Articles | Volume 17, issue 4
https://doi.org/10.5194/gmd-17-1667-2024
© Author(s) 2024. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/gmd-17-1667-2024
© Author(s) 2024. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Development and technical paper
| 
26 Feb 2024
Development and technical paper |
| 26 Feb 2024
| 26 Feb 2024
High-resolution multi-scaling of outdoor human thermal comfort and its intra-urban variability based on machine learning
Ferdinand Briegel
CORRESPONDING AUTHOR
Chair of Environmental Meteorology, Faculty of Environment and Natural Resources, University of Freiburg, Freiburg im Breisgau, Germany
Jonas Wehrle
Chair of Environmental Meteorology, Faculty of Environment and Natural Resources, University of Freiburg, Freiburg im Breisgau, Germany
Dirk Schindler
Chair of Environmental Meteorology, Faculty of Environment and Natural Resources, University of Freiburg, Freiburg im Breisgau, Germany
Andreas Christen
Chair of Environmental Meteorology, Faculty of Environment and Natural Resources, University of Freiburg, Freiburg im Breisgau, Germany
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Cited
16 citations as recorded by crossref.
- Coupling effects of building-vegetation-land on seasonal land surface temperature on street-level: A study from a campus in Beijing S. Zhang et al. 10.1016/j.buildenv.2024.111790
- Approach for the vertical wind speed profile implemented in the UTCI basics blocks UTCI applications at the urban pedestrian level H. Lee et al. 10.1007/s00484-024-02835-x
- Advancements in supervised machine learning for outdoor thermal comfort: A comprehensive systematic review of scales, applications, and data types T. Luo & M. Chen 10.1016/j.enbuild.2024.115255
- Applications of local climate zone classification in European cities: A review of in situ and mobile monitoring methods in urban climate studies V. Milica et al. 10.1515/geo-2025-0878
- Evaluation of the Urban Canopy Scheme TERRA-URB in the ICON Model at Hectometric Scale over the Naples Metropolitan Area D. Cinquegrana et al. 10.3390/atmos15091119
- Planning for cooler cities: A multimodal AI framework for hyperlocal spatio-temporal urban heat stress prediction and mitigation S. Yi et al. 10.1016/j.ufug.2025.129101
- Introducing new morphometric parameters to improve urban canopy air flow modeling: A CFD to machine-learning study in real urban environments J. Wehrle et al. 10.1016/j.uclim.2024.102173
- Mitigating urban heat stress through green infrastructure: A climate service approach G. Oukawa et al. 10.1016/j.uclim.2025.102384
- Is satellite land surface temperature an appropriate proxy for intra-urban variability of daytime heat stress? F. Briegel et al. 10.1016/j.rse.2025.115045
- Simplifying heat stress assessment: Evaluating meteorological variables as single indicators of outdoor thermal comfort in urban environments J. Anders et al. 10.1016/j.buildenv.2025.112658
- Multisensory Urban Climate Zones (MUCZ): A Framework for Mapping Dynamic Multidomain Human Comfort in Complex Urban Fabrics beyond Urban Morphology C. Grapas et al. 10.1016/j.scs.2025.106673
- Deep learning enables city-wide climate projections of street-level heat stress F. Briegel et al. 10.1016/j.uclim.2025.102564
- Toward the Next-Generation of Heat-Health Warning Systems and Action Plans A. Matzarakis & C. Giannaros 10.3390/atmos16080938
- Machine learning predicts pedestrian wind flow from urban morphology and prevailing wind direction J. Lu et al. 10.1088/1748-9326/adc148
- Application of human-centric digital twins: Predicting outdoor thermal comfort distribution in Singapore using multi-source data and machine learning X. Liu et al. 10.1016/j.uclim.2024.102210
- Towards Universal Thermal Climate Index Prediction via machine learning approaches O. Veisi et al. 10.1016/j.rser.2025.115680
16 citations as recorded by crossref.
- Coupling effects of building-vegetation-land on seasonal land surface temperature on street-level: A study from a campus in Beijing S. Zhang et al. 10.1016/j.buildenv.2024.111790
- Approach for the vertical wind speed profile implemented in the UTCI basics blocks UTCI applications at the urban pedestrian level H. Lee et al. 10.1007/s00484-024-02835-x
- Advancements in supervised machine learning for outdoor thermal comfort: A comprehensive systematic review of scales, applications, and data types T. Luo & M. Chen 10.1016/j.enbuild.2024.115255
- Applications of local climate zone classification in European cities: A review of in situ and mobile monitoring methods in urban climate studies V. Milica et al. 10.1515/geo-2025-0878
- Evaluation of the Urban Canopy Scheme TERRA-URB in the ICON Model at Hectometric Scale over the Naples Metropolitan Area D. Cinquegrana et al. 10.3390/atmos15091119
- Planning for cooler cities: A multimodal AI framework for hyperlocal spatio-temporal urban heat stress prediction and mitigation S. Yi et al. 10.1016/j.ufug.2025.129101
- Introducing new morphometric parameters to improve urban canopy air flow modeling: A CFD to machine-learning study in real urban environments J. Wehrle et al. 10.1016/j.uclim.2024.102173
- Mitigating urban heat stress through green infrastructure: A climate service approach G. Oukawa et al. 10.1016/j.uclim.2025.102384
- Is satellite land surface temperature an appropriate proxy for intra-urban variability of daytime heat stress? F. Briegel et al. 10.1016/j.rse.2025.115045
- Simplifying heat stress assessment: Evaluating meteorological variables as single indicators of outdoor thermal comfort in urban environments J. Anders et al. 10.1016/j.buildenv.2025.112658
- Multisensory Urban Climate Zones (MUCZ): A Framework for Mapping Dynamic Multidomain Human Comfort in Complex Urban Fabrics beyond Urban Morphology C. Grapas et al. 10.1016/j.scs.2025.106673
- Deep learning enables city-wide climate projections of street-level heat stress F. Briegel et al. 10.1016/j.uclim.2025.102564
- Toward the Next-Generation of Heat-Health Warning Systems and Action Plans A. Matzarakis & C. Giannaros 10.3390/atmos16080938
- Machine learning predicts pedestrian wind flow from urban morphology and prevailing wind direction J. Lu et al. 10.1088/1748-9326/adc148
- Application of human-centric digital twins: Predicting outdoor thermal comfort distribution in Singapore using multi-source data and machine learning X. Liu et al. 10.1016/j.uclim.2024.102210
- Towards Universal Thermal Climate Index Prediction via machine learning approaches O. Veisi et al. 10.1016/j.rser.2025.115680
Latest update: 30 Oct 2025
Short summary
We present a new approach to model heat stress in cities using artificial intelligence (AI). We show that the AI model is fast in terms of prediction but accurate when evaluated with measurements. The fast-predictive AI model enables several new potential applications, including heat stress prediction and warning; downscaling of potential future climates; evaluation of adaptation effectiveness; and, more fundamentally, development of guidelines to support urban planning and policymaking.
We present a new approach to model heat stress in cities using artificial intelligence (AI). We...
