Cloud Area Research Articles

Shape feature-assisted discrimination of cloud and snow in GF-1 spectral images of mountainous areas

ABSTRACTIn this study, we propose a new method for cloud and snow discrimination, which is based on spectral, texture, and shape features and combined with support vector machine multi-classification strategy. A new feature called curvature histogram is designed to describe edge shape. First, the region of interest test is applied to extract cloud and snow area. Then, the extracted area is combined with the segmentation results using the mean shift algorithm to obtain the object of interest while calculating the feature values. The complexity of surfaces in the cloud and snow area is classified into four types, namely, thick cloud, thin cloud, snow, and snow-covered land, such that six kinds of classifiers are obtained by designing a classifier between every two categories. Through six classifiers and calculating the sum of confidence coefficients for each category, every superpixel is classified into the class with the highest confidence coefficient and a rough cloud and snow mask is obtained. Finally, the GrabCut algorithm is applied to optimize the classification results at the pixel level. The experiments on the images of China’s GF-1 satellite, indicate that the proposed method is effective for cloud and snow discrimination on multispectral high-resolution satellites images in mountainous area.

Three-dimensional optical observations of an upward lightning triggered by positive cloud-to-ground lightning

Based on dual-station optical records, electric field and radar data, a detailed analysis of an instance of upward lightning initiated from Canton Tower, in the city of Guangzhou, Guangdong Province, China, is presented. The upward lightning was triggered by a positive cloud-to-ground (PCG) flash nearby. The upward lightning channel and part of the horizontal PCG lightning channel obtained by dual-station optical systems were reconstructed in three dimensions, providing us with spatial channel information on how the PCG lightning process triggered the upward lightning from Canton Tower. The upward lightning occurred in the trailing stratiform cloud area behind a squall line system. The radar echo intensity in this area was relatively weak (30–40 dBZ), and the height of the strongest radar echo center was about 4 km. From the optical records, the two-dimensional length of the horizontal PCG lightning channel was longer than 17.2 km. The length of the three-dimensional reconstructed part of the PCG lightning channel was about 3660 m. Its height increased from 3410 m to 4170 m along the development direction, with an average height of 3640 m. Based on the above result, as well as previous studies on the charge structure of the stratiform region, we infer that the height of the positive charge layer was roughly consistent with the height of the strongest echo center.

Role of clouds in accelerating cold‐season warming during 2000–2015 over the Tibetan Plateau

With the global warming slowdown in the twenty‐first century, a huge discrepancy in regional climate warming has been identified over the main region of the Tibetan Plateau (TP). Compared with the +0.04 °C/decade warming from 1961 to 1999, the warming greatly accelerated for the period 2000–2015 at a rate of +0.30 °C/decade. During the same period, warming in the cold season (November to March) was more pronounced than in the warm season (May to September) over the TP. The results also indicated that the middle‐level cloud (middle cloud) decreased (−0.359%/year), while the high‐level cloud (high cloud) increased (+0.241%/year) over almost all the TP during the cold season. Further analysis showed positive net cloud radiative forcing over the western TP from 2000–2015, that is, a heating effect of clouds, especially in the cold season. Combining the trends of the increase in high cloud and the decrease in middle cloud over most parts of the TP, the decreased albedo effect of middle cloud and the increased longwave greenhouse effect of high cloud may have partially contributed to the sustained warming, especially in the cold season from 2000 to 2015. Meanwhile, the results showed that the warming rate and cloud area fraction changes were significantly amplified with elevation. The analysis based on a model of Coupled Model Intercomparison Project Phase 5 shows that the decreased middle cloud plays more important role than the increased high cloud in modulating the enhanced warming over the TP, especially in the cold season.

Cloud Area Distributions of Shallow Cumuli: A New Method for Ground-Based Images

We develop a new approach that resolves cloud area distributions of single-layer shallow cumuli from ground-based observations. Our simple and computationally inexpensive approach uses images obtained from a Total Sky Imager (TSI) and complementary information on cloud base height provided by lidar measurements to estimate cloud equivalent diameter (CED) over a wide range of cloud sizes (about 0.01–3.5 km) with high temporal resolution (30 s). We illustrate the feasibility of our approach by comparing the estimated CEDs with those derived from collocated and coincident high-resolution (0.03 km) Landsat cloud masks with different spatial and temporal patterns of cloud cover collected over the Atmospheric Radiation Measurement (ARM) Southern Great Plains (SGP) site. We demonstrate that (1) good (~7%) agreement between TSI and Landsat characteristic cloud size can be obtained for clouds that fall within the region of the sky observable by the TSI and (2) large clouds that extend beyond this region are responsible for noticeable (~16%) underestimation of the TSI characteristic cloud size. Our approach provides a previously unavailable dataset for process studies in the convective boundary layer and evaluation of shallow cumuli in cloud-resolving models.

Performance Evaluation of Unstructured PBRA for Bigdata with Cassandra and MongoDB in Cloud

In this article, performance evaluation of web collection data in data stores, such as NoSQL-Cassandra and MongoDB is presented, yielding scalability of applications. In addition to scalability, security of NoSQL databases remains highly unproved. It is noteworthy that existing works in the area of cloud with NoSQL focus on either scalability or security but not both aspects. Also, security, if provided, is at minor interface levels. In this article, the PBRA system is designed to deal with highly unstructured big data emerging from the twitter social networking service, which is new of its kind to strengthen the bigdata security. PBRA is Passphrase Based REST API model where the REST API methods are integrated with the user generated passphrase in addition to the private key for a set of records of user desirable number before storing into the Cassandra and MongoDB databases. Results are presented to illustrate the same for nearly 1 million records and the efficiency of Cassandra over MongoDB is observed. It is observed from the results that though the time taken to load and retrieve bulk data records is higher than dealing with cipher text, Cassandra performs better than MongoDB with the proposed security model.

USE OF INTELLEGENT AND BLOCK CHAIN TECHNOLOGIES IN INFORMATION MANAGEMENT Minsk Innovation University

The aim of this work is the analysis of methods, approaches, technologies, means of information management (IM), using both conventional technologies and adding new intellectual and block chain. Trends in the use of intelligent technologies in information management are given. The developments in the field of intelligent agents based on semantic web, web-services and semantic web-services, the use of cloud computing (CC) are shown. The main ideas of semantic technologies, in which a page of the semantic network contains information in two languages: the natural and special, understood only by intelligent software agents (IA) are discussed.The use of block chain technology for the control of various material and non-material assets are done. Technically, the block chain technology is another application layer on top of the stack of the Internet protocols and can be integrated with the semantic level. The use of intelligent technologies in the information management in the cloud area with the work of IA. The intelligent web (web 3.0), which became the next stage in the development of the Internet is discussed. It forms the semantics on the ontology dase, creating new opportunities for IA to perform various user requests. Analyzes the major developments in the field of intellectualization of IM, and the main tendencies of their development. The result was a list of criteria and their values, which must meet an intelligent system for IM.As trends in the development of IM is considered the improvement of models and methods of joint activity of IA in the cloud area using block chain technology. Three directions of development of intellectual control system are proposed. As the development of information management proposed the creation of an instrumental platform based on multi agent approach, integrating semantic and block chain technology in the cloud area.

Key based Access Control Policies to Solve Security in Cloud Data Sharing

Cloud achieves not just diverse levels of accommodation and proficiency issues albeit persistently advancing in such manner, additionally extraordinary difficulties in the field of information assurance. SaaS based distributed computing stockpiling suppliers, for example, google, send space have been there for calm at some point with the security viewpoint continually being disregarded. So we propose a cloud construction modeling that addresses the security perspective as for encryption, access control and with respect to risk assessment, key controls, and monitoring and reporting execution sign. We additionally broaden it with data transmission estimation plan which is another key execution pointer of mists. Contrasted with before methodologies this plan has a lesser calculation overhead and is considered cutting edge because of usage of every conceivable execution parameter of cloud area.

MESH MODELLING OF 3D POINT CLOUD FROM UAV IMAGES BY POINT CLASSIFICATION AND GEOMETRIC CONSTRAINTS

Abstract. The point cloud generated by multiple image matching is classified as an unstructured point cloud because it is not regularly point spaced and has multiple viewpoints. The surface reconstruction technique is used to generate mesh model using unstructured point clouds. In the surface reconstruction process, it is important to calculate correct surface normals. The point cloud extracted from multi images contains position and color information of point as well as geometric information of images used in the step of point cloud generation. Thus, the surface normal estimation based on the geometric constraints is possible. However, there is a possibility that a direction of the surface normal is incorrectly estimated by noisy vertical area of the point cloud. In this paper, we propose an improved method to estimate surface normals of the vertical points within an unstructured point cloud. The proposed method detects the vertical points, adjust their normal vectors by analyzing surface normals of nearest neighbors. As a result, we have found almost all vertical points through point type classification, detected the points with wrong normal vectors and corrected the direction of the normal vectors. We compared the quality of mesh models generated with corrected surface normals and uncorrected surface normals. Result of comparison showed that our method could correct wrong surface normal successfully of vertical points and improve the quality of the mesh model.

Special Issue on Large-Scale Point Cloud Processing

Recently, terrestrial laser scanners have been significantly improved in terms of accuracy, measurement distance, measurement speed, and resolution. They enable us to capture dense 3D point clouds of large-scale objects and fields, such as factories, engineering plants, large equipment, and transport ships. In addition, the mobile mapping system, which is a vehicle equipped with laser scanners and GPSs, can be used for capturing large-scale point clouds from a wide range of roads, buildings, and roadside objects. Large-scale point clouds are useful in a variety of applications, such as renovation and maintenance of facilities, engineering simulation, asset management, and 3D mapping. To realize these applications, new techniques must be developed for processing large-scale point clouds. So far, point processing has been studied mainly for relatively small objects in the field of computer-aided design and computer graphics. However, in recent years, the application areas of point clouds are not limited to conventional domains, but also include manufacturing, civil engineering, construction, transportation, forestry, and so on. This is because the state-of-the-art laser scanner can be used to represent large objects or fields as dense point clouds. We believe that discussing new techniques and applications related to large-scale point clouds beyond the boundaries of traditional academic fields is very important.This special issue addresses the latest research advances in large-scale point cloud processing. This covers a wide area of point processing, including shape reconstruction, geometry processing, object recognition, registration, visualization, and applications. The papers will help readers explore and share their knowledge and experience in technologies and development techniques.All papers were refereed through careful peer reviews. We would like to express our sincere appreciation to the authors for their submissions and to the reviewers for their invaluable efforts for ensuring the success of this special issue.

Research on FAE Cloud Image Processing Method Based on Background Subtraction and Region Growing

After the first initiation, the Fuel Air Explosive (FAE) cloud formed through fuel explosion dispersal and it will generate tremendous damaging power after being detonated the second time. As the damaging power is closely related to the determination of reinitiation time, it is of great significance to study the growth principle of FAE cloud by means of analyzing FAE cloud images. Combining with background subtraction and region growing, an improved region growing image processing method was proposed, in which the seeds of region growing abstracted through background subtraction method and the growing criterion was modified. With this method, the integrate area of cloud can be obtained for extracting geometric parameters. Experiments were carried out on both cloudy and sunny days, and image overlap score was used to quantitatively evaluate the accuracy of images segmentation. The result indicated that this image processing method has advantages of high precision and robustness. In addition, the computation burden is reduced significantly compared with traditional region growing method.

An Economical Analytical Equation for the Integrated Vertical Overlap of Cumulus and Stratus

AbstractBy extending the previously proposed heuristic parameterization, the author derived an analytical equation computing the overlap areas between the precipitation (or radiation) areas and the cloud areas in a cloud system consisting of cumulus and stratus. The new analytical equation is accurate and much more efficient than the previous heuristic equation, which suffers from the truncation error in association with the digitalization of the overlap areas. Global test simulations with the new analytical formula in an offline mode showed that the maximum cumulus overlap simulates more surface precipitation flux than the random cumulus overlap. On the other hand, the maximum stratus overlap simulates less surface precipitation flux than random stratus overlap, which is due to the increase in the evaporation rate of convective precipitation from the random to maximum stratus overlap. The independent precipitation approximation (IPA) marginally decreases the surface precipitation flux, implying that IPA works well with other parameterizations. In contrast to the net production rate of precipitation and surface precipitation flux that increase when the cumulus and stratus are maximally and randomly overlapped, respectively, the global mean net radiative cooling and longwave cloud radiative forcing (LWCF) increase when the cumulus and stratus are randomly overlapped. On the global average, the vertical cloud overlap exerts larger impacts on the precipitation flux than on the radiation flux. The radiation scheme taking the subgrid variability of water vapor between the cloud and clear portions into account substantially increases the global mean LWCF in tropical deep convection and midlatitude storm track regions.

A Study on Particle Swarm based Load Balancing Algorithms in Cloud Computing

Cloud computing is a new and innovative perspective for large scale parallel and distributed computing. The dependence of user or load on the cloud is growing enormously with the enlargement of new applications. Load balancing is a significant area of cloud computing environment which ensures that all connected devices or processors carry out same amount of work in equal time. With an aim to make cloud resources and services accessible to the cloud user easily and conveniently, different algorithms and models for load balancing in cloud computing is being developed. There are so many algorithms are available for proper load balancing but in this paper particle swarm based algorithm is focused that can balance the load in cloud computing so that resources are easily available for users. This paper aims to develop an efficient load balancing algorithm using particle swarm based to minimize performance parameters like make span, latency, total execution time.

Clouds, Circulation, and Climate Sensitivity in a Radiative‐Convective Equilibrium Channel Model

AbstractTropical cloud and circulation changes are large sources of uncertainty in future climate change. This problem owes partly to the scale separation between large‐scale tropical dynamics ( km) and convective dynamics ( km), which generally requires parameterizing convection in models that resolve large‐scale dynamics, or parameterizing (or omitting) large‐scale dynamics in models that permit convection. Here we discuss simulations of radiative‐convective equilibrium (RCE) across a wide range of surface temperatures in long‐channel geometry—where the domain size and resolution marginally resolve both large‐scale dynamics and convection. Self‐aggregation of convection in these simulations spontaneously produces realistic dynamical regimes of large‐scale vertical motion. The circulation weakens with surface warming but changes in the degree of self‐aggregation depend on the metric that is used; there is no obvious trend in aggregation with warming. Surface warming causes an upward shift and decrease in area of high clouds, and a sharp decline in midlevel clouds, but no systematic trend in low cloud cover. We introduce a method for approximate radiative kernel feedback analysis in RCE, and apply it to both simulations in long‐channel geometry and in a smaller square domain. The kernel‐corrected cloud feedback is positive but its magnitude varies across temperatures. Compared to simulations that do not have aggregation, there is a more negative net feedback due to the effects of aggregation on relative humidity and cloud cover. These results are consistent with the hypothesis that self‐aggregation moderately reduces climate sensitivity.

A Security Perspective on Adoption and Migration to Mobile Cloud Technology

Security is one of the main concerns of those who want to adopt and migrate to cloud computing technology. Security issues raised by cloud technology reveals that mobile cloud computing is raising the privacy and security issues such as identification and authentication issue, as sometimes the identity and the authentication of the owner of the device or the owner of the data contained in the cloud was not strictly remote. These are some examples that could be considered as major setbacks to the mobile cloud computing adaptation and the reason why some entities are still reluctant of embracing, adopting and migrating to this technology. This research reviews the phenomenon of mobile cloud computing, and the security and privacy issues intrinsic within the area of mobile application and cloud computing with more emphasis on the security and privacy considerations to embrace and migrate to Mobile Cloud Computing.

Retrieval of outgoing longwave radiation at top-of-atmosphere using Himawari-8 AHI data

This study proposes an algorithm to retrieve the outgoing longwave radiation at the top of the atmosphere (TOA OLR) using the advanced Himawari imager (AHI) unit that is a narrowband sensor installed in Himawari-8, a geostationary satellite providing window and water vapor imagery. The outgoing longwave radiation at the top of the atmosphere (TOA OLR) was retrieved by an empirical method where a radiative transfer model (RTM) was used to simulate various atmospheric conditions, such as the surface temperature, water vapor, and cloud characteristics (e.g., cloud optical thickness and cloud height). The results of an algorithm using a single channel (OLR12.4) and two channels (OLR6.2+12.4) were converted into Terra Cloud and Earth's Radiant Energy System (CERES) TOA OLR for the daytime (0105–0135 UTC) and nighttime (1325–1355 UTC) cases of a scene analysis (typhoon Goni on August 18, 2015). Long-term analysis (August 2015–July 2016) and statistical analysis (i.e., mean, bias, root mean square error (RMSE), and correlation coefficient) were conducted. The analysis results showed that the largest error was produced in the cloudy areas (overcast areas), and the minimum RMSE and a high correlation coefficient were observed over an ocean area. In the scene analysis, however, the daytime and nighttime average RMSE of the CERES TOA OLR and OLR6.2+12.4 was 12.21Wm−2 and showed the correlation coefficient of 0.971. It was an improved result over the RMSE (12.33Wm−2) and correlation coefficient (0.967) shown in the analysis of OLR12.4. In the long-term analysis, the average RMSE was 11.83Wm−2 and showed a correlation coefficient of 0.972 (OLR12.4 showed 12.17Wm−2 and 0.969). Furthermore, to minimize the difference in the cloud area that had the largest error, the study analyzed TOA OLR at the significance level of 95% (±2 RMSE). As a result, the scene analysis showed an RMSE of 10.54Wm−2 and correlation coefficient of 0.977, and the long-term data showed an RMSE of 10.23Wm−2 and correlation coefficient of 0.979.

A Heuristic Parameterization for the Integrated Vertical Overlap of Cumulus and Stratus

AbstractThe author developed a heuristic parameterization to handle the contrasting vertical overlap structures of cumulus and stratus in an integrated way. The parameterization assumes that cumulus is maximum‐randomly overlapped with adjacent cumulus; stratus is maximum‐randomly overlapped with adjacent stratus; and radiation and precipitation areas at each model interface are grouped into four categories, that is, convective, stratiform, mixed, and clear areas. For simplicity, thermodynamic scalars within individual portions of cloud, radiation, and precipitation areas are assumed to be internally homogeneous. The parameterization was implemented into the Seoul National University Atmosphere Model version 0 (SAM0) in an offline mode and tested over the globe. The offline control simulation reasonably reproduces the online surface precipitation flux and longwave cloud radiative forcing (LWCF). Although the cumulus fraction is much smaller than the stratus fraction, cumulus dominantly contributes to precipitation production in the tropics. For radiation, however, stratus is dominant. Compared with the maximum overlap, the random overlap of stratus produces stronger LWCF and, surprisingly, more precipitation flux due to less evaporation of convective precipitation. Compared with the maximum overlap, the random overlap of cumulus simulates stronger LWCF and weaker precipitation flux. Compared with the control simulation with separate cumulus and stratus, the simulation with a single‐merged cloud substantially enhances the LWCF in the tropical deep convection and midlatitude storm track regions. The process‐splitting treatment of convective and stratiform precipitation with an independent precipitation approximation (IPA) simulates weaker surface precipitation flux than the control simulation in the tropical region.

The Cloud Area Padovana: from pilot to production

The Cloud Area Padovana has been running for almost two years. This is an OpenStack-based scientific cloud, spread across two different sites: the INFN Padova Unit and the INFN Legnaro National Labs. The hardware resources have been scaled horizontally and vertically, by upgrading some hypervisors and by adding new ones: currently it provides about 1100 cores. Some in-house developments were also integrated in the OpenStack dashboard, such as a tool for user and project registrations with direct support for the INFN-AAI Identity Provider as a new option for the user authentication. In collaboration with the EU-funded Indigo DataCloud project, the integration with Docker-based containers has been experimented with and will be available in production soon. This computing facility now satisfies the computational and storage demands of more than 70 users affiliated with about 20 research projects.We present here the architecture of this Cloud infrastructure, the tools and procedures used to operate it. We also focus on the lessons learnt in these two years, describing the problems that were found and the corrective actions that had to be applied. We also discuss about the chosen strategy for upgrades, which combines the need to promptly integrate the OpenStack new developments, the demand to reduce the downtimes of the infrastructure, and the need to limit the effort requested for such updates. We also discuss how this Cloud infrastructure is being used. In particular we focus on two big physics experiments which are intensively exploiting this computing facility: CMS and SPES. CMS deployed on the cloud a complex computational infrastructure, composed of several user interfaces for job submission in the Grid environment/local batch queues or for interactive processes; this is fully integrated with the local Tier-2 facility. To avoid a static allocation of the resources, an elastic cluster, based on cernVM, has been configured: it allows to automatically create and delete virtual machines according to the user needs. SPES, using a client-server system called TraceWin, exploits INFN’s virtual resources performing a very large number of simulations on about a thousand nodes elastically managed.

Improved algorithm for image haze removal based on dark channel priority

An improved algorithm is proposed for image haze removal based on dark channel priority to avoid the color distortion of haze removing about sky, white cloud or bright areas. The improved algorithm is mainly reflected in the following two aspects. One is the refined atmospheric transmittance of the hazy images with non-sky obtained using guided filter. The other is that the atmospheric transmittance of the hazy images with the sky, white cloud or the bright areas is estimated by a variety of classified statistics for haze-free outdoor images with non-sky or the haze-free outdoor images with sky. Compared with He algorithm, the weakness of atmospheric transmission of the hazy images with the sky, white cloud or the bright areas is remedied. Experimental results show that the improved algorithm is efficient to image haze removal.

Comparative evaluation of commercial softwares in UAV imagery for cultural heritage recording: case study for traditional building in South Korea

In recent years, many studies have conducted to 3D modeling based on images because of developments in hardware such as computers, non-metric digital cameras, and UAV (drone), and improvements in the performance of image processing software. These studies have shown the possibility of 3D modelling using the digital camera for record, preservation of cultural heritage. The purpose of this paper is to estimate the results of three commercial software using vertical, oblique, terrestrial images of a traditional building, and evaluate the possibility of 3D modelling based on images. The three software are ContextCapture, PhotoScan, Pix4Dmapper, the processes of three software consist of bundle block adjustment, point cloud generation, and 3D model generation. In order to verify the accuracy and precision results of each software, this paper measured values of ground control points, check points for spatial accuracy, density in some area of point clouds, and distance between feature points on 3D models.

A SIMPLE CLOUD DETECTION METHOD FOR GF-1 WFV IMAGERY

Abstract. Identification of clouds in optical images is often a necessary step toward their use. However, aimed at the cloud detection methods used on GF-1 is relatively less. In order to meet the requirement of accurate cloud detection in GF-1 WFV imagery, a new method based on the combination of band operation and spatial texture feature (BOTF) is proposed in this paper. First of all, the BOTF algorithm minimize interference of highlight surface and cloud regions by the band operation, and then distinguish between cloud area and non-cloud area with spatial texture feature. Finally, the cloud mask can be acquired by threshold segmentation method. The method was validated using scenes. The results indicate that the BOTF performs well under normal conditions, and the average overall accuracy of BOTF cloud detection is better than 90 %. The proposed method can meet the needs of routine work.