A Bayesian data assimilation framework for lake 3D hydrodynamic models with a physics-preserving particle filtering method using SPUX-MITgcm v1 Safin, Artur Ramón Casañas, Cintia Luz 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. 2022-11-18T11:34:16Z 2022-11-18T11:34:16Z 2022-10-21 journal article 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] https://hdl.handle.net/10481/78038 10.5194/gmd-15-7715-2022 eng http://creativecommons.org/licenses/by/4.0/ open access Atribución 4.0 Internacional Copernicus