A Short-Long Term Sequence Learning Network for Precipitation Nowcasting

Precipitation nowcasting is a critical task for various applications, such as disaster mitigation, water resource management, traffic safety, and agricultural planning. In recent years, deep learning methods equipped with long short-term memory (LSTM) have become a mainstream method for this task. Typically, these methods take as input a radar echo sequence and focus on learning the temporal features of precipitation. However, due to the limited spatial modeling ability of the current LSTM modules, they cannot sufficiently learn the spatial–temporal features of precipitation. To address this issue, we propose in this article a short-long term sequence learning (SLTSL) network. SLTSL mainly consists of the short-term sequence learning module (SSLM) and the long-term sequence learning module (LSLM). SSLM weights and integrates the spatial distribution features of precipitation at each location of the short-term sequence by various weighted operations, where the short-term sequence is obtained by SSLM through matrix concatenation of the feature maps of four adjacent moments. LSLM integrates all feature maps into a long-term sequence through matrix fusion and then captures the temporal features of precipitation at all moments from the long-term sequence by means of multistate transitions and aggregation. In order to test the performance of the proposed network, we carry out experiments on three widely used datasets, including RadarCIKM, TAASRAD19, and RadarKNMI. The experimental results demonstrate that our proposed network can achieve superior or comparable performance to several state-of-the-art baseline methods.