Cloud Processing Research Articles

Quantitative attribution of 2016 extreme Arctic warmth: Comparison between late winter and early spring

Abstract A deep, large-scale warmth occurred in the Arctic from January to April 2016, but the roles of various physical processes in this period have not been quantified. Here, we utilize an updated version of the coupled atmosphere‒surface climate feedback response analysis method to quantitatively attribute the extreme warmth. Our results show distinct characteristics associated with the warm anomaly in January‒February and March‒April. This extreme Arctic warmth is largely explained by the positive contributions of atmospheric dynamics, which are dominated by horizontal advection in January‒February and by adiabatic heating and vertical terms in March‒April. Compared with January‒February, an increase in solar radiation leads to an enhanced positive contribution from surface albedo processes in March‒April. Water vapor processes provide considerable positive contribution during both periods. In contrast, surface dynamic processes provide positive contribution in January‒February but negative contribution in March‒April, while cloud processes provide nearly negative contribution during both periods, primarily through their longwave effects.

TLSynth: A Novel Blender Add-On for Real-Time Point Cloud Generation from 3D Models

Point clouds are a crucial element in the process of scanning and reconstructing 3D environments, such as buildings or heritage sites. They allow for the creation of 3D models that can be used in a wide range of applications. In some cases, however, only the 3D model of an environment is available, and it is necessary to obtain point clouds with the same characteristics as those captured by a laser scanner. For instance, point clouds may be required for surveys, performance optimization, site scan planning, or validation of point cloud processing algorithms. This paper presents a new terrestrial laser scanner (TLS) simulator, designed as a Blender add-on, that produces synthetic point clouds from 3D models in real time. The simulator allows users to adjust a set of parameters to replicate real-world scanning conditions, such as noise generation, ensuring the synthetic point clouds closely mirror those produced by actual laser scanners. The target meshes may be derived from either a real-world scan or 3D designs created using design software. By replicating the spatial distributions and attributes of real laser scanner outputs and supporting real-time generation, the simulator serves as a valuable tool for scan planning and the development of synthetic point cloud repositories, advancing research and practical applications in 3D computer vision.

The Application of Three Dimensional Digital Technologies in Historic Gardens and Related Cultural Heritage: A Scoping Review

This paper presents a comprehensive scoping review of the application of 3D digital technologies in the documentation, conservation, and management of historic gardens and related cultural heritage. By analyzing a curated selection of literature, this study assessed the current state of research, highlighting trends in publications, the geographic distribution of contributors, and the key technologies employed. Using bibliometric methods and visualization tools, followed by a case study review, this review identified significant research hotspots and technical methodologies, particularly focusing on advanced techniques such as mobile laser scanning, UAV photogrammetry, and point cloud processing and their relationships with end users. The findings emphasize the importance of integrating multiple technologies to capture the diverse elements of historic gardens, including architectural features, vegetation, and topography. This review also underscores the significance of dynamic landscapes facing challenges posed by environmental degradation and urban development pressures. Moreover, it discusses the limitations of existing research and outlines future opportunities, such as the development of 4D documentation systems and the incorporation of AI for improving heritage management. This paper concludes by recommending interdisciplinary collaboration and public engagement to enhance the accessibility, understanding, and sustainable management of historic gardens through innovative technological applications.

Optimizing healthcare big data performance through regional computing

The healthcare sector is experiencing a digital transformation propelled by the Internet of Medical Things (IOMT), real-time patient monitoring, robotic surgery, Electronic Health Records (EHR), medical imaging, and wearable technologies. This proliferation of digital tools generates vast quantities of healthcare data. Efficient and timely analysis of this data is critical for enhancing patient outcomes and optimizing care delivery. Real-time processing of Healthcare Big Data (HBD) offers significant potential for improved diagnostics, continuous monitoring, and effective surgical interventions. However, conventional cloud-based processing systems face challenges due to the sheer volume and time-sensitive nature of this data. The migration of large datasets to centralized cloud infrastructures often results in latency, which impedes real-time applications. Furthermore, network congestion exacerbates these challenges, delaying access to vital insights necessary for informed decision-making. Such limitations hinder healthcare professionals from fully leveraging the capabilities of emerging technologies and big data analytics. To mitigate these issues, this paper proposes a Regional Computing (RC) paradigm for the management of HBD. The RC framework establishes strategically positioned regional servers capable of regionally collecting, processing, and storing medical data, thereby reducing dependence on centralized cloud resources, especially during peak usage periods. This innovative approach effectively addresses the constraints of traditional cloud processing, facilitating real-time data analysis at the regional level. Ultimately, it empowers healthcare providers with the timely information required to deliver data-driven, personalized care and optimize treatment strategies.

Effects of fine terrain complexity on cloud and precipitation changes over the Tibetan Plateau: a modeling study

Inaccurate characterization of complex topography leads to the wet bias in climate models, particularly affecting terrain effects in regions like the Tibetan Plateau (TP). This study utilizes the Weather Research and Forecasting (WRF) model with multiple terrain datasets and introduces the terrain complexity index (TCI) to quantify the degree of terrain changes, aiming to evaluate how terrain complexity affects the cloud and precipitation processes over the TP. The results indicate that fine terrain complexity primarily causes earlier cloud formation and precipitation, resulting in more heavy precipitation on the southern slope of the TP (SSTP) and more light precipitation on the TP platform. The structure of moisture transport and microphysical processes further reveals that this promotes the formation of more medium and high clouds, increasing the proportion of solid precipitation over the SSTP. Over the TP platform, the restriction of medium and high cloud development with enhancing the proportion of low clouds for more liquid precipitation. These findings deepen the understanding of the TP’s complex terrain effect on cloud and precipitation changes in the Asian water cycle.

Analysis and Evaluation of Intel Software Guard Extension-Based Trusted Execution Environment Usage in Edge Intelligence and Internet of Things Scenarios

With the extensive deployment and application of the Internet of Things (IoT), 5G and 6G technologies and edge intelligence, the volume of data generated by IoT and the number of intelligence applications derived from these data are rapidly growing. However, the absence of effective mechanisms to safeguard the vast data generated by IoT, along with the security and privacy of edge intelligence applications, hinders their further development and adoption. In recent years, Trusted Execution Environment (TEE) has emerged as a promising technology for securing cloud data storage and cloud processing, demonstrating significant potential for ensuring data and application confidentiality in more scenarios. Nevertheless, applying TEE technology to enhance security in IoT and edge intelligence scenarios still presents several challenges. This paper investigates the technical challenges faced by current TEE solutions, such as performance overhead and I/O security issues, in the context of the resource constraints and data mobility that are inherent to IoT and edge intelligence applications. Using Intel Software Guard Extensions (SGX) technology as a case study, this paper validates these challenges through extensive experiments. The results provide critical assessments and analyses essential for advancing the development and usage of TEE in IoT and edge intelligence scenarios.

Terrain Traversability via Sensed Data for Robots Operating Inside Heterogeneous, Highly Unstructured Spaces.

This paper presents a comprehensive approach to evaluating the ability of multi-legged robots to traverse confined and geometrically complex unstructured environments. The proposed approach utilizes advanced point cloud processing techniques integrating voxel-filtered cloud, boundary and mesh generation, and dynamic traversability analysis to enhance the robot's terrain perception and navigation. The proposed framework was validated through rigorous simulation and experimental testing with humanoid robots, showcasing the potential of the proposed approach for use in applications/environments characterized by complex environmental features (navigation inside collapsed buildings). The results demonstrate that the proposed framework provides the robot with an enhanced capability to perceive and interpret its environment and adapt to dynamic environment changes. This paper contributes to the advancement of robotic navigation and path-planning systems by providing a scalable and efficient framework for environment analysis. The integration of various point cloud processing techniques into a single architecture not only improves computational efficiency but also enhances the robot's interaction with its environment, making it more capable of operating in complex, hazardous, unstructured settings.

Estimating Phase Transition Rates in Shallow Cumulus Clouds from Mass Flux. Part II: Vertically Dependent Formulation

Abstract This work continued the investigation of the relationship between phase transition rates and mass flux in trade wind cumulus clouds. The latter was simulated by an LES model initialized with soundings from the Rain in Cumulus over the Ocean (RICO) field project. In Part I, we demonstrated that a very high correlation exists between integral phase transition rates and upward mass flux. In this study, we focused on the vertically dependent variables and showed that a similar high correlation exists between the condensation rate and the upward mass flux . Based on condensation theory, we showed that under quasi-steady approximation condensation rates can be calculated by a linear function of with the slope coefficient dependent only on temperature and pressure. The model data showed that the error of such approximation is less than a few tenths of a percent. The parameterization of the evaporation process is more complex, mostly because of the slow evaporation of raindrops as they fall through the cloud’s unsaturated areas. Nevertheless, it was possible to define the fraction of the evaporation to condensation rate as a function of vertical coordinate z and cloud thickness H. This function can be quite accurately approximated by the third-order polynomials of z and H. It is suggested that the proposed formulation of evaporation together with the quasi-steady formulation of condensation can serve as a parameterization of water phase transition rates in shallow cumulus clouds. Significance Statement This study investigated condensation/evaporation processes in tropical cumulus clouds. The energy exchanged during these processes is an important driving force behind a wide range of atmospheric phenomena. It was found that the vertical distribution of this energy source can be expressed as a linear function of cloud updrafts. This finding suggests a new approach to calculate cloud energy transformations in numerical weather prediction models.

Digital Transformation and Optimization Framework for Advancing SME Growth and Operational Effectiveness

This paper explores the pivotal role of digital transformation and optimization frameworks in driving the growth and operational effectiveness of small and medium-sized enterprises (SMEs). SMEs are critical contributors to global economic development but often face challenges such as limited resources, market competition, and operational inefficiencies. The study highlights how adopting digital technologies and structured optimization frameworks can address these challenges, enabling SMEs to streamline processes, enhance productivity, and remain competitive in dynamic markets. The research identifies key enablers of successful digital transformation, including strategic planning, workforce upskilling, data-driven decision-making, and the integration of scalable technologies like cloud computing, artificial intelligence, and process automation. Case studies illustrate the tangible benefits SMEs have realized, such as increased revenue growth, cost savings, and improved customer satisfaction. Moreover, the paper presents a comprehensive framework for SMEs to effectively implement digital transformation, emphasizing the importance of aligning technological adoption with business objectives. It also examines common barriers, including resistance to change, high implementation costs, and cyber security risks, offering practical solutions to mitigate these challenges. The findings underscore that a well-executed digital transformation strategy, coupled with continuous optimization, can empower SMEs to achieve sustainable growth and operational excellence. This paper concludes with policy recommendations and actionable insights for SMEs, industry stakeholders, and policymakers to foster an ecosystem conducive to digital innovation and scalability. By bridging theoretical concepts and real-world applications, this study provides a valuable roadmap for SMEs navigating the complexities of the digital era.

Cloud processing dominates the vertical profiles of aerosols in marine air masses over the Great Barrier Reef

Cloud processing dominates the vertical profiles of aerosols in marine air masses over the Great Barrier Reef

How Much Convective Environment Subgrid Spatial Variability Is Missing Within Atmospheric Reanalysis Data Sets?

AbstractConvective cloud processes are sensitive to environmental conditions that vary on scales smaller than reanalysis data sets (sub‐reanalysis scales). Convective environment variability within areas representative of reanalysis data sets is quantified using large‐domain, high‐resolution (∆x = 100 m) simulations of convective cloud systems throughout the tropics and subtropics. Even after removing locations of resolved clouds and precipitation, convective environment parameters vary significantly on these scales. For example, for half of the simulated data, 500 hPa relative humidity varies by ∼30% within a typical reanalysis area. Surface winds, convective available potential energy, and middle‐tropospheric moisture are the most variable convective environment parameters for both continental and maritime regimes, while above‐surface temperature and winds are the least variable. While high‐resolution, sub‐reanalysis‐scale extrema are well‐correlated with the reanalysis‐area mean values, some of the most extreme convective environments can occur within regions with moderate reanalysis‐area mean values, particularly for continental regions.

Retention of α-pinene oxidation products and nitro-aromatic compounds during riming

Abstract. Riming is an important growth process of graupel and hailstones in the mixed-phase zones of clouds, during which supercooled liquid droplets freeze on the surface of ice particles by contact. Compounds dissolved in the supercooled cloud droplets can remain in the ice or be released to the gas phase during freezing, which might play an important role in the vertical redistribution of these compounds in the atmosphere by convective cloud processes. This is important for estimating the availability of these compounds in the upper troposphere, where organic matter can promote new particle formation and growth. The amount of organic material remaining in the ice phase can be described by the retention coefficient. Experiments were performed in the Mainz vertical wind tunnel under dry and wet growth conditions (temperature from −12 to −3 °C and a liquid water content (LWC) of 0.9±0.2 g m−3 and 2.2±0.2 g m−3) as well as with different pH values (4 and 5.6) to obtain the retention coefficients of α-pinene oxidation products and nitro-aromatic compounds. For cis-pinic acid, cis-pinonic acid, and (−)-pinanediol, mean retention coefficients of 0.96±0.07, 0.92±0.11, and 0.98±0.08 were obtained. 4-Nitrophenol, 4-nitrocatechol, 2-nitrobenzoic acid, and 2-nitrophenol showed mean retention coefficients of 1.01±0.07, 1.01±0.14, 0.99±0.04, and 0.21±0.12. Only the retention coefficient of 2-nitrophenol showed a dependence on temperature, growth regime, and pH. This is in accordance with previous studies, which showed a dependence between the dimensionless effective Henry's law constant H* and the retention coefficient for inorganic and small organic molecules. Our results reveal that this correlation can also be applied to more complex organic molecules and that retention under these conditions is not a significant factor for molecules with H* below 103, while retention close to 1 can be expected for compounds with H* above 108.

A global climatology of sting-jet extratropical cyclones

Abstract. Sting jets have been identified in the most damaging extratropical cyclones impacting northwest Europe. Unlike the cold conveyor belt and other long-lived cyclone wind jets, sting jets can lead to regions of exceptionally strong near-surface winds and damaging gusts over just a few hours and with much smaller wind “footprints”. They descend into the frontal-fracture region found in warm-seclusion cyclones. Previous research has focused almost exclusively on North Atlantic–European cyclones, but there are no known physical reasons why sting jets should not develop elsewhere, and recognition of their existence can inform weather nowcasting and wind warnings. We have produced the first climatology of sting-jet cyclones over the major ocean basins. A sting-jet precursor diagnostic has been applied to more than 10 000 warm-seclusion cyclones in the top intensity decile, tracked using 43 extended winters of ERA5 reanalysis data. Cyclones with sting-jet precursors are found to occur over the North Pacific and Southern oceans for the first time, and they are more prevalent in the Northern Hemisphere (27 % of all top decile cyclones) compared to the Southern Hemisphere (15 %). These cyclones have distinct characteristics to those without the precursor, including initiating closer to the Equator, deepening faster in mean-sea-level pressure (MSLP), and having stronger near-surface winds, even in the reanalysis data that are too coarse to fully resolve sting jets. Composite analysis reveals systematic differences in structural evolution, including in potential vorticity (PV) and jet crossing. These differences evidence the climatological consequences of strong diabatic cloud processes on cyclone characteristics, implying that sting jets are likely to be enhanced by climate change.

IoRT-Based Middleware for Heterogeneous Multi-Robot Systems

The concurrence of social robots with different functionalities and cyber-physical systems in indoor environments has recently been increasing in many fields, such as medicine, education, and industry. In such scenarios, the collaboration of such heterogeneous robots demands effective communication for task completion. The concept of the Internet of Robotic Things (IoRT) is introduced as a potential solution, leveraging technologies like Artificial Intelligence, Cloud Computing, and Mesh Networks. This paper proposes an IoRT-based middleware that allows the communication of different types of robot operating systems in dynamic environments, using a cloud-based protocol. This middleware facilitates task assignment, training, and planning for heterogeneous robots, while enabling distributed communication via WiFi. The system operates in two control modes: local and cloud-based, for flexible communication and information distribution. This work highlights the challenges of current communication methods, particularly in ensuring information reach, agility, and handling diverse robots. To demonstrate the middleware suitability and applicability, an implementation of a proof-of-concept is shown in a touristic scenario where several guide robots can collaborate by effectively sharing information gathered from their heterogeneous sensor systems, with the aid of cloud processing or even internal communication processes. Results show that the performance of the middleware allows real-time applications for heterogeneous multi-robot systems in different domains.

Equations to Predict Carbon Monoxide Emissions from Amazon Rainforest Fires

Earth systems models (ESMs), which can simulate the complex feedbacks between climate and fires, struggle to predict fires well for tropical rainforests. This study provides equations that predict historic carbon monoxide emissions from Amazon rainforest fires for 2003–2018, which could be implemented within ESMs’ current structures. We also include equations to convert the predicted emissions to burned area. Regressions of varying mathematical forms are fitted to one or both of two fire CO emission inventories. Equation accuracy is scored on r2, bias of the mean prediction, and ratio of explained variances. We find that one equation is best for studying smoke consequences that scale approximately linearly with emissions, or for a fully coupled ESM with online meteorology. Compared to the deforestation fire equation in the Community Land Model ver. 4.5, this equation’s linear-scale accuracies are higher for both emissions and burned area. A second equation, more accurate when evaluated on a log scale, may better support studies of certain health or cloud process consequences of fires. The most accurate recommended equation requires that meteorology be known before emissions are calculated. For all three equations, both deforestation rates and meteorological variables are key groups of predictors. Predictions nevertheless fail to reproduce most of the variation in emissions. The highest linear r2s for monthly and annual predictions are 0.30 and 0.41, respectively. The impossibility of simultaneously matching both emission inventories limits achievable fit. One key cause of the remaining unexplained variability appears to be noise inherent to pan-tropical data, especially meteorology.

Synergistic image and point cloud processing of UAV data for urban flood modeling: point cloud smart thinning and curb mapping

Abstract. We propose an integrated approach for automatic point cloud thinning and curb mapping in Uncrewed Aerial Vehicle - Structure from Motion (UAV-SfM) point clouds to enhance hydrological modeling in flood-prone urban areas. UAV flights were conducted to generate an initial orthoimage, which was used to train a convolutional neural network (CNN) segmentation model. The trained model was then applied to the UAV images to produce two binary mask sets: one for vegetation and one for streets and sidewalks. These masks were incorporated during photogrammetric 3D reconstruction to estimate camera geometry and generate a dense point cloud. Our results show that vegetation masks did not improve camera geometry estimation. However, by applying UAV masks, we achieved a 15% reduction in total processing time and decreased the number of points by a factor of 2.7. This targeted approach enabled curb detection by focusing on expected curb locations. Curb candidate points were proposed using geometric characteristics of the point cloud, including normal values, linearity, and verticality. Our rule-based method effectively mapped even subtle curb features, providing a rapid, cost-effective solution for large-area curb mapping. Further, we explored the potential of random forest for curb mapping, with promising results. Our approach can support urban flood modeling efforts and strengthen urban resilience for flood-prone communities.

Summertime Continental Shallow Cumulus Cloud Detection Using GOES‐16 Satellite and Ground‐Based Ceilometer at North Alabama

AbstractAccurate simulations of boundary layer cloud processes remain challenging in Earth system modeling. Observations are essential to evaluate and improve models of such processes. This study introduces a comprehensive validation framework for a satellite‐based detection algorithm of continental shallow cumulus (ShCu) clouds during the daytime, which was initially developed using ground‐based observations of stereo cameras at the Department of Energy Atmospheric Radiation Measurement (ARM) Southern Great Plains site (J. Tian, Zhang, Klein, & Schumacher, 2021, https://doi.org/10.3390/rs13122309, 2022, https://doi.org/10.1029/2021gl097070). To validate this algorithm, the framework employs ground‐based ceilometer measurements from North Alabama (NA) where ShCu populations are prevalent. This study first generates clear‐sky surface reflectance maps at NA and identifies ShCu pixels with a detection threshold using Geostationary Operational Environmental Satellite (GOES) reflectance data. The obtained cloud fractions (CFs) are then compared against CFs from a ground‐based ceilometer, considering factors such as observed area differences, satellite parallax issue, and systematic biases. We found that with a detection threshold (∆R) of 0.05, the ShCu detection algorithm is effective for NA, enabling the reproduction of hourly ShCu CFs using GOES. Our framework is straightforward and easily repeatable to evaluate the effectiveness of a ∆R threshold for detecting ShCu clouds in various geographic regions where ceilometers are deployed. This satellite detection of ShCu provides a crucial regional context for ground‐based measurements, facilitating the tracking of convection initiation and its coupling with land surface conditions. Integrating localized ground‐based and regional satellite data will enhance our ability to conduct thorough studies of cloud morphology and land‐atmosphere interactions in North Alabama.

Numerical simulation of aerosol concentration effects on cloud droplet size spectrum evolutions of warm stratiform clouds in Jiangxi, China

Abstract. Changes in aerosol amount and size distribution significantly impact cloud droplet size distribution, as aerosols act as cloud condensation nuclei (CCNs) and influence the relative dispersion (ε) of cloud droplet spectra. Relative dispersion plays a key role in parameterizing cloud processes in general circulation models (GCMs) and microphysical schemes, affecting precipitation estimates and climate predictions. However, the effects of varying aerosol modes on cloud microphysics remain debated, depending on thermodynamic conditions and cloud type. This study simulates a warm stratiform cloud in Jiangxi, China, using the Weather Research and Forecasting (WRF) Spectra–Bin Microphysics scheme (SBM-FAST) from 18:00 on 24 December 2014 to 06:00 on 25 December 2014 (UTC). Satellite and aircraft observations were used to validate the simulation, showing good agreement in cloud structure. Sensitivity experiments were conducted by increasing nucleation, accumulation, and coarse-mode aerosols 5-fold and by reducing the total aerosol concentration to 1/5 of the control. Results show that higher aerosol concentrations enhance cloud formation and broaden droplet spectra, while lower concentrations suppress cloud development. Accumulation-mode aerosols increase small-droplet concentrations, while nucleation- and coarse-mode aerosols favor larger droplets. The correlation between ε and volume-weighted radius (Rv) shifts from positive to negative as Rv increases. This transition is driven by cloud droplet collision–coalescence, condensation, and activation. Increased accumulation-mode aerosol concentrations shift the ε–Rv correlation from negative to positive in the Rv range of 4.5–8 µm, while reduced aerosol concentrations strengthen the negative correlation. Regardless of different coalescence intensities, ε converges with the increase in number concentration of cloud droplets (Nc).

Kinetics and Oligomer Products of the Multiphase Reactions of Hydroxyacetone with Atmospheric Amines, Ammonium Sulfate, and Cloud Processing

Kinetics and Oligomer Products of the Multiphase Reactions of Hydroxyacetone with Atmospheric Amines, Ammonium Sulfate, and Cloud Processing

Calculating the Optimal Point Cloud Density for Airborne LiDAR Landslide Investigation: An Adaptive Approach

Ensuring that ground point density after raw point cloud processing meets the accuracy requirements for subsequent DEM construction represents a challenge for field operators during airborne LiDAR data acquisition. In this study, we propose a method to quantify DEM quality by combining the RMSE of elevation and terrain complexity, analyzing the DEM quality error curves constructed with different point cloud densities by a discrete difference peak-seeking method, to determine the optimal ground point density, and then constructing an ICP-NN algorithm for predicting the collected point cloud density. After analysis of DEM quality at eight point cloud dilution levels, the optimal ground point cloud densities were determined to be 2.43 pts/m2 (0.2 m resolution), 2.08 pts/m2 (1 m and 0.5 m resolution), and 1.84 pts/m2 (2 m resolution). Using the obtained optimal ground point densities, survey area slopes, canopy density, and elevation differences as eigenvalues, the ICP-NN model can be used to directly predict the collected point cloud density intervals in other regions, and the model has interval lengths ranging from 36 to 70.33 pts/m2 at 5 CLs. This method solves the problem of determining point cloud density in landslide surveys using airborne LiDAR and provides direct guidance for practical applications.