Geoscientific Model Development
Geoscientific Model Development
GMD
Articles | Volume 18, issue 10
Articles | Volume 18, issue 10
https://doi.org/10.5194/gmd-18-3003-2025
© Author(s) 2025. This work is distributed under
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/gmd-18-3003-2025
© Author(s) 2025. This work is distributed under
the Creative Commons Attribution 4.0 License.
Articles | Volume 18, issue 10
Development and technical paper
 | 
26 May 2025
Development and technical paper |  | 26 May 2025

A method for quantifying uncertainty in spatially interpolated meteorological data with application to daily maximum air temperature

Conor T. Doherty, Weile Wang, Hirofumi Hashimoto, and Ian G. Brosnan

Viewed

Total article views: 4,780 (including HTML, PDF, and XML)
HTML PDF XML Total Supplement BibTeX EndNote
3,345 799 636 4,780 74 125 214
  • HTML: 3,345
  • PDF: 799
  • XML: 636
  • Total: 4,780
  • Supplement: 74
  • BibTeX: 125
  • EndNote: 214
Views and downloads (calculated since 17 Jul 2024)
Cumulative views and downloads (calculated since 17 Jul 2024)

Viewed (geographical distribution)

Total article views: 4,780 (including HTML, PDF, and XML) Thereof 4,754 with geography defined and 26 with unknown origin.
Country # Views %
  • 1
1
 
 
 
 

Saved (final revised paper)

Latest update: 02 May 2026
Download
Short summary
We present, analyze, and validate a methodology for quantifying uncertainty in gridded meteorological data products produced by spatial interpolation. In a validation case study using daily maximum near-surface air temperature (Tmax), the method works well and produces predictive distributions with closely matching theoretical versus actual coverage levels. Application of the method reveals that the magnitude of uncertainty in interpolated Tmax varies significantly in both space and time.
Read more
Share