Modelling long-term thermal comfort conditions in urban environments using a deep convolutional encoder-decoder as a computational shortcut

Different urban microscale models exist to model street-level mean radiation temperature (Tmrt). However, these models are computationally expensive, albeit to varying degrees. We present a computational shortcut using a convolutional encoder-decoder network (U-Net) to predict pedestrian level (1.1 m a.g.l.) Tmrt at a building-resolved scale (1 x 1 m). SOLWEIG is used to create spatial training data for 68 days at hourly resolution in the city of Freiburg, Germany. Validation of the model was carried out in two steps: First, SOLWEIG (and U-Net) were validated against Tmrt point measurements. Second, U-Net was validated against SOLWEIG on 6 areas and 12 days resulting in a MAE of 2.4 K. The U-Net is 22 times faster than SOLWEIG, and thus able to emulate a micrometeorological physical model with computational superiority. As a demon-stration case, U-Net is applied to model Tmrt for the urbanized area of Freiburg for two complete 30-year periods (1961-1990, 1991-2020) driven by hourly ERA5-Land reanalysis data. Sum-mertime daily maximum Tmrt increased on average by 2.5 K, whereas summertime daily maximum air temperature increased by only 1.5 K. Maximum Tmrt increase is stronger on non -tree covered paved areas (2.8 K) than on tree covered grassy areas (1.8 K).

Automated summary

A convolutional encoder-decoder network (U-Net) is used to predict pedestrian level mean radiation temperature (Tmrt) at a building-resolved scale. The model is validated against point measurements and SOLWEIG, achieving a Mean Absolute Error (MAE) of 2.4 K. U-Net is 22 times faster than SOLWEIG and can effectively model Tmrt for urban areas.