Estimating Boundary Layer Height from LiDAR Data under Complex Atmospheric Conditions Using Machine Learning

Abstract

Reliable estimation of the atmospheric boundary layer height (ABLH) is critical for a range of meteorological applications, including air quality assessment and weather forecasting. Several algorithms have been proposed to detect ABLH from aerosol LiDAR backscatter data. However, most of these focus on cloud-free conditions or use other ancillary instruments due to strong interference from clouds or residual layer aerosols. In this paper, a machine learning method named the Mahalanobis transform K-near-means (MKnm) algorithm is first proposed to derive ABLH under complex atmospheric conditions using only LiDAR-based instruments. It was applied to the micro pulse LiDAR data obtained at the Southern Great Plains site of the Atmospheric Radiation Measurement (ARM) program. The diurnal cycles of ABLH from cloudy weather were detected by using the gradient method (GM), wavelet covariance transform method (WM), K-means, and MKnm. Meanwhile, the ABLH obtained by these four methods under cloud or residual layer conditions based on micropulse LiDAR data were compared with the reference height retrieved from radiosonde data. The results show that MKnm was good at tracking the diurnal variation of ABLH, and the ABLHs obtained by it have remarkable correlation coefficients and smaller mean absolute error and mean deviation with the radiosonde-derived ABLHs than those measured by other three methods. We conclude that MKnm is a promising algorithm to estimate ABLH under cloud or residual layer conditions.