Articles | Volume 10, issue 11
Research article
14 Nov 2017
Research article |  | 14 Nov 2017

Characterisation of the artificial neural network CiPS for cirrus cloud remote sensing with MSG/SEVIRI

Johan Strandgren, Jennifer Fricker, and Luca Bugliaro


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 (18 Sep 2017)  Author's response    Manuscript
ED: Publish as is (19 Sep 2017) by Gianfranco Vulpiani
Short summary
We characterise the the performance of a set of artificial neural networks used for the remote sensing of cirrus clouds from the geostationary Meteosat Second Generation satellites. The retrievals show little interference with the underlying land surface type as well as with possible liquid water clouds or aerosol layers below the cirrus cloud. We also characterise the retrievals as a funtion of optical thickness and top height and gain better understanding of the retrival uncertainties of CiPS