Geoscientific Model Development
Geoscientific Model Development
GMD
Articles | Volume 17, issue 2
Articles | Volume 17, issue 2
https://doi.org/10.5194/gmd-17-621-2024
© Author(s) 2024. This work is distributed under
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/gmd-17-621-2024
© Author(s) 2024. This work is distributed under
the Creative Commons Attribution 4.0 License.
Articles | Volume 17, issue 2
Development and technical paper
 | 
26 Jan 2024
Development and technical paper |  | 26 Jan 2024

Computationally efficient parameter estimation for high-dimensional ocean biogeochemical models

Skyler Kern, Mary E. McGuinn, Katherine M. Smith, Nadia Pinardi, Kyle E. Niemeyer, Nicole S. Lovenduski, and Peter E. Hamlington

Viewed

Total article views: 1,450 (including HTML, PDF, and XML)
HTML PDF XML Total BibTeX EndNote
1,017 366 67 1,450 63 69
  • HTML: 1,017
  • PDF: 366
  • XML: 67
  • Total: 1,450
  • BibTeX: 63
  • EndNote: 69
Views and downloads (calculated since 15 Jun 2023)
Cumulative views and downloads (calculated since 15 Jun 2023)

Viewed (geographical distribution)

Total article views: 1,450 (including HTML, PDF, and XML) Thereof 1,400 with geography defined and 50 with unknown origin.
Country # Views %
  • 1
1
 
 
 
 
Latest update: 17 Jan 2025
Download
Short summary
Computational models are used to simulate the behavior of marine ecosystems. The models often have unknown parameters that need to be calibrated to accurately represent observational data. Here, we propose a novel approach to simultaneously determine a large set of parameters for a one-dimensional model of a marine ecosystem in the surface ocean at two contrasting sites. By utilizing global and local optimization techniques, we estimate many parameters in a computationally efficient manner.
Read more