Geoscientific Model Development
Geoscientific Model Development
GMD
Articles | Volume 16, issue 13
Articles | Volume 16, issue 13
https://doi.org/10.5194/gmd-16-3785-2023
© Author(s) 2023. This work is distributed under
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/gmd-16-3785-2023
© Author(s) 2023. This work is distributed under
the Creative Commons Attribution 4.0 License.
Articles | Volume 16, issue 13
Model description paper
 | 
11 Jul 2023
Model description paper |  | 11 Jul 2023

Deep learning for stochastic precipitation generation – deep SPG v1.0

Leroy J. Bird, Matthew G. W. Walker, Greg E. Bodeker, Isaac H. Campbell, Guangzhong Liu, Swapna Josmi Sam, Jared Lewis, and Suzanne M. Rosier

Viewed

Total article views: 1,962 (including HTML, PDF, and XML)
HTML PDF XML Total BibTeX EndNote
1,525 386 51 1,962 52 51
  • HTML: 1,525
  • PDF: 386
  • XML: 51
  • Total: 1,962
  • BibTeX: 52
  • EndNote: 51
Views and downloads (calculated since 18 Jul 2022)
Cumulative views and downloads (calculated since 18 Jul 2022)

Viewed (geographical distribution)

Total article views: 1,962 (including HTML, PDF, and XML) Thereof 1,878 with geography defined and 84 with unknown origin.
Country # Views %
  • 1
1
 
 
 
 

Cited

Latest update: 13 Dec 2024
Download
Short summary
Deriving the statistics of expected future changes in extreme precipitation is challenging due to these events being rare. Regional climate models (RCMs) are computationally prohibitive for generating ensembles capable of capturing large numbers of extreme precipitation events with statistical robustness. Stochastic precipitation generators (SPGs) provide an alternative to RCMs. We describe a novel single-site SPG that learns the statistics of precipitation using a machine-learning approach.
Read more