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A New Satellite-Based Retrieval of Low-Cloud Liquid-Water Path Using Machine Learning and Meteosat SEVIRI Data

Kim, Miae; Cermak, Jan; Andersen, Hendrik; Fuchs, Julia; Stirnberg, Roland

Abstract:
Clouds are one of the major uncertainties of the climate system. The study of cloud processes requires information on cloud physical properties, in particular liquid water path (LWP). This parameter is commonly retrieved from satellite data using look-up table approaches. However, existing LWP retrievals come with uncertainties related to assumptions inherent in physical retrievals. Here, we present a new retrieval technique for cloud LWP based on a statistical machine learning model. The approach utilizes spectral information from geostationary satellite channels of Meteosat Spinning-Enhanced Visible and Infrared Imager (SEVIRI), as well as satellite viewing geometry. As ground truth, data from CloudNet stations were used to train the model. We found that LWP predicted by the machine-learning model agrees substantially better with CloudNet observations than a current physics-based product, the Climate Monitoring Satellite Application Facility (CM SAF) CLoud property dAtAset using SEVIRI, edition 2 (CLAAS-2), highlighting the potential of such approaches for future retrieval developments.

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Verlagsausgabe §
DOI: 10.5445/IR/1000125206
Veröffentlicht am 23.10.2020
Cover der Publikation
Zugehörige Institution(en) am KIT Institut für Photogrammetrie und Fernerkundung (IPF)
Institut für Meteorologie und Klimaforschung - Atmosphärische Spurenstoffe und Fernerkundung (IMK-ASF)
Publikationstyp Zeitschriftenaufsatz
Publikationsjahr 2020
Sprache Englisch
Identifikator ISSN: 2072-4292
KITopen-ID: 1000125206
HGF-Programm 12.01.01 (POF III, LK 01)
Clouds in a pertubed atmosphere
Erschienen in Remote sensing
Band 12
Heft 21
Seiten Article: 3475
Vorab online veröffentlicht am 22.10.2020
Schlagwörter liquid water path; geostationary satellite; SEVIRI; CM SAF CLAAS-2; CloudNet; machine learning; gradient boosted regression trees
Nachgewiesen in Scopus
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