Atmospheric Measurement Techniques
Atmospheric Measurement Techniques
AMT
Articles | Volume 15, issue 17
Articles | Volume 15, issue 17
https://doi.org/10.5194/amt-15-5181-2022
© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.
Special issue:
https://doi.org/10.5194/amt-15-5181-2022
© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.
Articles | Volume 15, issue 17
Research article
 | 
14 Sep 2022
Research article |  | 14 Sep 2022

Segmentation-based multi-pixel cloud optical thickness retrieval using a convolutional neural network

Vikas Nataraja, Sebastian Schmidt, Hong Chen, Takanobu Yamaguchi, Jan Kazil, Graham Feingold, Kevin Wolf, and Hironobu Iwabuchi

Viewed

Total article views: 1,998 (including HTML, PDF, and XML)
HTML PDF XML Total BibTeX EndNote
1,396 551 51 1,998 39 34
  • HTML: 1,396
  • PDF: 551
  • XML: 51
  • Total: 1,998
  • BibTeX: 39
  • EndNote: 34
Views and downloads (calculated since 15 Feb 2022)
Cumulative views and downloads (calculated since 15 Feb 2022)

Viewed (geographical distribution)

Total article views: 1,998 (including HTML, PDF, and XML) Thereof 2,066 with geography defined and -68 with unknown origin.
Country # Views %
  • 1
1
 
 
 
 

Cited

Latest update: 24 Apr 2024
Download
Short summary
A convolutional neural network (CNN) is introduced to retrieve cloud optical thickness (COT) from passive cloud imagery. The CNN, trained on large eddy simulations from the Sulu Sea, learns from spatial information at multiple scales to reduce cloud inhomogeneity effects. By considering the spatial context of a pixel, the CNN outperforms the traditional independent pixel approximation (IPA) across several cloud morphology metrics.
Read more
Special issue
Cloud, Aerosol and Monsoon Processes Philippines Experiment...