Articles | Volume 17, issue 2
https://doi.org/10.5194/gmd-17-621-2024
https://doi.org/10.5194/gmd-17-621-2024
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: 1,316 (including HTML, PDF, and XML)
HTML PDF XML Total BibTeX EndNote
924 329 63 1,316 57 63
  • HTML: 924
  • PDF: 329
  • XML: 63
  • Total: 1,316
  • BibTeX: 57
  • EndNote: 63
Views and downloads (calculated since 15 Jun 2023)
Cumulative views and downloads (calculated since 15 Jun 2023)

Viewed (geographical distribution)

Total article views: 1,316 (including HTML, PDF, and XML) Thereof 1,275 with geography defined and 41 with unknown origin.
Country # Views %
  • 1
1
 
 
 
 
Latest update: 04 Oct 2024
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.