Geoscientific Model Development
Geoscientific Model Development
GMD
Articles | Volume 15, issue 20
Articles | Volume 15, issue 20
https://doi.org/10.5194/gmd-15-7715-2022
© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.
Special issue:
https://doi.org/10.5194/gmd-15-7715-2022
© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.
Articles | Volume 15, issue 20
Model description paper
 | 
21 Oct 2022
Model description paper |  | 21 Oct 2022

A Bayesian data assimilation framework for lake 3D hydrodynamic models with a physics-preserving particle filtering method using SPUX-MITgcm v1

Artur Safin, Damien Bouffard, Firat Ozdemir, Cintia L. Ramón, James Runnalls, Fotis Georgatos, Camille Minaudo, and Jonas Šukys

Viewed

Total article views: 2,393 (including HTML, PDF, and XML)
HTML PDF XML Total Supplement BibTeX EndNote
1,786 520 87 2,393 176 47 42
  • HTML: 1,786
  • PDF: 520
  • XML: 87
  • Total: 2,393
  • Supplement: 176
  • BibTeX: 47
  • EndNote: 42
Views and downloads (calculated since 12 Nov 2021)
Cumulative views and downloads (calculated since 12 Nov 2021)

Viewed (geographical distribution)

Total article views: 2,393 (including HTML, PDF, and XML) Thereof 2,281 with geography defined and 112 with unknown origin.
Country # Views %
  • 1
1
 
 
 
 

Cited

Latest update: 20 Nov 2024
Download
Short summary
Reconciling the differences between numerical model predictions and observational data is always a challenge. In this paper, we investigate the viability of a novel approach to the calibration of a three-dimensional hydrodynamic model of Lake Geneva, where the target parameters are inferred in terms of distributions. We employ a filtering technique that generates physically consistent model trajectories and implement a neural network to enable bulk-to-skin temperature conversion.
Read more
Special issue
Modelling inland waters in a changing climate (GMD/ESD/TC...