Estimate of Boundary-Layer Depth Over Beijing, China, Using Doppler Lidar Data During SURF-2015

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• 作者：Meng Huang ; Zhiqiu Gao ; Shiguang Miao ; Fei Chen…
• 关键词：Doppler lidar ; Turbulence ; Urban boundary layer
• 刊名：Boundary-Layer Meteorology
• 出版年：2017
• 出版时间：March 2017
• 年：2017
• 卷：162
• 期：3
• 页码：503-522
• 全文大小：2615KB
• 刊物类别：Earth and Environmental Science
• 刊物主题：Atmospheric Sciences; Meteorology; Atmospheric Protection/Air Quality Control/Air Pollution;
• 出版者：Springer Netherlands
• ISSN：1573-1472
• 卷排序：162

文摘

Planetary boundary-layer (PBL) structure was investigated using observations from a Doppler lidar and the 325-m Institute of Atmospheric Physics (IAP) meteorological tower in the centre of Beijing during the summer 2015 Study of Urban-impacts on Rainfall and Fog/haze (SURF-2015) field campaign. Using six fair-weather days of lidar and tower data under clear to cloudy skies, we evaluate the ability of the Doppler lidar to probe the urban boundary-layer structure, and then propose a composite method for estimating the diurnal cycle of the PBL depth using the Doppler lidar. For the convective boundary layer (CBL), a threshold method using vertical velocity variance \((\sigma _w^2 >0.1\,\hbox {m}^{2}\hbox {s}^{-2})\) is used, since it provides more reliable CBL depths than a conventional maximum wind-shear method. The nocturnal boundary-layer (NBL) depth is defined as the height at which \(\sigma _w^2\) decreases to 10 % of its near-surface maximum minus a background variance. The PBL depths determined by combining these methods have average values ranging from \(\approx \)270 to \(\approx \)1500 m for the six days, with the greatest maximum depths associated with clear skies. Release of stored and anthropogenic heat contributes to the maintenance of turbulence until late evening, keeping the NBL near-neutral and deeper at night than would be expected over a natural surface. The NBL typically becomes more shallow with time, but grows in the presence of low-level nocturnal jets. While current results are promising, data over a broader range of conditions are needed to fully develop our PBL-depth algorithms.