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

Institute of Meteorology and Climate Research, Karlsruhe Institute of Technology (KIT), Hermann-von-Helmholtz-Platz 1, 76344 Eggenstein-Leopoldshafen, Germany

Institute of Photogrammetry and Remote Sensing, Karlsruhe Institute of Technology (KIT), Kaiserstr. 12, 76131 Karlsruhe, Germany

Author to whom correspondence should be addressed.

Remote Sens. 2020, 12(21), 3475; https://doi.org/10.3390/rs12213475

Received: 1 September 2020 / Revised: 19 October 2020 / Accepted: 20 October 2020 / Published: 22 October 2020

(This article belongs to the Special Issue Remote Sensing of Water Cycle Essential Climate Variables and Their Applications)

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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. View Full-Text

Keywords: liquid water path; geostationary satellite; SEVIRI; CM SAF CLAAS-2; CloudNet; machine learning; gradient boosted regression trees

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MDPI and ACS Style

Kim, M.; Cermak, J.; Andersen, H.; Fuchs, J.; Stirnberg, R. A New Satellite-Based Retrieval of Low-Cloud Liquid-Water Path Using Machine Learning and Meteosat SEVIRI Data. Remote Sens. 2020, 12, 3475. https://doi.org/10.3390/rs12213475

AMA Style

Kim M, Cermak J, Andersen H, Fuchs J, Stirnberg R. A New Satellite-Based Retrieval of Low-Cloud Liquid-Water Path Using Machine Learning and Meteosat SEVIRI Data. Remote Sensing. 2020; 12(21):3475. https://doi.org/10.3390/rs12213475

Chicago/Turabian Style

Kim, Miae, Jan Cermak, Hendrik Andersen, Julia Fuchs, and Roland Stirnberg. 2020. "A New Satellite-Based Retrieval of Low-Cloud Liquid-Water Path Using Machine Learning and Meteosat SEVIRI Data" Remote Sensing 12, no. 21: 3475. https://doi.org/10.3390/rs12213475

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

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