A LiDAR-Based Clustering Method for Automated Atmospheric Boundary Layer Height Detection Over Complex Terrain

Abstract. The planetary boundary layer height (PBLH) is a critical physical parameter for characterizing atmospheric dynamics, thermodynamic structures, and pollutant dispersion. LiDAR, as a high-spatiotemporal-resolution remote sensing tool, is widely employed to retrieve PBLH by analyzing backscatter signals that reflect aerosol concentrations. However, in complex terrains such as valleys, the boundary layer structure exhibits significant non-homogeneity and complexity due to local terrain-induced dynamic effects. This complexity renders traditional retrieval methods susceptible to interference from multi-layer aerosols and low signal-to-noise ratios (SNR). To address these challenges, this paper proposes an automated identification method—the Physics-Constrained Adaptive K-means (PCA-Kmeans) clustering analysis—specifically tailored for complex valley environments. The PCA-Kmeans framework first constructs a multi-feature scoring mechanism—incorporating signal intensity, vertical gradients, temporal continuity, and vertical compactness—to apply physical constraints for screening clustering results. Subsequently, a gradient-weighted selection and median smoothing strategy are introduced to pinpoint the PBLH from the optimal cluster, effectively mitigating the interference of complex stratification induced by valley topography. The proposed method was validated using LiDAR observation data from the Vipava Valley, Slovenia, from 2018 to 2019. Using Radiosonde (RS) measurements as the reference benchmark, a comparative analysis was conducted between the PCA-Kmeans method, the Gradient Method (GRD), and the Standard Deviation Method (STD). The results indicate that in valley atmospheres influenced by thermally driven local circulations, the PCA-Kmeans method demonstrates superior robustness under low SNR and multi-layer aerosol conditions. The inversion results show higher consistency with RS data, effectively overcoming the identification biases of traditional algorithms caused by residual aerosol layers. This study provides a reliable and automated technical solution for the long-term monitoring of valley boundary layers under the influence of complex local circulations.