Year: 2025
Type: preprint
Abstract: 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.
Source: arXiv (Cornell University)
Authors: Andry, Gérôme, Lewin, Sacha, Rozet, François, Rochman, Omer, Mangeleer, Victor, Pirlet, Matthias, Faulx, Elise, Grégoire, Marilaure, Louppe, Gilles
Language: English
Subfield: Atmospheric Science
Field: Earth and Planetary Sciences
Domain: Physical Sciences
SDG: Climate action
Open Access status: green
Award: G5418511742
OpenAlex ID: W4416358264