SOON: 用于全球亚季节到季节气候预测的对称正交运算网络
Symmetric Composition of Anisotropic Operators for Global Subseasonal-to-Seasonal Climate Forecasting
- The Hong Kong University of Science（香港科学与技术大学）
- DAMO Academy, Alibaba Group, Hangzhou, China（阿里云联合实验室，阿里集团，杭州，中国）
机构由 AI 辅助整理，请以论文原文为准。
AI总结：
SOON通过各向异性嵌入和对称分解技术，提升全球亚季节到季节气候预测的准确性和效率。
AI中文摘要：
准确的全球亚季节到季节（S2S）气候预测对于灾害准备和资源管理至关重要，但仍然具有挑战性，因为大气动力学具有混沌特性。现有模型大多将大气场视为各向同性的图像，将东西向波传播和纬向输送的物理过程混为一谈，导致对各向异性动力学的建模不理想。本文提出了一种用于全球S2S气候预测的对称正交运算网络（SOON）。它结合：（1）一种各向异性嵌入策略，将全球网格划分为纬度环，保留东西向周期结构的完整性；以及（2）一系列SOON模块，通过对称分解建模东西向和纬向运算的交替交互，结构上缓解长期积分中固有的离散化误差。对地球再分析5数据集的广泛实验表明，SOON建立了一个新的最先进的状态，显著优于现有方法在预测准确性和计算效率方面。
英文摘要：
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. Despite advances in geometry-aware representations, existing methods do not specify how the zonal and meridional interactions that govern real atmospheric dynamics should be explicitly coordinated and composed, leaving an important architectural gap for S2S forecasting. In this paper, we propose AnisoCast, which explicitly models anisotropic global atmospheric dynamics for accurate 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) stacked Aniso Blocks that arrange latent Zonal and Meridional Operators in a weight-shared palindromic composition inspired by symmetric operator splitting. Extensive experiments on the ERA5 reanalysis dataset demonstrate that AnisoCast establishes a new state-of-the-art, significantly outperforming existing methods in both forecasting accuracy and computational efficiency.
