Atmospheric Measurement Techniques
Atmospheric Measurement Techniques
AMT
Articles | Volume 13, issue 12
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.
https://doi.org/10.5194/amt-13-6989-2020
© Author(s) 2020. This work is distributed under
the Creative Commons Attribution 4.0 License.
Articles | Volume 13, issue 12
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

Viewed

Total article views: 2,222 (including HTML, PDF, and XML)
HTML PDF XML Total BibTeX EndNote
1,383 777 62 2,222 61 58
  • HTML: 1,383
  • PDF: 777
  • XML: 62
  • Total: 2,222
  • BibTeX: 61
  • EndNote: 58
Views and downloads (calculated since 31 Mar 2020)
Cumulative views and downloads (calculated since 31 Mar 2020)

Viewed (geographical distribution)

Total article views: 2,222 (including HTML, PDF, and XML) Thereof 2,070 with geography defined and 152 with unknown origin.
Country # Views %
  • 1
1
 
 
 
 

Cited

Latest update: 19 Apr 2024
Download
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.
Read more