Geoscientific Model Development
Geoscientific Model Development
GMD
Articles | Volume 15, issue 14
Articles | Volume 15, issue 14
https://doi.org/10.5194/gmd-15-5713-2022
© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.
Special issue:
https://doi.org/10.5194/gmd-15-5713-2022
© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.
Articles | Volume 15, issue 14
Development and technical paper
 | 
22 Jul 2022
Development and technical paper |  | 22 Jul 2022

Use of genetic algorithms for ocean model parameter optimisation: a case study using PISCES-v2_RC for North Atlantic particulate organic carbon

Marcus Falls, Raffaele Bernardello, Miguel Castrillo, Mario Acosta, Joan Llort, and Martí Galí

Viewed

Total article views: 4,211 (including HTML, PDF, and XML)
HTML PDF XML Total BibTeX EndNote
3,080 1,044 87 4,211 88 133
  • HTML: 3,080
  • PDF: 1,044
  • XML: 87
  • Total: 4,211
  • BibTeX: 88
  • EndNote: 133
Views and downloads (calculated since 06 Aug 2021)
Cumulative views and downloads (calculated since 06 Aug 2021)

Viewed (geographical distribution)

Total article views: 4,211 (including HTML, PDF, and XML) Thereof 4,010 with geography defined and 201 with unknown origin.
Country # Views %
  • 1
1
 
 
 
 
Latest update: 17 Nov 2025
Download
Short summary
This paper describes and tests a method which uses a genetic algorithm (GA), a type of optimisation algorithm, on an ocean biogeochemical model. The aim is to produce a set of numerical parameters that best reflect the observed data of particulate organic carbon in a specific region of the ocean. We show that the GA can provide optimised model parameters in a robust and efficient manner and can also help detect model limitations, ultimately leading to a reduction in the model uncertainties.
Read more
Share
Special issue
Nucleus for European Modelling of the Ocean - NEMO