Articles | Volume 26, issue 11
https://doi.org/10.5194/hess-26-2923-2022
https://doi.org/10.5194/hess-26-2923-2022
Research article
 | 
14 Jun 2022
Research article |  | 14 Jun 2022

Quantifying the uncertainty of precipitation forecasting using probabilistic deep learning

Lei Xu, Nengcheng Chen, Chao Yang, Hongchu Yu, and Zeqiang Chen

Viewed

Total article views: 2,790 (including HTML, PDF, and XML)
HTML PDF XML Total BibTeX EndNote
1,942 802 46 2,790 31 34
  • HTML: 1,942
  • PDF: 802
  • XML: 46
  • Total: 2,790
  • BibTeX: 31
  • EndNote: 34
Views and downloads (calculated since 25 Aug 2021)
Cumulative views and downloads (calculated since 25 Aug 2021)

Viewed (geographical distribution)

Total article views: 2,790 (including HTML, PDF, and XML) Thereof 2,534 with geography defined and 256 with unknown origin.
Country # Views %
  • 1
1
 
 
 
 

Cited

Latest update: 18 Apr 2024
Download
Short summary
Precipitation forecasting has potential uncertainty due to data and model uncertainties. Here, an integrated predictive uncertainty modeling framework is proposed by jointly considering data and model uncertainties through an uncertainty propagation theorem. The results indicate an effective predictive uncertainty estimation for precipitation forecasting, indicating the great potential for uncertainty quantification of numerous predictive applications.