用稀疏海面数据重建高分辨率三维海洋状态
High-resolution probabilistic estimation of three-dimensional regional ocean dynamics from sparse surface observations
- 基于条件去噪扩散模型，从99.9%稀疏的海面观测中恢复三维海洋场
- 在墨西哥湾成功重建多深度的温盐和流速场，还原大尺度环流与多尺度变化
- 无需物理模型，可泛化到未见深度，适合气候监测与预报应用
海洋内部调节地球气候，但因现场观测有限而难以全面获取，卫星观测也仅限于表层。本文提出一种深度感知的生成框架，仅依靠极稀疏的海面高度与温度观测（最高可达99.9%缺失），即可重建高分辨率三维海洋状态。该方法采用条件去噪扩散概率模型（DDPM），通过连续深度嵌入学习统一的垂直表示，无需依赖背景动力模型，且能泛化至未见深度。在墨西哥湾的应用中，模型准确重构了多个深度的亚表层温盐与速度场。统计指标、谱分析及热通量诊断评估表明，模型有效恢复了大尺度环流与多尺度变率。结果表明，生成扩散模型是数据匮乏环境下概率性海洋重建的可扩展方案，对气候监测与预测具有重要意义。
原文摘要 · Abstract (English)
The ocean interior regulates Earth's climate but remains sparsely observed due to limited in situ measurements, while satellite observations are restricted to the surface. We present a depth-aware generative framework for reconstructing high-resolution three-dimensional ocean states from extremely sparse surface data. Our approach employs a conditional denoising diffusion probabilistic model (DDPM) trained on sea surface height and temperature observations with up to 99.9 percent sparsity, without reliance on a background dynamical model. By incorporating continuous depth embeddings, the model learns a unified vertical representation of the ocean states and generalizes to previously unseen depths. Applied to the Gulf of Mexico, the framework accurately reconstructs subsurface temperature, salinity, and velocity fields across multiple depths. Evaluations using statistical metrics, spectral analysis, and heat transport diagnostics demonstrate recovery of both large-scale circulation and multiscale variability. These results establish generative diffusion models as a scalable approach for probabilistic ocean reconstruction in data-limited regimes, with implications for climate monitoring and forecasting.
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