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

Data sets

Hydrodynamic model predictions (2019) Artur Safin and James Runnalls

Model code and software

SPUX data assimilation package source code Artur Safin, Jonas Sukys, Firat Ozdemir, and Fotis Georgatos

MITgcm compilation and running scripts Artur Safin and Jonas Sukys

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

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.