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

Deep learning has advanced weather forecasting, but accurate prediction requires identifying the current state of the atmosphere from observational data. We introduce Appa, a score-based data assimilation model generating global atmospheric trajectories at 0.25° resolution and 1-hour intervals. Powered by a 565M-parameter latent diffusion model trained on ERA5, Appa can be conditioned on arbitrary observations to infer posterior distributions of plausible states without retraining. Our probabilistic framework handles reanalysis, filtering, and forecasting, within a single model, producing physically consistent reconstructions from various inputs. Results establish latent score-based data assimilation as a promising foundation for future global atmospheric modeling systems.