baumgarten
AR by Verena Schenzinger on behalf of the Authors (23 Feb 2021)
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Short summary
When measuring the aerosol optical depth of the atmosphere, clouds in front of the sun lead to erroneously high values. Therefore, measurements that are potentially affected by clouds need to be removed from the dataset by an automatic process. As the currently used algorithm cannot reliably identify thin clouds, we developed a new one based on a method borrowed from machine learning. Tests with 10 years of data show improved performance of the new routine and therefore higher data quality.