Appa: Bending Weather Dynamics with Latent Diffusion Models for Global Data Assimilation

Deep learning has transformed weather forecasting by improving both its
accuracy and computational efficiency. However, before any forecast can begin,
weather centers must identify the current atmospheric state from vast amounts
of observational data. To address this challenging problem, we introduce Appa,
a score-based data assimilation model producing global atmospheric trajectories
at 0.25-degree resolution and 1-hour intervals. Powered by a 1.5B-parameter
spatio-temporal latent diffusion model trained on ERA5 reanalysis data, Appa
can be conditioned on any type of observations to infer the posterior
distribution of plausible state trajectories, without retraining. Our unified
probabilistic framework flexibly tackles multiple inference tasks --
reanalysis, filtering, and forecasting -- using the same model, eliminating the
need for task-specific architectures or training procedures. Experiments
demonstrate physical consistency on a global scale and good reconstructions
from observations, while showing competitive forecasting skills. Our results
establish latent score-based data assimilation as a promising foundation for
future global atmospheric modeling systems.