A novel lidar gradient cluster analysis method of nocturnal boundary layer detection during air pollution episodes

Zhang, Yinchao; Chen, Su; Chen, Siying; Chen, He; Guo, Pan

The observation of the nocturnal boundary layer height (NBLH) plays an important role in air pollution and monitoring. Through 39 inline-formulad of heavy pollution observation experiments in Beijing (China), as well as an exhaustive evaluation of the gradient, wavelet covariance transform, and cubic root gradient methods, a novel algorithm based on the cluster analysis of the gradient method (CA-GM) of lidar signals is developed to capture the multilayer structure and achieve night-time stability. The CA-GM highlights its performance compared with radiosonde data, and the best correlation (0.85), weakest root-mean-square error (203 inline-formulam), and an improved 25 % correlation coefficient are achieved via the GM. Compared with the 39 inline-formulad experiments using other algorithms, reasonable parameter selection can help in distinguishing between layers with different properties, such as the cloud layer, elevated aerosol layers, and random noise. Consequently, the CA-GM can automatically address the uncertainty with multiple structures and obtain a stable NBLH with a high temporal resolution, which is expected to contribute to air pollution monitoring and climatology, as well as model verification.

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Zhang, Yinchao / Chen, Su / Chen, Siying / et al: A novel lidar gradient cluster analysis method of nocturnal boundary layer detection during air pollution episodes. 2020. Copernicus Publications.

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