Atmospheric Chemistry and Physics
Atmospheric Chemistry and Physics
ACP
Articles | Volume 20, issue 4
Articles | Volume 20, issue 4
https://doi.org/10.5194/acp-20-2303-2020
© Author(s) 2020. This work is distributed under
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/acp-20-2303-2020
© Author(s) 2020. This work is distributed under
the Creative Commons Attribution 4.0 License.
Articles | Volume 20, issue 4
Technical note
 | 
26 Feb 2020
Technical note |  | 26 Feb 2020

Technical note: Deep learning for creating surrogate models of precipitation in Earth system models

Theodore Weber, Austin Corotan, Brian Hutchinson, Ben Kravitz, and Robert Link

Viewed

Total article views: 2,520 (including HTML, PDF, and XML)
HTML PDF XML Total BibTeX EndNote
1,685 802 33 2,520 58 48
  • HTML: 1,685
  • PDF: 802
  • XML: 33
  • Total: 2,520
  • BibTeX: 58
  • EndNote: 48
Views and downloads (calculated since 11 Apr 2019)
Cumulative views and downloads (calculated since 11 Apr 2019)

Viewed (geographical distribution)

Total article views: 2,520 (including HTML, PDF, and XML) Thereof 2,302 with geography defined and 218 with unknown origin.
Country # Views %
  • 1
1
 
 
 
 

Cited

Latest update: 27 May 2023
Download
Short summary
Climate model emulators can save computer time but are less accurate than full climate models. We use neural networks to build emulators of precipitation, trained on existing climate model runs. By doing so, we can capture nonlinearities and how the past state of a model (to some degree) shapes the future state. Our emulator outperforms a persistence forecast of precipitation.
Read more
Altmetrics
Final-revised paper
Preprint