Hydrology and Earth System Sciences
Hydrology and Earth System Sciences
HESS
Articles | Volume 28, issue 24
Articles | Volume 28, issue 24
https://doi.org/10.5194/hess-28-5401-2024
© Author(s) 2024. This work is distributed under
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/hess-28-5401-2024
© Author(s) 2024. This work is distributed under
the Creative Commons Attribution 4.0 License.
Articles | Volume 28, issue 24
Research article
 | 
17 Dec 2024
Research article |  | 17 Dec 2024

Estimating global precipitation fields by interpolating rain gauge observations using the local ensemble transform Kalman filter and reanalysis precipitation

Yuka Muto and Shunji Kotsuki

Viewed

Total article views: 4,436 (including HTML, PDF, and XML)
HTML PDF XML Total BibTeX EndNote
3,092 1,146 198 4,436 171 311
  • HTML: 3,092
  • PDF: 1,146
  • XML: 198
  • Total: 4,436
  • BibTeX: 171
  • EndNote: 311
Views and downloads (calculated since 23 Apr 2024)
Cumulative views and downloads (calculated since 23 Apr 2024)

Viewed (geographical distribution)

Total article views: 4,436 (including HTML, PDF, and XML) Thereof 4,312 with geography defined and 124 with unknown origin.
Country # Views %
  • 1
1
 
 
 
 

Cited

Saved (final revised paper)

Latest update: 03 Sep 2026
Download
Short summary
It is crucial to improve global precipitation estimates to understand water-related disasters and water resources. This study proposes a new methodology to interpolate global precipitation fields from ground rain gauge observations using ensemble data assimilation and the precipitation of a numerical weather prediction model. Our estimates agree better with independent rain gauge observations than existing precipitation estimates, especially in mountainous or rain-gauge-sparse regions.
Read more
Share