Cirrus cloud retrieval with MSG/SEVIRI using artificial neural networks

Strandgren, Johan; Bugliaro, Luca; Sehnke, Frank; Schröder, Leon

doi:https://doi.org/10.5194/amt-10-3547-2017

Articles | Volume 10, issue 9

https://doi.org/10.5194/amt-10-3547-2017

© Author(s) 2017. This work is distributed under
the Creative Commons Attribution 3.0 License.

https://doi.org/10.5194/amt-10-3547-2017

© Author(s) 2017. This work is distributed under
the Creative Commons Attribution 3.0 License.

Articles | Volume 10, issue 9

Research article

|

29 Sep 2017

Research article |

| 29 Sep 2017

Cirrus cloud retrieval with MSG/SEVIRI using artificial neural networks

Johan Strandgren, Luca Bugliaro, Frank Sehnke, and Leon Schröder

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Final revised paper (published on 29 Sep 2017)
Preprint (discussion started on 05 Apr 2017)

Interactive discussion

Status: closed

AC: Author comment | RC: Referee comment | SC: Short comment | EC: Editor comment

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- Supplement

RC1: 'Good study in need of only minor clarifications', Anonymous Referee #1, 24 Apr 2017
- AC1: 'Response to Anonymous Referee #1', Johan Strandgren, 26 Jun 2017
RC2: 'Review', Anonymous Referee #3, 12 Jul 2017
- AC2: 'Response to Anonymous Referee #3', Johan Strandgren, 08 Aug 2017

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

AR by Johan Strandgren on behalf of the Authors (24 Aug 2017)

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.