Articles | Volume 15, issue 19
https://doi.org/10.5194/gmd-15-7353-2022
https://doi.org/10.5194/gmd-15-7353-2022
Development and technical paper
 | 
05 Oct 2022
Development and technical paper |  | 05 Oct 2022

Repeatable high-resolution statistical downscaling through deep learning

Dánnell Quesada-Chacón, Klemens Barfus, and Christian Bernhofer

Viewed

Total article views: 2,828 (including HTML, PDF, and XML)
HTML PDF XML Total BibTeX EndNote
2,003 765 60 2,828 52 51
  • HTML: 2,003
  • PDF: 765
  • XML: 60
  • Total: 2,828
  • BibTeX: 52
  • EndNote: 51
Views and downloads (calculated since 26 Apr 2022)
Cumulative views and downloads (calculated since 26 Apr 2022)

Viewed (geographical distribution)

Total article views: 2,828 (including HTML, PDF, and XML) Thereof 2,705 with geography defined and 123 with unknown origin.
Country # Views %
  • 1
1
 
 
 
 

Cited

Latest update: 17 Jul 2024
Download
Short summary
We improved the performance of past perfect prognosis statistical downscaling methods while achieving full model repeatability with GPU-calculated deep learning models using the TensorFlow, climate4R, and VALUE frameworks. We employed the ERA5 reanalysis as predictors and ReKIS (eastern Ore Mountains, Germany, 1 km resolution) as precipitation predictand, while incorporating modern deep learning architectures. The achieved repeatability is key to accomplish further milestones with deep learning.