Assessing CALIOP-Derived Planetary Boundary Layer Height Using Ground-Based Lidar

A mixed layer height parameterization in a 3-D chemical transport model: Implications for gas and aerosol simulations

Vertical mixing within the planetary boundary layer (PBL) is crucial for determining surface-level pollutant concentrations. However, standard PBL schemes in chemical transport models (CTMs) often fail to adequately define the upper bounds of vertical mixing, particularly at night. This limitation frequently results in overestimated nocturnal concentrations of pollutants near the surface. To address this issue, we propose a parameterization of mixed layer height (MLH) derived from the Yonsei University (YSU) PBL scheme and thoroughly evaluate it by comparing simulations with various observations. We utilized the Weather Research and Forecasting model coupled with GEOS-Chem (WRF-GC) to simulate gas and aerosol distributions over South Korea during the Satellite Integrated Joint Monitoring of Air Quality (SIJAQ) campaign in 2021. The WRF-GC simulations incorporating the MLH parameterization improved the excessive titration of O3 and the overproduction of HNO3 and NO3- in the model. Consequently, the model performances in gaseous and aerosol simulations showed a better agreement with observations, with changes in normalized mean biases (NMBs) of NOX (from 50 % to -27 %), O3 (from -49 % to -28 %), NO3- (from 126 % to 91 %), NH4+ (from 113 % to 85 %), BC (from 322 % to 135 %), and PM2.5 (from 58 % to 28 %).

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

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.