urban climate; modeling; dynamic downscaling; morphing; data analysis
Recently, the interest in urban weather modeling methods has been steadily increasing. This is in part due to the insight that thermal building performance simulations are typically undertaken with standardized weather files that provide a rather general perspective on urban weather conditions. This may lead toward errors in conclusions drawn from modeling efforts. In this context, present contribution reports on the potential of different approaches to generate location-dependent urban meteorological data. We compare the meteorological output generated with the Weather Research and Forecasting (WRF) model, Urban Weather Generator, and morphing approach. These methods were compared based on empirical data (air temperature, humidity, and wind speed) collected from two distinct urban locations in Vienna, Austria, over 5 study periods. Our results suggest significant temporal and spatial discrepancies in resulting modeling output. Results further suggest better predictive performance in the case of high-density urban areas and under warmer and extreme conditions in spring and summer periods, respectively.