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• W4220658052 startingPage "101385" @default.
• W4220658052 abstract "Accurate modeling of urban climate is essential to predict potential environmental risks in cities. Urban datasets, such as urban land use and urban canopy parameters (UCPs), are key input data for urban climate models and largely affect their performance. However, access to reliable urban datasets is a challenge, especially in fast urbanizing countries. In this study, we developed a high-resolution national urban dataset in China (NUDC) for the WRF/urban modeling system and evaluated its effect on urban climate modeling. Specifically, an optimization method based on building morphology was proposed to classify urban land use types. The key UCPs, including building height and width, street width, surface imperviousness, and anthropogenic heat flux, were calculated for both single-layer Urban Canopy Model (UCM) and multiple-layer Building Energy Parameterization (BEP). The results show that the derived morphological-based urban land use classification could better reflect the urban characteristics, compared to the socioeconomic-function-based classification. The UCPs varied largely in spatial within and across the cities. The integration of the developed urban land use and UCPs datasets significantly improved the representation of urban canopy characteristics, contributing to a more accurate modeling of near-surface air temperature, humidity, and wind in urban areas. The UCM performed better in the modeling of air temperature and humidity, while the BEP performed better in the modeling of wind speed. The newly developed NUDC can advance the study of urban climate and improve the prediction of potential urban environmental risks in China." @default.
• W4220658052 created "2022-04-03" @default.
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• W4220658052 date "2022-07-01" @default.
• W4220658052 modified "2023-09-26" @default.
• W4220658052 title "Improving the WRF/urban modeling system in China by developing a national urban dataset" @default.
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• W4220658052 doi "https://doi.org/10.1016/j.gsf.2022.101385" @default.
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