Articles | Volume 17, issue 14
https://doi.org/10.5194/amt-17-4337-2024
© Author(s) 2024. 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-17-4337-2024
© Author(s) 2024. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
23 Jul 2024
Research article |
| 23 Jul 2024
| 23 Jul 2024
The Chalmers Cloud Ice Climatology: retrieval implementation and validation
Adrià Amell
Department of Space, Earth and Environment, Chalmers University of Technology, Gothenburg, Sweden
Simon Pfreundschuh
CORRESPONDING AUTHOR
Department of Space, Earth and Environment, Chalmers University of Technology, Gothenburg, Sweden
Department of Atmospheric Science, Colorado State University, Fort Collins, USA
Patrick Eriksson
Department of Space, Earth and Environment, Chalmers University of Technology, Gothenburg, Sweden
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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.
The representation of clouds in numerical weather and climate models remains a major challenge...
