  Data assimilation, in its most comprehensive form, addresses the Bayesian
inverse problem of identifying plausible state trajectories that explain noisy
or incomplete observations of stochastic dynamical systems. Various approaches
have been proposed to solve this problem, including particle-based and
variational methods. However, most algorithms depend on the transition dynamics
for inference, which becomes intractable for long time horizons or for
high-dimensional systems with complex dynamics, such as oceans or atmospheres.
In this work, we introduce score-based data assimilation for trajectory
inference. We learn a score-based generative model of state trajectories based
on the key insight that the score of an arbitrarily long trajectory can be
decomposed into a series of scores over short segments. After training,
inference is carried out using the score model, in a non-autoregressive manner
by generating all states simultaneously. Quite distinctively, we decouple the
observation model from the training procedure and use it only at inference to
guide the generative process, which enables a wide range of zero-shot
observation scenarios. We present theoretical and empirical evidence supporting
the effectiveness of our method.
