Articles | Volume 28, issue 3
Nonlin. Processes Geophys., 28, 423–443, 2021
https://doi.org/10.5194/npg-28-423-2021
Nonlin. Processes Geophys., 28, 423–443, 2021
https://doi.org/10.5194/npg-28-423-2021
Research article
10 Sep 2021
Research article | 10 Sep 2021

Enhancing geophysical flow machine learning performance via scale separation

Davide Faranda et al.

Viewed

Total article views: 2,155 (including HTML, PDF, and XML)
HTML PDF XML Total Supplement BibTeX EndNote
1,798 332 25 2,155 38 34 23
  • HTML: 1,798
  • PDF: 332
  • XML: 25
  • Total: 2,155
  • Supplement: 38
  • BibTeX: 34
  • EndNote: 23
Views and downloads (calculated since 17 Sep 2020)
Cumulative views and downloads (calculated since 17 Sep 2020)

Viewed (geographical distribution)

Total article views: 2,155 (including HTML, PDF, and XML) Thereof 1,872 with geography defined and 283 with unknown origin.
Country # Views %
  • 1
1
 
 
 
 

Cited

Latest update: 02 Dec 2022
Download
Short summary
Machine learning approaches are spreading rapidly in climate sciences. They are of great help in many practical situations where using the underlying equations is difficult because of the limitation in computational power. Here we use a systematic approach to investigate the limitations of the popular echo state network algorithms used to forecast the long-term behaviour of chaotic systems, such as the weather. Our results show that noise and intermittency greatly affect the performances.