Showing papers on "Cloud computing published in 2005"

Virtual computing infrastructure

Abstract: A cloud computing environment having a plurality of computing nodes is described. A plurality of permissions associated with a cloud customer is created. A first set of permissions from the plurality of permissions is associated with one or more objects. Each of the first set of permissions describes an action performed on an object. A second set of permissions from the plurality of permissions is associated with one or more users. Each of the second set of permissions describes an action to be performed by one or more users.

Analysis of Load Balancing Techniques in Cloud Computing

Abstract: Cloud Computing is an emerging computing paradigm. It aims to share data, calculations, and service transparently over a scalable network of nodes. Since Cloud computing stores the data and disseminated resources in the open environment. So, the amount of data storage increases quickly. In the cloud storage, load balancing is a key issue. It would consume a lot of cost to maintain load information, since the system is too huge to timely disperse load. Load balancing is one of the main challenges in cloud computing which is required to distribute the dynamic workload across multiple nodes to ensure that no single node is overwhelmed. It helps in optimal utilization of resources and hence in enhancing the performance of the system. A few existing scheduling algorithms can maintain load balancing and provide better strategies through efficient job scheduling and resource allocation techniques as well. In order to gain maximum profits with optimized load balancing algorithms, it is necessary to utilize resources efficiently. This paper discusses some of the existing load balancing algorithms in cloud computing and also their challenges.

The Limitations of the Cloud Point Measurement Techniques and the Influence of the Oil Composition on Its Detection

Abstract: In the petroleum industry, cloud points are one of the main guides to evaluate the wax precipitation potential of a fluid. The planning of the exploration of a reservoir or the design of its pipelines are based on the measured cloud points for the reservoir oil. It is known that each measuring technique will provide a different cloud point temperature, yet although some of these techniques seem to be more accurate than others, no definite conclusion was established on how cloud points should be measured. On this work, several cloud point measurement techniques are discussed and compared. It will be shown that some of these techniques, such as viscosity, filter plugging, and differential scanning calorimetry (DSC) can only be used under very favorable circumstances, but it will be argued that because every technique requires some finite, often large, amount of solid to detect the presence of a new phase, the cloud point, defined as the temperature for which the first solid appears in the oil, is n...

The Internet of Things in the Cloud: A Middleware Perspective

Abstract: Although the Internet of Things (IoT) is a vast and dynamic territory that is evolving rapidly, there has been a need for a book that offers a holistic view of the technologies and applications of the entire IoT spectrum. Filling this void, The Internet of Things in the Cloud: A Middleware Perspective provides a comprehensive introduction to the IoT and its development worldwide.It gives you a panoramic view of the IoT landscapefocusing on the overall technological architecture and design of a tentatively unified IoT framework underpinned by Cloud computing from a middleware perspective. Organized into three sections, it: Describes the many facets of Internet of Thingsincluding the four pillars of IoT and the three layer value chain of IoT Focuses on middleware, the glue and building blocks of a holistic IoT system on every layer of the architecture Explores Cloud computing and IoT as well as their synergy based on the common background of distributed processing The book is based on the authors two previous bestselling books (in Chinese) on IoT and Cloud computing and more than two decades of hands-on software/middleware programming and architecting experience at organizations such as the Oak Ridge National Laboratory, IBM, BEA Systems, and Silicon Valley startup Doubletwist. Tapping into this wealth of knowledge, the book categorizes the many facets of the IoT and proposes a number of paradigms and classifications about Internet of Things' mass and niche markets and technologies.

Cloud Computing Issues

Abstract: Cloud computing is Internet-based computing, whereby shared resources, software and information, are provided to computers and devices on-demand, like the electricity grid. It aims to construct a perfect system with powerful computing capability through a large number of relatively low-cost computing entity, and using the advanced business models like SaaS (Software as a Service), PaaS (Platform as a Service), IaaS (Infrastructure as a Service) to distribute the powerful computing capacity to end users’ hands. Cloud Computing represents a new computing model that poses many demanding security issues at all levels, e.g., network, host, application, and data levels. The variety of the delivery models presents different security challenges depending on the model and consumers’ Quality of Service (QoS) requirements. Confidentiality, Integrity, Availability, Authenticity, and Privacy are essential concerns for both Cloud providers and consumers as well. This paper introduces the existing issues in cloud computing such as security, privacy, reliability and so on. This paper surveys the security problems of current cloud computing.

The time-space exchangeability of satellite retrieved relations between cloud top temperature and particle effective radius

Abstract: . A 3-minute 3-km rapid scan of the METEOSAT Second Generation geostationary satellite over southern Africa was applied to tracking the evolution of cloud top temperature (T) and particle effective radius (re) of convective elements. The evolution of T-re relations showed little dependence on time, leaving re to depend almost exclusively on T. Furthermore, cloud elements that fully grew to large cumulonimbus stature had the same T-re relations as other clouds in the same area with limited development that decayed without ever becoming a cumulonimbus. Therefore, a snap shot of T-re relations over a cloud field provides the same relations as composed from tracking the time evolution of T and re of individual clouds, and then compositing them. This is the essence of exchangeability of time and space scales, i.e., ergodicity, of the T-re relations for convective clouds. This property has allowed inference of the microphysical evolution of convective clouds with a snap shot from a polar orbiter. The fundamental causes for the ergodicity are suggested to be the observed stability of re for a given height above cloud base in a convective cloud, and the constant renewal of growing cloud tops with cloud bubbles that replace the cloud tops with fresh cloud matter from below.

Some Statistical Analysis of the Normal Cloud Model

Abstract: The probability distributions of the cloud drop and its certainty degree are analyzed. Then the definition of the expectation curve of the normal cloud model is given. Finally, the trends and rules of the normal cloud model, of which the shapes envolve as the parameters change, are discussed. All the above stochastic analysis has some values in theory and application, and will help to develop and perfect the normal cloud model in a wider and higher level.

Cluster analysis of cloud regimes and characteristic dynamics of midlatitude synoptic systems in observations and a model

Abstract: [1] Global climate models typically do not correctly simulate cloudiness associated with midlatitude synoptic systems because coarse grid spacing prevents them from resolving dynamics occurring at smaller scales and there exist no adequate parameterizations for the effects of these subgrid-scale dynamics. Comparison of modeled and observed cloud properties averaged over similar regimes (e.g., compositing) aids the diagnosis of simulation errors and identification of meteorological forcing responsible for producing particular cloud conditions. This study uses a k-means clustering algorithm to objectively classify satellite cloud scenes into distinct regimes based on grid box mean cloud fraction, cloud reflectivity, and cloud top pressure. The spatial domain is the densely instrumented southern Great Plains site of the Atmospheric Radiation Measurement Program, and the time period is the cool season months (November–March) of 1999–2001. As a complement to the satellite retrievals of cloud properties, lidar and cloud radar data are analyzed to examine the vertical structure of the cloud layers. Meteorological data from the constraint variational analysis is averaged for each cluster to provide insight on the large-scale dynamics and advective tendencies coincident with specific cloud types. Meteorological conditions associated with high and low subgrid spatial variability are also investigated for each cluster. Cloud outputs from a single-column model version of the GFDL AM2 atmospheric model forced with meteorological boundary conditions derived from observations and a numerical weather prediction model were compared to observations for each cluster in order to determine the accuracy with which the model reproduces attributes of specific cloud regimes.

Cache Clouds: Cooperative Caching of Dynamic Documents in Edge Networks

Abstract: Caching on the edge of the Internet is becoming a popular technique to improve the scalability and efficiency of delivering dynamic Web content. In this paper, we study the challenges in designing a large scale cooperative edge cache network, focusing on mechanisms and methodologies for efficient cooperation among caches to improve the overall performance of the edge cache network. This paper makes three original contributions. First, we introduce the concept of cache clouds, which forms the fundamental framework for cooperation among caches in the edge network. Second, we present dynamic hashing-based protocols for document lookups and updates within each cache cloud, which are not only efficient, but also effective in dynamically balancing lookup and update loads among the caches in the cloud. Third, we outline a utility-based mechanism for placing dynamic documents within a cache cloud. Our experiments indicate that these techniques can significantly improve the performance of the edge cache networks

On multiple attribute group decision making with linguistic assessment information based on cloud model

Abstract: Abstrcat To the problem of aggregation and expression of bias with linguistic assessment information in the multi-attribute group decision making, the bias expression,the bias aggregation and the alternative selection of experts based on cloud model are studied. Cloud model is used to express the linguistic assessment information given by each decision maker. The power of attribute and decision maker is calculated by the mood arithmetic of cloud. The bias aggregation is executed by means of floating cloud. The order and alternative of selection is determined according to the relative distance of cloud model. The burring and randomicity of assessment is fully expressed in this method.

Case study of inhomogeneous cloud parameter retrieval from MODIS data

Abstract: [1] Cloud parameter retrieval of inhomogeneous and fractional clouds is performed for a stratocumulus scene observed by MODIS at a solar zenith angle near 60°. The method is based on the use of neural network technique with multispectral and multiscale information. It allows to retrieve six cloud parameters, i.e. pixel means and standard deviations of optical thickness and effective radius, fractional cloud cover, and cloud top temperature. Retrieved cloud optical thickness and effective radius are compared to those retrieved with a classical method based on the homogeneous cloud assumption. Subpixel fractional cloud cover and optical thickness inhomogeneity are compared with their estimates obtained from 250m pixel observations; this comparison shows a fairly good agreement. The cloud top temperature appears also retrieved quite suitably.

Thermodynamic and cloud parameter retrieval using infrared spectral data

Abstract: High-resolution infrared radiance spectra obtained from near nadir observations provide atmospheric, surface, and cloud property information. A fast radiative transfer model, including cloud effects, is used for atmospheric profile and cloud parameter retrieval. The retrieval algorithm is presented along with its application to recent field experiment data from the NPOESS Airborne Sounding Testbed - Interferometer (NAST-I). The retrieval accuracy dependence on cloud properties is discussed. It is shown that relatively accurate temperature and moisture retrievals can be achieved below optically thin clouds. For optically thick clouds, accurate temperature and moisture profiles down to cloud top level are obtained. For both optically thin and thick cloud situations, the cloud top height can be retrieved with an accuracy of approximately 1.0 km. Preliminary NAST-I retrieval results from the recent Atlantic-THORPEX Regional Campaign (ATReC) are presented and compared with coincident observations obtained from dropsondes and the nadir-pointing Cloud Physics Lidar (CPL).

Deep-space to ground laser communications in a cloudy world

Abstract: Future deep-space communications will require the collection and transmission of data from high-bandwidth links. NASA's Jet Propulsion Laboratory (JPL) is investigating the utility of laser communications for future missions to Mars and for future communication stations on the moon. Cloud cover impacts the availability of space to ground optical communications. Mitigating these impacts requires a geographically diverse network of ground communication. Selecting the number and location of stations for a network requires an optimization algorithm that can distinguish and rank site availability based on multi-year cloud climatologies for many locations around the globe. The optimization algorithm must also consider the movement and location of a space-borne probe. In this JPL-funded study, the TASC Lasercom Network Optimization Tool (LNOT) is used to determine optimal networks of receiving stations by analyzing cloud mask data from the continental United States, Hawaii, South America, Europe, northern and southern Africa, the Middle East, central and eastern Asia, and Australia. To generate cloud masks, raw visible and infrared radiance data from GOES (Geostationary Operational Environmental Satellite) and Meteosat satellites are compared to predicted clear sky background values. Several threshold tests in the Cloud Mask Generator (CMG) involving radiance-derived cloud identification tools (e.g., fog product, albedo product) are used to estimate the probability of cloud cover for a given pixel of a satellite image. When stations are chosen from a list of sites of interest, six stations are needed to achieve a network availability of 90 % or better.

Determining cloud forcing by cloud type from geostationary satellite data

Abstract: [1] To fully attribute differences in cloud forcing between regions, or between models and observations, requires separation of the effects of different cloud types. This can only be achieved using data which resolve both day to day and diurnal variations. Here we propose a method which takes advantage of the high temporal resolution of geostationary data to estimate the contribution of individual cloud types to the monthly mean and monthly time-step mean cloud forcing in each grid-box. Details of the method are outlined, and data from the Meteosat-8 satellite for June 2004 are used to demonstrate its benefits.

CBTM: a trust model with uncertainty quantification and reasoning for pervasive computing

Abstract: This paper presents a novel trust model in which we model trust based on an exotic uncertainty theory, namely cloud model. We regard trust between entities as a cloud that is called as trust cloud. Based on such a quantification model of trust, we further propose the algorithms to compute propagated trust relationships and aggregated trust relationships, which are needed for trust reasoning in pervasive computing environments. Finally, we compare the proposed trust model with other three typical models in simulation experiments, and the results shows the cloud-based trust model performs better in a total sense.

Method of providing storage to virtual computer cluster within shared computing environment

Abstract: An embodiment of a method of providing storage to a virtual computer cluster within a shared computing environment (200) begins with a first step of combining storage resources within the shared computing environment (200) into a virtual storage pool. The virtual storage pool comprises at least portions of storage devices (204, 220) in which at least one of the storage devices (204, 220) is not directly accessible by all computers (202) which directly access any of the storage devices (204, 220). The method continues with a second step of partitioning a virtual storage volume from the virtual storage pool. In a third step, the method assigns the virtual storage volume to the virtual computer cluster. The method concludes with a fourth step of maiking the virtual storage volume accessible to computing platforms (306, 406) of the virtual computer cluster using software. The software allows access to the virtual storage volume by computing platforms (306, 406) while precluding access to remaining storage within the shared computing environment (200) by the computing platforms (306, 406).

Visible Infrared Imager Radiometer Suite: A New Operational Cloud Imager

Abstract: INTRODUCTION Satellite Meteorology Overview of Numerical Weather Prediction Modeling Evolution of Observational Data for Numerical Weather Prediction Modeling Additional Applications of Meteorological Satellite Data METEOROLOGICAL SATELLITE SYSTEMS Evolution of Satellites and Sensors The National Polar-Orbiting Operational Environmental Satellite System (NPOESS) VIIRS IMAGERY DESIGN ANALYSIS VIIRS Environmental Data Record Requirements Overview VIIRS Imagery Requirements Cloud Applications-Related Imagery Requirements Value of Manually Generated Cloud Analyses VIIRS IMAGERY REQUIREMENTS ANALYSIS Theoretical Basis for Manual Cloud Analyses Overview of Approach to Instrument Design Cloud Truth Data Sets to Flowdown Sensor Requirements Derivation of Sensing Requirements from Analysis Requirements Overview of VIIRS Hardware Design PRINCIPLES IN IMAGE INTERPRETATION Introduction VIIRS Imagery Data VIIRS Imagery Assist Data MULTICOLOR COMPOSITES OF MULTISPECTRAL IMAGERY Introduction Color Composites of (0.645-mm, 0.865-mm, and 12.0-mm) Surface Vegetation and Cloud Classifications Color Composites (3.7-mm albedo, 0.865- mm, 12.0- mm) for Snow Detection Color Composites of (0.645-mm, 0.645-mm, 3.7-mm albedo) Snow Mapping Through Thin Cirrus Clouds Color Composites of (0.412-mm, 0.865-mm, and 0.645-mm) Clouds Over Arid Regions CASE STUDIES IN THE USE OF MULTICOLOR COMPOSITES FOR SCENE INTERPRETATION Overview MODIS Airborne Simulation Data Over Alaska MODIS Airborne Simulation Success Data Collected Over Colorado AUTOMATED 3-D CLOUD ANALYSES FROM NPOESS Architecture for 3-D Cloud Analyses Automated Cloud Detection Cloud Top Phase Classifications Cloud Optical (Thickness and Particle Size) Properties Cloud Top (Temperature, Pressure, and Height) Parameters Cloud Base Heights REFERENCES INDEX

Mitigating the impact of clouds on optical communications

Abstract: NASA Jet Propulsion Laboratory (JPL) is interested in adding optical communications to its deep space communications network. Clouds adversely affect the transmission of optical communications. Mitigating the effects of clouds to achieve reliable communications requires a geographically diverse set of ground receiver stations. To study cloud effects on optical communications we have developed a high-resolution cloud climatology based on NOAA Geostationary Environmental Operational Satellite (GOES) imager data. The GOES imager includes multi-spectral channels, one visible and four infrared, at 4-km spatial resolution and 15-minute temporal resolution. Cloud detection is accomplished by modeling the radiance of the ground in the absence of clouds and comparing these to the actual radiance values from the imagery. A composite cloud decision is formed by objectively combining the results of the tests from the individual channels. Ground site selection studies are accomplished using the Lasercom Network Optimization Tool (LNOT). LNOT applies a discrete optimization algorithm to the cloud climatology dataset to find the optimal number and locations of ground stations for a given concept of operations. Applying LNOT to the JPL problem, we find that 90% availability could be achieved with 4-5 ground stations in the continental US (CONUS) and Hawaii. We also present the results of a pilot study that includes 6 months of cloud data over South America. We are continuing to expand our study by developing a global cloud analysis database, which will be used to identify the number and locations of global optical ground stations needed to achieve 90-97% availability. Currently, we are developing cloud detection capability over regions of interest in Europe, Africa, the Middle East, Asia and Australia based on the European Organization for the Exploitation of Meteorological Satellites (EUMETSAT) satellites: Meteosat-5, -7, -8 and NOAA GOES-9 imagery currently being operated by the Japanese Meteorological Agency (JMA)

Evaluation of a GCM subgrid cloud‐radiation interaction parameterization using cloud‐resolving model simulations

Abstract: [1] The mosaic approach of Liang and Wang (1997) for the general circulation model (GCM) parameterization of subgrid cloud-radiation interactions is evaluated against the validated cloud-resolving model (CRM) simulation of the Atmospheric Radiation Measurement (ARM) intensive observation period (IOP, June 22–July 17, 1997) at the Southern Great Plains (SGP) site. The CRM-generated cloud statistics determines the required characteristic structure differences between three primary cloud genera (convective, anvil and stratiform). It is demonstrated that the mosaic approach with the CRM cloud statistics well simulates the CRM domain-averaged radiative quantities. The result indicates that the mosaic approach of the cloud overlap based on the cloud genera differing in formation mechanisms and of the optical inhomogeneity by cloud water path scaling can capture, respectively, the dominant effects of the cloud geometric association and optical property variability within a GCM grid.

A Comparison of GOES Sounder– and Cloud Lidar- and Radar-Retrieved Cloud-Top Heights

Abstract: An assessment of the performance of the Geostationary Operational Environmental Satellite (GOES) sounder cloud-top pressure product is presented. GOES sounder cloud-top-height data were compared with values derived from a consensus cloud boundary dataset that utilizes data from a cloud lidar and a cloud radar located at the U.S. Department of Energy’s (DOE’s) Atmospheric Radiation Measurement (ARM) Program’s Cloud and Radiation Test Bed (CART) site in Lamont, Oklahoma. Comparisons were performed from April 2000 to March 2002. A temporal filtering process was applied to the cloud lidar and cloud radar output so that a representative picture of the cloud field on the same spatial scale of the GOES sounder could be derived. Comparisons between the GOES sounder and ground-based cloud boundary measurements yielded a mean difference of 1772 m and a standard deviation of 1733 m. The difference between GOES cloud-top-height and ground-based retrievals is within ±500 m for 22% of the retrievals and within...

Prototyping automatic cloud cover assessment (ACCA) algorithm for remote sensing on-board processing on a reconfigurable computer

Abstract: Clouds have a critical role in many studies, e.g. weather- and climate-related studies. However, they represent a source of errors in many applications, and the presence of cloud contamination can hinder the use of satellite data. This requires a cloud detection process to mask out cloudy pixels from further processing. The trend for remote sensing satellite missions has always been towards smaller size, lower cost, more flexibility, and higher computational power. Reconfigurable computers (RCs) combine the flexibility of traditional microprocessors with the power of field programmable gate arrays (FPGAs). Therefore, RCs are a promising candidate for on-board preprocessing. This paper presents the design and implementation of an RC-based real-time cloud detection system. We investigate the potential of using RCs for on-board preprocessing by prototyping the Landsat 7 ETM+ ACCA algorithm on one of the state-of-the art reconfigurable platforms, SRC-6E. Although a reasonable amount of investigations of the ACCA cloud detection algorithm using FPGAs has been reported in the literature, very few details/results were provided and/or limited contributions were accomplished. Our work has been proven to provide higher performance and higher detection accuracy

Cloud Properties Simulated by a Single-Column Model. Part I: Comparison to Cloud Radar Observations of Cirrus Clouds

Abstract: This study describes and demonstrates a new method for identifying deficiencies in how cloud processes are represented in large-scale models. Kilometer-scale-resolving cloud radar observations and cloud-resolving model (CRM) simulations were used to evaluate the representation of cirrus clouds in the single-column model (SCM) version of the National Centers for Environmental Prediction Global Forecast System model for a 29-day period during June and July 1997 at the Atmospheric Radiation Measurement Program site in Oklahoma. To produce kilometer-scale cirrus statistics from the SCM results, synthetic subgrid-scale (SGS) cloud fields were generated using the SCM’s cloud fraction and hydrometeor content profiles, and the SCM’s cloud overlap and horizontal inhomogeneity assumptions. Three sets of SCM synthetic SGS cloud fields were analyzed. Two NOSNOW sets were produced in which clouds did not include snow; one set used random overlap, the other, maximum/random. In the SNOW set, clouds included sno...

Application of an automated cloud-tracking algorithm on satellite imagery for tracking and monitoring small mesoscale convective cloud systems

Abstract: In this study, an automatic algorithm for tracking convective cloud cells, on the basis of infrared and water vapour Meteosat images, is applied in the case of intense precipitation events of 26 and 27 January 1996 in Greece, and the results are presented. The case presented in this study has the particularity of consisting of several localized maximum precipitation events that resulted from small mesoscale convective systems. The ability of the algorithm to detect and track in Meteosat images, in real mode, propagating cloud systems of this size, through the monitoring of several cloud parameters that express cloud development and movement, is examined. It was found that the algorithm was capable of identifying small mesoscale cloud cells and tracking them consistently to the point of dissipation. Moreover, the introduction in the algorithm of new cloud parameters, which are directly related to the cloud‐top structure, has proved very valuable in providing additional information on the convective potenti...

A Survey of Virtualization Techniques Focusing on Secure On-Demand Cluster Computing

Abstract: Virtualization, a technique once used to multiplex the resources of high-priced mainframe hardware, is seeing a resurgence in applicability with the increasing computing power of commodity computers. By inserting a layer of software between the machine and traditional operating systems, this technology allows access to a shared computing medium in a manner that is secure, resource-controlled, and efficient. These properties are attractive in the field of on-demand computing, where the fine-grained subdivision of resources provided by virtualized systems allows potentially higher utilization of computing resources. It this work, we survey a number of virtual machine systems with the goal of finding an appropriate candidate to serve as the basis for the On-Demand Secure Cluster Computing project at the National Center for Supercomputing Applications. Contenders are reviewed on a number of desirable properties including portability and security. We conclude with a comparison and justification of our choice.

Cloud detection for CHRIS/Proba hyperspectral images

Abstract: Accurate and automatic detection of clouds in satellite scenes is a key issue for a wide range of remote sensing applications. With no accurate cloud masking, undetected clouds are one of the most significant source of error in both sea and land cover biophysical parameter retrieval. Sensors with spectral channels beyond 1 um have demonstrated good capabilities to perform cloud masking. This spectral range can not be exploited by recently developed hyperspectral sensors that work in the spectral range between 400- 1000 nm. However, one can take advantage of their high number of channels and spectral resolution to increase the cloud detection accuracy, and to describe properly the detected clouds (cloud type, height, subpixel coverage, could shadows, etc.) In this paper, we present a methodology for cloud detection that could be used by sensors working in the VNIR range. First, physically-inspired features are extracted (TOA reflectance and their spectral derivatives, atmospheric oxygen and water vapour absorptions, etc). Second, growing maps are built from cloud-like pixels to select regions which potentially could contain clouds. Then, an unsupervised clustering algorithm is applied in these regions using all extracted features. The obtained clusters are labeled into geo-physical classes taking into account the spectral signature of the cluster centers. Finally, an spectral unmixing algorithm is applied to the segmented image in order to obtain an abundance map of the cloud content in the cloud pixels. As a direct consequence of the detection scheme, the proposed system is capable to yield probabilistic outputs on cloud detected pixels in the image, rather than flags. Performance of the proposed algorithm is tested on six CHRIS/Proba Mode 1 images, which presents a spatial resolution of 32 m, 62 spectral bands with 6-20 nm bandwidth, and multiangularity.

Towards evaluating cloud response to climate change using clustering technique identification of cloud regimes

Abstract: Most of the discrepancies in the climate sensitivity of general circulation models (GCMs) are believed to be due to differences in cloud radiative feedback. Analysis of cloud response to climate change in different ‘regimes’ may offer a more detailed understanding of how the cloud response differs between GCMs. In which case, evaluation of simulated cloud regimes against observations in terms of both their cloud properties and frequency of occurrence will assist in assessing confidence in the cloud response to climate change in a particular GCM. In this study, we use a clustering technique on International Satellite Cloud Climatology Project (ISCCP) data and on ISCCP-like diagnostics from two versions of the Hadley Centre GCM to identify cloud regimes over four different geographical regions. The two versions of the model are evaluated against observational data and their cloud response to climate change compared within the cloud regime framework. It is found that cloud clusters produced by the more recent GCM, HadSM4, compare more favourably with observations than HadSM3. In response to climate change, although the net cloud response over particular regions is often different in the two models, in several instances the same basic processes may be seen to be operating. Overall, both changes in the frequency of occurrence of cloud regimes and changes in the properties (optical depth and cloud top height) of the cloud regimes contribute to the cloud response to climate change.

Adding new features in a peer-to-peer distributed computing framework

Abstract: The aim of P2P computing is to build virtual computing systems dedicated to large-scale computational problems. This kind of architecture will contain several thousands of computing nodes, which need to be organized. Moreover, the architecture will deal with node's dynamism and network scalability. JXTA proposes an underlying infrastructure, which builds a virtual network of peers on top of a physical network. This structure allows, among other features, to create peer groups. JNGI, which is one of the first P2P decentralized computing platforms, is already using these groups to structure the computing system. This article aims to demonstrate how to build new types of groups called similarity groups into the JNGI project. A similarity group is a peer group where all the peers have common characteristics like CPU speed or memory size. This is done in order to increase the relevance of task dispatching and therefore to increase the performance of JNGI.

Feature tracking in VR for cumulus cloud life-cycle studies

Abstract: Feature tracking in large data sets is traditionally an off-line, batch processing operation while virtual reality typically focuses on highly interactive tasks and applications. This paper presents an approach that uses a combination of off-line preprocessing and interactive visualization in VR to simplify and speed up the identification of interesting features for further study. We couch the discussion in terms of our collaborative research on using virtual reality for cumulus cloud life-cycle studies, where selecting suitable clouds for study is simple for the skilled observer but difficult to formalize. The preprocessing involves identifying individual clouds within the data set through a 4D connected components algorithm, and then saving isosurface, bounding box, and volume information. This information is then interactively visualized in our VR Cloud Explorer with various tools and information displays to identify the most interesting clouds. In a small pilot study, reasonable performance, both in the preprocessing phase and the visualization phase, has been measured.