Geoscientific Model Development
Geoscientific Model Development
Geoscientific Model Development
Articles | Volume 13, issue 9
Articles | Volume 13, issue 9
Geosci. Model Dev., 13, 4399–4412, 2020
https://doi.org/10.5194/gmd-13-4399-2020
© Author(s) 2020. This work is distributed under
the Creative Commons Attribution 4.0 License.
Geosci. Model Dev., 13, 4399–4412, 2020
https://doi.org/10.5194/gmd-13-4399-2020
© Author(s) 2020. This work is distributed under
the Creative Commons Attribution 4.0 License.
Articles | Volume 13, issue 9
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 et al.

Viewed

Total article views: 1,388 (including HTML, PDF, and XML)
HTML PDF XML Total BibTeX EndNote
888 452 48 1,388 49 41
  • HTML: 888
  • PDF: 452
  • XML: 48
  • Total: 1,388
  • BibTeX: 49
  • EndNote: 41
Views and downloads (calculated since 21 Jan 2020)
Cumulative views and downloads (calculated since 21 Jan 2020)

Viewed (geographical distribution)

Total article views: 1,241 (including HTML, PDF, and XML) Thereof 1,238 with geography defined and 3 with unknown origin.
Country # Views %
  • 1
1
 
 
 
 

Cited

Latest update: 26 Jun 2022
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.
Read more