Computational Framework Overview

The implemented computational framework calculates the Universal Thermal Climate Index (UTCI) for urban-scale thermal comfort analysis by integrating three primary data sources: meteorological data from EPW weather files, urban geometry inputs that define building context and analysis planes, and boundary conditions specifying material and ground properties.

The core calculation process uses Radiance ray-tracing to determine sky view factors and solar irradiance patterns across the urban environment. These radiative components are combined with boundary conditions to calculate Mean Radiant Temperature (MRT) using the Effective Radiant Field methodology (MRT = Surface Temperature + Solar Irradiance + Sky View Factors). The final UTCI values integrate MRT, which is the most impactful variable for UTCI, with standard meteorological parameters - air temperature, relative humidity, and wind speed - providing spatially-resolved thermal comfort assessment.

This approach enables precise evaluation of heat stress conditions across complex urban morphologies, supporting evidence-based urban climate design decisions.

Validation and Quality Assurance

To ensure the accuracy and reliability of our UTCI calculation methodology, we conducted comprehensive validation studies in three climatically distinct locations, each representing different Köppen climate classification. This multi-climate validation approach ensures our methodology performs accurately across diverse global conditions.

Validation Locations and Climate Classification

Our validation study encompassed three strategically selected cities representing major Köppen climate zone.

Stockholm, Sweden (Dfb - Humid Continental)

• Cold winters with significant heating demands
• Moderate summers with low cooling requirements
• High seasonal variation in solar angles and daylight hours
• Frequent cloud cover affecting sky temperature calculations

Vienna, Austria (Cfb - Oceanic Climate)

• Temperate climate with mild winters and warm summers
• Moderate humidity levels throughout the year
• Balanced seasonal patterns representative of Central Europe
• Varied sky conditions providing diverse radiative environments

Singapore (Af - Tropical Rainforest)

• Consistently high temperatures and humidity year-round
• Minimal seasonal variation in temperature
• Intense solar radiation near the equator
• Frequent cloud cover and precipitation influencing sky emissivity

Global benchmarking

Seasonal comfort assessment (cold vs. hot) based on EnergyPlus simulations and Fanger Method:

Image to Image analysis

Modeling in EnergyPlus was conducted by assigning a single material (concrete) to the ground zone.

Comparison with other softwares

Different microclimate simulation engines vary in how they handle human body radiation exchange, as well as shortwave and longwave inputs when calculating UTCI.

Most platforms account for the fundamental components of direct and diffuse shortwave radiation, while their approaches to reflected radiation from buildings, vegetation, and ground surfaces differ. Some tools simplify reflections or exclude certain sources such as free-standing objects, while others employ methods like fisheye photographs or ray tracing to approximate view factors.

For longwave radiation, the general trend is to include heat exchange with the sky and built surfaces, whereas processes like vegetation transpiration, ground evaporation, or radiation from free-standing objects are often simplified or omitted. Treatment of local wind conditions also varies, with some platforms neglecting wind speed and direction effects altogether.

Among open-source platforms, Ladybug Tools is widely used in both research and practice, offering accessible radiation workflows for architects and designers. However, its radiation models are simplified compared to those of dedicated microclimate engines.

infrared.city adopts a balanced approach that prioritizes computational efficiency while maintaining accuracy for the most critical radiation processes influencing human thermal comfort. The platform employs ray tracing for precise view factor calculations and fully accounts for key radiation sources often simplified or omitted by other tools, particularly reflections from free-standing objects and ground surfaces. This technical approach enables detailed radiation modeling without compromising performance for real-time urban climate analysis.

In its current implementation, infrared.city uses a simplified representation of body shape and position, while shortwave absorption and longwave emissivity are fully accounted for. For shortwave radiation, direct and diffuse sky radiation are explicitly modeled. Reflected radiation is handled with a mix of detail and simplification: reflections from buildings and vegetation are simplified, while reflections from free-standing objects and ground surfaces are included. To capture view factors accurately, ray tracing is applied for both sky and surface calculations. Longwave exchange with the sky is fully accounted for, while interactions with buildings, free-standing objects, vegetation, and ground surfaces are treated in a simplified way.

While the current implementation focuses on the primary radiation exchange mechanisms, ongoing development efforts are directed toward incorporating additional processes such as vegetation transpiration, ground evaporation, and local wind effects to further enhance the comprehensiveness of the thermal comfort assessment framework.

Conclusion

This methodology provides a comprehensive framework for quantifying outdoor thermal comfort at the urban scale. By integrating advanced radiation modeling with Radiance and established thermal comfort indices, it enables detailed analysis of urban design interventions and climate adaptation strategies.

The complexity of the MRT calculation, particularly the solar irradiance component, reflects the physical reality of outdoor thermal environments where radiation exchange is the dominant factor influencing human thermal sensation. Through the use of sophisticated simulation techniques, this approach achieves the level of accuracy required for evidence-based urban climate design.

References

State-of-art Global UTCI models

• Di Napoli, C., Barnard, C., Prudhomme, C., Cloke, H. L., & Pappenberger, F. (2020). ERA5-HEAT: A global gridded historical dataset of human thermal comfort indices from climate reanalysis. Geoscience Data Journal, 8(1), 10–22. https://doi.org/10.1002/gdj3.102.
• Yang, Z., Peng, J., Liu, Y., Jiang, S., Cheng, X., Liu, X., Dong, J., Hua, T., & Yu, X. (2024). GloUTCI-M: a global monthly 1 km Universal Thermal Climate Index dataset from 2000 to 2022. Earth System Science Data, 16(5), 2407–2424. https://doi.org/10.5194/essd-16-2407-2024.
• Kaltenborn, J., Lange, C., Ramesh, V., Brouillard, P., Gurwicz, Y., Nagda, C., Runge, J., Nowack, P., & Rolnick, D. (2023). ClimateSet: A Large-Scale Climate Model Dataset for Machine Learning. Advances in Neural Information Processing Systems, 36. https://proceedings.neurips.cc/paper_files/paper/2023/file/3z9YV29Ogn_Paper.pdf.
• Gibson, P. B., Chapman, W. E., Altinok, A., Delle Monache, L., DeFlorio, M. J., & Waliser, D. E. (2021). Training machine learning models on climate model output yields skillful interpretable seasonal precipitation forecasts. Communications Earth & Environment, 2(159), 1-10. https://doi.org/10.1038/s43247-021-00225-4.

State-of-art MRT Approximation

• Kastner, P., & Dogan, T. (2020). Predicting space usage by multi-objective assessment of outdoor thermal comfort around a university campus. SimAUD 2020 Proceedings, 85-91. https://www.researchgate.net/publication/346039200_Predicting_space_usage_by_multi-objective_assessment_of_outdoor_thermal_comfort_around_a_university_campus
• Cui, Z., Leduc, T., Rodler, A., & Musy, M. (2023). Development of a composite model for predicting urban surface temperature distribution in the context of GIS. Journal of Physics: Conference Series, 2600(9). https://iopscience.iop.org/article/10.1088/1742-6596/2600/9/092026
• Lindberg, F., Holmer, B., & Thorsson, S. (2008). SOLWEIG 1.0 – Modelling spatial variations of 3D radiant fluxes and mean radiant temperature in complex urban settings. International Journal of Biometeorology, 52, 697-713. https://www.researchgate.net/publication/51400140_SOLWEIG_10_-_Modelling_spatial_variations_of_3D_radiant_fluxes_and_mean_radiant_temperature_in_complex_urban_settings
• Thorsson, S., Lindberg, F., Eliasson, I., & Holmer, B. (2007). Different methods for estimating the mean radiant temperature in an outdoor urban setting. International Journal of Climatology, 27(14), 1983-1993. https://scispace.com/papers/different-methods-for-estimating-the-mean-radiant-2kmp84yyys
• Kastner, P., & Dogan, T. (2019).** Towards high-resolution annual outdoor thermal comfort mapping in urban design. *Building Simulation 2019*. https://publications.ibpsa.org/conference/paper/?id=bs2019_210458
• Huang, J., Cedeno-Laurent, J. G., & Spengler, J. D. (2013). CityComfort: A simulation-based method for predicting mean radiant temperature in dense urban areas. Building and Environment, 60, 156-166. https://doi.org/10.1016/j.buildenv.2012.10.027