Articles | Volume 18, issue 14
https://doi.org/10.5194/gmd-18-4625-2025
https://doi.org/10.5194/gmd-18-4625-2025
Development and technical paper
 | 
29 Jul 2025
Development and technical paper |  | 29 Jul 2025

Interpolating turbulent heat fluxes missing from a prairie observation on the Tibetan Plateau using artificial intelligence models

Quanzhe Hou, Zhiqiu Gao, Zexia Duan, and Minghui Yu

Data sets

Complete dataset of turbulent heat flux for the QOMS site Q. Hou and Z. Gao https://doi.org/10.5281/zenodo.10005741

Model code and software

Complete dataset of turbulent heat flux for the QOMS site Q. Hou and Z. Gao Gao, Zhiqiu https://doi.org/10.5281/zenodo.10005741

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Short summary
This study evaluates various machine learning and statistical methods for interpolating turbulent heat flux data over the Tibetan Plateau. The Transformer model showed the best performance, leading to the development of the Transformer_CNN model, which combines global and local attention mechanisms. Results show that Transformer_CNN outperforms the other models and was successfully applied to interpolate heat flux data from 2007 to 2016.
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