Appa: Bending Weather Dynamics with Latent Diffusion Models for Global Data Assimilation
This work addresses the need for accurate initial conditions in weather forecasting for meteorologists and climate scientists, presenting a novel probabilistic framework that integrates reanalysis, filtering, and forecasting into a single model, though it appears incremental as it builds on existing deep learning and diffusion model techniques.
The paper tackled the problem of identifying the current state of the atmosphere from observational data for weather forecasting by introducing Appa, a score-based data assimilation model that generates global atmospheric trajectories at 0.25° resolution and 1-hour intervals, using a 565M-parameter latent diffusion model trained on ERA5 to infer plausible trajectories from arbitrary observations without retraining.
Deep learning has advanced weather forecasting, but accurate predictions first require identifying the current state of the atmosphere from observational data. In this work, we introduce Appa, a score-based data assimilation model generating global atmospheric trajectories at 0.25\si{\degree} 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 plausible trajectories, 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.