Accurate weather data is essential for building energy modeling (BEM), yet the actual urban local weather condition is often overlooked. This study developed an approach to generate local weather data using ERA5, a global atmospheric reanalysis dataset as input for two urban land surface models, Urban Tethys-Chloris (UT&C) and Urban Weather Generator (UWG). The generated datasets (UT&C-ERA5 and UWG-ERA5) are compared to locally measured weather data for a university campus in Singapore. Results show that the original ERA5 underestimates the diurnal temperature range. UT&C-ERA5 significantly improves hourly dry bulb temperature, reducing Mean Absolute Error (MAE) from 1.73 to 1.32 and Root Mean Square Error (RMSE) from 2.31 to 1.67, while UWG-ERA5 shows modest improvements (MAE from 1.73 to 1.70, RMSE from 2.31 to 2.22). UT&C-ERA5 also improves wind speed, lowering MAE from 2.85 to 1.54 and RMSE from 3.23 to 1.79. Subsequently, these weather datasets are employed as inputs for a calibrated BEM. Compared to the original ERA5, UT&C-ERA5 reduces CV (RMSE) of building cooling load from 17.13 % to 15.45 %. By leveraging the global availability of atmospheric reanalysis datasets, this approach can support building energy design and improve energy efficiency in tropical cities.

Building energy modeling (BEM) is essential for predicting energy use and improving thermal performance in buildings. Traditionally, weather data for BEM comes from built-in tool datasets. Additionally, global atmospheric reanalysis datasets like ERA5, have been used in recent years for BEM. However, the spatial resolution of global atmospheric reanalysis datasets is generally coarse relative to cities, limiting their accuracy in capturing local urban climate effects. Adopting ERA5 as the forcing data, this study examines the use of two urban land surface models, Urban Tethys-Chloris (UT&C) and Urban Weather Generator (UWG), to generate localized weather data for Singapore. The generated local weather data are compared with the data from an on-campus weather station and other weather datasets. Subsequently, these weather datasets are employed as input for an educational building’s energy model that has been validated with energy meter data. The results demonstrate a better agreement between the generated local weather data and locally measured data, compared to the original ERA5 data and typical meteorological year weather data. This leads to an improved accuracy in building energy prediction. By leveraging the global availability of atmospheric reanalysis datasets, this framework for generating local weather data can serve as a universally applicable approach to support building energy design in tropical cities.