Geoscientific Model Development
Geoscientific Model Development
GMD
Articles | Volume 14, issue 10
Articles | Volume 14, issue 10
https://doi.org/10.5194/gmd-14-6355-2021
© Author(s) 2021. This work is distributed under
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/gmd-14-6355-2021
© Author(s) 2021. This work is distributed under
the Creative Commons Attribution 4.0 License.
Articles | Volume 14, issue 10
Development and technical paper
 | 
22 Oct 2021
Development and technical paper |  | 22 Oct 2021

Fast and accurate learned multiresolution dynamical downscaling for precipitation

Jiali Wang, Zhengchun Liu, Ian Foster, Won Chang, Rajkumar Kettimuthu, and V. Rao Kotamarthi

Viewed

Total article views: 7,944 (including HTML, PDF, and XML)
HTML PDF XML Total BibTeX EndNote
4,666 3,136 142 7,944 126 166
  • HTML: 4,666
  • PDF: 3,136
  • XML: 142
  • Total: 7,944
  • BibTeX: 126
  • EndNote: 166
Views and downloads (calculated since 27 Jan 2021)
Cumulative views and downloads (calculated since 27 Jan 2021)

Viewed (geographical distribution)

Total article views: 7,944 (including HTML, PDF, and XML) Thereof 7,426 with geography defined and 518 with unknown origin.
Country # Views %
  • 1
1
 
 
 
 
Latest update: 02 Mar 2026
Download
Short summary
Downscaling, the process of generating a higher spatial or time dataset from a coarser observational or model dataset, is a widely used technique. Two common methodologies for performing downscaling are to use either dynamic (physics-based) or statistical (empirical). Here we develop a novel methodology, using a conditional generative adversarial network (CGAN), to perform the downscaling of a model's precipitation forecasts and describe the advantages of this method compared to the others.
Read more
Share