动态优化气象区域边界，提升全球与局部天气预测精度
STCast: Adaptive Boundary Alignment for Global and Regional Weather Forecasting
- 用空间对齐注意力机制自适应调整区域边界
- 按月动态分配专家模型，捕捉不同季节气候特征
- 适用于极端天气与集合预报，效果优于现有方法
为获得更精细的区域天气预报，现有方法多从全球大气中整合区域信息，如物理方法求解边界方程，或数据驱动方法直接裁剪全球预报。然而，这些方法常受限于静态且不精确的区域边界，导致泛化能力差。为此，我们提出STCast，一种新型的AI驱动框架，实现自适应区域边界优化与动态月度预报分配。具体地，采用空间对齐注意力（SAA）机制，对齐全球与区域空间分布以初始化边界，并基于注意力生成的对齐模式自适应优化边界；同时设计时间混合专家（TMoE）模块，利用离散高斯分布将不同月份的大气变量动态路由至专用专家，增强模型对时间模式的捕捉能力。除全局与区域预报外，还在极端事件预测与集合预报任务上评估了STCast。实验结果表明，在四项任务中均持续优于当前最优方法。代码：https://github.com/chenhao-zju/STCast
原文摘要 · Abstract (English)
To gain finer regional forecasts, many works have explored the regional integration from the global atmosphere, e.g., by solving boundary equations in physics-based methods or cropping regions from global forecasts in data-driven methods. However, the effectiveness of these methods is often constrained by static and imprecise regional boundaries, resulting in poor generalization ability. To address this issue, we propose Spatial-Temporal Weather Forecasting (STCast), a novel AI-driven framework for adaptive regional boundary optimization and dynamic monthly forecast allocation. Specifically, our approach employs a Spatial-Aligned Attention (SAA) mechanism, which aligns global and regional spatial distributions to initialize boundaries and adaptively refines them based on attention-derived alignment patterns. Furthermore, we design a Temporal Mixture-of-Experts (TMoE) module, where atmospheric variables from distinct months are dynamically routed to specialized experts using a discrete Gaussian distribution, enhancing the model's ability to capture temporal patterns. Beyond global and regional forecasting, we evaluate our STCast on extreme event prediction and ensemble forecasting. Experimental results demonstrate consistent superiority over state-of-the-art methods across all four tasks. Code: https://github.com/chenhao-zju/STCast
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