Geoscientific Model Development
Geoscientific Model Development
GMD
Articles | Volume 18, issue 14
Articles | Volume 18, issue 14
https://doi.org/10.5194/gmd-18-4625-2025
© Author(s) 2025. This work is distributed under
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/gmd-18-4625-2025
© Author(s) 2025. This work is distributed under
the Creative Commons Attribution 4.0 License.
Articles | Volume 18, issue 14
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

Viewed

Total article views: 928 (including HTML, PDF, and XML)
HTML PDF XML Total Supplement BibTeX EndNote
661 223 44 928 3 46 54
  • HTML: 661
  • PDF: 223
  • XML: 44
  • Total: 928
  • Supplement: 3
  • BibTeX: 46
  • EndNote: 54
Views and downloads (calculated since 08 Jan 2024)
Cumulative views and downloads (calculated since 08 Jan 2024)

Viewed (geographical distribution)

Total article views: 928 (including HTML, PDF, and XML) Thereof 926 with geography defined and 2 with unknown origin.
Country # Views %
  • 1
1
 
 
 
 
Latest update: 01 Aug 2025
Download
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.
Read more
Share