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Joint Intensity and Spatio-Temporal Representation Learning for Extreme Precipitation Nowcasting
Pan, Zefeng Hang, Renlong Liu, Qingshan Shi, Chunxiang Xu, Zhiqiang Yuan, Xiao-Tong
Pan, Zefeng
Hang, Renlong
Liu, Qingshan
Shi, Chunxiang
Xu, Zhiqiang
Yuan, Xiao-Tong
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Department
Machine Learning
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Type
Journal article
Date
2025
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Language
English
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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 multi-temporal 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 (JISTRL). 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 (CSI) at the highest threshold, and an average improvement of 81.7% in Structural Similarity (SSIM).
Citation
Z. Pan, R. Hang, Q. Liu, C. Shi, Z. Xu and X. -T. Yuan, "Joint Intensity and Spatio-Temporal Representation Learning for Extreme Precipitation Nowcasting," in IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, doi: 10.1109/JSTARS.2025.3590059
Source
IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing
Conference
Keywords
Extreme precipitation nowcasting, intensity features, spatio-temporal coherence
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Source
Publisher
IEEE