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Atmospheric Measurement Techniques An interactive open-access journal of the European Geosciences Union
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AMT | Articles | Volume 11, issue 6
Atmos. Meas. Tech., 11, 3373–3396, 2018
https://doi.org/10.5194/amt-11-3373-2018
© Author(s) 2018. This work is distributed under
the Creative Commons Attribution 4.0 License.
Atmos. Meas. Tech., 11, 3373–3396, 2018
https://doi.org/10.5194/amt-11-3373-2018
© Author(s) 2018. This work is distributed under
the Creative Commons Attribution 4.0 License.

Research article 13 Jun 2018

Research article | 13 Jun 2018

The Community Cloud retrieval for CLimate (CC4CL) – Part 1: A framework applied to multiple satellite imaging sensors

Oliver Sus et al.
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Status: closed
Status: closed
AC: Author comment | RC: Referee comment | SC: Short comment | EC: Editor comment
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Peer review completion
AR: Author's response | RR: Referee report | ED: Editor decision
AR by Oliver Sus on behalf of the Authors (09 Apr 2018)  Author's response    Manuscript
ED: Referee Nomination & Report Request started (23 Apr 2018) by Brian Kahn
RR by Anonymous Referee #3 (04 May 2018)
RR by Anonymous Referee #1 (04 May 2018)
RR by Anonymous Referee #2 (09 May 2018)
ED: Publish subject to minor revisions (review by editor) (21 May 2018) by Brian Kahn
AR by Oliver Sus on behalf of the Authors (30 May 2018)  Author's response    Manuscript
ED: Publish as is (05 Jun 2018) by Brian Kahn
Publications Copernicus
Short summary
This paper presents a new cloud detection and classification framework, CC4CL. It applies a sophisticated optimal estimation method to derive cloud variables from satellite data of various polar-orbiting platforms and sensors (AVHRR, MODIS, AATSR). CC4CL provides explicit uncertainty quantification and long-term consistency for decadal timeseries at various spatial resolutions. We analysed 5 case studies to show that cloud height estimates are very realistic unless optically thin clouds overlap.
This paper presents a new cloud detection and classification framework, CC4CL. It applies a...
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