用预训练扩散模型实现无需训练的数据同化，提升天气预测精度。
Training-Free Data Assimilation with GenCast
- 基于粒子滤波框架，利用预训练扩散模型直接进行状态估计。
- 在气象预报中显著优于传统方法，无需额外训练即可应用。
- 适合需要快速部署的动态系统建模场景，如气象与机器人领域。
数据同化广泛应用于气象学、海洋学和机器人等领域，用于从噪声观测中估计动态系统的状态。本文提出一种轻量且通用的方法，利用预先训练的扩散模型（用于模拟动态系统）实现数据同化，不需任何额外训练。该方法基于粒子滤波这一类数据同化算法，并以生成全球集合天气预报的扩散模型GenCast作为主要示例，展示了其在真实场景中的有效性。
原文摘要 · Abstract (English)
Data assimilation is widely used in many disciplines such as meteorology, oceanography, and robotics to estimate the state of a dynamical system from noisy observations. In this work, we propose a lightweight and general method to perform data assimilation using diffusion models pre-trained for emulating dynamical systems. Our method builds on particle filters, a class of data assimilation algorithms, and does not require any further training. As a guiding example throughout this work, we illustrate our methodology on GenCast, a diffusion-based model that generates global ensemble weather forecasts.
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