Igor Veselovskii’s research while affiliated with Russian Academy of Sciences and other places

Mineral dust is a key atmospheric aerosol agent that impacts the radiation budget and plays a significant role in cloud formation. However, studies on retrieving height-resolved microphysical properties of dust aerosols, which are crucial for understanding dust evolution and transport processes, from lidar measurements are still insufficient. Here, we retrieve dust aerosol microphysical properties, including the volume size distribution, volume concentration, effective radius (reff), refractive index and single-scattering albedo, from spectral extinction, backscattering and depolarization lidar measurements. We evaluate the performance of three particle scattering models – Sphere, Spheroid and Irregular–Hexahedral (IH) models in terms of mimicking dust optical properties and deriving retrieval results. We also explore the influence of inverting different measurement sets, namely the conventional 3β (backscattering coefficients at 355, 532 and 1064 nm) + 2α (extinction coefficients at 355 and 532 nm) and the expanded 3β + 2α + 3δ (depolarization ratio at 355, 532 and 1064 nm) measurements, on the retrieval. Both simulations and inversions of real lidar measurements show that it is necessary to use non-spherical models and incorporate 3δ measurements to improve the retrieval accuracy. An increase of discrepancy in depolarization ratio produced by the IH and Spheroid models is observed for reff > 0.5 μm, resulting in larger retrieval difference between the two non-spherical models after the inclusion of 3δ. The study demonstrates the prospect of retrieving height-resolved dust microphysical properties from lidar measurements, as well as potential limitations of the prevailing scattering models in simulating particle backscattering properties.

The fluorescence lidar at the Prokhorov General Physics Institute (Moscow) was utilized to study transported smoke during the wildfire season from May to September 2023. The lidar system, based on a tripled Nd:YAG laser, performs fluorescence measurements at wavelengths of 438, 472, 513, 560, and 614 nm. This configuration enables the assessment of the spectral dependence of fluorescence backscattering from the planetary boundary layer (PBL) to the upper troposphere and lower stratosphere (UTLS). The fluorescence capacity of smoke, defined as the ratio of fluorescence backscattering to aerosol backscattering at the laser wavelength, exhibits significant variation in the UTLS, with changes of up to a factor of 3. This variation is likely indicative of differences in the relative concentration of organic compounds within the smoke. Analysis of more than 40 smoke episodes enabled an evaluation of the height dependence of smoke fluorescence properties. Observations reveal that the fluorescence capacity generally increases with altitude, suggesting a higher concentration of organic compounds in the UTLS compared to the lower troposphere. Additionally, the measurements consistently show differences in the fluorescence spectra of smoke and urban aerosol. Urban aerosol fluorescence tends to decrease gradually with wavelength, whereas the peak of smoke fluorescence is observed at the 513 or 560 nm channels. This spectral distinction provides an effective means of separating smoke from urban aerosol. The technique was applied to events where smoke from the upper troposphere descended into the PBL and mixed with urban particles, demonstrating its utility in distinguishing between these aerosol types.

In the atmosphere, aerosols can originate from numerous sources, leading to the mixing of different particle types. This paper introduces an approach to the partitioning of aerosol mixtures in terms of backscattering coefficients. The method utilizes data collected from the Mie–Raman–fluorescence lidar, with the primary input information being the aerosol backscattering coefficient (β), particle depolarization ratio (δ), and fluorescence capacity (GF). The fluorescence capacity is defined as the ratio of the fluorescence backscattering coefficient to the particle backscattering coefficient at the laser wavelength. By solving a system of equations that model these three properties (β, δ and GF), it is possible to characterize a three-component aerosol mixture. Specifically, the paper assesses the contributions of smoke, urban, and dust aerosols to the overall backscattering coefficient at 532 nm. It is important to note that aerosol properties (δ and GF) may exhibit variations even within a specified aerosol type. To estimate the associated uncertainty, we employ the Monte Carlo technique, which assumes that GF and δ are random values uniformly distributed within predefined intervals. In each Monte Carlo run, a solution is obtained. Rather than relying on a singular solution, an average is computed across the whole set of solutions, and their dispersion serves as a metric for method uncertainty. This methodology was tested using observations conducted at the ATOLL (ATmospheric Observation at liLLe) observatory, Laboratoire d'Optique Atmosphérique, University of Lille, France.

This study explores the potential of Raman lidar technology for vertical profiling CO2 mixing ratios in the atmosphere. Measurements taken in 2023 at the ATOLL observatory, Laboratoire d’Optique Atmosphérique, University of Lille, France, demonstrate the promising capabilities of a Raman lidar system. This system is equipped with a 355 nm laser emitting 90 mJ pulses and a receiving telescope with a 400 mm aperture. It effectively derives vertical CO2 distributions with a vertical resolution of 200 meters up to heights of 4 kilometers, given several hours of signal accumulation. Notably, the lidar operates without calibrating CO2 concentrations, allowing only for the assessment of relative changes in CO2 mixing ratios with height. However, aerosol fluorescence introduces uncertainties in the CO2 measurements. To address this issue, a correction approach using single-channel fluorescence measurements is proposed, which could potentially enhance the measurement precision.

This study explores the potential of Raman lidar technology for vertical profiling CO2 mixing ratios in the atmosphere. Measurements taken in 2023 at the ATOLL observatory, Laboratoire d’Optique Atmosphérique, University of Lille, France, demonstrate the promising capabilities of a Raman lidar system. This system is equipped with a 355 nm laser emitting 90 mJ pulses and a receiving telescope with a 400 mm aperture. It effectively derives vertical CO2 distributions with a vertical resolution of 200 meters up to heights of 4 kilometers, given several hours of signal accumulation. Notably, the lidar operates without calibrating CO2 concentrations, allowing only for the assessment of relative changes in CO2 mixing ratios with height. However, aerosol fluorescence introduces uncertainties in the CO2 measurements. To address this issue, a correction approach using single-channel fluorescence measurements is proposed, which could potentially enhance the measurement precision.

Aerosol microphysical properties, including aerosol particle size distribution, complex refractive index and concentration properties, are key parameters evaluating the impact of aerosols on climate, meteorology, and human health. High Spectral Resolution Lidar (HSRL) is an efficient tool for probing the vertical optical properties of aerosol particles, including the aerosol backscatter coefficient (β) and extinction coefficient (α), at multiple wavelengths. To swiftly process vast data volumes, address the ill-posedness of retrieval problems, and suit simpler lidar systems, this study proposes an algorithm (modified algorithm) for retrieving microphysical property profiles from the HSRL optical data targeting fine-mode aerosols, building upon a previous algorithm (basic algorithm). The modified algorithm is based on a look-up table (LUT) approach, combined with the k-nearest neighbor (k-NN) and random forest (RF) algorithms, and it optimizes the decision tree generation strategy, incorporating a self-posed scheme. In numerical simulation tests for different lidar configurations, the modified algorithm reduced retrieval errors by 41%, 30%, and 32% compared to the basic algorithm for 3β + 2α, 3β + 1α, and 2β + 1α, respectively, with a remarkable improvement of stability. In two observation scenes of a field campaign, the median relative errors of the effective radius for 3β + 2α were 6% and −3%, and the median absolute errors of single-scattering albedo were 0.012 and 0.005. This method represents a further step toward the use of the LUT approach, with the potential to provide effective and efficient aerosol microphysical retrieval for simpler lidar systems, which could advance our understanding of aerosols’ climatic, meteorological, and health impacts.

Based on satellite observations in the Arctic stratosphere at latitudes from 61° to 66°N in the second half of 2019, Boone et al. (2022, https://doi.org/10.1029/2022jd036600) provide the impression that the aerosol in the upper troposphere and lower stratosphere (UTLS) over the entire Arctic consisted of sulfate aerosol originating from the Raikoke volcanic eruption in the summer of 2019. Here, we show that this was most probably not the case and the aerosol layering conditions were much more complex. By combining the stratospheric aerosol typing results of Boone et al. (2022, https://doi.org/10.1029/2022jd036600) with lidar observations at 85°–86°N of Ohneiser et al. (2021, https://doi.org/10.5194/acp‐21‐15783‐2021) of a dominating wildfire smoke layer in the UTLS height range, we demonstrate that the Arctic UTLS aerosol most likely consisted of Siberian wildfire smoke in the lower part and sulfate aerosol in the upper part of the aerosol layer which extended from 7 to 19 km height and was well observable until May 2020. The smoke‐ and sulfate‐related aerosol optical thickness (AOT) fractions were about 0.7–0.8 and 0.2–0.3, respectively, according to our analysis. The sulfate AOT is in good agreement with model‐based predictions of the Raikoke sulfate AOT.

This study focuses on the characterization of aerosol hygroscopicity using remote sensing techniques. We employ a Mie–Raman–fluorescence lidar (Lille Lidar for Atmospheric Study, LILAS), developed at the ATOLL platform, Laboratoire d'Optique Atmosphérique, Lille, France, in combination with the RPG-HATPRO-G5 microwave radiometer to enable continuous aerosol and water vapor monitoring. We identify hygroscopic growth cases when an aerosol layer exhibits an increase in both aerosol backscattering coefficient and relative humidity. By examining the fluorescence backscattering coefficient, which remains unaffected by the presence of water vapor, the potential temperature, and the absolute humidity, we verify the homogeneity of the aerosol layer. Consequently, the change in the backscattering coefficient is solely attributed to water uptake. The Hänel theory is employed to describe the evolution of the backscattering coefficient with relative humidity and introduces a hygroscopic coefficient, γ, which depends on the aerosol type. The particularity of this method revolves around the use of the fluorescence which is employed to take into account and correct the aerosol concentration variations in the layer. Case studies conducted on 29 July and 9 March 2021 examine, respectively, an urban and a smoke aerosol layer. For the urban case, γ is estimated as 0.47 ± 0.03 at 532 nm; as for the smoke case, the estimation of γ is 0.5 ± 0.3. These values align with those reported in the literature for urban and smoke particles. Our findings highlight the efficiency of the Mie–Raman–fluorescence lidar and microwave radiometer synergy in characterizing aerosol hygroscopicity. The results contribute to advance our understanding of atmospheric processes, aerosol–cloud interactions, and climate modeling.

Polarization properties of the fluorescence induced by polarized laser radiation are widely considered in laboratory studies. In lidar observations, however, only the total backscattered power of fluorescence is analyzed. In this paper we present results obtained with a modified Mie–Raman–fluorescence lidar operated at the ATOLL observatory, Laboratoire d'Optique Atmosphérique, University of Lille, France, allowing us to measure depolarization ratios of fluorescence at 466 nm (δF) and of water vapor Raman backscatter. Measurements were performed in May–June 2023 during the Alberta forest fires season when smoke plumes were almost continuously transported over the Atlantic Ocean towards Europe. During the same period, smoke plumes from the same sources were also detected and analyzed in Moscow, at the General Physics Institute (GPI), with a five-channel fluorescence lidar able to measure fluorescence backscattering at 438, 472, 513, 560 and 614 nm. Results demonstrate that, inside the planetary boundary layer (PBL), the urban aerosol fluorescence is maximal at 438 nm, and then it gradually decreases with the increase in wavelength. The smoke layers observed within 4–6 km height present a maximum fluorescence at 513 nm, while in the upper troposphere, fluorescence maximum shifts to 560 nm. Regarding the fluorescence depolarization ratio, for smoke its value typically varies within the 45 %–55 % range. The depolarization ratio of the water vapor Raman backscattering at 408 nm is shown to be quite low (2±0.5 %) in the absence of fluorescence because the narrowband interference filter (0.3 nm) in the water vapor channel selects only the strongest vibrational lines of the Raman spectrum. As a result, the depolarization ratio at the water vapor Raman channel is sensitive to the presence of strongly depolarized fluorescence backscattering and can be used for the evaluation of the aerosol fluorescence contribution to measured water vapor mixing ratio.

In the atmosphere, aerosols can originate from numerous sources, leading to the mixing of different particle types. This paper introduces an approach to the partitioning of aerosol mixtures in terms of backscattering coefficients. The method utilizes data collected from the Mie-Raman-fluorescence lidar, with the primary input information being the aerosol backscattering coefficient, particle depolarization ratio (δ), and fluorescence capacity (GF). The fluorescence capacity is defined as the ratio of the fluorescence backscattering coefficient to the particle backscattering coefficient at the laser wavelength. By solving a system of equations that model these three properties (bF, δ and GF), it is possible to characterize a three-component aerosol mixture. Specifically, the paper assesses the contributions of smoke, urban, and dust aerosols to the overall backscattering coefficient at 532 nm. It is important to note that aerosol properties (δ and GF) may exhibit variations even within a specified aerosol type. To estimate the associated uncertainty, we employ the Monte Carlo technique, which assumes that GF and δ are random values uniformly distributed within predefined intervals. In each Monte Carlo run, a solution is obtained. Rather than relying on a singular solution, an average is computed across the whole set of solutions, and their dispersion serves as a metric for method uncertainty. This methodology was tested using observations conducted at the ATOLL observatory, Laboratoire d'Optique Atmosphérique, University of Lille, France.