LEVDA: Latent Ensemble Variational Data Assimilation via Differentiable Dynamics

• URL: http://arxiv.org/abs/2602.19406v1
• Date: Mon, 23 Feb 2026 00:54:59 GMT
• Title: LEVDA: Latent Ensemble Variational Data Assimilation via Differentiable Dynamics
• Authors: Phillip Si, Peng Chen,
• Abstract summary: We propose Latent Ensemble Data Assimilation (LEVDA), an ensemble-space variational smoother.<n>It assimilates states and unknown parameters without the need for adjoint code or auxiliary observation-to-latent encoders.<n>It substantially improved assimilation accuracy and computational efficiency compared to full-state 4DEnVar.
• Score: 6.953554594702111
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Long-range geophysical forecasts are fundamentally limited by chaotic dynamics and numerical errors. While data assimilation can mitigate these issues, classical variational smoothers require computationally expensive tangent-linear and adjoint models. Conversely, recent efficient latent filtering methods often enforce weak trajectory-level constraints and assume fixed observation grids. To bridge this gap, we propose Latent Ensemble Variational Data Assimilation (LEVDA), an ensemble-space variational smoother that operates in the low-dimensional latent space of a pretrained differentiable neural dynamics surrogate. By performing four-dimensional ensemble-variational (4DEnVar) optimization within an ensemble subspace, LEVDA jointly assimilates states and unknown parameters without the need for adjoint code or auxiliary observation-to-latent encoders. Leveraging the fully differentiable, continuous-in-time-and-space nature of the surrogate, LEVDA naturally accommodates highly irregular sampling at arbitrary spatiotemporal locations. Across three challenging geophysical benchmarks, LEVDA matches or outperforms state-of-the-art latent filtering baselines under severe observational sparsity while providing more reliable uncertainty quantification. Simultaneously, it achieves substantially improved assimilation accuracy and computational efficiency compared to full-state 4DEnVar.

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