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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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...
