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

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Latest update: 12 Oct 2026
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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.
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