Articles | Volume 17, issue 14
https://doi.org/10.5194/amt-17-4337-2024
https://doi.org/10.5194/amt-17-4337-2024
Research article
 | 
23 Jul 2024
Research article |  | 23 Jul 2024

The Chalmers Cloud Ice Climatology: retrieval implementation and validation

Adrià Amell, Simon Pfreundschuh, and Patrick Eriksson

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Cited articles

Abel, S. J. and Boutle, I. A.: An improved representation of the raindrop size distribution for single-moment microphysics schemes, Q. J. Roy. Meteor. Soc., 138, 2151–2162, https://doi.org/10.1002/qj.1949, 2012. a
Amell, A. and Pfreundschuh, S.: SEE-GEO/ccic: Paper publication, Version 0.1, Zenodo [code], https://doi.org/10.5281/zenodo.8278127, 2023. a, b, c, d
Amell, A., Eriksson, P., and Pfreundschuh, S.: Ice water path retrievals from Meteosat-9 using quantile regression neural networks, Atmos. Meas. Tech., 15, 5701–5717, https://doi.org/10.5194/amt-15-5701-2022, 2022. a, b
Ba, J. L., Kiros, J. R., and Hinton, G. E.: Layer Normalization, arXiv [preprint], https://doi.org/10.48550/arXiv.1607.06450, 21 July 2016. a
Benas, N., Solodovnik, I., Stengel, M., Hüser, I., Karlsson, K.-G., Håkansson, N., Johansson, E., Eliasson, S., Schröder, M., Hollmann, R., and Meirink, J. F.: CLAAS-3: the third edition of the CM SAF cloud data record based on SEVIRI observations, Earth Syst. Sci. Data, 15, 5153–5170, https://doi.org/10.5194/essd-15-5153-2023, 2023. a
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Short summary
The representation of clouds in numerical weather and climate models remains a major challenge that is difficult to address because of the limitations of currently available data records of cloud properties. In this work, we address this issue by using machine learning to extract novel information on ice clouds from a long record of satellite observations. Through extensive validation, we show that this novel approach provides surprisingly accurate estimates of clouds and their properties.
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