What question did this study set out to answer?

The aim is to enhance meteorological data interpolation in complex terrains using a novel deep learning approach.

February 12, 2026Applied Sciences0 citationsOpen Access

Spatiotemporal Interpolation of Meteorological Fields in Complex Terrain Using Deep Graph Neural Networks

Key Points

The aim is to enhance meteorological data interpolation in complex terrains using a novel deep learning approach.
Developed a spatiotemporal model integrating diffusion graph convolution and residual learning.
Utilized data from 198 stations in Quanzhou, China, from 2020 to 2024.
Conducted ensemble experiments over 100 iterations to validate model performance.
DG model improves mountainous temperature predictions by 23.4% compared to KCN baseline.
Outperforms Ordinary Kriging in stability and accuracy.
Reduces ERA5 reanalysis bias by integrating historical station data.

Abstract

To address sparse meteorological data and the “smoothing effect” over complex terrain, this study proposes a spatiotemporal model based on a Diffusion Graph Convolutional Network (DG model). Focusing on Quanzhou, China, and using 2020–2024 data from 198 stations, the model integrates diffusion graph convolution and residual learning to capture nonlinear meteorological patterns. Ensemble experiments (100 iterations) demonstrate that the DG model significantly outperforms Ordinary Kriging and the KCN baseline in stability and accuracy. Specifically, it improves mountainous temperature prediction by 23.4% (40.0% vs. KCN) through terrain-adaptive weighting, effectively reproducing physical distribution characteristics. Furthermore, the model reduces inherent ERA5 reanalysis bias by integrating historical station data while maintaining background consistency. Validated against spatial-only (OSI) and temporal-only (OTI) variants, the DG model offers a robust approach for high-resolution meteorological reconstruction in complex terrain.

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