This article describes the first thermal method (2025) and an early check against EnergyPlus. For the current engine, with stored heat, evaporation and local wind, read advanced thermal comfort. The test against street measurements is on the Validation page.
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.
Early checks
We made early checks in three climatically distinct locations, each in a different Köppen climate class. These checks compare our results with EnergyPlus. They are not tests against field instruments. For the test against field measurements, see the Validation page.
Validation Locations and Climate Classification
The early checks used three 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
Seasonal comparison
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 modelling the most important radiation processes that influence 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.
Since this method was written, the engine has gained more physics. Infrared Core now adds heat stored in walls and ground, irrigation and evaporation of watered surfaces, and, as an option, local wind for each 1 m cell from our AI wind model, so wind in the UTCI is no longer one value for the whole site. The advanced thermal comfort article describes the current engine and its physics levels, and Simulation Engine Basics gives the overview. Results differ between physics levels, so always state the level that produced a result.
Conclusion
This method gives a 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. This approach supports the comparison of places and design options.
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 ↗