这篇论文用流匹配生成模型做大气数据同化，拿ERA5先验加NOAA观测，不用传统递推迭代，能直接做滤波平滑和集合预报，还挺巧妙的。
本研究提出一种基于潜视频流匹配的统一大气数据同化方法。该方法使用ERA5再分析数据（69个变量、8天窗口）训练先验模型。通过后验采样，可同化NOAA综合探空仪档案和地面站数据等真实观测。由于先验生成连续轨迹，能自然实现滤波、平滑等任务，并支持从稀疏观测直接生成集合预报。
Multimodal Spatiotemporal Atmospheric Data Assimilation with Latent Flow-matching
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.