Research on an adaptive filter for the Mie lidar signal

Song, Yuming; Zhou, Yudong; Liu, Pengbo; Shi, G.-Y.; Wang, Yufeng; Di, Huige; Hua, Dengxin

doi:10.1364/ao.58.000062

Cited by 3 publications

(1 citation statement)

References 15 publications

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“…In order to obtain aerosol optical properties accurately, various methods have been used to improve the SNR of the lidar signals, such as the application of wavelet transform (WT) filter, ensemble Kalman filter, empirical mode decomposition (EMD) approach, and adaptive filter [13][14][15][16]. These methods employ the low-pass filters at the lidar signals across the temporal and spatial axes, which is beneficial to the retrieval of optical properties.…”

Section: Introductionmentioning

confidence: 99%

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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Section: Introductionmentioning

confidence: 99%

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

et al. 2020

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Joint multiscale cloud detection algorithm for ground-based lidar

Xu¹,

Zhang²,

Mao³

et al. 2022

Opt. Express

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A ground-based lidar is a powerful tool for studying the vertical structure and optical properties of clouds. A layer detection algorithm is important to determine the presence and spatial position of clouds from vast lidar signals. However, current detection algorithms for ground-based lidar still involve substantial missing and false detections for tenuous layers and layer edges. Here, a joint multiscale cloud layer detection algorithm is proposed. The algorithm can effectively capture the tenuous layers and layer edges by using joint multiscale detection methods based on a trend function and the Bernoulli distribution assumption. Results show that the proposed algorithm detects 10.45% more cloud layers than the official cloud product of Micro Pulse Lidar Network (MPLNET) does. Specifically, 7.93% and 12.57% more cloud layers are detected at daytime and nighttime, respectively. The evaluation based on depolarization properties proves that the additional cloud layers detected by the joint multiscale algorithm are reliable. These additional detected clouds have important implications for cloud climatology and climate change research. The new algorithm remarkably enhances the cloud detection capability of ground-based lidar and potentially be widely used by the community.

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