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[2509.25210] STCast: Adaptive Boundary Alignment for Global and Regional Weather Forecasting
Summary
The paper introduces STCast, an AI-driven framework for adaptive boundary alignment in weather forecasting, enhancing regional forecasts through innovative spatial and temporal mechanisms.
Why It Matters
Accurate weather forecasting is crucial for disaster preparedness and resource management. STCast addresses limitations in current forecasting methods by optimizing regional boundaries, potentially improving prediction accuracy for extreme weather events.
Key Takeaways
- STCast employs a Spatial-Aligned Attention mechanism for boundary optimization.
- The Temporal Mixture-of-Experts module enhances the model's ability to capture seasonal atmospheric patterns.
- Experimental results show STCast outperforms existing forecasting methods in multiple tasks.
Computer Science > Machine Learning arXiv:2509.25210 (cs) [Submitted on 21 Sep 2025 (v1), last revised 23 Feb 2026 (this version, v2)] Title:STCast: Adaptive Boundary Alignment for Global and Regional Weather Forecasting Authors:Hao Chen, Tao Han, Jie Zhang, Song Guo, Lei Bai View a PDF of the paper titled STCast: Adaptive Boundary Alignment for Global and Regional Weather Forecasting, by Hao Chen and 4 other authors View PDF HTML (experimental) 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 m...