Atmospheric Chemistry and Physics
Atmospheric Chemistry and Physics
ACP
Articles | Volume 26, issue 2
Articles | Volume 26, issue 2
https://doi.org/10.5194/acp-26-947-2026
© Author(s) 2026. This work is distributed under
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/acp-26-947-2026
© Author(s) 2026. This work is distributed under
the Creative Commons Attribution 4.0 License.
Articles | Volume 26, issue 2
Research article
 | 
21 Jan 2026
Research article |  | 21 Jan 2026

Intra-city scale graph neural networks enhance short-term air temperature forecasting

Han Wang, Jianheng Tang, Jize Zhang, and Jiachuan Yang

Viewed

Total article views: 8,696 (including HTML, PDF, and XML)
HTML PDF XML Total Supplement BibTeX EndNote
6,860 1,525 311 8,696 486 335 358
  • HTML: 6,860
  • PDF: 1,525
  • XML: 311
  • Total: 8,696
  • Supplement: 486
  • BibTeX: 335
  • EndNote: 358
Views and downloads (calculated since 25 Aug 2025)
Cumulative views and downloads (calculated since 25 Aug 2025)

Viewed (geographical distribution)

Total article views: 8,696 (including HTML, PDF, and XML) Thereof 8,606 with geography defined and 90 with unknown origin.
Country # Views %
  • 1
1
 
 
 
 

Saved (final revised paper)

Latest update: 02 Nov 2026
Download
Short summary
Air temperature (Ta) varies substantially within cities, yet physics-based models struggle to capture this fine-scale variability. We demonstrate that a graph-based approach – leveraging spatial information from sensor networks – can significantly enhance Ta forecasting at individual locations. Furthermore, we introduce an automated framework to streamline graph construction and model implementation. This study also demonstrates the distinct spatiotemporal dynamics of intra-city Ta patterns.
Read more
Share
Altmetrics
Final-revised paper
Preprint