Work

Joint Intensity and Spatio-Temporal Representation Learning for Extreme Precipitation Nowcasting

Year: 2025

Type: article

Abstract: As a result of global warming, the intensity and frequency of extreme precipitation events have increased, posing significant threats to human life and property. Currently, precipitation nowcasting methods based on long short-term memory (LSTM) or Vision Transformers (ViT) are becoming mainstream. While they accurately predict ordinary precipitation events, these methods often fall short in nowcasting extreme precipitation events. This discrepancy can be attributed to the substantially higher intensity of extreme precipitation relative to ordinary precipitation, which presents greater challenges for accurate nowcasting. However, the existing methods tend to inadequately weigh precipitation intensity features by only implicitly learning and modeling these features within the spatial distribution. To address this issue, we design an Intensity Trend Module (ITM) to explicitly learn and model the intensity features of extreme precipitation events. ITM employs intensity trend factors to capture the intensity features of extreme precipitation, thereby mitigating intensity misestimation. Moreover, the current architectures of LSTM and ViT often compromise the spatial structure of precipitation, thereby contributing to the inadequate performance in the nowcasting of extreme precipitation events. Hence, we also design a Spatio-Temporal Coherence Module (STCM), which exploits voxel flow to capture spatio-temporal coherence features. Subsequently, by processing multitemporal state features and integrating coherence features, STCM can preserve the spatio-temporal structure of extreme precipitation events. Finally, building upon ITM and STCM, we propose a framework of Joint Intensity and Spatio-Temporal Representation Learning for Extreme Precipitation Nowcasting. Experimental results indicate that our method is capable of nowcasting up to 4 hours. Across three datasets, our method surpasses state-of-the-art methods by achieving an average increase of 10.28% in the Critical Success Index at the highest threshold, and an average improvement of 81.7% in Structural Similarity.

Source: IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing

DOI: 10.1109/jstars.2025.3590059

Authors: Zefeng Pan, Renlong Hang, Qingshan Liu, Chunxiang Shi, Zhiqiang Xu, Xiao-Tong Yuan