Hydrology and Earth System Sciences
Hydrology and Earth System Sciences
HESS
Articles | Volume 26, issue 19
Articles | Volume 26, issue 19
https://doi.org/10.5194/hess-26-4975-2022
© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/hess-26-4975-2022
© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.
Articles | Volume 26, issue 19
Research article
 | 
10 Oct 2022
Research article |  | 10 Oct 2022

Probabilistic subseasonal precipitation forecasts using preceding atmospheric intraseasonal signals in a Bayesian perspective

Yuan Li, Zhiyong Wu, Hai He, and Hao Yin

Viewed

Total article views: 3,089 (including HTML, PDF, and XML)
HTML PDF XML Total Supplement BibTeX EndNote
2,258 751 80 3,089 94 89 108
  • HTML: 2,258
  • PDF: 751
  • XML: 80
  • Total: 3,089
  • Supplement: 94
  • BibTeX: 89
  • EndNote: 108
Views and downloads (calculated since 03 Mar 2022)
Cumulative views and downloads (calculated since 03 Mar 2022)

Viewed (geographical distribution)

Total article views: 3,089 (including HTML, PDF, and XML) Thereof 2,956 with geography defined and 133 with unknown origin.
Country # Views %
  • 1
1
 
 
 
 
Latest update: 23 Nov 2025
Download
Short summary
The relationship between atmospheric intraseasonal signals and precipitation is highly uncertain and depends on the region and lead time. In this study, we develop a spatiotemporal projection, based on a Bayesian hierarchical model (STP-BHM), to address the above challenge. The results suggest that the STP-BHM model is skillful and reliable for probabilistic subseasonal precipitation forecasts over China during the boreal summer monsoon season.
Read more
Share