Articles | Volume 17, issue 19
https://doi.org/10.5194/amt-17-5957-2024
https://doi.org/10.5194/amt-17-5957-2024
Research article
 | 
11 Oct 2024
Research article |  | 11 Oct 2024

The Ice Cloud Imager: retrieval of frozen water column properties

Eleanor May, Bengt Rydberg, Inderpreet Kaur, Vinia Mattioli, Hanna Hallborn, and Patrick Eriksson

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The Ice Cloud Imager: retrieval of frozen water mass profiles
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This preprint is open for discussion and under review for Atmospheric Measurement Techniques (AMT).
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Cited articles

Accadia, C., Mattioli, V., Colucci, P., Schlüssel, P., D'Addio, S., Klein, U., Wehr, T., and Donlon, C.: Microwave and Sub-mm Wave Sensors: A European Perspective, in: Satellite Precipitation Measurement: Volume 1, edited by: Levizzani, V., Kidd, C., Kirschbaum, D. B., Kummerow, C. D., Nakamura, K., and Turk, F. J., Advances in Global Change Research, Springer International Publishing, Cham, 83–97, https://doi.org/10.1007/978-3-030-24568-9_5, 2020. a
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
Barker, H. W., Jerg, M. P., Wehr, T., Kato, S., Donovan, D. P., and Hogan, R. J.: A 3D cloud-construction algorithm for the EarthCARE satellite mission, Q. J. Roy. Meteor. Soc., 137, 1042–1058, https://doi.org/10.1002/qj.824, 2011. a, b
Barlakas, V. and Eriksson, P.: Three dimensional radiative effects in passive millimeter/sub-millimeter all-sky observations, Remote Sens.-Basel, 12, 531, https://doi.org/10.3390/rs12030531, 2020. a, b, c
Barlakas, V., Geer, A. J., and Eriksson, P.: Introducing hydrometeor orientation into all-sky microwave and submillimeter assimilation, Atmos. Meas. Tech., 14, 3427–3447, https://doi.org/10.5194/amt-14-3427-2021, 2021. a, b
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Short summary
The upcoming Ice Cloud Imager (ICI) mission is set to improve measurements of atmospheric ice through passive microwave and sub-millimetre wave observations. In this study, we perform detailed simulations of ICI observations. Machine learning is used to characterise the atmospheric ice present for a given simulated observation. This study acts as a final pre-launch assessment of ICI's capability to measure atmospheric ice, providing valuable information to climate and weather applications.
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