Abstract: We propose a framework to perform probabilistic data assimilation (DA) using conditional score-based diffusion models. Conditional score-based diffusion models are generative models that learn to approximate the score function of a conditional distribution using samples from the joint distribution. We apply this approach to approximate the update step of the Bayes filter and arrive at a particle-based DA approach. The application of this approach only requires simulating the forward model. Hence, the proposed approach can accommodate black-box forward dynamics models and complex measurement noise. We demonstrate the efficacy of the proposed approach on canonical and challenging DA problems. The results show that the proposed framework can solve large-scale physics-based DA problems efficiently.