A framework for urban meteorological simulations integrating multi-scale modeling and UAV aerial photography

Sophisticated urban climate systems emerge from the intricate interplay between mesoscale circulations and microscale forcing, making it difficult to fully capture their effects using conventional mesoscale or microscale models alone. Furthermore, high-quality urban morphology data are crucial for quantifying the highly heterogeneous and fragmented urban thermal environment. Here, we present an innovative multi-scale modeling framework that integrates a coupled mesoscale–microscale numerical model with unmanned aerial vehicle (UAV)-based remote sensing to acquire high-resolution urban surface data. This approach enables the accurate representation of fine-scale urban microclimate dynamics within a mesoscale meteorological context. Applying this framework to a heatwave event in Xiong'an New Area, China, we find that riverfront areas exhibit lower temperatures, enhanced ventilation, higher humidity, and reduced thermal stress compared to both high-density and low-density urban zones, with physiological equivalent temperature (PET) reduced by 2.3°C. In high-density built-up areas, radiative energy is primarily stored as ground heat (Q_s) and released as sensible heat (Q_h) at night, exacerbating nocturnal heat stress. By contrast, in riverfront zones, a greater fraction of radiative energy is converted into latent heat flux, mitigating heat stress. Additionally, shaded surfaces exhibit significantly lower heat fluxes than sunlit areas. These findings highlight the potential of this multi-scale framework as a powerful tool for sustainable urban climate design, offering new insights into the sensitivity of microclimatic processes to urban morphology, new developments, and the spatial configuration of blue-green infrastructure.