Approaches to Urban Weather Modeling: A Vienna Case Study

Abstract

Given the adverse implications of both urbanization and global climate change for cities, specifically regarding issues such as human health and comfort, local air quality, and increased summertime energy use in buildings, it is becoming imperative to develop models that can accurately predict the complex and nonlinear interactions between the surrounding urban fabric and local climatic context. Over the past years, a number of comprehensive tools have been widely applied for the generation of near-surface urban climatic information. In this paper, we report on the potential of two alternative approaches to urban climate modeling. Specifically, we compare the climatic output generated with Urban Weather Generator (UWG) and the Weather Research and Forecasting (WRF) model. The WRF model has been widely applied due to its capability of downscaling global weather data to finer resolutions, thus representing the location-specific microclimatic information, while considering the interactions with the surrounding urban and regional context. However, this approach is computationally intensive. The UWG was recently introduced as a simpler alternative to such complex models. The tool morphs rural weather data to represent urban conditions given a set of location-specific morphological parameters. In the present paper, WRF and UWG methods were compared based on empirical data pertaining to air temperature, wind speed, and humidity, collected from 12 locations in the city of Vienna, Austria, over 5 distinct time periods. In general, our results suggest that, as compared to the WRF model, the UWG model results are closer to monitored data. However, during the extreme conditions in summer, the WRF model was found to perform better. It was further noted that the discrepancy between the two models increases with decreasing temperatures, thus revealing a higher offset between UWG and WRF output during the winter period.