Weather Foundation Models (WFMs) have recently attracted significant attention for their exceptional performance and inference efficiency in global-scale weather forecasting. However, their coarse spatial resolution and inherent biases constrain their utility for station-level forecasting, which is crucial for applications such as renewable energy management and aviation safety. To address these limitations, we propose the Adaptive Spatiotemporal Alignment Fusion Network (ASTAFN), a novel framework designed for accurate station-level weather forecasting through the synergistic integration of WFMs and station observations. ASTAFN incorporates two complementary data sources: (1) recent station observations, which offer fine-grained local trend information, and (2) WFM-generated forecasts, which provide broad-scale weather patterns. The core innovation of ASTAFN lies in its proxy station learning mechanism, which aligns the spatial structure and corrects the biases of WFMs relative to actual station data, facilitating the extraction of homogeneous spatiotemporal features from both sources. These features are dynamically fused at each forecasting step using an adaptive strategy, effectively compensating for WFM biases and enhancing predictive accuracy. Experimental evaluations on three real-world datasets demonstrate that ASTAFN reduces mean absolute error by 20%–35% compared to baseline WFMs for station-level wind speed forecasting. ASTAFN has been deployed on the regional station-level weather forecasting and analysis platform of the Chinese Academy of Meteorological Sciences, currently serving the Guangdong and Yunnan provinces in southern China.