Articles | Volume 28, issue 24
https://doi.org/10.5194/hess-28-5401-2024
https://doi.org/10.5194/hess-28-5401-2024
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: 741 (including HTML, PDF, and XML)
HTML PDF XML Total BibTeX EndNote
552 139 50 741 27 26
  • HTML: 552
  • PDF: 139
  • XML: 50
  • Total: 741
  • BibTeX: 27
  • EndNote: 26
Views and downloads (calculated since 23 Apr 2024)
Cumulative views and downloads (calculated since 23 Apr 2024)

Viewed (geographical distribution)

Total article views: 741 (including HTML, PDF, and XML) Thereof 746 with geography defined and -5 with unknown origin.
Country # Views %
  • 1
1
 
 
 
 
Latest update: 17 Dec 2024
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.