BOUNDARY-LAYER HEIGHT ESTIMATED BY CEILOMETER

msra(2009)

引用 24|浏览11
暂无评分
摘要
Boundary-layer height, being one of the most important parameters in air-quality modeling, can be evaluated by several different device and methods. However, none of these methods alone can be used as an operative regime. The idealized 3-step method partly fulfills the gap. The method fits the backscattering profile of ceilometer measurements into a weighted profile of three error functions, thus it has potential for determining three vertical aerosol layer heights including boundary-layer height, residual layer and surface layer. The method has been tested by ceilometer and radiosounding monitoring data of the Helsinki Testbed Campaign. The results show a strong correlation between the 3-step method and soundings (correlation coefficient r = 0.89, N = 89). One of the remote sensing instruments meeting these conditions is a ceilometer, which measures the atmospheric backscattering profile. The measured backscatter intensity depends mainly on particulate concentration in the air. Since in general aerosol concentrations in free atmosphere are lower than in boundary layer where most of the sources of aerosols are located, the boundary layer height can be distinguished by a strong gradient in the vertical back-scattering profile. In this work we have developed and evaluated a novel method for estimating the MH from ceilometer observations in clear sky situations. The applied ceilometer-method requires that vertical distribution of aerosols in the atmosphere includes one or more normally distributed aerosol layers. The intensity of backscattered laser beams is illustrated by a series of cumulative normal distributions (figure 1), which is also the basis of the applied fitting function. Fitted ceilometer data determines the heights of the aerosol layers including MH. The reference MH is evaluated using radiosounding data. The detailed treatment of the methods is presented in section 2.
更多
查看译文
关键词
ceilometer,mixing height,normal distribution,fitness function,surface layer,cumulant,boundary layer
AI 理解论文
溯源树
样例
生成溯源树,研究论文发展脉络
Chat Paper
正在生成论文摘要