TPDTC-Net: A Decoupled Spatial-Temporal Network for Precipitation Nowcasting

Precipitation nowcasting is a highly challenging task in weather forecasting and plays a crucial role in protecting lives and property. However, autoregressive methods encounter training difficulties and are prone to error accumulation, whereas non-autoregressive models struggle to effectively utilize temporal information. To address these issues, this paper proposes a novel decoupled spatiotemporal network, TPDTC-Net, specifically for precipitation nowcasting. TPDTC-Net introduces an encoder–temporal predictor–decoder architecture based on a non-autoregressive model to prevent error accumulation, combining a time predictor and an adaptive dynamic weighting module that integrates the strengths of Transformer and convolutional neural networks, thereby improving the accuracy of precipitation nowcasting. From the spatial perspective, TPDTC-Net utilizes a multi-scale self-attention module in the encoder to extract global spatial features and employs a multiplicative convolution module to capture detailed features. In particular, the proposed adaptive dynamic weighting module effectively combines global and local features, allowing the encoder to dynamically adjust the weights based on different inputs and scenarios while learning feature fusion strategies. This enhances the supplementary role of convolution-extracted detail features to the global features extracted by the self-attention mechanism. From the temporal perspective, the time predictor adopts a Fourier self-attention mechanism and reshapes the feature maps passed from the encoder into abstract multivariate time series prediction tasks. By transforming unordered temporal information into ordered sequences, the time predictor effectively captures temporal dependencies, enabling the decoder to generate more accurate predictions. Extensive experiments on benchmark datasets show that TPDTC-Net significantly outperforms state-of-the-art networks in precipitation nowcasting. Specifically, on the KNMI dataset, compared to the second-best baseline model LPT-QPN ( r ≥ 10), the critical success index (CSI) and Heidke skill score (HSS) of TPDTC-Net increase by 10.35% and 9.37%, respectively. Besides, the balanced mean square error (BMSE) and balanced mean absolute error (BMAE) of TPDTC-Net decrease to 15.4787 and 1.1535. Similarly, on the CIKM AnalytiCup 2017 dataset, TPDTC-Net also delivers the best performance, with comparable performance trends observed. These results demonstrate the superior performance in terms of prediction accuracy of the proposed TPDTC-Net.