Geoscientific Model Development
Geoscientific Model Development
GMD
Articles | Volume 16, issue 16
Articles | Volume 16, issue 16
https://doi.org/10.5194/gmd-16-4639-2023
© Author(s) 2023. This work is distributed under
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/gmd-16-4639-2023
© Author(s) 2023. This work is distributed under
the Creative Commons Attribution 4.0 License.
Articles | Volume 16, issue 16
Methods for assessment of models
 | 
18 Aug 2023
Methods for assessment of models |  | 18 Aug 2023

Using Probability Density Functions to Evaluate Models (PDFEM, v1.0) to compare a biogeochemical model with satellite-derived chlorophyll

Bror F. Jönsson, Christopher L. Follett, Jacob Bien, Stephanie Dutkiewicz, Sangwon Hyun, Gemma Kulk, Gael L. Forget, Christian Müller, Marie-Fanny Racault, Christopher N. Hill, Thomas Jackson, and Shubha Sathyendranath

Viewed

Total article views: 1,858 (including HTML, PDF, and XML)
HTML PDF XML Total BibTeX EndNote
1,398 394 66 1,858 63 56
  • HTML: 1,398
  • PDF: 394
  • XML: 66
  • Total: 1,858
  • BibTeX: 63
  • EndNote: 56
Views and downloads (calculated since 08 Nov 2022)
Cumulative views and downloads (calculated since 08 Nov 2022)

Viewed (geographical distribution)

Total article views: 1,858 (including HTML, PDF, and XML) Thereof 1,820 with geography defined and 38 with unknown origin.
Country # Views %
  • 1
1
 
 
 
 
Latest update: 17 Nov 2024
Download
Short summary
While biogeochemical models and satellite-derived ocean color data provide unprecedented information, it is problematic to compare them. Here, we present a new approach based on comparing probability density distributions of model and satellite properties to assess model skills. We also introduce Earth mover's distances as a novel and powerful metric to quantify the misfit between models and observations. We find that how 3D chlorophyll fields are aggregated can be a significant source of error.
Read more