High-resolution air-temperature fields are essential for climate, hydrologic, and ecological applications in complex terrain, yet operational products often lack the spatial detail to resolve topographic effects. We develop an observation-driven reconstruction of daily air temperature fields for South Korea (2024) using ordinary kriging with lapse-rate correction (OKLR), integrating a dense network of over 500 stations from the Automatic Mountain Meteorology Observation System (AMOS) and the Automated Surface Observing System (ASOS). The OKLR framework systematically removes elevation-driven trends using a physically based fixed lapse rate (–6.5 °C km−1), performs kriging on detrended residuals, and reapplies Digital Elevation Model (DEM)-based corrections to generate high-fidelity daily fields at a 270 m grid spacing. Unlike numerical weather prediction (NWP) models that simulate atmospheric processes, this approach reconstructs spatially continuous fields directly from dense in situ observations, ensuring empirical grounding. Extensive daily spatial cross-validation (n = 37,813) demonstrates that OKLR (MAE = 0.656 °C) significantly outperforms elevation-unadjusted ordinary kriging by ≈37% and the operational 1.5 km LDAPS product (MAE = 0.895 °C) by 27%. This performance gain is particularly pronounced in high-elevation zones (>700 m) and natural surfaces (≈73% of the study area), where topographic complexity is greatest. The final observation-constrained reconstruction attains a robust MAE of 0.462 °C with near-zero bias over 188,318 station–days. As the first nationwide daily temperature dataset for South Korea at 270 m resolution, this study provides a critical foundation for precision agriculture, ecosystem monitoring, and climate change adaptation in topographically diverse environments.
Youn et al. (Thu,) studied this question.