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[2602.15040] SOON: Symmetric Orthogonal Operator Network for Global Subseasonal-to-Seasonal Climate Forecasting
Summary
The paper introduces the Symmetric Orthogonal Operator Network (SOON) for improved global Subseasonal-to-Seasonal climate forecasting, addressing limitations in existing models.
Why It Matters
Accurate S2S climate forecasting is essential for disaster preparedness and resource management. This research presents a novel approach that enhances forecasting accuracy and computational efficiency, potentially transforming how climate predictions are made.
Key Takeaways
- SOON utilizes an Anisotropic Embedding strategy to improve modeling of atmospheric dynamics.
- The network significantly outperforms existing forecasting methods in accuracy and efficiency.
- The approach mitigates discretization errors through a symmetric decomposition of operators.
Physics > Atmospheric and Oceanic Physics arXiv:2602.15040 (physics) [Submitted on 4 Feb 2026] Title:SOON: Symmetric Orthogonal Operator Network for Global Subseasonal-to-Seasonal Climate Forecasting Authors:Ziyu Zhou, Tian Zhou, Shiyu Wang, James Kwok, Yuxuan Liang View a PDF of the paper titled SOON: Symmetric Orthogonal Operator Network for Global Subseasonal-to-Seasonal Climate Forecasting, by Ziyu Zhou and 4 other authors View PDF HTML (experimental) Abstract:Accurate global Subseasonal-to-Seasonal (S2S) climate forecasting is critical for disaster preparedness and resource management, yet it remains challenging due to chaotic atmospheric dynamics. Existing models predominantly treat atmospheric fields as isotropic images, conflating the distinct physical processes of zonal wave propagation and meridional transport, and leading to suboptimal modeling of anisotropic dynamics. In this paper, we propose the Symmetric Orthogonal Operator Network (SOON) for global S2S climate forecasting. It couples: (1) an Anisotropic Embedding strategy that tokenizes the global grid into latitudinal rings, preserving the integrity of zonal periodic structures; and (2) a stack of SOON Blocks that models the alternating interaction of Zonal and Meridional Operators via a symmetric decomposition, structurally mitigating discretization errors inherent in long-term integration. Extensive experiments on the Earth Reanalysis 5 dataset demonstrate that SOON establishes a new state-of-the-art, si...