Climate Physics Dynamic Matching

2026-08-27

AI Summary

为解决天气预报中物理模型不完整和数据驱动模型黑盒问题，提出ClimPhyDM框架，结合物理先验与数据驱动，在ERA5基准上表现优异。

Abstract

Deep generative models such as flow matching and diffusion models have shown potential for learning complex dynamical systems, but typically act as black boxes that neglect underlying physical structure, while physics-based models governed by partial differential equations are often incomplete due to missing source terms, or uncertain parametrisations. We present Climate Physics Dynamic Matching (ClimPhyDM), a variational simulation-free dynamics informed framework for weather forecasting that combines an advection-type physics prior with data-driven components in a variational framework. % to capture the stochasticity and multi-modality of unresolved atmospheric dynamics. On the ERA5 benchmark at hourly (42-hour) and monthly (5-month) resolutions, ClimPhyDM outperforms ClimODE, and GB-DM, keeping the lower error at extended horizon, indicating improved temporal stability and resistance to error accumulation, while its simulation-free paradigm also enables training on a single modest 12 GB consumer GPU.

Problem

• climate physics
• dynamical systems
• weather forecasting
• partial differential equations
• stochasticity

Innovation

• variational simulation-free dynamics
• advection-type physics prior
• data-driven components
• temporal stability
• error accumulation resistance