# Training-Free Data Assimilation with GenCast

**Authors:** Thomas Savary, François Rozet, Gilles Louppe  
**arXiv:** [2509.18811](https://arxiv.org/abs/2509.18811)  
**Date:** 2025-09-23  
**Summary page:** https://www.summarizepaper.com/fr/paper/2509.18811v1/

## 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.