STGAF-Net: A Multi-Source Spatiotemporal Gated Adaptive Fusion Network for Wind Speed Nowcasting

Abstract

Accurate wind speed nowcasting is critical for wind-risk assessment, traffic management, and emergency response. However, existing deep learning methods still suffer from inadequate multi-source meteorological data fusion, multi-step error accumulation, and performance decay at high-wind-speed thresholds. To address these issues, this paper proposes STGAF-Net, a multi-source spatiotemporal gated adaptive fusion network. It uses radar reflectivity and surface wind speed as complementary inputs, employs collaborative local–global encoding to capture local details and long-range spatial dependencies, and adaptively integrates multi-source features through stage-wise gating. Temporally, non-recurrent spatiotemporal modeling dynamically adjusts the contribution of each historical time step based on sequence evolution and time intervals, enabling parallel prediction of future wind fields. This end-to-end framework effectively bolsters spatial structure preservation and long-range dependency modeling for severe winds. The experimental results demonstrate that STGAF-Net achieves the strongest overall performance among seven learning-based baselines and a non-learning persistence forecast on two datasets, particularly at high-wind-speed thresholds and at later lead times within the 2-h nowcasting horizon.