Interpolation of Temperature in a Mountainous Region Using Heterogeneous Observation Networks

Abstract

Accurately generating high-resolution surface grid datasets often involves merging multiple weather observation networks and addressing the challenge of network heterogeneity. This study aims to tackle the problem of accurately interpolating temperature data in regions with a complex topography. To achieve this, we introduce a deterministic interpolation method that incorporates elevation to enhance the accuracy of temperature datasets. This method is particularly valuable for areas with intricate terrains. Our robust methodology integrates a network harmonization method with radial basis function (RBF) interpolation for complex topographical regions. The method was tested on 10 min average temperature data from Jeju Island, South Korea, over 2 years that had a spatial resolution of 100 m. The results show a significant reduction of 5.5% in error rates, from an average of 0.73 °C to 0.69 °C, by incorporating all adjusted data. Integrating a parameterized nonlinear temperature profile further enhances accuracy, yielding an average reduction of 4.4% in error compared to the linear model. The spatial interpolation method, based on regression-based radial basis functions, demonstrates a 6.7% improvement over regression-based kriging for the same temperature profile. This research offers a valuable approach for precise temperature interpolation, especially in regions with a complex topography.

Keywords

• spatial interpolation
• radial basis function
• temperature inversion
• mountainous region
• merging networks