Articles | Volume 15, issue 13
Geosci. Model Dev., 15, 5407–5419, 2022
Geosci. Model Dev., 15, 5407–5419, 2022
Development and technical paper
15 Jul 2022
Development and technical paper | 15 Jul 2022

RAP-Net: Region Attention Predictive Network for precipitation nowcasting

Zheng Zhang et al.

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Cited articles

Alibaba Cloud: CIKM AnalytiCup2017 competition, Alibaba Cloud [data set],, last access: 2022. a
Ayzel, G., Scheffer, T., and Heistermann, M.: RainNet v1.0: a convolutional neural network for radar-based precipitation nowcasting, Geosci. Model Dev., 13, 2631–2644,, 2020. a
Chai, Z., Yuan, C., Lin, Z., and Bai, Y.: CMS-LSTM: Context-Embedding and Multi-Scale Spatiotemporal-Expression LSTM for Video Prediction, arXiv [preprint],, April 2022. a
Fu, J., Liu, J., Tian, H., Li, Y., Bao, Y., Fang, Z., and Lu, H.: Dual attention network for scene segmentation, in: CVPR, pp. 3146–3154,, 2019. a
Guen, V. L. and Thome, N.: Disentangling physical dynamics from unknown factors for unsupervised video prediction, in: CVPR, pp. 11474–11484,, 2020.​​​​​​​ a
Short summary
In this paper, we develop a model to predict radar echo sequences and apply it in the precipitation nowcasting field. Different from existing models, we propose two new attention modules. By introducing them, the performance of RAP-Net outperforms other models, especially in those regions with moderate and heavy rainfall. Considering that these regions cause more threats to human activities, the research in our work is significant for preventing natural disasters caused by heavy rainfall.