CC BY 4.0Free to use and share, including commercially, as long as you credit the creators.
Size
2.4 GB
How to cite
meng. (2025). Training and Testing Data, Associated Code for UCNN(a deep learning-based urban canopy model designed for complex building morphologies) [Data set]. Zenodo. https://doi.org/10.5281/zenodo.18001508
Check the repository for the citation its authors prefer.
Large-eddy simulations of turbulent airflow over 314 realistic urban neighbourhoods from 62 cities in 20 countries, with plan-area densities from 0.05 to 0.75 and several wind directions. Provides canopy-averaged statistics and flow fields, which are useful for canopy parameterisation, model validation and machine learning of urban wind.
PALM LES output for an urban crossing and a Berlin square (Ernst-Reuter-Platz) with different radiative transfer parameterisations. Useful for urban microclimate model comparison.
Data and scripts to develop the dyOHM storage heat flux parameterisation and apply it in SUEWS at neighbourhood scale. Useful for urban surface energy balance modelling.
From Zenodo, added 2026-11-07.
Listed there as “Training and Testing Data, Associated Code for UCNN(a deep learning-based urban canopy model designed for complex building morphologies)”. Tags proposed by AI and not yet checked by hand. Spotted a mistake? Tell the portal contacts.