Investigating atmospheric and convective boundary layer heights via active optical sensor signals and statistical techniques

Abstract

The mixing of air layers, wind shear components, mountain waves, aerosol particles, and other pollutants causes rotational turbulence that affects the lowest and densest layer of the earth's surface troposphere. This turbulence results in a significant change in the height of the convective boundary layer (CBLH) over a day. To analyze the behavior of the convective boundary layer, a statistical technique is used to observe the variation in peak positions of Light Detection and Ranging (LiDAR) back-scatter signals. Furthermore, a statistical method is provided to obtain the best-fit distribution to demonstrate how the result leads to the physical observation of the data. This method involves selecting a suitable distribution for the dataset and defining the percentile bins to use a specific range of the data. The observed frequencies and expected frequencies are then calculated to formulate the chi-square statistics, which are used to determine the fitness of the distribution. Finally, a histogram with the best-fit distribution curve is plotted to examine whether the formulation of the function is appropriate. This approach provides a comprehensive understanding of the behavior of the entire boundary layer and can be used to inform further analysis and interpretation of the data.