3 papers
physics.ao-ph2025
A Hierarchical Deep Learning Model for Predicting Pedestrian-Level Urban Winds
Reda Snaiki, Jiachen Lu, Shaopeng Li +1
Deep learning-based surrogate models offer a computationally efficient alternative to high-fidelity computational fluid dynamics (CFD) simulations for predicting urban wind flow. H…
physics.ao-ph2025
Satellite-derived Land Surface Temperatures Strongly Mischaracterise Urban Heat Hazard
Wenfeng Zhan, Benjamin Bechtel, Huilin Du +10
Escalating urban heat, driven by the convergence of global warming and rapid urbanization, is a profound threat to billions of city dwellers. The science directing urban heat adapt…
physics.ao-ph2025
Strengthening national capability in urban climate science: an Australian perspective
Negin Nazarian, Andy J Pitman, Mathew J Lipson +11
Cities are experiencing significant warming and more frequent climate extremes, raising risks for over 90% of Australians living in cities. Yet many of our tools for climate predic…