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

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

<p>We present a Bayesian inference for a three-dimensional hydrodynamic model of Lake Geneva with stochastic weather forcing and high-frequency observational datasets. This is achieved by coupling a Bayesian inference package, <code>SPUX</code>, with a hydrodynamics package, <co...

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Bibliographic Details
Main Authors: A. Safin, D. Bouffard, F. Ozdemir, C. L. Ramón, J. Runnalls, F. Georgatos, C. Minaudo, J. Šukys
Format: Article
Language:English
Published: Copernicus Publications 2022-10-01
Series:Geoscientific Model Development
Online Access:https://gmd.copernicus.org/articles/15/7715/2022/gmd-15-7715-2022.pdf

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