A Hybrid Transformer-CNN Model for Interpolating Meteorological Data on the Tibetan Plateau

High-quality observational data play a crucial role in deepening the investigation of the Tibetan Plateau’s influence on the Asian climate. This study employs eight machine learning models (support vector regression (SVR), k-nearest neighbors (KNN), extreme gradient boosting (XGBoost), random forest (RF), long short-term memory (LSTM), gated recurrent unit (GRU), Transformer, and Transformer–convolutional neural network (Transformer-CNN)) to interpolate missing observational data on surface net radiation (Rn), soil surface temperature (Ts), soil water content (SWC), air temperature (Ta), relative humidity (RH), and wind speed (WS) from the QOMS observation site. The data covers the period from 1 January 2007 through to 31 December 2016. A comparative evaluation of these models shows that the Transformer-CNN model consistently outperforms the other models in terms of prediction accuracy. On the test dataset, the coefficients of determination for the interpolated results of Ta, RH, WS, SWC, Ts, and Rn were 0.97, 0.92, 0.97, 0.79, 0.93, and 0.98, respectively. Secondly, the Transformer-CNN model was then applied to generate a complete meteorological dataset for the full period. A time series analysis of this dataset reveals statistically significant trends over the past decade: air temperature (Ta) increased by 0.60 °C (p = 0.022) and soil temperature (Ts) by 1.85 °C (p = 1.37 × 10−5). Meanwhile, wind speed (WS), soil water content (SWC), and net radiation (Rn) declined by 0.42 m/s (p = 1.18 × 10−12), 1.24% (p < 0.001), and 9.21 W/m2 (p = 8.81 × 10−6), respectively.