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

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Latest update: 18 Apr 2024
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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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