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: 1,288 (including HTML, PDF, and XML)
HTML PDF XML Total Supplement BibTeX EndNote
1,083 193 12 1,288 29 20 13
  • HTML: 1,083
  • PDF: 193
  • XML: 12
  • Total: 1,288
  • Supplement: 29
  • BibTeX: 20
  • EndNote: 13
Views and downloads (calculated since 17 Sep 2020)
Cumulative views and downloads (calculated since 17 Sep 2020)

Viewed (geographical distribution)

Total article views: 1,073 (including HTML, PDF, and XML) Thereof 1,071 with geography defined and 2 with unknown origin.
Country # Views %
  • 1
1
 
 
 
 
Latest update: 17 Oct 2021
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.