Articles | Volume 13, issue 4
https://doi.org/10.5194/gmd-13-2109-2020
https://doi.org/10.5194/gmd-13-2109-2020
Model experiment description paper
 | 
28 Apr 2020
Model experiment description paper |  | 28 Apr 2020

Configuration and intercomparison of deep learning neural models for statistical downscaling

Jorge Baño-Medina, Rodrigo Manzanas, and José Manuel Gutiérrez

Viewed

Total article views: 10,224 (including HTML, PDF, and XML)
HTML PDF XML Total BibTeX EndNote
7,149 2,977 98 10,224 138 113
  • HTML: 7,149
  • PDF: 2,977
  • XML: 98
  • Total: 10,224
  • BibTeX: 138
  • EndNote: 113
Views and downloads (calculated since 28 Oct 2019)
Cumulative views and downloads (calculated since 28 Oct 2019)

Viewed (geographical distribution)

Total article views: 10,224 (including HTML, PDF, and XML) Thereof 9,154 with geography defined and 1,070 with unknown origin.
Country # Views %
  • 1
1
 
 
 
 

Cited

Latest update: 24 Jul 2024
Download
Short summary
In this study we intercompare different deep learning topologies for statistical downscaling purposes. As compared to the top-ranked methods in the largest-to-date downscaling intercomparison study, our results better predict the local climate variability. Moreover, deep learning approaches can be suitably applied to large regions (e.g., continents), which can therefore foster the use of statistical downscaling in flagship initiatives such as CORDEX.