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Special Issue "Development of LIDAR Techniques for Atmospheric Remote Sensing"

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A special issue of Atmosphere (ISSN 2073-4433). This special issue belongs to the section "Atmospheric Techniques, Instruments, and Modeling".

Deadline for manuscript submissions: 30 June 2023 | Viewed by 2584

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Dr. Xin Ma

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State Key Laboratory of Information Engineering in Surveying, Mapping and Remote Sensing, Wuhan University, Wuhan 430072, China
Interests: LIDAR; atmosphere; greenhouse gases; aerosol detection; atmospheric model

Special Issue Information

Dear Colleagues,

As an important tool of active remote sensing, LIDAR can monitor the contents of the atmosphere, such as aerosols, temperature, polluting gases, and greenhouse gases during day and night. Moreover, it can also acquire the distribution of atmospheric compositions with spatial information and high accuracy. Data measured by LIADR can help us analyze the causes of extreme pollution cases, carbon cycle, and global climate change.

This Special Issue aims to present the latest research in the system development and applications of LIDAR in atmosphere. We would like to invite you to submit articles on your recent research on LIDAR system development with respect to the following topics:

Innovative methods for monitoring atmospheric composition;
Hardware development for LIDAR systems;
Models for quantifying gas fluxes;
Collaborative observation of greenhouse and pollution gases;
Measurements for stratospheric meteorology.

Dr. Xin Ma
Guest Editor

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Keywords

atmospheric composition
hardware development for LIDAR
gas fluxes
collaborative observation
meteorology

Published Papers (4 papers)

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Research

Open AccessArticle

Computation of the Attenuated Backscattering Coefficient by the Backscattering Lidar Signal Simulator (BLISS) in the Framework of the CALIOP/CALIPSO Observations

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Atmosphere 2023, 14(2), 249; https://doi.org/10.3390/atmos14020249 - 27 Jan 2023

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Abstract

This paper presents the Backscattering Lidar Signal Simulator (BLISS), an end-to-end lidar simulator developed by the Centre National d’Etudes Spatiales (CNES). We computed the constant multiple-scattering (MS) coefficient of BLISS with a Monte Carlo (MC) code in the framework of CALIOP/CALIPSO observations for different homogeneous and plane-parallel stratocumulus and cirrus cloud geophysical scenes. The MS coefficient varies from 0.46 to 0.63. Then we evaluated the Level 1 products of BLISS. Above and in-cloud relative difference between the attenuated backscattering coefficient vertical profile simulated by BLISS and by the MC code is smaller than 0.5% under single-scattering regime and smaller than 10% (30% if optical depth of cirrus is large) under multiple-scattering regime, thus confirming the robustness of BLISS. Full article

(This article belongs to the Special Issue Development of LIDAR Techniques for Atmospheric Remote Sensing)

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Open AccessArticle

A Method for Assessing Background Concentrations near Sources of Strong CO₂ Emissions

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Atmosphere 2023, 14(2), 200; https://doi.org/10.3390/atmos14020200 - 18 Jan 2023

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Abstract

In the quantification model of emission intensity of emission sources, the estimation of the background concentration of greenhouse gases near an emission source is an important problem. The traditional method of estimating the background concentration of greenhouse gases through statistical information often results in a certain deviation. In order to solve this problem, we propose an adaptive estimation method of CO₂ background concentrations near emission sources in this work, which takes full advantage of robust local regression and a Gaussian mixture model to achieve accurate estimations of greenhouse gas background concentrations. It is proved by experiments that when the measurement error is 0.2 ppm, the background concentration estimation error is only 0.08 mg/m³, and even when the measurement error is 1.2 ppm, the background concentration estimation error is less than 0.4 mg/m³. The CO₂ concentration measurement data all show a good background concentration assessment effect, and the accuracy of top-down carbon emission quantification based on actual measurements should be effectively improved in the future. Full article

(This article belongs to the Special Issue Development of LIDAR Techniques for Atmospheric Remote Sensing)

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Open AccessArticle

Aerosol Property Analysis Based on Ground-Based Lidar in Sansha, China

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Atmosphere 2022, 13(9), 1511; https://doi.org/10.3390/atmos13091511 - 16 Sep 2022

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Abstract

Marine aerosol is one of the most important natural aerosols. It has a significant impact on marine climate change, biochemical cycling and marine ecosystems. Previous studies on marine aerosols, especially in the South China Sea, were carried out by satellite and shipborne measurements. The above methods have drawbacks, such as low temporal–spatial resolution and signal interference. However, lidar has high accuracy and high temporal–spatial resolution, so it is suitable for high-precision long-term observations. In this work, we obtain marine aerosol data using Mie Lidar in Sansha, an island in the South Chain Sea. Firstly, by comparing boundary layer height (BLH) between Sansha and Hefei, we found that Sansha’s boundary layer height has significant differences with that of inland China. Secondly, we compare the aerosol extinction coefficients and their variation with height in Sansha and Hefei. Finally, we obtain hourly averaged aerosol optical depth at Sansha and explore its relation with weather. To analyze the AOD–weather relation, we select three meteorological factors (sea surface temperature, mean sea level pressure and 10 m u-component of wind) based on their feature importance, which is determined by random forest regression. We also analyze the relationship between AOD and the above meteorological factors in each season separately. The results show that there is a strong relation between the meteorological factors and AOD in spring and summer, while there is no clear correlation in fall and winter. These analyses can provide valid data for future researches on marine aerosols in the South China Sea. Full article

(This article belongs to the Special Issue Development of LIDAR Techniques for Atmospheric Remote Sensing)

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Open AccessArticle

Echo-Signal De-Noising of CO₂-DIAL Based on the Ensemble Empirical Mode Decomposition

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Atmosphere 2022, 13(9), 1361; https://doi.org/10.3390/atmos13091361 - 25 Aug 2022

Viewed by 597

Abstract

The carbon dioxide (CO₂) differential absorption lidar echo signal is susceptible to noise and must satisfy the high demand for signal-retrieval precision. Thus, a proper de-noising method should be selected to improve the inversion result. In this paper, we simultaneously decompose three signal pairs into different intrinsic mode functions (IMFs) using the method of ensemble empirical mode decomposition (EEMD). Further, the correlation coefficients of the IMFs with the same temporal scale are regarded as the criterion to determine the components that need removal. This method not only retains the useful information effectively but also removes the noise component. A significant improvement in the R² of the differential absorption optical depth (DAOD) of the de-noised signals is obtained. The results of the simulated and observed analysis signal demonstrated improvement both in the SNR and in the retrieval precision. Full article

(This article belongs to the Special Issue Development of LIDAR Techniques for Atmospheric Remote Sensing)

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