National Library of Energy BETA

Sample records for time series datastreams

  1. Denoising Deterministic Time Series

    E-Print Network [OSTI]

    Steven P. Lalley; Andrew B. Nobel

    2006-04-21

    This paper is concerned with the problem of recovering a finite, deterministic time series from observations that are corrupted by additive, independent noise. A distinctive feature of this problem is that the available data exhibit long-range dependence and, as a consequence, existing statistical theory and methods are not readily applicable. This paper gives an analysis of the denoising problem that extends recent work of Lalley, but begins from first principles. Both positive and negative results are established. The positive results show that denoising is possible under somewhat restrictive conditions on the additive noise. The negative results show that, under more general conditions on the noise, no procedure can recover the underlying deterministic series.

  2. Time Series Analysis 1 Time series in astronomy

    E-Print Network [OSTI]

    Babu, G. Jogesh

    (supernovae, gamma-ray bursts) Difficulties in astronomical time series Gapped data streams: Diurnal & monthly phenomena: thermonuclear (novae, X-ray bursts), magnetic reconnection (solar/stellar flares), star death); pulsation (helioseismology, Cepheids) Stochastic phenomena: accretion (CVs, X-ray binaries, Seyfert gals

  3. Regression quantiles for time series

    E-Print Network [OSTI]

    Cai, Zongwu

    2002-02-01

    ~see, e+g+, Ibragimov and Linnik, 1971, p+ 316!+ Namely, partition REGRESSION QUANTILES FOR TIME SERIES 187 $1, + + + , n% into 2qn 1 1 subsets with large block of size r 5 rn and small block of size s 5 sn+ Set q 5 qn 5 ? n rn 1 sn? , (A.7) where {x...! are the standard Lindeberg–Feller conditions for asymptotic normality of Qn,1 for the independent setup+ Let us first establish ~A+8!+ To this effect, we define the large-block size rn by rn 5 {~nhn!102} and the small-block size sn 5 {~nhn!1020log n}+ Then, as n r...

  4. Detection Methods for Astronomical Time Series

    E-Print Network [OSTI]

    Coehlo, Nathan Kirk

    2010-01-01

    Time Series by Nathan Kirk Coehlo A dissertation submittedCopyright 2010 by Nathan Kirk Coehlo Abstract DetectionTime Series by Nathan Kirk Coehlo Doctor of Philosophy in

  5. Statistical criteria for characterizing irradiance time series.

    SciTech Connect (OSTI)

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

    2010-10-01

    We propose and examine several statistical criteria for characterizing time series of solar irradiance. Time series of irradiance are used in analyses that seek to quantify the performance of photovoltaic (PV) power systems over time. Time series of irradiance are either measured or are simulated using models. Simulations of irradiance are often calibrated to or generated from statistics for observed irradiance and simulations are validated by comparing the simulation output to the observed irradiance. Criteria used in this comparison should derive from the context of the analyses in which the simulated irradiance is to be used. We examine three statistics that characterize time series and their use as criteria for comparing time series. We demonstrate these statistics using observed irradiance data recorded in August 2007 in Las Vegas, Nevada, and in June 2009 in Albuquerque, New Mexico.

  6. Multivariate Time Series Forecasting in Incomplete Environments

    E-Print Network [OSTI]

    Roberts, Stephen

    Multivariate Time Series Forecasting in Incomplete Environments Technical Report PARG 08-03 Seung of Oxford December 2008 #12;Seung Min Lee and Stephen J. Roberts Technical Report PARG 08-03 Multivariate missing observations and forecasting future values in incomplete multivariate time series data. We study

  7. ARM - Instrument Datastreams

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

    AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE: Alternative Fuels Data Center Homesum_a_epg0_fpd_mmcf_m.xls" ,"Available from WebQuantity ofkandz-cm11 Outreach HomeA Better Anode DesigngovCampaignsSpring Single ColumngovCampaignsWinter 1994DatastreamsCloud

  8. Integrated method for chaotic time series analysis

    DOE Patents [OSTI]

    Hively, L.M.; Ng, E.G.

    1998-09-29

    Methods and apparatus for automatically detecting differences between similar but different states in a nonlinear process monitor nonlinear data are disclosed. Steps include: acquiring the data; digitizing the data; obtaining nonlinear measures of the data via chaotic time series analysis; obtaining time serial trends in the nonlinear measures; and determining by comparison whether differences between similar but different states are indicated. 8 figs.

  9. Integrated method for chaotic time series analysis

    DOE Patents [OSTI]

    Hively, Lee M. (Philadelphia, TN); Ng, Esmond G. (Concord, TN)

    1998-01-01

    Methods and apparatus for automatically detecting differences between similar but different states in a nonlinear process monitor nonlinear data. Steps include: acquiring the data; digitizing the data; obtaining nonlinear measures of the data via chaotic time series analysis; obtaining time serial trends in the nonlinear measures; and determining by comparison whether differences between similar but different states are indicated.

  10. Can biomass time series be reliably assessed from CPUE time series data Francis Lalo1

    E-Print Network [OSTI]

    Hawai'i at Manoa, University of

    1 Can biomass time series be reliably assessed from CPUE time series data only? Francis Laloë1 to abundance. This means (i) that catchability is constant and (ii) that all the biomass is catchable. If so, relative variations in CPUE indicate the same relative variations in biomass. Myers and Worm consider

  11. Nonlinear time-series analysis revisited

    E-Print Network [OSTI]

    Elizabeth Bradley; Holger Kantz

    2015-03-25

    In 1980 and 1981, two pioneering papers laid the foundation for what became known as nonlinear time-series analysis: the analysis of observed data---typically univariate---via dynamical systems theory. Based on the concept of state-space reconstruction, this set of methods allows us to compute characteristic quantities such as Lyapunov exponents and fractal dimensions, to predict the future course of the time series, and even to reconstruct the equations of motion in some cases. In practice, however, there are a number of issues that restrict the power of this approach: whether the signal accurately and thoroughly samples the dynamics, for instance, and whether it contains noise. Moreover, the numerical algorithms that we use to instantiate these ideas are not perfect; they involve approximations, scale parameters, and finite-precision arithmetic, among other things. Even so, nonlinear time-series analysis has been used to great advantage on thousands of real and synthetic data sets from a wide variety of systems ranging from roulette wheels to lasers to the human heart. Even in cases where the data do not meet the mathematical or algorithmic requirements to assure full topological conjugacy, the results of nonlinear time-series analysis can be helpful in understanding, characterizing, and predicting dynamical systems.

  12. Segmenting Time Series for Weather Forecasting

    E-Print Network [OSTI]

    Reiter, Ehud

    summarisation. We found three alternative ways in which we could model data summarisation. One approach is based turbines. In the domain of meteorology, time series data produced by numerical weather prediction (NWP) models is summarised as weather forecast texts. In the domain of gas turbines, sensor data from

  13. Random Matrix Spectra as a Time Series

    E-Print Network [OSTI]

    Ruben Fossion; Gamaliel Torres Vargas; Juan Carlos López Vieyra

    2013-11-23

    Spectra of ordered eigenvalues of finite Random Matrices are interpreted as a time series. Dataadaptive techniques from signal analysis are applied to decompose the spectrum in clearly differentiated trend and fluctuation modes, avoiding possible artifacts introduced by standard unfolding techniques. The fluctuation modes are scale invariant and follow different power laws for Poisson and Gaussian ensembles, which already during the unfolding allows to distinguish the two cases.

  14. Normalizing the causality between time series

    E-Print Network [OSTI]

    Liang, X San

    2015-01-01

    Recently, a rigorous yet concise formula has been derived to evaluate the information flow, and hence the causality in a quantitative sense, between time series. To assess the importance of a resulting causality, it needs to be normalized. The normalization is achieved through distinguishing three types of fundamental mechanisms that govern the marginal entropy change of the flow recipient. A normalized or relative flow measures its importance relative to other mechanisms. In analyzing realistic series, both absolute and relative information flows need to be taken into account, since the normalizers for a pair of reverse flows belong to two different entropy balances; it is quite normal that two identical flows may differ a lot in relative importance in their respective balances. We have reproduced these results with several autoregressive models. We have also shown applications to a climate change problem and a financial analysis problem. For the former, reconfirmed is the role of the Indian Ocean Dipole as ...

  15. DCU Library User Guide -DataStream Advance 5.1 What is DataStream?

    E-Print Network [OSTI]

    Humphrys, Mark

    on Criteria Search, enter your search terms and click on Search. 5. Double-click on the DS Mnemonic you want on the designated DataStream PC in the Library's information commons. You'll be prompted to "Enter Password". Type is not broken!). 2. At Novell Login: "Workstation only" must be ticked. This automatically enters "datastream

  16. Univariate Modeling and Forecasting of Monthly Energy Demand Time Series

    E-Print Network [OSTI]

    Abdel-Aal, Radwan E.

    Univariate Modeling and Forecasting of Monthly Energy Demand Time Series Using Abductive and Neural networks, Neural networks, Modeling, Forecasting, Energy demand, Time series forecasting, Power system demand time series based only on data for six years to forecast the demand for the seventh year. Both

  17. Exact Primitives for Time Series Data Mining

    E-Print Network [OSTI]

    Mueen, Abdullah Al

    2012-01-01

    G. Silva, and Rui M. M. Brito. Mining approximate motifs intime series. In Data Mining, 2001. ICDM 2001, Proceedingson Knowledge discovery and data mining, KDD, pages 947–956,

  18. Mining Time Series Data: Flying Insect Classification and Detection

    E-Print Network [OSTI]

    Chen, Yanping

    2015-01-01

    Application of Data Mining. ” KDD'11: 761-764, 2011. G.Wang, E. J. Keogh. “Querying and mining of time series data.Mining

  19. Time Series Photometry Data: Standard Access, Standard Formats

    E-Print Network [OSTI]

    Holl, András

    Time Series Photometry Data: Standard Access, Standard Formats Andr#19; as Holl Konkoly Observatory a discussion on data access and #12;le format aspects of photometry. Introduction Presently there is time series photometry data available in public databases, but the access to these varies from one collection

  20. Efficient Mining of Partial Periodic Patterns in Time Series Database

    E-Print Network [OSTI]

    Dong, Guozhu

    Efficient Mining of Partial Periodic Patterns in Time Series Database In ICDE 99 Jiawei Han \\Lambda peri­ odic patterns in time­series databases, is an interesting data mining problem. Previous studies several algorithms for efficient mining of par­ tial periodic patterns, by exploring some interesting

  1. A Multivariate Approach to Estimate Complexity of FMRI Time Series

    E-Print Network [OSTI]

    A Multivariate Approach to Estimate Complexity of FMRI Time Series Henry Sch¨utze1,2 , Thomas (MPSE), a multivariate entropy ap- proach that estimates spatio-temporal complexity of fMRI time series. In a temporally sliding window, MPSE measures the differential entropy of an assumed multivariate Gaussian density

  2. Multiple Time Series 7.1 Introduction

    E-Print Network [OSTI]

    Penny, Will

    , W.D. Penny, April 2000. 7.2.1 Cross­correlation is asymmetric First, we re­cap as to why the auto, W.D. Penny, April 2000. 89 (a) 0 20 40 60 80 100 -2 -1 0 1 2 3 4 Figure 7.1: Signals x t (top) and y this should occur ? #12; 90 Signal Processing Course, W.D. Penny, April 2000. 7.2.3 Time­Delay Estimation

  3. Times Series Study of Effects of Petroleum Production on GDP 

    E-Print Network [OSTI]

    Ballinger, Leslie 1991-

    2012-05-02

    development. The countries studied include: Argentina, Canada, Colombia, the United States, Mexico, Venezuela, Peru, and Indonesia. The dates of analysis are different for every country due to data reliability. This paper focuses mainly on a time series...

  4. 14.384 Time Series Analysis, Fall 2008

    E-Print Network [OSTI]

    Schrimpf, Paul

    The course provides a survey of the theory and application of time series methods in econometrics. Topics covered will include univariate stationary and non-stationary models, vector autoregressions, frequency domain ...

  5. Modeling Time Series of Real Systems using Genetic Programming

    E-Print Network [OSTI]

    Dilip P. Ahalpara; Jitendra C. Parikh

    2006-07-14

    Analytic models of two computer generated time series (Logistic map and Rossler system) and two real time series (ion saturation current in Aditya Tokamak plasma and NASDAQ composite index) are constructed using Genetic Programming (GP) framework. In each case, the optimal map that results from fitting part of the data set also provides a very good description of rest of the data. Predictions made using the map iteratively range from being very good to fair.

  6. First time-series optical photometry from Antarctica

    E-Print Network [OSTI]

    K. G. Strassmeier; R. Briguglio; T. Granzer; G. Tosti; I. DiVarano; I. Savanov; M. Bagaglia; S. Castellini; A. Mancini; G. Nucciarelli; O. Straniero; E. Distefano; S. Messina; G. Cutispoto

    2008-07-18

    Beating the Earth's day-night cycle is mandatory for long and continuous time-series photometry and had been achieved with either large ground-based networks of observatories at different geographic longitudes or when conducted from space. A third possibility is offered by a polar location with astronomically-qualified site characteristics. Aims. In this paper, we present the first scientific stellar time-series optical photometry from Dome C in Antarctica and analyze approximately 13,000 CCD frames taken in July 2007. We conclude that high-precision CCD photometry with exceptional time coverage and cadence can be obtained at Dome C in Antarctica and be successfully used for time-series astrophysics.

  7. Resampling Methodology in Spatial Prediction and Repeated Measures Time Series 

    E-Print Network [OSTI]

    Rister, Krista Dianne

    2012-02-14

    of an SEM image. . . . . . . . . . . . . . . . . . . . . . . . 56 10 Framework of time time series representation of SEM image. . . . . . 57 11 Approximate values of f(L; ?, ?) for L = 200 for varying values of ?. 63 12 Bootstrap variance estimates... Cor- rected Predictor . . . . . . . . . . . . . . . . . . . . . 20 3. Bias of Predictors . . . . . . . . . . . . . . . . . . . . 21 F. Simulations . . . . . . . . . . . . . . . . . . . . . . . . . . 26 1. Framework...

  8. Feature-preserving interpolation and filtering of environmental time series

    E-Print Network [OSTI]

    Mariethoz, Gregoire; Jougnot, Damien; Rezaee, Hassan

    2015-01-01

    We propose a method for filling gaps and removing interferences in time series for applications involving continuous monitoring of environmental variables. The approach is non-parametric and based on an iterative pattern-matching between the affected and the valid parts of the time series. It considers several variables jointly in the pattern matching process and allows preserving linear or non-linear dependences between variables. The uncertainty in the reconstructed time series is quantified through multiple realizations. The method is tested on self-potential data that are affected by strong interferences as well as data gaps, and the results show that our approach allows reproducing the spectral features of the original signal. Even in the presence of intense signal perturbations, it significantly improves the signal and corrects bias introduced by asymmetrical interferences. Potential applications are wide-ranging, including geophysics, meteorology and hydrology.

  9. NONLINEAR MULTIVARIATE AND TIME SERIES ANALYSIS BY NEURAL NETWORK METHODS

    E-Print Network [OSTI]

    Hsieh, William

    NONLINEAR MULTIVARIATE AND TIME SERIES ANALYSIS BY NEURAL NETWORK METHODS William W. Hsieh] Methods in multivariate statistical analysis are essential for working with large amounts of geophysical multivariate statistical analysis, there is a hierarchy of methods, starting with linear regression at the base

  10. Time series of a CME blasting out from the Sun

    E-Print Network [OSTI]

    Christian, Eric

    #12;Time series of a CME blasting out from the Sun Composite image of the Sun in UV light with the naked eye, the Sun seems static, placid, constant. From the ground, the only notice- able variations in the Sun are its location (where will it rise and set today?) and its color (will clouds cover

  11. Symbolic Dynamic Analysis of Transient Time Series for Fault

    E-Print Network [OSTI]

    Ray, Asok

    Symbolic Dynamic Analysis of Transient Time Series for Fault Detection in Gas Turbine Engines paper presents a symbolic dynamics-based method for detection of incipient faults in gas turbine engines dynamics, fault detection, aircraft gas turbine engines 1 Introduction Performance monitoring of aircraft

  12. Chaotic time series prediction using artificial neural networks

    SciTech Connect (OSTI)

    Bartlett, E.B.

    1991-12-31

    This paper describes the use of artificial neural networks to model the complex oscillations defined by a chaotic Verhuist animal population dynamic. A predictive artificial neural network model is developed and tested, and results of computer simulations are given. These results show that the artificial neural network model predicts the chaotic time series with various initial conditions, growth parameters, or noise.

  13. Chaotic time series prediction using artificial neural networks

    SciTech Connect (OSTI)

    Bartlett, E.B.

    1991-01-01

    This paper describes the use of artificial neural networks to model the complex oscillations defined by a chaotic Verhuist animal population dynamic. A predictive artificial neural network model is developed and tested, and results of computer simulations are given. These results show that the artificial neural network model predicts the chaotic time series with various initial conditions, growth parameters, or noise.

  14. Discrimination of Locally Stationary Time Series Based on the Excess Mass Functional

    E-Print Network [OSTI]

    Polonik, Wolfgang

    Discrimination of Locally Stationary Time Series Based on the Excess Mass Functional Gabriel CHANDLER and Wolfgang POLONIK Discrimination of time series is an important practical problem the time series. Instead, features are measured for each time series, and discrimination is based

  15. Nonlinear chaos in temperature time series: Part I: Case studies

    E-Print Network [OSTI]

    Yaron Rosenstein; Gal Zahavi

    2012-11-27

    In this work we present 3 case studies of local temperature time series obtained from stations in Europe and Israel. The nonlinear nature of the series is presented along with model based forecasting. Data is nonlinearly filtered using high dimensional projection and analysis is performed on the filtered data. A lorenz type model of 3 first order ODEs is then fitted. Forecasts are shown for periods of 100 days ahead, outperforming any existing forecast method known today. While other models fail at forecasting periods above 11 days, ours shows remarkable stability 100 days ahead. Thus finally a local dynamical system if found for local temperature forecasting not requiring solution of Navier-Stokes equations. Thus saving computational costs.

  16. Characterizing Weak Chaos using Time Series of Lyapunov Exponents

    E-Print Network [OSTI]

    R. M. da Silva; C. Manchein; M. W. Beims; E. G. Altmann

    2015-06-13

    We investigate chaos in mixed-phase-space Hamiltonian systems using time series of the finite- time Lyapunov exponents. The methodology we propose uses the number of Lyapunov exponents close to zero to define regimes of ordered (stickiness), semi-ordered (or semi-chaotic), and strongly chaotic motion. The dynamics is then investigated looking at the consecutive time spent in each regime, the transition between different regimes, and the regions in the phase-space associated to them. Applying our methodology to a chain of coupled standard maps we obtain: (i) that it allows for an improved numerical characterization of stickiness in high-dimensional Hamiltonian systems, when compared to the previous analyses based on the distribution of recurrence times; (ii) that the transition probabilities between different regimes are determined by the phase-space volume associated to the corresponding regions; (iii) the dependence of the Lyapunov exponents with the coupling strength.

  17. Time series power flow analysis for distribution connected PV generation.

    SciTech Connect (OSTI)

    Broderick, Robert Joseph; Quiroz, Jimmy Edward; Ellis, Abraham; Reno, Matthew J.; Smith, Jeff; Dugan, Roger

    2013-01-01

    Distributed photovoltaic (PV) projects must go through an interconnection study process before connecting to the distribution grid. These studies are intended to identify the likely impacts and mitigation alternatives. In the majority of the cases, system impacts can be ruled out or mitigation can be identified without an involved study, through a screening process or a simple supplemental review study. For some proposed projects, expensive and time-consuming interconnection studies are required. The challenges to performing the studies are twofold. First, every study scenario is potentially unique, as the studies are often highly specific to the amount of PV generation capacity that varies greatly from feeder to feeder and is often unevenly distributed along the same feeder. This can cause location-specific impacts and mitigations. The second challenge is the inherent variability in PV power output which can interact with feeder operation in complex ways, by affecting the operation of voltage regulation and protection devices. The typical simulation tools and methods in use today for distribution system planning are often not adequate to accurately assess these potential impacts. This report demonstrates how quasi-static time series (QSTS) simulation and high time-resolution data can be used to assess the potential impacts in a more comprehensive manner. The QSTS simulations are applied to a set of sample feeders with high PV deployment to illustrate the usefulness of the approach. The report describes methods that can help determine how PV affects distribution system operations. The simulation results are focused on enhancing the understanding of the underlying technical issues. The examples also highlight the steps needed to perform QSTS simulation and describe the data needed to drive the simulations. The goal of this report is to make the methodology of time series power flow analysis readily accessible to utilities and others responsible for evaluating potential PV impacts.

  18. EMCS and time-series energy data analysis in a large government office building

    E-Print Network [OSTI]

    Piette, Mary Ann; Kinney, Satkartar; Friedman, Hannah

    2001-01-01

    th EMCS and Time-Series Energy Data Analysis in a LargeEMCS and Time-Series Energy Data Analysis in a Largeyears of utility bill energy data to evaluate whole-building

  19. New Problems for an Old Design: Time-Series Analyses of Air Pollution and Health

    E-Print Network [OSTI]

    Dominici, Francesca

    New Problems for an Old Design: Time-Series Analyses of Air Pollution and Health Jonathan M. Samet1 of particulate air pollution on the same or recent days (1;2). Studies of similar time-series design of morbidity for adverse effects of particulate air pollution on the public's health. The daily time-series studies of air

  20. Exposure Measurement Error in Time-Series Studies of Air Pollution: Concepts and Consequences

    E-Print Network [OSTI]

    Dominici, Francesca

    1 Exposure Measurement Error in Time-Series Studies of Air Pollution: Concepts and Consequences S in time-series studies 1 11/11/99 Keywords: measurement error, air pollution, time series, exposure of air pollution and health. Because measurement error may have substantial implications for interpreting

  1. February 8, 2015 16:49 World Scientific Review Volume -9in x 6in "time-series classification" page 1 Sparse Representation for Time-Series Classification

    E-Print Network [OSTI]

    Ray, Asok

    February 8, 2015 16:49 World Scientific Review Volume - 9in x 6in "time-series classification" page:49 World Scientific Review Volume - 9in x 6in "time-series classification" page 2 2 S. Bahrampour and N. M

  2. Changes to MFRSRCLDOD1MIN Datastream

    SciTech Connect (OSTI)

    S McFarlane and Y Shi

    2012-05-23

    Significant updates were made to the multifilter rotating shadowband radiometer (MFRSR) cloud optical depth (MFRSRCLDOD) value-added product (VAP) in 2011. The original intent of the update was to add quality control (QC) flags and to update the VAP to use the improved retrievals of liquid water path (LWP) from the Microwave Radiometer Retrievals (MWRRET) VAP rather than the statistical retrievals of LWP from the Microwave Radiometer Line of Sight (MWRLOS) datastream. Although this was originally intended to be a straightforward update of the code, it became more complicated due to the following factors: (1) a new developer and translator team were working with the code; (2) numerous small changes had to be made to the code to consistently implement the QC flags; and (3) ARM standards have changed over the years since the code was originally developed.

  3. SumTime-Turbine: A Knowledge-Based System to Communicate Gas Turbine Time-Series Data

    E-Print Network [OSTI]

    Reiter, Ehud

    SumTime-Turbine: A Knowledge-Based System to Communicate Gas Turbine Time-Series Data Jin Yu of Aberdeen Aberdeen, AB24 3UE, UK {jyu, ereiter, jhunter, ssripada}@csd.abdn.ac.uk Abstract: SumTime-Turbine produces textual summaries of archived time- series data from gas turbines. These summaries should help

  4. On the long-term correlations and multifractal properties of electric arc furnace time series

    E-Print Network [OSTI]

    Livi, Lorenzo; Rizzi, Antonello; Sadeghian, Alireza

    2015-01-01

    In this paper, we study long-term correlations and multifractal properties elaborated from time series of three-phase current signals coming from an industrial electric arc furnace plant. Implicit sinusoidal trends are suitably detected in the scaling of the fluctuation function of such time series. Time series are then initially filtered via a Fourier based analysis, removing hence such strong periodicities. In the filtered time series we detected long-term, positive correlations. The presence of persistent correlations is in agreement with the typical V--I characteristic (hysteresis) of the electric arc furnace, justifying thus the memory effects found in the current time series. The multifractal signature is strong enough in the filtered time series to be effectively classified as multifractal.

  5. Image/Time Series Mining Algorithms: Applications to Developmental Biology, Document Processing and Data Streams

    E-Print Network [OSTI]

    Tataw, Oben Moses

    2013-01-01

    International Conference on Data Mining (2001). Khairy, K. ,and Eamonn Keogh (2011). Mining Historical Documents forWang, E. J. Keogh. Querying and mining of time series data.

  6. 348 Book Reviews Nonlinear Time Series: Nonparametric and Parametric Methods.

    E-Print Network [OSTI]

    Fan, Jianqing

    diagnostics, and then computes optimal predictors for future values of the series. Autoregressive moving aver of topics covered in this book makes for a large and awk- ward load. It is like coming home from the grocery, and breaks, and some items are lost altogether. This book has scratches scattered throughout, in the form

  7. Input Data Reduction for the Prediction of Financial Time Series

    E-Print Network [OSTI]

    Verleysen, Michel

    on data from the BEL20 market index. 1. Introduction Since the beginning of this century, the question of the predictability of financial series (at least of stock market prices) has been the subject of a highly. To perform the transformation between the initial inputs and the new variables, we may choose to use a linear

  8. ARM: Millimeter Wave Cloud Radar (MMCR), replaces mmcrcal and mmcrmoments datastreams following C-40 processor upgrade of 2003.09.09

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

    Widener, Kevin; Bharadwaj, Nitin; Johnson, Karen

    Millimeter Wave Cloud Radar (MMCR), replaces mmcrcal and mmcrmoments datastreams following C-40 processor upgrade of 2003.09.09

  9. ARM: Millimeter Wave Cloud Radar (MMCR), replaces mmcrcal and mmcrmoments datastreams following C-40 processor upgrade of 2003.09.09

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

    Karen Johnson; Nitin Bharadwaj

    1990-01-01

    Millimeter Wave Cloud Radar (MMCR), replaces mmcrcal and mmcrmoments datastreams following C-40 processor upgrade of 2003.09.09

  10. Granger Causality in Multi-variate Time Series using a Time Ordered Restricted Vector Autoregressive Model

    E-Print Network [OSTI]

    Siggiridou, Elsa

    2015-01-01

    Granger causality has been used for the investigation of the inter-dependence structure of the underlying systems of multi-variate time series. In particular, the direct causal effects are commonly estimated by the conditional Granger causality index (CGCI). In the presence of many observed variables and relatively short time series, CGCI may fail because it is based on vector autoregressive models (VAR) involving a large number of coefficients to be estimated. In this work, the VAR is restricted by a scheme that modifies the recently developed method of backward-in-time selection (BTS) of the lagged variables and the CGCI is combined with BTS. Further, the proposed approach is compared favorably to other restricted VAR representations, such as the top-down strategy, the bottom-up strategy, and the least absolute shrinkage and selection operator (LASSO), in terms of sensitivity and specificity of CGCI. This is shown by using simulations of linear and nonlinear, low and high-dimensional systems and different t...

  11. Functional Coefficient Regression Models for Non-linear Time Series: A Polynomial

    E-Print Network [OSTI]

    Shen, Haipeng

    Functional Coefficient Regression Models for Non-linear Time Series: A Polynomial Spline Approach of functional coefficient regression models for non-linear time series. Consistency and rate of convergence to estimate the coefficient functions. Cai et al. (2000) and Chen & Liu (2001) used the local linear method

  12. A regression model with a hidden logistic process for feature extraction from time series

    E-Print Network [OSTI]

    Chamroukhi, Faicel

    A regression model with a hidden logistic process for feature extraction from time series Faicel from time series is proposed in this paper. This approach consists of a specific regression model Reweighted Least-Squares (IRLS) algorithm. A piecewise regression algorithm and its iterative variant have

  13. Discrimination and Classification of Nonstationary Time Series using the SLEX Model

    E-Print Network [OSTI]

    Discrimination and Classification of Nonstationary Time Series using the SLEX Model Hsiao-Yun Huang scheme based on the SLEX (Smooth Localized Complex EXponential) library. The SLEX library forms domains. Thus, the SLEX library has the ability to extract local spectral features of the time series

  14. Discrimination and Classification of Nonstationary Time Series Using the SLEX Model

    E-Print Network [OSTI]

    Discrimination and Classification of Nonstationary Time Series Using the SLEX Model Hsiao-Yun HUANG a discriminant scheme based on the SLEX (smooth localized complex exponential) library. The SLEX library forms domains. Thus, the SLEX library has the ability to extract local spectral features of the time series

  15. A test for second order stationarity of a time series based on the Discrete Fourier Transform

    E-Print Network [OSTI]

    Subba Rao, Suhasini

    A test for second order stationarity of a time series based on the Discrete Fourier Transform property, we construct a Portmanteau type test statistic for testing stationarity of the time series. It is shown that under the null of stationarity, the test statistic is approximately a chi square distribution

  16. Abstract--We present a method to integrate environmental time series

    E-Print Network [OSTI]

    89 Abstract--We present a method to integrate environmental time series into stock assessment). A general framework for integrating environmental time series into stock assessment models: model models and to test the significance of correlations between population processes and the environmental

  17. SVG and Geo Web Services for visualization of time series data of flood risk Barend Kbben

    E-Print Network [OSTI]

    Köbben, Barend

    SVG and Geo Web Services for visualization of time series data of flood risk Barend Köbben. The Open Geospatial Consortiums Web Map Service (WMS) specification is no doubt the most widely implemented webservices Open Web Services specifications Time series in WMS RIMapperWMS: SVG from WMS SVG animation of Geo-WebServices

  18. Detecting Climate Change in Multivariate Time Series Data by Novel Clustering and Cluster Tracing Techniques

    E-Print Network [OSTI]

    Detecting Climate Change in Multivariate Time Series Data by Novel Clustering and Cluster Tracing Aachen University, Germany {kremer, guennemann, seidl}@cs.rwth-aachen.de Abstract--Climate change can series, and trace the clusters over time. A climate pattern is categorized as a changing pattern

  19. Stochastic Generation of Synthetic Precipitation Time Series with High Temporal and Spatial Resolution for Engineering Practice

    E-Print Network [OSTI]

    Cirpka, Olaf Arie

    Stochastic Generation of Synthetic Precipitation Time Series with High Temporal and Spatial.Brommundt@iws.uni-stuttgart.de Introduction The stochastic precipitation time series generator, NiedSim, has been developed and installed been generated. In the year 2004 NiedSim was set up for Hessen and Rheinland-Pfalz. The total project

  20. Towards Never-Ending Learning from Time Series Streams , Yanping Chen*

    E-Print Network [OSTI]

    Zordan, Victor

    that an email is sent to the building supervisor with a picture of the patterns and any other useful metadata a time series produced by a light sensor at Soda Hall in Berkley. While the sensor will produce data sensors at Soda Hall produce a never- ending time series, of which we can cache only a small subset main

  1. Lean Blow-Out Prediction in Gas Turbine Combustors Using Symbolic Time Series Analysis

    E-Print Network [OSTI]

    Ray, Asok

    Lean Blow-Out Prediction in Gas Turbine Combustors Using Symbolic Time Series Analysis Achintya of lean blowout in gas turbine combustors based on symbolic analysis of time series data from optical. For the purpose of detecting lean blowout in gas turbine combustors, the state probability vector obtained

  2. A METHOD FOR IDENTIFYING REPETITION STRUCTURE IN MUSICAL AUDIO BASED ON TIME SERIES PREDICTION

    E-Print Network [OSTI]

    Dixon, Simon

    A METHOD FOR IDENTIFYING REPETITION STRUCTURE IN MUSICAL AUDIO BASED ON TIME SERIES PREDICTION This paper investigates techniques for determining the repeti- tion structure of musical audio. In particular. To this end, we propose a novel approach based on multivari- ate time series modelling of audio features

  3. Summarizing Neonatal Time Series Data Somayajulu G. Sripada, Ehud Reiter, Jim Hunter and Jin Yu

    E-Print Network [OSTI]

    a time series. We are building a system to summarize physiological times series data such as heart rate forecasts from weather data such as wind speed, wind direction, and wave heights. In gas turbines we pressure sampled every second for three hours. Figure 2 shows its summary extracted from a small corpus

  4. Summarizing Neonatal Time Series Data Somayajulu G. Sripada, Ehud Reiter, Jim Hunter and Jin Yu

    E-Print Network [OSTI]

    Reiter, Ehud

    a time series. We are building a system to summarize physiological times series data such as heart rate forecasts from weather data such as wind speed, wind direction, and wave heights. In gas turbines we for three hours. Figure 2 shows its summary extracted from a small corpus of human written summaries we

  5. Modelling signal interactions with application to financial time series

    E-Print Network [OSTI]

    Jain, Bonny

    2014-01-01

    In this thesis, we concern ourselves with the problem of reasoning over a set of objects evolving over time that are coupled through interaction structures that are themselves changing over time. We focus on inferring ...

  6. A New Architecture for Summarising Time Series Data

    E-Print Network [OSTI]

    Sripada, Yaji

    of the systems developed in the SumTime Project2 ) summarises sensor data from gas turbines. This is challenging because of the large amount of data being summarised; a typical gas turbine has 250 ana- logue data generation techniques to produce summaries of such data. A short extract from SumTime-Turbine's input data

  7. A FAST MODEL-BUILDING METHOD FOR TIME SERIES USING GENETIC PROGRAMMING

    E-Print Network [OSTI]

    Fernandez, Thomas

    generation, initial values of the parameters in an model (offspring) are inherited from the parents. step-2 series and applied it to lots of time series: (1) computer generated chaos e.g. Logistic, Loessler is made by using a set of finite number of past values measured from the system )x,,x,(xfx~ n-t2-t1-tt

  8. IMPROVED SEMI-PARAMETRIC TIME SERIES MODELS OF AIR POLLUTION AND MORTALITY

    E-Print Network [OSTI]

    Dominici, Francesca

    IMPROVED SEMI-PARAMETRIC TIME SERIES MODELS OF AIR POLLUTION AND MORTALITY Francesca Dominici series analyses of air pollution and health attracted the attention of the scientific community, policy makers, the press, and the diverse stakeholders con- cerned with air pollution. As the Environmental

  9. 2001, Applied Statistics, 50, 143-154. Nonlinear autoregressive time series with multivariate

    E-Print Network [OSTI]

    Glasbey, Chris

    series is proposed to model solar radiation data, by specifying joint marginal distributions at low lags, Multiprocess dynamic linear model, Solar radiation 1 Introduction Knowledge of the statistical characteristics of time series of solar radiation has many uses, one of which is in the design and evaluation of solar

  10. Topic Time Series Analysis of Microblogs ellai@uci.edu

    E-Print Network [OSTI]

    Bertozzi, Andrea L.

    -worthy phenomena. However, transforming raw, free-form, real time text into meaningful in- formation remains spatially, temporally or both might be of interest to analysts, marketers, researchers, law enforcement of microblog topics, where edges represent the predictive power of one topic for another. Recovery

  11. Dynamic dependence analysis : modeling and inference of changing dependence among multiple time-series

    E-Print Network [OSTI]

    Siracusa, Michael Richard, 1980-

    2009-01-01

    In this dissertation we investigate the problem of reasoning over evolving structures which describe the dependence among multiple, possibly vector-valued, time-series. Such problems arise naturally in variety of settings. ...

  12. Time Series Methods for ForecastingElectricityMarket Pricing Zoran Obradovic Kevin Tomsovic

    E-Print Network [OSTI]

    Obradovic, Zoran

    of traditional commodities, such as,oil or agricultural products. Clearly, assessing the effectivenessTime Series Methods for ForecastingElectricityMarket Pricing Zoran Obradovic Kevin Tomsovic PO Box

  13. Time-Series Classification of High-Temporal Resolution AVHRR NDVI Imagery of Mexico

    E-Print Network [OSTI]

    Egbert, Stephen L.; Ortega-Huerta, Miguel; Martí nez-Meyer, Enrique; Price, Kevin P.; Peterson, A. Townsend

    2000-01-01

    Time-series data from wide-field sensors, acquired for the period of a growing season or longer, capitalize on phenological changes in vegetation and make it possible to identify vegetated land cover types in greater detail. ...

  14. Generalized Volterra-Wiener and surrogate data methods for complex time series analysis

    E-Print Network [OSTI]

    Shashidhar, Akhil

    2006-01-01

    This thesis describes the current state-of-the-art in nonlinear time series analysis, bringing together approaches from a broad range of disciplines including the non-linear dynamical systems, nonlinear modeling theory, ...

  15. Time Series and the Dynamics of Demand Pacing Daniel T. Kaplan

    E-Print Network [OSTI]

    Kaplan, Daniel T.

    Time Series and the Dynamics of Demand Pacing Daniel T. Kaplan Dept. of Mathematics & Computer on in Kaplan2 and Christini and Kaplan3 . Along with the theory, I will detail some of the signal processing

  16. Modeling Gene Regulatory Networks from Time Series Data using Particle Filtering 

    E-Print Network [OSTI]

    Noor, Amina

    2012-10-19

    This thesis considers the problem of learning the structure of gene regulatory networks using gene expression time series data. A more realistic scenario where the state space model representing a gene network evolves nonlinearly is considered while...

  17. Mining Time Series Data: Moving from Toy Problems to Realistic Deployments

    E-Print Network [OSTI]

    Hu, Bing

    2013-01-01

    Conference on Data Mining, 2010 V. Chandola, A. Banerjee,and E. Keogh. “ Querying and Mining of Time Series Data:2 nd Workshop on Temporal Data Mining, 2002 K. Malatesta, S.

  18. A Time Series Analysis of Microarray Data Selnur Erdal1,2

    E-Print Network [OSTI]

    Ferhatosmanoglu, Hakan

    by algorithms that can extract and cluster related behaviors from the full population of time-series behaviors observed. Although traditional clus- tering techniques have shown to be effective for certain types

  19. Analysis of MODIS 250 m NDVI Using Different Time-Series Data for Crop Type Separability

    E-Print Network [OSTI]

    Lee, Eunmok

    2014-08-31

    The primary objectives of this research were to: (1) investigate the use of different compositing periods of NDVI values of time-series MODIS 250 m data for distinguishing major crop types on the central Great Plains of ...

  20. Nonparametric estimation of additive nonlinear ARX time series: Local Linear Fitting and Projections

    E-Print Network [OSTI]

    Cai, Zongwu; Masry, Elias

    2000-08-01

    We consider the estimation and identification of the components (endogenous and exogenous) of additive nonlinear ARX time series models. We employ a local polynomial fitting scheme coupled with projections. We establish ...

  1. Desertification of high latitude ecosystems: conceptual models, time-series analyses and experiments 

    E-Print Network [OSTI]

    Thorsson, Johann

    2009-05-15

    -1 DESERTIFICATION OF HIGH LATITUDE ECOSYSTEMS: CONCEPTUAL MODELS, TIME-SERIES ANALYSES AND EXPERIMENTS A Dissertation by JOHANN THORSSON Submitted to the Office of Graduate Studies of Texas A&M University in partial fulfillment of the requirements... for the degree of DOCTOR OF PHILOSOPHY December 2008 Major Subject: Rangeland Ecology and Management DESERTIFICATION OF HIGH LATITUDE ECOSYSTEMS: CONCEPTUAL MODELS, TIME-SERIES ANALYSES AND EXPERIMENTS A Dissertation by JOHANN...

  2. The application of complex network time series analysis in turbulent heated jets

    SciTech Connect (OSTI)

    Charakopoulos, A. K.; Karakasidis, T. E. Liakopoulos, A.; Papanicolaou, P. N.

    2014-06-15

    In the present study, we applied the methodology of the complex network-based time series analysis to experimental temperature time series from a vertical turbulent heated jet. More specifically, we approach the hydrodynamic problem of discriminating time series corresponding to various regions relative to the jet axis, i.e., time series corresponding to regions that are close to the jet axis from time series originating at regions with a different dynamical regime based on the constructed network properties. Applying the transformation phase space method (k nearest neighbors) and also the visibility algorithm, we transformed time series into networks and evaluated the topological properties of the networks such as degree distribution, average path length, diameter, modularity, and clustering coefficient. The results show that the complex network approach allows distinguishing, identifying, and exploring in detail various dynamical regions of the jet flow, and associate it to the corresponding physical behavior. In addition, in order to reject the hypothesis that the studied networks originate from a stochastic process, we generated random network and we compared their statistical properties with that originating from the experimental data. As far as the efficiency of the two methods for network construction is concerned, we conclude that both methodologies lead to network properties that present almost the same qualitative behavior and allow us to reveal the underlying system dynamics.

  3. Engine Control Improvement through Application of Chaotic Time Series Analysis

    SciTech Connect (OSTI)

    Green, J.B., Jr.; Daw, C.S.

    2003-07-15

    The objective of this program was to investigate cyclic variations in spark-ignition (SI) engines under lean fueling conditions and to develop options to reduce emissions of nitrogen oxides (NOx) and particulate matter (PM) in compression-ignition direct-injection (CIDI) engines at high exhaust gas recirculation (EGR) rates. The CIDI activity builds upon an earlier collaboration between ORNL and Ford examining combustion instabilities in SI engines. Under the original CRADA, the principal objective was to understand the fundamental causes of combustion instability in spark-ignition engines operating with lean fueling. The results of this earlier activity demonstrated that such combustion instabilities are dominated by the effects of residual gas remaining in each cylinder from one cycle to the next. A very simple, low-order model was developed that explained the observed combustion instability as a noisy nonlinear dynamical process. The model concept lead to development of a real-time control strategy that could be employed to significantly reduce cyclic variations in real engines using existing sensors and engine control systems. This collaboration led to the issuance of a joint patent for spark-ignition engine control. After a few years, the CRADA was modified to focus more on EGR and CIDI engines. The modified CRADA examined relationships between EGR, combustion, and emissions in CIDI engines. Information from CIDI engine experiments, data analysis, and modeling were employed to identify and characterize new combustion regimes where it is possible to simultaneously achieve significant reductions in NOx and PM emissions. These results were also used to develop an on-line combustion diagnostic (virtual sensor) to make cycle-resolved combustion quality assessments for active feedback control. Extensive experiments on engines at Ford and ORNL led to the development of the virtual sensor concept that may be able to detect simultaneous reductions in NOx and PM emissions under low temperature combustion (LTC) regimes. An invention disclosure was submitted to ORNL for the virtual sensor under the CRADA. Industrial in-kind support was available throughout the project period. Review of the research results were carried out on a regular basis (annual reports and meetings) followed by suggestions for improvement in ongoing work and direction for future work. A significant portion of the industrial support was in the form of experimentation, data analysis, data exchange, and technical consultation.

  4. Iterative prediction of chaotic time series using a recurrent neural network

    SciTech Connect (OSTI)

    Essawy, M.A.; Bodruzzaman, M. [Tennessee State Univ., Nashville, TN (United States). Dept. of Electrical and Computer Engineering; Shamsi, A.; Noel, S. [USDOE Morgantown Energy Technology Center, WV (United States)

    1996-12-31

    Chaotic systems are known for their unpredictability due to their sensitive dependence on initial conditions. When only time series measurements from such systems are available, neural network based models are preferred due to their simplicity, availability, and robustness. However, the type of neutral network used should be capable of modeling the highly non-linear behavior and the multi-attractor nature of such systems. In this paper the authors use a special type of recurrent neural network called the ``Dynamic System Imitator (DSI)``, that has been proven to be capable of modeling very complex dynamic behaviors. The DSI is a fully recurrent neural network that is specially designed to model a wide variety of dynamic systems. The prediction method presented in this paper is based upon predicting one step ahead in the time series, and using that predicted value to iteratively predict the following steps. This method was applied to chaotic time series generated from the logistic, Henon, and the cubic equations, in addition to experimental pressure drop time series measured from a Fluidized Bed Reactor (FBR), which is known to exhibit chaotic behavior. The time behavior and state space attractor of the actual and network synthetic chaotic time series were analyzed and compared. The correlation dimension and the Kolmogorov entropy for both the original and network synthetic data were computed. They were found to resemble each other, confirming the success of the DSI based chaotic system modeling.

  5. Anomaly detection in thermal pulse combustors using symbolic time series analysis

    E-Print Network [OSTI]

    Ray, Asok

    pulse combustor. Results are presented to exemplify early detection of combustion instability due339 Anomaly detection in thermal pulse combustors using symbolic time series analysis S Gupta1 for anomaly detection in thermal pulse combustors. The anomaly detection method has been tested on the time

  6. Climate signal detection using wavelet transform: How to make a time series sing

    SciTech Connect (OSTI)

    Lau, K.M.; Weng, H.

    1995-12-01

    In this paper, the application of the wavelet transform (WT) to climate time series analyses is introduced. A tutorial description of the basic concept of WT, compared with similar concepts used in music, is also provided. Using an analogy between WT representation of a time series and a music score, the authors illustrate the importance of local versus global information in the time-frequency localization of climate signals. Examples of WT applied to climate data analysis are demonstrated using analytic signals as well as real climate time series. Results of WT applied to two climate time series-that is, a proxy paleoclimate time series with a 2.5-Myr deep-sea sediment record of {gamma}{sup 18}O and a 140-yr monthly record of Northern Hemisphere surface temperature-are presented. The former shows the presence of a 40-kyr and a 100-kyr oscillation and an abrupt transition in the oscillation regime at 0.7 Myr before the present, consistent with previous studies. The latter possesses a myriad of oscillatory modes f rom interannual (2-5 yr), interdecadal (10-12 yr, 20-25 yr, and 40-60 yr), and century ({approximately}180 yr) scales at different periods of the data record. In spite of the large difference in timescales, common features in time-frequency characteristics of these two time series have been identified. These features suggest that the variations of the earth`s climate are consistent with those exhibited by a nonlinear dynamical system under external forcings. 32 refs., 9 figs.

  7. Time Series Evaluation of Radiation Portal Monitor Data for Point Source Detection

    SciTech Connect (OSTI)

    Robinson, Sean M.; Bender, Sarah E.; Flumerfelt, Eric L.; Lopresti, Charles A.; Woodring, Mitchell L.

    2009-12-08

    The time series of data from a Radiation Portal Monitor (RPM) system are evaluated for the presence of point sources by isolating the contribution of anomalous radiation. Energy-windowed background spectra taken from the RPM are compared with the observed spectra at each time step during a vehicle drive-through. The total signal is turned into a spectral distance index using this method. This provides a time series with reduced systematic fluctuations due to background attenuation by the vehicle, and allows for point source detection by time-series analyses. The anomalous time series is reanalyzed by using a wavelet filter function of similar size to the expected source profile. A number of real drive-through data sets taken at a U.S. port of entry are analyzed in this way. A set of isotopes are injected into the data set, and the resultant benign and injected data sets are analyzed with gross-counting, spectral-ratio, and time-based algorithms. Spectral and time methods together offer a significant increase to detection performance.

  8. Unified functional network and nonlinear time series analysis for complex systems science: The pyunicorn package

    E-Print Network [OSTI]

    Donges, Jonathan F; Beronov, Boyan; Wiedermann, Marc; Runge, Jakob; Feng, Qing Yi; Tupikina, Liubov; Stolbova, Veronika; Donner, Reik V; Marwan, Norbert; Dijkstra, Henk A; Kurths, Jürgen

    2015-01-01

    We introduce the pyunicorn (Pythonic unified complex network and recurrence analysis toolbox) open source software package for applying and combining modern methods of data analysis and modeling from complex network theory and nonlinear time series analysis. pyunicorn is a fully object-oriented and easily parallelizable package written in the language Python. It allows for the construction of functional networks such as climate networks in climatology or functional brain networks in neuroscience representing the structure of statistical interrelationships in large data sets of time series and, subsequently, investigating this structure using advanced methods of complex network theory such as measures and models for spatial networks, networks of interacting networks, node-weighted statistics or network surrogates. Additionally, pyunicorn provides insights into the nonlinear dynamics of complex systems as recorded in uni- and multivariate time series from a non-traditional perspective by means of recurrence qua...

  9. A non subjective approach to the GP algorithm for analysing noisy time series

    E-Print Network [OSTI]

    K. P. Harikrishnan; R. Misra; G. Ambika; A. K. Kembhavi

    2006-03-11

    We present an adaptation of the standard Grassberger-Proccacia (GP) algorithm for estimating the Correlation Dimension of a time series in a non subjective manner. The validity and accuracy of this approach is tested using different types of time series, such as, those from standard chaotic systems, pure white and colored noise and chaotic systems added with noise. The effectiveness of the scheme in analysing noisy time series, particularly those involving colored noise, is investigated. An interesting result we have obtained is that, for the same percentage of noise addition, data with colored noise is more distinguishable from the corresponding surrogates, than data with white noise. As examples for real life applications, analysis of data from an astrophysical X-ray object and human brain EEG, are presented.

  10. TIME SERIES MODELS OF THREE SETS OF RXTE OBSERVATIONS OF 4U 1543-47

    SciTech Connect (OSTI)

    Koen, C.

    2013-03-01

    The X-ray nova 4U 1543-47 was in a different physical state (low/hard, high/soft, and very high) during the acquisition of each of the three time series analyzed in this paper. Standard time series models of the autoregressive moving average (ARMA) family are fitted to these series. The low/hard data can be adequately modeled by a simple low-order model with fixed coefficients, once the slowly varying mean count rate has been accounted for. The high/soft series requires a higher order model, or an ARMA model with variable coefficients. The very high state is characterized by a succession of 'dips', with roughly equal depths. These seem to appear independently of one another. The underlying stochastic series can again be modeled by an ARMA form, or roughly as the sum of an ARMA series and white noise. The structuring of each model in terms of short-lived aperiodic and 'quasi-periodic' components is discussed.

  11. Detrended partial cross-correlation analysis of two time series influenced by common external forces

    E-Print Network [OSTI]

    Qian, Xi-Yuan; Jiang, Zhi-Qiang; Podobnik, Boris; Zhou, Wei-Xing; Stanley, H Eugene

    2015-01-01

    We propose a new method, detrended partial cross-correlation analysis (DPXA), to uncover the intrinsic power-law cross-correlations between two simultaneously recorded time series in the presence of nonstationarity after removing the effects of other time series acting as common forces. The DPXA method is a generalization of the detrended cross-correlation analysis by taking into account the partial correlation analysis. We illustrate the performance of the method using bivariate fractional Brownian motions and multifractal binomial measures with analytical expressions and apply it to extract the intrinsic cross-correlation between crude oil and gold futures by considering the impact of the US dollar index.

  12. Nonlinear analysis of time series of vibration data from a friction brake: SSA, PCA, and MFDFA

    E-Print Network [OSTI]

    Nikolay K. Vitanov; Norbert P. Hoffmann; Boris Wernitz

    2014-10-23

    We use the methodology of singular spectrum analysis (SSA), principal component analysis (PCA), and multi-fractal detrended fluctuation analysis (MFDFA), for investigating characteristics of vibration time series data from a friction brake. SSA and PCA are used to study the long time-scale characteristics of the time series. MFDFA is applied for investigating all time scales up to the smallest recorded one. It turns out that the majority of the long time-scale dynamics, that is presumably dominated by the structural dynamics of the brake system, is dominated by very few active dimensions only and can well be understood in terms of low dimensional chaotic attractors. The multi-fractal analysis shows that the fast dynamical processes originating in the friction interface are in turn truly multi-scale in nature.

  13. Time-varying Spectral Analysis in Neurophysiological Time Series Using Hilbert

    E-Print Network [OSTI]

    Whitcher, Brandon

    ability to detect time-varying coherence and phase properties. Key words: Coherence, electromyographic

  14. Aalborg Universitet ARIMA-Based Time Series Model of Stochastic Wind Power Generation

    E-Print Network [OSTI]

    Bak-Jensen, Birgitte

    the nonstationarity and physical limits of stochastic wind power generation. The model is constructed based on wind power measurement of one year from the Nysted offshore wind farm in Denmark. The proposed limitedAalborg Universitet ARIMA-Based Time Series Model of Stochastic Wind Power Generation Chen, Peiyuan

  15. Neural networks as a tool for constructing continuous NDVI time series from AVHRR and MODIS

    E-Print Network [OSTI]

    Neural networks as a tool for constructing continuous NDVI time series from AVHRR and MODIS M. E-stationary aspect of NDVI, we use an artificial neural network (ANN) to map the NDVI indices from AVHRR to those the AVHRR record. Four years of overlap between the two sensors is used to train a neural network to remove

  16. Railway Subsidence Monitoring by High Resolution INSAR Time Series Analysis in Tianjin

    E-Print Network [OSTI]

    Perissin, Daniele

    Railway Subsidence Monitoring by High Resolution INSAR Time Series Analysis in Tianjin Qingli Luo1 and the development of urban are seriously affected by the subsidence of them. Permanent Scatterers (PS) technology was developed as a powerful tool for subsidence monitoring. High resolution of 1m data can be provided by Terra

  17. Time Series Measurements of Temperature, Current Velocity, and Sediment Resuspension in Saginaw Bay

    E-Print Network [OSTI]

    Time Series Measurements of Temperature, Current Velocity, and Sediment Resuspension in Saginaw Bay and verification. These measurements will be made as part of this project. Measurements of sediment resuspension sediment resuspension in the bay during the spring. Measurements of sediment resuspension are important

  18. Evolving Neural Network Weights for Time-Series Prediction of General Aviation Flight Data

    E-Print Network [OSTI]

    Hu, Wen-Chen

    and predictive maintenance systems, reducing accident rates and saving lives. Keywords: Time-Series Prediction and lucrative industry, it has the highest accident rates within civil aviation [21]. For many years between 0.08% for altitude to 2% for roll. Cross validation of the best neural networks indicate

  19. DAMAGE DETECTION IN A WIND TURBINE BLADE BASED ON TIME SERIES Simon Hoell, Piotr Omenzetter

    E-Print Network [OSTI]

    Boyer, Edmond

    DAMAGE DETECTION IN A WIND TURBINE BLADE BASED ON TIME SERIES METHODS Simon Hoell, Piotr Omenzetter (SHM) systems. Different methods have been developed for detection of damages in WT blades. However a damage detection method based on autocorrelations of response accelerations. The damage sensitive feature

  20. Mapping Deforestation and Forest Degradation Using Landsat Time Series: a Case of Sumatra--Indonesia

    E-Print Network [OSTI]

    20 Mapping Deforestation and Forest Degradation Using Landsat Time Series: a Case of Sumatra--Indonesia Belinda Arunarwati Margono1, 2 Abstract Indonesia experiences the second highest rate of deforestation monitoring system, in addition to the problem of cloud cover in Indonesia. In this work, we demonstrate

  1. Indexing of Time Series by Major Minima and Maxima Eugene Fink

    E-Print Network [OSTI]

    Fink, Eugene

    sets: stock prices, air and sea temperatures, and wind speeds. Keywords: Compression, indexing.ics.uci.edu). Wind speeds: We have used daily wind speeds at twelve sites in Ireland, from 1961 to 1978, ob- tained. Indexing: The indexing of a time-series database is based on the notion of major inclines, illustrated

  2. Indexing of Time Series by Major Minima and Maxima Eugene Fink

    E-Print Network [OSTI]

    Fink, Eugene

    sets: stock prices, air and sea temperatures, and wind speeds. Keywords: Compression, indexing.ics.uci.edu). Wind speeds: We have used daily wind speeds at twelve sites in Ireland, from 1961 to 1978, ob­ tained. Indexing: The indexing of a time­series database is based on the notion of major inclines, illustrated

  3. OUTPUT-ONLY STATISTICAL TIME SERIES METHODS FOR STRUCTURAL HEALTH MONITORING: A COMPARATIVE STUDY

    E-Print Network [OSTI]

    Paris-Sud XI, Université de

    OUTPUT-ONLY STATISTICAL TIME SERIES METHODS FOR STRUCTURAL HEALTH MONITORING: A COMPARATIVE STUDY-STSMs) for Structural Health Monitoring (SHM) is presented via damage de- tection and identification in a GARTEUR type for Structural Health Monitoring (SHM). Their use is of high importance for structures such as bridges, aircraft

  4. Recognising Visual Patterns to Communicate Gas Turbine Time-Series Data

    E-Print Network [OSTI]

    Reiter, Ehud

    Recognising Visual Patterns to Communicate Gas Turbine Time-Series Data Jin Yu, Jim Hunter, Ehud analogue channels are sampled once per second and archived by the Tiger system for monitoring gas turbines that it is very important to identify such patterns in any attempt at summarisation. In the gas turbine domain

  5. Creating and Using Geospatial Ontology Time Series in a Semantic Cultural Heritage Portal

    E-Print Network [OSTI]

    Hyvönen, Eero

    Creating and Using Geospatial Ontology Time Series in a Semantic Cultural Heritage Portal Tomi annotations in semantic cultural heritage portals commonly make spatiotemporal references to historical heritage portal CULTURESAMPO to sup- port faceted semantic search of contents and to visualize historical

  6. INHERENT WATER OPTICAL PROPERTIES AT THE CARIBBEAN TIME SERIES STATION (CaTS)

    E-Print Network [OSTI]

    Gilbes, Fernando

    INHERENT WATER OPTICAL PROPERTIES AT THE CARIBBEAN TIME SERIES STATION (CaTS) Fernando Gilbes Rico 00681 ABSTRACT The temporal variability of the inherent water optical properties at the Caribbean wavelengths, but in all cases, the values were less than one. The correlation between bio-optical properties

  7. Ultrasound radio-frequency time series for finding malignant breast lesions

    E-Print Network [OSTI]

    Freitas, Nando de

    -based solutions for breast lesion characterization to reduce the patient recall rate after mammography screening. In this work, ultrasound radio frequency time series analysis is performed for sepa- rating benign framework can help in differentiating malignant from benign breast lesions. 1 Introduction In the United

  8. Monitoring water stress using time series of observed to unstressed surface temperature difference

    E-Print Network [OSTI]

    Gentine, Pierre

    Monitoring water stress using time series of observed to unstressed surface temperature difference to monitor stress have shifted from establishing empirical relationships between combined vegetation cover/temperature surface temperature as a baseline to monitor water stress. The unstressed temperature is the equilibrium

  9. Time-series validation of MODIS land biophysical products in a Kalahari woodland, Africa

    E-Print Network [OSTI]

    Myneni, Ranga B.

    Time-series validation of MODIS land biophysical products in a Kalahari woodland, Africa K. F MODIS variables are produced from the same algorithm. Solar zenith angle effects, differences between the two sides of the leaves are not symmetrical; 3. horizontally projected LAI is the area of `shadow

  10. Time Series Analysis with R A. Ian McLeod, Hao Yu, Esam Mahdi

    E-Print Network [OSTI]

    McLeod, Ian

    it is built on a solid foundation of core statistical and numerical algorithms. The R programming languageTime Series Analysis with R A. Ian McLeod, Hao Yu, Esam Mahdi Department of Statistical out some other key features of this quantitative programming environment (QPE). R is an open source

  11. 1.2000-2009 time-series return information for Snake River: a. Fall Chinook Salmon

    E-Print Network [OSTI]

    #12;Content: 1.2000-2009 time-series return information for Snake River: a. Fall Chinook Salmon b. Sockeye Salmon c. Summer Steelhead d. Spring/Summer Chinook Salmon 2.2010 run-size forecasts for: a. Sockeye Salmon b. Spring/Summer Chinook Salmon #12;#12;Species: Run: Origin: Period: Chinook Salmon Fall

  12. The Fourier Transform The Fourier transform is crucial to any discussion of time series analysis, and this chapter discusses the

    E-Print Network [OSTI]

    Masci, Frank

    The Fourier Transform The Fourier transform is crucial to any discussion of time series analysis: Fourier Series Fourier Transform Example and Interpretation Oddness and Evenness The Convolution Theorem Discrete Fourier Transforms Definitions Example Implementation Author à Fourier Series Recall the Fourier

  13. Detecting and interpreting distortions in hierarchical organization of complex time series

    E-Print Network [OSTI]

    Dro?d?, Stanis?aw

    2015-01-01

    Hierarchical organization is a cornerstone of complexity and multifractality constitutes its central quantifying concept. For model uniform cascades the corresponding singularity spectra are symmetric while those extracted from empirical data are often asymmetric. Using the selected time series representing such diverse phenomena like price changes and inter-transaction times in the financial markets, sentence length variability in the narrative texts, Missouri River discharge and Sunspot Number variability as examples, we show that the resulting singularity spectra appear strongly asymmetric, more often left-sided but in some cases also right-sided. We present a unified view on the origin of such effects and indicate that they may be crucially informative for identifying composition of the time series. One particularly intriguing case of this later kind of asymmetry is detected in the daily reported Sunspot Number variability. This signals that either the commonly used famous Wolf formula distorts the real d...

  14. Time series analysis of ionization waves in dc neon glow discharge

    SciTech Connect (OSTI)

    Hassouba, M. A.; Al-Naggar, H. I.; Al-Naggar, N. M.; Wilke, C. [Department of Physics, Faculty of Science, Benha University (Egypt); Institute of Physics, E. M. A. University, Domstrasse 10a, 17489 Greifswald (Germany)

    2006-07-15

    The dynamics of dc neon glow discharge is examined by calculating a Lyapunov exponent spectrum (LES) and correlation dimension (D{sub corr}) from experimental time series. The embedding theory is used to reconstruct an attractor with the delay coordinate method. The analysis refers to periodic, chaotic, and quasi-periodic attractors. The results obtained are confirmed by a comparison with other methods of time series analysis such as the Fourier power spectrum and autocorrelation function. The main object of the present work is the positive column of a dc neon glow discharge. The positive column is an excellent model for the study of a non-linearity plasma system because it is nonisothermal plasma far from equilibrium.

  15. A test for second order stationarity of a time series based on the Discrete Fourier Transform -Technical report

    E-Print Network [OSTI]

    Subba Rao, Suhasini

    A test for second order stationarity of a time series based on the Discrete Fourier Transform stationary. Exploiting this important property, we construct a Portmanteau type test statistic for testing stationarity of the time series. It is shown that under the null of stationarity, the test statistic has

  16. PUSHING THE LIMITS OF GROUND-BASED PHOTOMETRIC PRECISION: SUBMILLIMAGNITUDE TIME-SERIES PHOTOMETRY OF THE OPEN CLUSTER NGC 6791

    E-Print Network [OSTI]

    Gaudi, B. Scott

    PUSHING THE LIMITS OF GROUND-BASED PHOTOMETRIC PRECISION: SUBMILLIMAGNITUDE TIME-SERIES PHOTOMETRY of this study was to demonstrate the ability to obtain very high precision photometry for a large number to obtain high-precision (millimagnitude, or less than 1%) time-series, optical and infrared photometry

  17. Mesoscale variability in time series data: Satellite-based estimates for the U.S. JGOFS Bermuda Atlantic

    E-Print Network [OSTI]

    Mesoscale variability in time series data: Satellite-based estimates for the U.S. JGOFS Bermuda TOPEX/Poseidon­ERS-1/2) are used to characterize, statistically, the mesoscale variability about the U to better understand the contribution of mesoscale eddies to the time series record and the model- data

  18. Multifractal analysis of stress time series during ultrathin lubricant film melting

    E-Print Network [OSTI]

    A. V. Khomenko; I. A. Lyashenko; V. N. Borisyuk

    2010-07-20

    Melting of an ultrathin lubricant film confined between two atomically flat surfaces is we studied using the rheological model for viscoelastic matter approximation. Phase diagram with domains, corresponding to sliding, dry, and two types of $stick-slip$ friction regimes has been built taking into account additive noises of stress, strain, and temperature of the lubricant. The stress time series have been obtained for all regimes of friction using the Stratonovich interpretation. It has been shown that self-similar regime of lubricant melting is observed when intensity of temperature noise is much larger than intensities of strain and stress noises. This regime is defined by homogenous distribution, at which characteristic stress scale is absent. We study stress time series obtained for all friction regimes using multifractal detrended fluctuation analysis. It has been shown that multifractality of these series is caused by different correlations that are present in the system and also by a power-law distribution. Since the power-law distribution is related to small stresses, this case corresponds to self-similar solid-like lubricant.

  19. Time Series Evaluation of Radiation Portal Monitor Data for Point Source Discrimination.

    SciTech Connect (OSTI)

    Robinson, Sean M.; Bender, Sarah E.; Flumerfelt, Eric L.; Lopresti, Charles A.; Woodring, Mitchell L.

    2009-07-20

    A novel algorithm approach to evaluating data from PVT-based Radiation Portal Monitor (RPM) systems is established. Time series of data from RPMs are evaluated for the presence of sources of interest by comparing the background to the vehicle spectrum at each successive time step, isolating the contribution of anomalous radiation. At each time in the data sequence, a “spectral distance” index is calculated using this method. This method may dramatically reduce systematic fluctuations due to background attenuation by a vehicle (the so-called “shadow shielding” effect), and allow for time-series matched filtering for discrimination of compact anomalous sources. This is attempted by using a wavelet filter function of similar size to the expected source profile on the output of the spectral distance method. Performance of this method is shown by analysis (injection studies) of a number of real drive-through data sets taken at a U.S. port of entry. Spectra from isotopes of interest are injected into the data set, and the resultant “benign” and “injected” data sets are analyzed with gross-counting, spectral distance, and spatial algorithms. The combination of spectral and spatial analysis methods showed a significant increase to detection performance.

  20. Time cartogram series to explore differences in the level of railway services: a case of the Overijssel railways

    E-Print Network [OSTI]

    Time cartogram series to explore differences in the level of railway services: a case, population or travelling-time. A time cartogram is a type of cartogram in which the geographic-distance between locations is replaced by a time-related attribute (e.g., travelling-time) and the geography

  1. Transportation Energy Futures Series: Vehicle Technology Deployment Pathways: An Examination of Timing and Investment Constraints

    SciTech Connect (OSTI)

    Plotkin, S.; Stephens, T.; McManus, W.

    2013-03-01

    Scenarios of new vehicle technology deployment serve various purposes; some will seek to establish plausibility. This report proposes two reality checks for scenarios: (1) implications of manufacturing constraints on timing of vehicle deployment and (2) investment decisions required to bring new vehicle technologies to market. An estimated timeline of 12 to more than 22 years from initial market introduction to saturation is supported by historical examples and based on the product development process. Researchers also consider the series of investment decisions to develop and build the vehicles and their associated fueling infrastructure. A proposed decision tree analysis structure could be used to systematically examine investors' decisions and the potential outcomes, including consideration of cash flow and return on investment. This method requires data or assumptions about capital cost, variable cost, revenue, timing, and probability of success/failure, and would result in a detailed consideration of the value proposition of large investments and long lead times. This is one of a series of reports produced as a result of the Transportation Energy Futures (TEF) project, a Department of Energy-sponsored multi-agency effort to pinpoint underexplored strategies for abating GHGs and reducing petroleum dependence related to transportation.

  2. Transportation Energy Futures Series. Vehicle Technology Deployment Pathways. An Examination of Timing and Investment Constraints

    SciTech Connect (OSTI)

    Plotkin, Steve; Stephens, Thomas; McManus, Walter

    2013-03-01

    Scenarios of new vehicle technology deployment serve various purposes; some will seek to establish plausibility. This report proposes two reality checks for scenarios: (1) implications of manufacturing constraints on timing of vehicle deployment and (2) investment decisions required to bring new vehicle technologies to market. An estimated timeline of 12 to more than 22 years from initial market introduction to saturation is supported by historical examples and based on the product development process. Researchers also consider the series of investment decisions to develop and build the vehicles and their associated fueling infrastructure. A proposed decision tree analysis structure could be used to systematically examine investors' decisions and the potential outcomes, including consideration of cash flow and return on investment. This method requires data or assumptions about capital cost, variable cost, revenue, timing, and probability of success/failure, and would result in a detailed consideration of the value proposition of large investments and long lead times. This is one of a series of reports produced as a result of the Transportation Energy Futures (TEF) project, a Department of Energy-sponsored multi-agency effort to pinpoint underexplored strategies for abating GHGs and reducing petroleum dependence related to transportation.

  3. Interpretation of engine cycle-to-cycle variation by chaotic time series analysis

    SciTech Connect (OSTI)

    Daw, C.S.; Kahl, W.K.

    1990-01-01

    In this paper we summarize preliminary results from applying a new mathematical technique -- chaotic time series analysis (CTSA) -- to cylinder pressure data from a spark-ignition (SI) four-stroke engine fueled with both methanol and iso-octane. Our objective is to look for the presence of deterministic chaos'' dynamics in peak pressure variations and to investigate the potential usefulness of CTSA as a diagnostic tool. Our results suggest that sequential peak cylinder pressures exhibit some characteristic features of deterministic chaos and that CTSA can extract previously unrecognized information from such data. 18 refs., 11 figs., 2 tabs.

  4. Fast detection of nonlinearity and nonstationarity in short and noisy time series

    E-Print Network [OSTI]

    M. De Domenico; V. Latora

    2010-07-07

    We introduce a statistical method to detect nonlinearity and nonstationarity in time series, that works even for short sequences and in presence of noise. The method has a discrimination power similar to that of the most advanced estimators on the market, yet it depends only on one parameter, is easier to implement and faster. Applications to real data sets reject the null hypothesis of an underlying stationary linear stochastic process with a higher confidence interval than the best known nonlinear discriminators up to date.

  5. Analysis and synthesis of the variability of irradiance and PV power time series with the wavelet transform

    SciTech Connect (OSTI)

    Perpinan, O.; Lorenzo, E.

    2011-01-15

    The irradiance fluctuations and the subsequent variability of the power output of a PV system are analysed with some mathematical tools based on the wavelet transform. It can be shown that the irradiance and power time series are nonstationary process whose behaviour resembles that of a long memory process. Besides, the long memory spectral exponent {alpha} is a useful indicator of the fluctuation level of a irradiance time series. On the other side, a time series of global irradiance on the horizontal plane can be simulated by means of the wavestrapping technique on the clearness index and the fluctuation behaviour of this simulated time series correctly resembles the original series. Moreover, a time series of global irradiance on the inclined plane can be simulated with the wavestrapping procedure applied over a signal previously detrended by a partial reconstruction with a wavelet multiresolution analysis, and, once again, the fluctuation behaviour of this simulated time series is correct. This procedure is a suitable tool for the simulation of irradiance incident over a group of distant PV plants. Finally, a wavelet variance analysis and the long memory spectral exponent show that a PV plant behaves as a low-pass filter. (author)

  6. TiP: Analyzing Periodic Time Series Patterns Thomas Bernecker, Hans-Peter Kriegel, Peer Kroger, and Matthias Renz

    E-Print Network [OSTI]

    Kriegel, Hans-Peter

    , evolution of stock charts, research on medical behavior of organisms, or analysis and detec- tion of motion of environmental data. 1 Introduction In a large range of application domains, e.g. environmental analysis series. Overall, TiP serves as a framework to effectively and efficiently manage dual- domain time series

  7. A case study of an expert mathematics teacher's interactive decision-making system using physiological and behavioral time series data 

    E-Print Network [OSTI]

    Jensen, Deborah Larkey

    2005-02-17

    . The mean and standard deviation of observable decision-action rates on teacher-identified ?teaching days? were higher than the rates on ?guiding? days. Bivariate time series analysis of decision-action rates and physiological response rates showed a...

  8. Precursory signatures of protein folding/unfolding: From time series correlation analysis to atomistic mechanisms

    SciTech Connect (OSTI)

    Hsu, P. J.; Lai, S. K., E-mail: sklai@coll.phy.ncu.edu.tw [Complex Liquids Laboratory, Department of Physics, National Central University, Chungli 320 Taiwan (China); Molecular Science and Technology Program, Taiwan International Graduate Program, Academia Sinica, Taipei 115, Taiwan (China); Cheong, S. A. [Division of Physics and Applied Physics, School of Physical and Mathematical Sciences, Nanyang Technological University, 21 Nanyang Link, Singapore 637371 (Singapore)

    2014-05-28

    Folded conformations of proteins in thermodynamically stable states have long lifetimes. Before it folds into a stable conformation, or after unfolding from a stable conformation, the protein will generally stray from one random conformation to another leading thus to rapid fluctuations. Brief structural changes therefore occur before folding and unfolding events. These short-lived movements are easily overlooked in studies of folding/unfolding for they represent momentary excursions of the protein to explore conformations in the neighborhood of the stable conformation. The present study looks for precursory signatures of protein folding/unfolding within these rapid fluctuations through a combination of three techniques: (1) ultrafast shape recognition, (2) time series segmentation, and (3) time series correlation analysis. The first procedure measures the differences between statistical distance distributions of atoms in different conformations by calculating shape similarity indices from molecular dynamics simulation trajectories. The second procedure is used to discover the times at which the protein makes transitions from one conformation to another. Finally, we employ the third technique to exploit spatial fingerprints of the stable conformations; this procedure is to map out the sequences of changes preceding the actual folding and unfolding events, since strongly correlated atoms in different conformations are different due to bond and steric constraints. The aforementioned high-frequency fluctuations are therefore characterized by distinct correlational and structural changes that are associated with rate-limiting precursors that translate into brief segments. Guided by these technical procedures, we choose a model system, a fragment of the protein transthyretin, for identifying in this system not only the precursory signatures of transitions associated with ? helix and ? hairpin, but also the important role played by weaker correlations in such protein folding dynamics.

  9. Experimental nonlinear dynamical studies in cesium magneto-optical trap using time-series analysis

    SciTech Connect (OSTI)

    Anwar, M. Islam, R.; Faisal, M.; Sikandar, M.; Ahmed, M.

    2015-03-30

    A magneto-optical trap of neutral atoms is essentially a dissipative quantum system. The fast thermal atoms continuously dissipate their energy to the environment via spontaneous emissions during the cooling. The atoms are, therefore, strongly coupled with the vacuum reservoir and the laser field. The vacuum fluctuations as well as the field fluctuations are imparted to the atoms as random photon recoils. Consequently, the external and internal dynamics of atoms becomes stochastic. In this paper, we have investigated the stochastic dynamics of the atoms in a magneto-optical trap during the loading process. The time series analysis of the fluorescence signal shows that the dynamics of the atoms evolves, like all dissipative systems, from deterministic to the chaotic regime. The subsequent disappearance and revival of chaos was attributed to chaos synchronization between spatially different atoms in the magneto-optical trap.

  10. Forecasting of preprocessed daily solar radiation time series using neural networks

    SciTech Connect (OSTI)

    Paoli, Christophe; Muselli, Marc; Nivet, Marie-Laure [University of Corsica, CNRS UMR SPE, Corte (France); Voyant, Cyril [University of Corsica, CNRS UMR SPE, Corte (France); Hospital of Castelluccio, Radiotherapy Unit, Ajaccio (France)

    2010-12-15

    In this paper, we present an application of Artificial Neural Networks (ANNs) in the renewable energy domain. We particularly look at the Multi-Layer Perceptron (MLP) network which has been the most used of ANNs architectures both in the renewable energy domain and in the time series forecasting. We have used a MLP and an ad hoc time series pre-processing to develop a methodology for the daily prediction of global solar radiation on a horizontal surface. First results are promising with nRMSE {proportional_to} 21% and RMSE {proportional_to} 3.59 MJ/m{sup 2}. The optimized MLP presents predictions similar to or even better than conventional and reference methods such as ARIMA techniques, Bayesian inference, Markov chains and k-Nearest-Neighbors. Moreover we found that the data pre-processing approach proposed can reduce significantly forecasting errors of about 6% compared to conventional prediction methods such as Markov chains or Bayesian inference. The simulator proposed has been obtained using 19 years of available data from the meteorological station of Ajaccio (Corsica Island, France, 41 55'N, 8 44'E, 4 m above mean sea level). The predicted whole methodology has been validated on a 1.175 kWc mono-Si PV power grid. Six prediction methods (ANN, clear sky model, combination..) allow to predict the best daily DC PV power production at horizon d + 1. The cumulated DC PV energy on a 6-months period shows a great agreement between simulated and measured data (R{sup 2} > 0.99 and nRMSE < 2%). (author)

  11. Iterative prediction of chaotic time series using a recurrent neural network. Quarterly progress report, January 1, 1995--March 31, 1995

    SciTech Connect (OSTI)

    Bodruzzaman, M.; Essawy, M.A.

    1996-03-31

    Chaotic systems are known for their unpredictability due to their sensitive dependence on initial conditions. When only time series measurements from such systems are available, neural network based models are preferred due to their simplicity, availability, and robustness. However, the type of neural network used should be capable of modeling the highly non-linear behavior and the multi- attractor nature of such systems. In this paper we use a special type of recurrent neural network called the ``Dynamic System Imitator (DSI)``, that has been proven to be capable of modeling very complex dynamic behaviors. The DSI is a fully recurrent neural network that is specially designed to model a wide variety of dynamic systems. The prediction method presented in this paper is based upon predicting one step ahead in the time series, and using that predicted value to iteratively predict the following steps. This method was applied to chaotic time series generated from the logistic, Henon, and the cubic equations, in addition to experimental pressure drop time series measured from a Fluidized Bed Reactor (FBR), which is known to exhibit chaotic behavior. The time behavior and state space attractor of the actual and network synthetic chaotic time series were analyzed and compared. The correlation dimension and the Kolmogorov entropy for both the original and network synthetic data were computed. They were found to resemble each other, confirming the success of the DSI based chaotic system modeling.

  12. Assessment of Time-Series MODIS Data for Cropland Mapping in the U.S. Central Great Plains

    E-Print Network [OSTI]

    Masialeti, Iwake

    2008-07-23

    .1 Introduction 141 4.2 Research Objectives 143 4.3 Study Area 144 4.4 Data and Methods 145 4.4.1 Time-Series MODIS NDVI Data 146 4.4.2 Common Land Unit (CLU) and Field Site Database for 2005 147 4.4.3 Kansas Average NDVI... because it is expensive to collect, inadequate in spatial and temporal coverage, inadvertently inaccurate, outdated, legally restricted, or non-existent; 2) Evaluate the applicability of time-series MODIS 250-m NDVI data for crop-type discrimination...

  13. A Scientific Data Processing Framework for Time Series NetCDF Data

    SciTech Connect (OSTI)

    Gaustad, Krista L.; Shippert, Timothy R.; Ermold, Brian D.; Beus, Sherman J.; Daily, Jeffrey A.; Borsholm, Atle; Fox, Kevin M.

    2014-10-01

    ARM Data Integrator (ADI) is a framework to streamline the development of scientific algorithms that analyze time-series NetCDF data, and to improve the content and consistency of the output data products produced by these algorithms. ADI achieves these goals by automating the process of retrieving and preparing data for analysis, supporting the definition of output data products through a graphical interface, and providing a modular, flexible software development architecture. The input data, preprocessing, and output data specifications are defined through a graphical interface and stored in a database. ADI also includes a workflow for data integration, a library of software modules to support the workflow, and a source code generator that produces C, IDL and Python templates. Data preparation support includes automated retrieval of data from input files, merging the retrieved data into appropriately sized chunks, and transformation of the data onto a common coordinate system grid. Through the graphical interface, users can view the details of both their data products and those in the ARM catalog. The variable and attribute definitions of the existing data products can be used to build new output data products. In addition, the rules that make up the ARM archive’s data standards are laid on top of the view of the new data product providing the user with a visual cue indicating where their output violates an archive standard. The necessary configurations are stored in a database that is accessed by the ADI libraries. This paper discusses the ADI framework, its supporting components, and how ADI can significantly decrease the time and cost of implementing scientific algorithms while improving the ability of scientists to disseminate their results.

  14. Validation of Danish wind time series from a new global renewable energy atlas for energy system analysis

    E-Print Network [OSTI]

    Andresen, Gorm Bruun; Greiner, Martin

    2014-01-01

    We present a new global high-resolution renewable energy atlas (REatlas) that can be used to calculate customised hourly time series of wind and solar PV power generation. In this paper, the atlas is applied to produce 32-year-long hourly model wind power time series for Denmark for each historical and future year between 1980 and 2035. These are calibrated and validated against real production data from the period 2000 to 2010. The high number of years allows us to discuss how the characteristics of Danish wind power generation varies between individual weather years. As an example, the annual energy production is found to vary by $\\pm10\\%$ from the average. Furthermore, we show how the production pattern change as small onshore turbines are gradually replaced by large onshore and offshore turbines. In most energy system analysis tools, fixed hourly time series of wind power generation are used to model future power systems with high penetrations of wind energy. Here, we compare the wind power time series fo...

  15. How Representative is a Time Series Derived from a A Study at a Low Accumulation Site on the Antarctic

    E-Print Network [OSTI]

    Percival, Don

    signal. We find that common signals in electrical records have time scales of approxi- mately 1­3 years of Washington, Seattle, WA 98195­ 5640, USA #12;Abstract The acquisition and interpretation of increasingly high series of electrical and oxygen isotope mea- surements from a spatial array of firn cores with 3.5­7 km

  16. IGR For GR/M76881/01: Generating Summaries of Time-Series Data (SumTime) Background/Context

    E-Print Network [OSTI]

    Sripada, Yaji

    of numerical time-series data. The modern world is being flooded with such data. For example, a typical gas-turbine worked in three domains: weather forecasts, summaries of gas-turbine sensor data, and summaries of sensor number of input data values; this meant it could not be used in our hospital and gas-turbine domains

  17. LAMORE: A Stable, Scalable Approach to Latent Vector Autoregressive Modeling of Categorical Time Series

    E-Print Network [OSTI]

    Kaski, Samuel

    Series Yubin Park1 Carlos M. Carvalho2 Joydeep Ghosh1 1 Department of Electrical Computer Engineering by the authors. In the fraud detection community, purchase records and click streams have been key factors: interpretability and extensibility. Indeed, the VAR process has a rich history with parsimonious the- oretical

  18. An Approach to Generating Summaries of Time Series Data in the Gas Turbine Domain Jin Yu and Jim Hunter and Ehud Reiter and Somayajulu Sripada

    E-Print Network [OSTI]

    Sripada, Yaji

    An Approach to Generating Summaries of Time Series Data in the Gas Turbine Domain Jin Yu and Jim an approach to generating summaries of time series data in the gas turbine domain using AI techniques. Through), both domain knowledge from experts about how to solve problems in the gas turbine and information about

  19. A study of the effect of nonrandom sampling on the estimation of exposure variability using exposure-time series from the OSHA IMIS health inspection database

    SciTech Connect (OSTI)

    Martinez, L.T.

    1997-05-01

    The objective of this study was to determine the effect of nonrandom sampling over time may have on the estimation of variability, namely the geometric standard deviation, using time series of personal exposure data.

  20. On the potential value of Ca II K spectroheliogram time-series for solar activity and irradiance studies

    E-Print Network [OSTI]

    I. Ermolli; S. K. Solanki; A. G. Tlatov; N. A. Krivova; R. K. Ulrich; J. Singh

    2008-02-26

    Various observatories around the globe started regular full-disk imaging of the solar atmosphere in the Ca II K line since the early decades of the 20th century. The archives made by these observations have the potential of providing far more detailed information on solar magnetism than just the sunspot number and area records to which most studies of solar activity and irradiance changes are restricted. We evaluate the image contents of three Ca II K spectroheliogram time-series, specifically those obtained by the digitization of the Arcetri, Kodaikanal, and Mt Wilson photographic archives. We describe the main problems afflicting these data and analyze their quality by expressing the image contents through several quantities. We compare the results obtained with those for similar present-day observations taken with the Meudon spectroheliograph and with the Rome-PSPT. We show that historic data suffer from stronger geometrical distortions and photometric uncertainties than similar present-day observations. The latter uncertainties mostly originate from the photographic calibration of the original data and from stray-light effects. We also show that the image contents of the three analyzed series vary in time. These variations are probably due to instrument changes and aging of the spectrographs used, as well as changes of the observing programs. Our results imply that the main challenge for the analysis of historic data is their accurate photometric calibration. This problem must be solved before they can provide reliable information about solar magnetism and activity over the last century. Moreover, inter-calibration of results obtained from independent time-series is required to reliably trace changes of solar properties with time from the analysis of such data.

  1. Time series analysis of the lead-lag relationship of freight futures and spot market prices

    E-Print Network [OSTI]

    Gavriilidis, Nikolaos

    2008-01-01

    This thesis analyzes the relationship between the physical and paper shipping markets. The main objective is to find if one market leads the other by a specific time period so that market players can take advantage from ...

  2. IMPROVED SEMI-PARAMETRIC TIME SERIES MODELS OF AIR POLLUTION AND MORTALITY

    E-Print Network [OSTI]

    Hastie, Trevor

    we provide improvements in semi-parametric regression directly relevant to risk estimation in time of an intense national debate, that has led to a high profile research agenda (National Research Council, 1998, 1999, 2001). In the United States and elsewhere, evidence

  3. Identification of mixing effects in stratified chilled-water storage tanks by analysis of time series temperature data

    SciTech Connect (OSTI)

    Caldwell, J.S.; Bahnfleth, W.P.

    1998-12-31

    Several one-dimensional models of mixing in stratified chilled-water thermal energy storage tanks have been proposed. In the simplest models, mixing is assumed to be uniform throughout the tank. Other models permit spatial variation of mixing intensity. Published models were developed by adjusting model parameters to achieve qualitative agreement with measured profiles. The literature does not describe quantitative criteria for evaluating the performance of mixing models. This paper describes a method that can be used to determine the relative spatial distribution of mixing effects directly from experimental data. It also illustrates a method for quantitative comparison of experimental and modeled temperature profiles. The mixing calculation procedure may be applied to instantaneous spatial temperature data if temperature sensor spacing is sufficiently small. When sensors are widely spaced, time series data taken at individual sensors provide better accuracy. A criterion for maximum sensor spacing is proposed. The application of these procedures to time series charge-cycle operating data from a full-scale chilled-water thermal storage system serving a large medical center is described. Results of this analysis indicate that mixing is localized near the inlet diffuser and that one-dimensional flow with streamwise conduction predominates in most of the tank.

  4. Maximum likelihood parameter estimation in time series models using sequential Monte Carlo

    E-Print Network [OSTI]

    Yildirim, Sinan

    2013-06-11

    , respectively. This approach is useful to handle the case where the columns of Y are generated sequentially in time, such as in audio signal processing. Usually very large number of columns in Y leads to the necessity of online algorithms to learn the model... .6 (dashed lines). For illustrative purposes, every 1000th estimate is shown . . . . . . . . . . . . . . . . . . . . . . . 130 6.1 Histograms of Monte Carlo estimates of gradients of log p?,?,?? (Y ?,?,?) w.r.t. the parameters of the ?-stable distribution...

  5. Time series analysis of AERI radiances for GCM testing and improvement

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

    AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE: Alternative Fuels Data Center Homesum_a_epg0_fpd_mmcf_m.xls" ,"Available from WebQuantityBonneville Power AdministrationRobust,Field-effectWorking With U.S.Week DayDr.Theories (Journal Article)Clean4, 9/26/14),Time

  6. Characterizing Multi-Scale Self-Similar Behavior and Non-Statistical Properties of Financial Time Series

    E-Print Network [OSTI]

    Ghosh, Sayantan; Panigrahi, Prasanta K

    2010-01-01

    We make use of wavelet transform to study the multi-scale, self similar behavior and deviations thereof, in the stock prices of large companies, belonging to different economic sectors. The stock market returns exhibit multi-fractal characteristics, with some of the companies showing deviations at small and large scales. The fact that, the wavelets belonging to the Daubechies' (Db) basis enables one to isolate local polynomial trends of different degrees, plays the key role in isolating fluctuations at different scales. We make use of Db4 and Db6 basis sets to respectively isolate local linear and quadratic trends at different scales in order to study the statistical characteristics of these financial time series. The fluctuations reveal fat tail non-Gaussian behavior, unstable periodic modulations, at finer scales, from which the characteristic $k^{-3}$ power law behavior emerges at sufficiently large scales. We further identify stable periodic behavior through the continuous Morlet wavelet.

  7. Prediction of global solar irradiance based on time series analysis: Application to solar thermal power plants energy production planning

    SciTech Connect (OSTI)

    Martin, Luis; Marchante, Ruth; Cony, Marco; Zarzalejo, Luis F.; Polo, Jesus; Navarro, Ana

    2010-10-15

    Due to strong increase of solar power generation, the predictions of incoming solar energy are acquiring more importance. Photovoltaic and solar thermal are the main sources of electricity generation from solar energy. In the case of solar thermal energy plants with storage energy system, its management and operation need reliable predictions of solar irradiance with the same temporal resolution as the temporal capacity of the back-up system. These plants can work like a conventional power plant and compete in the energy stock market avoiding intermittence in electricity production. This work presents a comparisons of statistical models based on time series applied to predict half daily values of global solar irradiance with a temporal horizon of 3 days. Half daily values consist of accumulated hourly global solar irradiance from solar raise to solar noon and from noon until dawn for each day. The dataset of ground solar radiation used belongs to stations of Spanish National Weather Service (AEMet). The models tested are autoregressive, neural networks and fuzzy logic models. Due to the fact that half daily solar irradiance time series is non-stationary, it has been necessary to transform it to two new stationary variables (clearness index and lost component) which are used as input of the predictive models. Improvement in terms of RMSD of the models essayed is compared against the model based on persistence. The validation process shows that all models essayed improve persistence. The best approach to forecast half daily values of solar irradiance is neural network models with lost component as input, except Lerida station where models based on clearness index have less uncertainty because this magnitude has a linear behaviour and it is easier to simulate by models. (author)

  8. Time series association learning

    DOE Patents [OSTI]

    Papcun, George J. (Santa Fe, NM)

    1995-01-01

    An acoustic input is recognized from inferred articulatory movements output by a learned relationship between training acoustic waveforms and articulatory movements. The inferred movements are compared with template patterns prepared from training movements when the relationship was learned to regenerate an acoustic recognition. In a preferred embodiment, the acoustic articulatory relationships are learned by a neural network. Subsequent input acoustic patterns then generate the inferred articulatory movements for use with the templates. Articulatory movement data may be supplemented with characteristic acoustic information, e.g. relative power and high frequency data, to improve template recognition.

  9. Model-Based Clustering of Regression Time Series Data via APECM -- An AECM Algorithm Sung to an Even Faster Beat

    SciTech Connect (OSTI)

    Chen, Wei-Chen [ORNL; Maitra, Ranjan [Iowa State University

    2011-01-01

    We propose a model-based approach for clustering time series regression data in an unsupervised machine learning framework to identify groups under the assumption that each mixture component follows a Gaussian autoregressive regression model of order p. Given the number of groups, the traditional maximum likelihood approach of estimating the parameters using the expectation-maximization (EM) algorithm can be employed, although it is computationally demanding. The somewhat fast tune to the EM folk song provided by the Alternating Expectation Conditional Maximization (AECM) algorithm can alleviate the problem to some extent. In this article, we develop an alternative partial expectation conditional maximization algorithm (APECM) that uses an additional data augmentation storage step to efficiently implement AECM for finite mixture models. Results on our simulation experiments show improved performance in both fewer numbers of iterations and computation time. The methodology is applied to the problem of clustering mutual funds data on the basis of their average annual per cent returns and in the presence of economic indicators.

  10. A Bayesian method for characterizing distributed micro-releases: II. inference under model uncertainty with short time-series data.

    SciTech Connect (OSTI)

    Marzouk, Youssef; Fast P. (Lawrence Livermore National Laboratory, Livermore, CA); Kraus, M.; Ray, J. P.

    2006-01-01

    Terrorist attacks using an aerosolized pathogen preparation have gained credibility as a national security concern after the anthrax attacks of 2001. The ability to characterize such attacks, i.e., to estimate the number of people infected, the time of infection, and the average dose received, is important when planning a medical response. We address this question of characterization by formulating a Bayesian inverse problem predicated on a short time-series of diagnosed patients exhibiting symptoms. To be of relevance to response planning, we limit ourselves to 3-5 days of data. In tests performed with anthrax as the pathogen, we find that these data are usually sufficient, especially if the model of the outbreak used in the inverse problem is an accurate one. In some cases the scarcity of data may initially support outbreak characterizations at odds with the true one, but with sufficient data the correct inferences are recovered; in other words, the inverse problem posed and its solution methodology are consistent. We also explore the effect of model error-situations for which the model used in the inverse problem is only a partially accurate representation of the outbreak; here, the model predictions and the observations differ by more than a random noise. We find that while there is a consistent discrepancy between the inferred and the true characterizations, they are also close enough to be of relevance when planning a response.

  11. Jin Yu, Ehud Reiter, Jim Hunter and Chris Mellish Choosing the content of textual summaries of large time-series data sets

    E-Print Network [OSTI]

    Sripada, Yaji

    generates summaries of sensor data from a gas turbine. Table 1. Part of a sample of time series data describe and evaluate SumTime-Turbine, a prototype system which uses this architecture to generate textual summaries of sensor data from gas turbines. 1 Introduction It is often said in the NLP community

  12. Jin Yu, Ehud Reiter, Jim Hunter and Chris Mellish Choosing the content of textual summaries of large time-series data sets

    E-Print Network [OSTI]

    Reiter, Ehud

    describe and evaluate SumTime-Turbine, a prototype system which uses this architecture to generate textual summaries of sensor data from gas turbines. 1 Introduction It is often said in the NLP community simply from sensors in aircraft engines (Hey and Trefethen 2003). Currently numeric time-series data

  13. IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS--I: FUNDAMENTAL THEORY AND APPLICATIONS, VOL. 50, NO. 5, MAY 2003 663 Detecting Determinism in Time Series: The Method

    E-Print Network [OSTI]

    Tse, Chi K. "Michael"

    IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS--I: FUNDAMENTAL THEORY AND APPLICATIONS, VOL. 50, NO. 5 that may be applied to determine whether an observed time series is inconsis- tent with a specific class to the residuals of nonlinear models is equiv- alent to fitting that model subject to an information theoretic

  14. Seminar Series: MAE Seminar, 2015 spring quarter Date and Time: 05/08/2015 -10:30 am11:30 am

    E-Print Network [OSTI]

    Mease, Kenneth D.

    In this seminar, I will first discuss our research results of photoelectrochemical generation of hydrogen some of my research into the design and development of quinoline-based organic semiconducting materialsSeminar Series: MAE Seminar, 2015 spring quarter Date and Time: 05/08/2015 - 10:30 am­11:30 am

  15. Introduction to Ocean Station Time Series CD-ROM The National Oceanographic Data Center (NODC) and the World Data Center for

    E-Print Network [OSTI]

    Introduction to Ocean Station Time Series CD-ROM The National Oceanographic Data Center (NODC) and the World Data Center for Oceanography (WDC) have compiled from the NODC Oceanographic Station Data File a set of oceanographic data having repetitive samples along ocean sections or at fixed locations

  16. March 3, 2005 14:40 WSPC/Trim Size: 9in x 6in for Review Volume m-bickel04 EFFICIENT ESTIMATORS FOR TIME SERIES

    E-Print Network [OSTI]

    Schick, Anton

    ESTIMATORS FOR TIME SERIES Anton Schick Department of Mathematical Sciences, Binghamtom University Binghamton is semiparametric, with 1 #12;March 3, 2005 14:40 WSPC/Trim Size: 9in x 6in for Review Volume m-bickel04 2 A. Schick

  17. Using remote-sensing data to determine equilibrium-line altitude and mass-balance time series: validation on three French glaciers,

    E-Print Network [OSTI]

    Rabatel, Antoine

    Using remote-sensing data to determine equilibrium-line altitude and mass-balance time series to calculate glacier mass balance using remote-sensing data. Snowline measurements from remotely sensed images by ground measurements and remote sensing are compared and show excellent correlation (r2 > 0.89), both

  18. A time series approach for profiling attack Liran Lerman1,2 and Gianluca Bontempi2 and Souhaib Ben Taieb2 and Olivier Markowitch1

    E-Print Network [OSTI]

    International Association for Cryptologic Research (IACR)

    analysis, machine learning, time series classification. 1 Introduction Embedded devices such as smart cards operations allowing to secure, for example, bank transfers, buildings and cars. A modern bank card embeds securely a secret information allowing in fine to transfer cash. This operation is allowed by the smart

  19. Abstract--In this study, we analyzed a dataset of time-series vital-signs data collected by standard Propaq travel monitor

    E-Print Network [OSTI]

    Abstract--In this study, we analyzed a dataset of time-series vital-signs data collected combination of the automatically-collected and -qualified vital signs provides the best discrimination between of the vital-signs variables, and used the area under the receiver operating characteristic curve (ROC AUC

  20. A combined method to estimate parameters of the thalamocortical model from a heavily noise-corrupted time series of action potential

    SciTech Connect (OSTI)

    Wang, Ruofan; Wang, Jiang; Deng, Bin Liu, Chen; Wei, Xile; Tsang, K. M.; Chan, W. L.

    2014-03-15

    A combined method composing of the unscented Kalman filter (UKF) and the synchronization-based method is proposed for estimating electrophysiological variables and parameters of a thalamocortical (TC) neuron model, which is commonly used for studying Parkinson's disease for its relay role of connecting the basal ganglia and the cortex. In this work, we take into account the condition when only the time series of action potential with heavy noise are available. Numerical results demonstrate that not only this method can estimate model parameters from the extracted time series of action potential successfully but also the effect of its estimation is much better than the only use of the UKF or synchronization-based method, with a higher accuracy and a better robustness against noise, especially under the severe noise conditions. Considering the rather important role of TC neuron in the normal and pathological brain functions, the exploration of the method to estimate the critical parameters could have important implications for the study of its nonlinear dynamics and further treatment of Parkinson's disease.

  1. VisIO: enabling interactive visualization of ultra-scale, time-series data via high-bandwidth distributed I/O systems

    SciTech Connect (OSTI)

    Mitchell, Christopher J; Ahrens, James P; Wang, Jun

    2010-10-15

    Petascale simulations compute at resolutions ranging into billions of cells and write terabytes of data for visualization and analysis. Interactive visuaUzation of this time series is a desired step before starting a new run. The I/O subsystem and associated network often are a significant impediment to interactive visualization of time-varying data; as they are not configured or provisioned to provide necessary I/O read rates. In this paper, we propose a new I/O library for visualization applications: VisIO. Visualization applications commonly use N-to-N reads within their parallel enabled readers which provides an incentive for a shared-nothing approach to I/O, similar to other data-intensive approaches such as Hadoop. However, unlike other data-intensive applications, visualization requires: (1) interactive performance for large data volumes, (2) compatibility with MPI and POSIX file system semantics for compatibility with existing infrastructure, and (3) use of existing file formats and their stipulated data partitioning rules. VisIO, provides a mechanism for using a non-POSIX distributed file system to provide linear scaling of 110 bandwidth. In addition, we introduce a novel scheduling algorithm that helps to co-locate visualization processes on nodes with the requested data. Testing using VisIO integrated into Para View was conducted using the Hadoop Distributed File System (HDFS) on TACC's Longhorn cluster. A representative dataset, VPIC, across 128 nodes showed a 64.4% read performance improvement compared to the provided Lustre installation. Also tested, was a dataset representing a global ocean salinity simulation that showed a 51.4% improvement in read performance over Lustre when using our VisIO system. VisIO, provides powerful high-performance I/O services to visualization applications, allowing for interactive performance with ultra-scale, time-series data.

  2. Spot evolution on the red giant star XX Triangulum. A starspot-decay analysis based on time-series Doppler imaging

    E-Print Network [OSTI]

    Künstler, A; Strassmeier, K G

    2015-01-01

    Solar spots appear to decay linearly proportional to their size. The decay rate of solar spots is directly related to magnetic diffusivity, which itself is a key quantity for the length of a magnetic-activity cycle. Is a linear spot decay also seen on other stars, and is this in agreement with the large range of solar and stellar activity cycle lengths? We investigate the evolution of starspots on the rapidly-rotating ($P_{\\rm rot}$ $\\approx$ 24 d) K0 giant XX Tri, using consecutive time-series Doppler images. Our aim is to obtain a well-sampled movie of the stellar surface over many years, and thereby detect and quantify a starspot decay law for further comparison with the Sun. We obtained continuous high-resolution and phase-resolved spectroscopy with the 1.2-m robotic STELLA telescope on Tenerife over six years. For each observing season, we obtained between 5 to 7 independent Doppler images, one per stellar rotation, making up a total of 36 maps. To quantify starspot area decay and growth, we match the ob...

  3. TIME-SERIES PHOTOMETRY OF GLOBULAR CLUSTERS: M62 (NGC 6266), THE MOST RR LYRAE-RICH GLOBULAR CLUSTER IN THE GALAXY?

    SciTech Connect (OSTI)

    Contreras, R.; Catelan, M.; Smith, H. A.; Kuehn, C. A.; Pritzl, B. J.; Borissova, J.

    2010-12-15

    We present new time-series CCD photometry, in the B and V bands, for the moderately metal-rich ([Fe/H] {approx_equal} -1.3) Galactic globular cluster M62 (NGC 6266). The present data set is the largest obtained so far for this cluster and consists of 168 images per filter, obtained with the Warsaw 1.3 m telescope at the Las Campanas Observatory and the 1.3 m telescope of the Cerro Tololo Inter-American Observatory, in two separate runs over the time span of 3 months. The procedure adopted to detect the variable stars was the optimal image subtraction method (ISIS v2.2), as implemented by Alard. The photometry was performed using both ISIS and Stetson's DAOPHOT/ALLFRAME package. We have identified 245 variable stars in the cluster fields that have been analyzed so far, of which 179 are new discoveries. Of these variables, 133 are fundamental mode RR Lyrae stars (RRab), 76 are first overtone (RRc) pulsators, 4 are type II Cepheids, 25 are long-period variables (LPVs), 1 is an eclipsing binary, and 6 are not yet well classified. Such a large number of RR Lyrae stars places M62 among the top two most RR Lyrae-rich (in the sense of total number of RR Lyrae stars present) globular clusters known in the Galaxy, second only to M3 (NGC 5272) with a total of 230 known RR Lyrae stars. Since this study covers most but not all of the cluster area, it is not unlikely that M62 is in fact the most RR Lyrae-rich globular cluster in the Galaxy. In like vein, thanks to the time coverage of our data sets, we were also able to detect the largest sample of LPVs known so far in a Galactic globular cluster. We analyze a variety of Oosterhoff type indicators for the cluster, including mean periods, period distribution, Bailey diagrams, and Fourier decomposition parameters (as well as the physical parameters derived therefrom). All of these indicators clearly show that M62 is an Oosterhoff type I system. This is in good agreement with the moderately high metallicity of the cluster, in spite of its predominantly blue horizontal branch morphology-which is more typical of Oosterhoff type II systems. We thus conclude that metallicity plays a key role in defining Oosterhoff type. Finally, based on an application of the 'A-method', we conclude that the cluster RR Lyrae stars have a similar He abundance as M3, although more work on the temperatures of the M62 RR Lyrae is needed before this result can be conclusively established.

  4. VOLUME 76, NUMBER 18 P H Y S I C A L R E V I E W L E T T E R S 29 APRIL 1996 Nonlinear Control of Dynamical Systems from Time Series

    E-Print Network [OSTI]

    Showalter, Kenneth

    developments and experimental applications of feedback control to nonlinear dynamical systems [2­11]. Recent of Dynamical Systems from Time Series Valery Petrov and Kenneth Showalter* Department of Chemistry, West of multidimensional, nonlinear single-input single-output systems is formulated in terms of an invariant hypersurface

  5. Fourier Series Autumn 2006

    E-Print Network [OSTI]

    Wehlau, David

    Math 421 Fourier Series Autumn 2006 Text: Fourier Series, by Rajendra Bhatia, Math. Assoc. of America, 2005. Topics Covered: ­ Ch. 1, Fourier series and the heat equation ­ Ch. 2, Convergence of Fourier series ­ Ch. 3, Sine and cosine series, arbitrary pe- riods, sin(x)/x, Gibbs's phenomenon ­ Ch. 4

  6. Fourier Series and Integrals Fourier Series

    E-Print Network [OSTI]

    Mehta, Pankaj

    Fourier Series and Integrals Fourier Series Let f(x) be a piecewise linear function on [-L, L(x) can be expanded in a Fourier series f(x) = a0 2 + n=1 an cos nx L + bn sin nx L , (1a) or schematic form of the Fourier series is f(x) = n (an^cn + bn^sn). (2) This emphasizes that the Fourier

  7. This is the full version. An extended abstract to be presented in CANS 2014 Private and Dynamic Time-Series Data Aggregation

    E-Print Network [OSTI]

    International Association for Cryptologic Research (IACR)

    inputs collected through a smart metering network. The main shortcomings of this solution are its environment whereby data is produced by a set of users that hold smart meters. Smart meters can report accurately at specific time intervals energy, gas or water consumption. Considering electricity consumption

  8. Power Series Introduction

    E-Print Network [OSTI]

    Vickers, James

    Power Series 16.4 Introduction In this section we consider power series. These are examples of infinite series where each term contains a variable, x, raised to a positive integer power. We use the ratio test to obtain the radius of convergence R, of the power series and state the important result

  9. Six-week time series of eddy covariance CO2 flux at Mammoth Mountain, California: performance evaluation and role of meteorological forcing

    SciTech Connect (OSTI)

    Lewicki, Jennifer; Lewicki, J.L.; Fischer, M.L.; Hilley, G.E.

    2007-10-15

    CO{sub 2} and heat fluxes were measured over a six-week period (09/08/2006 to 10/24/2006) by the eddy covariance (EC) technique at the Horseshoe Lake tree kill (HLTK), Mammoth Mountain, CA, a site with complex terrain and high, spatially heterogeneous CO{sub 2} emission rates. EC CO{sub 2} fluxes ranged from 218 to 3500 g m{sup -2} d{sup -1} (mean = 1346 g m{sup -2} d{sup -1}). Using footprint modeling, EC CO{sub 2} fluxes were compared to CO{sub 2} fluxes measured by the chamber method on a grid repeatedly over a 10-day period. Half-hour EC CO{sub 2} fluxes were moderately correlated (R{sup 2} = 0.42) with chamber fluxes, whereas average-daily EC CO{sub 2} fluxes were well correlated (R{sup 2} = 0.70) with chamber measurements. Average daily EC CO{sub 2} fluxes were correlated with both average daily wind speed and atmospheric pressure; relationships were similar to those observed between chamber CO{sub 2} fluxes and the atmospheric parameters over a comparable time period. Energy balance closure was assessed by statistical regression of EC energy fluxes (sensible and latent heat) against available energy (net radiation, less soil heat flux). While incomplete (R{sup 2} = 0.77 for 1:1 line), the degree of energy balance closure fell within the range observed in many investigations conducted in contrasting ecosystems and climates. Results indicate that despite complexities presented by the HLTK, EC can be reliably used to monitor background variations in volcanic CO{sub 2} fluxes associated with meteorological forcing, and presumably changes related to deeply derived processes such as volcanic activity.

  10. Series Packages MASTERWORKS

    E-Print Network [OSTI]

    Arnold, Jonathan

    Series Packages MASTERWORKS · Atlanta Symphony Orchestra and Chorus ­ September 20 · Atlanta PACKAGE · Includes all of the concerts on both the Masterworks and Classics series. Ten performances · This package includes all of the events on the Show Biz and Celebrity Evenings series. Ten performances in all

  11. Learning connections in financial time series

    E-Print Network [OSTI]

    Gartheeban, Ganeshapillai

    2014-01-01

    Much of modern financial theory is based upon the assumption that a portfolio containing a diversified set of equities can be used to control risk while achieving a good rate of return. The basic idea is to choose equities ...

  12. Exact Primitives for Time Series Data Mining

    E-Print Network [OSTI]

    Mueen, Abdullah Al

    2012-01-01

    on Knowledge discovery and data mining, KDD, pages 947–956,on Knowledge discovery and data mining, KDD ’11, pages [15]on Knowledge discovery and data mining, KDD ’03, pages 493–

  13. Text Queries on Document Time Series

    E-Print Network [OSTI]

    Baeza-Yates, Ricardo

    Retrieved for Query "iraq war" #12;Smoothed Timeline for "iraq war" #12;Temporal Models Goal: Estimate;Documents Retrieved for Query "iraq war" #12;Weighted by Document Relevance #12;Smoothed with Background

  14. Series Transmission Line Transformer

    DOE Patents [OSTI]

    Buckles, Robert A. (Livermore, CA); Booth, Rex (Livermore, CA); Yen, Boris T. (El Cerrito, CA)

    2004-06-29

    A series transmission line transformer is set forth which includes two or more of impedance matched sets of at least two transmissions lines such as shielded cables, connected in parallel at one end ans series at the other in a cascading fashion. The cables are wound about a magnetic core. The series transmission line transformer (STLT) which can provide for higher impedance ratios and bandwidths, which is scalable, and which is of simpler design and construction.

  15. Introduction to Fourier Series

    E-Print Network [OSTI]

    2014-10-15

    Oct 15, 2014 ... The Basics. Fourier series ... period L. Sine and cosine are the most “basic” periodic functions! .... So just sit back, relax, and enjoy the ride!

  16. ARM XDC Datastreams

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

    AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE: Alternative Fuels Data Center Homesum_a_epg0_fpd_mmcf_m.xls" ,"Available from WebQuantity ofkandz-cm11 OutreachProductswsicloudwsicloudden Documentation Data ManagementStreamsWater Profiles from GOES Satellite Data

  17. ARM XDC Datastreams

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

    AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE: Alternative Fuels Data Center Homesum_a_epg0_fpd_mmcf_m.xls" ,"Available from WebQuantity ofkandz-cm11 OutreachProductswsicloudwsicloudden Documentation Data ManagementStreamsWater Profiles from GOES Satellite

  18. ARM XDC Datastreams

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

    AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE: Alternative Fuels Data Center Homesum_a_epg0_fpd_mmcf_m.xls" ,"Available from WebQuantity ofkandz-cm11 OutreachProductswsicloudwsicloudden Documentation Data ManagementStreamsWater Profiles from GOES

  19. ARM XDC Datastreams

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

    AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE: Alternative Fuels Data Center Homesum_a_epg0_fpd_mmcf_m.xls" ,"Available from WebQuantity ofkandz-cm11 OutreachProductswsicloudwsicloudden Documentation Data ManagementStreamsWater Profiles from GOESgovExternal Data

  20. ARM XDC Datastreams

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

    AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE: Alternative Fuels Data Center Homesum_a_epg0_fpd_mmcf_m.xls" ,"Available from WebQuantity ofkandz-cm11 OutreachProductswsicloudwsicloudden Documentation Data ManagementStreamsWater Profiles from GOESgovExternal

  1. ARM XDC Datastreams

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

    AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE: Alternative Fuels Data Center Homesum_a_epg0_fpd_mmcf_m.xls" ,"Available from WebQuantity ofkandz-cm11 OutreachProductswsicloudwsicloudden Documentation Data ManagementStreamsWater Profiles from

  2. ARM XDC Datastreams

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

    AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE: Alternative Fuels Data Center Homesum_a_epg0_fpd_mmcf_m.xls" ,"Available from WebQuantity ofkandz-cm11 OutreachProductswsicloudwsicloudden Documentation Data ManagementStreamsWater Profiles fromStreamsModel Output

  3. ARM XDC Datastreams

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

    AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE: Alternative Fuels Data Center Homesum_a_epg0_fpd_mmcf_m.xls" ,"Available from WebQuantity ofkandz-cm11 OutreachProductswsicloudwsicloudden Documentation Data ManagementStreamsWater Profiles fromStreamsModel

  4. ARM XDC Datastreams

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

    AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE: Alternative Fuels Data Center Homesum_a_epg0_fpd_mmcf_m.xls" ,"Available from WebQuantity ofkandz-cm11 OutreachProductswsicloudwsicloudden Documentation Data ManagementStreamsWater Profiles

  5. ARM XDC Datastreams

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

    AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE: Alternative Fuels Data Center Homesum_a_epg0_fpd_mmcf_m.xls" ,"Available from WebQuantity ofkandz-cm11 OutreachProductswsicloudwsicloudden Documentation Data ManagementStreamsWater ProfilesStreamsNational Centers for

  6. ARM XDC Datastreams

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

    AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE: Alternative Fuels Data Center Homesum_a_epg0_fpd_mmcf_m.xls" ,"Available from WebQuantity ofkandz-cm11 OutreachProductswsicloudwsicloudden Documentation Data ManagementStreamsWater ProfilesStreamsNational Centers

  7. ARM XDC Datastreams

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

    AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE: Alternative Fuels Data Center Homesum_a_epg0_fpd_mmcf_m.xls" ,"Available from WebQuantity ofkandz-cm11 OutreachProductswsicloudwsicloudden Documentation Data ManagementStreamsWater ProfilesStreamsNational

  8. ARM XDC Datastreams

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

    AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE: Alternative Fuels Data Center Homesum_a_epg0_fpd_mmcf_m.xls" ,"Available from WebQuantity ofkandz-cm11 OutreachProductswsicloudwsicloudden Documentation Data ManagementStreamsWater

  9. ARM XDC Datastreams

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

    AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE: Alternative Fuels Data Center Homesum_a_epg0_fpd_mmcf_m.xls" ,"Available from WebQuantity ofkandz-cm11 OutreachProductswsicloudwsicloudden Documentation Data ManagementStreamsWaterStreamsNOAA Surface Meteorology Data,

  10. ARM XDC Datastreams

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

    AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE: Alternative Fuels Data Center Homesum_a_epg0_fpd_mmcf_m.xls" ,"Available from WebQuantity ofkandz-cm11 OutreachProductswsicloudwsicloudden Documentation Data ManagementStreamsWaterStreamsNOAA Surface Meteorology

  11. ARM XDC Datastreams

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

    AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE: Alternative Fuels Data Center Homesum_a_epg0_fpd_mmcf_m.xls" ,"Available from WebQuantity ofkandz-cm11 OutreachProductswsicloudwsicloudden Documentation Data ManagementStreamsWaterStreamsNOAA Surface

  12. ARM XDC Datastreams

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

    AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE: Alternative Fuels Data Center Homesum_a_epg0_fpd_mmcf_m.xls" ,"Available from WebQuantity ofkandz-cm11 OutreachProductswsicloudwsicloudden Documentation Data ManagementStreamsWaterStreamsNOAA SurfaceStreamsOzone

  13. ARM XDC Datastreams

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

    AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE: Alternative Fuels Data Center Homesum_a_epg0_fpd_mmcf_m.xls" ,"Available from WebQuantity ofkandz-cm11 OutreachProductswsicloudwsicloudden Documentation Data ManagementStreamsWaterStreamsNOAA

  14. ARM XDC Datastreams

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

    AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE: Alternative Fuels Data Center Homesum_a_epg0_fpd_mmcf_m.xls" ,"Available from WebQuantity ofkandz-cm11 OutreachProductswsicloudwsicloudden Documentation Data ManagementStreamsWaterStreamsNOAAUpdate Cycle Model Data

  15. ARM XDC Datastreams

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

    AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE: Alternative Fuels Data Center Homesum_a_epg0_fpd_mmcf_m.xls" ,"Available from WebQuantity ofkandz-cm11 OutreachProductswsicloudwsicloudden Documentation Data ManagementStreamsWaterStreamsNOAAUpdate Cycle Model

  16. ARM XDC Datastreams

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

    AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE: Alternative Fuels Data Center Homesum_a_epg0_fpd_mmcf_m.xls" ,"Available from WebQuantity ofkandz-cm11 OutreachProductswsicloudwsicloudden Documentation Data ManagementStreamsWaterStreamsNOAAUpdate Cycle

  17. ARM XDC Datastreams

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

    AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE: Alternative Fuels Data Center Homesum_a_epg0_fpd_mmcf_m.xls" ,"Available from WebQuantity ofkandz-cm11 OutreachProductswsicloudwsicloudden Documentation Data ManagementStreamsWaterStreamsNOAAUpdate

  18. ARM XDC Datastreams

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

    AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE: Alternative Fuels Data Center Homesum_a_epg0_fpd_mmcf_m.xls" ,"Available from WebQuantity ofkandz-cm11 OutreachProductswsicloudwsicloudden Documentation Data ManagementStreamsWaterStreamsNOAAUpdateStreamsTropical

  19. ARM XDC Datastreams

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

    AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE: Alternative Fuels Data Center Homesum_a_epg0_fpd_mmcf_m.xls" ,"Available from WebQuantity ofkandz-cm11 OutreachProductswsicloudwsicloudden Documentation Data

  20. ARM XDC Datastreams

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

    AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE: Alternative Fuels Data Center Homesum_a_epg0_fpd_mmcf_m.xls" ,"Available from WebQuantity ofkandz-cm11 OutreachProductswsicloudwsicloudden Documentation DataStreamsTotal Ozone Mapping Spectrometer Documentation TOMS

  1. ARM XDC Datastreams

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

    AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE: Alternative Fuels Data Center Homesum_a_epg0_fpd_mmcf_m.xls" ,"Available from WebQuantity ofkandz-cm11 OutreachProductswsicloudwsicloudden Documentation DataStreamsTotal Ozone Mapping Spectrometer Documentation

  2. ARM XDC Datastreams

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

    AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE: Alternative Fuels Data Center Homesum_a_epg0_fpd_mmcf_m.xls" ,"Available from WebQuantity ofkandz-cm11 OutreachProductswsicloudwsicloudden Documentation DataStreamsTotal Ozone Mapping Spectrometer

  3. ARM XDC Datastreams

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

    AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE: Alternative Fuels Data Center Homesum_a_epg0_fpd_mmcf_m.xls" ,"Available from WebQuantity ofkandz-cm11 OutreachProductswsicloudwsicloudden Documentation DataStreamsTotal Ozone Mapping SpectrometerDemo Network

  4. ARM XDC Datastreams

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

    AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE: Alternative Fuels Data Center Homesum_a_epg0_fpd_mmcf_m.xls" ,"Available from WebQuantity ofkandz-cm11 OutreachProductswsicloudwsicloudden Documentation DataStreamsTotal Ozone Mapping SpectrometerDemo

  5. ARM XDC Datastreams

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

    AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE: Alternative Fuels Data Center Homesum_a_epg0_fpd_mmcf_m.xls" ,"Available from WebQuantity ofkandz-cm11 OutreachProductswsicloudwsicloudden Documentation Data Management Facility009 ARMStreamsArkansas-Red Basin River

  6. ARM XDC Datastreams

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

    AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE: Alternative Fuels Data Center Homesum_a_epg0_fpd_mmcf_m.xls" ,"Available from WebQuantity ofkandz-cm11 OutreachProductswsicloudwsicloudden Documentation Data Management Facility009 ARMStreamsArkansas-Red Basin

  7. ARM XDC Datastreams

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

    AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE: Alternative Fuels Data Center Homesum_a_epg0_fpd_mmcf_m.xls" ,"Available from WebQuantity ofkandz-cm11 OutreachProductswsicloudwsicloudden Documentation Data Management Facility009 ARMStreamsArkansas-Red

  8. ARM XDC Datastreams

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

    AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE: Alternative Fuels Data Center Homesum_a_epg0_fpd_mmcf_m.xls" ,"Available from WebQuantity ofkandz-cm11 OutreachProductswsicloudwsicloudden Documentation Data Management Facility009 ARMStreamsArkansas-RedStreamsC-Band

  9. ARM XDC Datastreams

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

    AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE: Alternative Fuels Data Center Homesum_a_epg0_fpd_mmcf_m.xls" ,"Available from WebQuantity ofkandz-cm11 OutreachProductswsicloudwsicloudden Documentation Data Management Facility009

  10. ARM XDC Datastreams

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

    AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE: Alternative Fuels Data Center Homesum_a_epg0_fpd_mmcf_m.xls" ,"Available from WebQuantity ofkandz-cm11 OutreachProductswsicloudwsicloudden Documentation Data Management Facility009Diagnostic Analyses Documentation

  11. ARM XDC Datastreams

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

    AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE: Alternative Fuels Data Center Homesum_a_epg0_fpd_mmcf_m.xls" ,"Available from WebQuantity ofkandz-cm11 OutreachProductswsicloudwsicloudden Documentation Data Management Facility009Diagnostic Analyses

  12. ARM XDC Datastreams

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

    AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE: Alternative Fuels Data Center Homesum_a_epg0_fpd_mmcf_m.xls" ,"Available from WebQuantity ofkandz-cm11 OutreachProductswsicloudwsicloudden Documentation Data Management Facility009Diagnostic AnalysesStreamsFluxNet:

  13. ARM XDC Datastreams

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

    AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE: Alternative Fuels Data Center Homesum_a_epg0_fpd_mmcf_m.xls" ,"Available from WebQuantity ofkandz-cm11 OutreachProductswsicloudwsicloudden Documentation Data Management Facility009Diagnostic

  14. ARM XDC Datastreams

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

    AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE: Alternative Fuels Data Center Homesum_a_epg0_fpd_mmcf_m.xls" ,"Available from WebQuantity ofkandz-cm11 OutreachProductswsicloudwsicloudden Documentation Data Management Facility009DiagnosticStreamsGeostationary

  15. ARM XDC Datastreams

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

    AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE: Alternative Fuels Data Center Homesum_a_epg0_fpd_mmcf_m.xls" ,"Available from WebQuantity ofkandz-cm11 OutreachProductswsicloudwsicloudden Documentation Data Management

  16. ARM - Datastreams - omi

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

    AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE: Alternative Fuels Data Center Homesum_a_epg0_fpd_mmcf_m.xls" ,"Available from WebQuantity ofkandz-cm11 Outreach HomeA Better Anode Design to Improve4AJ01) (See EnergyCurrent : 0.0September4compositeDatastreamsomi

  17. ARM - Datastreams - toms

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

    AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE: Alternative Fuels Data Center Homesum_a_epg0_fpd_mmcf_m.xls" ,"Available from WebQuantity ofkandz-cm11 Outreach HomeA Better Anode Design to Improve4AJ01) (See EnergyCurrent :Datastreamstoms Documentation XDC

  18. ARM - Instrument Datastreams

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

    AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE: Alternative Fuels Data Center Homesum_a_epg0_fpd_mmcf_m.xls" ,"Available from WebQuantity ofkandz-cm11 Outreach HomeA Better Anode DesigngovCampaignsSpring Single ColumngovCampaignsWinter 1994

  19. ARM - Instrument Datastreams

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

    AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE: Alternative Fuels Data Center Homesum_a_epg0_fpd_mmcf_m.xls" ,"Available from WebQuantity ofkandz-cm11 Outreach HomeA Better Anode DesigngovCampaignsSpring Single ColumngovCampaignsWinter 1994

  20. ARM - Instrument Datastreams

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

    AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE: Alternative Fuels Data Center Homesum_a_epg0_fpd_mmcf_m.xls" ,"Available from WebQuantity ofkandz-cm11 Outreach HomeA Better Anode DesigngovCampaignsSpring Single ColumngovCampaignsWinter 1994

  1. ARM - Instrument Datastreams

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

    AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE: Alternative Fuels Data Center Homesum_a_epg0_fpd_mmcf_m.xls" ,"Available from WebQuantity ofkandz-cm11 Outreach HomeA Better Anode DesigngovCampaignsSpring Single ColumngovCampaignsWinter

  2. ARM - Instrument Datastreams

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

    AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE: Alternative Fuels Data Center Homesum_a_epg0_fpd_mmcf_m.xls" ,"Available from WebQuantity ofkandz-cm11 Outreach HomeA Better Anode DesigngovCampaignsSpring Single ColumngovCampaignsWinter

  3. ARM - Datastreams - avhrrhovmuller

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

    AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE: Alternative Fuels Data Center Homesum_a_epg0_fpd_mmcf_m.xls" ,"Available from WebQuantity ofkandz-cm11 Outreach HomeA Better Anode Design to Improve4AJ01) (See EnergyCurrent : 0.0September 12,Datastreamsavhrrhovmuller

  4. ARM - Datastreams - avhrrkwaj12

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

    AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE: Alternative Fuels Data Center Homesum_a_epg0_fpd_mmcf_m.xls" ,"Available from WebQuantity ofkandz-cm11 Outreach HomeA Better Anode Design to Improve4AJ01) (See EnergyCurrent : 0.0September

  5. ARM - Datastreams - avhrrkwaj14

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

    AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE: Alternative Fuels Data Center Homesum_a_epg0_fpd_mmcf_m.xls" ,"Available from WebQuantity ofkandz-cm11 Outreach HomeA Better Anode Design to Improve4AJ01) (See EnergyCurrent : 0.0September4 Documentation XDC

  6. ARM - Datastreams - avhrrkwaj16

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

    AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE: Alternative Fuels Data Center Homesum_a_epg0_fpd_mmcf_m.xls" ,"Available from WebQuantity ofkandz-cm11 Outreach HomeA Better Anode Design to Improve4AJ01) (See EnergyCurrent : 0.0September4 Documentation XDC6

  7. ARM - Datastreams - avhrrkwaj17

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

    AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE: Alternative Fuels Data Center Homesum_a_epg0_fpd_mmcf_m.xls" ,"Available from WebQuantity ofkandz-cm11 Outreach HomeA Better Anode Design to Improve4AJ01) (See EnergyCurrent : 0.0September4 Documentation XDC67

  8. ARM - Datastreams - avhrrkwaj18

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

    AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE: Alternative Fuels Data Center Homesum_a_epg0_fpd_mmcf_m.xls" ,"Available from WebQuantity ofkandz-cm11 Outreach HomeA Better Anode Design to Improve4AJ01) (See EnergyCurrent : 0.0September4 Documentation XDC678

  9. ARM XDC Datastreams

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

    AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE: Alternative Fuels Data Center Homesum_a_epg0_fpd_mmcf_m.xls" ,"Available from WebQuantity ofkandz-cm11 Outreach Home Room News Publications Traditional Knowledge Kiosks Barrow, AlaskaDatabaseSearchTipsWith the3

  10. ARM - Datastreams - avhrr17

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

    AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE: Alternative Fuels Data Center Homesum_a_epg0_fpd_mmcf_m.xls" ,"Available from WebQuantity ofkandz-cm11 Comments? We would love to hear from you!ch4 Documentation XDC documentation Datalacman Documentation7

  11. ARM - Datastreams - avhrr18

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

    AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE: Alternative Fuels Data Center Homesum_a_epg0_fpd_mmcf_m.xls" ,"Available from WebQuantity ofkandz-cm11 Comments? We would love to hear from you!ch4 Documentation XDC documentation Datalacmanrad Documentation XDC

  12. ARM - Datastreams - avhrr9

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

    AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE: Alternative Fuels Data Center Homesum_a_epg0_fpd_mmcf_m.xls" ,"Available from WebQuantity ofkandz-cm11 Comments? We would love to hear from you!ch4 Documentation XDC documentation Datalacmanrad Documentation XDCrad

  13. ARM - Datastreams - avhrrdar

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

    AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE: Alternative Fuels Data Center Homesum_a_epg0_fpd_mmcf_m.xls" ,"Available from WebQuantity ofkandz-cm11 Comments? We would love to hear from you!ch4 Documentation XDC documentation Datalacmanrad Documentation

  14. ARM - Datastreams - blc

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

    AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE: Alternative Fuels Data Center Homesum_a_epg0_fpd_mmcf_m.xls" ,"Available from WebQuantity ofkandz-cm11 Comments? We would love to hear from you!ch4 Documentation XDC documentation Datalacmanrad

  15. ARM - Datastreams - blcprof

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

    AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE: Alternative Fuels Data Center Homesum_a_epg0_fpd_mmcf_m.xls" ,"Available from WebQuantity ofkandz-cm11 Comments? We would love to hear from you!ch4 Documentation XDC documentation DatalacmanradDatastreamsblcprof

  16. ARM - Datastreams - brs

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

    AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE: Alternative Fuels Data Center Homesum_a_epg0_fpd_mmcf_m.xls" ,"Available from WebQuantity ofkandz-cm11 Comments? We would love to hear from you!ch4 Documentation XDC documentation

  17. ARM - Datastreams - bsrn

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

    AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE: Alternative Fuels Data Center Homesum_a_epg0_fpd_mmcf_m.xls" ,"Available from WebQuantity ofkandz-cm11 Comments? We would love to hear from you!ch4 Documentation XDC documentationDatastreamsbrs60s Documentation

  18. ARM - Datastreams - ceil

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

    AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE: Alternative Fuels Data Center Homesum_a_epg0_fpd_mmcf_m.xls" ,"Available from WebQuantity ofkandz-cm11 Comments? We would love to hear from you!ch4 Documentation XDC documentationDatastreamsbrs60s

  19. ARM - Datastreams - ceilpblht

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

    AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE: Alternative Fuels Data Center Homesum_a_epg0_fpd_mmcf_m.xls" ,"Available from WebQuantity ofkandz-cm11 Comments? We would love to hear from you!ch4 Documentation XDC documentationDatastreamsbrs60sDatastreamsceilpblht

  20. ARM - Datastreams - cmh

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

    AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE: Alternative Fuels Data Center Homesum_a_epg0_fpd_mmcf_m.xls" ,"Available from WebQuantity ofkandz-cm11 Comments? We would love to hear from you!ch4 Documentation XDC

  1. ARM - Datastreams - co

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

    AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE: Alternative Fuels Data Center Homesum_a_epg0_fpd_mmcf_m.xls" ,"Available from WebQuantity ofkandz-cm11 Comments? We would love to hear from you!ch4 Documentation XDCDatastreamsco Documentation Data Quality Plots

  2. ARM - Datastreams - csaprrhi

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

    AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE: Alternative Fuels Data Center Homesum_a_epg0_fpd_mmcf_m.xls" ,"Available from WebQuantity ofkandz-cm11 Comments? We would love to hear from you!ch4 Documentation XDCDatastreamsco Documentation Data Quality

  3. ARM - Datastreams - csaprsec

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

    AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE: Alternative Fuels Data Center Homesum_a_epg0_fpd_mmcf_m.xls" ,"Available from WebQuantity ofkandz-cm11 Comments? We would love to hear from you!ch4 Documentation XDCDatastreamsco Documentation Data

  4. ARM - Datastreams - csaprsur

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

    AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE: Alternative Fuels Data Center Homesum_a_epg0_fpd_mmcf_m.xls" ,"Available from WebQuantity ofkandz-cm11 Comments? We would love to hear from you!ch4 Documentation XDCDatastreamsco Documentation DataDatastreamscsaprsur

  5. ARM - Datastreams - csaprvert

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

    AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE: Alternative Fuels Data Center Homesum_a_epg0_fpd_mmcf_m.xls" ,"Available from WebQuantity ofkandz-cm11 Comments? We would love to hear from you!ch4 Documentation XDCDatastreamsco Documentation

  6. ARM - Datastreams - csphotalm

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

    AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE: Alternative Fuels Data Center Homesum_a_epg0_fpd_mmcf_m.xls" ,"Available from WebQuantity ofkandz-cm11 Comments? We would love to hear from you!ch4 Documentation XDCDatastreamsco DocumentationDatastreamscsphotalm

  7. ARM - Datastreams - csphotaot

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

    AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE: Alternative Fuels Data Center Homesum_a_epg0_fpd_mmcf_m.xls" ,"Available from WebQuantity ofkandz-cm11 Comments? We would love to hear from you!ch4 Documentation XDCDatastreamscoDatastreamscsphotaot Documentation Data

  8. ARM - Datastreams - csphotaotfilt

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

    AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE: Alternative Fuels Data Center Homesum_a_epg0_fpd_mmcf_m.xls" ,"Available from WebQuantity ofkandz-cm11 Comments? We would love to hear from you!ch4 Documentation XDCDatastreamscoDatastreamscsphotaot Documentation

  9. ARM - Datastreams - csphotaotfiltqa

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

    AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE: Alternative Fuels Data Center Homesum_a_epg0_fpd_mmcf_m.xls" ,"Available from WebQuantity ofkandz-cm11 Comments? We would love to hear from you!ch4 Documentation XDCDatastreamscoDatastreamscsphotaot

  10. ARM - Datastreams - csphotpfn1

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

    AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE: Alternative Fuels Data Center Homesum_a_epg0_fpd_mmcf_m.xls" ,"Available from WebQuantity ofkandz-cm11 Comments? We would love to hear from you!ch4 Documentation

  11. ARM - Datastreams - csphotpp

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

    AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE: Alternative Fuels Data Center Homesum_a_epg0_fpd_mmcf_m.xls" ,"Available from WebQuantity ofkandz-cm11 Comments? We would love to hear from you!ch4 DocumentationDatastreamscsphotpp Documentation Data Quality Plots

  12. ARM - Datastreams - csphotsize

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

    AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE: Alternative Fuels Data Center Homesum_a_epg0_fpd_mmcf_m.xls" ,"Available from WebQuantity ofkandz-cm11 Comments? We would love to hear from you!ch4 DocumentationDatastreamscsphotpp Documentation Data Quality

  13. ARM - Datastreams - disdrometer

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

    AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE: Alternative Fuels Data Center Homesum_a_epg0_fpd_mmcf_m.xls" ,"Available from WebQuantity ofkandz-cm11 Comments? We would love to hear from you!ch4 DocumentationDatastreamscsphotpp Documentation Data

  14. ARM - Datastreams - dlcal1

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

    AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE: Alternative Fuels Data Center Homesum_a_epg0_fpd_mmcf_m.xls" ,"Available from WebQuantity ofkandz-cm11 Comments? We would love to hear from you!ch4 DocumentationDatastreamscsphotpp Documentation DataDatastreamsdlcal1

  15. ARM - Datastreams - dlcal2

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

    AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE: Alternative Fuels Data Center Homesum_a_epg0_fpd_mmcf_m.xls" ,"Available from WebQuantity ofkandz-cm11 Comments? We would love to hear from you!ch4 DocumentationDatastreamscsphotpp Documentation

  16. ARM - Datastreams - dlfpt

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

    AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE: Alternative Fuels Data Center Homesum_a_epg0_fpd_mmcf_m.xls" ,"Available from WebQuantity ofkandz-cm11 Comments? We would love to hear from you!ch4 DocumentationDatastreamscsphotpp DocumentationDatastreamsdlfpt

  17. ARM - Datastreams - dlppi

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

    AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE: Alternative Fuels Data Center Homesum_a_epg0_fpd_mmcf_m.xls" ,"Available from WebQuantity ofkandz-cm11 Comments? We would love to hear from you!ch4 DocumentationDatastreamscsphotpp

  18. ARM - Datastreams - dlrhi

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

    AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE: Alternative Fuels Data Center Homesum_a_epg0_fpd_mmcf_m.xls" ,"Available from WebQuantity ofkandz-cm11 Comments? We would love to hear from you!ch4 DocumentationDatastreamscsphotppDatastreamsdlrhi Documentation Data

  19. ARM - Datastreams - dlrhi2

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

    AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE: Alternative Fuels Data Center Homesum_a_epg0_fpd_mmcf_m.xls" ,"Available from WebQuantity ofkandz-cm11 Comments? We would love to hear from you!ch4 DocumentationDatastreamscsphotppDatastreamsdlrhi Documentation

  20. ARM - Datastreams - dlusr

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

    AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE: Alternative Fuels Data Center Homesum_a_epg0_fpd_mmcf_m.xls" ,"Available from WebQuantity ofkandz-cm11 Comments? We would love to hear from you!ch4 DocumentationDatastreamscsphotppDatastreamsdlrhi

  1. ARM - Datastreams - ecmwfflx

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

    AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE: Alternative Fuels Data Center Homesum_a_epg0_fpd_mmcf_m.xls" ,"Available from WebQuantity ofkandz-cm11 Comments? We would love to hear from you!ch4 DocumentationDatastreamscsphotppDatastreamsdlrhiDatastreamsecmwfflx

  2. ARM - Datastreams - ecmwfsfc

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

    AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE: Alternative Fuels Data Center Homesum_a_epg0_fpd_mmcf_m.xls" ,"Available from WebQuantity ofkandz-cm11 Comments? We would love to hear from you!ch4

  3. ARM - Datastreams - ecmwfsfce

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

    AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE: Alternative Fuels Data Center Homesum_a_epg0_fpd_mmcf_m.xls" ,"Available from WebQuantity ofkandz-cm11 Comments? We would love to hear from you!ch4Datastreamsecmwfsfc1l Documentation XDC documentation Data

  4. ARM - Datastreams - ecmwfsfcml

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

    AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE: Alternative Fuels Data Center Homesum_a_epg0_fpd_mmcf_m.xls" ,"Available from WebQuantity ofkandz-cm11 Comments? We would love to hear from you!ch4Datastreamsecmwfsfc1l Documentation XDC documentation

  5. ARM - Datastreams - ecmwfsupp

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

    AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE: Alternative Fuels Data Center Homesum_a_epg0_fpd_mmcf_m.xls" ,"Available from WebQuantity ofkandz-cm11 Comments? We would love to hear from you!ch4Datastreamsecmwfsfc1l Documentation XDC

  6. ARM - Datastreams - ecmwfsurf

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

    AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE: Alternative Fuels Data Center Homesum_a_epg0_fpd_mmcf_m.xls" ,"Available from WebQuantity ofkandz-cm11 Comments? We would love to hear from you!ch4Datastreamsecmwfsfc1l Documentation XDCDatastreamsecmwfsurf

  7. ARM - Datastreams - ecmwfupa

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

    AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE: Alternative Fuels Data Center Homesum_a_epg0_fpd_mmcf_m.xls" ,"Available from WebQuantity ofkandz-cm11 Comments? We would love to hear from you!ch4Datastreamsecmwfsfc1l Documentation

  8. ARM - Datastreams - ecmwfvar

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

    AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE: Alternative Fuels Data Center Homesum_a_epg0_fpd_mmcf_m.xls" ,"Available from WebQuantity ofkandz-cm11 Comments? We would love to hear from you!ch4Datastreamsecmwfsfc1l DocumentationDatastreamsecmwfvar Documentation

  9. ARM - Datastreams - fslruc60

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

    AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE: Alternative Fuels Data Center Homesum_a_epg0_fpd_mmcf_m.xls" ,"Available from WebQuantity ofkandz-cm11 Comments? We would love to hear from you!ch4Datastreamsecmwfsfc1l DocumentationDatastreamsecmwfvar

  10. ARM - Datastreams - gms5

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

    AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE: Alternative Fuels Data Center Homesum_a_epg0_fpd_mmcf_m.xls" ,"Available from WebQuantity ofkandz-cm11 Comments? We would love to hear from you!ch4Datastreamsecmwfsfc1lch2 Documentation XDC

  11. ARM - Datastreams - goes10

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

    AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE: Alternative Fuels Data Center Homesum_a_epg0_fpd_mmcf_m.xls" ,"Available from WebQuantity ofkandz-cm11 Comments? We would love to hear from you!ch4Datastreamsecmwfsfc1lch2 Documentation XDCDatastreamsgndrad60s

  12. ARM - Datastreams - goes12

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

    AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE: Alternative Fuels Data Center Homesum_a_epg0_fpd_mmcf_m.xls" ,"Available from WebQuantity ofkandz-cm11 Comments? We would love to hear from you!ch4Datastreamsecmwfsfc1lch2 Documentation XDCDatastreamsgndrad60svis

  13. ARM - Datastreams - goes13

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

    AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE: Alternative Fuels Data Center Homesum_a_epg0_fpd_mmcf_m.xls" ,"Available from WebQuantity ofkandz-cm11 Comments? We would love to hear from you!ch4Datastreamsecmwfsfc1lch2 Documentation XDCDatastreamsgndrad60svisvis

  14. ARM - Datastreams - goes14

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

    AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE: Alternative Fuels Data Center Homesum_a_epg0_fpd_mmcf_m.xls" ,"Available from WebQuantity ofkandz-cm11 Comments? We would love to hear from you!ch4Datastreamsecmwfsfc1lch2 Documentation Documentation XDC documentation

  15. ARM - Datastreams - goes8

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

    AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE: Alternative Fuels Data Center Homesum_a_epg0_fpd_mmcf_m.xls" ,"Available from WebQuantity ofkandz-cm11 Comments? We would love to hear from you!ch4Datastreamsecmwfsfc1lch2 Documentation Documentation XDC Documentation

  16. ARM - Datastreams - goes9

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

    AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE: Alternative Fuels Data Center Homesum_a_epg0_fpd_mmcf_m.xls" ,"Available from WebQuantity ofkandz-cm11 Comments? We would love to hear from you!ch4Datastreamsecmwfsfc1lch2 Documentation Documentation

  17. ARM - Datastreams - gvr

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

    AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE: Alternative Fuels Data Center Homesum_a_epg0_fpd_mmcf_m.xls" ,"Available from WebQuantity ofkandz-cm11 Comments? We would love to hear from you!ch4Datastreamsecmwfsfc1lch2 Documentation DocumentationDatastreamsgvr

  18. ARM - Datastreams - gvrp

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

    AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE: Alternative Fuels Data Center Homesum_a_epg0_fpd_mmcf_m.xls" ,"Available from WebQuantity ofkandz-cm11 Comments? We would love to hear from you!ch4Datastreamsecmwfsfc1lch2 Documentation

  19. ARM - Datastreams - hsrl

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

    AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE: Alternative Fuels Data Center Homesum_a_epg0_fpd_mmcf_m.xls" ,"Available from WebQuantity ofkandz-cm11 Comments? We would love to hear from you!ch4Datastreamsecmwfsfc1lch2 DocumentationDatastreamshsrl Documentation

  20. ARM - Datastreams - iap

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

    AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE: Alternative Fuels Data Center Homesum_a_epg0_fpd_mmcf_m.xls" ,"Available from WebQuantity ofkandz-cm11 Comments? We would love to hear from you!ch4Datastreamsecmwfsfc1lch2 DocumentationDatastreamshsrl

  1. ARM - Datastreams - iapavg

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

    AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE: Alternative Fuels Data Center Homesum_a_epg0_fpd_mmcf_m.xls" ,"Available from WebQuantity ofkandz-cm11 Comments? We would love to hear from you!ch4Datastreamsecmwfsfc1lch2 DocumentationDatastreamshsrlDatastreamsiapavg

  2. ARM - Datastreams - iapmfr

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

    AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE: Alternative Fuels Data Center Homesum_a_epg0_fpd_mmcf_m.xls" ,"Available from WebQuantity ofkandz-cm11 Comments? We would love to hear from you!ch4Datastreamsecmwfsfc1lch2

  3. ARM - Datastreams - irt

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

    AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE: Alternative Fuels Data Center Homesum_a_epg0_fpd_mmcf_m.xls" ,"Available from WebQuantity ofkandz-cm11 Comments? We would love to hear from you!ch4Datastreamsecmwfsfc1lch2Datastreamsirt Documentation Data Quality

  4. ARM - Datastreams - isssonde

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

    AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE: Alternative Fuels Data Center Homesum_a_epg0_fpd_mmcf_m.xls" ,"Available from WebQuantity ofkandz-cm11 Comments? We would love to hear from you!ch4Datastreamsecmwfsfc1lch2Datastreamsirt Documentation

  5. ARM - Datastreams - kasacrawrhi

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

    AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE: Alternative Fuels Data Center Homesum_a_epg0_fpd_mmcf_m.xls" ,"Available from WebQuantity ofkandz-cm11 Comments? We would love to hear from you!ch4Datastreamsecmwfsfc1lch2DatastreamsirtDatastreamskasacrawrhi

  6. ARM - Datastreams - kasacrblrhi

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

    AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE: Alternative Fuels Data Center Homesum_a_epg0_fpd_mmcf_m.xls" ,"Available from WebQuantity ofkandz-cm11 Comments? We would love to hear from

  7. ARM - Datastreams - kasacrcrcal

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

    AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE: Alternative Fuels Data Center Homesum_a_epg0_fpd_mmcf_m.xls" ,"Available from WebQuantity ofkandz-cm11 Comments? We would love to hear fromDatastreamskasacrcrcal Documentation Data Quality Plots Citation DOI:

  8. ARM - Datastreams - kasacrcrraster

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

    AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE: Alternative Fuels Data Center Homesum_a_epg0_fpd_mmcf_m.xls" ,"Available from WebQuantity ofkandz-cm11 Comments? We would love to hear fromDatastreamskasacrcrcal Documentation Data Quality Plots Citation

  9. ARM - Datastreams - kasacrcrrhi

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

    AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE: Alternative Fuels Data Center Homesum_a_epg0_fpd_mmcf_m.xls" ,"Available from WebQuantity ofkandz-cm11 Comments? We would love to hear fromDatastreamskasacrcrcal Documentation Data Quality Plots

  10. ARM - Datastreams - kasacrcwrhi

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

    AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE: Alternative Fuels Data Center Homesum_a_epg0_fpd_mmcf_m.xls" ,"Available from WebQuantity ofkandz-cm11 Comments? We would love to hear fromDatastreamskasacrcrcal Documentation Data Quality PlotsDatastreamskasacrcwrhi

  11. ARM - Datastreams - kasacrhsrhi

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  12. ARM - Datastreams - kasacrppi

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  13. ARM - Datastreams - kasacrrhi

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  14. ARM - Datastreams - kasacrslr

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  17. ARM - Datastreams - kasacrvpt

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  18. ARM - Datastreams - kasacrzppi

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  19. ARM - Datastreams - kazrge

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  20. ARM - Datastreams - kazrhi

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  1. ARM - Datastreams - kazrmd

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  5. ARM - Datastreams - kazrspeccmaskcicopol

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  6. ARM - Datastreams - kazrspeccmaskcixpol

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  7. ARM - Datastreams - kazrspeccmaskgecopol

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  8. ARM - Datastreams - kazrspeccmaskgexpol

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  9. ARM - Datastreams - kazrspeccmaskhicopol

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  11. ARM - Datastreams - kazrspeccmaskmdxpol

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  12. ARM - Datastreams - maps60

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  13. ARM - Datastreams - marinemet

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  14. ARM - Datastreams - met

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  15. ARM - Datastreams - mettiptwr

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  16. ARM - Datastreams - mettwr

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  17. ARM - Datastreams - mfraaf

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  18. ARM - Datastreams - mfrsr

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  19. ARM - Datastreams - mmcrcal

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  20. ARM - Datastreams - mmcrmom

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  1. ARM - Datastreams - mmcrmoments

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  2. ARM - Datastreams - mmcrspeccmaskbl

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  3. ARM - Datastreams - mmcrspeccmaskci

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  5. ARM - Datastreams - mmcrspectra

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  7. ARM - Datastreams - moltsedassfcclass1

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  10. ARM - Datastreams - mpl

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  13. ARM - Datastreams - mplps

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  16. ARM - Datastreams - mwacrspeccmaskcopol

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  18. ARM - Datastreams - mwrlos

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  19. ARM - Datastreams - mwrp

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  20. ARM - Datastreams - nav

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  1. ARM - Datastreams - navgps

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  2. ARM - Datastreams - ncepgfsatkflx

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  3. ARM - Datastreams - ncepgfsatkpprof

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  4. ARM - Datastreams - ncepgfsatksfc

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  5. ARM - Datastreams - ncepgfsatksprof

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  6. ARM - Datastreams - ncepgfsatkzprof

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  7. ARM - Datastreams - ncepgfsbrwflx

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  8. ARM - Datastreams - ncepgfsbrwpprof

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  9. ARM - Datastreams - ncepgfsbrwsfc

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  10. ARM - Datastreams - ncepgfsbrwsprof

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  11. ARM - Datastreams - ncepgfsbrwzprof

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  12. ARM - Datastreams - ncepgfsdarflx

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  13. ARM - Datastreams - ncepgfsdarpprof

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  14. ARM - Datastreams - ncepgfsdarsfc

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  15. ARM - Datastreams - ncepgfsdarsprof

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  16. ARM - Datastreams - ncepgfsdarzprof

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  17. ARM - Datastreams - ncepgfsflx

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  18. ARM - Datastreams - ncepgfsmanflx

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  19. ARM - Datastreams - ncepgfsmanpprof

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  20. ARM - Datastreams - ncepgfsmansfc

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  1. ARM - Datastreams - ncepgfsmansprof

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  2. ARM - Datastreams - ncepgfsmanzprof

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  3. ARM - Datastreams - ncepgfsnauflx

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  4. ARM - Datastreams - ncepgfsnaupprof

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  5. ARM - Datastreams - ncepgfsnausfc

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  6. ARM - Datastreams - ncepgfsnausprof

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  7. ARM - Datastreams - ncepgfsnauzprof

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  8. ARM - Datastreams - ncepgfspprof

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  9. ARM - Datastreams - ncepgfssfc

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  10. ARM - Datastreams - ncepgfssprof

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  11. ARM - Datastreams - ncepgfszprof

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  12. ARM - Datastreams - nfov

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  13. ARM - Datastreams - ngm250

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  14. ARM - Datastreams - nimfr

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  15. ARM - Datastreams - noaaaos

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  16. ARM - Datastreams - noaaaosavg

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  17. ARM - Datastreams - noaaaosccn100

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  18. ARM - Datastreams - noaaradbrw

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  19. ARM - Datastreams - org

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  20. ARM - Datastreams - pars2

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  1. ARM - Datastreams - pgs

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  2. ARM - Datastreams - precnet

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  3. ARM - Datastreams - prpfrsr

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  4. ARM - Datastreams - prprad

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  5. ARM - Datastreams - pws

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  6. ARM - Datastreams - rad

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  7. ARM - Datastreams - rain

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  8. ARM - Datastreams - rp

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  9. ARM - Datastreams - rphcontrol

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  10. ARM - Datastreams - rphtilt

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  11. ARM - Datastreams - rss

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  12. ARM - Datastreams - ruc60

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  13. ARM - Datastreams - sashemfr

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  14. ARM - Datastreams - sashenir

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  15. ARM - Datastreams - sashenirhisun

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  16. ARM - Datastreams - sashenirlowsun

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  17. ARM - Datastreams - sashevis

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  18. ARM - Datastreams - sashevishisun

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  19. ARM - Datastreams - sashevislowsun

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  20. ARM - Datastreams - saszefilterbands

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    AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-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 ofkandz-cm11 Comments? We wouldDatastreamsnoaaradbrw Documentation XDCDatastreamssashenirhisunDatastreamssaszefilterbands

  1. ARM - Datastreams - saszenir

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  2. ARM - Datastreams - saszevis

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    AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-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 ofkandz-cm11 Comments? We wouldDatastreamsnoaaradbrw DocumentationDatastreamssaszevis Documentation Data Quality Plots Citation

  3. ARM - Datastreams - sebs

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    AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-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 ofkandz-cm11 Comments? We wouldDatastreamsnoaaradbrw DocumentationDatastreamssaszevis Documentation Data Quality Plots

  4. ARM - Datastreams - sirs

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  5. ARM - Datastreams - snodep

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  6. ARM - Datastreams - sodar

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  7. ARM - Datastreams - sonde

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  8. ARM - Datastreams - sondewnpn

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  9. ARM - Datastreams - sondewnpr

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  10. ARM - Datastreams - sondewrpn

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  11. ARM - Datastreams - sondewrpr

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  12. ARM - Datastreams - surflog

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  13. ARM - Datastreams - surthref

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  14. ARM - Datastreams - swacrblrhi

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  15. ARM - Datastreams - swacrcal

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  16. ARM - Datastreams - swacrcwrhi

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  17. ARM - Datastreams - swacrfpt

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  18. ARM - Datastreams - swacrhsrhi

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  19. ARM - Datastreams - swacrppi

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  20. ARM - Datastreams - swacrspeccmaskcopol

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  1. ARM - Datastreams - swacrspeccmaskxpol

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  2. ARM - Datastreams - swacrvad

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  3. ARM - Datastreams - swacrvpt

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  4. ARM - Datastreams - swats

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  5. ARM - Datastreams - swatspcp

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  6. ARM - Datastreams - sws

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  7. ARM - Datastreams - tdmahyg

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  8. ARM - Datastreams - tdmasize

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  9. ARM - Datastreams - thwaps

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  10. ARM - Datastreams - tlcv

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  11. ARM - Datastreams - tps

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  12. ARM - Datastreams - tsicldmask

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  13. ARM - Datastreams - tsimovie

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    AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-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 ofkandz-cm11 Comments? WeDatastreamstps Documentation Data Quality Plots Citation DOI: 10.5439/1025305 [ What is

  14. ARM - Datastreams - tsiskycover

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

    AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE: Alternative Fuels Data Center Homesum_a_epg0_fpd_mmcf_m.xls" ,"Available from WebQuantity ofkandz-cm11 Comments? WeDatastreamstps Documentation Data Quality Plots Citation DOI: 10.5439/1025305 [ What

  15. ARM - Datastreams - tsiskyimage

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

    AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE: Alternative Fuels Data Center Homesum_a_epg0_fpd_mmcf_m.xls" ,"Available from WebQuantity ofkandz-cm11 Comments? WeDatastreamstps Documentation Data Quality Plots Citation DOI: 10.5439/1025305 [

  16. ARM - Datastreams - twr

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

    AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE: Alternative Fuels Data Center Homesum_a_epg0_fpd_mmcf_m.xls" ,"Available from WebQuantity ofkandz-cm11 Comments? WeDatastreamstps Documentation Data Quality Plots Citation DOI: 10.5439/1025305 [Datastreamstwr

  17. ARM - Datastreams - usdarad

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

    AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE: Alternative Fuels Data Center Homesum_a_epg0_fpd_mmcf_m.xls" ,"Available from WebQuantity ofkandz-cm11 Comments? WeDatastreamstps Documentation Data Quality Plots Citation DOI:

  18. ARM - Datastreams - usdaradaux

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    AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-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 ofkandz-cm11 Comments? WeDatastreamstps Documentation Data Quality Plots Citation DOI:Datastreamsusdaradaux Documentation XDC

  19. ARM - Datastreams - usdauvmfrsr

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  20. ARM - Datastreams - usdauvmfrsraux

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    AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-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 ofkandz-cm11 Comments? WeDatastreamstps Documentation Data Quality Plots Citation DOI:Datastreamsusdaradaux

  1. ARM - Datastreams - vdis

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

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  2. ARM - Datastreams - vdisdrops

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  3. ARM - Datastreams - wacr

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

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  4. ARM - Datastreams - wacrspeccmaskcopol

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  5. ARM - Datastreams - wacrspeccmaskxpol

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

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  6. ARM - Datastreams - wacrspecmom

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  7. ARM - Datastreams - wsacrawrhi

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  8. ARM - Datastreams - wsacrblrhi

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  9. ARM - Datastreams - wsacrcrcal

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    AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-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 ofkandz-cm11 Comments? WeDatastreamstps Documentation Data Quality PlotsDatastreamswsacrcrcal Documentation Data Quality Plots

  10. ARM - Datastreams - wsacrcrraster

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  11. ARM - Datastreams - wsacrcrrhi

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  12. ARM - Datastreams - wsacrcwrhi

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    AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-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 ofkandz-cm11 Comments? WeDatastreamstps Documentation Data Quality PlotsDatastreamswsacrcrcal Documentation

  13. ARM - Datastreams - wsacrhsrhi

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  14. ARM - Datastreams - wsacrppi

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

    AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE: Alternative Fuels Data Center Homesum_a_epg0_fpd_mmcf_m.xls" ,"Available from WebQuantity ofkandz-cm11 Comments? WeDatastreamstps Documentation Data Quality PlotsDatastreamswsacrcrcalDatastreamswsacrppi Documentation

  15. ARM - Datastreams - wsacrrhi

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  16. ARM - Datastreams - wsacrslr

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  17. ARM - Datastreams - wsacrspeccmaskcopol

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

    AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE: Alternative Fuels Data Center Homesum_a_epg0_fpd_mmcf_m.xls" ,"Available from WebQuantity ofkandz-cm11 Comments? WeDatastreamstps Documentation Data QualityDatastreamswsacrspeccmaskcopol Documentation Data Quality

  18. ARM - Datastreams - wsacrspeccmaskxpol

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

    AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE: Alternative Fuels Data Center Homesum_a_epg0_fpd_mmcf_m.xls" ,"Available from WebQuantity ofkandz-cm11 Comments? WeDatastreamstps Documentation Data QualityDatastreamswsacrspeccmaskcopol Documentation Data

  19. ARM - Datastreams - wsacrvpt

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    AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-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 ofkandz-cm11 Comments? WeDatastreamstps Documentation Data QualityDatastreamswsacrspeccmaskcopol Documentation

  20. ARM - Datastreams - xsacrawrhi

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    AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-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 ofkandz-cm11 Comments? WeDatastreamstps Documentation Data QualityDatastreamswsacrspeccmaskcopol

  1. ARM - Datastreams - xsacrblrhi

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    AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-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 ofkandz-cm11 Comments? WeDatastreamstps Documentation Data QualityDatastreamswsacrspeccmaskcopolDatastreamsxsacrblrhi

  2. ARM - Datastreams - xsacrcrraster

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

    AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE: Alternative Fuels Data Center Homesum_a_epg0_fpd_mmcf_m.xls" ,"Available from WebQuantity ofkandz-cm11 Comments? WeDatastreamstps Documentation Data

  3. ARM - Datastreams - xsacrcwrhi

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  4. ARM - Datastreams - xsacrhsrhi

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  5. ARM - Datastreams - xsacrppi

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

    AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE: Alternative Fuels Data Center Homesum_a_epg0_fpd_mmcf_m.xls" ,"Available from WebQuantity ofkandz-cm11 Comments? WeDatastreamstps Documentation DataDatastreamsxsacrcwrhi Documentation Data Quality Plots

  6. ARM - Datastreams - xsacrrhi

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    AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-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 ofkandz-cm11 Comments? WeDatastreamstps Documentation DataDatastreamsxsacrcwrhi Documentation Data Quality

  7. ARM - Datastreams - xsacrslr

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    AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-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 ofkandz-cm11 Comments? WeDatastreamstps Documentation DataDatastreamsxsacrcwrhi Documentation Data QualityDatastreamsxsacrslr

  8. ARM - Datastreams - xsacrspeccmaskcopol

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  9. ARM - Datastreams - xsacrspeccmaskxpol

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    AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-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 ofkandz-cm11 Comments? WeDatastreamstps Documentation DataDatastreamsxsacrcwrhi Documentation DataDatastreamsxsacrspeccmaskxpol

  10. ARM - Datastreams - xsacrvpt

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  11. ARM - Datastreams - xsacrzppi

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  12. ARM - Datastreams - xsaprhsrhi

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  13. ARM - Datastreams - xsaprppi

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  14. ARM - Datastreams - xsaprrhi

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  15. ARM - Datastreams - xsaprsur

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  16. ARM - Datastreams - xsaprvad

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  17. ARM - Datastreams - xsaprvpt

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  1. ARM - Datastreams - 06fslwpdnmet

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

    AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE: Alternative Fuels Data Center Homesum_a_epg0_fpd_mmcf_m.xls" ,"Available from WebQuantity ofkandz-cm11 Comments? We would love to hear from you! Send use0n Documentation XDC documentation Data Quality5okmfslwpdnmet

  2. ARM - Datastreams - 06fslwpdnrass

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

    AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE: Alternative Fuels Data Center Homesum_a_epg0_fpd_mmcf_m.xls" ,"Available from WebQuantity ofkandz-cm11 Comments? We would love to hear from you! Send use0n Documentation XDC documentation Data

  3. ARM - Datastreams - 06wpdnmmts

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

    AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE: Alternative Fuels Data Center Homesum_a_epg0_fpd_mmcf_m.xls" ,"Available from WebQuantity ofkandz-cm11 Comments? We would love to hear from you! Send use0n Documentation XDC documentation Datawpdnmmts Documentation

  4. ARM - Datastreams - 10wsipartradjpg

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

    AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE: Alternative Fuels Data Center Homesum_a_epg0_fpd_mmcf_m.xls" ,"Available from WebQuantity ofkandz-cm11 Comments? We would love to hear from you! Send use0n Documentation XDC documentationn Documentation

  5. ARM - Datastreams - 10wsipartradmpg

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

    AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE: Alternative Fuels Data Center Homesum_a_epg0_fpd_mmcf_m.xls" ,"Available from WebQuantity ofkandz-cm11 Comments? We would love to hear from you! Send use0n Documentation XDC documentationn Documentationwsipartradmpg

  6. ARM - Datastreams - 1290bsrwpprecipavg

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

    AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE: Alternative Fuels Data Center Homesum_a_epg0_fpd_mmcf_m.xls" ,"Available from WebQuantity ofkandz-cm11 Comments? We would love to hear from you! Send use0n Documentation XDC documentationn

  7. ARM - Datastreams - 1290bsrwpwindavg

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

    AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE: Alternative Fuels Data Center Homesum_a_epg0_fpd_mmcf_m.xls" ,"Available from WebQuantity ofkandz-cm11 Comments? We would love to hear from you! Send use0n Documentation XDC documentationnbsrwpwindavg Documentation

  8. ARM - Datastreams - 1290rwpprecipcon

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

    AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE: Alternative Fuels Data Center Homesum_a_epg0_fpd_mmcf_m.xls" ,"Available from WebQuantity ofkandz-cm11 Comments? We would love to hear from you! Send use0n Documentation XDC documentationnbsrwpwindavg

  9. ARM - Datastreams - 1290rwpprecipmom

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

    AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE: Alternative Fuels Data Center Homesum_a_epg0_fpd_mmcf_m.xls" ,"Available from WebQuantity ofkandz-cm11 Comments? We would love to hear from you! Send use0n Documentation XDC documentationnbsrwpwindavgrwpprecipmom

  10. ARM - Datastreams - 1290rwpprecipspec

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

    AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE: Alternative Fuels Data Center Homesum_a_epg0_fpd_mmcf_m.xls" ,"Available from WebQuantity ofkandz-cm11 Comments? We would love to hear from you! Send use0n Documentation XDC

  11. ARM - Datastreams - 1290rwpwindcon

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

    AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE: Alternative Fuels Data Center Homesum_a_epg0_fpd_mmcf_m.xls" ,"Available from WebQuantity ofkandz-cm11 Comments? We would love to hear from you! Send use0n Documentation XDCrwpwindcon Documentation Data Quality Plots

  12. ARM - Datastreams - 1290rwpwindmom

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

    AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE: Alternative Fuels Data Center Homesum_a_epg0_fpd_mmcf_m.xls" ,"Available from WebQuantity ofkandz-cm11 Comments? We would love to hear from you! Send use0n Documentation XDCrwpwindcon Documentation Data Quality

  13. ARM - Datastreams - 1290rwpwindspec

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

    AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE: Alternative Fuels Data Center Homesum_a_epg0_fpd_mmcf_m.xls" ,"Available from WebQuantity ofkandz-cm11 Comments? We would love to hear from you! Send use0n Documentation XDCrwpwindcon Documentation Data

  14. ARM - Datastreams - 15okm

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

    AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE: Alternative Fuels Data Center Homesum_a_epg0_fpd_mmcf_m.xls" ,"Available from WebQuantity ofkandz-cm11 Comments? We would love to hear from you! Send use0n Documentation XDCrwpwindcon Documentation

  15. ARM - Datastreams - 1smos

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

    AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE: Alternative Fuels Data Center Homesum_a_epg0_fpd_mmcf_m.xls" ,"Available from WebQuantity ofkandz-cm11 Comments? We would love to hear from you! Send use0n Documentation XDCrwpwindcon Documentationsmos Documentation

  16. ARM - Datastreams - 1sprprad

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

    AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE: Alternative Fuels Data Center Homesum_a_epg0_fpd_mmcf_m.xls" ,"Available from WebQuantity ofkandz-cm11 Comments? We would love to hear from you! Send use0n Documentation XDCrwpwindcon Documentationsmos

  17. ARM - Datastreams - 30ebbr

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

    AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE: Alternative Fuels Data Center Homesum_a_epg0_fpd_mmcf_m.xls" ,"Available from WebQuantity ofkandz-cm11 Comments? We would love to hear from you! Send use0n Documentation XDCrwpwindconflx25m Documentation

  18. ARM - Datastreams - 30ecor

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

    AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE: Alternative Fuels Data Center Homesum_a_epg0_fpd_mmcf_m.xls" ,"Available from WebQuantity ofkandz-cm11 Comments? We would love to hear from you! Send use0n Documentation XDCrwpwindconflx25m Documentationecor

  19. ARM - Datastreams - 30okm

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

    AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE: Alternative Fuels Data Center Homesum_a_epg0_fpd_mmcf_m.xls" ,"Available from WebQuantity ofkandz-cm11 Comments? We would love to hear from you! Send use0n Documentation XDCrwpwindconflx25m Documentationecorokm

  20. ARM - Datastreams - 30smos

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

    AFDC Printable Version Share this resource Send a link to EERE: Alternative Fuels Data Center Home Page to someone by E-mail Share EERE: Alternative Fuels Data Center Home Page on Facebook Tweet about EERE: Alternative Fuels Data Center Home Page on Twitter Bookmark EERE: Alternative Fuels Data Center Homesum_a_epg0_fpd_mmcf_m.xls" ,"Available from WebQuantity ofkandz-cm11 Comments? We would love to hear from you! Send use0n Documentation XDCrwpwindconflx25m Documentationecorokmsmos