Cloud Layer Research Articles

Use of VEGA data to analyse balloon options for possible subsequent long endurance Venus cloud layer missions

Abstract The vertical motions and buoyancy variations of the two VEGA super-pressure balloons, flown in the middle cloud layer of Venus, are discussed. Using data derived from these 1985 nightside flights, estimates are made of the energy required to operate some alternative balloon platform schemes under consideration for future-proposed Venus-atmosphere in situ science missions. Despite the dissimilarity of these alternative platform schemes, the energy inputs required to operate each scheme on the Venus nightside are found to be similar. Estimates of the associated mass penalties of the associated energy sources are also made. Further investigation of a vertical propulsive assist scheme is recommended.

Momentum Transport in Organized Shallow Cumulus Convection

Abstract This study investigates momentum transport in shallow cumulus clouds as simulated with the Dutch Atmospheric Large Eddy Simulation (DALES) for a 150 × 150 km2 domain east of Barbados during 9 days of EUREC4A. DALES is initialized and forced with the mesoscale weather model HARMONIE–AROME and subjectively reproduces observed cloud patterns. This study examines the evolution of momentum transport, which scales contribute to it, and how they modulate the trade winds. Daily-mean momentum flux profiles show downgradient zonal momentum transport in the subcloud layer, which turns countergradient in the cloud layer. The meridional momentum transport is nontrivial, with mostly downgradient transport throughout the trade wind layer except near the top of the surface layer and near cloud tops. Substantial spatial and temporal heterogeneity in momentum flux is observed with much stronger tendencies imposed in areas of organized convection. The study finds that while scales < 2 km dominate momentum flux at 200 m in unorganized fields, submesoscales carry up to 50% of the zonal momentum flux in the cloud layer in organized fields. For the meridional momentum flux, this fraction is even larger near the surface and in the subcloud layer. The scale dependence of the momentum flux is not explained by changes in convective or boundary layer depth. Instead, the results suggest the importance of spatial heterogeneity, increasing horizontal length scales, and countergradient transport in the presence of organized convection.

An intercomparison of EarthCARE cloud, aerosol, and precipitation retrieval products

Abstract. The objective of the Earth Cloud, Aerosol, and Radiation Explorer (EarthCARE) mission is to infer attributes of cloud, aerosol, precipitation, and radiation from observations made by four complementary instruments. This requires the development of single-instrument and multiple-instrument (i.e. synergistic) retrieval algorithms that employ measurements made by one, or more, of EarthCARE's cloud-profiling radar (CPR), atmospheric lidar (ATLID), and multi-spectral imager (MSI); its broadband radiometer (BBR) places the retrieved quantities in the context of the surface–atmosphere radiation budget. To facilitate the development and evaluation of ESA's EarthCARE production model prior to launch, sophisticated instrument simulators were developed to produce realistic synthetic EarthCARE measurements for simulated conditions provided by cloud-resolving models. While acknowledging that the physical and radiative representations of cloud, aerosol, and precipitation in the test scenes are based on numerical models, the opportunity to perform detailed evaluations wherein the “truth” is known provides insights into the performance of EarthCARE's instruments and retrieval algorithms. This level of omniscience will not be available for the evaluation of in-flight EarthCARE retrieval products, even during validation activities coordinated with ground-based and airborne measurements. In this study, we compare EarthCARE retrieval products both statistically across all simulated scenes and from a specific time series from a single scene. For ice clouds, it is shown that retrieved profiles of ice water content and effective particle size made by the ATLID-CPR-MSI cloud, aerosols, and precipitation (ACM-CAP) synergistic algorithm are consistently more accurate than those from its single-instrument counterparts. While liquid clouds are often difficult to detect from satellite-borne sensors, especially for multi-layered clouds, ACM-CAP benefits from combined constraints from lidar backscatter, solar radiances, and radar-path-integrated attenuation but still exhibits non-trivial random error. For precipitation retrievals, the CPR cloud and precipitation product (C-CLD) and ACM-CAP have a similar performance when well-constrained by CPR measurements. The greatest differences are in coverage, with ACM-CAP reporting retrievals in the melting layer, and in heavy precipitation, where CPR signals are dominated by multiple scattering and attenuation. Aerosol retrievals from ATLID compensate for a high degree of measurement noise in a number of ways, with the ATLID extinction, backscatter, and depolarisation (A-EBD) product and ACM-CAP demonstrating similar performance. The multi-spectral imager (MSI) cloud optical properties (M-COP) product performs very well for unambiguous cloud layers. Similarly, the MSI aerosol optical thickness (M-AOT) product performs well when radiances are unaffected by cloud, but both products provide little information about vertical profiles of properties. Finally, a summary of the performance of all retrieval products and their random errors is provided.

Lightning Flares in the Cloud Layer of Venus Detected in the Near-Infrared Range

Lightning Flares in the Cloud Layer of Venus Detected in the Near-Infrared Range

Comparison of Forecasting Biases Over New York State Mesonet: A Wet Summer Versus a Dry Summer

AbstractExtreme weather events are occurring with increasing frequent due to the climate change. This increasing frequency may introduce more uncertainty in weather forecasting model performance, particularly when considering the intricate relationship of the land surface and atmosphere coupling system. In this study, we utilize data from the sophisticated New York State Mesonet to evaluate the performance of a forecasting system based on WRF Version 4 model, drawing insights from both dry and wet summers. Additionally, the model's performance is assessed on two land surface types: forest and farmland, to provide a comprehensive evaluation of impact of land surface heterogeneity. The surface meteorology, fluxes, and cloud development are assessed. The coupling between surface and atmosphere is diagnosed using a mixing diagram which serves to represent surface thermodynamic properties. The results reveal a systematic increase in warm season dry and warm biases, especially for forested sites during a drought year. The model exhibits heightened sensitivity to drought conditions, resulting in a substantial underestimation of latent heat fluxes during such period. During days with boundary layer clouds, the mixing diagram shows a notably slower growth of moist static energy in the model compared to observation. It is possible that these biases partly attribute to the underestimation of cloud optical depth due to not enough energy for the cloud development.

Charge in non-thunderstorm clouds and fogs

The global atmospheric electric circuit links charge separation in thunderclouds with current flow throughout the atmosphere. This current flow causes charge accumulation on the upper and lower boundaries of clouds and fogs. Thunderclouds are therefore not the only charged clouds in the atmosphere, and other forms of clouds, especially extensive horizontal layer clouds (stratus) are much more abundant globally. To investigate possible effects of charging of natural droplets in clouds, experiments have been undertaken using corona ion emission into fogs, releasing from within and above the fog. During the corona ion release the droplet size distribution was modified, with an increase in small drops. Further, when the ions were released over the fog, the reflectivity of the fog increased by ∼2%, about 30s later.

On the relationship between mesoscale cellular convection and meteorological forcing: comparing the Southern Ocean against the North Pacific

Abstract. Marine atmospheric boundary layer (MABL) clouds cover vast areas over the ocean and have important radiative effects on the Earth's climate system. These radiative effects are known to be sensitive to the local organization, or structure, of the mesoscale cellular convection (MCC). A convolutional neural network model is used to identify the two idealized classes of MCC clouds, namely open and closed, over the Southern Ocean (SO) and Northwest Pacific (NP) from high-frequency geostationary Himawari-8 satellite observations. The results of the climatology show that MCC clouds are evenly distributed over the mid-latitude storm tracks for both hemispheres, with peaks poleward of the 40∘ latitude. Open-MCC clouds are more prevalent than closed MCC in both regions. An examination of the presumed meteorological forcing associated with open- and closed-MCC clouds is conducted to illustrate the influence of large-scale meteorological conditions. We establish the importance of the Kuroshio western boundary current in the spatial coverage of open and closed MCC across the NP, presumably through the supply of strong heat and moisture fluxes during marine cold-air outbreaks events. In regions where static stability is higher, we observe a more frequent occurrence of closed MCCs. This behavior contrasts markedly with that of open MCCs, whose formation and persistence are significantly influenced by the difference in temperature between the air and the sea surface. The occurrence frequency of closed MCC over the SO exhibits a significant diurnal cycle, while the diurnal cycle of closed MCC over the NP is less noticeable.

The Future of Electronic Commerce in the IoT Environment

The Internet of Things (IoT) was born from the fusion of virtual and physical space and became the initiator of many scientific fields. Economic sustainability is the key to further development and progress. To keep up with the changes, it is necessary to adapt economic models and concepts to meet the requirements of future smart environments. Today, the need for electronic commerce (e-commerce) has become an economic priority during the transition between Industry 4.0 and Industry 5.0. Unlike mass production in Industry 4.0, customized production in Industry 5.0 should gain additional benefits in vertical management and decision-making concepts. The authors’ research is focused on e-commerce in a three-layer vertical IoT environment. The vertical IoT concept is composed of edge, fog, and cloud layers. Given the ubiquity of artificial intelligence in data processing, economic analysis, and predictions, this paper presents a few state-of-the-art machine learning (ML) algorithms facilitating the transition from a flat to a vertical e-commerce concept. The authors also propose hands-on ML algorithms for a few e-commerce types: consumer–consumer and consumer–company–consumer relationships. These algorithms are mainly composed of convolutional neural networks (CNNs), natural language understanding (NLU), sequential pattern mining (SPM), reinforcement learning (RL for agent training), algorithms for clicking on the item prediction, consumer behavior learning, etc. All presented concepts, algorithms, and models are described in detail.

New Particle Formation Events Can Reduce Cloud Droplets in Boundary Layer Clouds at the Continental Scale

AbstractNew particle formation (NPF) substantially contributes to global cloud condensation nuclei (CCN), and their climate impacts. Individual NPF events are also thought to increase local CCN, cloud droplet number (CDN), and cloud albedo. High resolution simulations however go against the latter, showing that radiatively important stratiform clouds can experience a systematic and substantial decrease in CDN during and after NPF events. CDN drops because particles too small to act as CCN uptake condensable material, and stunt the growth of particles that would otherwise form droplets. Convective clouds however experience modest increases in CDN—consistent with established views on the NPF‐cloud link. Together, these results reshape our conceptual understanding of NPF impacts on clouds, as the newly discovered duality of responses would drive cloud systems in a fundamentally different manner than thought.

Characterizing Ice Nucleating Particles Over the Southern Ocean Using Simultaneous Aircraft and Ship Observations

AbstractSupercooled liquid clouds are ubiquitous over the Southern Ocean (SO), even to temperatures below −20°C, and comprise a large fraction of the marine boundary layer (MBL) clouds. Earth system models and reanalysis products have struggled to reproduce the observed cloud phase distribution and occurrence of cloud ice in the region. Recent simulations found the microphysical representation of ice nucleation and growth has a large impact on these properties, however, measurements of SO ice nucleating particles (INPs) to validate simulations are sparse. This study presents measurements of INPs from simultaneous aircraft and ship campaigns conducted over the SO in austral summer 2018, which include the first in situ observations in and above cloud in the region. Our results confirm recent observations that INP concentrations are uniformly lower than measurements made in the late 1960s. While INP concentrations below and above cloud are similar, higher ice nucleation efficiency above cloud supports model simulations that the dominant INP composition varies with height. Model parameterizations based solely on aerosol properties capture the mean relationship between INP concentration and temperature but not the observed variability, which is likely related to the only modest correlations observed between INPs and environmental or aerosol metrics. Including wind speed in addition to activation temperature in a marine INP parameterization reduces bias but does not explain the large range of observed INP concentrations. Direct and indirect inference of marine INP size suggests MBL INPs, at least during Austral summer, are dominated by particles with diameters smaller than 500 nm.

Understanding changes in cloud simulations from E3SM version 1 to version 2

Abstract. This study documents clouds simulated by the Energy Exascale Earth System Model (E3SM) version 2 (E3SMv2) and attempts to understand what causes the model behavior change in clouds relative to E3SMv1. This is done by analyzing the last 30-year (1985–2014) data from the 165-year historical simulations using E3SMv1 and v2 and four sensitivity tests to isolate the impact of changes in model parameter choices in its turbulence, shallow convection, and cloud macrophysics parameterization (Cloud Layers Unified By Binormals, CLUBB); microphysical parameterization (MG2); and deep-convection scheme (ZM), as well as model physics changes in convective triggering. It is shown that E3SMv2 significantly improves the simulation of subtropical coastal stratocumulus clouds and clouds with optical depth larger than 3.6 over the stratocumulus-to-cumulus transition regimes, where the shortwave cloud radiative effect (SWCRE) is also improved, and the Southern Ocean (SO) while seeing an overall slight degradation in low clouds over other tropical and subtropical oceans. The better performance in E3SMv1 over those regions is partially due to error compensation between its simulated optically thin and intermediate low clouds for which E3SMv2 actually improves simulation of optically intermediate low clouds. Sensitivity tests indicate that the changes in low clouds are primarily due to the tuning done in CLUBB. The impact of the ZM tuning is mainly on optically intermediate and thick high clouds, contributing to an improved SWCRE and longwave cloud radiative effect (LWCRE). The impact of the MG2 tuning and the new convective trigger is primarily on the high latitudes and the SO. They have a relatively smaller impact on clouds than CLUBB tuning and ZM tuning do. This study offers additional insights into clouds simulated in E3SMv2 by utilizing multiple data sets and the Cloud Feedback Model Intercomparison Project (CFMIP) Observation Simulator Package (COSP) diagnostic tool as well as sensitivity tests. The improved understanding will benefit future E3SM developments.

Cloud Overlap Features from Multi-Year Cloud Radar Observations at the SACOL Site and Comparison with Satellites

Cloud overlap, referring to distinct cloud layers occurring over the same location, is essential for accurately calculating the atmospheric radiation transfer in numerical models, which, in turn, enhances our ability to predict future climate change. In this study, we analyze multi-year cloud overlap properties observed from the Ka-band Zenith Radar (KAZR) at the Semi-Arid Climate and Environment Observatory of Lanzhou University’s (SACOL) site. We conduct a series of statistical analyses and determine the suitable temporal-spatial resolution of 1 h with a 360 m scale for data analysis. Our findings show that the cloud overlap parameter and total cloud fraction are maximized during winter-spring and minimized in summer-autumn, and the extreme value of decorrelation length usually lags one or two seasons. Additionally, we find the cloud overlap assumption has distinct effects on the cloud fraction bias for different cloud types. The random overlap leads to the minimum bias of the cloud fraction for Low-Middle-High (LMH), Low-Middle (LM), and Middle-High (MH) clouds, while the maximum overlap is for Low (L), Middle (M), and High (H) clouds. We also incorporate observations from satellite-based active sensors, including CloudSat, Cloud-Aerosol Lidar, and Infrared Pathfinder Satellite Observations (CALIPSO), to refine our study area and specific cases by considering the total cloud fraction and sample size from different datasets. Our analysis reveals that the representativeness of random overlap strengthens and then weakens with increasing layer separations. The decorrelation length varies with the KAZR, CloudSat-CALIPSO, CloudSat, and CALIPSO datasets, measuring 1.43 km, 2.18 km, 2.58 km, and 1.11 km, respectively. For H, MH, and LMH clouds, the average cloud overlap parameter from CloudSat-CALIPSO aligns closely with KAZR. For L, M, and LM clouds, when the level separation of cloud layer pairs are less than 1 km, the representative assumption obtained from different datasets are maximum overlap.

Optimizing Data Processing: A Comparative Study of Big Data Platforms in Edge, Fog, and Cloud Layers

Intelligent applications in several areas increasingly rely on big data solutions to improve their efficiency, but the processing and management of big data incur high costs. Although cloud-computing-based big data management and processing offer a promising solution to provide scalable and abundant resources, the current cloud-based big data management platforms do not properly address the high latency, privacy, and bandwidth consumption challenges that arise when sending large volumes of user data to the cloud. Computing in the edge and fog layers is quickly emerging as an extension of cloud computing used to reduce latency and bandwidth consumption, resulting in some of the processing tasks being performed in edge/fog-layer devices. Although these devices are resource-constrained, recent increases in resource capacity provide the potential for collaborative big data processing. We investigated the deployment of data processing platforms based on three different computing paradigms, namely batch processing, stream processing, and function processing, by aggregating the processing power from a diverse set of nodes in the local area. Herein, we demonstrate the efficacy and viability of edge-/fog-layer big data processing across a variety of real-world applications and in comparison to the cloud-native approach in terms of performance.

Image-registration-based solar meridian detection for accurate and robust polarization navigation.

Skylight polarization, inspired by the foraging behavior of insects, has been widely used for navigation for various platforms, such as robots, unmanned aerial vehicles, and others, owing to its stability and non-error-accumulation. Among the characteristics of skylight-polarized patterns, the angle of polarization (AOP) and the degree of polarization (DOP) are two of the most significant characteristics that provide abundant information regarding the position of the sun. In this study, we propose an accurate method for detecting the solar meridian for real-time bioinspired navigation through image registration. This method uses the AOP pattern to detect the solar meridian and eliminates the ambiguity between anti-solar meridian and solar meridian using the DOP pattern, resulting in an accurate heading of the observer. Simulation experiments demonstrated the superior performance of the proposed method compared to the alternative approaches. Field experiments demonstrate that the proposed method achieves real-time, robust, and accurate performance under different weather conditions with a root mean square error of 0.1° under a clear sky, 0.18° under an overcast sky with a thin layer of clouds, and 0.32° under an isolated thick cloud cover. Our findings suggest that the proposed method can be potentially used in skylight polarization for real-time and accurate navigation in GPS-denied environments.

AI-Assisted Deep NLP-Based Approach for Prediction of Fake News From Social Media Users

Social networking websites are now considered to be the best platforms for the dissemination of news articles. However, information sharing in social media platforms leads to explosion of fake news. Traditional detection methods were focusing on content analysis, while the current researchers examining social features of the news. In this work, we proposed a novel artificial intelligence (AI)-assisted fake news detection with deep natural language processing (NLP) model. The proposed work is characterized in four layers: publisher layer, social media networking layer, enabled edge layer, and cloud layer. In this work, four steps were carried out: 1) data acquisition; 2) information retrieval (IR); 3) NLP-based data processing and feature extraction; and 4) deep learning-based classification model that classifies news articles as fake or real using credibility score of publishers, users, messages, headlines, and so on. Three datasets, such as Buzzface, FakeNewsNet, and Twitter, were used for evaluation of the proposed model, and simulation results were computed. This proposed model obtained an average accuracy of 99.72% and an <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$F1$</tex-math> </inline-formula> score of 98.33%, which outperforms other existing methods.

A Framework of Real-Time Intelligent Transportation System Based on Hybrid Fog-Cloud Computing

Recent years have witnessed a mounting increase in the loss of lives and properties resulted from low-efficient and unsafe traffic management. However, traditional Intelligent Transportation System (ITS) cannot meet the strict requirements of transportation management on the efficiency, especially in terms of the latency. The emergence of fog computing provides a feasible solution to the aforementioned contradiction. In this paper, a framework of Real-time Intelligent Transportation System based on Hybrid Fog-cloud Computing System (RITS-HF) is proposed. All the components of RITS-HF, including the user layer, the fog layer, and the cloud layer in RITS-HF are presented clearly. Subsequently, main functions of RITS-HF are described, which are expected to greatly improve traffic efficiency and strengthen safety. In the meantime, the detailed processes and functions of each layer under different traffic configurations are presented. Finally, the challenges faced by RITS-HF are pointed out and analyzed in detail. RITS-HF is expected to play a vital role in improving the driving safety, increasing traffic efficiency, along with reducing energy consumption to some extent.

Spatio-temporal information analytics based performance-driven industrial process monitoring framework with cloud–edge-device collaboration

Smart manufacturing in the context of Industry 4.0 presents new characteristics, such as large-scale, complex sub-processes cooperation, and integrated information management, which makes comprehensive performance-driven process monitoring difficult to be achieved in practice. Cloud–edge-device collaboration brings new perspectives for modern industries. This paper presents a performance-driven process monitoring framework in cloud–edge-device collaborative architecture, including a spatio-temporal information analytics based method and its application to a hot strip mill process (HSMP). Firstly, an integrated performance-driven process monitoring framework is proposed, where different monitoring tasks are deployed in the device, edge, and cloud layers, respectively. Secondly, a new performance-related spatio-temporal information analytics based fault detection and localization method is developed. In detail, temporal kernel canonical correlation analysis (CCA) is employed to extract the canonical correlation features with nonlinearities in the temporal domains, and spatial CCA is designed to form the spatial features. Based on the spatio-temporal features, the equipment operation condition monitoring model is developed in the device layer for real-time fault localization, and the process monitoring model is deployed in the edge for fault detection. Furthermore, to give a high-level evaluation of the operating status of the process, a qualitative trend analysis based operation risks evaluation model is deployed in the cloud. Different from the traditional architectures, the data processing capability of each layer is fully utilized in the proposed one, and the function is relevant to the enterprise management system. It provides a topological basis with clear priorities for subsequent process regulation and optimization. Finally, an actual HSMP is introduced to testify the performance of the framework. From the results, the fault detection rates are improved in comparison with traditional methods, and the fault localization results are consistent with the actual situations.

Enhancing IoT systems through Cloud-Fog-Edge architectures challenges and opportunities

This article examines the deployment and implications of Cloud- Fog-Edge architectures in Internet of Things (IoT) systems, highlighting their significance in enhancing data management and system security across diverse sectors. As IoT ecosystems expand, the necessity for architectures that efficiently handle large volumes of data and ensure real-time processing capabilities becomes paramount. The Cloud-Fog-Edge architecture addresses these needs by distributing computing resources across three layers—cloud, fog, and edge—each optimized for specific tasks within the IoT workflow. We discuss the challenges and solutions associated with interoperability in such multi-layered systems, emphasizing the need for standardized communication protocols and data formats to facilitate seamless interactions between heterogeneous devices and platforms. Furthermore, the article delves into the critical aspects of security within these architectures, outlining strategies for robust data encryption, access management, regular security updates, and comprehensive network activity monitoring to safeguard against unauthorized access and cyber threats. The integration of Cloud-Fog-Edge architectures not only promises enhanced operational efficiency and scalability but also significantly boosts the adaptability of IoT systems to meet evolving technological and operational demands. By providing a detailed analysis of the functionalities, integration challenges, and security practices associated with each architectural layer, this article contributes to a deeper understanding of how Cloud-Fog-Edge frameworks can be optimized to bolster the reliability, efficiency, and security of modern IoT environments.

Cloud-based smart agriculture framework: Optimizing load balancing efficiency via integrated scheduling algorithm

The rise of smart agriculture has revolutionized farming through advanced technology, boosting efficiency, productivity, and sustainability. This study introduces a Cloud-Based Smart Agriculture Framework that optimizes load-balancing efficiency through integrated hybrid scheduling algorithms. The four-layer framework aims to transform agricultural practices by combining IoT, Edge, Cloud, and Network layers. The research explores the crucial roles of IoT and Cloud Computing in Agriculture, emphasizing their contributions to data acquisition, analysis, and management. Integrating GA+ IRR in the IoT layer enhances load balancing and system optimization, improving resource management and optimizing data processing in agricultural IoT devices.

Physico-chemical analysis and characterization of the flammability of metallic powders

When fine particles known as flammable powders are suspended in the air under specific circumstances, they have the potential to explode. There could be many serious fatalities, injuries, and structural damage as a result of a dust explosion. By being aware of the flammability parameters and the physico-chemical analysis for flammable powders and taking additional protective measures as a result of the occurrence of these kinds of catastrophic mixes, the risk of such incidents can be decreased. The goal of this study was to determine the flammability characteristics of two combustible powders, magnesium and aluminum powder. These characteristics included the lowest ignition temperature of the dust cloud and dust layer as well as certain physicochemical studies of the composition.