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

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

doi:https://doi.org/10.5194/gmd-18-3003-2025

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

Supplement

https://doi.org/10.5194/gmd-18-3003-2025-supplement

Data sets

Daymet: Station-Level Inputs and Cross-Validation for North America, Version 4 R1 M. M. Thornton et al. https://doi.org/10.3334/ORNLDAAC/2132

Elevation in the Western United States (90 meter DEM) S. E. Hanser https://www.sciencebase.gov/catalog/item/542aebf9e4b057766eed286a

Model code and software

Code for revision of "A Method for Quantifying Uncertainty in Spatially Interpolated Meteorological Data with Application to Daily Maximum Air Temperature" Conor T. Doherty https://doi.org/10.5281/zenodo.14602669

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 (T_max), 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 T_max varies significantly in both space and time.