Ground-based Lidar Research Articles

Limb Temperature Observations in the Stratosphere and Mesosphere Derived from the OMPS Sensor

Molecular scattering (Rayleigh scattering) has been extensively used from the ground with lidars and from space to observe the limb, thereby deriving vertical temperature profiles between 30 and 80 km. In this study, we investigate how temperature can be measured using the new Ozone Mapping and Profiler Suite (OMPS) sensor, aboard the Suomi NPP and NOAA-21 satellites. The OMPS consists of three instruments whose main purpose is to study the composition of the stratosphere. One of these, the Limb Profiler (LP), measures the radiance of the limb of the middle atmosphere (stratosphere and mesosphere, 12 to 90 km altitude) at wavelengths from 290 to 1020 nm. This new data set has been used with a New Simplified Radiative Transfer Model (NSRTM) to derive temperature profiles with a vertical resolution of 1 km. To validate the method, the OMPS-derived temperature profiles were compared with data from four ground-based lidars and the ERA5 and MSIS models. The results show that OMPS and the lidars are in agreement within a range of about 5 K from 30 to 80 km. Comparisons with the models also show similar results, except for ERA5 beyond 50 km. We investigated various sources of bias, such as different attenuation sources, which can produce errors of up to 120 K in the UV range, instrumental errors around 0.8 K and noise problems of up to 150 K in the visible range for OMPS. This study also highlighted the interest in developing a new miniaturised instrument that could provide real-time observation of atmospheric vertical temperature profiles using a constellation of CubeSats with our NSRTM.

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.

Summer daily dynamics of the vertical structure of atmospheric aerosol over Baikal coastal area from ground-based lidar measurements

ABSTRACT The spatial structure of atmospheric aerosol over Baikal in summer is studied on a regular basis with the use of ground-based LOSA-M2 lidar measurements at the Boyarsky scientific station of the Institute of Physical Material Science, Siberian Branch, Russian Academy of Sciences (51.83°N, 106.06°E, 456 m a.s.l.), Republic of Buryatia, Russia. The lake is located in a basin surrounded by mountain ranges on all side; its water volume is large. The lidar data are analysed along with the regional meteorological situation and meteorological parameters of the atmosphere. The daily dynamics of aerosol distribution over the atmospheric is examined for the period from 2015 to 2023 accounting the effect of different air masses in the region. Three main typical air circulation types are identified for the coastal zone of southern Baikal in summer, which determine the atmospheric aerosol generation and transport. The first and most common type relates to breeze circulation, where the main changes in the spatial structure of aerosol occur in the 2–3-km layer and are determined by changes in the transport direction within a breeze cell during the day. The main feature of this type is a decrease in the altitude of an air layer which contains most of aerosol in the lower atmosphere up to 1 km. The second type refers to southwestern transport under the presence of an anticyclone over the region. The maximal altitude of the aerosol layers attains 7 km in this case. The most complex and dynamic changes in the daily spatial structure of atmospheric aerosol occurred under transport of cyclones and associated atmospheric fronts over Lake Baikal. Under this third type, the altitude of the aerosol layers decreases throughout the day within the altitude range from the surface to 3–4 km.

Statistically Resolved Planetary Boundary Layer Height Diurnal Variability Using Spaceborne Lidar Data

The Planetary Boundary Layer Height (PBLH) significantly impacts weather, climate, and air quality. Understanding the global diurnal variation of the PBLH is particularly challenging due to the necessity of extensive observations and suitable retrieval algorithms that can adapt to diverse thermodynamic and dynamic conditions. This study utilized data from the Cloud-Aerosol Transport System (CATS) to analyze the diurnal variation of PBLH in both continental and marine regions. By leveraging CATS data and a modified version of the Different Thermo-Dynamics Stability (DTDS) algorithm, along with machine learning denoising, the study determined the diurnal variation of the PBLH in continental mid-latitude and marine regions. The CATS DTDS-PBLH closely matches ground-based lidar and radiosonde measurements at the continental sites, with correlation coefficients above 0.6 and well-aligned diurnal variability, although slightly overestimated at nighttime. In contrast, PBLH at the marine site was consistently overestimated due to the viewing geometry of CATS and complex cloud structures. The study emphasizes the importance of integrating meteorological data with lidar signals for accurate and robust PBLH estimations, which are essential for effective boundary layer assessment from satellite observations.

An Improved CH4 Profile Retrieving Method for Ground-Based Differential Absorption Lidar

Range-resolved CH4 concentration measurement is important prior data for atmospheric physical and chemical models. Ground-based differential absorption lidar (DIAL) can measure the vertical distribution of CH4 concentration in the atmosphere. The traditional method uses lidar observational data and the lidar equation to calculate profiles, but the inversion accuracy is greatly affected by noise. Although some denoising methods can improve accuracy at low altitudes, the low signal-to-noise ratio caused by the effect of aerosol Mie scattering and lower aerosol concentrations at high altitudes cannot be solved. Here, an improved cubic smoothing spline fitting CH4 concentration profile inversion method is proposed to address this challenge. By adding a penalty term of the second derivative of the conventional cubic spline function to the objective function, this penalty term acts to smooth the fitting, allowing the fitting function to avoid necessarily passing through those noisy sampling points. This avoids the large fluctuations caused by noisy sampling points, effectively suppresses noise, captures signals with lower noise levels, and thereby enhances the inversion accuracy of the profiles. Simulations and case studies demonstrated the superiority of the proposed method. Compared with the traditional method, cubic smoothing spline fitting can reduce the mean error of the whole CH4 profile by 85.54%. The standard deviation of CH4 concentration retrieved is 3.59 ppb–90.29 ppb and 0.01 ppb–6.75 ppb smaller than the traditional method and Chebyshev fitting, respectively. Three real cases also indicate that the CH4 concentration retrieved by cubic smoothing spline fitting is more consistent with in-situ measurements. In addition, long-term DIAL observations have also revealed notable diurnal and seasonal trends in CH4 concentration at observation sites.

Proof of concept demonstration of a metastable helium fluorescence lidar for thermospheric wind and temperature measurements.

We report on the first, to the best of our knowledge, spectral measurements of terrestrial thermospheric metastable helium using ground-based lidar. By stimulating fluorescence of He(23S) at four closely spaced wavelengths within the He line around 1083 nm and measuring the lidar returns, we measured the He(23S) spectrum at 600 km, providing coarse constraints on the He(23S) temperature and vertical wind speed. This work serves as a proof of concept and precursor experiment for future, more powerful helium lidar systems capable of measuring vertical profiles of neutral wind and temperature in the upper terrestrial thermosphere.

Nonuniformity of diffractive splitting and receiver-transmitter alignment for large-field-of-view hundred-beam-scale LiDAR

To meet the spatial perception requirements for autonomous ship navigation in common scenarios such as ocean navigation, port entry and exit, and lock passage, commercial vehicle-mounted LiDAR technology falls short of demands in aspects such as sensing distance, field of view, angular resolution, and spatial sampling density. This paper proposes a hundred-beam-scale LiDAR scheme based on large-field-of-view diffractive beam splitting and a fiber array for echo reception and presents an in-depth investigation of the angular nonuniformity of diffractive beam splitting and the microradian-scale alignment for such hundred-beam-scale large-field-of-view LiDAR. This paper considers a combination of split-beam transmission based on a high-order diffractive optical element (DOE) and echo reception based on a high-precision fiber-optic line array. The nonlinear angular characteristics of the DOE are deduced and analyzed for a large field of view. The units of the receiving fiber-optic array are designed to offset the influence of the angular nonlinearity of the DOE, ensuring high-precision receiver–transmitter alignment of the hundred-beam-scale LiDAR for beams at any order of diffraction and helping to reduce system errors. The above-described LiDAR system has undergone laboratory testing and practical engineering verification, and it provides a new optical solution for LiDAR systems at the hundred-beam scale with a large field of view, a small divergence angle, and high sampling density. The presented system achieves a registration accuracy of 66.5 μrad with 128 beams and a 10-degree field of view, greatly improving signal reception efficiency. Such LiDAR systems have a wide range of applications, including space docking, target identification, lunar and planetary exploration, and ground-based vehicle-mounted LiDAR.

EcoLiDAR: An economical LiDAR scanner for ecological research.

Despite recent popularization and widespread use in modern electronic devices, LiDAR technology remains expensive for research purposes, in part due to the very high performance offered by commercially available LiDAR scanners. However, such high performance is not always needed, and the expensive price ends up making LiDAR scanners inaccessible for research projects with reduced budget, such as those in developing countries. Here we designed and built a simple ground-based LiDAR scanner, with performance sufficient to fulfil the requirements for a variety of ecological research projects, while being cheap and easy to build. We managed to assemble a LiDAR scanner under 400 USD (as of 2021), and it is simple enough to be built by personnel with minimal engineering background. We also demonstrated the quality of the resulting point clouds by scanning a test site and producing some common LiDAR products. Although not adequate for mapping large area due to its limited range, our LiDAR design is open, customizable, and can produce adequate results while costing ~1% of "low-cost" scanners available in the market. As such, our LiDAR scanner opens a world of new opportunities, particularly for projects in developing countries.

Modelling polarized pulsed LiDAR signals in scattering media: an approach for optimizing systems in real-world scenarios

Adverse weather conditions present a primary challenge for ground-based LiDAR imaging systems in outdoor applications. The use of polarization has been proposed as an effective filtering mechanism. However, the number of potential situations is large, complex and difficult to parameterize with accuracy. In such conditions, advanced simulation methods enable the testing of different experimental configurations and the determination of the best possible setup. With this purpose, a Monte Carlo algorithm is presented for modeling polarized pulsed LiDAR signals in turbid media. This algorithm is designed for immediate applicability, incorporating realistic media characterization and accounting for the attributes of existing prototypes. It allows testing various experimental configurations, managing optical obstacles, adjusting polarization arrangements, and different geometries of particles within the medium. The developed algorithm accurately characterizes backscattering signals, revealing their dependence on medium properties. A relationship between visibility and backscattering energy is identified, offering insights for sensor optimization. Polarization analysis highlights the efficacy of circular polarization in mitigating scattering effects and establishes a connection with the polarimetric characteristics of imaged targets. The algorithm's application to irregular particles reveals also an unexpected behavior of polarized light, challenging established strategies. These diverse use cases exemplify the algorithm's capability to model real-world circumstances, emphasizing its significance in predicting cutting-edge situations when designing optical systems for complex and demanding outdoor scenarios.

Characteristics and seasonal variations of cirrus clouds from ground-based lidar and satellite observations over Shouxian Area, China

This study investigates the macroscopic and optical properties of cirrus clouds in the 32N region from July 2016 to May 2017, leveraging data from ground-based lidar observations and CALIOP to overcome the inconsistencies in detected cirrus cloud samples. Through extensive data analysis, statistical characteristics of cirrus clouds were discerned, revealing lidar ratio values of 28.5 ± 10.8 from ground-based lidar and 27.4 ± 11.2 from CALIOP. Validation with a decade of CALIOP data (2008-2018) confirmed these findings, presenting a consistent lidar ratio of 27.4 ± 12.0. A significant outcome of the analysis was the identification of a positive correlation between the lidar ratio and cloud centroid temperature, indicating a gradual decrease in the lidar ratio as temperatures dropped. The study established a fundamental consistency in their macroscopic properties, including cloud base height, cloud top height, cloud thickness, cloud centroid height, and cloud centroid temperature. The results for ground-based lidar (CALIOP) are: 10.0 ± 2.1 km (10.0 ± 2.2 km), 11.8 ± 2.1 km (11.5 ± 2.3 km), 1.87 ± 0.83 km (1.52 ± 0.71 km), and 10.5 ± 2.2 km, -46.9 ± 9.7°C (-47.1 ± 10.0°C).These properties exhibited seasonal variations, with cirrus clouds reaching higher altitudes in summer and lower in winter, influenced by the height of the tropopause. The optical properties of cirrus clouds were also analyzed, showing an annual average optical depth of 0.31 ± 0.35 for ground-based lidar and 0.32 ± 0.44 for CALIOP. The study highlighted the distribution of subvisible, thin, and thick cirrus clouds, with a notable prevalence of subvisible clouds during summer, suggesting their frequent formation above 14 km. Furthermore, the study observed linear growth in geometric thickness and optical depth up to 2.5 km from CALIOP and 2.9 km from ground-based lidar. Maximum optical depth was observed at cloud centroid temperatures of -35°C for CALIOP and -40°C for ground-based lidar, with optical depth decreasing as temperatures fell. This suggests that fully glaciated cirrus clouds exhibit the highest optical depth at warmer temperatures, within the complete glaciation temperature range of -35°C to -40°C.

Multiple-scattering effects on single-wavelength lidar sounding of multi-layered clouds

Abstract. We performed Monte Carlo simulations of single-wavelength lidar signals from multi-layered clouds with special attention focused on the multiple-scattering (MS) effect in regions of the cloud-free molecular atmosphere (i.e. between layers or outside a cloud system). Despite the fact that the strength of lidar signals from the molecular atmosphere is much lower compared to the in-cloud intervals, studies of MS effects in such regions are of interest from scientific and practical points of view. The MS effect on lidar signals always decreases with the increasing distance from the cloud far edge. The decrease is the direct consequence of the fact that the forward peak of particle phase functions is much larger than the receiver field of view (RFOV). Therefore, the photons scattered within the forward peak escape the sampling volume formed by the RFOV (i.e. the escape effect). We demonstrated that the escape effect is an inherent part of MS properties within the free atmosphere beyond the cloud far edge. In the cases of the ground-based lidar, the MS contribution is lower than 5 % within the regions of the cloud-free molecular atmosphere with a distance from the cloud far edge of about 1 km or higher. In the cases of the space-borne lidar, the rate of decrease of the MS contribution is so slow that the threshold of 5 % can hardly be reached. In addition, the effect of non-uniform beam filling is extremely strong. Therefore, practitioners should employ, with proper precautions, lidar data from regions below the cloud base when treating data of a space-borne lidar. In the case of two-layered cloud, the distance of 1 km is sufficiently large so that the scattered photons emerging from the first layer do not affect signals from the second layer when we are dealing with the ground-based lidar. In contrast, signals from the near edge of the second cloud layer are severely affected by the photons emerging from the first layer in the case of a space-borne lidar. We evaluated the Eloranta model (EM) in extreme conditions and showed its good performance in the cases of ground-based and space-borne lidars. At the same time, we revealed the shortcoming that can affect practical applications of the EM. Namely, values of the key parameters – i.e. the ratios of phase functions in the backscatter direction for the nth-order-scattered photon and a singly scattered photon – depend not only on the particle phase function but also on the distance from a lidar to the cloud and the receiver field of view. Those ratios vary within a quite large range, and the MS contribution to lidar signals can be largely overestimated or underestimated if erroneous values of the ratios are assigned to the EM.

A novel framework and validation for the dynamic characterization of civil structures via ground-based lidar

While there has been substantial research conducted on the use of Ground-Based Lidar (GBL; i.e., Terrestrial Laser Scanners) in monitoring the static deformation of civil structures, its application in monitoring dynamic vibrations of structures, which is critical for structural health monitoring (SHM), has only been studied to a limited extent. Traditional contact-based SHM frameworks are constrained by a limited number of sensors and the need for physical access for instrumentation placement. Although recent case studies demonstrate GBL's potential in quantifying dynamic displacements, there is a need for more comprehensive research to validate the accuracy of GBL-based dynamic measurements under various GBL- and structure-based parameters in an autonomous and scalable framework. Therefore, the main objective of this study is to develop and comprehensively validate a novel end-to-end framework to monitor the dynamic vibrations of structures using GBL through extensive experimentation in a controlled laboratory environment. In this study, a novel two-step spatio-temporal algorithm was developed to extract the dynamic vibrations of structures from the dynamic point clouds. The framework leverages the Density-based Spatial Clustering of Applications with Noise (DBSCAN) and change detection algorithms. The impact of several GBL-based parameters on the accuracy of the operational modal analysis results was investigated across six single-degree-of-freedom structures with unique natural frequencies. The GBL-based parameters included the resolution, quality, and point-to-point distance of the dynamic point clouds. Accelerometers and infrared-based sensors were used for the validation of GBL measurements and operational modal analysis results. To validate GBL's full-field mode shapes across the tested specimen configurations, analytical and finite element models were constructed to provide high-fidelity mode shapes as ground-truth data. The results show that the GBL can detect sub-millimeter structural vibrations, and that the resulting natural frequencies and operational deflected shapes closely match those of traditional sensing modalities, and the analytical and finite element models. This study concludes that GBL can be used reliably for remotely monitoring the dynamic response of structures at a high spatial resolution. However, further research is warranted to evaluate the full extents of the proposed framework.

An iterative algorithm to simultaneously retrieve aerosol extinction and effective radius profiles using CALIOP

Abstract. The Cloud-Aerosol Lidar with Orthogonal Polarization (CALIOP) on board the Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observation (CALIPSO) satellite has been widely used in climate and environment studies to obtain the vertical profiles of atmospheric aerosols. To retrieve the vertical profile of aerosol extinction, the CALIOP algorithm assumes column-averaged lidar ratios based on a clustering of aerosol optical properties measured at surface stations. On one hand, these lidar ratio assumptions may not be appropriate or representative at certain locations. One the other hand, the two-wavelength design of CALIOP has the potential to constrain aerosol size information, which has not been considered in the operational algorithm. In this study, we present a modified inversion algorithm to simultaneously retrieve aerosol extinction and effective radius profiles using two-wavelength elastic lidars such as CALIOP. Specifically, a lookup table is built to relate the lidar ratio with the Ångström exponent calculated using aerosol extinction at the two wavelengths, and the lidar ratio is then determined iteratively without a priori assumptions. The retrieved two-wavelength extinction at each layer is then converted to the particle effective radius assuming a lognormal distribution. The algorithm is tested on synthetic data, Raman lidar measurements and then finally the real CALIOP backscatter measurements. Results show improvements over the CALIPSO operational algorithm by comparing with ground-based Raman lidar profiles.

Revealing forest structural "fingerprints": An integration of LiDAR and deep learning uncovers topographical influences on Central Amazon forests

Amazon forests are characterized by rich structural diversity. However, the influence of factors such as topography, soil attributes, and external disturbances on structural variability is not always well characterized, and traditional structural metrics may be inadequate to capture this type of complexity. While LiDAR offers expanded structural insights, traditional parameters used in LiDAR analysis, such as mean or maximum canopy height, are not always well directly linked to environmental variables like topography. Emerging approaches merge LiDAR with machine learning to uncover deeper structural complexities. However, work to date may fail to fully utilize the potential of fine-scale LiDAR information. Here we introduce a novel approach, leveraging 2D point cloud images derived from a profiling canopy LiDAR (PCL). The technique targets intricate details within LiDAR point clouds by using deep learning algorithms. With a dataset from the Central Amazon comprising 18 multitemporal transects of 450 m in length, our objective was to detect structural "fingerprints" of varied topographical types along a hillslope, comprising: Riparian, White-sand, and Plateau, and to detect any gradient of structural shifts based on terrain variations here represented by the height above the nearest drainage (HAND). The dataset was trained and tested using a leave-one-group-out approach (LOGO) in which, for each iteration, a complete 450 m multitemporal transect was excluded from training and tested after each iteration. The fast.ai platform and a ResNet-34 architecture, coupled with transfer learning, were used to perform a classification to distinguish between three topographical types. Furthermore, a hybrid model combining a Convolutional Autoencoder, and Partial Least Square (PLS) regression was designed to detect forest structural gradient correlations with HAND variation. Cross-validation achieved a promising high weighted F1 score of 0.83 to classify forests based on the topographical types. Additionally, a combined Convolutional Autoencoder and PLS regression revealed a strong correlation (R2 = 0.76) between actual and predicted HAND. Innovatively combining deep learning with ground-based PCL LiDAR, our study revealed unique Amazon Forest structures connected to topographic variation. Our findings underscore the transformative potential of such integrative approaches for investigating forest dynamics and promise a powerful new tool for understanding climate-related forest structure change.

Tree parameter extraction method based on new remote sensing technology and terrestrial laser scanning technology

Ground LiDAR is a terrestrial LiDAR system that is often used for terrain and geomorphic mapping. Ground-based LiDAR can be used to collect more local and short-range data, making it ideal for mapping smaller areas with high precision. In order to solve the rapid extraction of tree parameters in the national public welfare forest survey, the ground-based LIDAR was used to obtain the point cloud of trees, and the point cloud data was registered, denoised, normalized, sliced, parameter extracted, etc., and the parameters of individual trees in the forest were obtained. The Bland-Altman consistency test is used to test whether the method of extracting tree parameters from point clouds is consistent with the traditional measurement method. The experimental results show that the point cloud data obtained by the ground-based LIDAR can quickly, conveniently and accurately extract the tree parameters, which is consistent with the traditional tree parameter extraction method, and has the advantages than the traditional tree parameter measurement, such as point cloud, image and traceability. It has a unique advantage in establishing a tree database. It is suggested that LIDAR should be used for forest survey in the future.

Trans-Boundary Dust Transport of Dust Storms in Northern China: A Study Utilizing Ground-Based Lidar Network and CALIPSO Satellite

During 14–16 March 2021, a large-scale dust storm event occurred in the northern region of China, and it was considered the most intense event in the past decade. This study employs observation data for PM2.5 and PM10 from the air quality monitoring station, the HYSPLIT model, ground-based polarized Lidar networks, AGRI payload data from Fengyun satellites and CALIPSO satellite Lidar data to jointly explore and scrutinize the three-dimensional spatial and temporal characteristics of aerosol transport. Firstly, by integrating meteorological data for PM2.5 and PM10, the air quality is assessed across six stations within the Lidar network during the dust storm. Secondly, employing a backward trajectory tracking model, the study elucidates sources of dust at the Lidar network sites. Thirdly, deploying a newly devised portable infrared 1064 nm Lidar and a pulsed 532 nm Lidar, a ground-based Lidar observation network is established for vertical probing of transboundary dust transport within the observed region. Finally, by incorporating cloud imagery from Fengyun satellites and CALIPSO satellite Lidar data, this study revealed the classification of dust and the height distribution of dust layers at pertinent sites within the Lidar observation network. The findings affirm that the eastward movement and southward compression of the intensifying Mongolian cyclone led to severe dust storm weather in western and southern Mongolia, as well as Inner Mongolia, further transporting dust into northern, northwestern, and northeastern parts of China. This dust event wielded a substantial impact on a broad expanse in northern China, manifesting in localized dust storms in Inner Mongolia, Beijing, Gansu, and surrounding areas. In essence, the dust emanated from the deserts in Mongolia and northwest China, encompassing both deserts and the Gobi region. The amalgamation of ground-based and spaceborne Lidar observations conclusively establishes that the distribution height of dust in the source region ranged from 3 to 5 km. Influenced by high-pressure systems, the protracted transport of dust over extensive distances prompted a gradual reduction in its distribution height owing to sedimentation. The comprehensive analysis of pertinent research data and information collectively affirms the precision and efficacy of the three-dimensional aerosol monitoring conducted by the ground-based Lidar network within the region.

Vertical Profiles of Aerosols Induced by Dust, Smoke, and Fireworks in the Cold Region of Northeast China

Despite the long-term implementation of air pollution control policies in northeast China, severe haze pollution continues to occur frequently. With the adoption of a megacity (Changchun) in northeast China, we analysed the vertical characteristics of aerosols and the causes of aerosol pollution throughout the year using multisource data for providing recommendations for controlling pollution events (i.e., straw burning and fireworks). Based on a ground-based LiDAR, it was found that the extinction coefficient (EC) of aerosols at a height of 300 m in Changchun was highest in winter (0.44 km−1), followed by summer (0.28 km−1), with significant differences from those in warmer regions, such as the Yangtze River Delta. Therefore, it is recommended that air pollution control policies be differentiated between winter and summer. On Chinese New Year’s Eve in Changchun, the ignition of firecrackers during the day and night caused increases in the EC at a height of 500 m to 0.37 and 0.88 km−1, respectively. It is suggested that the regulation of firecracker ignition should be reduced during the day and strengthened at night. Based on the CALIPSO and backward trajectory analysis results, two events of dust–biomass-burning composite pollution were observed in March and April. In March, the primary aerosol component was dust from western Changchun, whereas in April, the main aerosol component was biomass-burning aerosols originating from northern and eastern Changchun. Hence, reducing the intensity of spring biomass burning can mitigate the occurrence of dust–biomass-burning composite pollution. These findings can provide emission policy suggestions for areas facing similar issues regarding biomass-burning transmission pollution and firework emissions.

Validation of initial observation from the first spaceborne high-spectral-resolution lidar with a ground-based lidar network

Abstract. On 16 April 2022, China successfully launched the world's first spaceborne high-spectral-resolution lidar (HSRL), which is called the Aerosol and Carbon Detection Lidar (ACDL), on board the Atmospheric Environment Monitoring Satellite known as Daqi-1 (DQ-1). The ACDL is expected to precisely detect the three-dimensional distribution of aerosol and cloud globally with high spatial–temporal resolutions. To assess the performance of the newly launched satellite lidar, the ACDL-retrieved observations were compared with ground-based lidar measurements of atmospheric aerosol and cloud over northwest China from May to July 2022 using the Belt and Road lidar network (BR-lidarnet) initiated by Lanzhou University in China and the Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observation (CALIPSO) lidar observations. A total of six cases in the daytime and nighttime, including clear days, dust events, and cloudy conditions, were selected for further analysis. Moreover, profiles of the total attenuated backscatter coefficient (TABC) and the volume depolarization ratio (VDR) at 532 nm measured by the ACDL, the CALIPSO lidar, and ground-based lidar are compared in detail. Comparison is made between the 532 nm extinction coefficient and lidar ratio obtained from ACDL HSRL retrieval and the Raman retrieval results obtained from BR-lidarnet. The achieved results revealed that the ACDL observations were in good agreement with the ground-based lidar measurements during dust events with a relative deviation of about −10.5 ± 25.4 % for the TABC and −6.0 ± 38.5 % for the VDR. Additionally, the heights of the cloud top and bottom from these two measurements were well matched and comparable. Compared with the observation of CALIPSO, the ACDL also shows high consistency. This study proves that the ACDL provides reliable observations of aerosol and cloud in the presence of various climatic conditions, which helps to further evaluate the impacts of aerosol on climate and the environment, as well as on the ecosystem in the future.

The Tibetan Plateau space-based tropospheric aerosol climatology: 2007–2020

Abstract. A comprehensive and robust dataset of tropospheric aerosol properties is important for understanding the effects of aerosol–radiation feedback on the climate system and reducing the uncertainties of climate models. The “Third Pole” of Earth (Tibetan Plateau, TP) is highly challenging for obtaining long-term in situ aerosol data due to its harsh environmental conditions. Here, we provide the more reliable new vertical aerosol index (AI) parameter from the spaceborne-based lidar CALIOP (Cloud-Aerosol Lidar with Orthogonal Polarization) on board CALIPSO (Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observations) for daytime and nighttime to investigate the aerosol's climatology over the TP region during 2007–2020. The calculated vertical AI was derived from the aerosol extinction coefficient (EC), which was rigorously quality-checked and validated for passive satellite sensors (MODIS) and ground-based lidar measurements. Generally, our results demonstrated that there was agreement of the AI dataset with the CALIOP and ground-based lidar. In addition, the results showed that, after removing the low-reliability aerosol target signal, the optimized data can obtain the aerosol characteristics with higher reliability. The data also reveal the patterns and concentrations of high-altitude vertical structure characteristics of the tropospheric aerosol over the TP. They will also help to update and make up the observational aerosol data in the TP. We encourage climate modelling groups to consider new analyses of the AI vertical patterns, comparing the more accurate datasets, with the potential to increase our understanding of the aerosol–cloud interaction (ACI) and aerosol–radiation interaction (ARI) and their climate effects. Data described in this work are available at https://doi.org/10.11888/Atmos.tpdc.300614 (Huang, 2023).

Estimates of Southern Hemispheric Gravity Wave Momentum Fluxes across Observations, Reanalyses, and Kilometer-Scale Numerical Weather Prediction Model

Abstract Gravity waves (GWs) are among the key drivers of the meridional overturning circulation in the mesosphere and upper stratosphere. Their representation in climate models suffers from insufficient resolution and limited observational constraints on their parameterizations. This obscures assessments of middle atmospheric circulation changes in a changing climate. This study presents a comprehensive analysis of stratospheric GW activity above and downstream of the Andes from 1 to 15 August 2019, with special focus on GW representation ranging from an unprecedented kilometer-scale global forecast model (1.4 km ECMWF IFS), ground-based Rayleigh lidar (CORAL) observations, modern reanalysis (ERA5), to a coarse-resolution climate model (EMAC). Resolved vertical flux of zonal GW momentum (GWMF) is found to be stronger by a factor of at least 2–2.5 in IFS compared to ERA5. Compared to resolved GWMF in IFS, parameterizations in ERA5 and EMAC continue to inaccurately generate excessive GWMF poleward of 60°S, yielding prominent differences between resolved and parameterized GWMFs. A like-to-like validation of GW profiles in IFS and ERA5 reveals similar wave structures. Still, even at ∼1 km resolution, the resolved waves in IFS are weaker than those observed by lidar. Further, GWMF estimates across datasets reveal that temperature-based proxies, based on midfrequency approximations for linear GWs, overestimate GWMF due to simplifications and uncertainties in GW wavelength estimation from data. Overall, the analysis provides GWMF benchmarks for parameterization validation and calls for three-dimensional GW parameterizations, better upper-boundary treatment, and vertical resolution increases commensurate with increases in horizontal resolution in models, for a more realistic GW analysis. Significance Statement Gravity wave–induced momentum forcing forms a key component of the middle atmospheric circulation. However, complete knowledge of gravity waves, their atmospheric effects, and their long-term trends are obscured due to limited global observations, and the inability of current climate models to fully resolve them. This study combines a kilometer-scale forecast model, modern reanalysis, and a coarse-resolution climate model to first compare the resolved and parameterized momentum fluxes by gravity waves generated over the Andes, and then evaluate the fluxes using a state-of-the-art ground-based Rayleigh lidar. Our analysis reveals shortcomings in current model parameterizations of gravity waves in the middle atmosphere and highlights the sensitivity of the estimated flux to the formulation used.