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

Abstract

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 × 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 demonstration 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. Summertime 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).