Depolarization Ratio Research Articles

Long-term (2010–2021) lidar observations of stratospheric aerosols in Wuhan, China

Abstract. This study analyzes the vertical distribution, optical properties, radiative forcing, and several perturbation events of stratospheric aerosols using observations from a ground-based polarization lidar in Wuhan (30.5° N, 114.4° E) from 2010 to 2021. The background stratospheric aerosol optical depth (sAOD) was 0.0044 ± 0.0019 at 532 nm, as calculated during a stratosphere-quiescent period from January 2013 to August 2017. In addition, several cases of volcanic aerosol and wildfire-induced smoke were observed. Volcanic aerosols from the Nabro (2011) and Raikoke (2019) eruptions (both in boreal summer) increased the sAOD to 2.9 times the background level. Tracers of smoke from the Canadian wildfire in the summer of 2017 were observed twice, at 19–21 km on 14–17 September and at 20–23 km on 28–31 October, with a plume-isolated aerosol optical depth (AOD) of 0.002–0.010 and a particle linear depolarization ratio δp of 0.14–0.18, indicating the dominance of non-aged smoke particles. During these summertime events, the injected stratospheric aerosols were captured by the large-scale Asian monsoon anticyclone (AMA), confining the transport pathway to mid-latitude Asia. On 8–9 November 2020, smoke plumes originating from the California wildfire in October 2020 appeared at 16–17 km, with a mean δp of 0.13. Regarding seasonal variation, the sAOD in the cold half-year (0.0054) is 69 % larger than in the warm half-year (0.0032) due to stronger meridional transport of stratospheric aerosols from the tropics to middle latitudes. The stratospheric radiative forcing was −0.11 W m−2 during the stratosphere-quiescent period and increased to −0.31 W m−2 when volcanic aerosols were largely injected. These findings contribute to our understanding of the sources and transport patterns of stratospheric aerosols over mid-latitude Asia and serve as an important database for the validation of model outputs.

Aerosol type classification and its temporal distribution in Kanpur using ground-based remote sensing

Based on AERONET version 3 level 2 inversion products, we classify aerosol types and investigate their temporal distribution in the atmosphere using particle linear depolarization ratio (PLDR) and single scattering albedo (SSA) at the wavelength of 1020 nm over Kanpur. It is for the first time over the North Indian region the work has been emphasized. The remarkable findings over Kanpur station indicate that SSA and coarse-mode particles in the atmosphere increased with increasing PLDR at 440, 675, 870, and 1020 nm wavelengths. It is observed in the 2-dimensional histogram that the rate of occurrence of aerosols is high when the fine mode fraction (FMF) is high and the dust ratio (Rd) is low. The atmosphere of Kanpur is partially influenced by dust-dominated mixture (DDM), pollution-dominated mixture (PDM), and pure dust (PD) with 53% whereas, the rest of the dust-free pollution aerosols are 47%. The annual mean occurrence rate for different aerosol types is 5% for Strongly Absorbing (SA), 20% for Moderately Absorbing (MA), 19% for Weakly Absorbing (WA), 3% for Non-Absorbing (NA), 27% for DDM, 22% for PDM, and 4% for PD, ranging from January 2001 to January 2022. There is a variation in the distribution of various types of pollution particles, which is influenced by the changing seasons. The rate of occurrence of dust-free pollution aerosols is 47%, mostly observed throughout the post-monsoon and winter seasons. The PLDR values in the atmosphere of Kanpur are almost balanced equally because it is affected by both (dust and dust-free) pollution aerosols and the changes can be seen due to the frequent occurrence of dust storms and anthropogenic activities.

Backscattering Linear Depolarization Ratio of Smoke Aerosols From Biomass Burning

AbstractThe linear depolarization ratio (LDR) of backscattering is a key parameter for distinguishing particle types. The measured LDRs of smoke in previous studies are highly variable and different from each other. Existing research on smoke aerosols considers only the internal mixing state of the black carbon (BC) particle population either by ignoring the externally mixed organic carbon (OC) particle population or by evaluating the LDR of smoke aerosols using the results of individual particles rather than the particle population. The field‐measured LDR of smoke then becomes difficult to interpret properly. The recent prescribed forest burning experiment in China showed that the LDR of freshly emitted smoke varies between 0.0% and 20.1% in wavelength (λ) of 532 nm. Electron microscopy images also showed that coated BC and pure OC exist simultaneously in biomass burning aerosols. Therefore, this study evaluates the influence of various parameters on the LDR of smoke by considering the internal and external mixing states. The calculated LDRs of the smoke aerosols were found to vary between 0.0% and 28.2% when λ is equal to 532 nm. The calculation results indicate that the LDR of smoke is slightly influenced by BC, considerably affected by the externally mixed OC, and is essentially dominated by the latter's morphology and particle size distribution. High levels and rapid changes of LDR of smoke can be well explained by nonsphericity and particle size distribution of externally mixed OC. This study advances the research on the measurement and evaluation of the LDR of smoke aerosols.

The First Validation of Aerosol Optical Parameters Retrieved from the Terrestrial Ecosystem Carbon Inventory Satellite (TECIS) and Its Application

In August 2022, China successfully launched the Terrestrial Ecosystem Carbon Inventory Satellite (TECIS). The primary payload of this satellite is an onboard multi-beam lidar system, which is capable of observing aerosol optical parameters on a global scale. This pioneering study used the Fernald forward integration method to retrieve aerosol optical parameters based on the Level 2 data of the TECIS, including the aerosol depolarization ratio, aerosol backscatter coefficient, aerosol extinction coefficient, and aerosol optical depth (AOD). The validation of the TECIS-retrieved aerosol optical parameters was conducted using CALIPSO Level 1 and Level 2 data, with relative errors within 30%. A comparison of the AOD retrieved from the TECIS with the AERONET and MODIS AOD products yielded correlation coefficients greater than 0.7 and 0.6, respectively. The relative error of aerosol optical parameter profiles compared with ground-based measurements for CALIPSO was within 40%. Additionally, the correlation coefficients R2 with MODIS and AERONET AOD were approximately between 0.5 and 0.7, indicating the high accuracy of TECIS retrievals. Utilizing the TECIS retrieval results, combined with ground air quality monitoring data and HYSPLIT outcomes, a typical dust transport event was analyzed from 2 to 7 April 2023. The results indicate that dust was transported from the Taklamakan Desert in Xinjiang, China, to Henan and Anhui provinces, with a gradual decrease in the aerosol depolarization ratio and backscatter coefficient during the transport process, causing varying degrees of pollution in the downstream regions. This research verifies the accuracy of the retrieval algorithm through multi-source data comparison and demonstrates the potential application of the TECIS in the field of aerosol science for the first time. It enables the fine-scale regional monitoring of atmospheric aerosols and provides reliable data support for the three-dimensional distribution of global aerosols and related scientific applications.

Large-Scale Network-Based Observations of a Saharan Dust Event across the European Continent in Spring 2022

Between 14 March and 21 April 2022, an extensive investigation of an extraordinary Saharan dust intrusion over Europe was performed based on lidar measurements obtained by the European Aerosol Research Lidar Network (EARLINET). The dust episode was divided into two distinct periods, one in March and one in April, characterized by different dust transport paths. The dust aerosol layers were studied over 18 EARLINET stations, examining aerosol characteristics during March and April in four different regions (M-I, M-II, M-III, and M-IV and A-I, A-II, A-III, and A-IV, respectively), focusing on parameters such as aerosol layer thickness, center of mass (CoM), lidar ratio (LR), particle linear depolarization ratio (PLDR), and Ångström exponents (ÅE). In March, regions exhibited varying dust geometrical and optical properties, with mean CoM values ranging from approximately 3.5 to 4.8 km, and mean LR values typically between 36 and 54 sr. PLDR values indicated the presence of both pure and mixed dust aerosols, with values ranging from 0.20 to 0.32 at 355 nm and 0.24 to 0.31 at 532 nm. ÅE values suggested a range of particle sizes, with some regions showing a predominance of coarse particles. Aerosol Optical Depth (AOD) simulations from the NAAPS model indicated significant dust activity across Europe, with AOD values reaching up to 1.60. In April, dust aerosol layers were observed between 3.2 to 5.2 km. Mean LR values typically ranged from 35 to 51 sr at both 355 nm and 532 nm, while PLDR values confirmed the presence of dust aerosols, with mean values between 0.22 and 0.31 at 355 nm and 0.25 to 0.31 at 532 nm. The ÅE values suggested a mixture of particle sizes. The AOD values in April were generally lower, not exceeding 0.8, indicating a less intense dust presence compared to March. The findings highlight spatial and temporal variations in aerosol characteristics across the regions, during the distinctive periods. From 15 to 16 March 2022, Saharan dust significantly reduced UV-B radiation by approximately 14% over the ATZ station (Athens, GR). Backward air mass trajectories showed that the dust originated from the Western and Central Sahara when, during this specific case, the air mass trajectories passed over GRA (Granada, ES) and PAY (Payerne, CH) before reaching ATZ, maintaining high relative humidity and almost stable aerosol properties throughout its transport. Lidar data revealed elevated aerosol backscatter (baer) and PLDR values, combined with low LR and ÅE values, indicative of pure dust aerosols.

Observation and Classification of Low-Altitude Haze Aerosols Using Fluorescence–Raman–Mie Polarization Lidar in Beijing during Spring 2024

Haze aerosols have a profound impact on air quality and pose serious health risks to the public. Due to its geographical location, Beijing experienced haze events in the spring of 2024. Lidar is an active remote sensing technology with a high spatiotemporal resolution and the ability to classify aerosols, and it is essential for effective haze monitoring. This study utilizes fluorescence–Raman–Mie polarization lidar with an emission wavelength of 355 nm, employing the δp-Gf method based on the particle depolarization ratio at 355 nm (δp355) and the fluorescence capacity (Gf), and combines meteorological data and backward-trajectory analysis to observe and classify low-altitude haze aerosols in Beijing during the spring of 2024. Notably, a mining dust event with strong fluorescence backscatter was detected. The haze aerosols were categorized into three types: pollution aerosols, desert dust, and mining dust. Their optical properties were summarized and compared. Desert dust showed a particle depolarization ratio range of 0.23–0.39 and a fluorescence capacity range from 0.18 × 10−4 to 0.63 × 10−4. Pollution aerosols had a larger fluorescence capacity but a lower depolarization ratio compared to desert dust, with a fluorescence capacity ranging from 0.55 × 10−4 to 1.10 × 10−4 and a depolarization ratio ranging from 0.10 to 0.17. Mining dust shared similar depolarization characteristics with desert dust but had a larger fluorescence capacity, ranging from 0.71 × 10−4 to 1.23 × 10−4, with a depolarization ratio range of 0.30–0.39. This study validates the effectiveness of the δp355-Gf method in classifying low-altitude haze aerosols in Beijing. Additionally, it offers a new perspective for more detailed dust classification using lidar. Furthermore, utilizing the δp355-Gf classification method and the δp355-Gf distributions of three typical aerosol samples, we developed a set of equations for the analysis of mixed aerosols. This method facilitates the separation and fraction analysis of aerosol components under various mixing scenarios. It enables the characterization of variations in the three types of haze aerosols at different altitudes and times, offering valuable insights into the interactions between desert dust, mining dust, and pollution aerosols in Beijing.

Polarization lidar observations of diurnal and seasonal variations in the atmospheric mixing layer above a tropical rural place gadanki, India

This study presents the daily and seasonal variation of the atmospheric mixing layer height (MLH) over Gadanki, India (13.45°N, 79.18°E), a tropical rural location based on polarization lidar observations. The observations spanned the years 2009–2014, encompassing 303 instances, and coinciding with radiosonde and surface weather station measurements. The MLH was determined through the analysis of aerosol profiles and confirmed with the MLH values derived from radiosonde data. The lidar depolarization ratio was employed to characterize aerosol shape. This study aims to establish a connection between aerosol backscatter and its shape through lidar observations, considering diurnal and seasonal variations, while also identifying the influencing factors. This study illustrates four distinct case studies conducted during different seasons to depict aerosol behavior in both convectively active and non-active periods. These case studies unveil the influence of aerosol shape on water intake and subsequent residual layer and cloud formation. The observed fluctuations in MLH and aerosol shape suggest a dynamic relationship between local meteorology and long-range aerosol transport.

Design and synthesis of new benzothiazole-piperazine derivatives and in vitro and in silico investigation of their anticancer activity

In this work, novel benzothiazole-piperazine acetamide (2a-2l) analogs were synthesized and evaluated their anticancer activity. The 1HNMR , 13CNMR and LC-MS/MS spectral data and elemental analyses were used to clarify the structure of the final molecules. The title compounds were procured by reacting 2‑chloro-N-(6-substituted benzothiazole-2-yl) acetamide with some piperazine derivatives. Cytotoxicity and apoptosis parameters including Annexin V binding capacities, effect on cell cycle, caspase-3 activation and mitochondrial membrane depolarization effect were evaluated to determine anticancer profile of the final molecules on A549 (human lung adenocarcinoma), C6 (glioma cell line) and NIH/3T3 (mouse embryoblast cell line). Mostly, the final molecules showed significant cytotoxic profile with prominent selectivity. 2c (IC50: 7.23±1.17, SI: 6.93), 2d (IC50: 17.50±4.95; SI: 11.59), and 2 h (IC50: 10.83±0.76; SI: 29.23) showed significant and selective cytotoxic activity. Compounds 2b, 2c and 2e provoked apoptosis of A549 cells with potential higher than cisplatin as determined by flow cytometry. The cell cycle analysis of 2b, 2e, 2 g, and 2 h suggested that the A549 cells were affected during G0/G1 phase. Caspase-3 activation was observed on cells by compounds 2a, 2e, 2i and 2l at most. The highest mitochondrial membrane depolarization ratios were seen treated by compounds 2e, 2 h, and 2j. None of the analogs displayed any obvious inhibition activity on MMP-9. The physicochemical properties were predicted for all compounds and also molecular docking and dynamics simulation studies were realized for compound 2e, namely N-(6-methoxybenzothiazol-2-yl)-2-(4-(p-tolyl) piperazin-1-yl) acetamide. The results indicated that merging benzothiazole and piperazine derivatives via acetamide bridge is promising in cancer research because these moieties can interact with the loop and ß-sheet regions of MMP-9. So, 2e was considered as a potential therapeutic agent against lung carcinoma.

Profiling of particulate matter transport flux based on dual-wavelength lidar and ensemble learning algorithm

Transport flux (TF) is a significant particulate matter (PM) characteristic. This paper introduces an advanced dual-wavelength polarization aerosol and wind lidar (Wind Flux 3000) capable of independently observing the PM TF. The PM TF observation capability, which allows for simultaneous aerosol and wind measurements, was achieved by integrating a Mie-polarization particle lidar module and a coherent Doppler wind lidar module into a single lidar system. The primary measurement products of the Wind Flux 3000 include particulate extinction coefficient at 532 nm and 1550 nm, volume linear depolarization ratio at 532 nm (δp,532), wind speed (WS), wind direction (WD), vertical speed (VS), turbulence intensity (TI) and mixing layer height (MLH), retrieved by physical and proven algorithms. The PM concentration scales with the measured optical parameters and is also impacted by other environmental or meteorological parameters. Under the framework of the potential relationship between the PM concentration and the above parameters, the PM2.5 and PM10 concentration retrieval models were established using the stacking method of the ensemble learning technique; the models were trained using the in-situ data as true values, while the signal-to-noise ratio (SNR) at 1550 nm, δp,532, WS, WD, VS, the standard deviation of VS, TI, MLH provided by the Wind Flux 3000, as well as the relative humidity and temperature from ERA5, the hours of the day, and a “dust day” flag were used as inputs. The R2, RMSE, and MAE for the comparison between the predicted and true values of the PM2.5 test set are 0.857, 13.52 µg · m- 3, 9.16 µg · m- 3, and those of the PM10 test set are 0.926, 24.75 µg · m- 3, 14.39 µg · m- 3, respectively. The performance of the PM2.5 and PM10 concentration retrieval ensemble models is better than that of individual machine learning algorithms and better than that of the linear model. On 15th March 2023, a strong southeastward dust transport process with dust plume deposition was observed at Qingdao by the Wind Flux 3000. The analyses of the dust event demonstrated the Wind Flux 3000's ability to evaluate the transports of PM quantitatively.

Polarization characterization of a nonspherical sea salt aerosol model

The T-matrix method was utilized to study the polarization characteristics of nonspherical sea salt aerosol models within the wavelength range of 0.48–2.5 µm. Analysis was conducted on the polarization characteristics of nonspherical sea salt aerosols across different wavelengths as a function of scattering angle. This included scrutinizing linear depolarization ratios under typical visible and near-infrared wavelengths for various aspect ratios. The impact of particle nonsphericity on the linear depolarization ratios of monodisperse and polydisperse sea salt aerosol particles was examined. The results indicate: (1) In the analysis of the polarization characteristics of sea salt aerosols, the trends of polarization properties are similar between monodisperse and polydisperse systems. The scattering phase function P11(θ) is predominantly more significant in the forward-scattering direction. P11(θ) is insensitive to wavelength changes in the backward-scattering direction. P33(θ)/P11(θ) varies across different bands; in the visible light spectrum, there are significant fluctuating changes, while in the infrared spectrum, it trends towards nearly linear changes. The variation trends of −P12(θ)/P11(θ) and P34(θ)/P11(θ) with scattering angle are similar, and both are significantly affected by changes in wavelength. (2) Regarding the depolarization ratio of sea salt aerosols, the value for polydisperse systems is more than twice that of monodisperse systems, and the greater the nonsphericity, the higher the linear depolarization ratio. In monodisperse systems, at a wavelength of 0.633 µm for visible light and an aspect ratio of 0.4, the maximum depolarization ratio is around 118.82, while at 1.65 µm in the near-infrared, with an aspect ratio of 0.2, the maximum depolarization ratio is near 97.52; under polydisperse conditions, at 0.633 µm for visible light and an aspect ratio of 0.4, the maximum depolarization ratio is around 117.18, while at 1.65 µm in the near-infrared, with an aspect ratio of 0.2, the maximum linear depolarization ratio is near 215.66. Investigating the polarization characteristics and linear depolarization ratios of nonspherical spheroid sea salt aerosol particle models at all scattering angles is important for remote-sensing detection, high-precision calibration, and other optoelectronic applications.

Retrieval and analysis of the composition of an aerosol mixture through Mie–Raman–fluorescence lidar observations

Abstract. 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.

Enhancing Identification of Meteorological and Biological Targets Using the Depolarization Ratio for Weather Radar: A Case Study of FAW Outbreak in Rwanda

Leveraging weather radar technology for environmental monitoring, particularly the detection of biometeors like birds, bats, and insects, presents a significant challenge due to the dynamic nature of their behavior. Unlike hydrometeor targets, biometeor targets exhibit arbitrary changes in direction and position, which significantly alter radar wave polarization upon scattering. This study addresses this challenge by introducing a novel methodology utilizing Rwanda’s C-Band Polarization Radar. Our approach exploits the capabilities of dual-polarization radar by analyzing parameters such as differential reflectivity (ZDR) and correlation coefficient (RHOHV) to derive the Depolarization Ratio (DR). While existing radar metrics offer valuable insights, they have limitations in fully capturing depolarization effects. To address this, we propose an advanced fuzzy logic algorithm (FL_DR) integrating the DR parameter. The FL_DR’s performance was rigorously evaluated against a standard FL algorithm. Leveraging a substantial dataset comprising nocturnal clear air radar echoes collected during a Fall Armyworm (FAW) outbreak in maize fields from September 2020 to January 2021, the FL_DR demonstrated a notable improvement in accuracy compared to the existing FL algorithm. This improvement is evident in the Fraction of Echoes Correctly Identified (FEI), which increased from 98.42% to 98.93% for biological radar echoes and from 87.02% to 95.81% for meteorological radar echoes. This enhanced detection capability positions FL_DR as a valuable system for monitoring, identification, and warning of environmental phenomena in regions similar to tropical areas facing FAW outbreaks. Additionally, it could be tested and further refined for other migrating biological targets such as birds, insects, or bats.

On-line coupling of hollow-fiber flow field-flow fractionation and depolarized multi-angle static light scattering (HF5/D-MALS). Proof of principle

Introduced here is the on-line coupling of hollow-fiber flow field-flow fractionation (HF5) to depolarized multi-angle static light scattering (D-MALS). HF5 is a size-based separation alternative to size-exclusion and hydrodynamic chromatography and asymmetric flow field-flow fractionation. HF5 can separate larger sizes than its chromatographic counterparts and provides several advantages over its fractionation counterpart, including reduced sample consumption and greater ease of operation. D-MALS is a variant of MALS in which the depolarized scattering from the analyte solution is measured at a variety of angles simultaneously. Measurements of depolarized scattering have previously been employed in studying the optical properties of solutions or suspensions, to determine the length of rod-like analytes, and to gain increased accuracy in the determination of analyte molar mass. The coupling HF5/D-MALS allows for the depolarization ratio of a solution or suspension to be measured continuously across the fractogram. This is demonstrated here for a Teflon latex the size range of which extends beyond that accessible to commercial size-exclusion columns. The results presented provide the first reported on-line HF5/D-MALS coupling, showing the feasibility of the technique as well as its realized potential for providing continuous depolarization measurements, inter alia.

Characteristics of Cloud and Aerosol Derived from Lidar Observations during Winter in Lhasa, Tibetan Plateau

In order to investigate the variations of cloud and aerosol vertical profiles over the Tibetan Plateau (TP) in winter, we performed ground-based lidar observations in Lhasa, a city on the TP, from November 2021 to January 2022. The profiles of extinction coefficient, depolarization ratio, and signal-to-noise ratio (SNR) were retrieved using the atmospheric echo signals collected by the lidar. Clouds were identified by the range-correction echo signals and classified into water clouds, mixed clouds, horizontally oriented ice crystal clouds (HOICC), and ice clouds by the depolarization ratio and the hourly temperature from the European Centre for Medium-Range Weather Forecasts Reanalysis version 5 (ERA5). The clouds mainly appeared at a height of 3~5 km from 14:00–22:00 Beijing Time throughout the field campaign. The height and frequency (~30%) for cloud appearance were significantly lower than that reported in previous studies in summer. The cloud categories were dominated by mixed clouds and ice clouds during the observation period. The proportions of ice clouds gradually increased with increasing heights. After eliminating profiles influenced by clouds, the aerosol extinction coefficient and depolarization ratio were obtained, and the atmospheric boundary layer height (ABLH) was calculated. The aerosol extinction coefficient decreased with increasing height in the ABLH, and there were no obvious changes for the aerosol extinction coefficient above the ABL. The aerosol extinction coefficients near the Earth’s surface presented two peaks, appearing in the morning and evening, respectively. The high aerosols at the surface in the morning continually spread upward for 4–5 h and finally reached an altitude of 1 km with the development of ABLH. In addition, the depolarization ratio of aerosols decreased slowly with increasing altitudes. There was no obvious diurnal variation for depolarization ratios, indicating partly that the source of aerosols did not change significantly. These results are beneficial in understanding the evolution of cloud and aerosol vertical profiles over the TP.

Accelerating Molecular Vibrational Spectra Simulations with a Physically Informed Deep Learning Model.

In recent years, machine learning (ML) surrogate models have emerged as an indispensable tool to accelerate simulations of physical and chemical processes. However, there is still a lack of ML models that can accurately predict molecular vibrational spectra. Here, we present a highly efficient multitask ML surrogate model termed Vibrational Spectra Neural Network (VSpecNN), to accurately calculate infrared (IR) and Raman spectra based on dipole moments and polarizabilities obtained on-the-fly via ML-enhanced molecular dynamics simulations. The methodology is applied to pyrazine, a prototypical polyatomic chromophore. The VSpecNN-predicted energies are well within the chemical accuracy (1 kcal/mol), and the errors for VSpecNN-predicted forces are only half of those obtained from a popular high-performance ML model. Compared to the ab initio reference, the VSpecNN-predicted frequencies of IR and Raman spectra differ only by less than 5.87 cm-1, and the intensities of IR spectra and the depolarization ratios of Raman spectra are well reproduced. The VSpecNN model developed in this work highlights the importance of constructing highly accurate neural network potentials for predicting molecular vibrational spectra.

Enhancing mobile aerosol monitoring with CE376 dual-wavelength depolarization lidar

Abstract. We present the capabilities of a compact dual-wavelength depolarization lidar to assess the spatiotemporal variations in aerosol properties aboard moving vectors. Our approach involves coupling the lightweight Cimel CE376 lidar, which provides measurements at 532 and 808 nm and depolarization at 532 nm, with a photometer to monitor aerosol properties. The assessments, both algorithmic and instrumental, were conducted at ATOLL (ATmospheric Observatory of LiLle) platform operated by the Laboratoire d'Optique Atmosphérique (LOA), in Lille, France. An early version of the CE376 lidar co-located with the CE318-T photometer and with a multi-wavelength Raman lidar were considered for comparisons and validation. We developed a modified Klett inversion method for simultaneous two-wavelength elastic lidar and photometer measurements. Using this setup, we characterized aerosols during two distinct events of Saharan dust and dust smoke aerosols transported over Lille in spring 2021 and summer 2022. For validation purposes, comparisons against the Raman lidar were performed, demonstrating good agreement in aerosol properties with relative differences of up to 12 % in the depolarization measurements. Moreover, a first dataset of CE376 lidar and photometer performing on-road measurements was obtained during the FIREX-AQ (Fire Influence on Regional to Global Environments and Air Quality) field campaign deployed in summer 2019 over the northwestern USA. By lidar and photometer mapping in 3D, we investigated the transport of released smoke from active fire spots at William Flats (northeast WA, USA). Despite extreme environmental conditions, our study enabled the investigation of aerosol optical properties near the fire source, distinguishing the influence of diffuse, convective, and residual smoke. Backscatter, extinction profiles, and column-integrated lidar ratios at 532 and 808 nm were derived for a quality-assured dataset. Additionally, the extinction Ångström exponent (EAE), color ratio (CR), attenuated color ratio (ACR), and particle linear depolarization ratio (PLDR) were derived. In this study, we discuss the capabilities (and limitations) of the CE376 lidar in bridging observational gaps in aerosol monitoring, providing valuable insights for future research in this field.

Assessing Lidar Ratio Impact on CALIPSO Retrievals Utilized for the Estimation of Aerosol SW Radiative Effects across North Africa, the Middle East, and Europe

North Africa, the Middle East, and Europe (NAMEE domain) host a variety of suspended particles characterized by different optical and microphysical properties. In the current study, we investigate the importance of the lidar ratio (LR) on Cloud-Aerosol Lidar with Orthogonal Polarization–Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observations (CALIOP-CALIPSO) aerosol retrievals towards assessing aerosols’ impact on the Earth-atmosphere radiation budget. A holistic approach has been adopted involving collocated Aerosol Robotic Network (AERONET) observations, Radiative Transfer Model (RTM) simulations, as well as reference radiation measurements acquired using spaceborne (Clouds and the Earth’s Radiant Energy System-CERES) and ground-based (Baseline Surface Radiation Network-BSRN) instruments. We are assessing the clear-sky shortwave (SW) direct radiative effects (DREs) on 550 atmospheric scenes, identified within the 2007–2020 period, in which the primary tropospheric aerosol species (dust, marine, polluted continental/smoke, elevated smoke, and clean continental) are probed using CALIPSO. RTM runs have been performed relying on CALIOP retrievals in which the default and the DeLiAn (Depolarization ratio, Lidar ratio, and Ångström exponent)-based aerosol-speciated LRs are considered. The simulated fields from both configurations are compared against those produced when AERONET AODs are applied. Overall, the DeLiAn LRs leads to better results mainly when mineral particles are either solely recorded or coexist with other aerosol species (e.g., sea-salt). In quantitative terms, the errors in DREs are reduced by ~26–27% at the surface (from 5.3 to 3.9 W/m2) and within the atmosphere (from −3.3 to −2.4 W/m2). The improvements become more significant (reaching up to ~35%) for moderate-to-high aerosol loads (AOD ≥ 0.2).

Combining deep learning with physical parameters in POC and PIC inversion from spaceborne lidar CALIOP

POC and PIC are indispensable components in the global ocean carbon cycle, their transport and space distribution being driven by the biological carbon pump and the carbonate pump. However, passive ocean color remote sensing, usually employed for POC and PIC research, experiences serious shortcomings in polar winter conditions due to its reliance on sunlight, leading to scarce data coverage in the polar regions. In contrast, CALIOP has shown considerable promise in high-latitude ocean observing. Past approaches to estimate POC from CALIOP data relied on bbp measurements obtained through the application of algorithms that presume an empirical linear correlation between bbp and the backscatter coefficient measured at 180°. This method does not account for any spatiotemporal variability in the conversion coefficient. Furthermore, the potential of CALIOP to provide estimates of PIC has not been explored yet. Here, we developed an innovative Two-Branch-Two-Step (TBTS) model to estimate POC and PIC from CALIOP data, which effectively expands the spatial coverage of the MODIS products. This method exploits the strength of deep learning while encapsulating the generalizability of physical parameters. This method consists of two branches: (1) a deep learning branch based on lidar attenuated backscatter waveform and (2) a branch focusing on physical parameters, including the total column-integrated depolarization ratio and the subsurface cross-polarized component of column-integrated backscatter. The model’s generalizability and accuracy are confirmed through the evaluation of a test dataset and validation using in-situ measurements. Our model outperforms several other prevalent machine learning models. We also dissected the importance of different parts of the input data using SHAP tools, thereby providing insights into the black-box nature of deep learning models. Using CALIOP products, we put forth the inaugural estimation of interannually resolved PIC and POC standing stocks in polar regions. The implementation of CALIOP in polar regions bridges the gap inherent in passive ocean color measurements. Lidar-derived total global PIC standing stock is estimated to be 8% higher than that from MODIS, while the POC standing stock is boosted by 17.2%. The carbon standing stock in polar regions exhibits significant inter-annual variability and apparent seasonal periodicity. Hence, results from this research effort clearly reveal that the exploitation of the CALIOP-derived POC and PIC measurements, in combination with the application of new approaches and algorithms to future space lidar data, will undeniably enhance our comprehension of the polar ocean carbon cycle. However, it is important to acknowledge that the CALIOP products may inherit biases from the MODIS data used for training. Hence, where MODIS data is available, it’s still the “better” product to use, but where there isn’t MODIS data, the CALIOP product is extremely useful, especially in the polar regions.

Tropospheric sulfate from Cumbre Vieja (La Palma) observed over Cabo Verde contrasted with background conditions: a lidar case study of aerosol extinction, backscatter, depolarization and lidar ratio profiles at 355, 532 and 1064 nm

Abstract. In September 2021, volcanic aerosol (mainly freshly formed sulfate plumes) originating from the eruption of Cumbre Vieja on La Palma, Canary Islands, Spain, crossed Cabo Verde at altitudes below 2 km. On 24 September 2021, an extraordinary large aerosol optical depth (AOD) close to 1 (daily mean at 500 nm) was observed at Mindelo, Cabo Verde. This event provided favorable conditions to obtain lidar-derived profiles of extinction and backscatter coefficients, lidar ratio, and depolarization ratio at 355, 532 and 1064 nm in the sulfate aerosol plume. A novel feature of the lidar system operated at Mindelo is the availability of extinction, lidar ratio and depolarization measurements at 1064 nm in addition to the standard wavelengths of 355 and 532 nm. Having measurements of these parameters at all three wavelengths is a major advantage for the aerosol characterization and in aerosol typing efforts as the lidar ratio and the particle linear depolarization ratio are key parameters for this purpose. In this article, we present the key results of the lidar observations obtained on one specific day, namely on 24 September 2021 at 04:38–05:57 UTC, including the first ever measurements of the particle extinction coefficient, the lidar ratio and the depolarization ratio at 1064 nm for volcanic sulfate, and discuss the findings in terms of aerosol optical properties and mass concentrations by comparison with a reference observation (16 September 2021) representing the typical background conditions before the start of the eruptions. We found an unusual high particle extinction coefficient of 721 ± 51, 549 ± 38 and 178 ± 13 Mm−1, as well as an enhanced lidar ratio of 66.9 ± 10.1, 60.2 ± 9.2 and 30.8 ± 8.7 sr at 355, 532 and 1064 nm, respectively, in the sulfate-dominated planetary boundary layer (PBL). The particle linear depolarization ratio was ≤ 0.9 % at all respective wavelengths. It is the first time that lidar-derived intensive aerosol optical properties could be derived for volcanic sulfate at all three wavelengths, and thus it is a highly valuable data set for global aerosol characterization. The lidar analysis also revealed a sulfate-related AOD of about 0.35 ± 0.03 at 532 nm of the total PBL-related AOD of 0.43. The rest of the AOD contribution was caused by a lofted Saharan dust layer extending from 1.4 to 5 km and leading to a total AOD of 0.79 at 532 nm. Volcanic ash contribution to the observed aerosol plumes could be mostly excluded based on trajectory analysis and the observed optical properties. Peak mass concentration was 178.5 ± 44.6 µg m−3 in the volcanic-influenced and sulfate-dominated polluted PBL, showing the hazardous potential of such sulfate plumes to significantly worsen local air quality even at remote locations.

Properties of Cirrus Cloud Observed over Koror, Palau (7.3°N, 134.5°E), in Tropical Western Pacific Region

This study presented an analysis of the geometric and optical properties of cirrus clouds with data produced by Compact Cloud-Aerosol Lidar (ComCAL) over Koror, Palau (7.3°N, 134.5°E), in the Tropical Western Pacific region. The lidar measurement dataset covers April 2018 to May 2019 and includes data collected during March, July and August 2022. The results show that cirrus clouds occur approximately 47.9% of the lidar sampling time, predominantly between altitudes of 15 and 18 km. Seasonal variations in cirrus top height closely align with those of the cold point tropopause. Most cirrus clouds exhibit low cloud optical depth (COD < 0.1), with an annual mean depolarization ratio of 31 ± 19%. Convective-forming cirrus clouds during the summer monsoon season exhibit a larger size by notably lower values in terms of color ratio. Extremely thin cirrus clouds (COD < 0.005) constituting 1.6% of total cirrus occurrences are frequently observed at 1–2 km above the cold point, particularly during winter and summer, suggesting significant stratosphere–troposphere exchange. The coldest and highest tropopause over Palau is persistent during winter, and related to the pathway of tropospheric air entering the stratosphere through the cold trap. In summer, the extremely thin cirrus above the cold point is likely correlated with equatorial Kelvin waves induced by western Pacific monsoon convection.