Title: Training-Free Data Assimilation with GenCast Show Chinese title

Title: 无需训练的数据同化与GenCast

Abstract: 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. Show Chinese abstract

Abstract: 数据同化广泛应用于气象学、海洋学和机器人技术等多个学科，以从噪声观测中估计动态系统状态。 在本工作中，我们提出了一种轻量且通用的方法，使用为模拟动态系统预训练的扩散模型来进行数据同化。 我们的方法建立在粒子滤波器上，这是一种数据同化算法，并且不需要任何进一步的训练。 作为本工作的指导示例，我们在GenCast上展示了我们的方法，GenCast是一个基于扩散模型生成全球集合天气预测的模型。