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Cirrus cloud retrieval with MSG/SEVIRI using artificial neural networks

Strandgren, Johan and Bugliaro Goggia, Luca and Sehnke, Frank and Schröder, Leon (2017) Cirrus cloud retrieval with MSG/SEVIRI using artificial neural networks. Atmospheric Measurement Techniques (AMT), 10 (9), pp. 3547-3573. Copernicus Publications. doi: 10.5194/amt-10-3547-2017. ISSN 1867-1381.

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Official URL: http://dx.doi.org/10.5194/amt-10-3547-2017


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.

Item URL in elib:https://elib.dlr.de/114488/
Document Type:Article
Title:Cirrus cloud retrieval with MSG/SEVIRI using artificial neural networks
AuthorsInstitution or Email of AuthorsAuthor's ORCID iD
Strandgren, Johandlr, ipahttps://orcid.org/0000-0001-7876-5845
Bugliaro Goggia, LucaDLR, IPAhttps://orcid.org/0000-0003-4793-0101
Sehnke, FrankZentrum für Sonnenenergie- und Wasserstoff-Forschung, StuttgartUNSPECIFIED
Schröder, LeonZentrum für Sonnenenergie- und Wasserstoff-Forschung, StuttgartUNSPECIFIED
Date:29 September 2017
Journal or Publication Title:Atmospheric Measurement Techniques (AMT)
Refereed publication:Yes
Open Access:Yes
Gold Open Access:Yes
In ISI Web of Science:Yes
DOI :10.5194/amt-10-3547-2017
Page Range:pp. 3547-3573
Publisher:Copernicus Publications
Keywords:cirrus clouds, remote sensing, SEVIRI, neural networks
HGF - Research field:Aeronautics, Space and Transport
HGF - Program:Space
HGF - Program Themes:Earth Observation
DLR - Research area:Raumfahrt
DLR - Program:R EO - Earth Observation
DLR - Research theme (Project):R - Atmospheric and climate research
Location: Oberpfaffenhofen
Institutes and Institutions:Institute of Atmospheric Physics > Atmospheric Remote Sensing
Deposited By: Strandgren, Johan
Deposited On:23 Oct 2017 08:52
Last Modified:08 Mar 2018 18:10

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