Articles | Volume 13, issue 12
https://doi.org/10.5194/amt-13-6989-2020
© Author(s) 2020. 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-13-6989-2020
© Author(s) 2020. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
21 Dec 2020
Research article |
| 21 Dec 2020
| 21 Dec 2020
Applying deep learning to NASA MODIS data to create a community record of marine low-cloud mesoscale morphology
Earth Science Division, NASA Goddard Space Flight Center, Greenbelt, Maryland, USA
Joint Center for Earth Systems Technology, University of Maryland,
Baltimore County, Baltimore, Maryland, USA
Science Systems and Applications, Inc., Lanham, Maryland, USA
Robert Wood
Department of Atmospheric Sciences, University of Washington, Seattle, Washington, USA
Johannes Mohrmann
Department of Atmospheric Sciences, University of Washington, Seattle, Washington, USA
Kerry Meyer
Earth Science Division, NASA Goddard Space Flight Center, Greenbelt, Maryland, USA
Lazaros Oreopoulos
Earth Science Division, NASA Goddard Space Flight Center, Greenbelt, Maryland, USA
Steven Platnick
Earth Science Division, NASA Goddard Space Flight Center, Greenbelt, Maryland, USA
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Cited
14 citations as recorded by crossref.
- A Novel Approach for Reliable Classification of Marine Low Cloud Morphologies with Vision–Language Models E. Erfani & F. Hosseinpour https://doi.org/10.3390/atmos16111252
- On the relationship between mesoscale cellular convection and meteorological forcing: comparing the Southern Ocean against the North Pacific F. Lang et al. https://doi.org/10.5194/acp-24-1451-2024
- A survey of radiative and physical properties of North Atlantic mesoscale cloud morphologies from multiple identification methodologies R. Eastman et al. https://doi.org/10.5194/acp-24-6613-2024
- The Role of Mesoscale Cloud Morphology in the Shortwave Cloud Feedback I. McCoy et al. https://doi.org/10.1029/2022GL101042
- A global classification dataset of daytime and nighttime marine low-cloud mesoscale morphology based on deep-learning methods Y. Wu et al. https://doi.org/10.5194/essd-17-3243-2025
- Beyond discrete stratocumulus regimes: a ternary continuum of morphology reveals within-regime variability in cloud susceptibilities T. Goren et al. https://doi.org/10.5194/acp-26-7193-2026
- 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
- Environmental and Internal Controls on Lagrangian Transitions from Closed Cell Mesoscale Cellular Convection over Subtropical Oceans R. Eastman et al. https://doi.org/10.1175/JAS-D-20-0277.1
- Impacts of Mesoscale Cloud Organization on Aerosol‐Induced Cloud Water Adjustment and Cloud Brightness X. Zhou & G. Feingold https://doi.org/10.1029/2023GL103417
- AICCA: AI-Driven Cloud Classification Atlas T. Kurihana et al. https://doi.org/10.3390/rs14225690
- Identifying meteorological influences on marine low-cloud mesoscale morphology using satellite classifications J. Mohrmann et al. https://doi.org/10.5194/acp-21-9629-2021
- Cloud Mesoscale Cellular Classification and Diurnal Cycle Using a Convolutional Neural Network (CNN) M. Segal Rozenhaimer et al. https://doi.org/10.3390/rs15061607
- Increased dynamic efficiency in mesoscale organized trade wind cumulus clouds I. McCoy et al. https://doi.org/10.5194/acp-25-16233-2025
- A climatology of open and closed mesoscale cellular convection over the Southern Ocean derived from Himawari-8 observations F. Lang et al. https://doi.org/10.5194/acp-22-2135-2022
14 citations as recorded by crossref.
- A Novel Approach for Reliable Classification of Marine Low Cloud Morphologies with Vision–Language Models E. Erfani & F. Hosseinpour https://doi.org/10.3390/atmos16111252
- On the relationship between mesoscale cellular convection and meteorological forcing: comparing the Southern Ocean against the North Pacific F. Lang et al. https://doi.org/10.5194/acp-24-1451-2024
- A survey of radiative and physical properties of North Atlantic mesoscale cloud morphologies from multiple identification methodologies R. Eastman et al. https://doi.org/10.5194/acp-24-6613-2024
- The Role of Mesoscale Cloud Morphology in the Shortwave Cloud Feedback I. McCoy et al. https://doi.org/10.1029/2022GL101042
- A global classification dataset of daytime and nighttime marine low-cloud mesoscale morphology based on deep-learning methods Y. Wu et al. https://doi.org/10.5194/essd-17-3243-2025
- Beyond discrete stratocumulus regimes: a ternary continuum of morphology reveals within-regime variability in cloud susceptibilities T. Goren et al. https://doi.org/10.5194/acp-26-7193-2026
- 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
- Environmental and Internal Controls on Lagrangian Transitions from Closed Cell Mesoscale Cellular Convection over Subtropical Oceans R. Eastman et al. https://doi.org/10.1175/JAS-D-20-0277.1
- Impacts of Mesoscale Cloud Organization on Aerosol‐Induced Cloud Water Adjustment and Cloud Brightness X. Zhou & G. Feingold https://doi.org/10.1029/2023GL103417
- AICCA: AI-Driven Cloud Classification Atlas T. Kurihana et al. https://doi.org/10.3390/rs14225690
- Identifying meteorological influences on marine low-cloud mesoscale morphology using satellite classifications J. Mohrmann et al. https://doi.org/10.5194/acp-21-9629-2021
- Cloud Mesoscale Cellular Classification and Diurnal Cycle Using a Convolutional Neural Network (CNN) M. Segal Rozenhaimer et al. https://doi.org/10.3390/rs15061607
- Increased dynamic efficiency in mesoscale organized trade wind cumulus clouds I. McCoy et al. https://doi.org/10.5194/acp-25-16233-2025
- A climatology of open and closed mesoscale cellular convection over the Southern Ocean derived from Himawari-8 observations F. Lang et al. https://doi.org/10.5194/acp-22-2135-2022
Saved (final revised paper)
Latest update: 03 Jun 2026
Short summary
We use deep transfer learning techniques to classify satellite cloud images into different morphology types. It achieves the state-of-the-art results and can automatically process a large amount of satellite data. The algorithm will help low-cloud researchers to better understand their mesoscale organizations.
We use deep transfer learning techniques to classify satellite cloud images into different...
