Articles | Volume 18, issue 23
https://doi.org/10.5194/amt-18-7243-2025
© Author(s) 2025. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/amt-18-7243-2025
© Author(s) 2025. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
02 Dec 2025
Research article |
| 02 Dec 2025
| 02 Dec 2025
The Ice Cloud Imager: retrieval of frozen water mass profiles
Department of Space, Earth and Environment, Chalmers University of Technology, Gothenburg, Sweden
Patrick Eriksson
Department of Space, Earth and Environment, Chalmers University of Technology, Gothenburg, Sweden
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Our study shows that accurately representing atmospheric ice masses remains a major challenge. We compared climate models to satellite data, finding that conventional models consistently underestimate the amount of ice. While new, higher-resolution models perform better, both models and observations still have significant discrepancies. These shortcomings limit our confidence in cloud-related climate feedbacks, which are critical for our predictions of the future climate.
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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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Our study shows that accurately representing atmospheric ice masses remains a major challenge. We compared climate models to satellite data, finding that conventional models consistently underestimate the amount of ice. While new, higher-resolution models perform better, both models and observations still have significant discrepancies. These shortcomings limit our confidence in cloud-related climate feedbacks, which are critical for our predictions of the future climate.
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Short summary
The vertical distribution of atmospheric ice impacts Earth's weather and climate. The Ice Cloud Imager (ICI) will measure at microwave and sub-millimetre frequencies, which are well suited to detect atmospheric ice. In this study, a machine learning model is trained on ICI simulations. Results show that the vertical distribution of ice can be derived from ICI observations, and that ICI could offer a valuable data source that complements existing radar- and lidar-based measurements.
The vertical distribution of atmospheric ice impacts Earth's weather and climate. The Ice Cloud...
