Development of ZJU high-spectral-resolution lidar for aerosol and cloud: Feature detection and classification

Wang, Nanchao; Shen, Xiaojing; Xiao, Da; Veselovskii, Igor; Zhao, Chuanfeng; Chen, Feitong; Liu, Chong; Rong, Yuhang; Ke, Ju; Wang, Binyu; Liu, Dong

doi:10.1016/j.jqsrt.2021.107513

Cited by 19 publications

(15 citation statements)

References 32 publications

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“…However, to assess ACI this method requires a priori assumptions about the lidar ratio and subadiabatic cloud conditions. The impact of the a priori assumptions on the aerosol and cloud retrievals has been widely discussed (10,40,41). In general, all existing multiple-FOV lidar-based techniques have their advantages and also limitations.…”

Section: Significancementioning

confidence: 99%

Dual-field-of-view high-spectral-resolution lidar: Simultaneous profiling of aerosol and water cloud to study aerosol–cloud interaction

Wang

Zhang

Shen

et al. 2022

Proc. Natl. Acad. Sci. U.S.A.

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Significance Aerosol–cloud interaction affects the cooling of Earth’s climate, mostly by activation of aerosols as cloud condensation nuclei that can increase the amount of sunlight reflected back to space. But the controlling physical processes remain uncertain in current climate models. We present a lidar-based technique as a unique remote-sensing tool without thermodynamic assumptions for simultaneously profiling diurnal aerosol and water cloud properties with high resolution. Direct lateral observations of cloud properties show that the vertical structure of low-level water clouds can be far from being perfectly adiabatic. Furthermore, our analysis reveals that, instead of an increase of liquid water path (LWP) as proposed by most general circulation models, elevated aerosol loading can cause a net decrease in LWP.

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Section: Significancementioning

confidence: 99%

Dual-field-of-view high-spectral-resolution lidar: Simultaneous profiling of aerosol and water cloud to study aerosol–cloud interaction

Wang

Zhang

Shen

et al. 2022

Proc. Natl. Acad. Sci. U.S.A.

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“…This case study will also demonstrate that the optical properties retrieved by the IIR method are more stable compared to the standard method. In this case study, observations of the ZJU HSRL at 532 nm were used [21]. We used daytime scenes with a high vertical resolution of 7.5 m and a temporal resolution of 120 s to compare the IIR method and the standard method, since the noisy observations from daytime reflect the performance of different methods under the condition of lower SNR.…”

Section: Resultsmentioning

confidence: 99%

“…The characteristics of the proposed method can be summarized into two points: (1) considering the relation of the lidar signals and utilizing the feature detection method to obtain the "feature" of the "image", (2) transforming the ill-posed problem of the numerical derivative into a well-posed one of the estimates of the lidar ratio. This "feature" is identified with the Feature Detection Algorithm from a previous work to represent the data associated with aerosols and clouds [21]. The region where the scattering ratio profile is greater than one standard deviation from the expected clear-sky is determined as the "feature."…”

Section: Iterative Image Reconstruction (Iir) Methodsmentioning

confidence: 99%

“…What we concerned most is the region containing a "feature," i.e., dense aerosols and clouds. Therefore, a feature detection method is utilized to exclude the clear-sky region [21]. It is obvious that the structures of the extinction coefficient and the lidar ratio obtained by the IIR method are closer to the true values than those obtained by the standard method.…”

Section: Comparison Between the Standard Methods And Iir Methodsmentioning

confidence: 99%

“…Taking advantage of these properties of lidar signals, the IIR method reconstructs the "image" of the "feature" of the lidar signals on the basis of reasonable estimates of the lidar ratio by minimizing the difference between the reconstructed signals with the observations under an appropriate assumption of signal noises. The "feature" is identified with a scattering ratio profile greater than one standard deviation from the expected clear-sky [21]. Using an iterative regularization method to carry out the best optimization estimates of the lidar ratio, the IIR method transforms the ill-posed problem of the numerical derivative into a well-posed problem of the image reconstruction.…”

Section: Introductionmentioning

confidence: 99%

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Development of ZJU High-Spectral-Resolution Lidar for Aerosol and Cloud: Extinction Retrieval

et al. 2020

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The retrieval of the extinction coefficients of aerosols and clouds without assumptions is the most important advantage of the high-spectral-resolution lidar (HSRL). The standard method to retrieve the extinction coefficient from HSRL signals depends heavily on the signal-to-noise ratio (SNR). In this work, an iterative image reconstruction (IIR) method is proposed for the retrieval of the aerosol extinction coefficient based on HSRL data, this proposed method manages to minimize the difference between the reconstructed and raw signals based on reasonable estimates of the lidar ratio. To avoid the ill-posed solution, a regularization method is adopted to reconstruct the lidar signals in the IIR method. The results from Monte-Carlo (MC) simulations applying both standard and IIR methods are compared and these comparisons demonstrate that the extinction coefficient and the lidar ratio retrieved by the IIR method have smaller root mean square error (RMSE) and relative bias values than the standard method. A case study of measurements made by Zhejiang University (ZJU) HSRL is presented, and their results show that the IIR method not only obtains a finer structure of the aerosol layer under the condition of low SNR, but it is also able to retrieve more reasonable values of the lidar ratio.

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The preliminary measurements of water cloud microphysical properties using multiple scattering Raman lidar

Qi,

Yang,

Mao

et al. 2024

Optics Communications

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Development of ZJU high-spectral-resolution lidar for aerosol and cloud: Feature detection and classification

Cited by 19 publications

References 32 publications

Dual-field-of-view high-spectral-resolution lidar: Simultaneous profiling of aerosol and water cloud to study aerosol–cloud interaction

Dual-field-of-view high-spectral-resolution lidar: Simultaneous profiling of aerosol and water cloud to study aerosol–cloud interaction

Development of ZJU High-Spectral-Resolution Lidar for Aerosol and Cloud: Extinction Retrieval

The preliminary measurements of water cloud microphysical properties using multiple scattering Raman lidar

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