STCast: Adaptive Boundary Alignment for Global and Regional Weather Forecasting

• URL: http://arxiv.org/abs/2509.25210v1
• Date: Sun, 21 Sep 2025 05:27:52 GMT
• Title: STCast: Adaptive Boundary Alignment for Global and Regional Weather Forecasting
• Authors: Hao Chen, Tao Han, Jie Zhang, Song Guo, Lei Bai,
• Abstract summary: We propose a novel AI-driven framework for adaptive regional boundary optimization and dynamic monthly forecast allocation.<n>Specifically, our approach employs a Spatial-Aligned Attention (SAA) mechanism, which aligns global and regional spatial distributions.<n>We also design a Temporal Mixture-of-Experts (TMoE) module, where atmospheric variables from distinct months are dynamically routed to specialized experts.
• Score: 35.64570745321468
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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.

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