the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Intra-city Scale Graph Neural Networks Enhance Short-term Air Temperature Forecasting
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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Status: final response (author comments only)
- RC1: 'Comment on egusphere-2025-3429', Anonymous Referee #2, 16 Sep 2025
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RC2: 'Comment on egusphere-2025-3429', Yuanjian Yang, 04 Oct 2025
This work demonstrated that graph neural networks (GNNs) can harnessobservation network information to refine Ta prediction at individual locations and elucidate underlying mechanisms. The topic is very interesting and has important implications in prediction of urban weather. The paper is well organized and written. The findings of this study are worth of publication in the ACP after minor revision as following:
1. The rolesof Urban morphology, local circulation, anthropogenic heat release and land use and their impacts on local climate in the Hong Kong should be compared with other regions, that is, can LOI defined in the present work be related to these local background factors? Just for wind or altitude?
Effects of anthropogenic heat release upon the urban climate in a Japanese megacity; Diurnal variation of amplified canopy urban heat island in Beijing megacity during heat wave periods: Roles of mountain-valley circulation and urban morphology; Simulating the Regional Impacts of Urbanization and Anthropogenic Heat Release on Climate across China;Unevenly spatiotemporal distribution of urban excess warming in coastal Shanghai megacity, China: Roles of geophysical environment, ventilation and sea breezes;Adjustment of the urbanization bias in surface air temperature series based on urban spatial morphologies and using machine learning; Impacts of Various Local Climate Zones on Canopy Urban Heat Island and Dry Island: Spatial Heterogeneity and Relative Contributions in Beijing2. About overfitting issues of machine learninglearning models, how did you address or aovid it?
3. Station ID can be removed in Figures 2 and 6b for clear show.
Citation: https://doi.org/10.5194/egusphere-2025-3429-RC2
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This study introduces a novel Mix-n-Scale framework with GNNs for short-term air temperature (Ta) forecasting at the intra-city scale. The authors convincingly demonstrate that incorporating spatial interactions improves forecast skill, achieving more than a 12 % reduction in RMSE compared with a conventional LSTM-only baseline. They provide a thorough evaluation of model performance and present well-structured analyses. Overall, this is a solid paper and is a meaningful contribution with clear potential to advance short-term forecasting in urban areas. However, there are several areas where additional experiment and discussion would strengthen the manuscript. I would support publication after minor revisions.
Major comments:
Minor comments: