Abstract High velocity clouds supply the Milky Way with gas that sustains star formation over cosmic timescales. Precise distance measurements are therefore essential to quantify their mass inflow rates and gauge their exact contribution to the Galaxy’s gas supply. We use a sample of 594 SDSS-V BOSS stellar spectra within 10° of the high-velocity Smith Cloud (SC) to trace Na I absorption and dust extinction as functions of distance. By fitting interstellar-medium-corrected MaStar templates to each spectrum, we isolate residual equivalent widths and extinction and then compare trends in the SC region to a same-latitude control field. Stars beyond 1 kpc toward the SC exhibit a significant Na I equivalent width excess (>0.2 Å, >3 σ ) relative to the control. Two-component linear fits of Na I equivalent width and A V against both low and high-velocity H I column densities show that the low-velocity component is strongly correlated with both quantities, while the high-velocity term is marginally significant in extinction and Na I , consistent with a patchy, low dust-to-gas ratio. Given that the excess Na I begins at distances <2 kpc uniquely in the direction of the SC, and previous estimates of the SC place it at 12.4 ± 1.3 kpc, further investigation of its distance is warranted.

Abstract Satellite observations of artificial light sources at night are increasing. Clouds, as the most important radiative modulators in the Earth‐atmosphere system, significantly influence the observed values and spatial distribution of the artificial light coming from the ground. However, the mechanisms by which clouds affect the radiative transmission of ground‐based light sources remain poorly understood. Consequently, many quantitative remote sensing applications at night are restricted to cloud‐free conditions. Conversely, ground‐based light can cause anomalous radiation distributions in clouds, hindering studies on their characteristics. In this study, we employed Monte Carlo radiative transfer simulations to investigate nighttime light radiation through complex three‐dimensional cloud structures. The simulations incorporated clouds modeled using large eddy simulation (LES) and custom cloud configurations with varying radii, thickness, base heights, and relative positions between clouds and lighting to elucidate the underlying physical mechanisms. The results indicate that cloud layers exhibit dual effects on radiance distribution: They reduce nadir radiance through absorption and scattering while amplifying surrounding brightness via scattering. Additionally, increasing cloud thickness attenuates overhead radiance, whereas it enhances pronounced peaks at cloud boundaries. Low clouds intercept and modulate upward radiance more effectively than high clouds due to geometric constraints. Source displacement breaks radiance symmetry, enhancing cloud edge brightness. Multiple scattering within clouds redistributes ground‐based light signals, expanding compact point sources into extended radiance patterns observable by satellites.

Abstract. This study investigated the potential of cloud seeding to mitigate extreme rainfall localization (i.e., overseeding) associated with mesoscale convective systems in Japan. Using a numerical weather prediction model, we conducted cloud seeding experiments by artificially increasing ice nuclei concentrations in a double-moment microphysics scheme for the heavy rainfall event in Hiroshima Prefecture, Japan, in August 2014. We examined the sensitivity of rainfall changes to altitude and area of the seeding. The results showed that seeding in the mid–upper troposphere (7.2–8.6 km), where air temperature ranged from −22 to −12 °C, resulted in the most pronounced changes in rainfall amount. At these levels, high supercooled cloud water content and strong updrafts favoured heterogeneous freezing, resulting in a depletion of moisture and suppression of graupel growth. The cloud seeding led to reduced rainfall within the heavy rainfall region and increased rainfall downwind, demonstrating the hypothesized dispersal mechanism of “overseeding”. Expanding the seeding to cover the upstream region of the heavy rainfall area had a greater impact than increasing vertical thickness of the seeding. The most effective seeding configuration (24 km × 24 km area at 7.2 km) achieved an 11.5 % decrease in area-averaged 3-h accumulated rainfall and a maximum reduction of 32 % in 3-h accumulated rainfall over the heavy rainfall region. Future work should consider more realistic representations of seeding substance (i.e., transport, dispersion, and interactions) and explore a wider range of rainfall events to generalize the applicability of this approach.

Abstract Cloud formation is due to a combination between water uptake and aerosol distribution and characteristics. Globally, the state of the terrestrial atmosphere is influenced by solar activity and galactic cosmic rays through the global electric circuit. This paper investigates the possible link between cloud cover and solar proxies, namely sunspot number (SSN), solar/plasma wind speed (PWS), and the associated interplanetary electric and magnetic fields (IEF, IMF), respectively, for two solar cycles. The seasonal variation on possible links is also investigated. Each solar driver may influence the atmospheric electricity, thus cloud formation and cloud cover. The study uses the first long-term cloud database, as provided by the International Satellite Cloud Climatology Project (ISCCP), since these data had sufficient time for validation and are ready to use as such. Solar proxies were taken from NASA’s OMNIWeb database, from measurements with instruments onboard several spacecraft with geocentric orbits. Cloud types were individually considered, and global distribution of cloud cover was analyzed. The study reveals that the cloud cover response to changes in various solar indicator depends on local conditions, and varies with season. E.g., high clouds cover exhibited anticorrelation with IEF in January on large areas, while low cloud cover was moderately positively correlated with PWS on extended regions in July.

Abstract This study systematically investigates biases in cloud radiative effects (CREs) within the recently released Energy Exascale Earth System Model version 3 (E3SMv3). Compared to its previous version (E3SMv2), E3SMv3 shows excessively strong shortwave CRE over tropical and subtropical oceans, the Southern Ocean, and the Northern Hemisphere storm tracks, which is primarily caused by an overestimation of optically intermediate low clouds. The model also displays excessive longwave CRE over the Maritime Continent and other tropical deep convection regions, resulting from an overestimation of optically thick high clouds. Implementing the Predicted Particle Properties (P3) scheme for stratiform clouds played the most significant role in these cloud changes. In addition, using a double‐moment scheme for convective clouds contributed to the increase in intermediate low clouds and optically thick clouds in tropical deep convection regions. Error metrics for total cloud amount (E TCA ), cloud properties (E ctp‐τ ), and cloud properties weighted by their SW and LW radiative impacts (E SW and E LW ) indicate that E3SMv3's ability to reproduce observed cloud radiative effect of low, middle, and high clouds has not been improved compared to E3SMv2. Nevertheless, the performance of E3SMv3 remains well within the spread of CMIP6 models, with E SW and E LW values smaller than those in most CMIP6 models. This study underscores the importance of integrating diverse satellite observations for robust cloud evaluation and using cloud‐radiation relationships as consistency check for model errors. It suggests that further model developments focus on improving cloud microphysics and their interactions with radiation.

CARE: a next-generation high resolution cloud and radiation remote sensing product and its Earth system applications.

Abstract. The Arctic is experiencing unprecedented environmental changes with rapidly rising temperatures. Emissions of methane (CH4) – a potent greenhouse gas – may be increasing from the region, making accurate monitoring essential. The TROPOspheric Monitoring Instrument (TROPOMI) instrument offers high spatial and temporal coverage of CH4 column mole fractions. However, its data in the Arctic has historically exhibited seasonal and latitudinal biases and low-quality retrievals. A major challenge is the lack of ground-based validation data in high-latitude regions, which are used to improve satellite retrievals. This study evaluates inverse modelling to estimate CH4 emissions using TROPOMI measurements over the North Slope of Alaska. Using two retrieval products – the operational SRON product and the scientific WFMD product from the University of Bremen – we assess the alignment of derived emissions with surface measurement-derived inversions over 2018–2020 and test their robustness through sensitivity analyses. Our results show that tundra emissions from SRON inversions align more closely with surface measurement-derived emissions than WFMD inversions. Both TROPOMI-product derived emissions have anomalously low emissions in August 2018 compared to surface measurement-derived emissions, likely due to low data density resulting from high cloud cover. TROPOMI inversions provided stronger constraints on fugitive anthropogenic emissions compared to surface inversions. However, each retrieval produced different emission estimates, highlighting retrieval-dependent differences. Sensitivity tests revealed a strong prior dependence in both retrievals, raising concerns about robustness in northern high latitudes. This study highlights the importance of using multiple retrievals and rigorous sensitivity testing in high-latitude satellite inversions.

Abstract JWST observations provide for the first time evidence for an atmosphere on a rocky exoplanet - 55 Cnc e. The atmosphere of 55 Cnc e is hot with Teq > 2000 K and shows strong variability, for which cloud formation above a molten crust could be one possible explanation. The composition of the atmosphere of 55 Cnc e is still unknown but suggests the presence of volatiles. We have run cloud formation models on a grid of N-dominated, O-dominated, C-dominated and H-dominated atmospheres to investigate which type of cloud we could expect on hot super-Earths and hot sub-Neptunes (1000 K < T < 3000 K). Our models combine radiative transfer with equilibrium chemistry of the gaseous and condensed phases, vertical mixing of condensable species, sedimentation, nucleation and coagulation. We find that the condensability of species is highly dependent on the oxygen abundance of an atmosphere. Oxygen poor atmospheres can be heated by UV and optical absorbers PS, TiO and CN which create temperature inversions. These inhibit condensation. Oxygen rich atmospheres are colder without temperature inversions, and are therefore more favourable environments for cloud formation. The major expected cloud component in O-dominated atmospheres with solar refractory abundance is TiO2(s). Spectral features of clouds in these worlds are stronger in transmission than in emission, in particular at short wavelengths. We find a lack of optical data of solid species in comparison to the variety of stable cloud components which can form on hot, rocky planets.

Abstract Cloud radiative effects (CREs) play a critical role in Earth's energy balance and climate variability, yet the variability and specific contributions of distinct cloud types remain poorly understood. Using the Clouds and the Earth's Radiant Energy System FluxByCldTyp data set, this study investigates how temporal variations in total top‐of‐the‐atmosphere CREs are influenced by changes in the physical properties and fractional coverages of 42 individual cloud types and their broader categories over a 19‐year period. The analysis spans the tropical belt (25°S–25°N) and several convectively active regions, including the Tropical Western Pacific (TWP) and Africa. Our results show that variability in total CREs is primarily driven by changes in cloud fraction rather than microphysical properties. High clouds—particularly cirrostratus and deep convective clouds—exert strong negative correlations with shortwave CREs and strong positive correlations with longwave CREs, with correlation magnitudes reaching ±0.90 in the TWP. Low clouds, especially shallow cumulus, exhibit opposite correlations, partly due to obscuration by upper‐level clouds. While properties like total cloud water path, optical depth, and particle size influence cloud type‐mean CREs, their correlations with total CRE are relatively weak and largely due to co‐variability with total cloud amount. These correlations are generally more distinct and stronger within regional domains than across the tropical mean. Additionally, strong interrelationships are found among cloud categories, with high and low clouds often varying inversely. These results underscore the importance of cloud type‐specific contributions to radiative budget variability, providing observational benchmarks for climate model evaluation and cloud feedback studies.

Abstract. In Earth's radiative budget, clouds play a central role but their representation in General Circulation Models (GCMs) remains a major source of uncertainty for climate projection. Here, we used spaceborne lidar observations to assess cloud distribution in the IPSL-CM6-LR model using the CFMIP Observation Simulator Package (COSP). We focused on the lidars onboard CALIPSO and AEOLUS satellites during 2006–2023 and 2018–2023. While CALIPSO has been widely used for GCMs evaluation, AEOLUS was originally designed for wind profiling. However, studies have demonstrated its potential to retrieve reliable cloud profiles. A new module was developed to simulate AEOLUS observations within COSP-lidar, extending original implementations made for CALIPSO, including wavelength change (532 to 355 nm), viewing geometry (35° off-nadir) and specific parameters adjustments related to sensivity and resolution. We compared our simulations to 1-year observations for both instruments. Results show that AEOLUS observations can effectively evaluate clouds in GCMs, as it shows similar cloud fraction biases in IPSL-CM6-LR to those obtained with CALIPSO. Significant underestimations of low (up to 20 %) and high clouds in certain regions (e.g. warm pool) were re-assessed for this model. Sensitivity analyses highlighted the small role of instrument-specific parameters in COSP-lidar: viewing geometry, multiple scattering coefficient and cloud detection threshold (associated with wavelength and sensivity). This work lays the foundation for a consistent multi-decades evaluation of cloud representation using different lidar missions, and supports the integration of EarthCARE/ATLID in COSP-lidar for further model evaluation.

This study examines how frequently the specular reflection of sunlight—that is, sun glint—reveals the presence of ice crystals that maintain a steady horizontal orientation. The study analyzes data from the Earth Polychromatic Imaging Camera (EPIC) onboard the Deep Space Climate Observatory (DSCOVR) spacecraft and from collocated images taken by geostationary satellites. The analysis of spatio-temporal variations in glint frequency over vegetated land surfaces reveals that (a) year-to-year variations are modest with no clear trends; (b) glints typically occur 7%–8% more frequently than previously estimated; (c) glints are most frequently observed during the May-August period, and over Asia. The results also show that glint frequency drops for very high (>12–13 km) clouds but otherwise displays little sensitivity to geostationary satellite-provided cloud parameters, namely altitude, optical thickness, and particle size. This is because glints come from horizontal crystals near cloud tops whereas geostationary satellites characterize the entire cloudy column. This suggests that glint-free passive satellite observations are not well-suited for estimating the likelihood of horizontal ice crystals and underlines the importance of analyzing direct sun glint observations from satellite instruments such as EPIC.

This study examines how Artificial Intelligence (AI) adoption in digital marketing enhances the competitiveness of small retail businesses (SRBs) in Cape Town, South Africa. Guided by an integrated Technology–Organisation–Environment (TOE) and Unified Theory of Acceptance and Use of Technology (UTAUT) framework, a systematic review following PRISMA 2020 guidelines was conducted across eight databases, resulting in the inclusion of 37 empirical and conceptual studies published between 2015 and 2025. Findings show that AI adoption remains modest, with only 39% of small businesses planning to develop internal AI capabilities by 2025. The most common entry points are ChatGPT, text-to-image generators, predictive analytics systems, and WhatsApp-based chatbots. Businesses using these tools reported an average 11% increase in sales and a 28% reduction in marketing costs within six months. However, adoption is constrained by infrastructure challenges (patchy broadband, power cuts, high cloud costs), organisational limitations (low digital literacy, managerial scepticism, fear of automation), and socio-cultural barriers (language diversity and customer mistrust of chatbots). This study provides the first Cape Town-focused synthesis linking AI adoption to SME competitiveness through the TOE–UTAUT framework. It recommends policy measures to improve digital infrastructure and data governance, practical initiatives to strengthen AI literacy and ethical awareness, and theoretical extensions to contextualise socio-cultural dimensions of AI use. Collectively, these insights contribute to advancing inclusive, trust-based digital transformation in Cape Town’s small business ecosystem.

ABSTRACTInvasive species, loss of habitat, and climate change are just some of the many threats accelerating biodiversity loss, and understanding their impacts on endangered species is key to implementing effective conservation. The endemic Grenada frog (Pristimantis euphronides) is found only in high elevation cloud forests, habitat that is being invaded by the introduced Lesser Antillean frog (Eleutherodactylus johnstonei) and threatened by climate change. Between 2004 and 2020, our field team surveyed three key sites in the central highlands of Grenada to monitor populations of both frogs. We used generalized linear models and Spearman's rank analysis to evaluate the effects of site and invasive frog relative abundance on the endemic frog. Although the relative abundances of the two species were negatively correlated overall (ρ = −0.501, 95% CI: (−0.660, −0.300)), the relationship between them was weakly positive in the model that included site as a covariate. The two species appear to respond similarly to environmental fluctuations at local scales, but the negative overall correlation implies that competition with the Lesser Antillean frog may have affected the distribution of the Grenada frog across the island. Grenada frogs were much more abundant than Lesser Antillean frogs at the highest elevation site, while the reverse was true at the lower sites. If higher elevation sites are indeed acting as refugia for the Grenada frog from its invasive competitor, the effects of climate change at those high elevation sites will likely be critical to the future of the species.

Which photovoltaic technology delivers superior performance under the demanding conditions of tropical climates? This research compares monocrystalline and polycrystalline solar panel efficiency and energy yield under Malaysian equatorial conditions characterized by high temperatures, intense humidity, and variable cloud cover patterns. The investigation monitored matched panel arrays over a complete annual cycle to capture seasonal variations and establish performance expectations for solar installations throughout the tropical Southeast Asian region [1]. The experimental program was conducted at the Kuala Lumpur Institute of Technology renewable energy research facility from January through December 2024, employing parallel monitoring of identical-capacity monocrystalline and polycrystalline arrays under identical environmental exposure. Continuous data acquisition recorded electrical output, irradiance levels, ambient temperature, module temperature, and humidity at five-minute intervals throughout the monitoring period. Results demonstrated that monocrystalline panels achieved 12.5% higher annual energy yield compared to polycrystalline alternatives despite both technologies experiencing significant efficiency derating under tropical conditions. Monocrystalline arrays delivered average efficiency of 16.2% compared to nameplate rating of 21.3%, while polycrystalline arrays achieved 14.1% efficiency against 17.8% rated specification. The efficiency gap between technologies remained relatively consistent across seasons, though both experienced minimum performance during the hot humid period from April through September. Thermal losses emerged as the dominant performance limitation, with module temperatures regularly exceeding 55 degrees Celsius during peak irradiance periods. The temperature coefficient disadvantage of monocrystalline silicon produced greater absolute efficiency reduction, yet higher baseline efficiency maintained overall performance advantage. Soiling losses averaging 2.4% required bi-weekly cleaning intervention to maintain near-optimal output. The findings support continued preference for monocrystalline technology in space-constrained Malaysian installations despite higher initial cost, while polycrystalline panels remain economically attractive for larger ground-mounted systems where area availability reduces the premium on conversion efficiency. Annual energy yield projections of 1,322 kWh per kilowatt-peak for monocrystalline and 1,175 kWh per kilowatt-peak for polycrystalline arrays provide planning guidance for solar system sizing in tropical climates.

Colored point cloud comprising geometry and attribute components is one of the mainstream representations enabling realistic and immersive 3D applications. To generate large-scale and denser colored point clouds, we propose a deep learning-based Joint Geometry and Attribute Up-sampling (JGAU) method, which learns to model both geometry and attribute patterns and leverages the spatial attribute correlation. Firstly, we establish and release a large-scale dataset for colored point cloud up-sampling, named SYSU-PCUD, which has 121 large-scale colored point clouds with diverse geometry and attribute complexities in six categories and four sampling rates. Secondly, to improve the quality of up-sampled point clouds, we propose a deep learning-based JGAU framework to up-sample the geometry and attribute jointly. It consists of a geometry up-sampling network and an attribute up-sampling network, where the latter leverages the up-sampled auxiliary geometry to model neighborhood correlations of the attributes. Thirdly, we propose two coarse attribute up-sampling methods, Geometric Distance Weighted Attribute Interpolation (GDWAI) and Deep Learning-based Attribute Interpolation (DLAI), to generate coarsely up-sampled attributes for each point. Then, we propose an attribute enhancement module to refine the up-sampled attributes and generate high quality point clouds by further exploiting intrinsic attribute and geometry patterns. Extensive experiments show that Peak Signal-to-Noise Ratio (PSNR) achieved by the proposed JGAU are 33.90 dB, 32.10 dB, 31.10 dB, and 30.39 dB when up-sampling rates are $4\times $ , $8\times $ , $12\times $ , and $16\times $ , respectively. Compared to the state-of-the-art schemes, the JGAU achieves an average of 2.32 dB, 2.47 dB, 2.28 dB and 2.11 dB PSNR gains at four up-sampling rates, respectively, which are significant. The code is released with https://github.com/SYSU-Video/JGAU.

Conventional end-to-end learning-based point cloud compression requires training multiple models to adapt to different target bit rates. Moreover, the rate difference between geometry and attribute components of point clouds is not well-considered. In this paper, we propose an end-to-end Rate-Reconfigurable Deep Point Cloud Compression (RR-DPCC) with on/off-line Perceptual Bit Allocation Optimization (PBAO-ON/OFF), which achieves arbitrary bit rate control with one trained deep model and high efficiency joint geometry and attribute coding. First, we propose the framework of the RR-DPCC using PBAO-ON/OFF, which includes Point Cloud Quality Assessment (PCQA) for perceptual quality measurement, PBAO-ON/OFF modules for bit allocation and RR-DPCC for high efficiency point cloud coding. Second, we propose a one-stream network of the RR-DPCC to encode the attribute and geometry of point clouds jointly. Moreover, in RR-DPCC, a bitrate reconfigurable module is proposed to encode multiple fine-grained bitrate points with one trained model and a rate allocation module is proposed to allocate bits between geometry and attribute. Third, we propose on/off-line PBAO algorithms to maximize the perceptual quality of the reconstructed point cloud, where the bits are properly allocated based on the importance of geometry and attribute. Meanwhile, rate-distortion models (R- $\alpha $ / $\beta $ and D- $\alpha $ / $\beta $ ) are derived for high accuracy rate control and bit allocation. Experimental results show that the proposed RR-DPCC achieves fine-grained bitrate control and allocation through a single trained model. When combined the proposed RR-DPCC with PBAO-ON, it reduces -6.56% and -18.68% bit rate on average as comparing with the state-of-the-art V-PCC and Deep Joint Geometry and Attribute Compression (Deep-JGAC), respectively. When combined with the PBAO-OFF, it achieves -4.90% and -15.34% bit rate reductions on average, and reduces 98.38%/22.05% and 53.75%/10.04% encoding/decoding time on average with respect to V-PCC and Deep-JGAC.

Cloud computing, edge computing, and vehicle-to-vehicle (V2V) communication are essential for intelligent vehicular task processing, offering powerful computation and cooperative capabilities. However, dynamic traffic conditions, limited edge server coverage, and high cloud latency pose significant challenges for task offloading—especially in complex intersections prone to communication disruptions. To address these issues, we propose a cloud-edge collaborative offloading framework that takes into account real-world traffic environments and regulations. We model the problem as a distributed online service optimization problem and prove it NP-hard. To address this issue, we design predictive models for vehicle steering and passing time, and introduce the Traffic-Aware Distributed Online Offloading (TDOO) algorithm. Experiments using real traffic data show that TDOO significantly reduces task latency and outperforms existing methods.

The thermal forcing of the Tibetan Plateau (TP) significantly influences the Asian summer monsoon. However, its interaction with cloud feedbacks remains unclear due to the limitations of synchronous analysis and traditional cloud classification over the TP. By applying an improved cloud-classification algorithm—which integrates cloud microphysical properties to improve low-cloud detection—to CERES data (2001–2023), we generated a long-term cloud-type dataset. Combined with ERA5 reanalysis data, we systematically analyzed the trends and lead–lag relationships among cloud vertical structure, surface radiation, cloud radiative forcing (CRF), heat fluxes, snowfall, and the TP Monsoon Index (TPMI). Results indicate a vertical cloud redistribution over the TP, with high cloud cover (HCC) decreasing and low cloud cover (LCC) increasing. HCC is strongly synchronized with snowfall and significantly affects surface radiation, while net CRF and sensible heat flux show delayed responses, peaking when HCC leads by about one month. A composite analysis of winter low-HCC events reveals that reduced HCC suppresses snowfall, weakens net CRF, and reduces sensible heat flux after approximately 1–2 months, while the TPMI shows a significant response around month zero. These findings highlight the key role of cloud–radiation–snowfall interactions in modulating TP thermal forcing.

Abstract. We present theory and simulations to show that at frequencies of order 200 GHz (G-band) the radar cross section (σr) of ice particles larger than ∼ a quarter wavelength (0.375 mm) is nearly directly proportional to their mass (m). Hence measurements of radar reflectivity (Z) at this frequency are directly proportional to the ice water content (IWC), with no other assumptions about the shape or breadth of the particle size distribution required. For the same reason, vertically pointing Doppler velocities at this frequency provide the mass-weighted mean vertical velocity of the particles, and the product of Z with the mean Doppler velocity (MDV) is proportional to the snowfall rate (S). This presents the opportunity for straightforward and accurate retrievals of ice microphysics. We explore the sensitivity of such retrievals to the scattering model for ice particles. We find that all seven models examined, four with random orientation and three with horizontal orientation, have σr∝m in this regime, but that the coefficient of proportionality varies between models. The dominant factor controlling this coefficient is the mass-size relationship for the ice particles, and specifically the mass of a wavelength-sized ice particle. If this information is known, or can be assumed, then the ice population parameters above can be retrieved with high accuracy. For mass-weighted mean diameters Dm>0.5 mm the variation in the IWC–Z relationship is within ≈ 30 %, and the variation in the S–(Z×MDV) relationship is within ≈ 15 %. The method is applied to retrieve IWC and S during two case studies, with measurements from the GRaCE 200 GHz Doppler radar at Chilbolton Observatory in the UK. In the first of these case studies, retrieved snowfall rates from particles falling aloft in a precipitating ice cloud were compared to gauge data at the surface. In the second case study, retrieved ice water contents from a deep non-precipitating stratiform ice cloud were compared to measurements made using an evaporative water content probe on board the Facility for Airborne Atmospheric Measurements (FAAM) BAe-146 instrumented research aircraft. In both cases a statistical comparison was necessary because of imperfect colocation of the radar measurements and in-situ/gauge sampling. The measurements fall within the distributions of the retrieved water content and snowfall fields, and follow consistent trends with time (Case 1) and height (Case 2), providing evidence that this method produces realistic retrievals. Application of the same technique at even higher radar frequencies would allow clouds with smaller particles (e.g. in high altitude cirrus clouds) to be characterised. Because of the increased gaseous attenuation at such frequencies, the latter may be more practical from airborne or spaceborne platforms.

This article provides a comparative analysis of modern 3D reconstruction methods based on laser scanning (LiDAR) and multi-camera triangulation. 3D reconstruction is a vital tool in various fields such as architecture, industrial design, cultural heritage preservation, autonomous navigation, and virtual reality. Laser scanning delivers high precision and dense point clouds, making it ideal for scanning large and complex structures, yet it requires expensive equipment and longer processing times. In contrast, multi-camera triangulation, relying on the processing of images captured from several cameras or viewpoints, is effective in generating detailed 3D models of textured surfaces, although its accuracy depends significantly on lighting conditions and camera configuration. The article examines key stages of each method, including pre-calibration, segmentation, point cloud alignment, and mesh generation. Algorithms such as Structure from Motion (SfM), Multi-View Stereo (MVS), and Iterative Closest Point (ICP) are reviewed, along with recent hybrid approaches that combine the strengths of both techniques. A comparative assessment is provided in terms of accuracy, speed, scalability, and implementation cost. Real-world applications are illustrated for each method, including digital reconstruction of architectural heritage, forensic analysis, quality control in manufacturing, and automated mapping. The findings indicate that the choice of a 3D reconstruction technique depends on required accuracy, equipment availability, scanning environment, and object characteristics. Future directions suggest the development of hybrid systems integrating LiDAR and photogrammetry to achieve optimal accuracy and efficiency.