Uncertainty-aware data assimilation through variational inference

• URL: http://arxiv.org/abs/2510.17268v1
• Date: Mon, 20 Oct 2025 07:54:35 GMT
• Title: Uncertainty-aware data assimilation through variational inference
• Authors: Anthony Frion, David S Greenberg,
• Abstract summary: We propose a variational inference-based extension to the deterministic machine learning approach.<n>We show that our new model enables to obtain nearly perfectly calibrated predictions.
• Score: 1.0214749455979089
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Data assimilation, consisting in the combination of a dynamical model with a set of noisy and incomplete observations in order to infer the state of a system over time, involves uncertainty in most settings. Building upon an existing deterministic machine learning approach, we propose a variational inference-based extension in which the predicted state follows a multivariate Gaussian distribution. Using the chaotic Lorenz-96 dynamics as a testing ground, we show that our new model enables to obtain nearly perfectly calibrated predictions, and can be integrated in a wider variational data assimilation pipeline in order to achieve greater benefit from increasing lengths of data assimilation windows. Our code is available at https://github.com/anthony-frion/Stochastic_CODA.

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