Quantifying urban heat island intensity and its physical mechanism using WRF/UCM

Li, Huidong; Zhou, Yuyu; Wang, Xun; Zhou, Xu; Zhang, Huiwen; Sodoudi, Sahar

doi:10.1016/j.scitotenv.2018.10.025

Cited by 125 publications

(60 citation statements)

References 49 publications

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“…While ENVI-met is regarded as a reliable and validated microclimate simulation tool at early urban design stages, it doesn't support bigger climate scales' simulations with regard to the city and regional scales which can be generated through field measurements in order to either project them in future with consideration to climate change scenarios, downscale them in order to generate recent weather files or couple different climate scales models' generated by them, the work of Li, H., et al, is just an example not to mention [122,123]. On the other hand, microclimate simulation tools have limitations with regard to computational optimization of urban form designs according to environmental conditions.…”

Section: Limitations Of Simulation Toolsmentioning

confidence: 99%

A Review and Insights for Eleven Years of Urban Microclimate Research Towards a New Egyptian ERA of Low Carbon, Comfortable and Energy-Efficient Housing Typologies

et al. 2020

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Since the mid-1970s, urban development in Egypt has sprawled far from the distinguished compact arid built environment, as the court-yarded housing typologies that completed the vernacular picture of desert architecture have been discarded in the early 20th century. This has motivated urban microclimate research in Egypt. The main objective was initially to improve outdoor thermal comfort. Therefore, Egyptian research started with assessing different existing patterns for the sake of climate responsive and sustainable urban design practice characterized with low carbon, thermal comfort and energy efficiency in such a hot arid conditions. That is why the review workflow of this article has followed a design progress workflow that led to solving design complexities with regard to generating housing urban forms on a microclimate basis rather than an article regular review workflow in order to extract the research gaps and conclude insights. After discussing a general framework for generating housing sustainable design identified from the concluded gaps, the main conclusion is a vision and a call to integrate the Urban microclimate-Building passiveness-Renewables design dimensions, UBR, towards the evolution of a new era of energy efficient housing typologies and a 5th generation of Egyptian sustainable cities where the 1st generation of new Egyptian cities started 1970s.

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Section: Limitations Of Simulation Toolsmentioning

confidence: 99%

A Review and Insights for Eleven Years of Urban Microclimate Research Towards a New Egyptian ERA of Low Carbon, Comfortable and Energy-Efficient Housing Typologies

et al. 2020

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“…In addition, advanced computer technology along with high spatial and temporal resolution of satellite imagery has been used to measure UHIs in more detail [52,53]. The UHI phenomenon can also be studied by using numerical atmospheric simulation (climate) models [54][55][56][57][58][59][60]. The latter approach provides a unique ability to study the response of various land-cover change scenarios on UHI.…”

Section: Introductionmentioning

confidence: 99%

The Sensitivity of Urban Heat Island to Urban Green Space—A Model-Based Study of City of Colombo, Sri Lanka

et al. 2019

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Urbanization continues to trigger massive land-use land-cover change that transforms natural green environments to impermeable paved surfaces. Fast-growing cities in Asia experience increased urban temperature indicating the development of urban heat islands (UHIs) because of decreased urban green space, particularly in recent decades. This paper investigates the existence of UHIs and the impact of green areas to mitigate the impacts of UHIs in Colombo, Sri Lanka, using UrbClim, a boundary climate model that runs two classes of simulations, namely urbanization impact simulations, and greening simulations. The urbanization impact simulation results show that UHIs spread spatially with the reduction of vegetation cover, and increases the average UHI intensity. The greening simulations show that increasing green space up to 30% in urban areas can decrease the average air temperature by 0.1 °C. On the other hand, converting entire green areas into urban areas in suburban areas increases the average temperature from 27.75 °C to 27.78 °C in Colombo. This demonstrates the sensitivity of UHI to vegetation cover in both urban and suburban areas. These seemingly small changes are average grid values and may indicate much higher impacts at sub-grid levels.

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“…is effect can directly and indirectly affect regional climate [4], energy use [5], air quality [6], urban hydrology [7], soil physicochemical properties [8], creature distribution and activities [9], and human health, comfort, and quality of life [10]. e approaches used for studying urban heat island (UHI) include weather station observations [11], fixed-point field measurements [12], mobile belt transect surveys [13], numerical modelling [14], and remote sensing monitoring, which have been widely accepted and adopted [15,16]. Although land surface temperatures (LSTs) derived by remote sensing are not identical to above-ground air temperatures, they are closely related [17,18].…”

Section: Introductionmentioning

confidence: 99%

Associated Determinants of Surface Urban Heat Islands across 1449 Cities in China

Wang

Liu

et al. 2019

Advances in Meteorology

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The thermal environment is closely related to human well-being. Determinants of surface urban heat islands (SUHIs) have been extensively studied. Nevertheless, some research fields remain blank or have conflicting findings, which need to be further addressed. Particularly, few studies focus on drivers of SUHIs in massive cities with different sizes under various contexts at large scales. Using multisource data, we explored 11 determinants of surface urban heat island intensity (SUHII) for 1449 cities in different ecological contexts throughout China in 2010, adopting the Spearman and partial correlation analysis and machine learning method. The main results were as follows: (1) Significant positive partial correlations existed between daytime SUHII and the differences in nighttime light intensity and built-up intensity between cities and their corresponding villages except in arid or semiarid western China. The differences in the enhanced vegetation index were generally partially negatively correlated with daytime and nighttime SUHII. The differences in white sky albedo were usually partially negatively correlated with nighttime SUHII. The mean air temperature was partially positively correlated with nighttime SUHII in 40% of cases. Only a few significant partial relationships existed between SUHII and urban area, total population, and differences in aerosol optical depth. The explanation rates during daytime were larger than during nighttime in 72% of cases. The largest and smallest rates occurred during summer days in humid cold northeastern China (63.84%) and in southern China (10.44%), respectively. (2) Both the daytime and nighttime SUHII could be well determined by drivers using the machine learning method. The RMSE ranged from 0.49°C to 1.54°C at a national scale. The simulation SUHII values were always significantly correlated with the actual SUHII values. The simulation accuracies were always higher during nighttime than daytime. The highest accuracies occurred in central-northern China and were lowest in western China during both daytime and nighttime.

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Quantifying urban heat island intensity and its physical mechanism using WRF/UCM

Cited by 125 publications

References 49 publications

A Review and Insights for Eleven Years of Urban Microclimate Research Towards a New Egyptian ERA of Low Carbon, Comfortable and Energy-Efficient Housing Typologies

A Review and Insights for Eleven Years of Urban Microclimate Research Towards a New Egyptian ERA of Low Carbon, Comfortable and Energy-Efficient Housing Typologies

The Sensitivity of Urban Heat Island to Urban Green Space—A Model-Based Study of City of Colombo, Sri Lanka

Associated Determinants of Surface Urban Heat Islands across 1449 Cities in China

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