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

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Cited articles

Alabert, F.: The practice of fast conditional simulations through the LU decomposition of the covariance matrix, Math. Geol., 19, 369–386, https://doi.org/10.1007/BF00897191, 1987. 
Anderes, E. B.: Kriging, in: Encyclopedia of Environmetrics, edited by: El-Shaarawi, A. H. and Piegorsch, W. W., Wiley, https://doi.org/10.1002/9780470057339.vak003.pub2, 2012. 
Bezanson, J., Edelman, A., Karpinski, S., and Shah, V. B.: Julia: A fresh approach to numerical computing, SIAM Rev., 59, 65–98, 2017. 
Burke, M., Hsiang, S. M., and Miguel, E.: Global non-linear effect of temperature on economic production, Nature, 527, 235–239, https://doi.org/10.1038/nature15725, 2015. 
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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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