Articles | Volume 11, issue 12
https://doi.org/10.5194/gmd-11-4739-2018
https://doi.org/10.5194/gmd-11-4739-2018
Development and technical paper
 | 
30 Nov 2018
Development and technical paper |  | 30 Nov 2018

Land surface model parameter optimisation using in situ flux data: comparison of gradient-based versus random search algorithms (a case study using ORCHIDEE v1.9.5.2)

Vladislav Bastrikov, Natasha MacBean, Cédric Bacour, Diego Santaren, Sylvain Kuppel, and Philippe Peylin

Viewed

Total article views: 3,090 (including HTML, PDF, and XML)
HTML PDF XML Total Supplement BibTeX EndNote
1,976 1,027 87 3,090 408 90 95
  • HTML: 1,976
  • PDF: 1,027
  • XML: 87
  • Total: 3,090
  • Supplement: 408
  • BibTeX: 90
  • EndNote: 95
Views and downloads (calculated since 30 Jul 2018)
Cumulative views and downloads (calculated since 30 Jul 2018)

Viewed (geographical distribution)

Total article views: 3,090 (including HTML, PDF, and XML) Thereof 2,830 with geography defined and 260 with unknown origin.
Country # Views %
  • 1
1
 
 
 
 

Cited

Latest update: 20 Nov 2024
Download
Short summary
In this study, we compare different methods for optimising parameters of the ORCHIDEE land surface model (LSM) using in situ observations. We use two minimisation methods - local gradient-based and global random search - applied either at each individual site or a group of sites characterised by one plant functional type. We demonstrate the advantages and challenges of different techniques and provide some advice on using it for the LSM parameters optimisation.