Articles | Volume 18, issue 10
https://doi.org/10.5194/gmd-18-3003-2025
https://doi.org/10.5194/gmd-18-3003-2025
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

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

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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.
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