Articles | Volume 13, issue 9
https://doi.org/10.5194/gmd-13-4399-2020
https://doi.org/10.5194/gmd-13-4399-2020
Model description paper
 | 
21 Sep 2020
Model description paper |  | 21 Sep 2020

RadNet 1.0: exploring deep learning architectures for longwave radiative transfer

Ying Liu, Rodrigo Caballero, and Joy Merwin Monteiro

Viewed

Total article views: 2,111 (including HTML, PDF, and XML)
HTML PDF XML Total BibTeX EndNote
1,398 640 73 2,111 71 62
  • HTML: 1,398
  • PDF: 640
  • XML: 73
  • Total: 2,111
  • BibTeX: 71
  • EndNote: 62
Views and downloads (calculated since 21 Jan 2020)
Cumulative views and downloads (calculated since 21 Jan 2020)

Viewed (geographical distribution)

Total article views: 2,111 (including HTML, PDF, and XML) Thereof 1,936 with geography defined and 175 with unknown origin.
Country # Views %
  • 1
1
 
 
 
 

Cited

Latest update: 07 Nov 2024
Download
Short summary
The calculation of atmospheric radiative transfer is the most computationally expensive part of climate models. Reducing this computational burden could potentially improve our ability to simulate the earth's climate at finer scales. We propose using a statistical model – a deep neural network – to compute approximate radiative transfer in the earth's atmosphere. We demonstrate a significant reduction in computational burden as compared to a traditional scheme, especially when using GPUs.