Articles | Volume 15, issue 19
https://doi.org/10.5194/amt-15-5701-2022
https://doi.org/10.5194/amt-15-5701-2022
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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Cited articles

AERIS/ICARE Data and Services Center: ICARE On-line Data Archive, ftp://ftp.icare.univ-lille1.fr/SPACEBORNE/MULTI_SENSOR/DARDAR_CLOUD.v2.1.1 (last access: 29 September 2022), 2019. a
Amell, A.: Ice water path retrievals from Meteosat-9 with quantile regression neural networks: code and models, Zenodo [code], https://doi.org/10.5281/zenodo.6570587, 2022a. a
Amell, A.: Ice water path retrievals from Meteosat-9 with quantile regression neural networks: video supplement, Zenodo [video], https://doi.org/10.5281/zenodo.6639443, 2022b. a
Aminou, D. M. A., Jacquet, B., and Pasternak, F.: Characteristics of the Meteosat Second Generation (MSG) radiometer/imager: SEVIRI, in: Sensors, Systems, and Next-Generation Satellites, edited by: Fujisada, H., International Society for Optics and Photonics, SPIE, 3221, 19–31, https://doi.org/10.1117/12.298084, 1997. a
Benas, N., Finkensieper, S., Stengel, M., van Zadelhoff, G.-J., Hanschmann, T., Hollmann, R., and Meirink, J. F.: The MSG-SEVIRI-based cloud property data record CLAAS-2, Earth Syst. Sci. Data, 9, 415–434, https://doi.org/10.5194/essd-9-415-2017, 2017. a, b, c, d
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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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