Conference
Appa: Bending Weather Dynamics with Latent Diffusion Models for Global Data Assimilation
| Τίτλος: | Appa: Bending Weather Dynamics with Latent Diffusion Models for Global Data Assimilation |
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| Συγγραφείς: | Andry, Gérôme, Lewin, Sacha, Rozet, François, Rochman, Omer, Mangeleer, Victor, Pirlet, Matthias, Faulx, Elise, Grégoire, Marilaure, Louppe, Gilles |
| Πηγή: | Machine Learning and the Physical Sciences Workshop (NeurIPS 2025), San Diego, United States - California [US-CA], 06/12/2025 |
| Έτος έκδοσης: | 2025 |
| Θεματικοί όροι: | Computer Science - Learning, Physics - Atmospheric and Oceanic Physics, Engineering, computing & technology, Computer science, Ingénierie, informatique & technologie, Sciences informatiques |
| Περιγραφή: | 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. |
| Τύπος εγγράφου: | conference paper not in proceedings http://purl.org/coar/resource_type/c_18cp conferencePaper peer reviewed |
| Γλώσσα: | English |
| DOI: | 10.48550/arXiv.2504.18720 |
| Σύνδεσμος πρόσβασης: | https://orbi.uliege.be/handle/2268/342181 |
| Rights: | open access http://purl.org/coar/access_right/c_abf2 info:eu-repo/semantics/openAccess |
| Αριθμός Καταχώρησης: | edsorb.342181 |
| Βάση Δεδομένων: | ORBi |
| DOI: | 10.48550/arXiv.2504.18720 |
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