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https://doi.org/10.5194/egusphere-2025-3429
https://doi.org/10.5194/egusphere-2025-3429
25 Aug 2025
 | 25 Aug 2025

Intra-city Scale Graph Neural Networks Enhance Short-term Air Temperature Forecasting

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

Abstract. Air temperature (Ta) has critical implications for various socioeconomic sectors, yet its dynamics are particularly complex in urban areas due to heterogeneous built environments, landscapes, and diverse anthropogenic activities. Physics-based models struggle with intra-city Ta forecasts due to inadequate urban representation and limited spatial resolution. While weather observation networks offer promising alternatives for direct local Ta modeling, an effective framework to leverage these intra-city data remains lacking. Here, we demonstrate that graph neural networks (GNNs) can harness observation network information to refine Ta prediction at individual locations and elucidate underlying mechanisms. Our novel Mix-n-Scale framework with GNNs achieves over 12 % improvement in short-term Ta forecasts compared to conventional time-series approaches. Further model evaluation disentangles performance variations with local Ta variability in diverse spatiotemporal contexts, indicating distinct patterns of intra-city heterogeneity across seasonal and diurnal scales. Our findings establish graph-based approaches for leveraging proliferating urban sensor data and advancing understanding of Ta spatiotemporal dynamics in complex urban environments.

Competing interests: The authors declare no conflicts of interest relevant to this study.

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

Status: final response (author comments only)

Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor | : Report abuse
  • RC1: 'Comment on egusphere-2025-3429', Anonymous Referee #2, 16 Sep 2025
  • RC2: 'Comment on egusphere-2025-3429', Yuanjian Yang, 04 Oct 2025
Han Wang, Jianheng Tang, Jize Zhang, and Jiachuan Yang
Han Wang, Jianheng Tang, Jize Zhang, and Jiachuan Yang

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Short summary
Air temperature (Ta) varies substantially within cities, yet physics-based models often struggle to capture this fine-scale variability. We demonstrate that a deep learning graph-based approach—leveraging observational sensor networks—can significantly enhance short-term Ta forecasting by refining local amplitude patterns. Furthermore, we introduce an automated framework to streamline graph construction. This study also demonstrates the distinct spatiotemporal dynamics of intra-city Ta patterns.
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