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Ormel, Chris W., and Michiel Min. "ARCiS framework for exoplanet atmospheres." Astronomy & Astrophysics 622 (February 2019): A121. http://dx.doi.org/10.1051/0004-6361/201833678.
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Context. Understanding of clouds is instrumental in interpreting current and future spectroscopic observations of exoplanets. Modeling clouds consistently is complex, since it involves many facets of chemistry, nucleation theory, condensation physics, coagulation, and particle transport. Aims. We aim to develop a simple physical model for cloud formation and transport, efficient and versatile enough that it can be used, in modular fashion for parameter optimization searches of exoplanet atmosphere spectra. In this work we present the cloud model and investigate the dependence of key parameters as the cloud diffusivity K and the nuclei injection rate Σn. on the planet’s observational characteristics. Methods. The transport equations are formulated in 1D, accounting for sedimentation and diffusion. The grain size is obtained through a moment method. For simplicity, only one cloud species is considered and the nucleation rate is parametrized. From the resulting physical profiles we simulate transmission spectra covering the visual to mid-IR wavelength range. Results. We apply our models toward KCl clouds in the atmosphere of GJ1214 b and toward MgSiO3 clouds of a canonical hot-Jupiter. We find that larger K increases the thickness of the cloud, pushing the τ = 1 surface to a lower pressure layer higher in the atmosphere. A larger nucleation rate also increases the cloud thickness while it suppresses the grain size. Coagulation is most important at high Σn. and low K. We find that the investigated combinations of K and Σn. greatly affect the transmission spectra in terms of the slope at near-IR wavelength (a proxy for grain size), the molecular features seen at approximately μm (which disappear for thick clouds, high in the atmosphere), and the 10 μm silicate feature, which becomes prominent for small grains high in the atmosphere. Conclusions. Clouds have a major impact on the atmospheric characteristics of hot-Jupiters, and models as those presented here are necessary to reveal the underlying properties of exoplanet atmospheres. The result of our hybrid approach – aimed to provide a good balance between physical consistency and computational efficiency – is ideal toward interpreting (future) spectroscopic observations of exoplanets.
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Chang, Victor, Yen-Hung Kuo, and Muthu Ramachandran. "Cloud computing adoption framework: A security framework for business clouds." Future Generation Computer Systems 57 (April 2016): 24–41. http://dx.doi.org/10.1016/j.future.2015.09.031.
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Zhao, X., Z. Gao, W. Sun, and F. Wen. "A COARSE-TO-FINE BAND REGISTRATION FRAMEWORK FOR MULTI/HYPERSPECTRAL REMOTE SENSING IMAGES CONSIDERING CLOUD INFLUENCE." ISPRS Annals of Photogrammetry, Remote Sensing and Spatial Information Sciences V-3-2020 (August 3, 2020): 201–8. http://dx.doi.org/10.5194/isprs-annals-v-3-2020-201-2020.
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Abstract. Band registration is one of the most critical steps in the production of multi/hyperspectral images and determines the accuracy of applications directly. Because of the characteristics of imaging devices in some multi/hyperspectral satellites, there may be a time difference between bands during push-broom imaging, which leads to displacements of moving clouds with respect to the ground. And a large number of feature points may gather around cloud contours due to the high contrast and rich texture, resulting in building a transformation more suitable for moving clouds and making ground objects ghosted and blurred. This brings a big challenge for registration methods based on feature extraction and matching. In this paper, we propose a novel coarse-to-fine band registration framework for multi/hyperspectral images containing moving clouds. In the coarse registration stage, a cloud mask is generated by grayscale stretching, morphology and other operations. Based on this mask, a coarse matching of cloud-free regions is performed to eliminate large misalignment between bands. In the refinement stage, low-rank analysis and RASL (Robust Alignment by Sparse and Low-rank decomposition) are used to optimize the rank of coarse results to achieve fine registration between bands. After experiments on a total of 102 images (83 cloudy images and 19 cloud-free images with all 32 bands) from Zhuhai-1 hyperspectral satellite, our method can achieve a registration accuracy of 0.6 pixels in cloudy images, 0.41 pixels in cloud-free images, which is enough for subsequent applications.
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Aazam, Mohammad, and Eui-Nam Huh. "Framework of Resource Management for Intercloud Computing." Mathematical Problems in Engineering 2014 (2014): 1–9. http://dx.doi.org/10.1155/2014/108286.
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There has been a very rapid increase in digital media content, due to which media cloud is gaining importance. Cloud computing paradigm provides management of resources and helps create extended portfolio of services. Through cloud computing, not only are services managed more efficiently, but also service discovery is made possible. To handle rapid increase in the content, media cloud plays a very vital role. But it is not possible for standalone clouds to handle everything with the increasing user demands. For scalability and better service provisioning, at times, clouds have to communicate with other clouds and share their resources. This scenario is called Intercloud computing or cloud federation. The study on Intercloud computing is still in its start. Resource management is one of the key concerns to be addressed in Intercloud computing. Already done studies discuss this issue only in a trivial and simplistic way. In this study, we present a resource management model, keeping in view different types of services, different customer types, customer characteristic, pricing, and refunding. The presented framework was implemented using Java and NetBeans 8.0 and evaluated using CloudSim 3.0.3 toolkit. Presented results and their discussion validate our model and its efficiency.
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Vijaya, Aparna, and Neelanarayanan V. "A Model Driven Framework for Portable Cloud Services." International Journal of Electrical and Computer Engineering (IJECE) 6, no. 2 (April 1, 2016): 708. http://dx.doi.org/10.11591/ijece.v6i2.8270.
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<p class="Abstract">Cloud Computing is an evolving technology as it offers significant benefits like pay only for what you use, scale the resources according to the needs and less in-house staff and resources. These benefits have resulted in tremendous increase in the number of applications and services hosted in the cloud which inturn has resulted in increase in the number of cloud providers in the market. Cloud service providers have a lot of heterogeneity in the resources they use. They have their own servers, different cloud infrastructures, API’s and methods to access the cloud resources. Despite its benefits; lack of standards among service providers has caused a high level of vendor lock-in when a software developer tries to change its cloud provider. In this paper we give an overview on the ongoing and current trends in the area of cloud service portability and we also propose a new cloud portability platform. Our new platform is based on establishing feature models which offers the desired cloud portability. Our solution DSkyL uses feature models and domain model analysis to support development, customization and deployment of application components across multiple clouds. The main goal of our approach is to reduce the effort and time needed for porting applications across different clouds. This paper aims to give an overview on DSkyL.</p>
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Vijaya, Aparna, and Neelanarayanan V. "A Model Driven Framework for Portable Cloud Services." International Journal of Electrical and Computer Engineering (IJECE) 6, no. 2 (April 1, 2016): 708. http://dx.doi.org/10.11591/ijece.v6i2.pp708-716.
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<p class="Abstract">Cloud Computing is an evolving technology as it offers significant benefits like pay only for what you use, scale the resources according to the needs and less in-house staff and resources. These benefits have resulted in tremendous increase in the number of applications and services hosted in the cloud which inturn has resulted in increase in the number of cloud providers in the market. Cloud service providers have a lot of heterogeneity in the resources they use. They have their own servers, different cloud infrastructures, API’s and methods to access the cloud resources. Despite its benefits; lack of standards among service providers has caused a high level of vendor lock-in when a software developer tries to change its cloud provider. In this paper we give an overview on the ongoing and current trends in the area of cloud service portability and we also propose a new cloud portability platform. Our new platform is based on establishing feature models which offers the desired cloud portability. Our solution DSkyL uses feature models and domain model analysis to support development, customization and deployment of application components across multiple clouds. The main goal of our approach is to reduce the effort and time needed for porting applications across different clouds. This paper aims to give an overview on DSkyL.</p>
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Patil, Raj Kumar, and Rehaman Pasha. "A Framework for Cloud Information Accountability." International Journal of Scientific Research 2, no. 10 (June 1, 2012): 1–4. http://dx.doi.org/10.15373/22778179/oct2013/51.
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Zbořil, Martin, and Vlasta Svatá. "Cloud Adoption Framework." Procedia Computer Science 207 (2022): 483–93. http://dx.doi.org/10.1016/j.procs.2022.09.103.
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Harshvardhan, Guang Guo, Robert N. Green, Zheng Qu, and Takashi Y. Nakajima. "Remotely Sensed Microphysical and Thermodynamic Properties of Nonuniform Water Cloud Fields." Journal of the Atmospheric Sciences 61, no. 21 (November 1, 2004): 2574–87. http://dx.doi.org/10.1175/jas3301.1.
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Abstract Visible and near-infrared reflected radiances have been used to estimate the cloud optical depth and effective radius of cloud-filled global area coverage (GAC) pixels from the Advanced Very High Resolution Radiometer (AVHRR) for two cases in the North Atlantic Ocean. One is representative of clouds having low concentrations of cloud condensation nuclei (CCN), while the other is an example of maritime clouds forming in continental air, in this case, intruding from Europe around a cutoff low pressure system. It is shown that an estimate of the cloud drop concentration can be obtained from remotely sensed cloud radiative properties and standard meteorological analyses. These concentrations show very clearly the influence of enhanced CCN on cloud microphysics. However, conclusions regarding the indirect radiative effect of aerosol on cloud must wait for the development of a framework for analyzing changes in cloud liquid water path (LWP). It is shown that estimates of LWP are greatly influenced by the scheme that is used to identify cloudy pixels at the AVHRR GAC resolution. Application of a very strict thermal channel spatial coherence criterion for identifying cloud-filled pixels yields mean LWP estimates for cloudy pixels alone that are 40%–75% higher than mean LWP estimates for the much larger sample of possibly cloudy pixels identified by a reflectance threshold criterion.
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Sharma, Puneet, Peter Dalin, and Ingrid Mann. "Towards a Framework for Noctilucent Cloud Analysis." Remote Sensing 11, no. 23 (November 22, 2019): 2743. http://dx.doi.org/10.3390/rs11232743.
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In this paper, we present a framework to study the spatial structure of noctilucent clouds formed by ice particles in the upper atmosphere at mid and high latitudes during summer. We studied noctilucent cloud activity in optical images taken from three different locations and under different atmospheric conditions. In order to identify and distinguish noctilucent cloud activity from other objects in the scene, we employed linear discriminant analysis (LDA) with feature vectors ranging from simple metrics to higher-order local autocorrelation (HLAC), and histogram of oriented gradients (HOG). Finally, we propose a convolutional neural networks (CNN)-based method for the detection of noctilucent clouds. The results clearly indicate that the CNN-based approach outperforms the LDA-based methods used in this article. Furthermore, we outline suggestions for future research directions to establish a framework that can be used for synchronizing the optical observations from ground-based camera systems with echoes measured with radar systems like EISCAT in order to obtain independent additional information on the ice clouds.
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K., Arul Jothy, Sivakumar K., and Delsey M.J. "Distributed System Framework for Mobile Cloud Computing." Bonfring International Journal of Research in Communication Engineering 8, no. 1 (February 28, 2018): 05–09. http://dx.doi.org/10.9756/bijrce.8357.
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Sun, Rui Qi, Jie Yang, Zhan Gao, and Zhi Qiang He. "A Framework of Automatic Deployment for Cloud Management Platforms." Applied Mechanics and Materials 631-632 (September 2014): 218–21. http://dx.doi.org/10.4028/www.scientific.net/amm.631-632.218.
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With the widespread adoption of Cloud infrastructure, there are many famous open source Cloud management platforms, but it is not a trivial task to build Infrastructure as a Service Clouds using them. Moreover, during the developing and testing phase, Cloud platforms need to be built frequently. Existing automatic deployment tools require users to have programming skills using high-level programming language. However, many Cloud administrators are not related programming experience. Therefore, they must repeatedly manually deploy the Cloud platforms step by step. In this paper, we propose a visualized framework of automatic deployment system for Cloud management platforms. It allows users to design templates of Cloud management platforms through a Web based interface without high-level programming, and it can automatically deploy Cloud platforms from templates. The experimental results show that a person without high-level programming experience can design a template of CloudStack system with load balance and high availability in less than 30 minutes, and the template can be used to deploy real world CloudStack systems repeatedly automatically.
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Achar, Sandesh. "Network Security Framework, Techniques, and Design for Hybrid Cloud." Engineering International 9, no. 2 (December 31, 2021): 141–52. http://dx.doi.org/10.18034/ei.v9i2.642.
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Network security is a framework that deals with issuing procedures and policies that will be used to establish and maintain security protocols in a particular organization. The functions related to the network security framework are oriented toward insulating the specific organization from external cyber threats and adversaries. On the other hand, a hybrid cloud is a type of cloud whose function is to allow the running and operating of different applications in various and different environments. The primary technique associated with developing hybrid clouds is the conjunctions between private and public clouds that will allow application portability and management for better and more efficient working of the clouds.
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Mahdavi-Hezavehi, S., Y. Alimardani, and R. Rahmani. "An Efficient Framework for a Third Party Auditor in Cloud Computing Environments." Computer Journal 63, no. 9 (May 13, 2019): 1285–97. http://dx.doi.org/10.1093/comjnl/bxz045.
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Abstract Cloud Service Providers supply services to clients in terms of their demands. They need to be constantly under monitoring for their services with respect to consensus agreements between clients and service providers. A Third Party Auditor or TPA as a trusted organization appears to be necessary to monitor executing agreements of cloud services. Using a third party as an extra component creates cost overheads for clients in a cloud environment. Thus, introducing a cost efficient framework for a cloud environment which includes a third party is an eminent achievement to make a TPA feasible and practical in cloud environments. In this paper, we propose a TPA framework for monitoring service level agreements between cloud service providers and cloud clients using several cloud resources. This framework employs different types of service deployments from various cloud service providers excluding the cloud service provider which is being monitored. Then, we demonstrate that the framework can mitigate costs of a third party auditor in a cloud environment. Simulations of trends for costs exhibits cost efficiency of at least forty percent over ten years when a TPA follows our proposed framework in comparison to other frameworks. Finally, we provide an analysis to compare characteristics of our framework with other frameworks and discuss the advantages of our proposed framework. Our results indicate that TPA as a component of the framework not only reduces overall costs of its presentation in a cloud environment but additionally improves management efficiency and security.
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Yao, Yulin. "Cloud Computing." International Journal of Organizational and Collective Intelligence 5, no. 3 (July 2015): 32–43. http://dx.doi.org/10.4018/ijoci.2015070103.
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This paper provides an overview of Cloud Computing between Year 2009 and Year 2015. It presents the past and current literature and practices for Cloud Computing. Literatrue review is undertaken to identify key areas relevant to current Cloud Computing development. Review on frameworks for Cloud computing has also been presented to illustrate the good use of structured and valid framework approaches. Topics of discussion demonstrate that Cloud computing can provide added values not only to businesses but also Higher Education. Future directions and conclusion have been presented and acknowledged, including the development of a new framework. Cloud Computing is concluded to provide better services and integrations with other emerging technologies such as Big Data.
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Szczap, F., Y. Gour, T. Fauchez, C. Cornet, T. Faure, O. Jourdan, and P. Dubuisson. "A flexible three-dimensional stratocumulus, cumulus and cirrus cloud generator (3DCLOUD) based on drastically simplified atmospheric equations and Fourier transform framework." Geoscientific Model Development Discussions 7, no. 1 (January 14, 2014): 295–337. http://dx.doi.org/10.5194/gmdd-7-295-2014.
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Abstract. The 3DCLOUD algorithm for generating stochastic three-dimensional (3-D) cloud fields is described in this paper. The generated outputs are 3-D optical depth (τ) for stratocumulus and cumulus fields and 3-D ice water content (IWC) for cirrus clouds. This model is designed to generate cloud fields that share some statistical properties observed in real clouds such as the inhomogeneity parameter ρ (standard deviation normalized by the mean of the studied quantity), the Fourier spectral slope β close to −5/3 between the smallest scale of the simulation to the outer Lout (where the spectrum becomes flat). Firstly, 3DCLOUD assimilates meteorological profiles (humidity, pressure, temperature and wind velocity). The cloud coverage C, defined by the user, can also be assimilated, but only for stratocumulus and cumulus regime. 3DCLOUD solves drastically simplified basic atmospheric equations, in order to simulate 3-D cloud structures of liquid or ice water content. Secondly, Fourier filtering method is used to constrain intensity of ρ, β, Lout and mean of τ or IWC of these 3-D cloud structures. 3DCLOUD model was developed to run on a personnel computer under Matlab environment with the Matlab statistics toolbox. It is used to study 3-D interactions between cloudy atmosphere and radiation.
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Bathini, Ravinder, and Naresh Vurukonda. "A survey to build framework for optimize and secure migration and transmission of cloud data." Bulletin of Electrical Engineering and Informatics 13, no. 2 (April 1, 2024): 812–20. http://dx.doi.org/10.11591/eei.v13i2.5181.
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In the recent era of computational technologies, the internet is needed daily. The data generated is enormous and primarily stored on dedicated servers or clouds. Data migration and transfer are significant tasks for maintaining consistency and updating data. The data is the most critical component in any cloud service. There are various methods to protect data, like secure transfer, encryption, and authentication. These techniques are used as per need and transmission of the data. As data grows on a server or cloud, it must be migrated securely. Here, the exhaustive survey is provided for building a framework for migrating and transmitting cloud data. The framework should be sustainable and adaptable for load-balancing recovery and secure transmission. Various security load balancing parameters must be considered to obtain these state-of-the-art functionalities in the framework. The existing similar frameworks are studied, and findings are proposed in the paper to develop the framework.
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Liu, K., and J. Boehm. "A New Framework For Interactive Segmentation of Point Clouds." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XL-5 (June 6, 2014): 357–62. http://dx.doi.org/10.5194/isprsarchives-xl-5-357-2014.
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Point cloud segmentation is a fundamental problem in point processing. Segmenting a point cloud fully automatically is very challenging due to the property of point cloud as well as different requirements of distinct users. In this paper, an interactive segmentation method for point clouds is proposed. Only two strokes need to be drawn intuitively to indicate the target object and the background respectively. The draw strokes are sparse and don't necessarily cover the whole object. Given the strokes, a weighted graph is built and the segmentation is formulated as a minimization problem. The problem is solved efficiently by using the Max Flow Min Cut algorithm. In the experiments, the mobile mapping data of a city area is utilized. The resulting segmentations demonstrate the efficiency of the method that can be potentially applied for general point clouds.
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Szczap, F., Y. Gour, T. Fauchez, C. Cornet, T. Faure, O. Jourdan, G. Penide, and P. Dubuisson. "A flexible three-dimensional stratocumulus, cumulus and cirrus cloud generator (3DCLOUD) based on drastically simplified atmospheric equations and the Fourier transform framework." Geoscientific Model Development 7, no. 4 (August 25, 2014): 1779–801. http://dx.doi.org/10.5194/gmd-7-1779-2014.
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Abstract. The 3DCLOUD algorithm for generating stochastic three-dimensional (3-D) cloud fields is described in this paper. The generated outputs are 3-D optical depth (τ) for stratocumulus and cumulus fields and 3-D ice water content (IWC) for cirrus clouds. This model is designed to generate cloud fields that share some statistical properties observed in real clouds such as the inhomogeneity parameter ρ (standard deviation normalized by the mean of the studied quantity), the Fourier spectral slope β close to −5/3 between the smallest scale of the simulation to the outer Lout (where the spectrum becomes flat). Firstly, 3DCLOUD assimilates meteorological profiles (humidity, pressure, temperature and wind velocity). The cloud coverage C, defined by the user, can also be assimilated, but only for stratocumulus and cumulus regime. 3DCLOUD solves drastically simplified basic atmospheric equations, in order to simulate 3-D cloud structures of liquid or ice water content. Secondly, the Fourier filtering method is used to constrain the intensity of ρ, β, Lout and the mean of τ or IWC of these 3-D cloud structures. The 3DCLOUD model was developed to run on a personal computer under Matlab environment with the Matlab statistics toolbox. It is used to study 3-D interactions between cloudy atmosphere and radiation.
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Ravinder, Rao, and V. Sucharita. "A secure cloud service deployment framework for DevOps." Indonesian Journal of Electrical Engineering and Computer Science 21, no. 2 (February 1, 2021): 874. http://dx.doi.org/10.11591/ijeecs.v21.i2.pp874-885.
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<span>The advancements in cloud computing and leveraging the benefits from cloud computing to the service providers have increased the deployment of traditional applications to the cloud. The applications once deployed on the cloud, due to various reasons, need migration from development infrastructure to operational infrastructure, one operational instance to other operational instances due to load balancing and the cycle continues due to the use of DevOps as development strategies for cloud computing applications. Advocates of hybrid and public clouds observe cloud computing makes it possible for organizations to avert or minimize upfront IT infrastructure expenses. Proponents also assert that cloud computing systems permit businesses to receive their software up and running faster, using improved manageability and less maintenance, so it empowers IT teams to rapidly adapt tools to meet the varying and unpredictable requirements. DevOps is a lot of practices that mechanizes the procedures between programming improvement and IT groups, all together that they can fabricate, test, and discharge programming quicker and even more dependably. The idea of DevOps is established on building a culture of a joint effort between groups that generally worked in relative siloes. The guaranteed advantages incorporate expanded trust, quicker programming discharges, capacity to explain basic issues rapidly and better oversee impromptu work. Thus, this work identifies the need for providing multiple security protocols during the complete life cycle of cloud application development and deployment. This work proposes a novel framework for automatic selection and deployment of the security protocols during cloud service deployments. The framework identifies the need for security aspects and selects the appropriate security algorithms for virtual machines. The proposed framework demonstrates nearly 80% improvement over the security policy deployment time. </span>
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Schmidt, Clinton T., and Timothy J. Garrett. "A Simple Framework for the Dynamic Response of Cirrus Clouds to Local Diabatic Radiative Heating." Journal of the Atmospheric Sciences 70, no. 5 (April 23, 2013): 1409–22. http://dx.doi.org/10.1175/jas-d-12-056.1.
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Abstract This paper presents a simple analytical framework for the dynamic response of cirrus to a local radiative flux convergence, expressible in terms of three independent modes of cloud evolution. Horizontally narrow and tenuous clouds within a stable environment adjust to radiative heating by ascending gradually across isentropes while spreading sufficiently fast that isentropic surfaces stay nearly flat. Alternatively, optically dense clouds experience very concentrated heating, and if they are also very broad, they develop a convecting mixed layer. Along-isentropic spreading still occurs, but in the form of turbulent density currents rather than laminar flows. A third adjustment mode relates to evaporation, which erodes cloudy air as it lofts, regardless of its optical density. The dominant mode is determined from two dimensionless numbers, whose predictive power is shown in comparisons with high-resolution numerical cloud simulations. The power and simplicity of the approach hints that fast, subgrid-scale radiative–dynamic atmospheric interactions might be efficiently parameterized within slower, coarse-grid climate models.
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Yuan, T. "Cloud macroscopic organization: order emerging from randomness." Atmospheric Chemistry and Physics 11, no. 15 (August 1, 2011): 7483–90. http://dx.doi.org/10.5194/acp-11-7483-2011.
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Abstract. Clouds play a central role in many aspects of the climate system and their forms and shapes are remarkably diverse. Appropriate representation of clouds in climate models is a major challenge because cloud processes span at least eight orders of magnitude in spatial scales. Here we show that there exists order in cloud size distribution of low-level clouds, and that it follows a power-law distribution with exponent γ close to 2. γ is insensitive to yearly variations in environmental conditions, but has regional variations and land-ocean contrasts. More importantly, we demonstrate this self-organizing behavior of clouds emerges naturally from a complex network model with simple, physical organizing principles: random clumping and merging. We also demonstrate symmetry between clear and cloudy skies in terms of macroscopic organization because of similar fundamental underlying organizing principles. The order in the apparently complex cloud-clear field thus has its root in random local interactions. Studying cloud organization with complex network models is an attractive new approach that has wide applications in climate science. We also propose a concept of cloud statistic mechanics approach. This approach is fully complementary to deterministic models, and the two approaches provide a powerful framework to meet the challenge of representing clouds in our climate models when working in tandem.
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Khan, Noreen, Shahab Haider, Thar Baker, Ghulam Abbas, and Ziaul Haq Abbas. "MACRS: An Enhanced Directory-Based Resource Sharing Framework for Mobile Ad Hoc Networks." Electronics 11, no. 5 (February 26, 2022): 725. http://dx.doi.org/10.3390/electronics11050725.
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Recent technological developments have caused a rapid increase in the use of portable devices around the globe. However, these devices comprise limited processing resources that restrict their performance. To overcome this issue, the existing literature provides several frameworks that enable resource sharing through ad hoc clouds. However, these frameworks lack the ability to cater to the omni-directional movements of devices, which adversely affects the cloud stability, thereby, restricting the resource sharing process. To this end, this paper proposes a novel framework, namely Mobility-aware Ad hoc Cloud-based Resource Sharing (MACRS), which aims to enhance resource sharing among devices. To achieve this aim, MACRS proposes a new mobility-aware clustering algorithm that improves cloud stability. Moreover, the proposed framework prevents unfair resource exploitation and introduces an enhanced technique to handle emergency tasks. Furthermore, we employed event-triggered energy valuations’ synchronization, instead of periodic updates, which minimizes network congestion, hence staving off bandwidth wastage. Additionally, MACRS proposes to maintain the local directory at each node, instead of the cellular service provider, to reduce end-to-end delay during energy valuations’ verification and to minimize the overall execution time of tasks. The simulation results demonstrated that MACRS provides considerably improved cloud stability and resource sharing in comparison with eminent frameworks.
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Shuang, Liang. "The Design and Realization of Cloud Computing Framework Model Based on SOA." Advanced Materials Research 171-172 (December 2010): 696–701. http://dx.doi.org/10.4028/www.scientific.net/amr.171-172.696.
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Cloud computing is a system which provide hardware services, infrastructure services, platform services, software services, storage services to a variety of Internet applications, and SOA is a component model, it will rely on well-defined interfaces between services and linked contract applications. In this paper, combined cloud computing and SOA together closely to form a cloud computing framework model based on SOA, So that the clouds provide in a simple and flexible way to offer services. And it gives a realization of the model. Experiments show that the model is simple, practical and fully reflects the cloud computing and service-oriented architecture advantages.
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Jones, William K., Matthew W. Christensen, and Philip Stier. "A semi-Lagrangian method for detecting and tracking deep convective clouds in geostationary satellite observations." Atmospheric Measurement Techniques 16, no. 4 (March 2, 2023): 1043–59. http://dx.doi.org/10.5194/amt-16-1043-2023.
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Abstract. Automated methods for the detection and tracking of deep convective clouds in geostationary satellite imagery have a vital role in both the forecasting of severe storms and research into their behaviour. Studying the interactions and feedbacks between multiple deep convective clouds (DCC), however, poses a challenge for existing algorithms due to the necessary compromise between false detection and missed detection errors. We utilise an optical flow method to determine the motion of deep convective clouds in GOES-16 ABI imagery in order to construct a semi-Lagrangian framework for the motion of the cloud field, independently of the detection and tracking of cloud objects. The semi-Lagrangian framework allows severe storms to be simultaneously detected and tracked in both spatial and temporal dimensions. For the purpose of this framework we have developed a novel Lagrangian convolution method and a number of novel implementations of morphological image operations that account for the motion of observed objects. These novel methods allow the accurate extension of computer vision techniques to the temporal domain for moving objects such as DCCs. By combining this framework with existing methods for detecting DCCs (including detection of growing cores through cloud top cooling and detection of anvil clouds using brightness temperature), we show that the novel framework enables reductions in errors due to both false and missed detections compared to any of the individual methods, reducing the need to compromise when compared with existing frameworks. The novel framework enables the continuous tracking of anvil clouds associated with detected deep convection after convective activity has stopped, enabling the study of the entire life cycle of DCCs and their associated anvils. Furthermore, we expect this framework to be applicable to a wide range of cases including the detection and tracking of low-level clouds and other atmospheric phenomena. In addition, this framework may be used to combine observations from multiple sources, including satellite observations, weather radar and reanalysis model data.
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Yuan, T. "Cloud macroscopic organization: order emerging from randomness." Atmospheric Chemistry and Physics Discussions 11, no. 1 (January 17, 2011): 1105–19. http://dx.doi.org/10.5194/acpd-11-1105-2011.
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Abstract. Clouds play a central role in many aspects of the climate system and their forms and shapes are remarkably diverse. Appropriate representation of clouds in climate models is a major challenge because cloud processes span at least eight orders of magnitude in spatial scales. Here we show that there exists order in cloud size distribution of low-level clouds and it follows a power-law distribution with exponent γ close to 2. γ is insensitive to yearly variations in environmental conditions, but has regional variations and land-ocean contrasts. More importantly, we demonstrate this self-organizing behavior of clouds emerges naturally from a complex network model with simple, physical organizing principles: random clumping and merging. We also show clear-cloudy sky symmetry in terms of macroscopic organization because of similar fundamental underlying organizing principles. The order in the apparently complex cloud-clear field thus has its root in random simple interactions. Studying cloud organization with complex network models is an attractive new approach that has wide applications in climate science. This approach is fully complementary to deterministic models and the two approaches provide a powerful framework to meet the challenge of representing clouds in our climate models when working in tandem.
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Saadatdoost, Robab, Alex Tze Hiang Sim, Hosein Jafarkarimi, Jee Mei Hee, and Leila Saadatdoost. "Cloud Computing for Teaching Practice." International Journal of Web-Based Learning and Teaching Technologies 9, no. 4 (October 2014): 50–68. http://dx.doi.org/10.4018/ijwltt.2014100104.
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Recently researchers have shown an increased interest in cloud computing technology. It is becoming increasingly difficult to ignore cloud computing technology in education context. However rapid changes in information technology are having a serious effect on teaching framework designs. So far, however, there has been little discussion about cloud computing benefits in domains of teaching frameworks which propels us to study and redesign teaching frameworks considering cloud computing. The purpose of this paper is to review recent research into cloud computing and features which can be improved with this new technology. This paper studied several researches through literature to determine the main impact of cloud computing on “planning and preparation” and “instruction” domains as two main domains of teaching framework. Light will be then shed on the impact and potential benefits of cloud computing on teaching framework. The paper closes by proposing to design an evaluation table based on cloud computing artifacts to enhance teaching practice and highlights its offerings for educational institutions.
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Rosenfeld, D., E. Williams, M. O. Andreae, E. Freud, U. Pöschl, and N. O. Rennó. "The scientific basis for a satellite mission to retrieve CCN concentrations and their impacts on convective clouds." Atmospheric Measurement Techniques 5, no. 8 (August 23, 2012): 2039–55. http://dx.doi.org/10.5194/amt-5-2039-2012.
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Abstract. The cloud-mediated aerosol radiative forcing is widely recognized as the main source of uncertainty in our knowledge of the anthropogenic forcing on climate. The current challenges for improving our understanding are (1) global measurements of cloud condensation nuclei (CCN) in the cloudy boundary layer from space, and (2) disentangling the effects of aerosols from the thermodynamic and meteorological effects on the clouds. Here, we present a new conceptual framework to help us overcome these two challenges, using relatively simple passive satellite measurements in the visible and infared (IR). The idea is to use the clouds themselves as natural CCN chambers by retrieving simultaneously the number of activated aerosols at cloud base, Na, and the cloud base updraft speed. The Na is obtained by analyzing the distribution of cloud drop effective radius in convective elements as a function of distance above cloud base. The cloud base updraft velocities are estimated by double stereoscopic viewing and tracking of the evolution of cloud surface features just above cloud base. In order to resolve the vertical dimension of the clouds, the field of view will be 100 m for the microphysical retrievals, and 50 m for the stereoscopic measurements. The viewing geometry will be eastward and 30 degrees off nadir, with the Sun in the back at 30 degrees off zenith westward, requiring a Sun-synchronous orbit at 14 LST. Measuring simultaneously the thermodynamic environment, the vertical motions of the clouds, their microstructure and the CCN concentration will allow separating the dynamics from the CCN effects. This concept is being applied in the proposed satellite mission named Clouds, Hazards and Aerosols Survey for Earth Researchers (CHASER).
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Koushik S. and Annapurna P. Patil. "An Approach to Ensure Secure Inter-Cloud Data and Application Migration Using End-to-End Encryption and Content Verification." International Journal of Ambient Computing and Intelligence 13, no. 1 (January 2022): 1–21. http://dx.doi.org/10.4018/ijaci.293148.
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Cloud Computing is one of the most popular platforms in recent times and has many services to offer. The resources are deployed on the Cloud and are made available to cloud users over high-speed internet connectivity. Many enterprises think of migrating the data or application hosted from one Cloud to another based on the requirements. Migration from one Cloud to another Cloud requires security as it is vital for any data. This article presents a novel secure framework called ‘InterCloudFramework,’ considering well-established criteria to migrate various services across clouds with minimal supervision and interruption. Security is the primary concern to migrate the data among inter-clouds. The study incorporates the Elliptical-Curve Diffie-Hellman algorithm to encrypt the data and Merkle Hash Trees to check the integrity of the data. In addition to security during migration, the framework reduces the migration time of data or applications.
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Feng, Yu-Cheng, Sheng-Yun Zeng, and Tyng-Yeu Liang. "Part2Point: A Part-Oriented Point Cloud Reconstruction Framework." Sensors 24, no. 1 (December 20, 2023): 34. http://dx.doi.org/10.3390/s24010034.
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Three-dimensional object modeling is necessary for developing virtual and augmented reality applications. Traditionally, application engineers must manually use art software to edit object shapes or exploit LIDAR to scan physical objects for constructing 3D models. This is very time-consuming and costly work. Fortunately, GPU recently provided a cost-effective solution for massive data computation. With GPU support, many studies have proposed 3D model generators based on different learning architectures, which can automatically convert 2D object pictures into 3D object models with good performance. However, as the demand for model resolution increases, the required computing time and memory space increase as significantly as the parameters of the learning architecture, which seriously degrades the efficiency of 3D model construction and the feasibility of resolution improvement. To resolve this problem, this paper proposes a part-oriented point cloud reconstruction framework called Part2Point. This framework segments the object’s parts, reconstructs the point cloud for individual object parts, and combines the part point clouds into the complete object point cloud. Therefore, it can reduce the number of learning network parameters at the exact resolution, effectively minimizing the calculation time cost and the required memory space. Moreover, it can improve the resolution of the reconstructed point cloud so that the reconstructed model can present more details of object parts.
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Abulaish, Muhammad, and Tarique Anwar. "A Keyphrase-Based Tag Cloud Generation Framework to Conceptualize Textual Data." International Journal of Adaptive, Resilient and Autonomic Systems 4, no. 2 (April 2013): 72–93. http://dx.doi.org/10.4018/jaras.2013040104.
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Tag clouds have become an effective tool to quickly perceive the most prominent terms embedded within textual data. Tag clouds help grasp the main theme of a corpus without exploring the pile of documents. However, the effectiveness of tag clouds to conceptualize text corpora is directly proportional to the quality of the tags. In this paper, the authors propose a keyphrase-based tag cloud generation framework. In contrast to existing tag cloud generation systems that use single words as tags and their frequency counts to determine the font size of the tags, the proposed framework identifies feasible keyphrases and uses them as tags. The font-size of a keyphrase is determined as a function of its relevance weight. Instead of using partial or full parsing, which is inefficient for lengthy sentences and inaccurate for the sentences that do not follow proper grammatical structure, the proposed method applies n-gram techniques followed by various heuristics-based refinements to identify candidate phrases from text documents. A rich set of lexical and semantic features are identified to characterize the candidate phrases and determine their keyphraseness and relevance weights. The authors also propose a font-size determination function, which utilizes the relevance weights of the keyphrases to determine their relative font size for tag cloud visualization. The efficacy of the proposed framework is established through experimentation and its comparison with the existing state-of-the-art tag cloud generation methods.
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Malik, Saadia, Nadia Tabassum, Muhammad Saleem, Tahir Alyas, Muhammad Hamid, and Umer Farooq. "Cloud-IoT Integration: Cloud Service Framework for M2M Communication." Intelligent Automation & Soft Computing 31, no. 1 (2022): 471–80. http://dx.doi.org/10.32604/iasc.2022.019837.
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Abhishek, Kumar, Manish Kumar Verma, Kumar Shivam, Vinit Kumar, and Adarsh Mohan. "Integrated Hadoop Cloud Framework (IHCF)." Indian Journal of Science and Technology 10, no. 10 (February 1, 2017): 1–8. http://dx.doi.org/10.17485/ijst/2017/v10i10/107943.
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Bhavani Dasari, Durga. "Cloud-Based Venue Recommendation Framework." International Journal of Cloud-Computing and Super-Computing 4, no. 1 (July 30, 2017): 21–26. http://dx.doi.org/10.21742/ijcs.2017.4.1.04.
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Hung Kao, Chia. "Cloud-Assisted Safe Driving Framework." International Journal of Modeling and Optimization 7, no. 6 (December 2017): 346–51. http://dx.doi.org/10.7763/ijmo.2017.v7.610.
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Alqahtani, Jawaher. "Security Framework for Cloud Computing." IARJSET 6, no. 12 (December 30, 2019): 27–31. http://dx.doi.org/10.17148/iarjset.2019.61207.
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Munir, Kashif, and Sellapan Palaniappan. "Framework for Secure Cloud Computing." International Journal on Cloud Computing: Services and Architecture 3, no. 2 (April 30, 2013): 21–35. http://dx.doi.org/10.5121/ijccsa.2013.3202.
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Alex, M. Edington, and R. Kishore. "Forensics framework for cloud computing." Computers & Electrical Engineering 60 (May 2017): 193–205. http://dx.doi.org/10.1016/j.compeleceng.2017.02.006.
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Sanagavarapu, Lalit Mohan, Gangadharan G.R., and Raghu Reddy Y. "KIET Framework for Cloud Adoption." International Journal of Cloud Applications and Computing 8, no. 4 (October 2018): 72–87. http://dx.doi.org/10.4018/ijcac.2018100105.
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The expenses in the sustenance of IT investments has become a major ledger item in businesses to the extent that in some cases business priorities had to be changed for sustaining IT systems. Cloud computing, a disruptive technology, is changing the sustenance model with on-demand and metered service approach. However, the adoption of this technology has not been consistent across sectors due to fear on loss of control and changes required in application development and deployment. Authors propose KIET (Knowing, Initiating, Evolving and Transforming) framework based on diffusion theory for adoption of cloud computing in organizations that have strong regulatory framework. Authors implemented the proposed framework on the Indian Banking sector, with majority of the banks being in the public sector. After the implementation of the framework, 49.4% of the banks have adopted cloud computing and another 27.8% of the banks have started the initial steps for adoption.
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Kumar, Ashok, Anju Sharma, and Rajesh Kumar. "SERVmegh: framework for green cloud." Concurrency and Computation: Practice and Experience 29, no. 4 (June 30, 2016): e3903. http://dx.doi.org/10.1002/cpe.3903.
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Jarecka, Dorota, Wojciech W. Grabowski, and Hanna Pawlowska. "Modeling of Subgrid-Scale Mixing in Large-Eddy Simulation of Shallow Convection." Journal of the Atmospheric Sciences 66, no. 7 (July 1, 2009): 2125–33. http://dx.doi.org/10.1175/2009jas2929.1.
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Abstract This paper discusses an extension of the approach proposed previously to represent the delay of cloud water evaporation and buoyancy reversal due to the cloud–environment mixing in bulk microphysics large-eddy simulation of clouds. In the original approach, an additional equation for the mean spatial scale of cloudy filaments was introduced to represent the progress toward microscale homogenization of a volume undergoing turbulent cloud–environment mixing, with the evaporation of cloud water allowed only when the filament scale approached the Kolmogorov microscale. Here, it is shown through a posteriori analysis of model simulations that one should also predict the volume fraction of the cloudy air that was diagnosed in the original approach. The resulting model of turbulent mixing and homogenization, referred to as the λ–β model, is applied in a series of shallow convection simulations using various spatial resolutions and compared to the traditional bulk model. This work represents an intermediate step in the development of a modeling framework to simulate characteristics of microphysical transformations during entrainment and subgrid-scale turbulent mixing.
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Yin, Kai Ze, and Hai Hang Wang. "MCACM: A Cloud Storage Access Control Model for Multi-Clouds Environment Based on XACML." Applied Mechanics and Materials 713-715 (January 2015): 2451–54. http://dx.doi.org/10.4028/www.scientific.net/amm.713-715.2451.
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Cloud storage as a popular application in cloud services, are developing from single cloud service provider towards multiple cloud service providers. As a consequence, users need to manage their files from different sources and share in multi-clouds. While the conventional access control models are no longer suitable in multi-clouds environment. Therefore, a multi-clouds access control model based on XACML, named MCACM, is proposed here to implement access control in multi-clouds environment. In this model, cross-clouds access control is enabled through extending standard XACML framework. At last, we implemented a prototype of MCACM, and performance evaluation results show that our scheme is efficient.
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Gautam, Pratiksha, Mohd Dilshad Ansari, and Surender Kumar Sharma. "Enhanced Security for Electronic Health Care Information Using Obfuscation and RSA Algorithm in Cloud Computing." International Journal of Information Security and Privacy 13, no. 1 (January 2019): 59–69. http://dx.doi.org/10.4018/ijisp.2019010105.
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Recently, the electronic heath record (EHR) has become the chosen method to maintain a patient's health information. The advancement of cloud computing enables users to access their data with flexibility, providing large storage capability at low costs, which encourages EHR maintainers to consider shifting from their own storage to the cloud. In cloud computing, it is no doubt that securing EHRs poses a gigantic challenge. Various security properties like access control, data privacy, and scalable access between different clouds needs to be satisfied. This article presents a secure and efficient blueprint for securing data confidentiality on cloud computing storage. The proposed framework is carried out for EHR confidential data on cloud storage. Moreover, the proposed approach combines the obfuscation and RSA encryption together to enforce confidentiality and authentication. Through this framework, the data confidentiality and authentication scheme on EHR information can be enforced on clouds storage.
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Marinou, Eleni, Kalliopi Artemis Voudouri, Ioanna Tsikoudi, Eleni Drakaki, Alexandra Tsekeri, Marco Rosoldi, Dragos Ene, et al. "Geometrical and Microphysical Properties of Clouds Formed in the Presence of Dust above the Eastern Mediterranean." Remote Sensing 13, no. 24 (December 9, 2021): 5001. http://dx.doi.org/10.3390/rs13245001.
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In this work, collocated lidar–radar observations are used to retrieve the vertical profiles of cloud properties above the Eastern Mediterranean. Measurements were performed in the framework of the PRE-TECT experiment during April 2017 at the Greek atmospheric observatory of Finokalia, Crete. Cloud geometrical and microphysical properties at different altitudes were derived using the Cloudnet target classification algorithm. We found that the variable atmospheric conditions that prevailed above the region during April 2017 resulted in complex cloud structures. Mid-level clouds were observed in 38% of the cases, high or convective clouds in 58% of the cases, and low-level clouds in 2% of the cases. From the observations of cloudy profiles, pure ice phase occurred in 94% of the cases, mixed-phase clouds were observed in 27% of the cases, and liquid clouds were observed in 8.7% of the cases, while Drizzle or rain occurred in 12% of the cases. The significant presence of Mixed-Phase Clouds was observed in all the clouds formed at the top of a dust layer, with three times higher abundance than the mean conditions (26% abundance at −15 °C). The low-level clouds were formed in the presence of sea salt and continental particles with ice abundance below 30%. The derived statistics on clouds’ high-resolution vertical distributions and thermodynamic phase can be combined with Cloudnet cloud products and lidar-retrieved aerosol properties to study aerosol-cloud interactions in this understudied region and evaluate microphysics parameterizations in numerical weather prediction and global climate models.
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Lin, Mingwei, and Shuyu Chen. "An Efficient Anomaly Detection Framework for Cloud Computing Environment." Journal of Computers 10, no. 3 (2015): 155–65. http://dx.doi.org/10.17706/jcp.10.3.155-165.
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Diamond, Michael S., Amie Dobracki, Steffen Freitag, Jennifer D. Small Griswold, Ashley Heikkila, Steven G. Howell, Mary E. Kacarab, James R. Podolske, Pablo E. Saide, and Robert Wood. "Time-dependent entrainment of smoke presents an observational challenge for assessing aerosol–cloud interactions over the southeast Atlantic Ocean." Atmospheric Chemistry and Physics 18, no. 19 (October 12, 2018): 14623–36. http://dx.doi.org/10.5194/acp-18-14623-2018.
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Abstract. The colocation of clouds and smoke over the southeast Atlantic Ocean during the southern African biomass burning season has numerous radiative implications, including microphysical modulation of the clouds if smoke is entrained into the marine boundary layer. NASA's ObseRvations of Aerosols above CLouds and their intEractionS (ORACLES) campaign is studying this system with aircraft in three field deployments between 2016 and 2018. Results from ORACLES-2016 show that the relationship between cloud droplet number concentration and smoke below cloud is consistent with previously reported values, whereas cloud droplet number concentration is only weakly associated with smoke immediately above cloud at the time of observation. By combining field observations, regional chemistry–climate modeling, and theoretical boundary layer aerosol budget equations, we show that the history of smoke entrainment (which has a characteristic mixing timescale on the order of days) helps explain variations in cloud properties for similar instantaneous above-cloud smoke environments. Precipitation processes can obscure the relationship between above-cloud smoke and cloud properties in parts of the southeast Atlantic, but marine boundary layer carbon monoxide concentrations for two case study flights suggest that smoke entrainment history drove the observed differences in cloud properties for those days. A Lagrangian framework following the clouds and accounting for the history of smoke entrainment and precipitation is likely necessary for quantitatively studying this system; an Eulerian framework (e.g., instantaneous correlation of A-train satellite observations) is unlikely to capture the true extent of smoke–cloud interaction in the southeast Atlantic.
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Han, Jungsu, Sun Park, and JongWon Kim. "Dynamic OverCloud: Realizing Microservices-Based IoT-Cloud Service Composition over Multiple Clouds." Electronics 9, no. 6 (June 11, 2020): 969. http://dx.doi.org/10.3390/electronics9060969.
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With the expansion of cloud-leveraged Information and Communications Technology (ICT) convergence trend, cloud-native computing is starting to be the de-facto paradigm together with MSA(Microservices Architecture)-based service composition for agility and efficiency. Moreover, by bridging the Internet of Things (IoT) and cloud together, a variety of cloud applications are explosively emerging. As an example, the so-called IoT-Cloud services, which are cloud-leveraged inter-connected services with distributed IoT devices, dynamically utilize geographically-distributed multiple clouds since mobile IoT devices can selectively connect to the near-by cloud resources for low-latency and high-throughput connectivity. In comparison, most public cloud providers may cause vendor lock-in problems that limit the inter-operable service compositions. Thus, in this paper, we propose a new overlay approach to address the above limitations, denoted as Dynamic OverCloud, which is a specially-arranged razor-thin overlay layer that provides users with an inter-operable and visibility-supported environment for MSA-based IoT-Cloud service composition over the existing multiple clouds. Then, we design a software framework that dynamically builds the proposed concept. We also describe a detailed implementation of the software framework with workflows. Finally, we verify its feasibility by realizing a smart energy IoT-Cloud service with the suggested operation lifecycle.
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Xu, Dongmei, Thomas Auligné, Gaël Descombes, and Chris Snyder. "A method for retrieving clouds with satellite infrared radiances using the particle filter." Geoscientific Model Development 9, no. 11 (November 2, 2016): 3919–32. http://dx.doi.org/10.5194/gmd-9-3919-2016.
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Abstract. Ensemble-based techniques have been widely utilized in estimating uncertainties in various problems of interest in geophysical applications. A new cloud retrieval method is proposed based on the particle filter (PF) by using ensembles of cloud information in the framework of Gridpoint Statistical Interpolation (GSI) system. The PF cloud retrieval method is compared with the Multivariate Minimum Residual (MMR) method that was previously established and verified. Cloud retrieval experiments involving a variety of cloudy types are conducted with the PF and MMR methods with measurements of infrared radiances on multi-sensors onboard both geostationary and polar satellites, respectively. It is found that the retrieved cloud masks with both methods are consistent with other independent cloud products. MMR is prone to producing ambiguous small-fraction clouds, while PF detects clearer cloud signals, yielding closer heights of cloud top and cloud base to other references. More collections of small-fraction particles are able to effectively estimate the semi-transparent high clouds. It is found that radiances with high spectral resolutions contribute to quantitative cloud top and cloud base retrievals. In addition, a different way of resolving the filtering problem over each model grid is tested to better aggregate the weights with all available sensors considered, which is proven to be less constrained by the ordering of sensors. Compared to the MMR method, the PF method is overall more computationally efficient, and the cost of the model grid-based PF method scales more directly with the number of computing nodes.
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Christensen, Matthew W., Po-Lun Ma, Peng Wu, Adam C. Varble, Johannes Mülmenstädt, and Jerome D. Fast. "Evaluation of aerosol–cloud interactions in E3SM using a Lagrangian framework." Atmospheric Chemistry and Physics 23, no. 4 (March 2, 2023): 2789–812. http://dx.doi.org/10.5194/acp-23-2789-2023.
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Abstract. A Lagrangian framework is used to evaluate aerosol–cloud interactions in the U.S. Department of Energy's Energy Exascale Earth System Model (E3SM) version 1 (E3SMv1) for measurements taken at Graciosa Island in the Azores where a U.S. Department of Energy Atmosphere Radiation Measurement (ARM) site is located. This framework uses direct measurements of cloud condensation nuclei (CCN) concentration (instead of relying on satellite retrievals of aerosol optical depth) and incorporates a suite of ground-based ARM measurements, satellite retrievals, and meteorological reanalysis products that when applied to over a 1500 trajectories provides key insights into the evolution of low-level clouds and aerosol radiative forcing that is not feasible from a traditional Eulerian analysis framework. Significantly lower concentrations (40 %) of surface CCN concentration are measured when precipitation rates in 48 h back trajectories average above 1.2 mm d−1 in the Integrated Multi-satellitE Retrievals for Global Precipitation Measurement (IMERG) product. The depletion of CCN concentration when precipitation rates are elevated is nearly twice as large in the ARM observations compared to E3SMv1 simulations. The model CCN concentration bias remains significant despite modifying the autoconversion and accretion rates in warm clouds. As the clouds in trajectories associated with larger surface-based CCN concentration advect away from Graciosa Island, they maintain higher values of droplet number concentrations (Nd) over multiple days in observations and E3SM simulations compared to trajectories that start with lower CCN concentrations. The response remains robust even after controlling for meteorological factors such as lower troposphere stability, the degree of cloud coupling with the surface, and island wake effects. E3SMv1 simulates a multi-day aerosol effect on clouds and a Twomey radiative effect that is within 30 % of the ARM and satellite observations. However, the mean cloud droplet concentration is more than 2–3 times larger than in the observations. While Twomey radiative effects are similar amongst autoconversion and accretion sensitivity experiments, the liquid water path and cloud fraction adjustments are positive when using a regression model as opposed to negative when using the present-day minus pre-industrial aerosol emissions approach. This result suggests that tuning the autoconversion and accretion alone is unlikely to produce the desired aerosol susceptibilities in E3SMv1.
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Kuba, N., and Y. Fujiyoshi. "Development of a cloud microphysical model and parameterizations to describe the effect of CCN on warm cloud." Atmospheric Chemistry and Physics Discussions 6, no. 1 (February 21, 2006): 1413–53. http://dx.doi.org/10.5194/acpd-6-1413-2006.
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Abstract. First, a hybrid cloud microphysical model was developed that incorporates both Lagrangian and Eulerian frameworks to study quantitatively the effect of cloud condensation nuclei (CCN) on the precipitation of warm clouds. A parcel model and a grid model comprise the cloud model. The condensation growth of CCN in each parcel is estimated in a Lagrangian framework. Changes in cloud droplet size distribution arising from condensation and coalescence are calculated on grid points using a two-moment bin method in a semi-Lagrangian framework. Sedimentation and advection are estimated in the Eulerian framework between grid points. Results from the cloud model show that an increase in the number of CCN affects both the amount and the location of precipitation. Additionally, results from the hybrid microphysical model and Kessler's parameterization were compared. Second, new parameterizations were developed that estimate the number and size distribution of cloud droplets given the updraft velocity and the number of CCN. The parameterizations were derived from the results of numerous numerical experiments that used the cloud microphysical parcel model. The input information of CCN for these parameterizations is only several values of CCN spectrum (they are given by CCN counter for example). It is more convenient than conventional parameterizations those need values concerned with CCN spectrum, C and k in the equation of N=CSk, or, breadth, total number and median radius, for example. The new parameterizations' predictions of initial cloud droplet size distribution for the bin method were verified by using the aforesaid hybrid microphysical model. The newly developed parameterizations will save computing time, and can effectively approximate components of cloud microphysics in a non-hydrostatic cloud model. The parameterizations are useful not only in the bin method in the regional cloud-resolving model but also both for a two-moment bulk microphysical model and for a global model. The effects of sea salt, sulfate, and organic carbon particles were also studied with these parameterizations and global model.