Improved mixing height estimates from atmospheric LiDAR measurements

Abstract

In this study, an improvement in the estimation of the mixing height is carried out by introducing a time-dependent maximum and minimum analysis altitude (TDMMAA) in the Haar wavelet covariance transform (WCT) technique applied to atmospheric light detection and ranging (LiDAR) measurements generally used in mixing height estimations. Results showed that the standard method usually overestimates the mixing height and that the proposed algorithm is more robust against clouds and residual layers in the boundary layer that generally occur in the nighttime and early morning. The TDMMAA method does have a bit of subjectivity especially in defining the analysis periods as well as the top and bottom of the analysis altitudes as it needs user experience and guidance. Moreover, the algorithm needs to be further objectively refined for automation and operational use, validated with in-situ profile measurements, and tested during different atmospheric conditions.