<![CDATA[Accurate air temperature monitoring is essential for understanding climate dynamics and microclimates, particularly in regions with diverse topography. The limited number of observation stations often results in data that do not fully represent actual conditions. To address this gap, combining in-situ measurements with ERA5-Land reanalysis presents a promising alternative, although ERA5-Land may still exhibit biases in mountainous or urban areas. This study applies Kriging with External Drift (KED) to improve temperature estimation, focusing on identifying the most suitable semivariogram model. Daily and monthly analyses were conducted, with performance evaluated using RMSE, MAE, and MSE. The results indicate that the Spherical model consistently performs best for average and maximum temperatures, while the Exponential model provides better estimates for minimum temperature at the daily scale, and the Linear model at the monthly scale. These findings demonstrate that KED can significantly enhance temperature estimation in areas with sparse observations, while also highlighting the most reliable semivariogram models for different temperature parameters.]]>
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