用生成模型从极稀疏的海洋观测中重建高分辨率动力状态
Generative Lagrangian data assimilation for ocean dynamics under extreme sparsity
- 结合神经算子与扩散模型，从稀疏拉格朗日数据生成海洋状态
- 在99%稀疏合成数据和99.9%真实卫星数据下仍能还原小尺度湍流
- 适合处理极端稀疏观测的海洋预报与气候建模任务
从观测数据重构海洋动力过程受限于空间采样稀疏、不规则且为拉格朗日式，尤其在次表层和偏远区域。这种稀疏性极大挑战了涡旋脱落和巨浪等关键现象的预测。传统数据同化方法与深度学习模型在该条件下难以恢复中尺度湍流。本文提出一种深度学习框架，融合神经算子与去噪扩散概率模型（DDPM），从极稀疏拉格朗日观测中重建高分辨率海洋状态。通过将生成模型以神经算子输出为条件，该框架在合成数据上实现99%稀疏度、真实卫星数据上实现99.9%稀疏度下仍能准确捕捉小尺度、高波数动态。在基准系统、合成浮标观测及真实卫星数据上验证，性能显著优于其他深度学习基线。
原文摘要 · Abstract (English)
Reconstructing ocean dynamics from observational data is fundamentally limited by the sparse, irregular, and Lagrangian nature of spatial sampling, particularly in subsurface and remote regions. This sparsity poses significant challenges for forecasting key phenomena such as eddy shedding and rogue waves. Traditional data assimilation methods and deep learning models often struggle to recover mesoscale turbulence under such constraints. We leverage a deep learning framework that combines neural operators with denoising diffusion probabilistic models (DDPMs) to reconstruct high-resolution ocean states from extremely sparse Lagrangian observations. By conditioning the generative model on neural operator outputs, the framework accurately captures small-scale, high-wavenumber dynamics even at $99\%$ sparsity (for synthetic data) and $99.9\%$ sparsity (for real satellite observations). We validate our method on benchmark systems, synthetic float observations, and real satellite data, demonstrating robust performance under severe spatial sampling limitations as compared to other deep learning baselines.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。