用视频流匹配生成连续大气状态轨迹，实现高效多源观测融合。
Multimodal Spatiotemporal Atmospheric Data Assimilation with Latent Video Flow-matching
- 基于潜空间视频流匹配构建先验轨迹，支持时序一致性采样。
- 仅需稀疏观测即可生成全状态集合预报，性能媲美顶尖模型。
- 统一框架可灵活完成滤波、平滑等任务，适配气象预测与研究者。
数据同化（DA）通过贝叶斯推断将观测数据融入数值预报模型状态。本文提出一种全新的统一大气数据同化方法：利用潜空间视频流匹配技术，从基于ERA5再分析数据（69个变量，8天时间窗）训练的先验模型中采样时序一致的状态轨迹；同时采用后验采样融合真实观测数据，如来自美国国家海洋和大气管理局（NOAA）的综合全球探空档案（IGRA）和综合地表数据库（ISD）。由于先验生成连续轨迹，天然实现了观测帧与未观测帧间的信息传递，因此仅需调整观测帧即可完成滤波、平滑等多种数据同化任务。此外，可直接从稀疏观测生成完整状态集合预报，性能达到当前最优观测-预报模型水平。
原文摘要 · Abstract (English)
Data assimilation (DA) uses Bayesian inference to update the state of a numerical forecast model with observed data. In this study, we propose a fundamentally different, unified approach to atmospheric data assimilation. We use latent video flow-matching to sample temporally consistent trajectories from a prior trained using ERA5 reanalysis (69 variables over an 8-day window). We also use posterior sampling to assimilate real observation sources, such as those from the NOAA Integrated Global Radiosonde Archive and the Integrated Surface Database. Because the prior generates a continuous trajectory, it naturally propagates information between observed and unobserved frames. Therefore, we can perform various DA tasks, such as filtering and smoothing, simply by changing the observed frames. Moreover, we generate full-state ensemble forecasts directly from sparse observations, achieving performance competitive with state-of-the-art observation-to-forecast models.
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