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...
| Main Authors: | , , , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Copernicus Publications
2022-10-01
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| Series: | Geoscientific Model Development |
| Online Access: | https://gmd.copernicus.org/articles/15/7715/2022/gmd-15-7715-2022.pdf |