Abstract
Abstract. The observation of the nocturnal boundary layer height (NBLH) plays an important role in air pollution and monitoring. Through 39 d 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 m), and an improved 25 % correlation coefficient are achieved via the GM. Compared with the 39 d 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.
Highlights
Air pollution has an important impact on human health, climatic patterns, and the ecological environment (Shi et al, 2019; Su et al, 2020a; Wang et al, 2020)
The cluster analysis of the gradient method (CA-gradient method (GM)) had the highest consistency among the classical methods, with the highest correlation coefficient (0.85), the weakest root-mean-square error (RMSE) (203 m), the smaller mean bias (28 m), and the minimum mean relative absolute difference (PRD) (17 %) (Table 2)
Compared with the wavelet covariance transform (WCT) and cubicroot gradient method (CRGM), the former underestimated the nocturnal boundary layer height (NBLH) by approximately 13 m, whereas the latter overestimated the altitude by 186 m
Summary
Air pollution has an important impact on human health, climatic patterns, and the ecological environment (Shi et al, 2019; Su et al, 2020a; Wang et al, 2020). Some graph theory methods, such as the extended Kalman filter (Banks et al, 2014), Pathfinder and PathfinderTURB (de Bruine et al, 2017; Poltera et al, 2017), k-means clustering (Liu et al, 2018; Toledo et al, 2014), and the STRAT2D algorithm (Haeffelin et al, 2012) have been proposed to yield promising results via an automated method that reduces the incorrect detection of ABLH These techniques strongly depend on the vertical distribution of particle layers (aerosols and clouds) and are prone to increase the uncertainty under complicated multilayer conditions. More instrument and multi-wavelength lidar systems are combined to obtain more accurate results identify the EALs (Liu et al, 2019; Ortega et al, 2016) Digressing from these previous efforts to estimate the ABLH, we present a new approach – cluster analysis of the gradient method (CA-GM) – to overcome the multilayer structure and remove the noise fluctuation of NBLH with raw data resolution. The results were evaluated by comparison with the nearby radiosonde site, and they were confirmed through continuous observation via traditional methods in different atmospheric layers
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