Articles | Volume 17, issue 1
https://doi.org/10.5194/gmd-17-399-2024
https://doi.org/10.5194/gmd-17-399-2024
Model description paper
 | 
16 Jan 2024
Model description paper |  | 16 Jan 2024

GAN-argcPredNet v2.0: a radar echo extrapolation model based on spatiotemporal process enhancement

Kun Zheng, Qiya Tan, Huihua Ruan, Jinbiao Zhang, Cong Luo, Siyu Tang, Yunlei Yi, Yugang Tian, and Jianmei Cheng

Viewed

Total article views: 1,481 (including HTML, PDF, and XML)
HTML PDF XML Total Supplement BibTeX EndNote
1,000 423 58 1,481 33 43 57
  • HTML: 1,000
  • PDF: 423
  • XML: 58
  • Total: 1,481
  • Supplement: 33
  • BibTeX: 43
  • EndNote: 57
Views and downloads (calculated since 16 Jan 2023)
Cumulative views and downloads (calculated since 16 Jan 2023)

Viewed (geographical distribution)

Total article views: 1,481 (including HTML, PDF, and XML) Thereof 1,407 with geography defined and 74 with unknown origin.
Country # Views %
  • 1
1
 
 
 
 

Cited

Latest update: 14 Oct 2024
Download
Short summary
Radar echo extrapolation is the common method in precipitation nowcasting. Deep learning has potential in extrapolation. However, the existing models have low prediction accuracy for heavy rainfall. In this study, the prediction accuracy is improved by suppressing the blurring effect of rain distribution and reducing the negative bias. The results show that our model has better performance, which is useful for urban operation and flood prevention.