Toward a new paradigm for urban climate modelling: challenges and opportunities

초록

Urban climate modelling has become essential for simulating localized climate behaviour and supporting building performance simulation, UHI mitigation, and outdoor thermal comfort assessments. Despite advances in urban canopy, computational fluid dynamics, and data-driven models, significant challenges remain - particularly in terms of physical realism, scalability, and generalizability. This viewpoint article argues that prevailing urban climate models are constrained primarily by oversimplified representations of urban form, scalability limitations due to high computational costs, and poor generalizability across diverse urban and climatic conditions. In response, we propose a shift toward hybrid physics - data modelling as a promising paradigm. By combining physically grounded simulations with data-driven learning, hybrid models offer greater potential to capture the complexity of real-world urban climates while supporting more interpretable and transferable outcomes. We outline key priorities for advancing this modelling direction, including scalable physics-based foundations, improved parameter calibration, and methods for interpreting model bias.

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