Articles | Volume 17, issue 4
https://doi.org/10.5194/gmd-17-1667-2024
https://doi.org/10.5194/gmd-17-1667-2024
Development and technical paper
 | 
26 Feb 2024
Development and technical paper |  | 26 Feb 2024

High-resolution multi-scaling of outdoor human thermal comfort and its intra-urban variability based on machine learning

Ferdinand Briegel, Jonas Wehrle, Dirk Schindler, and Andreas Christen

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Interactive discussion

Status: closed

Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor | : Report abuse
  • RC1: 'Comment on gmd-2023-122', Laura Muntjewerf, 13 Oct 2023
    • AC1: 'Reply on RC1', Ferdinand Briegel, 02 Nov 2023
  • RC2: 'Comment on gmd-2023-122', Krzysztof Fortuniak, 29 Nov 2023
    • AC2: 'Reply on RC2', Ferdinand Briegel, 05 Dec 2023

Peer review completion

AR: Author's response | RR: Referee report | ED: Editor decision | EF: Editorial file upload
AR by Ferdinand Briegel on behalf of the Authors (05 Dec 2023)  Author's response   Author's tracked changes   Manuscript 
ED: Referee Nomination & Report Request started (14 Dec 2023) by Jinkyu Hong
RR by Laura Muntjewerf (28 Dec 2023)
RR by Krzysztof Fortuniak (01 Jan 2024)
ED: Publish as is (13 Jan 2024) by Jinkyu Hong
AR by Ferdinand Briegel on behalf of the Authors (15 Jan 2024)  Author's response   Manuscript 
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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.