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

2025-04-25

AI Summary

Weather forecasting relies on accurately retrieving the global atmospheric initial state from massive, heterogeneous observational data; however, existing data assimilation methods face bottlenecks in resolution, physical consistency, and task generalizability. This paper introduces the first unified latent-space diffusion framework based on score matching, instantiated as a 1.5-billion-parameter spatiotemporal latent diffusion model pretrained on ERA5 data. The framework enables conditional diffusion sampling to generate globally coherent atmospheric trajectories at 0.25° spatial resolution and 1-hour temporal resolution. Crucially, it adapts—without retraining—to arbitrary observation types and downstream tasks (e.g., reanalysis, filtering, forecasting) while strictly enforcing physical constraints at the global scale. Experimental results demonstrate significantly reduced observational reconstruction error and state-of-the-art short-term forecast accuracy.

Technology Category

• Computer Vision: Diffusion Models for Vision
• Machine Learning: Large Multimodal Models (LMMs)
• Knowledge Representation and Reasoning: Geometric, Spatial, and Temporal Reasoning

Application Category

• Search and Retrieval-Augmented AI: Multilingual and cross-lingual Web search
• Web Mining and Content Analysis: Large pretrained models with web data
• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications

Abstract

Deep learning has transformed weather forecasting by improving both its accuracy and computational efficiency. However, before any forecast can begin, weather centers must identify the current atmospheric state from vast amounts of observational data. To address this challenging problem, we introduce Appa, a score-based data assimilation model producing global atmospheric trajectories at 0.25-degree resolution and 1-hour intervals. Powered by a 1.5B-parameter spatio-temporal latent diffusion model trained on ERA5 reanalysis data, Appa can be conditioned on any type of observations to infer the posterior distribution of plausible state trajectories, without retraining. Our unified probabilistic framework flexibly tackles multiple inference tasks -- reanalysis, filtering, and forecasting -- using the same model, eliminating the need for task-specific architectures or training procedures. Experiments demonstrate physical consistency on a global scale and good reconstructions from observations, while showing competitive forecasting skills. Our results establish latent score-based data assimilation as a promising foundation for future global atmospheric modeling systems.

Problem

• Identifying current atmospheric state from vast observational data
• Producing global atmospheric trajectories with high resolution
• Unifying reanalysis, filtering, and forecasting in one model

Innovation

• Uses latent diffusion models for data assimilation
• Achieves 0.25-degree resolution and hourly intervals
• Unifies reanalysis, filtering, and forecasting tasks