Urban-scale air temperature estimation: development of an empirical model based on mobile transects
The urban microclimate is influenced by many factors which trigger the well-known Urban Heat Island (UHI) phenomenon. Different approaches have been developed in the literature for estimating urban temperatures, but a compromise is always needed, either spatial or temporal. To solve this issue, this...
| Authors: | , , , |
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| Format: | article |
| Status: | Versión aceptada para publicación |
| Publication Date: | 2020 |
| Country: | España |
| Institution: | Universidad de Sevilla (US) |
| Repository: | idUS. Depósito de Investigación de la Universidad de Sevilla |
| OAI Identifier: | oai:idus.us.es:11441/166670 |
| Online Access: | https://hdl.handle.net/11441/166670 https://doi.org/10.1016/j.scs.2020.102471 |
| Access Level: | Open access |
| Keyword: | Urban climate Urban Heat Island Microclimate Mobile transects GIS Temperature forecasting |
| Summary: | The urban microclimate is influenced by many factors which trigger the well-known Urban Heat Island (UHI) phenomenon. Different approaches have been developed in the literature for estimating urban temperatures, but a compromise is always needed, either spatial or temporal. To solve this issue, this work presents a new way for estimating urban temperatures with a fine spatial resolution (even specific streets) while also keeping a high temporal resolution (hourly time-steps) for prolonged periods. This is done by developing an empirical model, based on the measurements of a reference weather station and data taken from mobile transects. The proposed method was tested in Seville (Spain). The validation was done comparing the prediction of the model with the measurements of a fixed temperature sensor, from the 7th of June 2019 until the 7th of December 2019 (4390 hours). The results showed a high R2 coefficient of 0.976 and a low RMSE of 1.21, improving the accuracy of previous literature for estimating urban temperatures. The methodology is applicable for any geographical location around the world, with different climates or population densities. In addition, it offers a precise way to verify the real impact of UHI mitigation strategies and concentrate climate change mitigation efforts. |
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