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Boundary Layer Height as Estimated from Radar Wind Profilers in Four Cities in China: Relative Contributions from Aerosols and Surface Features

Authors :
Boming Liu
Jianping Guo
Wei Gong
Yifan Shi
Shikuan Jin
Source :
Remote Sensing, Vol 12, Iss 10, p 1657 (2020)
Publication Year :
2020
Publisher :
MDPI AG, 2020.

Abstract

The turbulent mixing and dispersion of air pollutants is strongly dependent on the vertical structure of the wind, which constitutes one of the major challenges affecting the determination of boundary layer height (BLH). Here, an adaptive method is proposed to estimate BLH from measurements of radar wind profilers (RWPs) in Beijing (BJ), Nanjing (NJ), Chongqing (CQ), and Wulumuqi (WQ), China, during the summer of 2019. Validation against simultaneous BLH estimates from radiosondes (RSs) yielded a correlation coefficient of 0.66, indicating that the method can be used to derive BLH from RWPs. Diurnal variations of BLH and the ventilation coefficient (VC) at four sites were then examined. A distinct diurnal cycle of BLH was observed over all four cities; BLH gradually increased from sunset, reached a maximum in the afternoon, and then dropped sharply after sunset. The maximum hourly average BLH (1.426 ± 0.46 km) occurred in WQ, consistent with the maximum hourly mean VC larger than 5000 m2/s observed there. By comparison, the diurnal variation of VC was not strong, with values ranging between 2000 and 3000 m2/s, likely owing to the high-humidity environment. Furthermore, surface sensible heat flux, latent heat flux, and dry mass of particulate matter with aerodynamic diameter ≤2.5 µm (PM2.5) concentrations were found to somehow affect the vertical structure of wind and thermodynamic features, leading to a difference between RS and RWP BLH estimates. This indicates that the atmospheric environment can affect BLH estimates using RWP data. The BLH results from RWPs were better in some specific cases. These findings show great potential of RWP measurements in air quality research, and will provide key data references for policy-making toward emission reductions.

Details

Language :
English
ISSN :
20724292
Volume :
12
Issue :
10
Database :
Directory of Open Access Journals
Journal :
Remote Sensing
Publication Type :
Academic Journal
Accession number :
edsdoj.352e304ec1d8468fb3eb170b263a0602
Document Type :
article
Full Text :
https://doi.org/10.3390/rs12101657