Articles | Volume 10, issue 12
https://doi.org/10.5194/amt-10-4747-2017
https://doi.org/10.5194/amt-10-4747-2017
Research article
 | 
05 Dec 2017
Research article |  | 05 Dec 2017

Feasibility study of multi-pixel retrieval of optical thickness and droplet effective radius of inhomogeneous clouds using deep learning

Rintaro Okamura, Hironobu Iwabuchi, and K. Sebastian Schmidt

Viewed

Total article views: 3,193 (including HTML, PDF, and XML)
HTML PDF XML Total BibTeX EndNote
1,991 1,114 88 3,193 100 97
  • HTML: 1,991
  • PDF: 1,114
  • XML: 88
  • Total: 3,193
  • BibTeX: 100
  • EndNote: 97
Views and downloads (calculated since 30 Jun 2017)
Cumulative views and downloads (calculated since 30 Jun 2017)

Viewed (geographical distribution)

Total article views: 3,193 (including HTML, PDF, and XML) Thereof 3,131 with geography defined and 62 with unknown origin.
Country # Views %
  • 1
1
 
 
 
 

Cited

Latest update: 02 Nov 2024
Download
Short summary
Three-dimensional (3-D) radiative transfer effects are a major source of retrieval errors in satellite-based optical remote sensing of clouds. Multi-pixel, multispectral approaches based on deep learning are proposed for retrieval of cloud optical thickness and droplet effective radius. A feasibility test shows that proposed retrieval methods are effective to obtain accurate cloud properties. Use of the convolutional neural network is effective to reduce 3-D radiative transfer effects.