Geoscientific Model Development
Geoscientific Model Development
GMD
Articles | Volume 15, issue 13
Articles | Volume 15, issue 13
https://doi.org/10.5194/gmd-15-5407-2022
© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/gmd-15-5407-2022
© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.
Articles | Volume 15, issue 13
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, Chuyao Luo, Shanshan Feng, Rui Ye, Yunming Ye, and Xutao Li

Viewed

Total article views: 2,833 (including HTML, PDF, and XML)
HTML PDF XML Total BibTeX EndNote
1,980 771 82 2,833 69 110
  • HTML: 1,980
  • PDF: 771
  • XML: 82
  • Total: 2,833
  • BibTeX: 69
  • EndNote: 110
Views and downloads (calculated since 11 Feb 2022)
Cumulative views and downloads (calculated since 11 Feb 2022)

Viewed (geographical distribution)

Total article views: 2,833 (including HTML, PDF, and XML) Thereof 2,633 with geography defined and 200 with unknown origin.
Country # Views %
  • 1
1
 
 
 
 
Latest update: 22 Nov 2025
Download
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.
Read more
Share