SEARCH · Engineering Papers

Results for “cloud database”

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.

SourceResearch connectionFrom yearThrough year

Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.

At least 271 records · Page 15

All sources NASA NTRS DOE OSTI

NASA NTRSDate not supplied

Heat-death by volcano - how Venus went rogue?

The idea of a habitable Venus epoch has gained traction in recent years via 1-D and 3-D General Circulation Modeling (GCM) efforts [1,2,3]. Although recent work has supported an alternative permanent hot and dry scenario [4]. However, the habitable scenario presents us with a conundrum - how does a terrestrial planet transform from temperate to hot-house? For decades it was proposed that the gradual brightening of the sun was the probable cause [5]. Yet 4 billion years ago Venus was receiving nearly 1.4 times the insolation that Earth receives today, and many studies have put Earth at the inner boundary of the habitable zone today [6]. The newer 3-D GCM efforts have demonstrated that if Venus had an early habitable period, that the cloud-albedo feedback responsible for maintaining temperate surface conditions [7] could still be in operation today. From this perspective increasing insolation through time cannot be an answer to the transition from habitable to hot-house. We propose that the 'Great Climate Transition' (GCT) was trigged by simultaneous large igneous provinces (LIPs) akin to those like the Siberian Traps responsible for the End Permian [8]. We have taken the most up to date LIP database for Earth [9] and characterized their distribution through time as random or nearly random. Next we initiate a large suite of Monte Carlo simulations based on this record and generate the likelihood for simultaneous, or environmentally overlapping events in this hypothetical setup. We find the probability of such events to be quite high, a probable cause for Venus' GCT, and a possible harbinger of things to come for Earth.

Venus↗

DOE OSTI2015

Spatial Clustering Techniques for Access to Large and Dynamic Datasets from Mobile Devices Over 4G Networks RSLN-25-15, Year 1 of 1

Presentation describes project to develop a framework for access to large datasets (i.e., millions of database objects, dynamically calculated values, or raster datasets that exceed 100s of Megabytes) from smart mobile devices with dynamic network bandwidths.

97 MATHEMATICS AND COMPUTING↗

NASA NTRS2004

Precipitation and Latent Heating Distributions from Satellite Passive Microwave Radiometry: Evaluation of Estimates Using Independent Data - Part 2

Rainfall rate estimates from space-borne k&ents are generally accepted as reliable by a majority of the atmospheric science commu&y. One-of the Tropical Rainfall Measuring Mission (TRh4M) facility rain rate algorithms is based upon passive microwave observations fiom the TRMM Microwave Imager (TMI). Part I of this study describes improvements in the TMI algorithm that are required to introduce cloud latent heating and drying as additional algorithm products. Here, estimates of surface rain rate, convective proportion, and latent heating are evaluated using independent ground-based estimates and satellite products. Instantaneous, OP5resolution estimates of surface rain rate over ocean fiom the improved TMI algorithm are well correlated with independent radar estimates (r approx. 0.88 over the Tropics), but bias reduction is the most significant improvement over forerunning algorithms. The bias reduction is attributed to the greater breadth of cloud-resolving model simulations that support the improved algorithm, and the more consistent and specific convective/stratiform rain separation method utilized. The bias of monthly, 2.5 deg. -resolution estimates is similarly reduced, with comparable correlations to radar estimates. Although the amount of independent latent heating data are limited, TMI estimated latent heating profiles compare favorably with instantaneous estimates based upon dual-Doppler radar observations, and time series of surface rain rate and heating profiles are generally consistent with those derived from rawinsonde analyses. Still, some biases in profile shape are evident, and these may be resolved with: (a) additional contextual information brought to the estimation problem, and/or; (b) physically-consistent and representative databases supporting the algorithm. A model of the random error in instantaneous, 0.5 deg-resolution rain rate estimates appears to be consistent with the levels of error determined from TMI comparisons to collocated radar. Error model modifications for non-raining situations will be required, however. Sampling error appears to represent only a fraction of the total error in monthly, 2S0-resolution TMI estimates; the remaining error is attributed to physical inconsistency or non-representativeness of cloud-resolving model simulated profiles supporting the algorithm.

Yang, Song↗

NASA NTRS2010

ICESat Lidar and Global Digital Elevation Models: Application to DESDynI

Geodetic control is extremely important in the production and quality control of topographic data sets, enabling elevation results to be referenced to an absolute vertical datum. Global topographic data with improved geodetic accuracy achieved using global Ground Control Point (GCP) databases enable more accurate characterization of land topography and its change related to solid Earth processes, natural hazards and climate change. The multiple-beam lidar instrument that will be part of the NASA Deformation, Ecosystem Structure and Dynamics of Ice (DESDynI) mission will provide a comprehensive, global data set that can be used for geodetic control purposes. Here we illustrate that potential using data acquired by NASA's Ice, Cloud and land Elevation Satellite (ICEsat) that has acquired single-beam, globally distributed laser altimeter profiles (+/-86deg) since February of 2003 [1, 2]. The profiles provide a consistently referenced elevation data set with unprecedented accuracy and quantified measurement errors that can be used to generate GCPs with sub-decimeter vertical accuracy and better than 10 m horizontal accuracy. Like the planned capability for DESDynI, ICESat records a waveform that is the elevation distribution of energy reflected within the laser footprint from vegetation, where present, and the ground where illuminated through gaps in any vegetation cover [3]. The waveform enables assessment of Digital Elevation Models (DEMs) with respect to the highest, centroid, and lowest elevations observed by ICESat and in some cases with respect to the ground identified beneath vegetation cover. Using the ICESat altimetry data we are developing a comprehensive database of consistent, global, geodetic ground control that will enhance the quality of a variety of regional to global DEMs. Here we illustrate the accuracy assessment of the Shuttle Radar Topography Mission (SRTM) DEM produced for Australia, documenting spatially varying elevation biases of several meters in magnitude.

Carabajal, Claudia C.↗

DOE OSTI2024

MLClouds [SWR-24-24]

The National Solar Radiation Database (NSRDB) is NREL’s flagship solar data resource. With over 20 years of high-resolution surface irradiance data covering most of the western hemisphere, the NSRDB is a crucial public data asset. A fundamental input to accurate surface irradiance in the NSRDB is high quality cloud property data. Cloud properties are used in radiative transfer calculations and are sourced from satellite imagery. Improving the accuracy of cloud property inputs is a tractable method for improving the accuracy of the irradiance data in the NSRDB. For example, in July of 2018, an average location in the Continental United States is missing cloud property data for nearly one quarter of all daylight cloudy timesteps. This project aims to improve the cloud data inputs to the NSRDB by using machine learning techniques to exploit the NSRDB’s massive data resources. More accurate cloud property input data will yield more accurate surface irradiance data in the NSRDB, providing direct benefit to researchers at NREL and to public data users everywhere.

Buster, Grant↗

DOE OSTI2015

High Performance Data Analytics on a Commodity Private Cloud

Sandia is researching novel methods of analysis for large datasets. The term “Big Data” has been used frequently in recent years to describe a discipline of computer science that attempts to distil an increasing quantity of data into a result or meaningful subset that can be easily visualized and acted upon by human consumers. These data are frequently characterized by one or more of the following: high volume, velocity, variety, and veracity. Sandia scientists and researchers are no strangers to large datasets, which often typify the result of high-performance computing workloads. The challenge for Sandia data scientists, however, comes at the arrival of exascale computing, which has become mandated by executive order for national computing centers. Because of power and networking limitations, exascale computing precludes the movement of datasets at exascale from data production to data analysis. Such analysis must be done in-situ, to mitigate the time and power costs of network transport. Currently, many of Sandia’s data analytics environments exist as independent entities, often specialized for individual use cases, interconnected to other data analytics, high-performance computing, and storage systems. Sandia is procuring, installing, and researching future platforms for such data analytics. A memory footprint of 256 GB per node, fourteen data rate (FDR) InfiniBand networking, PCIe connected non-volatile memory (NVMe) data storage, and an object data store will characterize the coming institutional data analytics platforms. The combined systems will contain 300 compute nodes. A cloud-inspired software stack resulting from collaboration between the Sandia centers will allow the systems to support a flexible mix of user and system-facing workloads and use cases including code development and testing in R and Python, Apache Hadoop and Spark, distributed in-memory databases, graph analytics, emulytics, system log and predictive analytics, and data visualization for institutional and mission customers. These systems will arrive at the end of FY 2015 and production is planned by the spring of FY 2016.

97 MATHEMATICS AND COMPUTING↗

NASA NTRS2009

Exploring Alternate Parameterizations for Snowfall with Validation from Satellite and Terrestrial Radars

Increases in computational resources have allowed operational forecast centers to pursue experimental, high resolution simulations that resolve the microphysical characteristics of clouds and precipitation. These experiments are motivated by a desire to improve the representation of weather and climate, but will also benefit current and future satellite campaigns, which often use forecast model output to guide the retrieval process. Aircraft, surface and radar data from the Canadian CloudSat/CALIPSO Validation Project are used to check the validity of size distribution and density characteristics for snowfall simulated by the NASA Goddard six-class, single-moment bulk water microphysics scheme, currently available within the Weather Research and Forecast (WRF) Model. Widespread snowfall developed across the region on January 22, 2007, forced by the passing of a midlatitude cyclone, and was observed by the dual-polarimetric, C-band radar King City, Ontario, as well as the NASA 94 GHz CloudSat Cloud Profiling Radar. Combined, these data sets provide key metrics for validating model output: estimates of size distribution parameters fit to the inverse-exponential equations prescribed within the model, bulk density and crystal habit characteristics sampled by the aircraft, and representation of size characteristics as inferred by the radar reflectivity at C- and W-band. Specified constants for distribution intercept and density differ significantly from observations throughout much of the cloud depth. Alternate parameterizations are explored, using column-integrated values of vapor excess to avoid problems encountered with temperature-based parameterizations in an environment where inversions and isothermal layers are present. Simulation of CloudSat reflectivity is performed by adopting the discrete-dipole parameterizations and databases provided in literature, and demonstrate an improved capability in simulating radar reflectivity at W-band versus Mie scattering assumptions.

Molthan, Andrew L.↗

NASA NTRS2009

Exploring Alternative Parameterizations for Snowfall with Validation from Satellite and Terrestrial Radars

Increases in computational resources have allowed operational forecast centers to pursue experimental, high resolution simulations that resolve the microphysical characteristics of clouds and precipitation. These experiments are motivated by a desire to improve the representation of weather and climate, but will also benefit current and future satellite campaigns, which often use forecast model output to guide the retrieval process. The combination of reliable cloud microphysics and radar reflectivity may constrain radiative transfer models used in satellite simulators during future missions, including EarthCARE and the NASA Global Precipitation Measurement. Aircraft, surface and radar data from the Canadian CloudSat/CALIPSO Validation Project are used to check the validity of size distribution and density characteristics for snowfall simulated by the NASA Goddard six-class, single moment bulk water microphysics scheme, currently available within the Weather Research and Forecast (WRF) Model. Widespread snowfall developed across the region on January 22, 2007, forced by the passing of a mid latitude cyclone, and was observed by the dual-polarimetric, C-band radar King City, Ontario, as well as the NASA 94 GHz CloudSat Cloud Profiling Radar. Combined, these data sets provide key metrics for validating model output: estimates of size distribution parameters fit to the inverse-exponential equations prescribed within the model, bulk density and crystal habit characteristics sampled by the aircraft, and representation of size characteristics as inferred by the radar reflectivity at C- and W-band. Specified constants for distribution intercept and density differ significantly from observations throughout much of the cloud depth. Alternate parameterizations are explored, using column-integrated values of vapor excess to avoid problems encountered with temperature-based parameterizations in an environment where inversions and isothermal layers are present. Simulation of CloudSat reflectivity is performed by adopting the discrete-dipole parameterizations and databases provided in literature, and demonstrate an improved capability in simulating radar reflectivity at W-band versus Mie scattering assumptions.

Molthan, Andrew L.↗

NASA NTRSDate not supplied

Comparison of Bimodal Ice Shapes on Full-Chord and Hybrid CRM Midspan Models

An ice shape database has been created to document ice accretions on a full-chord length and hybrid versions of a 65 percent scale size of the mid-span section of the Common Research Model resulting from an exposure to a Super-cooled Large Drop (SLD) icing cloud with a bimodal drop size distribution. The ice shapes created were documented with photographs, laser scanned surface measurements over a section of the model span, and measurement of the ice mass over the same section of each ice accretion. Drop distribution effects were evaluated by using the same IRT icing conditions except with either monomodal or bimodal drop distributions. Ice shapes resulting from the bimodal distribution as well as from monomodal distributions of identical cloud droplet Median Volume Diameter (MVD) were obtained and compared. Results indicated that the ice shapes resulting from the monomodal and bimodal drop size distributions have similar 2D Maximum Combined Cross Section, or MCCS, ice shape around the model leading edge region and equivalent ice mass measurement per unit span to within the IRT SLD cloud calibration uncertainty, as expected with this approach, but the bimodal distributions have distinctly larger ice feathers that extended further back on the chord of the model. For most test conditions, the overall size and shape of the main ice accretions compared well between the full-chord and hybrid models.

Icing↗

NASA NTRSDate not supplied

Comparison of Bimodal Ice Shapes on Full-Chord and Hybrid CRM Midspan Models

Icing , Simulation, Validation↗

NASA NTRSDate not supplied

Comparison of Bimodal Ice Shapes on Full-Chord and Hybrid CRM Midspan Models

Icing↗

NASA NTRS2006

Precipitation and Latent Heating Distributions from Satellite Passive Microwave Radiometry. Part II: Evaluation of Estimates Using Independent Data

Rainfall rate estimates from spaceborne microwave radiometers are generally accepted as reliable by a majority of the atmospheric science community. One of the Tropical Rainfall Measuring Mission (TRMM) facility rain-rate algorithms is based upon passive microwave observations from the TRMM Microwave Imager (TMI). In Part I of this series, improvements of the TMI algorithm that are required to introduce latent heating as an additional algorithm product are described. Here, estimates of surface rain rate, convective proportion, and latent heating are evaluated using independent ground-based estimates and satellite products. Instantaneous, 0.5 deg. -resolution estimates of surface rain rate over ocean from the improved TMI algorithm are well correlated with independent radar estimates (r approx. 0.88 over the Tropics), but bias reduction is the most significant improvement over earlier algorithms. The bias reduction is attributed to the greater breadth of cloud-resolving model simulations that support the improved algorithm and the more consistent and specific convective/stratiform rain separation method utilized. The bias of monthly 2.5 -resolution estimates is similarly reduced, with comparable correlations to radar estimates. Although the amount of independent latent heating data is limited, TMI-estimated latent heating profiles compare favorably with instantaneous estimates based upon dual-Doppler radar observations, and time series of surface rain-rate and heating profiles are generally consistent with those derived from rawinsonde analyses. Still, some biases in profile shape are evident, and these may be resolved with (a) additional contextual information brought to the estimation problem and/or (b) physically consistent and representative databases supporting the algorithm. A model of the random error in instantaneous 0.5 deg. -resolution rain-rate estimates appears to be consistent with the levels of error determined from TMI comparisons with collocated radar. Error model modifications for nonraining situations will be required, however. Sampling error represents only a portion of the total error in monthly 2.5 -resolution TMI estimates; the remaining error is attributed to random and systematic algorithm errors arising from the physical inconsistency and/or nonrepresentativeness of cloud-resolving-model-simulated profiles that support the algorithm.

Yang, Song↗

NASA NTRS2022

Constraining Clouds and Convective Parameterizations in A Climate Model Using Paleoclimate Data

Cloud and convective parameterizations strongly influence uncertainties in equilibrium climate sensitivity. We provide a proof-of-concept study to constrain these parameterizations in a perturbed parameter ensemble of the atmosphere-only version of the Goddard Institute for Space Studies Model E2.1 simulations by evaluating model biases in the present-day runs using multiple satellite climatologies and by comparing simulated δ 18 O of precipitation (δ 18 O p ), known to be sensitive to parameterization schemes, with a global database of speleothem δ 18 O records covering the Last Glacial Maximum (LGM), mid-Holocene (MH) and pre-industrial (PI) periods. Relative to modern interannual variability, paleoclimate simulations show greater sensitivity to parameter changes, allowing for an evaluation of model uncertainties over a broader range of climate forcing and the identification of parts of the world that are parameter sensitive. Certain simulations reproduced absolute δ 18 O p values across all time periods, along with LGM and MH δ 18 O p anomalies relative to the PI, better than the default parameterization. No single set of parameterizations worked well in all climate states, likely due to the non-stationarity of cloud feedbacks under varying boundary conditions. Future work that involves varying multiple parameter sets simultaneously with coupled ocean feedbacks will likely provide improved constraints on cloud and convective parameterizations.

cloud and convective parameterization↗

DOE OSTI2019

Cloud Atlas: Hubble Space Telescope Near-infrared Spectral Library of Brown Dwarfs, Planetary-mass Companions, and Hot Jupiters

Bayesian atmospheric retrieval tools can place constraints on the properties of brown dwarfs' and hot Jupiters' atmospheres. To fully exploit these methods, high signal-to-noise spectral libraries with well-understood uncertainties are essential. We present a high signal-to-noise spectral library (1.10–1.69 μm) of the thermal emission of 76 brown dwarfs and hot Jupiters. All our spectra have been acquired with the Hubble Space Telescope’s Wide Field Camera 3 instrument and its G141 grism. The near-infrared spectral types of these objects range from L4 to Y1. Eight of our targets have estimated masses below the deuterium-burning limit. We analyze the database to identify peculiar objects and/or multiple systems, concluding that this sample includes two very-low-surface-gravity objects and five intermediate-surface-gravity objects. In addition, spectral indices designed to search for composite-atmosphere brown dwarfs indicate that eight objects in our sample are strong candidates to have such atmospheres. None of these objects are overluminous, so their composite atmospheres are unlikely to be companion-induced artifacts. Five of the eight confirmed candidates have been reported as photometrically variable, suggesting that composite atmospheric indices are useful in identifying brown dwarfs with strongly heterogeneous cloud covers. We compare hot Jupiters and brown dwarfs in a near-infrared color–magnitude diagram. We confirm that the coldest hot Jupiters in our sample have spectra similar to mid-L dwarfs, and the hottest hot Jupiters have spectra similar to those of M-dwarfs. Our sample provides a uniform data set of a broad range of ultracool atmospheres, allowing large-scale comparative studies and providing an HST legacy spectral library.

79 ASTRONOMY AND ASTROPHYSICS↗

DOE OSTI2015

Variable stars in metal-rich globular clusters. IV. Long-period variables in NGC 6496

We present VI-band photometry for stars in the metal-rich globular cluster NGC 6496. Our time-series data were cadenced to search for long-period variables (LPVs) over a span of nearly two years, and our variability search yielded the discovery of 13 new variable stars, of which 6 are LPVs, 2 are suspected LPVs, and 5 are short-period eclipsing binaries. An additional star was found in the ASAS database, and we clarify its type and period. We argue that all of the eclipsing binaries are field stars, while five to six of the LPVs are members of NGC 6496. We compare the period–luminosity distribution of these LPVs with those of LPVs in the Large Magellanic Cloud and 47 Tucanae, and with theoretical pulsation models. We also present a VI color–magnitude diagram, display the evolutionary states of the variables, and match isochrones to determine a reddening of E(B−V)= 0.21±0.02 mag and apparent distance modulus of 15.60±0.15 mag.

79 ASTRONOMY AND ASTROPHYSICS↗

NASA NTRS2011

Redefining Tactical Operations for MER Using Cloud Computing

The Mars Exploration Rover Mission (MER) includes the twin rovers, Spirit and Opportunity, which have been performing geological research and surface exploration since early 2004. The rovers' durability well beyond their original prime mission (90 sols or Martian days) has allowed them to be a valuable platform for scientific research for well over 2000 sols, but as a by-product it has produced new challenges in providing efficient and cost-effective tactical operational planning. An early stage process adaptation was the move to distributed operations as mission scientists returned to their places of work in the summer of 2004, but they would still came together via teleconference and connected software to plan rover activities a few times a week. This distributed model has worked well since, but it requires the purchase, operation, and maintenance of a dedicated infrastructure at the Jet Propulsion Laboratory. This server infrastructure is costly to operate and the periodic nature of its usage (typically heavy usage for 8 hours every 2 days) has made moving to a cloud based tactical infrastructure an extremely tempting proposition. In this paper we will review both past and current implementations of the tactical planning application focusing on remote plan saving and discuss the unique challenges present with long-latency, distributed operations. We then detail the motivations behind our move to cloud based computing services and as well as our system design and implementation. We will discuss security and reliability concerns and how they were addressed

Mars↗

DOE OSTI2021

Reinspection of a Clinical Proteomics Tumor Analysis Consortium (CPTAC) Dataset with Cloud Computing Reveals Abundant Post-Translational Modifications and Protein Sequence Variants

The Clinical Proteomic Tumor Analysis Consortium (CPTAC) has provided some of the most in-depth analyses of the phenotypes of human tumors ever constructed. Today, the majority of proteomic data analysis is still performed using software housed on desktop computers which limits the number of sequence variants and post-translational modifications that can be considered. The original CPTAC studies limited the search for PTMs to only samples that were chemically enriched for those modified peptides. Similarly, the only sequence variants considered were those with strong evidence at the exon or transcript level. In this multi-institutional collaborative reanalysis, we utilized unbiased protein databases containing millions of human sequence variants in conjunction with hundreds of common post-translational modifications. Using these tools, we identified tens of thousands of high-confidence PTMs and sequence variants. We identified 4132 phosphorylated peptides in nonenriched samples, 93% of which were confirmed in the samples which were chemically enriched for phosphopeptides. In addition, our results also cover 90% of the high-confidence variants reported by the original proteogenomics study, without the need for sample specific next-generation sequencing. Finally, we report fivefold more somatic and germline variants that have an independent evidence at the peptide level, including mutations in ERRB2 and BCAS1. In this reanalysis of CPTAC proteomic data with cloud computing, we present an openly available and searchable web resource of the highest-coverage proteomic profiling of human tumors described to date.

60 APPLIED LIFE SCIENCES↗

NASA NTRS2016

Ice-Accretion Test Results for Three Large-Scale Swept-Wing Models in the NASA Icing Research Tunnel

Icing simulation tools and computational fluid dynamics codes are reaching levels of maturity such that they are being proposed by manufacturers for use in certification of aircraft for flight in icing conditions with increasingly less reliance on natural-icing flight testing and icing-wind-tunnel testing. Sufficient high-quality data to evaluate the performance of these tools is not currently available. The objective of this work was to generate a database of ice-accretion geometry that can be used for development and validation of icing simulation tools as well as for aerodynamic testing. Three large-scale swept wing models were built and tested at the NASA Glenn Icing Research Tunnel (IRT). The models represented the Inboard (20 percent semispan), Midspan (64 percent semispan) and Outboard stations (83 percent semispan) of a wing based upon a 65 percent scale version of the Common Research Model (CRM). The IRT models utilized a hybrid design that maintained the full-scale leading-edge geometry with a truncated afterbody and flap. The models were instrumented with surface pressure taps in order to acquire sufficient aerodynamic data to verify the hybrid model design capability to simulate the full-scale wing section. A series of ice-accretion tests were conducted over a range of total temperatures from -23.8 to -1.4 C with all other conditions held constant. The results showed the changing ice-accretion morphology from rime ice at the colder temperatures to highly 3-D scallop ice in the range of -11.2 to -6.3 C. Warmer temperatures generated highly 3-D ice accretion with glaze ice characteristics. The results indicated that the general scallop ice morphology was similar for all three models. Icing results were documented for limited parametric variations in angle of attack, drop size and cloud liquid-water content (LWC). The effect of velocity on ice accretion was documented for the Midspan and Outboard models for a limited number of test cases. The data suggest that there are morphological characteristics of glaze and scallop ice accretion on these swept-wing models that are dependent upon the velocity. This work has resulted in a large database of ice-accretion geometry on large-scale, swept-wing models.

Wing Aerodynamics↗