Abstract:

The atmospheric boundary layer has an important feature, that is, at the top of the boundary layer, there is a significant abrupt change in the vertical distribution of aerosol concentration.Using this feature, the active remote sensing device is used to remove the noise data through window smoothing, and an improved atmospheric boundary layer height inversion method based on gradient method-the window standard deviation method was proposed.Based on the backscattering profile data of the laser ceilometer, this method was used to retrieve the height of the boundary layer.In the case when the atmospheric aerosol is uniformly mixed at the boundary layer height, the inversion result is ideal.On this basis, comparing the inversion results of the window standard deviation method with the inversion results of the stepwise curve fitting method, it is found that the two methods have a good correlation, with a correlation coefficient of 0.94.The reason for the difference between the two methods is that the window standard deviation method does not consider the thickness of the entrainment layer while the stepwise curve fitting method considers the thickness of the entrainment layer.The window standard deviation method can reduce the influence of high-altitude background light noise on the inversion results.The height of the boundary layer retrieved by this method has the characteristics of strong continuity in time series, and the inversion results are more conducive to the study of the temporal change trend of the height of the atmospheric boundary layer.

Key words: Lidar, Boundary layer height inversion, Standard deviation, Sliding window, Ceilometer