mlsanityData to 7 Oct 2026

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

arXiv 2310.01853 ↗First posted 3 Oct 2023stat.ML cs.LG physics.ao-phcited by 11

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Abstract (arXiv)

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.

Key facts. Cited by 11 papers in the map (data through 7 Oct 2026). Builds on 16 of its 33 references that are in the map. First posted on arXiv 3 Oct 2023.

Builds on

16 of 33 references.

References · 16

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4 more
  1. 2021Factorized Fourier Neural Operatorscited by 237
  2. 2022It's Raw! Audio Generation with State-Space Modelscited by 171
  3. 2023Score-based Data Assimilationcited by 91
  4. 2016Memory-Efficient Backpropagation Through Timecited by 78

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2024: 5; 2025: 5; 2026: 1.

11 papers.