Search Results (251)

Aerosols play an important role in global meteorology and climate, as well as in air transport and human health, but there are still many unknowns on their effects and importance, in particular for the coarser (giant and ultragiant) aerosol particles. In this study, we aim to exploit the synergy between Raman lidar and Ka-band cloud radar to enlarge the size range in which aerosols can be observed and characterized. To this end, we developed an inversion technique that retrieves the aerosol microphysical properties based on cloud radar reflectivity and linear depolarization ratio. We applied this technique to a 6-year-long dataset, which was created using a recently developed methodology for the identification of giant aerosols in cloud radar measurements, with measurements from Potenza in Italy. Similarly, using collocated and concurrent lidar profiles, a dataset of aerosol microphysical properties using a widely used inversion technique complements the radar-retrieved dataset. Hence, we demonstrate that the combined use of lidar- and radar-derived aerosol properties enables the inclusion of particles with radii up to 12 µm, which is twice the size typically observed using atmospheric lidar alone. Full article

This study addresses collision detection in the unmanned loading of ore from load-haul-dump (LHD) machines into mining trucks in underground metal mines. Such environments present challenges like heavy dust, confined spaces, sensor occlusions, and poor lighting. This work identifies two primary collision risks and proposes corresponding detection strategies. First, for collisions between the bucket and tunnel walls, LiDAR is used to collect 3D point cloud data. The point cloud is processed through filtering, downsampling, clustering, and segmentation to isolate the bucket and tunnel wall. A KD-tree algorithm is then used to compute distances to assess collision risk. Second, for collisions between the bucket and the mining truck, a kinematic model of the LHD’s working device is established using the Denavit–Hartenberg (DH) method. Combined with inclination sensor data and geometric parameters, a formula is derived to calculate the pose of the bucket’s tip. Key points from the bucket and truck are then extracted to perform collision detection using the oriented bounding box (OBB) and the separating axis theorem (SAT). Simulation results confirm that the derived pose estimation formula yields a maximum error of 0.0252 m, and both collision detection algorithms demonstrate robust performance. Full article

Accurate projection of future climate trends in arid regions critically depends on reliable precipitation simulations. However, most Coupled Model Intercomparison Project Phase 6 (CMIP6) models exhibit systematic overestimations of precipitation in Northwest China, a bias that undermines the credibility of climate projections for this vulnerable region. This persistent bias likely stems from the omission of key physical processes in traditional models. In this study, we incorporate a dust–ice-cloud interaction scheme into the Community Atmosphere Model version 5 (CAM5) model to investigate its role in regulating precipitation over dust-rich arid regions. This physical mechanism, which is rarely included in conventional models, is particularly relevant for Northwest China where dust aerosols are abundant. Our results show that accounting for dust-induced ice nucleation leads to a significant reduction in total precipitation, especially in the convective component, thereby alleviating the longstanding wet bias in the region. These findings underscore the critical importance of dust–ice-cloud interactions in simulating precipitation in arid environments. To improve the accuracy of future climate projections in Northwest China, climate models must incorporate realistic representations of dust-related microphysical processes. Full article

This study presents a global aerosol climatology derived from six years (October 2018–October 2024) of the Ice, Cloud, and land Elevation Satellite-2 (ICESat-2) observations, using a U-Net Convolutional Neural Network (CNN) machine learning algorithm for Cloud–Aerosol Discrimination (CAD). Despite ICESat-2’s design primarily as an altimetry mission with a single-wavelength, low-power, high-repetition-rate laser, ICESat-2 effectively captures global aerosol distribution patterns and can provide valuable insights to bridge the observational gap between the Cloud–Aerosol Lidar and Infrared Pathfinder Satellite Observations (CALIPSO) and Earth Cloud, Aerosol and Radiation Explorer (EarthCARE) missions to support future spaceborne lidar mission design. The machine learning approach outperforms traditional thresholding methods, particularly in complex conditions of cloud embedded in aerosol, owing to a finer spatiotemporal resolution. Our results show that annually, between 60°S and 60°N, 78.4%, 17.0%, and 4.5% of aerosols are located within the 0–2 km, 2–4 km, and 4–6 km altitude ranges, respectively. Regional analyses cover the Arabian Sea (ARS), Arabian Peninsula (ARP), South Asia (SAS), East Asia (EAS), Southeast Asia (SEA), the Americas, and tropical oceans. Vertical aerosol structures reveal strong trans-Atlantic dust transport from the Sahara in summer and biomass burning smoke transport from the Savanna during dry seasons. Marine aerosol belts are most prominent in the tropics, contrasting with earlier reports of the Southern Ocean maxima. This work highlights the importance of vertical aerosol distributions needed for more accurate quantification of the aerosol–cloud interaction influence on radiative forcing for improving global climate models. Full article

Aerosols play a critical role in modulating the land–atmosphere energy balance, influencing regional climate dynamics, and affecting air quality. Xinjiang, a typical arid and semi-arid region in China, frequently experiences dust events and complex aerosol transport processes. This study provides a comprehensive analysis of the spatiotemporal evolution and potential source regions of aerosols in Xinjiang from 2005 to 2023, based on Moderate Resolution Imaging Spectroradiometer (MODIS) aerosol products (MCD19A2), Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observation (CALIPSO) vertical profiles, ground-based PM_2.5 and PM₁₀ concentrations, MERRA-2 and ERA5 reanalysis datasets, and HYSPLIT backward trajectory simulations. The results reveal pronounced spatial and temporal heterogeneity in aerosol optical depth (AOD). In Northern Xinjiang (NXJ), AOD exhibits relatively small seasonal variation with a wintertime peak, while Southern Xinjiang (SXJ) shows significant seasonal and interannual variability, characterized by high AOD in spring and a minimum in winter, without a clear long-term trend. Dust is the dominant aerosol type, accounting for 96.74% of total aerosol content, and AOD levels are consistently higher in SXJ than in NXJ. During winter, aerosols are primarily deposited in the near-surface layer as a result of local and short-range transport processes, whereas in spring, long-range transport at higher altitudes becomes more prominent. In NXJ, air masses are primarily sourced from local regions and Central Asia, with stronger pollution levels observed in winter. In contrast, springtime pollution in Kashgar is mainly influenced by dust emissions from the Taklamakan Desert, exceeding winter levels. These findings provide important scientific insights for atmospheric environment management and the development of targeted dust mitigation strategies in arid regions. Full article

This study investigates the impact of desert dust on precipitation patterns using multi-model simulations. Dust-based processes of formation/removal of ice nuclei (IN) and cloud condensation nuclei (CCN) are investigated by using both the online access model WRF-CHIMERE and the online integrated model WRF-Chem. Comparisons of model predictions with rainfall measurements (GRISO: Spatial Interpolation Generator from Rainfall Observations) over the Italian peninsula show the models’ ability to reproduce heavy orographic precipitation in alpine regions. To quantify the impact of the mineral dust transport concomitant to the atmospheric river (AR) on cloud formation, a sensitivity study is performed by using the WRF-CHIMERE model (i) by setting dust concentrations to zero and (ii) by modifying the settings of the Thompson Aerosol-Aware microphysics scheme. Statistical comparisons revealed that WRF-CHIMERE outperformed WRF-Chem. It achieved a correlation coefficient of up to 0.77, mean bias (MB) between +3.56 and +5.01 mm/day, and lower RMSE and MAE values (~32 mm and ~22 mm, respectively). Conversely, WRF-Chem displayed a substantial underestimation, with an MB of −25.22 mm/day and higher RMSE and MAE values. Our findings show that, despite general agreement in spatial precipitation patterns, both models significantly underestimated the peak daily rainfall in pre-alpine regions (e.g., 216 mm observed at Malga Valine vs. 130–140 mm simulated, corresponding to a 35–40% underestimation). Although important instantaneous changes in precipitation and temperature were modeled at a local scale, no significant total changes in precipitation or air temperature averaged over the entire domain were observed. These results underline the complexity of aerosol–cloud interactions and the need for improved parameterizations in coupled meteorological models. Full article

Mineral dust particles significantly influence the Earth’s climate through direct and semi-direct radiative effects. This study investigates these effects and their meteorological impacts during a dust intrusion and heatwave over the Iberian Peninsula in summer 2019 using a regional climate model. Three simulations with different spectral nudging configurations are evaluated. During the central period, the mean direct and semi-direct radiative effects in the shortwave spectrum at the top of the atmosphere (bottom of the atmosphere) are −0.4 ± 0.4 (−3.9 ± 2.3) Wm⁻² and +0.1 ± 1.7 (−0.1 ± 1.9) Wm⁻², respectively. In the longwave spectrum, these effects are +0.1 ± 0.1 (+0.3 ± 0.1) WmWm⁻² and 0.0 ± 0.6 (+0.9 ± 1.1) Wm⁻², respectively. The semi-direct effect mitigates 18.8% of the dust-induced warming in the full atmosphere and alters meteorological variables. The liquid water path decreases by −0.2 ± 4.5 mg m⁻², the cloud fraction in the upper (lower) troposphere reduces (increases) by −0.2 ± 1.2 (+0.1 ± 1.3) %, and the near-surface air temperature drops slightly by −0.2 ± 0.2 °C. The results highlight substantial spatial variability and underscore the importance of considering semi-direct radiative effects in radiative analysis. Full article

The Barcelona Raman LIDAR (BRL) will provide continuous monitoring of the aerosol extinction profile along the line of sight of the Cherenkov Telescope Array Observatory (CTAO). It will be located at its Northern site (CTAO-N) on the Observatorio del Roque de Los Muchachos. This article presents the performance of the pathfinder Barcelona Raman LIDAR (pBRL), a prototype instrument for the final BRL. Power budget simulations were carried out for the pBRL operating under various conditions, including clear nights, moon conditions, and dust intrusions. The LIDAR PreProcessing (LPP) software suite is presented, which includes several new statistical methods for background subtraction, signal gluing, ground layer and cloud detection and inversion, based on two elastic and one Raman lines. Preliminary test campaigns were conducted, first close to Barcelona and later at CTAO-N, albeit during moonlit nights only. The pBRL, under these non-optimal conditions, achieves maximum ranges up to about 35 km, range resolution of about 50 m for strongly absorbing dust layers, and 500 m for optically thin clouds with the Raman channel only, leading to similar resolutions for the LIDAR ratios and Ångström exponents. Given the reasonable agreement between the extinction coefficients obtained from the Raman and elastic lines independently, an accuracy of aerosol optical depth retrieval in the order of 0.05 can be assumed with the current setup. The results show that the pBRL can provide valuable scientific results on aerosol characteristics and structure, although not all performance requirements could be validated under the conditions found at the two test sites. Several moderate hardware improvements are planned for its final upgraded version, such as gated PMTs for the elastic channels and a reduced-power laser with a higher repetition rate, to ensure that the data acquisition system is not saturated and therefore not affected by residual ringing. Full article

Long-term investigations of Aerosol Optical Depth (AOD) across Asia are crucial for understanding its regional impacts on the global climate system. However, satellite-derived AOD datasets frequently suffer from missing values due to factors such as cloud cover, algorithmic limitations, and various atmospheric conditions. To overcome these challenges, this study employs the deep learning model TabNet, incorporating Digital Elevation Model (DEM) data and ERA5 meteorological variables, to fuse MERRA-2 AOD with MODIS MAIAC AOD observations. The resulting integration yields a high-resolution, seamless daily AOD dataset for Asia spanning the period from 2001 to 2024. The fused dataset demonstrates significant improvements over the original MERRA-2 AOD, with an increase in the coefficient of determination (R²) by 0.1065 and a reduction in root mean square error (RMSE) by 0.0369. Spatio-temporal analysis, conducted using Empirical Orthogonal Function (EOF) decomposition, reveals that AOD concentrations across Asia are strongly influenced by anthropogenic factors, including industrial activities, transportation emissions, and biomass burning. The results indicate a generally increasing trend in AOD from 2001 to 2014, followed by a declining trend from 2015 to 2024. Notably, EOF results show a marked rise in AOD levels in Mongolia after 2020, likely attributable to an uptick in dust storm activity. This research offers valuable insights into the spatiotemporal trends of aerosols across Asia, underscoring the need for sustained air quality measures to mitigate pollution and protect public health. Full article

Aerosols and clouds influence Earth’s radiative energy budget, but their regional radiative impacts remain insufficiently understood. This study investigates the spatial distribution patterns and long-term trends of radiative fluxes over China from March 2000 to February 2023 using CERES-SYN data. Notable decreasing trends in the net radiative fluxes over China at the top of the atmosphere (−0.38 W m⁻² year⁻¹) and the surface (−0.35 W m⁻² year⁻¹) during the study period have been observed. Cloud properties from CERES-SYN and aerosol properties from MERRA-2 are used to assess the impacts of aerosols and clouds on radiative flux variations. Results show that aerosols are the primary drivers of radiative flux variations across China, while cloud changes exert notable but regionally dependent influences. In southern China, reductions in black carbon and organic carbon aerosols substantially influence radiative flux variations, along with contributions from changes in mid-high, mid-low, and low clouds. In northern China, decreases in dust and organic carbon aerosols primarily drive radiative flux trends. Over the Tibetan Plateau, variations in mid-high clouds predominantly affect radiative flux changes. In Xinjiang and Inner Mongolia, fluctuations in high, mid-high, and mid-low clouds, along with dust and sulfate aerosols, jointly contribute to the radiative flux variations, although the overall impacts remain relatively small. Full article

The response of low-level cloud properties to aerosol loading remains ambiguous, particularly due to the confounding influence of meteorological factors and water vapor availability. We utilize long-term data from Ka-band Zenith Radar, Clouds and the Earth’s Radiant Energy System, Modern-Era Retrospective analysis for Research and Applications Version 2, and European Centre for Medium-Range Weather Forecasts Reanalysis v5 to evaluate aerosol’s effects on low-level clouds under the constrains of meteorological conditions and liquid water path (LWP) over the Semi-Arid Climate and Environment Observatory of Lanzhou University during 2014–2019. To better constrain meteorological variability, we apply Principal Component Analysis to derive the first principal component (PC1), which strongly correlates with cloud properties, thereby enabling more accurate assessment of aerosol–cloud interaction (ACI) under constrained meteorological conditions delineated by PC1. Analysis suggests that under favorable meteorological conditions for low-level cloud formation (low PC1) and moderate LWP levels (25–150 g/m²), ACI is characterized by a significantly negative ACI index, with the cloud effective radius (CER) increasing in response to rising aerosol concentrations. When constrained by both PC1 and LWP, the relationship between CER and the aerosol optical depth shows a distinct bifurcation into positive and negative correlations. Different aerosol types show contrasting effects: dust aerosols increase CER under favorable meteorological conditions, whereas sulfate, organic carbon, and black carbon aerosols consistently decrease it, even under high-LWP conditions. Full article

This study presents a high-precision warehouse material level monitoring method that integrates millimeter-wave radar with 3D surface reconstruction to address the limitations of LiDAR, which is highly susceptible to dust and haze interference in complex storage environments. The proposed method employs Chirp-Z Transform (CZT) super-resolution processing to enhance spectral resolution and measurement accuracy. To improve grain surface identification, an anomalous signal correction method based on angle–range feature fusion is introduced, mitigating errors caused by weak reflections and multipath effects. The point cloud data acquired by the radar undergo denoising, smoothing, and enhancement using statistical filtering, Moving Least Squares (MLS) smoothing, and bicubic spline interpolation to ensure data continuity and accuracy. A Poisson Surface Reconstruction algorithm is then applied to generate a continuous 3D model of the grain heap. The vector triple product method is used to estimate grain volume. Experimental results show a reconstruction volume error within 3%, demonstrating the method’s accuracy, robustness, and adaptability. The reconstructed surface accurately represents grain heap geometry, making this approach well suited for real-time warehouse monitoring and providing reliable support for material balance and intelligent storage management. Full article

Gravitational fragmentation of a protoplanetary disk is considered a possible mechanism for the formation of planets and brown dwarfs. In this process, transitory objects are formed that are known as clumps, which are compact gas–dust condensations with a size of several astronomical units. The contraction of these clumps to planetary sizes via the dissociation of molecular hydrogen or tidal downsizing can ultimately lead to planet or brown dwarf formation. Here, we present a comprehensive numerical and statistical study of the clump properties in protoplanetary disks formed from cloud cores of similar mass (0.9–1.0 $M_{\odot }$). We focus on possible differences in their characteristics depending on the metallicity of the parental disk. We show that notable differences can be expected in the clump characteristics in terms of their number, internal energetics, mass, and distance to the star. For all metallicities considered, the propensity to forming planets or brown dwarfs via disk fragmentation is challenged by large amounts of gravitationally unbound clumps. We conclude that giant planet formation via disk fragmentation is possible down to 1/100 solar metallicity but it should be a rare outcome. Brown dwarf formation via disk fragmentation is possible only down to 1/10 solar metallicity. Our results stand for similar masses of the central star on the order of the Sun. Full article

Vertically retrieved optical properties by Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observations (CALIPSO) were investigated in the case of three selected events over Athens and Thessaloniki with documented high pollen concentrations. Hirst-type volumetric samplers were used to detect and characterize the pollen during the CALIPSO overpasses. Only cases with a total pollen concentration greater than 400 grains m⁻³ for at least two hours per day were considered severe pollen events, while model simulations were used to exclude the presence of other depolarizing aerosol types. This study provides mean values of lidar-derived optical properties inside the detected pollen layers; i.e., optical values represent the atmosphere with the presence of pollen, in urban cities of Greece. Specifically, three observed aerosol layers, one over Athens and two over Thessaloniki with particulate color ratios of 0.652 ± 0.194, 0.638 ± 0.362, and 0.456 ± 0.284, and depolarization ratios of 8.70 ± 6.26%, 28.30 ± 14.16%, and 8.96 ± 6.87%, respectively, were misclassified by CALIPSO as marine-dusty marine, dust, and polluted dust. In cases of intense pollen presence, CALIPSO vertical profiles and aerobiological monitoring methods may be used synergistically to better characterize the atmospheric pollen layers. Full article

Tropospheric aerosols play an important role in the notable warming phenomenon and climate change occurring in the Arctic. The accuracy of Cloud–Aerosol Lidar with Orthogonal Polarization (CALIOP) aerosol optical depth (AOD) and the distribution of Arctic AOD based on the CALIOP Level 2 aerosol products and the Aerosol Robotic Network (AERONET) AOD data during 2006–2021 were analyzed. The distributions, trends, and three-dimensional (3D) structures of the frequency of occurrences (FoOs) of different aerosol subtypes during 2006–2021 are also discussed. We found that the CALIOP AOD exhibited a high level of agreement with AERONET AOD, with a correlation coefficient of approximately 0.67 and an RMSE of less than 0.1. However, CALIOP usually underestimated AOD over the Arctic, especially in wet conditions during the late spring and early summer. Moreover, the Arctic AOD was typically higher in winter than in autumn, summer, and spring. Specifically, polluted dust (PD), dust, and clean marine (CM) were the dominant aerosol types in spring, autumn, and winter, while in summer, ES (elevated smoke) from frequent wildfires reached the highest FoOs. There were increasing trends in the FoOs of CM and dust, with decreasing trends in the FoOs of PD, PC (polluted continental), and DM (dusty marine) due to Arctic amplification. In general, the vertical distribution patterns of different aerosol types showed little seasonal variation, but their horizontal distribution patterns at various altitudes varied by season. Furthermore, locally sourced aerosols such as dust in Greenland, PD in eastern Siberia, and ES in middle Siberia can spread to surrounding areas and accumulate further north, affecting a broader region in the Arctic. Full article