Energies, Vol. 19, Pages 724: Confronting Land Surface Temperature and Ground Station Data for Urban Heat Island Assessment and Urban Building Energy Modeling—A Case Study for Northern Italy
Energies doi: 10.3390/en19030724
Authors:
Mario Alves da da Silva
Gregorio Borelli
Andrea Gasparella
Giovanni Pernigotto
Data scarcity limits robust assessment of urban overheating and its implications for building energy use, especially in complex-terrain cities such as those in mountain environments. In this context, Land Surface Temperature (LST) from thermal remote sensing can be used to map urban hotspots at high spatial resolution. Nevertheless, it does not provide the full set of hourly atmospheric variables required to run building energy simulations aimed at quantifying their impact and defining mitigation measures. Given these premises, this study proposes a methodology combining satellite-derived LST with ground meteorological measurements to assess Urban Heat Island (UHI) patterns and quantify how measured weather data selection affects urban building energy modeling (UBEM) outcomes. After selecting as a case study Bolzano, an Alpine city in Northern Italy, ECOSTRESS LST (2019–2025, May–August) was first processed and quality-screened to (1) compute ΔLST (urban–rural) and (2) identify diurnal and spatial overheating patterns across the building stock. Second, four measured weather datasets—one rural station and three urban stations located in the city core, in the industrial district, and in the urban edge—were used as boundary conditions in an EnergyPlus-based UBEM parametric campaign for 253 residential buildings, covering multiple envelope insulation levels and window-to-wall ratios. Results show strong diurnal asymmetry in surface overheating, with the largest contrasts in the afternoon and prominent industrial hotspots. Ground measurements confirm persistent intra-urban microclimatic differences, and the choice of measured weather dataset causes systematic shifts in simulated cooling demand and thermal comfort. The study highlights the need for weather data selection strategies based on microclimatic context rather than simple proximity, improving representativeness in UBEM applications for Alpine and other heterogeneous urban environments.