Constructing Gridded Daily Oklahoma Mesonet DATA for AGRO-Hydrological Applications.

Gridded models, such as crop models and hydrologic models require meteorological forcing data with matching spatio-temporal resolution and coverage. Regional, automated meteorological networks, such as the Oklahoma Mesonet, can potentially provide high quality forcing data for gridded models, but proven methods of interpolating weather variables between the station locations are needed. The objective of this study was to compare four interpolation methods for creating daily gridded weather datasets for agro-hydrological applications. Daily meteorological variables from the Oklahoma Mesonet for the period 1997-2014 were interpolated using geoprocessing tool in ArcGIS with python as the scripting language. Ordinary kriging (OK) and empirical Bayesian kriging (EBK) with and without the use of climate imprints (CI) were compared. Cross-validation metrics for all interpolation approaches showed R² values of 0.99 and 0.98 for maximum (TMAX) and minimum (TMIN), with mean absolute error (MAE) ranging from ±0.45–0.50°C for TMAX and ±0.77–0.80°C for TMIN. Likewise, for SRAD R² values of 0.94 and 0.93 showed overall good prediction accuracy with MAE values 1.00 MJ m^-2 d^-1 and 1.01 MJ m^-2 d^-1 for EBK and OK respectively. However, for rainfall, all methods yielded R² values ≤0.67, suggesting a need for more effective interpolation methods. Therefore, based in its lower computational time and lower input data requirement, OK appears preferable to the other approaches tested here to meet the daily weather data for gridded models in Oklahoma and other regions with similar monitoring networks.