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<p>Natural ventilation is widely used for low-carbon building design. Its potential is influenced largely by the building’s micrometeorological context. Traditionally, weather data used in building energy simulation are observed at rural sites which are far from the site of interest and not representative of the area’s surroundings. Here we combine the Surface Urban Energy and Water Balance Scheme (SUEWS) and the building energy simulation tool, EnergyPlus, to predict the natural ventilation potential (NVP) in buildings located in urban areas in five representative Chinese cities in different climate zones. The meteorological data required by EnergyPlus (e.g. air temperature, relative humidity, wind speed profile) are modelled by SUEWS. The dense urban areas (building fraction <em>λ<sub>P</sub></em> = 0.6) have an overall warmer and less windy environment compared to rural areas. In summer, the urban-rural natural ventilation hour differences are -3% to -85% (cf. rural) across all climates, while in spring/autumn differences are -25% to 42%. The method is intended to improve the accuracy of NVP prediction using EnergyPlus in cities.</p>

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<p>Accurate and agile modelling of weather, climate, hydrology and air quality in cities is essential for delivering integrated urban services. SUEWS (Surface Urban Energy and Water balance Scheme) allows simulation of urban–atmospheric interactions by quantifying the energy, water and carbon fluxes.  SuPy (SUEWS in Python) provides the SUEWS computation kernel, a Python-based data stack that streamlines pre-processing, computation and post-processing to facilitate common urban climate modelling. This paper documents the recent developments in both SuPy and SUEWS, and the background principles of their interface, F2PY (Fortran to Python) configuration and Python front-end implementation. SuPy is deployed via PyPI (Python Package Index) allowing an automated workflow for cross-platform compilation on all mainstream operating systems (Windows, Linux and macOS). The online tutorials, using Jupyter Notebooks, allow users to become familiar with SuPy. A brief overview of other complementary SUEWS developments will be given, and include within canopy layer profiles of temperature, humidity, wind, and radiation that are supporting a wide range of applications; and database developments for obtaining model parameters.</p>

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