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Score-based Data Assimilation for a Two-Layer Quasi-Geostrophic Model

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arxiv 2310.01853 v2 pith:77ZHPPZC submitted 2023-10-03 stat.ML cs.LGphysics.ao-ph

François Rozet , Gilles Louppe This is my paper · ORCID
classification stat.MLcs.LGphysics.ao-ph
keywords assimilationdatasystemsdynamicalmodelquasi-geostrophicscore-basedtwo-layer
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Data assimilation addresses the problem of identifying plausible state trajectories of dynamical systems given noisy or incomplete observations. In geosciences, it presents challenges due to the high-dimensionality of geophysical dynamical systems, often exceeding millions of dimensions. This work assesses the scalability of score-based data assimilation (SDA), a novel data assimilation method, in the context of such systems. We propose modifications to the score network architecture aimed at significantly reducing memory consumption and execution time. We demonstrate promising results for a two-layer quasi-geostrophic model.

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Cited by 3 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. Align-DA: Align Score-based Atmospheric Data Assimilation with Multiple Preferences

    physics.ao-ph 2025-05 conditional novelty 6.0 of 10

    Align-DA uses direct preference optimization to align a score-based data assimilation prior with assimilation accuracy, forecast skill, and physical adherence rewards, improving analysis quality over unaligned diffusi...

  2. Generating time-consistent dynamics with discriminator-guided image diffusion models

    cs.LG 2025-05 conditional novelty 6.0 of 10

    A time-consistency discriminator guides a pretrained image diffusion model at inference time to generate realistic, stable spatiotemporal sequences without finetuning the diffusion model.

  3. PhyDA: Physics-Guided Diffusion Models for Data Assimilation in Atmospheric Systems

    cs.LG 2025-05 conditional novelty 5.0 of 10

    PhyDA adds PDE-residual regularization and a multi-scale latent encoder to a score-based diffusion model, and reports lower error and lower z500 spectral divergence than prior deep learning baselines on ERA5.

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