National Library of Energy BETA

Sample records for wrf model output

  1. Air pollution forecasting by coupled atmosphere-fire model WRF and SFIRE with WRF-Chem

    E-Print Network [OSTI]

    Kochanski, Adam K; Mandel, Jan; Clements, Craig B

    2013-01-01

    Atmospheric pollution regulations have emerged as a dominant obstacle to prescribed burns. Thus, forecasting the pollution caused by wildland fires has acquired high importance. WRF and SFIRE model wildland fire spread in a two-way interaction with the atmosphere. The surface heat flux from the fire causes strong updrafts, which in turn change the winds and affect the fire spread. Fire emissions, estimated from the burning organic matter, are inserted in every time step into WRF-Chem tracers at the lowest atmospheric layer. The buoyancy caused by the fire then naturally simulates plume dynamics, and the chemical transport in WRF-Chem provides a forecast of the pollution spread. We discuss the choice of wood burning models and compatible chemical transport models in WRF-Chem, and demonstrate the results on case studies.

  2. Coupling the high-complexity land surface model ACASA to the mesoscale model WRF

    E-Print Network [OSTI]

    Pyles, R. D.

    In this study, the Weather Research and Forecasting (WRF) model is coupled with the Advanced Canopy–Atmosphere–Soil Algorithm (ACASA), a high-complexity land surface model. Although WRF is a state-of-the-art regional ...

  3. Coupling the High Complexity Land Surface Model ACASA to the Mesoscale Model WRF

    E-Print Network [OSTI]

    Xu, L.

    In this study, the Weather Research and Forecasting Model (WRF) is coupled with the Advanced Canopy-Atmosphere-Soil Algorithm (ACASA), a high complexity land surface model. Although WRF is a state-of-the-art regional ...

  4. Verification of the WRF model during a high ozone event over Houston, TX 

    E-Print Network [OSTI]

    Ames, Douglas Seeley

    2003-01-01

    High ozone values were observed in Houston, TX during August 25 - September 1, 2000. A comparison of WRF data with observations and MM5 data was conducted to determine the WRF model's performance in simulating the meteorological conditions...

  5. The Lagrangian particle dispersion model FLEXPART-WRF VERSION 3.1

    SciTech Connect (OSTI)

    Brioude, J.; Arnold, D.; Stohl, A.; Cassiani, M.; Morton, Don; Seibert, P.; Angevine, W. M.; Evan, S.; Dingwell, A.; Fast, Jerome D.; Easter, Richard C.; Pisso, I.; Bukhart, J.; Wotawa, G.

    2013-11-01

    The Lagrangian particle dispersion model FLEXPART was originally designed for cal- culating long-range and mesoscale dispersion of air pollutants from point sources, such as after an accident in a nuclear power plant. In the meantime FLEXPART has evolved into a comprehensive tool for atmospheric transport modeling and analysis at different scales. This multiscale need from the modeler community has encouraged new developments in FLEXPART. In this document, we present a version that works with the Weather Research and Forecasting (WRF) mesoscale meteoro- logical model. Simple procedures on how to run FLEXPART-WRF are presented along with special options and features that differ from its predecessor versions. In addition, test case data, the source code and visualization tools are provided to the reader as supplementary material.

  6. Simulations of organic aerosol concentrations in Mexico City using the WRF-CHEM model during the MCMA-2006/MILAGRO campaign

    E-Print Network [OSTI]

    Molina, Luisa Tan

    Organic aerosol concentrations are simulated using the WRF-CHEM model in Mexico City during the period from 24 to 29 March in association with the MILAGRO-2006 campaign. Two approaches are employed to predict the variation ...

  7. Regional Modeling of Dust Mass Balance and Radiative Forcing over East Asia using WRF-Chem

    SciTech Connect (OSTI)

    Chen, Siyu; Zhao, Chun; Qian, Yun; Leung, Lai-Yung R.; Huang, J.; Huang, Zhongwei; Bi, Jianrong; Zhang, Wu; Shi, Jinsen; Yang, Lei; Li, Deshuai; Li, Jinxin

    2014-12-01

    The Weather Research and Forecasting model with Chemistry (WRF-Chem) is used to investigate the seasonal and annual variations of mineral dust over East Asia during 2007-2011, with a focus on the dust mass balance and radiative forcing. A variety of measurements from in-stu and satellite observations have been used to evaluate simulation results. Generally, WRF-Chem reproduces not only the column variability but also the vertical profile and size distribution of mineral dust over and near the dust source regions of East Asia. We investigate the dust lifecycle and the factors that control the seasonal and spatial variations of dust mass balance and radiative forcing over the seven sub-regions of East Asia, i.e. source regions, the Tibetan Plateau, Northern China, Southern China, the ocean outflow region, and Korea-Japan regions. Results show that, over the source regions, transport and dry deposition are the two dominant sinks. Transport contributes to ~30% of the dust sink over the source regions. Dust results in a surface cooling of up to -14 and -10 W m-2, atmospheric warming of up to 20 and 15 W m-2, and TOA cooling of -5 and -8 W m-2 over the two major dust source regions of East Asia, respectively. Over the Tibetan Plateau, transport is the dominant source with a peak in summer. Over identified outflow regions, maximum dust mass loading in spring is contributed by the transport. Dry and wet depositions are the comparably dominant sinks, but wet deposition is larger than dry deposition over the Korea-Japan region, particularly in spring (70% versus 30%). The WRF-Chem simulations can generally capture the measured features of dust aerosols and its radaitve properties and dust mass balance over East Asia, which provides confidence for use in further investigation of dust impact on climate over East Asia.

  8. Parametric Sensitivity and Calibration for Kain-Fritsch Convective Parameterization Scheme in the WRF Model

    SciTech Connect (OSTI)

    Yan, Huiping; Qian, Yun; Lin, Guang; Leung, Lai-Yung R.; Yang, Ben; Fu, Q.

    2014-03-25

    Convective parameterizations used in weather and climate models all display sensitivity to model resolution and variable skill in different climatic regimes. Although parameters in convective schemes can be calibrated using observations to reduce model errors, it is not clear if the optimal parameters calibrated based on regional data can robustly improve model skill across different model resolutions and climatic regimes. In this study, this issue is investigated using a regional modeling framework based on the Weather Research and Forecasting (WRF) model. To quantify the response and sensitivity of model performance to model parameters, we identified five key input parameters and specified their ranges in the Kain-Fritsch (KF) convection scheme in WRF and calibrated them across different spatial resolutions, climatic regimes, and radiation schemes using observed precipitation data. Results show that the optimal values for the five input parameters in the KF scheme are close and model sensitivity and error exhibit similar dependence on the input parameters for all experiments conducted in this study despite differences in the precipitation climatology. We found that the model overall performances in simulating precipitation are more sensitive to the coefficients of downdraft (Pd) and entrainment (Pe) mass flux and starting height of downdraft (Ph). However, we found that rainfall biases, which are probably more related to structural errors, still exist over some regions in the simulation even with the optimal parameters, suggesting further studies are needed to identify the sources of uncertainties and reduce the model biases or structural errors associated with missed or misrepresented physical processes and/or potential problems with the modeling framework.

  9. Development and Initial Application of the Global-Through-Urban Weather Research1 and Forecasting Model with Chemistry (GU-WRF/Chem)2

    E-Print Network [OSTI]

    Nenes, Athanasios

    1 Development and Initial Application of the Global-Through-Urban Weather Research1 and Forecasting-cloud-radiation-precipitation-climate interactions. In this work, a global-through-urban33 WRF/Chem model (i.e., GU-WRF/Chem) has been developed photolysis rate, near-surface temperature, wind speed at 10-m, planetary boundary layer height,40

  10. Fire weather simulation skill by the Weather Research and Forecasting (WRF) model over south-east Australia

    E-Print Network [OSTI]

    Evans, Jason

    Fire weather simulation skill by the Weather Research and Forecasting (WRF) model over south, Australia. D Corresponding author. Email: h.clarke@student.unsw.edu.au Abstract. The fire weather of south of the McArthur Forest Fire Danger Index (FFDI) using probability density function skill scores, annual

  11. Effects of vegetation and soil moisture on the simulated land surface processes from the coupled WRF/Noah model

    E-Print Network [OSTI]

    Small, Eric

    WRF/Noah model Seungbum Hong,1 Venkat Lakshmi,1 Eric E. Small,2 Fei Chen,3 Mukul Tewari,3 and Kevin W. Lakshmi, E. E. Small, F. Chen, M. Tewari, and K. W. Manning (2009), Effects of vegetation and soil

  12. Continuous VRML output fromContinuous VRML output from regional circulation models: aregional circulation models: a

    E-Print Network [OSTI]

    Continuous VRML output fromContinuous VRML output from regional circulation models: aregional and volume to viewview ·· Generate Virtual Reality Modeling LanguageGenerate Virtual Reality ModelingDesktop or laptop PC with web browser ­­ High speed/large RAM not essentialHigh speed/large RAM not essential

  13. Aerosol Indirect Effect on the Grid-scale Clouds in the Two-way Coupled WRF-CMAQ: Model Description, Development, Evaluation and Regional Analysis

    SciTech Connect (OSTI)

    Yu, Shaocai; Mathur, Rohit; Pleim, Jonathan; Wong, David; Gilliam, R.; Alapaty, Kiran; Zhao, Chun; Liu, Xiaohong

    2014-10-24

    This study implemented first, second and glaciations aerosol indirect effects (AIE) on resolved clouds in the two-way coupled WRF-CMAQ modeling system by including parameterizations for both cloud drop and ice number concentrations on the basis of CMAQpredicted aerosol distributions and WRF meteorological conditions. The performance of the newly-developed WRF-CMAQ model, with alternate CAM and RRTMG radiation schemes, was evaluated with the observations from the CERES satellite and surface monitoring networks (AQS, IMPROVE, CASTNet, STN, and PRISM) over the continental U.S. (CONUS) (12-km resolution) and eastern Texas (4-km resolution) during August and September of 2006. The results at the AQS surface sites show that in August, the NMB values for PM2.5 over the eastern/western U.S (EUS/WUS) and western U.S. (WUS) are 5.3% (?0.1%) and 0.4% (-5.2%) for WRF-CMAQ/CAM (WRF-CMAQ/RRTMG), respectively. The evaluation of PM2.5 chemical composition reveals that in August, WRF-CMAQ/CAM (WRF-CMAQ/RRTMG) consistently underestimated the observed SO4 2? by -23.0% (-27.7%), -12.5% (-18.9%) and -7.9% (-14.8%) over the EUS at the CASTNet, IMPROVE and STN sites, respectively. Both models (WRF-CMAQ/CAM, WRF-CMAQ/RRTMG) overestimated the observed mean OC, EC and TC concentrations over the EUS in August at the IMPROVE sites. Both models generally underestimated the cloud field (SWCF) over the CONUS in August due to the fact that the AIE on the subgrid convective clouds was not considered when the model simulations were run at the 12 km resolution. This is in agreement with the fact that both models captured SWCF and LWCF very well for the 4-km simulation over the eastern Texas when all clouds were resolved by the finer domain. Both models generally overestimated the observed precipitation by more than 40% mainly because of significant overestimation in the southern part of the CONUS in August. The simulations of WRF-CMAQ/CAM and WRF-CMAQ/RRTMG show dramatic improvements for SWCF, LWCF, COD, cloud fractions and precipitation over the ocean relative to those of WRF default cases in August. The model performance in September is similar to that in August except for greater overestimation of PM2.5 due to the overestimations of SO4 2-, NH4 +, NO3 -, and TC over the EUS, less underestimation of clouds (SWCF) over the land areas due to about 10% lower SWCF values and less convective clouds in September.

  14. Impacts of WRF Physics and Measurement Uncertainty on California Wintertime Model Wet Bias

    SciTech Connect (OSTI)

    Chin, H S; Caldwell, P M; Bader, D C

    2009-07-22

    The Weather and Research Forecast (WRF) model version 3.0.1 is used to explore California wintertime model wet bias. In this study, two wintertime storms are selected from each of four major types of large-scale conditions; Pineapple Express, El Nino, La Nina, and synoptic cyclones. We test the impacts of several model configurations on precipitation bias through comparison with three sets of gridded surface observations; one from the National Oceanographic and Atmospheric Administration, and two variations from the University of Washington (without and with long-term trend adjustment; UW1 and UW2, respectively). To simplify validation, California is divided into 4 regions (Coast, Central Valley, Mountains, and Southern California). Simulations are driven by North American Regional Reanalysis data to minimize large-scale forcing error. Control simulations are conducted with 12-km grid spacing (low resolution) but additional experiments are performed at 2-km (high) resolution to evaluate the robustness of microphysics and cumulus parameterizations to resolution changes. We find that the choice of validation dataset has a significant impact on the model wet bias, and the forecast skill of model precipitation depends strongly on geographic location and storm type. Simulations with right physics options agree better with UW1 observations. In 12-km resolution simulations, the Lin microphysics and the Kain-Fritsch cumulus scheme have better forecast skill in the coastal region while Goddard, Thompson, and Morrison microphysics, and the Grell-Devenyi cumulus scheme perform better in the rest of California. The effect of planetary boundary layer, soil-layer, and radiation physics on model precipitation is weaker than that of microphysics and cumulus processes for short- to medium-range low-resolution simulations. Comparison of 2-km and 12-km resolution runs suggests a need for improvement of cumulus schemes, and supports the use of microphysics schemes in coarser-grid applications.

  15. The widely used Weather Research and Forecasting (WRF) model provides a few land surface schemes

    E-Print Network [OSTI]

    Menut, Laurent

    is com- puted from the surface energy balance using a force- restore algorithm for heat exchange within linearised surface energy balance equation representing the combined ground-vegetation sur- face. Soil (USA) and widely used in the World meteorolo- gical and air quality communities. WRF provides a choice

  16. WRF-Var implementation for data assimilation experimentation at MIT

    E-Print Network [OSTI]

    Williams, John K. (John Kenneth)

    2008-01-01

    The goal of this Masters project is to implement the WRF model with 3D variational assimilation (3DVAR) at MIT. A working version of WRF extends the scope of experimentation to mesoscale problems in both real and idealized ...

  17. WRF-Fire Applied in Bulgaria Nina Dobrinkova1

    E-Print Network [OSTI]

    Borissova, Daniela

    WRF-Fire Applied in Bulgaria Nina Dobrinkova1 Georgi Jordanov2 Jan Mandel3 1 Institute and Statistical Sciences University of Colorado Denver jan.mandel@ucdenver.edu Abstract. WRF-Fire consists of the WRF (Weather Research and Forecasting Model) coupled with a fire spread model, based on the level- set

  18. Gaseous Chemistry and Aerosol Mechanism Developments for Version 3.5.1 of the Online Regional Model, WRF-Chem

    SciTech Connect (OSTI)

    Archer-Nicholls, Scott; Lowe, Douglas; Utembe, Steve; Allan, James D.; Zaveri, Rahul A.; Fast, Jerome D.; Hodnebrog, Oivind; Denier van der Gon, Hugo; McFiggans, Gordon

    2014-11-08

    We have made a number of developments in the regional coupled model WRF-Chem, with the aim of making the model more suitable for prediction of atmospheric composition and of interactions between air quality and weather. We have worked on the European domain, with a particular focus on making the model suitable for the study of night time chemistry and oxidation by the nitrate radical in the UK atmosphere. A reduced form of the Common Reactive Intermediates gas-phase chemical mechanism (CRIv2-R5) has been implemented to enable more explicit simulation of VOC degradation. N2O5 heterogeneous chemistry has been added to the existing sectional MOSAIC aerosol module, and coupled to both the CRIv2-R5 and existing CBM-Z gas phase scheme. Modifications have also been made to the sea-spray aerosol emission representation, allowing the inclusion of primary organic material in sea-spray aerosol. Driven by appropriate emissions, wind fields and chemical boundary conditions, implementation of the different developments is illustrated in order to demonstrate the impact that these changes have in the North-West European domain. These developments are now part of the freely available WRF-Chem distribution.

  19. Tracking tropical cloud systems for the diagnosis of simulations by the weather research and forecasting (WRF) model

    SciTech Connect (OSTI)

    Vogelmann, A.M.; Lin, W.; Cialella, A.; Luke, E. P.; Jensen, M. P.; Zhang, M. H.; Boer, E.

    2010-06-27

    To aid in improving model parameterizations of clouds and convection, we examine the capability of models, using explicit convection, to simulate the life cycle of tropical cloud systems in the tropical warm pool. The cloud life cycle is determined using a satellite cloud tracking algorithm (Boer and Ramanathan, J. Geophys. Res., 1997), and the statistics are compared to those of simulations using the Weather Research and Forecasting (WRF) Model. Using New York Blue, a Blue Gene/L supercomputer that is co-operated by Brookhaven and Stony Brook, simulations are run at a resolution comparable to the observations. Initial results suggest that the organization of the mesoscale convective systems is particularly sensitive to the cloud microphysics parameterization used.

  20. Tracking tropical cloud systems - Observations for the diagnosis of simulations by the Weather Research and Forecasting (WRF) Model

    SciTech Connect (OSTI)

    Vogelmann, A.M.; Lin, W.; Cialella, A.; Luke, E.; Jensen, M.; Zhang, M.

    2010-03-15

    To aid in improving model parameterizations of clouds and convection, we examine the capability of models, using explicit convection, to simulate the life cycle of tropical cloud systems in the vicinity of the ARM Tropical Western Pacific sites. The cloud life cycle is determined using a satellite cloud tracking algorithm (Boer and Ramanathan, 1997), and the statistics are compared to those of simulations using the Weather Research and Forecasting (WRF) Model. Using New York Blue, a Blue Gene/L supercomputer that is co-operated by Brookhaven and Stony Brook, simulations are run at a resolution comparable to the observations. Initial results suggest a computational paradox where, even though the size of the simulated systems are about half of that observed, their longevities are still similar. The explanation for this seeming incongruity will be explored.

  1. Assessing the CAM5 Physics Suite in the WRF-Chem Model: Implementation, Resolution Sensitivity, and a First Evaluation for a Regional Case Study

    SciTech Connect (OSTI)

    Ma, Po-Lun; Rasch, Philip J.; Fast, Jerome D.; Easter, Richard C.; Gustafson, William I.; Liu, Xiaohong; Ghan, Steven J.; Singh, Balwinder

    2014-05-06

    A suite of physical parameterizations (deep and shallow convection, turbulent boundary layer, aerosols, cloud microphysics, and cloud fraction) from the global climate model Community Atmosphere Model version 5.1 (CAM5) has been implemented in the regional model Weather Research and Forecasting with chemistry (WRF-Chem). A downscaling modeling framework with consistent physics has also been established in which both global and regional simulations use the same emissions and surface fluxes. The WRF-Chem model with the CAM5 physics suite is run at multiple horizontal resolutions over a domain encompassing the northern Pacific Ocean, northeast Asia, and northwest North America for April 2008 when the ARCTAS, ARCPAC, and ISDAC field campaigns took place. These simulations are evaluated against field campaign measurements, satellite retrievals, and ground-based observations, and are compared with simulations that use a set of common WRF-Chem Parameterizations. This manuscript describes the implementation of the CAM5 physics suite in WRF-Chem provides an overview of the modeling framework and an initial evaluation of the simulated meteorology, clouds, and aerosols, and quantifies the resolution dependence of the cloud and aerosol parameterizations. We demonstrate that some of the CAM5 biases, such as high estimates of cloud susceptibility to aerosols and the underestimation of aerosol concentrations in the Arctic, can be reduced simply by increasing horizontal resolution. We also show that the CAM5 physics suite performs similarly to a set of parameterizations commonly used in WRF-Chem, but produces higher ice and liquid water condensate amounts and near-surface black carbon concentration. Further evaluations that use other mesoscale model parameterizations and perform other case studies are needed to infer whether one parameterization consistently produces results more consistent with observations.

  2. Community Climate System Model (CCSM) Experiments and Output Data

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

    The CCSM web makes the source code of various versions of the model freely available and provides access to experiments that have been run and the resulting output data.

  3. Ecosystem feedbacks to climate change in California: Development, testing, and analysis using a coupled regional atmosphere and land-surface model (WRF3-CLM3.5)

    SciTech Connect (OSTI)

    Subin, Z.M.; Riley, W.J.; Kueppers, L.M.; Jin, J.; Christianson, D.S.; Torn, M.S.

    2010-11-01

    A regional atmosphere model [Weather Research and Forecasting model version 3 (WRF3)] and a land surface model [Community Land Model, version 3.5 (CLM3.5)] were coupled to study the interactions between the atmosphere and possible future California land-cover changes. The impact was evaluated on California's climate of changes in natural vegetation under climate change and of intentional afforestation. The ability of WRF3 to simulate California's climate was assessed by comparing simulations by WRF3-CLM3.5 and WRF3-Noah to observations from 1982 to 1991. Using WRF3-CLM3.5, the authors performed six 13-yr experiments using historical and future large-scale climate boundary conditions from the Geophysical Fluid Dynamics Laboratory Climate Model version 2.1 (GFDL CM2.1). The land-cover scenarios included historical and future natural vegetation from the Mapped Atmosphere-Plant-Soil System-Century 1 (MC1) dynamic vegetation model, in addition to a future 8-million-ha California afforestation scenario. Natural vegetation changes alone caused summer daily-mean 2-m air temperature changes of -0.7 to +1 C in regions without persistent snow cover, depending on the location and the type of vegetation change. Vegetation temperature changes were much larger than the 2-m air temperature changes because of the finescale spatial heterogeneity of the imposed vegetation change. Up to 30% of the magnitude of the summer daily-mean 2-m air temperature increase and 70% of the magnitude of the 1600 local time (LT) vegetation temperature increase projected under future climate change were attributable to the climate-driven shift in land cover. The authors projected that afforestation could cause local 0.2-1.2 C reductions in summer daily-mean 2-m air temperature and 2.0-3.7 C reductions in 1600 LT vegetation temperature for snow-free regions, primarily because of increased evapotranspiration. Because some of these temperature changes are of comparable magnitude to those projected under climate change this century, projections of climate and vegetation change in this region need to consider these climate-vegetation interactions.

  4. PLANNEN MET WRF OP HET ITC JANNEKE ETTEMA, VALENTIJN VENUS, BOB SU EN VICTOR JETTEN

    E-Print Network [OSTI]

    -OUT Inleiding over het ITC WRF-model in Water Resources afdeling WRF-model in Natural Resources afdeling WRF-model Systems Analyses Natural Resources Urban and Regional Planning and Geo- information Management Water Resources Research themes 5 #12;Wind SolarRadiation SensibleHeat (warming) Gases SoilHeat (Conduction

  5. PV output variability modeling using satellite imagery.

    SciTech Connect (OSTI)

    Stein, Joshua S.; Ellis, Abraham; Reno, Matthew J.

    2010-11-01

    High frequency irradiance variability measured on the ground is caused by the formation, dissipation, and passage of clouds in the sky. If we can identify and associate different cloud types/patterns from satellite imagery, we may be able to predict irradiance variability in areas lacking sensors. With satellite imagery covering the entire U.S., this allows for more accurate integration planning and power flow modeling over wide areas. Satellite imagery from southern Nevada was analyzed at 15 minute intervals over a year. Methods for image stabilization, cloud detection, and textural classification of clouds were developed and tested. High Performance Computing parallel processing algorithms were also investigated and tested. Artificial Neural Networks using imagery as inputs were trained on ground-based measurements of irradiance to model the variability and were tested to show some promise as a means for predicting irradiance variability.

  6. On-line Chemistry within WRF: Description and Evaluation of a State-of-the-Art Multiscale Air Quality and Weather Prediction Model

    SciTech Connect (OSTI)

    Grell, Georg; Fast, Jerome D.; Gustafson, William I.; Peckham, Steven E.; McKeen, Stuart A.; Salzmann, Marc; Freitas, Saulo

    2010-01-01

    This is a conference proceeding that is now being put together as a book. This is chapter 2 of the book: "INTEGRATED SYSTEMS OF MESO-METEOROLOGICAL AND CHEMICAL TRANSPORT MODELS" published by Springer. The chapter title is "On-line Chemistry within WRF: Description and Evaluation of a State-of-the-Art Multiscale Air Quality and Weather Prediction Model." The original conference was the COST-728/NetFAM workshop on Integrated systems of meso-meteorological and chemical transport models, Danish Meteorological Institute, Copenhagen, May 21-23, 2007.

  7. A Spatial Analysis of Multivariate Output from Regional Climate Models

    E-Print Network [OSTI]

    Sain, Steve

    , Columbus, OH 43210, ncressie@stat.osu.edu. 1 #12;1 Introduction Many processes in the Earth system cannot, etc. Climate models attempt to represent this system, as well as to incorporate anthropogenic forcingsA Spatial Analysis of Multivariate Output from Regional Climate Models Stephan R. Sain,1 Reinhard

  8. THE NOAA HAZARDOUS WEATHER TESTBED: COLLABORATIVE TESTING OF ENSEMBLE AND CONVECTION-ALLOWING WRF MODELS AND SUBSEQUENT

    E-Print Network [OSTI]

    Xue, Ming

    THE NOAA HAZARDOUS WEATHER TESTBED: COLLABORATIVE TESTING OF ENSEMBLE AND CONVECTION-ALLOWING WRF NOAA's Hazardous Weather Testbed (HWT) is a joint facility managed by the National Severe Storms Laboratory (NSSL), the Storm Prediction Center (SPC), and the NWS Oklahoma City/Norman Weather Forecast

  9. Supplement S1. Description of the WRF performance

    E-Print Network [OSTI]

    Meskhidze, Nicholas

    and simulated wind speed and directions by CAMS stations and WRF model. The blue dots show the CAMS sites.83 and increased to 0.89 when the urban sites were excludedThe agreement for the wind speed in all sitesSupplement S1. Description of the WRF performance The wind fields were computed

  10. Modeling of passive microwave responses in convective situations using output from mesoscale models

    E-Print Network [OSTI]

    Pardo-Carrión, Juan R.

    Modeling of passive microwave responses in convective situations using output from mesoscale models using output from nonhydrostatic mesoscale atmospheric model, Meso-NH, simulations. The radiative for a systematic evaluation of the mesoscale cloud models. An overall good agreement is obtained for both

  11. Statistical post processing of model output from the air quality model LOTOS-EUROS

    E-Print Network [OSTI]

    Stoffelen, Ad

    are calculated with R, a language for statistical computing. The routine STEP in R is used to remove variablesStatistical post processing of model output from the air quality model LOTOS-EUROS Annemiek Pijnappel De Bilt, 2011 | Stageverslag #12;#12;Statistical post processing of model output from the air

  12. A Deep Learning Model for Structured Outputs with High-order Interaction

    E-Print Network [OSTI]

    Toronto, University of

    A Deep Learning Model for Structured Outputs with High-order Interaction Hongyu Guo , Xiaodan Zhu to generate a powerful nonlinear functional mapping from structured input to structured output. More nonlinear functional mapping from high-order structured input to high-order structured output. To this end

  13. Modeling of passive microwave responses in convective situations using output from mesoscale models

    E-Print Network [OSTI]

    Chaboureau, Jean-Pierre

    Modeling of passive microwave responses in convective situations using output from mesoscale models 2003; revised 27 January 2004; accepted 5 February 2004; published 30 March 2004. [1] Passive microwave, which essentially sense cloud tops. Therefore passive microwave observations are a very promising tool

  14. An Advanced simulation Code for Modeling Inductive Output Tubes

    SciTech Connect (OSTI)

    Thuc Bui; R. Lawrence Ives

    2012-04-27

    During the Phase I program, CCR completed several major building blocks for a 3D large signal, inductive output tube (IOT) code using modern computer language and programming techniques. These included a 3D, Helmholtz, time-harmonic, field solver with a fully functional graphical user interface (GUI), automeshing and adaptivity. Other building blocks included the improved electrostatic Poisson solver with temporal boundary conditions to provide temporal fields for the time-stepping particle pusher as well as the self electric field caused by time-varying space charge. The magnetostatic field solver was also updated to solve for the self magnetic field caused by time changing current density in the output cavity gap. The goal function to optimize an IOT cavity was also formulated, and the optimization methodologies were investigated.

  15. Use of Advanced Meteorological Model Output for Coastal Ocean Modeling in Puget Sound

    SciTech Connect (OSTI)

    Yang, Zhaoqing; Khangaonkar, Tarang; Wang, Taiping

    2011-06-01

    It is a great challenge to specify meteorological forcing in estuarine and coastal circulation modeling using observed data because of the lack of complete datasets. As a result of this limitation, water temperature is often not simulated in estuarine and coastal modeling, with the assumption that density-induced currents are generally dominated by salinity gradients. However, in many situations, temperature gradients could be sufficiently large to influence the baroclinic motion. In this paper, we present an approach to simulate water temperature using outputs from advanced meteorological models. This modeling approach was applied to simulate annual variations of water temperatures of Puget Sound, a fjordal estuary in the Pacific Northwest of USA. Meteorological parameters from North American Region Re-analysis (NARR) model outputs were evaluated with comparisons to observed data at real-time meteorological stations. Model results demonstrated that NARR outputs can be used to drive coastal ocean models for realistic simulations of long-term water-temperature distributions in Puget Sound. Model results indicated that the net flux from NARR can be further improved with the additional information from real-time observations.

  16. Methods to Register Models and Input/Output Parameters for Integrated Modeling

    SciTech Connect (OSTI)

    Droppo, James G.; Whelan, Gene; Tryby, Michael E.; Pelton, Mitchell A.; Taira, Randal Y.; Dorow, Kevin E.

    2010-07-10

    Significant resources can be required when constructing integrated modeling systems. In a typical application, components (e.g., models and databases) created by different developers are assimilated, requiring the framework’s functionality to bridge the gap between the user’s knowledge of the components being linked. The framework, therefore, needs the capability to assimilate a wide range of model-specific input/output requirements as well as their associated assumptions and constraints. The process of assimilating such disparate components into an integrated modeling framework varies in complexity and difficulty. Several factors influence the relative ease of assimilating components, including, but not limited to, familiarity with the components being assimilated, familiarity with the framework and its tools that support the assimilation process, level of documentation associated with the components and the framework, and design structure of the components and framework. This initial effort reviews different approaches for assimilating models and their model-specific input/output requirements: 1) modifying component models to directly communicate with the framework (i.e., through an Application Programming Interface), 2) developing model-specific external wrappers such that no component model modifications are required, 3) using parsing tools to visually map pre-existing input/output files, and 4) describing and linking models as dynamic link libraries. Most of these approaches are illustrated using the widely distributed modeling system called Framework for Risk Analysis in Multimedia Environmental Systems (FRAMES). The review concludes that each has its strengths and weakness, the factors that determine which approaches work best in a given application.

  17. WRF-Chem model predictions of the regional impacts of N2O5 heterogeneous processes on night-time chemistry over north-western Europe

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

    Lowe, Douglas; Archer-Nicholls, Scott; Morgan, Will; Allan, James D.; Utembe, Steve; Ouyang, Bin; Aruffo, Eleonora; Le Breton, Michael; Zaveri, Rahul A.; di Carlo, Piero; et al

    2015-02-09

    Chemical modelling studies have been conducted over north-western Europe in summer conditions, showing that night-time dinitrogen pentoxide (N2O5) heterogeneous reactive uptake is important regionally in modulating particulate nitrate and has a~modest influence on oxidative chemistry. Results from Weather Research and Forecasting model with Chemistry (WRF-Chem) model simulations, run with a detailed volatile organic compound (VOC) gas-phase chemistry scheme and the Model for Simulating Aerosol Interactions and Chemistry (MOSAIC) sectional aerosol scheme, were compared with a series of airborne gas and particulate measurements made over the UK in July 2010. Modelled mixing ratios of key gas-phase species were reasonably accurate (correlationsmore »with measurements of 0.7–0.9 for NO2 and O3). However modelled loadings of particulate species were less accurate (correlation with measurements for particulate sulfate and ammonium were between 0.0 and 0.6). Sulfate mass loadings were particularly low (modelled means of 0.5–0.7 ?g kg?1air, compared with measurements of 1.0–1.5 ?g kg?1air). Two flights from the campaign were used as test cases – one with low relative humidity (RH) (60–70%), the other with high RH (80–90%). N2O5 heterogeneous chemistry was found to not be important in the low-RH test case; but in the high-RH test case it had a strong effect and significantly improved the agreement between modelled and measured NO3 and N2O5. When the model failed to capture atmospheric RH correctly, the modelled NO3 and N2O5 mixing ratios for these flights differed significantly from the measurements. This demonstrates that, for regional modelling which involves heterogeneous processes, it is essential to capture the ambient temperature and water vapour profiles. The night-time NO3 oxidation of VOCs across the whole region was found to be 100–300 times slower than the daytime OH oxidation of these compounds. The difference in contribution was less for alkenes (× 80) and comparable for dimethylsulfide (DMS). However the suppression of NO3 mixing ratios across the domain by N2O5 heterogeneous chemistry has only a very slight, negative, influence on this oxidative capacity. The influence on regional particulate nitrate mass loadings is stronger. Night-time N2O5 heterogeneous chemistry maintains the production of particulate nitrate within polluted regions: when this process is taken into consideration, the daytime peak (for the 95th percentile) of PM10 nitrate mass loadings remains around 5.6 ?g kg?1air, but the night-time minimum increases from 3.5 to 4.6 ?g kg?1air. The sustaining of higher particulate mass loadings through the night by this process improves model skill at matching measured aerosol nitrate diurnal cycles and will negatively impact on regional air quality, requiring this process to be included in regional models.« less

  18. Recursive Modeling of Stateflow as Input/Output-Extended Automaton

    E-Print Network [OSTI]

    Kumar, Ratnesh

    for their offline analysis (testing/verification) or online analysis (monitoring), the Stateflow model must-time system and the translated model preserves all the discrete-time behaviors. This work complements our development tool for many industrial domains, such as power systems, aircraft, automotives and chemical plants

  19. Investigation of the aerosol-cloud interaction using the WRF framework 

    E-Print Network [OSTI]

    Li, Guohui

    2009-05-15

    In this dissertation, a two-moment bulk microphysical scheme with aerosol effects is developed and implemented into the Weather Research and Forecasting (WRF) model to investigate the aerosol-cloud interaction. Sensitivities ...

  20. Neural Networks for Post-processing Model Output: Caren Marzban

    E-Print Network [OSTI]

    Marzban, Caren

    variables to the neural network are: Forecast hour, model forecast temperature, relative humidity, wind direction and speed, mean sea level pressure, cloud cover, and precipitation rate and amount. The single to being able to approximate a large class of functions, they are less inclined to overfit data than some

  1. Application of an online-coupled regional climate model, WRF-CAM5, over East Asia for examination of ice nucleation schemes: Part I. Comprehensive model evaluation and trend analysis for 2006 and 2011

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

    Chen, Ying; Zhang, Yang; Fan, Jiwen; Leung, Lai -Yung; Zhang, Qiang; He, Kebin

    2015-08-18

    Online-coupled climate and chemistry models are necessary to realistically represent the interactions between climate variables and chemical species and accurately simulate aerosol direct and indirect effects on cloud, precipitation, and radiation. In this Part I of a two-part paper, simulations from the Weather Research and Forecasting model coupled with the physics package of Community Atmosphere Model (WRF-CAM5) are conducted with the default heterogeneous ice nucleation parameterization over East Asia for two full years: 2006 and 2011. A comprehensive model evaluation is performed using satellite and surface observations. The model shows an overall acceptable performance for major meteorological variables at themore »surface and in the boundary layer, as well as column variables (e.g., precipitation, cloud fraction, precipitating water vapor, downward longwave and shortwave radiation). Moderate to large biases exist for cloud condensation nuclei over oceanic areas, cloud variables (e.g., cloud droplet number concentration, cloud liquid and ice water paths, cloud optical depth, longwave and shortwave cloud forcing). These biases indicate a need to improve the model treatments for cloud processes, especially cloud droplets and ice nucleation, as well as to reduce uncertainty in the satellite retrievals. The model simulates well the column abundances of chemical species except for column SO2 but relatively poor for surface concentrations of several species such as CO, NO2, SO2, PM2.5, and PM10. Several reasons could contribute to the underestimation of major chemical species in East Asia including underestimations of anthropogenic emissions and natural dust emissions, uncertainties in the spatial and vertical distributions of the anthropogenic emissions, as well as biases in meteorological, radiative, and cloud predictions. Despite moderate to large biases in the chemical predictions, the model performance is generally consistent with or even better than that reported for East Asia with only a few exceptions. The model generally reproduces the observed seasonal variations and the difference between 2006 and 2011 for most variables or chemical species. Overall, these results demonstrate promising skills of WRF-CAM5 for long-term simulations at a regional scale and suggest several areas of potential improvements.« less

  2. Supplementary Materials to "Adaptive Output-Feedback Control for Relative Degree Two Systems Based on Closed-Loop Reference Models"

    E-Print Network [OSTI]

    Qu, Zheng

    2015-09-14

    Abstract--- In this paper, a new adaptive output-feedback controller for multi-input-multi-output (MIMO) linear plant models with relative degree two is developed. The adaptive controller includes a baseline design based ...

  3. PV Output Variability Modeling Using Satellite Imagery and Neural Matthew J. Reno1,2

    E-Print Network [OSTI]

    PV Output Variability Modeling Using Satellite Imagery and Neural Networks Matthew J. Reno1, Albuquerque, NM, USA Abstract -- Variability and ramp rates of PV systems are increasingly important to understand and model for grid stability as PV penetration levels rise. Using satellite imagery to identify

  4. Impact of natural and anthropogenic aerosols on stratocumulus and precipitation in the Southeast Pacific: A regional modeling study using WRF-Chem

    SciTech Connect (OSTI)

    Yang, Qing; Gustafson, William I.; Fast, Jerome D.; Wang, Hailong; Easter, Richard C.; Wang, Minghuai; Ghan, Steven J.; Berg, Larry K.; Leung, Lai-Yung R.; Morrison, H.

    2012-09-28

    Cloud-system resolving simulations with the chemistry version of the Weather Research and Forecasting (WRF-Chem) model are used to quantify the impacts of regional anthropogenic and oceanic emissions on changes in aerosol properties, cloud macro- and microphysics, and cloud radiative forcing over the Southeast Pacific (SEP) during the VAMOS Ocean-Cloud-Atmosphere-Land Study Regional Experiment (VOCALS-REx) (15 Oct–Nov 16, 2008). The effects of oceanic aerosols on cloud properties, precipitation, and the shortwave forcing counteract those of anthropogenic aerosols. Despite the relatively small changes in Na concentrations (2-12%) from regional oceanic emissions, their net effect (direct and indirect) on the surface shortwave forcing is opposite and comparable or even larger in magnitude compared to those of regional anthropogenic emissions over the SEP. Two distinct regions are identified in the VOCALS-REx domain. The near-coast polluted region is characterized with strong droplet activation suppression of small particles by sea-salt particles, the more important role of the first than the second indirect effect, low surface precipitation rate, and low aerosol-cloud interaction strength associated with anthropogenic emissions. The relatively clean remote region is characterized with large contributions of Cloud Condensation Nuclei (CCN, number concentration denoted by NCCN) and droplet number concentrations (Nd) from non-local sources (lateral boundaries), a significant amount of surface precipitation, and high aerosol-cloud interactions under a scenario of five-fold increase in anthropogenic emissions. In the clean region, cloud properties have high sensitivity (e.g., 13% increase in cloud-top height and a 9% surface albedo increase) to the moderate increase in CCN concentration (?Nccn = 13 cm-3; 25%) produced by a five-fold increase in regional anthropogenic emissions. The increased anthropogenic aerosols reduce the precipitation amount over the relatively clean remote ocean. The reduction of precipitation (as a cloud water sink) more than doubles the wet scavenging timescale, resulting in an increased aerosol lifetime in the marine boundary layer. Therefore, the aerosol impacts on precipitation are amplified by the positive feedback of precipitation on aerosol. The positive feedback ultimately alters the cloud micro- and macro-properties, leading to strong aerosol-cloud-precipitation interactions. The higher sensitivity of clouds to anthropogenic aerosols over this region is also related to a 16% entrainment rate increase due to anthropogenic aerosols. The simulated aerosol-cloud-precipitation interactions are stronger at night over the clean marine region, while during the day, solar heating results in more frequent decoupling, thinner clouds, reduced precipitation, and reduced sensitivity to anthropogenic emissions. The simulated high sensitivity to the increased anthropogenic emissions over the clean region suggests that the perturbation of the clean marine environment with anthropogenic aerosols may have a larger effect on climate than that of already polluted marine environments.

  5. Weather Research and Forecasting Model's Community Variational/Ensemble Data Assimilation System: WRFDA

    SciTech Connect (OSTI)

    Barker, D.; Huang, X. Y.; Liu, Z. Q.; Auligne, T.; Zhang, X.; Rugg, S.; Ajjaji, R.; Bourgeois, A.; Bray, J.; Chen, Y. S.; Demirtas, M.; Guo, Y. R.; Henderson, T.; Huang, W.; Lin, H. C.; Michalakes, J.; Rizvi, S.; Zhang, X. Y.

    2012-06-01

    Data assimilation is the process by which observations are combined with short-range NWP model output to produce an analysis of the state of the atmosphere at a specified time. Since its inception in the late 1990s, the multiagency Weather Research and Forecasting (WRF) model effort has had a strong data assimilation component, dedicating two working groups to the subject. This article documents the history of the WRF data assimilation effort, and discusses the challenges associated with balancing academic, research, and operational data assimilation requirements in the context of the WRF effort to date. The WRF Model's Community Variational/Ensemble Data Assimilation System (WRFDA) has evolved over the past 10 years, and has resulted in over 30 refereed publications to date, as well as implementation in a wide range of real-time and operational NWP systems.

  6. Characteristic Operator Functions for Quantum Input-Plant-Output Models & Coherent Control

    E-Print Network [OSTI]

    J. E. Gough

    2015-01-09

    We introduce the characteristic operator as the generalization of the usual concept of a transfer function of linear input-plant-output systems to arbitrary quantum nonlinear Markovian input-output models. This is intended as a tool in the characterization of quantum feedback control systems that fits in with the general theory of networks. The definition exploits the linearity of noise differentials in both the plant Heisenberg equations of motion and the differential form of the input-output relations. Mathematically, the characteristic operator is a matrix of dimension equal to the number of outputs times the number of inputs (which must coincide), but with entries that are operators of the plant system. In this sense the characteristic operator retains details of the effective plant dynamical structure and is an essentially quantum object. We illustrate the relevance to model reduction and simplification by showing that the convergence of the characteristic operator in adiabatic elimination limit models requires the same conditions and assumptions appearing in the work on limit quantum stochastic differential theorems of Bouten and Silberfarb. This approach also shows in a natural way that the limit coefficients of the quantum stochastic differential equations in adiabatic elimination problems arise algebraically as Schur complements, and amounts to a model reduction where the fast degrees of freedom are decoupled from the slow ones, and eliminated.

  7. Using Weather Data and Climate Model Output in Economic Analyses of Climate Change

    SciTech Connect (OSTI)

    Auffhammer, Maximilian; Hsiang, Solomon M.; Schlenker, Wolfram; Sobel, Adam H.

    2013-06-28

    Economists are increasingly using weather data and climate model output in analyses of the economic impacts of climate change. This article introduces a set of weather data sets and climate models that are frequently used, discusses the most common mistakes economists make in using these products, and identifies ways to avoid these pitfalls. We first provide an introduction to weather data, including a summary of the types of datasets available, and then discuss five common pitfalls that empirical researchers should be aware of when using historical weather data as explanatory variables in econometric applications. We then provide a brief overview of climate models and discuss two common and significant errors often made by economists when climate model output is used to simulate the future impacts of climate change on an economic outcome of interest.

  8. An Eta-model output study of frontogenesis conditions favoring development of a troposphere-spanning front 

    E-Print Network [OSTI]

    Stewart, Jeffrey Paul

    1999-01-01

    the formation of a troposphere spanning front, tilting, confluent and shear deformation terms of the Petterssen and other applicable frontogenesis equations were evaluated utilizing numerical model output of the Eta model. The outcome of this study indicates...

  9. Application of an online-coupled regional climate model, WRF-CAM5, over East Asia for examination of ice nucleation schemes. Part II. Sensitivity to heterogeneous ice nucleation parameterizations and dust emissions

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

    Zhang, Yang; Chen, Ying; Fan, Jiwen; Leung, Lai -Yung

    2015-09-14

    Aerosol particles can affect cloud microphysical properties by serving as ice nuclei (IN). Large uncertainties exist in the ice nucleation parameterizations (INPs) used in current climate models. In this Part II paper, to examine the sensitivity of the model predictions to different heterogeneous INPs, WRF-CAM5 simulation using the INP of Niemand et al. (N12) [1] is conducted over East Asia for two full years, 2006 and 2011, and compared with simulation using the INP of Meyers et al. (M92) [2], which is the original INP used in CAM5. M92 calculates the nucleated ice particle concentration as a function of icemore »supersaturation, while N12 represents the nucleated ice particle concentration as a function of temperature and the number concentrations and surface areas of dust particles. Compared to M92, the WRF-CAM5 simulation with N12 produces significantly higher nucleated ice crystal number concentrations (ICNCs) in the northern domain where dust sources are located, leading to significantly higher cloud ice number and mass concentrations and ice water path, but the opposite is true in the southern domain where temperatures and moistures play a more important role in ice formation. Overall, the simulation with N12 gives lower downward shortwave radiation but higher downward longwave radiation, cloud liquid water path, cloud droplet number concentrations, and cloud optical depth. The increase in cloud optical depth and the decrease in downward solar flux result in a stronger shortwave and longwave cloud forcing, and decreases temperature at 2-m and precipitation. Changes in temperature and radiation lower surface concentrations of OH, O?, SO?˛?, and PM2.5, but increase surface concentrations of CO, NO?, and SO? over most of the domain. By acting as cloud condensation nuclei (CCN) and IN, dust particles have different impacts on cloud water and ice number concentrations, radiation, and temperature at 2-m and precipitation depending on whether the dominant role of dust is CCN or IN. These results indicate the importance of the heterogeneous ice nucleation treatments and dust emissions in accurately simulating regional climate and air quality.« less

  10. Artificial neural networks for input-output dynamic modeling of nonlinear processes 

    E-Print Network [OSTI]

    Sarimveis, Haralambos

    1992-01-01

    ARTIFICIAL NEURAL NETWORKS FOR INPUT-OUTPUT DYNAMIC MODELING OF NONLINEAR PROCESSES A Th& vis HARALA'&IBOS SARIlcIVEIS Snhnuttect to th&e Otftc& of Cire&11&nt&' Stll&heb of T& x?. Akrr I L'rrrrc& re& tv in pnrtinl frrlfilhrreut of the rc turr...&iveil as to style anel e&a&t& nt hy. 'liM 1 i'll&. hael Nil&a)sou (Chaii of Comuiitte& ) A Teil Watson (I&lenih&a ) Al& ~ii&)e& G. Parloa ( Ileiubei. ) Rayiinai&1 W. F'lumcifelt ( H& a&1 of Defa& it &neat ) A. u ust 10l1a ABSTRACT Artih& ial Neural...

  11. WRF-Chem model predictions of the regional impacts of N2O5 heterogeneous processes on night-time chemistry over north-western Europe

    SciTech Connect (OSTI)

    Lowe, Douglas; Archer-Nicholls, Scott; Morgan, Will; Allan, James D.; Utembe, Steve; Ouyang, Bin; Aruffo, Eleonora; Le Breton, Michael; Zaveri, Rahul A.; di Carlo, Piero; Percival, Carl; Coe, H.; Jones, Roderic L.; McFiggans, Gordon

    2015-01-01

    Chemical modelling studies have been conducted over north-western Europe in summer conditions, showing that night-time dinitrogen pentoxide (N2O5) heterogeneous reactive uptake is important regionally in modulating particulate nitrate and has a~modest influence on oxidative chemistry. Results from Weather Research and Forecasting model with Chemistry (WRF-Chem) model simulations, run with a detailed volatile organic compound (VOC) gas-phase chemistry scheme and the Model for Simulating Aerosol Interactions and Chemistry (MOSAIC) sectional aerosol scheme, were compared with a series of airborne gas and particulate measurements made over the UK in July 2010. Modelled mixing ratios of key gas-phase species were reasonably accurate (correlations with measurements of 0.7–0.9 for NO2 and O3). However modelled loadings of particulate species were less accurate (correlation with measurements for particulate sulfate and ammonium were between 0.0 and 0.6). Sulfate mass loadings were particularly low (modelled means of 0.5–0.7 ?g kg?1air, compared with measurements of 1.0–1.5 ?g kg?1air). Two flights from the campaign were used as test cases – one with low relative humidity (RH) (60–70%), the other with high RH (80–90%). N2O5 heterogeneous chemistry was found to not be important in the low-RH test case; but in the high-RH test case it had a strong effect and significantly improved the agreement between modelled and measured NO3 and N2O5. When the model failed to capture atmospheric RH correctly, the modelled NO3 and N2O5 mixing ratios for these flights differed significantly from the measurements. This demonstrates that, for regional modelling which involves heterogeneous processes, it is essential to capture the ambient temperature and water vapour profiles.

    The night-time NO3 oxidation of VOCs across the whole region was found to be 100–300 times slower than the daytime OH oxidation of these compounds. The difference in contribution was less for alkenes (× 80) and comparable for dimethylsulfide (DMS). However the suppression of NO3 mixing ratios across the domain by N2O5 heterogeneous chemistry has only a very slight, negative, influence on this oxidative capacity. The influence on regional particulate nitrate mass loadings is stronger. Night-time N2O5 heterogeneous chemistry maintains the production of particulate nitrate within polluted regions: when this process is taken into consideration, the daytime peak (for the 95th percentile) of PM10 nitrate mass loadings remains around 5.6 ?g kg?1air, but the night-time minimum increases from 3.5 to 4.6 ?g kg?1air. The sustaining of higher particulate mass loadings through the night by this process improves model skill at matching measured aerosol nitrate diurnal cycles and will negatively impact on regional air quality, requiring this process to be included in regional models.

  12. Robust UAV Coordination for Target Tracking using Output-Feedback Model Predictive Control with Moving Horizon Estimation

    E-Print Network [OSTI]

    Hespanha, Joăo Pedro

    Robust UAV Coordination for Target Tracking using Output-Feedback Model Predictive Control consider the control of two UAVs tracking an evasive moving ground vehicle. The UAVs are small fixed-wing aircraft equipped with gimbaled cameras and must coordinate their control actions so that at least one UAV

  13. Errors Characteristics of Two Grid Refinement Approaches in Aquaplanet Simulations: MPAS-A and WRF

    SciTech Connect (OSTI)

    Hagos, Samson M.; Leung, Lai-Yung R.; Rauscher, Sara; Ringler, Todd

    2013-09-01

    This study compares the error characteristics associated with two grid refinement approaches including global variable resolution and nesting for high resolution regional climate modeling. The global variable resolution model, Model for Prediction Across Scales-Atmosphere (MPAS-A), and the limited area model, Weather Research and Forecasting (WRF) model, are compared in an idealized aqua-planet context. For MPAS-A, simulations have been performed with a quasi-uniform resolution global domain at coarse (1°) and high (0.25°) resolution, and a variable resolution domain with a high resolution region at 0.25° configured inside a coarse resolution global domain at 1° resolution. Similarly, WRF has been configured to run on a coarse (1°) and high (0.25°) tropical channel domain as well as a nested domain with a high resolution region at 0.25° nested two-way inside the coarse resolution (1°) tropical channel. The variable resolution or nested simulations are compared against the high resolution simulations. Both models respond to increased resolution with enhanced precipitation. Limited and significant reduction in the ratio of convective to non-convective precipitation. The limited area grid refinement induces zonal asymmetry in precipitation (heating), accompanied by zonal anomalous Walker like circulations and standing Rossby wave signals. Within the high resolution limited area, the zonal distribution of precipitation is affected by advection in MPAS-A and by the nesting strategy in WRF. In both models, 20 day Kelvin waves propagate through the high-resolution domains fairly unaffected by the change in resolution (and the presence of a boundary in WRF) but increased resolution strengthens eastward propagating inertio-gravity waves.

  14. A New WRF-Chem Treatment for Studying Regional Scale Impacts of Cloud-Aerosol Interactions in Parameterized Cumuli

    SciTech Connect (OSTI)

    Berg, Larry K.; Shrivastava, ManishKumar B.; Easter, Richard C.; Fast, Jerome D.; Chapman, Elaine G.; Liu, Ying

    2015-01-01

    A new treatment of cloud-aerosol interactions within parameterized shallow and deep convection has been implemented in WRF-Chem that can be used to better understand the aerosol lifecycle over regional to synoptic scales. The modifications to the model to represent cloud-aerosol interactions include treatment of the cloud dropletnumber mixing ratio; key cloud microphysical and macrophysical parameters (including the updraft fractional area, updraft and downdraft mass fluxes, and entrainment) averaged over the population of shallow clouds, or a single deep convective cloud; and vertical transport, activation/resuspension, aqueous chemistry, and wet removal of aerosol and trace gases in warm clouds. Thesechanges have been implemented in both the WRF-Chem chemistry packages as well as the Kain-Fritsch cumulus parameterization that has been modified to better represent shallow convective clouds. Preliminary testing of the modified WRF-Chem has been completed using observations from the Cumulus Humilis Aerosol Processing Study (CHAPS) as well as a high-resolution simulation that does not include parameterized convection. The simulation results are used to investigate the impact of cloud-aerosol interactions on the regional scale transport of black carbon (BC), organic aerosol (OA), and sulfate aerosol. Based on the simulations presented here, changes in the column integrated BC can be as large as -50% when cloud-aerosol interactions are considered (due largely to wet removal), or as large as +35% for sulfate in non-precipitating conditions due to the sulfate production in the parameterized clouds. The modifications to WRF-Chem version 3.2.1 are found to account for changes in the cloud drop number concentration (CDNC) and changes in the chemical composition of cloud-drop residuals in a way that is consistent with observations collected during CHAPS. Efforts are currently underway to port the changes described here to WRF-Chem version 3.5, and it is anticipated that they will be included in a future public release of WRF-Chem.

  15. Mesoscale & Microscale Meteorological Division / NCAR WRF Nature Run

    E-Print Network [OSTI]

    Michalakes, John

    Mesoscale & Microscale Meteorological Division / NCAR WRF Nature Run John Michalakes Josh Hacker overview and petascale issues Nature run methodology Results and conclusion #12;Mesoscale & Microscale's atmosphere #12;Mesoscale & Microscale Meteorological Division / NCAR Description of Science · Kinetic energy

  16. A WRF Ensemble for Improved Wind Speed Forecasts at Turbine Height ADAM J. DEPPE AND WILLIAM A. GALLUS JR.

    E-Print Network [OSTI]

    McCalley, James D.

    University, Ames, Iowa (Manuscript received 2 October 2011, in final form 15 May 2013) ABSTRACT The Weather Research and Forecasting Model (WRF) with 10-km horizontal grid spacing was used to explore improvements.S. Department of Energy goal of having 20% of the nation's electrical energy from wind by 2030 will require

  17. Modelling Dynamic Constraints in Electricity Markets and the Costs of Uncertain Wind Output

    E-Print Network [OSTI]

    Musgens, Felix; Neuhoff, Karsten

    2006-03-14

    define model demand as German demand net of CHP, run off river hydro, expected wind generation and international power exchange. Hourly wind forecasts and realisations are provided by ISET e.V. Generation plant data are taken from EWI’s plant data base... one model region and endogenously determine international power exchange. In addition, the model is directly applicable to many other empirical questions, such as the effect of CO2emission costs on plant dispatch and costs or competitive bench...

  18. Evaluating WRF-Chem aerosol indirect effects in Southeast Pacific marine stratocumulus during VOCALS-REx

    SciTech Connect (OSTI)

    Saide P. E.; Springston S.; Spak, S. N.; Carmichael, G. R.; Mena-Carrasco, M. A.; Yang, Q.; Howell, S.; Leon, D. C.; Snider, J. R.; Bandy, A. R.; Collett, J. L.; Benedict, K. B.; de Szoeke, S. P.; Hawkins, L. N.; Allen, G.; Crawford, I.; Crosier, J.

    2012-03-29

    We evaluate a regional-scale simulation with the WRF-Chem model for the VAMOS (Variability of the American Monsoon Systems) Ocean-Cloud-Atmosphere-Land Study Regional Experiment (VOCALS-REx), which sampled the Southeast Pacific's persistent stratocumulus deck. Evaluation of VOCALS-REx ship-based and three aircraft observations focuses on analyzing how aerosol loading affects marine boundary layer (MBL) dynamics and cloud microphysics. We compare local time series and campaign-averaged longitudinal gradients, and highlight differences in model simulations with (W) and without (NW) wet deposition processes. The higher aerosol loadings in the NW case produce considerable changes in MBL dynamics and cloud microphysics, in accordance with the established conceptual model of aerosol indirect effects. These include increase in cloud albedo, increase in MBL and cloud heights, drizzle suppression, increase in liquid water content, and increase in cloud lifetime. Moreover, better statistical representation of aerosol mass and number concentration improves model fidelity in reproducing observed spatial and temporal variability in cloud properties, including top and base height, droplet concentration, water content, rain rate, optical depth (COD) and liquid water path (LWP). Together, these help to quantify confidence in WRF-Chem's modeled aerosol-cloud interactions, especially in the activation parameterization, while identifying structural and parametric uncertainties including: irreversibility in rain wet removal; overestimation of marine DMS and sea salt emissions, and accelerated aqueous sulfate conversion. Our findings suggest that WRF-Chem simulates marine cloud-aerosol interactions at a level sufficient for applications in forecasting weather and air quality and studying aerosol climate forcing, and may do so with the reliability required for policy analysis.

  19. Evaluating WRF-Chem aerosol indirect effects in Southeast Pacific marine stratocumulus during VOCALS-REx

    SciTech Connect (OSTI)

    Saide, Pablo; Spak, S. N.; Carmichael, Gregory; Mena-Carrasco, M. A.; Yang, Qing; Howell, S. G.; Leon, Dolislager; Snider, Jefferson R.; Bandy, Alan R.; Collett, Jeffrey L.; Benedict, K. B.; de Szoeke, S.; Hawkins, Lisa; Allen, Grant; Crawford, I.; Crosier, J.; Springston, S. R.

    2012-03-30

    We evaluate a regional-scale simulation with the WRF-Chem model for the VAMOS (Variability of the American Monsoon Systems) Ocean-Cloud-Atmosphere-Land Study Regional Experiment (VOCALS-REx), which sampled the Southeast Pacific's persistent stratocumulus deck. Evaluation of VOCALS-REx ship-based and aircraft observations focuses on analyzing how aerosol loading affects marine boundary layer (MBL) dynamics and cloud microphysics. We compare local time series and campaign averaged longitudinal gradients, and highlight differences in model simulations with (W) and without wet (NW) deposition processes. The higher aerosol loadings in the NW case produce considerable changes in MBL dynamics and cloud microphysics, in accordance with the established conceptual model of aerosol indirect effects. These include increase in cloud albedo, increase in MBL and cloud heights, drizzle suppression, increase in liquid water content, and increase in cloud lifetime. Moreover, better statistical representation of aerosol mass and number concentration improves model fidelity in reproducing observed spatial and temporal variability in cloud properties, including top and base height, droplet concentration, water content, rain rate, optical depth (COD) and liquid water path (LWP). Together, these help to quantify confidence in WRF-Chem's modeled aerosol-cloud interactions, while identifying structural and parametric uncertainties including: irreversibility in rain wet removal; overestimation of marine DMS and sea salt emissions and accelerated aqueous sulfate conversion. Our findings suggest that WRF-Chem simulates marine cloud-aerosol interactions at a level sufficient for applications in forecasting weather and air quality and studying aerosol climate forcing, including the reliability required for policy analysis and geo-engineering applications.

  20. Model Validation and Spatial Interpolation by Combining Observations with Outputs from Numerical

    E-Print Network [OSTI]

    Washington at Seattle, University of

    ""r,c,rn The authors are for hel]JfuI #12;Abstract Constructing maps of pollution levels is vital for air quality concentrations. Key tlJords: air pollution, Ba~yesian inference, change of support, likelihood approaches, Matern Resolutions 2.5 Modeling a Nonstationary Covariance . 3 Estimation 3.1 Algorithm 4 Application: Air Pollution

  1. USE OF GENERAL CIRCULATION MODEL OUTPUT IN THE CREATION OF CLIMATE CHANGE SCENARIOS

    E-Print Network [OSTI]

    Robock, Alan

    , Sub-Saharan Africa and Venezuela, for use in biological effects models. By combining the general, and possible solar variations, and all agree that surface air temperatures will rise, pre- cipitation patterns will change, and sea level will rise. Even though such projections of the future are relatively crude

  2. Inclusion of biomass burning in WRF-Chem: Impact of wildfires on weather forecasts

    SciTech Connect (OSTI)

    Grell, G. A.; Freitas, Saulo; Stuefer, Martin; Fast, Jerome D.

    2011-06-06

    A plume rise algorithm for wildfires was included in WRF-Chem, and applied to look at the impact of intense wildfires during the 2004 Alaska wildfire season on weather forecasts using model resolutions of 10km and 2km. Biomass burning emissions were estimated using a biomass burning emissions model. In addition, a 1-D, time-dependent cloud model was used online in WRF-Chem to estimate injection heights as well as the final emission rates. It was shown that with the inclusion of the intense wildfires of the 2004 fire season in the model simulations, the interaction of the aerosols with the atmospheric radiation led to significant modifications of vertical profiles of temperature and moisture in cloud-free areas. On the other hand, when clouds were present, the high concentrations of fine aerosol (PM2.5) and the resulting large numbers of Cloud Condensation Nuclei (CCN) had a strong impact on clouds and microphysics, with decreased precipitation coverage and precipitation amounts during the first 12 hours of the integration, but significantly stronger storms during the afternoon hours.

  3. Prediction of Lumen Output and Chromaticity Shift in LEDs Using Kalman Filter and Extended Kalman Filter Based Models

    SciTech Connect (OSTI)

    Lall, Pradeep; Wei, Junchao; Davis, J Lynn

    2014-06-24

    Abstract— Solid-state lighting (SSL) luminaires containing light emitting diodes (LEDs) have the potential of seeing excessive temperatures when being transported across country or being stored in non-climate controlled warehouses. They are also being used in outdoor applications in desert environments that see little or no humidity but will experience extremely high temperatures during the day. This makes it important to increase our understanding of what effects high temperature exposure for a prolonged period of time will have on the usability and survivability of these devices. Traditional light sources “burn out” at end-of-life. For an incandescent bulb, the lamp life is defined by B50 life. However, the LEDs have no filament to “burn”. The LEDs continually degrade and the light output decreases eventually below useful levels causing failure. Presently, the TM-21 test standard is used to predict the L70 life of LEDs from LM-80 test data. Several failure mechanisms may be active in a LED at a single time causing lumen depreciation. The underlying TM-21 Model may not capture the failure physics in presence of multiple failure mechanisms. Correlation of lumen maintenance with underlying physics of degradation at system-level is needed. In this paper, Kalman Filter (KF) and Extended Kalman Filters (EKF) have been used to develop a 70-percent Lumen Maintenance Life Prediction Model for LEDs used in SSL luminaires. Ten-thousand hour LM-80 test data for various LEDs have been used for model development. System state at each future time has been computed based on the state space at preceding time step, system dynamics matrix, control vector, control matrix, measurement matrix, measured vector, process noise and measurement noise. The future state of the lumen depreciation has been estimated based on a second order Kalman Filter model and a Bayesian Framework. Life prediction of L70 life for the LEDs used in SSL luminaires from KF and EKF based models have been compared with the TM-21 model predictions and experimental data.

  4. Development and initial application of the global-through-urban weather research and forecasting model with chemistry

    E-Print Network [OSTI]

    Zhang, Yang

    Development and initial application of the global-through-urban weather research and forecasting application of the global-through-urban weather research and forecasting model with chemistry (GU-WRF/Chem), J. In this work, a global-through-urban WRF/Chem model (i.e., GU-WRF/Chem) has been developed to provide

  5. An Electricity-focused Economic Input-output Model: Life-cycle Assessment and Policy Implications of Future Electricity Generation Scenarios

    E-Print Network [OSTI]

    on the environmental impacts associated with electricity consumption, and that interstate trading tends to makeAn Electricity-focused Economic Input-output Model: Life-cycle Assessment and Policy Implications of Future Electricity Generation Scenarios Joe Marriott Submitted in Partial Fulfillment of the Requirements

  6. Two-Way Integration of WRF and CCSM for Regional Climate Simulations...

    Office of Scientific and Technical Information (OSTI)

    the process of dynamical downscaling by avoiding massive intermediate model outputs at high frequency that are typically required for offline regional downscaling. The inline...

  7. Twelve-month, 12 km resolution North American WRF-Chem v3.4 air quality simulation: performance evaluation

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

    Tessum, C. W.; Hill, J. D.; Marshall, J. D.

    2014-12-02

    We present results from and evaluate the performance of a 12 month, 12 km horizontal resolution air pollution simulation for the contiguous United States using the WRF-Chem (Weather Research and Forecasting with Chemistry) meteorology and chemical transport model (CTM). We employ the 2005 US National Emissions Inventory, the Regional Atmospheric Chemistry Mechanism (RACM), and the Modal Aerosol Dynamics Model for Europe (MADE) with a Volatility Basis Set (VBS) secondary aerosol module. Overall, model performance is comparable to contemporary models used for regulatory and health-effects analysis, with an annual average daytime ozone (O3) mean fractional bias (MFB) of 12% and anmore »annual average fine particulate matter (PM2.5) MFB of ?1%. WRF-Chem, as configured here, tends to overpredict total PM2.5 at some high concentration locations, and generally overpredicts average 24 h O3 concentrations, with better performance at predicting average daytime and daily peak O3 concentrations. Predictive performance for PM2.5 subspecies is mixed: the model overpredicts particulate sulfate (MFB = 65%), underpredicts particulate nitrate (MFB = ?110%) and organic carbon (MFB = ?65%), and relatively accurately predicts particulate ammonium (MFB = 3%) and elemental carbon (MFB = 3%), so that the accuracy in total PM2.5 predictions is to some extent a function of offsetting over- and underpredictions of PM2.5 subspecies. Model predictive performance for PM2.5 and its subspecies is in general worse in winter and in the western US than in other seasons and regions, suggesting spatial and temporal opportunities for future WRF-Chem model development and evaluation.« less

  8. Twelve-month, 12 km resolution North American WRF-Chem v3.4 air quality simulation: performance evaluation

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

    Tessum, C. W.; Hill, J. D.; Marshall, J. D.

    2015-04-07

    We present results from and evaluate the performance of a 12-month, 12 km horizontal resolution year 2005 air pollution simulation for the contiguous United States using the WRF-Chem (Weather Research and Forecasting with Chemistry) meteorology and chemical transport model (CTM). We employ the 2005 US National Emissions Inventory, the Regional Atmospheric Chemistry Mechanism (RACM), and the Modal Aerosol Dynamics Model for Europe (MADE) with a volatility basis set (VBS) secondary aerosol module. Overall, model performance is comparable to contemporary modeling efforts used for regulatory and health-effects analysis, with an annual average daytime ozone (O3) mean fractional bias (MFB) of 12%more »and an annual average fine particulate matter (PM2.5) MFB of ?1%. WRF-Chem, as configured here, tends to overpredict total PM2.5 at some high concentration locations and generally overpredicts average 24 h O3 concentrations. Performance is better at predicting daytime-average and daily peak O3 concentrations, which are more relevant for regulatory and health effects analyses relative to annual average values. Predictive performance for PM2.5 subspecies is mixed: the model overpredicts particulate sulfate (MFB = 36%), underpredicts particulate nitrate (MFB = ?110%) and organic carbon (MFB = ?29%), and relatively accurately predicts particulate ammonium (MFB = 3%) and elemental carbon (MFB = 3%), so that the accuracy in total PM2.5 predictions is to some extent a function of offsetting over- and underpredictions of PM2.5 subspecies. Model predictive performance for PM2.5 and its subspecies is in general worse in winter and in the western US than in other seasons and regions, suggesting spatial and temporal opportunities for future WRF-Chem model development and evaluation.« less

  9. A new WRF-Chem treatment for studying regional-scale impacts of cloud processes on aerosol and trace gases in parameterized cumuli

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

    Berg, L. K.; Shrivastava, M.; Easter, R. C.; Fast, J. D.; Chapman, E. G.; Liu, Y.; Ferrare, R. A.

    2015-02-24

    A new treatment of cloud effects on aerosol and trace gases within parameterized shallow and deep convection, and aerosol effects on cloud droplet number, has been implemented in the Weather Research and Forecasting model coupled with Chemistry (WRF-Chem) version 3.2.1 that can be used to better understand the aerosol life cycle over regional to synoptic scales. The modifications to the model include treatment of the cloud droplet number mixing ratio; key cloud microphysical and macrophysical parameters (including the updraft fractional area, updraft and downdraft mass fluxes, and entrainment) averaged over the population of shallow clouds, or a single deep convectivemore »cloud; and vertical transport, activation/resuspension, aqueous chemistry, and wet removal of aerosol and trace gases in warm clouds. These changes have been implemented in both the WRF-Chem chemistry packages as well as the Kain–Fritsch (KF) cumulus parameterization that has been modified to better represent shallow convective clouds. Testing of the modified WRF-Chem has been completed using observations from the Cumulus Humilis Aerosol Processing Study (CHAPS). The simulation results are used to investigate the impact of cloud–aerosol interactions on regional-scale transport of black carbon (BC), organic aerosol (OA), and sulfate aerosol. Based on the simulations presented here, changes in the column-integrated BC can be as large as –50% when cloud–aerosol interactions are considered (due largely to wet removal), or as large as +40% for sulfate under non-precipitating conditions due to sulfate production in the parameterized clouds. The modifications to WRF-Chem are found to account for changes in the cloud droplet number concentration (CDNC) and changes in the chemical composition of cloud droplet residuals in a way that is consistent with observations collected during CHAPS. Efforts are currently underway to port the changes described here to the latest version of WRF-Chem, and it is anticipated that they will be included in a future public release of WRF-Chem.« less

  10. Study of Multi-Scale Cloud Processes Over the Tropical Western Pacific Using Cloud-Resolving Models Constrained by Satellite Data

    SciTech Connect (OSTI)

    Dudhia, Jimy

    2013-03-12

    Clouds in the tropical western Pacific are an integral part of the large scale environment. An improved understanding of the multi-scale structure of clouds and their interactions with the environment is critical to the ARM (Atmospheric Radiation Measurement) program for developing and evaluating cloud parameterizations, understanding the consequences of model biases, and providing a context for interpreting the observational data collected over the ARM Tropical Western Pacific (TWP) sites. Three-dimensional cloud resolving models (CRMs) are powerful tools for developing and evaluating cloud parameterizations. However, a significant challenge in using CRMs in the TWP is that the region lacks conventional data, so large uncertainty exists in defining the large-scale environment for clouds. This project links several aspects of the ARM program, from measurements to providing improved analyses, and from cloud-resolving modeling to climate-scale modeling and parameterization development, with the overall objective to improve the representations of clouds in climate models and to simulate and quantify resolved cloud effects on the large-scale environment. Our objectives will be achieved through a series of tasks focusing on the use of the Weather Research and Forecasting (WRF) model and ARM data. Our approach includes: -- Perform assimilation of COSMIC GPS radio occultation and other satellites products using the WRF Ensemble Kalman Filter assimilation system to represent the tropical large-scale environment at 36 km grid resolution. This high-resolution analysis can be used by the community to derive forcing products for single-column models or cloud-resolving models. -- Perform cloud-resolving simulations using WRF and its nesting capabilities, driven by the improved regional analysis and evaluate the simulations against ARM datasets such as from TWP-ICE to optimize the microphysics parameters for this region. A cirrus study (Mace and co-authors) already exists for TWP-ICE using satellite and ground-based observations. -- Perform numerical experiments using WRF to investigate how convection over tropical islands in the Maritime Continent interacts with large-scale circulation and affects convection in nearby regions. -- Evaluate and apply WRF as a testbed for GCM cloud parameterizations, utilizing the ability of WRF to run on multiple scales (from cloud resolving to global) to isolate resolution and physics issues from dynamical and model framework issues. Key products will be disseminated to the ARM and larger community through distribution of data archives, including model outputs from the data assimilation products and cloud resolving simulations, and publications.

  11. A high-resolution, cloud-assimilating numerical weather prediction model for solar irradiance forecasting

    E-Print Network [OSTI]

    Mathiesen, Patrick; Collier, Craig; Kleissl, Jan

    2013-01-01

    of the WRF model solar irradiance forecasts in Andalusia (Beyer, H. , 2009.    Irradiance forecasting for the power dependent probabilistic irradiance  forecasts for coastal 

  12. Linear output nitinol engine

    SciTech Connect (OSTI)

    Banks, R.M.

    1986-01-14

    This patent describes a linear output nitinol engine consisting of a number of integrated communicating parts. The engine has an external support framework which is described in detail. The patent further describes a wire transport mechanism, a pair of linkage levers with a loom secured to them, a number of nitinol wires strung between the looms, and a power takeoff block secured to the linkage levers. A pulley positioned in a flip-flop supporting bracket and a power takeoff modality including a tension member connected to a power output cable in order to provide linear power output transmission is described. A method for biasing the timing and the mechanism for timing the synchronization of the throw over arms and the flip-flop of the pulley are also described.

  13. WRF-Chem Simulations of Aerosols and Anthropogenic Aerosol Radiative Forcing in East Asia

    SciTech Connect (OSTI)

    Gao, Yi; Zhao, Chun; Liu, Xiaohong; Zhang, Meigen; Leung, Lai-Yung R.

    2014-08-01

    This study aims to provide a first comprehensive evaluation of WRF-Chem for modeling aerosols and anthropogenic aerosol radiative forcing (RF) over East Asia. Several numerical experiments were conducted from November 2007 to December 2008. Comparison between model results and observations shows that the model can generally reproduce the observed spatial distributions of aerosol concentration, aerosol optical depth (AOD) and single scattering albedo (SSA) from measurements at different sites, including the relatively higher aerosol concentration and AOD over East China and the relatively lower AOD over Southeast Asia, Korean, and Japan. The model also depicts the seasonal variation and transport of pollutions over East Asia. Particulate matter of 10 um or less in the aerodynamic diameter (PM10), black carbon (BC), sulfate (SO42-), nitrate (NO3-) and ammonium (NH4+) concentrations are higher in spring than other seasons in Japan due to the pollutant transport from polluted area of East Asia. AOD is high over Southwest and Central China in winter, spring and autumn and over North China in summer while is low over South China in summer due to monsoon precipitation. SSA is lowest in winter and highest in summer. The model also captures the dust events at the Zhangye site in the semi-arid region of China. Anthropogenic aerosol RF is estimated to range from -5 to -20 W m-2 over land and -20 to -40 W m-2 over ocean at the top of atmosphere (TOA), 5 to 30 W m-2 in the atmosphere (ATM) and -15 to -40 W m-2 at the bottom (BOT). The warming effect of anthropogenic aerosol in ATM results from BC aerosol while the negative aerosol RF at TOA is caused by scattering aerosols such as SO4 2-, NO3 - and NH4+. Positive BC RF at TOA compensates 40~50% of the TOA cooling associated with anthropogenic aerosol.

  14. Comparing urban solid waste recycling from the viewpoint of urban metabolism based on physical input-output model: A case of Suzhou in China

    SciTech Connect (OSTI)

    Liang Sai; Zhang Tianzhu

    2012-01-15

    Highlights: Black-Right-Pointing-Pointer Impacts of solid waste recycling on Suzhou's urban metabolism in 2015 are analyzed. Black-Right-Pointing-Pointer Sludge recycling for biogas is regarded as an accepted method. Black-Right-Pointing-Pointer Technical levels of reusing scrap tires and food wastes should be improved. Black-Right-Pointing-Pointer Other fly ash utilization methods should be exploited. Black-Right-Pointing-Pointer Secondary wastes from reusing food wastes and sludge should be concerned. - Abstract: Investigating impacts of urban solid waste recycling on urban metabolism contributes to sustainable urban solid waste management and urban sustainability. Using a physical input-output model and scenario analysis, urban metabolism of Suzhou in 2015 is predicted and impacts of four categories of solid waste recycling on urban metabolism are illustrated: scrap tire recycling, food waste recycling, fly ash recycling and sludge recycling. Sludge recycling has positive effects on reducing all material flows. Thus, sludge recycling for biogas is regarded as an accepted method. Moreover, technical levels of scrap tire recycling and food waste recycling should be improved to produce positive effects on reducing more material flows. Fly ash recycling for cement production has negative effects on reducing all material flows except solid wastes. Thus, other fly ash utilization methods should be exploited. In addition, the utilization and treatment of secondary wastes from food waste recycling and sludge recycling should be concerned.

  15. CONSTRUCTION OF WRF/CAM TWO-WAY COUPLING SYSTEM AND PRELIMINARY RESULTS

    E-Print Network [OSTI]

    /Atmospheric Sciences Division Brookhaven National Laboratory U.S. Department of Energy Office of Science ABSTRACT A WRF Associates, LLC under Contract No. DE-AC02- 98CH10886 with the U.S. Department of Energy. The publisher-exclusive, paid-up, irrevocable, world-wide license to publish or reproduce the published form of this manuscript

  16. WRF/Chem Simulations Over Fairbanks, AK Atmospheric Stability and Energy Correlation

    E-Print Network [OSTI]

    Moelders, Nicole

    1 WRF/Chem Simulations Over Fairbanks, AK Atmospheric Stability and Energy Correlation Analysis deposition. #12;3 The interactions and cycles of energy, water and trace gas components are also simulated, Alaska, that is characteristic of the Tanana valley; Specifically, Turbulent Kinetic Energy (TKE

  17. Commissioning of output factors for uniform scanning proton beams

    SciTech Connect (OSTI)

    Zheng Yuanshui; Ramirez, Eric; Mascia, Anthony; Ding Xiaoning; Okoth, Benny; Zeidan, Omar; Hsi Wen; Harris, Ben; Schreuder, Andries N.; Keole, Sameer [ProCure Proton Therapy Center, 5901 West Memorial Road, Oklahoma City, Oklahoma 73142 (United States); ProCure Treatment Centers, 420 North Walnut Street, Bloomington, Indiana 47404 (United States); ProCure Proton Therapy Center, 5901 West Memorial Road, Oklahoma City, Oklahoma 73142 (United States)

    2011-04-15

    Purpose: Current commercial treatment planning systems are not able to accurately predict output factors and calculate monitor units for proton fields. Patient-specific field output factors are thus determined by either measurements or empirical modeling based on commissioning data. The objective of this study is to commission output factors for uniform scanning beams utilized at the ProCure proton therapy centers. Methods: Using water phantoms and a plane parallel ionization chamber, the authors first measured output factors with a fixed 10 cm diameter aperture as a function of proton range and modulation width for clinically available proton beams with ranges between 4 and 31.5 cm and modulation widths between 2 and 15 cm. The authors then measured the output factor as a function of collimated field size at various calibration depths for proton beams of various ranges and modulation widths. The authors further examined the dependence of the output factor on the scanning area (i.e., uncollimated proton field), snout position, and phantom material. An empirical model was developed to calculate the output factor for patient-specific fields and the model-predicted output factors were compared to measurements. Results: The output factor increased with proton range and field size, and decreased with modulation width. The scanning area and snout position have a small but non-negligible effect on the output factors. The predicted output factors based on the empirical modeling agreed within 2% of measurements for all prostate treatment fields and within 3% for 98.5% of all treatment fields. Conclusions: Comprehensive measurements at a large subset of available beam conditions are needed to commission output factors for proton therapy beams. The empirical modeling agrees well with the measured output factor data. This investigation indicates that it is possible to accurately predict output factors and thus eliminate or reduce time-consuming patient-specific output measurements for proton treatments.

  18. Implementation and assessment of turbine wake models in the Weather Research and Forecasting model for both mesoscale and large-eddy simulation

    SciTech Connect (OSTI)

    Singer, M; Mirocha, J; Lundquist, J; Cleve, J

    2010-03-03

    Flow dynamics in large wind projects are influenced by the turbines located within. The turbine wakes, regions characterized by lower wind speeds and higher levels of turbulence than the surrounding free stream flow, can extend several rotor diameters downstream, and may meander and widen with increasing distance from the turbine. Turbine wakes can also reduce the power generated by downstream turbines and accelerate fatigue and damage to turbine components. An improved understanding of wake formation and transport within wind parks is essential for maximizing power output and increasing turbine lifespan. Moreover, the influence of wakes from large wind projects on neighboring wind farms, agricultural activities, and local climate are all areas of concern that can likewise be addressed by wake modeling. This work describes the formulation and application of an actuator disk model for studying flow dynamics of both individual turbines and arrays of turbines within wind projects. The actuator disk model is implemented in the Weather Research and Forecasting (WRF) model, which is an open-source atmospheric simulation code applicable to a wide range of scales, from mesoscale to large-eddy simulation. Preliminary results demonstrate the applicability of the actuator disk model within WRF to a moderately high-resolution large-eddy simulation study of a small array of turbines.

  19. Assessing regional scale predictions of aerosols, marine stratocumulus, and their interactions during VOCALS-REx using WRF-Chem

    SciTech Connect (OSTI)

    Yang Q.; Lee Y.; Gustafson Jr., W. I.; Fast, J. D.; Wang, H.; Easter, R. C.; Morrison, H.; Chapman, E. G.; Spak, S. N.; Mena-Carrasco, M. A.

    2011-12-02

    This study assesses the ability of the recent chemistry version (v3.3) of the Weather Research and Forecasting (WRF-Chem) model to simulate boundary layer structure, aerosols, stratocumulus clouds, and energy fluxes over the Southeast Pacific Ocean. Measurements from the VAMOS Ocean-Cloud-Atmosphere-Land Study Regional Experiment (VOCALS-REx) and satellite retrievals (i.e., products from the MODerate resolution Imaging Spectroradiometer (MODIS), Clouds and Earth's Radiant Energy System (CERES), and GOES-10) are used for this assessment. The Morrison double-moment microphysics scheme is newly coupled with interactive aerosols in the model. The 31-day (15 October-16 November 2008) WRF-Chem simulation with aerosol-cloud interactions (AERO hereafter) is also compared to a simulation (MET hereafter) with fixed cloud droplet number concentrations in the microphysics scheme and simplified cloud and aerosol treatments in the radiation scheme. The well-simulated aerosol quantities (aerosol number, mass composition and optical properties), and the inclusion of full aerosol-cloud couplings lead to significant improvements in many features of the simulated stratocumulus clouds: cloud optical properties and microphysical properties such as cloud top effective radius, cloud water path, and cloud optical thickness. In addition to accounting for the aerosol direct and semi-direct effects, these improvements feed back to the simulation of boundary-layer characteristics and energy budgets. Particularly, inclusion of interactive aerosols in AERO strengthens the temperature and humidity gradients within the capping inversion layer and lowers the marine boundary layer (MBL) depth by 130 m from that of the MET simulation. These differences are associated with weaker entrainment and stronger mean subsidence at the top of the MBL in AERO. Mean top-of-atmosphere outgoing shortwave fluxes, surface latent heat, and surface downwelling longwave fluxes are in better agreement with observations in AERO, compared to the MET simulation. Nevertheless, biases in some of the simulated meteorological quantities (e.g., MBL temperature and humidity) and aerosol quantities (e.g., underestimations of accumulation mode aerosol number) might affect simulated stratocumulus and energy fluxes over the Southeastern Pacific, and require further investigation. The well-simulated timing and outflow patterns of polluted and clean episodes demonstrate the model's ability to capture daily/synoptic scale variations of aerosol and cloud properties, and suggest that the model is suitable for studying atmospheric processes associated with pollution outflow over the ocean. The overall performance of the regional model in simulating mesoscale clouds and boundary layer properties is encouraging and suggests that reproducing gradients of aerosol and cloud droplet concentrations and coupling cloud-aerosol-radiation processes are important when simulating marine stratocumulus over the Southeast Pacific.

  20. Simulating 3-D Radiative Transfer Effects over the Sierra Nevada Mountains using WRF

    SciTech Connect (OSTI)

    Gu, Yu; Liou, K. N.; Lee, W- L.; Leung, Lai-Yung R.

    2012-10-30

    A surface solar radiation parameterization based on deviations between 3-D and conventional plane-parallel radiative transfer models has been incorporated into the Weather Research and Forecasting (WRF) model to understand the solar insolation over mountain/snow areas and to investigate the impact of the spatial and temporal distribution and variation of surface solar fluxes on land-surface processes. Using the Sierra-Nevada in the western United States as a testbed, we show that mountain effect could produce up to ?50 to + 50Wm?2 deviations in the surface solar fluxes over the mountain areas, resulting in a temperature increase of up to 1 °C on the sunny side. Upward surface sensible and latent heat fluxes are modulated accordingly to compensate for the change in surface solar fluxes. Snow water equivalent and surface albedo both show decreases on the sunny side of the mountains, indicating more snowmelt and hence reduced snow albedo associated with more solar insolation due to mountain effect. Soil moisture increases on the sunny side of the mountains due to enhanced snowmelt, while decreases on the shaded side. Substantial differences are found in the morning hours from 8-10 a.m. and in the afternoon around 3-5 p.m., while differences around noon and in the early morning and late afternoon are comparatively smaller. Variation in the surface energy balance can also affect atmospheric processes, such as cloud fields, through the modulation of vertical thermal structure. Negative changes of up to ?40 gm?2 are found in the cloud water path, associated with reductions in the surface insolation over the cloud region. The day-averaged deviations in the surface solar flux are positive over the mountain areas and negative in the valleys, with a range between ?12~12Wm?2. Changes in sensible and latent heat fluxes and surface skin temperature follow the solar insolation pattern. Differences in the domain-averaged diurnal variation over the Sierras show that the mountain area receives more solar insolation during early morning and late afternoon, resulting in enhanced upward sensible heat and latent heat fluxes from the surface and a corresponding increase in surface skin temperature. During the middle of the day, however, the surface insolation and heat fluxes show negative changes, indicating a cooling effect. Hence overall, the diurnal variations of surface temperature and surface fluxes in the Sierra-Nevada are reduced through the interactions of radiative transfer and mountains. The hourly differences of the surface solar insolation in higher elevated regions, however, show smaller magnitude in negative changes during the middle of the day and possibly more solar fluxes received during the whole day.

  1. Enhanced performance CCD output amplifier

    DOE Patents [OSTI]

    Dunham, Mark E. (Los Alamos, NM); Morley, David W. (Santa Fe, NM)

    1996-01-01

    A low-noise FET amplifier is connected to amplify output charge from a che coupled device (CCD). The FET has its gate connected to the CCD in common source configuration for receiving the output charge signal from the CCD and output an intermediate signal at a drain of the FET. An intermediate amplifier is connected to the drain of the FET for receiving the intermediate signal and outputting a low-noise signal functionally related to the output charge signal from the CCD. The amplifier is preferably connected as a virtual ground to the FET drain. The inherent shunt capacitance of the FET is selected to be at least equal to the sum of the remaining capacitances.

  2. UFO - The Universal FeynRules Output

    E-Print Network [OSTI]

    Céline Degrande; Claude Duhr; Benjamin Fuks; David Grellscheid; Olivier Mattelaer; Thomas Reiter

    2012-07-31

    We present a new model format for automatized matrix-element generators, the so- called Universal FeynRules Output (UFO). The format is universal in the sense that it features compatibility with more than one single generator and is designed to be flexible, modular and agnostic of any assumption such as the number of particles or the color and Lorentz structures appearing in the interaction vertices. Unlike other model formats where text files need to be parsed, the information on the model is encoded into a Python module that can easily be linked to other computer codes. We then describe an interface for the Mathematica package FeynRules that allows for an automatic output of models in the UFO format.

  3. UFO - The Universal FeynRules Output

    E-Print Network [OSTI]

    Degrande, Céline; Fuks, Benjamin; Grellscheid, David; Mattelaer, Olivier; Reiter, Thomas

    2011-01-01

    We present a new model format for automatized matrix-element generators, the so- called Universal FeynRules Output (UFO). The format is universal in the sense that it features compatibility with more than one single generator and is designed to be flexible, modular and agnostic of any assumption such as the number of particles or the color and Lorentz structures appearing in the interaction vertices. Unlike other model formats where text files need to be parsed, the information on the model is encoded into a Python module that can easily be linked to other computer codes. We then describe an interface for the Mathematica package FeynRules that allows for an automatic output of models in the UFO format.

  4. Evaluation of WRF predicted near hub-height winds and ramp events over a Pacific Northwest site with complex terrain

    SciTech Connect (OSTI)

    Yang, Qing; Berg, Larry K.; Pekour, Mikhail S.; Fast, Jerome D.; Newsom, Rob K.; Stoelinga, Mark; Finley, Cathy

    2013-08-16

    The WRF model version 3.3 is used to simulate near hub-height winds and power ramps utilizing three commonly used planetary boundary-layer (PBL) schemes: Mellor-Yamada-Janji? (MYJ), University of Washington (UW), and Yonsei University (YSU). The predicted winds have small mean biases compared with observations. Power ramps and step changes (changes within an hour) consistently show that the UW scheme performed better in predicting up ramps under stable conditions with higher prediction accuracy and capture rates. Both YSU and UW scheme show good performance predicting up- and down- ramps under unstable conditions with YSU being slightly better for ramp durations longer than an hour. MYJ is the most successful simulating down-ramps under stable conditions. The high wind speed and large shear associated with low-level jets are frequently associated with power ramps, and the biases in predicted low-level jet explain some of the shown differences in ramp predictions among different PBL schemes. Low-level jets were observed as low as ~200 m in altitude over the Columbia Basin Wind Energy Study (CBWES) site, located in an area of complex terrain. The shear, low-level peak wind speeds, as well as the height of maximum wind speed are not well predicted. Model simulations with 3 PBL schemes show the largest variability among them under stable conditions.

  5. Evaluation of and Suggested Improvements to the WSM6 Microphysics in WRF- ARW Using Synthetic and Observed GOES-13 Imagery

    SciTech Connect (OSTI)

    Grasso, Lewis; Lindsey, Daniel T.; Lim, Kyo-Sun; Clark, Adam; Bikos, Dan; Dembek, Scott R.

    2014-10-01

    Synthetic satellite imagery can be employed to evaluate simulated cloud fields. Past studies have revealed that the Weather Research and Forecasting (WRF) WRF Single-Moment 6-class (WSM6) microphysics in WRF-ARW produces less upper level ice clouds within synthetic images compared to observations. Synthetic Geostationary Operational Environmental Satellite (GOES)-13 imagery at 10.7 ?m of simulated cloud fields from the 4 km National Severe Storms Laboratory (NSSL) WRF-ARW is compared to observed GOES-13 imagery. Histograms suggest that too few points contain upper level simulated ice clouds. In particular, side-by-side examples are shown of synthetic and observed convective anvils. Such images illustrate the lack of anvil cloud associated with convection produced by the NSSL WRF-ARW. A vertical profile of simulated hydrometeors suggests that too much cloud water mass may be converted into graupel mass, effectively reducing the main source of ice mass in a simulated anvil. Further, excessive accretion of ice by snow removes ice from an anvil by precipitation settling. Idealized sensitivity tests reveal that a 50% reduction of the conversion of cloud water mass to graupel and a 50% reduction of the accretion rate of ice by snow results in a significant increase in anvil ice of a simulated storm. Such results provide guidance as to which conversions could be reformulated, in a more physical manner, to increase simulated ice mass in the upper troposphere.

  6. Maximum output at minimum cost

    E-Print Network [OSTI]

    Firestone, Jeremy

    Gamesa G90-2.0 MW #12;Maximum output at minimum cost per kWh for low wind sites ®® Class IIIA mast and the electrical substation. This innovative modular design based on TCP/IP architecture has

  7. Assimilation of T-TREC-retrieved wind data with WRF 3DVAR for the short-term forecasting of typhoon Meranti

    E-Print Network [OSTI]

    Xue, Ming

    Assimilation of T-TREC-retrieved wind data with WRF 3DVAR for the short-term forecasting of typhoon Research and Forecasting). The T-TREC winds or the original Vr data from a single coastal Doppler radar of T-TREC-retrieved wind data with WRF 3DVAR for the short-term forecasting of typhoon Meranti (2010

  8. Assessment of multi-decadal WRF-CMAQ simulations for understanding direct aerosol effects on radiation "brightening" in the United States

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

    Gan, C.-M.; Pleim, J.; Mathur, R.; Hogrefe, C.; Long, C. N.; Xing, J.; Wong, D.; Gilliam, R.; Wei, C.

    2015-07-01

    Multi-decadal simulations with the coupled WRF-CMAQ model have been conducted to systematically investigate the changes in anthropogenic emissions of SO2 and NOx over the past 21 years (1990–2010) across the United States (US), their impacts on anthropogenic aerosol loading over North America, and subsequent impacts on regional radiation budgets. In particular, this study attempts to determine the consequences of the changes in tropospheric aerosol burden arising from substantial reductions in emissions of SO2 and NOx associated with control measures under the Clean Air Act (CAA) especially on trends in solar radiation. Extensive analyses conducted by Gan et al. (2014) utilizingmore »observations (e.g. SURFRAD, CASTNET, IMPROVE and ARM) over the past 16 years (1995–2010) indicate a shortwave (SW) radiation (both all-sky and clear-sky) "brightening" in the US. The relationship of the radiation brightening trend with decreases in the aerosol burden is less apparent in the western US. One of the main reasons for this is that the emission controls under the CAA were aimed primarily at reducing pollutants in areas violating national air quality standards, most of which were located in the eastern US while the relatively less populated areas in the western US were less polluted at the beginning of this study period. Comparisons of model results with observations of aerosol optical depth (AOD), aerosol concentration, and radiation demonstrate that the coupled WRF-CMAQ model is capable of replicating the trends well even through it tends to underestimate the AOD. In particular, the sulfate concentration predictions were well matched with the observations. The discrenpancies found in the clear-sky diffuse SW radiation are likely due to several factors such as potential increase of ice particles associated with increasing air traffic, the definition of "clear-sky" in the radiation retrieval methodology and aerosol semi-direct and/or indirect effects which cannot be readily isolated from the observed data.« less

  9. Effect of Terrestrial and Marine Organic Aerosol on Regional and Global Climate: Model Development, Application, and Verification with Satellite Data

    SciTech Connect (OSTI)

    Meskhidze, Nicholas; Zhang, Yang; Kamykowski, Daniel

    2012-03-28

    In this DOE project the improvements to parameterization of marine primary organic matter (POM) emissions, hygroscopic properties of marine POM, marine isoprene derived secondary organic aerosol (SOA) emissions, surfactant effects, new cloud droplet activation parameterization have been implemented into Community Atmosphere Model (CAM 5.0), with a seven mode aerosol module from the Pacific Northwest National Laboratory (PNNL)���¢��������s Modal Aerosol Model (MAM7). The effects of marine aerosols derived from sea spray and ocean emitted biogenic volatile organic compounds (BVOCs) on microphysical properties of clouds were explored by conducting 10 year CAM5.0-MAM7 model simulations at a grid resolution 1.9�������°��������2.5�������° with 30 vertical layers. Model-predicted relationship between ocean physical and biological systems and the abundance of CCN in remote marine atmosphere was compared to data from the A-Train satellites (MODIS, CALIPSO, AMSR-E). Model simulations show that on average, primary and secondary organic aerosol emissions from the ocean can yield up to 20% increase in Cloud Condensation Nuclei (CCN) at 0.2% Supersaturation, and up to 5% increases in droplet number concentration of global maritime shallow clouds. Marine organics were treated as internally or externally mixed with sea salt. Changes associated with cloud properties reduced (absolute value) the model-predicted short wave cloud forcing from -1.35 Wm-2 to -0.25 Wm-2. By using different emission scenarios, and droplet activation parameterizations, this study suggests that addition of marine primary aerosols and biologically generated reactive gases makes an important difference in radiative forcing assessments. All baseline and sensitivity simulations for 2001 and 2050 using global-through-urban WRF/Chem (GU-WRF) were completed. The main objective of these simulations was to evaluate the capability of GU-WRF for an accurate representation of the global atmosphere by exploring the most accurate configuration of physics options in GWRF for global scale modeling in 2001 at a horizontal grid resolution of 1�������° x 1�������°. GU-WRF model output was evaluated using observational datasets from a variety of sources including surface based observations (NCDC and BSRN), model reanalysis (NCEP/ NCAR Reanalysis and CMAP), and remotely-sensed data (TRMM) to evaluate the ability of GU-WRF to simulate atmospheric variables at the surface as well as aloft. Explicit treatment of nanoparticles produced from new particle formation in GU-WRF/Chem-MADRID was achieved by expanding particle size sections from 8 to 12 to cover particles with the size range of 1.16 nm to 11.6 �������µm. Simulations with two different nucleation parameterizations were conducted for August 2002 over a global domain at a 4�������º by 5�������º horizontal resolution. The results are evaluated against field measurement data from the 2002 Aerosol Nucleation and Real Time Characterization Experiment (ANARChE) in Atlanta, Georgia, as well as satellite and reanalysis data. We have also explored the relationship between ���¢��������clean marine���¢������� aerosol optical properties and ocean surface wind speed using remotely sensed data from the Cloud-Aerosol Lidar with Orthogonal Polarization (CALIOP) on board the CALIPSO satellite and the Advanced Microwave Scanning Radiometer (AMSR-E) on board the AQUA satellite. Detailed data analyses

  10. Calibrated Probabilistic Mesoscale Weather Field Forecasting: The Geostatistical Output Perturbation

    E-Print Network [OSTI]

    Raftery, Adrian

    Calibrated Probabilistic Mesoscale Weather Field Forecasting: The Geostatistical Output. This is typically not feasible for mesoscale weather prediction carried out locally by organizations without by simulating realizations of the geostatistical model. The method is applied to 48-hour mesoscale forecasts

  11. Using Mesoscale Weather Model Output as Boundary Conditions for Atmospheric Large-Eddy Simulations and Wind-Plant Aerodynamic Simulations (Presentation)

    SciTech Connect (OSTI)

    Churchfield, M. J.; Michalakes, J.; Vanderwende, B.; Lee, S.; Sprague, M. A.; Lundquist, J. K.; Moriarty, P. J.

    2013-10-01

    Wind plant aerodynamics are directly affected by the microscale weather, which is directly influenced by the mesoscale weather. Microscale weather refers to processes that occur within the atmospheric boundary layer with the largest scales being a few hundred meters to a few kilometers depending on the atmospheric stability of the boundary layer. Mesoscale weather refers to large weather patterns, such as weather fronts, with the largest scales being hundreds of kilometers wide. Sometimes microscale simulations that capture mesoscale-driven variations (changes in wind speed and direction over time or across the spatial extent of a wind plant) are important in wind plant analysis. In this paper, we present our preliminary work in coupling a mesoscale weather model with a microscale atmospheric large-eddy simulation model. The coupling is one-way beginning with the weather model and ending with a computational fluid dynamics solver using the weather model in coarse large-eddy simulation mode as an intermediary. We simulate one hour of daytime moderately convective microscale development driven by the mesoscale data, which are applied as initial and boundary conditions to the microscale domain, at a site in Iowa. We analyze the time and distance necessary for the smallest resolvable microscales to develop.

  12. IEEE TRANSACTIONS ON PLASMA SCIENCE, VOL. 24, NO. 3, JUNE 1996 879 A Neural-Network Model of th.e Input/Output

    E-Print Network [OSTI]

    discusses an approach to model the inputloutput characteristics of the Sinus-6 electron beam accelerator to accurately describe the inputloutput behavior of the Sinus-6-driven backward wave oscillator (BWO of New Mexico Pulsed Power and TPlasma Science Laboratory, in collaboration with the systems group

  13. Impact of WRF Physics and Grid Resolution on Low-level Wind Prediction: Towards the Assessment of Climate Change Impact on Future Wind Power

    SciTech Connect (OSTI)

    Chin, H S; Glascoe, L; Lundquist, J; Wharton, S

    2010-02-24

    The Weather Research and Forecast (WRF) model is used in short-range simulations to explore the sensitivity of model physics and horizontal grid resolution. We choose five events with the clear-sky conditions to study the impact of different planetary boundary layer (PBL), surface and soil-layer physics on low-level wind forecast for two wind farms; one in California (CA) and the other in Texas (TX). Short-range simulations are validated with field measurements. Results indicate that the forecast error of the CA case decreases with increasing grid resolution due to the improved representation of valley winds. Besides, the model physics configuration has a significant impact on the forecast error at this location. In contrast, the forecast error of the TX case exhibits little dependence on grid resolution and is relatively independent of physics configuration. Therefore, the occurrence frequency of lowest root mean square errors (RMSEs) at this location is used to determine an optimal model configuration for subsequent decade-scale regional climate model (RCM) simulations. In this study, we perform two sets of 20-year RCM simulations using the data from the NCAR Global Climate Model (GCM) simulations; one set models the present climate and the other simulates the future climate. These RCM simulations will be used to assess the impact of climate change on future wind energy.

  14. Modeling the Direct and Indirect Effects of Atmospheric Aerosols on Tropical Cyclones 

    E-Print Network [OSTI]

    Lee, Keun-Hee

    2012-02-14

    The direct and indirect effects of aerosols on the hurricane ‘Katrina’ have been investigated using the WRF model with a two-moment bulk microphysical scheme and modified Goddard shortwave radiation scheme. Simulations of the hurricane ‘Katrina...

  15. Exploring the sensitivity of precipitation behavior using a single-column model

    E-Print Network [OSTI]

    Clark, Kathryn

    2013-05-31

    Relationships between land-surface conditions, boundary layer (PBL) growth, atmospheric stability, and convective precipitation behavior are explored using the Weather Research and Forecasting Model (ARW WRF) in single ...

  16. A Framework to Determine the Probability Density Function for the Output Power of Wind Farms

    E-Print Network [OSTI]

    Dominguez-Garcia, Alejandro

    A Framework to Determine the Probability Density Function for the Output Power of Wind Farms Sairaj to the power output of a wind farm while factoring in the availability of the wind turbines in the farm availability model for the wind turbines, we propose a method to determine the wind-farm power output pdf

  17. Assessment of long-term WRF–CMAQ simulations for understanding direct aerosol effects on radiation "brightening" in the United States

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

    Gan, C.-M.; Pleim, J.; Mathur, R.; Hogrefe, C.; Long, C. N.; Xing, J.; Wong, D.; Gilliam, R.; Wei, C.

    2015-11-03

    Long-term simulations with the coupled WRF–CMAQ (Weather Research and Forecasting–Community Multi-scale Air Quality) model have been conducted to systematically investigate the changes in anthropogenic emissions of SO2 and NOx over the past 16 years (1995–2010) across the United States (US), their impacts on anthropogenic aerosol loading over North America, and subsequent impacts on regional radiation budgets. In particular, this study attempts to determine the consequences of the changes in tropospheric aerosol burden arising from substantial reductions in emissions of SO2 and NOx associated with control measures under the Clean Air Act (CAA) especially on trends in solar radiation. Extensive analysesmore »conducted by Gan et al. (2014a) utilizing observations (e.g., SURFRAD, CASTNET, IMPROVE, and ARM) over the past 16 years (1995–2010) indicate a shortwave (SW) radiation (both all-sky and clear-sky) "brightening" in the US. The relationship of the radiation brightening trend with decreases in the aerosol burden is less apparent in the western US. One of the main reasons for this is that the emission controls under the CAA were aimed primarily at reducing pollutants in areas violating national air quality standards, most of which were located in the eastern US, while the relatively less populated areas in the western US were less polluted at the beginning of this study period. Comparisons of model results with observations of aerosol optical depth (AOD), aerosol concentration, and radiation demonstrate that the coupled WRF–CMAQ model is capable of replicating the trends well even though it tends to underestimate the AOD. In particular, the sulfate concentration predictions were well matched with the observations. The discrepancies found in the clear-sky diffuse SW radiation are likely due to several factors such as the potential increase of ice particles associated with increasing air traffic, the definition of "clear-sky" in the radiation retrieval methodology, and aerosol semi-direct and/or indirect effects which cannot be readily isolated from the observed data.« less

  18. A WRF Simulation of the Impact of 3-D Radiative Transfer on Surface Hydrology over the Rocky Mountains and Sierra Nevada

    SciTech Connect (OSTI)

    Liou, K. N.; Gu, Y.; Leung, Lai-Yung R.; Lee, W- L.; Fovell, R. G.

    2013-12-03

    We investigate 3-D mountains/snow effects on solar flux distributions and their impact on surface hydrology over the western United States, specifically the Rocky Mountains and Sierra Nevada. The Weather Research and Forecasting (WRF) model, applied at a 30 km grid resolution, is used in conjunction with a 3-D radiative transfer parameterization covering a time period from 1 November 2007 to 31 May 2008, during which abundant snowfall occurred. A comparison of the 3-D WRF simulation with the observed snow water equivalent (SWE) and precipitation from Snowpack Telemetry (SNOTEL) sites shows reasonable agreement in terms of spatial patterns and daily and seasonal variability, although the simulation generally has a positive precipitation bias. We show that 3-D mountain features have a profound impact on the diurnal and monthly variation of surface radiative and heat fluxes, and on the consequent elevation dependence of snowmelt and precipitation distributions. In particular, during the winter months, large deviations (3-DPP, in which PP denotes the plane-parallel approach) of the monthly mean surface solar flux are found in the morning and afternoon hours due to shading effects for elevations below 2.5 km. During spring, positive deviations shift to the earlier morning. Over mountaintops higher than 3 km, positive deviations are found throughout the day, with the largest values of 40-60Wm?2 occurring at noon during the snowmelt season of April to May. The monthly SWE deviations averaged over the entire domain show an increase in lower elevations due to reduced snowmelt, which leads to a reduction in cumulative runoff. Over higher elevation areas, positive SWE deviations are found because of increased solar radiation available at the surface. Overall, this study shows that deviations of SWE due to 3-D radiation effects range from an increase of 18%at the lowest elevation range (1.5-2 km) to a decrease of 8% at the highest elevation range (above 3 km). Since lower elevation areas occupy larger fractions of the land surface, the net effect of 3-D radiative transfer is to extend snowmelt and snowmelt-driven runoff into the warm season. Because 60-90% of water resources originate from mountains worldwide, the aforementioned differences in simulated hydrology due solely to 3-D interactions between solar radiation and mountains/snow merit further investigation in order to understand the implications of modeling mountain water resources, and these resources’ vulnerability to climate change and air pollution.

  19. Control of fuel cell power output Federico Zenith, Sigurd Skogestad *

    E-Print Network [OSTI]

    Skogestad, Sigurd

    Control of fuel cell power output Federico Zenith, Sigurd Skogestad * Department of Chemical A simplified dynamic model for fuel cells is developed, based on the concept of instantaneous characteristic, which is the set of values of current and voltage that a fuel cell can reach instantaneously

  20. Simulation of Aerosol-Cloud Interactions in the WRF Model at the Southern Great Plains Site 

    E-Print Network [OSTI]

    Vogel, Jonathan 1988-

    2012-08-21

    for the aerosol indirect effect and a modified Goddard shortwave radiation scheme for the aerosol direct effect. The three cases studied include a developing low pressure system, a low precipitation event of mainly cirrus clouds, and a cold frontal passage. Three...

  1. Modeling passive scalar dispersion in the atmospheric boundary layer with WRF large-eddy simulation

    E-Print Network [OSTI]

    Nottrott, Anders; Kleissl, Jan; Keeling, Ralph

    2014-01-01

    self-similarity only in the crosswind direction. Thoseas the vertical and crosswind dimensions of the plumeObukhov length streamwise, crosswind horizontal and vertical

  2. Evaluated Crop Evapotranspiration over a Region of Irrigated Orchards with the Improved ACASA–WRF Model

    E-Print Network [OSTI]

    Falk, Matthias

    Among the uncertain consequences of climate change on agriculture are changes in timing and quantity of precipitation together with predicted higher temperatures and changes in length of growing season. The understanding ...

  3. CLOUD RESOLVING SIMULATIONS USING THE WRF MODEL DRIVEN BY LARGE-SCALE FORCINGS

    E-Print Network [OSTI]

    Department/Atmospheric Sciences Division Brookhaven National Laboratory U.S. Department of Energy Office Associates, LLC under Contract No. DE-AC02- 98CH10886 with the U.S. Department of Energy. The publisher-exclusive, paid-up, irrevocable, world-wide license to publish or reproduce the published form of this manuscript

  4. Modeling of gas and aerosol with WRF/Chem over Europe: Evaluation and sensitivity study

    E-Print Network [OSTI]

    Curci, Gabriele

    average temperature shows a very small negative bias, the relative humidity and the wind speed at curbside sites, 18 at near-city and urban background sites [Van Dingenen et al., 2004]. Chemical speciation

  5. PNNL-Weather Research and Forecasting (WRF)-Chem Modeling in Mexico | Open

    Open Energy Info (EERE)

    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: Alternative Fuels Data Center Home Page on Google Bookmark EERE: Alternative Fuels Data Center Home Page on QA:QA J-E-1 SECTION J APPENDIXsourceII JumpQuarterly Smart GridNorth Carolina: Energy ResourcesLLC JumpMILAGRO

  6. OUTPUT REGULATION OF NONLINEAR NEUTRAL SYSTEMS

    E-Print Network [OSTI]

    Fridman, Emilia

    OUTPUT REGULATION OF NONLINEAR NEUTRAL SYSTEMS Emilia Fridman1 Department of Electrical Engineering, Tel-Aviv University Ramat-Aviv, Tel-Aviv 69978, Israel emilia@eng.tau.ac.il Summary. Output regulation regulation, regulator equations, center manifold 1 Introduction One of the most important problems in control

  7. High Energy Output Marx Generator Design

    SciTech Connect (OSTI)

    Monty Lehmann

    2011-07-01

    High Energy Output Marx Generator Design a design of a six stage Marx generator that has a unipolar pulse waveform of 200 kA in a 50×500 microsecond waveform is presented. The difficulties encountered in designing the components to withstand the temperatures and pressures generated during the output pulse are discussed. The unique methods and materials used to successfully overcome these problems are given. The steps necessary to increase the current output of this Marx generator design to the meg-ampere region or higher are specified.

  8. A Program to Output Stored Pictures

    E-Print Network [OSTI]

    Woodham, Robert J.

    A program called LPTSEE has been written for use with the MIT vision system. LPTSEE makes use of the overprint capability of the line printer to allow the user to output a stored picture image.

  9. InMAP: a new model for air pollution interventions

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

    Tessum, C. W.; Hill, J. D.; Marshall, J. D.

    2015-10-29

    Mechanistic air pollution models are essential tools in air quality management. Widespread use of such models is hindered, however, by the extensive expertise or computational resources needed to run most models. Here, we present InMAP (Intervention Model for Air Pollution), which offers an alternative to comprehensive air quality models for estimating the air pollution health impacts of emission reductions and other potential interventions. InMAP estimates annual-average changes in primary and secondary fine particle (PM2.5) concentrations – the air pollution outcome generally causing the largest monetized health damages – attributable to annual changes in precursor emissions. InMAP leverages pre-processed physical andmore »chemical information from the output of a state-of-the-science chemical transport model (WRF-Chem) within an Eulerian modeling framework, to perform simulations that are several orders of magnitude less computationally intensive than comprehensive model simulations. InMAP uses a variable resolution grid that focuses on human exposures by employing higher spatial resolution in urban areas and lower spatial resolution in rural and remote locations and in the upper atmosphere; and by directly calculating steady-state, annual average concentrations. In comparisons run here, InMAP recreates WRF-Chem predictions of changes in total PM2.5 concentrations with population-weighted mean fractional error (MFE) and bias (MFB) R2 ~ 0.99. Among individual PM2.5 species, the best predictive performance is for primary PM2.5 (MFE: 16 %; MFB: 13 %) and the worst predictive performance is for particulate nitrate (MFE: 119 %; MFB: 106 %). Potential uses of InMAP include studying exposure, health, and environmental justice impacts of potential shifts in emissions for annual-average PM2.5. Features planned for future model releases include a larger spatial domain, more temporal information, and the ability to predict ground-level ozone (O3) concentrations. The InMAP model source code and input data are freely available online.« less

  10. WRF/MM5 User's Workshop, Boulder, CO, June 22-25, 2004, pp. 225-228. A Better Understanding of the Effects of

    E-Print Network [OSTI]

    Berleant, Daniel

    WRF/MM5 User's Workshop, Boulder, CO, June 22-25, 2004, pp. 225-228. A Better Understanding the impact of bugs in a well-known weather simulation system, MM5. The findings help fill a gap in knowledge questions. In the research reported here, bugs were artificially added to MM5. Their effects were analyzed

  11. ASSIMILATION OF DOPPLER RADAR DATA INTO NUMERICAL WEATHER MODELS

    SciTech Connect (OSTI)

    Chiswell, S.; Buckley, R.

    2009-01-15

    During the year 2008, the United States National Weather Service (NWS) completed an eight fold increase in sampling capability for weather radars to 250 m resolution. This increase is expected to improve warning lead times by detecting small scale features sooner with increased reliability; however, current NWS operational model domains utilize grid spacing an order of magnitude larger than the radar data resolution, and therefore the added resolution of radar data is not fully exploited. The assimilation of radar reflectivity and velocity data into high resolution numerical weather model forecasts where grid spacing is comparable to the radar data resolution was investigated under a Laboratory Directed Research and Development (LDRD) 'quick hit' grant to determine the impact of improved data resolution on model predictions with specific initial proof of concept application to daily Savannah River Site operations and emergency response. Development of software to process NWS radar reflectivity and radial velocity data was undertaken for assimilation of observations into numerical models. Data values within the radar data volume undergo automated quality control (QC) analysis routines developed in support of this project to eliminate empty/missing data points, decrease anomalous propagation values, and determine error thresholds by utilizing the calculated variances among data values. The Weather Research and Forecasting model (WRF) three dimensional variational data assimilation package (WRF-3DVAR) was used to incorporate the QC'ed radar data into input and boundary conditions. The lack of observational data in the vicinity of SRS available to NWS operational models signifies an important data void where radar observations can provide significant input. These observations greatly enhance the knowledge of storm structures and the environmental conditions which influence their development. As the increase in computational power and availability has made higher resolution real-time model simulations possible, the need to obtain observations to both initialize numerical models and verify their output has become increasingly important. The assimilation of high resolution radar observations therefore provides a vital component in the development and utility of numerical model forecasts for both weather forecasting and contaminant transport, including future opportunities to improve wet deposition computations explicitly.

  12. Predicting the Energy Output of Wind Farms Based on Weather Data: Important Variables and their Correlation

    E-Print Network [OSTI]

    Vladislavleva, Katya; Neumann, Frank; Wagner, Markus

    2011-01-01

    Wind energy plays an increasing role in the supply of energy world-wide. The energy output of a wind farm is highly dependent on the weather condition present at the wind farm. If the output can be predicted more accurately, energy suppliers can coordinate the collaborative production of different energy sources more efficiently to avoid costly overproductions. With this paper, we take a computer science perspective on energy prediction based on weather data and analyze the important parameters as well as their correlation on the energy output. To deal with the interaction of the different parameters we use symbolic regression based on the genetic programming tool DataModeler. Our studies are carried out on publicly available weather and energy data for a wind farm in Australia. We reveal the correlation of the different variables for the energy output. The model obtained for energy prediction gives a very reliable prediction of the energy output for newly given weather data.

  13. PV output smoothing with energy storage.

    SciTech Connect (OSTI)

    Ellis, Abraham; Schoenwald, David Alan

    2012-03-01

    This report describes an algorithm, implemented in Matlab/Simulink, designed to reduce the variability of photovoltaic (PV) power output by using a battery. The purpose of the battery is to add power to the PV output (or subtract) to smooth out the high frequency components of the PV power that that occur during periods with transient cloud shadows on the PV array. The control system is challenged with the task of reducing short-term PV output variability while avoiding overworking the battery both in terms of capacity and ramp capability. The algorithm proposed by Sandia is purposely very simple to facilitate implementation in a real-time controller. The control structure has two additional inputs to which the battery can respond. For example, the battery could respond to PV variability, load variability or area control error (ACE) or a combination of the three.

  14. Design of a next-generation regional weather research and forecast model.

    SciTech Connect (OSTI)

    Michalakes, J.

    1999-01-13

    The Weather Research and Forecast (WRF) model is a new model development effort undertaken jointly by the National Center for Atmospheric Research (NCAR), the National Oceanic and Atmospheric Administration (NOAA), and a number of collaborating institutions and university scientists. The model is intended for use by operational NWP and university research communities, providing a common framework for idealized dynamical studies, fill physics numerical weather prediction, air-quality simulation, and regional climate. It will eventually supersede large, well-established but aging regional models now maintained by the participating institutions. The WRF effort includes re-engineering the underlying software architecture to produce a modular, flexible code designed from the outset to provide portable performance across diverse computing architectures. This paper outlines key elements of the WRF software design.

  15. Multiple output timing and trigger generator

    SciTech Connect (OSTI)

    Wheat, Robert M. [Los Alamos National Laboratory; Dale, Gregory E [Los Alamos National Laboratory

    2009-01-01

    In support of the development of a multiple stage pulse modulator at the Los Alamos National Laboratory, we have developed a first generation, multiple output timing and trigger generator. Exploiting Commercial Off The Shelf (COTS) Micro Controller Units (MCU's), the timing and trigger generator provides 32 independent outputs with a timing resolution of about 500 ns. The timing and trigger generator system is comprised of two MCU boards and a single PC. One of the MCU boards performs the functions of the timing and signal generation (the timing controller) while the second MCU board accepts commands from the PC and provides the timing instructions to the timing controller. The PC provides the user interface for adjusting the on and off timing for each of the output signals. This system provides 32 output or timing signals which can be pre-programmed to be in an on or off state for each of 64 time steps. The width or duration of each of the 64 time steps is programmable from 2 {micro}s to 2.5 ms with a minimum time resolution of 500 ns. The repetition rate of the programmed pulse train is only limited by the time duration of the programmed event. This paper describes the design and function of the timing and trigger generator system and software including test results and measurements.

  16. Porous radiant burners having increased radiant output

    DOE Patents [OSTI]

    Tong, Timothy W. (Tempe, AZ); Sathe, Sanjeev B. (Tempe, AZ); Peck, Robert E. (Tempe, AZ)

    1990-01-01

    Means and methods for enhancing the output of radiant energy from a porous radiant burner by minimizing the scattering and increasing the adsorption, and thus emission of such energy by the use of randomly dispersed ceramic fibers of sub-micron diameter in the fabrication of ceramic fiber matrix burners and for use therein.

  17. Adjoint Error Correction for Integral Outputs

    E-Print Network [OSTI]

    Pierce, Niles A.

    University Computing Laboratory, Oxford OX1 3QD, United Kingdom 2 Applied & Computational Mathematics problem gives the e#11;ect of numerical approximations on the output func- tional of interest and nonlinear di#11;erential equations, incor- porating a range of numerical examples illustrating the ability

  18. Generalized Input/Output Equations and Nonlinear Realizability

    E-Print Network [OSTI]

    Wang, Yuan

    operator satisfies a possibly high-order differential input/output equation, then it is locally realizable-9108250 and DMS-9403924 Keywords: Generating series, local realization of input/output operators, input/outputGeneralized Input/Output Equations and Nonlinear Realizability Yuan Wang Mathematics Department

  19. Multiple-Input Multiple-Output (MIMO) Linear Systems Extreme Inputs/Outputs

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

    Smallwood, David O.

    2007-01-01

    A linear structure is excited at multiple points with a stationary normal random process. The response of the structure is measured at multiple outputs. If the autospectral densities of the inputs are specified, the phase relationships between the inputs are derived that will minimize or maximize the trace of the autospectral density matrix of the outputs. If the autospectral densities of the outputs are specified, the phase relationships between the outputs that will minimize or maximize the trace of the input autospectral density matrix are derived. It is shown that other phase relationships and ordinary coherence less than one willmore »result in a trace intermediate between these extremes. Least favorable response and some classes of critical response are special cases of the development. It is shown that the derivation for stationary random waveforms can also be applied to nonstationary random, transients, and deterministic waveforms.« less

  20. Output Feedback Stabilization of Linear PDEs with Finite Dimensional Input-Output Maps and Kelvin-Voigt Damping

    E-Print Network [OSTI]

    Chung, Soon-Jo

    Output Feedback Stabilization of Linear PDEs with Finite Dimensional Input-Output Maps and Kelvin with respect to x. The finite dimensional input-output (FDIO) map makes it tempting to design an output of Technology Bombay, Mumbai, India. Member, IEEE. paranjape@aero.iitb.ac.in. Soon-Jo Chung

  1. Evaluating climate models: Should we use weather or climate observations?

    SciTech Connect (OSTI)

    Oglesby, Robert J [ORNL; Erickson III, David J [ORNL

    2009-12-01

    Calling the numerical models that we use for simulations of climate change 'climate models' is a bit of a misnomer. These 'general circulation models' (GCMs, AKA global climate models) and their cousins the 'regional climate models' (RCMs) are actually physically-based weather simulators. That is, these models simulate, either globally or locally, daily weather patterns in response to some change in forcing or boundary condition. These simulated weather patterns are then aggregated into climate statistics, very much as we aggregate observations into 'real climate statistics'. Traditionally, the output of GCMs has been evaluated using climate statistics, as opposed to their ability to simulate realistic daily weather observations. At the coarse global scale this may be a reasonable approach, however, as RCM's downscale to increasingly higher resolutions, the conjunction between weather and climate becomes more problematic. We present results from a series of present-day climate simulations using the WRF ARW for domains that cover North America, much of Latin America, and South Asia. The basic domains are at a 12 km resolution, but several inner domains at 4 km have also been simulated. These include regions of complex topography in Mexico, Colombia, Peru, and Sri Lanka, as well as a region of low topography and fairly homogeneous land surface type (the U.S. Great Plains). Model evaluations are performed using standard climate analyses (e.g., reanalyses; NCDC data) but also using time series of daily station observations. Preliminary results suggest little difference in the assessment of long-term mean quantities, but the variability on seasonal and interannual timescales is better described. Furthermore, the value-added by using daily weather observations as an evaluation tool increases with the model resolution.

  2. SARAH 3.2: Dirac Gauginos, UFO output, and more

    E-Print Network [OSTI]

    Florian Staub

    2013-02-12

    SARAH is a Mathematica package optimized for the fast, efficient and precise study of supersymmetric models beyond the MSSM: a new model can be defined in a short form and all vertices are derived. This allows SARAH to create model files for FeynArts/FormCalc, CalcHep/CompHep and WHIZARD/OMEGA. The newest version of SARAH now provides the possibility to create model files in the UFO format which is supported by MadGraph 5, MadAnalysis, GoSam, and soon by Herwig++. Furthermore, SARAH also calculates the mass matrices, RGEs and one-loop corrections to the mass spectrum. This information is used to write source code for SPheno in order to create a precision spectrum generator for the given model. This spectrum-generator-generator functionality as well as the output of WHIZARD and CalcHep model files have seen further improvement in this version. Also models including Dirac Gauginos are supported with the new version of SARAH, and additional checks for the consistency of model implementations have been created.

  3. Bandwidth Guaranteed Multicast Scheduling for Virtual Output Queued Packet Switches

    E-Print Network [OSTI]

    Pan, Deng

    Bandwidth Guaranteed Multicast Scheduling for Virtual Output Queued Packet Switches Deng Pan Dept in Internet multimedia applications. Although several multicast scheduling schemes for packet switches have output queued (VOQ) switches. We propose the Credit based Multicast Fair scheduling (CMF) algorithm

  4. Modeling the Atmospheric Boundary Layer Wind Response to Mesoscale Sea Surface Temperature Perturbations

    E-Print Network [OSTI]

    Kurapov, Alexander

    Modeling the Atmospheric Boundary Layer Wind Response to Mesoscale Sea Surface Temperature received 25 October 2013, in final form 24 July 2014) ABSTRACT The wind speed response to mesoscale SST Research and Forecasting (WRF) Model and the U.S. Navy Coupled Ocean­Atmosphere Mesoscale Prediction System

  5. Seasonal trends in air temperature and precipitation in IPCC AR4 GCM output for Kansas, USA: evaluation and implications

    E-Print Network [OSTI]

    Brunsell, Nathaniel A.; Jones, Aubrey R.; Feddema, Johannes J.

    2009-01-08

    averaged monthly output of 21 global climate models under the Special Report on Emissions Scenarios A1B scenario used in the Intergovernmental Panel of Climate Change Assessment Report 4 are examined for six grid cells representing Kansas. To ascertain...

  6. Simulating Quantum Circuits with Sparse Output Distributions

    E-Print Network [OSTI]

    Martin Schwarz; Maarten Van den Nest

    2013-10-24

    We show that several quantum circuit families can be simulated efficiently classically if it is promised that their output distribution is approximately sparse i.e. the distribution is close to one where only a polynomially small, a priori unknown subset of the measurement probabilities are nonzero. Classical simulations are thereby obtained for quantum circuits which---without the additional sparsity promise---are considered hard to simulate. Our results apply in particular to a family of Fourier sampling circuits (which have structural similarities to Shor's factoring algorithm) but also to several other circuit families, such as IQP circuits. Our results provide examples of quantum circuits that cannot achieve exponential speed-ups due to the presence of too much destructive interference i.e. too many cancelations of amplitudes. The crux of our classical simulation is an efficient algorithm for approximating the significant Fourier coefficients of a class of states called computationally tractable states. The latter result may have applications beyond the scope of this work. In the proof we employ and extend sparse approximation techniques, in particular the Kushilevitz-Mansour algorithm, in combination with probabilistic simulation methods for quantum circuits.

  7. Effect of parameterized sub-grid-scale diffusions on the source-receptor relationship in the WRF model

    E-Print Network [OSTI]

    Chen, Shu-Hua

    is the consequence of wave breaking under certain flow regimes when mountain waves are present [Peltier and Clark, and mountain peak height, respectively. Below this critical level, either self-induced or specified [Peltier and Clark, 1979; Clark and Peltier, 1984; Durran, 1986; Peltier and Scinocca, 1990; Scinocca and Peltier

  8. Megacity impacts on regional ozone formation: observations and WRF-Chem modeling for the MIRAGE-Shanghai field campaign

    E-Print Network [OSTI]

    2013-01-01

    4 September, when the city pollution was defined as “cleanSeptember, when the city pollution was defined as “pollutedpollution control not only in the urban area of large cities,

  9. Megacity impacts on regional ozone formation: observations and WRF-Chem modeling for the MIRAGE-Shanghai field campaign

    E-Print Network [OSTI]

    2013-01-01

    Tie, X. : Analysis of ozone and VOCs measured in Shanghai: AMegacity impacts on regional ozone formation terminations inand Tie, X. : Study of ozone “weekend effect” in Shanghai,

  10. Compact waveguide power divider with multiple isolated outputs

    DOE Patents [OSTI]

    Moeller, Charles P. (Del Mar, CA)

    1987-01-01

    A waveguide power divider (10) for splitting electromagnetic microwave power and directionally coupling the divided power includes an input waveguide (21) and reduced height output waveguides (23) interconnected by axial slots (22) and matched loads (25) and (26) positioned at the unused ends of input and output guides (21) and (23) respectively. The axial slots are of a length such that the wave in the input waveguide (21) is directionally coupled to the output waveguides (23). The widths of input guide (21) and output guides (23) are equal and the width of axial slots (22) is one half of the width of the input guide (21).

  11. Predicted Radiation Output from Several Kilograms of Plutonium...

    Office of Scientific and Technical Information (OSTI)

    Cycle & Fuel Materials(11); Nuclear Physics & Radiation Physics(73) radiation output, passive signature, plutonium oxide Word Cloud More Like This Full Text File size NAView Full...

  12. Research Outputs Archive Journal Articles 2007

    E-Print Network [OSTI]

    New South Wales, University of

    design using a shared IFC building model-Learning from experience', Automation in Construction, vol 16 fibre- reinforced polymers (CFRP) tendons', Construction and Building Materials, vol 21, pp 777-788 *Kordjamshidi, M, *King, SE, *Zehner, RB & *Prasad, DK 2007, 'Modeling efficient building design: A comparison

  13. Using Extra Output Learning to Insert a Symbolic Theory into a Connectionist Network

    E-Print Network [OSTI]

    Dawson, Michael

    technique called extra output learning. In Study 1, standard machine learning techniques were used to create of Alberta, Edmonton, Alberta, Canada T6G 2E9 (E-mail: mike@bcp.psych.ualberta.ca); 2Center for the Neural whether a classical model could be translated into a PDP network using a standard connectionist training

  14. Output from microturbulence simulation of a fusion plasma showing 22 of the 400 million plasma

    E-Print Network [OSTI]

    Utah, University of

    instabilities is critical in the design of fusion reactors such as the International Thermonuclear ExperimentalOutput from microturbulence simulation of a fusion plasma showing 22 of the 400 million plasma particles simulated. Petascale-class machines help fusion scientists model their simulations with higher

  15. Analytical input-output and supply chain study of China's coke and steel sectors

    E-Print Network [OSTI]

    Li, Yu, 1976-

    2004-01-01

    I design an input-output model to investigate the energy supply chain of coal-coke-steel in China. To study the demand, supply, and energy-intensity issues for coal and coke from a macroeconomic perspective, I apply the ...

  16. Halbach array generator/motor having mechanically regulated output voltage and mechanical power output

    DOE Patents [OSTI]

    Post, Richard F.

    2005-06-14

    A motor/generator has its stationary portion, i.e., the stator, positioned concentrically within its rotatable element, i.e., the rotor, along the axis of rotation of the rotor. The rotor includes a Halbach array of magnets. The voltage and power outputs are regulated by varying the radial gap in between the stator windings and the rotating Halbach array. The gap is varied by extensible and retractable supports attached to the stator windings that can move the windings in a radial direction.

  17. On the Multi-output Filtering Model and Its Applications

    E-Print Network [OSTI]

    International Association for Cryptologic Research (IACR)

    it on studying TUAK's f1 algorithm, AES, KASUMI and PRESENT. We demonstrate that the success rate of the attack on KASUMI and PRESENT is non-negligible, but f1 and AES are resistant to this attack. Second, we study and Teng Wu Department of Electrical and Computer Engineering University of Waterloo, Canada {ggong

  18. Visual Analytics Approach for the Assessment of Simulation Model Output

    E-Print Network [OSTI]

    Freytag, Johann-Christoph

    Collaborator was able to identify Green = Central Pacific El Nino Red = Eastern Pacific El Nino #12;

  19. Bayesian approaches for combining computational model output and physical

    Office of Scientific and Technical Information (OSTI)

    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: Alternative Fuels Data Center Homesum_a_epg0_fpd_mmcf_m.xls" ,"Available from WebQuantity of NaturalDukeWakefield MunicipalTechnical Report: Achievements of structural genomicsOffice ofobservations (Conference) |

  20. Bayesian approaches for combining computational model output and physical

    Office of Scientific and Technical Information (OSTI)

    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: Alternative Fuels Data Center Homesum_a_epg0_fpd_mmcf_m.xls" ,"Available from WebQuantity of NaturalDukeWakefieldSulfate Reducing Bacteria (Technical Report) | SciTechReport) | SciTech

  1. SWAT 2012 Input/Output Documentation 

    E-Print Network [OSTI]

    Arnold, J.G.; Kiniry, J.R.; Srinivasan, R.; Williams, J.R.; Haney, E.B.; Neitsch, S.L.

    2013-03-04

    The Soil and Water Assessment Tool (SWAT) is a comprehensive model that requires a diversity of information in order to run. Novice users may feel overwhelmed by the variety and number of inputs when they first begin to ...

  2. Challenges in Predicting Power Output from Offshore Wind Farms

    E-Print Network [OSTI]

    Pryor, Sara C.

    Challenges in Predicting Power Output from Offshore Wind Farms R. J. Barthelmie1 and S. C. Pryor2 Abstract: Offshore wind energy is developing rapidly in Europe and the trend is towards large wind farms an offshore wind farm, accurate assessment of the wind resource/power output from the wind farm is a necessity

  3. Toward Controlled Wind Farm Output: Adjustable Power Filtering

    E-Print Network [OSTI]

    Maggiore, Manfredi

    1 Toward Controlled Wind Farm Output: Adjustable Power Filtering Barry G. Rawn, Student Member research into the limits on controllable power output from wind energy conversion systems. The viewpoint methodology that specifies the delivered power as a filtered version of available wind power. Simulation

  4. channels enable burst output in rat cerebellar Purkinje cells

    E-Print Network [OSTI]

    Turner, Ray

    to generate an appropriate spike output depends on a balance between membrane depolarizationsKv3 K+ channels enable burst output in rat cerebellar Purkinje cells B. E. McKay and R. W. Turner and the repolarizing actions of K+ currents. The high-voltage-activated Kv3 class of K+ channels repolarizes Na+ spikes

  5. POLE PLACEMENT VIA OUTPUT FEEDBACK: A METHODOLOGY BASED ON

    E-Print Network [OSTI]

    Orsi, Robert

    POLE PLACEMENT VIA OUTPUT FEEDBACK: A METHODOLOGY BASED ON PROJECTIONS Kaiyang Yang and Robert Orsi feedback pole placement problems of the following rather general form: given n subsets of the complex plane, find a static output feedback that places in each of these subsets a pole of the closed loop system

  6. Using Economic Input/Output Tables to Predict a Country’s Nuclear Status

    SciTech Connect (OSTI)

    Weimar, Mark R.; Daly, Don S.; Wood, Thomas W.

    2010-07-15

    Both nuclear power and nuclear weapons programs should have (related) economic signatures which are detectible at some scale. We evaluated this premise in a series of studies using national economic input/output (IO) data. Statistical discrimination models using economic IO tables predict with a high probability whether a country with an unknown predilection for nuclear weapons proliferation is in fact engaged in nuclear power development or nuclear weapons proliferation. We analyzed 93 IO tables, spanning the years 1993 to 2005 for 37 countries that are either members or associates of the Organization for Economic Cooperation and Development (OECD). The 2009 OECD input/output tables featured 48 industrial sectors based on International Standard Industrial Classification (ISIC) Revision 3, and described the respective economies in current country-of-origin valued currency. We converted and transformed these reported values to US 2005 dollars using appropriate exchange rates and implicit price deflators, and addressed discrepancies in reported industrial sectors across tables. We then classified countries with Random Forest using either the adjusted or industry-normalized values. Random Forest, a classification tree technique, separates and categorizes countries using a very small, select subset of the 2304 individual cells in the IO table. A nation’s efforts in nuclear power, be it for electricity or nuclear weapons, are an enterprise with a large economic footprint -- an effort so large that it should discernibly perturb coarse country-level economics data such as that found in yearly input-output economic tables. The neoclassical economic input-output model describes a country’s or region’s economy in terms of the requirements of industries to produce the current level of economic output. An IO table row shows the distribution of an industry’s output to the industrial sectors while a table column shows the input required of each industrial sector by a given industry.

  7. Perceptrons without output codes , A. Echeverria2

    E-Print Network [OSTI]

    Sierra, Alejandro

    incurred by assigning patterns to the class of the closest projected mean. In this way, we obtain two or construct models as combinations of binary classifiers. The "one-versus-all" strategy [1] and error means divided by the dispersion around the means. Typically, once a projection is found, patterns

  8. Saving Output to a File (Using Codeblocks or Dev-C++) Saving Your Output to a File

    E-Print Network [OSTI]

    Sokol, Dina

    Saving Output to a File (Using Codeblocks or Dev-C++) Saving Your Output to a File To save | New | Source File. d. In the new window, right-click and select Paste. e. Then select "File | Save as" to save and name the file. i. In the window that pops up, the bottom fill-in box is labelled "Save as type

  9. Development and initial application of a sub-grid scale plume treatment in a state-of-the-art online Multi-scale Air Quality and Weather Prediction Model

    E-Print Network [OSTI]

    Zhang, Yang

    grid models applies to both "off- line" models, in which externally derived meteorology is usedDevelopment and initial application of a sub-grid scale plume treatment in a state of plume-in-grid (PinG) treatment in online-coupled WRF/Chem. grid

  10. Simulation of the output power of copper bromide lasers by the MARS method

    SciTech Connect (OSTI)

    Iliev, I P; Voynikova, D S; Gocheva-Ilieva, S G

    2012-04-30

    The dependence of the output power of CuBr lasers (operating at wavelengths of 510.6 and 578.2 nm) on ten input physical parameters has been statistically analysed based on a large amount of experimental data accumulated for these lasers. Regression models have been built using the flexible nonparametric method of multivariate adaptive regression splines (MARS) to describe both linear and nonlinear local dependences. These models cover more than 97% initial data with an error comparable with the experimental error; they are applied to estimate and predict the output powers of both existing and future lasers. The advantage of the models constructed for estimating laser parameters over the standard parametric methods of multivariate factor and regression analysis is demonstrated.

  11. Dual output acoustic wave sensor for molecular identification

    DOE Patents [OSTI]

    Frye, Gregory C. (Cedar Crest, NM); Martin, Stephen J. (Albuquerque, NM)

    1991-01-01

    A method of identification and quantification of absorbed chemical species by measuring changes in both the velocity and the attenuation of an acoustic wave traveling through a thin film into which the chemical species is sorbed. The dual output response provides two independent sensor responses from a single sensing device thereby providing twice as much information as a single output sensor. This dual output technique and analysis allows a single sensor to provide both the concentration and the identity of a chemical species or permits the number of sensors required for mixtures to be reduced by a factor of two.

  12. Passive states optimize the output of bosonic Gaussian quantum channels

    E-Print Network [OSTI]

    Giacomo De Palma; Dario Trevisan; Vittorio Giovannetti

    2015-11-01

    An ordering between the quantum states emerging from a single mode gauge-covariant bosonic Gaussian channel is proven. Specifically, we show that within the set of input density matrices with the same given spectrum, the element passive with respect to the Fock basis (i.e. diagonal with decreasing eigenvalues) produces an output which majorizes all the other outputs emerging from the same set. When applied to pure input states, our finding includes as a special case the result of A. Mari, et al., Nat. Comm. 5, 3826 (2014) which implies that the output associated to the vacuum majorizes the others.

  13. Correction method for in-air output ratio for output variations occurring with changes in backscattered radiation

    SciTech Connect (OSTI)

    Tajiri, Minoru; Tokiya, Yuji; Watanabe, Kazuhiro [Research Center Hospital for Charged Particle Therapy, National Institute of Radiological Sciences, 4-9-1, Anagawa, Inage-ku, Chiba 263-8555 (Japan); International University of Health and Welfare, 1-4-3, Mita, Minato-ku, Tokyo 108-8329 (Japan); Research Center Hospital for Charged Particle Therapy, National Institute of Radiological Sciences, 4-9-1, Anagawa, Inage-ku, Chiba 263-8555 (Japan)

    2012-02-15

    Purpose: The in-air output ratio (S{sub c}) for a rectangular field is usually obtained using an equivalent square field formula. However, it is well-known that S{sub c} obtained using an equivalent square field formula differs slightly from the measured S{sub c}. Though several correction methods have been suggested for the monitor-backscatter effect, the authors propose a more simple correction method for a rectangular field. Methods: For rectangular fields and equivalent square fields, the authors assumed that the output variation was the product of six output variations for each backscattering area at the top of the collimator jaws, and the correction factor was the ratio of the output variation for a rectangular field to the output variation for an equivalent square field. The output variation was measured by using a telescope measurement. Results: The differences between the measured and corrected S{sub c} ranged from -0.20% to 0.28% for symmetric rectangular fields by applying the correction factor to S{sub c} obtained using an equivalent square field formula. This correction method is also available for asymmetric rectangular fields. Conclusions: The authors propose a method to correct S{sub c} obtained using an equivalent square field formula, and a method to obtain the output variation for a field defined by collimator jaws.

  14. Grid adaptation for functional outputs of compressible flow simulations

    E-Print Network [OSTI]

    Venditti, David Anthony, 1973-

    2002-01-01

    An error correction and grid adaptive method is presented for improving the accuracy of functional outputs of compressible flow simulations. The procedure is based on an adjoint formulation in which the estimated error in ...

  15. Tomography of photon-number resolving continuous-output detectors

    E-Print Network [OSTI]

    Peter C. Humphreys; Benjamin J. Metcalf; Thomas Gerrits; Thomas Hiemstra; Adriana E. Lita; Joshua Nunn; Sae Woo Nam; Animesh Datta; W. Steven Kolthammer; Ian A. Walmsley

    2015-02-26

    We report a comprehensive approach to analysing continuous-output photon detectors. We employ principal component analysis to maximise the information extracted, followed by a novel noise-tolerant parameterised approach to the tomography of PNRDs. We further propose a measure for rigorously quantifying a detector's photon-number-resolving capability. Our approach applies to all detectors with continuous-output signals. We illustrate our methods by applying them to experimental data obtained from a transition-edge sensor (TES) detector.

  16. Current Output and Adjustment The 0-20mA current output is derived from the 0-10V output.

    E-Print Network [OSTI]

    Landers, Robert G.

    . There is no power supplied to the unit or power connections are reversed. The range LEDs are designed such that at least one of the LEDs will be lit whenever power is applied. · Sudden output change and/or no response reviewing these hints, call Lion Precision for assistance at 651-484-6544. · None of the range LEDs are lit

  17. Collaborative Research: Towards Advanced Understanding and Predictive Capability of Climate Change in the Arctic Using a High-Resolution Regional Arctic Climate Model

    SciTech Connect (OSTI)

    Cassano, John

    2013-06-30

    The primary research task completed for this project was the development of the Regional Arctic Climate Model (RACM). This involved coupling existing atmosphere, ocean, sea ice, and land models using the National Center for Atmospheric Research (NCAR) Community Climate System Model (CCSM) coupler (CPL7). RACM is based on the Weather Research and Forecasting (WRF) atmospheric model, the Parallel Ocean Program (POP) ocean model, the CICE sea ice model, and the Variable Infiltration Capacity (VIC) land model. A secondary research task for this project was testing and evaluation of WRF for climate-scale simulations on the large pan-Arctic model domain used in RACM. This involved identification of a preferred set of model physical parameterizations for use in our coupled RACM simulations and documenting any atmospheric biases present in RACM.

  18. Cavity-output-field control via interference effects

    E-Print Network [OSTI]

    Viorel Ciornea; Mihai A. Macovei

    2014-10-25

    We show how interference effects are responsible for manipulating the output electromagnetic field of an optical micro-resonator in the good-cavity limit. The system of interest consists in a moderately strongly pumped two-level emitter embedded in the optical cavity. When an additional weaker laser of the same frequency is pumping the combined system through one of the resonator's mirrors then the output cavity electromagnetic field can be almost completely suppressed or enhanced. This is due to the interference among the scattered light by the strongly pumped atom into the cavity mode and the incident weaker laser field. The result applies to photonic crystal environments as well.

  19. Development of a 402.5 MHz 140 kW Inductive Output Tube

    SciTech Connect (OSTI)

    R. Lawrence Ives; Michael Read, Robert Jackson

    2012-05-09

    This report contains the results of Phase I of an SBIR to develop a Pulsed Inductive Output Tube (IOT) with 140 kW at 400 MHz for powering H-proton beams. A number of sources, including single beam and multiple beam klystrons, can provide this power, but the IOT provides higher efficiency. Efficiencies exceeding 70% are routinely achieved. The gain is typically limited to approximately 24 dB; however, the availability of highly efficient, solid state drivers reduces the significance of this limitation, particularly at lower frequencies. This program initially focused on developing a 402 MHz IOT; however, the DOE requirement for this device was terminated during the program. The SBIR effort was refocused on improving the IOT design codes to more accurately simulate the time dependent behavior of the input cavity, electron gun, output cavity, and collector. Significant improvement was achieved in modeling capability and simulation accuracy.

  20. Output-Based Regulations: A Handbook for Air Regulators (U.S. EPA), August 2004

    Office of Energy Efficiency and Renewable Energy (EERE)

    Handbook providing practical information to help regulators decide if they want to use output-based regulations and explains how to develop an output-based emission standard

  1. Submodule construction for specifications with input assumptions and output guarantees *

    E-Print Network [OSTI]

    von Bochmann, Gregor

    bisimulation as conformance relation), and by synchronous finite state machines [Kim 97]. A restricted with synchronous communication or interleaving semantics. The paper also provides a new solution formula input and output. A new submodule construction algorithm for synchronous, partially defined input

  2. APPROXIMATION THEORY OF OUTPUT STATISTICS Dept. Information Systems

    E-Print Network [OSTI]

    Verdú, Sergio

    . In order to generate a ran- dom process we assume that a primary random source with an equiprobableAPPROXIMATION THEORY OF OUTPUT STATISTICS Te Sun Han Dept. Information Systems Senshu University-length) source coding rate of any finite-alphabet source, and a strong converse of the identijication coding

  3. ANALOG-DIGITAL INPUT OUTPUT SYSTEM FOR APPLE CO

    E-Print Network [OSTI]

    Groppi, Christopher

    Initialization Program - ADIOS INITB Appendix 2 Test Program - ADIOS TEST Appendix 3 AND9513 Utilization Appendix HI-506A. Multiplexer F. Sprague UHP -507 Relay Driver G. Teledyne Solid-State Relays H. Advanced bus driver, a 4-bit relay driver, or two solid-state relays. Three of the digital output bits can

  4. Enhanced immune function does not depress reproductive output

    E-Print Network [OSTI]

    Christians, Julian

    by a physiological (resource allocation) trade-oˇ between immune function and reproductive eˇort, and several recent, based on a reciprocal, negative relationship between resources allocated to immune functionEnhanced immune function does not depress reproductive output T. D. Williams* , J. K. Christians, J

  5. TRICOLOR LIGHT EMITTING DIODE DOT MATRIX DISPLAY SYSTEM WITHAUDIO OUTPUT

    E-Print Network [OSTI]

    Pang, Grantham

    1 TRICOLOR LIGHT EMITTING DIODE DOT MATRIX DISPLAY SYSTEM WITHAUDIO OUTPUT Grantham Pang, Chi emitting diodes; tricolor display; audio communication. I. Introduction This paper relates to a tricolor broadcasting through the visible light rays transmitted by the display panel or assembly. Keywords: light

  6. Design of Controllers for a Multiple Input Multiple Output System 

    E-Print Network [OSTI]

    Harris, Amanda Lynne

    2012-07-16

    A method of controller design for multiple input multiple output (MIMO) system is needed that will not give the high order controllers of modern control theory but will be more systematic than the “ad hoc” method. The objective of this method...

  7. Computability in Anonymous Networks: Revocable vs. Irrecovable Outputs

    E-Print Network [OSTI]

    Computability in Anonymous Networks: Revocable vs. Irrecovable Outputs Yuval Emek 1 Jochen Seidel 2 Zurich ­ Distributed Computing Group ­ www.disco.ethz.ch #12;Anonymous Networks #12;Computability in Anonymous Networks #12;Computability in Anonymous Networks Computable Not Computable #12;Computability

  8. POLE PLACEMENT BY STATIC OUTPUT FEEDBACK FOR GENERIC LINEAR SYSTEMS

    E-Print Network [OSTI]

    Eremenko, Alexandre

    POLE PLACEMENT BY STATIC OUTPUT FEEDBACK FOR GENERIC LINEAR SYSTEMS A. EREMENKO AND A. GABRIELOV of such systems, where the real pole placement map is not surjective. It follows that for each system in U, there exists an open set of pole configurations, symmetric with respect to the real line, which cannot

  9. Counterexamples to pole placement by static output feedback

    E-Print Network [OSTI]

    Eremenko, Alexandre

    Counterexamples to pole placement by static output feedback A. Eremenko # and A. Gabrielov + 6 these conditions, there is a non­empty open subset U of such systems, where the real pole placement map is not surjective. It follows that for systems in U , there exist open sets of pole configurations which cannot

  10. Counterexamples to pole placement by static output feedback

    E-Print Network [OSTI]

    Eremenko, Alexandre

    Counterexamples to pole placement by static output feedback A. Eremenko and A. Gabrielov 6 these conditions, there is a non-empty open subset U of such systems, where the real pole placement map is not surjective. It follows that for systems in U, there exist open sets of pole configurations which cannot

  11. POLE PLACEMENT BY STATIC OUTPUT FEEDBACK FOR GENERIC LINEAR SYSTEMS

    E-Print Network [OSTI]

    Eremenko, Alexandre

    POLE PLACEMENT BY STATIC OUTPUT FEEDBACK FOR GENERIC LINEAR SYSTEMS A. EREMENKO # AND A. GABRIELOV of such systems, where the real pole placement map is not surjective. It follows that for each system in U , there exists an open set of pole configurations, symmetric with respect to the real line, which cannot

  12. Implementation of the Immersed Boundary Method in the Weather Research and Forecasting model

    SciTech Connect (OSTI)

    Lundquist, K A

    2006-12-07

    Accurate simulations of atmospheric boundary layer flow are vital for predicting dispersion of contaminant releases, particularly in densely populated urban regions where first responders must react within minutes and the consequences of forecast errors are potentially disastrous. Current mesoscale models do not account for urban effects, and conversely urban scale models do not account for mesoscale weather features or atmospheric physics. The ultimate goal of this research is to develop and implement an immersed boundary method (IBM) along with a surface roughness parameterization into the mesoscale Weather Research and Forecasting (WRF) model. IBM will be used in WRF to represent the complex boundary conditions imposed by urban landscapes, while still including forcing from regional weather patterns and atmospheric physics. This document details preliminary results of this research, including the details of three distinct implementations of the immersed boundary method. Results for the three methods are presented for the case of a rotation influenced neutral atmospheric boundary layer over flat terrain.

  13. KHAMIS, LAMPERT: CO-CLASSIFICATION WITH OUTPUT SPACE REGULARIZATION 1 CoConut: Co-Classification with Output

    E-Print Network [OSTI]

    Daume III, Hal

    KHAMIS, LAMPERT: CO-CLASSIFICATION WITH OUTPUT SPACE REGULARIZATION 1 CoConut: Co, otherwise independent, data samples. The method we present, named CoConut, is based on the idea of adding on the class labels. CoConut can build on existing classi- fiers without making any assumptions on how

  14. Review of Wind Energy Forecasting Methods for Modeling Ramping Events

    SciTech Connect (OSTI)

    Wharton, S; Lundquist, J K; Marjanovic, N; Williams, J L; Rhodes, M; Chow, T K; Maxwell, R

    2011-03-28

    Tall onshore wind turbines, with hub heights between 80 m and 100 m, can extract large amounts of energy from the atmosphere since they generally encounter higher wind speeds, but they face challenges given the complexity of boundary layer flows. This complexity of the lowest layers of the atmosphere, where wind turbines reside, has made conventional modeling efforts less than ideal. To meet the nation's goal of increasing wind power into the U.S. electrical grid, the accuracy of wind power forecasts must be improved. In this report, the Lawrence Livermore National Laboratory, in collaboration with the University of Colorado at Boulder, University of California at Berkeley, and Colorado School of Mines, evaluates innovative approaches to forecasting sudden changes in wind speed or 'ramping events' at an onshore, multimegawatt wind farm. The forecast simulations are compared to observations of wind speed and direction from tall meteorological towers and a remote-sensing Sound Detection and Ranging (SODAR) instrument. Ramping events, i.e., sudden increases or decreases in wind speed and hence, power generated by a turbine, are especially problematic for wind farm operators. Sudden changes in wind speed or direction can lead to large power generation differences across a wind farm and are very difficult to predict with current forecasting tools. Here, we quantify the ability of three models, mesoscale WRF, WRF-LES, and PF.WRF, which vary in sophistication and required user expertise, to predict three ramping events at a North American wind farm.

  15. Simulation of one-minute power output from utility-scale photovoltaic generation systems.

    SciTech Connect (OSTI)

    Stein, Joshua S.; Ellis, Abraham; Hansen, Clifford W.

    2011-08-01

    We present an approach to simulate time-synchronized, one-minute power output from large photovoltaic (PV) generation plants in locations where only hourly irradiance estimates are available from satellite sources. The approach uses one-minute irradiance measurements from ground sensors in a climatically and geographically similar area. Irradiance is translated to power using the Sandia Array Performance Model. Power output is generated for 2007 in southern Nevada are being used for a Solar PV Grid Integration Study to estimate the integration costs associated with various utility-scale PV generation levels. Plant designs considered include both fixed-tilt thin-film, and single-axis-tracked polycrystalline Si systems ranging in size from 5 to 300 MW{sub AC}. Simulated power output profiles at one-minute intervals were generated for five scenarios defined by total PV capacity (149.5 MW, 222 WM, 292 MW, 492 MW, and 892 MW) each comprising as many as 10 geographically separated PV plants.

  16. Calculation of output characteristics of semiconductor quantum-well lasers with account for both electrons and holes

    SciTech Connect (OSTI)

    Sokolova, Z N; Tarasov, I S; Asryan, L V

    2014-09-30

    Using an extended theoretical model, which includes the rate equations for both electrons and holes, we have studied the output characteristics of semiconductor quantum-well lasers. We have found non-trivial dependences of electron and hole concentrations in the waveguide region of the laser on the capture velocities of both types of carriers from the waveguide region into the quantum well. We have obtained the dependences of the internal differential quantum efficiency and optical output power of the laser on the capture velocities of electrons and holes. An increase in the capture velocities has been shown to result in suppression of parasitic recombination in the waveguide region and therefore in a substantial increase in the quantum efficiency and output power. (lasers)

  17. Optical device with conical input and output prism faces

    DOE Patents [OSTI]

    Brunsden, Barry S. (Chicago, IL)

    1981-01-01

    A device for radially translating radiation in which a right circular cylinder is provided at each end thereof with conical prism faces. The faces are oppositely extending and the device may be severed in the middle and separated to allow access to the central part of the beam. Radiation entering the input end of the device is radially translated such that radiation entering the input end at the perimeter is concentrated toward the output central axis and radiation at the input central axis is dispersed toward the output perimeter. Devices are disclosed for compressing beam energy to enhance drilling techniques, for beam manipulation of optical spatial frequencies in the Fourier plane and for simplification of dark field and color contrast microscopy. Both refracting and reflecting devices are disclosed.

  18. Light-operated proximity detector with linear output

    DOE Patents [OSTI]

    Simpson, M.L.; McNeilly, D.R.

    1984-01-01

    A light-operated proximity detector is described in which reflected light intensity from a surface whose proximity to the detector is to be gauged is translated directly into a signal proportional to the distance of the detector from the surface. A phototransistor is used to sense the reflected light and is connected in a detector circuit which maintains the phtotransistor in a saturated state. A negative feedback arrangement using an operational amplifier connected between the collector and emitter of the transistor provides an output at the output of the amplifier which is linearly proportional to the proximity of the surface to the detector containing the transistor. This direct proportional conversion is true even though the light intensity is varying with the proximity in proportion to the square of the inverse of the distance. The detector may be used for measuring the distance remotely from any target surface.

  19. Light-operated proximity detector with linear output

    DOE Patents [OSTI]

    Simpson, Marc L. (Harriman, TN); McNeilly, David R. (Maryville, TN)

    1985-01-01

    A light-operated proximity detector is described in which reflected light intensity from a surface whose proximity to the detector is to be gauged is translated directly into a signal proportional to the distance of the detector from the surface. A phototransistor is used to sense the reflected light and is connected in a detector circuit which maintains the phototransistor in a saturated state. A negative feedback arrangement using an operational amplifier connected between the collector and emitter of the transistor provides an output at the output of the amplifier which is linearly proportional to the proximity of the surface to the detector containing the transistor. This direct proportional conversion is true even though the light intensity is varying with the proximity in proportion to the square of the inverse of the distance. The detector may be used for measuring the distance remotely from any target surface.

  20. Quantum Discrete Fourier Transform with Classical Output for Signal Processing

    E-Print Network [OSTI]

    Chao-Yang Pang; Ben-Qiong Hu

    2007-06-17

    Discrete Fourier transform (DFT) is the base of modern signal or information processing. 1-Dimensional fast Fourier transform (1D FFT) and 2D FFT have time complexity O(NlogN) and O(N^2logN) respectively. Quantum 1D and 2D DFT algorithms with classical output (1D QDFT and 2D QDFT) are presented in this paper. And quantum algorithm for convolution estimation is also presented in this paper. Compared with FFT, QDFT has two advantages at least. One of advantages is that 1D and 2D QDFT has time complexity O(sqrt(N)) and O(N) respectively. The other advantage is that QDFT can process very long signal sequence at a time. QDFT and quantum convolution demonstrate that quantum signal processing with classical output is possible.

  1. The effects of output transformers on distortion in audio amplifiers 

    E-Print Network [OSTI]

    Lanier, Ross Edwin

    1949-01-01

    in Pig. 8. The audio frequency amplifier is a conventional single-ended, fixed bias, power amplifier using a 6?6 tube connected for triode operation and driven by a low impedance audio frequency oscillator. The amplifier was coupled to its recommended... distortion in either the primary or the output of the transformer. 52 INTZRMODUIATION DISTORTION Hewlett-Packer Audio Frequenc Oscillator odel 200 Regulate Power Supply Aud o Frequenc Am lifie rane orme under Hew e -Pac sr Harmonic lliave...

  2. Development of a high-output dual-fuel engine

    SciTech Connect (OSTI)

    Danyluk, P.R. . Fairbanks Morse Engineering Division)

    1993-10-01

    This paper presents the results of a new dual-fuel engine development program. The engine is the largest commercially available in terms of power output (650 hp/cyl) and features very low emissions (1 g/hp-hr NO[sub x]) and excellent fuel consumption (43 percent thermal efficiency). A two-cylinder turbocharged prototype was designed and built for the initial development. Results from testing on 18-cylinder production versions are also reported.

  3. A Wavelet-Based Variability Model (WVM) for Solar PV Power Plants

    E-Print Network [OSTI]

    Lave, Matthew; Kleissl, Jan; Stein, Joshua S

    2013-01-01

    Model (WVM) for Solar PV Power Plants Matthew Lave, Jansolar photovoltaic (PV) power plant output given a singleproduce a simulated power plant output. The WVM is validated

  4. A Wavelet-Based Variability Model (WVM) for Solar PV Power Plants

    E-Print Network [OSTI]

    Lave, Matthew; Kleissl, Jan; Stein, Joshua S

    2013-01-01

    Model (WVM) for Solar PV Power Plants Matthew Lave, Jansimulating solar photovoltaic (PV) power plant output giventhe power output of a solar photovoltaic (PV) plant was

  5. Application of WRF/Chem-MADRID for real-time air quality forecasting over the Southeastern United States

    E-Print Network [OSTI]

    Zhang, Yang

    States Ming-Tung Chuang a , Yang Zhang a,*, Daiwen Kang b a Air Quality Forecasting Lab, North Carolina on a three-dimensional air quality model provides a powerful tool to forecast air quality and advise, inaccuracies in simulated meteorological variables such as 2-m temperature, 10-m wind speed, and precipitation

  6. Method and system for managing an electrical output of a turbogenerator

    DOE Patents [OSTI]

    Stahlhut, Ronnie Dean (Bettendorf, IA); Vuk, Carl Thomas (Denver, IA)

    2009-06-02

    The system and method manages an electrical output of a turbogenerator in accordance with multiple modes. In a first mode, a direct current (DC) bus receives power from a turbogenerator output via a rectifier where turbogenerator revolutions per unit time (e.g., revolutions per minute (RPM)) or an electrical output level of a turbogenerator output meet or exceed a minimum threshold. In a second mode, if the turbogenerator revolutions per unit time or electrical output level of a turbogenerator output are less than the minimum threshold, the electric drive motor or a generator mechanically powered by the engine provides electrical energy to the direct current bus.

  7. Method and system for managing an electrical output of a turbogenerator

    DOE Patents [OSTI]

    Stahlhut, Ronnie Dean (Bettendorf, IA); Vuk, Carl Thomas (Denver, IA)

    2010-08-24

    The system and method manages an electrical output of a turbogenerator in accordance with multiple modes. In a first mode, a direct current (DC) bus receives power from a turbogenerator output via a rectifier where turbogenerator revolutions per unit time (e.g., revolutions per minute (RPM)) or an electrical output level of a turbogenerator output meet or exceed a minimum threshold. In a second mode, if the turbogenerator revolutions per unit time or electrical output level of a turbogenerator output are less than the minimum threshold, the electric drive motor or a generator mechanically powered by the engine provides electrical energy to the direct current bus.

  8. Reliable Gas Turbine Output: Attaining Temperature Independent Performance 

    E-Print Network [OSTI]

    Neeley, J. E.; Patton, S.; Holder, F.

    1992-01-01

    stream_source_info ESL-IE-92-04-10.pdf.txt stream_content_type text/plain stream_size 16831 Content-Encoding ISO-8859-1 stream_name ESL-IE-92-04-10.pdf.txt Content-Type text/plain; charset=ISO-8859-1 RELIABLE GAS TURBINE... OUTPUT; ATTAINING TEMPERATURE INDEPENDENT PERFORMANCE James E. Neeley, P.E. Power Plant Engineer Public Utility Commission of Texas Austin, Texas ABSTRACT Improvements in gas turbine efficiency, coupled with dropping gas prices, has made gas...

  9. Enhancing e-waste estimates: Improving data quality by multivariate Input–Output Analysis

    SciTech Connect (OSTI)

    Wang, Feng, E-mail: fwang@unu.edu [Institute for Sustainability and Peace, United Nations University, Hermann-Ehler-Str. 10, 53113 Bonn (Germany); Design for Sustainability Lab, Faculty of Industrial Design Engineering, Delft University of Technology, Landbergstraat 15, 2628CE Delft (Netherlands); Huisman, Jaco [Institute for Sustainability and Peace, United Nations University, Hermann-Ehler-Str. 10, 53113 Bonn (Germany); Design for Sustainability Lab, Faculty of Industrial Design Engineering, Delft University of Technology, Landbergstraat 15, 2628CE Delft (Netherlands); Stevels, Ab [Design for Sustainability Lab, Faculty of Industrial Design Engineering, Delft University of Technology, Landbergstraat 15, 2628CE Delft (Netherlands); Baldé, Cornelis Peter [Institute for Sustainability and Peace, United Nations University, Hermann-Ehler-Str. 10, 53113 Bonn (Germany); Statistics Netherlands, Henri Faasdreef 312, 2492 JP Den Haag (Netherlands)

    2013-11-15

    Highlights: • A multivariate Input–Output Analysis method for e-waste estimates is proposed. • Applying multivariate analysis to consolidate data can enhance e-waste estimates. • We examine the influence of model selection and data quality on e-waste estimates. • Datasets of all e-waste related variables in a Dutch case study have been provided. • Accurate modeling of time-variant lifespan distributions is critical for estimate. - Abstract: Waste electrical and electronic equipment (or e-waste) is one of the fastest growing waste streams, which encompasses a wide and increasing spectrum of products. Accurate estimation of e-waste generation is difficult, mainly due to lack of high quality data referred to market and socio-economic dynamics. This paper addresses how to enhance e-waste estimates by providing techniques to increase data quality. An advanced, flexible and multivariate Input–Output Analysis (IOA) method is proposed. It links all three pillars in IOA (product sales, stock and lifespan profiles) to construct mathematical relationships between various data points. By applying this method, the data consolidation steps can generate more accurate time-series datasets from available data pool. This can consequently increase the reliability of e-waste estimates compared to the approach without data processing. A case study in the Netherlands is used to apply the advanced IOA model. As a result, for the first time ever, complete datasets of all three variables for estimating all types of e-waste have been obtained. The result of this study also demonstrates significant disparity between various estimation models, arising from the use of data under different conditions. It shows the importance of applying multivariate approach and multiple sources to improve data quality for modelling, specifically using appropriate time-varying lifespan parameters. Following the case study, a roadmap with a procedural guideline is provided to enhance e-waste estimation studies.

  10. New Research Center to Increase Safety and Power Output of U...

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

    New Research Center to Increase Safety and Power Output of U.S. Nuclear Reactors New Research Center to Increase Safety and Power Output of U.S. Nuclear Reactors May 3, 2011 -...

  11. Output-Based Regulations: A Handbook for Air Regulators (U.S...

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

    Output-Based Regulations: A Handbook for Air Regulators (U.S. EPA), August 2004 Output-Based Regulations: A Handbook for Air Regulators (U.S. EPA), August 2004 The U.S....

  12. Stochastic Analysis of Stable Marriages in Combined Input Output Queued Switches

    E-Print Network [OSTI]

    Goel, Ashish

    Stochastic Analysis of Stable Marriages in Combined Input Output Queued Switches Ashish Goel 1 Balaji Prabhakar 2 Abstract Traditionally, Output Queued switch architectures have been proposed to implement Quality of Service schemes such as Weighted Fair Queueing. Output Queued switches with N input

  13. SOLAR ENERGY (conditionally accepted 1/2010) QUANTIFYING PV POWER OUTPUT VARIABILITY

    E-Print Network [OSTI]

    Perez, Richard R.

    SOLAR ENERGY (conditionally accepted 1/2010) QUANTIFYING PV POWER OUTPUT VARIABILITY Thomas E power output variability from a fleet of photovoltaic (PV) systems, ranging from a single central station to a set of distributed PV systems. The approach demonstrates that the relative power output

  14. Aalborg Universitet On the Impact of Partial Shading on PV Output Power

    E-Print Network [OSTI]

    Sera, Dezso

    Aalborg Universitet On the Impact of Partial Shading on PV Output Power Sera, Dezso; Baghzouz version (APA): Sera, D., & Baghzouz, Y. (2008). On the Impact of Partial Shading on PV Output Power from vbn.aau.dk on: juli 06, 2015 #12;On the Impact of Partial Shading on PV Output Power DEZSO SERA

  15. A 400 to 500-MHz CMOS Power Amplifier with Multi-Watt Output

    E-Print Network [OSTI]

    Kuhn, William B.

    PA cells are power-combined to generate approximately 5-Watt output. The same PA chip is used, the authors demonstrated a fully integrated CMOS 1-Watt PA with high-Q 1:3 turns- ratio output balun [8A 400 to 500-MHz CMOS Power Amplifier with Multi-Watt Output Jeongmin Jeon, Student Member, IEEE

  16. Flexible High-Output Nanogenerator Based on Lateral ZnO Nanowire Array

    E-Print Network [OSTI]

    Wang, Zhong L.

    Flexible High-Output Nanogenerator Based on Lateral ZnO Nanowire Array Guang Zhu, Rusen Yang scalable sweeping-printing-method, for fabricating flexible high- output nanogenerator (HONG) that can-circuit voltage of up to 2.03 V and a peak output power density of 11 mW/cm3 have been achieved. The generated

  17. Supplementary Information Flexible high-output nanogenerator based on lateral ZnO nanowire array

    E-Print Network [OSTI]

    Wang, Zhong L.

    1 Supplementary Information Flexible high-output nanogenerator based on lateral ZnO nanowire array voltage input was provided by a functional generator (peak value of 2 V and 0.5 Hz); and the output direction is along the c-axis. #12;3 Durability test Figure S3. Result of the durability test on the output

  18. Augmenting Test Suites Effectiveness by Increasing Output Diversity Nadia Alshahwan and Mark Harman

    E-Print Network [OSTI]

    Harman, Mark

    that output uniqueness shall be used to augment a traditional test generation adequacy criterion not replace for relatively high coverage test suites. In this paper we use output uniqueness to augment a test suiteAugmenting Test Suites Effectiveness by Increasing Output Diversity Nadia Alshahwan and Mark Harman

  19. PARALLEL HIGH THROUGHPUT SOFT-OUTPUT SPHERE DECODER Q. Qi, C. Chakrabarti

    E-Print Network [OSTI]

    Kambhampati, Subbarao

    - putation complexity of only the list generator. Recently, a high speed systolic-like soft-output spherePARALLEL HIGH THROUGHPUT SOFT-OUTPUT SPHERE DECODER Q. Qi, C. Chakrabarti School of Electrical,chaitali}@asu.edu ABSTRACT Multiple-Input-Multiple-Output communication systems de- mand fast sphere decoding with high

  20. Toward high output-power nanogenerator Peng Fei,1,2

    E-Print Network [OSTI]

    Wang, Zhong L.

    Toward high output-power nanogenerator Jin Liu,1 Peng Fei,1,2 Jun Zhou,1 Rao Tummala,1 and Zhong contributed by all of the NWs while the output voltage is determined by the voltage generated by a single NW 2008; published online 29 April 2008 In this paper, the factors that determine the power output

  1. Dosimetric characterization and output verification for conical brachytherapy surface applicators. Part I. Electronic brachytherapy source

    SciTech Connect (OSTI)

    Fulkerson, Regina K. Micka, John A.; DeWerd, Larry A.

    2014-02-15

    Purpose: Historically, treatment of malignant surface lesions has been achieved with linear accelerator based electron beams or superficial x-ray beams. Recent developments in the field of brachytherapy now allow for the treatment of surface lesions with specialized conical applicators placed directly on the lesion. Applicators are available for use with high dose rate (HDR){sup 192}Ir sources, as well as electronic brachytherapy sources. Part I of this paper will discuss the applicators used with electronic brachytherapy sources; Part II will discuss those used with HDR {sup 192}Ir sources. Although the use of these applicators has gained in popularity, the dosimetric characteristics including depth dose and surface dose distributions have not been independently verified. Additionally, there is no recognized method of output verification for quality assurance procedures with applicators like these. Existing dosimetry protocols available from the AAPM bookend the cross-over characteristics of a traditional brachytherapy source (as described by Task Group 43) being implemented as a low-energy superficial x-ray beam (as described by Task Group 61) as observed with the surface applicators of interest. Methods: This work aims to create a cohesive method of output verification that can be used to determine the dose at the treatment surface as part of a quality assurance/commissioning process for surface applicators used with HDR electronic brachytherapy sources (Part I) and{sup 192}Ir sources (Part II). Air-kerma rate measurements for the electronic brachytherapy sources were completed with an Attix Free-Air Chamber, as well as several models of small-volume ionization chambers to obtain an air-kerma rate at the treatment surface for each applicator. Correction factors were calculated using MCNP5 and EGSnrc Monte Carlo codes in order to determine an applicator-specific absorbed dose to water at the treatment surface from the measured air-kerma rate. Additionally, relative dose measurements of the surface dose distributions and characteristic depth dose curves were completed in-phantom. Results: Theoretical dose distributions and depth dose curves were generated for each applicator and agreed well with the measured values. A method of output verification was created that allows users to determine the applicator-specific dose to water at the treatment surface based on a measured air-kerma rate. Conclusions: The novel output verification methods described in this work will reduce uncertainties in dose delivery for treatments with these kinds of surface applicators, ultimately improving patient care.

  2. High lumen compact fluorescents boost light output in new fixtures

    SciTech Connect (OSTI)

    1992-12-31

    Some compact fluorescent lamps aren`t so compact. General Electric (GE), OSRAM, and Philips have been expanding offerings in longer, more powerful, hard wired CFLs that generate enough light to serve applications once limited to conventional fluorescents and metal halide systems. All three of these manufacturers have for some time offered 18- to 40-watt high-output CFLs, which use a fluorescent tube doubled back on itself to produce a lot of light in a compact source. Now GE has introduced an even larger, more powerful 50-watt unit, and OSRAM is soon to follow suit with a 55-watt lamp. These new entries to the field of turbocharged CFLs can provide general lighting at ceiling heights of 12 feet or more as well as indirect lighting, floodlighting, and wall washing. They are such a concentrated source of light that they can provide the desired illumination using fewer lamps and fixtures than would be needed with competing sources.

  3. This study considered the impact of grid resolution on wind velocity simulated by Weather Research and Forecasting (WRF) model. The period simulated spanned November 2009 through January

    E-Print Network [OSTI]

    the entire domains and the wind velocity observed solely over offshore locations. Wind velocity was observed locations for wind farms. The results go further to suggest the ideal location for these potential wind farms will be at offshore locations. Mechanical Engineering Master's Defense Impact of Grid Resolution

  4. WRF-Chem model predictions of the regional impacts of N2O5 heterogeneous processes on night-time chemistry over north-western Europe

    E-Print Network [OSTI]

    Lowe, D.; Archer-Nicholls, S.; Morgan, W.; Allan, J.; Utembe, S.; Ouyang, B.; Aruffo, E.; Le Breton, M.; Zaveri, R. A.; Di Carlo, P.; Percival, C.; Coe, H.; Jones, R.; McFiggans, G.

    2015-02-09

    The hydroxyl radical (OH) dominates the oxidative control of the composition of the troposphere, primarily through its established role as a daytime oxidant. However, night-time chemistry, driven primarily by the nitrate radical (NO3), has been increasingly...

  5. Ecosystem feedbacks to climate change in California: Development, testing, and analysis using a coupled regional atmosphere and land-surface model (WRF3-CLM3.5)

    E-Print Network [OSTI]

    Subin, Z.M.

    2012-01-01

    shrub Whiteleaf manzanita (EGS) Mountain misery Ceanothustemperate coniferous forest 17% EGS; 18% BG Red fir forestmosaic 36% TEGC; 15% EGS; 9% Temperate conifer xeromorphic

  6. Updated Eastern Interconnect Wind Power Output and Forecasts for ERGIS: July 2012

    SciTech Connect (OSTI)

    Pennock, K.

    2012-10-01

    AWS Truepower, LLC (AWST) was retained by the National Renewable Energy Laboratory (NREL) to update wind resource, plant output, and wind power forecasts originally produced by the Eastern Wind Integration and Transmission Study (EWITS). The new data set was to incorporate AWST's updated 200-m wind speed map, additional tall towers that were not included in the original study, and new turbine power curves. Additionally, a primary objective of this new study was to employ new data synthesis techniques developed for the PJM Renewable Integration Study (PRIS) to eliminate diurnal discontinuities resulting from the assimilation of observations into mesoscale model runs. The updated data set covers the same geographic area, 10-minute time resolution, and 2004?2006 study period for the same onshore and offshore (Great Lakes and Atlantic coast) sites as the original EWITS data set.

  7. Fail safe controllable output improved version of the Electromechanical battery

    DOE Patents [OSTI]

    Post, Richard F. (Walnut Creek, CA)

    1999-01-01

    Mechanical means are provided to control the voltages induced in the windings of a generator/motor. In one embodiment, a lever is used to withdraw or insert the entire stator windings from the cavity where the rotating field exists. In another embodiment, voltage control and/or switching off of the output is achievable with a variable-coupling generator/motor. A stator is made up of two concentric layers of windings, with a larger number of turns on the inner layer of windings than the outer layer of windings. The windings are to be connected in series electrically, that is, their voltages add vectorially. The mechanical arrangement is such that one or both of the windings can be rotated with respect to the other winding about their common central axis. Another improved design for the stator assembly of electromechanical batteries provides knife switch contacts that are in electrical contact with the stator windings. The operation of this embodiment depends on the fact that an abnormally large torque will be exerted on the stator structure during any short-circuit condition.

  8. Fail safe controllable output improved version of the electromechanical battery

    DOE Patents [OSTI]

    Post, R.F.

    1999-01-19

    Mechanical means are provided to control the voltages induced in the windings of a generator/motor. In one embodiment, a lever is used to withdraw or insert the entire stator windings from the cavity where the rotating field exists. In another embodiment, voltage control and/or switching off of the output is achievable with a variable-coupling generator/motor. A stator is made up of two concentric layers of windings, with a larger number of turns on the inner layer of windings than the outer layer of windings. The windings are to be connected in series electrically, that is, their voltages add vectorially. The mechanical arrangement is such that one or both of the windings can be rotated with respect to the other winding about their common central axis. Another improved design for the stator assembly of electromechanical batteries provides knife switch contacts that are in electrical contact with the stator windings. The operation of this embodiment depends on the fact that an abnormally large torque will be exerted on the stator structure during any short-circuit condition. 4 figs.

  9. HSPICE and WaveView Tutorial Hspice is used for circuit simulation and WaveView is used to view output waveforms.

    E-Print Network [OSTI]

    Mahmoodi, Hamid

    NNano-E Hs Electron Sc San Fr S spice Q Mich Hamid nics & C chool of rancisco San Fra Spr Quick By be downloaded from the following website: http://ptm.asu.edu/ Click on the latest models and download 16nm PTM "hspice job aborted". In that case, please open the output file (inv.out) and search for error to see

  10. Quasi-output-buffered switches Cheng-Shang Chang, Jay Cheng, Duan-Shin Lee and Chi-Feung Wu

    E-Print Network [OSTI]

    Chang, Cheng-Shang

    Quasi-output-buffered switches Cheng-Shang Chang, Jay Cheng, Duan-Shin Lee and Chi-Feung Wu--Output-buffered switches are known to have better performance than other switch architectures. However, output- buffered switches also suffer from the notorious scalability prob- lem, and direct constructions of large output

  11. ARM: ARSCL: multiple outputs from first Clothiaux algorithms on Vaisala or Belfort ceilometers, Micropulse lidar, and MMCR

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

    Karen Johnson; Michael Jensen

    ARSCL: multiple outputs from first Clothiaux algorithms on Vaisala or Belfort ceilometers, Micropulse lidar, and MMCR

  12. Evaluation of Rate-based Adaptivity in Asynchronous Data Stream Beth Plale and Nithya Vijayakumar

    E-Print Network [OSTI]

    Plale, Beth

    , mesoscale meteorology simulations, such as WRF [13], are forecast models used to predict the occurrence

  13. Weather Effects on European Agricultural Output 1850-1913

    E-Print Network [OSTI]

    Solomou, Solomos; Wu, Weike

    2004-06-16

    This paper compares the effects of weather shocks on agricultural production in Britain, France and Germany during the late nineteenth century. Using semi- parametric models to estimate the non-linear agro-weather relationship, we find...

  14. Gamma-ray Output Spectra from 239 Pu Fission

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

    Ullmann, John

    2015-05-25

    Gamma-ray multiplicities, individual gamma-ray energy spectra, and total gamma energy spectra following neutron-induced fission of 239Pu were measured using the DANCE detector at Los Alamos. Corrections for detector response were made using a forward-modeling technique based on propagating sets of gamma rays generated from a paramaterized model through a GEANT model of the DANCE array and adjusting the parameters for best fit to the measured spectra. The results for the gamma-ray spectrum and multiplicity are in general agreement with previous results, but the measured total gamma-ray energy is about 10% higher. A dependence of the gamma-ray spectrum on the gamma-raymore »multplicity was also observed. Global model calculations of the multiplicity and gamma energy distributions are in good agreement with the data, but predict a slightly softer total-energy distribution.« less

  15. Output Field-Quadrature Measurements and Squeezing in Ultrastrong Cavity-QED

    E-Print Network [OSTI]

    Roberto Stassi; Salvatore Savasta; Luigi Garziano; Bernardo Spagnolo; Franco Nori

    2015-09-30

    We study the squeezing of output quadratures of an electro-magnetic field escaping from a resonator coupled to a general quantum system with arbitrary interaction strengths. The generalized theoretical analysis of output squeezing proposed here is valid for all the interaction regimes of cavity-quantum electrodynamics: from the weak to the strong, ultrastrong, and deep coupling regimes. For coupling rates comparable or larger then the cavity resonance frequency, the standard input-output theory for optical cavities fails to calculate the correct output field-quadratures and predicts a non-negligible amount of output squeezing, even if the system is in its ground state. Here we show that, for arbitrary interactions and cavity-embedded quantum systems, no squeezing can be found in the output-field quadratures if the system is in its ground state. We also apply the proposed theoretical approach to study the output squeezing produced by: (i) an artificial two-level atom embedded in a coherently-excited cavity; and (ii) a cascade-type three-level system interacting with a cavity field mode. In the latter case the output squeezing arises from the virtual photons of the atom-cavity dressed states. This work extends the possibility of predicting and analyzing continuous-variable optical quantum-state tomography when optical resonators interact very strongly with other quantum systems.

  16. Quasi-Output-Buffered Switches Cheng-Shang Chang, Fellow, IEEE, Jay Cheng, Senior Member, IEEE,

    E-Print Network [OSTI]

    Chang, Cheng-Shang

    1 Quasi-Output-Buffered Switches Cheng-Shang Chang, Fellow, IEEE, Jay Cheng, Senior Member, IEEE-buffered switches have better performance than other switch architectures. However, output-buffered switches also-buffered switches are difficult. In this paper, we study the problem of constructing scalable switches that have

  17. Optimizing the Output of a Human-Powered Energy Harvesting System with Miniaturization and Integrated Control

    E-Print Network [OSTI]

    Potkonjak, Miodrag

    1 Optimizing the Output of a Human-Powered Energy Harvesting System with Miniaturization]. Aside from reducing power consumption in such systems, har- vesting human energy output as a potential mechanical energy from human foot-strikes and explore its configuration and control towards optimized energy

  18. Distinguishers for the Compression Function and Output Transformation of Hamsi-256

    E-Print Network [OSTI]

    International Association for Cryptologic Research (IACR)

    Distinguishers for the Compression Function and Output Transformation of Hamsi-256 Jean, with digital signatures and integrity checks as their main applications. Collision attacks on the deployed as the full 6-round output transformation. The former gives near-collisions on (256 - 25) bits

  19. Code optimization and analysis for multiple-input and multiple-output communication systems 

    E-Print Network [OSTI]

    Yue, Guosen

    2005-11-01

    at the output of the soft-input soft-output (SISO) multiuser detectors as a function of the pdf of input extrinsic messages, user spreading codes, channel impulse responses, and signal-to-noise ratios. Using these techniques, we are able to accurately compute...

  20. On the Communication Complexity of Secure Function Evaluation with Long Output

    E-Print Network [OSTI]

    International Association for Cryptologic Research (IACR)

    On the Communication Complexity of Secure Function Evaluation with Long Output Pavel Hub´acek Daniel Wichs Abstract We study the communication complexity of secure function evaluation (SFE). Consider SFE protocols have communication complexity that scales with size of the output y, which can

  1. Quality assurance of solar thermal systems with the ISFH-Input/Output-Procedure

    E-Print Network [OSTI]

    Quality assurance of solar thermal systems with the ISFH- Input/Output-Procedure Peter Paerisch/Output-Controllers for in situ and automatic function control of solar thermal systems that were developed within the research project have been installed in 12 systems. After five years seven solar thermal systems benefited from

  2. Mode competition and output power in regular and chaotic dielectric cavity lasers

    E-Print Network [OSTI]

    Stone, A. Douglas

    Mode competition and output power in regular and chaotic dielectric cavity lasers Hakan E. T many modes and their competition can address the mode selection and output power of these lasers lasing patterns of two- dimensional dielectric microcavity lasers of different shape, including

  3. High power CW Tm:YLF laser with a holographic output Alex Dergachev, Peter F. Moulton

    E-Print Network [OSTI]

    Glebov, Leon

    High power CW Tm:YLF laser with a holographic output coupler Alex Dergachev, Peter F. Moulton Q with output power exceeding 30 W. 2003 Optical Society of America OCIS codes: (140.3580) Lasers, solid yet reported of a high power Tm-doped bulk laser operated with a bulk holographic Bragg grating

  4. Method for leveling the power output of an electromechanical battery as a function of speed

    DOE Patents [OSTI]

    Post, Richard F. (Walnut Creek, CA)

    1999-01-01

    The invention is a method of leveling the power output of an electromechanical battery during its discharge, while at the same time maximizing its power output into a given load. The method employs the concept of series resonance, employing a capacitor the parameters of which are chosen optimally to achieve the desired near-flatness of power output over any chosen charged-discharged speed ratio. Capacitors are inserted in series with each phase of the windings to introduce capacitative reactances that act to compensate the inductive reactance of these windings. This compensating effect both increases the power that can be drawn from the generator before inductive voltage drops in the windings become dominant and acts to flatten the power output over a chosen speed range. The values of the capacitors are chosen so as to optimally flatten the output of the generator over the chosen speed range.

  5. Method for leveling the power output of an electromechanical battery as a function of speed

    DOE Patents [OSTI]

    Post, R.F.

    1999-03-16

    The invention is a method of leveling the power output of an electromechanical battery during its discharge, while at the same time maximizing its power output into a given load. The method employs the concept of series resonance, employing a capacitor the parameters of which are chosen optimally to achieve the desired near-flatness of power output over any chosen charged-discharged speed ratio. Capacitors are inserted in series with each phase of the windings to introduce capacitative reactances that act to compensate the inductive reactance of these windings. This compensating effect both increases the power that can be drawn from the generator before inductive voltage drops in the windings become dominant and acts to flatten the power output over a chosen speed range. The values of the capacitors are chosen so as to optimally flatten the output of the generator over the chosen speed range. 3 figs.

  6. Biomolecular Filters for Improved Separation of Output Signals in Enzyme Logic Systems Applied to Biomedical Analysis

    E-Print Network [OSTI]

    Jan Halamek; Jian Zhou; Lenka Halamkova; Vera Bocharova; Vladimir Privman; Joseph Wang; Evgeny Katz

    2011-10-08

    Biomolecular logic systems processing biochemical input signals and producing "digital" outputs in the form of YES/NO were developed for analysis of physiological conditions characteristic of liver injury, soft tissue injury and abdominal trauma. Injury biomarkers were used as input signals for activating the logic systems. Their normal physiological concentrations were defined as logic-0 level, while their pathologically elevated concentrations were defined as logic-1 values. Since the input concentrations applied as logic 0 and 1 values were not sufficiently different, the output signals being at low and high values (0, 1 outputs) were separated with a short gap making their discrimination difficult. Coupled enzymatic reactions functioning as a biomolecular signal processing system with a built-in filter property were developed. The filter process involves a partial back-conversion of the optical-output-signal-yielding product, but only at its low concentrations, thus allowing the proper discrimination between 0 and 1 output values.

  7. System for adjusting frequency of electrical output pulses derived from an oscillator

    DOE Patents [OSTI]

    Bartholomew, David B.

    2006-11-14

    A system for setting and adjusting a frequency of electrical output pulses derived from an oscillator in a network is disclosed. The system comprises an accumulator module configured to receive pulses from an oscillator and to output an accumulated value. An adjustor module is configured to store an adjustor value used to correct local oscillator drift. A digital adder adds values from the accumulator module to values stored in the adjustor module and outputs their sums to the accumulator module, where they are stored. The digital adder also outputs an electrical pulse to a logic module. The logic module is in electrical communication with the adjustor module and the network. The logic module may change the value stored in the adjustor module to compensate for local oscillator drift or change the frequency of output pulses. The logic module may also keep time and calculate drift.

  8. Energy output from a single outer hair cell

    E-Print Network [OSTI]

    Iwasa, Kuni H

    2016-01-01

    Electromotility of outer hair cells (OHCs) has been extensively studied with in vitro experiments because of its physiological significance in the cochlear amplifier, which provides the exquisite sensitivity and frequency selectivity of the mammalian ear. However, these studies have been performed largely under load-free conditions or with static load, while these cells function in vivo in a dynamic environment, receiving electrical energy to enhance mechanical oscillation in the inner ear. This gap leaves uncertainties in addressing a key issue, how much mechanical energy an OHC provides. The present report is an attempt of bridging the gap by introducing a simple one-dimensional model for electromotility of OHC in a dynamic environment. This model incorporates a feedback loop involving the receptor potential and the mechanical load on OHC, and leads to an analytical expression for the membrane capacitance, which explicitly describes the dependence on the elastic load, viscous drag, and the mass. The derived...

  9. Numerical simulations of output pulse extraction from a high-power microwave compressor with a plasma switch

    SciTech Connect (OSTI)

    Shlapakovski, Anatoli; Beilin, Leonid; Bliokh, Yuri; Donskoy, Moshe; Krasik, Yakov E. [Physics Department, Technion, Haifa 32000 (Israel); Hadas, Yoav [Department of Applied Physics, Rafael, PO Box 2250, Haifa 31021 (Israel); Schamiloglu, Edl [Department of Electrical and Computer Engineering, University of New Mexico, Albuquerque, New Mexico 87131 (United States)

    2014-05-07

    Numerical simulations of the process of electromagnetic energy release from a high-power microwave pulse compressor comprising a gas-filled cavity and interference switch were carried out. A microwave plasma discharge in a rectangular waveguide H-plane tee was modeled with the use of the fully electromagnetic particle-in-cell code MAGIC. The gas ionization, plasma evolution, and interaction with RF fields accumulated within the compressor were simulated using different approaches provided by the MAGIC code: particle-in-cell approach accounting for electron-neutral collisions, gas conductivity model based on the concept of mobility, and hybrid modeling. The dependences of the microwave output pulse peak power and waveform on parameters that can be controlled in experiments, such as an external ionization rate, RF field amplitude, and background gas pressure, were investigated.

  10. Finding the quantum thermoelectric with maximal efficiency and minimal entropy production at given power output

    E-Print Network [OSTI]

    Robert S. Whitney

    2015-03-16

    We investigate the nonlinear scattering theory for quantum systems with strong Seebeck and Peltier effects, and consider their use as heat-engines and refrigerators with finite power outputs. This article gives detailed derivations of the results summarized in Phys. Rev. Lett. 112, 130601 (2014). It shows how to use the scattering theory to find (i) the quantum thermoelectric with maximum possible power output, and (ii) the quantum thermoelectric with maximum efficiency at given power output. The latter corresponds to a minimal entropy production at that power output. These quantities are of quantum origin since they depend on system size over electronic wavelength, and so have no analogue in classical thermodynamics. The maximal efficiency coincides with Carnot efficiency at zero power output, but decreases with increasing power output. This gives a fundamental lower bound on entropy production, which means that reversibility (in the thermodynamic sense) is impossible for finite power output. The suppression of efficiency by (nonlinear) phonon and photon effects is addressed in detail; when these effects are strong, maximum efficiency coincides with maximum power. Finally, we show in particular limits (typically without magnetic fields) that relaxation within the quantum system does not allow the system to exceed the bounds derived for relaxation-free systems, however, a general proof of this remains elusive.

  11. X-ray source assembly having enhanced output stability, and fluid stream analysis applications thereof

    DOE Patents [OSTI]

    Radley, Ian; Bievenue, Thomas J.; Burdett Jr., John H.; Gallagher, Brian W.; Shakshober, Stuart M.; Chen, Zewu; Moore, Michael D.

    2007-04-24

    An x-ray source assembly (2700) and method of operation are provided having enhanced output stability. The assembly includes an anode (2125) having a source spot upon which electrons (2120) impinge and a control system (2715/2720) for controlling position of the anode source spot relative to an output structure. The control system can maintain the anode source spot location relative to the output structure (2710) notwithstanding a change in one or more operating conditions of the x-ray source assembly. One aspect of the disclosed invention is most amenable to the analysis of sulfur in petroleum-based fuels.

  12. X-ray source assembly having enhanced output stability, and fluid stream analysis applications thereof

    DOE Patents [OSTI]

    Radley, Ian (Glenmont, NY); Bievenue, Thomas J. (Delmar, NY); Burdett, John H. (Charlton, NY); Gallagher, Brian W. (Guilderland, NY); Shakshober, Stuart M. (Hudson, NY); Chen, Zewu (Schenectady, NY); Moore, Michael D. (Alplaus, NY)

    2008-06-08

    An x-ray source assembly and method of operation are provided having enhanced output stability. The assembly includes an anode having a source spot upon which electrons impinge and a control system for controlling position of the anode source spot relative to an output structure. The control system can maintain the anode source spot location relative to the output structure notwithstanding a change in one or more operating conditions of the x-ray source assembly. One aspect of the disclosed invention is most amenable to the analysis of sulfur in petroleum-based fuels.

  13. Optimization of the output and efficiency of a high power cascaded arc hydrogen plasma source

    SciTech Connect (OSTI)

    Vijvers, W. A. J.; Gils, C. A. J. van; Goedheer, W. J.; Meiden, H. J. van der; Veremiyenko, V. P.; Westerhout, J.; Lopes Cardozo, N. J.; Rooij, G. J. van; Schram, D. C.

    2008-09-15

    The operation of a cascaded arc hydrogen plasma source was experimentally investigated to provide an empirical basis for the scaling of this source to higher plasma fluxes and efficiencies. The flux and efficiency were determined as a function of the input power, discharge channel diameter, and hydrogen gas flow rate. Measurements of the pressure in the arc channel show that the flow is well described by Poiseuille flow and that the effective heavy particle temperature is approximately 0.8 eV. Interpretation of the measured I-V data in terms of a one-parameter model shows that the plasma production is proportional to the input power, to the square root of the hydrogen flow rate, and is independent of the channel diameter. The observed scaling shows that the dominant power loss mechanism inside the arc channel is one that scales with the effective volume of the plasma in the discharge channel. Measurements on the plasma output with Thomson scattering confirm the linear dependence of the plasma production on the input power. Extrapolation of these results shows that (without a magnetic field) an improvement in the plasma production by a factor of 10 over where it was in van Rooij et al. [Appl. Phys. Lett. 90, 121501 (2007)] should be possible.

  14. Augmentation of Power Output of Axisymmetric Ducted Wind Turbines by Porous Trailing Edge Disks

    E-Print Network [OSTI]

    widnall, sheila

    2014-06-30

    This paper presents analytical and experimental results that demonstrated that the power output from a ducted wind turbine can be dramatically increased by the addition of a trailing edge device such as a porous disk. In ...

  15. The New Active Output Filter for Variable Speed Constant Frequency Aerospace Applications 

    E-Print Network [OSTI]

    Alhuwaishel, Fahad

    2015-08-06

    capacitor filter. Moreover, the main square wave inverter has a capability to regulate the output voltage depending on the Load requirements providing a wide range of modulation index. The operation principle of the concept is demonstrated for Variable Speed...

  16. Design of Dual-Output Alternators With Switched-Mode Rectification

    E-Print Network [OSTI]

    Hassan, Gimba

    The push to introduce dual-voltage (42 V/14 V) automotive electrical systems necessitates power generation solutions capable of supplying power to multiple outputs. A number of approaches for implementing dual-voltage ...

  17. Motor output in a bimanual continuation-tapping task is independent of visual cues 

    E-Print Network [OSTI]

    Miller, Louisa

    2009-07-03

    Presented are two studies examining the role of vision on motor output in the continuation-tapping paradigm (Stevens, 1886). The role of vision is measured by comparisons of motor performance under three visual feedback conditions: freeview...

  18. Examining the Variability of Wind Power Output in the Regulation Time Frame: Preprint

    SciTech Connect (OSTI)

    Hodge, B. M.; Shedd, S.; Florita, A.

    2012-08-01

    This work examines the distribution of changes in wind power for different time scales in the regulation time frame as well as the correlation of changes in power output for individual wind turbines in a wind plant.

  19. Output of an Ultrasonic Wave-Driven Nanogenerator in a Confined Tube

    E-Print Network [OSTI]

    Wang, Zhong L.

    with theoretical simulations directly proves the relationship between the input wave energy and the output power of Technology, Atlanta, Georgia 30332-0245, USA 2 Department of Materials Science and Engineering, University

  20. A Hardware Implementation of the Soft Output Viterbi Algorithm for Serially Concatenated Convolutional Codes

    E-Print Network [OSTI]

    Werling, Brett William

    2010-06-28

    two important algorithms for decoding convo- lutional codes: the Viterbi algoritm and soft output Viterbi algorithm. 4.1 Viterbi Algorithm The most common method for decoding a convolutionally-encoded sequence is the Viterbi algorithm, which was first...

  1. Biomolecular Filters for Improved Separation of Output Signals in Enzyme Logic Systems Applied to Biomedical Analysis

    E-Print Network [OSTI]

    Halamek, Jan; Halamkova, Lenka; Bocharova, Vera; Privman, Vladimir; Wang, Joseph; Katz, Evgeny

    2011-01-01

    Biomolecular logic systems processing biochemical input signals and producing "digital" outputs in the form of YES/NO were developed for analysis of physiological conditions characteristic of liver injury, soft tissue injury and abdominal trauma. Injury biomarkers were used as input signals for activating the logic systems. Their normal physiological concentrations were defined as logic-0 level, while their pathologically elevated concentrations were defined as logic-1 values. Since the input concentrations applied as logic 0 and 1 values were not sufficiently different, the output signals being at low and high values (0, 1 outputs) were separated with a short gap making their discrimination difficult. Coupled enzymatic reactions functioning as a biomolecular signal processing system with a built-in filter property were developed. The filter process involves a partial back-conversion of the optical-output-signal-yielding product, but only at its low concentrations, thus allowing the proper discrimination betw...

  2. Code design for multiple-input multiple-output broadcast channels 

    E-Print Network [OSTI]

    Uppal, Momin Ayub

    2009-06-02

    Recent information theoretical results indicate that dirty-paper coding (DPC) achieves the entire capacity region of the Gaussian multiple-input multiple-output (MIMO) broadcast channel (BC). This thesis presents practical ...

  3. Investigation of various ways of obtaining output waveforms of CMOS digital circuits by explicit methods 

    E-Print Network [OSTI]

    Ong, Lian Wah

    1989-01-01

    INVESTIGATION OF VARIOUS WAYS OF OBTAINING OUTPUT WAVEFORMS OF CMOS DIGITAL CIRCUITS BY EXPLICIT METHODS A Thesis by LIAN WAH ONG Submitted to the Office of Graduate Studies of Texas Agi. M University in partial fulfillment... of the requirement for the degree of iMASTER OF SCIENCE December 1989 Major Subject: Electrical Engineering INVESTIGATION OF VARIOUS WAYS OF OBTAINING OUTPUT WAVEFORMS OF CMOS DIGITAL CIRCUITS BY EXPLICIT METHODS A Thesis by LIAN-WAH ONG Approved...

  4. Water Power Calculator Temperature and Analog Input/Output Module Ambient Temperature Testing

    SciTech Connect (OSTI)

    Mark D. McKay

    2011-02-01

    Water Power Calculator Temperature and Analog input/output Module Ambient Temperature Testing A series of three ambient temperature tests were conducted for the Water Power Calculator development using the INL Calibration Laboratory’s Tenney Environmental Chamber. The ambient temperature test results demonstrate that the Moore Industries Temperature Input Modules, Analog Input Module and Analog Output Module, ambient temperature response meet or exceed the manufactures specifications

  5. Optimization of the LCLS X-ray FEL output performance in the presence of strong undulator wakefields

    E-Print Network [OSTI]

    Reiche, S; Emma, P; Fawley, W M; Huang, Z; Nuhn, H D; Stupakov, G V

    2005-01-01

    Optimization of the LCLS X-ray FEL output performance in the presence of strong undulator wakefields

  6. A Low Voltage, Rail-to-Rail, Class AB CMOS Amplifier With High Drive and Low Output Impedance Characteristics

    E-Print Network [OSTI]

    Rincon-Mora, Gabriel A.

    1 A Low Voltage, Rail-to-Rail, Class AB CMOS Amplifier With High Drive and Low Output Impedance describes a CMOS rail-to-rail class AB operational amplifier designed to have extremely low output impedance. The source follower ensures low output impedance, which enables it to drive relatively large load capacitors

  7. Output Harmonic Termination Techniques for AlGaN/GaN HEMT Power Amplifiers Using Active Integrated Antenna Approach

    E-Print Network [OSTI]

    Itoh, Tatsuo

    Output Harmonic Termination Techniques for AlGaN/GaN HEMT Power Amplifiers Using Active Integrated 1200, Los Angeles, CA 90045 Abstract -- In this paper, effects of output harmonic terminations on PAE termination, we observe a substantial increase in PAE and output power. Further, we demonstrate the high

  8. A Fully Integrated Switched-Capacitor DC-DC Converter with Dual Output for Low Power Application

    E-Print Network [OSTI]

    Ayers, Joseph

    . The entire converter system uses two 2-to-1 converter blocks. The upper output voltage (3.2V) is generated from the 2-to-1_up converter by means of averaging the 5V input and the generated lower output voltage is designed using high-voltage 0.35m BCDMOS technology. Both output voltages are regulated by means of pulse

  9. A framework for interpreting climate model outputs Nadja A. Leith and Richard E. Chandler

    E-Print Network [OSTI]

    Guillas, Serge

    , the Intergovernmental Panel on Climate Change (IPCC) recently asserted that "the observed pattern of tropospheric warming and stratospheric cooling is very likely due to the influence of anthropogenic forcing" (Hegerl et

  10. FRACTIONAL ORDER MODELING AND CONTROL OF MULTI-INPUT-MULTI-OUTPUT PROCESSES

    E-Print Network [OSTI]

    Li, Zhuo

    2015-01-01

    order MPC using RIOTS on the Peltier platform Simulation ofconfiguration of the Peltier cold plate platform. . . . . .and principle of Peltier heat pumping. . . . . . . . . . .

  11. FRACTIONAL ORDER MODELING AND CONTROL OF MULTI-INPUT-MULTI-OUTPUT PROCESSES

    E-Print Network [OSTI]

    Li, Zhuo

    2015-01-01

    based extremum seeking,” Mechatronics, vol. 23, pp. 863–872,YangQuan Chen, “Take-home mechatronics control labs: a low-disc-drive servo system mechatronics,” Mechatronics, vol.

  12. FRACTIONAL ORDER MODELING AND CONTROL OF MULTI-INPUT-MULTI-OUTPUT PROCESSES

    E-Print Network [OSTI]

    Li, Zhuo

    2015-01-01

    L´evy flight based PSO . . . . . . . . . The snapshot of thea particle swarm optimization (PSO) algorithm,” Journal ofOO pdf PDF PE PFR PID PRBS PSO RF RGA RIOTS R-L RL RMS RTW

  13. Dissemination of Climate Model Output to the Public and Commercial Sector

    SciTech Connect (OSTI)

    Robert Stockwell, PhD

    2010-09-23

    Climate is defined by the Glossary of Meteorology as the mean of atmospheric variables over a period of time ranging from as short as a few months to multiple years and longer. Although the term climate is often used to refer to long-term weather statistics, the broader definition of climate is the time evolution of a system consisting of the atmosphere, hydrosphere, lithosphere, and biosphere. Physical, chemical, and biological processes are involved in interactions among the components of the climate system. Vegetation, soil moisture, and glaciers are part of the climate system in addition to the usually considered temperature and precipitation (Pielke, 2008). Climate change refers to any systematic change in the long-term statistics of climate elements (such as temperature, pressure, or winds) sustained over several decades or longer. Climate change can be initiated by external forces, such as cyclical variations in the Earth's solar orbit that are thought to have caused glacial and interglacial periods within the last 2 million years (Milankovitch, 1941). However, a linear response to astronomical forcing does not explain many other observed glacial and interglacial cycles (Petit et al., 1999). It is now understood that climate is influenced by the interaction of solar radiation with atmospheric greenhouse gasses (e.g., carbon dioxide, chlorofluorocarbons, methane, nitrous oxide, etc.), aerosols (airborne particles), and Earth's surface. A significant aspect of climate are the interannual cycles, such as the El Nino La Nina cycle which profoundly affects the weather in North America but is outside the scope of weather forecasts. Some of the most significant advances in understanding climate change have evolved from the recognition of the influence of ocean circulations upon the atmosphere (IPCC, 2007). Human activity can affect the climate system through increasing concentrations of atmospheric greenhouse gases, air pollution, increasing concentrations of aerosol, and land alteration. A particular concern is that atmospheric levels of CO{sub 2} may be rising faster than at any time in Earth's history, except possibly following rare events like impacts from large extraterrestrial objects (AMS, 2007). Atmospheric CO{sub 2} concentrations have increased since the mid-1700s through fossil fuel burning and changes in land use, with more than 80% of this increase occurring since 1900. The increased levels of CO{sub 2} will remain in the atmosphere for hundreds to thousands of years. The complexity of the climate system makes it difficult to predict specific aspects of human-induced climate change, such as exactly how and where changes will occur, and their magnitude. The Intergovernmental Panel for Climate Change (IPCC) was established by World Meteorological Organization (WMO) and the United Nations in 1988. The IPCC was tasked with assessing the scientific, technical and socioeconomic information needed to understand the risk of human-induced climate change, its observed and projected impacts, and options for adaptation and mitigation. The IPCC concluded in its Fourth Assessment Report (AR4) that warming of the climate system is unequivocal, and that most of the observed increase in globally averaged temperatures since the mid-20th century is very likely due to the observed increased in anthropogenic greenhouse gas concentrations (IPCC, 2007).

  14. Distributed quantitative precipitation forecasts combining information from radar and numerical weather prediction model outputs

    E-Print Network [OSTI]

    Ganguly, Auroop Ratan

    2002-01-01

    Applications of distributed Quantitative Precipitation Forecasts (QPF) range from flood forecasting to transportation. Obtaining QPF is acknowledged to be one of the most challenging areas in hydrology and meteorology. ...

  15. FRACTIONAL ORDER MODELING AND CONTROL OF MULTI-INPUT-MULTI-OUTPUT PROCESSES

    E-Print Network [OSTI]

    Li, Zhuo

    2015-01-01

    fixed-wing unmanned aerial vehicle,” Control Engineeringof a T-tail unmanned aerial vehicle”, Journal of Intelligent

  16. System and method for cancelling the effects of stray magnetic fields from the output of a variable reluctance sensor

    DOE Patents [OSTI]

    Chen, Chingchi (Ann Arbor, MI); Degner, Michael W. (Farmington Hills, MI)

    2002-11-19

    A sensor system for sensing a rotation of a sensing wheel is disclosed. The sensor system has a sensing coil in juxtaposition with the sensing wheel. Moreover, the sensing coil has a sensing coil output signal indicative of the rotational speed of the sensing wheel. Further, a cancellation coil is located remotely from the sensing coil and connected in series therewith. Additionally, the cancellation coil has a cancellation coil output signal indicative of an environmental disturbance which is effecting the sensing coil output signal. The cancellation coil output signal operates to cancel the effects of the environmental disturbance on the sensing coil output signal.

  17. A combined compensation method for the output voltage of an insulated core transformer power supply

    SciTech Connect (OSTI)

    Yang, L.; Yang, J., E-mail: jyang@mail.hust.edu.cn; Liu, K. F.; Qin, B.; Chen, D. Z. [State Key Laboratory of Advanced Electromagnetic Engineering and Technology, Huazhong University of Science and Technology, Wuhan 430074 (China)

    2014-06-15

    An insulated core transformer (ICT) power supply is an ideal high-voltage generator for irradiation accelerators with energy lower than 3 MeV. However, there is a significant problem that the structure of the segmented cores leads to an increase in the leakage flux and voltage differences between rectifier disks. A high level of consistency in the output of the disks helps to achieve a compact structure by improving the utilization of both the rectifier components and the insulation distances, and consequently increase the output voltage of the power supply. The output voltages of the disks which are far away from the primary coils need to be improved to reduce their inhomogeneity. In this study, by investigating and comparing the existing compensation methods, a new combined compensation method is proposed, which increases the turns on the secondary coils and employs parallel capacitors to improve the consistency of the disks, while covering the entire operating range of the power supply. This method turns out to be both feasible and effective during the development of an ICT power supply. The non-uniformity of the output voltages of the disks is less than 3.5% from no-load to full-load, and the power supply reaches an output specification of 350 kV/60 mA.

  18. Comparison of Mixed Layer Heights from Airborne High Spectral Resolution Lidar, Ground-based Measurements, and the WRP-Chem Model during CalNex and CARES

    SciTech Connect (OSTI)

    Scarino, Amy Jo; Obland, Michael; Fast, Jerome D.; Burton, S. P.; Ferrare, R. A.; Hostetler, Chris A.; Berg, Larry K.; Lefer, Barry; Haman, C.; Hair, John; Rogers, Ray; Butler, Carolyn; Cook, A. L.; Harper, David

    2014-06-05

    The California Research at the Nexus of Air Quality and Climate Change (CalNex) and Carbonaceous Aerosol and Radiative Effects Study (CARES) field campaigns during May and June 2010 provided a data set appropriate for studying characteristics of the planetary boundary layer (PBL). The NASA Langley Research Center (LaRC) airborne High Spectral Resolution Lidar (HSRL) was deployed to California onboard the NASA LaRC B-200 aircraft to aid incharacterizing aerosol properties during these two field campaigns. Measurements of aerosol extinction (532 nm), backscatter (532 and 1064 nm), and depolarization (532 and 1064 nm) profiles during 31 flights, many in coordination with other research aircraft and ground sites, constitute a diverse data set for use in characterizing the spatial and temporal distribution of aerosols, as well as the depth and variability of the daytime mixed layer (ML), which is a subset within the PBL. This work illustrates the temporal and spatial variability of the ML in the vicinity of Los Angeles and Sacramento, CA. ML heights derived from HSRL measurements are compared to PBL heights derived from radiosonde profiles, ML heights measured from ceilometers, and simulated PBL heights from the Weather Research and Forecasting Chemistry (WRF-Chem) community model. Comparisons between the HSRL ML heights and the radiosonde profiles in Sacramento result in a correlation coefficient value (R) of 0.93 (root7 mean-square (RMS) difference of 157 m and bias difference (HSRL radiosonde) of 5 m). HSRL ML heights compare well with those from the ceilometer in the LA Basin with an R of 0.89 (RMS difference of 108 m and bias difference (HSRL Ceilometer) of -9.7 m) for distances of up to 30 km between the B-200 flight track and the ceilometer site. Simulated PBL heights from WRF-Chem were compared with those obtained from all flights for each campaign, producing an R of 0.58 (RMS difference of 604 m and a bias difference (WRF-Chem HSRL) of -157 m) for CalNex and 0.59 (RMS difference of 689 m and a bias difference (WRF-Chem HSRL) of 220 m) for CARES. Aerosol backscatter simulations are also available from WRF15 Chem and are compared to those from HSRL to examine differences among the methods used to derive ML heights.

  19. SAS Output

    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: Alternative Fuels Data Center Home Page on Google Bookmark EERE: Alternative Fuels Data Center Home Page| Open Energy Informationmonthly gasoline price to fall to $3.43 byInformation7,propane pricesRetail PowerRisingCoal

  20. SAS Output

    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: Alternative Fuels Data Center Home Page on Google Bookmark EERE: Alternative Fuels Data Center Home Page| Open Energy Informationmonthly gasoline price to fall to $3.43 byInformation7,propane pricesRetail

  1. SAS Output

    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: Alternative Fuels Data Center Home Page on Google Bookmark EERE: Alternative Fuels Data Center Home Page| Open Energy Informationmonthly gasoline price to fall to $3.43 byInformation7,propane pricesRetail Productive

  2. SAS Output

    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: Alternative Fuels Data Center Home Page on Google Bookmark EERE: Alternative Fuels Data Center Home Page| Open Energy Informationmonthly gasoline price to fall to $3.43 byInformation7,propane pricesRetail ProductiveCapacity

  3. SAS Output

    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: Alternative Fuels Data Center Home Page on Google Bookmark EERE: Alternative Fuels Data Center Home Page| Open Energy Informationmonthly gasoline price to fall to $3.43 byInformation7,propane pricesRetail

  4. SAS Output

    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: Alternative Fuels Data Center Home Page on Google Bookmark EERE: Alternative Fuels Data Center Home Page| Open Energy Informationmonthly gasoline price to fall to $3.43 byInformation7,propane pricesRetailRecoverable Coal

  5. SAS Output

    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: Alternative Fuels Data Center Home Page on Google Bookmark EERE: Alternative Fuels Data Center Home Page| Open Energy Informationmonthly gasoline price to fall to $3.43 byInformation7,propane pricesRetailRecoverable

  6. SAS Output

    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: Alternative Fuels Data Center Home Page on Google Bookmark EERE: Alternative Fuels Data Center Home Page| Open Energy Informationmonthly gasoline price to fall to $3.43 byInformation7,propane

  7. SAS Output

    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: Alternative Fuels Data Center Home Page on Google Bookmark EERE: Alternative Fuels Data Center Home Page| Open Energy Informationmonthly gasoline price to fall to $3.43 byInformation7,propaneRecoverable Coal Reserves and

  8. SAS Output

    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: Alternative Fuels Data Center Home Page on Google Bookmark EERE: Alternative Fuels Data Center Home Page| Open Energy Informationmonthly gasoline price to fall to $3.43 byInformation7,propaneRecoverable Coal Reserves

  9. SAS Output

    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: Alternative Fuels Data Center Home Page on Google Bookmark EERE: Alternative Fuels Data Center Home Page| Open Energy Informationmonthly gasoline price to fall to $3.43 byInformation7,propaneRecoverable Coal Reserves19.

  10. SAS Output

    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: Alternative Fuels Data Center Home Page on Google Bookmark EERE: Alternative Fuels Data Center Home Page| Open Energy Informationmonthly gasoline price to fall to $3.43 byInformation7,propaneRecoverable Coal Reserves19.Coal

  11. SAS Output

    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: Alternative Fuels Data Center Home Page on Google Bookmark EERE: Alternative Fuels Data Center Home Page| Open Energy Informationmonthly gasoline price to fall to $3.43 byInformation7,propaneRecoverable Coal

  12. SAS Output

    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: Alternative Fuels Data Center Home Page on Google Bookmark EERE: Alternative Fuels Data Center Home Page| Open Energy Informationmonthly gasoline price to fall to $3.43 byInformation7,propaneRecoverable CoalCoal

  13. SAS Output

    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: Alternative Fuels Data Center Home Page on Google Bookmark EERE: Alternative Fuels Data Center Home Page| Open Energy Informationmonthly gasoline price to fall to $3.43 byInformation7,propaneRecoverable CoalCoal2.

  14. SAS Output

    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: Alternative Fuels Data Center Home Page on Google Bookmark EERE: Alternative Fuels Data Center Home Page| Open Energy Informationmonthly gasoline price to fall to $3.43 byInformation7,propaneRecoverable CoalCoal2.3. Coal

  15. SAS Output

    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: Alternative Fuels Data Center Home Page on Google Bookmark EERE: Alternative Fuels Data Center Home Page| Open Energy Informationmonthly gasoline price to fall to $3.43 byInformation7,propaneRecoverable CoalCoal2.3. Coal4.

  16. SAS Output

    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: Alternative Fuels Data Center Home Page on Google Bookmark EERE: Alternative Fuels Data Center Home Page| Open Energy Informationmonthly gasoline price to fall to $3.43 byInformation7,propaneRecoverable CoalCoal2.3.

  17. SAS Output

    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: Alternative Fuels Data Center Home Page on Google Bookmark EERE: Alternative Fuels Data Center Home Page| Open Energy Informationmonthly gasoline price to fall to $3.43 byInformation7,propaneRecoverable CoalCoal2.3.6. U.S.

  18. SAS Output

    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: Alternative Fuels Data Center Home Page on Google Bookmark EERE: Alternative Fuels Data Center Home Page| Open Energy Informationmonthly gasoline price to fall to $3.43 byInformation7,propaneRecoverable CoalCoal2.3.6.

  19. SAS Output

    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: Alternative Fuels Data Center Home Page on Google Bookmark EERE: Alternative Fuels Data Center Home Page| Open Energy Informationmonthly gasoline price to fall to $3.43 byInformation7,propaneRecoverable CoalCoal2.3.6.8.

  20. SAS Output

    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: Alternative Fuels Data Center Home Page on Google Bookmark EERE: Alternative Fuels Data Center Home Page| Open Energy Informationmonthly gasoline price to fall to $3.43 byInformation7,propaneRecoverable CoalCoal2.3.6.8.9.

  1. SAS Output

    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: Alternative Fuels Data Center Home Page on Google Bookmark EERE: Alternative Fuels Data Center Home Page| Open Energy Informationmonthly gasoline price to fall to $3.43 byInformation7,propaneRecoverable

  2. SAS Output

    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: Alternative Fuels Data Center Home Page on Google Bookmark EERE: Alternative Fuels Data Center Home Page| Open Energy Informationmonthly gasoline price to fall to $3.43 byInformation7,propaneRecoverable0. Average Sales

  3. SAS Output

    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: Alternative Fuels Data Center Home Page on Google Bookmark EERE: Alternative Fuels Data Center Home Page| Open Energy Informationmonthly gasoline price to fall to $3.43 byInformation7,propaneRecoverable0. Average Sales1.

  4. SAS Output

    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: Alternative Fuels Data Center Home Page on Google Bookmark EERE: Alternative Fuels Data Center Home Page| Open Energy Informationmonthly gasoline price to fall to $3.43 byInformation7,propaneRecoverable0. Average Sales1.2.

  5. SAS Output

    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: Alternative Fuels Data Center Home Page on Google Bookmark EERE: Alternative Fuels Data Center Home Page| Open Energy Informationmonthly gasoline price to fall to $3.43 byInformation7,propaneRecoverable0. Average

  6. SAS Output

    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: Alternative Fuels Data Center Home Page on Google Bookmark EERE: Alternative Fuels Data Center Home Page| Open Energy Informationmonthly gasoline price to fall to $3.43 byInformation7,propaneRecoverable0. Average4. Average

  7. SAS Output

    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: Alternative Fuels Data Center Home Page on Google Bookmark EERE: Alternative Fuels Data Center Home Page| Open Energy Informationmonthly gasoline price to fall to $3.43 byInformation7,propaneRecoverable0. Average4.

  8. SAS Output

    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: Alternative Fuels Data Center Home Page on Google Bookmark EERE: Alternative Fuels Data Center Home Page| Open Energy Informationmonthly gasoline price to fall to $3.43 byInformation7,propaneRecoverable0. Average4.Coal

  9. SAS Output

    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: Alternative Fuels Data Center Home Page on Google Bookmark EERE: Alternative Fuels Data Center Home Page| Open Energy Informationmonthly gasoline price to fall to $3.43 byInformation7,propaneRecoverable0. Average4.CoalCoal

  10. SAS Output

    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: Alternative Fuels Data Center Home Page on Google Bookmark EERE: Alternative Fuels Data Center Home Page| Open Energy Informationmonthly gasoline price to fall to $3.43 byInformation7,propaneRecoverable0.

  11. SAS Output

    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: Alternative Fuels Data Center Home Page on Google Bookmark EERE: Alternative Fuels Data Center Home Page| Open Energy Informationmonthly gasoline price to fall to $3.43 byInformation7,propaneRecoverable0.Coal Disposition by

  12. SAS Output

    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: Alternative Fuels Data Center Home Page on Google Bookmark EERE: Alternative Fuels Data Center Home Page| Open Energy Informationmonthly gasoline price to fall to $3.43 byInformation7,propaneRecoverable0.Coal Disposition

  13. SAS Output

    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: Alternative Fuels Data Center Home Page on Google Bookmark EERE: Alternative Fuels Data Center Home Page| Open Energy Informationmonthly gasoline price to fall to $3.43 byInformation7,propaneRecoverable0.Coal

  14. SAS Output

    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: Alternative Fuels Data Center Home Page on Google Bookmark EERE: Alternative Fuels Data Center Home Page| Open Energy Informationmonthly gasoline price to fall to $3.43 byInformation7,propaneRecoverable0.CoalU.S.

  15. SAS Output

    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: Alternative Fuels Data Center Home Page on Google Bookmark EERE: Alternative Fuels Data Center Home Page| Open Energy Informationmonthly gasoline price to fall to $3.43 byInformation7,propaneRecoverable0.CoalU.S.Average

  16. SAS Output

    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: Alternative Fuels Data Center Home Page on Google Bookmark EERE: Alternative Fuels Data Center Home Page| Open Energy Informationmonthly gasoline price to fall to $3.43 byInformation7,propaneRecoverable0.CoalU.S.AverageU.S.

  17. SAS Output

    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: Alternative Fuels Data Center Home Page on Google Bookmark EERE: Alternative Fuels Data Center Home Page| Open Energy Informationmonthly gasoline price to fall to $3.43 byInformation7,propaneRecoverable0.CoalU.S.AverageU.S.

  18. SAS Output

    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: Alternative Fuels Data Center Home Page on Google Bookmark EERE: Alternative Fuels Data Center Home Page| Open Energy Informationmonthly gasoline price to fall to $3.43

  19. SAS Output

    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: Alternative Fuels Data Center Home Page on Google Bookmark EERE: Alternative Fuels Data Center Home Page| Open Energy Informationmonthly gasoline price to fall to $3.43U.S. Coke Exports" "(short tons)"

  20. SAS Output

    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: Alternative Fuels Data Center Home Page on Google Bookmark EERE: Alternative Fuels Data Center Home Page| Open Energy Informationmonthly gasoline price to fall to $3.43U.S. Coke Exports" "(short tons)"Average

  1. SAS Output

    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: Alternative Fuels Data Center Home Page on Google Bookmark EERE: Alternative Fuels Data Center Home Page| Open Energy Informationmonthly gasoline price to fall to $3.43U.S. Coke Exports" "(short tons)"Average

  2. SAS Output

    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: Alternative Fuels Data Center Home Page on Google Bookmark EERE: Alternative Fuels Data Center Home Page| Open Energy Informationmonthly gasoline price to fall to $3.43U.S. Coke Exports" "(short

  3. SAS Output

    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: Alternative Fuels Data Center Home Page on Google Bookmark EERE: Alternative Fuels Data Center Home Page| Open Energy Informationmonthly gasoline price to fall to $3.43U.S. Coke Exports" "(shortCoal Production,

  4. SAS Output

    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: Alternative Fuels Data Center Home Page on Google Bookmark EERE: Alternative Fuels Data Center Home Page| Open Energy Informationmonthly gasoline price to fall to $3.43U.S. Coke Exports" "(shortCoal

  5. SAS Output

    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: Alternative Fuels Data Center Home Page on Google Bookmark EERE: Alternative Fuels Data Center Home Page| Open Energy Informationmonthly gasoline price to fall to $3.43U.S. Coke Exports" "(shortCoal U.S. Coke

  6. SAS Output

    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: Alternative Fuels Data Center Home Page on Google Bookmark EERE: Alternative Fuels Data Center Home Page| Open Energy Informationmonthly gasoline price to fall to $3.43U.S. Coke Exports" "(shortCoal U.S.

  7. SAS Output

    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: Alternative Fuels Data Center Home Page on Google Bookmark EERE: Alternative Fuels Data Center Home Page| Open Energy Informationmonthly gasoline price to fall to $3.43U.S. Coke Exports" "(shortCoal U.S.2. Coal

  8. SAS Output

    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: Alternative Fuels Data Center Home Page on Google Bookmark EERE: Alternative Fuels Data Center Home Page| Open Energy Informationmonthly gasoline price to fall to $3.43U.S. Coke Exports" "(shortCoal U.S.2. Coal

  9. SAS Output

    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: Alternative Fuels Data Center Home Page on Google Bookmark EERE: Alternative Fuels Data Center Home Page| Open Energy Informationmonthly gasoline price to fall to $3.43U.S. Coke Exports" "(shortCoal U.S.2.

  10. SAS Output

    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: Alternative Fuels Data Center Home Page on Google Bookmark EERE: Alternative Fuels Data Center Home Page| Open Energy Informationmonthly gasoline price to fall to $3.43U.S. Coke Exports" "(shortCoal U.S.2.Quantity

  11. SAS Output

    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: Alternative Fuels Data Center Home Page on Google Bookmark EERE: Alternative Fuels Data Center Home Page| Open Energy Informationmonthly gasoline price to fall to $3.43U.S. Coke Exports" "(shortCoal

  12. SAS Output

    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: Alternative Fuels Data Center Home Page on Google Bookmark EERE: Alternative Fuels Data Center Home Page| Open Energy Informationmonthly gasoline price to fall to $3.43U.S. Coke Exports" "(shortCoalAverage Price

  13. SAS Output

    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: Alternative Fuels Data Center Home Page on Google Bookmark EERE: Alternative Fuels Data Center Home Page| Open Energy Informationmonthly gasoline price to fall to $3.43U.S. Coke Exports" "(shortCoalAverage Price

  14. SAS Output

    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: Alternative Fuels Data Center Home Page on Google Bookmark EERE: Alternative Fuels Data Center Home Page| Open Energy Informationmonthly gasoline price toStocks 2009CubicAnalysis &V 1997 Housingb.Million

  15. SAS Output

    Gasoline and Diesel Fuel Update (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: Alternative Fuels Data Center Homesum_a_epg0_fpd_mmcf_m.xls" ,"Available from WebQuantity of Natural GasAdjustments (Billion Cubic Feet) Wyoming Dry Natural GasNaturalOctoberheating13, 2014propanepropane2,165.

  16. SAS Output

    Gasoline and Diesel Fuel Update (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: Alternative Fuels Data Center Homesum_a_epg0_fpd_mmcf_m.xls" ,"Available from WebQuantity of Natural GasAdjustments (Billion Cubic Feet) Wyoming Dry Natural GasNaturalOctoberheating13, 2014propanepropane2,165.1.

  17. SAS Output

    Gasoline and Diesel Fuel Update (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: Alternative Fuels Data Center Homesum_a_epg0_fpd_mmcf_m.xls" ,"Available from WebQuantity of Natural GasAdjustments (Billion Cubic Feet) Wyoming Dry Natural GasNaturalOctoberheating13, 2014propanepropane2,165.1.2.

  18. SAS Output

    Gasoline and Diesel Fuel Update (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: Alternative Fuels Data Center Homesum_a_epg0_fpd_mmcf_m.xls" ,"Available from WebQuantity of Natural GasAdjustments (Billion Cubic Feet) Wyoming963 1.969 1.979 1.988 1.996 2.003 1990-2016 East) e j )Million1. Total

  19. SAS Output

    Gasoline and Diesel Fuel Update (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: Alternative Fuels Data Center Homesum_a_epg0_fpd_mmcf_m.xls" ,"Available from WebQuantity of Natural GasAdjustments (Billion Cubic Feet) Wyoming963 1.969 1.979 1.988 1.996 2.003 1990-2016 East) e j )Million1. Total2.

  20. SAS Output

    Gasoline and Diesel Fuel Update (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: Alternative Fuels Data Center Homesum_a_epg0_fpd_mmcf_m.xls" ,"Available from WebQuantity of Natural GasAdjustments (Billion Cubic Feet) Wyoming963 1.969 1.979 1.988 1.996 2.003 1990-2016 East) e j )Million1.

  1. SAS Output

    Gasoline and Diesel Fuel Update (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: Alternative Fuels Data Center Homesum_a_epg0_fpd_mmcf_m.xls" ,"Available from WebQuantity of Natural GasAdjustments (Billion Cubic Feet) Wyoming963 1.969 1.979 1.988 1.996 2.003 1990-2016 East) e j )Million1.. Number

  2. SAS Output

    Gasoline and Diesel Fuel Update (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: Alternative Fuels Data Center Homesum_a_epg0_fpd_mmcf_m.xls" ,"Available from WebQuantity of Natural GasAdjustments (Billion Cubic Feet) Wyoming963 1.969 1.979 1.988 1.996 2.003 1990-2016 East) e j )Million1..

  3. SAS Output

    Gasoline and Diesel Fuel Update (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: Alternative Fuels Data Center Homesum_a_epg0_fpd_mmcf_m.xls" ,"Available from WebQuantity of Natural GasAdjustments (Billion Cubic Feet) Wyoming963 1.969 1.979 1.988 1.996 2.003 1990-2016 East) e j )Million1..3.

  4. SAS Output

    Gasoline and Diesel Fuel Update (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: Alternative Fuels Data Center Homesum_a_epg0_fpd_mmcf_m.xls" ,"Available from WebQuantity of Natural GasAdjustments (Billion Cubic Feet) Wyoming963 1.969 1.979 1.988 1.996 2.003 1990-2016 East) e j )Million1..3.4.

  5. SAS Output

    Gasoline and Diesel Fuel Update (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: Alternative Fuels Data Center Homesum_a_epg0_fpd_mmcf_m.xls" ,"Available from WebQuantity of Natural GasAdjustments (Billion Cubic Feet) Wyoming963 1.969 1.979 1.988 1.996 2.003 1990-2016 East) e j )Million1..3.4.5.

  6. SAS Output

    Gasoline and Diesel Fuel Update (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: Alternative Fuels Data Center Homesum_a_epg0_fpd_mmcf_m.xls" ,"Available from WebQuantity of Natural GasAdjustments (Billion Cubic Feet) Wyoming963 1.969 1.979 1.988 1.996 2.003 1990-2016 East) e j )Million1..3.4.5.6.

  7. SAS Output

    Gasoline and Diesel Fuel Update (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: Alternative Fuels Data Center Homesum_a_epg0_fpd_mmcf_m.xls" ,"Available from WebQuantity of Natural GasAdjustments (Billion Cubic Feet) Wyoming963 1.969 1.979 1.988 1.996 2.003 1990-2016 East) e j

  8. SAS Output

    Gasoline and Diesel Fuel Update (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: Alternative Fuels Data Center Homesum_a_epg0_fpd_mmcf_m.xls" ,"Available from WebQuantity of Natural GasAdjustments (Billion Cubic Feet) Wyoming963 1.969 1.979 1.988 1.996 2.003 1990-2016 East) e j8. Retail Sales of

  9. SAS Output

    Gasoline and Diesel Fuel Update (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: Alternative Fuels Data Center Homesum_a_epg0_fpd_mmcf_m.xls" ,"Available from WebQuantity of Natural GasAdjustments (Billion Cubic Feet) Wyoming963 1.969 1.979 1.988 1.996 2.003 1990-2016 East) e j8. Retail Sales of9.

  10. SAS Output

    Gasoline and Diesel Fuel Update (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: Alternative Fuels Data Center Homesum_a_epg0_fpd_mmcf_m.xls" ,"Available from WebQuantity of Natural GasAdjustments (Billion Cubic Feet) Wyoming963 1.969 1.979 1.988 1.996 2.003 1990-2016 East) e j8. Retail Sales

  11. SAS Output

    Gasoline and Diesel Fuel Update (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: Alternative Fuels Data Center Homesum_a_epg0_fpd_mmcf_m.xls" ,"Available from WebQuantity of Natural GasAdjustments (Billion Cubic Feet) Wyoming963 1.969 1.979 1.988 1.996 2.003 1990-2016 East) e j8. Retail Sales1.

  12. SAS Output

    Gasoline and Diesel Fuel Update (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: Alternative Fuels Data Center Homesum_a_epg0_fpd_mmcf_m.xls" ,"Available from WebQuantity of Natural GasAdjustments (Billion Cubic Feet) Wyoming963 1.969 1.979 1.988 1.996 2.003 1990-2016 East) e j8. Retail Sales1.2.

  13. SAS Output

    Gasoline and Diesel Fuel Update (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: Alternative Fuels Data Center Homesum_a_epg0_fpd_mmcf_m.xls" ,"Available from WebQuantity of Natural GasAdjustments (Billion Cubic Feet) Wyoming963 1.969 1.979 1.988 1.996 2.003 1990-2016 East) e j8. Retail

  14. SAS Output

    Gasoline and Diesel Fuel Update (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: Alternative Fuels Data Center Homesum_a_epg0_fpd_mmcf_m.xls" ,"Available from WebQuantity of Natural GasAdjustments (Billion Cubic Feet) Wyoming963 1.969 1.979 1.988 1.996 2.003 1990-2016 East) e j8. Retail4. Green

  15. SAS Output

    Gasoline and Diesel Fuel Update (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: Alternative Fuels Data Center Homesum_a_epg0_fpd_mmcf_m.xls" ,"Available from WebQuantity of Natural GasAdjustments (Billion Cubic Feet) Wyoming963 1.969 1.979 1.988 1.996 2.003 1990-2016 East) e j8. Retail4. GreenA.

  16. SAS Output

    Gasoline and Diesel Fuel Update (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: Alternative Fuels Data Center Homesum_a_epg0_fpd_mmcf_m.xls" ,"Available from WebQuantity of Natural GasAdjustments (Billion Cubic Feet) Wyoming963 1.969 1.979 1.988 1.996 2.003 1990-2016 East) e j8. Retail4.

  17. SAS Output

    Gasoline and Diesel Fuel Update (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: Alternative Fuels Data Center Homesum_a_epg0_fpd_mmcf_m.xls" ,"Available from WebQuantity of Natural GasAdjustments (Billion Cubic Feet) Wyoming963 1.969 1.979 1.988 1.996 2.003 1990-2016 East) e j8. Retail4.A. Net

  18. SAS Output

    Gasoline and Diesel Fuel Update (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: Alternative Fuels Data Center Homesum_a_epg0_fpd_mmcf_m.xls" ,"Available from WebQuantity of Natural GasAdjustments (Billion Cubic Feet) Wyoming963 1.969 1.979 1.988 1.996 2.003 1990-2016 East) e j8. Retail4.A. NetB.

  19. SAS Output

    Gasoline and Diesel Fuel Update (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: Alternative Fuels Data Center Homesum_a_epg0_fpd_mmcf_m.xls" ,"Available from WebQuantity of Natural GasAdjustments (Billion Cubic Feet) Wyoming963 1.969 1.979 1.988 1.996 2.003 1990-2016 East) e j8. Retail4.A.

  20. SAS Output

    Gasoline and Diesel Fuel Update (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: Alternative Fuels Data Center Homesum_a_epg0_fpd_mmcf_m.xls" ,"Available from WebQuantity of Natural GasAdjustments (Billion Cubic Feet) Wyoming963 1.969 1.979 1.988 1.996 2.003 1990-2016 East) e j8. Retail4.A.B. Net

  1. SAS Output

    Gasoline and Diesel Fuel Update (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: Alternative Fuels Data Center Homesum_a_epg0_fpd_mmcf_m.xls" ,"Available from WebQuantity of Natural GasAdjustments (Billion Cubic Feet) Wyoming963 1.969 1.979 1.988 1.996 2.003 1990-2016 East) e j8. Retail4.A.B.

  2. SAS Output

    Gasoline and Diesel Fuel Update (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: Alternative Fuels Data Center Homesum_a_epg0_fpd_mmcf_m.xls" ,"Available from WebQuantity of Natural GasAdjustments (Billion Cubic Feet) Wyoming963 1.969 1.979 1.988 1.996 2.003 1990-2016 East) e j8. Retail4.A.B.B.

  3. SAS Output

    Gasoline and Diesel Fuel Update (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: Alternative Fuels Data Center Homesum_a_epg0_fpd_mmcf_m.xls" ,"Available from WebQuantity of Natural GasAdjustments (Billion Cubic Feet) Wyoming963 1.969 1.979 1.988 1.996 2.003 1990-2016 East) e j8. Retail4.A.B.B.A.

  4. SAS Output

    Gasoline and Diesel Fuel Update (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: Alternative Fuels Data Center Homesum_a_epg0_fpd_mmcf_m.xls" ,"Available from WebQuantity of Natural GasAdjustments (Billion Cubic Feet) Wyoming963 1.969 1.979 1.988 1.996 2.003 1990-2016 East) e j8.

  5. SAS Output

    Gasoline and Diesel Fuel Update (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: Alternative Fuels Data Center Homesum_a_epg0_fpd_mmcf_m.xls" ,"Available from WebQuantity of Natural GasAdjustments (Billion Cubic Feet) Wyoming963 1.969 1.979 1.988 1.996 2.003 1990-2016 East) e j8.6. Net Generation

  6. SAS Output

    Gasoline and Diesel Fuel Update (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: Alternative Fuels Data Center Homesum_a_epg0_fpd_mmcf_m.xls" ,"Available from WebQuantity of Natural GasAdjustments (Billion Cubic Feet) Wyoming963 1.969 1.979 1.988 1.996 2.003 1990-2016 East) e j8.6. Net

  7. SAS Output

    Gasoline and Diesel Fuel Update (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: Alternative Fuels Data Center Homesum_a_epg0_fpd_mmcf_m.xls" ,"Available from WebQuantity of Natural GasAdjustments (Billion Cubic Feet) Wyoming963 1.969 1.979 1.988 1.996 2.003 1990-2016 East) e j8.6. Net8. Net

  8. SAS Output

    Gasoline and Diesel Fuel Update (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: Alternative Fuels Data Center Homesum_a_epg0_fpd_mmcf_m.xls" ,"Available from WebQuantity of Natural GasAdjustments (Billion Cubic Feet) Wyoming963 1.969 1.979 1.988 1.996 2.003 1990-2016 East) e j8.6. Net8. Net9. Net

  9. SAS Output

    Gasoline and Diesel Fuel Update (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: Alternative Fuels Data Center Homesum_a_epg0_fpd_mmcf_m.xls" ,"Available from WebQuantity of Natural GasAdjustments (Billion Cubic Feet) Wyoming963 1.969 1.979 1.988 1.996 2.003 1990-2016 East) e j8.6. Net8. Net9.

  10. SAS Output

    Gasoline and Diesel Fuel Update (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: Alternative Fuels Data Center Homesum_a_epg0_fpd_mmcf_m.xls" ,"Available from WebQuantity of Natural GasAdjustments (Billion Cubic Feet) Wyoming963 1.969 1.979 1.988 1.996 2.003 1990-2016 East) e j8.6. Net8. Net9.1.

  11. SAS Output

    Gasoline and Diesel Fuel Update (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: Alternative Fuels Data Center Homesum_a_epg0_fpd_mmcf_m.xls" ,"Available from WebQuantity of Natural GasAdjustments (Billion Cubic Feet) Wyoming963 1.969 1.979 1.988 1.996 2.003 1990-2016 East) e j8.6. Net8. Net9.1.2.

  12. SAS Output

    Gasoline and Diesel Fuel Update (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: Alternative Fuels Data Center Homesum_a_epg0_fpd_mmcf_m.xls" ,"Available from WebQuantity of Natural GasAdjustments (Billion Cubic Feet) Wyoming963 1.969 1.979 1.988 1.996 2.003 1990-2016 East) e j8.6. Net8.

  13. SAS Output

    Gasoline and Diesel Fuel Update (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: Alternative Fuels Data Center Homesum_a_epg0_fpd_mmcf_m.xls" ,"Available from WebQuantity of Natural GasAdjustments (Billion Cubic Feet) Wyoming963 1.969 1.979 1.988 1.996 2.003 1990-2016 East) e j8.6. Net8.4. Net

  14. SAS Output

    Gasoline and Diesel Fuel Update (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: Alternative Fuels Data Center Homesum_a_epg0_fpd_mmcf_m.xls" ,"Available from WebQuantity of Natural GasAdjustments (Billion Cubic Feet) Wyoming963 1.969 1.979 1.988 1.996 2.003 1990-2016 East) e j8.6. Net8.4. Net5.

  15. SAS Output

    Gasoline and Diesel Fuel Update (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: Alternative Fuels Data Center Homesum_a_epg0_fpd_mmcf_m.xls" ,"Available from WebQuantity of Natural GasAdjustments (Billion Cubic Feet) Wyoming963 1.969 1.979 1.988 1.996 2.003 1990-2016 East) e j8.6. Net8.4. Net5.6.

  16. SAS Output

    Gasoline and Diesel Fuel Update (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: Alternative Fuels Data Center Homesum_a_epg0_fpd_mmcf_m.xls" ,"Available from WebQuantity of Natural GasAdjustments (Billion Cubic Feet) Wyoming963 1.969 1.979 1.988 1.996 2.003 1990-2016 East) e j8.6. Net8.4.

  17. SAS Output

    Gasoline and Diesel Fuel Update (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: Alternative Fuels Data Center Homesum_a_epg0_fpd_mmcf_m.xls" ,"Available from WebQuantity of Natural GasAdjustments (Billion Cubic Feet) Wyoming963 1.969 1.979 1.988 1.996 2.003 1990-2016 East) e j8.6. Net8.4.8. Net

  18. SAS Output

    Gasoline and Diesel Fuel Update (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: Alternative Fuels Data Center Homesum_a_epg0_fpd_mmcf_m.xls" ,"Available from WebQuantity of Natural GasAdjustments (Billion Cubic Feet) Wyoming963 1.969 1.979 1.988 1.996 2.003 1990-2016 East) e j8.6. Net8.4.8. Net9.

  19. SAS Output

    Gasoline and Diesel Fuel Update (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: Alternative Fuels Data Center Homesum_a_epg0_fpd_mmcf_m.xls" ,"Available from WebQuantity of Natural GasAdjustments (Billion Cubic Feet) Wyoming963 1.969 1.979 1.988 1.996 2.003 1990-2016 East) e j8.6. Net8.4.8.

  20. SAS Output

    Gasoline and Diesel Fuel Update (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: Alternative Fuels Data Center Homesum_a_epg0_fpd_mmcf_m.xls" ,"Available from WebQuantity of Natural GasAdjustments (Billion Cubic Feet) Wyoming963 1.969 1.979 1.988 1.996 2.003 1990-2016 East) e j8.6. Net8.4.8.1.

  1. SAS Output

    Gasoline and Diesel Fuel Update (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: Alternative Fuels Data Center Homesum_a_epg0_fpd_mmcf_m.xls" ,"Available from WebQuantity of Natural GasAdjustments (Billion Cubic Feet) Wyoming963 1.969 1.979 1.988 1.996 2.003 1990-2016 East) e j8.6. Net8.4.8.1.2.

  2. SAS Output

    Gasoline and Diesel Fuel Update (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: Alternative Fuels Data Center Homesum_a_epg0_fpd_mmcf_m.xls" ,"Available from WebQuantity of Natural GasAdjustments (Billion Cubic Feet) Wyoming963 1.969 1.979 1.988 1.996 2.003 1990-2016 East) e j8.6. Net8.4.8.1.2.3.

  3. SAS Output

    Gasoline and Diesel Fuel Update (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: Alternative Fuels Data Center Homesum_a_epg0_fpd_mmcf_m.xls" ,"Available from WebQuantity of Natural GasAdjustments (Billion Cubic Feet) Wyoming963 1.969 1.979 1.988 1.996 2.003 1990-2016 East) e j8.6.

  4. SAS Output

    Gasoline and Diesel Fuel Update (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: Alternative Fuels Data Center Homesum_a_epg0_fpd_mmcf_m.xls" ,"Available from WebQuantity of Natural GasAdjustments (Billion Cubic Feet) Wyoming963 1.969 1.979 1.988 1.996 2.003 1990-2016 East) e j8.6.. Count of

  5. SAS Output

    Gasoline and Diesel Fuel Update (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: Alternative Fuels Data Center Homesum_a_epg0_fpd_mmcf_m.xls" ,"Available from WebQuantity of Natural GasAdjustments (Billion Cubic Feet) Wyoming963 1.969 1.979 1.988 1.996 2.003 1990-2016 East) e j8.6.. Count ofA.

  6. SAS Output

    Gasoline and Diesel Fuel Update (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: Alternative Fuels Data Center Homesum_a_epg0_fpd_mmcf_m.xls" ,"Available from WebQuantity of Natural GasAdjustments (Billion Cubic Feet) Wyoming963 1.969 1.979 1.988 1.996 2.003 1990-2016 East) e j8.6.. Count ofA.B.

  7. SAS Output

    Gasoline and Diesel Fuel Update (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: Alternative Fuels Data Center Homesum_a_epg0_fpd_mmcf_m.xls" ,"Available from WebQuantity of Natural GasAdjustments (Billion Cubic Feet) Wyoming963 1.969 1.979 1.988 1.996 2.003 1990-2016 East) e j8.6.. Count ofA.B.3.

  8. SAS Output

    Gasoline and Diesel Fuel Update (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: Alternative Fuels Data Center Homesum_a_epg0_fpd_mmcf_m.xls" ,"Available from WebQuantity of Natural GasAdjustments (Billion Cubic Feet) Wyoming963 1.969 1.979 1.988 1.996 2.003 1990-2016 East) e j8.6.. Count

  9. SAS Output

    Gasoline and Diesel Fuel Update (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: Alternative Fuels Data Center Homesum_a_epg0_fpd_mmcf_m.xls" ,"Available from WebQuantity of Natural GasAdjustments (Billion Cubic Feet) Wyoming963 1.969 1.979 1.988 1.996 2.003 1990-2016 East) e j8.6.. Count5.

  10. SAS Output

    Gasoline and Diesel Fuel Update (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: Alternative Fuels Data Center Homesum_a_epg0_fpd_mmcf_m.xls" ,"Available from WebQuantity of Natural GasAdjustments (Billion Cubic Feet) Wyoming963 1.969 1.979 1.988 1.996 2.003 1990-2016 East) e j8.6.. Count5.6.

  11. SAS Output

    Gasoline and Diesel Fuel Update (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: Alternative Fuels Data Center Homesum_a_epg0_fpd_mmcf_m.xls" ,"Available from WebQuantity of Natural GasAdjustments (Billion Cubic Feet) Wyoming963 1.969 1.979 1.988 1.996 2.003 1990-2016 East) e j8.6.. Count5.6.A.

  12. SAS Output

    Gasoline and Diesel Fuel Update (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: Alternative Fuels Data Center Homesum_a_epg0_fpd_mmcf_m.xls" ,"Available from WebQuantity of Natural GasAdjustments (Billion Cubic Feet) Wyoming963 1.969 1.979 1.988 1.996 2.003 1990-2016 East) e j8.6.. Count5.6.A.B.

  13. SAS Output

    Gasoline and Diesel Fuel Update (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: Alternative Fuels Data Center Homesum_a_epg0_fpd_mmcf_m.xls" ,"Available from WebQuantity of Natural GasAdjustments (Billion Cubic Feet) Wyoming963 1.969 1.979 1.988 1.996 2.003 1990-2016 East) e j8.6..

  14. SAS Output

    Gasoline and Diesel Fuel Update (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: Alternative Fuels Data Center Homesum_a_epg0_fpd_mmcf_m.xls" ,"Available from WebQuantity of Natural GasAdjustments (Billion Cubic Feet) Wyoming963 1.969 1.979 1.988 1.996 2.003 1990-2016 East) e j8.6..9. Total

  15. SAS Output

    Gasoline and Diesel Fuel Update (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: Alternative Fuels Data Center Homesum_a_epg0_fpd_mmcf_m.xls" ,"Available from WebQuantity of Natural GasAdjustments (Billion Cubic Feet) Wyoming963 1.969 1.979 1.988 1.996 2.003 1990-2016 East) e j8.6..9. Total0. Net

  16. SAS Output

    Gasoline and Diesel Fuel Update (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: Alternative Fuels Data Center Homesum_a_epg0_fpd_mmcf_m.xls" ,"Available from WebQuantity of Natural GasAdjustments (Billion Cubic Feet) Wyoming963 1.969 1.979 1.988 1.996 2.003 1990-2016 East) e j8.6..9. Total0.

  17. SAS Output

    Gasoline and Diesel Fuel Update (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: Alternative Fuels Data Center Homesum_a_epg0_fpd_mmcf_m.xls" ,"Available from WebQuantity of Natural GasAdjustments (Billion Cubic Feet) Wyoming963 1.969 1.979 1.988 1.996 2.003 1990-2016 East) e j8.6..9. Total0.2.

  18. SAS Output

    Gasoline and Diesel Fuel Update (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: Alternative Fuels Data Center Homesum_a_epg0_fpd_mmcf_m.xls" ,"Available from WebQuantity of Natural GasAdjustments (Billion Cubic Feet) Wyoming963 1.969 1.979 1.988 1.996 2.003 1990-2016 East) e j8.6..9. Total0.2.3.

  19. SAS Output

    Gasoline and Diesel Fuel Update (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: Alternative Fuels Data Center Homesum_a_epg0_fpd_mmcf_m.xls" ,"Available from WebQuantity of Natural GasAdjustments (Billion Cubic Feet) Wyoming963 1.969 1.979 1.988 1.996 2.003 1990-2016 East) e j8.6..9.

  20. SAS Output

    Gasoline and Diesel Fuel Update (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: Alternative Fuels Data Center Homesum_a_epg0_fpd_mmcf_m.xls" ,"Available from WebQuantity of Natural GasAdjustments (Billion Cubic Feet) Wyoming963 1.969 1.979 1.988 1.996 2.003 1990-2016 East) e j8.6..9.A. Coal:

  1. SAS Output

    Gasoline and Diesel Fuel Update (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: Alternative Fuels Data Center Homesum_a_epg0_fpd_mmcf_m.xls" ,"Available from WebQuantity of Natural GasAdjustments (Billion Cubic Feet) Wyoming963 1.969 1.979 1.988 1.996 2.003 1990-2016 East) e j8.6..9.A. Coal:B.

  2. SAS Output

    Gasoline and Diesel Fuel Update (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: Alternative Fuels Data Center Homesum_a_epg0_fpd_mmcf_m.xls" ,"Available from WebQuantity of Natural GasAdjustments (Billion Cubic Feet) Wyoming963 1.969 1.979 1.988 1.996 2.003 1990-2016 East) e j8.6..9.A. Coal:B.C.

  3. SAS Output

    Gasoline and Diesel Fuel Update (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: Alternative Fuels Data Center Homesum_a_epg0_fpd_mmcf_m.xls" ,"Available from WebQuantity of Natural GasAdjustments (Billion Cubic Feet) Wyoming963 1.969 1.979 1.988 1.996 2.003 1990-2016 East) e j8.6..9.A.

  4. SAS Output

    Gasoline and Diesel Fuel Update (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: Alternative Fuels Data Center Homesum_a_epg0_fpd_mmcf_m.xls" ,"Available from WebQuantity of Natural GasAdjustments (Billion Cubic Feet) Wyoming963 1.969 1.979 1.988 1.996 2.003 1990-2016 East) e j8.6..9.A.E. Coal:

  5. SAS Output

    Gasoline and Diesel Fuel Update (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: Alternative Fuels Data Center Homesum_a_epg0_fpd_mmcf_m.xls" ,"Available from WebQuantity of Natural GasAdjustments (Billion Cubic Feet) Wyoming963 1.969 1.979 1.988 1.996 2.003 1990-2016 East) e j8.6..9.A.E. Coal:F.

  6. SAS Output

    Gasoline and Diesel Fuel Update (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: Alternative Fuels Data Center Homesum_a_epg0_fpd_mmcf_m.xls" ,"Available from WebQuantity of Natural GasAdjustments (Billion Cubic Feet) Wyoming963 1.969 1.979 1.988 1.996 2.003 1990-2016 East) e j8.6..9.A.E.

  7. SAS Output

    Gasoline and Diesel Fuel Update (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: Alternative Fuels Data Center Homesum_a_epg0_fpd_mmcf_m.xls" ,"Available from WebQuantity of Natural GasAdjustments (Billion Cubic Feet) Wyoming963 1.969 1.979 1.988 1.996 2.003 1990-2016 East) e j8.6..9.A.E.B.

  8. SAS Output

    Gasoline and Diesel Fuel Update (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: Alternative Fuels Data Center Homesum_a_epg0_fpd_mmcf_m.xls" ,"Available from WebQuantity of Natural GasAdjustments (Billion Cubic Feet) Wyoming963 1.969 1.979 1.988 1.996 2.003 1990-2016 East) e j8.6..9.A.E.B.C.

  9. SAS Output

    Gasoline and Diesel Fuel Update (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: Alternative Fuels Data Center Homesum_a_epg0_fpd_mmcf_m.xls" ,"Available from WebQuantity of Natural GasAdjustments (Billion Cubic Feet) Wyoming963 1.969 1.979 1.988 1.996 2.003 1990-2016 East) e j8.6..9.A.E.B.C.D.

  10. SAS Output

    Gasoline and Diesel Fuel Update (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: Alternative Fuels Data Center Homesum_a_epg0_fpd_mmcf_m.xls" ,"Available from WebQuantity of Natural GasAdjustments (Billion Cubic Feet) Wyoming963 1.969 1.979 1.988 1.996 2.003 1990-2016 East) e j8.6..9.A.E.B.C.D.E.

  11. SAS Output

    Gasoline and Diesel Fuel Update (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: Alternative Fuels Data Center Homesum_a_epg0_fpd_mmcf_m.xls" ,"Available from WebQuantity of Natural GasAdjustments (Billion Cubic Feet) Wyoming963 1.969 1.979 1.988 1.996 2.003 1990-2016 East) e

  12. SAS Output

    Gasoline and Diesel Fuel Update (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: Alternative Fuels Data Center Homesum_a_epg0_fpd_mmcf_m.xls" ,"Available from WebQuantity of Natural GasAdjustments (Billion Cubic Feet) Wyoming963 1.969 1.979 1.988 1.996 2.003 1990-2016 East) eA. Petroleum Coke:

  13. SAS Output

    Gasoline and Diesel Fuel Update (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: Alternative Fuels Data Center Homesum_a_epg0_fpd_mmcf_m.xls" ,"Available from WebQuantity of Natural GasAdjustments (Billion Cubic Feet) Wyoming963 1.969 1.979 1.988 1.996 2.003 1990-2016 East) eA. Petroleum Coke:B.

  14. SAS Output

    Gasoline and Diesel Fuel Update (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: Alternative Fuels Data Center Homesum_a_epg0_fpd_mmcf_m.xls" ,"Available from WebQuantity of Natural GasAdjustments (Billion Cubic Feet) Wyoming963 1.969 1.979 1.988 1.996 2.003 1990-2016 East) eA. Petroleum Coke:B.C.

  15. SAS Output

    Gasoline and Diesel Fuel Update (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: Alternative Fuels Data Center Homesum_a_epg0_fpd_mmcf_m.xls" ,"Available from WebQuantity of Natural GasAdjustments (Billion Cubic Feet) Wyoming963 1.969 1.979 1.988 1.996 2.003 1990-2016 East) eA. Petroleum

  16. SAS Output

    Gasoline and Diesel Fuel Update (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: Alternative Fuels Data Center Homesum_a_epg0_fpd_mmcf_m.xls" ,"Available from WebQuantity of Natural GasAdjustments (Billion Cubic Feet) Wyoming963 1.969 1.979 1.988 1.996 2.003 1990-2016 East) eA. PetroleumE.

  17. SAS Output

    Gasoline and Diesel Fuel Update (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: Alternative Fuels Data Center Homesum_a_epg0_fpd_mmcf_m.xls" ,"Available from WebQuantity of Natural GasAdjustments (Billion Cubic Feet) Wyoming963 1.969 1.979 1.988 1.996 2.003 1990-2016 East) eA. PetroleumE.F.

  18. SAS Output

    Gasoline and Diesel Fuel Update (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: Alternative Fuels Data Center Homesum_a_epg0_fpd_mmcf_m.xls" ,"Available from WebQuantity of Natural GasAdjustments (Billion Cubic Feet) Wyoming963 1.969 1.979 1.988 1.996 2.003 1990-2016 East) eA. PetroleumE.F.A.

  19. SAS Output

    Gasoline and Diesel Fuel Update (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: Alternative Fuels Data Center Homesum_a_epg0_fpd_mmcf_m.xls" ,"Available from WebQuantity of Natural GasAdjustments (Billion Cubic Feet) Wyoming963 1.969 1.979 1.988 1.996 2.003 1990-2016 East) eA. PetroleumE.F.A.B.

  20. SAS Output

    Gasoline and Diesel Fuel Update (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: Alternative Fuels Data Center Homesum_a_epg0_fpd_mmcf_m.xls" ,"Available from WebQuantity of Natural GasAdjustments (Billion Cubic Feet) Wyoming963 1.969 1.979 1.988 1.996 2.003 1990-2016 East) eA. PetroleumE.F.A.B.C.

  1. SAS Output

    Gasoline and Diesel Fuel Update (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: Alternative Fuels Data Center Homesum_a_epg0_fpd_mmcf_m.xls" ,"Available from WebQuantity of Natural GasAdjustments (Billion Cubic Feet) Wyoming963 1.969 1.979 1.988 1.996 2.003 1990-2016 East) eA.

  2. SAS Output

    Gasoline and Diesel Fuel Update (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: Alternative Fuels Data Center Homesum_a_epg0_fpd_mmcf_m.xls" ,"Available from WebQuantity of Natural GasAdjustments (Billion Cubic Feet) Wyoming963 1.969 1.979 1.988 1.996 2.003 1990-2016 East) eA.E. Natural Gas:

  3. SAS Output

    Gasoline and Diesel Fuel Update (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: Alternative Fuels Data Center Homesum_a_epg0_fpd_mmcf_m.xls" ,"Available from WebQuantity of Natural GasAdjustments (Billion Cubic Feet) Wyoming963 1.969 1.979 1.988 1.996 2.003 1990-2016 East) eA.E. Natural Gas:F.

  4. SAS Output

    Gasoline and Diesel Fuel Update (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: Alternative Fuels Data Center Homesum_a_epg0_fpd_mmcf_m.xls" ,"Available from WebQuantity of Natural GasAdjustments (Billion Cubic Feet) Wyoming963 1.969 1.979 1.988 1.996 2.003 1990-2016 East) eA.E. Natural Gas:F.D.

  5. SAS Output

    Gasoline and Diesel Fuel Update (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: Alternative Fuels Data Center Homesum_a_epg0_fpd_mmcf_m.xls" ,"Available from WebQuantity of Natural GasAdjustments (Billion Cubic Feet) Wyoming963 1.969 1.979 1.988 1.996 2.003 1990-2016 East) eA.E. Natural

  6. SAS Output

    Gasoline and Diesel Fuel Update (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: Alternative Fuels Data Center Homesum_a_epg0_fpd_mmcf_m.xls" ,"Available from WebQuantity of Natural GasAdjustments (Billion Cubic Feet) Wyoming963 1.969 1.979 1.988 1.996 2.003 1990-2016 East) eA.E. NaturalF. Wood /

  7. SAS Output

    Gasoline and Diesel Fuel Update (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: Alternative Fuels Data Center Homesum_a_epg0_fpd_mmcf_m.xls" ,"Available from WebQuantity of Natural GasAdjustments (Billion Cubic Feet) Wyoming963 1.969 1.979 1.988 1.996 2.003 1990-2016 East) eA.E. NaturalF. Wood

  8. SAS Output

    Gasoline and Diesel Fuel Update (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: Alternative Fuels Data Center Homesum_a_epg0_fpd_mmcf_m.xls" ,"Available from WebQuantity of Natural GasAdjustments (Billion Cubic Feet) Wyoming963 1.969 1.979 1.988 1.996 2.003 1990-2016 East) eA.E. NaturalF. WoodB.

  9. SAS Output

    Gasoline and Diesel Fuel Update (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: Alternative Fuels Data Center Homesum_a_epg0_fpd_mmcf_m.xls" ,"Available from WebQuantity of Natural GasAdjustments (Billion Cubic Feet) Wyoming963 1.969 1.979 1.988 1.996 2.003 1990-2016 East) eA.E. NaturalF.

  10. SAS Output

    Gasoline and Diesel Fuel Update (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: Alternative Fuels Data Center Homesum_a_epg0_fpd_mmcf_m.xls" ,"Available from WebQuantity of Natural GasAdjustments (Billion Cubic Feet) Wyoming963 1.969 1.979 1.988 1.996 2.003 1990-2016 East) eA.E. NaturalF.D.

  11. SAS Output

    Gasoline and Diesel Fuel Update (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: Alternative Fuels Data Center Homesum_a_epg0_fpd_mmcf_m.xls" ,"Available from WebQuantity of Natural GasAdjustments (Billion Cubic Feet) Wyoming963 1.969 1.979 1.988 1.996 2.003 1990-2016 East) eA.E. NaturalF.D.E.

  12. SAS Output

    Gasoline and Diesel Fuel Update (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: Alternative Fuels Data Center Homesum_a_epg0_fpd_mmcf_m.xls" ,"Available from WebQuantity of Natural GasAdjustments (Billion Cubic Feet) Wyoming963 1.969 1.979 1.988 1.996 2.003 1990-2016 East) eA.E. NaturalF.D.E.F.

  13. SAS Output

    Gasoline and Diesel Fuel Update (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: Alternative Fuels Data Center Homesum_a_epg0_fpd_mmcf_m.xls" ,"Available from WebQuantity of Natural GasAdjustments (Billion Cubic Feet) Wyoming963 1.969 1.979 1.988 1.996 2.003 1990-2016 East) eA.E. NaturalF.D.E.F.A.

  14. SAS Output

    Gasoline and Diesel Fuel Update (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: Alternative Fuels Data Center Homesum_a_epg0_fpd_mmcf_m.xls" ,"Available from WebQuantity of Natural GasAdjustments (Billion Cubic Feet) Wyoming963 1.969 1.979 1.988 1.996 2.003 1990-2016 East) eA.E.

  15. SAS Output

    Gasoline and Diesel Fuel Update (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: Alternative Fuels Data Center Homesum_a_epg0_fpd_mmcf_m.xls" ,"Available from WebQuantity of Natural GasAdjustments (Billion Cubic Feet) Wyoming963 1.969 1.979 1.988 1.996 2.003 1990-2016 East) eA.E.C. Biogenic

  16. SAS Output

    Gasoline and Diesel Fuel Update (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: Alternative Fuels Data Center Homesum_a_epg0_fpd_mmcf_m.xls" ,"Available from WebQuantity of Natural GasAdjustments (Billion Cubic Feet) Wyoming963 1.969 1.979 1.988 1.996 2.003 1990-2016 East) eA.E.C. BiogenicD.

  17. SAS Output

    Gasoline and Diesel Fuel Update (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: Alternative Fuels Data Center Homesum_a_epg0_fpd_mmcf_m.xls" ,"Available from WebQuantity of Natural GasAdjustments (Billion Cubic Feet) Wyoming963 1.969 1.979 1.988 1.996 2.003 1990-2016 East) eA.E.C. BiogenicD.E.

  18. SAS Output

    Gasoline and Diesel Fuel Update (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: Alternative Fuels Data Center Homesum_a_epg0_fpd_mmcf_m.xls" ,"Available from WebQuantity of Natural GasAdjustments (Billion Cubic Feet) Wyoming963 1.969 1.979 1.988 1.996 2.003 1990-2016 East) eA.E.C. BiogenicD.E.F.

  19. SAS Output

    Gasoline and Diesel Fuel Update (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: Alternative Fuels Data Center Homesum_a_epg0_fpd_mmcf_m.xls" ,"Available from WebQuantity of Natural GasAdjustments (Billion Cubic Feet) Wyoming963 1.969 1.979 1.988 1.996 2.003 1990-2016 East) eA.E.C.

  20. SAS Output

    Gasoline and Diesel Fuel Update (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: Alternative Fuels Data Center Homesum_a_epg0_fpd_mmcf_m.xls" ,"Available from WebQuantity of Natural GasAdjustments (Billion Cubic Feet) Wyoming963 1.969 1.979 1.988 1.996 2.003 1990-2016 East) eA.E.C.E. Other Waste

  1. SAS Output

    Gasoline and Diesel Fuel Update (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: Alternative Fuels Data Center Homesum_a_epg0_fpd_mmcf_m.xls" ,"Available from WebQuantity of Natural GasAdjustments (Billion Cubic Feet) Wyoming963 1.969 1.979 1.988 1.996 2.003 1990-2016 East) eA.E.C.E. Other WasteF.

  2. SAS Output

    Gasoline and Diesel Fuel Update (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: Alternative Fuels Data Center Homesum_a_epg0_fpd_mmcf_m.xls" ,"Available from WebQuantity of Natural GasAdjustments (Billion Cubic Feet) Wyoming963 1.969 1.979 1.988 1.996 2.003 1990-2016 East) eA.E.C.E. Other

  3. SAS Output

    Gasoline and Diesel Fuel Update (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: Alternative Fuels Data Center Homesum_a_epg0_fpd_mmcf_m.xls" ,"Available from WebQuantity of Natural GasAdjustments (Billion Cubic Feet) Wyoming963 1.969 1.979 1.988 1.996 2.003 1990-2016 East) eA.E.C.E. Other0.

  4. SAS Output

    Gasoline and Diesel Fuel Update (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: Alternative Fuels Data Center Homesum_a_epg0_fpd_mmcf_m.xls" ,"Available from WebQuantity of Natural GasAdjustments (Billion Cubic Feet) Wyoming963 1.969 1.979 1.988 1.996 2.003 1990-2016 East) eA.E.C.E. Other0.1.

  5. SAS Output

    Gasoline and Diesel Fuel Update (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: Alternative Fuels Data Center Homesum_a_epg0_fpd_mmcf_m.xls" ,"Available from WebQuantity of Natural GasAdjustments (Billion Cubic Feet) Wyoming963 1.969 1.979 1.988 1.996 2.003 1990-2016 East) eA.E.C.E. Other0.1.2.

  6. SAS Output

    Gasoline and Diesel Fuel Update (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: Alternative Fuels Data Center Homesum_a_epg0_fpd_mmcf_m.xls" ,"Available from WebQuantity of Natural GasAdjustments (Billion Cubic Feet) Wyoming963 1.969 1.979 1.988 1.996 2.003 1990-2016 East) eA.E.C.E. Other0.1.2.3.

  7. SAS Output

    Gasoline and Diesel Fuel Update (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: Alternative Fuels Data Center Homesum_a_epg0_fpd_mmcf_m.xls" ,"Available from WebQuantity of Natural GasAdjustments (Billion Cubic Feet) Wyoming963 1.969 1.979 1.988 1.996 2.003 1990-2016 East) eA.E.C.E.

  8. SAS Output

    Gasoline and Diesel Fuel Update (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: Alternative Fuels Data Center Homesum_a_epg0_fpd_mmcf_m.xls" ,"Available from WebQuantity of Natural GasAdjustments (Billion Cubic Feet) Wyoming963 1.969 1.979 1.988 1.996 2.003 1990-2016 East) eA.E.C.E.1. Stocks of

  9. SAS Output

    Gasoline and Diesel Fuel Update (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: Alternative Fuels Data Center Homesum_a_epg0_fpd_mmcf_m.xls" ,"Available from WebQuantity of Natural GasAdjustments (Billion Cubic Feet) Wyoming963 1.969 1.979 1.988 1.996 2.003 1990-2016 East) eA.E.C.E.1. Stocks of2

  10. SAS Output

    Gasoline and Diesel Fuel Update (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: Alternative Fuels Data Center Homesum_a_epg0_fpd_mmcf_m.xls" ,"Available from WebQuantity of Natural GasAdjustments (Billion Cubic Feet) Wyoming963 1.969 1.979 1.988 1.996 2.003 1990-2016 East) eA.E.C.E.1. Stocks of23

  11. SAS Output

    Gasoline and Diesel Fuel Update (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: Alternative Fuels Data Center Homesum_a_epg0_fpd_mmcf_m.xls" ,"Available from WebQuantity of Natural GasAdjustments (Billion Cubic Feet) Wyoming963 1.969 1.979 1.988 1.996 2.003 1990-2016 East) eA.E.C.E.1. Stocks

  12. SAS Output

    Gasoline and Diesel Fuel Update (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: Alternative Fuels Data Center Homesum_a_epg0_fpd_mmcf_m.xls" ,"Available from WebQuantity of Natural GasAdjustments (Billion Cubic Feet) Wyoming963 1.969 1.979 1.988 1.996 2.003 1990-2016 East) eA.E.C.E.1. Stocks.

  13. SAS Output

    Gasoline and Diesel Fuel Update (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: Alternative Fuels Data Center Homesum_a_epg0_fpd_mmcf_m.xls" ,"Available from WebQuantity of Natural GasAdjustments (Billion Cubic Feet) Wyoming963 1.969 1.979 1.988 1.996 2.003 1990-2016 East) eA.E.C.E.1. Stocks.3.

  14. SAS Output

    Gasoline and Diesel Fuel Update (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: Alternative Fuels Data Center Homesum_a_epg0_fpd_mmcf_m.xls" ,"Available from WebQuantity of Natural GasAdjustments (Billion Cubic Feet) Wyoming963 1.969 1.979 1.988 1.996 2.003 1990-2016 East) eA.E.C.E.1. Stocks.3.4.

  15. SAS Output

    Gasoline and Diesel Fuel Update (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: Alternative Fuels Data Center Homesum_a_epg0_fpd_mmcf_m.xls" ,"Available from WebQuantity of Natural GasAdjustments (Billion Cubic Feet) Wyoming963 1.969 1.979 1.988 1.996 2.003 1990-2016 East) eA.E.C.E.1.

  16. SAS Output

    Gasoline and Diesel Fuel Update (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: Alternative Fuels Data Center Homesum_a_epg0_fpd_mmcf_m.xls" ,"Available from WebQuantity of Natural GasAdjustments (Billion Cubic Feet) Wyoming963 1.969 1.979 1.988 1.996 2.003 1990-2016 East) eA.E.C.E.1.6. Receipts,

  17. SAS Output

    Gasoline and Diesel Fuel Update (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: Alternative Fuels Data Center Homesum_a_epg0_fpd_mmcf_m.xls" ,"Available from WebQuantity of Natural GasAdjustments (Billion Cubic Feet) Wyoming963 1.969 1.979 1.988 1.996 2.003 1990-2016 East) eA.E.C.E.1.6.

  18. SAS Output

    Gasoline and Diesel Fuel Update (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: Alternative Fuels Data Center Homesum_a_epg0_fpd_mmcf_m.xls" ,"Available from WebQuantity of Natural GasAdjustments (Billion Cubic Feet) Wyoming963 1.969 1.979 1.988 1.996 2.003 1990-2016 East) eA.E.C.E.1.6.8.

  19. SAS Output

    Gasoline and Diesel Fuel Update (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: Alternative Fuels Data Center Homesum_a_epg0_fpd_mmcf_m.xls" ,"Available from WebQuantity of Natural GasAdjustments (Billion Cubic Feet) Wyoming963 1.969 1.979 1.988 1.996 2.003 1990-2016 East) eA.E.C.E.1.6.8.9.

  20. SAS Output

    Gasoline and Diesel Fuel Update (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: Alternative Fuels Data Center Homesum_a_epg0_fpd_mmcf_m.xls" ,"Available from WebQuantity of Natural GasAdjustments (Billion Cubic Feet) Wyoming963 1.969 1.979 1.988 1.996 2.003 1990-2016 East) eA.E.C.E.1.6.8.9.0.

  1. SAS Output

    Gasoline and Diesel Fuel Update (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: Alternative Fuels Data Center Homesum_a_epg0_fpd_mmcf_m.xls" ,"Available from WebQuantity of Natural GasAdjustments (Billion Cubic Feet) Wyoming963 1.969 1.979 1.988 1.996 2.003 1990-2016 East) eA.E.C.E.1.6.8.9.0.1.

  2. SAS Output

    Gasoline and Diesel Fuel Update (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: Alternative Fuels Data Center Homesum_a_epg0_fpd_mmcf_m.xls" ,"Available from WebQuantity of Natural GasAdjustments (Billion Cubic Feet) Wyoming963 1.969 1.979 1.988 1.996 2.003 1990-2016 East) eA.E.C.E.1.6.8.9.0.1.2.

  3. SAS Output

    Gasoline and Diesel Fuel Update (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: Alternative Fuels Data Center Homesum_a_epg0_fpd_mmcf_m.xls" ,"Available from WebQuantity of Natural GasAdjustments (Billion Cubic Feet) Wyoming963 1.969 1.979 1.988 1.996 2.003 1990-2016 East)

  4. SAS Output

    Gasoline and Diesel Fuel Update (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: Alternative Fuels Data Center Homesum_a_epg0_fpd_mmcf_m.xls" ,"Available from WebQuantity of Natural GasAdjustments (Billion Cubic Feet) Wyoming963 1.969 1.979 1.988 1.996 2.003 1990-2016 East)4. Receipts of Petroleum

  5. SAS Output

    Gasoline and Diesel Fuel Update (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: Alternative Fuels Data Center Homesum_a_epg0_fpd_mmcf_m.xls" ,"Available from WebQuantity of Natural GasAdjustments (Billion Cubic Feet) Wyoming963 1.969 1.979 1.988 1.996 2.003 1990-2016 East)4. Receipts of

  6. SAS Output

    Gasoline and Diesel Fuel Update (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: Alternative Fuels Data Center Homesum_a_epg0_fpd_mmcf_m.xls" ,"Available from WebQuantity of Natural GasAdjustments (Billion Cubic Feet) Wyoming963 1.969 1.979 1.988 1.996 2.003 1990-2016 East)4. Receipts of6.

  7. SAS Output

    Gasoline and Diesel Fuel Update (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: Alternative Fuels Data Center Homesum_a_epg0_fpd_mmcf_m.xls" ,"Available from WebQuantity of Natural GasAdjustments (Billion Cubic Feet) Wyoming963 1.969 1.979 1.988 1.996 2.003 1990-2016 East)4. Receipts of6.7.

  8. SAS Output

    Gasoline and Diesel Fuel Update (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: Alternative Fuels Data Center Homesum_a_epg0_fpd_mmcf_m.xls" ,"Available from WebQuantity of Natural GasAdjustments (Billion Cubic Feet) Wyoming963 1.969 1.979 1.988 1.996 2.003 1990-2016 East)4. Receipts of6.7.8.

  9. SAS Output

    Gasoline and Diesel Fuel Update (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: Alternative Fuels Data Center Homesum_a_epg0_fpd_mmcf_m.xls" ,"Available from WebQuantity of Natural GasAdjustments (Billion Cubic Feet) Wyoming963 1.969 1.979 1.988 1.996 2.003 1990-2016 East)4. Receipts of6.7.8.9.

  10. SAS Output

    Gasoline and Diesel Fuel Update (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: Alternative Fuels Data Center Homesum_a_epg0_fpd_mmcf_m.xls" ,"Available from WebQuantity of Natural GasAdjustments (Billion Cubic Feet) Wyoming963 1.969 1.979 1.988 1.996 2.003 1990-2016 East)4. Receipts of6.7.8.9.0.

  11. SAS Output

    Gasoline and Diesel Fuel Update (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: Alternative Fuels Data Center Homesum_a_epg0_fpd_mmcf_m.xls" ,"Available from WebQuantity of Natural GasAdjustments (Billion Cubic Feet) Wyoming963 1.969 1.979 1.988 1.996 2.003 1990-2016 East)4. Receipts

  12. SAS Output

    Gasoline and Diesel Fuel Update (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: Alternative Fuels Data Center Homesum_a_epg0_fpd_mmcf_m.xls" ,"Available from WebQuantity of Natural GasAdjustments (Billion Cubic Feet) Wyoming963 1.969 1.979 1.988 1.996 2.003 1990-2016 East)4. Receipts2. Receipts

  13. SAS Output

    Gasoline and Diesel Fuel Update (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: Alternative Fuels Data Center Homesum_a_epg0_fpd_mmcf_m.xls" ,"Available from WebQuantity of Natural GasAdjustments (Billion Cubic Feet) Wyoming963 1.969 1.979 1.988 1.996 2.003 1990-2016 East)4. Receipts2. Receipts3.

  14. SAS Output

    Gasoline and Diesel Fuel Update (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: Alternative Fuels Data Center Homesum_a_epg0_fpd_mmcf_m.xls" ,"Available from WebQuantity of Natural GasAdjustments (Billion Cubic Feet) Wyoming963 1.969 1.979 1.988 1.996 2.003 1990-2016 East)4. Receipts2.

  15. SAS Output

    Gasoline and Diesel Fuel Update (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: Alternative Fuels Data Center Homesum_a_epg0_fpd_mmcf_m.xls" ,"Available from WebQuantity of Natural GasAdjustments (Billion Cubic Feet) Wyoming963 1.969 1.979 1.988 1.996 2.003 1990-2016 East)4. Receipts2.5. Receipts

  16. SAS Output

    Gasoline and Diesel Fuel Update (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: Alternative Fuels Data Center Homesum_a_epg0_fpd_mmcf_m.xls" ,"Available from WebQuantity of Natural GasAdjustments (Billion Cubic Feet) Wyoming963 1.969 1.979 1.988 1.996 2.003 1990-2016 East)4. Receipts2.5.

  17. SAS Output

    Gasoline and Diesel Fuel Update (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: Alternative Fuels Data Center Homesum_a_epg0_fpd_mmcf_m.xls" ,"Available from WebQuantity of Natural GasAdjustments (Billion Cubic Feet) Wyoming963 1.969 1.979 1.988 1.996 2.003 1990-2016 East)4. Receipts2.5.2.

  18. SAS Output

    Gasoline and Diesel Fuel Update (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: Alternative Fuels Data Center Homesum_a_epg0_fpd_mmcf_m.xls" ,"Available from WebQuantity of Natural GasAdjustments (Billion Cubic Feet) Wyoming963 1.969 1.979 1.988 1.996 2.003 1990-2016 East)4. Receipts2.5.2.3.

  19. SAS Output

    Gasoline and Diesel Fuel Update (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: Alternative Fuels Data Center Homesum_a_epg0_fpd_mmcf_m.xls" ,"Available from WebQuantity of Natural GasAdjustments (Billion Cubic Feet) Wyoming963 1.969 1.979 1.988 1.996 2.003 1990-2016 East)4. Receipts2.5.2.3.4.

  20. SAS Output

    Gasoline and Diesel Fuel Update (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: Alternative Fuels Data Center Homesum_a_epg0_fpd_mmcf_m.xls" ,"Available from WebQuantity of Natural GasAdjustments (Billion Cubic Feet) Wyoming963 1.969 1.979 1.988 1.996 2.003 1990-2016 East)4. Receipts2.5.2.3.4.5.

  1. SAS Output

    Gasoline and Diesel Fuel Update (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: Alternative Fuels Data Center Homesum_a_epg0_fpd_mmcf_m.xls" ,"Available from WebQuantity of Natural GasAdjustments (Billion Cubic Feet) Wyoming963 1.969 1.979 1.988 1.996 2.003 1990-2016 East)4.

  2. SAS Output

    Gasoline and Diesel Fuel Update (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: Alternative Fuels Data Center Homesum_a_epg0_fpd_mmcf_m.xls" ,"Available from WebQuantity of Natural GasAdjustments (Billion Cubic Feet) Wyoming963 1.969 1.979 1.988 1.996 2.003 1990-2016 East)4.B. Noncoincident Peak

  3. SAS Output

    Gasoline and Diesel Fuel Update (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: Alternative Fuels Data Center Homesum_a_epg0_fpd_mmcf_m.xls" ,"Available from WebQuantity of Natural GasAdjustments (Billion Cubic Feet) Wyoming963 1.969 1.979 1.988 1.996 2.003 1990-2016 East)4.B. Noncoincident

  4. SAS Output

    Gasoline and Diesel Fuel Update (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: Alternative Fuels Data Center Homesum_a_epg0_fpd_mmcf_m.xls" ,"Available from WebQuantity of Natural GasAdjustments (Billion Cubic Feet) Wyoming963 1.969 1.979 1.988 1.996 2.003 1990-2016 East)4.B. NoncoincidentB. Net

  5. SAS Output

    Gasoline and Diesel Fuel Update (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: Alternative Fuels Data Center Homesum_a_epg0_fpd_mmcf_m.xls" ,"Available from WebQuantity of Natural GasAdjustments (Billion Cubic Feet) Wyoming963 1.969 1.979 1.988 1.996 2.003 1990-2016 East)4.B. NoncoincidentB.

  6. SAS Output

    Gasoline and Diesel Fuel Update (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: Alternative Fuels Data Center Homesum_a_epg0_fpd_mmcf_m.xls" ,"Available from WebQuantity of Natural GasAdjustments (Billion Cubic Feet) Wyoming963 1.969 1.979 1.988 1.996 2.003 1990-2016 East)4.B. NoncoincidentB.B.

  7. SAS Output

    Gasoline and Diesel Fuel Update (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: Alternative Fuels Data Center Homesum_a_epg0_fpd_mmcf_m.xls" ,"Available from WebQuantity of Natural GasAdjustments (Billion Cubic Feet) Wyoming963 1.969 1.979 1.988 1.996 2.003 1990-2016 East)4.B. NoncoincidentB.B.A.

  8. SAS Output

    Gasoline and Diesel Fuel Update (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: Alternative Fuels Data Center Homesum_a_epg0_fpd_mmcf_m.xls" ,"Available from WebQuantity of Natural GasAdjustments (Billion Cubic Feet) Wyoming963 1.969 1.979 1.988 1.996 2.003 1990-2016 East)4.B.

  9. SAS Output

    Gasoline and Diesel Fuel Update (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: Alternative Fuels Data Center Homesum_a_epg0_fpd_mmcf_m.xls" ,"Available from WebQuantity of Natural GasAdjustments (Billion Cubic Feet) Wyoming963 1.969 1.979 1.988 1.996 2.003 1990-2016 East)4.B.A. Existing

  10. SAS Output

    Gasoline and Diesel Fuel Update (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: Alternative Fuels Data Center Homesum_a_epg0_fpd_mmcf_m.xls" ,"Available from WebQuantity of Natural GasAdjustments (Billion Cubic Feet) Wyoming963 1.969 1.979 1.988 1.996 2.003 1990-2016 East)4.B.A. ExistingB.

  11. SAS Output

    Gasoline and Diesel Fuel Update (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: Alternative Fuels Data Center Homesum_a_epg0_fpd_mmcf_m.xls" ,"Available from WebQuantity of Natural GasAdjustments (Billion Cubic Feet) Wyoming963 1.969 1.979 1.988 1.996 2.003 1990-2016 East)4.B.A. ExistingB.A. U.S.

  12. SAS Output

    Gasoline and Diesel Fuel Update (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: Alternative Fuels Data Center Homesum_a_epg0_fpd_mmcf_m.xls" ,"Available from WebQuantity of Natural GasAdjustments (Billion Cubic Feet) Wyoming963 1.969 1.979 1.988 1.996 2.003 1990-2016 East)4.B.A. ExistingB.A.

  13. SAS Output

    Gasoline and Diesel Fuel Update (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: Alternative Fuels Data Center Homesum_a_epg0_fpd_mmcf_m.xls" ,"Available from WebQuantity of Natural GasAdjustments (Billion Cubic Feet) Wyoming963 1.969 1.979 1.988 1.996 2.003 1990-2016 East)4.B.A. ExistingB.A.A.

  14. SAS Output

    Gasoline and Diesel Fuel Update (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: Alternative Fuels Data Center Homesum_a_epg0_fpd_mmcf_m.xls" ,"Available from WebQuantity of Natural GasAdjustments (Billion Cubic Feet) Wyoming963 1.969 1.979 1.988 1.996 2.003 1990-2016 East)4.B.A. ExistingB.A.A.B.

  15. SAS Output

    Gasoline and Diesel Fuel Update (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: Alternative Fuels Data Center Homesum_a_epg0_fpd_mmcf_m.xls" ,"Available from WebQuantity of Natural GasAdjustments (Billion Cubic Feet) Wyoming963 1.969 1.979 1.988 1.996 2.003 1990-2016 East)4.B.A.

  16. SAS Output

    Gasoline and Diesel Fuel Update (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: Alternative Fuels Data Center Homesum_a_epg0_fpd_mmcf_m.xls" ,"Available from WebQuantity of Natural GasAdjustments (Billion Cubic Feet) Wyoming963 1.969 1.979 1.988 1.996 2.003 1990-2016 East)4.B.A.B. U.S.

  17. SAS Output

    Gasoline and Diesel Fuel Update (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: Alternative Fuels Data Center Homesum_a_epg0_fpd_mmcf_m.xls" ,"Available from WebQuantity of Natural GasAdjustments (Billion Cubic Feet) Wyoming963 1.969 1.979 1.988 1.996 2.003 1990-2016 East)4.B.A.B. U.S.3. Quantity

  18. SAS Output

    Gasoline and Diesel Fuel Update (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: Alternative Fuels Data Center Homesum_a_epg0_fpd_mmcf_m.xls" ,"Available from WebQuantity of Natural GasAdjustments (Billion Cubic Feet) Wyoming963 1.969 1.979 1.988 1.996 2.003 1990-2016 East)4.B.A.B. U.S.3.

  19. SAS Output

    Gasoline and Diesel Fuel Update (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: Alternative Fuels Data Center Homesum_a_epg0_fpd_mmcf_m.xls" ,"Available from WebQuantity of Natural GasAdjustments (Billion Cubic Feet) Wyoming963 1.969 1.979 1.988 1.996 2.003 1990-2016 East)4.B.A.B. U.S.3.5.

  20. SAS Output

    Gasoline and Diesel Fuel Update (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: Alternative Fuels Data Center Homesum_a_epg0_fpd_mmcf_m.xls" ,"Available from WebQuantity of Natural GasAdjustments (Billion Cubic Feet) Wyoming963 1.969 1.979 1.988 1.996 2.003 1990-2016 East)4.B.A.B. U.S.3.5..

  1. SAS Output

    Gasoline and Diesel Fuel Update (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: Alternative Fuels Data Center Homesum_a_epg0_fpd_mmcf_m.xls" ,"Available from WebQuantity of Natural GasAdjustments (Billion Cubic Feet) Wyoming963 1.969 1.979 1.988 1.996 2.003 1990-2016 East)4.B.A.B. U.S.3.5..2.

  2. SAS Output

    Gasoline and Diesel Fuel Update (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: Alternative Fuels Data Center Homesum_a_epg0_fpd_mmcf_m.xls" ,"Available from WebQuantity of Natural GasAdjustments (Billion Cubic Feet) Wyoming963 1.969 1.979 1.988 1.996 2.003 1990-2016 East)4.B.A.B. U.S.3.5..2.3.

  3. SAS Output

    Gasoline and Diesel Fuel Update (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: Alternative Fuels Data Center Homesum_a_epg0_fpd_mmcf_m.xls" ,"Available from WebQuantity of Natural GasAdjustments (Billion Cubic Feet) Wyoming963 1.969 1.979 1.988 1.996 2.003 1990-2016 East)4.B.A.B. U.S.3.5..2.3.4.

  4. SAS Output

    Gasoline and Diesel Fuel Update (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: Alternative Fuels Data Center Homesum_a_epg0_fpd_mmcf_m.xls" ,"Available from WebQuantity of Natural GasAdjustments (Billion Cubic Feet) Wyoming963 1.969 1.979 1.988 1.996 2.003 1990-2016 East)4.B.A.B.

  5. SAS Output

    Gasoline and Diesel Fuel Update (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: Alternative Fuels Data Center Homesum_a_epg0_fpd_mmcf_m.xls" ,"Available from WebQuantity of Natural GasAdjustments (Billion Cubic Feet) Wyoming963 1.969 1.979 1.988 1.996 2.003 1990-2016 East)4.B.A.B.6. Energy

  6. SAS Output

    Gasoline and Diesel Fuel Update (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: Alternative Fuels Data Center Homesum_a_epg0_fpd_mmcf_m.xls" ,"Available from WebQuantity of Natural GasAdjustments (Billion Cubic Feet) Wyoming963 1.969 1.979 1.988 1.996 2.003 1990-2016 East)4.B.A.B.6. Energy7.

  7. SAS Output

    Gasoline and Diesel Fuel Update (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: Alternative Fuels Data Center Homesum_a_epg0_fpd_mmcf_m.xls" ,"Available from WebQuantity of Natural GasAdjustments (Billion Cubic Feet) Wyoming963 1.969 1.979 1.988 1.996 2.003 1990-2016 East)4.B.A.B.6. Energy7.8.

  8. SAS Output

    Gasoline and Diesel Fuel Update (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: Alternative Fuels Data Center Homesum_a_epg0_fpd_mmcf_m.xls" ,"Available from WebQuantity of Natural GasAdjustments (Billion Cubic Feet) Wyoming963 1.969 1.979 1.988 1.996 2.003 1990-2016 East)4.B.A.B.6. Energy7.8.9.

  9. SAS Output

    Gasoline and Diesel Fuel Update (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: Alternative Fuels Data Center Homesum_a_epg0_fpd_mmcf_m.xls" ,"Available from WebQuantity of Natural GasAdjustments (Billion Cubic Feet) Wyoming963 1.969 1.979 1.988 1.996 2.003 1990-2016 East)4.B.A.B.6.

  10. SAS Output

    Gasoline and Diesel Fuel Update (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: Alternative Fuels Data Center Homesum_a_epg0_fpd_mmcf_m.xls" ,"Available from WebQuantity of Natural GasAdjustments (Billion Cubic Feet) Wyoming963 1.969 1.979 1.988 1.996 2.003 1990-2016 East)4.B.A.B.6.1. Sulfur

  11. SAS Output

    Gasoline and Diesel Fuel Update (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: Alternative Fuels Data Center Homesum_a_epg0_fpd_mmcf_m.xls" ,"Available from WebQuantity of Natural GasAdjustments (Billion Cubic Feet) Wyoming963 1.969 1.979 1.988 1.996 2.003 1990-2016 East)4.B.A.B.6.1. Sulfur2.

  12. SAS Output

    Gasoline and Diesel Fuel Update (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: Alternative Fuels Data Center Homesum_a_epg0_fpd_mmcf_m.xls" ,"Available from WebQuantity of Natural GasAdjustments (Billion Cubic Feet) Wyoming963 1.969 1.979 1.988 1.996 2.003 1990-2016 East)4.B.A.B.6.1. Sulfur2.3.

  13. SAS Output

    Gasoline and Diesel Fuel Update (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: Alternative Fuels Data Center Homesum_a_epg0_fpd_mmcf_m.xls" ,"Available from WebQuantity of Natural GasAdjustments (Billion Cubic Feet) Wyoming963 1.969 1.979 1.988 1.996 2.003 1990-2016 East)4.B.A.B.6.1.

  14. SAS Output

    Gasoline and Diesel Fuel Update (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: Alternative Fuels Data Center Homesum_a_epg0_fpd_mmcf_m.xls" ,"Available from WebQuantity of Natural GasAdjustments (Billion Cubic Feet) Wyoming963 1.969 1.979 1.988 1.996 2.003 1990-2016 East)4.B.A.B.6.1.5. Unit of

  15. SU-E-T-257: Output Constancy: Reducing Measurement Variations in a Large Practice Group

    SciTech Connect (OSTI)

    Hedrick, K; Fitzgerald, T; Miller, R [Northwest Medical Physics Center, Lynnwood, WA (United States)

    2014-06-01

    Purpose: To standardize output constancy check procedures in a large medical physics practice group covering multiple sites, in order to identify and reduce small systematic errors caused by differences in equipment and the procedures of multiple physicists. Methods: A standardized machine output constancy check for both photons and electrons was instituted within the practice group in 2010. After conducting annual TG-51 measurements in water and adjusting the linac to deliver 1.00 cGy/MU at Dmax, an acrylic phantom (comparable at all sites) and PTW farmer ion chamber are used to obtain monthly output constancy reference readings. From the collected charge reading, measurements of air pressure and temperature, and chamber Ndw and Pelec, a value we call the Kacrylic factor is determined, relating the chamber reading in acrylic to the dose in water with standard set-up conditions. This procedure easily allows for multiple equipment combinations to be used at any site. The Kacrylic factors and output results from all sites and machines are logged monthly in a central database and used to monitor trends in calibration and output. Results: The practice group consists of 19 sites, currently with 34 Varian and 8 Elekta linacs (24 Varian and 5 Elekta linacs in 2010). Over the past three years, the standard deviation of Kacrylic factors measured on all machines decreased by 20% for photons and high energy electrons as systematic errors were found and reduced. Low energy electrons showed very little change in the distribution of Kacrylic values. Small errors in linac beam data were found by investigating outlier Kacrylic values. Conclusion: While the use of acrylic phantoms introduces an additional source of error through small differences in depth and effective depth, the new standardized procedure eliminates potential sources of error from using many different phantoms and results in more consistent output constancy measurements.

  16. High-output microwave detector using voltage-induced ferromagnetic resonance

    SciTech Connect (OSTI)

    Shiota, Yoichi Suzuki, Yoshishige; Miwa, Shinji; Tamaru, Shingo; Nozaki, Takayuki; Kubota, Hitoshi; Fukushima, Akio; Yuasa, Shinji

    2014-11-10

    We investigated the voltage-induced ferromagnetic resonance (FMR) with various DC bias voltage and input RF power in magnetic tunnel junctions. We found that the DC bias monotonically increases the homodyne detection voltage due to the nonlinear FMR originating in an asymmetric magnetization-potential in the free layer. In addition, the linear increase of an output voltage to the input RF power in the voltage-induced FMR is more robust than that in spin-torque FMR. These characteristics enable us to obtain an output voltage more than ten times than that of microwave detectors using spin-transfer torque.

  17. Chow's Team Petri Net Models discrete event

    E-Print Network [OSTI]

    Kaber, David B.

    ", and "high" plate contents CELISCA: collection of physiology data based on NCSU prototype Output1 CELISCA: collection of physiology data based on New NCSU prototype Output2 #12;k Chow's Team Petri Net Models ­ discrete event stochastic models (set fixed time interval updates

  18. Caste ratios affect the reproductive output of social trematode T. KAMIYA & R. POULIN

    E-Print Network [OSTI]

    Poulin, Robert

    Caste ratios affect the reproductive output of social trematode colonies T. KAMIYA & R. POULIN Department of Zoology, University of Otago, Dunedin, New Zealand Keywords: caste ratio; optimal ratio theory phenotypic diversification in social organisms often leads to formation of physical castes which

  19. A Program for Automatically Selecting the Best Output from Multiple Machine Translation Engines

    E-Print Network [OSTI]

    Plotkin, Joshua B.

    A Program for Automatically Selecting the Best Output from Multiple Machine Translation Engines translation engines. The program is simplified by assuming that the most fluent item in the set is the best the program performs for human ranked data as compared to each of its constituent engines. Keywords machine

  20. Low Power Architecture of the SoftOutput Viterbi David Garrett and Mircea Stan

    E-Print Network [OSTI]

    Stan, Mircea R.

    Low Power Architecture of the Soft­Output Viterbi Algorithm David Garrett and Mircea Stan@virginia.edu mircea@virginia.edu 1 ABSTRACT This paper investigates the low power imple­ mentation issues of the soft parallel access across sequentially received data. 1.1 Keywords SOVA, turbo codes, VA, low power. 2