Next Article in Journal
Consistency Analysis and Accuracy Assessment of Three Global 30-m Land-Cover Products over the European Union using the LUCAS Dataset
Next Article in Special Issue
The Passive Microwave Neural Network Precipitation Retrieval Algorithm for Climate Applications (PNPR-CLIM): Design and Verification
Previous Article in Journal
Automatic Identification and Dynamic Monitoring of Open-Pit Mines Based on Improved Mask R-CNN and Transfer Learning
Previous Article in Special Issue
Evaluation the Performance of Several Gridded Precipitation Products over the Highland Region of Yemen for Water Resources Management
Technical Note

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

1
Institute of Meteorology and Climate Research, Karlsruhe Institute of Technology (KIT), Hermann-von-Helmholtz-Platz 1, 76344 Eggenstein-Leopoldshafen, Germany
2
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
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 liquid water path; geostationary satellite; SEVIRI; CM SAF CLAAS-2; CloudNet; machine learning; gradient boosted regression trees
Show Figures

Graphical abstract

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

Find Other Styles
Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Article Access Map by Country/Region

1
Back to TopTop