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Atmospheric Measurement Techniques An interactive open-access journal of the European Geosciences Union

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Atmos. Meas. Tech., 10, 3547-3573, 2017
https://doi.org/10.5194/amt-10-3547-2017
© Author(s) 2017. This work is distributed under
the Creative Commons Attribution 3.0 License.
Research article
29 Sep 2017
Cirrus cloud retrieval with MSG/SEVIRI using artificial neural networks
Johan Strandgren et al.
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Interactive discussionStatus: closed
AC: Author comment | RC: Referee comment | SC: Short comment | EC: Editor comment
Printer-friendly Version - Printer-friendly version      Supplement - Supplement
 
RC1: 'Good study in need of only minor clarifications', Anonymous Referee #1, 24 Apr 2017 Printer-friendly Version 
AC1: 'Response to Anonymous Referee #1', Johan Strandgren, 26 Jun 2017 Printer-friendly Version 
 
RC2: 'Review', Anonymous Referee #3, 12 Jul 2017 Printer-friendly Version 
AC2: 'Response to Anonymous Referee #3', Johan Strandgren, 08 Aug 2017 Printer-friendly Version 
Peer review completion
AR: Author's response | RR: Referee report | ED: Editor decision
AR by Johan Strandgren on behalf of the Authors (08 Aug 2017)  Author's response  Manuscript
ED: Publish as is (24 Aug 2017) by Alexander Kokhanovsky
CC BY 4.0
Publications Copernicus
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Short summary
The new algorithm CiPS is presented and validated. CiPS detects cirrus clouds, identifies opaque pixels and retrieves the corresponding optical thickness, cloud top height and ice water path from the geostationary imager MSG/SEVIRI. CiPS utilises a set of four artificial neural networks trained with space-borne lidar data, thermal MSG/SEVIRI observations, model data and auxiliary data. To demonstrate the capabilities of CiPS, the life cycle of a thin cirrus cloud is analysed.
The new algorithm CiPS is presented and validated. CiPS detects cirrus clouds, identifies opaque...
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