Aerosol characteristics inversion based on the improved lidar ratio profile with the ground-based rotational Raman–Mie lidar

Ji, Hongzhu; Zhang, Yinchao; Chen, Siying; Chen, He; Guo, Pan

doi:10.1016/j.optcom.2018.02.003

Cited by 15 publications

(3 citation statements)

References 25 publications

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“…A single-wavelength Raman-Mie lidar is operated in the campus of Beijing institute of Technology, providing aerosol, cloud, ABLHs and temperature measurements. This lidar system has been continuously improved to capture the loading of aerosols (Chen et al, 2014;Ji et al, 2018a). The standardized range-corrected signal (RCS) is subjected to correlation lidar factor correction (correction of electronic noise error, background noise error, overlap factor) and distance correction.…”

Section: The Bit-lidar Systemmentioning

confidence: 99%

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

Zhang¹,

Chen²,

Chen³

et al. 2020

Preprint

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Abstract. The observation of the nocturnal boundary layer height (NBLH) plays an important role in air pollution and monitoring. Through 39 days heavily polluted observation experiment over Beijing (China) and exhaustive evaluation of gradient method (GM), wavelet covariance transform method (WCT) and cubic roots gradient method (CRGM), a novel algorithm based on cluster analysis of gradient method (CA-GM) of lidar signal is developed to capture the multilayer structure and achieve stability in the nighttime. The CA-GM highlights its performance in comparison with radiosonde data, the best correlation (0.85), the weakest root mean square error (203 m), and the improved 25 % correlation coefficient by the GM. In comparison with long-term experiments with other algorithms, a reasonable parameter selection can distinguish layers with different properties, such as the cloud layer, elevated aerosol layers, and random noise. Consequently, the CA-GM can automatically deal with the uncertainty of the multiple structures and obtain a stable NBLH with a high time resolution, which expected to contribute to air pollution monitoring and climatologies, as well as model verification.

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Section: The Bit-lidar Systemmentioning

confidence: 99%

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

Zhang¹,

Chen²,

Chen³

et al. 2020

Preprint

Self Cite

View full text Add to dashboard Cite

show abstract

“…Therefore, a study of the aerosol extinction coefficient can indirectly lead to a prediction of air quality. Moreover, accurate inversion of the extinction coefficient can also support the study of atmospheric visibility and particle size, which is also very important [14]. However, for Mie-scattering lidar, the lidar return equation contains two unknowns, i.e., the aerosol extinction coefficient and the backscattering coefficient; therefore, it is impossible to obtain the inversion results directly.…”

Section: Introductionmentioning

confidence: 99%

Novel Inversion Algorithm for the Atmospheric Aerosol Extinction Coefficient Based on an Improved Genetic Algorithm

Mao

et al. 2022

Photonics

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As an important atmospheric component, aerosols play a very important role in the radiation budget balance of the earth–atmosphere system. To study the optical characteristics of aerosols, it is necessary to use an inversion algorithm to process the lidar return signal to obtain both the aerosol extinction coefficient and the backscattering coefficient. However, the lidar return power equation is ill-conditioned and contains two unknown parameters, meaning that traditional inversion algorithms must be solved by adopting certain assumptions (e.g., a uniform atmosphere and the lidar ratio), which to a certain extent can seriously affect the inversion accuracy. Here, to improve the accuracy of the aerosol extinction coefficient inversion, an inversion method based on an improved genetic algorithm is proposed. Using the U.S. Standard Atmosphere model and the return power equation, the aerosol extinction coefficient and the backscattering coefficient are independent variables that randomly provide initial values to simulate the theoretical lidar power. Then, the genetic algorithm is used to approximate the theoretical lidar power to the measured lidar return power with height; when the two are infinitely close, the values of the corresponding two independent variables (i.e., the extinction and backscattering coefficients) are inverted. Experiments performed to compare the different effects between a simple genetic algorithm and the improved genetic algorithm showed the proposed method capable of inverting the aerosol extinction coefficient without reliance on traditional inversion methods, representing a novel approach to the inversion of the aerosol extinction coefficient and the backscattering coefficient.

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“…The primary anthropogenic emission source is particulate matter (PM), which is the major source of severe haze in Beijing (Lv et al, 2020;Ma et al, 2019). Many passive and active remote sensing instruments have been combined to observe aerosol optical and microphysical properties (Ji et al, 2018b;Wang et al, 2019), the relationships between PM and meteorology (Li et al, 2019;Zhang et al, 2015), and aerosol-atmospheric boundary layer height (ABLH) interactions (Dong et al, 2017;Su et al, 2020b). With the development in star and moon photometry, continuous day-to-night detection has improved the estimation of column-integrated aerosol properties at night.…”

Section: Introductionmentioning

confidence: 99%

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

Ying-chao

Chen

et al. 2020

Atmos. Meas. Tech.

Self Cite

View full text Add to dashboard Cite

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.

show abstract

Aerosol characteristics inversion based on the improved lidar ratio profile with the ground-based rotational Raman–Mie lidar

Cited by 15 publications

References 25 publications

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

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

Novel Inversion Algorithm for the Atmospheric Aerosol Extinction Coefficient Based on an Improved Genetic Algorithm

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

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