DFST-GAN: A Dynamic Flow Spatio-Temporal Generative Adversarial Network for High-Quality Precipitation Nowcasting

This paper proposes a Dynamic Flow Spatio-Temporal Generative Adversarial Network (DFST-GAN) model for high-quality precipitation nowcasting. Current spatio-temporal prediction models struggle with two key limitations: the inability to adaptively capture complex motion patterns and the tendency to generate blurry predictions over time. To address these challenges, DFST-GAN integrates a dynamic flow feature extraction mechanism with a novel specialized meteorological discriminator, enabling adaptive modeling of complex precipitation system trajectories and generating sharper, physically consistent predictions. We evaluate our approach on the HKO-7 dataset using metrics including CSI, HSS, POD, FAR and ETS. Experimental results demonstrate that DFST-GAN consistently outperforms existing methods across all evaluation metrics, with particularly notable improvements for moderate to heavy rainfall events (dBZ ≥ 50), showing a 18.8% relative improvement in CSI compared to PredRNN-V2. The ablation studies confirm that each component makes a meaningful contribution to overall performance, validating the potential of our approach for operational precipitation nowcasting applications.