Sample records for total cloud cover

  1. Changes in Cloud Cover and Cloud Types Over the Ocean from Surface

    E-Print Network [OSTI]

    Hochberg, Michael

    Total cloud cover 54 68 Clear sky (frequency) 22 3 #12;Low Clouds & Solar Radiation Low clouds scatterChanges in Cloud Cover and Cloud Types Over the Ocean from Surface Observations, 1954-2008 Ryan This produces a weak net warming effect in the atmosphere, since more radiation comes in, and less goes out

  2. Decomposing aerosol cloud radiative effects into cloud cover, liquid water path and Twomey components

    E-Print Network [OSTI]

    Daniel, Rosenfeld

    Decomposing aerosol cloud radiative effects into cloud cover, liquid water path and Twomey interactions radiative effects, i.e., the cloud cover, liquid water path (LWP) and cloud drop radius (Twomey negative radiative forcing on the global scale, mainly due to the cloud cover effect. © 2013 Elsevier B

  3. CLOUD COVER REPORTING BIAS AT MAJOR AIRPORTS Richard Perez

    E-Print Network [OSTI]

    Perez, Richard R.

    CLOUD COVER REPORTING BIAS AT MAJOR AIRPORTS Richard Perez Joshua A. Bonaventura-Sparagna & Marek Kmiecik ASRC, SUNY, Albany, NY Ray George & David Renné NREL, Golden, CO ABSTRACT Cloud cover has been generated all or in part from cloud cover measurements [1,2]. This paper presents evidence

  4. Enhancement of cloud cover and suppression of nocturnal drizzle in stratocumulus polluted by haze

    E-Print Network [OSTI]

    to amplify the negative radiative forcing by increasing cloud cover and cloud water [Albrecht, 1989]. [3] We in ship tracks [Ackerman et al., 2000]. Evidence for secondary effects is ambiguous. Cloud cover is seenEnhancement of cloud cover and suppression of nocturnal drizzle in stratocumulus polluted by haze A

  5. Cloud cover increase with increasing aerosol absorptivity: A counterexample to the conventional semidirect aerosol effect

    E-Print Network [OSTI]

    humidity. The net effect is more low cloud cover with increasing aerosol absorption. The higher specific by dust radiative heating. Although in some areas our model exhibits a reduction of low cloud cover due are expected to have a similar effect. Citation: Perlwitz, J., and R. L. Miller (2010), Cloud cover increase

  6. A Survey of Changes in Cloud Cover and Cloud Types over Land from Surface Observations, 197196

    E-Print Network [OSTI]

    Hochberg, Michael

    of their effects on solar radiation, terrestrial radiation, and precipitation. These effects depend on cloud height, and the season of the year and time of day. The effect of clouds on the earth's radiation budget, the "cloud to be a useful classification in studies of cloud processes (Houze 1993). The climatic effects of clouds further

  7. Interannual Variations of Arctic Cloud Types

    E-Print Network [OSTI]

    Hochberg, Michael

    Sciences #12;Changes in Arctic Climate What is the role of cloud cover in Arctic climate change? What is the Cloud Radiative Effect (CRE) in the Arctic? #12;CRE depends on season, cloud type CRE ­ whether clouds specifically chosen to include nighttime obs Total cloud cover and nine cloud types: - High cloud (cirriform

  8. Interannual Variations of Arctic Cloud Types

    E-Print Network [OSTI]

    Hochberg, Michael

    Declining September sea-ice extent #12;Clouds & Changes in Arctic Climate What is the role of cloud cover in Arctic climate change? What is the Cloud Radiative Effect (CRE) in the Arctic? #12;CRE Defined CRE nighttime obs Total cloud cover and nine cloud types: - High cloud (cirriform) - Middle Clouds: Altocumulus

  9. ARM: Fractional cloud cover, clear-sky and all-sky shortwave flux for each of 25 individual SGP facilities.

    DOE Data Explorer [Office of Scientific and Technical Information (OSTI)]

    Gaustad, Krista; Gaustad, Krista; McFarlane, Sally; McFarlane, Sally

    Fractional cloud cover, clear-sky and all-sky shortwave flux for each of 25 individual SGP facilities.

  10. Climatological data for clouds over the globe from surface observations, 1982--1991: The total cloud edition

    SciTech Connect (OSTI)

    Hahn, C.J. [Colorado Univ., Boulder, CO (United States). Cooperative Inst. for Research in Environmental Sciences] [Colorado Univ., Boulder, CO (United States). Cooperative Inst. for Research in Environmental Sciences; Warren, S.G. [Washington Univ., Seattle, WA (United States). Dept. of Atmospheric Sciences] [Washington Univ., Seattle, WA (United States). Dept. of Atmospheric Sciences; London, J. [Colorado Univ., Boulder, CO (United States). Dept. of Astrophysical, Planetary, and Atmospheric Sciences] [Colorado Univ., Boulder, CO (United States). Dept. of Astrophysical, Planetary, and Atmospheric Sciences

    1994-10-01T23:59:59.000Z

    Routine, surface synoptic weather reports from ships and land stations over the entire globe, for the ten-year period December 1981 through November 1991, were processed for total cloud cover and the frequencies of occurrence of clear sky, precipitation, and sky-obscured due to fog. Archived data, consisting of various annual, seasonal and monthly averages, are provided in grid boxes that are typically 2.5{degrees} {times} 2.5{degrees} for land and 5{degrees} {times} 5{degrees} for ocean. Day and nighttime averages are also given separately for each season. Several derived quantities, such as interannual variations and annual and diurnal harmonics, are provided as well. This data set incorporates an improved representation of nighttime cloudiness by utilizing only those nighttime observations for which the illuminance due to moonlight exceeds a specified threshold. This reduction in the night-detection bias increases the computed global average total cloud cover by about 2%. The impact on computed diurnal cycles is even greater, particularly over the oceans where is found, in contrast to previous surface-based climatologies, that cloudiness is often greater at night than during the day.

  11. Variations in Cloud Cover and Cloud Types over the Ocean from Surface Observations, 19542008

    E-Print Network [OSTI]

    Hochberg, Michael

    ). MSC therefore have a cooling ef- fect on climate [negative cloud radiative effect (CRE)]. Randall et in climate, affecting both radiation fluxes and latent heat fluxes, but the various cloud types affect marine. By contrast, high (cirriform) clouds are thinner and colder, so their longwave effect dominates, giving them

  12. Improving total column ozone retrievals by using cloud pressures derived from Raman scattering in the UV

    E-Print Network [OSTI]

    Joiner, Joanna

    Improving total column ozone retrievals by using cloud pressures derived from Raman scattering resolution, coverage, and sampling of the Aura satellite ozone monitoring instrument (OMI), as compared with the total ozone mapping spectrometer (TOMS) should allow for improved ozone retrievals. By default, the TOMS

  13. ARM: Gridded (0.25 x 0.25 lat/lon) fractional cloud cover, clear-sky and all-sky shortwave flux over the SGP site.

    DOE Data Explorer [Office of Scientific and Technical Information (OSTI)]

    Gaustad, Krista; Gaustad, Krista; McFarlane, Sally; McFarlane, Sally

    Gridded (0.25 x 0.25 lat/lon) fractional cloud cover, clear-sky and all-sky shortwave flux over the SGP site.

  14. CLOUD FRACTION: CAN IT BE DEFINED, CAN IT BE MEASURED, AND IF WE KNEW IT

    E-Print Network [OSTI]

    Schwartz, Stephen E.

    droplets and/or ice particles in the atmosphere above the earth's surface. Total cloud cover: Fraction, JGR (ERBE, 1988) Cloud cover is a loosely defined term. Potter Stewart (U.S. Supreme Court, 1964) I feedbacks. Accurate representation of cloud radiative effects is essential in climate models. Getting cloud

  15. Investigation of a cloud-cover modification to SPCTRAL2, SERI's simple model for cloudless-sky, spectral solar irradiance

    SciTech Connect (OSTI)

    Bird, R.E.; Riordan, C.J.; Myers, D.R.

    1987-06-01T23:59:59.000Z

    This report summarizes the investigation of a cloud-cover modification to SPCTRAL2, SERI's simple model for cloudless-sky, spectral solar irradiance. Our approach was to develop a modifier that relies on commonly acquired meteorological and broadband-irradiance data rather than detailed cloud properties that are generally not available. The method was to normalize modeled, cloudless-sky spectral irradiance to a measured broadband-irradiance value under cloudy skies, and then to compare the normalized, modeled data with measured spectral-irradiance data to empirically derive spectral modifiers that improve the agreement between modeled and measured data. Results indicate the possible form of the spectral corrections; however, we must analyze additional data to develop a spectral transmission function for cloudy-sky conditions.

  16. Remote sensing of total integrated water vapor, wind speed, and cloud liquid water over the ocean using the Special Sensor Microwave/Imager (SSM/I)

    E-Print Network [OSTI]

    Manning, Norman Willis William

    1997-01-01T23:59:59.000Z

    A modified D-matrix retrieval method is the basis of the refined total integrated water vapor (TIWV), total integrated cloud liquid water (CLW), and surface wind speed (WS) retrieval methods that are developed. The 85 GHZ polarization difference...

  17. Water vapor, cloud liquid water paths, and rain rates over northern high latitude open seas

    E-Print Network [OSTI]

    Zuidema, Paquita

    longwave radiation caused by differences in cloud cover can produce an JOURNAL OF GEOPHYSICAL RESEARCH, VOL-level stratus con- tribute the most to the total Arctic cloud cover of any cloud type according to surface presence during summertime but otherwise the Wentz internal sea-ice screening appears effective

  18. Cloud Services Cloud Services

    E-Print Network [OSTI]

    Cloud Services Cloud Services In 2012 UCD IT Services launched an exciting new set of cloud solutions called CloudEdu, which includes cloud servers, cloud storage, cloud hosting and cloud network. The CloudEdu package includes a consultancy service in design, deployment, management and utilisation

  19. A comparative analysis of total lightning observations and cloud-to-ground lightning observations in the Southeastern United States region

    E-Print Network [OSTI]

    Hugo, Keith Michael

    1998-01-01T23:59:59.000Z

    Flashes of April 20, 1996, Orbit 13 After Performing Collocation Procedure. 19 21 5 OTD Versus NLDN Lightning Flashes. 24 6 OTD and NLDN Lightning Flashes of December 20, 1995, Orbit 2. 26 7 OTD and NLDN Lightning Flashes of July 23, 1996, Orbit 2... prototype for the Lightning Imaging Sensor launched aboard the Tropical Rainfall Measuring Mission (TRMM) [Goodman et aL, 1996], has enabled the detection of total lightning from space and allowed interesting comparisons of data coincident with ground...

  20. Microphysical Effects Determine Macrophysical Response for Aerosol Impacts on Deep Convective Clouds

    SciTech Connect (OSTI)

    Fan, Jiwen; Leung, Lai-Yung R.; Rosenfeld, Daniel; Chen, Qian; Li, Zhanqing; Zhang, Jinqiang; Yan, Hongru

    2013-11-26T23:59:59.000Z

    Deep convective clouds (DCCs) play a crucial role in the general circulation and energy and hydrological cycle of our climate system. Anthropogenic and natural aerosol particles can influence DCCs through changes in cloud properties, precipitation regimes, and radiation balance. Modeling studies have reported both invigoration and suppression of DCCs by aerosols, but none has fully quantified aerosol impacts on convection life cycle and radiative forcing. By conducting multiple month-long cloud-resolving simulations with spectral-bin cloud microphysics that capture the observed macro- and micro-physical properties of summer convective clouds in the tropics and mid-latitudes, this study provides the first comprehensive look at how aerosols affect cloud cover, cloud top height (CTH), and radiative forcing. Observations validate these simulation results. We find that microphysical aerosol effects contribute predominantly to increased cloud cover and CTH by inducing larger amount of smaller but longer lasting ice particles in the stratiform/anvils of DCCs with dynamical aerosol effects contributing at most ~ 1/4 of the total increase of cloud cover. The overall effect is a radiative warming in the atmosphere (3 to 5 W m-2) with strong surface cooling (-5 to -8 W m-2). Herein we clearly identified mechanisms more important than and additional to the invigoration effects hypothesized previously that explain the consistent signatures of increased cloud tops area and height by aerosols in DCCs revealed by observations.

  1. ARM - Measurement - Total cloud water

    Broader source: All U.S. Department of Energy (DOE) Office Webpages (Extended Search)

    AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE: Alternative1 First Use of Energy for All Purposes (Fuel and Nonfuel), 2002; Level: National5Sales for4,645 3,625govInstrumentstdmadap Documentation TDMADAP : XDCnarrowbandheat flux ARM Data Discovery Browseenergy

  2. Urban slum structure: integrating socioeconomic and land cover data to model slum evolution in Salvador, Brazil

    E-Print Network [OSTI]

    2013-01-01T23:59:59.000Z

    roofs, white-painted roofs, pavement, and cloud land covers.white-painted roofs, pavement, vegetation, water, sand exposed soil and clouds.

  3. Characterization of melting level clouds over the tropical western pacific warm pool

    SciTech Connect (OSTI)

    Jensen, M.; Johnson, K.; Billings, J.; Troyan, D.; Long, C.; Comstock, J.

    2010-03-15T23:59:59.000Z

    A cursory examination of historical ARSCL data indicates a common cloud feature in the tropics are thin detrainment shelves (Attendant Shelf Clouds, or ASCs) near the melting level (see figure for example). We use the ARSCL product to identify ASCs by defining them as cloud layers with bases above 4 km, a corresponding top below 6 km, and a thickness of less than 1 km. In order to prevent biases in determination of the diurnal cycle of cloud occurrence, we require that both the MMCR and MPL are operating well. In this study we use a total of 55 months of data collected over 14 years of deployments at the Manus, Nauru, and Darwin ARM sites in the Tropical Western Pacific to define the frequency of occurrence (~ 14% of the time) and diurnal cycle of these clouds, along with the atmospheric thermodynamic profile. We further investigate the horizontal extent, cloud radiative forcing, and cloud particle phase through a series of “golden cases” where there is a general absence of additional cloud types in the column and nearby deep convection. These cases indicate that the clouds can cover horizontal areas on the order of a GCM gridbox, have significant (but not always) cloud radiative forcing, and may be composed of liquid or ice water.

  4. The effect of smoke, dust, and pollution aerosol on shallow cloud development over the Atlantic Ocean

    E-Print Network [OSTI]

    Daniel, Rosenfeld

    radiation by aerosols, however, can reduce the cloud cover. The net aerosol effect on clouds is currently- induced cloud changes, and 1 3 is due to aerosol direct radiative effect. cloud cover cloud height understand the processes. The radiative effect at the top of the atmosphere incurred by the aerosol effect

  5. Total Light Management

    Broader source: Energy.gov [DOE]

    Presentation covers total light management, and is given at the Spring 2010 Federal Utility Partnership Working Group (FUPWG) meeting in Providence, Rhode Island.

  6. EVENT CLOUDS : lighter than air architectural structures

    E-Print Network [OSTI]

    Peydro Duclos, Ignacio

    2014-01-01T23:59:59.000Z

    EVENT CLOUD is a versatile covering system that allows events to happen independently to weather conditions. It consists of a lighter than air pneumatic structure, filled either with helium or hot air, that covers spaces ...

  7. TGRS-2010-00092.R1 1 Abstract--Cloud properties were retrieved by applying the

    E-Print Network [OSTI]

    Dong, Xiquan

    cover (~59%) is divided equally between liquid and ice clouds. Global mean cloud effective heights , respectively, for liquid clouds and 8.3 km, 12.7, 52.2 µm, and 230 gm-2 for ice clouds. Cloud droplet effective radiation processes requires determination of cloud property distributions and the radiation budget

  8. LES Simulations of Roll Clouds Observed During Mixed- Phase Arctic Cloud Experiment

    SciTech Connect (OSTI)

    Greenberg, S.D.; Harrington, J.Y.; Prenni, A.; DeMott, P.

    2005-03-18T23:59:59.000Z

    Roll clouds, and associated roll convection, are fairly common features of the atmospheric boundary layer. While these organized cumuliform clouds are found over many regions of the planet, they are quite ubiquitous near the edge of the polar ice sheets. In particular, during periods of off-ice flow, when cold polar air flows from the ice pack over the relatively warm ocean water, strong boundary layer convection develops along with frequent rolls. According to Bruemmer and Pohlman (2000), most of the total cloud cover in the Arctic is due to roll clouds. In an effort to examine the influences of mixed-phase microphysics on the boundary layer evolution of roll clouds during off-ice flow, Olsson and Harrington (2000) used a 2D mesoscale model coupled to a bulk microphysical scheme (see Section 2). Their results showed that mixed-phase clouds produced more shallow boundary layers with weaker turbulence than liquid-phase cases. Furthermore, their results showed that because of th e reduced turbulent drag on the atmosphere in the mixed-phase case, regions of mesoscale divergence in the marginal ice-zone were significantly affected. A follow-up 2D study (Harrington and Olsson 2001) showed that the reduced turbulent intensity in mixed-phase cases was due to precipitation. Ice precipitation caused downdraft stabilization which fed back and caused a reduction in the surface heat fluxes. In this work, we extend the work of Olsson and Harrington (2000) and Harrington and Olsson (2001) by examining the impacts of ice microphysics on roll convection. We will present results that illustrate how microphysics alters roll cloud structure and dynamics.

  9. Separating real and apparent effects of cloud, humidity, and dynamics on aerosol optical thickness near cloud edges

    E-Print Network [OSTI]

    Li, Zhanqing

    have reported correlations between AOT and cloud cover, pointing to potential cloud contamination of Energy's Atmospheric Radiation Measurement Program. It was found that aerosol humidification effects can explain about one fourth of the correlation between the cloud cover and AOT. New particle genesis

  10. Changes in high cloud conditions

    E-Print Network [OSTI]

    Himebrook, Richard Frank

    1974-01-01T23:59:59.000Z

    ). When the effect of unknowns is added to the data (Figs. 3(a) and 3(b), p, 21), the period with most high-cloud cover seems to alter- nate back and forth almost monthly, The average, global, solar radiation (Fig. 3(c), p. 21) depicts a decrease from... radiation, per cent possible sunshine, and average sky cover. The increases in high-cloud cover occurred in areas with the following characteristics: strong upper-air flow; frequent jet ' aircraft traffic; coverage of less than half the sky; late...

  11. LETTER The incidence and implications of clouds for cloud forest plant water relations

    E-Print Network [OSTI]

    Goldsmith, Greg

    , the montane forest experienced higher precipi- tation, cloud cover and leaf wetting events of longer duration for an improved understanding of clouds and their effects on cloud forest plant functioning. As summarised below (VPD) and photosynthetically active radiation. In turn, this decreases plant water demand. The suppres

  12. Interannual variations of Arctic cloud types in relation to Ryan Eastman

    E-Print Network [OSTI]

    Hochberg, Michael

    increasing cloud cover, which may promote ice loss by the longwave effect. The trends are positive in all in sea ice extent and thickness may be affected by cloud radiative effect (CRE), and seaice changes may in turn impart changes to cloud cover. Visual cloud reports from land and ocean regions of the Arctic

  13. Cloud Computing

    SciTech Connect (OSTI)

    Pete Beckman and Ian Foster

    2009-12-04T23:59:59.000Z

    Chicago Matters: Beyond Burnham (WTTW). Chicago has become a world center of "cloud computing." Argonne experts Pete Beckman and Ian Foster explain what "cloud computing" is and how you probably already use it on a daily basis.

  14. Relationships between Arctic Sea Ice and Clouds during Autumn AXEL J. SCHWEIGER AND RON W. LINDSAY

    E-Print Network [OSTI]

    Francis, Jennifer

    , as the direct radiative effects of cloud cover changes are compensated for by changes in the temperature The connection between sea ice variability and cloud cover over the Arctic seas during autumn is investigated that cloud cover variability near the sea ice margins is strongly linked to sea ice variability. Sea ice

  15. Understanding biases in shortwave cloud radiative forcing in the National Center for Atmospheric Research Community Atmosphere

    E-Print Network [OSTI]

    Zhang, Guang Jun

    in response to El Nin~o warming. The vast cloud cover in the region leads to much stronger cloud greenhouse effect in longwave radiation (longwave cloud radiative forcing) and cloud shielding effect in shortwaveUnderstanding biases in shortwave cloud radiative forcing in the National Center for Atmospheric

  16. Parameterizing the Difference in Cloud Fraction Defined by Area and by Volume as Observed with Radar and Lidar

    E-Print Network [OSTI]

    Reading, University of

    partially cloudy grid boxes by weighting clear and cloudy fluxes by the fractional area of cloud cover (Ca cloud cover from 53% to 63%, and so is of similar importance to the cloud overlap assumption. A simple for calculating the radiative effect of cloud (Stephens 1984; Edwards and Slingo 1996) and the representation

  17. Transmission of Solar Radiation by Clouds over Snow and Ice Surfaces. Part II: Cloud Optical Depth and Shortwave Radiative Forcing from Pyranometer

    E-Print Network [OSTI]

    Warren, Stephen

    coincident hourly sea ice reports, instantaneous cloud radiative forcing and effective cloud optical depth. "Effective" optical depths (for a radiatively equivalent horizontally homogeneous cloud) are classified a characteristic optical depth of 15 at 47°S, increasing to 24 in the region of maximum cloud cover at 58°S

  18. Aerosol Effects on Clouds, Energy & Hydrologic Cycle Steven Ghan, Trond Iversen, Jon Egill Kristjansson, Athanasios Nenes, Joyce Penner

    E-Print Network [OSTI]

    cycle and a "semi-direct" effect by suppressing cloud formation due to absorption of solar radiation cloud coverage. The increased cloud albedo and cloud cover decrease solar insolation at the surfaceAerosol Effects on Clouds, Energy & Hydrologic Cycle Steven Ghan, Trond Iversen, Jon Egill

  19. Effective Radius of Cloud Droplets by Ground-Based Remote Sensing: Relationship to Aerosol

    E-Print Network [OSTI]

    Schwartz, Stephen E.

    albedo and radiative forcing for a given LWP are highly sensitive to effective radius; for solar zenith and the average cloud cover on earth. Additionally, reduction in cloud cover caused by absorption of solarEffective Radius of Cloud Droplets by Ground-Based Remote Sensing: Relationship to Aerosol Byung

  20. Polar Cloud Detection using Satellite Data with Analysis and Application of Kernel Learning Algorithms

    E-Print Network [OSTI]

    Shi, Tao

    Abstract Polar Cloud Detection using Satellite Data with Analysis and Application of Kernel Professor Bin Yu, Chair Clouds play a major role in Earth's climate and cloud detection is a crucial step climate model studies. Cloud detection is particularly difficult in the snow- and ice-covered polar

  1. DRAFT, Revised June 2012 Aerosol cloud-mediated radiative forcing: highly uncertain and

    E-Print Network [OSTI]

    Daniel, Rosenfeld

    drops, adding more cloud water, and increasing the cloud cover. Aerosols affect these components1 DRAFT, Revised June 2012 Aerosol cloud-mediated radiative forcing: highly uncertain and opposite effects from shallow and deep clouds Daniel Rosenfeld1 , Robert Wood2 , Leo Donner3 , Steven Sherwood4 1

  2. Back Cover Front Cover Office of Continuing

    E-Print Network [OSTI]

    Goodman, Robert M.

    Back Cover Front Cover Office of Continuing Professional Education 2012­2013 Professional Landscape of Golf Course Irrigation Systems (p. 13) · Basics of Turf Management (p. 21) · Turfgrass Establishment (p

  3. Cloud a particle beam facility to investigate the influence of cosmic rays on clouds

    E-Print Network [OSTI]

    Kirkby, Jasper

    2001-01-01T23:59:59.000Z

    Palaeoclimatic data provide extensive evidence for solar forcing of the climate during the Holocene and the last ice age, but the underlying mechanism remains a mystery. However recent observations suggest that cosmic rays may play a key role. Satellite data have revealed a surprising correlation between cosmic ray intensity and the fraction of the Earth covered by low clouds \\cite{svensmark97,marsh}. Since the cosmic ray intensity is modulated by the solar wind, this may be an important clue to the long-sought mechanism for solar-climate variability. In order to test whether cosmic rays and clouds are causally linked and, if so, to understand the microphysical mechanisms, a novel experiment known as CLOUD\\footnotemark\\ has been proposed \\cite{cloud_proposal}--\\cite{cloud_addendum_2}. CLOUD proposes to investigate ion-aerosol-cloud microphysics under controlled laboratory conditions using a beam from a particle accelerator, which provides a precisely adjustable and measurable artificial source of cosmic rays....

  4. Effects of biomass-burning-derived aerosols on precipitation and clouds in the Amazon Basin: a satellite-based empirical study

    E-Print Network [OSTI]

    Pielke, Roger A.

    in both 2000 and 2003. With enhanced ta, cloud cover increased significantly, and cloud top temperature convection, leading to higher clouds, enhanced cloud cover, and stronger rainfall. We speculate that changes radiative and hydrological effects on the Amazonian climate system. The accelerated forest burning

  5. Cloud Computing Adam Barker

    E-Print Network [OSTI]

    St Andrews, University of

    Cloud Computing 1 Adam Barker #12;Overview · Introduction to Cloud computing · Enabling technologies · Di erent types of cloud: IaaS, PaaS and SaaS · Cloud terminology · Interacting with a cloud: management consoles · Launching an instance · Connecting to an instance · Running your application · Clouds

  6. Global cloud liquid water path simulations

    SciTech Connect (OSTI)

    Lemus, L. [Southern Hemisphere Meteorology, Clayton, Victoria (Australia)] [Southern Hemisphere Meteorology, Clayton, Victoria (Australia); Rikus, L. [Bureau of Meteorology Research Centre, Melbourne, Victoria (Australia)] [Bureau of Meteorology Research Centre, Melbourne, Victoria (Australia); Martin, C.; Platt, R. [CSIRO, Aspendale, Victoria (Australia)] [CSIRO, Aspendale, Victoria (Australia)

    1997-01-01T23:59:59.000Z

    A new parameterization of cloud liquid water and ice content has been included in the Bureau of Meteorology Global Assimilation and Prediction System. The cloud liquid water content is derived from the mean cloud temperatures in the model using an empirical relationship based on observations. The results from perpetual January and July simulations are presented and show that the total cloud water path steadily decreases toward high latitudes, with two relative maxima at midlatitudes and a peak at low latitudes. To validate the scheme, the simulated fields need to be processed to produce liquid water paths that can be directly compared with the corresponding field derived from Special Sensor Microwave/Imager (SSM/I) data. This requires the identification of cloud ice water content within the parameterization and a prescription to account for the treatment of strongly precipitating subgrid-scale cloud. The resultant cloud liquid water paths agree qualitatively with the SSM/I data but show some systematic errors that are attributed to corresponding errors in the model`s simulation of cloud amounts. Given that a more quantitative validation requires substantial improvement in the model`s diagnostic cloud scheme, the comparison with the SSM/I data indicates that the cloud water path, derived from the cloud liquid water content parameterization introduced in this paper, is consistent with the observations and can be usefully incorporated in the prediction system. 40 refs., 11 figs., 1 tab.

  7. Transforming the representation of the boundary layer and low clouds for high-resolution regional climate modeling: Final report

    SciTech Connect (OSTI)

    Huang, Hsin-Yuan; Hall, Alex

    2013-07-24T23:59:59.000Z

    Stratocumulus and shallow cumulus clouds in subtropical oceanic regions (e.g., Southeast Pacific) cover thousands of square kilometers and play a key role in regulating global climate (e.g., Klein and Hartmann, 1993). Numerical modeling is an essential tool to study these clouds in regional and global systems, but the current generation of climate and weather models has difficulties in representing them in a realistic way (e.g., Siebesma et al., 2004; Stevens et al., 2007; Teixeira et al., 2011). While numerical models resolve the large-scale flow, subgrid-scale parameterizations are needed to estimate small-scale properties (e.g. boundary layer turbulence and convection, clouds, radiation), which have significant influence on the resolved scale due to the complex nonlinear nature of the atmosphere. To represent the contribution of these fine-scale processes to the resolved scale, climate models use various parameterizations, which are the main pieces in the model that contribute to the low clouds dynamics and therefore are the major sources of errors or approximations in their representation. In this project, we aim to 1) improve our understanding of the physical processes in thermal circulation and cloud formation, 2) examine the performance and sensitivity of various parameterizations in the regional weather model (Weather Research and Forecasting model; WRF), and 3) develop, implement, and evaluate the advanced boundary layer parameterization in the regional model to better represent stratocumulus, shallow cumulus, and their transition. Thus, this project includes three major corresponding studies. We find that the mean diurnal cycle is sensitive to model domain in ways that reveal the existence of different contributions originating from the Southeast Pacific land-masses. The experiments suggest that diurnal variations in circulations and thermal structures over this region are influenced by convection over the Peruvian sector of the Andes cordillera, while the mostly dry mountain-breeze circulations force an additional component that results in semi-diurnal variations near the coast. A series of numerical tests, however, reveal sensitivity of the simulations to the choice of vertical grid, limiting the possibility of solid quantitative statements on the amplitudes and phases of the diurnal and semidiurnal components across the domain. According to our experiments, the Mellor-Yamada-Nakanishi-Niino (MYNN) boundary layer scheme and the WSM6 microphysics scheme is the combination of schemes that performs best. For that combination, mean cloud cover, liquid water path, and cloud depth are fairly wellsimulated, while mean cloud top height remains too low in comparison to observations. Both microphysics and boundary layer schemes contribute to the spread in liquid water path and cloud depth, although the microphysics contribution is slightly more prominent. Boundary layer schemes are the primary contributors to cloud top height, degree of adiabaticity, and cloud cover. Cloud top height is closely related to surface fluxes and boundary layer structure. Thus, our study infers that an appropriate tuning of cloud top height would likely improve the low-cloud representation in the model. Finally, we show that entrainment governs the degree of adiabaticity, while boundary layer decoupling is a control on cloud cover. In the intercomparison study using WRF single-column model experiments, most parameterizations show a poor agreement of the vertical boundary layer structure when compared with large-eddy simulation models. We also implement a new Total-Energy/Mass- Flux boundary layer scheme into the WRF model and evaluate its ability to simulate both stratocumulus and shallow cumulus clouds. Result comparisons against large-eddy simulation show that this advanced parameterization based on the new Eddy-Diffusivity/Mass-Flux approach provides a better performance than other boundary layer parameterizations.

  8. Lecture Ch. 8 Cloud Classification

    E-Print Network [OSTI]

    Russell, Lynn

    clouds Middle clouds Grayish, block the sun, sometimes patchy Sharp outlines, rising, bright white1 Lecture Ch. 8 · Cloud Classification ­ Descriptive approach to clouds · Drop Growth and Precipitation Processes ­ Microphysical characterization of clouds · Complex (i.e. Real) Clouds ­ Examples

  9. A New Double-Moment Microphysics Parameterization for Application in Cloud and Climate Models. Part II: Single-Column Modeling of Arctic Clouds

    E-Print Network [OSTI]

    Shupe, Matthew

    of the arctic bound- ary layer, the presence of leads (cracks) in the sea ice surface, the persistence of mixed-phaseA New Double-Moment Microphysics Parameterization for Application in Cloud and Climate Models. Part- dicted cloud boundaries and total cloud fraction compare reasonably well with observations. Cloud phase

  10. Cloud Controlling Factors --Low Clouds BJORN STEVENS,

    E-Print Network [OSTI]

    Stevens, Bjorn

    Cloud Controlling Factors -- Low Clouds BJORN STEVENS, Department of Atmospheric and Oceanic) clouds is reviewed, with an emphasis on factors that may be expected to change in a changing climate of low-cloud control- ling processes are offered: these include renewing our focus on theory, model

  11. Cloud Controlling Factors --Low Clouds BJORN STEVENS,

    E-Print Network [OSTI]

    Stevens, Bjorn

    Cloud Controlling Factors -- Low Clouds BJORN STEVENS, Department of Atmospheric and Oceanic conspire to determine the statistics and cli- matology of layers of shallow (boundary layer) clouds of low-cloud control- ling processes are offered: these include renewing our focus on theory, model

  12. Cloud Tracking in Cloud-Resolving Models

    E-Print Network [OSTI]

    Plant, Robert

    Cloud Tracking in Cloud-Resolving Models RMetS Conference 4th September 2007 Bob Plant Department of Meteorology, University of Reading, UK #12;Introduction Obtain life cycle statistics for clouds in CRM simulations What is the distribution of cloud lifetimes? What factors determine the lifetime of an individual

  13. Cloud Security by Max Garvey

    E-Print Network [OSTI]

    Tolmach, Andrew

    Cloud Security Survey by Max Garvey #12;Cloudy Cloud is Cloudy What is the cloud? On Demand Service Network access Resource pooling Elasticity of Resources Measured Service #12;Cloud Types/Variants Iaa Cloud Public Cloud Hybrid Cloud combination. Private cloud with overflow going to public cloud. #12

  14. Multiple layer insulation cover

    DOE Patents [OSTI]

    Farrell, James J. (Livingston Manor, NY); Donohoe, Anthony J. (Ovid, NY)

    1981-11-03T23:59:59.000Z

    A multiple layer insulation cover for preventing heat loss in, for example, a greenhouse, is disclosed. The cover is comprised of spaced layers of thin foil covered fabric separated from each other by air spaces. The spacing is accomplished by the inflation of spaced air bladders which are integrally formed in the cover and to which the layers of the cover are secured. The bladders are inflated after the cover has been deployed in its intended use to separate the layers of the foil material. The sizes of the material layers are selected to compensate for sagging across the width of the cover so that the desired spacing is uniformly maintained when the cover has been deployed. The bladders are deflated as the cover is stored thereby expediting the storage process and reducing the amount of storage space required.

  15. ICE AND DUST IN THE PRESTELLAR DARK CLOUD LYNDS 183: PREPLANETARY MATTER AT THE LOWEST TEMPERATURES

    SciTech Connect (OSTI)

    Whittet, D. C. B.; Poteet, C. A.; Bajaj, V. M.; Horne, D. [Department of Physics, Applied Physics and Astronomy and New York Center for Astrobiology, Rensselaer Polytechnic Institute, 110 Eighth Street, Troy, NY 12180 (United States); Chiar, J. E. [SETI Institute, Carl Sagan Center, 189 Bernardo Avenue, Mountain View, CA 94043 (United States); Pagani, L. [LERMA, UMR 8112 du CNRS, Observatoire de Paris, 61 Av. de l'Observatoire, F-75014 Paris (France); Shenoy, S. S. [SOFIA Science Center, NASA Ames Research Center, MS 232-12, Moffett Field, CA 94035 (United States); Adamson, A. J. [Gemini Observatory, Southern Operations Center, Casilla 603, La Serena (Chile)

    2013-09-10T23:59:59.000Z

    Dust grains are nucleation centers and catalysts for the growth of icy mantles in quiescent interstellar clouds, the products of which may accumulate into preplanetary matter when new stars and solar systems form within the clouds. In this paper, we present the first spectroscopic detections of silicate dust and the molecular ices H{sub 2}O, CO, and CO{sub 2} in the vicinity of the prestellar core L183 (L134N). An infrared photometric survey of the cloud was used to identify reddened background stars, and we present spectra covering solid-state absorption features in the wavelength range 2-20 {mu}m for nine of them. The mean composition of the ices in the best-studied line of sight (toward J15542044-0254073) is H{sub 2}O:CO:CO{sub 2} Almost-Equal-To 100:40:24. The ices are amorphous in structure, indicating that they have been maintained at low temperature ({approx}< 15 K) since formation. The ice column density N(H{sub 2}O) correlates with reddening by dust, exhibiting a threshold effect that corresponds to the transition from unmantled grains in the outer layers of the cloud to ice-mantled grains within, analogous to that observed in other dark clouds. A comparison of results for L183 and the Taurus and IC 5146 dark clouds suggests common behavior, with mantles first appearing in each case at a dust column corresponding to a peak optical depth {tau}{sub 9.7} = 0.15 {+-} 0.03 in the silicate feature. Our results support a previous conclusion that the color excess E{sub J-K} does not obey a simple linear correlation with the total dust column in lines of sight that intercept dense clouds. The most likely explanation is a systematic change in the optical properties of the dust as the density increases.

  16. Beam Measurements of a CLOUD (Cosmics Leaving OUtdoor Droplets) Chamber

    E-Print Network [OSTI]

    Kirkby, Jasper

    2001-01-01T23:59:59.000Z

    A striking correlation has recently been observed between global cloud cover and the flux of incident cosmic rays. The effect of natural variations in the cosmic ray flux is large, causing estimated changes in the Earth's energy radiation balance that are comparable to those attributed to greenhouse gases from the burning of fossil fuels since the Industrial Revolution. However a direct link between cosmic rays and cloud formation has not been unambiguously established. We therefore propose to experimentally measure cloud (water droplet) formation under controlled conditions in a test beam at CERN with a CLOUD chamber, duplicating the conditions prevailing in the troposphere. These data, which have never been previously obtained, will allow a detailed understanding of the possible effects of cosmic rays on clouds and confirm, or otherwise, a direct link between cosmic rays, global cloud cover and the Earth's climate. The measurements will, in turn, allow more reliable calculations to be made of the residual e...

  17. Cloud and Aerosol Properties, Precipitable Water, and Profiles of Temperature and Water Vapor from MODIS

    E-Print Network [OSTI]

    Sheridan, Jennifer

    Cloud and Aerosol Properties, Precipitable Water, and Profiles of Temperature and Water Vapor from such as cloud mask, atmos- pheric profiles, aerosol properties, total precipitable water, and cloud properties vapor amount, aerosol particles, and the subsequently formed clouds [9]. Barnes et al. [2] provide

  18. Cloud Computing og availability

    E-Print Network [OSTI]

    Christensen, Henrik Bærbak

    Cloud Computing og availability Projekt i pålidelighed Henrik Lavdal - 20010210 Søren Bardino Kaa - 20011654 Gruppe 8 19-03-2010 #12;Cloud Computing og availability Side 2 af 28 Indholdsfortegnelse as a Service (SaaS) ...................................................................9 Availability i cloud

  19. Ad hoc cloud computing 

    E-Print Network [OSTI]

    McGilvary, Gary Andrew

    2014-11-27T23:59:59.000Z

    Commercial and private cloud providers offer virtualized resources via a set of co-located and dedicated hosts that are exclusively reserved for the purpose of offering a cloud service. While both cloud models appeal to ...

  20. P2.11 AN ANNUAL CYCLE OF ARCTIC CLOUD MICROPHYSICS Matthew D. Shupe*

    E-Print Network [OSTI]

    Shupe, Matthew

    to classify cloud scenes as all- ice, all-liquid, mixed-phase, or precipitating so that the appropriate ice/snow-covered surfaces. Several studies have demonstrated the importance of specific cloud microphysical properties on cloud-radiation and ice-albedo feedback mechanisms; these in turn have bearing

  1. Aerosol Effects on Cloud Emissivity and Surface Longwave Heating in the Arctic TIMOTHY J. GARRETT1,*

    E-Print Network [OSTI]

    ) studies show that in the Arctic cloud cover generally acts to warm the surface, while coolingAerosol Effects on Cloud Emissivity and Surface Longwave Heating in the Arctic TIMOTHY J. GARRETT1 in the atmosphere tend to increase the reflectance of solar (shortwave) radiation from water clouds, which can lead

  2. On Demand Surveillance Service in Vehicular Cloud

    E-Print Network [OSTI]

    Weng, Jui-Ting

    2013-01-01T23:59:59.000Z

    Toward Vehicular Service Cloud . . . . . . . . . . . . . . .4.2 Open Mobile Cloud Requirement . . . . .3.1 Mobile Cloud

  3. Cirrus cloud simulations using WRF with improved radiation parameterization and increased vertical resolution

    E-Print Network [OSTI]

    Liou, K. N.

    and aerosolcloudradiation interactions. With the newly implemented radiation scheme, the simulations of cloud cover:10.1029/2010JD014574. 1. Introduction [2] Cirrus clouds cover about 20% of the Earth's surface and showed that the effects of radiative processes and vertical transports are both significant in cirrus

  4. Covering Walls With Fabrics.

    E-Print Network [OSTI]

    Anonymous,

    1979-01-01T23:59:59.000Z

    TDOC . Z TA24S.7 8873 NO.1227 WALLS with ;FABRICS Texas Agricultural Extension Service . The Texas A&M University System Daniel C. Pfannstiel, Director, College Station, Texas Covering Walls with Fabrics* When tastefully applied, fabrics... it is applied, fabric-covered walls improve the sound-absorbing acoustical properties of a room. Also, fabrics can be used for covering walls of either textured gypsum board or wood paneling. Home decorating magazines are good sources for ideas about fabric...

  5. Using Radar, Lidar, and Radiometer measurements to Classify Cloud Type and Study Middle-Level Cloud Properties

    SciTech Connect (OSTI)

    Wang, Zhien

    2010-06-29T23:59:59.000Z

    The project is mainly focused on the characterization of cloud macrophysical and microphysical properties, especially for mixed-phased clouds and middle level ice clouds by combining radar, lidar, and radiometer measurements available from the ACRF sites. First, an advanced mixed-phase cloud retrieval algorithm will be developed to cover all mixed-phase clouds observed at the ACRF NSA site. The algorithm will be applied to the ACRF NSA observations to generate a long-term arctic mixed-phase cloud product for model validations and arctic mixed-phase cloud processes studies. To improve the representation of arctic mixed-phase clouds in GCMs, an advanced understanding of mixed-phase cloud processes is needed. By combining retrieved mixed-phase cloud microphysical properties with in situ data and large-scale meteorological data, the project aim to better understand the generations of ice crystals in supercooled water clouds, the maintenance mechanisms of the arctic mixed-phase clouds, and their connections with large-scale dynamics. The project will try to develop a new retrieval algorithm to study more complex mixed-phase clouds observed at the ACRF SGP site. Compared with optically thin ice clouds, optically thick middle level ice clouds are less studied because of limited available tools. The project will develop a new two wavelength radar technique for optically thick ice cloud study at SGP site by combining the MMCR with the W-band radar measurements. With this new algorithm, the SGP site will have a better capability to study all ice clouds. Another area of the proposal is to generate long-term cloud type classification product for the multiple ACRF sites. The cloud type classification product will not only facilitates the generation of the integrated cloud product by applying different retrieval algorithms to different types of clouds operationally, but will also support other research to better understand cloud properties and to validate model simulations. The ultimate goal is to improve our cloud classification algorithm into a VAP.

  6. Influence of clouds and diffuse radiation on ecosystem-atmosphere CO 2 and CO 18 O exchanges

    E-Print Network [OSTI]

    2009-01-01T23:59:59.000Z

    cover, radiation, meteorological and water isotope data tohere, radiation, cloud property, and aerosol data wereData were obtained from the Atmospheric Radiation

  7. Cloud Computing For Bioinformatics

    E-Print Network [OSTI]

    Ferrara, Katherine W.

    Cloud Computing For Bioinformatics EC2 and AMIs #12;Quick-starting an EC2 instance (let's get our feet wet!) Cloud Computing #12;Cloud Computing: EC2 instance Quick Start · On EC2 console, we can click on Launch Instance · This will let us get up and going quickly #12;Cloud Computing: EC2 instance

  8. Bolocam Survey for 1.1 mm Dust Continuum Emission in the c2d Legacy Clouds. II. Ophiuchus

    E-Print Network [OSTI]

    K. E. Young; M. L. Enoch; N. J. Evans II; J. Glenn; A. Sargent; T. Huard; J. Aguirre; S. Golwala; D. Haig; P. Harvey; G. Laurent; P. Mauskopf; J. Sayers

    2006-02-11T23:59:59.000Z

    We present a large-scale millimeter continuum map of the Ophiuchus molecular cloud. Nearly 11 square degrees, including all of the area in the cloud with visual extinction more than 3 magnitudes, was mapped at 1.1 mm with Bolocam on the Caltech Submillimeter Observatory (CSO). By design, the map also covers the region mapped in the infrared with the Spitzer Space Telescope. We detect 44 definite sources, and a few likely sources are also seen along a filament in the eastern streamer. The map indicates that dense cores in Ophiuchus are very clustered and often found in filaments within the cloud. Most sources are round, as measured at the half power point, but elongated when measured at lower contour levels, suggesting spherical sources lying within filaments. The masses, for an assumed dust temperature of 10 K, range from 0.24 to 3.9 solar masses, with a mean value of 0.96 solar masses. The total mass in distinct cores is 42 solar masses, 0.5 to 2% of the total cloud mass, and the total mass above 4 sigma is about 80 solar masses. The mean densities in the cores are quite high, with an average of 1.6 x 10^6 per cc, suggesting short free-fall times. The core mass distribution can be fitted with a power law with slope of 2.1 plus or minus 0.3 for M>0.5 solar masses, similar to that found in other regions, but slightly shallower than that of some determinations of the local IMF. In agreement with previous studies, our survey shows that dense cores account for a very small fraction of the cloud volume and total mass. They are nearly all confined to regions with visual extinction at least 9 mag, a lower threshold than found previously.

  9. Cloud-Scale Vertical Velocity and Turbulent Dissipation Rate Retrievals

    DOE Data Explorer [Office of Scientific and Technical Information (OSTI)]

    Shupe, Matthew

    Time-height fields of retrieved in-cloud vertical wind velocity and turbulent dissipation rate, both retrieved primarily from vertically-pointing, Ka-band cloud radar measurements. Files are available for manually-selected, stratiform, mixed-phase cloud cases observed at the North Slope of Alaska (NSA) site during periods covering the Mixed-Phase Arctic Cloud Experiment (MPACE, late September through early November 2004) and the Indirect and Semi-Direct Aerosol Campaign (ISDAC, April-early May 2008). These time periods will be expanded in a future submission.

  10. DO AEROSOLS CHANGE CLOUD COVER AND AFFECT CLIMATE?

    E-Print Network [OSTI]

    Schwartz, Stephen E.

    BALANCE Global and annual average energy fluxes in watts per square meter Schwartz, 1996, modified from;AEROSOL INFLUENCES ON CLIMATE AND CLIMATE CHANGE #12;DMS #12;AEROSOL IN MEXICO CITY BASIN #12;AEROSOL IN MEXICO CITY BASIN Light scattering by aerosols decreases absorption of solar radiation. #12;AEROSOLS

  11. Cloud fraction, liquid and ice water contents derived from long-term radar, lidar, and microwave radiometer data are systematically compared to models to quantify and

    E-Print Network [OSTI]

    Hogan, Robin

    Cloud fraction, liquid and ice water contents derived from long-term radar, lidar, and microwave a systematic evaluation of clouds in forecast models. Clouds and their associated microphysical processes for end users of weather forecasts, who may be interested not only in cloud cover, but in other variables

  12. Tripleclouds: An Efficient Method for Representing Horizontal Cloud Inhomogeneity in 1D Radiation Schemes by Using Three Regions at Each Height

    E-Print Network [OSTI]

    Hogan, Robin

    that a mere 4% increase in global cloud cover could counter- act the warming caused by a doubling of carbon the effect of in- homogeneity on the radiative properties of high cloud. They used cloud radar data to inferTripleclouds: An Efficient Method for Representing Horizontal Cloud Inhomogeneity in 1D Radiation

  13. Retrieval of Cloud Phase Using the Moderate Resolution Imaging Spectroradiometer Data during the Mixed-Phase Arctic Cloud Experiment

    SciTech Connect (OSTI)

    Spangenberg, D.; Minnis, P.; Shupe, M.; Uttal, T.; Poellot, M.

    2005-03-18T23:59:59.000Z

    Improving climate model predictions over Earth's polar regions requires a comprehensive knowledge of polar cloud microphysics. Over the Arctic, there is minimal contrast between the clouds and background snow surface, making it difficult to detect clouds and retrieve their phase from space. Snow and ice cover, temperature inversions, and the predominance of mixed-phase clouds make it even more difficult to determine cloud phase. Also, since determining cloud phase is the first step toward analyzing cloud optical depth, particle size, and water content, it is vital that the phase be correct in order to obtain accurate microphysical and bulk properties. Changes in these cloud properties will, in turn, affect the Arctic climate since clouds are expected to play a critical role in the sea ice albedo feedback. In this paper, the IR trispectral technique (IRTST) is used as a starting point for a WV and 11-{micro}m brightness temperature (T11) parameterization (WVT11P) of cloud phase using MODIS data. In addition to its ability to detect mixed-phase clouds, the WVT11P also has the capability to identify thin cirrus clouds overlying mixed or liquid phase clouds (multiphase ice). Results from the Atmospheric Radiation Measurement (ARM) MODIS phase model (AMPHM) are compared to the surface-based cloud phase retrievals over the ARM North Slope of Alaska (NSA) Barrow site and to in-situ data taken from University of North Dakota Citation (CIT) aircraft which flew during the Mixed-Phase Arctic Cloud Experiment (MPACE). It will be shown that the IRTST and WVT11P combined to form the AMPHM can achieve a relative high accuracy of phase discrimination compared to the surface-based retrievals. Since it only uses MODIS WV and IR channels, the AMPHM is robust in the sense that it can be applied to daytime, twilight, and nighttime scenes with no discontinuities in the output phase.

  14. CoverSheet

    Broader source: All U.S. Department of Energy (DOE) Office Webpages (Extended Search)

    overseen by MST-6, that is available for use by qualified users. In FY12 the EML service contract costs were covered by funds from LDRD, BES, NE and other programs. Users...

  15. TOTAL M F Total M F Total M F Total M F Total M F Total M F Total M F Total M F Total M F Total M F Total M F Total M F Total Spring 2010

    E-Print Network [OSTI]

    Hayes, Jane E.

    202 51 *total new freshmen 684: 636 Lexington campus, 48 Paducah campus MS Total 216 12 5 17 2 0 2 40 248 247 648 45 210 14 *total new freshmen 647: 595 Lexington campus, 52 Paducah campus MS Total 192 14

  16. An Assessment of the Parameterization of Subgrid-Scale Cloud Effects on Radiative Transfer. Part II: Horizontal Inhomogeneity

    E-Print Network [OSTI]

    Stephens, Graeme L.

    in downwelling radiative fluxes at the surface induced by changes in cloud cover and water vapor distributions. 1An Assessment of the Parameterization of Subgrid-Scale Cloud Effects on Radiative Transfer. Part II form 5 January 2005) ABSTRACT The role of horizontal inhomogeneity in radiative transfer through cloud

  17. Use of a Lidar Forward Model for Global Comparisons of Cloud Fraction between the ICESat Lidar and the ECMWF Model

    E-Print Network [OSTI]

    Hogan, Robin

    to underestimate cloud cover in the extra-tropical oceans, the trade wind cumulus, the stratocumulus sheets off-to-backscatter ratio and effective radius affect the forward modeled mean cloud fraction by no more than 10%. 1. Introduction Clouds play a major role in the Earth's radiation bud- get and predictions of future climate

  18. RISK ASSESSMENT CLOUD COMPUTING

    E-Print Network [OSTI]

    Columbia University

    SECURITY RESEARCH PRIVACY RISK ASSESSMENT AMC DATA FISMA CLOUD COMPUTING MOBILE DEVICES OPERATIONS application hosted in the cloud · Alaska DHHS fined $1.7M ­ Portable device stolen from vehicle · Mass Eye

  19. XSEDE Cloud Survey Report

    E-Print Network [OSTI]

    Walter, M.Todd

    XSEDE Cloud Survey Report David Lifka, Cornell Center for Advanced Computing Ian Foster, ANL, ANL and The University of Chicago A National Science Foundation-sponsored cloud user survey was conducted from September 2012 to April 2013 by the XSEDE Cloud Integration Investigation Team to better

  20. Research Cloud Computing Recommendations

    E-Print Network [OSTI]

    Qian, Ning

    Research Cloud Computing Recommendations SRCPAC December 3, 2014 #12;Mandate and Membership SRCPAC convened this committee in Sept 2014 to investigate the role that cloud computing should play in our & Academic Affairs (Social Work) #12;Questions discussed · What cloud resources are available? · Which kinds

  1. Covered Product Category: Displays

    Broader source: Energy.gov [DOE]

    FEMP provides acquisition guidance and Federal efficiency requirements across a variety of product categories, including displays, which are covered by the ENERGY STAR program. Federal laws and requirements mandate that agencies meet these efficiency requirements in all procurement and acquisition actions that are not specifically exempted by law.

  2. Working inside the Cloud: Developing a Cloud Computing Infrastructure

    E-Print Network [OSTI]

    Krause, Rolf

    UROP 2012 Working inside the Cloud: Developing a Cloud Computing Infrastructure Cloud computing and live-migration of running VM. USI participates to the development of the first European Cloud computing for a motivated student that will have a chance to improve his/her knowledge on Cloud computing, Java and/or Ruby

  3. Dynamic Cloud Resource Reservation via Cloud Brokerage

    E-Print Network [OSTI]

    Li, Baochun

    Department of Electrical and Computer Engineering, University of Toronto Department of Electrical@eecg.toronto.edu, liang@utoronto.ca Abstract--Infrastructure-as-a-Service clouds offer diverse pric- ing options

  4. Coverable functions Petr Kucera,

    E-Print Network [OSTI]

    of clauses needed to represent f by a CNF. ess(f) - maximum number of pairwise disjoint essential sets of implicates of f. A function f is coverable, if cnf(f)=ess(f). #12;Talk outline We already know from Horn functions. X E ess(f) = ess(X) + k #12;CNF Graph For a Horn CNF let be the digraph defined as: N

  5. An Autonomous Reliabilit Cloud Comput

    E-Print Network [OSTI]

    Buyya, Rajkumar

    An Autonomous Reliabilit Ami Cloud Comput Department of Computing and Informa Abstract--Cloud computing paradigm allo based access to computing and storages s Internet. Since with advances of Cloud. Keywords- Cloud computing; SLA negotiat I. INTRODUCTION Cloud computing has transferred the services

  6. Radiative Effects of Dust Aerosols, Natural Cirrus Clouds and Contrails: Broadband Optical Properties and Sensitivity Studies

    E-Print Network [OSTI]

    Yi, Bingqi

    2013-07-09T23:59:59.000Z

    This dissertation aims to study the broadband optical properties and radiative effects of dust aerosols and ice clouds. It covers three main topics: the uncertainty of dust optical properties and radiative effects from the dust particle shape...

  7. Radiative Effects of Dust Aerosols, Natural Cirrus Clouds and Contrails: Broadband Optical Properties and Sensitivity Studies 

    E-Print Network [OSTI]

    Yi, Bingqi

    2013-07-09T23:59:59.000Z

    This dissertation aims to study the broadband optical properties and radiative effects of dust aerosols and ice clouds. It covers three main topics: the uncertainty of dust optical properties and radiative effects from the dust particle shape...

  8. Clouds up close | EMSL

    Broader source: All U.S. Department of Energy (DOE) Office Webpages (Extended Search)

    interactions that affect clouds and thus improve climate projections. Contact Heng Xiao Pacific Northwest National Laboratory 902 Battelle Blvd., PO Box 999 MSIN: K9-30...

  9. Finance Idol Word Cloud

    Broader source: Energy.gov [DOE]

    This word cloud represents the topics discussed during the Big and Small Ideas: How to Lower Solar Financing Costs breakout session at the SunShot Grand Challenge.

  10. SURFACE CLOUD RADIATIVE FORCING, CLOUD FRACTION AND CLOUD ALBEDO: THEIR RELATIONSHIP AND MULTISCALE VARIATION

    E-Print Network [OSTI]

    that have been used to quantify the effect of clouds on radiation budget in both modeling and observationalSURFACE CLOUD RADIATIVE FORCING, CLOUD FRACTION AND CLOUD ALBEDO: THEIR RELATIONSHIP AND MULTISCALE/Atmospheric Sciences Division Brookhaven National Laboratory P.O. Box, Upton, NY www.bnl.gov ABSTRACT Cloud-radiation

  11. Parameterizing Size Distribution in Ice Clouds

    SciTech Connect (OSTI)

    DeSlover, Daniel; Mitchell, David L.

    2009-09-25T23:59:59.000Z

    PARAMETERIZING SIZE DISTRIBUTIONS IN ICE CLOUDS David L. Mitchell and Daniel H. DeSlover ABSTRACT An outstanding problem that contributes considerable uncertainty to Global Climate Model (GCM) predictions of future climate is the characterization of ice particle sizes in cirrus clouds. Recent parameterizations of ice cloud effective diameter differ by a factor of three, which, for overcast conditions, often translate to changes in outgoing longwave radiation (OLR) of 55 W m-2 or more. Much of this uncertainty in cirrus particle sizes is related to the problem of ice particle shattering during in situ sampling of the ice particle size distribution (PSD). Ice particles often shatter into many smaller ice fragments upon collision with the rim of the probe inlet tube. These small ice artifacts are counted as real ice crystals, resulting in anomalously high concentrations of small ice crystals (D < 100 µm) and underestimates of the mean and effective size of the PSD. Half of the cirrus cloud optical depth calculated from these in situ measurements can be due to this shattering phenomenon. Another challenge is the determination of ice and liquid water amounts in mixed phase clouds. Mixed phase clouds in the Arctic contain mostly liquid water, and the presence of ice is important for determining their lifecycle. Colder high clouds between -20 and -36 oC may also be mixed phase but in this case their condensate is mostly ice with low levels of liquid water. Rather than affecting their lifecycle, the presence of liquid dramatically affects the cloud optical properties, which affects cloud-climate feedback processes in GCMs. This project has made advancements in solving both of these problems. Regarding the first problem, PSD in ice clouds are uncertain due to the inability to reliably measure the concentrations of the smallest crystals (D < 100 µm), known as the “small mode”. Rather than using in situ probe measurements aboard aircraft, we employed a treatment of ice cloud optical properties formulated in terms of PSD parameters in combination with remote measurements of thermal radiances to characterize the small mode. This is possible since the absorption efficiency (Qabs) of small mode crystals is larger at 12 µm wavelength relative to 11 µm wavelength due to the process of wave resonance or photon tunneling more active at 12 µm. This makes the 12/11 µm absorption optical depth ratio (or equivalently the 12/11 µm Qabs ratio) a means for detecting the relative concentration of small ice particles in cirrus. Using this principle, this project tested and developed PSD schemes that can help characterize cirrus clouds at each of the three ARM sites: SGP, NSA and TWP. This was the main effort of this project. These PSD schemes and ice sedimentation velocities predicted from them have been used to test the new cirrus microphysics parameterization in the GCM known as the Community Climate Systems Model (CCSM) as part of an ongoing collaboration with NCAR. Regarding the second problem, we developed and did preliminary testing on a passive thermal method for retrieving the total water path (TWP) of Arctic mixed phase clouds where TWPs are often in the range of 20 to 130 g m-2 (difficult for microwave radiometers to accurately measure). We also developed a new radar method for retrieving the cloud ice water content (IWC), which can be vertically integrated to yield the ice water path (IWP). These techniques were combined to determine the IWP and liquid water path (LWP) in Arctic clouds, and hence the fraction of ice and liquid water. We have tested this approach using a case study from the ARM field campaign called M-PACE (Mixed-Phase Arctic Cloud Experiment). This research led to a new satellite remote sensing method that appears promising for detecting low levels of liquid water in high clouds typically between -20 and -36 oC. We hope to develop this method in future research.

  12. NERSC Journal Cover Stories

    Broader source: All U.S. Department of Energy (DOE) Office Webpages (Extended Search)

    AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE: Alternative1 First Use of Energy for All Purposes (Fuel and Nonfuel), 2002; Level: National5Sales for4,645U.S. DOE Office of Science (SC)Integrated Codes |IsLove Your1AllocationsNOVA Portal:Ott2006.jpg A NewCEN-Cover.png

  13. METR 3223: Physical Meteorology II: Cloud Physics, Atmospheric Electricity and Optics

    E-Print Network [OSTI]

    Droegemeier, Kelvin K.

    METR 3223: Physical Meteorology II: Cloud Physics, Atmospheric Electricity and Optics CLASS: Monday as atmospheric electricity and optics. Specific topics that will be covered are as follows: Cloud physics: Review Lightening Atmospheric optics: Reflection and refraction Optical phenomena GRADES Homework problems: 20% Quiz

  14. A06: Analysis of GRAPE data The effects of anthropogenic aerosols on cloud microphysical properties.

    E-Print Network [OSTI]

    Oxford, University of

    the radiative balance of the atmosphere. This effect is known as the `first direct radiative forcing'[4 of this warming is to reduce the upward movement of moisture and in turn reduce the cloud cover[5]. This `semiA06: Analysis of GRAPE data The effects of anthropogenic aerosols on cloud microphysical properties

  15. Radiative and microphysical properties of Arctic stratus clouds from multiangle downwelling infrared radiances

    E-Print Network [OSTI]

    Shupe, Matthew

    climate is strongly influenced by an extensive and persistent pattern of cloud cover [Francis, 1997 properties can have significant effects on long- wave radiation, which dominates the radiation energy budgetRadiative and microphysical properties of Arctic stratus clouds from multiangle downwelling

  16. GROUND-BASED CLOUD IMAGES AND SKY RADIANCES IN THE VISIBLE AND NEAR INFRARED REGION FROM

    E-Print Network [OSTI]

    Shields, Janet

    the atmospheric heating rates as well as the amount of solar radiation including biologically effective UV preliminary comparisons with model calculations and cloud cover data both from another type of sky imager data are of specific importance to study the role of clouds on the radiation balance of the earth

  17. Inconsistencies between satellite estimates of longwave cloud forcing and dynamical fields from reanalyses

    E-Print Network [OSTI]

    Allan, Richard P.

    of cloud cover. We argue that monthly mean clear-sky outgoing longwave radiation (OLRc) measurements] The greenhouse effect of cloud may be quantified as the difference between outgoing longwave radiation (OLR longwave radiative effect is made which is directly comparable with standard climate model diagnostics

  18. Sandia Energy - Cloud Computing Services

    Broader source: All U.S. Department of Energy (DOE) Office Webpages (Extended Search)

    Cloud Computing Services Home Stationary Power Safety, Security & Resilience of Energy Infrastructure Grid Modernization Cyber Security for Electric Infrastructure Cloud Computing...

  19. Profiling clouds' inner life | EMSL

    Broader source: All U.S. Department of Energy (DOE) Office Webpages (Extended Search)

    life Released: May 29, 2014 Subgrid modeling pinpoints cloud transformation to uncover true reflective power An accurate understanding of clouds over the ocean is important for...

  20. CONTRIBUTED Green Cloud Computing

    E-Print Network [OSTI]

    Tucker, Rod

    to manage energy consumption across the entire information and communications technology (ICT) sector. While considers both public and private clouds, and includes energy consumption in switching and transmission to energy consumption and cloud computing seems to be an alternative to office-based computing. By Jayant

  1. Toward Securing Sensor Clouds

    E-Print Network [OSTI]

    · 32 GB microSDHC storage 2 Image from http://hothardware.com/News/Leaked-Motorola-DROID-X-2-Daytona Computer Mini Computer External Storage External Storage Router Router Router Router Cloud Computing Cloud: micro surveys, amber alerts 4 #12;Router Router Router Router Mini Computer Mini Computer Mini Computer

  2. July 2012July 2012 Cloud Computing and Virtualization:Cloud Computing and Virtualization

    E-Print Network [OSTI]

    Liu, Jiangchuan (JC)

    July 2012July 2012 Cloud Computing and Virtualization:Cloud Computing and Virtualization/26/2633 Recent: CloudRecent: Cloud The fast growth of cloud computing Cloud file storage/synchronization services Google entries about cloud computing: 184,000,000 #12;July 2012July 2012 44/26/2644 Our CloudOur Cloud 7

  3. Beam Measurements of a CLOUD (Cosmics Leaving OUtdoor Droplets) Chamber

    E-Print Network [OSTI]

    Jasper Kirkby

    2001-04-27T23:59:59.000Z

    A striking correlation has recently been observed between global cloud cover and the flux of incident cosmic rays. The effect of natural variations in the cosmic ray flux is large, causing estimated changes in the Earth's energy radiation balance that are comparable to those attributed to greenhouse gases from the burning of fossil fuels since the Industrial Revolution. However a direct link between cosmic rays and cloud formation has not been unambiguously established. We therefore propose to experimentally measure cloud (water droplet) formation under controlled conditions in a test beam at CERN with a CLOUD chamber, duplicating the conditions prevailing in the troposphere. These data, which have never been previously obtained, will allow a detailed understanding of the possible effects of cosmic rays on clouds and confirm, or otherwise, a direct link between cosmic rays, global cloud cover and the Earth's climate. The measurements will, in turn, allow more reliable calculations to be made of the residual effect on global temperatures of the burning of fossil fuels, an issue of profound importance to society. Furthermore, light radio-isotope records indicate a correlation has existed between global climate and the cosmic ray flux extending back over the present inter-glacial and perhaps earlier. This suggests it may eventually become possible to make long-term (10-1,000 year) predictions of changes in the Earth's climate, provided a deeper understanding can be achieved of the ``geomagnetic climate'' of the Sun and Earth that modulates the cosmic-ray flux.

  4. Total solar irradiance during the Holocene F. Steinhilber,1

    E-Print Network [OSTI]

    Wehrli, Bernhard

    Total solar irradiance during the Holocene F. Steinhilber,1 J. Beer,1 and C. Fro¨hlich2 Received 20 solar irradiance covering 9300 years is presented, which covers almost the entire Holocene. This reconstruction is based on a recently observationally derived relationship between total solar irradiance

  5. When Clouds become Green: the Green Open Cloud Architecture

    E-Print Network [OSTI]

    Boyer, Edmond

    of a new original energy-efficient Cloud infrastructure called Green Open Cloud. Keywords. Energy with the support of energy-efficient frameworks dedicated to Cloud architectures. Virtualization is a key feature of the energy-aware Cloud infras- tructure that we propose. The conclusion and future works are reviewed

  6. Cloud is not a silver bullet: A case study of cloud-based mobile browsing

    E-Print Network [OSTI]

    Greenberg, Albert

    and device energy con- sumption. While these efforts have adopted different ap- proaches to cloud not provide clear benefits over Direct either in energy or download time. For e.g. while CB decreases for other pages. Sim- ilarly while CB decreases the total energy by up to 20.77J compared to Direct for 52

  7. Attribution Analysis of Cloud Feedback

    E-Print Network [OSTI]

    Zhou, Chen

    2014-07-15T23:59:59.000Z

    -term global warming. If the EIS-low cloud fraction relationship holds under global warming, it is likely that the tropical low cloud fraction change is non-negative. Climate models without significant negative low cloud fraction change suggest that the cloud...

  8. Convective Cloud Lifecycles Lunchtime seminar

    E-Print Network [OSTI]

    Plant, Robert

    Convective Cloud Lifecycles Lunchtime seminar 19th May 2009 Bob Plant Department of Meteorology, University of Reading, UK #12;Introduction Obtain life cycle statistics for clouds in CRM simulations Why Conclusions Convective Cloud Lifecycles ­ p.1/3 #12;Why bother? Convective Cloud Lifecycles ­ p.2/3 #12;Some

  9. Identifying clouds over the Pierre Auger Observatory using infrared satellite data

    SciTech Connect (OSTI)

    Abreu, Pedro; et al.,

    2013-12-01T23:59:59.000Z

    We describe a new method of identifying night-time clouds over the Pierre Auger Observatory using infrared data from the Imager instruments on the GOES-12 and GOES-13 satellites. We compare cloud identifications resulting from our method to those obtained by the Central Laser Facility of the Auger Observatory. Using our new method we can now develop cloud probability maps for the 3000 km^2 of the Pierre Auger Observatory twice per hour with a spatial resolution of ~2.4 km by ~5.5 km. Our method could also be applied to monitor cloud cover for other ground-based observatories and for space-based observatories.

  10. First observations of tracking clouds using scanning ARM cloud radars

    DOE Public Access Gateway for Energy & Science Beta (PAGES Beta)

    Borque, Paloma; Giangrande, Scott; Kollias, Pavlos

    2014-12-01T23:59:59.000Z

    Tracking clouds using scanning cloud radars can help to document the temporal evolution of cloud properties well before large drop formation (‘‘first echo’’). These measurements complement cloud and precipitation tracking using geostationary satellites and weather radars. Here, two-dimensional (2-D) Along-Wind Range Height Indicator (AW-RHI) observations of a population of shallow cumuli (with and without precipitation) from the 35-GHz scanning ARM cloud radar (SACR) at the DOE Atmospheric Radiation Measurements (ARM) program Southern Great Plains (SGP) site are presented. Observations from the ARM SGP network of scanning precipitation radars are used to provide the larger scale context of the cloud fieldmore »and to highlight the advantages of the SACR to detect the numerous, small, non-precipitating cloud elements. A new Cloud Identification and Tracking Algorithm (CITA) is developed to track cloud elements. In CITA, a cloud element is identified as a region having a contiguous set of pixels exceeding a preset reflectivity and size threshold. The high temporal resolution of the SACR 2-D observations (30 sec) allows for an area superposition criteria algorithm to match cloud elements at consecutive times. Following CITA, the temporal evolution of cloud element properties (number, size, and maximum reflectivity) is presented. The vast majority of the designated elements during this cumulus event were short-lived non-precipitating clouds having an apparent life cycle shorter than 15 minutes. The advantages and disadvantages of cloud tracking using an SACR are discussed.« less

  11. First observations of tracking clouds using scanning ARM cloud radars

    DOE Public Access Gateway for Energy & Science Beta (PAGES Beta)

    Borque, Paloma [McGill Univ., Montreal, QC (Canada); Giangrande, Scott [Brookhaven National Lab. (BNL), Upton, NY (United States); Kollias, Pavlos [McGill Univ., Montreal, QC (Canada)

    2014-12-01T23:59:59.000Z

    Tracking clouds using scanning cloud radars can help to document the temporal evolution of cloud properties well before large drop formation (‘‘first echo’’). These measurements complement cloud and precipitation tracking using geostationary satellites and weather radars. Here, two-dimensional (2-D) Along-Wind Range Height Indicator (AW-RHI) observations of a population of shallow cumuli (with and without precipitation) from the 35-GHz scanning ARM cloud radar (SACR) at the DOE Atmospheric Radiation Measurements (ARM) program Southern Great Plains (SGP) site are presented. Observations from the ARM SGP network of scanning precipitation radars are used to provide the larger scale context of the cloud field and to highlight the advantages of the SACR to detect the numerous, small, non-precipitating cloud elements. A new Cloud Identification and Tracking Algorithm (CITA) is developed to track cloud elements. In CITA, a cloud element is identified as a region having a contiguous set of pixels exceeding a preset reflectivity and size threshold. The high temporal resolution of the SACR 2-D observations (30 sec) allows for an area superposition criteria algorithm to match cloud elements at consecutive times. Following CITA, the temporal evolution of cloud element properties (number, size, and maximum reflectivity) is presented. The vast majority of the designated elements during this cumulus event were short-lived non-precipitating clouds having an apparent life cycle shorter than 15 minutes. The advantages and disadvantages of cloud tracking using an SACR are discussed.

  12. Migrating enterprise storage applications to the cloud

    E-Print Network [OSTI]

    Vrable, Michael Daniel

    2011-01-01T23:59:59.000Z

    2.1 Cloud Providers . . . . . . . . . . . .2.1.1 Cloud Storage . . . . . . . . .2.1.2 Cloud Computation . . . . . . 2.2 Enterprise Storage

  13. A developer's survey on different cloud platforms

    E-Print Network [OSTI]

    Doan, Dzung

    2009-01-01T23:59:59.000Z

    1 Introduction Cloud computing is a computing paradigm inFor this reason, cloud computing has also been describedparallel processing. Cloud computing can be contrasted with

  14. Covered Product Category: Commercial Fryers

    Broader source: Energy.gov [DOE]

    The Federal Energy Management Program (FEMP) provides acquisition guidance for commercial fryers, which is a product category covered by the ENERGY STAR program.

  15. Covered Product Category: Commercial Griddles

    Broader source: Energy.gov [DOE]

    The Federal Energy Management Program (FEMP) provides acquisition guidance for commercial griddles, which is a product category covered by the ENERGY STAR program

  16. A High Resolution Hydrometer Phase Classifier Based on Analysis of Cloud Radar Doppler Spectra.

    SciTech Connect (OSTI)

    Luke,E.; Kollias, P.

    2007-08-06T23:59:59.000Z

    The lifecycle and radiative properties of clouds are highly sensitive to the phase of their hydrometeors (i.e., liquid or ice). Knowledge of cloud phase is essential for specifying the optical properties of clouds, or else, large errors can be introduced in the calculation of the cloud radiative fluxes. Current parameterizations of cloud water partition in liquid and ice based on temperature are characterized by large uncertainty (Curry et al., 1996; Hobbs and Rangno, 1998; Intriery et al., 2002). This is particularly important in high geographical latitudes and temperature ranges where both liquid droplets and ice crystal phases can exist (mixed-phase cloud). The mixture of phases has a large effect on cloud radiative properties, and the parameterization of mixed-phase clouds has a large impact on climate simulations (e.g., Gregory and Morris, 1996). Furthermore, the presence of both ice and liquid affects the macroscopic properties of clouds, including their propensity to precipitate. Despite their importance, mixed-phase clouds are severely understudied compared to the arguably simpler single-phase clouds. In-situ measurements in mixed-phase clouds are hindered due to aircraft icing, difficulties distinguishing hydrometeor phase, and discrepancies in methods for deriving physical quantities (Wendisch et al. 1996, Lawson et al. 2001). Satellite-based retrievals of cloud phase in high latitudes are often hindered by the highly reflecting ice-covered ground and persistent temperature inversions. From the ground, the retrieval of mixed-phase cloud properties has been the subject of extensive research over the past 20 years using polarization lidars (e.g., Sassen et al. 1990), dual radar wavelengths (e.g., Gosset and Sauvageot 1992; Sekelsky and McIntosh, 1996), and recently radar Doppler spectra (Shupe et al. 2004). Millimeter-wavelength radars have substantially improved our ability to observe non-precipitating clouds (Kollias et al., 2007) due to their excellent sensitivity that enables the detection of thin cloud layers and their ability to penetrate several non-precipitating cloud layers. However, in mixed-phase clouds conditions, the observed Doppler moments are dominated by the highly reflecting ice crystals and thus can not be used to identify the cloud phase. This limits our ability to identify the spatial distribution of cloud phase and our ability to identify the conditions under which mixed-phase clouds form.

  17. ARM - Midlatitude Continental Convective Clouds (jensen-sonde)

    SciTech Connect (OSTI)

    Jensen, Mike; Comstock, Jennifer; Genio, Anthony Del; Giangrande, Scott; Kollias, Pavlos

    2012-01-19T23:59:59.000Z

    A major component of the Mid-latitude Continental Convective Clouds Experiment (MC3E) field campaign was the deployment of an enhanced radiosonde array designed to capture the vertical profile of atmospheric state variables (pressure, temperature, humidity wind speed and wind direction) for the purpose of deriving the large-scale forcing for use in modeling studies. The radiosonde array included six sites (enhanced Central Facility [CF-1] plus five new sites) launching radiosondes at 3-6 hour sampling intervals. The network will cover an area of approximately (300)2 km2 with five outer sounding launch sites and one central launch location. The five outer sounding launch sites are: S01 Pratt, KS [ 37.7oN, 98.75oW]; S02 Chanute, KS [37.674, 95.488]; S03 Vici, Oklahoma [36.071, -99.204]; S04 Morris, Oklahoma [35.687, -95.856]; and S05 Purcell, Oklahoma [34.985, -97.522]. Soundings from the SGP Central Facility during MC3E can be retrieved from the regular ARM archive. During routine MC3E operations 4 radiosondes were launched from each of these sites (approx. 0130, 0730, 1330 and 1930 UTC). On days that were forecast to be convective up to four additional launches were launched at each site (approx. 0430, 1030, 1630, 2230 UTC). There were a total of approximately 14 of these high frequency launch days over the course of the experiment.

  18. ARM - Midlatitude Continental Convective Clouds (jensen-sonde)

    DOE Data Explorer [Office of Scientific and Technical Information (OSTI)]

    Jensen, Mike; Comstock, Jennifer; Genio, Anthony Del; Giangrande, Scott; Kollias, Pavlos

    A major component of the Mid-latitude Continental Convective Clouds Experiment (MC3E) field campaign was the deployment of an enhanced radiosonde array designed to capture the vertical profile of atmospheric state variables (pressure, temperature, humidity wind speed and wind direction) for the purpose of deriving the large-scale forcing for use in modeling studies. The radiosonde array included six sites (enhanced Central Facility [CF-1] plus five new sites) launching radiosondes at 3-6 hour sampling intervals. The network will cover an area of approximately (300)2 km2 with five outer sounding launch sites and one central launch location. The five outer sounding launch sites are: S01 Pratt, KS [ 37.7oN, 98.75oW]; S02 Chanute, KS [37.674, 95.488]; S03 Vici, Oklahoma [36.071, -99.204]; S04 Morris, Oklahoma [35.687, -95.856]; and S05 Purcell, Oklahoma [34.985, -97.522]. Soundings from the SGP Central Facility during MC3E can be retrieved from the regular ARM archive. During routine MC3E operations 4 radiosondes were launched from each of these sites (approx. 0130, 0730, 1330 and 1930 UTC). On days that were forecast to be convective up to four additional launches were launched at each site (approx. 0430, 1030, 1630, 2230 UTC). There were a total of approximately 14 of these high frequency launch days over the course of the experiment.

  19. Thin Cloud Length Scales Using CALIPSO and CloudSat Data

    E-Print Network [OSTI]

    Solbrig, Jeremy E.

    2010-10-12T23:59:59.000Z

    Thin clouds are the most difficult cloud type to observe. The recent availability of joint cloud products from the active remote sensing instruments aboard CloudSat and the Cloud-Aerosol Lidar and Infrared Pathfinder Satellite (CALIPSO) facilitates...

  20. Using cloud resolving model simulations of deep convection to inform cloud parameterizations in large-scale models

    SciTech Connect (OSTI)

    Klein, Stephen A.; Pincus, Robert; Xu, Kuan-man

    2003-06-23T23:59:59.000Z

    Cloud parameterizations in large-scale models struggle to address the significant non-linear effects of radiation and precipitation that arise from horizontal inhomogeneity in cloud properties at scales smaller than the grid box size of the large-scale models. Statistical cloud schemes provide an attractive framework to self-consistently predict the horizontal inhomogeneity in radiation and microphysics because the probability distribution function (PDF) of total water contained in the scheme can be used to calculate these non-linear effects. Statistical cloud schemes were originally developed for boundary layer studies so extending them to a global model with many different environments is not straightforward. For example, deep convection creates abundant cloudiness and yet little is known about how deep convection alters the PDF of total water or how to parameterize these impacts. These issues are explored with data from a 29 day simulation by a cloud resolving model (CRM) of the July 1997 ARM Intensive Observing Period at the Southern Great Plains site. The simulation is used to answer two questions: (a) how well can the beta distribution represent the PDFs of total water relative to saturation resolved by the CRM? (b) how can the effects of convection on the PDF be parameterized? In addition to answering these questions, additional sections more fully describe the proposed statistical cloud scheme and the CRM simulation and analysis methods.

  1. Development of an objective method for forecasting "Gulf" stratus clouds at Bryan Air Base, Texas, in the summer season

    E-Print Network [OSTI]

    Jenrette, James Prentiss

    1958-01-01T23:59:59.000Z

    slopes in producing the stratus clouds, He also indicated that, in general, the presence of high clouds is fairly well correlated wtth nonexistence of stratus. Another important factor, ground ventilation, was discussed by Decker (32), In a study... radiation, up- slope movement of the prevailing wind, ground ventilation, and high cloud cover are factors that must be considered in solving the stratus problem. Obviously it would be difficulty if not impossible, to incor- porate parameters...

  2. A Parameterized Microwace Emission Model for Dry Snow Cover Lingmei JIANG1,2,3

    E-Print Network [OSTI]

    California at Santa Barbara, University of

    , and the measurements can be carried out through cloud cover. When snow starts to melt, emission will significantly the vector radiative transfer equations to include the multi-scattering effects. This model uses 1) the dense the Dense Media Radiative Transfer Model (DMRT) and AIEM to simulation of dry snow emission with Matrix

  3. NOAA Technical Memorandum ERL GLERL-Y AN ANALYSIS OF GREAT LAKES ICE COVER

    E-Print Network [OSTI]

    ~ecting the passes to be used were: amount of cloud cover, availability of ground verification data, and number and the primary modes of interaction with incident radiation with respect to the satellite sensor. Table 3, and the path radiance. These effects mu*t be calculated for each frame; this can be achieved by measuring

  4. Ice Heating Up Cold Clouds | EMSL

    Broader source: All U.S. Department of Energy (DOE) Office Webpages (Extended Search)

    Ice Heating Up Cold Clouds Ice Heating Up Cold Clouds Released: October 04, 2011 In a heated battle, ice crystals win the competition for cloud water vapor The mighty cloud ice...

  5. Cloud Based Applications and Platforms (Presentation)

    SciTech Connect (OSTI)

    Brodt-Giles, D.

    2014-05-15T23:59:59.000Z

    Presentation to the Cloud Computing East 2014 Conference, where we are highlighting our cloud computing strategy, describing the platforms on the cloud (including Smartgrid.gov), and defining our process for implementing cloud based applications.

  6. Attribution Analysis of Cloud Feedback 

    E-Print Network [OSTI]

    Zhou, Chen

    2014-07-15T23:59:59.000Z

    Uncertainty on cloud feedback is the primary contributor to the large spread of equilibrium climate sensitivity (ECS) in climate models. In this study, we compare the short-term cloud feedback in climate models with observations, and evaluate...

  7. Modeling Incoherent Electron Cloud Effects

    E-Print Network [OSTI]

    Benedetto, E.

    2008-01-01T23:59:59.000Z

    electron-cloud effects and synchrotron radiation can lead toelectron-cloud effects and synchrotron radiation can lead tocloud phenomena in positrons storage rings the effect of syn- chrotron radiation

  8. Secure Cloud Computing With Brokered Trusted

    E-Print Network [OSTI]

    ) ·Audio ·QualComm 7201 528MHZ ·64MB Ram ·MicroSD Slow Storage ·Currently NO SIM CHIPS Monday, March 29 External Storage External Storage Router Router Router Router Cloud Computing Cloud Computing Cloud Storage External Storage Router Router Router Router Cloud Computing Cloud Computing Cloud Computing Tower

  9. Opaque cloud detection

    DOE Patents [OSTI]

    Roskovensky, John K. (Albuquerque, NM)

    2009-01-20T23:59:59.000Z

    A method of detecting clouds in a digital image comprising, for an area of the digital image, determining a reflectance value in at least three discrete electromagnetic spectrum bands, computing a first ratio of one reflectance value minus another reflectance value and the same two values added together, computing a second ratio of one reflectance value and another reflectance value, choosing one of the reflectance values, and concluding that an opaque cloud exists in the area if the results of each of the two computing steps and the choosing step fall within three corresponding predetermined ranges.

  10. Command Line Tools Cloud Computing

    E-Print Network [OSTI]

    Ferrara, Katherine W.

    Command Line Tools Cloud Computing #12;Everybody (or nearly everybody) loves GUI. AWS Command Line of advanced features. After surviving the cloud computing class till now, Your are almost a command line guru! You need AWS command line tools, ec2-api-tools, to maximize the power of AWS cloud computing. Plugging

  11. 8, 96979729, 2008 FRESCO+ cloud

    E-Print Network [OSTI]

    Paris-Sud XI, Université de

    ACPD 8, 9697­9729, 2008 FRESCO+ cloud retrieval algorithm P. Wang et al. Title Page Abstract Chemistry and Physics Discussions FRESCO+: an improved O2 A-band cloud retrieval algorithm for tropospheric on behalf of the European Geosciences Union. 9697 #12;ACPD 8, 9697­9729, 2008 FRESCO+ cloud retrieval

  12. 3, 33013333, 2003 Cirrus cloud

    E-Print Network [OSTI]

    Paris-Sud XI, Université de

    ACPD 3, 3301­3333, 2003 Cirrus cloud occurrence as function of ambient relative humidity J. Str and Physics Discussions Cirrus cloud occurrence as function of ambient relative humidity: A comparison¨om (johan@itm.su.se) 3301 #12;ACPD 3, 3301­3333, 2003 Cirrus cloud occurrence as function of ambient

  13. Cloud Formation, Evolution and Destruction

    E-Print Network [OSTI]

    Estalella, Robert

    Chapter 4 Cloud Formation, Evolution and Destruction We now begin to trace the journey towards a star. How long does this take? The answer is surprisingly short: a good many clouds already contain new stars and these stars tend to be young. The typical cloud cannot spend long, if any time at all

  14. 5, 60136039, 2005 FRESCO cloud

    E-Print Network [OSTI]

    Paris-Sud XI, Université de

    ACPD 5, 6013­6039, 2005 FRESCO cloud algorithm N. Fournier et al. Title Page Abstract Introduction cloud information over deserts from SCIAMACHY O2 A-band N. Fournier 1 , P. Stammes 1 , M. de Graaf 1 , R, 6013­6039, 2005 FRESCO cloud algorithm N. Fournier et al. Title Page Abstract Introduction Conclusions

  15. NIST Cloud Computing Reference Architecture

    E-Print Network [OSTI]

    Perkins, Richard A.

    NIST Cloud Computing Reference Architecture Recommendations of the National Institute of Standards Publication 500-292 #12;i NIST Special Publication 500-292 NIST Cloud Computing Reference Architecture, John Messina, Lee Badger and Dawn Leaf Information Techonology Laboratory Cloud Computing Program

  16. Cover Crops for the Garden

    E-Print Network [OSTI]

    2008-01-01T23:59:59.000Z

    matter for your soil or compost pile. Organic matter is thatin the spring or made into compost, cover crops will act asgathered up and added to your compost pile. The first method

  17. On the Role of Massive Stars in the Support and Destruction of Giant Molecular Clouds

    E-Print Network [OSTI]

    Christopher D. Matzner

    2002-02-01T23:59:59.000Z

    We argue that massive stars are the dominant sources of energy for the turbulent motions within giant molecular clouds, and that the primary agent of feedback is the expansion of H II regions within the cloud volume. This conclusion is suggested by the low efficiency of star formation and corroborated by dynamical models of H II regions. We evaluate the turbulent energy input rate in clouds more massive than one third of a million solar masses, for which gravity does not significantly affect the expansion of H II regions. Such clouds achieve a balance between the decay of turbulent energy and its regeneration in H II regions; summed over clouds, the implied ionizing luminosity and star formation rate are roughly consistent with the Galactic total. H II regions also photoevaporate their clouds: we derive cloud destruction times somewhat shorter than those estimated by Williams and McKee. The upper mass limit for molecular clouds in the Milky Way may derive from the fact that larger clouds would destroy themselves in less than one crossing time. The conditions within starburst galaxies do not permit giant molecular clouds to be supported or destroyed by H II regions. This should lead to rapid cloud collapse and the efficient formation of massive star clusters, explaining some aspects of the starburst phenomenon.

  18. Stratocumulus Clouds ROBERT WOOD

    E-Print Network [OSTI]

    Wood, Robert

    by latent heating in updrafts and cooling in downdrafts. Turbulent eddies and evaporative cooling drives, stratification of the STBL, and in some cases cloud breakup. Feedbacks between radiative cooling, precipitation- way interactions may be a key driver of aerosol concentrations over the remote oceans. Aerosol

  19. Computing and Partitioning Cloud Feedbacks Using Cloud Property Histograms. Part I: Cloud Radiative Kernels

    E-Print Network [OSTI]

    Hartmann, Dennis

    radiative forcing. The global and annual mean model-simulated cloud feedback is dominated by contributions to a hypothetical cloudless but other- wise identical planet, the global and annual mean effect of clouds at the top is how cloud radiative effects will change as the planet warms because of long-lived greenhouse gases

  20. Cover

    Office of Environmental Management (EM)

    AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE: Alternative1 First Use of Energy for All Purposes (Fuel and Nonfuel), 2002; Level: National5Sales for4,645 3,625 1,006 492 742 33 1112011AT&T, Inc.'sEnergyTexas1.SpaceFluorControlsEnergy Copyin Salt |Course

  1. Cover

    Office of Environmental Management (EM)

    AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE: Alternative1 First Use of Energy for All Purposes (Fuel and Nonfuel), 2002; Level: National5Sales for4,645 3,625 1,006 492 742 33 111 1,613PortsmouthBartlesvilleAbout » Contact Us ContactPractices inCostsCourse Overview

  2. cover

    Broader source: All U.S. Department of Energy (DOE) Office Webpages (Extended Search)

    AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE: Alternative1 First Use of Energy for All Purposes (Fuel and Nonfuel), 2002; Level:Energy: Grid Integration Redefining What'sis Taking Over OurThe Iron4 Self-Scrubbing:,, , ., ..., ,+ .

  3. Reexamination of the State of the Art Cloud Modeling Shows Real Improvements

    SciTech Connect (OSTI)

    Muehlbauer, Andreas D.; Grabowski, Wojciech W.; Malinowski, S. P.; Ackerman, Thomas P.; Bryan, George; Lebo, Zachary; Milbrandt, Jason; Morrison, H.; Ovchinnikov, Mikhail; Tessendorf, Sarah; Theriault, Julie M.; Thompson, Gregory

    2013-05-25T23:59:59.000Z

    Following up on an almost thirty year long history of International Cloud Modeling Workshops, that started out with a meeting in Irsee, Germany in 1985, the 8th International Cloud Modeling Workshop was held in July 2012 in Warsaw, Poland. The workshop, hosted by the Institute of Geophysics at the University of Warsaw, was organized by Szymon Malinowski and his local team of students and co-chaired by Wojciech Grabowski (NCAR/MMM) and Andreas Muhlbauer (University of Washington). International Cloud Modeling Workshops have been held traditionally every four years typically during the week before the International Conference on Clouds and Precipitation (ICCP) . Rooted in the World Meteorological Organization’s (WMO) weather modification program, the core objectives of the Cloud Modeling Workshop have been centered at the numerical modeling of clouds, cloud microphysics, and the interactions between cloud microphysics and cloud dynamics. In particular, the goal of the workshop is to provide insight into the pertinent problems of today’s state-of-the-art of cloud modeling and to identify key deficiencies in the microphysical representation of clouds in numerical models and cloud parameterizations. In recent years, the workshop has increasingly shifted the focus toward modeling the interactions between aerosols and clouds and provided case studies to investigate both the effects of aerosols on clouds and precipitation as well as the impact of cloud and precipitation processes on aerosols. This time, about 60 (?) scientists from about 10 (?) different countries participated in the workshop and contributed with discussions, oral and poster presentations to the workshop’s plenary and breakout sessions. Several case leaders contributed to the workshop by setting up five observationally-based case studies covering a wide range of cloud types, namely, marine stratocumulus, mid-latitude squall lines, mid-latitude cirrus clouds, Arctic stratus and winter-time orographic clouds and precipitation. Interested readers are encouraged to visit the workshop website at http://www.atmos.washington.edu/~andreasm/workshop2012/ and browse through the list of case studies. The web page also provides a detailed list of participants and the workshop agenda. Aside from contributed oral and poster presentations during the workshop’s plenary sessions, parallel breakout sessions focused on presentations and discussions of the individual cases. A short summary and science highlights from each of the cases is presented below.

  4. EA-1852: Cloud County Community College Wind Energy Project,...

    Broader source: Energy.gov (indexed) [DOE]

    2: Cloud County Community College Wind Energy Project, Cloud County, Kansas EA-1852: Cloud County Community College Wind Energy Project, Cloud County, Kansas Summary This EA...

  5. Linking Chemical Changes in Soot and Polyaromatics to Cloud Droplet Formation

    E-Print Network [OSTI]

    Mason, Laura E.

    2010-01-14T23:59:59.000Z

    ?s important to study both mechanisms of cloud development. In this study, we employed four separate analytical techniques, horizontal attenuated total reflectance infrared spectroscopy (HATR), UV-VIS spectrophotometer, environmental scanning electron...

  6. Simulations of Arctic Mixed-Phase Clouds in Forecasts with CAM3 and AM2 for M-PACE

    SciTech Connect (OSTI)

    Xie, Shaocheng; Boyle, James; Klein, Stephen A.; Liu, Xiaohong; Ghan, Steven J.

    2008-02-29T23:59:59.000Z

    Simulations of mixed-phase clouds in short-range forecasts with the National Center for Atmospheric Research Community Atmosphere Model version 3 (CAM3) and the Geophysical Fluid Dynamics Laboratory (GFDL) climate model (AM2) for the Mixed-Phase Arctic Cloud Experiment (M-PACE) are performed under the DOE CCPP-ARM Parameterization Testbed (CAPT), which initializes the climate models with analysis data produced from numerical weather prediction (NWP) centers. It is shown that CAM3 significantly underestimates the observed boundary layer mixed-phase clouds and cannot realistically simulate the variations with temperature and cloud height of liquid water fraction in the total cloud condensate based an oversimplified cloud microphysical scheme. In contrast, AM2 reasonably reproduces the observed boundary layer clouds while its clouds contain much less cloud condensate than CAM3 and the observations. Both models underestimate the observed cloud top and base for the boundary layer clouds. The simulation of the boundary layer mixed-phase clouds and their microphysical properties is considerably improved in CAM3 when a new physically based cloud microphysical scheme is used. The new scheme also leads to an improved simulation of the surface and top of the atmosphere longwave radiative fluxes in CAM3. It is shown that the Bergeron-Findeisen process, i.e., the ice crystal growth by vapor deposition at the expense of coexisting liquid water, is important for the models to correctly simulate the characteristics of the observed microphysical properties in mixed-phase clouds. Sensitivity tests show that these results are not sensitive to the analysis data used for model initializations. Increasing model horizontal resolution helps capture the subgrid-scale features in Arctic frontal clouds but does not help improve the simulation of the single-layer boundary layer clouds. Ice crystal number density has large impact on the model simulated mixed-phase clouds and their microphysical properties and needs to be accurately represented in climate models.

  7. CloudAnalyst: A CloudSim-based Visual Modeller for Analysing Cloud Computing Environments and Applications

    E-Print Network [OSTI]

    Calheiros, Rodrigo N.

    CloudAnalyst: A CloudSim-based Visual Modeller for Analysing Cloud Computing Environments and Applications Bhathiya Wickremasinghe1 , Rodrigo N. Calheiros2 , and Rajkumar Buyya1 1 The Cloud Computing and Distributed Systems (CLOUDS) Laboratory Department of Computer Science and Software Engineering The University

  8. CloudSat Overview CloudSat will provide, from space, the first global survey of cloud profiles and

    E-Print Network [OSTI]

    on the radiative and water budgets of clouds are broadly referred to as indirect aerosol effects. The aerosol processes and their accumulated effects on the global scale. 2. Mission Description CloudSat is plannedCloudSat Overview CloudSat will provide, from space, the first global survey of cloud profiles

  9. Cloud computing and hyperbolic Voronoi diagrams on the sphere

    E-Print Network [OSTI]

    Pavel Bleher; Caroline Shouraboura

    2012-03-13T23:59:59.000Z

    In this work we study the minimization problem for the total distance in a cloud computing network on the sphere. We give a solution to this problem in terms of hyperbolic Voronoi diagrams on the sphere. We present results of computer simulations illustrating the solution.

  10. Total Space Heat-

    Annual Energy Outlook 2013 [U.S. Energy Information Administration (EIA)]

    Commercial Buildings Energy Consumption Survey: Energy End-Use Consumption Tables Total Space Heat- ing Cool- ing Venti- lation Water Heat- ing Light- ing Cook- ing Refrig- eration...

  11. Total Space Heat-

    Gasoline and Diesel Fuel Update (EIA)

    Revised: December, 2008 Total Space Heat- ing Cool- ing Venti- lation Water Heat- ing Light- ing Cook- ing Refrig- eration Office Equip- ment Com- puters Other All Buildings...

  12. A Catalog of HI Clouds in the Large Magellanic Cloud

    E-Print Network [OSTI]

    S. Kim; E. Rosolowsky; Y. Lee; Y. Kim; Y. C. Jung; M. A. Dopita; B. G. Elmegreen; K. C. Freeman; R. J. Sault; M. J. Kesteven; D. McConnell; Y. -H. Chu

    2007-06-28T23:59:59.000Z

    A 21 cm neutral hydrogen interferometric survey of the Large Magellanic Cloud (LMC) combined with the Parkes multi-beam HI single-dish survey clearly shows that the HI gas is distributed in the form of clumps or clouds. The HI clouds and clumps have been identified using a thresholding method with three separate brightness temperature thresholds ($T_b$). Each catalog of HI cloud candidates shows a power law relationship between the sizes and the velocity dispersions of the clouds roughly following the Larson Law scaling $\\sigma_v \\propto R^{0.5}$, with steeper indices associated with dynamically hot regions. The clouds in each catalog have roughly constant virial parameters as a function mass suggesting that that the clouds are all in roughly the same dynamical state, but the values of the virial parameter are significantly larger than unity showing that turbulent motions dominate gravity in these clouds. The mass distribution of the clouds is a power law with differential indices between -1.6 and -2.0 for the three catalogs. In contrast, the distribution of mean surface densities is a log-normal distribution.

  13. Covered Product Category: Imaging Equipment

    Broader source: Energy.gov [DOE]

    FEMP provides acquisition guidance and Federal efficiency requirements across a variety of product categories, including imaging equipment, which is covered by the ENERGY STAR® program. Federal laws and requirements mandate that agencies meet these efficiency requirements in all procurement and acquisition actions that are not specifically exempted by law.

  14. Deans Audit Cover Environmental Compliance

    E-Print Network [OSTI]

    Pawlowski, Wojtek

    facilities in central New York to comply with a New York State Department of Environmental Conservation (DECDeans Audit Cover Environmental Compliance Guidance Document Approved by: (Pat McNally) Last electronically at: http://sp.ehs.cornell.edu/env/general-environmental-management/environmental

  15. Final Scientific/Technical Report Grant title: Use of ARM Measurements of Spectral Zenith Radiance for Better Understanding of 3D Cloud-Radiation Processes and Aerosol-Cloud Interaction This is a collaborative project with the NASA GSFC project of Dr. A. Marshak and W. Wiscombe (PIs). This report covers BU activities from February 2011 to June 2011 and BU "Â?no-cost extension" activities from June 2011 to June 2012. This report summarizes results that complement a final technical report submitted by the PIs in 2011.

    SciTech Connect (OSTI)

    Knyazikhin, Y

    2012-09-10T23:59:59.000Z

    Main results are summarized for work in these areas: spectrally-invariant approximation within atmospheric radiative transfer; spectral invariance of single scattering albedo for water droplets and ice crystals at weakly absorbing wavelengths; seasonal changes in leaf area of Amazon forests from leaf flushing and abscission; and Cloud droplet size and liquid water path retrievals from zenith radiance measurements.

  16. Broken and inhomogeneous cloud impact on satellite cloud particle effective radius and cloudphase retrievals

    E-Print Network [OSTI]

    Stoffelen, Ad

    on the particle size distribution, height, and thermo- dynamic phase of clouds. Water and ice clouds have parameterizations is the global dis- tribution of cloud thermodynamic phase, i.e., whether a cloud is composed on satellitederived cloud particle effective radius (re) and cloud phase (CPH) for broken and overcast inhomogeneous

  17. RHIC PRESSURE RISE AND ELECTRON CLOUD.

    SciTech Connect (OSTI)

    Zhang, S Y; Blaskiewicz, M; Cameron, P; Drees, P; Afischer, W; Gassner, D; Gullotta, J; He, P; Hseuh, H; Chuang, H; Iriso-Aziz, U; Lee, R; Mackay, W; Woerter, B; Ptitsyn, V; Ponnaiyan, V; Roser, T; Satogata, T; Smart, L; Trbojevic, D

    2003-05-12T23:59:59.000Z

    In RHIC high intensity operation, two types of pressure rise are currently of concern. The first type is at the beam injection, which seems to be caused by the electron multipacting, and the second is the one at the beam transition, where the electron cloud is not the dominant cause. The first type of pressure rise is limiting the beam intensity and the second type might affect the experiments background for very high total beam intensity. In this article, the pressure rises at RHIC are described, and preliminary study results are reported. Some of the unsettled issues and questions are raised, and possible counter measures are discussed.

  18. Total aerosol effect: forcing or radiative flux perturbation?

    SciTech Connect (OSTI)

    Lohmann, Ulrike; Storelvmo, Trude; Jones, Andy; Rotstayn, Leon; Menon, Surabi; Quaas, Johannes; Ekman, Annica; Koch, Dorothy; Ruedy, Reto

    2009-09-25T23:59:59.000Z

    Uncertainties in aerosol forcings, especially those associated with clouds, contribute to a large extent to uncertainties in the total anthropogenic forcing. The interaction of aerosols with clouds and radiation introduces feedbacks which can affect the rate of rain formation. Traditionally these feedbacks were not included in estimates of total aerosol forcing. Here we argue that they should be included because these feedbacks act quickly compared with the time scale of global warming. We show that for different forcing agents (aerosols and greenhouse gases) the radiative forcings as traditionally defined agree rather well with estimates from a method, here referred to as radiative flux perturbations (RFP), that takes these fast feedbacks and interactions into account. Thus we propose replacing the direct and indirect aerosol forcing in the IPCC forcing chart with RFP estimates. This implies that it is better to evaluate the total anthropogenic aerosol effect as a whole.

  19. A Survey on Cloud Provider Security

    E-Print Network [OSTI]

    A Survey on Cloud Provider Security Measures Alex Pucher, Stratos Dimopoulos Abstract Cloud take advantage of this model already, but security and privacy concerns limit the further adoption agencies and start offering security certifications and separate tightly controlled "government" cloud

  20. Cicada: Predictive Guarantees for Cloud Network Bandwidth

    E-Print Network [OSTI]

    LaCurts, Katrina

    2014-03-24T23:59:59.000Z

    In cloud-computing systems, network-bandwidth guarantees have been shown to improve predictability of application performance and cost. Most previous work on cloud-bandwidth guarantees has assumed that cloud tenants know ...

  1. Electron-Cloud Build-Up: Summary

    E-Print Network [OSTI]

    Furman, M.A.

    2007-01-01T23:59:59.000Z

    Properties In?uencing Electron Cloud Phenomena,” Appl. Surf.Dissipation of the Electron Cloud,” Proc. PAC03 (Portland,is no signi?cant electron-cloud under nominal operating

  2. DIRSIG Cloud Modeling Capabilities; A Parametric Study

    E-Print Network [OSTI]

    Salvaggio, Carl

    1 DIRSIG Cloud Modeling Capabilities; A Parametric Study Kristen Powers powers:................................................................................................................... 13 Calculation of Sensor Reaching Radiance Truth Values for Cloudless & Stratus Cloud Scenes and Atmospheric Database Creation for Stratus Cloud Scene & Calculation of Associated Sensor Reaching Radiance

  3. Magellan: experiences from a Science Cloud

    E-Print Network [OSTI]

    Ramakrishnan, Lavanya

    2013-01-01T23:59:59.000Z

    2010. From Clusters To Clouds: xCAT 2 Is Out Of The Bag.Cost of Doing Science on the Cloud: The Montage Example. Incost of doing science on the cloud: the montage example. In

  4. The Cloud Computing and Other Variables

    E-Print Network [OSTI]

    Borjon-Kubota, Martha Estela

    2011-01-01T23:59:59.000Z

    12. Fragments in Six 13. Cloud Computing 14. Phase 15.Note 48. Devoured vi Cloud Computing and other Variables I.moment. Lasts hours. Cloud Computing Just there Over the

  5. The Magellan Final Report on Cloud Computing

    E-Print Network [OSTI]

    Coghlan, Susan

    2013-01-01T23:59:59.000Z

    4.3.1 Cloud Computing Attractive Features . 4.3.2A berkeley view of cloud computing. Technical Report UCB/matching computations on cloud computing platforms and hpc

  6. Total Synthesis of (?)-Himandrine

    E-Print Network [OSTI]

    Movassaghi, Mohammad

    We describe the first total synthesis of (?)-himandrine, a member of the class II galbulimima alkaloids. Noteworthy features of this chemistry include a diastereoselective Diels?Alder reaction in the rapid synthesis of the ...

  7. Sunlight Changes Aerosols in Clouds | EMSL

    Broader source: All U.S. Department of Energy (DOE) Office Webpages (Extended Search)

    Sunlight Changes Aerosols in Clouds Sunlight Changes Aerosols in Clouds Released: October 20, 2011 Scientists show how sunlight alters optical, chemical properties of atmospheric...

  8. Temporal Land Cover Analysis for Net Ecosystem Improvement

    SciTech Connect (OSTI)

    Ke, Yinghai; Coleman, Andre M.; Diefenderfer, Heida L.

    2013-04-09T23:59:59.000Z

    We delineated 8 watersheds contributing to previously defined river reaches within the 1,468-km2 historical floodplain of the tidally influenced lower Columbia River and estuary. We assessed land-cover change at the watershed, reach, and restoration site scales by reclassifying remote-sensing data from the National Oceanic and Atmospheric Administration Coastal Change Analysis Program’s land cover/land change product into forest, wetland, and urban categories. The analysis showed a 198.3 km2 loss of forest cover during the first 6 years of the Columbia Estuary Ecosystem Restoration Program, 2001–2006. Total measured urbanization in the contributing watersheds of the estuary during the full 1996-2006 change analysis period was 48.4 km2. Trends in forest gain/loss and urbanization differed between watersheds. Wetland gains and losses were within the margin of error of the satellite imagery analysis. No significant land cover change was measured at restoration sites, although it was visible in aerial imagery, therefore, the 30-m land-cover product may not be appropriate for assessment of early-stage wetland restoration. These findings suggest that floodplain restoration sites in reaches downstream of watersheds with decreasing forest cover will be subject to increased sediment loads, and those downstream of urbanization will experience effects of increased impervious surfaces on hydrologic processes.

  9. 3, 44614488, 2003 Cloud particle

    E-Print Network [OSTI]

    Paris-Sud XI, Université de

    effects. On one hand, clouds reflect the incoming solar radiation and thus cool the Earth significant effect on the radiation balance (Wielicki et al, 1996; Mitchell, 1989) due to two competing-Atmosphere system. On the other hand, clouds absorb longwave thermal radiation coming from the surface and then re

  10. EVOLUTIONARY COMPUTATION AND POST-WILDFIRE LAND-COVER MAPPING WITH MULTISPECTRAL IMAGERY.

    SciTech Connect (OSTI)

    Brumby, Steven P.; Koch, S. W. (Steven W.); Hansen, L. A. (Leslie A.)

    2001-01-01T23:59:59.000Z

    The Cerro Grande Los Alamos wildfire devastated approximately 43,000 acres (17,500 ha) of forested land, and destroyed over 200 structures in the town of Los Alamos. The need to monitor the continuing impact of the fire on the local environment has led to the application of a number of advanced remote sensing technologies. During and after the fire, remote-sensing data was acquired fiorn a variety of aircraft- and satellite-based sensors, including Landsat 7 Enhanced Thematic Mapper (ETM+). We now report on the application of a machine learning technique io the automated classification of land cover using multispectral imagery. We apply a hybrid gertelic programminghupervised classification technique to evolve automatic feature extraction algorithms. We use a software package we have developed at Los Alamos National Laboratory, called GENIE, to carry out this evolution. We use multispectral imagery fiom the Landsat 7 ETM+ instrument fiom before and after the wildfire. Using an existing land cover classification based on a Landsat 5 TM scene for our training data, we evolve algorithms that distinguish a range of land cover categories, along with clouds and cloud shadows. The details of our evolved classification are compared to the manually produced land-cover classification. Keywords: Feature Extraction, Genetic programming, Supervised classification, Multi-spectral imagery, Land cover, Wildfire.

  11. Microphysical effects determine macrophysical response for aerosol impacts on deep

    E-Print Network [OSTI]

    Li, Zhanqing

    cloud cover, cloud top height, and radiative forcing. We found that although the widely accepted theory. The thermodynamic invigoration effect contrib- utes up to 27% of total increase in cloud cover. The overall aerosol by aerosols that drives the dramatic increase in cloud cover, cloud top height, and cloud thickness

  12. Dynamical modeling of the Deep Impact dust ejecta cloud

    E-Print Network [OSTI]

    Tanyu Bonev; Nancy Ageorges; Stefano Bagnulo; Luis Barrera; Hermann B{ö}hnhardt; Olivier Hainaut; Emmanuel Jehin; Hans-Ullrich K{ä}ufl; Florian Kerber; Gaspare LoCurto; Jean Manfroid; Olivier Marco; Eric Pantin; Emanuela Pompei; Ivo Saviane; Fernando Selman; Chris Sterken; Heike Rauer; Gian Paolo Tozzi; Michael Weiler

    2007-03-21T23:59:59.000Z

    The collision of Deep Impact with comet 9P/Tempel 1 generated a bright cloud of dust which dissipated during several days after the impact. The brightness variations of this cloud and the changes of its position and shape are governed by the physical properties of the dust grains. We use a Monte Carlo model to describe the evolution of the post-impact dust plume. The results of our dynamical simulations are compared to the data obtained with FORS2, the FOcal Reducer and low dispersion Spectrograph for the VLT of the European Southern Observatory (ESO), to derive the particle size distribution and the total amount of material contained in the dust ejecta cloud.

  13. Total Energy Monitor

    SciTech Connect (OSTI)

    Friedrich, S

    2008-08-11T23:59:59.000Z

    The total energy monitor (TE) is a thermal sensor that determines the total energy of each FEL pulse based on the temperature rise induced in a silicon wafer upon absorption of the FEL. The TE provides a destructive measurement of the FEL pulse energy in real-time on a pulse-by-pulse basis. As a thermal detector, the TE is expected to suffer least from ultra-fast non-linear effects and to be easy to calibrate. It will therefore primarily be used to cross-calibrate other detectors such as the Gas Detector or the Direct Imager during LCLS commissioning. This document describes the design of the TE and summarizes the considerations and calculations that have led to it. This document summarizes the physics behind the operation of the Total Energy Monitor at LCLS and derives associated engineering specifications.

  14. Determination of Total Solids and Ash in Algal Biomass: Laboratory Analytical Procedure (LAP)

    SciTech Connect (OSTI)

    Van Wychen, S.; Laurens, L. M. L.

    2013-12-01T23:59:59.000Z

    This procedure describes the methods used to determine the amount of moisture or total solids present in a freeze-dried algal biomass sample, as well as the ash content. A traditional convection oven drying procedure is covered for total solids content, and a dry oxidation method at 575?C is covered for ash content.

  15. Tropical and subtropical cloud transitions in weather and climate prediction models: the GCSS/WGNE Pacific Cross-Section Intercomparison (GPCI)

    SciTech Connect (OSTI)

    Teixeira, J.; Cardoso, S.; Bonazzola, M.; Cole, Jason N.; DelGenio, Anthony D.; DeMott, C.; Franklin, A.; Hannay, Cecile; Jakob, Christian; Jiao, Y.; Karlsson, J.; Kitagawa, H.; Koehler, M.; Kuwano-Yoshida, A.; LeDrian, C.; Lock, Adrian; Miller, M.; Marquet, P.; Martins, J.; Mechoso, C. R.; Meijgaard, E. V.; Meinke, I.; Miranda, P.; Mironov, D.; Neggers, Roel; Pan, H. L.; Randall, David A.; Rasch, Philip J.; Rockel, B.; Rossow, William B.; Ritter, B.; Siebesma, A. P.; Soares, P.; Turk, F. J.; Vaillancourt, P.; Von Engeln, A.; Zhao, M.

    2011-11-01T23:59:59.000Z

    A model evaluation approach is proposed where weather and climate prediction models are analyzed along a Pacific Ocean cross-section, from the stratocumulus regions off the coast of California, across the shallow convection dominated trade-winds, to the deep convection regions of the ITCZ: the GCSS/WGNE Pacific Cross-section Intercomparison (GPCI). The main goal of GPCI is to evaluate, and help understand and improve the representation of tropical and sub-tropical cloud processes in weather and climate prediction models. In this paper, a detailed analysis of cloud regime transitions along the cross-section from the sub-tropics to the tropics for the season JJA of 1998 is presented. This GPCI study confirms many of the typical weather and climate prediction model problems in the representation of clouds: underestimation of clouds in the stratocumulus regime by most models with the corresponding consequences in terms of shortwave radiation biases; overestimation of clouds by the ECMWF Re-Analysis (ERA40) in the deep tropics (in particular) with the corresponding impact in the outgoing longwave radiation; large spread between the different models in terms of cloud cover, liquid water path and shortwave radiation; significant differences between the models in terms of vertical crosssections of cloud properties (in particular), vertical velocity and relative humidity. An alternative analysis of cloud cover mean statistics is proposed where sharp gradients in cloud cover along the GPCI transect are taken into account. This analysis shows that the negative cloud bias of some models and ERA40 in the stratocumulus regions (as compared to ISCCP) is associated not only with lower values of cloud cover in these regimes, but also with a stratocumulus-to-cumulus transition that occurs too early along the trade-wind Lagrangian trajectory. Histograms of cloud cover along the cross-section differ significantly between models. Some models exhibit a quasi-bimodal structure with cloud cover being either very large (close to 100%) or very small, while other models show a more continuous transition. The ISCCP observations suggest that reality is in-between these two extreme examples. These different patterns reflect the diverse nature of the cloud, boundary layer, and convection parameterizations in the participating weather and climate prediction models.

  16. Convective plumes and the scarcity of Titan's clouds Ralph D. Lorenz,1

    E-Print Network [OSTI]

    Lorenz, Ralph D.

    dynamical models and with the relative tropospheric cloud cover, which is only $1% on Titan. Rainstorms is significantly opaque to thermal infrared radiation, leading to a strong greenhouse effect. The equivalent grey is absorbed by methane in the troposphere. Only around 10% of the incident solar radiation reaches the surface

  17. Resolved Atomic Super-clouds in Spiral Galaxies

    E-Print Network [OSTI]

    Robert Braun

    1995-12-13T23:59:59.000Z

    High quality data are presented of neutral hydrogen emission and absorption in the fields of eleven of the nearest spiral galaxies. Multi-configuration VLA observations have provided angular resolution of 6~arcsec (corresponding to about 100~pc at the average galaxy distance of 3.5~Mpc) and velocity resolution of 6~km~s$^{-1}$, while accurately recovering the total line flux detected previously with filled apertures. Previous experience suggests that this physical resolution is sufficient to at least marginally resolve the \\ion{H}{1} super-cloud population which delineates regions of active star formation. A high brightness filamentary network of \\ion{H}{1} super-clouds is seen in each galaxy. Emission brightness temperatures in excess of 200~Kelvin are sometimes detected at large radii, even in relatively face-on systems. All galaxies display a systematic increase in the observed brightness temperature of super-clouds with radius, followed by a flattening and subsequent decline. In the few instances where background continuum sources allow detection of \\ion{H}{1} absorption, the indicative spin temperatures are consistent with the super-cloud brightness temperature seen in emission at similar radii. These data suggest substantial opacity of the \\ion{H}{1} in the super-cloud network.

  18. Total Precipitable Water

    SciTech Connect (OSTI)

    None

    2012-01-01T23:59:59.000Z

    The simulation was performed on 64K cores of Intrepid, running at 0.25 simulated-years-per-day and taking 25 million core-hours. This is the first simulation using both the CAM5 physics and the highly scalable spectral element dynamical core. The animation of Total Precipitable Water clearly shows hurricanes developing in the Atlantic and Pacific.

  19. Construction Costs of Six Landfill Cover Designs

    SciTech Connect (OSTI)

    Dwyer, S.F.

    1998-12-23T23:59:59.000Z

    A large-scale field demonstration comparing and contrasting final landfill cover designs has been constructed and is currently being monitored. Four alternative cover designs and two conventional designs (a RCRA Subtitle `D' Soil Cover and a RCRA Subtitle `C' Compacted Clay Cover) were constructed side-by-side for direct comparison. The demonstration is intended to evaluate the various cover designs based on their respective water balance performance, ease and reliability of construction, and cost. This paper provides an overview of the construction costs of each cover design.

  20. Cost comparisons of alternative landfill final covers

    SciTech Connect (OSTI)

    Dwyer, S.F.

    1997-02-01T23:59:59.000Z

    A large-scale field demonstration comparing and contrasting final landfill cover designs has been constructed and is currently being monitored. Four alternative cover designs and two conventional designs (a RCRA Subtitle ``D`` Soil Cover and a RCRA Subtitle ``C`` Compacted Clay Cover) were constructed of uniform size, side-by-side. The demonstration is intended to evaluate the various cover designs based on their respective water balance performance, ease and reliability of construction, and cost. This paper provides an overview of the construction costs of each cover design.

  1. Platform for Hybrid Cloud Technical White Paper

    E-Print Network [OSTI]

    Chaudhuri, Surajit

    Platform for Hybrid Cloud Technical White Paper Published: September 2013 (updated) Applies to: SQL Server and Windows Azure Summary: Cloud computing brings a new paradigm shift in computing in the cloud with greater scale and flexibility. Microsoft SQL Server runs very well in the cloud environment

  2. Cloud Computing An enterprise perspective Raghavan Subramanian

    E-Print Network [OSTI]

    Rajamani, Sriram K.

    Cloud Computing ­ An enterprise perspective Raghavan Subramanian Infosys Technologies Limited #12;2Infosys Confidential Overview of cloud computing? Cloud computing* Computing in which dynamically scalable of cloud computing 1. On-demand self-service 2. Ubiquitous network access 3. Location independent resource

  3. IBM Software Solution Brief Safeguarding the cloud

    E-Print Network [OSTI]

    IBM Software Solution Brief Safeguarding the cloud with IBM Security solutions Maintain visibility and control with proven security solutions for public, private and hybrid clouds Highlights Address cloud internal and external users, data, applications and workloads as they move to and from the cloud Regain

  4. 7, 1711717146, 2007 Dependence of cloud

    E-Print Network [OSTI]

    Paris-Sud XI, Université de

    ACPD 7, 17117­17146, 2007 Dependence of cloud fraction and cloud height on temperature T. Wagner et a Creative Commons License. Atmospheric Chemistry and Physics Discussions Dependence of cloud fraction and cloud top height on surface temperature derived from spectrally resolved UV/vis satellite observations T

  5. Draft NISTIR 80061 NIST Cloud Computing2

    E-Print Network [OSTI]

    Draft NISTIR 80061 NIST Cloud Computing2 Forensic Science Challenges NIST Cloud Computing Forensic Computing11 Forensic Science Challenges 12 NIST Cloud Computing Forensic Science Working Group13 Information challenges77 faced by experts when responding to incidents that have occurred in a cloud-computing ecosystem

  6. Cloud Data Management (CDM) Yunpeng Chai

    E-Print Network [OSTI]

    /W performance / Parallelism No/ Simple SQL operations 12 /26 Survey of CDM Cloud Storage: Architecture: Master#12;Cloud Data Management (CDM) Yunpeng Chai 2 /26 Outline Motivation of CDM Survey of CDM IBM SUR Cloud China Mobile National Health Care #12;9 /26 Outline Motivation of CDM Survey of CDM IBM SUR Cloud

  7. 6, 43414373, 2006 Cloud-borne aerosol

    E-Print Network [OSTI]

    Paris-Sud XI, Université de

    Discussions Impact of cloud-borne aerosol representation on aerosol direct and indirect effects S. J. Ghan of aerosols employ a variety of rep- resentations of such cloud-borne particles. Here we use a global aerosol- ulated aerosol, cloud and radiation fields to various approximations to the representa- tion of cloud

  8. Cloud radar Doppler spectra in drizzling stratiform clouds: 2. Observations and microphysical modeling of drizzle evolution

    E-Print Network [OSTI]

    Cloud radar Doppler spectra in drizzling stratiform clouds: 2. Observations and microphysical I, the influence of cloud microphysics and dynamics on the shape of cloud radar Doppler spectra in warm stratiform clouds was discussed. The traditional analysis of radar Doppler moments was extended

  9. Vision: Cloud-Powered Sight for All Showing the Cloud What You See

    E-Print Network [OSTI]

    Zhong, Lin

    Vision: Cloud-Powered Sight for All Showing the Cloud What You See Paramvir Bahl Matthai Philipose argue that for computers to do more for us, we need to show the cloud what we see and embrace cloud General Terms Algorithms, Design, Human Factors, Languages, Performance, Security Keywords Camera, cloud

  10. CLOUD, DRIZZLE, AND TURBULENCE OBSERVATIONS IN MARINE STRATOCUMULUS CLOUDS IN THE AZORES

    E-Print Network [OSTI]

    CLOUD, DRIZZLE, AND TURBULENCE OBSERVATIONS IN MARINE STRATOCUMULUS CLOUDS IN THE AZORES Jasmine at the Azores provided a unique, long-term record (May 2009 to December 2010) of cloud observations in a regime dominated by low-level stratiform clouds. First, a comprehensive cloud classification scheme that utilizes

  11. Cloud Futures Workshop 2010 Cloud Computing Support for Massively Social Gaming Alexandru Iosup

    E-Print Network [OSTI]

    Iosup, Alexandru

    1 Cloud Futures Workshop 2010 ­ Cloud Computing Support for Massively Social Gaming Alexandru Iosup Pierre (Vrije U.). Cloud Computing Support for Massively Social Gaming (Rain for the Thirsty) #12;Cloud Futures Workshop 2010 ­ Cloud Computing Support for Massively Social Gaming 2 Intermezzo: Tips on how

  12. Mixed phase clouds, cloud electrification and remote sensing.

    SciTech Connect (OSTI)

    Chylek, P. (Petr); Borel, C. C. (Christoph C.); Klett, James

    2004-01-01T23:59:59.000Z

    Most of hypothesis trying to explain charge separation in thunderstorm clouds require presence of ice and supercooled water. Thus the existence of ice or at least mixed phase regions near cloud tops should be a necessary (but not a sufficient) condition for development of lightning. We show that multispectral satellite based instruments, like the DOE MTI (Multispectral Thermal Imager) or NASA MODIS (Moderate Resolution Imaging Spectroradiometer), using the near infrared and visible spectral bands are able to distinguish between water, ice and mixed phase cloud regions. An analysis of the MTI images of mixed phase clouds - with spatial resolution of about 20 m - shows regions of pure water, pure ice as well as regions of water/ice mixtures. We suggest that multispectral satellite instruments may be useful for a short time forecast of lightning probabilities.

  13. COVER IMAGE Constraint-satisfaction problems

    E-Print Network [OSTI]

    Loss, Daniel

    : MÁRIA ERCSEY-RAVASZ COVER DESIGN: KAREN MOORE ON THE COVER Trilayer graphene A tale of two stackings Gorman, Ilya Drozdov, Yew San Hor, R. J. Cava and Ali Yazdani 944 Observation of an electrically tunable

  14. A Game Theoretic Framework of SLA-Based Resource Allocation for Competitive Cloud Service Providers

    E-Print Network [OSTI]

    Pedram, Massoud

    paradigm that allows the on-demand delivering of software, hardware, and data as services. It has attracted subtracted by the total energy cost. The total revenue depends on the average service request response time access, on- demand service, and transference of risk [1]-[4]. Cloud computing shifts the computation

  15. Covered Product Category: Uninterruptible Power Supplies (for...

    Office of Energy Efficiency and Renewable Energy (EERE) Indexed Site

    Applications) Covered Product Category: Uninterruptible Power Supplies (for Data Center, Computer, and Telecommunication Applications) The Federal Energy Management...

  16. How might a statistical cloud scheme be coupled to a mass-flux convection scheme?

    SciTech Connect (OSTI)

    Klein, Stephen A.; Pincus, Robert; Hannay, Cecile; Xu, Kuan-man

    2004-09-27T23:59:59.000Z

    The coupling of statistical cloud schemes with mass-flux convection schemes is addressed. Source terms representing the impact of convection are derived within the framework of prognostic equations for the width and asymmetry of the probability distribution function of total water mixing ratio. The accuracy of these source terms is quantified by examining output from a cloud resolving model simulation of deep convection. Practical suggestions for inclusion of these source terms in large-scale models are offered.

  17. 3D cloud detection and tracking system for solar forecast using multiple sky imagers

    DOE Public Access Gateway for Energy & Science Beta (PAGES Beta)

    Peng, Zhenzhou; Yu, Dantong; Huang, Dong; Heiser, John; Yoo, Shinjae; Kalb, Paul

    2015-08-01T23:59:59.000Z

    We propose a system for forecasting short-term solar irradiance based on multiple total sky imagers (TSIs). The system utilizes a novel method of identifying and tracking clouds in three-dimensional space and an innovative pipeline for forecasting surface solar irradiance based on the image features of clouds. First, we develop a supervised classifier to detect clouds at the pixel level and output cloud mask. In the next step, we design intelligent algorithms to estimate the block-wise base height and motion of each cloud layer based on images from multiple TSIs. This information is then applied to stitch images together into largermore »views, which are then used for solar forecasting. We examine the system’s ability to track clouds under various cloud conditions and investigate different irradiance forecast models at various sites. We confirm that this system can 1) robustly detect clouds and track layers, and 2) extract the significant global and local features for obtaining stable irradiance forecasts with short forecast horizons from the obtained images. Finally, we vet our forecasting system at the 32-megawatt Long Island Solar Farm (LISF). Compared with the persistent model, our system achieves at least a 26% improvement for all irradiance forecasts between one and fifteen minutes.« less

  18. Engineering guides for estimating cover material thickness and volume for uranium mill tailings

    SciTech Connect (OSTI)

    Rogers, V.C.; Nielson, K.K.; Merrell, G.B.

    1982-09-01T23:59:59.000Z

    Five nomographs have been prepared that facilitate the estimation of cover thickness and cover material volume for the Uranium Mill Tailing Remedial Action Program. Key parameters determined include the cover thickness with either a surface radon flux or a boundary radon air concentration criterion and the total volume of cover material required for two different treatments of the edge slopes. Also included in the engineering guide are descriptions and representative values for the radon source term, the diffusion coefficients and the key meteorological parameters. 16 refs., 7 figs., 2 tabs.

  19. Using Surface Remote Sensors to Derive Radiative Characteristics of Mixed-Phase Clouds: An Example from M-PACE

    SciTech Connect (OSTI)

    de Boer, Gijs; Collins, William D.; Menon, Surabi; Long, Charles N.

    2011-12-02T23:59:59.000Z

    Measurements from ground-based cloud radar, high spectral resolution lidar and microwave radiometer are used in conjunction with a column version of the Rapid Radiative Transfer Model (RRTMG) and radiosonde measurements to derive the surface radiative properties under mixed-phase cloud conditions. These clouds were observed during the United States Department of Energy (US DOE) Atmospheric Radiation Measurement (ARM) Mixed-Phase Arctic Clouds Experiment (M-PACE) between September and November of 2004. In total, sixteen half hour time periods are reviewed due to their coincidence with radiosonde launches. Cloud liquid (ice) water paths are found to range between 11.0-366.4 (0.5-114.1) gm-2, and cloud physical thicknesses fall between 286-2075 m. Combined with temperature and hydrometeor size estimates, this information is used to calculate surface radiative flux densities using RRTMG, which are demonstrated to generally agree with measured flux densities from surface-based radiometric instrumentation. Errors in longwave flux density estimates are found to be largest for thin clouds, while shortwave flux density errors are generally largest for thicker clouds. A sensitivity study is performed to understand the impact of retrieval assumptions and uncertainties on derived surface radiation estimates. Cloud radiative forcing is calculated for all profiles, illustrating longwave dominance during this time of year, with net cloud forcing generally between 50 and 90 Wm-2.

  20. Cloud Computing and Validation of Expandable In Silico Livers

    E-Print Network [OSTI]

    Ropella, Glen EP; Hunt, C Anthony

    2010-01-01T23:59:59.000Z

    benefit analysis of cloud computing versus desktop grids.as: Ropella and Hunt: Cloud computing and validation ofCloud computing and validation of expandable in silico

  1. TotalView Training

    Broader source: All U.S. Department of Energy (DOE) Office Webpages (Extended Search)

    AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE: Alternative1 First Use of Energy for All Purposes (Fuel and Nonfuel), 2002; Level: National5Sales for4,645U.S. DOE Office of ScienceandMesa del SolStrengthening a solidSynthesisAppliances » Top InnovativeTopoisomeraseTotalView

  2. Title: Networking the Cloud: Enabling Enterprise Computing and Storage Cloud computing has been changing how enterprises run and manage their IT systems. Cloud

    E-Print Network [OSTI]

    Title: Networking the Cloud: Enabling Enterprise Computing and Storage Abstract: Cloud computing has been changing how enterprises run and manage their IT systems. Cloud computing platforms provide introduction on Cloud Computing. We propose a Virtual Cloud Pool abstraction to logically unify cloud

  3. Cluster analysis of cloud properties : a method for diagnosing cloud-climate feedbacks

    E-Print Network [OSTI]

    Gordon, Neil D.

    2008-01-01T23:59:59.000Z

    represent cloud effects on gridbox mean visible radiationclouds and the resulting effect on the balance of radiationrepresent cloud effects on grid-box-mean visible radiation

  4. The Evolution of Cloud Computing in ATLAS

    E-Print Network [OSTI]

    Taylor, Ryan P.; The ATLAS collaboration; Love, Peter; Leblanc, Matthew Edgar; Di Girolamo, Alessandro; Paterson, Michael; Gable, Ian; Sobie, Randall; Field, Laurence

    2015-01-01T23:59:59.000Z

    The ATLAS experiment has successfully incorporated cloud computing technology and cloud resources into its primarily grid-based model of distributed computing. Cloud R&D activities continue to mature and transition into stable production systems, while ongoing evolutionary changes are still needed to adapt and refine the approaches used, in response to changes in prevailing cloud technology. In addition, completely new developments are needed to handle emerging requirements. This work will describe the overall evolution of cloud computing in ATLAS. The current status of the VM management systems used for harnessing IAAS resources will be discussed. Monitoring and accounting systems tailored for clouds are needed to complete the integration of cloud resources within ATLAS' distributed computing framework. We are developing and deploying new solutions to address the challenge of operation in a geographically distributed multi-cloud scenario, including a system for managing VM images across multiple clouds, ...

  5. A Comparison of Multiscale Variations of Decade-long Cloud Fractions from Six Different Platforms over the Southern Great Plains in the United States

    SciTech Connect (OSTI)

    Wu, Wei; Liu, Yangang; Jensen, Michael; Toto, Tami; Foster, Michael J.; Long, Charles N.

    2014-03-27T23:59:59.000Z

    This study investigates 1997-2011 observationally based cloud fraction estimates from different platforms over the Southern Great Plains, United States, including three ground-based estimates and three satellite-based estimates at multiple temporal and spatial scales. They are: 1) the Active Remotely Sensed Clouds Locations (ARSCL); 2) the Total Sky Imager (TSI); 3) the Radiative Flux Analysis (RFA); 4) Geostationary Operational Environmental Satellite (GOES); 5) the International Satellite Cloud Climatology Project (ISCCP); and 6) Advanced Very High Resolution Radiometer Pathfinder Atmospheres Extended (PATMOS-x). A substantial disagreement is evident among different estimates, especially for ISCCP and ARSCL with statistically significant larger cloud fractions than the other estimates. For example, ISCCP and ARSCL mean cloud fractions in January are ~21% and 8% larger than the average from all the other estimates, respectively. Three estimates (ISCCP, ARSCL, GOES) exhibit an 8%-10% overall increase in the annually averaged cloud fractions from 1998 to 2009; the other three estimates (TSI, RFA, and PATMOS-x) exhibit no significant tendency of increase in this decade. Monthly cloud fractions from all the estimates exhibit Gaussian-like distributions while the distributions of daily cloud fractions are dependent on spatial scales. Investigations of high-resolution cloud fractions reveal that the differences stem from the inconsistent definitions of cloud fraction. Findings from this study suggest caution when using observationally based cloud fraction estimates for climate studies, highlighting that the consistency in defining cloud fraction between models and observations is crucial for studying the Earth’s climate.

  6. An Analysis of Cloud Cover and Water Vapor for the ALMA Project

    E-Print Network [OSTI]

    (Chile), Chalviri (Bolivia) and Five Sites in Argentina using Satellite Data and a Verification and water vapor at Chajnantor (Chile), Chalviri (Bolivia) and four sites in Argentina. Since time

  7. Accounting for Circumsolar and Horizon Cloud Determination Errors in Sky Image Inferral of Sky Cover

    Broader source: All U.S. Department of Energy (DOE) Office Webpages (Extended Search)

    AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE: Alternative1 First Use of Energy for All Purposes (Fuel and Nonfuel), 2002; Level: National5Sales for4,645U.S. DOE Office511041cloth DocumentationProducts (VAP) VAP7-0973 1 Introduction In theACMEAccountable Property

  8. Equivalence demonstration of an alternative cover system 307 EQUIVALENCE DEMONSTRATION OF AN ALTERNATIVE COVER SYSTEM

    E-Print Network [OSTI]

    Zornberg, Jorge G.

    engineered components of municipal and hazardous waste landfills is the cover system. The cover system should systems for arid locations has been acknowledged by field experimental assessments (e.g., Anderson et al for final cover design at hazardous waste sites. Evapotranspirative covers are also referred

  9. Global Climate Change,Global Climate Change, Land Cover Change, andLand Cover Change, and

    E-Print Network [OSTI]

    1 Global Climate Change,Global Climate Change, Land Cover Change, andLand Cover Change Changes · Due to ­ Climate Change ­ Land Cover / Land Use Change ­ Interaction of Climate and Land Cover Change · Resolution ­ Space ­ Time Hydro-Climatic Change · Variability vs. Change (Trends) · Point data

  10. Dust takes detour on ice-cloud journey | EMSL

    Broader source: All U.S. Department of Energy (DOE) Office Webpages (Extended Search)

    Dust takes detour on ice-cloud journey Dust takes detour on ice-cloud journey Pollution-coated particles bypass ice formation, but influence clouds Cirrus clouds are composed of...

  11. Global Simulations of Ice nucleation and Ice Supersaturation with an Improved Cloud Scheme in the Community Atmosphere Model

    SciTech Connect (OSTI)

    Gettelman, A.; Liu, Xiaohong; Ghan, Steven J.; Morrison, H.; Park, Sungsu; Conley, Andrew; Klein, Stephen A.; Boyle, James; Mitchell, David; Li, J-L F.

    2010-09-28T23:59:59.000Z

    A process-based treatment of ice supersaturation and ice-nucleation is implemented in the National Center for Atmospheric Research (NCAR) Community Atmosphere Model (CAM). The new scheme is designed to allow (1) supersaturation with respect to ice, (2) ice nucleation by aerosol particles and (3) ice cloud cover consistent with ice microphysics. The scheme is implemented with a 4-class 2 moment microphysics code and is used to evaluate ice cloud nucleation mechanisms and supersaturation in CAM. The new model is able to reproduce field observations of ice mass and mixed phase cloud occurrence better than previous versions of the model. Simulations indicate heterogeneous freezing and contact nucleation on dust are both potentially important over remote areas of the Arctic. Cloud forcing and hence climate is sensitive to different formulations of the ice microphysics. Arctic radiative fluxes are sensitive to the parameterization of ice clouds. These results indicate that ice clouds are potentially an important part of understanding cloud forcing and potential cloud feedbacks, particularly in the Arctic.

  12. ANISOTROPY LENGTHENS THE DECAY TIME OF TURBULENCE IN MOLECULAR CLOUDS

    SciTech Connect (OSTI)

    Hansen, Charles E.; McKee, Christopher F.; Klein, Richard I. [Astronomy Department, University of California, Berkeley, CA 94720 (United States)

    2011-09-01T23:59:59.000Z

    The decay of isothermal turbulence with velocity anisotropy is investigated using computational simulations and synthetic observations. We decompose the turbulence into isotropic and anisotropic components with total velocity dispersions {sigma}{sub iso} and {sigma}{sub ani}, respectively. We find that the decay rate of the turbulence depends on the crossing time of the isotropic component only. A cloud of size L with significant anisotropy in its turbulence has a dissipation time, t{sub diss} = L/(2{sigma}{sub iso}). This translates into turbulent energy decay rates on the cloud scale that can be much lower for anisotropic turbulence than for isotropic turbulence. To help future observations determine whether observed molecular clouds have the level of anisotropy required to maintain the observed level of turbulence over their lifetimes, we performed a principal component analysis on our simulated clouds. Even with projection effects washing out the anisotropic signal, there is a measurable difference in the axis-constrained principal component analysis performed in directions parallel and perpendicular to the direction of maximum velocity dispersion. When this relative difference, {psi}, is 0.1, there is enough anisotropy for the dissipation time to triple the expected isotropic value. We provide a fit for converting {psi} into an estimate for the dissipation time, t{sub diss}.

  13. Covered Product Category: Residential Central Air Conditioners...

    Office of Energy Efficiency and Renewable Energy (EERE) Indexed Site

    Central Air Conditioners Covered Product Category: Residential Central Air Conditioners The Federal Energy Management Program (FEMP) provides acquisition guidance for residential...

  14. Covered Product Category: Hot Food Holding Cabinets

    Broader source: Energy.gov [DOE]

    The Federal Energy Management Program (FEMP) provides acquisition guidance for hot food holding cabinets, which are covered by the ENERGY STAR program.

  15. Covered Product Category: Commercial Steam Cookers

    Broader source: Energy.gov [DOE]

    The Federal Energy Management Program (FEMP) provides acquisition guidance for commercial steam cookers, which are covered by the ENERGY STAR program.

  16. Covered Product Category: Residential Electric Resistance Water...

    Office of Energy Efficiency and Renewable Energy (EERE) Indexed Site

    Electric Resistance Water Heaters Covered Product Category: Residential Electric Resistance Water Heaters The Federal Energy Management Program (FEMP) sets federal efficiency...

  17. Covered Product Category: Commercial Refrigerators and Freezers

    Broader source: Energy.gov [DOE]

    The Federal Energy Management Program (FEMP) provides acquisition guidance for commercial refrigerators and freezers, which are covered by the ENERGY STAR program.

  18. Socially Optimal Pricing of Cloud Computing Resources

    E-Print Network [OSTI]

    Menache, Ishai

    The cloud computing paradigm offers easily accessible computing resources of variable size and capabilities. We consider a cloud-computing facility that provides simultaneous service to a heterogeneous, time-varying ...

  19. The Evolution of Cloud Computing in ATLAS

    E-Print Network [OSTI]

    Taylor, Ryan P; The ATLAS collaboration; Brasolin, Franco; Cordeiro, Cristovao; Desmarais, Ron; Field, Laurence; Gable, Ian; Giordano, Domenico; Di Girolamo, Alessandro; Hover, John; Leblanc, Matthew Edgar; Love, Peter; Paterson, Michael; Sobie, Randall; Zaytsev, Alexandr

    2015-01-01T23:59:59.000Z

    The ATLAS experiment has successfully incorporated cloud computing technology and cloud resources into its primarily grid-based model of distributed computing. Cloud R&D activities continue to mature and transition into stable production systems, while ongoing evolutionary changes are still needed to adapt and refine the approaches used, in response to changes in prevailing cloud technology. In addition, completely new developments are needed to handle emerging requirements. This paper describes the overall evolution of cloud computing in ATLAS. The current status of the virtual machine (VM) management systems used for harnessing infrastructure as a service (IaaS) resources are discussed. Monitoring and accounting systems tailored for clouds are needed to complete the integration of cloud resources within ATLAS' distributed computing framework. We are developing and deploying new solutions to address the challenge of operation in a geographically distributed multi-cloud scenario, including a system for ma...

  20. Disruptive technology business models in cloud computing

    E-Print Network [OSTI]

    Krikos, Alexis Christopher

    2010-01-01T23:59:59.000Z

    Cloud computing, a term whose origins have been in existence for more than a decade, has come into fruition due to technological capabilities and marketplace demands. Cloud computing can be defined as a scalable and flexible ...

  1. SCANNING CLOUD RADAR OBSERVATIONS AT AZORES: PRELIMINARY 3D CLOUD PRODUCTS

    E-Print Network [OSTI]

    SCANNING CLOUD RADAR OBSERVATIONS AT AZORES: PRELIMINARY 3D CLOUD PRODUCTS P. Kollias, I. Jo, A, NY www.bnl.gov ABSTRACT The deployment of the Scanning W-Band ARM Cloud Radar (SWACR) during the AMF campaign at Azores signals the first deployment of an ARM Facility-owned scanning cloud radar and offers

  2. Cloud-Top Temperatures for Precipitating Winter Clouds JAY W. HANNA

    E-Print Network [OSTI]

    Schultz, David

    1 Cloud-Top Temperatures for Precipitating Winter Clouds JAY W. HANNA NOAA/NESDIS Satellite of satellite-derived cloud-top brightness temperatures from GOES longwave infrared (channel 4) satellite data, rain, freezing rain, and sleet. The distributions of cloud-top brightness temperatures were constructed

  3. Cloud networking and communications Cloud computing is having an important impact on

    E-Print Network [OSTI]

    Boutaba, Raouf

    Editorial Cloud networking and communications Cloud computing is having an important impact attention has been devoted to system aspects of Cloud computing. More recently, however, the focus is shifting towards Cloud net- working and communications with evolutionary and revo- lutionary propositions

  4. Cloud seeding as a technique for studying aerosol-cloud interactions in marine stratocumulus

    E-Print Network [OSTI]

    Miami, University of

    Cloud seeding as a technique for studying aerosol-cloud interactions in marine stratocumulus hygroscopic aerosols were introduced into a solid marine stratocumulus cloud (200 m thick) by burning hygroscopic flares mounted on an aircraft. The cloud microphysical response in two parallel seeding plumes

  5. Cloud radar Doppler spectra in drizzling stratiform clouds: 1. Forward modeling and remote sensing applications

    E-Print Network [OSTI]

    Cloud radar Doppler spectra in drizzling stratiform clouds: 1. Forward modeling and remote sensing broadening and drizzle growth in shallow liquid clouds remain not well understood. Detailed, cloudscale. Profiling, millimeterwavelength (cloud) radars can provide such observations. In particular, the first three

  6. The Cloud Adoption Toolkit: Supporting Cloud Adoption Decisions in the Enterprise

    E-Print Network [OSTI]

    Sommerville, Ian

    1 The Cloud Adoption Toolkit: Supporting Cloud Adoption Decisions in the Enterprise Ali Khajeh-Hosseini, David Greenwood, James W. Smith, Ian Sommerville Cloud Computing Co-laboratory, School of Computer Science University of St Andrews, UK {akh, dsg22, jws7, ifs}@cs.st-andrews.ac.uk Abstract Cloud computing

  7. CLOUD COMPUTING AND INFORMATION POLICY 1 Cloud Computing and Information Policy

    E-Print Network [OSTI]

    Lin, Jimmy

    CLOUD COMPUTING AND INFORMATION POLICY 1 Cloud Computing and Information Policy: Computing in a Policy Cloud? Forthcoming in the Journal of Information Technology and Politics, 5(3). Paul T. Jaeger University of Maryland Jimmy Lin University of Maryland Justin M. Grimes University of Maryland #12;CLOUD

  8. HPI Cloud Symposium ,Operating The Cloud` 25.09.2013, Hasso-Plattner-Institut, Auditorium Building

    E-Print Network [OSTI]

    Weske, Mathias

    Agenda HPI Cloud Symposium ,Operating The Cloud` 25.09.2013, Hasso-Plattner-Institut, Auditorium Building 09:30h Registration 10:00h Opening Prof. Dr. Christoph Meinel, HPI Potsdam 10:30h Cloud-RAID: Eine Methode zur Bereitstellung zuverlässiger Speicherressourcen in Öffentlichen Clouds Maxim Schnajkin, HPI

  9. Cloud Verifier: Verifiable Auditing Service for IaaS Clouds Joshua Schiffman

    E-Print Network [OSTI]

    Jaeger, Trent

    Cloud Verifier: Verifiable Auditing Service for IaaS Clouds Joshua Schiffman Security Architecture University Park, PA, USA yus138,hvijay,tjaeger@cse.psu.edu Abstract--Cloud computing has commoditized compute paradigm, its adoption has been stymied by cloud platform's lack of trans- parency, which leaves customers

  10. Cloud Tracking in Cloud-Resolving Models R. S. Plant1

    E-Print Network [OSTI]

    Plant, Robert

    Cloud Tracking in Cloud-Resolving Models R. S. Plant1 1 Department of Meteorology, University. INTRODUCTION In recent years Cloud Resolving Models (CRMs) have become an increasingly important tool for CRM data, which allows one to investigate statistical prop- erties of the lifecycles of the "clouds

  11. From mini-clouds to Cloud Computing Boris Mejias, Peter Van Roy

    E-Print Network [OSTI]

    Bonaventure, Olivier

    From mini-clouds to Cloud Computing Boris Mej´ias, Peter Van Roy Universit´e catholique de Louvain ­ Belgium {boris.mejias|peter.vanroy}@uclouvain.be Abstract Cloud computing has many definitions with different views within industry and academia, but everybody agrees on that cloud computing is the way

  12. AnonymousCloud: A Data Ownership Privacy Provider Framework in Cloud Computing

    E-Print Network [OSTI]

    Hamlen, Kevin W.

    AnonymousCloud: A Data Ownership Privacy Provider Framework in Cloud Computing Safwan Mahmud Khan their computation results are ultimately delivered. To provide this data ownership privacy, the cloud's distributed-anonymity; authentication; cloud computing; in- formation security; privacy; Tor I. INTRODUCTION Revolutionary advances

  13. Leveraging Platform Basic Services in Cloud Application Platforms for the Development of Cloud

    E-Print Network [OSTI]

    Simons, Anthony J. H.

    Leveraging Platform Basic Services in Cloud Application Platforms for the Development of Cloud.Simons@dcs.shef.ac.uk Abstract-- Cloud application platforms gain popularity and have the potential to alter the way service based cloud applications are developed involving utilisation of platform basic services. A platform

  14. Carbon Chemistry in interstellar clouds

    E-Print Network [OSTI]

    Maryvonne Gerin; David Fosse; Evelyne Roueff

    2002-12-03T23:59:59.000Z

    We discuss new developments of interstellar chemistry, with particular emphasis on the carbon chemistry. We confirm that carbon chains and cycles are ubiquitous in the ISM and closely chemically related to ea ch other, and to carbon. Investigation of the carbon budget in shielded and UV illuminated gas shows that the inventory of interstellar molecules is not complete and more complex molecules with 4 or more carbon atoms must be present. Finally we discuss the consequences for the evolution of clouds and conclude that the ubiquitous presence of carbon chains and cycles is not a necessary consequence of a very young age for interstellar clouds.

  15. Interdisciplinary Pest Management Potentials of Cover Cropping Systems

    E-Print Network [OSTI]

    Bachie, Oli Gurmu

    2011-01-01T23:59:59.000Z

    Cover Crops: Cowpea, Sunn Hemp, and Velvetbean. HottscienceCover Crops: Cowpea, Sunn Hemp, and Velvetbean. Hottsciencethan grasses using sun hemp mulches. While cover cropping

  16. Interactive physically-based cloud simulation

    E-Print Network [OSTI]

    Overby, Derek Robert

    2002-01-01T23:59:59.000Z

    of digital artistic media. Previous methods for modeling the growth of clouds do not account for the fluid interactions that are responsible for cloud formation in the physical atmosphere. We propose a model for simulating cloud formation based on a basic...

  17. Dynamics of Clouds Fall Semester 2012

    E-Print Network [OSTI]

    ATS712 Dynamics of Clouds Fall Semester 2012 Meeting Times: T/Th: 9-10:15am Room: ATS 101-2pm Course Description: This class focuses on the general dynamics of cloud systems. Models of fog and other Tools / Skills Cotton, W.R., G.H. Bryan, and S.C. van den Heever, 2010: Storm and Cloud Dynamics

  18. Microsoft Private Cloud Title of document

    E-Print Network [OSTI]

    Chaudhuri, Surajit

    Microsoft Private Cloud Title of document 1 1 Microsoft Private Cloud A Comparative Look at Functionality, Benefits, and Economics November2012 #12;Microsoft Private Cloud Title of document 2 2 Copyright Information © 2012 Microsoft Corporation. All rights reserved. This document is provided "as-is." Information

  19. Performance Engineering for Cloud Computing John Murphy

    E-Print Network [OSTI]

    Murphy, John

    Performance Engineering for Cloud Computing John Murphy Lero ­ The Irish Software Engineering.Murphy@ucd.ie Abstract. Cloud computing potentially solves some of the major challenges in the engineering of large efficient operation. This paper argues that cloud computing is an area where performance engineering must

  20. Level Set Implementations on Unstructured Point Cloud

    E-Print Network [OSTI]

    Duncan, James S.

    Level Set Implementations on Unstructured Point Cloud by HO, Hon Pong A Thesis Submitted;Level Set Implementations on Unstructured Point Cloud by HO, Hon Pong This is to certify that I have implementations on unstructured point cloud 15 3.1 Level set initialization

  1. 6, 93519388, 2006 Aerosol-cloud

    E-Print Network [OSTI]

    Paris-Sud XI, Université de

    ACPD 6, 9351­9388, 2006 Aerosol-cloud interaction inferred from MODIS and models G. Myhre et al Chemistry and Physics Discussions Aerosol-cloud interaction inferred from MODIS satellite data and global 6, 9351­9388, 2006 Aerosol-cloud interaction inferred from MODIS and models G. Myhre et al. Title

  2. Cloud Security: Issues and Concerns Pierangela Samarati*

    E-Print Network [OSTI]

    Samarati, Pierangela

    1 Cloud Security: Issues and Concerns Authors Pierangela Samarati* Università degli Studi di Milano, Italy sabrina.decapitani@unimi.it Keywords cloud security confidentiality integrity availability secure data storage and processing Summary The cloud has emerged as a successful computing paradigm

  3. Cloud Computing: Centralization and Data Sovereignty

    E-Print Network [OSTI]

    Paris-Sud XI, Université de

    1 Cloud Computing: Centralization and Data Sovereignty Primavera De Filippi, Smari McCarthy Abstract: Cloud computing can be defined as the provision of computing resources on-demand over and elasticity of costs, problems arise concerning the collection of personal information in the Cloud

  4. Optimizing Offloading Strategies in Mobile Cloud Computing

    E-Print Network [OSTI]

    Hyytiä, Esa

    Optimizing Offloading Strategies in Mobile Cloud Computing Esa Hyyti¨a Department of Communications Abstract--We consider a dynamic offloading problem arising in the context of mobile cloud computing (MCC consider the task assignment problem arising in the context of the mobile cloud computing (MCC). In MCC

  5. CONTROLLING DATA IN THE CLOUD: OUTSOURCING COMPUTATION

    E-Print Network [OSTI]

    Zou, Cliff C.

    #12;CONTROLLING DATA IN THE CLOUD: OUTSOURCING COMPUTATION WITHOUT OUTSOURCING CONTROL Paper By Laboratories Of America 2009 ACM WORKSHOP ON CLOUD COMPUTING SECURITY (CCSW 2009) Presented By Talal Basaif CAP that will arise later · New directions to solve some issues #12;INTRODUCTION · Cloud computing is one of desirable

  6. Towards a Ubiquitous Cloud Computing Infrastructure

    E-Print Network [OSTI]

    van der Merwe, Kobus

    Towards a Ubiquitous Cloud Computing Infrastructure Jacobus Van der Merwe, K.K. Ramakrishnan of a number of cloud computing use cases. We specifically consider cloudbursting and follow-the-sun and focus that are also network service providers. I. INTRODUCTION Cloud computing is rapidly gaining acceptance

  7. Cloud Computing: Legal Issues in Centralized Architectures

    E-Print Network [OSTI]

    Paris-Sud XI, Université de

    Cloud Computing: Legal Issues in Centralized Architectures Primavera DE FILIPPI1 , Smari McCARTHY2, Reykjavik, 101, Iceland - Email: smari@gmail.com Abstract: Cloud computing can be defined as the provision they can access their data and the extent to which parties can exploit it. Keywords: Cloud Computing

  8. Ice Cover on the Great Lakes NATIONALOCEANIC

    E-Print Network [OSTI]

    Ice Cover on the Great Lakes NATIONALOCEANIC AND ATMOSPHERIC ADMINISTRATION U.S. D EPARTMENT OF COMM ER CE Great Lakes Ice Cover facts since 1973 - 94.7% ice coverage in 1979 is the maximum on record - 9.5% ice coverage in 2002 is the lowest on record - 11.5% ice coverage in 1998, a strong El Nino

  9. Cloud Seeding By: Julie Walter

    E-Print Network [OSTI]

    Toohey, Darin W.

    , smoke, that then are cooled because of the high altitudes. As the water or condensation nuclei cool more pushed up enough the warm air that is filled with moisture should reach an optimum cooling point-based Western Weather Consultants, whose company supplied Vail Resorts with the cloud seeding generators

  10. Cloud and Autonomic Computing Center

    E-Print Network [OSTI]

    Gelfond, Michael

    boundary layers and wind turbine aerodynamics Siva Parameswarn, Ph.D. Professor in the Department vehicles » Wake development behind wind turbines PHYSICS Ismael Regis de Farias Jr., Ph.D. Associate in cloud environments » Intelligent data management & understanding » Automated web service composition

  11. Clouds are integral to the climate system. They are a crucial component of the global water cycle, vital

    E-Print Network [OSTI]

    Allan, Richard P.

    gives a value of 61.6% cloud cover over the period January 2001 to December 2010). These estimates were with the Earth's radiative energy balance. They cool the surface by shading it from the direct solar beam but almost as strongly enhance the greenhouse effect of the atmosphere by reducing the efficiency by which

  12. Evaluation of erosion and cover re-establishment following site preparation on east Texas forest lands

    E-Print Network [OSTI]

    Blume, Timothy Allen

    1979-01-01T23:59:59.000Z

    damage following mechanical site prepara- tion. (uantitative data characterizing the rate of recovery of soi. l protective cover, used in combination with erosion data, gives planners and forest managers an indication of the total impact of mechanical...EVALUATION OF EROSION AND COVER RE-ESTABLISHMENT 1'OLLOWING SITE PREPARATION ON EAST TEXAS FOREST LANDS A Thesis by Timothy Allen Blume Submitted to the Graduate College of Texas A&M Uniuersity in partial fullfillment of the requir ment...

  13. Radiation Parameterization for Three-Dimensional Inhomogeneous Cirrus Clouds Applied to ARM Data and Climate Models

    SciTech Connect (OSTI)

    Kuo-Nan Liou

    2003-12-29T23:59:59.000Z

    OAK-B135 (a) We developed a 3D radiative transfer model to simulate the transfer of solar and thermal infrared radiation in inhomogeneous cirrus clouds. The model utilized a diffusion approximation approach (four-term expansion in the intensity) employing Cartesian coordinates. The required single-scattering parameters, including the extinction coefficient, single-scattering albedo, and asymmetry factor, for input to the model, were parameterized in terms of the ice water content and mean effective ice crystal size. The incorporation of gaseous absorption in multiple scattering atmospheres was accomplished by means of the correlated k-distribution approach. In addition, the strong forward diffraction nature in the phase function was accounted for in each predivided spatial grid based on a delta-function adjustment. The radiation parameterization developed herein is applied to potential cloud configurations generated from GCMs to investigate broken clouds and cloud-overlapping effects on the domain-averaged heating rate. Cloud inhomogeneity plays an important role in the determination of flux and heating rate distributions. Clouds with maximum overlap tend to produce less heating than those with random overlap. Broken clouds show more solar heating as well as more IR cooling as compared to a continuous cloud field (Gu and Liou, 2001). (b) We incorporated a contemporary radiation parameterization scheme in the UCLA atmospheric GCM in collaboration with the UCLA GCM group. In conjunction with the cloud/radiation process studies, we developed a physically-based cloud cover formation scheme in association with radiation calculations. The model clouds were first vertically grouped in terms of low, middle, and high types. Maximum overlap was then used for each cloud type, followed by random overlap among the three cloud types. Fu and Liou's 1D radiation code with modification was subsequently employed for pixel-by-pixel radiation calculations in the UCLA GCM. We showed that the simulated cloud cover and OLR fields without special tuning are comparable to those of ISCCP dataset and the results derived from radiation budget experiments. Use of the new radiation and cloud schemes enhances the radiative warming in the middle to upper tropical troposphere and alleviates the cold bias in the UCLA atmospheric GCM. We also illustrated that ice crystal size and cloud inhomogeneous are significant factors affecting the radiation budgets at the top of the atmosphere and the surface (Gu et al. 2003). (c) An innovative approach has been developed to construct a 3D field of inhomogeneous clouds in general and cirrus in particular in terms of liquid/ice water content and particle size on the basis of a unification of satellite and ground-based cloud radar data. Satellite remote sensing employing the current narrow-band spectro-radiometers has limitation and only the vertically integrated cloud parameters (optical depth and mean particle size) can be determined. However, by combining the horizontal cloud mapping inferred from satellites with the vertical structure derived from the profiling Doppler cloud radar, a 3D cloud field can be constructed. This represents a new conceptual approach to 3D remote sensing and imaging and offers a new perspective in observing the cloud structure. We applied this novel technique to AVHRR/NOAA satellite and mm-wave cloud radar data obtained from the ARM achieve and assessed the 3D cirrus cloud field with the ice crystal size distributions independently derived from optical probe measurements aboard the University of North Dakota Citation. The retrieved 3D ice water content and mean effective ice crystal size involving an impressive cirrus cloud occurring on April 18, 1997, are shown to be comparable to those derived from the analysis of collocated and coincident in situ aircraft measurements (Liou et al. 2002). (d) Detection of thin cirrus with optical depths less than 0.5, particularly those occurring i n the tropics remains a fundamental problem in remote sensing. We developed a new detection scheme for the

  14. MUJERES TOTAL BIOLOGIA 16 27

    E-Print Network [OSTI]

    Autonoma de Madrid, Universidad

    , PLASTICA Y VISUAL 2 2 EDUCACION FISICA, DEPORTE Y MOTRICIDAD HUMANA 1 1 6 11 TOTAL CIENCIAS Nº DE TESIS

  15. MUJERES ( * ) TOTAL BIOLOGA 16 22

    E-Print Network [OSTI]

    Autonoma de Madrid, Universidad

    , DEPORTE Y MOTRICIDAD HUMANA 0 4 TOTAL FORMACIÓN DE PROFESORADO Y EDUCACIÓN 0 6 ANATOMÍA PATOLÓGICA 2 5

  16. The Total RNA Story Introduction

    E-Print Network [OSTI]

    Goldman, Steven A.

    The Total RNA Story Introduction Assessing RNA sample quality as a routine part of the gene about RNA sample quality. Data from a high quality total RNA preparation Although a wide variety RNA data interpretation and identify features from total RNA electropherograms that reveal information

  17. OGJ300; Smaller list, bigger financial totals

    SciTech Connect (OSTI)

    Beck, R.J.; Biggs, J.B.

    1991-09-30T23:59:59.000Z

    This paper reports on Oil and Gas Journal's list of the largest, publicly traded oil and gas producing companies in the U.S. which is both smaller and larger this year than it was in 1990. It's smaller because it covers fewer companies. Industry consolidation has slashed the number of public companies. As a result, the former OGJ400 has become the OGJ300, which includes the 30 largest limited partnerships. But the assets-ranked list is larger because important financial totals - representing 1990 results - are significantly higher than those of a year ago, despite the lower number of companies. Consolidation of the U.S. producing industry gained momentum throughout the 1980s. Unable to sustain profitability in a period of sluggish energy prices and, for many, rising costs, companies sought relief through mergers or liquidation of producing properties. As this year's list shows, however, surviving companies have managed to grow. Assets for the OGJ300 group totaled $499.3 billion in 1990 - up 6.3% from the 1989 total of last year's OGJ400. Stockholders' equity moved up 5.3% to $170.7 billion. Stockholders' equity was as high as $233.8 billion in 1983.

  18. Cloud speed impact on solar variability scaling â?? Application to the wavelet variability model

    E-Print Network [OSTI]

    Lave, Matthew; Kleissl, Jan

    2013-01-01T23:59:59.000Z

    Kleissl, J. , 2013. Deriving cloud velocity from an array ofCloud Speed Impact on Solar Variability Scaling -this work, we determine from cloud speeds. Cloud simulator

  19. SLA-based Optimization of Power and Migration Cost in Cloud Computing Hadi Goudarzi, Mohammad Ghasemazar and Massoud Pedram

    E-Print Network [OSTI]

    Pedram, Massoud

    the total energy cost of cloud computing system while meeting the specified client-level SLAs, and infrastructure-independent computing are examples of motivations of such systems. Electrical energy cost the system. These constraints result in a basic trade-off between the total energy cost and client

  20. Features . . . Cover Crop Value to Cotton

    E-Print Network [OSTI]

    Watson, Craig A.

    .............................................................................................Page 6 Fuel Prices Projections - Encouraging News .......................Page 7 Agronomy Notes VolumeFeatures . . . Cotton Cover Crop Value to Cotton Cotton Price and Rotation ..............................................................Page 5 Miscellaneous Large differences in nitrogen prices.......................................Page 6

  1. Special study on vegetative covers. [UMTRA Project

    SciTech Connect (OSTI)

    Not Available

    1988-11-01T23:59:59.000Z

    This report describes the findings of a special study on the use of vegetative covers to stabilize tailings piles for the Uranium Mill Tailings Remedial Action (UMTRA) Project. The principal rationale for using plants would be to establish a dynamic system for controlling water balance. Specifically, vegetation would be used to intercept and transpire precipitation to the atmosphere, rather than allowing water to drain into the tailings and mobilize contaminants. This would facilitate compliance with groundwater standards proposed for the UMTRA Project by the Environmental Protection Agency. The goals of the study were to evaluate the feasibility of using vegetative covers on UMTRA Project piles, define the advantages and disadvantages of vegetative covers, and develop general guidelines for their use when such use seems reasonable. The principal method for the study was to analyze and apply to the UMTRA Project the results of research programs on vegetative covers at other US Department of Energy (DOE) waste management facilities. The study also relied upon observations made of existing stabilized piles at UMTRA Project sites where natural vegetation is growing on the rock-covered surfaces. Water balance and erosion models were also used to quantify the long-term performance of vegetative covers planned for the topslopes of stabilized piles at Grand Junction and Durango, Colorado, two UMTRA Project sites where the decision was made during the course of this special study to use vegetative covers. Elements in the design and construction of the vegetative covers at these two sites are discussed in the report, with explanations of the differing features that reflect differing environmental conditions. 28 refs., 18 figs., 9 tabs.

  2. Vegetative covers: Special study. [Final report

    SciTech Connect (OSTI)

    Not Available

    1988-11-01T23:59:59.000Z

    This report describes the findings of a special study on the use of vegetative covers to stabilize tailings piles for the Uranium Mill Tailings Remedial Action (UMTRA) Project. The principal rationale for using plants would be to establish a dynamic system for controlling water balance. Specifically, vegetation would be used to intercept and transpire precipitation to the atmosphere, rather than allowing water to drain into the tailings and mobilize contaminants. This would facilitate compliance with groundwater standards proposed for the UMTRA Project by the Environmental Protection Agency. The goals of the study were to (1) evaluate the feasibility of using vegetative covers on UMTRA Project piles, (2) define the advantages and disadvantages of vegetative covers, and (3) develop general guidelines for their use when such use seems reasonable. The principal method for the study was to analyze and apply to the UMTRA Project the results of research programs on vegetative covers at other US Department of Energy (DOE) waste management facilities. The study also relied upon observations made of existing stabilized piles at UMTRA Project sites (Shiprock, New Mexico; Burrell, Pennsylvania; and Clive, Utah) where natural vegetation is growing on the rock-covered surfaces. Water balance and erosion models were also used to quantify the long-term performance of vegetative covers planned for the topslopes of stabilized piles at Grand Junction and Durango, Colorado, two UMTRA Project sites where the decision was made during the course of this special study to use vegetative covers. Elements in the design and construction of the vegetative covers at these two sites are discussed in the report, with explanations of the differing features that reflect differing environmental conditions.

  3. GALACTIC ALL-SKY SURVEY HIGH-VELOCITY CLOUDS IN THE REGION OF THE MAGELLANIC LEADING ARM

    SciTech Connect (OSTI)

    For, Bi-Qing; Staveley-Smith, Lister [International Centre for Radio Astronomy Research, University of Western Australia, 35 Stirling Hwy, Crawley, WA 6009 (Australia)] [International Centre for Radio Astronomy Research, University of Western Australia, 35 Stirling Hwy, Crawley, WA 6009 (Australia); McClure-Griffiths, N. M., E-mail: biqing.for@uwa.edu.au [Australia Telescope National Facility, CSIRO Astronomy and Space Science, PO Box 76, Epping, NSW 1710 (Australia)

    2013-02-10T23:59:59.000Z

    We present a catalog of high-velocity clouds in the region of the Magellanic Leading Arm. The catalog is based on neutral hydrogen (H I) observations from the Parkes Galactic All-Sky Survey. Excellent spectral resolution allows clouds with narrow-line components to be resolved. The total number of detected clouds is 419. We describe the method of cataloging and present the basic parameters of the clouds. We discuss the general distribution of the high-velocity clouds and classify the clouds based on their morphological type. The presence of a significant number of head-tail clouds and their distribution in the region is discussed in the context of Magellanic System simulations. We suggest that ram-pressure stripping is a more important factor than tidal forces for the morphology and formation of the Magellanic Leading Arm and that different environmental conditions might explain the morphological difference between the Magellanic Leading Arm and Magellanic Stream. We also discuss a newly identified population of clouds that forms the LA IV and a new diffuse bridge-like feature connecting the LA II and III complexes.

  4. Intercomparison of model simulations of mixed-phase clouds observed during the ARM Mixed-Phase Arctic Cloud Experiment. Part I: Single layer cloud

    E-Print Network [OSTI]

    Klein, Stephen A.

    2009-01-01T23:59:59.000Z

    cloud has the correct effect on surface fluxes of radiation.radiation is 200 W m –2 in clear-sky STREAMER calculations, the longwave cloud radiative effect

  5. Fine-scale Horizontal Structure of Arctic Mixed-Phase Clouds.

    SciTech Connect (OSTI)

    Rambukkange,M.; Verlinde, J.; Elorante, E.; Luke, E.; Kollias, P.; Shupe, M.

    2006-07-10T23:59:59.000Z

    Recent in situ observations in stratiform clouds suggest that mixed phase regimes, here defined as limited cloud volumes containing both liquid and solid water, are constrained to narrow layers (order 100 m) separating all-liquid and fully glaciated volumes (Hallett and Viddaurre, 2005). The Department of Energy Atmospheric Radiation Measurement Program's (DOE-ARM, Ackerman and Stokes, 2003) North Slope of Alaska (NSA) ARM Climate Research Facility (ACRF) recently started collecting routine measurement of radar Doppler velocity power spectra from the Millimeter Cloud Radar (MMCR). Shupe et al. (2004) showed that Doppler spectra has potential to separate the contributions to the total reflectivity of the liquid and solid water in the radar volume, and thus to investigate further Hallett and Viddaurre's findings. The Mixed-Phase Arctic Cloud Experiment (MPACE) was conducted along the NSA to investigate the properties of Arctic mixed phase clouds (Verlinde et al., 2006). We present surface based remote sensing data from MPACE to discuss the fine-scale structure of the mixed-phase clouds observed during this experiment.

  6. Star formation efficiencies of molecular clouds in a galactic center environment

    E-Print Network [OSTI]

    Bertram, Erik; Clark, Paul C; Klessen, Ralf S

    2015-01-01T23:59:59.000Z

    We use the Arepo moving mesh code to simulate the evolution of molecular clouds exposed to a harsh environment similar to that found in the galactic center (GC), in an effort to understand why the star formation efficiency (SFE) of clouds in this environment is so small. Our simulations include a simplified treatment of time-dependent chemistry and account for the highly non-isothermal nature of the gas and the dust. We model clouds with a total mass of 1.3x10^5 M_{sun} and explore the effects of varying the mean cloud density and the virial parameter, alpha = E_{kin}/|E_{pot}|. We vary the latter from alpha = 0.5 to alpha = 8.0, and so many of the clouds that we simulate are gravitationally unbound. We expose our model clouds to an interstellar radiation field (ISRF) and cosmic ray flux (CRF) that are both a factor of 1000 higher than the values found in the solar neighbourhood. As a reference, we also run simulations with local solar neighbourhood values of the ISRF and the CRF in order to better constrain ...

  7. Total..........................................................

    Broader source: All U.S. Department of Energy (DOE) Office Webpages (Extended Search)

    Q 0.4 3 or More Units... 5.4 0.3 Q Q Central Air-Conditioning Usage Air-Conditioned Floorspace (Square Feet)...

  8. Total..........................................................

    Broader source: All U.S. Department of Energy (DOE) Office Webpages (Extended Search)

    ... 1.9 1.1 Q Q 0.3 Q Do Not Use Central Air-Conditioning... 45.2 24.6 3.6 5.0 8.8 3.2 Use a Programmable...

  9. Total..........................................................

    Broader source: All U.S. Department of Energy (DOE) Office Webpages (Extended Search)

    Q 0.6 3 or More Units... 5.4 3.8 2.9 0.4 Q N 0.2 Central Air-Conditioning Usage Air-Conditioned Floorspace (Square Feet)...

  10. Total..........................................................

    Broader source: All U.S. Department of Energy (DOE) Office Webpages (Extended Search)

    1.3 Q 3 or More Units... 5.4 1.6 0.8 Q 0.3 0.3 Q Central Air-Conditioning Usage Air-Conditioned Floorspace (Square Feet)...

  11. Total..........................................................

    Broader source: All U.S. Department of Energy (DOE) Office Webpages (Extended Search)

    3 or More Units... 5.4 2.4 1.4 0.7 0.9 Central Air-Conditioning Usage Air-Conditioned Floorspace (Square Feet)...

  12. Total..........................................................

    Broader source: All U.S. Department of Energy (DOE) Office Webpages (Extended Search)

    3 or More Units... 5.4 2.3 1.7 0.6 Central Air-Conditioning Usage Air-Conditioned Floorspace (Square Feet)...

  13. Total..........................................................

    Broader source: All U.S. Department of Energy (DOE) Office Webpages (Extended Search)

    8.6 Have Equipment But Do Not Use it... 1.9 Q Q Q Q 0.6 0.4 0.3 Q Type of Air-Conditioning Equipment 1, 2 Central System......

  14. Total..........................................................

    Broader source: All U.S. Department of Energy (DOE) Office Webpages (Extended Search)

    3 or More Units... 5.4 2.1 0.9 0.2 1.0 Central Air-Conditioning Usage Air-Conditioned Floorspace (Square Feet)...

  15. Total..........................................................

    Broader source: All U.S. Department of Energy (DOE) Office Webpages (Extended Search)

    30.3 Have Equipment But Do Not Use it... 1.9 0.5 0.6 0.4 Q Q 0.5 0.8 Type of Air-Conditioning Equipment 1, 2 Central System......

  16. Total..........................................................

    Broader source: All U.S. Department of Energy (DOE) Office Webpages (Extended Search)

    0.3 3 or More Units... 5.4 0.7 0.5 Q Central Air-Conditioning Usage Air-Conditioned Floorspace (Square Feet)...

  17. Total..........................................................

    Broader source: All U.S. Department of Energy (DOE) Office Webpages (Extended Search)

    3 or More Units... 5.4 2.3 0.7 2.1 0.3 Central Air-Conditioning Usage Air-Conditioned Floorspace (Square Feet)...

  18. Total..........................................................

    Annual Energy Outlook 2013 [U.S. Energy Information Administration (EIA)]

    111.1 47.1 19.0 22.7 22.3 Personal Computers Do Not Use a Personal Computer... 35.5 16.9 6.5 4.6 7.6 Use a Personal Computer......

  19. Total..........................................................

    Gasoline and Diesel Fuel Update (EIA)

    26.7 28.8 20.6 13.1 22.0 16.6 38.6 Personal Computers Do Not Use a Personal Computer... 35.5 17.1 10.8 4.2 1.8 1.6 10.3 20.6 Use a Personal Computer......

  20. Total..........................................................

    Gasoline and Diesel Fuel Update (EIA)

    Personal Computers Do Not Use a Personal Computer... 35.5 14.2 7.2 2.8 4.2 Use a Personal Computer... 75.6...

  1. Total..........................................................

    Gasoline and Diesel Fuel Update (EIA)

    5.6 17.7 7.9 Personal Computers Do Not Use a Personal Computer... 35.5 8.1 5.6 2.5 Use a Personal Computer......

  2. Total..........................................................

    Annual Energy Outlook 2013 [U.S. Energy Information Administration (EIA)]

    4.2 7.6 16.6 Personal Computers Do Not Use a Personal Computer... 35.5 6.4 2.2 4.2 Use a Personal Computer......

  3. Total..........................................................

    Gasoline and Diesel Fuel Update (EIA)

    ..... 111.1 7.1 7.0 8.0 12.1 Personal Computers Do Not Use a Personal Computer... 35.5 3.0 2.0 2.7 3.1 Use a Personal Computer......

  4. Total..........................................................

    Annual Energy Outlook 2013 [U.S. Energy Information Administration (EIA)]

    25.6 40.7 24.2 Personal Computers Do Not Use a Personal Computer... 35.5 6.9 8.1 14.2 6.4 Use a Personal Computer......

  5. Total..........................................................

    Annual Energy Outlook 2013 [U.S. Energy Information Administration (EIA)]

    1.3 0.8 0.5 Once a Day... 19.2 4.6 3.0 1.6 Between Once a Day and Once a Week... 32.0 8.9 6.3 2.6 Once a...

  6. Total..........................................................

    Gasoline and Diesel Fuel Update (EIA)

    AppliancesTools.... 56.2 11.6 3.3 8.2 Other Appliances Used Auto BlockEngineBattery Heater... 0.8 0.2 Q 0.1 Hot Tub or Spa......

  7. Total..........................................................

    Annual Energy Outlook 2013 [U.S. Energy Information Administration (EIA)]

    Tools... 56.2 20.5 10.8 3.6 6.1 Other Appliances Used Auto BlockEngineBattery Heater... 0.8 N N N N Hot Tub or Spa......

  8. Total..........................................................

    Annual Energy Outlook 2013 [U.S. Energy Information Administration (EIA)]

    Tools... 56.2 27.2 10.6 9.3 9.2 Other Appliances Used Auto BlockEngineBattery Heater... 0.8 Q Q Q 0.4 Hot Tub or Spa......

  9. Total..........................................................

    U.S. Energy Information Administration (EIA) Indexed Site

    AppliancesTools.... 56.2 12.2 9.4 2.8 Other Appliances Used Auto BlockEngineBattery Heater... 0.8 Q Q Q Hot Tub or Spa......

  10. Total..........................................................

    Annual Energy Outlook 2013 [U.S. Energy Information Administration (EIA)]

    1.3 3.8 Table HC7.10 Home Appliances Usage Indicators by Household Income, 2005 Below Poverty Line Eligible for Federal Assistance 1 40,000 to 59,999 60,000 to 79,999 80,000...

  11. Total..............................................

    Broader source: All U.S. Department of Energy (DOE) Office Webpages (Extended Search)

    AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE: Alternative1 First Use of Energy for All Purposes (Fuel and Nonfuel), 2002; Level:Energy: Grid Integration Redefining What'sis Taking Over OurThe Iron Spin Transition in2, 2003Tool ofTopo II: AnPipeline. 111.1 86.6 2,720

  12. Total................................................

    Broader source: All U.S. Department of Energy (DOE) Office Webpages (Extended Search)

    AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE: Alternative1 First Use of Energy for All Purposes (Fuel and Nonfuel), 2002; Level:Energy: Grid Integration Redefining What'sis Taking Over OurThe Iron Spin Transition in2, 2003Tool ofTopo II: AnPipeline. 111.1 86.6 2,720..

  13. Total........................................................

    Broader source: All U.S. Department of Energy (DOE) Office Webpages (Extended Search)

    AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE: Alternative1 First Use of Energy for All Purposes (Fuel and Nonfuel), 2002; Level:Energy: Grid Integration Redefining What'sis Taking Over OurThe Iron Spin Transition in2, 2003Tool ofTopo II: AnPipeline. 111.1 86.6 2,720..

  14. Total..........................................................

    Broader source: All U.S. Department of Energy (DOE) Office Webpages (Extended Search)

    AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE: Alternative1 First Use of Energy for All Purposes (Fuel and Nonfuel), 2002; Level:Energy: Grid Integration Redefining What'sis Taking Over OurThe Iron Spin Transition in2, 2003Tool ofTopo II: AnPipeline. 111.1 86.6

  15. Total...........................................................

    Broader source: All U.S. Department of Energy (DOE) Office Webpages (Extended Search)

    AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE: Alternative1 First Use of Energy for All Purposes (Fuel and Nonfuel), 2002; Level:Energy: Grid Integration Redefining What'sis Taking Over OurThe Iron Spin Transition in2, 2003Tool ofTopo II: AnPipeline. 111.1 86.6Q Table

  16. Total...........................................................

    Broader source: All U.S. Department of Energy (DOE) Office Webpages (Extended Search)

    AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE: Alternative1 First Use of Energy for All Purposes (Fuel and Nonfuel), 2002; Level:Energy: Grid Integration Redefining What'sis Taking Over OurThe Iron Spin Transition in2, 2003Tool ofTopo II: AnPipeline. 111.1 86.6Q TableQ

  17. Total...........................................................

    Broader source: All U.S. Department of Energy (DOE) Office Webpages (Extended Search)

    AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE: Alternative1 First Use of Energy for All Purposes (Fuel and Nonfuel), 2002; Level:Energy: Grid Integration Redefining What'sis Taking Over OurThe Iron Spin Transition in2, 2003Tool ofTopo II: AnPipeline. 111.1 86.6Q

  18. Total...........................................................

    Broader source: All U.S. Department of Energy (DOE) Office Webpages (Extended Search)

    AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE: Alternative1 First Use of Energy for All Purposes (Fuel and Nonfuel), 2002; Level:Energy: Grid Integration Redefining What'sis Taking Over OurThe Iron Spin Transition in2, 2003Tool ofTopo II: AnPipeline. 111.1 86.6Q26.7

  19. Total............................................................

    Broader source: All U.S. Department of Energy (DOE) Office Webpages (Extended Search)

    AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE: Alternative1 First Use of Energy for All Purposes (Fuel and Nonfuel), 2002; Level:Energy: Grid Integration Redefining What'sis Taking Over OurThe Iron Spin Transition in2, 2003Tool ofTopo II: AnPipeline. 111.1

  20. Total............................................................

    Broader source: All U.S. Department of Energy (DOE) Office Webpages (Extended Search)

    AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE: Alternative1 First Use of Energy for All Purposes (Fuel and Nonfuel), 2002; Level:Energy: Grid Integration Redefining What'sis Taking Over OurThe Iron Spin Transition in2, 2003Tool ofTopo II: AnPipeline. 111.1

  1. Total.............................................................

    Broader source: All U.S. Department of Energy (DOE) Office Webpages (Extended Search)

    AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE: Alternative1 First Use of Energy for All Purposes (Fuel and Nonfuel), 2002; Level:Energy: Grid Integration Redefining What'sis Taking Over OurThe Iron Spin Transition in2, 2003Tool ofTopo II: AnPipeline. 111.126.7 28.8 20.6

  2. Total..............................................................

    Broader source: All U.S. Department of Energy (DOE) Office Webpages (Extended Search)

    AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE: Alternative1 First Use of Energy for All Purposes (Fuel and Nonfuel), 2002; Level:Energy: Grid Integration Redefining What'sis Taking Over OurThe Iron Spin Transition in2, 2003Tool ofTopo II: AnPipeline. 111.126.7 28.8

  3. Total..............................................................

    Broader source: All U.S. Department of Energy (DOE) Office Webpages (Extended Search)

    AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE: Alternative1 First Use of Energy for All Purposes (Fuel and Nonfuel), 2002; Level:Energy: Grid Integration Redefining What'sis Taking Over OurThe Iron Spin Transition in2, 2003Tool ofTopo II: AnPipeline. 111.126.7 28.8,171

  4. Total...............................................................

    Broader source: All U.S. Department of Energy (DOE) Office Webpages (Extended Search)

    AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE: Alternative1 First Use of Energy for All Purposes (Fuel and Nonfuel), 2002; Level:Energy: Grid Integration Redefining What'sis Taking Over OurThe Iron Spin Transition in2, 2003Tool ofTopo II: AnPipeline. 111.126.7

  5. Total...............................................................

    Broader source: All U.S. Department of Energy (DOE) Office Webpages (Extended Search)

    AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE: Alternative1 First Use of Energy for All Purposes (Fuel and Nonfuel), 2002; Level:Energy: Grid Integration Redefining What'sis Taking Over OurThe Iron Spin Transition in2, 2003Tool ofTopo II: AnPipeline. 111.126.70.7 21.7

  6. Total...............................................................

    Broader source: All U.S. Department of Energy (DOE) Office Webpages (Extended Search)

    AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE: Alternative1 First Use of Energy for All Purposes (Fuel and Nonfuel), 2002; Level:Energy: Grid Integration Redefining What'sis Taking Over OurThe Iron Spin Transition in2, 2003Tool ofTopo II: AnPipeline. 111.126.70.7

  7. Total...............................................................

    Broader source: All U.S. Department of Energy (DOE) Office Webpages (Extended Search)

    AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE: Alternative1 First Use of Energy for All Purposes (Fuel and Nonfuel), 2002; Level:Energy: Grid Integration Redefining What'sis Taking Over OurThe Iron Spin Transition in2, 2003Tool ofTopo II: AnPipeline. 111.126.70.747.1

  8. Total...............................................................

    Broader source: All U.S. Department of Energy (DOE) Office Webpages (Extended Search)

    AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE: Alternative1 First Use of Energy for All Purposes (Fuel and Nonfuel), 2002; Level:Energy: Grid Integration Redefining What'sis Taking Over OurThe Iron Spin Transition in2, 2003Tool ofTopo II: AnPipeline. 111.126.70.747.1Do

  9. Total................................................................

    Broader source: All U.S. Department of Energy (DOE) Office Webpages (Extended Search)

    AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE: Alternative1 First Use of Energy for All Purposes (Fuel and Nonfuel), 2002; Level:Energy: Grid Integration Redefining What'sis Taking Over OurThe Iron Spin Transition in2, 2003Tool ofTopo II: AnPipeline. 111.126.70.747.1Do

  10. Total.................................................................

    Broader source: All U.S. Department of Energy (DOE) Office Webpages (Extended Search)

    AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE: Alternative1 First Use of Energy for All Purposes (Fuel and Nonfuel), 2002; Level:Energy: Grid Integration Redefining What'sis Taking Over OurThe Iron Spin Transition in2, 2003Tool ofTopo II: AnPipeline.

  11. Total.................................................................

    Broader source: All U.S. Department of Energy (DOE) Office Webpages (Extended Search)

    AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE: Alternative1 First Use of Energy for All Purposes (Fuel and Nonfuel), 2002; Level:Energy: Grid Integration Redefining What'sis Taking Over OurThe Iron Spin Transition in2, 2003Tool ofTopo II: AnPipeline.14.7 7.4 12.5 12.5

  12. Total.................................................................

    Broader source: All U.S. Department of Energy (DOE) Office Webpages (Extended Search)

    AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE: Alternative1 First Use of Energy for All Purposes (Fuel and Nonfuel), 2002; Level:Energy: Grid Integration Redefining What'sis Taking Over OurThe Iron Spin Transition in2, 2003Tool ofTopo II: AnPipeline.14.7 7.4 12.5

  13. Total..................................................................

    Broader source: All U.S. Department of Energy (DOE) Office Webpages (Extended Search)

    AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE: Alternative1 First Use of Energy for All Purposes (Fuel and Nonfuel), 2002; Level:Energy: Grid Integration Redefining What'sis Taking Over OurThe Iron Spin Transition in2, 2003Tool ofTopo II: AnPipeline.14.7 7.4 12.578.1

  14. Total..................................................................

    Broader source: All U.S. Department of Energy (DOE) Office Webpages (Extended Search)

    AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE: Alternative1 First Use of Energy for All Purposes (Fuel and Nonfuel), 2002; Level:Energy: Grid Integration Redefining What'sis Taking Over OurThe Iron Spin Transition in2, 2003Tool ofTopo II: AnPipeline.14.7 7.4

  15. Total..................................................................

    Broader source: All U.S. Department of Energy (DOE) Office Webpages (Extended Search)

    AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE: Alternative1 First Use of Energy for All Purposes (Fuel and Nonfuel), 2002; Level:Energy: Grid Integration Redefining What'sis Taking Over OurThe Iron Spin Transition in2, 2003Tool ofTopo II: AnPipeline.14.7 7.4. 111.1 14.7

  16. Total...................................................................

    Broader source: All U.S. Department of Energy (DOE) Office Webpages (Extended Search)

    AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE: Alternative1 First Use of Energy for All Purposes (Fuel and Nonfuel), 2002; Level:Energy: Grid Integration Redefining What'sis Taking Over OurThe Iron Spin Transition in2, 2003Tool ofTopo II: AnPipeline.14.7 7.4. 111.1

  17. Total...................................................................

    Broader source: All U.S. Department of Energy (DOE) Office Webpages (Extended Search)

    AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE: Alternative1 First Use of Energy for All Purposes (Fuel and Nonfuel), 2002; Level:Energy: Grid Integration Redefining What'sis Taking Over OurThe Iron Spin Transition in2, 2003Tool ofTopo II: AnPipeline.14.7 7.4. 111.115.2

  18. Total...................................................................

    Broader source: All U.S. Department of Energy (DOE) Office Webpages (Extended Search)

    AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE: Alternative1 First Use of Energy for All Purposes (Fuel and Nonfuel), 2002; Level:Energy: Grid Integration Redefining What'sis Taking Over OurThe Iron Spin Transition in2, 2003Tool ofTopo II: AnPipeline.14.7 7.4.

  19. Total...................................................................

    Broader source: All U.S. Department of Energy (DOE) Office Webpages (Extended Search)

    AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE: Alternative1 First Use of Energy for All Purposes (Fuel and Nonfuel), 2002; Level:Energy: Grid Integration Redefining What'sis Taking Over OurThe Iron Spin Transition in2, 2003Tool ofTopo II: AnPipeline.14.7

  20. Total...................................................................

    Broader source: All U.S. Department of Energy (DOE) Office Webpages (Extended Search)

    AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE: Alternative1 First Use of Energy for All Purposes (Fuel and Nonfuel), 2002; Level:Energy: Grid Integration Redefining What'sis Taking Over OurThe Iron Spin Transition in2, 2003Tool ofTopo II: AnPipeline.14.72,033 1,618

  1. Total....................................................................

    Broader source: All U.S. Department of Energy (DOE) Office Webpages (Extended Search)

    AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE: Alternative1 First Use of Energy for All Purposes (Fuel and Nonfuel), 2002; Level:Energy: Grid Integration Redefining What'sis Taking Over OurThe Iron Spin Transition in2, 2003Tool ofTopo II: AnPipeline.14.72,033 1,61814.7

  2. Total.......................................................................

    Broader source: All U.S. Department of Energy (DOE) Office Webpages (Extended Search)

    AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE: Alternative1 First Use of Energy for All Purposes (Fuel and Nonfuel), 2002; Level:Energy: Grid Integration Redefining What'sis Taking Over OurThe Iron Spin Transition in2, 2003Tool ofTopo II: AnPipeline.14.72,033

  3. Total.......................................................................

    Broader source: All U.S. Department of Energy (DOE) Office Webpages (Extended Search)

    AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE: Alternative1 First Use of Energy for All Purposes (Fuel and Nonfuel), 2002; Level:Energy: Grid Integration Redefining What'sis Taking Over OurThe Iron Spin Transition in2, 2003Tool ofTopo II: AnPipeline.14.72,0335.6 17.7

  4. Total.......................................................................

    Broader source: All U.S. Department of Energy (DOE) Office Webpages (Extended Search)

    AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE: Alternative1 First Use of Energy for All Purposes (Fuel and Nonfuel), 2002; Level:Energy: Grid Integration Redefining What'sis Taking Over OurThe Iron Spin Transition in2, 2003Tool ofTopo II: AnPipeline.14.72,0335.6 17.74.2

  5. Total........................................................................

    Broader source: All U.S. Department of Energy (DOE) Office Webpages (Extended Search)

    AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE: Alternative1 First Use of Energy for All Purposes (Fuel and Nonfuel), 2002; Level:Energy: Grid Integration Redefining What'sis Taking Over OurThe Iron Spin Transition in2, 2003Tool ofTopo II: AnPipeline.14.72,0335.6

  6. Total........................................................................

    Broader source: All U.S. Department of Energy (DOE) Office Webpages (Extended Search)

    AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE: Alternative1 First Use of Energy for All Purposes (Fuel and Nonfuel), 2002; Level:Energy: Grid Integration Redefining What'sis Taking Over OurThe Iron Spin Transition in2, 2003Tool ofTopo II: AnPipeline.14.72,0335.615.1 5.5

  7. Total........................................................................

    Broader source: All U.S. Department of Energy (DOE) Office Webpages (Extended Search)

    AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE: Alternative1 First Use of Energy for All Purposes (Fuel and Nonfuel), 2002; Level:Energy: Grid Integration Redefining What'sis Taking Over OurThe Iron Spin Transition in2, 2003Tool ofTopo II: AnPipeline.14.72,0335.615.1

  8. Total........................................................................

    Broader source: All U.S. Department of Energy (DOE) Office Webpages (Extended Search)

    AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE: Alternative1 First Use of Energy for All Purposes (Fuel and Nonfuel), 2002; Level:Energy: Grid Integration Redefining What'sis Taking Over OurThe Iron Spin Transition in2, 2003Tool ofTopo II: AnPipeline.14.72,0335.615.10.7

  9. Total........................................................................

    Broader source: All U.S. Department of Energy (DOE) Office Webpages (Extended Search)

    AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE: Alternative1 First Use of Energy for All Purposes (Fuel and Nonfuel), 2002; Level:Energy: Grid Integration Redefining What'sis Taking Over OurThe Iron Spin Transition in2, 2003Tool ofTopo II:

  10. Total........................................................................

    Broader source: All U.S. Department of Energy (DOE) Office Webpages (Extended Search)

    AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE: Alternative1 First Use of Energy for All Purposes (Fuel and Nonfuel), 2002; Level:Energy: Grid Integration Redefining What'sis Taking Over OurThe Iron Spin Transition in2, 2003Tool ofTopo II:7.1 7.0 8.0 12.1 Do Not Have

  11. Total........................................................................

    Broader source: All U.S. Department of Energy (DOE) Office Webpages (Extended Search)

    AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE: Alternative1 First Use of Energy for All Purposes (Fuel and Nonfuel), 2002; Level:Energy: Grid Integration Redefining What'sis Taking Over OurThe Iron Spin Transition in2, 2003Tool ofTopo II:7.1 7.0 8.0 12.1 Do Not Have7.1

  12. Total.........................................................................

    Broader source: All U.S. Department of Energy (DOE) Office Webpages (Extended Search)

    AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE: Alternative1 First Use of Energy for All Purposes (Fuel and Nonfuel), 2002; Level:Energy: Grid Integration Redefining What'sis Taking Over OurThe Iron Spin Transition in2, 2003Tool ofTopo II:7.1 7.0 8.0 12.1 Do Not

  13. Total..........................................................................

    Broader source: All U.S. Department of Energy (DOE) Office Webpages (Extended Search)

    AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE: Alternative1 First Use of Energy for All Purposes (Fuel and Nonfuel), 2002; Level:Energy: Grid Integration Redefining What'sis Taking Over OurThe Iron Spin Transition in2, 2003Tool ofTopo II:7.1 7.0 8.0 12.1 Do Not25.6 40.7

  14. Total..........................................................................

    Broader source: All U.S. Department of Energy (DOE) Office Webpages (Extended Search)

    AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE: Alternative1 First Use of Energy for All Purposes (Fuel and Nonfuel), 2002; Level:Energy: Grid Integration Redefining What'sis Taking Over OurThe Iron Spin Transition in2, 2003Tool ofTopo II:7.1 7.0 8.0 12.1 Do Not25.6

  15. Total..........................................................................

    Broader source: All U.S. Department of Energy (DOE) Office Webpages (Extended Search)

    AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE: Alternative1 First Use of Energy for All Purposes (Fuel and Nonfuel), 2002; Level:Energy: Grid Integration Redefining What'sis Taking Over OurThe Iron Spin Transition in2, 2003Tool ofTopo II:7.1 7.0 8.0 12.1 Do Not25.65.6

  16. Total..........................................................................

    Broader source: All U.S. Department of Energy (DOE) Office Webpages (Extended Search)

    AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE: Alternative1 First Use of Energy for All Purposes (Fuel and Nonfuel), 2002; Level:Energy: Grid Integration Redefining What'sis Taking Over OurThe Iron Spin Transition in2, 2003Tool ofTopo II:7.1 7.0 8.0 12.1 Do

  17. Total..........................................................................

    Broader source: All U.S. Department of Energy (DOE) Office Webpages (Extended Search)

    AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE: Alternative1 First Use of Energy for All Purposes (Fuel and Nonfuel), 2002; Level:Energy: Grid Integration Redefining What'sis Taking Over OurThe Iron Spin Transition in2, 2003Tool ofTopo II:7.1 7.0 8.0 12.1 Do4.2 7.6 16.6

  18. Total..........................................................................

    Broader source: All U.S. Department of Energy (DOE) Office Webpages (Extended Search)

    AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE: Alternative1 First Use of Energy for All Purposes (Fuel and Nonfuel), 2002; Level:Energy: Grid Integration Redefining What'sis Taking Over OurThe Iron Spin Transition in2, 2003Tool ofTopo II:7.1 7.0 8.0 12.1 Do4.2 7.6

  19. Total..........................................................................

    Broader source: All U.S. Department of Energy (DOE) Office Webpages (Extended Search)

    AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE: Alternative1 First Use of Energy for All Purposes (Fuel and Nonfuel), 2002; Level:Energy: Grid Integration Redefining What'sis Taking Over OurThe Iron Spin Transition in2, 2003Tool ofTopo II:7.1 7.0 8.0 12.1 Do4.2 7.67.1

  20. Total...........................................................................

    Broader source: All U.S. Department of Energy (DOE) Office Webpages (Extended Search)

    AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE: Alternative1 First Use of Energy for All Purposes (Fuel and Nonfuel), 2002; Level:Energy: Grid Integration Redefining What'sis Taking Over OurThe Iron Spin Transition in2, 2003Tool ofTopo II:7.1 7.0 8.0 12.1 Do4.2 7.67.10.6

  1. Total...........................................................................

    Broader source: All U.S. Department of Energy (DOE) Office Webpages (Extended Search)

    AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE: Alternative1 First Use of Energy for All Purposes (Fuel and Nonfuel), 2002; Level:Energy: Grid Integration Redefining What'sis Taking Over OurThe Iron Spin Transition in2, 2003Tool ofTopo II:7.1 7.0 8.0 12.1 Do4.2

  2. Total...........................................................................

    Broader source: All U.S. Department of Energy (DOE) Office Webpages (Extended Search)

    AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE: Alternative1 First Use of Energy for All Purposes (Fuel and Nonfuel), 2002; Level:Energy: Grid Integration Redefining What'sis Taking Over OurThe Iron Spin Transition in2, 2003Tool ofTopo II:7.1 7.0 8.0 12.1 Do4.24.2 7.6

  3. Total.............................................................................

    Broader source: All U.S. Department of Energy (DOE) Office Webpages (Extended Search)

    AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE: Alternative1 First Use of Energy for All Purposes (Fuel and Nonfuel), 2002; Level:Energy: Grid Integration Redefining What'sis Taking Over OurThe Iron Spin Transition in2, 2003Tool ofTopo II:7.1 7.0 8.0 12.1 Do4.24.2

  4. Total.............................................................................

    Broader source: All U.S. Department of Energy (DOE) Office Webpages (Extended Search)

    AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE: Alternative1 First Use of Energy for All Purposes (Fuel and Nonfuel), 2002; Level:Energy: Grid Integration Redefining What'sis Taking Over OurThe Iron Spin Transition in2, 2003Tool ofTopo II:7.1 7.0 8.0 12.1 Do4.24.2Cooking

  5. Total.............................................................................

    Broader source: All U.S. Department of Energy (DOE) Office Webpages (Extended Search)

    AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE: Alternative1 First Use of Energy for All Purposes (Fuel and Nonfuel), 2002; Level:Energy: Grid Integration Redefining What'sis Taking Over OurThe Iron Spin Transition in2, 2003Tool ofTopo II:7.1 7.0 8.0 12.1

  6. Total.............................................................................

    Broader source: All U.S. Department of Energy (DOE) Office Webpages (Extended Search)

    AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE: Alternative1 First Use of Energy for All Purposes (Fuel and Nonfuel), 2002; Level:Energy: Grid Integration Redefining What'sis Taking Over OurThe Iron Spin Transition in2, 2003Tool ofTopo II:7.1 7.0 8.0 12.1Do Not Have

  7. Total.............................................................................

    Broader source: All U.S. Department of Energy (DOE) Office Webpages (Extended Search)

    AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE: Alternative1 First Use of Energy for All Purposes (Fuel and Nonfuel), 2002; Level:Energy: Grid Integration Redefining What'sis Taking Over OurThe Iron Spin Transition in2, 2003Tool ofTopo II:7.1 7.0 8.0 12.1Do Not HaveDo

  8. Total.............................................................................

    Broader source: All U.S. Department of Energy (DOE) Office Webpages (Extended Search)

    AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE: Alternative1 First Use of Energy for All Purposes (Fuel and Nonfuel), 2002; Level:Energy: Grid Integration Redefining What'sis Taking Over OurThe Iron Spin Transition in2, 2003Tool ofTopo II:7.1 7.0 8.0 12.1Do Not HaveDoDo

  9. Total.............................................................................

    Broader source: All U.S. Department of Energy (DOE) Office Webpages (Extended Search)

    AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE: Alternative1 First Use of Energy for All Purposes (Fuel and Nonfuel), 2002; Level:Energy: Grid Integration Redefining What'sis Taking Over OurThe Iron Spin Transition in2, 2003Tool ofTopo II:7.1 7.0 8.0 12.1Do Not

  10. Total.............................................................................

    Broader source: All U.S. Department of Energy (DOE) Office Webpages (Extended Search)

    AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE: Alternative1 First Use of Energy for All Purposes (Fuel and Nonfuel), 2002; Level:Energy: Grid Integration Redefining What'sis Taking Over OurThe Iron Spin Transition in2, 2003Tool ofTopo II:7.1 7.0 8.0 12.1Do NotDo Not

  11. Total..............................................................................

    Broader source: All U.S. Department of Energy (DOE) Office Webpages (Extended Search)

    AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE: Alternative1 First Use of Energy for All Purposes (Fuel and Nonfuel), 2002; Level:Energy: Grid Integration Redefining What'sis Taking Over OurThe Iron Spin Transition in2, 2003Tool ofTopo II:7.1 7.0 8.0 12.1Do NotDo Not

  12. Total..............................................................................

    Broader source: All U.S. Department of Energy (DOE) Office Webpages (Extended Search)

    AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE: Alternative1 First Use of Energy for All Purposes (Fuel and Nonfuel), 2002; Level:Energy: Grid Integration Redefining What'sis Taking Over OurThe Iron Spin Transition in2, 2003Tool ofTopo II:7.1 7.0 8.0 12.1Do NotDo Not20.6

  13. Total..............................................................................

    Broader source: All U.S. Department of Energy (DOE) Office Webpages (Extended Search)

    AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE: Alternative1 First Use of Energy for All Purposes (Fuel and Nonfuel), 2002; Level:Energy: Grid Integration Redefining What'sis Taking Over OurThe Iron Spin Transition in2, 2003Tool ofTopo II:7.1 7.0 8.0 12.1Do NotDo

  14. Total..............................................................................

    Broader source: All U.S. Department of Energy (DOE) Office Webpages (Extended Search)

    AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE: Alternative1 First Use of Energy for All Purposes (Fuel and Nonfuel), 2002; Level:Energy: Grid Integration Redefining What'sis Taking Over OurThe Iron Spin Transition in2, 2003Tool ofTopo II:7.1 7.0 8.0 12.1Do NotDo7.1 19.0

  15. Total.................................................................................

    Broader source: All U.S. Department of Energy (DOE) Office Webpages (Extended Search)

    AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE: Alternative1 First Use of Energy for All Purposes (Fuel and Nonfuel), 2002; Level:Energy: Grid Integration Redefining What'sis Taking Over OurThe Iron Spin Transition in2, 2003Tool ofTopo II:7.1 7.0 8.0 12.1Do NotDo7.1

  16. Total.................................................................................

    Broader source: All U.S. Department of Energy (DOE) Office Webpages (Extended Search)

    AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE: Alternative1 First Use of Energy for All Purposes (Fuel and Nonfuel), 2002; Level:Energy: Grid Integration Redefining What'sis Taking Over OurThe Iron Spin Transition in2, 2003Tool ofTopo II:7.1 7.0 8.0 12.1Do NotDo7.1...

  17. Total....................................................................................

    Broader source: All U.S. Department of Energy (DOE) Office Webpages (Extended Search)

    AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE: Alternative1 First Use of Energy for All Purposes (Fuel and Nonfuel), 2002; Level:Energy: Grid Integration Redefining What'sis Taking Over OurThe Iron Spin Transition in2, 2003Tool ofTopo II:7.1 7.0 8.0 12.1Do

  18. Total....................................................................................

    Broader source: All U.S. Department of Energy (DOE) Office Webpages (Extended Search)

    AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE: Alternative1 First Use of Energy for All Purposes (Fuel and Nonfuel), 2002; Level:Energy: Grid Integration Redefining What'sis Taking Over OurThe Iron Spin Transition in2, 2003Tool ofTopo II:7.1 7.0 8.0 12.1DoCooking

  19. Total....................................................................................

    Broader source: All U.S. Department of Energy (DOE) Office Webpages (Extended Search)

    AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE: Alternative1 First Use of Energy for All Purposes (Fuel and Nonfuel), 2002; Level:Energy: Grid Integration Redefining What'sis Taking Over OurThe Iron Spin Transition in2, 2003Tool ofTopo II:7.1 7.0 8.0 12.1DoCooking25.6

  20. Total....................................................................................

    Broader source: All U.S. Department of Energy (DOE) Office Webpages (Extended Search)

    AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE: Alternative1 First Use of Energy for All Purposes (Fuel and Nonfuel), 2002; Level:Energy: Grid Integration Redefining What'sis Taking Over OurThe Iron Spin Transition in2, 2003Tool ofTopo II:7.1 7.0 8.0 12.1DoCooking25.65.6

  1. Total....................................................................................

    Broader source: All U.S. Department of Energy (DOE) Office Webpages (Extended Search)

    AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE: Alternative1 First Use of Energy for All Purposes (Fuel and Nonfuel), 2002; Level:Energy: Grid Integration Redefining What'sis Taking Over OurThe Iron Spin Transition in2, 2003Tool ofTopo II:7.1 7.0 8.0

  2. Total....................................................................................

    Broader source: All U.S. Department of Energy (DOE) Office Webpages (Extended Search)

    AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE: Alternative1 First Use of Energy for All Purposes (Fuel and Nonfuel), 2002; Level:Energy: Grid Integration Redefining What'sis Taking Over OurThe Iron Spin Transition in2, 2003Tool ofTopo II:7.1 7.0 8.04.2 7.6 16.6 Personal

  3. Total....................................................................................

    Broader source: All U.S. Department of Energy (DOE) Office Webpages (Extended Search)

    AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE: Alternative1 First Use of Energy for All Purposes (Fuel and Nonfuel), 2002; Level:Energy: Grid Integration Redefining What'sis Taking Over OurThe Iron Spin Transition in2, 2003Tool ofTopo II:7.1 7.0 8.04.2 7.6 16.6 Personal

  4. Total.........................................................................................

    Broader source: All U.S. Department of Energy (DOE) Office Webpages (Extended Search)

    AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE: Alternative1 First Use of Energy for All Purposes (Fuel and Nonfuel), 2002; Level:Energy: Grid Integration Redefining What'sis Taking Over OurThe Iron Spin Transition in2, 2003Tool ofTopo II:7.1 7.0 8.04.2 7.6 16.6

  5. Total

    U.S. Energy Information Administration (EIA) Indexed Site

    AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE: Alternative1 First Use of Energy for All Purposes (Fuel and Nonfuel), 2002; Level: National5 Tables July 1996 Energy Information Administration Office ofthroughYear JanYear Jan Feb Mar Apr May(MillionFeet)July 23,

  6. Total

    U.S. Energy Information Administration (EIA) Indexed Site

    AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE: Alternative1 First Use of Energy for All Purposes (Fuel and Nonfuel), 2002; Level: National5 Tables July 1996 Energy Information Administration Office ofthroughYear JanYear Jan Feb Mar Apr May(MillionFeet)July 23,Product:

  7. Total..............................................

    Annual Energy Outlook 2013 [U.S. Energy Information Administration (EIA)]

    AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE: Alternative1 First Use of Energy for All Purposes (Fuel and Nonfuel), 2002; Level: National5Sales for4,645 3,625 1,006 492 742 33 111 1,613 122 40 Buildingto17questionnairesU.S. Weekly70516,2,730,770 111.1 86.6 2,720 1,970

  8. Total................................................

    Annual Energy Outlook 2013 [U.S. Energy Information Administration (EIA)]

    AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE: Alternative1 First Use of Energy for All Purposes (Fuel and Nonfuel), 2002; Level: National5Sales for4,645 3,625 1,006 492 742 33 111 1,613 122 40 Buildingto17questionnairesU.S. Weekly70516,2,730,770 111.1 86.6 2,720

  9. Total........................................................

    Annual Energy Outlook 2013 [U.S. Energy Information Administration (EIA)]

    AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE: Alternative1 First Use of Energy for All Purposes (Fuel and Nonfuel), 2002; Level: National5Sales for4,645 3,625 1,006 492 742 33 111 1,613 122 40 Buildingto17questionnairesU.S. Weekly70516,2,730,770 111.1 86.6 2,720 111.1

  10. Total..........................................................

    Annual Energy Outlook 2013 [U.S. Energy Information Administration (EIA)]

    AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE: Alternative1 First Use of Energy for All Purposes (Fuel and Nonfuel), 2002; Level: National5Sales for4,645 3,625 1,006 492 742 33 111 1,613 122 40 Buildingto17questionnairesU.S. Weekly70516,2,730,770 111.1 86.6 2,720

  11. Total...........................................................

    Annual Energy Outlook 2013 [U.S. Energy Information Administration (EIA)]

    AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE: Alternative1 First Use of Energy for All Purposes (Fuel and Nonfuel), 2002; Level: National5Sales for4,645 3,625 1,006 492 742 33 111 1,613 122 40 Buildingto17questionnairesU.S. Weekly70516,2,730,770 111.1 86.6 2,720Q Table

  12. Total...........................................................

    Annual Energy Outlook 2013 [U.S. Energy Information Administration (EIA)]

    AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE: Alternative1 First Use of Energy for All Purposes (Fuel and Nonfuel), 2002; Level: National5Sales for4,645 3,625 1,006 492 742 33 111 1,613 122 40 Buildingto17questionnairesU.S. Weekly70516,2,730,770 111.1 86.6 2,720Q

  13. Total...........................................................

    Annual Energy Outlook 2013 [U.S. Energy Information Administration (EIA)]

    AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE: Alternative1 First Use of Energy for All Purposes (Fuel and Nonfuel), 2002; Level: National5Sales for4,645 3,625 1,006 492 742 33 111 1,613 122 40 Buildingto17questionnairesU.S. Weekly70516,2,730,770 111.1 86.6 2,720Q14.7

  14. Total...........................................................

    Annual Energy Outlook 2013 [U.S. Energy Information Administration (EIA)]

    AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE: Alternative1 First Use of Energy for All Purposes (Fuel and Nonfuel), 2002; Level: National5Sales for4,645 3,625 1,006 492 742 33 111 1,613 122 40 Buildingto17questionnairesU.S. Weekly70516,2,730,770 111.1 86.6

  15. Total............................................................

    Annual Energy Outlook 2013 [U.S. Energy Information Administration (EIA)]

    AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE: Alternative1 First Use of Energy for All Purposes (Fuel and Nonfuel), 2002; Level: National5Sales for4,645 3,625 1,006 492 742 33 111 1,613 122 40 Buildingto17questionnairesU.S. Weekly70516,2,730,770 111.1

  16. Total............................................................

    Annual Energy Outlook 2013 [U.S. Energy Information Administration (EIA)]

    AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE: Alternative1 First Use of Energy for All Purposes (Fuel and Nonfuel), 2002; Level: National5Sales for4,645 3,625 1,006 492 742 33 111 1,613 122 40 Buildingto17questionnairesU.S. Weekly70516,2,730,770 111.1

  17. Total.............................................................

    Annual Energy Outlook 2013 [U.S. Energy Information Administration (EIA)]

    AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE: Alternative1 First Use of Energy for All Purposes (Fuel and Nonfuel), 2002; Level: National5Sales for4,645 3,625 1,006 492 742 33 111 1,613 122 40 Buildingto17questionnairesU.S. Weekly70516,2,730,770 111.126.7 28.8 20.6

  18. Total..............................................................

    Annual Energy Outlook 2013 [U.S. Energy Information Administration (EIA)]

    AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE: Alternative1 First Use of Energy for All Purposes (Fuel and Nonfuel), 2002; Level: National5Sales for4,645 3,625 1,006 492 742 33 111 1,613 122 40 Buildingto17questionnairesU.S. Weekly70516,2,730,770 111.126.7 28.8 20.6,171

  19. Total..............................................................

    Annual Energy Outlook 2013 [U.S. Energy Information Administration (EIA)]

    AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE: Alternative1 First Use of Energy for All Purposes (Fuel and Nonfuel), 2002; Level: National5Sales for4,645 3,625 1,006 492 742 33 111 1,613 122 40 Buildingto17questionnairesU.S. Weekly70516,2,730,770 111.126.7 28.8

  20. Total...............................................................

    Annual Energy Outlook 2013 [U.S. Energy Information Administration (EIA)]

    AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE: Alternative1 First Use of Energy for All Purposes (Fuel and Nonfuel), 2002; Level: National5Sales for4,645 3,625 1,006 492 742 33 111 1,613 122 40 Buildingto17questionnairesU.S. Weekly70516,2,730,770 111.126.7 28.820.6 25.6

  1. Total...............................................................

    Annual Energy Outlook 2013 [U.S. Energy Information Administration (EIA)]

    AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE: Alternative1 First Use of Energy for All Purposes (Fuel and Nonfuel), 2002; Level: National5Sales for4,645 3,625 1,006 492 742 33 111 1,613 122 40 Buildingto17questionnairesU.S. Weekly70516,2,730,770 111.126.7 28.820.6

  2. Total...............................................................

    Annual Energy Outlook 2013 [U.S. Energy Information Administration (EIA)]

    AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE: Alternative1 First Use of Energy for All Purposes (Fuel and Nonfuel), 2002; Level: National5Sales for4,645 3,625 1,006 492 742 33 111 1,613 122 40 Buildingto17questionnairesU.S. Weekly70516,2,730,770 111.126.7 28.820.626.7

  3. Total...............................................................

    Annual Energy Outlook 2013 [U.S. Energy Information Administration (EIA)]

    AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE: Alternative1 First Use of Energy for All Purposes (Fuel and Nonfuel), 2002; Level: National5Sales for4,645 3,625 1,006 492 742 33 111 1,613 122 40 Buildingto17questionnairesU.S. Weekly70516,2,730,770 111.126.7

  4. Total...............................................................

    Annual Energy Outlook 2013 [U.S. Energy Information Administration (EIA)]

    AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE: Alternative1 First Use of Energy for All Purposes (Fuel and Nonfuel), 2002; Level: National5Sales for4,645 3,625 1,006 492 742 33 111 1,613 122 40 Buildingto17questionnairesU.S. Weekly70516,2,730,770 111.126.747.1 19.0 22.7

  5. Total................................................................

    Annual Energy Outlook 2013 [U.S. Energy Information Administration (EIA)]

    AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE: Alternative1 First Use of Energy for All Purposes (Fuel and Nonfuel), 2002; Level: National5Sales for4,645 3,625 1,006 492 742 33 111 1,613 122 40 Buildingto17questionnairesU.S. Weekly70516,2,730,770 111.126.747.1 19.0 22.7

  6. Total.................................................................

    Annual Energy Outlook 2013 [U.S. Energy Information Administration (EIA)]

    AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE: Alternative1 First Use of Energy for All Purposes (Fuel and Nonfuel), 2002; Level: National5Sales for4,645 3,625 1,006 492 742 33 111 1,613 122 40 Buildingto17questionnairesU.S. Weekly70516,2,730,770 111.126.747.1 19.0

  7. Total.................................................................

    Annual Energy Outlook 2013 [U.S. Energy Information Administration (EIA)]

    AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE: Alternative1 First Use of Energy for All Purposes (Fuel and Nonfuel), 2002; Level: National5Sales for4,645 3,625 1,006 492 742 33 111 1,613 122 40 Buildingto17questionnairesU.S. Weekly70516,2,730,770 111.126.747.1 19.014.7

  8. Total.................................................................

    Annual Energy Outlook 2013 [U.S. Energy Information Administration (EIA)]

    AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE: Alternative1 First Use of Energy for All Purposes (Fuel and Nonfuel), 2002; Level: National5Sales for4,645 3,625 1,006 492 742 33 111 1,613 122 40 Buildingto17questionnairesU.S. Weekly70516,2,730,770 111.126.747.1

  9. Total..................................................................

    Annual Energy Outlook 2013 [U.S. Energy Information Administration (EIA)]

    AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE: Alternative1 First Use of Energy for All Purposes (Fuel and Nonfuel), 2002; Level: National5Sales for4,645 3,625 1,006 492 742 33 111 1,613 122 40 Buildingto17questionnairesU.S. Weekly70516,2,730,770 111.126.747.178.1 64.1

  10. Total..................................................................

    Annual Energy Outlook 2013 [U.S. Energy Information Administration (EIA)]

    AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE: Alternative1 First Use of Energy for All Purposes (Fuel and Nonfuel), 2002; Level: National5Sales for4,645 3,625 1,006 492 742 33 111 1,613 122 40 Buildingto17questionnairesU.S. Weekly70516,2,730,770 111.126.747.178.1

  11. Total..................................................................

    Annual Energy Outlook 2013 [U.S. Energy Information Administration (EIA)]

    AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE: Alternative1 First Use of Energy for All Purposes (Fuel and Nonfuel), 2002; Level: National5Sales for4,645 3,625 1,006 492 742 33 111 1,613 122 40 Buildingto17questionnairesU.S. Weekly70516,2,730,770 111.126.747.178.1.

  12. Total...................................................................

    Annual Energy Outlook 2013 [U.S. Energy Information Administration (EIA)]

    AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE: Alternative1 First Use of Energy for All Purposes (Fuel and Nonfuel), 2002; Level: National5Sales for4,645 3,625 1,006 492 742 33 111 1,613 122 40 Buildingto17questionnairesU.S. Weekly70516,2,730,770

  13. Total...................................................................

    Annual Energy Outlook 2013 [U.S. Energy Information Administration (EIA)]

    AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE: Alternative1 First Use of Energy for All Purposes (Fuel and Nonfuel), 2002; Level: National5Sales for4,645 3,625 1,006 492 742 33 111 1,613 122 40 Buildingto17questionnairesU.S. Weekly70516,2,730,77015.2 7.8 1.0 1.2 3.3 1.9

  14. Total...................................................................

    Annual Energy Outlook 2013 [U.S. Energy Information Administration (EIA)]

    AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE: Alternative1 First Use of Energy for All Purposes (Fuel and Nonfuel), 2002; Level: National5Sales for4,645 3,625 1,006 492 742 33 111 1,613 122 40 Buildingto17questionnairesU.S. Weekly70516,2,730,77015.2 7.8 1.0 1.2 3.3

  15. Total...................................................................

    Annual Energy Outlook 2013 [U.S. Energy Information Administration (EIA)]

    AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE: Alternative1 First Use of Energy for All Purposes (Fuel and Nonfuel), 2002; Level: National5Sales for4,645 3,625 1,006 492 742 33 111 1,613 122 40 Buildingto17questionnairesU.S. Weekly70516,2,730,77015.2 7.8 1.0 1.2 3.3Type

  16. Total...................................................................

    Annual Energy Outlook 2013 [U.S. Energy Information Administration (EIA)]

    AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE: Alternative1 First Use of Energy for All Purposes (Fuel and Nonfuel), 2002; Level: National5Sales for4,645 3,625 1,006 492 742 33 111 1,613 122 40 Buildingto17questionnairesU.S. Weekly70516,2,730,77015.2 7.8 1.0 1.2

  17. Total....................................................................

    Annual Energy Outlook 2013 [U.S. Energy Information Administration (EIA)]

    AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE: Alternative1 First Use of Energy for All Purposes (Fuel and Nonfuel), 2002; Level: National5Sales for4,645 3,625 1,006 492 742 33 111 1,613 122 40 Buildingto17questionnairesU.S. Weekly70516,2,730,77015.2 7.8 1.0 1.214.7 7.4

  18. Total.......................................................................

    Annual Energy Outlook 2013 [U.S. Energy Information Administration (EIA)]

    AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE: Alternative1 First Use of Energy for All Purposes (Fuel and Nonfuel), 2002; Level: National5Sales for4,645 3,625 1,006 492 742 33 111 1,613 122 40 Buildingto17questionnairesU.S. Weekly70516,2,730,77015.2 7.8 1.0 1.214.7

  19. Total.......................................................................

    Annual Energy Outlook 2013 [U.S. Energy Information Administration (EIA)]

    AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE: Alternative1 First Use of Energy for All Purposes (Fuel and Nonfuel), 2002; Level: National5Sales for4,645 3,625 1,006 492 742 33 111 1,613 122 40 Buildingto17questionnairesU.S. Weekly70516,2,730,77015.2 7.8 1.0 1.214.75.6

  20. Total.......................................................................

    Annual Energy Outlook 2013 [U.S. Energy Information Administration (EIA)]

    AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE: Alternative1 First Use of Energy for All Purposes (Fuel and Nonfuel), 2002; Level: National5Sales for4,645 3,625 1,006 492 742 33 111 1,613 122 40 Buildingto17questionnairesU.S. Weekly70516,2,730,77015.2 7.8 1.0

  1. Total........................................................................

    Annual Energy Outlook 2013 [U.S. Energy Information Administration (EIA)]

    AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE: Alternative1 First Use of Energy for All Purposes (Fuel and Nonfuel), 2002; Level: National5Sales for4,645 3,625 1,006 492 742 33 111 1,613 122 40 Buildingto17questionnairesU.S. Weekly70516,2,730,77015.2 7.8 1.025.6 40.7

  2. Total........................................................................

    Annual Energy Outlook 2013 [U.S. Energy Information Administration (EIA)]

    AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE: Alternative1 First Use of Energy for All Purposes (Fuel and Nonfuel), 2002; Level: National5Sales for4,645 3,625 1,006 492 742 33 111 1,613 122 40 Buildingto17questionnairesU.S. Weekly70516,2,730,77015.2 7.8 1.025.6

  3. Total........................................................................

    Annual Energy Outlook 2013 [U.S. Energy Information Administration (EIA)]

    AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE: Alternative1 First Use of Energy for All Purposes (Fuel and Nonfuel), 2002; Level: National5Sales for4,645 3,625 1,006 492 742 33 111 1,613 122 40 Buildingto17questionnairesU.S. Weekly70516,2,730,77015.2 7.8 1.025.65.6 17.7

  4. Total........................................................................

    Annual Energy Outlook 2013 [U.S. Energy Information Administration (EIA)]

    AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE: Alternative1 First Use of Energy for All Purposes (Fuel and Nonfuel), 2002; Level: National5Sales for4,645 3,625 1,006 492 742 33 111 1,613 122 40 Buildingto17questionnairesU.S. Weekly70516,2,730,77015.2 7.8 1.025.65.6

  5. Total........................................................................

    Annual Energy Outlook 2013 [U.S. Energy Information Administration (EIA)]

    AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE: Alternative1 First Use of Energy for All Purposes (Fuel and Nonfuel), 2002; Level: National5Sales for4,645 3,625 1,006 492 742 33 111 1,613 122 40 Buildingto17questionnairesU.S. Weekly70516,2,730,77015.2 7.8 1.025.65.64.2

  6. Total........................................................................

    Annual Energy Outlook 2013 [U.S. Energy Information Administration (EIA)]

    AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE: Alternative1 First Use of Energy for All Purposes (Fuel and Nonfuel), 2002; Level: National5Sales for4,645 3,625 1,006 492 742 33 111 1,613 122 40 Buildingto17questionnairesU.S. Weekly70516,2,730,77015.2 7.8

  7. Total........................................................................

    Annual Energy Outlook 2013 [U.S. Energy Information Administration (EIA)]

    AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE: Alternative1 First Use of Energy for All Purposes (Fuel and Nonfuel), 2002; Level: National5Sales for4,645 3,625 1,006 492 742 33 111 1,613 122 40 Buildingto17questionnairesU.S. Weekly70516,2,730,77015.2 7.87.1 19.0 22.7

  8. Total.........................................................................

    Annual Energy Outlook 2013 [U.S. Energy Information Administration (EIA)]

    AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE: Alternative1 First Use of Energy for All Purposes (Fuel and Nonfuel), 2002; Level: National5Sales for4,645 3,625 1,006 492 742 33 111 1,613 122 40 Buildingto17questionnairesU.S. Weekly70516,2,730,77015.2 7.87.1 19.0

  9. Total..........................................................................

    Annual Energy Outlook 2013 [U.S. Energy Information Administration (EIA)]

    AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE: Alternative1 First Use of Energy for All Purposes (Fuel and Nonfuel), 2002; Level: National5Sales for4,645 3,625 1,006 492 742 33 111 1,613 122 40 Buildingto17questionnairesU.S. Weekly70516,2,730,77015.2 7.87.1 19.025.6

  10. Total..........................................................................

    Annual Energy Outlook 2013 [U.S. Energy Information Administration (EIA)]

    AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE: Alternative1 First Use of Energy for All Purposes (Fuel and Nonfuel), 2002; Level: National5Sales for4,645 3,625 1,006 492 742 33 111 1,613 122 40 Buildingto17questionnairesU.S. Weekly70516,2,730,77015.2 7.87.1 19.025.6.

  11. Total..........................................................................

    Annual Energy Outlook 2013 [U.S. Energy Information Administration (EIA)]

    AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE: Alternative1 First Use of Energy for All Purposes (Fuel and Nonfuel), 2002; Level: National5Sales for4,645 3,625 1,006 492 742 33 111 1,613 122 40 Buildingto17questionnairesU.S. Weekly70516,2,730,77015.2 7.87.1 19.025.6.5.6

  12. Total..........................................................................

    Annual Energy Outlook 2013 [U.S. Energy Information Administration (EIA)]

    AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE: Alternative1 First Use of Energy for All Purposes (Fuel and Nonfuel), 2002; Level: National5Sales for4,645 3,625 1,006 492 742 33 111 1,613 122 40 Buildingto17questionnairesU.S. Weekly70516,2,730,77015.2 7.87.1

  13. Total..........................................................................

    Annual Energy Outlook 2013 [U.S. Energy Information Administration (EIA)]

    AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE: Alternative1 First Use of Energy for All Purposes (Fuel and Nonfuel), 2002; Level: National5Sales for4,645 3,625 1,006 492 742 33 111 1,613 122 40 Buildingto17questionnairesU.S. Weekly70516,2,730,77015.2 7.87.14.2 7.6 16.6

  14. Total..........................................................................

    Annual Energy Outlook 2013 [U.S. Energy Information Administration (EIA)]

    AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE: Alternative1 First Use of Energy for All Purposes (Fuel and Nonfuel), 2002; Level: National5Sales for4,645 3,625 1,006 492 742 33 111 1,613 122 40 Buildingto17questionnairesU.S. Weekly70516,2,730,77015.2 7.87.14.2 7.6

  15. Total..........................................................................

    Annual Energy Outlook 2013 [U.S. Energy Information Administration (EIA)]

    AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE: Alternative1 First Use of Energy for All Purposes (Fuel and Nonfuel), 2002; Level: National5Sales for4,645 3,625 1,006 492 742 33 111 1,613 122 40 Buildingto17questionnairesU.S. Weekly70516,2,730,77015.2 7.87.14.2 7.67.1

  16. Total...........................................................................

    Annual Energy Outlook 2013 [U.S. Energy Information Administration (EIA)]

    AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE: Alternative1 First Use of Energy for All Purposes (Fuel and Nonfuel), 2002; Level: National5Sales for4,645 3,625 1,006 492 742 33 111 1,613 122 40 Buildingto17questionnairesU.S. Weekly70516,2,730,77015.2 7.87.14.2 7.67.10.6

  17. Total...........................................................................

    Annual Energy Outlook 2013 [U.S. Energy Information Administration (EIA)]

    AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE: Alternative1 First Use of Energy for All Purposes (Fuel and Nonfuel), 2002; Level: National5Sales for4,645 3,625 1,006 492 742 33 111 1,613 122 40 Buildingto17questionnairesU.S. Weekly70516,2,730,77015.2 7.87.14.2

  18. Total...........................................................................

    Annual Energy Outlook 2013 [U.S. Energy Information Administration (EIA)]

    AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE: Alternative1 First Use of Energy for All Purposes (Fuel and Nonfuel), 2002; Level: National5Sales for4,645 3,625 1,006 492 742 33 111 1,613 122 40 Buildingto17questionnairesU.S. Weekly70516,2,730,77015.2 7.87.14.24.2 7.6

  19. Total.............................................................................

    Annual Energy Outlook 2013 [U.S. Energy Information Administration (EIA)]

    AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE: Alternative1 First Use of Energy for All Purposes (Fuel and Nonfuel), 2002; Level: National5Sales for4,645 3,625 1,006 492 742 33 111 1,613 122 40 Buildingto17questionnairesU.S. Weekly70516,2,730,77015.2 7.87.14.24.2 7.6Do

  20. Total.............................................................................

    Annual Energy Outlook 2013 [U.S. Energy Information Administration (EIA)]

    AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE: Alternative1 First Use of Energy for All Purposes (Fuel and Nonfuel), 2002; Level: National5Sales for4,645 3,625 1,006 492 742 33 111 1,613 122 40 Buildingto17questionnairesU.S. Weekly70516,2,730,77015.2 7.87.14.24.2

  1. Total.............................................................................

    Annual Energy Outlook 2013 [U.S. Energy Information Administration (EIA)]

    AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE: Alternative1 First Use of Energy for All Purposes (Fuel and Nonfuel), 2002; Level: National5Sales for4,645 3,625 1,006 492 742 33 111 1,613 122 40 Buildingto17questionnairesU.S. Weekly70516,2,730,77015.2 7.87.14.24.2Cooking

  2. Total.............................................................................

    Annual Energy Outlook 2013 [U.S. Energy Information Administration (EIA)]

    AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE: Alternative1 First Use of Energy for All Purposes (Fuel and Nonfuel), 2002; Level: National5Sales for4,645 3,625 1,006 492 742 33 111 1,613 122 40 Buildingto17questionnairesU.S. Weekly70516,2,730,77015.2

  3. Total.............................................................................

    Annual Energy Outlook 2013 [U.S. Energy Information Administration (EIA)]

    AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE: Alternative1 First Use of Energy for All Purposes (Fuel and Nonfuel), 2002; Level: National5Sales for4,645 3,625 1,006 492 742 33 111 1,613 122 40 Buildingto17questionnairesU.S. Weekly70516,2,730,77015.2Do Not Have Cooling

  4. Total.............................................................................

    Annual Energy Outlook 2013 [U.S. Energy Information Administration (EIA)]

    AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE: Alternative1 First Use of Energy for All Purposes (Fuel and Nonfuel), 2002; Level: National5Sales for4,645 3,625 1,006 492 742 33 111 1,613 122 40 Buildingto17questionnairesU.S. Weekly70516,2,730,77015.2Do Not Have

  5. Total.............................................................................

    Annual Energy Outlook 2013 [U.S. Energy Information Administration (EIA)]

    AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE: Alternative1 First Use of Energy for All Purposes (Fuel and Nonfuel), 2002; Level: National5Sales for4,645 3,625 1,006 492 742 33 111 1,613 122 40 Buildingto17questionnairesU.S. Weekly70516,2,730,77015.2Do Not HaveDo Not

  6. Total.............................................................................

    Annual Energy Outlook 2013 [U.S. Energy Information Administration (EIA)]

    AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE: Alternative1 First Use of Energy for All Purposes (Fuel and Nonfuel), 2002; Level: National5Sales for4,645 3,625 1,006 492 742 33 111 1,613 122 40 Buildingto17questionnairesU.S. Weekly70516,2,730,77015.2Do Not HaveDo NotDo

  7. Total..............................................................................

    Annual Energy Outlook 2013 [U.S. Energy Information Administration (EIA)]

    AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE: Alternative1 First Use of Energy for All Purposes (Fuel and Nonfuel), 2002; Level: National5Sales for4,645 3,625 1,006 492 742 33 111 1,613 122 40 Buildingto17questionnairesU.S. Weekly70516,2,730,77015.2Do Not HaveDo

  8. Total..............................................................................

    Annual Energy Outlook 2013 [U.S. Energy Information Administration (EIA)]

    AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE: Alternative1 First Use of Energy for All Purposes (Fuel and Nonfuel), 2002; Level: National5Sales for4,645 3,625 1,006 492 742 33 111 1,613 122 40 Buildingto17questionnairesU.S. Weekly70516,2,730,77015.2Do Not HaveDo0.7

  9. Total..............................................................................

    Annual Energy Outlook 2013 [U.S. Energy Information Administration (EIA)]

    AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE: Alternative1 First Use of Energy for All Purposes (Fuel and Nonfuel), 2002; Level: National5Sales for4,645 3,625 1,006 492 742 33 111 1,613 122 40 Buildingto17questionnairesU.S. Weekly70516,2,730,77015.2Do Not HaveDo0.7

  10. Total..............................................................................

    Annual Energy Outlook 2013 [U.S. Energy Information Administration (EIA)]

    AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE: Alternative1 First Use of Energy for All Purposes (Fuel and Nonfuel), 2002; Level: National5Sales for4,645 3,625 1,006 492 742 33 111 1,613 122 40 Buildingto17questionnairesU.S. Weekly70516,2,730,77015.2Do Not HaveDo0.77.1

  11. Total.................................................................................

    Annual Energy Outlook 2013 [U.S. Energy Information Administration (EIA)]

    AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE: Alternative1 First Use of Energy for All Purposes (Fuel and Nonfuel), 2002; Level: National5Sales for4,645 3,625 1,006 492 742 33 111 1,613 122 40 Buildingto17questionnairesU.S. Weekly70516,2,730,77015.2Do Not

  12. Total.................................................................................

    Annual Energy Outlook 2013 [U.S. Energy Information Administration (EIA)]

    AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE: Alternative1 First Use of Energy for All Purposes (Fuel and Nonfuel), 2002; Level: National5Sales for4,645 3,625 1,006 492 742 33 111 1,613 122 40 Buildingto17questionnairesU.S. Weekly70516,2,730,77015.2Do Not7.1 7.0 8.0

  13. Total....................................................................................

    Annual Energy Outlook 2013 [U.S. Energy Information Administration (EIA)]

    AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE: Alternative1 First Use of Energy for All Purposes (Fuel and Nonfuel), 2002; Level: National5Sales for4,645 3,625 1,006 492 742 33 111 1,613 122 40 Buildingto17questionnairesU.S. Weekly70516,2,730,77015.2Do Not7.1 7.0

  14. Total....................................................................................

    Annual Energy Outlook 2013 [U.S. Energy Information Administration (EIA)]

    AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE: Alternative1 First Use of Energy for All Purposes (Fuel and Nonfuel), 2002; Level: National5Sales for4,645 3,625 1,006 492 742 33 111 1,613 122 40 Buildingto17questionnairesU.S. Weekly70516,2,730,77015.2Do Not7.1 7.05.6

  15. Total....................................................................................

    Annual Energy Outlook 2013 [U.S. Energy Information Administration (EIA)]

    AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE: Alternative1 First Use of Energy for All Purposes (Fuel and Nonfuel), 2002; Level: National5Sales for4,645 3,625 1,006 492 742 33 111 1,613 122 40 Buildingto17questionnairesU.S. Weekly70516,2,730,77015.2Do Not7.1

  16. Total....................................................................................

    Annual Energy Outlook 2013 [U.S. Energy Information Administration (EIA)]

    AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE: Alternative1 First Use of Energy for All Purposes (Fuel and Nonfuel), 2002; Level: National5Sales for4,645 3,625 1,006 492 742 33 111 1,613 122 40 Buildingto17questionnairesU.S. Weekly70516,2,730,77015.2Do Not7.1Personal

  17. Total....................................................................................

    Annual Energy Outlook 2013 [U.S. Energy Information Administration (EIA)]

    AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE: Alternative1 First Use of Energy for All Purposes (Fuel and Nonfuel), 2002; Level: National5Sales for4,645 3,625 1,006 492 742 33 111 1,613 122 40 Buildingto17questionnairesU.S. Weekly70516,2,730,77015.2Do Not7.1Personal4.2

  18. Total....................................................................................

    Annual Energy Outlook 2013 [U.S. Energy Information Administration (EIA)]

    AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE: Alternative1 First Use of Energy for All Purposes (Fuel and Nonfuel), 2002; Level: National5Sales for4,645 3,625 1,006 492 742 33 111 1,613 122 40 Buildingto17questionnairesU.S. Weekly70516,2,730,77015.2Do

  19. Total....................................................................................

    Annual Energy Outlook 2013 [U.S. Energy Information Administration (EIA)]

    AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE: Alternative1 First Use of Energy for All Purposes (Fuel and Nonfuel), 2002; Level: National5Sales for4,645 3,625 1,006 492 742 33 111 1,613 122 40 Buildingto17questionnairesU.S. Weekly70516,2,730,77015.2Do 111.1 47.1 19.0

  20. Total.........................................................................................

    Annual Energy Outlook 2013 [U.S. Energy Information Administration (EIA)]

    AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE: Alternative1 First Use of Energy for All Purposes (Fuel and Nonfuel), 2002; Level: National5Sales for4,645 3,625 1,006 492 742 33 111 1,613 122 40 Buildingto17questionnairesU.S. Weekly70516,2,730,77015.2Do 111.1 47.1

  1. Determinating Timing Channels in Statistically Multiplexed Clouds

    E-Print Network [OSTI]

    Aviram, Amittai; Ford, Bryan; Gummadi, Ramakrishna

    2010-01-01T23:59:59.000Z

    Timing side-channels represent an insidious security challenge for cloud computing, because: (a) they enable one customer to steal information from another without leaving a trail or raising alarms; (b) only the cloud provider can feasibly detect and report such attacks, but the provider's incentives are not to; and (c) known general-purpose timing channel control methods undermine statistical resource sharing efficiency, and, with it, the cloud computing business model. We propose a new cloud architecture that uses provider-enforced deterministic execution to eliminate all timing channels internal to a shared cloud domain, without limiting internal resource sharing. A prototype determinism-enforcing hypervisor demonstrates that utilizing such a cloud might be both convenient and efficient. The hypervisor enables parallel guest processes and threads to interact via familiar shared memory and file system abstractions, and runs moderately coarse-grained parallel tasks as efficiently and scalably as current nond...

  2. Free-Floating HI Clouds in the M 81 Group

    E-Print Network [OSTI]

    Elias Brinks; Fabian Walter; Evan D. Skillman

    2007-08-21T23:59:59.000Z

    Recent VLA observations pointed at dwarf spheroidal (dSph) galaxies in the M 81 group reveal a hitherto hidden population of extremely low mass (~1e5 Msol) HI clouds with no obvious optical counterparts. We have searched 10 fields in the M81 group totalling 2.2 square degree, both targeting known dwarf spheroidal galaxies and blank fields around the central triplet. Our observations show that the new population of low-mass HI clouds appears to be confined to a region toward the South-East of the central triplet (at distances of ~100 kpc from M 81). Possible explanations for these free-floating HI clouds are that they are related to the dSphs found to the South-East of M 81, that they belong to the galaxies of the M 81 triplet (equivalent to HVCs), that they are of primordial nature and provide fresh, unenriched material falling into the M 81 group, or that they are tidal debris from the 3-body interaction involving M 81-M 82-NGC 3077. Based on circumstantial evidence, we currently favour the latter explanation.

  3. Analysis of cloud layer structure in Shouxian, China using RS92 radiosonde aided by 95 GHz cloud radar

    E-Print Network [OSTI]

    Li, Zhanqing

    Analysis of cloud layer structure in Shouxian, China using RS92 radiosonde aided by 95 GHz cloud to analyze cloud vertical structure over this area by taking advantage of the first direct measurements of cloud vertical layers from the 95 GHz radar. Singlelayer, twolayer, and threelayer clouds account for 28

  4. In Proceedings of APSEC 2010 Cloud Workshop, Sydney, Australia, 30th An Analysis of The Cloud Computing Security Problem

    E-Print Network [OSTI]

    Grundy, John

    of The Cloud Computing Security Problem Mohamed Al Morsy, John Grundy and Ingo Müller Computer Science to adopt IT without upfront investment. Despite the potential gains achieved from the cloud computing solution. Keywords: cloud computing; cloud computing security; cloud computing security management. I

  5. April 12, 2014: The Era of Cloud Computing is coming Headline: The Era of Cloud Computing is coming

    E-Print Network [OSTI]

    Buyya, Rajkumar

    April 12, 2014: The Era of Cloud Computing is coming #12;Headline: The Era of Cloud Computing of Cloud Computing at a seminar in MANIT and RGPV on Saturday. Inset headline: This is the right time to build a career in Cloud Computing Article: Prof. Rajkumar Buyya gave guidance to students about Cloud

  6. After the definition of Cloud Computing ... What has NIST done in the Cloud space lately? What's next?

    E-Print Network [OSTI]

    After the definition of Cloud Computing ... What has NIST done in the Cloud space lately? What Publication SP 500-292: Cloud Computing Reference Architecture. This document takes the NIST definition of Cloud Computing a step further by expanding the definition into a logical representation of the cloud

  7. Generated using version 3.0 of the official AMS LATEX template Computing and Partitioning Cloud Feedbacks using Cloud1

    E-Print Network [OSTI]

    Hartmann, Dennis

    by adjusting the change in cloud radiative forcing for non-cloud22 related effects as in Soden et al. (2008 planet, the global and annual mean effect40 of clouds at the top of atmosphere (TOA) is to increase Feedbacks using Cloud1 Property Histograms.2 Part I: Cloud Radiative Kernels3 Mark D. Zelinka Department

  8. Influence of Cloud-Top Height and Geometric Thickness on a MODIS Infrared-Based Ice Cloud Retrieval

    E-Print Network [OSTI]

    Baum, Bryan A.

    of the net cloud radiative forc- ing of these clouds requires a global, diurnal climatology, which can most and temporal scales. In this study, the sensitivity of an infrared-based ice cloud retrieval to effective cloud temperature is investigated, with a focus on the effects of cloud-top height and geometric thickness

  9. Alternative Landfill Cover. Innovative Technology Summary Report

    SciTech Connect (OSTI)

    NONE

    2000-12-01T23:59:59.000Z

    The primary purpose of an engineered cover is to isolate the underlying waste. A key element to isolating the wastes from the environment, engineered covers should minimize or prevent water from infiltrating into the landfill and coming into contact with the waste, thereby minimizing leachate generation. The U.S. EPA construction guidelines for soil hydraulic barriers specify that the soil moisture content and compactive effort may be increased to ensure that the barrier achieves a specified permeability of 1 x 10{sup {minus}7} cm/sec. However, constructing a soil barrier with high moisture content makes the soil more difficult to work and increases the required compactive effort to achieve the specified density, ultimately increasing the construction cost of the barrier. Alternative landfill cover designs rely on soil physical properties, hydraulic characteristics, and vegetation requirements to lower the flux rate of water through the cover. They can achieve greater reliability than the prescriptive RCRA Subtitle C design, especially under arid or semi-arid environmental conditions. With an alternative cover design, compacted soil barriers can be constructed with a soil moisture content that makes placement and compaction of the soil easier and less expensive. Under these conditions, the soil barrier has more capacity to absorb and control moisture within it, thereby enhancing the reliability of the barrier. This document contains information on the above-mentioned technology, including description, applicability, cost, and performance, data.

  10. CHARACTERIZATION OF CLOUDS IN TITAN'S TROPICAL ATMOSPHERE

    SciTech Connect (OSTI)

    Griffith, Caitlin A.; Penteado, Paulo [Department of Planetary Sciences, University of Arizona, Tucson, AZ 85719 (United States); Rodriguez, Sebastien [Laboratoire AIM, Universite Paris 7/CNRS/CEA-Saclay, DSM/IRFU/SAp (France); Le Mouelic, Stephane [Laboratoire de Planetologie et Geodynamique, CNRS, UMR-6112, Universite de Nantes, 44000 Nantes (France); Baines, Kevin H.; Buratti, Bonnie; Sotin, Christophe [Jet Propulsion Laboratory, Pasadena, CA 91109 (United States); Clark, Roger [U.S. Geological Survey, Denver, CO 80225 (United States); Nicholson, Phil [Department of Astronomy, Cornell University, Ithaca, NY (United States); Jaumann, Ralf [Institute of Planetary Exploration, Deutsche Zentrum, fuer Luft- und Raumfahrt (Germany)

    2009-09-10T23:59:59.000Z

    Images of Titan's clouds, possible over the past 10 years, indicate primarily discrete convective methane clouds near the south and north poles and an immense stratiform cloud, likely composed of ethane, around the north pole. Here we present spectral images from Cassini's Visual Mapping Infrared Spectrometer that reveal the increasing presence of clouds in Titan's tropical atmosphere. Radiative transfer analyses indicate similarities between summer polar and tropical methane clouds. Like their southern counterparts, tropical clouds consist of particles exceeding 5 {mu}m. They display discrete structures suggestive of convective cumuli. They prevail at a specific latitude band between 8 deg. - 20 deg. S, indicative of a circulation origin and the beginning of a circulation turnover. Yet, unlike the high latitude clouds that often reach 45 km altitude, these discrete tropical clouds, so far, remain capped to altitudes below 26 km. Such low convective clouds are consistent with the highly stable atmospheric conditions measured at the Huygens landing site. Their characteristics suggest that Titan's tropical atmosphere has a dry climate unlike the south polar atmosphere, and despite the numerous washes that carve the tropical landscape.

  11. Interstellar Turbulence, Cloud Formation and Pressure Balance

    E-Print Network [OSTI]

    Enrique Vazquez-Semadeni

    1998-10-23T23:59:59.000Z

    We discuss HD and MHD compressible turbulence as a cloud-forming and cloud-structuring mechanism in the ISM. Results from a numerical model of the turbulent ISM at large scales suggest that the phase-like appearance of the medium, the typical values of the densities and magnetic field strengths in the intercloud medium, as well as Larson's velocity dispersion-size scaling relation in clouds may be understood as consequences of the interstellar turbulence. However, the density-size relation appears to only hold for the densest simulated clouds, there existing a large population of small, low-density clouds, which, on the other hand, are hardest to observe. We then discuss several tests and implications of a fully dynamical picture of interstellar clouds. The results imply that clouds are transient, constantly being formed, distorted and disrupted by the turbulent velocity field, with a fraction of these fluctuations undergoing gravitational collapse. Simulated line profiles and estimated cloud lifetimes are consistent with observational data. In this scenario, we suggest it is quite unlikely that quasi-hydrostatic structures on any scale can form, and that the near pressure balance between clouds and the intercloud medium is an incidental consequence of the density field driven by the turbulence and in the presence of appropriate cooling, rather than a driving or confining mechanism.

  12. Intercomparison of cloud model simulations of Arctic mixed-phase boundary layer clouds observed during

    E-Print Network [OSTI]

    Zuidema, Paquita

    /crystal concentration also suggests the need for improved understanding of ice nucleation and its parameterizationIntercomparison of cloud model simulations of Arctic mixed-phase boundary layer clouds observed is presented. This case study is based on observations of a persistent mixed-phase boundary layer cloud

  13. Dark Clouds on the Horizon: Using Cloud Storage as Attack Vector and Online Slack Space

    E-Print Network [OSTI]

    Dark Clouds on the Horizon: Using Cloud Storage as Attack Vector and Online Slack Space Martin this as online slack space. We conclude by discussing security improvements for mod- ern online storage services protocol. With the advent of cloud computing and the shared usage of resources, these centralized storage

  14. To Cloud or Not to Cloud: A Mobile Device Perspective on Energy Consumption of Applications

    E-Print Network [OSTI]

    Namboodiri, Vinod

    To Cloud or Not to Cloud: A Mobile Device Perspective on Energy Consumption of Applications Vinod important criteria might be the energy consumed by the applications they run. The goal of this work is to characterize under what scenarios cloud-based applications would be relatively more energy-efficient for users

  15. Aircraft Microphysical Documentation from Cloud Base to Anvils of Hailstorm Feeder Clouds in Argentina

    E-Print Network [OSTI]

    Daniel, Rosenfeld

    in Argentina DANIEL ROSENFELD The Hebrew University of Jerusalem, Jerusalem, Israel WILLIAM L. WOODLEY Woodley, Argentina, with a cloud-physics jet aircraft penetrating the major feeder clouds from cloud base to the 45°C. Introduction The province of Mendoza in western Argentina (32°S, 68°W), which is known worldwide for its wine

  16. Investigating the Radiative Impact Clouds Using Retrieved Properties to Classify Cloud Type

    E-Print Network [OSTI]

    Hogan, Robin

    of Reading, RG6 6AL, UK Abstract. Active remote sensing allows cloud properties such as ice and liquid water remote sensing, Cloud categorization, Cloud properties, Radiative impact. PACS: 92.60. Vb. INTRODUCTION in a radiation scheme which can simulate the radiation budget and heating rates throughout the atmospheric

  17. The Design of a Community Science Cloud: The Open Science Data Cloud Perspective

    E-Print Network [OSTI]

    Grossman, Robert

    The Design of a Community Science Cloud: The Open Science Data Cloud Perspective Robert L. Grossman, Matthew Greenway, Allison P. Heath, Ray Powell, Rafael D. Suarez, Walt Wells, and Kevin White University Abstract--In this paper we describe the design, and implemen- tation of the Open Science Data Cloud

  18. From Grid to private Clouds, to interClouds. Project Team

    E-Print Network [OSTI]

    Vialle, Stéphane

    24/10/2011 1 From Grid to private Clouds, to interClouds. AlGorille Project Team An overviewGorille INRIA Project Team October 21, 2011 I Premise of Grid ComputingI Premise of Grid Computing... From Grid to private Clouds, to inter

  19. A 3D STOCHASTIC CLOUD MODEL FOR INVESTIGATING THE RADIATIVE PROPERTIES OF INHOMOGENEOUS CIRRUS CLOUDS

    E-Print Network [OSTI]

    Hogan, Robin

    A 3D STOCHASTIC CLOUD MODEL FOR INVESTIGATING THE RADIATIVE PROPERTIES OF INHOMOGENEOUS CIRRUS, Berkshire, United Kingdom 1 INTRODUCTION The importance of ice clouds on the earth's radiation budget for quantifying this effect, and several such models exist for boundary layer clouds, such as those of Cahalan et

  20. Assessing Cloud Spatial and Vertical Distribution with Infrared Cloud Analyzer

    Broader source: All U.S. Department of Energy (DOE) Office Webpages (Extended Search)

    AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE: Alternative1 First Use of Energy for All Purposes (Fuel and Nonfuel), 2002; Level: National5Sales for4,645U.S. DOE Office511041cloth DocumentationProductsAlternativeOperational Management »EnergyHubs | DepartmentCloud Spatial

  1. Shoring up Infrastructure Weaknesses with Hybrid Cloud Storage

    E-Print Network [OSTI]

    Chaudhuri, Surajit

    Shoring up Infrastructure Weaknesses with Hybrid Cloud Storage #12;2StorSimple White Pages: Shoring Up Infrastructure Weaknesses with Hybrid Cloud Storage Table of Contents The Hybrid Cloud Context for IT Managers ............................................................. 3 The Bottleneck of Managing Storage

  2. Satellite Remote Sensing of Mid-level Clouds

    E-Print Network [OSTI]

    Jin, Hongchun 1980-

    2012-11-07T23:59:59.000Z

    algorithm is evaluated using the CALIPSO cloud phase products for single-layer, heterogeneous, and multi-layer scenes. The AIRS phase algorithm has excellent performance (>90%) in detecting ice clouds compared to the CALIPSO ice clouds. It is capable...

  3. A cloud-assisted design for autonomous driving

    E-Print Network [OSTI]

    Suresh Kumar, Swarun

    This paper presents Carcel, a cloud-assisted system for autonomous driving. Carcel enables the cloud to have access to sensor data from autonomous vehicles as well as the roadside infrastructure. The cloud assists autonomous ...

  4. Aneka Cloud Application Platform and Its Integration with Windows Azure

    E-Print Network [OSTI]

    Melbourne, University of

    scheduling, and energy efficient resource utilization. The Aneka Cloud Application platform, together. Ltd., Melbourne, Victoria, Australia 2 Cloud Computing and Distributed Systems (CLOUDS) Laboratory, Department of Computer Science and Software Engineering, The University of Melbourne, Australia Abstract

  5. Fair-weather clouds hold dirty secret | EMSL

    Broader source: All U.S. Department of Energy (DOE) Office Webpages (Extended Search)

    Fair-weather clouds hold dirty secret Fair-weather clouds hold dirty secret Released: May 05, 2013 New study reveals particles that seed small-scale clouds over Oklahoma Air...

  6. E-Cloud Build-up in Grooved Chambers

    E-Print Network [OSTI]

    Venturini, Marco

    2007-01-01T23:59:59.000Z

    and F. Zimmermann, ”LC e-Cloud Activities at CERN”, talkal. , Simulations of the Electron Cloud for Vari- ous Con?E-CLOUD BUILD-UP IN GROOVED CHAMBERS ? M. Venturini † LBNL,

  7. Building Dynamic Computing Infrastructures over Distributed Clouds Pierre Riteau

    E-Print Network [OSTI]

    Paris-Sud XI, Université de

    Building Dynamic Computing Infrastructures over Distributed Clouds Pierre Riteau University--The emergence of cloud computing infrastructures brings new ways to build and manage computing systems objectives. First, leveraging virtualization and cloud computing infrastruc- tures to build distributed large

  8. Modelling Cloud Computing Infrastructure Marianne Hickey and Maher Rahmouni,

    E-Print Network [OSTI]

    Paris-Sud XI, Université de

    Modelling Cloud Computing Infrastructure Marianne Hickey and Maher Rahmouni, HP Labs, Long Down, and shared vocabularies. Keywords: Modelling, Cloud Computing, RDF, Ontology, Rules, Validation 1 Introduction There is currently a shift towards cloud computing, which changes the model of provision

  9. Consistent cloud computing storage as the basis for distributed applications

    E-Print Network [OSTI]

    Anderson, James William

    2011-01-01T23:59:59.000Z

    Messaging in Cloud Computing . . . . . . . . . .7 1.4Eucalyptus Open—Source Cloud—Computing System. In C'C&#http://www.eweek.com/c/a/Cloud-Computing/Amazons—Head—Start—

  10. On water ice formation in interstellar clouds

    E-Print Network [OSTI]

    Renaud Papoular

    2005-07-06T23:59:59.000Z

    A model is proposed for the formation of water ice mantles on grains in interstellar clouds. This occurs by direct accretion of monomers from the gas, be they formed by gas or surface reactions. The model predicts the existence of a threshold in interstellar light extinction, A(v), which is mainly determined by the adsorption energy of water molecules on the grain material; for hydrocarbon material, chemical simulation places this energy between 0.5 and 2 kcal/mole, which sets the visible exctinction threshold at a few magnitudes, as observed. Once the threshold is crossed, all available water molecules in the gas are quickly adsorbed, forming an ice mantle, because the grain cools down and the adsorption energy on ice is higher than on bare grain. The model also predicts that the thickness of the mantle, and, hence, the optical thickness at 3 mu, grow linearly with A(v), as observed, with a slope which depends upon the total amount of water in the gas. Chemical simulation was also used to determine the adsorption sites and energies of O and OH on hydrocarbons, and study the dynamics of formation of water molecules by surface reactions with gaseous H atoms, as well as their chances of sticking in situ.

  11. Covered Product Category: Light Fixtures (Luminaires)

    Broader source: Energy.gov [DOE]

    FEMP provides acquisition guidance and Federal efficiency requirements across a variety of product categories, including luminaires, or light fixtures. The luminaires product category is very broad and covers a wide variety of lighting products. Both ENERGY STAR® and FEMP provide programmatic guidance for various types of luminaires. See table 2 for more information about which types of light fixtures are covered by which program (FEMP or ENERGY STAR). Federal laws and requirements mandate that agencies meet these efficiency requirements in all procurement and acquisition actions that are not specifically exempted by law.

  12. Land Use and Land Cover Change

    SciTech Connect (OSTI)

    Brown, Daniel; Polsky, Colin; Bolstad, Paul V.; Brody, Samuel D.; Hulse, David; Kroh, Roger; Loveland, Thomas; Thomson, Allison M.

    2014-05-01T23:59:59.000Z

    A contribution to the 3rd National Climate Assessment report, discussing the following key messages: 1. Choices about land-use and land-cover patterns have affected and will continue to affect how vulnerable or resilient human communities and ecosystems are to the effects of climate change. 2. Land-use and land-cover changes affect local, regional, and global climate processes. 3. Individuals, organizations, and governments have the capacity to make land-use decisions to adapt to the effects of climate change. 4. Choices about land use and land management provide a means of reducing atmospheric greenhouse gas levels.

  13. Scientific Analysis Cover Sheet for Radionuclide Screening

    SciTech Connect (OSTI)

    G. Ragan

    2002-08-09T23:59:59.000Z

    The waste forms under consideration for disposal in the proposed repository at Yucca Mountain contain scores of radionuclides (Attachments V and VI). It would be impractical and highly inefficient to model all of these radionuclides in a total system performance assessment (TSPA). Thus, the purpose of this radionuclide screening analysis is to remove from further consideration (screen out) radionuclides that are unlikely to significantly contribute to radiation dose to the public from the proposed nuclear waste repository at Yucca Mountain. The remaining nuclides (those screened in) are recommended for consideration in TSPA modeling for license application. This analysis also covers radionuclides that are not screened in based on dose, but need to be included in TSPA modeling for other reasons. For example, U.S. Environmental Protection Agency (EPA) and U.S. Nuclear Regulatory Commission (NRC) regulations require consideration of the combined activity of Ra-226 and Ra-228 in groundwater (40 CFR 197.30, 10 CFR 63.331). Also, Cm-245, Pu-241, and U-235 decay indirectly to potentially important radionuclides, and are not identified by the screening analysis as important. The radionuclide screening analysis separately considers two different postclosure time periods: the 10,000-y regulatory period for the proposed repository at Yucca Mountain and the period after 10,000 y up to 1 million y after emplacement. The incremental effect of extending the screening for the regulatory period to 20,000 y is also addressed. Four release scenarios are considered: (1) the nominal scenario, which entails long-term degradation of disposal containers and waste forms, (2) a human-intrusion scenario, (3) an intrusive igneous event, and (4) an eruptive igneous event. Because the first three scenarios require groundwater transport, they are called groundwater scenarios below. The screening analysis considers the following waste forms: spent boiling water reactor (BWR) fuel, spent pressurized water reactor (PWR) fuel, U.S. Department of Energy (DOE) spent nuclear fuel (DSNF), and high-level waste (HLW). Average and outlying (high burnup, high initial enrichment, low age, or otherwise exceptional) forms of each waste-form type are considered. This analysis has been prepared in accordance with a technical work plan (BSC 2002c). In a review of Revision 00 of this radionuclide screening analysis, the NRC found that ''processes that affect transport in the biosphere, such as uptake by plants and bioaccumulation are not accounted for'' and that ''the direct exposure pathway is not accounted for'' (Beckman 2001, Section 5.3.2.1). The NRC also found that the solubility and sorption classes were too broadly defined, noting, for example, that Se is in the same solubility and sorptivity groups as Np and U, yet is ''more soluble than Np and U by several orders of magnitude'' (Beckman 2001, Section 5.3.2.1). This revision seeks to build upon the strengths of the earlier screening method while responding to the specific concerns raised by the NRC and other reviewers. In place of simple inhalation and ingestion dose conversion factors, the revised radionuclide screening uses screening factors that also take into account soil accumulation, uptake by plants, exposure to contaminated ground, and other features of the biosphere that were neglected in the previous screening. Whereas the previous screening analysis allowed only two solubility classes (soluble and insoluble), the revised screening introduces an intermediate solubility class to better segregate the radionuclides into transport groups.

  14. great basin naturalist 502 1990 ppap 121 134 FOLIAGE BIOMASS AND COVER relationships BETWEEN

    E-Print Network [OSTI]

    inin the great basin and southwest both species are aggressive and can nearly eliminate the previous the range of site conditions sampled treetiee dominated plots varied by about two to one cover inin shrub twototwotroto to one total foliage biomass inin both tree and shrub dominated plots correlated best

  15. Chapter 3: Evaluating the impacts of carbonaceous aerosols on clouds and climate

    SciTech Connect (OSTI)

    Menon, Surabi; Del Genio, Anthony D.

    2007-09-03T23:59:59.000Z

    Any attempt to reconcile observed surface temperature changes within the last 150 years to changes simulated by climate models that include various atmospheric forcings is sensitive to the changes attributed to aerosols and aerosol-cloud-climate interactions, which are the main contributors that may well balance the positive forcings associated with greenhouse gases, absorbing aerosols, ozone related changes, etc. These aerosol effects on climate, from various modeling studies discussed in Menon (2004), range from +0.8 to -2.4 W m{sup -2}, with an implied value of -1.0 W m{sup -2} (range from -0.5 to -4.5 W m{sup -2}) for the aerosol indirect effects. Quantifying the contribution of aerosols and aerosol-cloud interactions remain complicated for several reasons some of which are related to aerosol distributions and some to the processes used to represent their effects on clouds. Aerosol effects on low lying marine stratocumulus clouds that cover much of the Earth's surface (about 70%) have been the focus of most of prior aerosol-cloud interaction effect simulations. Since cumulus clouds (shallow and deep convective) are short lived and cover about 15 to 20% of the Earth's surface, they are not usually considered as radiatively important. However, the large amount of latent heat released from convective towers, and corresponding changes in precipitation, especially in biomass regions due to convective heating effects (Graf et al. 2004), suggest that these cloud systems and aerosol effects on them, must be examined more closely. The radiative heating effects for mature deep convective systems can account for 10-30% of maximum latent heating effects and thus cannot be ignored (Jensen and Del Genio 2003). The first study that isolated the sensitivity of cumulus clouds to aerosols was from Nober et al. (2003) who found a reduction in precipitation in biomass burning regions and shifts in circulation patterns. Aerosol effects on convection have been included in other models as well (cf. Jacobson, 2002) but the relative impacts on convective and stratiform processes were not separated. Other changes to atmospheric stability and thermodynamical quantities due to aerosol absorption are also known to be important in modifying cloud macro/micro properties. Linkages between convection and boreal biomass burning can also impact the upper troposphere and lower stratosphere, radiation and cloud microphysical properties via transport of tropospheric aerosols to the lower stratosphere during extreme convection (Fromm and Servranckx 2003). Relevant questions regarding the impact of biomass aerosols on convective cloud properties include the effects of vertical transport of aerosols, spatial and temporal distribution of rainfall, vertical shift in latent heat release, phase shift of precipitation, circulation and their impacts on radiation. Over land surfaces, a decrease in surface shortwave radiation ({approx} 3-6 W m{sup -2} per decade) has been observed between 1960 to 1990, whereas, increases of 0.4 K in land temperature during the same period that occurred have resulted in speculations that evaporation and precipitation should also have decreased (Wild et al. 2004). However, precipitation records for the same period over land do not indicate any significant trend (Beck et al. 2005). The changes in precipitation are thought to be related to increased moisture advection from the oceans (Wild et al. 2004), which may well have some contributions from aerosol-radiation-convection coupling that could modify circulation patterns and hence moisture advection in specific regions. Other important aspects of aerosol effects, besides the direct, semi-direct, microphysical and thermodynamical impacts include alteration of surface albedos, especially snow and ice covered surfaces, due to absorbing aerosols. These effects are uncertain (Jacobson, 2004) but may produce as much as 0.3 W m{sup -2} forcing in the Northern hemisphere that could contribute to melting of ice and permafrost and change in the length of the season (e.g. early arrival of Spring

  16. AEROSOL, CLOUDS, AND CLIMATE CHANGE

    SciTech Connect (OSTI)

    SCHWARTZ, S.E.

    2005-09-01T23:59:59.000Z

    Earth's climate is thought to be quite sensitive to changes in radiative fluxes that are quite small in absolute magnitude, a few watts per square meter, and in relation to these fluxes in the natural climate. Atmospheric aerosol particles exert influence on climate directly, by scattering and absorbing radiation, and indirectly by modifying the microphysical properties of clouds and in turn their radiative effects and hydrology. The forcing of climate change by these indirect effects is thought to be quite substantial relative to forcing by incremental concentrations of greenhouse gases, but highly uncertain. Quantification of aerosol indirect forcing by satellite- or ground-based remote sensing has proved quite difficult in view of inherent large variation in the pertinent observables such as cloud optical depth, which is controlled mainly by liquid water path and only secondarily by aerosols. Limited work has shown instances of large magnitude of aerosol indirect forcing, with local instantaneous forcing upwards of 50 W m{sup 66}-2. Ultimately it will be necessary to represent aerosol indirect effects in climate models to accurately identify the anthropogenic forcing at present and over secular time and to assess the influence of this forcing in the context of other forcings of climate change. While the elements of aerosol processes that must be represented in models describing the evolution and properties of aerosol particles that serve as cloud condensation particles are known, many important components of these processes remain to be understood and to be represented in models, and the models evaluated against observation, before such model-based representations can confidently be used to represent aerosol indirect effects in climate models.

  17. The Giant Molecular Cloud Environments of Infrared Dark Clouds

    E-Print Network [OSTI]

    Hernandez, Audra K

    2015-01-01T23:59:59.000Z

    We study the GMC environments surrounding 10 IRDCs, based on 13CO molecular line emission from the Galactic Ring Survey. Using a range of physical scales, we measure the physical properties of the IRDCs and their surrounding molecular material extending out to radii, R, of 30pc. By comparing different methods for defining cloud boundaries and for deriving mass surface densities, Sigma, and velocity dispersions, sigma, we settled on a preferred "CE,tau,G" method of "Connected Extraction" in position-velocity space along with Gaussian fitting to opacity-corrected line profiles for velocity dispersion and mass estimation. We examine how cloud definition affects measurements of the magnitude and direction of line of sight velocity gradients and velocity dispersions, including the associated dependencies on size scale. CE,tau,G-defined IRDCs and GMCs show velocity gradient versus size relations that scale approximately as dv_0/ds~s^(-1/2) and velocity dispersion versus size relations sigma~s^(1/2), which are consi...

  18. Cover Image: The cover shows the crystal structure of the alanate NaAlH4,

    E-Print Network [OSTI]

    of materials, hydrogen "encapsulates" Al to form a hydrogen-rich anion, AlH4 -, whose structure resembles is encapsulated by metal ions, and the hydrogen density is correspondingly lower. In the cover image, the diameter) - p13 (top): The estimated power output from 10% efficient solar cells covering 1.7% of the land area

  19. Electron Cloud Effects in Accelerators

    SciTech Connect (OSTI)

    Furman, M.A.

    2012-11-30T23:59:59.000Z

    Abstract We present a brief summary of various aspects of the electron-cloud effect (ECE) in accelerators. For further details, the reader is encouraged to refer to the proceedings of many prior workshops, either dedicated to EC or with significant EC contents, including the entire ?ECLOUD? series [1?22]. In addition, the proceedings of the various flavors of Particle Accelerator Conferences [23] contain a large number of EC-related publications. The ICFA Beam Dynamics Newsletter series [24] contains one dedicated issue, and several occasional articles, on EC. An extensive reference database is the LHC website on EC [25].

  20. ARM - Lesson Plans: Making Clouds

    Broader source: All U.S. Department of Energy (DOE) Office Webpages (Extended Search)

    AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE: Alternative1 First Use of Energy for All Purposes (Fuel and Nonfuel), 2002; Level: National5Sales for4,645 3,625govInstrumentstdmadap Documentation TDMADAP : XDC documentationBarrow, Alaska OutreachMaking Clouds Outreach Home

  1. Sandia Energy - Cloud Computing Services

    Broader source: All U.S. Department of Energy (DOE) Office Webpages (Extended Search)

    AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE: Alternative1 First Use of Energy for All Purposes (Fuel and Nonfuel), 2002; Level: National5Sales for4,645U.S. DOE Office of Scienceand RequirementsCoatings Initiated at PNNL's Sequim BayCaptureCloud Computing Services

  2. On Covering Points with Conics and Strips in the Plane

    E-Print Network [OSTI]

    Tiwari, Praveen 1985-

    2012-12-10T23:59:59.000Z

    Geometric covering problems have always been of focus in computer scientific research. The generic geometric covering problem asks to cover a set S of n objects with another set of objects whose cardinality is minimum, in a geometric setting. Many...

  3. The Formation and Fragmentation of Primordial Molecular Clouds

    E-Print Network [OSTI]

    Tom Abel; Greg L. Bryan; Michael L. Norman

    2000-02-07T23:59:59.000Z

    Many questions in physical cosmology regarding the thermal history of the intergalactic medium, chemical enrichment, reionization, etc. are thought to be intimately related to the nature and evolution of pregalactic structure. In particular the efficiency of primordial star formation and the primordial IMF are of special interest. We present results from high resolution three--dimensional adaptive mesh refinement simulations that follow the collapse of primordial molecular clouds and their subsequent fragmentation within a cosmologically representative volume. Comoving scales from 128 kpc down to 1 pc are followed accurately. Dark matter dynamics, hydrodynamics and all relevant chemical and radiative processes (cooling) are followed self-consistently for a cluster normalized CDM structure formation model. Primordial molecular clouds with ~10^5 solar masses are assembled by mergers of multiple objects that have formed hydrogen molecules in the gas phase with a fractional abundance of ~10^-4. As the subclumps merge cooling lowers the temperature to ~200 Kelvin in a `cold pocket' at the center of the halo. Within this cold pocket, a quasi-hydrostatically contracting core with ~200 solar mass and number densities > 10^5 cm^-3 is found. We find that less than 1% of the primordial gas in such small scale structures cools and collapses to sufficiently high densities to be available for primordial star formation. Furthermore, it is worthwhile to note that this study achieved the highest dynamic range covered by structured adaptive mesh techniques in cosmological hydrodynamics to date.

  4. 1CHANCELLOR'S REPORT 20112012 On the cover

    E-Print Network [OSTI]

    . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 14 Renewable Energy Fueled by a Cell1CHANCELLOR'S REPORT 2011­2012 #12;2 On the cover Examining the role that marine microbes play and Education (C-MORE) at the University of Hawai`i at Mnoa. The first of its kind to focus on microbes, C

  5. Covered Product Category: Commercial Gas Water Heaters

    Broader source: Energy.gov [DOE]

    FEMP provides acquisition guidance and Federal efficiency requirements across a variety of product categories, including commercial gas water heaters, which are covered by the ENERGY STAR® program. Federal laws and requirements mandate that agencies meet these efficiency requirements in all procurement and acquisition actions that are not specifically exempted by law.

  6. Corn Ethanol -April 2006 11 Cover Story

    E-Print Network [OSTI]

    Patzek, Tadeusz W.

    Corn Ethanol - April 2006 11 Cover Story orn ethanol is the fuel du jour. It's domestic. It oil into gasoline or diesel fuel. Ethanol refineries also use huge amounts of water. An average dry's not oil. Ethanol's going to help promote "energy independence." Magazines trumpet it as the motor vehicle

  7. Marine Fisheries On the cover, top to

    E-Print Network [OSTI]

    Marine Fisheries ~@WD@W On the cover, top to bollom: Yelloweye rock fish, Sebastes ruberrimus Maturity and Fecundity in the Rockfishes, Sebastes spp., a Review Joy Clark, Wade Griffin, Jerry Clark.25 foreign. Publication of material from sources outside the NMFS is not an endorsement and the NMFS

  8. Covered Product Category: Refrigerated Beverage Vending Machines

    Broader source: Energy.gov [DOE]

    FEMP provides acquisition guidance and Federal efficiency requirements across a variety of product categories, including refrigerated beverage vending machines, which are covered by the ENERGY STAR® program. Federal laws and requirements mandate that agencies meet these efficiency requirements in all procurement and acquisition actions that are not specifically exempted by law.

  9. CloneCloud: Boosting Mobile Device Applications Through Cloud Clone Execution

    E-Print Network [OSTI]

    Chun, Byung-Gon; Maniatis, Petros; Naik, Mayur

    2010-01-01T23:59:59.000Z

    Mobile applications are becoming increasingly ubiquitous and provide ever richer functionality on mobile devices. At the same time, such devices often enjoy strong connectivity with more powerful machines ranging from laptops and desktops to commercial clouds. This paper presents the design and implementation of CloneCloud, a system that automatically transforms mobile applications to benefit from the cloud. The system is a flexible application partitioner and execution runtime that enables unmodified mobile applications running in an application-level virtual machine to seamlessly off-load part of their execution from mobile devices onto device clones operating in a computational cloud. CloneCloud uses a combination of static analysis and dynamic profiling to optimally and automatically partition an application so that it migrates, executes in the cloud, and re-integrates computation in a fine-grained manner that makes efficient use of resources. Our evaluation shows that CloneCloud can achieve up to 21.2x s...

  10. Public Cloud B CarbonEmission

    E-Print Network [OSTI]

    Buyya, Rajkumar

    Sensors, Demand Prediction Power Capping, Green Software Services such as energy-efficient scientific) Request a Cloud service 4) Allocate service 5) Request service allocation 3) Request energy efficiency information Green Offer Directory 2) Request any `Green Offer' Routers Internet Green Broker #12;Cloud

  11. The CloudNets Network Virtualization Architecture

    E-Print Network [OSTI]

    Schmid, Stefan

    Nets Network Virtualization Architecture Johannes Grassler jgrassler@inet.tu-berlin.de 05. Februar, 2014 Johannes Grassler jgrassler@inet.tu-berlin.de The CloudNets Network Virtualization Architecture #12;..... . .... . .... . ..... . .... . .... . .... . ..... . .... . .... . .... . ..... . .... . .... . .... . ..... . .... . ..... . .... . .... . Johannes Grassler jgrassler@inet.tu-berlin.de The CloudNets Network Virtualization Architecture #12

  12. 7, 80878111, 2007 Influence of cloud top

    E-Print Network [OSTI]

    Paris-Sud XI, Université de

    ACPD 7, 8087­8111, 2007 Influence of cloud top variability on radiative transfer Richter, Barfus top variability from radar measurements on 3-D radiative transfer F. Richter 1 , K. Barfus 1 , F. H.richter@awi.de) 8087 #12;ACPD 7, 8087­8111, 2007 Influence of cloud top variability on radiative transfer Richter

  13. Verifiable Resource Accounting for Cloud Computing Services

    E-Print Network [OSTI]

    Maniatis, Petros

    Verifiable Resource Accounting for Cloud Computing Services Vyas Sekar Intel Labs Petros Maniatis Intel Labs ABSTRACT Cloud computing offers users the potential to reduce operating and capital expenses cause providers to incorrectly attribute resource consumption to customers or im- plicitly bear

  14. Compression of Antiproton Clouds for Antihydrogen Trapping

    E-Print Network [OSTI]

    G. B. Andresen; W. Bertsche; P. D. Bowe; C. C. Bray; E. Butler; C. L. Cesar; S. Chapman; M. Charlton; J. Fajans; M. C. Fujiwara; R. Funakoshi; D. R. Gill; J. S. Hangst; W. N. Hardy; R. S. Hayano; M. E. Hayden; R. Hydomako; M. J. Jenkins; L. V. Jorgensen; L. Kurchaninov; R. Lambo; N. Madsen; P. Nolan; K. Olchanski; A. Olin; A. Povilus; P. Pusa; F. Robicheaux; E. Sarid; S. Seif El Nasr; D. M. Silveira; J. W. Storey; R. I. Thompson; D. P. van der Werf; J. S. Wurtele; Y. Yamazaki

    2008-06-30T23:59:59.000Z

    Control of the radial profile of trapped antiproton clouds is critical to trapping antihydrogen. We report the first detailed measurements of the radial manipulation of antiproton clouds, including areal density compressions by factors as large as ten, by manipulating spatially overlapped electron plasmas. We show detailed measurements of the near-axis antiproton radial profile and its relation to that of the electron plasma.

  15. CLOUD COMPUTING INFRASTRUCTURE AND OPERATIONS PROGRAM

    E-Print Network [OSTI]

    Schaefer, Marcus

    theory and best practices, Cloud operations analytics, globally-responsive architecture, functional of Cloud infrastructures Best practices for building Infrastructure as a Service (IaaS), with an emphasis-distributed, responsive web application capable of massive scale with operational performance metrics. DePaul University

  16. Privacy in the Cloud Computing Era

    E-Print Network [OSTI]

    Narasayya, Vivek

    Privacy in the Cloud Computing Era A Microsoft Perspective November 2009 #12;The information information presented after the date of publication. This white paper is for informational purposes only. Microsoft Corp. · One Microsoft Way · Redmond, WA 98052-6399 · USA #12;Contents Cloud Computing and Privacy

  17. Residential Windows and Window Coverings: A Detailed View of...

    Office of Energy Efficiency and Renewable Energy (EERE) Indexed Site

    Residential Windows and Window Coverings: A Detailed View of the Installed Base and User Behavior Residential Windows and Window Coverings: A Detailed View of the Installed Base...

  18. TOWER OF COVERINGS OF QUASI-PROJECTIVE VARIETIES ...

    E-Print Network [OSTI]

    2012-04-25T23:59:59.000Z

    on a tower of coverings of a non-compact Kähler manifold of finite volume with reasonable geometric assumptions to its universal covering. Applicable examples ...

  19. Cloud-integrated Storage What & Why 2StoreSimple White Pages: Shoring Up Infrastructure Weaknesses with Cloud Storage

    E-Print Network [OSTI]

    Chaudhuri, Surajit

    Cloud-integrated Storage ­ What & Why #12;2StoreSimple White Pages: Shoring Up Infrastructure Weaknesses with Cloud Storage Overview..........................................................................................................3 Enterprise-class storage platform

  20. Final Technical Report for "Radiative Heating Associated with Tropical Convective Cloud Systems: Its Importance at Meso and Global Scales"

    SciTech Connect (OSTI)

    Schumacher, Courtney

    2012-12-13T23:59:59.000Z

    Heating associated with tropical cloud systems drive the global circulation. The overall research objectives of this project were to i) further quantify and understand the importance of heating in tropical convective cloud systems with innovative observational techniques, and ii) use global models to determine the large-scale circulation response to variability in tropical heating profiles, including anvil and cirrus cloud radiative forcing. The innovative observational techniques used a diversity of radar systems to create a climatology of vertical velocities associated with the full tropical convective cloud spectrum along with a dissection of the of the total heating profile of tropical cloud systems into separate components (i.e., the latent, radiative, and eddy sensible heating). These properties were used to validate storm-scale and global climate models (GCMs) and were further used to force two different types of GCMs (one with and one without interactive physics). While radiative heating was shown to account for about 20% of the total heating and did not have a strong direct response on the global circulation, the indirect response was important via its impact on convection, esp. in how radiative heating impacts the tilt of heating associated with the Madden-Julian Oscillation (MJO), a phenomenon that accounts for most tropical intraseasonal variability. This work shows strong promise in determining the sensitivity of climate models and climate processes to heating variations associated with cloud systems.