The impact of urbanization on air stagnation: Shenzhen as case study

Li, Zhiqiang; Zhou, Yulun; Wan, Biao; Qin-xin, Chen; Huang, Bo; Cui, Yuanzheng; Chung, Hopun

doi:10.1016/j.scitotenv.2019.01.232

Cited by 21 publications

(19 citation statements)

References 27 publications

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“…The influence of urbanization on the surface wind speed has been investigated in previous studies based on observations , Peng et al 2018 and model simulations (Hou et al 2013, Li et al 2019b. Contrasting the observed data at the urban stations against those at the rural (or suburban) stations, a typical method (Wu et al 2018) for identifying the effects of urbanization, reveals that the urban stations show larger decreases in the surface wind speed than the rural (or suburban) stations , Peng et al 2018.…”

Section: Discussionmentioning

confidence: 99%

“…This has important implications for urban developments and our lives. Firstly, it exacerbates the problem of urban air pollution (Li et al 2019b). Climate projections suggest that future weather conditions will be more conducive to forming severe haze (Cai et al 2017); reduced wind speed due to urbanization will worsen air quality in urban regions.…”

Section: Discussionmentioning

confidence: 99%

“…By analyzing the observations at 822 surface weather stations, Vautard et al (2010) showed that the surface wind speed has been reduced by 5%-15% over almost all continents in the Northern Hemisphere (NH) midlatitudes over . A recent study also showed a decrease in surface wind speeds and wind power potential in most of NH over the past four decades (Tian et al 2019) exerting adverse impacts on windgenerated electricity (Sherman et al 2017) and the dispersion of air pollutants (Li et al 2019b), reducing evaporation (McVicar et al 2012), and affecting soil erosion (Zhang et al 2019). However, the mechanisms related to the observed wind stilling are not well understood (Wu et al 2018, Zeng et al 2018.…”

Section: Introductionmentioning

confidence: 99%

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Urbanization has stronger impacts than regional climate change on wind stilling: a lesson from South Korea

Chen

Jeong

Park

et al. 2020

Environ. Res. Lett.

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Wind stilling has been observed in many regions across the Northern Hemisphere; however, the related mechanisms are not well understood. Analyses of the wind speed variations in South Korea during 1993-2015 in this study reveal that the annual-mean surface wind speeds at rural stations have increased by up to 0.41 m s −1 decade −1 , while those at urban stations have decreased by up to −0.63 m s −1 decade −1 . The local wind speed variations are found to be negatively correlated with the population density at the corresponding observation sites. Gustiness analyses show the increase in local surface roughness due to urbanization can explain the observed negative wind speed trends at urban stations as the urbanization effect overwhelms the positive wind speed trend due to climate change. The observed negative wind speed trend in urban areas are not found in the regional climate model simulations in the Coordinated Regional Climate Downscaling Experiment-East Asia (CORDEX-EA) as these models do not take into account the impact of urbanization on wind variations during the period. This study suggests that urbanization can play an important role in the recent wind stilling in rapidly developing regions such as South Korea. Our results suggest that future climate projections in CORDEX-EA may overestimate wind speeds in urban areas, and that future regional climate projections need to consider the effects of urbanization for a more accurate projection of wind speeds.

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Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Urbanization has stronger impacts than regional climate change on wind stilling: a lesson from South Korea

Chen

Jeong

Park

et al. 2020

Environ. Res. Lett.

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“…We also compared the simulation results using land surface data before and after the refinement to investigate the impact of incoming data quality on the accuracy and granularity of simulation results. Typically, the quality of urban climate modeled results should be evaluated by comparing the modeled near-surface variables with their corresponding observed ones (Li et al, 2019a). We extracted and compared five near-surface meteorological variables -surface temperature and near-surface air temperature, wind speed, precipitation, and relative humidity -along the spatial and temporal dimension.…”

Section: Evaluation Methods For Incoming Data Qualitymentioning

confidence: 99%

“…We extracted and compared five near-surface meteorological variables -surface temperature and near-surface air temperature, wind speed, precipitation, and relative humidity -along the spatial and temporal dimension. Moreover, these critical modeled nearsurface variables are also compared with their corresponding observed ones using the statistic tools suggested by Li et al (2019a), which include a temporal comparison of spatial variation (TCSV), Perkins skill score (PSS), and probability density function of difference (PDFD). For more details on the TCSV, PSS, and PDFD, please refer to the companion paper -"Model evaluation of high-resolution urban climate simulations: using the WRF/Noah LSM/SLUCM model (Version 3.7.1) as a case study" (Li et al, 2019b).…”

Section: Evaluation Methods For Incoming Data Qualitymentioning

confidence: 99%

Incoming data quality control in high-resolution urban climate simulations: a Hong Kong–Shenzhen area urban climate simulation as a case study using the WRF/Noah LSM/SLUCM model (Version 3.7.1)

Wan

Zhou

et al. 2020

Geosci. Model Dev.

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Abstract. The growth of computational power unleashed the potential of high-resolution urban climate simulations using limited-area models in recent years. This trend empowered us to deepen our understanding of urban-scale climatology with much finer spatial–temporal details. However, these high-resolution models would also be particularly sensitive to model uncertainties, especially in urbanizing cities where natural surface texture is changed artificially into impervious surfaces with extreme rapidity. These artificial changes always lead to dramatic changes in the land surface process. While models capturing detailed meteorological processes are being refined continuously, the input data quality has been the primary source of biases in modeling results but has received inadequate attention. To address this issue, we first examine the quality of the incoming static data in two cities in China, i.e., Shenzhen and Hong Kong SAR, provided by the WRF ARW model, a widely applied state-of-the-art mesoscale numerical weather simulation model. Shenzhen has gone through an unprecedented urbanization process in the past 30 years, and Hong Kong SAR is another well-urbanized city. A significant proportion of the incoming data is outdated, highlighting the necessity of conducting incoming data quality control in the region of Shenzhen and Hong Kong SAR. Therefore, we proposed a sophisticated methodology to develop a high-resolution land surface dataset in this region. We conducted urban climate simulations in this region using both the developed land surface dataset and the original dataset utilizing the WRF ARW model coupled with Noah LSM/SLUCM and evaluated the performance of modeling results. The performance of modeling results using the developed high-resolution urban land surface datasets is significantly improved compared to modeling results using the original land surface dataset in this region. This result demonstrates the necessity and effectiveness of the proposed methodology. Our results provide evidence of the effects of incoming land surface data quality on the accuracy of high-resolution urban climate simulations and emphasize the importance of the incoming data quality control.

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A High-Resolution Urban Land Surface Dataset for the Hong Kong-Shenzhen Area

Wan

2022

Business Information Systems Workshops

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The impact of urbanization on air stagnation: Shenzhen as case study

Cited by 21 publications

References 27 publications

Urbanization has stronger impacts than regional climate change on wind stilling: a lesson from South Korea

Urbanization has stronger impacts than regional climate change on wind stilling: a lesson from South Korea

Incoming data quality control in high-resolution urban climate simulations: a Hong Kong–Shenzhen area urban climate simulation as a case study using the WRF/Noah LSM/SLUCM model (Version 3.7.1)

A High-Resolution Urban Land Surface Dataset for the Hong Kong-Shenzhen Area

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