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<p>Urban weather and climate modeling is challenged by the highly heterogeneous and dynamic nature of cities. It exhibits a persistent trilemma between spatial granularity, spatiotemporal coverage, and physical interpretability. We articulate this challenge and propose a hybrid framework integrating physics-based models, urban observations, and machine learning. Framing this challenge as an integration problem across methods and scales, we provide a structured guide for next-generation, decision-relevant urban weather and climate modeling.</p>

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Unraveling the intractable trilemma in urban weather and climate modeling

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2026年4月18日

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  • Peiyuan Li,
  • Ashish Sharma,
  • Rao Kotamarthi,
  • Alberto Martilli,
  • Subimal Ghosh,
  • Cristina Negri,
  • Scott Collis,
  • Lee Chapman,
  • Fei Chen,
  • Luis M. A. Bettencourt,
  • Winston T. L. Chow

摘要

Urban weather and climate modeling is challenged by the highly heterogeneous and dynamic nature of cities. It exhibits a persistent trilemma between spatial granularity, spatiotemporal coverage, and physical interpretability. We articulate this challenge and propose a hybrid framework integrating physics-based models, urban observations, and machine learning. Framing this challenge as an integration problem across methods and scales, we provide a structured guide for next-generation, decision-relevant urban weather and climate modeling.