FourCastLSTM: A precipitation nowcasting model integrating global and local spatiotemporal features

Abstract

Accurate precipitation nowcasting is crucial for transportation, agriculture, urban planning, and tourism, and it is highly beneficial in disaster prevention, resource allocation, and service optimization. Existing precipitation nowcasting methods often integrate convolution neural networks and recurrent neural networks or employ vision transformers to capture spatiotemporal correlations. However, convolutional operators struggle to capture global information, and vision transformers based global modeling may overemphasize heavy rainfall while neglecting moderate and light precipitation. In this study, Fourier nowCasting LSTM (FourCastLSTM) is introduced to effectively capture and fusion spatiotemporal global and local features of precipitation, enhancing prediction accuracy for different precipitation intensities. A Fourier nowCasting LSTM Cell (FourCastCell), which combine the Adaptive Fourier Neural Operator (AFNO) with a simplified LSTM, is proposed to reinforce the representation of global spatiotemporal precipitation patterns by replacing traditional convolutional layers with AFNO. An Image Detail Enhancement module (IDE) is adopted to strengthen local precipitation detail features by integrating difference convolutional neural network. Finally, the adaptive feature fusion module embedded in the IDE, can dynamically adjust the integration weights of global and local features based on the specific spatiotemporal features of precipitation events, ensuring a balanced fusion of features with different intensities. Experiments on synthetic datasets (MovingMNIST++) and real-world datasets (RadarCIKM) demonstrate that the proposed FourCastLSTM outperforms state-of-the-art approaches by 15.6 % and 9.6 % in B-MAE and B-MSE metrics, respectively.