Articles | Volume 13, issue 12
https://doi.org/10.5194/amt-13-6989-2020
https://doi.org/10.5194/amt-13-6989-2020
Research article
 | 
21 Dec 2020
Research article |  | 21 Dec 2020

Applying deep learning to NASA MODIS data to create a community record of marine low-cloud mesoscale morphology

Tianle Yuan, Hua Song, Robert Wood, Johannes Mohrmann, Kerry Meyer, Lazaros Oreopoulos, and Steven Platnick

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Cited articles

Agee, E. M. and Dowell, K. E.: Observational Studies of Mesoscale Cellular Convection, J. Appl. Meteor., 13, 46–53, https://doi.org/10.1175/1520-0450(1974)013<0046:OSOMCC>2.0.CO;2, 1974. 
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Deng, J., Dong, W., Socher, R., Li, L.-J., Li, K., and Li, F. F.: ImageNet: A large-scale hierarchical image database, IEEE Computer Vision and Pattern Recognition (CVPR), 248–255, 2009. 
LAADS DAAC, NASA, Goddard Space Flight Center, https://ladsweb.modaps.eosdis.nasa.gov/, last access: 28 November 2020. 
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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.