02 Feb 2022
02 Feb 2022
Status: a revised version of this preprint is currently under review for the journal NPG.

Predicting Sea Surface Temperatures with Coupled Reservoir Computers

Benjamin Walleshauser1,3 and Erik Bollt2,3 Benjamin Walleshauser and Erik Bollt
  • 1Department of Physics and Department of Mechanical Engineering, Clarkson University
  • 2Department of Electrical and Computer Engineering, Clarkson University
  • 3Clarkson Center for Complex Systems Science

Abstract. Sea surface temperature (SST) is a key factor in understanding the greater climate of the Earth and is an important variable when making weather predictions. Methods of machine learning have become ever more present and important in data-driven science and engineering including in important areas for Earth Science. We propose here an efficient framework that allows us to make global SST forecasts by use of a coupled reservoir computer method that we have specialized to this domain allowing for template regions that accommodate irregular coastlines. Reservoir computing is an especially good method for forecasting spatiotemporally complex dynamical systems, as it is a machine learning method that despite many randomly selected weights, it is nonetheless highly accurate and easy to train. Our approach provides the benefit of a simple and computationally efficient model that is able to predict sea surface temperatures across the entire Earth’s oceans. The results are demonstrated to replicate the actual dynamics of the system over a forecasting period of several weeks.

Benjamin Walleshauser and Erik Bollt

Status: final response (author comments only)

Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor | : Report abuse
  • RC1: 'Comment on npg-2022-4', Anonymous Referee #1, 02 Mar 2022
    • AC1: 'Reply on RC1', Ben Walleshauser, 08 Apr 2022
  • RC2: 'Comment on npg-2022-4', Anonymous Referee #2, 10 Mar 2022
    • AC2: 'Reply on RC2', Ben Walleshauser, 08 Apr 2022

Benjamin Walleshauser and Erik Bollt

Benjamin Walleshauser and Erik Bollt


Total article views: 592 (including HTML, PDF, and XML)
HTML PDF XML Total BibTeX EndNote
500 81 11 592 4 2
  • HTML: 500
  • PDF: 81
  • XML: 11
  • Total: 592
  • BibTeX: 4
  • EndNote: 2
Views and downloads (calculated since 02 Feb 2022)
Cumulative views and downloads (calculated since 02 Feb 2022)

Viewed (geographical distribution)

Total article views: 557 (including HTML, PDF, and XML) Thereof 557 with geography defined and 0 with unknown origin.
Country # Views %
  • 1
Latest update: 23 May 2022
Short summary
As sea surface temperature is vital towards understanding the greater climate of the Earth and 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 sea surface temperature at specific locations, as well as over the greater domain of the Earth’s oceans.