SEARCH · Engineering Papers
Results for “Cloud Computing”
Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.
Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.
Satellite-tracked cumulus velocities
Basic problems in the interpretation of satellite-tracked low-cloud velocities are reviewed. The METRACOM system of cloud velocity computation is outlined, and caution is urged in converting cloud velocities into wind velocities. The motion of various cumulus cells over Springfield, Mo., Barbados, and Tampa, Fla., is analyzed. It is shown that multiturret cells do not always move with the wind, that addition and deletion of turrets belonging to a specific cell may cause erratic motion in a tracer cell, and that cumulus turrets between 0.3 and 2 miles in size are the best targets for inferring the mean wind velocity within the subcloud layers. It is concluded that the accuracy of wind velocity estimates will be no better than 2 meters/sec unless the physical and dynamic characteristics of cumulus motion are further investigated.
Arctic ocean radiative fluxes and cloud forcing estimated from the ISCCP C2 cloud dataset, 1983-1990
Radiative fluxes and cloud forcings for the ocean areas of the Arctic are computed from the monthly cloud product of the International Satellite Cloud Climatology Project (ISCCP) for 1983-90. Spatially averaged short-wave fluxes are compared well with climatological values, while downwelling longwave fluxes are significantly lower. This is probably due to the fact that the ISCCP cloud amounts are underestimates. Top-of-the-atmosphere radiative fluxes are in excellent agreement with measurements from the Earth Radiation Budget Experiment (ERBE). Computed cloud forcings indicate that clouds have a warming effect at the surface and at the top of the atmosphere during winter and a cooling effect during summer. The net radiative effect of clouds is larger at the surface during winter but greater at the top of the atmosphere during summer. Overall the net radiative effect of clouds at the top of the atmosphere is one of cooling. This is in contrast to a previous result from ERBE data showing arctic cloud forcings have a net warming effect. Sensitivities to errors in input parameters are generally greater during winter with cloud amount being the most important paarameter. During summer the surface radiation balance is most sensitive to errors in the measurements of surface reflectance. The results are encouraging, but the estimated error of 20 W/sq m in surface net radiative fluxes is too large, given that estimates of the net radiative warming effect due to a doubling of CO2 are on the order of 4 W/sq m. Because it is difficult to determine the accuracy of results with existing in situ observations, it is recommended that the development of improved algorithms for the retrieval of surface radiative properties be accompanied by the simultaneous assembly of validation datasets.
Game-Theoretic Strategies for Cyber-Physical Infrastructures Under Component Disruptions
In this work, networked infrastructures of recursively defined systems composed of discrete cyber and physical components are considered. The components of basic systems at the finest levels can be disrupted by cyber or physical means, and can be reinforced to survive at certain costs. A problem of ensuring the infrastructure performance is formulated as a game between a provider and an attacker, who probabilistically choose components to reinforce and attack, respectively. The disruptions of this infrastructure are characterized using the aggregate failure correlation function that specifies the conditional failure probability of the infrastructure given the failure of an individual system at that level. The survival probabilities of basic systems satisfy simple product-form, first-order differential equations expressed in terms of the multiplier functions. The utility functions of the provider and attacker are composed of the reward and cost terms, both expressed in terms of the component reinforcement and attack probabilities. The Nash equilibrium of this game is characterized, along with the sensitivity functions of the survival probabilities of basic systems that highlight their dependence individually on the cost-benefit terms, the correlation functions, and the multiplier functions. These results are illustrated using simplified models of a distributed cloud servers infrastructure, a 5G data network infrastructure, a high performance computing federation, and a smart energy grid infrastructure.
Enabling the rapid search of genetic sequence databases with Bloom filters [Slides]
Abstract not provided.
The Concept of a Quantum Edge Simulator: Edge Computing and Sensing in the Quantum Era
Sensors, enabling observations across vast spatial, spectral, and temporal scales, are major data generators for information technology (IT). Processing, storing, and communicating this ever-growing amount of data pose challenges for the current IT infrastructure. Edge computing—an emerging paradigm to overcome the shortcomings of cloud-based computing—could address these challenges. Furthermore, emerging technologies such as quantum computing, quantum sensing, and quantum communications have the potential to fill the performance gaps left by their classical counterparts. Here, we present the concept of an edge quantum computing (EQC) simulator—a platform for designing the next generation of edge computing applications. An EQC simulator is envisioned to integrate elements from both quantum technologies and edge computing to allow studies of quantum edge applications. The presented concept is motivated by the increasing demand for more sensitive and precise sensors that can operate faster at lower power consumption, generating both larger and denser datasets. These demands may be fulfilled with edge quantum sensor networks. Envisioning the EQC era, we present our view on how such a scenario may be amenable to quantification and design. Given the cost and complexity of quantum systems, constructing physical prototypes to explore design and optimization spaces is not sustainable, necessitating EQC infrastructure and component simulators to aid in co-design. We discuss what such a simulator may entail and possible use cases that invoke quantum computing at the edge integrated with new sensor infrastructures.
SMASH: Survey of the MAgellanic Stellar History
The Large and Small Magellanic Clouds are unique local laboratories for studying the formation and evolution of small galaxies in exquisite detail. The Survey of the MAgellanic Stellar History (SMASH) is an NOAO community Dark Energy Camera (DECam) survey of the Clouds mapping 480 deg{sup 2} (distributed over ∼2400 square degrees at ∼20% filling factor) to ∼24th mag in ugriz . The primary goals of SMASH are to identify low surface brightness stellar populations associated with the stellar halos and tidal debris of the Clouds, and to derive spatially resolved star formation histories. Here, we present a summary of the survey, its data reduction, and a description of the first public Data Release (DR1). The SMASH DECam data have been reduced with a combination of the NOAO Community Pipeline, the PHOTRED automated point-spread-function photometry pipeline, and custom calibration software. The astrometric precision is ∼15 mas and the accuracy is ∼2 mas with respect to the Gaia reference frame. The photometric precision is ∼0.5%–0.7% in griz and ∼1% in u with a calibration accuracy of ∼1.3% in all bands. The median 5 σ point source depths in ugriz are 23.9, 24.8, 24.5, 24.2, and 23.5 mag. The SMASH data have already been used to discover the Hydra II Milky Way satellite, the SMASH 1 old globular cluster likely associated with the LMC, and extended stellar populations around the LMC out to R ∼ 18.4 kpc. SMASH DR1 contains measurements of ∼100 million objects distributed in 61 fields. A prototype version of the NOAO Data Lab provides data access and exploration tools.
Cloud cover estimation using bispectral satellite measurements
An algorithm has been developed for cloud cover estimation using bispectral satellite measurements. Based on the distribution of pixels in albedo-brightness temperature space, a number of threshold values are applied to identify those pixels which are most likely totally cloud filled. Mean cloudy-column albedo in a region much larger than a single pixel is then estimated and cloud cover computed. The algorithm has been applied to the International Satellite Cloud Climatology Project Geostationary Meteorological Satellite B2 data. Locations of tropical convective cells and the passage of fronts in higher latitudes are identified. Since these features represent the states of large-scale atmospheric circulations, it is concluded that the algorithm can yield consistent cloud data sets useful for climate studies.
Relationship between the longwave cloud radiative forcing at the surface and the top of the atmosphere
An analysis is presented which suggests a technique that may be able to circumvent the problem of mapping the global longwave surface radiation budget from space in the presence of clouds. A theoretical framework is given that avoids the explicit computation of cloud fraction and the location of cloud base. It is found that in regions where a particular cloud regime exists preferentially, a relationship between the mean long range cloud radiative forcing (CRF) at the top of the atmosphere and at the surface can be shown to exist. Results from a general circulation model suggest that this relationship for monthly means is coherent over fairly large geographical areas. For example, in tropical convective areas, the longwave CRF at the top is very large, but at the surface it is quite small because of the high opacity of the lowest layers of the atmosphere. It is also found that, in areas of stratus over cool ocean surfaces, the longwave CRF at the top is very small but at the surface it is quite substantial.
HPC Obfuscation and Security Toolkit
Explore the source record for details and available documents.
Cloud object store for archive storage of high performance computing data using decoupling middleware
Cloud object storage is enabled for archived data, such as checkpoints and results, of high performance computing applications using a middleware process. A plurality of archived files, such as checkpoint files and results, generated by a plurality of processes in a parallel computing system are stored by obtaining the plurality of archived files from the parallel computing system; converting the plurality of archived files to objects using a log structured file system middleware process; and providing the objects for storage in a cloud object storage system. The plurality of processes may run, for example, on a plurality of compute nodes. The log structured file system middleware process may be embodied, for example, as a Parallel Log-Structured File System (PLFS). The log structured file system middleware process optionally executes on a burst buffer node.
World-wide cloud cover distribution for use in computer simulations Final report, 18 Jan. 1967 - 17 Jan. 1968
Probability distributions of world wide cloud cover for computer simulated observations of earth from space
Assessing the Amazon Cloud Suitability for CLARREO's Computational Needs
In this document we compare the performance of the Amazon Web Services (AWS), also known as Amazon Cloud, with the CLARREO (Climate Absolute Radiance and Refractivity Observatory) cluster and assess its suitability for computational needs of the CLARREO mission. A benchmark executable to process one month and one year of PARASOL (Polarization and Anistropy of Reflectances for Atmospheric Sciences coupled with Observations from a Lidar) data was used. With the optimal AWS configuration, adequate data-processing times, comparable to the CLARREO cluster, were found. The assessment of alternatives to the CLARREO cluster continues and several options, such as a NASA-based cluster, are being considered.
Cloud Bursting with GlideinWMS: Means to satisfy ever increasing computing needs for Scientific Workflows
Scientific communities have been in the forefront of adopting new technologies and methodologies in the computing. Scientific computing has influenced how science is done today, achieving breakthroughs that were impossible to achieve several decades ago. For the past decade several such communities in the Open Science Grid (OSG) and the European Grid Infrastructure (EGI) have been using GlideinWMS to run complex application workflows to effectively share computational resources over the grid. GlideinWMS is a pilot-based workload management system (WMS) that creates on demand, a dynamically sized overlay HTCondor batch system on grid resources. At present, the computational resources shared over the grid are just adequate to sustain the computing needs. We envision that the complexity of the science driven by 'Big Data' will further push the need for computational resources. To fulfill their increasing demands and/or to run specialized workflows, some of the big communities like CMS are investigating the use of cloud computing as Infrastructure-As-A-Service (IAAS) with GlideinWMS as a potential alternative to fill the void. Similarly, communities with no previous access to computing resources can use GlideinWMS to setup up a batch system on the cloud infrastructure. To enable this, the architecture of GlideinWMS has been extended to enable support for interfacing GlideinWMS with different Scientific and commercial cloud providers like HLT, FutureGrid, FermiCloud and Amazon EC2. In this paper, we describe a solution for cloud bursting with GlideinWMS. The paper describes the approach, architectural changes and lessons learned while enabling support for cloud infrastructures in GlideinWMS.
Quantum chemistry as a benchmark for near-term quantum computers
We present a quantum chemistry benchmark for noisy intermediate-scale quantum computers that leverages the variational quantum eigensolver, active-space reduction, a reduced unitary coupled cluster ansatz, and reduced density purification as error mitigation. We demonstrate this benchmark using 4 of the available qubits on the 20-qubit IBM Tokyo and 16-qubit Rigetti Aspen processors via the simulation of alkali metal hydrides (NaH, KH, RbH), with accuracy of the computed ground state energy serving as the primary benchmark metric. We further parameterize this benchmark suite on the trial circuit type, the level of symmetry reduction, and error mitigation strategies. Our results demonstrate the characteristically high noise level present in near-term superconducting hardware, but provide a relevant baseline for future improvement of the underlying hardware, and a means for comparison across near-term hardware types. We also demonstrate how to reduce the noise in post processing with specific error mitigation techniques. Particularly, the adaptation of McWeeny purification of noisy density matrices dramatically improves accuracy of quantum computations, which, along with adjustable active space, significantly extends the range of accessible molecular systems. We demonstrate that for specific benchmark settings and a selected range of problems, the accuracy metric can reach chemical accuracy when computing over the cloud on certain quantum computers.
Development of Multi-Sensor Global Cloud and Radiance Composites for Earth Radiation Budget Monitoring from DSCOVR
The Deep Space Climate Observatory (DSCOVR) enables analysis of the daytime Earth radiation budget via the onboard Earth Polychromatic Imaging Camera (EPIC) and National Institute of Standards and Technology Advanced Radiometer (NISTAR). Radiance observations and cloud property retrievals from low earth orbit and geostationary satellite imagers have to be co-located with EPIC pixels to provide scene identification in order to select anisotropic directional models needed to calculate shortwave and longwave fluxes. A new algorithm is proposed for optimal merging of selected radiances and cloud properties derived from multiple satellite imagers to obtain seamless global hourly composites at 5-kilometer resolution. An aggregated rating is employed to incorporate several factors and to select the best observation at the time nearest to the EPIC measurement. Spatial accuracy is improved using inverse mapping with gradient search during reprojection and bicubic interpolation for pixel resampling. The composite data are subsequently remapped into EPIC-view domain by convolving composite pixels with the EPIC point spread function (PSF) defined with a half-pixel accuracy. PSF-weighted average radiances and cloud properties are computed separately for each cloud phase. The algorithm has demonstrated contiguous global coverage for any requested time of day with a temporal lag of under 2 hours in over 95 percent of the globe.
An Assessment of New Satellite Data Products for the Development of a Long-Term Global Solar Resource at 10-100 km
A project representing an effort to reprocess the NASA based solar resource data sets is reviewed. The effort represented a collaboration between NASA, NOAA, NREL and the SUNY-Albany and aimed to deliver a 10 km resolution, 3-hourly data set spanning from 1983 through near-present. Part of the project was to transition project capability to NREL for annual processing to extend data set. Due to delays in the key input project called ISCCP, we evaluate only Beta versions of this data set and also introduce the potential use of another NASA Langley based cloud data set for the CERES project. The CERES project uses these cloud properties to compute global top-of-atmosphere and surface fluxes at the 1x1 degree resolution. Here, we also briefly discuss these data sets in potential usage for solar resource benchmarking.
StratuSim: A Configurable Interface for Cloud-Enabled HPC Workflows
Explore the source record for details and available documents.