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

Aug 5, 2026

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Heatwave frequency and severity are projected to rise, making accurate, high-resolution mapping and forecasting of outdoor human thermal comfort in cities essential. This machine-learning study develops an emulation-based thermal comfort model balancing computational cost, complexity, and accuracy with common numerical urban climate approaches. Four submodels predict air temperature, relative humidity, wind speed, and mean radiant temperature from meteorological forcing and geospatial building, land-cover, and vegetation inputs, which are combined into UTCI. Street-level evaluation in Freiburg yields a mean absolute error of 2.3 K. City-wide simulations show strong intra-urban heterogeneity and hotspot shifts between daytime and nighttime.

LanguageEnglish

CategoryResearch & Report

FormatPDF

Pages22

Reading Time55 min

AccessFree to download

Introduction

Heatwaves and urban heat exposure

Thermal comfort drivers and thermal indices

Multi-scale modelling focus and target variables

Existing urban modelling approaches

Geosci. Model Dev., 17, 1667–1688, 2024 [https://doi.org/10.5194/gmd-17-1667-2024](https://doi.org/10.5194/gmd-17-1667-2024)[ ](https://doi.org/10.5194/gmd-17-1667-2024)© Author(s) 2024 . This work is distributed under the Creative Commons Attribution 4 .0 License. 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 Chair of Environmental Meteorology, Faculty of Environment and Natural Resources, University of Freiburg, Freiburg im Breisgau, Germany Correspondence: Ferdinand Briegel ([ferdinand.briegel@meteo.uni-freiburg.de](ferdinand.briegel@meteo.uni-freiburg.de)) Received: 16 June 2023 – Discussion started: 26 July 2023 Revised: 5 December 2023 – Accepted: 13 January 2024 – Published: 26 February 2024 Abstract. As the frequency and intensity of heatwaveswill continue to increase in the future, accurate and highresolution mapping and forecasting of human outdoor thermal comfort in urban environments are of great importance. This study presents a machine-learning-based outdoor thermal comfort model with a good trade-off between computational cost, complexity, and accuracy compared to common numerical urban climate models. The machine learning approach is basically an emulation of different numerical urban climate models. The ﬁnal model consists of four submodels that predict air temperature, relative humidity, wind speed, and mean radiant temperature based on meteorological forcing and geospatial data on building forms, land cover, and vegetation. These variables are then combined into a thermal index (universal thermal climate index – UTCI) . All four submodel predictions and the ﬁnal model output are evaluated using street-level measurements from a dense urban sensor network in Freiburg, Germany. The ﬁnal model has a mean absolute error of 2.3 K. Based on a city-wide simulation for Freiburg, we demonstrate that the model is fast and versatile enough to simulate multiple years at hourly time steps to predict street-level UTCI at 1 m spatial resolution for an entire city. Simulations indicate that neighbourhood-averaged thermal comfort conditions vary widely between neighbourhoods, even if they are attributed to the same local climate zones, for example, due to differences in age and degree of urban vegetation. Simulations also show contrasting differences in the location of hotspots during the day and at night. 1 Introduction The frequency and severity of heatwaves have increased and are expected to increase even further due to human-caused climate change (IPCC, 2021) . In addition, heatwaves are occurring earlier in summer, resulting in a longer period of potential heat stress (IPCC, 2021) . Rousi et al. (2022) found that the frequency and intensity of heatwaves increased 3–4 times faster in Europe than in the rest of the mid-latitudes over the past decades. In 2020, heatwaves in western Europe accounted for 42 % of all reported global deaths from extreme weather events, with a total of 6340 deaths (CRED and UNDRR, 2021) . As the severity of heatwaves also depends on land cover and land use, urban areas are even more exposed to extreme heatwave events than rural areas due to their physical characteristics (Masson et al., 2020; Unger et al., 2020), including reduced nocturnal cooling and limited access to cool microenvironments for urban populations. Human thermal comfort is inﬂuenced not only by air temperature ( Ta ) but also by wind speed ( U ), radiation, and humidity. The variables expressing the effect of radiation and humidity are the mean radiant temperature ( Tmrt) and relative humidity (RH), respectively. Thermal indices combine these four environmental variables to describe the thermal comfort and overall thermal stress of an individual (Epstein and Moran, 2006) . Multiple thermal indices have been developed, such as the physiological equivalent temperatur

FAQs

What does the proposed model predict and how is it structured?

The model uses four submodels to predict air temperature, relative humidity, wind speed, and mean radiant temperature, which are then combined into the UTCI thermal index.

How is the model evaluated and what accuracy is reported?

All submodels and the final output are evaluated with street-level measurements from a dense urban sensor network in Freiburg, Germany, achieving a mean absolute error of 2.3 K.

What do the city-wide simulations reveal about thermal comfort in neighborhoods?

Neighbourhood-averaged conditions vary widely even under the same local climate zones, driven by differences such as age and degree of urban vegetation, and hotspots shift differently between day and night.

Aug 5, 2026

Upload by Eliana