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.
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.
the Creative Commons Attribution 4.0 License.
Intra-city scale graph neural networks enhance short-term air temperature forecasting
State Key Laboratory of Climate Resilience for Coastal Cities, Department of Civil and Environmental Engineering, The Hong Kong University of Science and Technology, Hong Kong, China
Department of Civil and Environmental Engineering, The Hong Kong University of Science and Technology, Hong Kong, China
Jianheng Tang
Division of Emerging Interdisciplinary Areas, The Hong Kong University of Science and Technology, Hong Kong, China
Jize Zhang
Department of Civil and Environmental Engineering, The Hong Kong University of Science and Technology, Hong Kong, China
State Key Laboratory of Climate Resilience for Coastal Cities, Department of Civil and Environmental Engineering, The Hong Kong University of Science and Technology, Hong Kong, China
Department of Civil and Environmental Engineering, The Hong Kong University of Science and Technology, Hong Kong, China
Cited articles
Arnfield, A. J.: Two decades of urban climate research: a review of turbulence, exchanges of energy and water, and the urban heat island, International Journal of Climatology, 23, 1–26, https://doi.org/10.1002/joc.859, 2003.
Bauer, T. J.: Interaction of Urban Heat Island Effects and Land–Sea Breezes during a New York City Heat Event, Journal of Applied Meteorology and Climatology, 59, 477–495, https://doi.org/10.1175/JAMC-D-19-0061.1, 2020.
Bergstra, J., Bardenet, R., Bengio, Y., and Kégl, B.: Algorithms for Hyper-Parameter Optimization, Advances in Neural Information Processing Systems, 24, https://papers.nips.cc/paper/4443-algorithms-for-hyper-parameter-optimization (last access: 14 January 2026), 2011.
Bi, K., Xie, L., Zhang, H., Chen, X., Gu, X., and Tian, Q.: Accurate medium-range global weather forecasting with 3D neural networks, Nature, 619, 533–538, https://doi.org/10.1038/s41586-023-06185-3, 2023.
Brody, S., Alon, U., and Yahav, E.: How Attentive are Graph Attention Networks?, in: International Conference on Learning Representations, https://openreview.net/forum?id=F72ximsx7C1 (last access: 14 January 2026), 2021.
Chapman, L. and Bell, S. J.: High-Resolution Monitoring of Weather Impacts on Infrastructure Networks Using the Internet of Things, Bulletin of the American Meteorological Society, 1147–1154, https://doi.org/10.1175/BAMS-D-17-0214.1, 2018.
Chen, B., Kong, F., Meadows, M. E., Pan, H., Zhu, A.-X., Chen, L., Yin, H., and Yang, L.: The evolution of social-ecological system interactions and their impact on the urban thermal environment, npj Urban Sustain, 4, 1–11, https://doi.org/10.1038/s42949-024-00141-4, 2024.
Chen, F., Kusaka, H., Bornstein, R., Ching, J., Grimmond, C. S. B., Grossman-Clarke, S., Loridan, T., Manning, K. W., Martilli, A., Miao, S., Sailor, D., Salamanca, F. P., Taha, H., Tewari, M., Wang, X., Wyszogrodzki, A. A., and Zhang, C.: The integrated WRF/urban modelling system: development, evaluation, and applications to urban environmental problems, International Journal of Climatology, 31, 273–288, https://doi.org/10.1002/joc.2158, 2011.
Chen, Y. and Wu, L.: Graph Neural Networks: Graph Structure Learning, in: Graph Neural Networks: Foundations, Frontiers, and Applications, edited by: Wu, L., Cui, P., Pei, J., and Zhao, L., Springer Nature Singapore, Singapore, 297–321, https://doi.org/10.1007/978-981-16-6054-2_14, 2022.
Effrosynidis, D., Spiliotis, E., Sylaios, G., and Arampatzis, A.: Time series and regression methods for univariate environmental forecasting: An empirical evaluation, Science of The Total Environment, 875, 162580, https://doi.org/10.1016/j.scitotenv.2023.162580, 2023.
Elsayed, S., Thyssens, D., Rashed, A., Jomaa, H. S., and Schmidt-Thieme, L.: Do We Really Need Deep Learning Models for Time Series Forecasting?, arXiv [preprint], https://doi.org/10.48550/arXiv.2101.02118, 2021.
Gao, S., Chen, Y., Chen, D., He, B., Gong, A., Hou, P., Li, K., and Cui, Y.: Urbanization-induced warming amplifies population exposure to compound heatwaves but narrows exposure inequality between global North and South cities, npj Clim. Atmos. Sci., 7, 1–10, https://doi.org/10.1038/s41612-024-00708-z, 2024.
Goodfellow, I., Bengio, Y., Courville, A., and Bengio, Y.: Deep learning, MIT press Cambridge, http://www.deeplearningbook.org (last access: 14 January 2026), 2016.
Greff, K., Srivastava, R. K., Koutník, J., Steunebrink, B. R., and Schmidhuber, J.: LSTM: A Search Space Odyssey, IEEE Transactions on Neural Networks and Learning Systems, 28, 2222–2232, https://doi.org/10.1109/TNNLS.2016.2582924, 2017.
Hamilton, W., Ying, Z., and Leskovec, J.: Inductive representation learning on large graphs, Advances in neural information processing systems, 30, https://papers.nips.cc/paper/6703-inductive-representation-learning-on-large-graphs (last access: 14 January 2026), 2017.
Haque, E., Tabassum, S., and Hossain, E.: A Comparative Analysis of Deep Neural Networks for Hourly Temperature Forecasting, IEEE Access, 9, 160646–160660, https://doi.org/10.1109/ACCESS.2021.3131533, 2021.
Hochreiter, S. and Schmidhuber, J.: Long Short-Term Memory, Neural Computation, 9, 1735–1780, https://doi.org/10.1162/neco.1997.9.8.1735, 1997.
Iakovlev, V. and Lähdesmäki, H.: Learning Spatiotemporal Dynamical Systems from Point Process Observations, arXiv [preprint], https://doi.org/10.48550/arXiv.2406.00368, 2024.
Kendon, E. J., Prein, A. F., Senior, C. A., and Stirling, A.: Challenges and outlook for convection-permitting climate modelling, Philosophical Transactions of the Royal Society A, https://doi.org/10.1098/rsta.2019.0547, 2021.
Kipf, T. N. and Welling, M.: Semi-Supervised Classification with Graph Convolutional Networks, arXiv [preprint], https://doi.org/10.48550/arXiv.1609.02907, 22 February 2017.
Knutti, R., Furrer, R., Tebaldi, C., Cermak, J., and Meehl, G. A.: Challenges in Combining Projections from Multiple Climate Models, Journal of Climate, https://doi.org/10.1175/2009JCLI3361.1, 2010.
Lam, R., Sanchez-Gonzalez, A., Willson, M., Wirnsberger, P., Fortunato, M., Alet, F., Ravuri, S., Ewalds, T., Eaton-Rosen, Z., Hu, W., Merose, A., Hoyer, S., Holland, G., Vinyals, O., Stott, J., Pritzel, A., Mohamed, S., and Battaglia, P.: GraphCast: Learning skillful medium-range global weather forecasting, arXiv [preprint], https://doi.org/10.48550/arXiv.2212.12794, 2023.
Li, D. and Bou-Zeid, E.: Synergistic Interactions between Urban Heat Islands and Heat Waves: The Impact in Cities Is Larger than the Sum of Its Parts, Journal of Applied Meteorology and Climatology, https://doi.org/10.1175/JAMC-D-13-02.1, 2013.
Li, P., Yu, Y., Huang, D., Wang, Z.-H., and Sharma, A.: Regional Heatwave Prediction Using Graph Neural Network and Weather Station Data, Geophysical Research Letters, 50, e2023GL103405, https://doi.org/10.1029/2023GL103405, 2023.
Li, P., Wang, Z.-H., and Wang, C.: The potential of urban irrigation for counteracting carbon-climate feedback, Nat. Commun., 15, 2437, https://doi.org/10.1038/s41467-024-46826-3, 2024.
Ma, M., Xie, P., Teng, F., Wang, B., Ji, S., Zhang, J., and Li, T.: HiSTGNN: Hierarchical spatio-temporal graph neural network for weather forecasting, Information Sciences, 648, 119580, https://doi.org/10.1016/j.ins.2023.119580, 2023.
Mora, C., Dousset, B., Caldwell, I. R., Powell, F. E., Geronimo, R. C., Bielecki, C. R., Counsell, C. W. W., Dietrich, B. S., Johnston, E. T., Louis, L. V., Lucas, M. P., McKenzie, M. M., Shea, A. G., Tseng, H., Giambelluca, T. W., Leon, L. R., Hawkins, E., and Trauernicht, C.: Global risk of deadly heat, Nature Clim. Change, 7, 501–506, https://doi.org/10.1038/nclimate3322, 2017.
Nogueira, M., Hurduc, A., Ermida, S., Lima, D. C. A., Soares, P. M. M., Johannsen, F., and Dutra, E.: Assessment of the Paris urban heat island in ERA5 and offline SURFEX-TEB (v8.1) simulations using the METEOSAT land surface temperature product, Geosci. Model Dev., 15, 5949–5965, https://doi.org/10.5194/gmd-15-5949-2022, 2022.
Oke, T. R., Mills, G., Christen, A., and Voogt, J. A.: Urban Climates, 1st Edn., Cambridge University Press, https://doi.org/10.1017/9781139016476, 2017.
Perera, A. T. D., Nik, V. M., Chen, D., Scartezzini, J.-L., and Hong, T.: Quantifying the impacts of climate change and extreme climate events on energy systems, Nat. Energy, 5, 150–159, https://doi.org/10.1038/s41560-020-0558-0, 2020.
Price, I., Sanchez-Gonzalez, A., Alet, F., Andersson, T. R., El-Kadi, A., Masters, D., Ewalds, T., Stott, J., Mohamed, S., Battaglia, P., Lam, R., and Willson, M.: GenCast: Diffusion-based ensemble forecasting for medium-range weather, arXiv [preprint], https://doi.org/10.48550/arXiv.2312.15796, 2024.
Salcedo-Sanz, S., Deo, R. C., Carro-Calvo, L., and Saavedra-Moreno, B.: Monthly prediction of air temperature in Australia and New Zealand with machine learning algorithms, Theor. Appl. Climatol., 125, 13–25, https://doi.org/10.1007/s00704-015-1480-4, 2016.
Schär, C., Fuhrer, O., Arteaga, A., Ban, N., Charpilloz, C., Girolamo, S. D., Hentgen, L., Hoefler, T., Lapillonne, X., Leutwyler, D., Osterried, K., Panosetti, D., Rüdisühli, S., Schlemmer, L., Schulthess, T. C., Sprenger, M., Ubbiali, S., and Wernli, H.: Kilometer-Scale Climate Models: Prospects and Challenges, Bulletin of the American Meteorological Society, https://doi.org/10.1175/BAMS-D-18-0167.1, 2020.
Scheitlin, K.: The Maritime Influence on Diurnal Temperature Range in the Chesapeake Bay Area, Earth Interactions, https://doi.org/10.1175/2013EI000546.1, 2013.
Sharma, A., Wuebbles, D. J., and Kotamarthi, R.: The Need for Urban-Resolving Climate Modeling Across Scales, AGU Advances, 2, e2020AV000271, https://doi.org/10.1029/2020AV000271, 2021.
Shi, T., Yang, Y., Qi, P., and Lolli, S.: Diurnal variation in an amplified canopy urban heat island during heat wave periods in the megacity of Beijing: roles of mountain–valley breeze and urban morphology, Atmos. Chem. Phys., 24, 12807–12822, https://doi.org/10.5194/acp-24-12807-2024, 2024.
Stewart, I. D. and Oke, T. R.: Local Climate Zones for Urban Temperature Studies, Bulletin of the American Meteorological Society, 93, 1879–1900, https://doi.org/10.1175/BAMS-D-11-00019.1, 2012.
The Hong Kong Observatory: The Weather of January 2021 in Hong Kong: https://www.hko.gov.hk/en/wxinfo/pastwx/mws2021/mws202101.htm, last access: 30 June 2025.
Tuholske, C., Caylor, K., Funk, C., Verdin, A., Sweeney, S., Grace, K., Peterson, P., and Evans, T.: Global urban population exposure to extreme heat, Proceedings of the National Academy of Sciences, 118, e2024792118, https://doi.org/10.1073/pnas.2024792118, 2021.
UN Statistics Division: Goal 11: Make cities and human settlements inclusive, safe, resilient and sustainable, https://unstats.un.org/sdgs/report/2023/goal-11/ (last access: 14 January 2026), 2023.
Veličković, P., Cucurull, G., Casanova, A., Romero, A., Liò, P., and Bengio, Y.: Graph Attention Networks, in: International Conference on Learning Representations, https://openreview.net/forum?id=rJXMpikCZ (last access: 14 January 2026), 2018.
Wang, C., Song, J., Shi, D., Reyna, J. L., Horsey, H., Feron, S., Zhou, Y., Ouyang, Z., Li, Y., and Jackson, R. B.: Impacts of climate change, population growth, and power sector decarbonization on urban building energy use, Nat. Commun., 14, 6434, https://doi.org/10.1038/s41467-023-41458-5, 2023a.
Wang, H.: Scripts for Intra-city GNN, Zenodo [data set and code], https://doi.org/10.5281/zenodo.17900788, 2025.
Wang, H., Yang, J., Chen, G., Ren, C., and Zhang, J.: Machine learning applications on air temperature prediction in the urban canopy layer: A critical review of 2011–2022, Urban Climate, 49, 101499, https://doi.org/10.1016/j.uclim.2023.101499, 2023b.
Wang, H., Zhang, J., and Yang, J.: Time series forecasting of pedestrian-level urban air temperature by LSTM: Guidance for practitioners, Urban Climate, 56, 102063, https://doi.org/10.1016/j.uclim.2024.102063, 2024.
Wang, S., Li, Y., Zhang, J., Meng, Q., Meng, L., and Gao, F.: PM2.5-GNN: A Domain Knowledge Enhanced Graph Neural Network For PM2.5 Forecasting, in: Proceedings of the 28th International Conference on Advances in Geographic Information Systems, 163–166, https://doi.org/10.1145/3397536.3422208, 2020.
Wilks, D. S.: Statistical methods in the atmospheric sciences, Academic press, ISBN 978-0-12-385022-5, 2011.
Wu, H., Zhou, H., Long, M., and Wang, J.: Interpretable weather forecasting for worldwide stations with a unified deep model, Nat. Mach. Intell., 5, 602–611, https://doi.org/10.1038/s42256-023-00667-9, 2023.
Xu, S., Zhang, Y., Chen, J., and Zhang, Y.: Short- to Medium-Term Weather Forecast Skill of the AI-Based Pangu-Weather Model Using Automatic Weather Stations in China, Remote Sensing, 17, 191, https://doi.org/10.3390/rs17020191, 2025.
Yang, Y., Guo, M., Wang, L., Zong, L., Liu, D., Zhang, W., Wang, M., Wan, B., and Guo, Y.: Unevenly spatiotemporal distribution of urban excess warming in coastal Shanghai megacity, China: Roles of geophysical environment, ventilation and sea breezes, Building and Environment, 235, 110180, https://doi.org/10.1016/j.buildenv.2023.110180, 2023.
Yano, J.-I., Ziemiański, M. Z., Cullen, M., Termonia, P., Onvlee, J., Bengtsson, L., Carrassi, A., Davy, R., Deluca, A., Gray, S. L., Homar, V., Köhler, M., Krichak, S., Michaelides, S., Phillips, V. T. J., Soares, P. M. M., and Wyszogrodzki, A. A.: Scientific Challenges of Convective-Scale Numerical Weather Prediction, Bulletin of the American Meteorological Society, https://doi.org/10.1175/BAMS-D-17-0125.1, 2018.
Yu, M., Xu, F., Hu, W., Sun, J., and Cervone, G.: Using Long Short-Term Memory (LSTM) and Internet of Things (IoT) for Localized Surface Temperature Forecasting in an Urban Environment, IEEE Access, 9, 137406–137418, https://doi.org/10.1109/ACCESS.2021.3116809, 2021.
Yuan, Y., Santamouris, M., Xu, D., Geng, X., Li, C., Cheng, W., Su, L., Xiong, P., Fan, Z., Wang, X., and Liao, C.: Surface urban heat island effects intensify more rapidly in lower income countries, npj Urban Sustain., 5, 1–11, https://doi.org/10.1038/s42949-025-00198-9, 2025.
Zeng, A., Chen, M., Zhang, L., and Xu, Q.: Are Transformers Effective for Time Series Forecasting?, arXiv [preprint], https://doi.org/10.48550/arXiv.2205.13504, 17 August 2022.
Zhang, J., You, Q., Ren, G., Ullah, S., Normatov, I., and Chen, D.: Inequality of Global Thermal Comfort Conditions Changes in a Warmer World, Earth's Future, 11, e2022EF003109, https://doi.org/10.1029/2022EF003109, 2023.
Zheng, Y., Zhang, Z., Wang, Z., Li, X., Luan, S., Peng, X., and Chen, L.: Rethinking Structure Learning For Graph Neural Networks, arXiv [preprint], https://doi.org/10.48550/arXiv.2411.07672, 12 November 2024.
Zhou, H., Zhang, F., Du, Z., and Liu, R.: A theory-guided graph networks based PM2.5 forecasting method, Environmental Pollution, 293, 118569, https://doi.org/10.1016/j.envpol.2021.118569, 2022.
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.
Air temperature (Ta) varies substantially within cities, yet physics-based models struggle to...
Altmetrics
Final-revised paper
Preprint