Geoscientific Model Development
Geoscientific Model Development
GMD
Articles | Volume 16, issue 10
Articles | Volume 16, issue 10
https://doi.org/10.5194/gmd-16-2737-2023
© Author(s) 2023. This work is distributed under
the Creative Commons Attribution 4.0 License.
Special issue:
https://doi.org/10.5194/gmd-16-2737-2023
© Author(s) 2023. This work is distributed under
the Creative Commons Attribution 4.0 License.
Articles | Volume 16, issue 10
Model experiment description paper
 | 
23 May 2023
Model experiment description paper |  | 23 May 2023

CLGAN: a generative adversarial network (GAN)-based video prediction model for precipitation nowcasting

Yan Ji, Bing Gong, Michael Langguth, Amirpasha Mozaffari, and Xiefei Zhi

Viewed

Total article views: 2,941 (including HTML, PDF, and XML)
HTML PDF XML Total BibTeX EndNote
2,197 663 81 2,941 75 68
  • HTML: 2,197
  • PDF: 663
  • XML: 81
  • Total: 2,941
  • BibTeX: 75
  • EndNote: 68
Views and downloads (calculated since 14 Nov 2022)
Cumulative views and downloads (calculated since 14 Nov 2022)

Viewed (geographical distribution)

Total article views: 2,941 (including HTML, PDF, and XML) Thereof 2,848 with geography defined and 93 with unknown origin.
Country # Views %
  • 1
1
 
 
 
 

Cited

Latest update: 26 Dec 2024
Download
Short summary
Formulating short-term precipitation forecasting as a video prediction task, a novel deep learning architecture (convolutional long short-term memory generative adversarial network, CLGAN) is proposed. A benchmark dataset is built on minute-level precipitation measurements. Results show that with the GAN component the model generates predictions sharing statistical properties with observations, resulting in it outperforming the baseline in dichotomous and spatial scores for heavy precipitation.
Read more
Special issue
Benchmark datasets and machine learning algorithms for Earth...