Aerosol light extinction and backscattering: A review with a lidar perspective

Ceolato, Romain; Berg, Matthew J.

doi:10.1016/j.jqsrt.2020.107492

Cited by 36 publications

(24 citation statements)

References 177 publications

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“…The elastic lidar equation can be derived from the Radiative Transfer Equation (RTE) 26 , which itself can be derived from first principles, i.e., from the Maxwell equations, as shown by Mishchenko 57 . The RTE usually assumes that the incident light is scattered only once, i.e., multiple-scattering events are ignored, and thus, can be analyzed as a link-budget for the backscattered power P(r) as a function of range r as:…”

Section: Inversion Methodsmentioning

confidence: 99%

“…1. Then, the backscattered specific intensity I bac (r, −q inc ,t) can be directly derived from the Radiative Transfer Equation (RTE) 26 as,…”

Section: Principle Of Operationmentioning

confidence: 99%

“…For the polarization insensitive measurements considered here, Eq. (1) can be simplified 26 by a scalar version of U as…”

Section: Principle Of Operationmentioning

confidence: 99%

“…The Colibri instrument is a forward-looking picosecond short-range elastic backscatter lidar (PSR-EBL) with a bi-static, multi-axial architecture. The system was developed by ONERA, The French Aerospace Lab, for remote measurements of aerosols with high range and temporal resolution 26,51 . Compared to other lidar systems for atmospheric studies, Colibri is lightweight, compact, and suitable for a mobile platform.…”

Section: Short-range Micro-lidar Instrumentmentioning

confidence: 99%

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Black carbon aerosol number and mass concentration measurements by picosecond short-range elastic backscatter lidar

Ceolato

Bedoya-Velásquez

Fossard

et al. 2021

Preprint

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Black carbon aerosol emissions are recognized as contributors to global warming and air pollution. There remains, however, a lack of in-situ techniques to remotely quantify black carbon aerosol particles with high range and time resolution. This article presents for the first time, to our knowledge, a direct and contact-free remote measurement of black carbon aerosol number and mass concentration less than ten of meters from the emission source. This is done with a novel picosecond short-range elastic backscatter lidar (PSR-EBL) technique. To address the complexity of retrieving lidar products at short measurement ranges, we apply a forward inversion method featuring radiometric lidar calibration. Our method is based on an extension of a well-established light-scattering model, the Rayleigh-Debye-Gans for Fractal-Aggregates (RDG-FA) theory, which computes an analytical expression for lidar parameters. These parameters are the backscattering cross-sections and the lidar ratio for black carbon fractal aggregates. Using a small-scale Jet A-1 kerosene pool fire, it is shown that our technique can quantify the aerosol number and mass concentration with centimetre range-resolution and millisecond time-resolution.

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Section: Inversion Methodsmentioning

confidence: 99%

“…1. Then, the backscattered specific intensity I bac (r, −q inc ,t) can be directly derived from the Radiative Transfer Equation (RTE) 26 as,…”

Section: Principle Of Operationmentioning

confidence: 99%

“…For the polarization insensitive measurements considered here, Eq. (1) can be simplified 26 by a scalar version of U as…”

Section: Principle Of Operationmentioning

confidence: 99%

Section: Short-range Micro-lidar Instrumentmentioning

confidence: 99%

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Black carbon aerosol number and mass concentration measurements by picosecond short-range elastic backscatter lidar

Ceolato

Bedoya-Velásquez

Fossard

et al. 2021

Preprint

Self Cite

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“…As can be seen from Table 1, for the samples with radar data, only 18.81% of them had icing risk, while 81.19% had no icing risk, indicating that those with and without ice accumulation should be separated as far as possible to reduce the false alarm rate. Millimeter wave has a better ability of penetrating clouds, but Lidar detection is easily affected by clouds and aerosols and may cause errors [39][40][41]. In the discussion in Section 3, it was found that the velocity standard deviation had little correlation and contribution to the icing index.…”

Section: Qualitative Classifications Of Radar Datamentioning

confidence: 99%

Study on Icing Environment Judgment Based on Radar Data

et al. 2021

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As a major threat to aviation flight safety, it is particularly important to make accurate judgments and forecasts of the ice accumulation environment. Radar is widely used in civil aviation and meteorology, and has the advantages of high timeliness and resolution. In this paper, a variety of machine learning methods are used to establish the relationship between radar data and icing index (Ic) to determine the ice accumulation environment. The research shows the following. (1) A linear model was established, based on the scattering rate factor (Zh), radial velocity (v), spectral width (w), velocity standard deviation (σ) detected by 94 GHz millimeter wave radar, and backward attenuation coefficient (β) detected by 905 nm lidar, so linear regression was carried out. After principal component analysis (PCA), the correction determination coefficient of the linear equation was increased from 0.7127 to 0.7240. (2) Ice accumulation was unlikely for samples that were significantly off-center. By clustering the data into three or four categories, the proportion of icing lattice points could be increased from 18.81% to 33.03%. If the clustering number was further increased, the ice accumulation ratio will not be further increased, and the increased classification is reflected in the classification of pairs of noises and the possibility of omission is also increased. (3) Considering the classification and nonlinear factors of ice accumulation risk, the neural network method was used to judge the ice accumulation environment. Two kinds of neural network structures were established for quantitative calculation: Structure 1 first distinguished whether there was ice accumulation, and further calculated the icing index for the points where there was ice accumulation; Structure 2 directly calculated the temperature and relative humidity, and calculated the icing index according to definition. The accuracy of the above two structures could reach nearly 60%, but the quantitative judgment of the ice accumulation index was not ideal. The reasons for this dissatisfaction may be the small number of variables and samples, the interval between time and space, the difference in instrument detection principle, and the representativeness of the ice accumulation index. Further research can be improved from the above four points. This study can provide a theoretical basis for the diagnosis and analysis of the aircraft ice accumulation environment.

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Extinction of Electromagnetic Waves

Markel

2023

Springer Series in Light Scattering

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Aerosol light extinction and backscattering: A review with a lidar perspective

Cited by 36 publications

References 177 publications

Black carbon aerosol number and mass concentration measurements by picosecond short-range elastic backscatter lidar

Black carbon aerosol number and mass concentration measurements by picosecond short-range elastic backscatter lidar

Study on Icing Environment Judgment Based on Radar Data

Extinction of Electromagnetic Waves

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