Urban heat island (UHI) is an environmental issue faced by worldwide cities. Simulating UHI accurately can not only improve the understanding of urban-induced thermal and dynamic effects but also be of great help for investigating UHI-induced problems (e.g., increasing cooling energy consumption and heat waves). The accuracy of UHI simulation depends on the parameterization of urban morphology, including roof width, building height, and ground width for three urban classes (i.e., low-intensity residential, high-intensity residential, and commercial). In this study, first, we generated real-time monthly green vegetation fraction, leaf area index and surface albedo datasets on the Google Earth Engine Platform using MODIS datasets. Second, we derived the detailed urban classes via Point-of-Interest data using text analysis and spatial density distribution analysis. Third, we obtained road and building footprint layers on the Baidu web mapping platform via web crawler techniques for improved information of urban morphology.
Results/Conclusions
Our result presents spatial patterns and temporal trends of UHI simulated by WRF and their differences between simulations using updated real-time datasets and default datasets. The resulted spatially explicit temperature can be used in city-scale building energy use modeling for an improved understanding of UHI impact on building energy use, which is important for developing feasible options to improve building energy efficiency, meet goals for reduction of greenhouse gas emissions, and mitigate health issues due to extreme weather events such as heat waves.