Vertical Structure of Dust Aerosols Observed by a Ground-Based Raman Lidar with Polarization Capabilities in the Center of the Taklimakan Desert

Lidar AOD inversion and aerosol extinction profile correction method based on GA-BP neural network

Lidar is an effective remote sensing method to obtain the vertical distribution of aerosols, and how to select the aerosol extinction-backscattering ratio (AE-BR) during the inversion process is a key step to guarantee the accuracy of the lidar inversion of aerosol optical thickness (AOD) and aerosol extinction coefficient profile (AECP). In this paper, an inversion algorithm for AOD and AECP based on a genetic BP (GA-BP) neural network is proposed. Simultaneous measurements are carried out using CE318 sun photometer and lidar, and the mapping relationship between the lidar echo signal and AOD is established based on the genetic BP (GA-BP) neural network method, which achieves the accurate inversion of AOD with an absolute error mean value of 0.0156. Based on the AOD output from the GA-BP neural network, the real-time best AE- BR to improve the inversion accuracy of AECP. Finally, practical tests show that the method achieves accurate inversion of AOD, determines the range of AE-BR from 20-50sr, realizes real-time dynamic correction of AECP, and has strong generalization ability and applicability in practical situations.

Measurements of particle extinction coefficients at 1064 nm with lidar: temperature dependence of rotational Raman channels

Aerosol intensive optical properties, including lidar ratio and particle depolarization ratio, are of vital importance for aerosol typing. However, aerosol intensive optical properties at near-infrared wavelength are less exploited by atmospheric lidar measurements, because of the comparably small backscatter cross section of Raman-scattering and a low efficiency of signal detection compared to what is commonly available at 355 nm and 532 nm. To obtain accurate optical properties of aerosols at near-infrared wavelength, we considered three factors: Raman-spectra selection, detector selection, and interference-filter optimization. Rotational Raman scattering has been chosen for Raman signal detection, because of the higher cross-section compared to vibrational Raman scattering. The optimization of the properties of the interference filter are based on a comprehensive consideration of both signal-to-noise ratio and temperature dependence of the simulated lidar signals. The interference filter that has eventually been chosen uses the central wavelength at 1056 nm and a filter bandwidth (full-width-at-half-maximum) of 6 nm. We built a 3-channel 1064-nm rotational Raman lidar. In this paper two methods are proposed to test the temperature dependence of the signal-detection unit and to evaluate the quality of the Raman signals. We performed two measurements to test the quality of the detection channel: cirrus clouds in the free troposphere and aerosols in the planetary boundary layer. Our analysis of the measured Raman signals shows a negligible temperature dependence of the Raman signals in our system. For cirrus measurements, the Raman signal profile did not show crosstalk even for the case of strong elastic backscatter from clouds, which was about 100 times larger than Rayleigh scattering in the case considered here. The cirrus-mean extinction-to-backscatter ratio (lidar ratio) was 27.8 ± 10.0 sr (1064 nm) at a height of 10.5-11.5 km above ground. For the aerosols in the planetary boundary layer, we found the mean lidar ratio of 38.9 ± 7.0 sr at a height of 1.0-3.0 km above ground.

Identification of fluorescent aerosol observed by a spectroscopic lidar over northwest China

Bioaerosols play a significant role in climate change and variation of ecological environment. To investigate characterization of atmospheric bioaerosols, we conducted lidar measurement for observing bioaerosols close to dust sources over northwest China in April, 2014. The developed lidar system can not only allowed us to measure the 32-channel fluorescent spectrum between 343 nm to 526 nm with a spectral resolution of 5.8 nm but also simultaneously detect polarisation measurements at 355 nm and 532 nm, as well as Raman scattering signals at 387 nm and 407 nm. According to the findings, the lidar system was able to pick up the robust fluorescence signal emitted by dust aerosols. Especially the polluted dust, the fluorescence efficiency could reach 0.17. In addition, the efficiency of single-band fluorescence typically rises as the wavelength goes up and the ratio of fluorescence efficiency of polluted dust, dust, air pollutant and background aerosols is about 4:3:8:2. Moreover, our results demonstrate that simultaneous measurements of depolarization at 532 nm and fluorescence could better distinguish fluorescent aerosols than those at 355 nm. This study enhances the ability of laser remote sensing for real-time detecting bioaerosol in the atmosphere.

Small lidar ratio of dust aerosol observed by Raman-polarization lidar near desert sources

Previous studies have shown that the lidar ratio has a significant influence on the retrieval of the aerosol extinction coefficient via the Fernald method, leading to a large uncertainty in the evaluation of dust radiative forcing. Here, we found that the lidar ratios of dust aerosol were only 18.16 ± 14.23sr, based on Raman-polarization lidar measurements in Dunhuang (94.6°E, 40.1°N) in April of 2022. These ratios are much smaller than other reported results (∼50 sr) for Asian dust. This finding is also confirmed by some previous results from lidar measurements under different conditions for dust aerosols. The particle depolarization ratio (PDR) at 532 nm and color ratio (CR, 1064 nm/532 nm) of dust aerosols are0.28 ± 0.013 and 0.5-0.6, respectively, indicating that extremely fine nonspherical particles exist. In addition, the dust extinction coefficients at 532 nm range from2 × 10^-4 to 6 × 10^-4m^-1for such small lidar ratio particles. Combining lidar measurements and model simulation by the T-matrix method, we further reveal that the reason for this phenomenon is mainly due to the relatively small effective radius and weak light absorption of dust particles. Our study provides a new insight into the wide variation in the lidar ratio for dust aerosols, which helps to better explain the impacts of dust aerosols on the climate and environment.

Simulated depolarization ratios for dust and smoke at laser wavelengths: implications for lidar application

Polarization measurements have been widely used to detect aerosol properties by remote sensing in recent decades. To better understand the polarization characteristics of aerosols by lidar, the numerically exact T-matrix method was used to simulate the depolarization ratio (DR) of dust and smoke aerosols at typical laser wavelengths in this study. The results show that the DRs of dust and smoke aerosols have obviously different spectral dependences. Moreover, the ratio of DRs at two wavelengths has an obvious linear relationship with the microphysical properties of aerosols, including aspect ratio, effective radius and complex refractive index. At short wavelengths, we can use it to invert the absorption characteristics of particles, further improving the detection ability of lidar. Comparing the simulation results of different channels, DR, (color ratio) CR and (lidar ratio) LR have a good logarithmic fitting relationship at 532 nm and 1064 nm, which helps to classify the aerosol types. On this basis, a new inversion algorithm, "1β+1α+2δ", was presented. By this algorithm, the backscattering coefficient (β), extinction coefficient (α), DR (δ) at 532 nm and 1064 nm can be used to expand the range of inversion and compare lidar data with different configurations to obtain more extensive optical characteristics of aerosols. Our study enhances the application of laser remote sensing in aerosol observations more accurately.