Articles | Volume 23, issue 1
https://doi.org/10.5194/acp-23-523-2023
© Author(s) 2023. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/acp-23-523-2023
© Author(s) 2023. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
13 Jan 2023
Research article |
| 13 Jan 2023
| 13 Jan 2023
Machine learning of cloud types in satellite observations and climate models
Department of Meteorology (MISU), Stockholm University, Stockholm, Sweden
Frida A.-M. Bender
Department of Meteorology (MISU), Stockholm University, Stockholm, Sweden
Alex Schuddeboom
School of Physical and Chemical Sciences, University of Canterbury, Christchurch, Aotearoa New Zealand
Adrian J. McDonald
School of Physical and Chemical Sciences, University of Canterbury, Christchurch, Aotearoa New Zealand
Øyvind Seland
Research and Development Department, Norwegian Meteorological Institute, Oslo, Norway
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Cited
15 citations as recorded by crossref.
- Quantum-Inspired Neural Radiative Transfer (QINRT): A Multi-Scale Computational Framework for Next-Generation Climate Intelligence M. Akhtar https://doi.org/10.3390/appliedmath5040145
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- Unraveling deep-learning detection of atmospheric Kármán vortex streets: dataset, baseline benchmarking, and optimization Y. Zhang et al. https://doi.org/10.1080/20964471.2025.2522492
- The CUISINES Framework for Conducting Exoplanet Model Intercomparison Projects, Version 1.0 L. Sohl et al. https://doi.org/10.3847/PSJ/ad5830
- Bringing it all together: science priorities for improved understanding of Earth system change and to support international climate policy C. Jones et al. https://doi.org/10.5194/esd-15-1319-2024
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- Surface Broadband Radiation Data from a Bipolar Perspective: Assessing Climate Change Through Machine Learning A. Cavaliere et al. https://doi.org/10.3390/cli13070147
- Re-appraisal of the global climatic role of natural forests for improved climate projections and policies A. Makarieva et al. https://doi.org/10.3389/ffgc.2023.1150191
- Recent Advances in the Observation and Modeling of Aerosol-Cloud Interactions, Cloud Feedbacks, and Earth’s Energy Imbalance: A Review T. Michibata et al. https://doi.org/10.1007/s40726-025-00382-6
- CloudViT: exploring cloud type classification with vision transformers in global satellite data J. Lenhardt et al. https://doi.org/10.5194/acp-26-5447-2026
- Cloud properties and their projected changes in CMIP models with low to high climate sensitivity L. Bock & A. Lauer https://doi.org/10.5194/acp-24-1587-2024
- Enhancing Kármán Vortex Street Detection via Auxiliary Networks Incorporating Key Atmospheric Parameters Y. Zhang et al. https://doi.org/10.3390/atmos16030338
- Physics-guided machine learning approach for reconstructing air temperature in warm permafrost on the Qinghai‒Xizang Plateau C. Peng et al. https://doi.org/10.1016/j.accre.2026.04.013
- Aerosol-related effects on the occurrence of heterogeneous ice formation over Lauder, New Zealand ∕ Aotearoa J. Hofer et al. https://doi.org/10.5194/acp-24-1265-2024
- The implications of maintaining Earth's hemispheric albedo symmetry for shortwave radiative feedbacks A. Jönsson & F. Bender https://doi.org/10.5194/esd-14-345-2023
15 citations as recorded by crossref.
- Quantum-Inspired Neural Radiative Transfer (QINRT): A Multi-Scale Computational Framework for Next-Generation Climate Intelligence M. Akhtar https://doi.org/10.3390/appliedmath5040145
- Opinion: Can uncertainty in climate sensitivity be narrowed further? S. Sherwood & C. Forest https://doi.org/10.5194/acp-24-2679-2024
- Unraveling deep-learning detection of atmospheric Kármán vortex streets: dataset, baseline benchmarking, and optimization Y. Zhang et al. https://doi.org/10.1080/20964471.2025.2522492
- The CUISINES Framework for Conducting Exoplanet Model Intercomparison Projects, Version 1.0 L. Sohl et al. https://doi.org/10.3847/PSJ/ad5830
- Bringing it all together: science priorities for improved understanding of Earth system change and to support international climate policy C. Jones et al. https://doi.org/10.5194/esd-15-1319-2024
- Characterizing clouds with the CCClim dataset, a machine learning cloud class climatology A. Kaps et al. https://doi.org/10.5194/essd-16-3001-2024
- Surface Broadband Radiation Data from a Bipolar Perspective: Assessing Climate Change Through Machine Learning A. Cavaliere et al. https://doi.org/10.3390/cli13070147
- Re-appraisal of the global climatic role of natural forests for improved climate projections and policies A. Makarieva et al. https://doi.org/10.3389/ffgc.2023.1150191
- Recent Advances in the Observation and Modeling of Aerosol-Cloud Interactions, Cloud Feedbacks, and Earth’s Energy Imbalance: A Review T. Michibata et al. https://doi.org/10.1007/s40726-025-00382-6
- CloudViT: exploring cloud type classification with vision transformers in global satellite data J. Lenhardt et al. https://doi.org/10.5194/acp-26-5447-2026
- Cloud properties and their projected changes in CMIP models with low to high climate sensitivity L. Bock & A. Lauer https://doi.org/10.5194/acp-24-1587-2024
- Enhancing Kármán Vortex Street Detection via Auxiliary Networks Incorporating Key Atmospheric Parameters Y. Zhang et al. https://doi.org/10.3390/atmos16030338
- Physics-guided machine learning approach for reconstructing air temperature in warm permafrost on the Qinghai‒Xizang Plateau C. Peng et al. https://doi.org/10.1016/j.accre.2026.04.013
- Aerosol-related effects on the occurrence of heterogeneous ice formation over Lauder, New Zealand ∕ Aotearoa J. Hofer et al. https://doi.org/10.5194/acp-24-1265-2024
- The implications of maintaining Earth's hemispheric albedo symmetry for shortwave radiative feedbacks A. Jönsson & F. Bender https://doi.org/10.5194/esd-14-345-2023
Saved (final revised paper)
Latest update: 21 Jul 2026
Short summary
We present a machine learning method for determining cloud types in climate model output and satellite observations based on ground observations of cloud genera. We analyse cloud type biases and changes with temperature in climate models and show that the bias is anticorrelated with climate sensitivity. Models simulating decreasing stratiform and increasing cumuliform clouds with increased CO2 concentration tend to have higher climate sensitivity than models simulating the opposite tendencies.
We present a machine learning method for determining cloud types in climate model output and...
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