Improved mixing height estimates from atmospheric LiDAR measurements

Ronald Macatangay, Worapop Thongsame, Raman Solanki, Ying Jen Wu, Sheng Hsiang Wang, Titaporn Supasri, Jirasak Noisapung

Research output: Contribution to journalConference articlepeer-review


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.

Original languageEnglish
Article number012053
JournalJournal of Physics: Conference Series
Issue number1
StatePublished - 7 Jan 2022
Event16th Siam Physics Congress, SPC 2021 - Virtual, Online
Duration: 24 May 202125 May 2021


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