Relevant bibliographies by topics / Multiple Cloud

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  1. Journal articles
  2. Dissertations / Theses
  3. Books
  4. Book chapters
  5. Conference papers
  6. Reports

Academic literature on the topic 'Multiple Cloud'

Author: Grafiati
Published: 3 June 2025
Last updated: 6 July 2025

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Journal articles on the topic "Multiple Cloud"

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Naud, C. M., J. P. Muller, E. E. Clothiaux, B. A. Baum, and W. P. Menzel. "Intercomparison of multiple years of MODIS, MISR and radar cloud-top heights." Annales Geophysicae 23, no. 7 (2005): 2415–24. http://dx.doi.org/10.5194/angeo-23-2415-2005.

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Abstract. Radar cloud-top heights were retrieved at both the Chilbolton Facility for Atmospheric and Radio Research, UK (CFARR) and the ARM Southern Great Plain site, USA (SGP), using millimetre wave cloud radars and identical algorithms. The resulting cloud-top heights were used for comparison with MODIS and MISR retrieved cloud-top heights, from March 2000 to October 2003. Both imaging instruments reside on the NASA Earth Observing System (EOS) Terra platform launched in 1999. MODIS and MISR cloud-top products were from the recent collections (4 and 3, respectively) that cover the entire mission. The cloud characteristics are different at each ground site, with clouds generally residing at higher altitudes at SGP, but with a greater occurrence of broken or multilayered clouds at CFARR. A method is presented to automatically eliminate scenes where clouds are of a broken nature, since it is difficult in these conditions to ensure that ground-based and satellite measurements refer to the same cloud deck. The intercomparison between MODIS and radar cloud-top heights reveals that MODIS cloud-top heights agree with radar within about 1km for mid- and high-level clouds. However, this accuracy is degraded to nearly 3 km for low-level clouds. MISR cloud-top heights are found to agree with radar cloud-top heights to within 0.6 km, which is in line with theoretical expectations. In single-level cloud situations MODIS and MISR cloud-top heights tend to agree within 1 km. This comparison also reveals that the loss of radar sensitivity during 2001 resulted in the CFARR instrument being less accurate for high-level cloud-top height measurements. Keywords. Atmospheric composition and structure (Instruments and techniques)
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Geer, Alan J., Peter Bauer, and Christopher W. O’Dell. "A Revised Cloud Overlap Scheme for Fast Microwave Radiative Transfer in Rain and Cloud." Journal of Applied Meteorology and Climatology 48, no. 11 (2009): 2257–70. http://dx.doi.org/10.1175/2009jamc2170.1.

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Abstract The assimilation of cloud- and precipitation-affected observations into weather forecasting systems requires very fast calculations of radiative transfer in the presence of multiple scattering. At the European Centre for Medium-Range Weather Forecasts (ECMWF), performance limitations mean that only a single cloudy calculation (including any precipitation) can be made, and the simulated radiance is a weighted combination of cloudy- and clear-sky radiances. Originally, the weight given to the cloudy part was the maximum cloud fraction in the atmospheric profile. However, this weighting was excessive, and because of nonlinear radiative transfer (the “beamfilling effect”) there were biases in areas of cloud and precipitation. A new approach instead uses the profile average cloud fraction, and decreases RMS errors by 40% in areas of rain or heavy clouds when “truth” comes from multiple independent column simulations. There is improvement all the way from low (e.g., 19 GHz) to high (e.g., 183 GHz) microwave frequencies. There is also improvement when truth comes from microwave imager observations. One minor problem is that biases increase slightly in mid- and upper-tropospheric sounding channels in light-cloud situations, which shows that future improvements will require the cloud fraction to vary according to the optical properties at different frequencies.
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Chou, Ming-Dah, Kyu-Tae Lee, Si-Chee Tsay, and Qiang Fu. "Parameterization for Cloud Longwave Scattering for Use in Atmospheric Models." Journal of Climate 12, no. 1 (1999): 159–69. http://dx.doi.org/10.1175/1520-0442-12.1.159.

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Abstract A parameterization for the scattering of thermal infrared (longwave) radiation by clouds has been developed based on discrete-ordinate multiple-scattering calculations. The effect of backscattering is folded into the emission of an atmospheric layer and the absorption between levels by scaling the cloud optical thickness. The scaling is a function of the single-scattering albedo and asymmetry factor. For wide ranges of cloud particle size, optical thickness, height, and atmospheric conditions, flux errors induced by the parameterization are small. They are <4 W m−2 (2%) in the upward flux at the top of the atmosphere and <2 W m−2 (1%) in the downward flux at the surface. Compared to the case that scattering by clouds is neglected, the flux errors are more than a factor of 2 smaller. The maximum error in cooling rate is ≈8%, which occurs at the top of clouds, as well as at the base of high clouds where the difference between the cloud and surface temperatures is large. With the scaling approximation, radiative transfer equations for a cloudy atmosphere are identical with those for a clear atmosphere, and the difficulties in applying a multiple-scattering algorithm to a partly cloudy atmosphere (assuming homogeneous clouds) are avoided. The computational efficiency is practically the same as that for a clear atmosphere. The parameterization represents a significant reduction in one source of the errors involved in the calculation of longwave cooling in cloudy atmospheres.
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Gielen, C., M. Van Roozendael, F. Hendrick, et al. "A simple and versatile cloud-screening method for MAX-DOAS retrievals." Atmospheric Measurement Techniques 7, no. 10 (2014): 3509–27. http://dx.doi.org/10.5194/amt-7-3509-2014.

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Abstract. We present a cloud-screening method based on differential optical absorption spectroscopy (DOAS) measurements, more specifically using intensity measurements and O4 differential slant-column densities (DSCDs). Using the colour index (CI), i.e. the ratio of the radiance at two wavelengths, we define different sky conditions including clear, thin clouds/polluted, fully-cloudy, and heavily polluted. We also flag the presence of broken and scattered clouds. The O4 absorption is a good tracer for cloud-induced light-path changes and is used to detect clouds and discriminate between instances of high aerosol optical depth (AOD) and high cloud optical depth (COD). We apply our cloud screening to MAX-DOAS (multi-axis DOAS) retrievals at three different sites with different typical meteorological conditions, more specifically suburban Beijing (39.75° N, 116.96° E), Brussels (50.78° N, 4.35° E) and Jungfraujoch (46.55° N, 7.98° E). We find that our cloud screening performs well characterizing the different sky conditions. The flags based on the colour index are able to detect changes in visibility due to aerosols and/or (scattered) clouds. The O4-based multiple-scattering flag is able to detect optically thick clouds, and is needed to correctly identify clouds for sites with extreme aerosol pollution. Removing data taken under cloudy conditions results in a better agreement, in both correlation and slope, between the MAX-DOAS AOD retrievals and measurements from other co-located instruments.
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Gielen, C., M. Van Roozendael, F. Hendrick, et al. "A simple and versatile cloud-screening method for MAX-DOAS retrievals." Atmospheric Measurement Techniques Discussions 7, no. 6 (2014): 5883–920. http://dx.doi.org/10.5194/amtd-7-5883-2014.

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Abstract. We present a cloud-screening method based on differential optical absorption spectroscopy (DOAS) measurements, more specifically using zenith sky spectra and O4 differential slant-column densities (DSCDs). Using the colour index (CI), i.e. the ratio of the radiance at two wavelengths, we define different sky conditions including clear, thin clouds/polluted, fully-cloudy, and heavily polluted. We also flag the presence of broken and scattered clouds. The O4 absorption is a good tracer for cloud-induced light-path changes and is used to detect clouds and discriminate between instances of high aerosol optical depth (AOD) and high cloud optical depth (COD). We apply our cloud screening to MAX-DOAS (multi-axis DOAS) retrievals at three different sites with different typical meteorological conditions, more specifically suburban Beijing (39.75° N, 116.96° E), Brussels (50.78° N, 4.35° E) and Jungfraujoch (46.55° N, 7.98° E). We find that our cloud screening performs well characterizing the different sky conditions. The flags based on the colour index are able to detect changes in visibility due to aerosols and/or (scattered) clouds. The O4-based multiple-scattering flag is able to detect optically thick clouds, and is needed to correctly identify clouds for sites with extreme aerosol pollution. Removing data taken under cloudy conditions results in a better agreement, in both correlation and slope, between the AOD retrievals and measurements from other co-located instruments.
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Joiner, J., A. P. Vasilkov, P. K. Bhartia, G. Wind, S. Platnick, and W. P. Menzel. "Detection of multi-layer and vertically-extended clouds using A-train sensors." Atmospheric Measurement Techniques 3, no. 1 (2010): 233–47. http://dx.doi.org/10.5194/amt-3-233-2010.

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Abstract. The detection of multiple cloud layers using satellite observations is important for retrieval algorithms as well as climate applications. In this paper, we describe a relatively simple algorithm to detect multiple cloud layers and distinguish them from vertically-extended clouds. The algorithm can be applied to coincident passive sensors that derive both cloud-top pressure from the thermal infrared observations and an estimate of solar photon pathlength from UV, visible, or near-IR measurements. Here, we use data from the A-train afternoon constellation of satellites: cloud-top pressure, cloud optical thickness, the multi-layer flag from the Aqua MODerate-resolution Imaging Spectroradiometer (MODIS) and the optical centroid cloud pressure from the Aura Ozone Monitoring Instrument (OMI). For the first time, we use data from the CloudSat radar to evaluate the results of a multi-layer cloud detection scheme. The cloud classification algorithms applied with different passive sensor configurations compare well with each other as well as with data from CloudSat. We compute monthly mean fractions of pixels containing multi-layer and vertically-extended clouds for January and July 2007 at the OMI spatial resolution (12 km×24 km at nadir) and at the 5 km×5 km MODIS resolution used for infrared cloud retrievals. There are seasonal variations in the spatial distribution of the different cloud types. The fraction of cloudy pixels containing distinct multi-layer cloud is a strong function of the pixel size. Globally averaged, these fractions are approximately 20% and 10% for OMI and MODIS, respectively. These fractions may be significantly higher or lower depending upon location. There is a much smaller resolution dependence for fractions of pixels containing vertically-extended clouds (~20% for OMI and slightly less for MODIS globally), suggesting larger spatial scales for these clouds. We also find higher fractions of vertically-extended clouds over land as compared with ocean, particularly in the tropics and summer hemisphere.
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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 (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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Sinclair, Kenneth, Bastiaan van Diedenhoven, Brian Cairns, John Yorks, Andrzej Wasilewski, and Matthew McGill. "Remote sensing of multiple cloud layer heights using multi-angular measurements." Atmospheric Measurement Techniques 10, no. 6 (2017): 2361–75. http://dx.doi.org/10.5194/amt-10-2361-2017.

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Abstract. Cloud top height (CTH) affects the radiative properties of clouds. Improved CTH observations will allow for improved parameterizations in large-scale models and accurate information on CTH is also important when studying variations in freezing point and cloud microphysics. NASA's airborne Research Scanning Polarimeter (RSP) is able to measure cloud top height using a novel multi-angular contrast approach. For the determination of CTH, a set of consecutive nadir reflectances is selected and the cross correlations between this set and collocated sets at other viewing angles are calculated for a range of assumed cloud top heights, yielding a correlation profile. Under the assumption that cloud reflectances are isotropic, local peaks in the correlation profile indicate cloud layers. This technique can be applied to every RSP footprint and we demonstrate that detection of multiple peaks in the correlation profile allows retrieval of heights of multiple cloud layers within single RSP footprints. This paper provides an in-depth description of the architecture and performance of the RSP's CTH retrieval technique using data obtained during the Studies of Emissions and Atmospheric Composition, Clouds and Climate Coupling by Regional Surveys (SEAC4RS) campaign. RSP-retrieved cloud heights are evaluated using collocated data from the Cloud Physics Lidar (CPL). The method's accuracy associated with the magnitude of correlation, optical thickness, cloud thickness and cloud height are explored. The technique is applied to measurements at a wavelength of 670 and 1880 nm and their combination. The 1880 nm band is virtually insensitive to the lower troposphere due to strong water vapor absorption. It is found that each band is well suitable for retrieving heights of cloud layers with optical thicknesses above about 0.1 and that RSP cloud layer height retrievals more accurately correspond to CPL cloud middle than cloud top. It is also found that the 1880 nm band yields the most accurate results for clouds at middle and high altitudes (4.0 to 17 km), while the 670 nm band is most accurate at low and middle altitudes (1.0–13.0 km). The dual band performs best over the broadest range and is suitable for accurately retrieving cloud layer heights between 1.0 and 16.0 km. Generally, the accuracy of the retrieved cloud top heights increases with increasing correlation value. Improved accuracy is achieved by using customized filtering techniques for each band with the most significant improvements occurring in the primary layer retrievals. RSP is able to measure a primary layer CTH with a median error of about 0.5 km when compared to CPL. For multilayered scenes, the second and third layer heights are determined median errors of about 1.5 and 2.0–2.5 km, respectively.
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Cao, Xianjie, Gefei Lu, Mengqi Li, and Jiayun Wang. "Statistical Characteristics of Cloud Heights over Lanzhou, China from Multiple Years of Micro-Pulse Lidar Observation." Atmosphere 12, no. 11 (2021): 1415. http://dx.doi.org/10.3390/atmos12111415.

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The macroscopic characteristics of clouds over Lanzhou, China were investigated using micro-pulse lidar data from September 2005 to November 2011. The results show that the mean of the cloud base height, cloud peak height, cloud top height and cloud thickness during the observation was 4.03 km, 4.81 km, 5.50 km and 1.47 km, respectively; the maximum frequency of the cloud base height, cloud peak height, cloud top height and cloud thickness was 25.7% in the range of 1–2 km, 16.2% in the range of 2–3 km, 14.6% in the range of 2–3 km and 42.2% in the range of 1–2 km, respectively; the maximum frequency of cloud base height was 24.2%, 24.6%, 29.7% and 21.4% in spring, summer, autumn and winter, respectively, all in the range of 1–2 km, and middle clouds occurred most frequently at 41.4%, followed by low clouds (33.7%) and high clouds (24.9%) during the observation period; the maximum frequency of cloud peak height was 15.8% in the range of 3–4 km, 18% in the range of 4–5 km, 20% in the range of 2–3 km in autumn and 18.6% in the range of 5–6 km in winter; the maximum frequency of cloud top height was 14% in the range of 3–4 km in spring, 16% in the range of 4–5 km in summer, 20.1% in the range of 2–3 km in autumn and 17.8% in the range of 7–8 km in winter; the maximum frequency of cloud thickness was 44.9%, 35.6% and 52% in the range of 1–2 km in spring, summer and winter, respectively, while it was 44.9% in the range of 0–1 km in autumn; the cloud thickness was mostly less than 3 km; generally, the thicker of cloud, the less the frequency.
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Min, Q., and R. Li. "Longwave indirect effect of mineral dusts on ice clouds." Atmospheric Chemistry and Physics Discussions 10, no. 1 (2010): 763–83. http://dx.doi.org/10.5194/acpd-10-763-2010.

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Abstract. In addition to microphysical changes in clouds, changes in nucleation processes of ice cloud due to aerosols would result in substantial changes in cloud top distribution as mildly supercooled clouds are glaciated through heterogonous nucleation processes. Measurements from multiple sensors on multiple observing platforms over the Atlantic Ocean show that the cloud effective temperature increases with mineral dust loading with a slope of +3.06 °C per unit AOD. The macrophysical changes in ice cloud top distributions as a consequence of mineral dust-cloud interaction exert a strong cooling effect (up to 16 w m−2) of thermal infrared radiation on cloud systems. Induced changes of ice particle size by mineral dusts influence cloud emissivity and play a minor role in modulating the outgoing longwave radiation for optically thin ice clouds. Such a strong cooling forcing of thermal infrared radiation would have significant impacts on cloud systems and subsequently on climate.
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Dissertations / Theses on the topic "Multiple Cloud"

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Delgado, Donate Eduardo Juan. "Multiple star formation in molecular cloud cores." Thesis, University of Cambridge, 2004. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.615675.

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Sellami, Rami. "Supporting multiple data stores based applications in cloud environments." Thesis, Université Paris-Saclay (ComUE), 2016. http://www.theses.fr/2016SACLL002/document.

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Avec l’avènement du cloud computing et des big data, de nouveaux systèmes de gestion de bases de données sont apparus, connus en général sous le vocable systèmes NoSQL. Par rapport aux systèmes relationnels, ces systèmes se distinguent par leur absence de schéma, une spécialisation pour des types de données particuliers (documents, graphes, clé/valeur et colonne) et l’absence de langages de requêtes déclaratifs. L’offre est assez pléthorique et il n’y a pas de standard aujourd’hui comme peut l’être SQL pour les systèmes relationnels. De nombreuses applications peuvent avoir besoin de manipuler en même temps des données stockées dans des systèmes relationnels et dans des systèmes NoSQL. Le programmeur doit alors gérer deux (au moins) modèles de données différents et deux (au moins) langages de requêtes différents pour pouvoir écrire son application. De plus, il doit gérer explicitement tout son cycle de vie. En effet, il a à (1) coder son application, (2) découvrir les services de base de données déployés dans chaque environnement Cloud et choisir son environnement de déploiement, (3) déployer son application, (4) exécuter des requêtes multi-sources en les programmant explicitement dans son application, et enfin le cas échéant (5) migrer son application d’un environnement Cloud à un autre. Toutes ces tâches sont lourdes et fastidieuses et le programmeur risque d’être perdu dans ce haut niveau d’hétérogénéité. Afin de pallier ces problèmes et aider le programmeur tout au long du cycle de vie des applications utilisant des bases de données multiples, nous proposons un ensemble cohérent de modèles, d’algorithmes et d’outils. En effet, notre travail dans ce manuscrit de thèse se présente sous forme de quatre contributions. Tout d’abord, nous proposons un modèle de données unifié pour couvrir l’hétérogénéité entre les modèles de données relationnelles et NoSQL. Ce modèle de données est enrichi avec un ensemble de règles de raffinement. En se basant sur ce modèle, nous avons défini notre algèbre de requêtes. Ensuite, nous proposons une interface de programmation appelée ODBAPI basée sur notre modèle de données unifié, qui nous permet de manipuler de manière uniforme n’importe quelle source de données qu’elle soit relationnelle ou NoSQL. ODBAPI permet de programmer des applications indépendamment des bases de données utilisées et d’exprimer des requêtes simples et complexes multi-sources. Puis, nous définissons la notion de bases de données virtuelles qui interviennent comme des médiateurs et interagissent avec les bases de données intégrées via ODBAPI. Ce dernier joue alors le rôle d’adaptateur. Les bases de données virtuelles assurent l’exécution des requêtes d’une façon optimale grâce à un modèle de coût et un algorithme de génération de plan d’exécution optimal que nous définis. Enfin, nous proposons une approche automatique de découverte de bases de données dans des environnements Cloud. En effet, les programmeurs peuvent décrire leurs exigences en termes de bases de données dans des manifestes, et grâce à notre algorithme d’appariement, nous sélectionnons l’environnement le plus adéquat à notre application pour la déployer. Ainsi, nous déployons l’application en utilisant une API générique de déploiement appelée COAPS. Nous avons étendue cette dernière pour pouvoir déployer les applications utilisant plusieurs sources de données. Un prototype de la solution proposée a été développé et mis en œuvre dans des cas d'utilisation du projet OpenPaaS. Nous avons également effectué diverses expériences pour tester l'efficacité et la précision de nos contributions<br>The production of huge amount of data and the emergence of Cloud computing have introduced new requirements for data management. Many applications need to interact with several heterogeneous data stores depending on the type of data they have to manage: traditional data types, documents, graph data from social networks, simple key-value data, etc. Interacting with heterogeneous data models via different APIs, and multiple data stores based applications imposes challenging tasks to their developers. Indeed, programmers have to be familiar with different APIs. In addition, the execution of complex queries over heterogeneous data models cannot, currently, be achieved in a declarative way as it is used to be with mono-data store application, and therefore requires extra implementation efforts. Moreover, developers need to master and deal with the complex processes of Cloud discovery, and application deployment and execution. In this manuscript, we propose an integrated set of models, algorithms and tools aiming at alleviating developers task for developing, deploying and migrating multiple data stores applications in cloud environments. Our approach focuses mainly on three points. First, we provide a unified data model used by applications developers to interact with heterogeneous relational and NoSQL data stores. This model is enriched by a set of refinement rules. Based on that, we define our query algebra. Developers express queries using OPEN-PaaS-DataBase API (ODBAPI), a unique REST API allowing programmers to write their applications code independently of the target data stores. Second, we propose virtual data stores, which act as a mediator and interact with integrated data stores wrapped by ODBAPI. This run-time component supports the execution of single and complex queries over heterogeneous data stores. It implements a cost model to optimally execute queries and a dynamic programming based algorithm to generate an optimal query execution plan. Finally, we present a declarative approach that enables to lighten the burden of the tedious and non-standard tasks of (1) discovering relevant Cloud environments and (2) deploying applications on them while letting developers to simply focus on specifying their storage and computing requirements. A prototype of the proposed solution has been developed and implemented use cases from the OpenPaaS project. We also performed different experiments to test the efficiency and accuracy of our proposals
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Bhattacharjee, Tirtha Pratim. "A dynamic middleware to integrate multiple cloud infrastructures with remote apllications." Thesis, Virginia Tech, 2014. http://hdl.handle.net/10919/71290.

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In an era with compelling need for greater computation power, the aggregation of software system components is becoming more challenging and diverse. The new-generation scientific applications are growing hub of complex and intense computation performed on huge data set with exponential growth. With the development of parallel algorithms, design of multi-user web applications and frequent changes in software architecture, there is a bigger challenge lying in front of the research institutes and organizations. Network science is an interesting field posing extreme computation demands to sustain complex large-scale networks. Several static or dynamic network analysis have to be performed through algorithms implementing complex graph theories, statistical mechanics, data mining and visualization. Similarly, high performance computation infrastructures are imbibing multiple characters and expanding in an unprecedented way. In this age, it's mandatory for all software solutions to migrate to scalable platforms and integrate cloud enabled data center clusters for higher computation needs. So, with aggressive adoption of cloud infrastructures and resource-intensive web-applications, there is a pressing need for a dynamic middleware to bridge the gap and effectively coordinate the integrated system. Such a heterogeneous environment encourages the devising of a transparent, portable and flexible solution stack. In this project, we propose adoption of Virtual Machine aware Portable Batch System Cluster (VM-aware PBS Cluster), a self-initiating and self-regulating cluster of Virtual Machines (VM) capable of operating and scaling on any cloud infrastructure. This is an unique but simple solution for large-scale softwares to migrate to cloud infrastructures retaining the most of the application stack intact. In this project, we have also designed and implemented Cloud Integrator Framework, a dynamic implementation of cloud aware middleware for the proposed VM-aware PBS cluster. This framework regulates job distribution in an aggregate of VMs and optimizes resource consumption through on-demand VM initialization and termination. The model was integrated into CINET system, a network science application. This model has enabled CINET to mediate large-scale network analysis and simulation tasks across varied cloud platforms such as OpenStack and Amazon EC2 for its computation requirements.<br>Master of Science
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Haywood, Dana. "The Relationship between Nonprofit Organizations and Cloud Adoption Concerns." ScholarWorks, 2017. https://scholarworks.waldenu.edu/dissertations/4372.

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Many leaders of nonprofit organizations (NPOs) in the United States do not have plans to adopt cloud computing. However, the factors accounting for their decisions is not known. This correlational study used the extended unified theory of acceptance and use of technology (UTAUT2) to examine whether performance expectancy, effort expectancy, social influence, facilitating conditions, hedonic motivation, price value, and habit can predict behavioral intention (BI) and use behavior (UB) of NPO information technology (IT) managers towards adopting cloud computing within the Phoenix metropolitan area of Arizona of the U.S. An existing UTAUT2 survey instrument was used with a sample of IT managers (N = 106) from NPOs. A multiple regression analysis confirmed a positive statistically significant relationship between predictors and the dependent variables of BI and UB. The first model significantly predicted BI, F (7,99) =54.239, p -?¤ .001, R^2=.795. Performance expectancy (β = .295, p = .004), social influence (β = .148, p = .033), facilitating conditions (β = .246, p = .007), and habit (β = .245, p = .002) were statistically significant predictors of BI at the .05 level. The second model significantly predicted UB, F (3,103) = 37.845, p -?¤ .001, R^2 = .527. Habit (β = .430, p = .001) was a statistically significant predictor for UB at a .05 level. Using the study results, NPO IT managers may be able to develop strategies to improve the adoption of cloud computing within their organization. The implication for positive social change is that, by using the study results, NPO leaders may be able to improve their IT infrastructure and services for those in need, while also reducing their organization's carbon footprint through use of shared data centers for processing.
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Algarni, Abdullah Fayez H. "A machine learning framework for optimising file distribution across multiple cloud storage services." Thesis, University of York, 2017. http://etheses.whiterose.ac.uk/17981/.

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Storing data using a single cloud storage service may lead to several potential problems for the data owner. Such issues include service continuity, availability, performance, security, and the risk of vendor lock-in. A promising solution is to distribute the data across multiple cloud storage services , similarly to the manner in which data are distributed across multiple physical disk drives to achieve fault tolerance and to improve performance . However, the distinguishing characteristics of different cloud providers, in term of pricing schemes and service performance, make optimising the cost and performance across many cloud storage services at once a challenge. This research proposes a framework for automatically tuning the data distribution policies across multiple cloud storage services from the client side, based on file access patterns. The aim of this work is to explore the optimisation of both the average cost per gigabyte and the average service performance (mainly latency time) on multiple cloud storage services . To achieve these aims, two machine learning algorithms were used: 1. supervised learning to predict file access patterns. 2. reinforcement learning to learn the ideal file distribution parameters. File distribution over several cloud storage services . The framework was tested in a cloud storage services emulator, which emulated a real multiple-cloud storage services setting (such as Google Cloud Storage, Amazon S3, Microsoft Azure Storage, and Rack- Space file cloud) in terms of service performance and cost. In addition, the framework was tested in various settings of several cloud storage services. The results of testing the framework showed that the multiple cloud approach achieved an improvement of about 42% for cost and 76% for performance. These findings indicate that storing data in multiple clouds is a superior approach, compared with the commonly used uniform file distribution and compared with a heuristic distribution method.
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Nogherotto, Rita. "A numerical framework for multiple phase cloud microphysics in regional and global atmospheric models." Doctoral thesis, Università degli studi di Trieste, 2015. http://hdl.handle.net/10077/11140.

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2012/2013<br>The Regional Climate Model RegCM4 (Giorgi et al., 2012) treats nonconvective clouds and precipitation following the Subgrid Explicit SUBEX param- eterization (Pal et al., 2000). This scheme includes a simple representation for the formation of raindrops and solves diagnostically the precipitation: rain forms when the cloud water content exceeds the autoconversion threshold, that is an increasing function of the temperature and assumes different values over the land and over the ocean to account for the difference in number of the cloud condensation nuclei over continental and oceanic regions. The SUBEX scheme does not account for the presence of clouds ice, and the fraction of ice is diagnosed as a function of temperature in the radiation scheme. Due to the increasing emphasis on cloud representations in the climate community and the forthcoming increasing resolution due to the inclusion, in the close future, of a non-hydrostatic compressible core, the treatment of the ice microphysics and a prognostic representation of the precipitation is required in RegCM4. This thesis presents the new parameterization for stratiform cloud microphysics and precipitation implemented in RegCM4. The approach of the new parameterization is based on an implicit numerical framework recently developed and implemented into the ECMWF operational forecasting model (Tiedtke, 1993). The new parameterization solves 5 prognostic equations for the water vapour, the liquid water, the rain, the ice and the snow mixing ratios. It allows a proper treatment of mixed-phase clouds and a more physically realistic representation of the precipitation as it is no more an instantaneous response to the microphysical processes occurring in clouds and is subjected to the horizontal advection. A first discussion of the results contains an evaluation of the vertical distributions of the main microphysical quantities, such as the liquid and ice water mixing ratios and the relative fractions. It also presents a series of sensitivity tests to understand how the moisture and radiation quantities respond to the variation of the microphysical parameters used in the scheme, such as the fall speeds of the falling categories, the autoconversion scheme and the evaporation coefficient. Cloud properties are afterwards evaluated through the implementation for RegCM4 of the new cloud evaluation COSP tool (Bodas-Salcedo et al., 2011), developed by the Cloud Feedback Model In- tercomparison Project (CFMIP), that facilitates the comparison of simulated clouds with observations from passive and active remote sensing by diagnosing from model outputs the quantities that would be observed from satellites if they were flying above an atmosphere similar to that predicted by the model. Different hypothesis are presented to explain the reasons for RegCM4 biases in representing different types of clouds over the tropical band and new prospectives for the future investigations designed to answer to the open questions are outlined.<br>XXVI Ciclo<br>1983
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Kini, Rohit Ravindranath. "Sensor Position Optimization for Multiple LiDARs in Autonomous Vehicles." Thesis, KTH, Skolan för elektroteknik och datavetenskap (EECS), 2020. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-289597.

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3D ranging sensor LiDAR, is an extensively used sensor in the autonomous vehicle industry, but LiDAR placement problem is not studied extensively. This thesis work proposes a framework in an open- source autonomous driving simulator (CARLA) that aims to solve LiDAR placement problem, based on the tasks that LiDAR is intended for in most of the autonomous vehicles. LiDAR placement problem is solved by improving point cloud density around the vehicle, and this is calculated by using LiDAR Occupancy Boards (LOB). Introducing LiDAR Occupancy as an objective function, the genetic algorithm is used to optimize this problem. This method can be extended for multiple LiDAR placement problem. Additionally, for multiple LiDAR placement problem, LiDAR scan registration algorithm (NDT) can also be used to find a better match for first or reference LiDAR. Multiple experiments are carried out in simulation with a different vehicle truck and car, different LiDAR sensors Velodyne 16 and 32 channel LiDAR, and, by varying Region Of Interest (ROI), for testing the scalability and technical robustness of the framework. Finally, this framework is validated by comparing the current and proposed LiDAR positions on the truck.<br>3D- sensor LiDAR, är en sensor som används i stor utsträckning inom den autonoma fordonsindustrin, men LiDAR- placeringsproblemet studeras inte i stor utsträckning. Detta uppsatsarbete föreslår en ram i en öppen källkod för autonom körningssimulator (CARLA) som syftar till att lösa LiDAR- placeringsproblem, baserat på de uppgifter som LiDAR är avsedda för i de flesta av de autonoma fordonen. LiDAR- placeringsproblem löses genom att förbättra punktmolntätheten runt fordonet, och detta beräknas med LiDAR Occupancy Boards (LOB). Genom att introducera LiDAR Occupancy som en objektiv funktion används den genetiska algoritmen för att optimera detta problem. Denna metod kan utökas för flera LiDAR- placeringsproblem. Dessutom kan LiDAR- scanningsalgoritm (NDT) för flera LiDAR- placeringsproblem också användas för att hitta en bättre matchning för LiDAR för första eller referens. Flera experiment utförs i simulering med ett annat fordon lastbil och bil, olika LiDAR-sensorer Velodyne 16 och 32kanals LiDAR, och, genom att variera intresseområde (ROI), för att testa skalbarhet och teknisk robusthet i ramverket. Slutligen valideras detta ramverk genom att jämföra de nuvarande och föreslagna LiDAR- positionerna på lastbilen.
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De, Souza Bento Da Silva Pedro Paulo. "On the mapping of distributed applications onto multiple Clouds." Thesis, Lyon, 2017. http://www.theses.fr/2017LYSEN089/document.

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Le Cloud est devenu une plate-forme très répandue pour le déploiement d'applications distribuées. Beaucoup d'entreprises peuvent sous-traiter leurs infrastructures d'hébergement et, ainsi, éviter des dépenses provenant d'investissements initiaux en infrastructure et de maintenance.Des petites et moyennes entreprises, en particulier, attirés par le modèle de coûts sur demande du Cloud, ont désormais accès à des fonctionnalités comme le passage à l'échelle, la disponibilité et la fiabilité, qui avant le Cloud étaient presque réservées à de grandes entreprises.Les services du Cloud peuvent être offerts aux utilisateurs de plusieurs façons. Dans cette thèse, nous nous concentrons sur le modèle d'Infrastructure sous Forme de Service. Ce modèle permet aux utilisateurs d’accéder à des ressources de calcul virtualisés sous forme de machine virtuelles (MVs).Pour installer une application distribuée, un client du Cloud doit d'abord définir l'association entre son application et l'infrastructure. Il est nécessaire de prendre en considération des contraintesde coût, de ressource et de communication pour pouvoir choisir un ensemble de MVs provenant d'opérateurs de Cloud publiques et privés le plus adaptés. Cependant, étant donné la quantité exponentiel de configurations, la définition manuelle de l'association entre application et infrastructure peut être un challenge dans des scénarios à large échelle ou ayant des contraintes importantes de temps. En effet, ce problème est une généralisation du problème de calcul de homomorphisme de graphes, qui est NP-complet.Dans cette thèse, nous adressons le problème de calculer des placements initiaux et de reconfiguration pour des applications distribuées sur potentiellement de multiples Clouds. L'objectif est de minimiser les coûts de location et de migration en satisfaisant des contraintes de ressources et communications. Pour cela, nous proposons des heuristiques performantes capables de calculer des placements de bonne qualité très rapidement pour des scénarios à petite et large échelles. Ces heuristiques, qui sont basées sur des algorithmes de partition de graphes et de vector packing, ont été évaluées en les comparant avec des approches de l'état de l'art comme des solveurs exactes et des méta-heuristiques. Nous montrons en utilisant des simulations que les heuristiques proposées arrivent à calculer des solutions de bonne qualité en quelques secondes tandis que des autres approches prennent des heures ou jours pour les calculer<br>The Cloud has become a very popular platform for deploying distributed applications. Today, virtually any credit card holder can have access to Cloud services. There are many different ways of offering Cloud services to customers. In this thesis we especially focus on theInfrastructure as a Service (IaaS), a model that, usually, proposes virtualized computing resources to costumers in the form of virtual machines (VMs). Thanks to its attractive pay-as-you-use cost model, it is easier for customers, specially small and medium companies, to outsource hosting infrastructures and benefit of savings related to upfront investments and maintenance costs. Also, customers can have access to features such as scalability, availability, and reliability, which previously were almost exclusive for large companies. To deploy a distributed application, a Cloud customer must first consider the mapping between her application (or its parts) to the target infrastructure. She needs to take into consideration cost, resource, and communication constraints to select the most suitable set of VMs, from private and public Cloud providers. However, defining a mapping manually may be a challenge in large-scale or time constrained scenarios since the number of possible configuration explodes. Furthermore, when automating this process, scalability issues must be taken into account given that this mapping problem is a generalization of the graph homomorphism problem, which is NP-complete.In this thesis we address the problem of calculating initial and reconfiguration placements for distributed applications over possibly multiple Clouds. Our objective is to minimize renting and migration costs while satisfying applications' resource and communication constraints. We concentrate on the mapping between applications and Cloud infrastructure. Using an incremental approach, we split the problem into three different parts and propose efficient heuristics that can compute good quality placements very quickly for small and large scenarios. These heuristics are based on graph partition and vector packing heuristics and have been extensively evaluated against state of the art approaches such as MIP solvers and meta-heuristics. We show through simulations that the proposed heuristics manage to compute solutions in a few seconds that would take many hours or days for other approaches to compute
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Schröder, Marc. "Multiple scattering and absorption of solar radiation in the presence of three-dimensional cloud fields." [S.l. : s.n.], 2004. http://www.diss.fu-berlin.de/2004/237/index.html.

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Mellas, Michael John. "Constructing Multiple Realities on Stage: Conceiving a Magical Realist Production of Jose Rivera's Cloud Tectonics." Miami University / OhioLINK, 2008. http://rave.ohiolink.edu/etdc/view?acc_num=miami1218129542.

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Books on the topic "Multiple Cloud"

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M, Welch Ronald, and United States. National Aeronautics and Space Administration., eds. Global single and multiple cloud classification with a fuzzy logic expert system. National Aeronautics and Space Administration, 1996.

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M, Welch Ronald, and United States. National Aeronautics and Space Administration., eds. Global single and multiple cloud classification with a fuzzy logic expert system. National Aeronautics and Space Administration, 1996.

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Doerr, Anthony. Cloud Cuckoo Land: A novel. Scribner, 2021.

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Dwyer, Pat. Bright clouds. Guajira Publications, 2004.

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Dwyer, Pat. Bright clouds. Guajira Publications, 2004.

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Doerr, Anthony. Cloud Cuckoo Land. HarperCollins Publishers Limited, 2023.

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Doerr, Anthony. Cloud Cuckoo Land. HarperCollins Publishers Limited, 2021.

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Doerr, Anthony. Cloud Cuckoo Land. HarperCollins Publishers Limited, 2021.

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Doerr, Anthony. Cloud Cuckoo Land. HarperCollins Publishers Limited, 2022.

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Doerr, Anthony. Cloud Cuckoo Land. HarperCollins Publishers Limited, 2021.

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Book chapters on the topic "Multiple Cloud"

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Kanemitsu, Hidehiro, Masaki Hanada, and Hidenori Nakazato. "Multiple Workflow Scheduling with Offloading Tasks to Edge Cloud." In Cloud Computing – CLOUD 2019. Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-23502-4_4.

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Xiong, Kaiqi. "Multiple-Class Customers." In Resource Optimization and Security for Cloud Services. John Wiley & Sons, Inc., 2014. http://dx.doi.org/10.1002/9781118898598.ch4.

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Tasci, Timur, Sara Höhr, and Stefan Magerstedt. "Datenprozessabbildung über multiple Cloud-Dienstleister." In Digitale Dienstleistungsinnovationen. Springer Berlin Heidelberg, 2019. http://dx.doi.org/10.1007/978-3-662-59517-6_18.

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Mahmoodi, Seyed Eman, Koduvayur Subbalakshmi, and R. N. Uma. "Cognitive Cloud Offloading Using Multiple Radios." In Spectrum-Aware Mobile Computing. Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-030-02411-6_4.

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Mu, Shuai, Maomeng Su, Pin Gao, Yongwei Wu, Keqin Li, and Albert Y. Zomaya. "Cloud Storage over Multiple Data Centers." In Handbook on Data Centers. Springer New York, 2015. http://dx.doi.org/10.1007/978-1-4939-2092-1_24.

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Yan, Shaojun, Haihua Liang, and Xinpeng Zhang. "Location Privacy-Preserving Scheme Based on Multiple Virtual Maps." In Cloud Computing and Security. Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-030-00018-9_39.

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Phan, Thomas, and Wen-Syan Li. "Vertical Load Distribution for Cloud Computing via Multiple Implementation Options." In Handbook of Cloud Computing. Springer US, 2010. http://dx.doi.org/10.1007/978-1-4419-6524-0_12.

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Tang, Xiaojing. "Deforestation Viewed from Multiple Sensors." In Cloud-Based Remote Sensing with Google Earth Engine. Springer International Publishing, 2023. http://dx.doi.org/10.1007/978-3-031-26588-4_50.

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AbstractCombining data from multiple sensors is the best way to increase data density and hence detect change faster. The purpose of this chapter is to demonstrate a simple method of combining Landsat, Sentinel-2, and Sentinel-1 data for monitoring tropical forest disturbance. You will learn how to import, preprocess, and fuse optical and synthetic aperture radar (SAR) remote sensing data. You will also learn how to monitor change using time-series models.
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Wang, Dingcheng, Yiyi Lu, Beijing Chen, and Liming Chen. "Research on Intuitionistic Fuzzy Multiple Output Least Squares Support Vector Regression." In Cloud Computing and Security. Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-030-00009-7_36.

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Liao, Niandong, Caixia Sun, Lingyun Xiang, and Feng Li. "A Multiple Watermarking Scheme for Content Authentication of OOXML Format Documents." In Cloud Computing and Security. Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-030-00015-8_13.

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Conference papers on the topic "Multiple Cloud"

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Nguyen, Dac Hoang, Nhu-Tai Do, and Huy Quoc Nguyen. "An Useful Approach for Multiple Cloud Platforms Deployment." In 2024 International Conference on Advanced Technologies for Communications (ATC). IEEE, 2024. https://doi.org/10.1109/atc63255.2024.10908170.

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Kokhanovsky, Alexander A., Wolfgang von Hoyningen-Huene, Vladimir V. Rozanov, Eleonora P. Zege, Heinrich Bovensmann, and John P. Burrows. "A cloud retrieval algorithm for SCIAMACHY." In Lidar Multiple Scattering Experiments, edited by Christian Werner, Ulrich G. Oppel, and Tom Rother. SPIE, 2003. http://dx.doi.org/10.1117/12.512351.

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von Laszewski, Gregor, Javier Diaz, Fugang Wang, and Geoffrey C. Fox. "Comparison of Multiple Cloud Frameworks." In 2012 IEEE 5th International Conference on Cloud Computing (CLOUD). IEEE, 2012. http://dx.doi.org/10.1109/cloud.2012.104.

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Jensen, Meiko, Jorg Schwenk, Jens-Matthias Bohli, Nils Gruschka, and Luigi Lo Iacono. "Security Prospects through Cloud Computing by Adopting Multiple Clouds." In 2011 IEEE 4th International Conference on Cloud Computing (CLOUD). IEEE, 2011. http://dx.doi.org/10.1109/cloud.2011.85.

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Gupta, Aditi, Sudip Mittal, Karuna P. Joshi, Claudia Pearce, and Anupam Joshi. "Streamlining Management of Multiple Cloud Services." In 2016 IEEE 9th International Conference on Cloud Computing (CLOUD). IEEE, 2016. http://dx.doi.org/10.1109/cloud.2016.0070.

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Houidi, Ines, Marouen Mechtri, Wajdi Louati, and Djamal Zeghlache. "Cloud Service Delivery across Multiple Cloud Platforms." In 2011 IEEE International Conference on Services Computing (SCC). IEEE, 2011. http://dx.doi.org/10.1109/scc.2011.107.

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Wu, Hsiang Huang, Tse Chen Yeh, and Chien Min Wang. "Multiple Two-Phase Data Processing with MapReduce." In 2014 IEEE 7th International Conference on Cloud Computing (CLOUD). IEEE, 2014. http://dx.doi.org/10.1109/cloud.2014.55.

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Matveev, Artem. "Cost-Efficient Data Privacy Protection in Multi Cloud Storage." In 3rd International Conference on Data Mining and Machine Learning (DMML 2022). Academy and Industry Research Collaboration Center (AIRCC), 2022. http://dx.doi.org/10.5121/csit.2022.120706.

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Data privacy in the cloud is a big concern for all of its users, especially for public clouds. Modern trends in studies utilise multiple clouds to achieve data privacy protection. Most of the present studies focus on business-oriented solutions, but current study aims to create a solution for individual users which would not increase the cost of ownership, and provide enough flexibility and privacy protection by combining password protection, key-derivation, multilayer encryption and key distribution across multiple clouds. New design allows to use single cloud to store protected user data, meanwhile use free plans on other clouds to store key information on others and thereby does not rise a cost of the solution. As a result, proposed design gives multiple layers of protection of Data Privacy while having a low cost of use. With some further adaptation it could be proposed as a business solution.
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Saripalli, Prasad, and Gopal Pingali. "MADMAC: Multiple Attribute Decision Methodology for Adoption of Clouds." In 2011 IEEE 4th International Conference on Cloud Computing (CLOUD). IEEE, 2011. http://dx.doi.org/10.1109/cloud.2011.61.

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Ellens, Wendy, Miroslav ivkovic, Jacob Akkerboom, Remco Litjens, and Hans van den Berg. "Performance of Cloud Computing Centers with Multiple Priority Classes." In 2012 IEEE 5th International Conference on Cloud Computing (CLOUD). IEEE, 2012. http://dx.doi.org/10.1109/cloud.2012.96.

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Reports on the topic "Multiple Cloud"

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Huang, Dong, Stephen E. Schwartz, and Dantong Yu. Determination of Cloud Base Height, Wind Velocity, and Short-Range Cloud Structure Using Multiple Sky Imagers Field Campaign Report. Office of Scientific and Technical Information (OSTI), 2016. http://dx.doi.org/10.2172/1294258.

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Mungiole, Michael, and Alan Wetmore. COMBIC Modifications to Determine Aerosol Cloud Densities for Multiple Obscurant Input Sources. Defense Technical Information Center, 2001. http://dx.doi.org/10.21236/ada392771.

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Sassen, K. Cloud and aerosol characterization for the ARM central facility: Multiple remote sensor techniques development. Office of Scientific and Technical Information (OSTI), 1992. http://dx.doi.org/10.2172/6955485.

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Trishchenko, A. P., Z. Li, and F. L. Chang. Cloud optical depths and TOA fluxes: Comparison between satellite and surface retrievals from multiple platforms. Natural Resources Canada/ESS/Scientific and Technical Publishing Services, 2001. http://dx.doi.org/10.4095/219748.

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Thomson, Ewen M. Location and Characterization of In-Cloud Lightning Currents by Multiple Station VHF and Electric Fields Measurements. Defense Technical Information Center, 1992. http://dx.doi.org/10.21236/ada264097.

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Thomson, Ewen M. The Physical Origin of In-Cloud Lightning Processes Determined from Multiple-Station Wideband Electric Field Research. Defense Technical Information Center, 1998. http://dx.doi.org/10.21236/ada340207.

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Sassen, K. Cloud and aerosol characterization for the ARM central facility: Multiple remote sensor techniques development. Final technical report. Office of Scientific and Technical Information (OSTI), 1993. http://dx.doi.org/10.2172/10105834.

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Sassen, K. Cloud and aerosol characterization for the ARM central facility: Multiple remote sensor techniques development. Technical progress report. Office of Scientific and Technical Information (OSTI), 1992. http://dx.doi.org/10.2172/10183917.

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Kleespies, Thomas J. Retrieval of Cloud Parameters by Multiple Observations in The Near- Infrared under Conditions of Varying Solar Illumination. Defense Technical Information Center, 1993. http://dx.doi.org/10.21236/ada264180.

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Li, Z., A. Trishchenko, H. W. Barker, G. L. Stephens, and P. Partain. Analysis of Atmospheric Radiation Measurement (ARM) program's Enhanced Shortwave Experiment (ARESE) multiple data sets for studying cloud absorption. Natural Resources Canada/ESS/Scientific and Technical Publishing Services, 1999. http://dx.doi.org/10.4095/219777.

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