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

We introduce a score-based data assimilation framework powered by a 565M-parameter latent diffusion model trained on ERA5 reanalysis data. Our model generates global atmospheric trajectories at 0.25 degree resolution and 1-hour intervals, and supports conditioning on arbitrary observations to infer plausible trajectories, without retraining. This unified probabilistic approach handles reanalysis, filtering, and forecasting tasks while maintaining physical consistency.