A Bayesian data assimilation framework for lake 3D hydrodynamic models with a physics-preserving particle filtering method using <span style="" class="text typewriter">SPUX-MITgcm</span> v1

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

doi:https://doi.org/10.5194/gmd-15-7715-2022

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:

Modelling inland waters in a changing climate (GMD/ESD/TC...

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

Supplement

https://doi.org/10.5194/gmd-15-7715-2022-supplement

Data sets

Hydrodynamic model predictions (2019) Artur Safin and James Runnalls https://doi.org/10.5281/zenodo.5642898

Model code and software

SPUX data assimilation package source code Artur Safin, Jonas Sukys, Firat Ozdemir, and Fotis Georgatos https://doi.org/10.5281/zenodo.5638312

MITgcm compilation and running scripts Artur Safin and Jonas Sukys https://doi.org/10.5281/zenodo.5637215

MITgcm source code modified to run with SPUX MITgcm developers (Jean-Michel Campin, Patrick Heimbach, Martin Losch, Gael Forget, Ed Hill, etc), and Artur Safin https://doi.org/10.5281/zenodo.5634041

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.