Scaled outdoor experimental studies of urban thermal environment in street canyon models with various aspect ratios and thermal storage

Chen, Guanwen; Wang, Dongyang; Wang, Qun; Li, Yuguo; Wang, Xuemei; Hang, Jian; Gao, Peng; Ou, Cuiyun; Wang, Kai

doi:10.1016/j.scitotenv.2020.138147

Cited by 105 publications

(38 citation statements)

References 84 publications

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“…Anthropogenic heat flux is closely related to the change in built-up area around the stations (Figure 1B). Therefore, anthropogenic heat flux can be considered to reliably reflect the effects of both anthropogenic emissions and land-use change related to latent heat flux and sensible heat flux (Jiang et al, 2019;Chen et al, 2020). Therefore, classified the stations by the mean anthropogenic heat fluxes around all the stations.…”

Section: Division and Matching Of Urban And Rural Stationsmentioning

confidence: 99%

Dual Effects of Synoptic Weather Patterns and Urbanization on Summer Diurnal Temperature Range in an Urban Agglomeration of East China

Guo

Zhang

Wang

et al. 2021

Front. Environ. Sci.

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Previous studies on the impact of urbanization on the diurnal temperature range (DTR) have mainly concentrated on the intra-seasonal and interannual–decadal scales, while relatively fewer studies have considered synoptic scales. In particular, the modulation of DTR by different synoptic weather patterns (SWPs) is not yet fully understood. Taking the urban agglomeration of the Yangtze River Delta region (YRDUA) in eastern China as an example, and by using random forest machine learning and objective weather classification methods, this paper analyzes the characteristics of DTR and its urban–rural differences (DTRU–R) in summer from 2013 to 2016, based on surface meteorological observations, satellite remote sensing, and reanalysis data. Ultimately, the influences of urbanization-related factors and different large-scale SWPs on DTR and DTRU–R are explored. Results show that YRDUA is controlled by four SWPs in the 850-hPa geopotential height field in summer, and the DTRs in three sub-regions are significantly different under the four SWPs, indicating that they play a role in regulating the DTR in YRDUA. In terms of the average DTR for each SWP, the southern sub-region of the YRDUA is the highest, followed by the northern sub-region, and the middle sub-region is the lowest, which is most significantly affected by high-level urbanization and high anthropogenic heat emission. The DTRU–R is negative and differs under the four different SWPs with variation in sunshine and rainfall. The difference in anthropogenic heat flux between urban and rural areas is one of the potentially important urbanization-related drivers for DTRU–R. Our findings help towards furthering our understanding of the response of DTR in urban agglomerations to different SWPs via the modulation of local meteorological conditions.

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Section: Division and Matching Of Urban And Rural Stationsmentioning

confidence: 99%

Dual Effects of Synoptic Weather Patterns and Urbanization on Summer Diurnal Temperature Range in an Urban Agglomeration of East China

Guo

Zhang

Wang

et al. 2021

Front. Environ. Sci.

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“…In addition, urbanization is not only reflected by the two-dimensional horizontal urban expansion but also by the vertical morphology of the three-dimensional urban spatial structure. Previous studies suggested that the vertical geometry of urban canopy building also had an impact on local microclimate (Oke, 2004;Bonacquisti et al, 2006;Chen et al, 2020). In the future, we will expand three-dimensional indicators to supply the indicators of urbanization bias correction.…”

Section: Discussionmentioning

confidence: 98%

“…Urbanization directly affects the types of land use/cover and anthropogenic heat emissions around meteorological stations, leading to major changes in the observation environment (Gallo et al, 1996;Peterson, 2006;Trusilova et al, 2008;Chen et al, 2020), which in turn has an important impact on the accuracy, representativeness, and homogeneity of meteorological observation data (Davey and Sr, 2005;Vose, 2005). The contribution of the socalled urbanization bias (the effect of urbanization on surface air temperature (SAT), the list of abbreviations used in this article and their expanded names can be found in Appendix A) to meteorological observation data usually stems from changes in the observation environment against the background of urbanized areas (Ren et al, 2017).…”

Section: Introductionmentioning

confidence: 99%

A New Method for Correcting Urbanization-Induced Bias in Surface Air Temperature Observations: Insights From Comparative Site-Relocation Data

Tao

Huang

Sun³

et al. 2021

Front. Environ. Sci.

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The effect of urbanization on surface air temperature (SAT) is one of the most important systematic biases in SAT series of urban stations. Correcting this so-called urbanization bias has the potential to provide accurate basic data for long-term climate change monitoring and research. In the western region of the Yangtze River Delta, 42 meteorological stations with site-relocation history from 2009 to 2018 were selected to analyze the statistical characteristics of the differences in comparative site-relocation daily average SAT. The annual average differences in comparative site-relocation SAT series between the old and the new stations (SATDON) were used to characterize the impact of urbanization bias on the air temperature observation series. Using remote sensing technology, spatial datasets of land-use, landscape, and geometric parameters of the underlying surface in the 5-km buffer zone around the station were established as the observed environmental factors of the site, and the differences in these observed environmental factors (DOEFs) between the old and the new stations were calculated to indicate the change induced by urbanization. Next, multiple linear regression models of SATDON and DOEFs were constructed, showing that the error range of the model for simulated SATDON was 3.66–18.21%, and the average error was 10.09%. Finally, this new correction method (NCM) and conventional correction method (CCM) were applied to the correction of the urbanization bias of SAT series at Hefei station. After comparison, it is found that the NCM could reveal clear contributions of the rapid and slow stages of the urbanization process and resultant environmental changes around the stations to the observed SAT. In summary, the NCM based on remote sensing technology can more reasonably and effectively correct the urbanization bias caused by local human activities, as well as reduce the error caused by the selection of reference stations via the conventional correction method.

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“…The Ten mean annual zonation is characterized by a major inner land zone with temperature below 22 C and a costal and mountain zone with a temperature between 23 and 27 C. The hotter Ted in mountain and coastal zones in comparison to Ten is due to the proximity to the sea and the higher elevations that leads to a temperate air temperature environment. DTR is an essential element of the climate system and was used for urban thermal environment analysis, for example, extensive heat waves, heat-related health issues, landscaping and recreation, and engineering of heat transfer efficiency in buildings and roads (Duan et al 2014;Wang and Upreti 2019;Yang et al 2020;Chen et al 2020). The pattern of DTR closely resembles the pattern of Ted, where the mountains and coastal zones indicate cooler temperatures than the inner land zones.…”

Section: (A)mentioning

confidence: 99%

Retrieval of monthly maximum and minimum air temperature using MODIS aqua land surface temperature data over the United Arab Emirates

Alqasemi

Hereher

Al-Quraishi³

et al. 2020

Geocarto International

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Spatially distributed air temperature (Ta) data are essential for environmental studies. Ta data are collected from meteorological stations of sparse distribution. This problem can be overcome by using remotely sensed datasets at different scales. This study used land-based temperature measurements and satellite data for estimating Ta distribution over the United Arab Emirates. Landbased Ta data from 11 weather stations during 2003 to 2019 were used with MODIS Aqua LST for both daytime (LSTd) and nighttime (LSTn) data. The results indicate a significant correlation between LST and Ta with regression coefficients R 2 > 0.94/0.96 and Root Mean Square Error about 1.75/0.97 C of LSTd/T max and LSTn/T min , respectively. Large variability was observed between the daytime and nighttime mean temperature distribution indicating the importance of MODIS LST as a proxy for Ta. These countrywide Ta grids provide vital tools for the planning of environmental and economic developments in the era of global climate change.

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Scaled outdoor experimental studies of urban thermal environment in street canyon models with various aspect ratios and thermal storage

Cited by 105 publications

References 84 publications

Dual Effects of Synoptic Weather Patterns and Urbanization on Summer Diurnal Temperature Range in an Urban Agglomeration of East China

Dual Effects of Synoptic Weather Patterns and Urbanization on Summer Diurnal Temperature Range in an Urban Agglomeration of East China

A New Method for Correcting Urbanization-Induced Bias in Surface Air Temperature Observations: Insights From Comparative Site-Relocation Data

Retrieval of monthly maximum and minimum air temperature using MODIS aqua land surface temperature data over the United Arab Emirates

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