Regional, automated meteorological networks, such as the Oklahoma Mesonet can potentially provide high quality forcing data for generating gridded surfaces, but proven methods of interpolating weather variables between the station locations are needed. We compared two interpolation methods, ordinary kriging (OK) and empirical Bayesian kriging (EBK), with and without using long‐term climate imprints (CI), for creating spatially continuous, daily weather datasets. Daily meteorological variables (maximum and minimum temperature, solar radiation, and precipitation) from the Oklahoma Mesonet for the period 1997–2014 were interpolated using geoprocessing tools in ArcGIS. Cross‐validation was used for evaluation of interpolation methods, with 90% of sites chosen randomly for the training set and the remaining 10% left for validation. For all interpolation approaches, cross‐validation showed coefficient of determination (
This content will become publicly available on August 1, 2024
- Award ID(s):
- 2033607
- NSF-PAR ID:
- 10451725
- Date Published:
- Journal Name:
- Remote Sensing
- Volume:
- 15
- Issue:
- 15
- ISSN:
- 2072-4292
- Page Range / eLocation ID:
- 3844
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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