Articles | Volume 10, issue 9
Atmos. Meas. Tech., 10, 3547–3573, 2017
https://doi.org/10.5194/amt-10-3547-2017
Atmos. Meas. Tech., 10, 3547–3573, 2017
https://doi.org/10.5194/amt-10-3547-2017

Research article 29 Sep 2017

Research article | 29 Sep 2017

Cirrus cloud retrieval with MSG/SEVIRI using artificial neural networks

Johan Strandgren et al.

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Interactive discussion

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 Johan Strandgren on behalf of the Authors (08 Aug 2017)  Author's response    Manuscript
ED: Publish as is (24 Aug 2017) by Alexander Kokhanovsky
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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.