Lidar Observations Research Articles

Quantifying the Impact of the Surface Roughness of Hexagonal Ice Crystals on Backscattering Properties for Lidar‐Based Remote Sensing Applications

AbstractImpacts of small‐scale surface irregularities, or surface roughness, of atmospheric ice crystals on lidar backscattering properties are quantified. Geometric ice crystal models with various degrees of surface roughness and state‐of‐the‐science light‐scattering computational capabilities are utilized to simulate the single‐scattering properties across the entire practical size parameter range. The simulated bulk lidar and depolarization ratios of polydisperse ice crystals at wavelength 532 nm are strongly sensitive to the degree of surface roughness. Comparisons of these quantities between the theoretical simulations and counterparts inferred from spaceborne lidar observations for cold cirrus clouds suggest a typical surface‐roughness‐degree range of 0.03–0.15 in the cases of compact hexagonal ice crystals, which is most consistent with direct measurements of scanning electron microscopic images. To properly interpret lidar backscattering observations of ice clouds, it is necessary to account for the degree of surface roughness in light‐scattering computations involving ice crystals.

Accounting for plume rise of aircraft emissions in AERMOD

The U.S. Federal Aviation Administration (FAA) uses the Aviation Environmental Design Tool (AEDT) to assess the impact of airport emissions on air quality in and around an airport. AEDT incorporates EPA's (Environmental Protection Agency) regulatory model, AERMOD. The area/volume source algorithm in AERMOD is currently used to treat most airport sources including aircraft, which contribute a major fraction of total airport emissions. Aircraft emissions have horizontal momentum corresponding to the forward thrust of the aircraft. In addition, they have buoyancy corresponding to the heat rejected from the aircraft engine. These plume dynamics are not included in the latest version (v22112) of the area/volume source algorithm in AERMOD; the effects of the plume on ground-level concentrations are accounted through an initial plume height and width based on LIDAR observations at the end of runways. Model predictions based on this approach are likely to lead to overestimates of ground-level concentrations because they do not account for the increase of plume height with distance from the source. This paper proposes a plume rise formulation for aircraft emissions modeled with AERMOD. Plume rise is modeled using the weighted average of the characteristics of the aircraft that pass through the area/volume source during 1 h, the averaging time used in AERMOD. The buoyancy parameter used to compute plume rise is estimated using the aircraft engine characteristics: thrust, fuel burn rate, the velocity of the aircraft, air-fuel ratio, and the engine bypass ratio. The proposed plume rise formulation for AERMOD is evaluated using SO2 observations from the 2012 Air Quality Source Apportionment Study (AQSAS) conducted at the Los Angeles International Airport (LAX). The results from the evaluation indicate that plume rise improves AERMOD's description of the qualitative behavior of concentrations measured in and around the airport.

The Benefit of NASA’s Atmosphere Observing System (AOS) Mission Lidar and Polarimeter Observations for Health and Air Quality Applications

The Benefit of NASA’s Atmosphere Observing System (AOS) Mission Lidar and Polarimeter Observations for Health and Air Quality Applications

Lidar Observations of Predawn Thermosphere‐Ionosphere Na (TINa) Layers Over Boulder (40.13°N, 105.24°W): Annual Phase Variations and Correlation With Sunrise and Tidal Winds

AbstractWe have discovered that the peak phase time of predawn thermosphere‐ionosphere Na (TINa) layers (∼110–150 km altitude) undergoes clear annual variations with the earliest occurrence in summer and latest in winter over Boulder (40.13°N, 105.24°W), which are closely correlated to annual phase variations of sunrise and tidal winds. Such discoveries were enabled by the first characterization of 12 monthly composites of TINa layers from January through December using 7 years of lidar observations (2011–2017). Despite their tenuous densities, the predawn TINa layers have nearly 100% occurrence rate (160 out of 164 nights of observations). Monthly composites show downward‐phase‐progression TINa descending at similar phase speeds as Climatological Tidal Model of the Thermosphere tidal winds. These TINa layers occur in ion convergence but neutral divergence regions, modeled using tidal winds. These results support the formation mechanism (neutralization of converged TINa+ forming TINa) proposed previously and suggest that migrating tidal winds experience annual phase variations.

Solitary wave characteristics on the fine structure of the mesospheric sporadic sodium layer

The so-called sporadic sodium layers (SSLs or NaS) are proposed to be strongly related to wave fluctuations. The solitary wave is a particular solution of the partial differential equation whose energy travels as a localized wave packet. A soliton, on the other hand, is a special type of solitary wave that exhibits a particle-like behavior with a strong stable form. For the first time, the solitary wave theory has been used in this research to study the fine structure of SSL/NaS. We performed soliton fitting processes on the observed data from the Andes Lidar Observatory and found out that 24/27 NaS events had exhibited similar features/characteristics to a soliton. Time series of the net anomaly of the NaS revealed the same variation process to the solution of a generalized five-order KdV equation. Our results, therefore, suggested that the NaS phenomenon would be a pertinent tracer for non-linear wave studies in the atmosphere.

Cloud top heights and aerosol layer properties from EarthCARE lidar observations: the A-CTH and A-ALD products

Abstract. The high-spectral-resolution Atmospheric Lidar (ATLID) on the Earth Cloud, Aerosol and Radiation Explorer (EarthCARE) provides vertically resolved information on aerosols and clouds with unprecedented accuracy. Together with the Cloud Profiling Radar (CPR), the Multi-Spectral Imager (MSI), and the Broad-Band Radiometer (BBR) on the same platform, it allows for a new synergistic view on atmospheric processes related to the interaction of aerosols, clouds, precipitation, and radiation at the global scale. This paper describes the algorithms for the determination of cloud top height and aerosol layer information from ATLID Level 1b (L1b) and Level 2a (L2a) input data. The ATLID L2a Cloud Top Height (A-CTH) and Aerosol Layer Descriptor (A-ALD) products are developed to ensure the provision of atmospheric layer products in continuation of the heritage from the Cloud–Aerosol Lidar and Infrared Pathfinder Satellite Observations (CALIPSO). Moreover, the products serve as input for synergistic algorithms that make use of data from ATLID and MSI. Therefore, the products are provided on the EarthCARE joint standard grid (JSG). A wavelet covariance transform (WCT) method with flexible thresholds is applied to determine layer boundaries from the ATLID Mie co-polar signal. Strong features detected with a horizontal resolution of 1 JSG pixel (approximately 1 km) or 11 JSG pixels are classified as thick or thin clouds, respectively. The top height of the uppermost cloud layer together with information on cloud layering are stored in the A-CTH product for further use in the generation of the ATLID-MSI Cloud Top Height (AM-CTH) synergy product. Aerosol layers are detected as weaker features at a resolution of 11 JSG pixels. Layer-mean optical properties are calculated from the ATLID L2a Extinction, Backscatter and Depolarization (A-EBD) product and stored in the A-ALD product, which also contains the aerosol optical thickness (AOT) of each layer, the stratospheric AOT, and the AOT of the entire atmospheric column. The latter parameter is used to produce the synergistic ATLID-MSI Aerosol Column Descriptor (AM-ACD) later in the processing chain. Several quality criteria are applied in the generation of A-CTH and A-ALD, and respective information is stored in the products. The functionality and performance of the algorithms are demonstrated by applying them to common EarthCARE test scenes. Conclusions are drawn for the application to real-world data and the validation of the products after the launch of EarthCARE.

Machine Learning Techniques for Vertical Lidar-Based Detection, Characterization, and Classification of Aerosols and Clouds: A Comprehensive Survey

This survey presents an in-depth analysis of machine learning techniques applied to lidar observations for the detection of aerosol and cloud optical, geometrical, and microphysical properties. Lidar technology, with its ability to probe the atmosphere at very high spatial and temporal resolution and measure backscattered signals, has become an invaluable tool for studying these atmospheric components. However, the complexity and diversity of lidar technology requires advanced data processing and analysis methods, where machine learning has emerged as a powerful approach. This survey focuses on the application of various machine learning techniques, including supervised and unsupervised learning algorithms and deep learning models, to extract meaningful information from lidar observations. These techniques enable the detection, classification, and characterization of aerosols and clouds by leveraging the rich features contained in lidar signals. In this article, an overview of the different machine learning architectures and algorithms employed in the field is provided, highlighting their strengths, limitations, and potential applications. Additionally, this survey examines the impact of machine learning techniques on improving the accuracy, efficiency, and robustness of aerosol and cloud real-time detection from lidar observations. By synthesizing the existing literature and providing critical insights, this survey serves as a valuable resource for researchers, practitioners, and students interested in the application of machine learning techniques to lidar technology. It not only summarizes current state-of-the-art methods but also identifies emerging trends, open challenges, and future research directions, with the aim of fostering advancements in this rapidly evolving field.

Climatology of aerosol types and their vertical distribution over Iran using CALIOP dataset during 2007–2021

The present study uses a Lidar-based aerosol type's extinction coefficient (EC) of the Cloud–Aerosol Lidar with Orthogonal Polarization (CALIOP) on the Cloud–Aerosol Lidar and Infrared Pathfinder Satellite Observation (CALIPSO) mission to investigate the spatio-temporal changes in the vertical cross section of aerosols from 2007 to 2021 in Iran. Aerosol characteristics i.e., the EC values (levels) and frequency are presented in a seasonal time scale. The trend of aerosol types EC in the vertical profile was estimated using Sen's slope estimator. Moreover a Pearson's correlation was employed to assess the link between climatic factors (temperature, pressure and relative humidity) and aerosol types EC. It is found that smoke followed by polluted continental have the largest EC levels than dust and polluted dust. The highest level and frequency of dust EC were observed in summer at an average altitude of 0.7 and 4.5 km, respectively in southern Iran. The highest level and greatest frequency of polluted continental were detected in summer and winter respectively at an altitude of below 2 km in the southern and northern polluted cities of Iran. The highest level and the greatest frequency of polluted dust EC were recorded in summer in the south of Iran at an average altitude of 4.5 km. Both the maximum smoke EC level and frequency were detected in winter and in the south of Iran at an average altitude of 4 km. A significant decreasing (increasing) trend of dust (polluted continental) EC was observed in spring and summer (summer) at an altitude below 5 (3) km in most regions (northern part) of Iran. Polluted dust showed an increasing trend in spring in most regions at an altitude below 5 km. The correlation between climatic factors and aerosol EC varied according to seasons, altitudes, and aerosol type. The common feature was the positive correlation between aerosol types EC and relative humidity in most regions, seasons and altitudes. In the final step, six polluted cities were analyzed for aerosol's trend and the correlations.

Distribution, Population Density, and Behavior of Dwarf Galagos (Paragalago sp.) in Taita Hills, Kenya

AbstractA good understanding of taxonomy, distribution, and population density is needed for conservation. Scientific knowledge of dwarf galagos (Paragalago sp.) remains limited. Two populations of Taita dwarf galagos survive in the largest remnants of moist montane forest in Taita Hills, Kenya. We obtained acoustic data on the two remaining populations using AudioMoths—small, passive recorders—more than 231 h in January and February 2021. We measured forest structure by using airborne LiDAR and ground-level observations and tested the influence of forest size and height, canopy coverage, and density on calls per hour as an indicator of population density. Forest size significantly affected population size. Taita dwarf galagos prefer dense canopy coverage, and they prefer the lower 20–30-m tall forest. Mbololo forest (185 ha) has steady population, whereas dwarf galagos are nearly extinct in Ngangao forest (120 ha). The calls of Taita dwarf galagos resemble those of Kenya coast dwarf galagos (Paragalago cocos). DNA studies are urgently needed to resolve the taxonomic status of both surviving populations of dwarf galagos in the Taita Hills. This simple study design should be conducted across African forests to obtain crucial knowledge for galago conservation.

Calculation of Resonance Fluorescence Scattering Cross Sections of Metal Particles in the Middle and Upper Atmosphere and Comparison of Their Detectability

Resonance fluorescence scattering is the physical mechanism with which lidar detects atmospheric metal layers. The resonance fluorescence scattering cross section is an important parameter for lidar data processing. In this work, the resonance fluorescence backscattering cross sections of most detectable metal atoms and ions in the atmosphere were calculated. The calculated maximum backscattering cross section of Na at the D2 line is 7.38 × 10−17 m2/sr; K at the D1 line is 7.37 × 10−17 m2/sr; Fe at the 372 nm line is 7.53 × 10−18 m2/sr; Fe at the 374 nm line is 6.98 × 10−18 m2/sr; Fe at the 386 nm line is 3.75 × 10−18 m2/sr; Ni at the 337 nm line is 4.05 × 10−18 m2/sr; and Ni at the 341 nm line is 2.05 × 10−17 m2/sr; Ca is 3.06 × 10−16 m2/sr; Ca+ is 1.12 × 10−16 m2/sr. The influence of the laser linewidth on the effective scattering cross section was discussed. If the laser linewidth is lower than 2 GHz to detect Na, the laser center frequency locked at the D2a line is a better option than the D2 line in order to obtain greater signals. If an unlocked lidar is used to detect Na, the frequency at D2a should be used as the laser center frequency when the effective scattering cross section of Na was calculated, because the absorption cross section of Na atom has two local maxima. This work proposes a quantifiable comparative method for assessing the observation difficulty of different metal particles by comparing their relative uncertainties in lidar observation. It is assumed that under the same observation conditions, the detectability of different metal atoms and ions is compared. Using Na as a basis for comparison, the relative uncertainty of Ni at 337 nm is the highest, about a factor of 21 larger than that of Na, indicating that it is the most difficult to be detected. The purpose of this work is to present a quantifiable comparison method for the detection difficulty of the metal particles by lidar in the middle and upper atmosphere, which has great significance for the design of the lidar system.

Combining Landsat time series and GEDI data for improved characterization of fuel types and canopy metrics in wildfire simulation

Wildfires in the Chornobyl Exclusion Zone (CEZ) and other radioactively contaminated areas threaten human health and well-being with the potential to resuspend radionuclides. Wildfire behavior simulation is a necessary tool to examine the efficiency of fuel treatments in the CEZ, but it requires systematically updated maps of fuel types and canopy metrics. The objective of this study was to demonstrate an effective approach for mapping fuel types, canopy height (CH), and canopy cover (CC) in territories contaminated by radionuclides using Landsat time series (LTS) and Global Ecosystem Dynamics Investigation (GEDI) LiDAR observations. We combined LTS and GEDI data to map fuel types and canopy metrics used in wildfire simulations within the CEZ. Our classification model showed an adequate overall accuracy (75%) in mapping land covers and associated fuel types. The phenology metrics extracted from LTS reliably distinguished spectrally similar vegetation types (such as grasslands and croplands) which exhibit different flammability through the year. We also predicted a suite of relative heights metrics and CC at Landsat 30-m pixel level (R2 = 0.23–0.26) using the nearest neighbor technique. The imputed maps adequately captured the dynamics of CH and CC in the CEZ after recent large wildfires occurred in 2015, 2020, and 2022. Thus, we illustrate a LTS processing approach to produce wall-to-wall maps of canopy characteristics that are important for wildfire simulations. We conclude that continuous updating of land cover and canopy fuel data is crucial to ensure relevant fire management of radioactively contaminated landscapes and support local decision-making.

Monitoring of nature-based solution for stabilizing eroded muddy coastline of the Chao Phraya Delta, Thailand

The Chao Phraya Delta (CPD), Thailand’s largest delta and low-lying muddy coast, has experienced shoreline recession for over six decades due to relative sea-level rise. A nature-based solution, bamboo fences, widely used in low-lying tropical areas, have been intensely applied along the CPD coastline during the last two decades. This study evaluated the effectiveness of bamboo fences in protecting eroded CPD coastlines using remote sensing data (aerial photographs, satellite imagery, and LiDAR observation data). Results from this study indicate that the effectiveness of bamboo fences in trapping sediment decreased over time due to the deterioration of bamboo, which generally deteriorates within four years. Additionally, the installation of multiple layers of bamboo fences did not directly increase the effectiveness of the structure in stabilizing the CPD coastline. It was also found that the mangrove shoreline can be successfully regenerated in the CPD coast where the seabed is higher than 0.5 m amsl. Because of the short life of the bamboo fences, the bamboo fence may not be the best solution for coastal protection and sustainable coastal management in a low-lying area with a high rate of shoreline recession and a low rate of sedimentation.

What CloudSat cannot see: liquid water content profiles inferred from MODIS and CALIOP observations

Abstract. Single-layer nonprecipitating warm clouds are integral to Earth's climate, and accurate estimates of cloud liquid water content for these clouds are critical for constraining cloud models and understanding climate feedbacks. As the only cloud-sensitive radar currently in space, CloudSat provides very important cloud-profiling capabilities. However, a significant fraction of clouds is missed by CloudSat because they are either too thin or too close to the Earth's surface. We find that the CloudSat Radar-Visible Optical Depth Cloud Water Content Product, 2B-CWC-RVOD, misses about 73 % of nonprecipitating liquid cloudy pixels and about 63 % of total nonprecipitating liquid cloud water content compared to coincident Moderate Resolution Imaging Spectroradiometer (MODIS) observations. Those percentages increase to 84 % and 69 %, respectively, if MODIS “partly cloudy” pixels are included. We develop a method, based on adiabatic parcel theory but modified to account for the fact that observed clouds are often subadiabatic, to estimate profiles of cloud liquid water content based on MODIS observations of cloud-top effective radius and cloud optical depth combined with lidar observations of cloud-top height. We find that, for cloudy pixels that are detected by CloudSat, the resulting subadiabatic profiles of cloud water are similar to what is retrieved from CloudSat. For cloudy pixels that are not detected by CloudSat, the subadiabatic profiles can be used to supplement the CloudSat profiles, recovering much of the missing cloud water and generating realistic-looking merged profiles of cloud water. Adding this missing cloud water to the CWC-RVOD product increases the mean cloud liquid water path by 228 % for single-layer nonprecipitating warm clouds. This method will be included in a subsequent reprocessing of the 2B-CWC-RVOD algorithm.

Horizontal Wavenumber Spectra Across the Middle Atmosphere From Airborne Lidar Observations During the 2019 Southern Hemispheric SSW

AbstractHorizontal wavenumber spectra across the middle atmosphere are investigated based on density measurements with the Airborne Lidar for Middle Atmosphere research (ALIMA) in the vicinity of the Southern Andes, the Drake passage and the Antarctic peninsula in September 2019. The probed horizontal scales range from 2000 to 25 km. Spectral slopes are close to k−5/3 in the stratosphere and get shallower for horizontal wavelengths <200 km in the mesosphere. The spectral slopes are shown to be statistically robust with the presented number of flight legs despite the unknown orientation of true wave vectors relative to the flight track using synthetic data and a Monte Carlo approach. The largest spectral amplitudes are found over the ocean rather than over topography. The 2019 sudden stratospheric warming caused a critical level for MWs and a reduction of spectral amplitudes at horizontal wavelengths of about 200 km in the mesosphere.

Particle Microphysical Parameters and the Complex Refractive Index from 3β + 2α HSRL/Raman Lidar Measurements: Conditions of Accurate Retrieval, Retrieval Uncertainties and Constraints to Suppress the Uncertainties

We study retrieval methods in regard to their potential to accurately retrieve particle microphysical parameters (PMP) from 3β + 2α HSRL/Raman lidar measurements. PMPs estimated with these methods are number, surface-area and volume concentrations, the effective radius, and complex refractive index of the investigated particle size distribution (PSD). The 3β + 2α optical data are particle backscatter coefficients at 355, 532 and 1064 nm and extinction coefficients at 355 and 532 nm. We present results that are fundamental for our understanding of how uncertainties of the optical data convert into uncertainties of PMPs. PMPs can only be retrieved with preset accuracy if the input optical data are accurate to at least eight significant digits, i.e., 10−6%. Such measurement accuracy cannot be achieved by currently existing lidar measurement techniques and the fact that atmospheric conditions are not static during lidar observations. Our analysis of the results derived with the novel approach shows that (a) the uncertainty of the retrieved surface-area concentration increases proportionally to the measurement uncertainty of the extinction coefficient at 355 nm, (b) the uncertainty of the effective radius is inversely proportional to the measurement uncertainty of the extinction-related Ångström exponent, (c) the uncertainty of volume concentration is close to the one of the effective radius, and (d) the uncertainty of number concentration is proportional to the inverse of the square value of the uncertainty of the effective radius. The complex refractive index (CRI) cannot be estimated without introducing extra constraints, even if measurement uncertainties of the optical data are as low as 1−3%. We tested constraints and their impact on the solution space, and in how far these constraints could allow us to restrict the retrieval uncertainties. For example, we used information about relative humidity that can be measured with Raman lidar. Relative humidity is an important piece of information that allows for more accurate aerosol typing and thus plays a vital role in any kind of aerosol characterization. The measurement example we used in this study shows that such a constraint can reduce the retrieval uncertainty of single scattering albedo (SSA) to as low as ±0.01–±0.025 (at 532 nm), on the condition that the uncertainty of the input optical data stays below 15%. The results will be used for uncertainty analysis of data products provided by future versions of the Tikhonov Advanced Regularization Algorithm (TiARA). This algorithm has evolved into a standard tool for the derivation of microphysical particle properties from multiwavelength High-Spectral-Resolution Lidar (HSRL)/Raman lidar operated in Europe, East Asia, and the US.

Non‐Orographic Gravity Waves and Turbulence Caused by Merging Jet Streams

AbstractJet streams are important sources of non‐orographic internal gravity waves and clear air turbulence (CAT). We analyze non‐orographic gravity waves and CAT during a merger of the polar front jet stream (PFJ) with the subtropical jet stream (STJ) above the southern Atlantic. Thereby, we use a novel combination of airborne observations covering the meso‐scale and turbulent scale in combination with high‐resolution deterministic short‐term forecasts. Coherent phase lines of temperature perturbations by gravity waves stretching along a highly sheared tropopause fold are simulated by the ECMWF IFS (integrated forecast system) forecasts. During the merging event, the PFJ reverses its direction from approximately antiparallel to parallel with respect to the STJ, going along with strong wind shear and horizontal deformation. Temperature perturbations in limb‐imaging and lidar observations onboard the research aircraft HALO during the SouthTRAC campaign show remarkable agreement with the IFS data. Ten hours earlier, the IFS data show an “X‐shaped” pattern in the temperature perturbations emanating from the sheared tropopause fold. Tendencies of the IFS wind components show that these gravity waves are excited by spontaneous emission adjusting the strongly divergent flow when the PFJ impinges the STJ. In situ observations of temperature and wind components at 100 Hz confirm upward propagation of the probed portion of the gravity waves. They furthermore reveal embedded episodes of light‐to‐moderate CAT, Kelvin Helmholtz waves, and indications for partial wave reflection. Patches of low Richardson numbers in the IFS data coincide with the CAT observations, suggesting that this event was accessible to turbulence forecasting.

Total water level controls on the trajectory of dune toe retreat

This study examines the trajectory (slope) of coastal foredune toe retreat in response to nine storm events that impacted the Outer Banks, North Carolina, USA. High resolution, three-dimensional, repeat mobile terrestrial lidar observations over a four kilometer stretch of coast were used to assess spatiotemporal beach and dune evolution at the storm timescale. Consistent with existing field observations from other sandy coastlines, an upward toe retreat was observed for most instances of dune retreat in the Outer Banks. However, these new topographic data indicate that the retreat can proceed steeply downward when the maximum total water level (TWL) defined by the 2% runup exceedance level is not high enough, for long enough, to erode the dune face. Non-linear relationships were found between the dune toe retreat trajectory as well as both the magnitude and duration of TWL above the dune toe, where instances of upward- and downward-directed retreat are best differentiated using the 7% runup exceedance level, rather than the commonly used 2% level. This physically justified non-linear relationship is shown to be consistent with observations from other studies, and could be a more effective parameterization for the retreat trajectory than those currently implemented in wave-impact dune erosion models.

Quantifying strong point sources emissions of CO2 using spaceborne LiDAR: Method development and potential analysis

Accurate reporting of point source emissions of CO2 is fundamental to addressing climate change. Currently, bottom-up verification methods based on inventory statistics face significant challenges in this area. Satellite remote sensing has emerged as a promising approach for cost-effective global-scale verification of point source emissions, with spaceborne LiDAR offering high spatial resolution ideal for this purpose. However, the inversion of CO2 emissions from spaceborne LiDAR CO2 concentration observations requires urgent attention, as existing methods heavily rely on prior information in diffusion models and the accuracy of meteorological data. In this work, a novel emission inversion method based on genetic algorithms and trust-region techniques is proposed to estimate CO2 emissions from point sources using spaceborne LiDAR observations. A comparison between the CO2 emission rates calculated from actual airborne LiDAR data (as a prototype of spaceborne LiDAR) and emission inventories for the Suizhong power plant showed a deviation of less than 7.0%. Observing system simulation experiment (OSSE) demonstrated that using DQ-1 (spaceborne LiDAR) observation data as input, the relative error of emission rates would be less than 0.6% when the distance between the emission source and the observation footprint is less than 10 km. Furthermore, the developed model mitigates the impact of uncertainties in meteorological data and IPDA (Integrated-Path Differential Absorption) LiDAR measurements on the final emission quantification. The proposed approach is expected to enable DQ-1 to provide affordable and accurate carbon verification services for over 20.0% of the world's strong point source emissions.

Lidar measurements of wake around a bridge deck

Remote wind sensing technologies allow for measurements in a spatial domain beyond the one accessible by anemometers fixed to a mast. Remote optical wind sensors installed at an existing bridge site can both provide new information on the wind flow upstream of the bridge and capture the disturbed flow around the structure. These possibilities are explored in the presented measurement campaign with three synchronised continuous-wave Doppler lidars at the Lysefjord bridge in Norway. In particular, the interaction between the oncoming wind flow and the bridge deck is studied in terms of the mean velocity deficit wake profile as well as the spectral turbulence characteristics. The lidar measurements are validated by comparing the lidar observations at locations undisturbed by the structure with corresponding sonic anemometer data recorded 6 m and 10 m above the deck. The consistency between the two sets of data is tested in terms of the turbulence time series, their spectral content and the statistical moments. Within the wake of the bridge, significant values of vertical velocity and inclination angles, as well as turbulence intensities are seen. Moreover, the wake region can be distinguished by its singular spectral content, cross-correlation coefficient, and coherence. The results show that the tailored configuration of the lidars succeeded in providing information on 3D turbulence around the bridge, thereby capturing the bridge aerodynamic characteristics in full-scale.

Mineral dust scavenges anthropogenic aerosols in polluted environment

Scavenging of anthropogenic aerosols by mineral dust in polluted environment was investigated in this work based on comprehensive observations of a long durative dust event from March 15 to 18, 2021 over north China. Combined observations of a Raman-depolarization lidar (355 nm) and a micro-pulse lidar (532 nm) show that dust particles become more spherical after mixing with anthropogenic pollutants in a polluted environment, likely due to mixing with anthropogenic aerosols. A sharply decease in anthropogenic aerosol in PM2.5 during dust event, especially for inorganic components (e.g., nitrate (NO3−), sulfate (SO42+), ammonium (NH4+)), supports the above viewpoint. The mass concentration of anthropogenic aerosol in PM2.5 decreased by 78% in late period of dust event. Further analyses suggest that such a decrease of anthropogenic aerosol in PM2.5 is caused by the coating of fine anthropogenic aerosols on coarse dust particles, rather than enhanced atmospheric diffusion capacity or weakened chemical reactions. Dust particles own high deposition velocity due to their coarse size. Hence, the coating of anthropogenic aerosols on dust particles enhance the scavenging of anthropogenic aerosols.