Atmospheric Measurement Techniques
Atmospheric Measurement Techniques
AMT
Articles | Volume 15, issue 19
Articles | Volume 15, issue 19
https://doi.org/10.5194/amt-15-5701-2022
© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/amt-15-5701-2022
© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.
Articles | Volume 15, issue 19
Research article
 | 
12 Oct 2022
Research article |  | 12 Oct 2022

Ice water path retrievals from Meteosat-9 using quantile regression neural networks

Adrià Amell, Patrick Eriksson, and Simon Pfreundschuh

Supplement

https://doi.org/10.5194/amt-15-5701-2022-supplement

Data sets

Ice water path retrievals from Meteosat-9 with quantile regression neural networks: code and models Adrià Amell https://doi.org/10.5281/zenodo.6570587

CLAAS-2.1: CM SAF CLoud property dAtAset using SEVIRI – Edition 2.1 Stephan Finkensieper, Jan-Fokke Meirink, Gerd-Jan van Zadelhoff, Timo Hanschmann, Nikolaos Benas, Martin Stengel, Petra Fuchs, Rainer Hollmann, Johannes Kaiser, and Martin Werscheck https://doi.org/10.5676/EUM_SAF_CM/CLAAS/V002_01

High Rate SEVIRI Level 1.5 Image Data - MSG - 0degree EUMETSAT https://data.eumetsat.int/product/EO:EUM:DAT:MSG:HRSEVIRI

Video supplement

Ice water path retrievals from Meteosat-9 with quantile regression neural networks: video supplement Adrià Amell https://doi.org/10.5281/zenodo.6639443

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Short summary
Geostationary satellites continuously image a given location on Earth, a feature that satellites designed to characterize atmospheric ice lack. However, the relationship between geostationary images and atmospheric ice is complex. Machine learning is used here to leverage such images to characterize atmospheric ice throughout the day in a probabilistic manner. Using structural information from the image improves the characterization, and this approach compares favourably to traditional methods.
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