Articles | Volume 29, issue 3
Nonlin. Processes Geophys., 29, 255–264, 2022
https://doi.org/10.5194/npg-29-255-2022
Nonlin. Processes Geophys., 29, 255–264, 2022
https://doi.org/10.5194/npg-29-255-2022
Research article
05 Jul 2022
Research article | 05 Jul 2022

Predicting sea surface temperatures with coupled reservoir computers

Benjamin Walleshauser and Erik Bollt

Viewed

Total article views: 1,177 (including HTML, PDF, and XML)
HTML PDF XML Total BibTeX EndNote
989 163 25 1,177 17 13
  • HTML: 989
  • PDF: 163
  • XML: 25
  • Total: 1,177
  • BibTeX: 17
  • EndNote: 13
Views and downloads (calculated since 02 Feb 2022)
Cumulative views and downloads (calculated since 02 Feb 2022)

Viewed (geographical distribution)

Total article views: 1,109 (including HTML, PDF, and XML) Thereof 1,109 with geography defined and 0 with unknown origin.
Country # Views %
  • 1
1
 
 
 
 
Latest update: 28 Sep 2022
Download
Short summary
As sea surface temperature (SST) is vital for understanding the greater climate of the Earth and is also an important variable in weather prediction, we propose a model that effectively capitalizes on the reduced complexity of machine learning models while still being able to efficiently predict over a large spatial domain. We find that it is proficient at predicting the SST at specific locations as well as over the greater domain of the Earth’s oceans.