提出SOON模型，更好捕捉气候系统的各向异性动态。
SOON: Symmetric Orthogonal Operator Network for Global Subseasonal-to-Seasonal Climate Forecasting
- 将全球网格按纬圈分块，保留经向周期结构。
- 通过对称正交算子交替建模，提升长期预测精度。
- 在地球再分析5数据集上表现领先，兼顾准确与效率。
精准的全球次季节至季节气候预报对防灾减灾和资源管理至关重要，但受大气混沌动力学影响仍具挑战。现有模型多将大气场视为各向同性图像，混淆了经向波传播与纬向输送的物理过程，导致各向异性动力建模不佳。本文提出对称正交算子网络（SOON）用于全球次季节至季节气候预报，包含：(1) 各向异性嵌入策略，将全球网格划分为纬圈，保持经向周期结构完整性；(2) 多层SOON模块，通过对称分解建模经向与纬向算子的交替作用，从结构上缓解长期积分中的离散化误差。在地球再分析5（ERA5）数据集上的大量实验表明，SOON达到新基准，显著优于现有方法，在预报精度与计算效率方面均有提升。
原文摘要 · Abstract (English)
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, significantly outperforming existing methods in both forecasting accuracy and computational efficiency.
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