Hydrology and Earth System Sciences
Hydrology and Earth System Sciences
HESS
Articles | Volume 27, issue 10
Articles | Volume 27, issue 10
https://doi.org/10.5194/hess-27-1945-2023
© Author(s) 2023. This work is distributed under
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/hess-27-1945-2023
© Author(s) 2023. This work is distributed under
the Creative Commons Attribution 4.0 License.
Articles | Volume 27, issue 10
Research article
 | 
22 May 2023
Research article |  | 22 May 2023

Statistical post-processing of precipitation forecasts using circulation classifications and spatiotemporal deep neural networks

Tuantuan Zhang, Zhongmin Liang, Wentao Li, Jun Wang, Yiming Hu, and Binquan Li

Viewed

Total article views: 2,871 (including HTML, PDF, and XML)
HTML PDF XML Total Supplement BibTeX EndNote
2,199 585 87 2,871 169 61 59
  • HTML: 2,199
  • PDF: 585
  • XML: 87
  • Total: 2,871
  • Supplement: 169
  • BibTeX: 61
  • EndNote: 59
Views and downloads (calculated since 02 Jan 2023)
Cumulative views and downloads (calculated since 02 Jan 2023)

Viewed (geographical distribution)

Total article views: 2,871 (including HTML, PDF, and XML) Thereof 2,762 with geography defined and 109 with unknown origin.
Country # Views %
  • 1
1
 
 
 
 

Cited

Latest update: 13 Nov 2024
Download
Short summary
We use circulation classifications and spatiotemporal deep neural networks to correct raw daily forecast precipitation by combining large-scale circulation patterns with local spatiotemporal information. We find that the method not only captures the westward and northward movement of the western Pacific subtropical high but also shows substantially higher bias-correction capabilities than existing standard methods in terms of spatial scale, timescale, and intensity.
Read more