Abstract Accurate radar echo extrapolation is critical for short‐term weather forecasting, yet existing deep learning methods often suffer from echo ambiguity, intensity decay, and insufficient global context utilization. To address these limitations, this paper proposes Global‐Frequency Spatiotemporal Long Short‐Term Memory ( GFST‐LSTM), a novel model that integrates a global attention mechanism and Fourier convolutional modules into the Spatiotemporal LSTM ( ST‐LSTM) architecture. The attention module dynamically weights multi‐scale spatiotemporal features by enhancing channel and spatial correlations, while the Fourier convolution module captures global periodic patterns via frequency‐domain transformations. Evaluated on the Moving Modified National Institute of Standards and Technology database (Moving MNIST) benchmark and Jiangsu Province radar data sets (2019–2021), GFST‐LSTM achieves a 22.9% improvement in Critical Success Index and 13.1% in Heidke Skill Score over Predictive Recurrent Neural Network at the 40 dBZ threshold. Notably, it excels in preserving strong echo regions during 60–120 min predictions, reducing positional bias by 6.6% compared to the Motion Gated Recurrent Unit (MotionGRU). Ablation studies confirm the synergistic effect of both modules, with the full model outperforming variants that lack either component.
Wu et al. (2026) studied this question.