A Bayesian data assimilation framework for lake 3D hydrodynamic models with a physics-preserving particle filtering method using SPUX-MITgcm v1
Metadatos
Mostrar el registro completo del ítemEditorial
Copernicus
Fecha
2022-10-21Referencia bibliográfica
Safin, A... [et al.]. 2022. A Bayesian data assimilation framework for lake 3D hydrodynamic models with a physics-preserving particle filtering method using SPUX-MITgcm v1, Geosci. Model Dev., 15, 7715–7730, [https://doi.org/10.5194/gmd-15-7715-2022]
Patrocinador
Swiss Data Science Center (SDSC) DATALAKES C17-17; Eawag Discretionary FundingResumen
We present a Bayesian inference for a threedimensional
hydrodynamic model of Lake Geneva with
stochastic weather forcing and high-frequency observational
datasets. This is achieved by coupling a Bayesian inference
package, SPUX, with a hydrodynamics package, MITgcm,
into a single framework, SPUX-MITgcm. To mitigate uncertainty
in the atmospheric forcing, we use a smoothed particle
Markov chain Monte Carlo method, where the intermediate
model state posteriors are resampled in accordance
with their respective observational likelihoods. To improve
the uncertainty quantification in the particle filter, we develop
a bi-directional long short-term memory (BiLSTM) neural
network to estimate lake skin temperature from a history of
hydrodynamic bulk temperature predictions and atmospheric
data. This study analyzes the benefit and costs of such a stateof-
the-art computationally expensive calibration and assimilation
method for lakes.