发表机构
Linköping University; Swedish Meteorological and Hydrological Institute（林雪平大学; 瑞典气象水文研究所）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
DAWIS提出一种统一的数据同化框架，通过多任务插值器实现窗口逆采样，支持滤波、平滑及预测集成，在非线性系统中优于现有基线。
AI 中文摘要
基于流和扩散的生成模型最近已成为动力系统灵活且高效的预测模型。当与推理时引导相结合时，它们为高维非高斯数据同化（DA）提供了一条有前景的途径，该问题涉及将预测与观测相结合以估计潜在系统状态。然而，现有滤波器基于固定历史进行条件化，仅同化最近的观测，导致它们在新观测到达时无法修正过去的状态。估计值因此始终依附于可能被后续观测矛盾的历史，误差在同化过程中不断累积。为此，我们引入了**DAWIS**，一种统一的数据同化方法，在单一框架内涵盖滤波、固定滞后平滑和块平滑。DAWIS将状态级先验的单一流时间替换为连续状态窗口上的多任务随机插值器，为每个状态分配独立的流时间。同化循环将窗口逆转为每个状态转折点的向量，并在观测引导下重新生成，转折点控制每个状态被固定、修正或从头生成的强度。相同的构造还可以将预测吸收到同化循环中，从而无需单独的预测模型。在具有挑战性的非线性系统上的实验表明，DAWIS在稀疏、有噪声和非线性观测下优于滤波和平滑基线。DAWIS的代码可在https://this URL获取。
英文摘要
Flow- and diffusion-based generative models have recently emerged as flexible and highly efficient forecasting models for dynamical systems. When combined with inference-time guidance, they offer a promising route to high-dimensional non-Gaussian data assimilation (DA), the problem of combining forecasts with observations to estimate latent system states. Existing filters, however, condition on a fixed history and assimilate only the most recent observation, leaving them unable to revise past states when new observations arrive. Estimates then stay tethered to a history that later observations may contradict, and errors accumulate over the assimilation run. To this end, we introduce **DAWIS**, a unified DA method covering filtering, fixed-lag smoothing, and block smoothing within a single framework. DAWIS replaces the single flow time of a state-level prior with a multitask stochastic interpolant over a window of consecutive states, assigning a separate flow time to each. An assimilation cycle inverts the window to a vector of per-state turning points and regenerates it under observation guidance, with the turning points controlling how strongly each state is held fixed, revised, or generated from scratch. The same construction can also absorb the forecast into the assimilation cycle, removing the need for a separate forecasting model. Experiments on challenging nonlinear systems show that DAWIS improves on both filtering and smoothing baselines under sparse, noisy, and nonlinear observations. The code for DAWIS is available at https://github.com/Erik-Wikingsson/DAWIS
