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Sample records for ensemble atmospheric modelingfor

  1. Advanced Atmospheric Ensemble Modeling Techniques

    Energy Technology Data Exchange (ETDEWEB)

    Buckley, R. [Savannah River Site (SRS), Aiken, SC (United States). Savannah River National Lab. (SRNL); Chiswell, S. [Savannah River Site (SRS), Aiken, SC (United States). Savannah River National Lab. (SRNL); Kurzeja, R. [Savannah River Site (SRS), Aiken, SC (United States). Savannah River National Lab. (SRNL); Maze, G. [Savannah River Site (SRS), Aiken, SC (United States). Savannah River National Lab. (SRNL); Viner, B. [Savannah River Site (SRS), Aiken, SC (United States). Savannah River National Lab. (SRNL); Werth, D. [Savannah River Site (SRS), Aiken, SC (United States). Savannah River National Lab. (SRNL)

    2017-09-29

    Ensemble modeling (EM), the creation of multiple atmospheric simulations for a given time period, has become an essential tool for characterizing uncertainties in model predictions. We explore two novel ensemble modeling techniques: (1) perturbation of model parameters (Adaptive Programming, AP), and (2) data assimilation (Ensemble Kalman Filter, EnKF). The current research is an extension to work from last year and examines transport on a small spatial scale (<100 km) in complex terrain, for more rigorous testing of the ensemble technique. Two different release cases were studied, a coastal release (SF6) and an inland release (Freon) which consisted of two release times. Observations of tracer concentration and meteorology are used to judge the ensemble results. In addition, adaptive grid techniques have been developed to reduce required computing resources for transport calculations. Using a 20- member ensemble, the standard approach generated downwind transport that was quantitatively good for both releases; however, the EnKF method produced additional improvement for the coastal release where the spatial and temporal differences due to interior valley heating lead to the inland movement of the plume. The AP technique showed improvements for both release cases, with more improvement shown in the inland release. This research demonstrated that transport accuracy can be improved when models are adapted to a particular location/time or when important local data is assimilated into the simulation and enhances SRNL’s capability in atmospheric transport modeling in support of its current customer base and local site missions, as well as our ability to attract new customers within the intelligence community.

  2. Ensemble data assimilation in the Red Sea: sensitivity to ensemble selection and atmospheric forcing

    KAUST Repository

    Toye, Habib

    2017-05-26

    We present our efforts to build an ensemble data assimilation and forecasting system for the Red Sea. The system consists of the high-resolution Massachusetts Institute of Technology general circulation model (MITgcm) to simulate ocean circulation and of the Data Research Testbed (DART) for ensemble data assimilation. DART has been configured to integrate all members of an ensemble adjustment Kalman filter (EAKF) in parallel, based on which we adapted the ensemble operations in DART to use an invariant ensemble, i.e., an ensemble Optimal Interpolation (EnOI) algorithm. This approach requires only single forward model integration in the forecast step and therefore saves substantial computational cost. To deal with the strong seasonal variability of the Red Sea, the EnOI ensemble is then seasonally selected from a climatology of long-term model outputs. Observations of remote sensing sea surface height (SSH) and sea surface temperature (SST) are assimilated every 3 days. Real-time atmospheric fields from the National Center for Environmental Prediction (NCEP) and the European Center for Medium-Range Weather Forecasts (ECMWF) are used as forcing in different assimilation experiments. We investigate the behaviors of the EAKF and (seasonal-) EnOI and compare their performances for assimilating and forecasting the circulation of the Red Sea. We further assess the sensitivity of the assimilation system to various filtering parameters (ensemble size, inflation) and atmospheric forcing.

  3. Ensemble data assimilation in the Red Sea: sensitivity to ensemble selection and atmospheric forcing

    Science.gov (United States)

    Toye, Habib; Zhan, Peng; Gopalakrishnan, Ganesh; Kartadikaria, Aditya R.; Huang, Huang; Knio, Omar; Hoteit, Ibrahim

    2017-07-01

    We present our efforts to build an ensemble data assimilation and forecasting system for the Red Sea. The system consists of the high-resolution Massachusetts Institute of Technology general circulation model (MITgcm) to simulate ocean circulation and of the Data Research Testbed (DART) for ensemble data assimilation. DART has been configured to integrate all members of an ensemble adjustment Kalman filter (EAKF) in parallel, based on which we adapted the ensemble operations in DART to use an invariant ensemble, i.e., an ensemble Optimal Interpolation (EnOI) algorithm. This approach requires only single forward model integration in the forecast step and therefore saves substantial computational cost. To deal with the strong seasonal variability of the Red Sea, the EnOI ensemble is then seasonally selected from a climatology of long-term model outputs. Observations of remote sensing sea surface height (SSH) and sea surface temperature (SST) are assimilated every 3 days. Real-time atmospheric fields from the National Center for Environmental Prediction (NCEP) and the European Center for Medium-Range Weather Forecasts (ECMWF) are used as forcing in different assimilation experiments. We investigate the behaviors of the EAKF and (seasonal-) EnOI and compare their performances for assimilating and forecasting the circulation of the Red Sea. We further assess the sensitivity of the assimilation system to various filtering parameters (ensemble size, inflation) and atmospheric forcing.

  4. Ensemble-based Experimental Atmospheric Reanalysis using a Global Coupled Atmosphere-Ocean GCM

    Science.gov (United States)

    Komori, N.; Enomoto, T.; Miyoshi, T.; Yamazaki, A.; Kuwano-Yoshida, A.; Taguchi, B.

    2016-02-01

    To enhance the capability of the local ensemble transform Kalman filter (LETKF) with the Atmospheric general circulation model (GCM) for the Earth Simulator (AFES), a new system has been developed by replacing AFES with the Coupled atmosphere-ocean GCM for the Earth Simulator (CFES). An initial test of the prototype of the CFES-LETKF system has been completed successfully, assimilating atmospheric observational data (NCEP PREPBUFR archived at UCAR) every 6 hours to update the atmospheric variables, whereas the oceanic variables are kept unchanged throughout the assimilation procedure. An experimental retrospective analysis-forecast cycle with the coupled system (CLERA-A) starts on August 1, 2008, and the atmospheric initial conditions (63 members) are taken from the second generation of AFES-LETKF experimental ensemble reanalysis (ALERA2). The ALERA2 analyses are also used as forcing of stand-alone 63-member ensemble simulations with the Ocean GCM for the Earth Simulator (EnOFES), from which the oceanic initial conditions for the CLERA-A are taken. The ensemble spread of SST is larger in CLERA-A than in EnOFES, suggesting positive feedback between the ocean and the atmosphere. Although SST in CLERA-A suffers from the common biases among many coupled GCMs, the ensemble spreads of air temperature and specific humidity in the lower troposphere are larger in CLERA-A than in ALERA2. Thus replacement of AFES with CFES successfully contributes to mitigate an underestimation of the ensemble spread near the surface resulting from the single boundary condition for all ensemble members and the lack of atmosphere-ocean interaction. In addition, the basin-scale structure of surface atmospheric variables over the tropical Pacific is well reconstructed from the ensemble correlation in CLERA-A but not ALERA2. This suggests that use of a coupled GCM rather than an atmospheric GCM could be important even for atmospheric reanalysis with an ensemble-based data assimilation system.

  5. Ensemble Atmospheric Properties of Small Planets around M Dwarfs

    Science.gov (United States)

    Guo, Xueying; Ballard, Sarah; Dragomir, Diana

    2018-01-01

    With the growing number of planets discovered by the Kepler mission and ground-base surveys, people start to try to understand the atmospheric features of those uncovered new worlds. While it has been found that hot Jupiters exhibit diverse atmosphere composition with both clear and cloudy/hazy atmosphere possible, similar studies on ensembles of smaller planets (Earth analogs) have been held up due to the faintness of most of their host stars. In this work, a sample of 20 Earth analogs of similar periods around M dwarfs with existing Kepler transit information and Spitzer observations is composed, complemented with previously studies GJ1214b and GJ1132b, as well as the recently announced 7 small planets in the TRAPPIST-1 system. We evaluate their transit depths with uncertainties on the Spitzer 4.5 micron band using the “pixel-level decorrelation” method, and together with their well analyzed Kepler data and Hubble data, we put constraints on their atmosphere haze slopes and cloud levels. Aside from improving the understanding of ensemble properties of small planets, this study will also provide clues of potential targets for detailed atmospheric studies using the upcoming James Webb Telescope.

  6. Ensemble data assimilation in the Whole Atmosphere Community Climate Model

    Science.gov (United States)

    Pedatella, N. M.; Raeder, K.; Anderson, J. L.; Liu, H.-L.

    2014-08-01

    We present results pertaining to the assimilation of real lower, middle, and upper atmosphere observations in the Whole Atmosphere Community Climate Model (WACCM) using the Data Assimilation Research Testbed (DART) ensemble adjustment Kalman filter. The ability to assimilate lower atmosphere observations of aircraft and radiosonde temperature and winds, satellite drift winds, and Constellation Observing System for Meteorology, Ionosphere, and Climate refractivity along with middle/upper atmosphere temperature observations from SABER and Aura MLS is demonstrated. The WACCM+DART data assimilation system is shown to be able to reproduce the salient features, and variability, of the troposphere present in the National Centers for Environmental Prediction/National Center for Atmospheric Research Re-Analysis. In the mesosphere, the fit of WACCM+DART to observations is found to be slightly worse when only lower atmosphere observations are assimilated compared to a control experiment that is reflective of the model climatological variability. This differs from previous results which found that assimilation of lower atmosphere observations improves the fit to mesospheric observations. This discrepancy is attributed to the fact that due to the gravity wave drag parameterizations, the model climatology differs significantly from the observations in the mesosphere, and this is not corrected by the assimilation of lower atmosphere observations. The fit of WACCM+DART to mesospheric observations is, however, significantly improved compared to the control experiment when middle/upper atmosphere observations are assimilated. We find that assimilating SABER observations reduces the root-mean-square error and bias of WACCM+DART relative to the independent Aura MLS observations by ˜50%, demonstrating that assimilation of middle/upper atmosphere observations is essential for accurate specification of the mesosphere and lower thermosphere region in WACCM+DART. Last, we demonstrate that

  7. Regionality of Dust Haze Transport in the Mars Atmosphere Revealed by Ensemble Simulations

    Science.gov (United States)

    Ogohara, K.

    2017-06-01

    Regionality of dust haze transport in the Mars atmosphere is investigated by ensemble simulations using a GCM. It is turned out that processes of dust haze dispersion by advection are categorized into a few cases.

  8. Dispersion of aerosol particles in the free atmosphere using ensemble forecasts

    Directory of Open Access Journals (Sweden)

    T. Haszpra

    2013-10-01

    Full Text Available The dispersion of aerosol particle pollutants is studied using 50 members of an ensemble forecast in the example of a hypothetical free atmospheric emission above Fukushima over a period of 2.5 days. Considerable differences are found among the dispersion predictions of the different ensemble members, as well as between the ensemble mean and the deterministic result at the end of the observation period. The variance is found to decrease with the particle size. The geographical area where a threshold concentration is exceeded in at least one ensemble member expands to a 5–10 times larger region than the area from the deterministic forecast, both for air column "concentration" and in the "deposition" field. We demonstrate that the root-mean-square distance of any particle from its own clones in the ensemble members can reach values on the order of one thousand kilometers. Even the centers of mass of the particle cloud of the ensemble members deviate considerably from that obtained by the deterministic forecast. All these indicate that an investigation of the dispersion of aerosol particles in the spirit of ensemble forecast contains useful hints for the improvement of risk assessment.

  9. An ensemble data assimilation system to estimate CO2 surface fluxes from atmospheric trace gas observations

    NARCIS (Netherlands)

    Peters, W.; Miller, J.B.; Whitaker, J.; Denning, A.S.; Hirsch, A.; Krol, M.C.; Zupanski, D.; Bruhwiler, L.; Tans, P.P.

    2005-01-01

    We present a data assimilation system to estimate surface fluxes of CO2 and other trace gases from observations of their atmospheric abundances. The system is based on ensemble data assimilation methods under development for Numerical Weather Prediction (NWP) and is the first of its kind to be used

  10. Can the confidence in long range atmospheric transport models be increased? The Pan-European experience of ENSEMBLE

    DEFF Research Database (Denmark)

    Galmarini, S.; Bianconi, R.; Klug, W.

    2004-01-01

    Is atmospheric dispersion forecasting an important asset of the early-phase nuclear emergency response management? Is there a 'perfect atmospheric dispersion model'? Is there a way to make the results of dispersion models more reliable and trustworthy? While seeking to answer these questions...... the multi-model ensemble dispersion forecast system ENSEMBLE will be presented....

  11. Can the confidence in long range atmospheric transport models be increased? The pan-european experience of ensemble.

    Science.gov (United States)

    Galmarini, S; Bianconi, R; Klug, W; Mikkelsen, T; Addis, R; Andronopoulos, S; Astrup, P; Baklanov, A; Bartniki, J; Bartzis, J C; Bellasio, R; Bompay, F; Buckley, R; Bouzom, M; Champion, H; D'Amours, R; Davakis, E; Eleveld, H; Geertsema, G T; Glaab, H; Kollax, M; Ilvonen, M; Manning, A; Pechinger, U; Persson, C; Polreich, E; Potemski, S; Prodanova, M; Saltbones, J; Slaper, H; Sofiev, M A; Syrakov, D; Sørensen, J H; Van der Auwera, L; Valkama, I; Zelazny, R

    2004-01-01

    Is atmospheric dispersion forecasting an important asset of the early-phase nuclear emergency response management? Is there a 'perfect atmospheric dispersion model'? Is there a way to make the results of dispersion models more reliable and trustworthy? While seeking to answer these questions the multi-model ensemble dispersion forecast system ENSEMBLE will be presented. Copyright 2004 Oxford University Press

  12. Regional effects of atmospheric aerosols on temperature: an evaluation of an ensemble of online coupled models

    Science.gov (United States)

    Baró, Rocío; Palacios-Peña, Laura; Baklanov, Alexander; Balzarini, Alessandra; Brunner, Dominik; Forkel, Renate; Hirtl, Marcus; Honzak, Luka; Pérez, Juan Luis; Pirovano, Guido; San José, Roberto; Schröder, Wolfram; Werhahn, Johannes; Wolke, Ralf; Žabkar, Rahela; Jiménez-Guerrero, Pedro

    2017-08-01

    The climate effect of atmospheric aerosols is associated with their influence on the radiative budget of the Earth due to the direct aerosol-radiation interactions (ARIs) and indirect effects, resulting from aerosol-cloud-radiation interactions (ACIs). Online coupled meteorology-chemistry models permit the description of these effects on the basis of simulated atmospheric aerosol concentrations, although there is still some uncertainty associated with the use of these models. Thus, the objective of this work is to assess whether the inclusion of atmospheric aerosol radiative feedbacks of an ensemble of online coupled models improves the simulation results for maximum, mean and minimum temperature at 2 m over Europe. The evaluated models outputs originate from EuMetChem COST Action ES1004 simulations for Europe, differing in the inclusion (or omission) of ARI and ACI in the various models. The cases studies cover two important atmospheric aerosol episodes over Europe in the year 2010: (i) a heat wave event and a forest fire episode (July-August 2010) and (ii) a more humid episode including a Saharan desert dust outbreak in October 2010. The simulation results are evaluated against observational data from the E-OBS gridded database. The results indicate that, although there is only a slight improvement in the bias of the simulation results when including the radiative feedbacks, the spatiotemporal variability and correlation coefficients are improved for the cases under study when atmospheric aerosol radiative effects are included.

  13. Regional effects of atmospheric aerosols on temperature: an evaluation of an ensemble of online coupled models

    Directory of Open Access Journals (Sweden)

    R. Baró

    2017-08-01

    Full Text Available The climate effect of atmospheric aerosols is associated with their influence on the radiative budget of the Earth due to the direct aerosol–radiation interactions (ARIs and indirect effects, resulting from aerosol–cloud–radiation interactions (ACIs. Online coupled meteorology–chemistry models permit the description of these effects on the basis of simulated atmospheric aerosol concentrations, although there is still some uncertainty associated with the use of these models. Thus, the objective of this work is to assess whether the inclusion of atmospheric aerosol radiative feedbacks of an ensemble of online coupled models improves the simulation results for maximum, mean and minimum temperature at 2 m over Europe. The evaluated models outputs originate from EuMetChem COST Action ES1004 simulations for Europe, differing in the inclusion (or omission of ARI and ACI in the various models. The cases studies cover two important atmospheric aerosol episodes over Europe in the year 2010: (i a heat wave event and a forest fire episode (July–August 2010 and (ii a more humid episode including a Saharan desert dust outbreak in October 2010. The simulation results are evaluated against observational data from the E-OBS gridded database. The results indicate that, although there is only a slight improvement in the bias of the simulation results when including the radiative feedbacks, the spatiotemporal variability and correlation coefficients are improved for the cases under study when atmospheric aerosol radiative effects are included.

  14. Global atmospheric carbon budget: results from an ensemble of atmospheric CO2 inversions

    Directory of Open Access Journals (Sweden)

    P. Peylin

    2013-10-01

    Full Text Available Atmospheric CO2 inversions estimate surface carbon fluxes from an optimal fit to atmospheric CO2 measurements, usually including prior constraints on the flux estimates. Eleven sets of carbon flux estimates are compared, generated by different inversions systems that vary in their inversions methods, choice of atmospheric data, transport model and prior information. The inversions were run for at least 5 yr in the period between 1990 and 2010. Mean fluxes for 2001–2004, seasonal cycles, interannual variability and trends are compared for the tropics and northern and southern extra-tropics, and separately for land and ocean. Some continental/basin-scale subdivisions are also considered where the atmospheric network is denser. Four-year mean fluxes are reasonably consistent across inversions at global/latitudinal scale, with a large total (land plus ocean carbon uptake in the north (−3.4 Pg C yr−1 (±0.5 Pg C yr−1 standard deviation, with slightly more uptake over land than over ocean, a significant although more variable source over the tropics (1.6 ± 0.9 Pg C yr−1 and a compensatory sink of similar magnitude in the south (−1.4 ± 0.5 Pg C yr−1 corresponding mainly to an ocean sink. Largest differences across inversions occur in the balance between tropical land sources and southern land sinks. Interannual variability (IAV in carbon fluxes is larger for land than ocean regions (standard deviation around 1.06 versus 0.33 Pg C yr−1 for the 1996–2007 period, with much higher consistency among the inversions for the land. While the tropical land explains most of the IAV (standard deviation ~ 0.65 Pg C yr−1, the northern and southern land also contribute (standard deviation ~ 0.39 Pg C yr−1. Most inversions tend to indicate an increase of the northern land carbon uptake from late 1990s to 2008 (around 0.1 Pg C yr−1, predominantly in North Asia. The mean seasonal cycle appears to be well constrained by the atmospheric data over

  15. Development of an ensemble Kalman filter data assimilation system for the Venusian atmosphere.

    Science.gov (United States)

    Sugimoto, Norihiko; Yamazaki, Akira; Kouyama, Toru; Kashimura, Hiroki; Enomoto, Takeshi; Takagi, Masahiro

    2017-08-24

    The size and mass of Venus is similar to those of the Earth; however, its atmospheric dynamics are considerably different and they are poorly understood due to limited observations and computational difficulties. Here, we developed a data assimilation system based on the local ensemble transform Kalman filter (LETKF) for a Venusian Atmospheric GCM for the Earth Simulator (VAFES), to make full use of the observational data. To examine the validity of the system, two datasets were assimilated separately into the VAFES forecasts forced with solar heating that excludes the diurnal component Qz; one was created from a VAFES run forced with solar heating that includes the diurnal component Qt, whereas the other was based on observations made by the Venus Monitoring Camera (VMC) onboard the Venus Express. The VAFES-LETKF system rapidly reduced the errors between the analysis and forecasts. In addition, the VAFES-LETKF system successfully reproduced the thermal tide excited by the diurnal component of solar heating, even though the second datasets only included horizontal winds at a single altitude on the dayside with a long interval of approximately one Earth day. This advanced system could be useful in the analysis of future datasets from the Venus Climate Orbiter 'Akatsuki'.

  16. On the Sensitivity of Atmospheric Ensembles to Cloud Microphysics in Long-Term Cloud-Resolving Model Simulations

    Science.gov (United States)

    Zeng, Xiping; Tao, Wei-Kuo; Lang, Stephen; Hou, Arthur Y.; Zhang, Minghua; Simpson, Joanne

    2008-01-01

    Month-long large-scale forcing data from two field campaigns are used to drive a cloud-resolving model (CRM) and produce ensemble simulations of clouds and precipitation. Observational data are then used to evaluate the model results. To improve the model results, a new parameterization of the Bergeron process is proposed that incorporates the number concentration of ice nuclei (IN). Numerical simulations reveal that atmospheric ensembles are sensitive to IN concentration and ice crystal multiplication. Two- (2D) and three-dimensional (3D) simulations are carried out to address the sensitivity of atmospheric ensembles to model dimensionality. It is found that the ensembles with high IN concentration are more sensitive to dimensionality than those with low IN concentration. Both the analytic solutions of linear dry models and the CRM output show that there are more convective cores with stronger updrafts in 3D simulations than in 2D, which explains the differing sensitivity of the ensembles to dimensionality at different IN concentrations.

  17. Land-total and Ocean-total Precipitation and Evaporation from a Community Atmosphere Model version 5 Perturbed Parameter Ensemble

    Energy Technology Data Exchange (ETDEWEB)

    Covey, Curt [Lawrence Livermore National Lab. (LLNL), Livermore, CA (United States); Lucas, Donald D. [Lawrence Livermore National Lab. (LLNL), Livermore, CA (United States); Trenberth, Kevin E. [National Center for Atmospheric Research, Boulder, CO (United States)

    2016-03-02

    This document presents the large scale water budget statistics of a perturbed input-parameter ensemble of atmospheric model runs. The model is Version 5.1.02 of the Community Atmosphere Model (CAM). These runs are the “C-Ensemble” described by Qian et al., “Parametric Sensitivity Analysis of Precipitation at Global and Local Scales in the Community Atmosphere Model CAM5” (Journal of Advances in Modeling the Earth System, 2015). As noted by Qian et al., the simulations are “AMIP type” with temperature and sea ice boundary conditions chosen to match surface observations for the five year period 2000-2004. There are 1100 ensemble members in addition to one run with default inputparameter values.

  18. Insights into the early Eocene hydrological cycle from an ensemble of atmosphere-ocean GCM simulations

    Science.gov (United States)

    Carmichael, Matthew; Lunt, Daniel; Pancost, Richard

    2015-04-01

    Recent studies utilising a range of geochemical proxies have indicated that a significant perturbation to global hydrology occurred at the Paleocene-Eocene Thermal Maximum (PETM; ~56 Ma). An enhanced hydrological cycle for the warm early Eocene is also suggested to have played a key role in maintaining high-latitude warmth during this interval. Comparisons of proxy data to General Circulation Model (GCM) simulated hydrology have not widely been made however, and inter-model variability remains poorly characterised despite significant differences in simulated surface temperatures. In this work, we address this by undertaking an intercomparison of GCM-derived precipitation distributions within the EoMIP ensemble (Lunt et al., 2012), which includes previously-published early Eocene simulations performed using five GCMs differing in boundary conditions, model structure and precipitation-relevant parameterisation schemes. We show that an intensified hydrological cycle is simulated for all Eocene simulations relative to preindustrial. This is primarily due to elevated atmospheric paleo-CO2, although the effects of differences in paleogeography/ice sheets are also of importance in some models. For a given CO2 level, globally-averaged precipitation rates vary widely between models, largely as a result of different climate sensitivities (dT/dCO2) and differing parameterisation schemes. Despite this, models with similar global precipitation sensitivities (dP/dT) display different regional responses for a given temperature change. Regions which are particularly model sensitive include the South Pacific, tropical Africa and the Tethys and may represent targets for future proxy acquisition. A comparison of leaf-fossil-derived precipitation estimates with GCM data illustrates that models tend to unanimously underestimate early Eocene precipitation rates at high latitudes. Models which warm these regions via elevated CO2 or by utilising alternative parameterisations are most

  19. Iterative ensemble Kalman filter for atmospheric dispersion in nuclear accidents: An application to Kincaid tracer experiment

    Energy Technology Data Exchange (ETDEWEB)

    Zhang, X.L.; Su, G.F.; Chen, J.G. [Institute of Public Safety Research, Department of Engineering Physics, Tsinghua University, Beijing 100084 (China); Raskob, W. [Institute for Nuclear and Energy Technologies, Karlsruhe Institute of Technology, Karlsruhe, D-76021 (Germany); Yuan, H.Y., E-mail: hy-yuan@outlook.com [Institute of Public Safety Research, Department of Engineering Physics, Tsinghua University, Beijing 100084 (China); Huang, Q.Y. [Institute of Public Safety Research, Department of Engineering Physics, Tsinghua University, Beijing 100084 (China)

    2015-10-30

    Highlights: • We integrate the iterative EnKF method into the POLYPHEMUS platform. • We thoroughly evaluate the data assimilation system against the Kincaid dataset. • The data assimilation system substantially improves the model predictions. • More than 60% of the retrieved emissions are within a factor two of actual values. • The results reveal that the boundary layer height is the key influential factor. - Abstract: Information about atmospheric dispersion of radionuclides is vitally important for planning effective countermeasures during nuclear accidents. Results of dispersion models have high spatial and temporal resolutions, but they are not accurate enough due to the uncertain source term and the errors in meteorological data. Environmental measurements are more reliable, but they are scarce and unable to give forecasts. In this study, our newly proposed iterative ensemble Kalman filter (EnKF) data assimilation scheme is used to combine model results and environmental measurements. The system is thoroughly validated against the observations in the Kincaid tracer experiment. The initial first-guess emissions are assumed to be six magnitudes underestimated. The iterative EnKF system rapidly corrects the errors in the emission rate and wind data, thereby significantly improving the model results (>80% reduction of the normalized mean square error, r = 0.71). Sensitivity tests are conducted to investigate the influence of meteorological parameters. The results indicate that the system is sensitive to boundary layer height. When the heights from the numerical weather prediction model are used, only 62.5% of reconstructed emission rates are within a factor two of the actual emissions. This increases to 87.5% when the heights derived from the on-site observations are used.

  20. EPA Contribution to Manuscript "Evaluation and Error Apportionment of an Ensemble of Atmospheric Chemistry Transport Modelling Systems: Multi-variable Temporal and Spatial Breakdown"

    Data.gov (United States)

    U.S. Environmental Protection Agency — This dataset contains the data contributed by EPA/ORD/NERL/CED researchers to the manuscript "Evaluation and Error Apportionment of an Ensemble of Atmospheric...

  1. Development and Testing of a Coupled Ocean-atmosphere Mesoscale Ensemble Prediction System

    Science.gov (United States)

    2011-06-28

    LK, Hodur R (2000) The interaction between hurricane Opal (1995) and a warm core ring in the Gulf of Mexico. Mon Wea Rev 128:1347–1365 Houtekamer PL...and moving and fixed nest options. Results from relatively coarse resolution (∼27-km) ensemble simulations of Hurricanes Hanna and Ike demonstrate...moving and fixed nest options. Results from relatively coarse resolution (???27-km) ensemble simulations of Hurricanes Hanna and Ike demonstrate that

  2. Removing Circulation Effects to Assess Central U.S. Land-Atmosphere Interactions in the CESM Large Ensemble

    Science.gov (United States)

    Merrifield, Anna; Lehner, Flavio; Xie, Shang-Ping; Deser, Clara

    2017-10-01

    Interannual variability of summer surface air temperature (SAT) in the central United States (U.S.) is influenced by atmospheric circulation and land surface feedbacks. Here a method of dynamical adjustment is used to remove the effects of circulation on summer SAT variability over North America in the Community Earth System Model Large Ensemble. The residual SAT variability is shown to reflect thermodynamic feedbacks associated with land surface conditions. In particular, the central U.S. is a "hot spot" of land-atmosphere interaction, with residual SAT accounting for more than half of the total SAT variability. Within the "hot spot," residual SAT anomalies show higher month-to-month persistence through the warm season and a redder spectrum than dynamically induced SAT anomalies. Residual SAT variability in this region is also shown to be related to preseason soil moisture conditions, surface flux variability, and local atmospheric pressure anomalies.

  3. A Hierarchical Multivariate Bayesian Approach to Ensemble Model output Statistics in Atmospheric Prediction

    Science.gov (United States)

    2017-09-01

    from this PDF . EMOS models also use multiple linear regression to characterize the sensitivity of a univariate weather quantity—that is, the...classical least-squares approach to multivariate multiple linear regression using both measures-oriented and distributions-oriented scoring rules...14. SUBJECT TERMS ensemble model output statistics, statistical post-processing, multivariate multiple linear regression, Bayesian data analysis

  4. Insights in time dependent cross compartment sensitivities from ensemble simulations with the fully coupled subsurface-land surface-atmosphere model TerrSysMP

    Science.gov (United States)

    Schalge, Bernd; Rihani, Jehan; Haese, Barbara; Baroni, Gabriele; Erdal, Daniel; Haefliger, Vincent; Lange, Natascha; Neuweiler, Insa; Hendricks-Franssen, Harrie-Jan; Geppert, Gernot; Ament, Felix; Kollet, Stefan; Cirpka, Olaf; Saavedra, Pablo; Han, Xujun; Attinger, Sabine; Kunstmann, Harald; Vereecken, Harry; Simmer, Clemens

    2017-04-01

    Currently, an integrated approach to simulating the earth system is evolving where several compartment models are coupled to achieve the best possible physically consistent representation. We used the model TerrSysMP, which fully couples subsurface, land surface and atmosphere, in a synthetic study that mimicked the Neckar catchment in Southern Germany. A virtual reality run at a high resolution of 400m for the land surface and subsurface and 1.1km for the atmosphere was made. Ensemble runs at a lower resolution (800m for the land surface and subsurface) were also made. The ensemble was generated by varying soil and vegetation parameters and lateral atmospheric forcing among the different ensemble members in a systematic way. It was found that the ensemble runs deviated for some variables and some time periods largely from the virtual reality reference run (the reference run was not covered by the ensemble), which could be related to the different model resolutions. This was for example the case for river discharge in the summer. We also analyzed the spread of model states as function of time and found clear relations between the spread and the time of the year and weather conditions. For example, the ensemble spread of latent heat flux related to uncertain soil parameters was larger under dry soil conditions than under wet soil conditions. Another example is that the ensemble spread of atmospheric states was more influenced by uncertain soil and vegetation parameters under conditions of low air pressure gradients (in summer) than under conditions with larger air pressure gradients in winter. The analysis of the ensemble of fully coupled model simulations provided valuable insights in the dynamics of land-atmosphere feedbacks which we will further highlight in the presentation.

  5. Atmospheric dynamical changes as a contributor to deglacial climate variability: results from an ensemble of transient deglacial simulations

    Science.gov (United States)

    Andres, Heather; Tarasov, Lev

    2017-04-01

    The atmosphere is often assumed to play a passive role in centennial- to millennial-timescale climate variations of the last deglaciation due to its short response times ( years) and the absence of abrupt changes in external climate forcings. Nevertheless, atmospheric dynamical responses to changes in ice sheet topography and albedo can affect the entire Northern Hemisphere through the altering of Rossby stationary wave patterns and changes to the North Atlantic eddy-driven jet. These responses appear sensitive to the particular configuration of Northern Hemisphere land ice, so small changes have the potential to reorganize atmospheric circulation with impacts on precipitation distributions, ocean surface currents and sea ice extent. Indirect proxy evidence, idealized theoretical studies, and "snapshot" simulations performed at different periods during the last glacial cycle indicate that between the Last Glacial Maximum and the preindustrial period the North Atlantic eddy-driven jet weakened, became less zonally-oriented, and exhibited greater variability. How the transition (or transitions) between the glacial atmospheric state and the interglacial state occurred is less clear. To address this question, we performed an ensemble of transient simulations of the last deglaciation using the Planet Simulator coupled atmosphere-ocean-vegetation-sea ice model (PlaSim, at an atmospheric resolution of T42) forced by variants of the GLAC1-D deglacial ice sheet chronology. We characterize simulated changes in stationary wave patterns over this period as well as changes in the strength and position of the North Atlantic eddy-driven jet. In particular, we document the range of timescales for these changes and compare the simulated climate signatures of these transitions to data archives of precipitation and sea ice extent.

  6. A new atmospheric proxy for sea level variability in the southeastern North Sea: observations and future ensemble projections

    Science.gov (United States)

    Dangendorf, Sönke; Wahl, Thomas; Nilson, Enno; Klein, Birgit; Jensen, Jürgen

    2014-07-01

    Atmosphere-ocean interactions are known to dominate seasonal to decadal sea level variability in the southeastern North Sea. In this study an atmospheric proxy for the observed sea level variability in the German Bight is introduced. Monthly mean sea level (MSL) time series from 13 tide gauges located in the German Bight and one virtual station record are evaluated in comparison to sea level pressure fields over the North Atlantic and Europe. A quasi-linear relationship between MSL in the German Bight and sea level pressure over Scandinavia and the Iberian Peninsula is found. This relationship is used (1) to evaluate the atmospheric contribution to MSL variability in hindcast experiments over the period from 1871-2008 with data from the twentieth century reanalysis v2 (20CRv2), (2) to isolate the high frequency meteorological variability of MSL from longer-term changes, (3) to derive ensemble projections of the atmospheric contribution to MSL until 2100 with eight different coupled global atmosphere-ocean models (AOGCM's) under the A1B emission scenario and (4) two additional projections for one AOGCM (ECHAM5/MPI-OM) under the B1 and A2 emission scenarios. The hindcast produces a reasonable good reconstruction explaining approximately 80 % of the observed MSL variability over the period from 1871 to 2008. Observational features such as the divergent seasonal trend development in the second half of the twentieth century, i.e. larger trends from January to March compared to the rest of the year, and regional variations along the German North Sea coastline in trends and variability are well described. For the period from 1961 to 1990 the Kolmogorov-Smirnow test is used to evaluate the ability of the eight AOGCMs to reproduce the observed statistical properties of MSL variations. All models are able to reproduce the statistical distribution of atmospheric MSL. For the target year 2100 the models point to a slight increase in the atmospheric component of MSL with

  7. An ensemble Kalman filter for atmospheric data assimilation: Application to wind tunnel data

    Science.gov (United States)

    Zheng, D. Q.; Leung, J. K. C.; Lee, B. Y.

    2010-05-01

    In the previous work ( Zheng et al., 2007, 2009), a data assimilation method, based on ensemble Kalman filter, has been applied to a Monte Carlo Dispersion Model (MCDM). The results were encouraging when the method was tested by the twin experiment and a short-range field experiment. In this technical note, the measured data collected in a wind tunnel experiment have been assimilated into the Monte Carlo dispersion model. The uncertain parameters in the dispersion model, including source term, release height, turbulence intensity and wind direction have been considered. The 3D parameters, i.e. the turbulence intensity and wind direction, have been perturbed by 3D random fields. In order to find the factors which may influence the assimilation results, eight tests with different specifications were carried out. Two strategies of constructing the 3D perturbation field of wind direction were proposed, and the result shows that the two level strategy performs better than the one level strategy. It is also found that proper standard deviation and the correlation radius of the perturbation field play an important role for the data assimilation results.

  8. A New Ensemble of Perturbed-Input-Parameter Simulations by the Community Atmosphere Model

    Energy Technology Data Exchange (ETDEWEB)

    Covey, C; Brandon, S; Bremer, P T; Domyancis, D; Garaizar, X; Johannesson, G; Klein, R; Klein, S A; Lucas, D D; Tannahill, J; Zhang, Y

    2011-10-27

    Uncertainty quantification (UQ) is a fundamental challenge in the numerical simulation of Earth's weather and climate, and other complex systems. It entails much more than attaching defensible error bars to predictions: in particular it includes assessing low-probability but high-consequence events. To achieve these goals with models containing a large number of uncertain input parameters, structural uncertainties, etc., raw computational power is needed. An automated, self-adapting search of the possible model configurations is also useful. Our UQ initiative at the Lawrence Livermore National Laboratory has produced the most extensive set to date of simulations from the US Community Atmosphere Model. We are examining output from about 3,000 twelve-year climate simulations generated with a specialized UQ software framework, and assessing the model's accuracy as a function of 21 to 28 uncertain input parameter values. Most of the input parameters we vary are related to the boundary layer, clouds, and other sub-grid scale processes. Our simulations prescribe surface boundary conditions (sea surface temperatures and sea ice amounts) to match recent observations. Fully searching this 21+ dimensional space is impossible, but sensitivity and ranking algorithms can identify input parameters having relatively little effect on a variety of output fields, either individually or in nonlinear combination. Bayesian statistical constraints, employing a variety of climate observations as metrics, also seem promising. Observational constraints will be important in the next step of our project, which will compute sea surface temperatures and sea ice interactively, and will study climate change due to increasing atmospheric carbon dioxide.

  9. ESPC Coupled Global Ensemble Design

    Science.gov (United States)

    2014-09-30

    square error of the ensemble mean (RMSE), Continuous Ranked Probability Skill score ( CRPS ), the bias of the ensemble mean, and a measure of the...boundary conditions use a multi-year history of global ocean model output. RESULTS Atmosphere component: 1) Milestone: Develop ensemble...significant improvements, mainly in the ensemble 10m wind speed RMSE and CRPS metrics. An example of these improvements is shown for the case of the

  10. Surface drift prediction in the Adriatic Sea using hyper-ensemble statistics on atmospheric, ocean and wave models: Uncertainties and probability distribution areas

    Science.gov (United States)

    Rixen, M.; Ferreira-Coelho, E.; Signell, R.

    2008-01-01

    Despite numerous and regular improvements in underlying models, surface drift prediction in the ocean remains a challenging task because of our yet limited understanding of all processes involved. Hence, deterministic approaches to the problem are often limited by empirical assumptions on underlying physics. Multi-model hyper-ensemble forecasts, which exploit the power of an optimal local combination of available information including ocean, atmospheric and wave models, may show superior forecasting skills when compared to individual models because they allow for local correction and/or bias removal. In this work, we explore in greater detail the potential and limitations of the hyper-ensemble method in the Adriatic Sea, using a comprehensive surface drifter database. The performance of the hyper-ensembles and the individual models are discussed by analyzing associated uncertainties and probability distribution maps. Results suggest that the stochastic method may reduce position errors significantly for 12 to 72??h forecasts and hence compete with pure deterministic approaches. ?? 2007 NATO Undersea Research Centre (NURC).

  11. Quality assessment of atmospheric surface fields over the Baltic Sea from an ensemble of regional climate model simulations with respect to ocean dynamics

    Directory of Open Access Journals (Sweden)

    H. E. Markus Meier

    2011-05-01

    Full Text Available Climate model results for the Baltic Sea region from an ensemble of eight simulations using the Rossby Centre Atmosphere model version 3 (RCA3 driven with lateral boundary data from global climate models (GCMs are compared with results from a downscaled ERA40 simulation and gridded observations from 1980-2006. The results showed that data from RCA3 scenario simulations should not be used as forcing for Baltic Sea models in climate change impact studies because biases of the control climate significantly affect the simulated changes of future projections. For instance, biases of the sea ice cover in RCA3 in the present climate affect the sensitivity of the model's response to changing climate due to the ice-albedo feedback. From the large ensemble of available RCA3 scenario simulations two GCMs with good performance in downscaling experiments during the control period 1980-2006 were selected. In this study, only the quality of atmospheric surface fields over the Baltic Sea was chosen as a selection criterion. For the greenhouse gas emission scenario A1B two transient simulations for 1961-2100 driven by these two GCMs were performed using the regional, fully coupled atmosphere-ice-ocean model RCAO. It was shown that RCAO has the potential to improve the results in downscaling experiments driven by GCMs considerably, because sea surface temperatures and sea ice concentrations are calculated more realistically with RCAO than when RCA3 has been forced with surface boundary data from GCMs. For instance, the seasonal 2 m air temperature cycle is closer to observations in RCAO than in RCA3 downscaling simulations. However, the parameterizations of air-sea fluxes in RCAO need to be improved.

  12. Global Ensemble Forecast System (GEFS) [1 Deg.

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — The Global Ensemble Forecast System (GEFS) is a weather forecast model made up of 21 separate forecasts, or ensemble members. The National Centers for Environmental...

  13. A Projection of Changes in Landfilling Atmospheric River Frequency and Extreme Precipitation over Western North America from the Large Ensemble CESM Simulations

    Energy Technology Data Exchange (ETDEWEB)

    Hagos, Samson M.; Leung, Lai-Yung R.; Yoon, Jin-Ho; Lu, Jian; Gao, Yang

    2016-02-06

    Simulations from the Community Earth System Model Large Ensemble project are analyzed to investigate the impact of global warming on atmospheric rivers (ARs). The model has notable biases in simulating the subtropical jet position and the relationship between extreme precipitation and moisture transport. After accounting for these biases, the model projects an ensemble mean increase of 35% in the number of landfalling AR days between the last twenty years of the 20th and 21st centuries. However, the number of AR associated extreme precipitation days increases only by 28% because the moisture transport required to produce extreme precipitation also increases with warming. Internal variability introduces an uncertainty of ±8% and ±7% in the projected changes in AR days and associated extreme precipitation days. In contrast, accountings for model biases only change the projected changes by about 1%. The significantly larger mean changes compared to internal variability and to the effects of model biases highlight the robustness of AR responses to global warming.

  14. Noise Reduction, Atmospheric Pressure Admittance Estimation and Long-Period Component Extraction in Time-Varying Gravity Signals Using Ensemble Empirical Mode Decomposition

    Directory of Open Access Journals (Sweden)

    Linsong Wang

    2015-01-01

    Full Text Available Time-varying gravity signals, with their nonlinear, non-stationary and multi-scale characteristics, record the physical responses of various geodynamic processes and consist of a blend of signals with various periods and amplitudes, corresponding to numerous phenomena. Superconducting gravimeter (SG records are processed in this study using a multi-scale analytical method and corrected for known effects to reduce noise, to study geodynamic phenomena using their gravimetric signatures. Continuous SG (GWR-C032 gravity and barometric data are decomposed into a series of intrinsic mode functions (IMFs using the ensemble empirical mode decomposition (EEMD method, which is proposed to alleviate some unresolved issues (the mode mixing problem and the end effect of the empirical mode decomposition (EMD. Further analysis of the variously scaled signals is based on a dyadic filter bank of the IMFs. The results indicate that removing the high-frequency IMFs can reduce the natural and man-made noise in the data, which are caused by electronic device noise, Earth background noise and the residual effects of pre-processing. The atmospheric admittances based on frequency changes are estimated from the gravity and the atmospheric pressure IMFs in various frequency bands. These time- and frequency-dependent admittance values can be used effectively to improve the atmospheric correction. Using the EEMD method as a filter, the long-period IMFs are extracted from the SG time-varying gravity signals spanning 7 years. The resulting gravity residuals are well correlated with the gravity effect caused by the _ polar motion after correcting for atmospheric effects.

  15. NYYD Ensemble

    Index Scriptorium Estoniae

    2002-01-01

    NYYD Ensemble'i duost Traksmann - Lukk E.-S. Tüüri teosega "Symbiosis", mis on salvestatud ka hiljuti ilmunud NYYD Ensemble'i CDle. 2. märtsil Rakvere Teatri väikeses saalis ja 3. märtsil Rotermanni Soolalaos, kavas Tüür, Kaumann, Berio, Reich, Yun, Hauta-aho, Buckinx

  16. Future projections of the surface heat and water budgets of the Mediterranean Sea in an ensemble of coupled atmosphere-ocean regional climate models

    Energy Technology Data Exchange (ETDEWEB)

    Dubois, C.; Somot, S.; Deque, M.; Sevault, F. [CNRM-GAME, Meteo-France, CNRS, Toulouse (France); Calmanti, S.; Carillo, A.; Dell' Aquilla, A.; Sannino, G. [ENEA, Rome (Italy); Elizalde, A.; Jacob, D. [Max Planck Institute for Meteorology, Hamburg (Germany); Gualdi, S.; Oddo, P.; Scoccimarro, E. [INGV, Bologna (Italy); L' Heveder, B.; Li, L. [Laboratoire de Meteorologie Dynamique, Paris (France)

    2012-10-15

    Within the CIRCE project ''Climate change and Impact Research: the Mediterranean Environment'', an ensemble of high resolution coupled atmosphere-ocean regional climate models (AORCMs) are used to simulate the Mediterranean climate for the period 1950-2050. For the first time, realistic net surface air-sea fluxes are obtained. The sea surface temperature (SST) variability is consistent with the atmospheric forcing above it and oceanic constraints. The surface fluxes respond to external forcing under a warming climate and show an equivalent trend in all models. This study focuses on the present day and on the evolution of the heat and water budget over the Mediterranean Sea under the SRES-A1B scenario. On the contrary to previous studies, the net total heat budget is negative over the present period in all AORCMs and satisfies the heat closure budget controlled by a net positive heat gain at the strait of Gibraltar in the present climate. Under climate change scenario, some models predict a warming of the Mediterranean Sea from the ocean surface (positive net heat flux) in addition to the positive flux at the strait of Gibraltar for the 2021-2050 period. The shortwave and latent flux are increasing and the longwave and sensible fluxes are decreasing compared to the 1961-1990 period due to a reduction of the cloud cover and an increase in greenhouse gases (GHGs) and SSTs over the 2021-2050 period. The AORCMs provide a good estimates of the water budget with a drying of the region during the twenty-first century. For the ensemble mean, he decrease in precipitation and runoff is about 10 and 15% respectively and the increase in evaporation is much weaker, about 2% compared to the 1961-1990 period which confirm results obtained in recent studies. Despite a clear consistency in the trends and results between the models, this study also underlines important differences in the model set-ups, methodology and choices of some physical parameters inducing

  17. Evaluation of ensemble atmospheric simulations in oil dispersion models at Itaguai Port region; Avaliacao do uso de resultados numericos de previsao atmosferica por conjunto na modelagem da dispersao de oleo na regiao do Porto de Itaguai

    Energy Technology Data Exchange (ETDEWEB)

    Santos, Renato Goncalves dos; Silva, Mariana P.R.; Silva, Ricardo Marcelo da; Torres Junior, Audalio R. [Universidade Federal do Rio de Janeiro (UFRJ), RJ (Brazil). Lab. de Modelagem de Processos Marinhos e Atmosfericos (LAMMA); Landau, Luiz [Universidade Federal do Rio de Janeiro (UFRJ), RJ (Brazil). Lab. de Metodos Computacinais em Engenharia (LAMCE); Sa, Reginaldo Ventura de; Hochleitner, Fabio; Correa, Eduardo Barbosa [AQUAMET Meteorologia e Projeto de Sistemas, Rio de Janeiro, RJ (Brazil)

    2008-07-01

    This work discusses the use of numerical prediction using ensemble as boundary condition in pollutants dispersion models, applied in a hypothetical case of an oil spill occurrence in Itaguai Port. The Princeton Ocean Model (POM) has been used to simulate hydrodynamics and NICOIL Eulerian model to forecast oil spill dispersion, and ensemble wind forecast from Global Forecast System (GFS), aiming to assess the importance of this parameter variability in oil dispersion at sea. The wind scenarios using ensemble members has showed significant dispersion when compared to control simulation, demonstrating that the uncertainty in the atmospheric modeling can generate considerable variations in the placement of the final spot of oil. The region of interest was the Sepetiba Bay, located on the southern coast of the Rio de Janeiro state; because of port operations carried out around the Port of Itaguai where they can, eventually, oil leaks occur. (author)

  18. Simulation of medicanes over the Mediterranean Sea in regional climate model ensembles: impact of ocean-atmosphere coupling and increased resolution

    Science.gov (United States)

    Gaertner, Miguel Angel; Jesús González-Alemán, Juan; Romera, Raquel; Domínguez, Marta; Gil, Victoria; Sánchez, Enrique; Gallardo, Clemente

    2017-04-01

    Some cyclones over the Mediterranean Sea occasionally develop a tropical structure. These cyclones, also called medicanes, can produce significant damage due to the combination of intense winds and heavy precipitation. The small size of medicanes and the importance of air-sea interaction in their formation and intensification represents a challenge for RCMs. Large ensembles of high resolution and ocean-atmosphere coupled RCM simulations are now available from MedCORDEX and EURO-CORDEX. We use these ensembles to analyze the ability of RCMs to reproduce the observed characteristics of medicanes, and to assess the impact of increasing resolution and using air-sea coupling on its simulation. As a reference for evaluating the simulations, we take the observational database of Miglietta et al. (2013), based on satellite images combined with very high resolution simulations. The simulated medicanes do not coincide in general on a case-by-case basis with the observed medicanes. This can be expected in climate mode simulations due to the small size of medicanes and the fact that they develop within the RCM domain. Therefore, the evaluation of medicanes in RCM simulations has to be done statistically. The observed spatial distribution of medicanes is well simulated in general. Regarding the monthly distribution, RCMs have difficulties in simulating the first medicanes appearing in September after the summer minimum. The use of higher horizontal resolution clearly increases the simulated frequency of medicanes, resulting in generally better frequency values. But the intensity, which is underestimated in low resolution runs, is not improved by most increased resolution simulations. A few RCMs show a clear intensity increase in the higher resolution runs, suggesting that model formulation is more important in this respect than high resolution alone. Air-sea interaction frequently produces a negative intensity feedback for tropical cyclones, which depends on the oceanic mixed

  19. Ensembl 2007.

    Science.gov (United States)

    Hubbard, T J P; Aken, B L; Beal, K; Ballester, B; Caccamo, M; Chen, Y; Clarke, L; Coates, G; Cunningham, F; Cutts, T; Down, T; Dyer, S C; Fitzgerald, S; Fernandez-Banet, J; Graf, S; Haider, S; Hammond, M; Herrero, J; Holland, R; Howe, K; Howe, K; Johnson, N; Kahari, A; Keefe, D; Kokocinski, F; Kulesha, E; Lawson, D; Longden, I; Melsopp, C; Megy, K; Meidl, P; Ouverdin, B; Parker, A; Prlic, A; Rice, S; Rios, D; Schuster, M; Sealy, I; Severin, J; Slater, G; Smedley, D; Spudich, G; Trevanion, S; Vilella, A; Vogel, J; White, S; Wood, M; Cox, T; Curwen, V; Durbin, R; Fernandez-Suarez, X M; Flicek, P; Kasprzyk, A; Proctor, G; Searle, S; Smith, J; Ureta-Vidal, A; Birney, E

    2007-01-01

    The Ensembl (http://www.ensembl.org/) project provides a comprehensive and integrated source of annotation of chordate genome sequences. Over the past year the number of genomes available from Ensembl has increased from 15 to 33, with the addition of sites for the mammalian genomes of elephant, rabbit, armadillo, tenrec, platypus, pig, cat, bush baby, common shrew, microbat and european hedgehog; the fish genomes of stickleback and medaka and the second example of the genomes of the sea squirt (Ciona savignyi) and the mosquito (Aedes aegypti). Some of the major features added during the year include the first complete gene sets for genomes with low-sequence coverage, the introduction of new strain variation data and the introduction of new orthology/paralog annotations based on gene trees.

  20. Global Ensemble Forecast System (GEFS) [2.5 Deg.

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — The Global Ensemble Forecast System (GEFS) is a weather forecast model made up of 21 separate forecasts, or ensemble members. The National Centers for Environmental...

  1. Observation bias correction with an ensemble Kalman filter

    OpenAIRE

    Fertig, Elana J.; Baek, Seung-Jong; Hunt, Brian R.; Ott, Edward; Szunyogh, Istvan; Aravéquia, José A.; Kalnay, Eugenia; Li, Hong; Liu, Junjie

    2009-01-01

    This paper considers the use of an ensemble Kalman filter to correct satellite radiance observations for state dependent biases. Our approach is to use state-space augmentation to estimate satellite biases as part of the ensemble data assimilation procedure. We illustrate our approach by applying it to a particular ensemble scheme—the local ensemble transform Kalman filter (LETKF)—to assimilate simulated biased atmospheric infrared sounder brightness temperature observations from 15 channels ...

  2. The diffuse ensemble filter

    Directory of Open Access Journals (Sweden)

    X. Yang

    2009-07-01

    Full Text Available A new class of ensemble filters, called the Diffuse Ensemble Filter (DEnF, is proposed in this paper. The DEnF assumes that the forecast errors orthogonal to the first guess ensemble are uncorrelated with the latter ensemble and have infinite variance. The assumption of infinite variance corresponds to the limit of "complete lack of knowledge" and differs dramatically from the implicit assumption made in most other ensemble filters, which is that the forecast errors orthogonal to the first guess ensemble have vanishing errors. The DEnF is independent of the detailed covariances assumed in the space orthogonal to the ensemble space, and reduces to conventional ensemble square root filters when the number of ensembles exceeds the model dimension. The DEnF is well defined only in data rich regimes and involves the inversion of relatively large matrices, although this barrier might be circumvented by variational methods. Two algorithms for solving the DEnF, namely the Diffuse Ensemble Kalman Filter (DEnKF and the Diffuse Ensemble Transform Kalman Filter (DETKF, are proposed and found to give comparable results. These filters generally converge to the traditional EnKF and ETKF, respectively, when the ensemble size exceeds the model dimension. Numerical experiments demonstrate that the DEnF eliminates filter collapse, which occurs in ensemble Kalman filters for small ensemble sizes. Also, the use of the DEnF to initialize a conventional square root filter dramatically accelerates the spin-up time for convergence. However, in a perfect model scenario, the DEnF produces larger errors than ensemble square root filters that have covariance localization and inflation. For imperfect forecast models, the DEnF produces smaller errors than the ensemble square root filter with inflation. These experiments suggest that the DEnF has some advantages relative to the ensemble square root filters in the regime of small ensemble size, imperfect model, and copious

  3. World Music Ensemble: Kulintang

    Science.gov (United States)

    Beegle, Amy C.

    2012-01-01

    As instrumental world music ensembles such as steel pan, mariachi, gamelan and West African drums are becoming more the norm than the exception in North American school music programs, there are other world music ensembles just starting to gain popularity in particular parts of the United States. The kulintang ensemble, a drum and gong ensemble…

  4. Towards a GME ensemble forecasting system: Ensemble initialization using the breeding technique

    Directory of Open Access Journals (Sweden)

    Jan D. Keller

    2008-12-01

    Full Text Available The quantitative forecast of precipitation requires a probabilistic background particularly with regard to forecast lead times of more than 3 days. As only ensemble simulations can provide useful information of the underlying probability density function, we built a new ensemble forecasting system (GME-EFS based on the GME model of the German Meteorological Service (DWD. For the generation of appropriate initial ensemble perturbations we chose the breeding technique developed by Toth and Kalnay (1993, 1997, which develops perturbations by estimating the regions of largest model error induced uncertainty. This method is applied and tested in the framework of quasi-operational forecasts for a three month period in 2007. The performance of the resulting ensemble forecasts are compared to the operational ensemble prediction systems ECMWF EPS and NCEP GFS by means of ensemble spread of free atmosphere parameters (geopotential and temperature and ensemble skill of precipitation forecasting. This comparison indicates that the GME ensemble forecasting system (GME-EFS provides reasonable forecasts with spread skill score comparable to that of the NCEP GFS. An analysis with the continuous ranked probability score exhibits a lack of resolution for the GME forecasts compared to the operational ensembles. However, with significant enhancements during the 3 month test period, the first results of our work with the GME-EFS indicate possibilities for further development as well as the potential for later operational usage.

  5. Ensemble global ocean forecasting

    Science.gov (United States)

    Brassington, G. B.

    2016-02-01

    A novel time-lagged ensemble system based on multiple independent cycles has been performed in operations at the Australian Bureau of Meteorology for the past 3 years. Despite the use of only four cycles the ensemble mean provided robustly higher skill and the ensemble variance was a reliable predictor of forecast errors. A spectral analysis comparing the ensemble mean with the members demonstrated the gradual increase in power of random errors with wavenumber up to a saturation length scale imposed by the resolution of the observing system. This system has been upgraded to a near-global 0.1 degree system in a new hybrid six-member ensemble system configuration including a new data assimilation system, cycling pattern and initialisation. The hybrid system consists of two ensemble members per day each with a 3 day cycle. We will outline the performance of both the deterministic and ensemble ocean forecast system.

  6. Online Learning with Ensembles

    OpenAIRE

    Urbanczik, R.

    1999-01-01

    Supervised online learning with an ensemble of students randomized by the choice of initial conditions is analyzed. For the case of the perceptron learning rule, asymptotically the same improvement in the generalization error of the ensemble compared to the performance of a single student is found as in Gibbs learning. For more optimized learning rules, however, using an ensemble yields no improvement. This is explained by showing that for any learning rule $f$ a transform $\\tilde{f}$ exists,...

  7. Multilevel ensemble Kalman filter

    KAUST Repository

    Chernov, Alexey

    2016-01-06

    This work embeds a multilevel Monte Carlo (MLMC) sampling strategy into the Monte Carlo step of the ensemble Kalman filter (EnKF). In terms of computational cost vs. approximation error the asymptotic performance of the multilevel ensemble Kalman filter (MLEnKF) is superior to the EnKF s.

  8. The Ensembl REST API: Ensembl Data for Any Language.

    Science.gov (United States)

    Yates, Andrew; Beal, Kathryn; Keenan, Stephen; McLaren, William; Pignatelli, Miguel; Ritchie, Graham R S; Ruffier, Magali; Taylor, Kieron; Vullo, Alessandro; Flicek, Paul

    2015-01-01

    We present a Web service to access Ensembl data using Representational State Transfer (REST). The Ensembl REST server enables the easy retrieval of a wide range of Ensembl data by most programming languages, using standard formats such as JSON and FASTA while minimizing client work. We also introduce bindings to the popular Ensembl Variant Effect Predictor tool permitting large-scale programmatic variant analysis independent of any specific programming language. The Ensembl REST API can be accessed at http://rest.ensembl.org and source code is freely available under an Apache 2.0 license from http://github.com/Ensembl/ensembl-rest. © The Author 2014. Published by Oxford University Press.

  9. Ensemble Data Mining Methods

    Data.gov (United States)

    National Aeronautics and Space Administration — Ensemble Data Mining Methods, also known as Committee Methods or Model Combiners, are machine learning methods that leverage the power of multiple models to achieve...

  10. Ensembl variation resources

    Directory of Open Access Journals (Sweden)

    Marin-Garcia Pablo

    2010-05-01

    Full Text Available Abstract Background The maturing field of genomics is rapidly increasing the number of sequenced genomes and producing more information from those previously sequenced. Much of this additional information is variation data derived from sampling multiple individuals of a given species with the goal of discovering new variants and characterising the population frequencies of the variants that are already known. These data have immense value for many studies, including those designed to understand evolution and connect genotype to phenotype. Maximising the utility of the data requires that it be stored in an accessible manner that facilitates the integration of variation data with other genome resources such as gene annotation and comparative genomics. Description The Ensembl project provides comprehensive and integrated variation resources for a wide variety of chordate genomes. This paper provides a detailed description of the sources of data and the methods for creating the Ensembl variation databases. It also explores the utility of the information by explaining the range of query options available, from using interactive web displays, to online data mining tools and connecting directly to the data servers programmatically. It gives a good overview of the variation resources and future plans for expanding the variation data within Ensembl. Conclusions Variation data is an important key to understanding the functional and phenotypic differences between individuals. The development of new sequencing and genotyping technologies is greatly increasing the amount of variation data known for almost all genomes. The Ensembl variation resources are integrated into the Ensembl genome browser and provide a comprehensive way to access this data in the context of a widely used genome bioinformatics system. All Ensembl data is freely available at http://www.ensembl.org and from the public MySQL database server at ensembldb.ensembl.org.

  11. The semantic similarity ensemble

    Directory of Open Access Journals (Sweden)

    Andrea Ballatore

    2013-12-01

    Full Text Available Computational measures of semantic similarity between geographic terms provide valuable support across geographic information retrieval, data mining, and information integration. To date, a wide variety of approaches to geo-semantic similarity have been devised. A judgment of similarity is not intrinsically right or wrong, but obtains a certain degree of cognitive plausibility, depending on how closely it mimics human behavior. Thus selecting the most appropriate measure for a specific task is a significant challenge. To address this issue, we make an analogy between computational similarity measures and soliciting domain expert opinions, which incorporate a subjective set of beliefs, perceptions, hypotheses, and epistemic biases. Following this analogy, we define the semantic similarity ensemble (SSE as a composition of different similarity measures, acting as a panel of experts having to reach a decision on the semantic similarity of a set of geographic terms. The approach is evaluated in comparison to human judgments, and results indicate that an SSE performs better than the average of its parts. Although the best member tends to outperform the ensemble, all ensembles outperform the average performance of each ensemble's member. Hence, in contexts where the best measure is unknown, the ensemble provides a more cognitively plausible approach.

  12. Ensemble Pulsar Time Scale

    Science.gov (United States)

    Yin, Dong-shan; Gao, Yu-ping; Zhao, Shu-hong

    2017-07-01

    Millisecond pulsars can generate another type of time scale that is totally independent of the atomic time scale, because the physical mechanisms of the pulsar time scale and the atomic time scale are quite different from each other. Usually the pulsar timing observations are not evenly sampled, and the internals between two data points range from several hours to more than half a month. Further more, these data sets are sparse. All this makes it difficult to generate an ensemble pulsar time scale. Hence, a new algorithm to calculate the ensemble pulsar time scale is proposed. Firstly, a cubic spline interpolation is used to densify the data set, and make the intervals between data points uniform. Then, the Vondrak filter is employed to smooth the data set, and get rid of the high-frequency noises, and finally the weighted average method is adopted to generate the ensemble pulsar time scale. The newly released NANOGRAV (North American Nanohertz Observatory for Gravitational Waves) 9-year data set is used to generate the ensemble pulsar time scale. This data set includes the 9-year observational data of 37 millisecond pulsars observed by the 100-meter Green Bank telescope and the 305-meter Arecibo telescope. It is found that the algorithm used in this paper can reduce effectively the influence caused by the noises in pulsar timing residuals, and improve the long-term stability of the ensemble pulsar time scale. Results indicate that the long-term (> 1 yr) stability of the ensemble pulsar time scale is better than 3.4 × 10-15.

  13. Decadal climate predictions improved by ocean ensemble dispersion filtering

    Science.gov (United States)

    Kadow, C.; Illing, S.; Kröner, I.; Ulbrich, U.; Cubasch, U.

    2017-06-01

    Decadal predictions by Earth system models aim to capture the state and phase of the climate several years in advance. Atmosphere-ocean interaction plays an important role for such climate forecasts. While short-term weather forecasts represent an initial value problem and long-term climate projections represent a boundary condition problem, the decadal climate prediction falls in-between these two time scales. In recent years, more precise initialization techniques of coupled Earth system models and increased ensemble sizes have improved decadal predictions. However, climate models in general start losing the initialized signal and its predictive skill from one forecast year to the next. Here we show that the climate prediction skill of an Earth system model can be improved by a shift of the ocean state toward the ensemble mean of its individual members at seasonal intervals. We found that this procedure, called ensemble dispersion filter, results in more accurate results than the standard decadal prediction. Global mean and regional temperature, precipitation, and winter cyclone predictions show an increased skill up to 5 years ahead. Furthermore, the novel technique outperforms predictions with larger ensembles and higher resolution. Our results demonstrate how decadal climate predictions benefit from ocean ensemble dispersion filtering toward the ensemble mean.Plain Language SummaryDecadal predictions aim to predict the climate several years in advance. Atmosphere-ocean interaction plays an important role for such climate forecasts. The ocean memory due to its heat capacity holds big potential skill. In recent years, more precise initialization techniques of coupled Earth system models (incl. atmosphere and ocean) have improved decadal predictions. Ensembles are another important aspect. Applying slightly perturbed predictions to trigger the famous butterfly effect results in an ensemble. Instead of evaluating one prediction, but the whole ensemble with its

  14. Multilevel ensemble Kalman filtering

    KAUST Repository

    Hoel, Haakon

    2016-01-08

    The ensemble Kalman filter (EnKF) is a sequential filtering method that uses an ensemble of particle paths to estimate the means and covariances required by the Kalman filter by the use of sample moments, i.e., the Monte Carlo method. EnKF is often both robust and efficient, but its performance may suffer in settings where the computational cost of accurate simulations of particles is high. The multilevel Monte Carlo method (MLMC) is an extension of classical Monte Carlo methods which by sampling stochastic realizations on a hierarchy of resolutions may reduce the computational cost of moment approximations by orders of magnitude. In this work we have combined the ideas of MLMC and EnKF to construct the multilevel ensemble Kalman filter (MLEnKF) for the setting of finite dimensional state and observation spaces. The main ideas of this method is to compute particle paths on a hierarchy of resolutions and to apply multilevel estimators on the ensemble hierarchy of particles to compute Kalman filter means and covariances. Theoretical results and a numerical study of the performance gains of MLEnKF over EnKF will be presented. Some ideas on the extension of MLEnKF to settings with infinite dimensional state spaces will also be presented.

  15. Neural Network Ensembles

    DEFF Research Database (Denmark)

    Hansen, Lars Kai; Salamon, Peter

    1990-01-01

    We propose several means for improving the performance an training of neural networks for classification. We use crossvalidation as a tool for optimizing network parameters and architecture. We show further that the remaining generalization error can be reduced by invoking ensembles of similar...... networks....

  16. Tailored Random Graph Ensembles

    Science.gov (United States)

    Roberts, E. S.; Annibale, A.; Coolen, A. C. C.

    2013-02-01

    Tailored graph ensembles are a developing bridge between biological networks and statistical mechanics. The aim is to use this concept to generate a suite of rigorous tools that can be used to quantify and compare the topology of cellular signalling networks, such as protein-protein interaction networks and gene regulation networks. We calculate exact and explicit formulae for the leading orders in the system size of the Shannon entropies of random graph ensembles constrained with degree distribution and degree-degree correlation. We also construct an ergodic detailed balance Markov chain with non-trivial acceptance probabilities which converges to a strictly uniform measure and is based on edge swaps that conserve all degrees. The acceptance probabilities can be generalized to define Markov chains that target any alternative desired measure on the space of directed or undirected graphs, in order to generate graphs with more sophisticated topological features.

  17. Kingston Soundpainting Ensemble

    OpenAIRE

    Minors, Helen Julia; Kingston Soundpainting Ensemble

    2012-01-01

    This performance is designed to introduce teachers and school musicians to this live multidisciplinary live composing sign language.\\ud \\ud Led by Dr. Helen Julia Minors (soundpainter, trumpet, voice), the Kingston Soundpainting Ensemble, led by Dr. Minors at Kington University, is representated by a section a varied set of performers, using woodwind, brass, voice and percussion, spanning popular, classical and world styles. This performance consists of:\\ud \\ud Philip Warda (electronic instru...

  18. Three-model ensemble wind prediction in southern Italy

    Directory of Open Access Journals (Sweden)

    R. C. Torcasio

    2016-03-01

    Full Text Available Quality of wind prediction is of great importance since a good wind forecast allows the prediction of available wind power, improving the penetration of renewable energies into the energy market. Here, a 1-year (1 December 2012 to 30 November 2013 three-model ensemble (TME experiment for wind prediction is considered. The models employed, run operationally at National Research Council – Institute of Atmospheric Sciences and Climate (CNR-ISAC, are RAMS (Regional Atmospheric Modelling System, BOLAM (BOlogna Limited Area Model, and MOLOCH (MOdello LOCale in H coordinates. The area considered for the study is southern Italy and the measurements used for the forecast verification are those of the GTS (Global Telecommunication System. Comparison with observations is made every 3 h up to 48 h of forecast lead time. Results show that the three-model ensemble outperforms the forecast of each individual model. The RMSE improvement compared to the best model is between 22 and 30 %, depending on the season. It is also shown that the three-model ensemble outperforms the IFS (Integrated Forecasting System of the ECMWF (European Centre for Medium-Range Weather Forecast for the surface wind forecasts. Notably, the three-model ensemble forecast performs better than each unbiased model, showing the added value of the ensemble technique. Finally, the sensitivity of the three-model ensemble RMSE to the length of the training period is analysed.

  19. Imprinting and recalling cortical ensembles.

    Science.gov (United States)

    Carrillo-Reid, Luis; Yang, Weijian; Bando, Yuki; Peterka, Darcy S; Yuste, Rafael

    2016-08-12

    Neuronal ensembles are coactive groups of neurons that may represent building blocks of cortical circuits. These ensembles could be formed by Hebbian plasticity, whereby synapses between coactive neurons are strengthened. Here we report that repetitive activation with two-photon optogenetics of neuronal populations from ensembles in the visual cortex of awake mice builds neuronal ensembles that recur spontaneously after being imprinted and do not disrupt preexisting ones. Moreover, imprinted ensembles can be recalled by single- cell stimulation and remain coactive on consecutive days. Our results demonstrate the persistent reconfiguration of cortical circuits by two-photon optogenetics into neuronal ensembles that can perform pattern completion. Copyright © 2016, American Association for the Advancement of Science.

  20. Multilevel ensemble Kalman filtering

    KAUST Repository

    Hoel, Hakon

    2016-06-14

    This work embeds a multilevel Monte Carlo sampling strategy into the Monte Carlo step of the ensemble Kalman filter (EnKF) in the setting of finite dimensional signal evolution and noisy discrete-time observations. The signal dynamics is assumed to be governed by a stochastic differential equation (SDE), and a hierarchy of time grids is introduced for multilevel numerical integration of that SDE. The resulting multilevel EnKF is proved to asymptotically outperform EnKF in terms of computational cost versus approximation accuracy. The theoretical results are illustrated numerically.

  1. Distinguishing Two Probability Ensembles with One Sample from each Ensemble

    NARCIS (Netherlands)

    Antunes, L.; Buhrman, H.; Matos, A.; Souto, A.; Teixeira, A.

    2016-01-01

    We introduced a new method for distinguishing two probability ensembles called one from each method, in which the distinguisher receives as input two samples, one from each ensemble. We compare this new method with multi-sample from the same method already exiting in the literature and prove that

  2. Distinguishing two probability ensembles with one sample from each ensemble

    NARCIS (Netherlands)

    L.F. Antunes (Luis); H. Buhrman (Harry); A. Matos; A. Souto (Andre); A. Teixeira

    2016-01-01

    htmlabstractWe introduced a new method for distinguishing two probability ensembles called one from each method, in which the distinguisher receives as input two samples, one from each ensemble. We compare this new method with multi-sample from the same method already exiting in the literature

  3. Climate Model Ensemble Methodology: Rationale and Challenges

    Science.gov (United States)

    Vezer, M. A.; Myrvold, W.

    2012-12-01

    A tractable model of the Earth's atmosphere, or, indeed, any large, complex system, is inevitably unrealistic in a variety of ways. This will have an effect on the model's output. Nonetheless, we want to be able to rely on certain features of the model's output in studies aiming to detect, attribute, and project climate change. For this, we need assurance that these features reflect the target system, and are not artifacts of the unrealistic assumptions that go into the model. One technique for overcoming these limitations is to study ensembles of models which employ different simplifying assumptions and different methods of modelling. One then either takes as reliable certain outputs on which models in the ensemble agree, or takes the average of these outputs as the best estimate. Since the Intergovernmental Panel on Climate Change's Fourth Assessment Report (IPCC AR4) modellers have aimed to improve ensemble analysis by developing techniques to account for dependencies among models, and to ascribe unequal weights to models according to their performance. The goal of this paper is to present as clearly and cogently as possible the rationale for climate model ensemble methodology, the motivation of modellers to account for model dependencies, and their efforts to ascribe unequal weights to models. The method of our analysis is as follows. We will consider a simpler, well-understood case of taking the mean of a number of measurements of some quantity. Contrary to what is sometimes said, it is not a requirement of this practice that the errors of the component measurements be independent; one must, however, compensate for any lack of independence. We will also extend the usual accounts to include cases of unknown systematic error. We draw parallels between this simpler illustration and the more complex example of climate model ensembles, detailing how ensembles can provide more useful information than any of their constituent models. This account emphasizes the

  4. Climate Prediction Center(CPC)Ensemble Canonical Correlation Analysis Forecast of Temperature

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — The Ensemble Canonical Correlation Analysis (ECCA) temperature forecast is a 90-day (seasonal) outlook of US surface temperature anomalies. The ECCA uses Canonical...

  5. Climate Prediction Center (CPC)Ensemble Canonical Correlation Analysis 90-Day Seasonal Forecast of Precipitation

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — The Ensemble Canonical Correlation Analysis (ECCA) precipitation forecast is a 90-day (seasonal) outlook of US surface precipitation anomalies. The ECCA uses...

  6. On Ensemble Nonlinear Kalman Filtering with Symmetric Analysis Ensembles

    KAUST Repository

    Luo, Xiaodong

    2010-09-19

    The ensemble square root filter (EnSRF) [1, 2, 3, 4] is a popular method for data assimilation in high dimensional systems (e.g., geophysics models). Essentially the EnSRF is a Monte Carlo implementation of the conventional Kalman filter (KF) [5, 6]. It is mainly different from the KF at the prediction steps, where it is some ensembles, rather then the means and covariance matrices, of the system state that are propagated forward. In doing this, the EnSRF is computationally more efficient than the KF, since propagating a covariance matrix forward in high dimensional systems is prohibitively expensive. In addition, the EnSRF is also very convenient in implementation. By propagating the ensembles of the system state, the EnSRF can be directly applied to nonlinear systems without any change in comparison to the assimilation procedures in linear systems. However, by adopting the Monte Carlo method, the EnSRF also incurs certain sampling errors. One way to alleviate this problem is to introduce certain symmetry to the ensembles, which can reduce the sampling errors and spurious modes in evaluation of the means and covariances of the ensembles [7]. In this contribution, we present two methods to produce symmetric ensembles. One is based on the unscented transform [8, 9], which leads to the unscented Kalman filter (UKF) [8, 9] and its variant, the ensemble unscented Kalman filter (EnUKF) [7]. The other is based on Stirling’s interpolation formula (SIF), which results in the divided difference filter (DDF) [10]. Here we propose a simplified divided difference filter (sDDF) in the context of ensemble filtering. The similarity and difference between the sDDF and the EnUKF will be discussed. Numerical experiments will also be conducted to investigate the performance of the sDDF and the EnUKF, and compare them to a well‐established EnSRF, the ensemble transform Kalman filter (ETKF) [2].

  7. The Polyanalytic Ginibre Ensembles

    Science.gov (United States)

    Haimi, Antti; Hedenmalm, Haakan

    2013-10-01

    For integers n, q=1,2,3,… , let Pol n, q denote the -linear space of polynomials in z and , of degree ≤ n-1 in z and of degree ≤ q-1 in . We supply Pol n, q with the inner product structure of the resulting Hilbert space is denoted by Pol m, n, q . Here, it is assumed that m is a positive real. We let K m, n, q denote the reproducing kernel of Pol m, n, q , and study the associated determinantal process, in the limit as m, n→+∞ while n= m+O(1); the number q, the degree of polyanalyticity, is kept fixed. We call these processes polyanalytic Ginibre ensembles, because they generalize the Ginibre ensemble—the eigenvalue process of random (normal) matrices with Gaussian weight. There is a physical interpretation in terms of a system of free fermions in a uniform magnetic field so that a fixed number of the first Landau levels have been filled. We consider local blow-ups of the polyanalytic Ginibre ensembles around points in the spectral droplet, which is here the closed unit disk . We obtain asymptotics for the blow-up process, using a blow-up to characteristic distance m -1/2; the typical distance is the same both for interior and for boundary points of . This amounts to obtaining the asymptotical behavior of the generating kernel K m, n, q . Following (Ameur et al. in Commun. Pure Appl. Math. 63(12):1533-1584, 2010), the asymptotics of the K m, n, q are rather conveniently expressed in terms of the Berezin measure (and density) [Equation not available: see fulltext.] For interior points | z|1. In contrast, for exterior points | z|>1, we have instead that , where is the harmonic measure at z with respect to the exterior disk . For boundary points, | z|=1, the Berezin measure converges to the unit point mass at z, as with interior points, but the blow-up to the scale m -1/2 exhibits quite different behavior at boundary points compared with interior points. We also obtain the asymptotic boundary behavior of the 1-point function at the coarser local scale q 1

  8. Diurnal Ensemble Surface Meteorology Statistics

    Data.gov (United States)

    U.S. Environmental Protection Agency — Excel file containing diurnal ensemble statistics of 2-m temperature, 2-m mixing ratio and 10-m wind speed. This Excel file contains figures for Figure 2 in the...

  9. PSO-Ensemble Demo Application

    DEFF Research Database (Denmark)

    2004-01-01

    Within the framework of the PSO-Ensemble project (FU2101) a demo application has been created. The application use ECMWF ensemble forecasts. Two instances of the application are running; one for Nysted Offshore and one for the total production (except Horns Rev) in the Eltra area. The output is a...... is available via two password-protected web-pages hosted at IMM and is used daily by Elsam and E2....

  10. Deformed Ginibre ensembles and integrable systems

    Energy Technology Data Exchange (ETDEWEB)

    Orlov, A.Yu., E-mail: orlovs@ocean.ru

    2014-01-17

    We consider three Ginibre ensembles (real, complex and quaternion-real) with deformed measures and relate them to known integrable systems by presenting partition functions of these ensembles in form of fermionic expectation values. We also introduce double deformed Dyson–Wigner ensembles and compare their fermionic representations with those of Ginibre ensembles.

  11. Adaptive correction of ensemble forecasts

    Science.gov (United States)

    Pelosi, Anna; Battista Chirico, Giovanni; Van den Bergh, Joris; Vannitsem, Stephane

    2017-04-01

    Forecasts from numerical weather prediction (NWP) models often suffer from both systematic and non-systematic errors. These are present in both deterministic and ensemble forecasts, and originate from various sources such as model error and subgrid variability. Statistical post-processing techniques can partly remove such errors, which is particularly important when NWP outputs concerning surface weather variables are employed for site specific applications. Many different post-processing techniques have been developed. For deterministic forecasts, adaptive methods such as the Kalman filter are often used, which sequentially post-process the forecasts by continuously updating the correction parameters as new ground observations become available. These methods are especially valuable when long training data sets do not exist. For ensemble forecasts, well-known techniques are ensemble model output statistics (EMOS), and so-called "member-by-member" approaches (MBM). Here, we introduce a new adaptive post-processing technique for ensemble predictions. The proposed method is a sequential Kalman filtering technique that fully exploits the information content of the ensemble. One correction equation is retrieved and applied to all members, however the parameters of the regression equations are retrieved by exploiting the second order statistics of the forecast ensemble. We compare our new method with two other techniques: a simple method that makes use of a running bias correction of the ensemble mean, and an MBM post-processing approach that rescales the ensemble mean and spread, based on minimization of the Continuous Ranked Probability Score (CRPS). We perform a verification study for the region of Campania in southern Italy. We use two years (2014-2015) of daily meteorological observations of 2-meter temperature and 10-meter wind speed from 18 ground-based automatic weather stations distributed across the region, comparing them with the corresponding COSMO

  12. Probabilistic seasonal prediction of summer rainfall over East China based on multi-model ensemble schemes

    Science.gov (United States)

    Li, Fang

    2011-06-01

    The skill of probability density function (PDF) prediction of summer rainfall over East China using optimal ensemble schemes is evaluated based on the precipitation data from five coupled atmosphere-ocean general circulation models that participate in the ENSEMBLES project. The optimal ensemble scheme in each region is the scheme with the highest skill among the four commonly-used ones: the equally-weighted ensemble (EE), EE for calibrated model-simulations (Cali-EE), the ensemble scheme based on multiple linear regression analysis (MLR), and the Bayesian ensemble scheme (Bayes). The results show that the optimal ensemble scheme is the Bayes in the southern part of East China; the Cali-EE in the Yangtze River valley, the Yangtze-Huaihe River basin, and the central part of northern China; and the MLR in the eastern part of northern China. Their PDF predictions are well calibrated, and are sharper than or have approximately equal interval-width to the climatology prediction. In all regions, these optimal ensemble schemes outperform the climatology prediction, indicating that current commonly-used multi-model ensemble schemes are able to produce skillful PDF prediction of summer rainfall over East China, even though more information for other model variables is not derived.

  13. Algorithms on ensemble quantum computers.

    Science.gov (United States)

    Boykin, P Oscar; Mor, Tal; Roychowdhury, Vwani; Vatan, Farrokh

    2010-06-01

    In ensemble (or bulk) quantum computation, all computations are performed on an ensemble of computers rather than on a single computer. Measurements of qubits in an individual computer cannot be performed; instead, only expectation values (over the complete ensemble of computers) can be measured. As a result of this limitation on the model of computation, many algorithms cannot be processed directly on such computers, and must be modified, as the common strategy of delaying the measurements usually does not resolve this ensemble-measurement problem. Here we present several new strategies for resolving this problem. Based on these strategies we provide new versions of some of the most important quantum algorithms, versions that are suitable for implementing on ensemble quantum computers, e.g., on liquid NMR quantum computers. These algorithms are Shor's factorization algorithm, Grover's search algorithm (with several marked items), and an algorithm for quantum fault-tolerant computation. The first two algorithms are simply modified using a randomizing and a sorting strategies. For the last algorithm, we develop a classical-quantum hybrid strategy for removing measurements. We use it to present a novel quantum fault-tolerant scheme. More explicitly, we present schemes for fault-tolerant measurement-free implementation of Toffoli and σ(z)(¼) as these operations cannot be implemented "bitwise", and their standard fault-tolerant implementations require measurement.

  14. Ensemble algorithms in reinforcement learning.

    Science.gov (United States)

    Wiering, Marco A; van Hasselt, Hado

    2008-08-01

    This paper describes several ensemble methods that combine multiple different reinforcement learning (RL) algorithms in a single agent. The aim is to enhance learning speed and final performance by combining the chosen actions or action probabilities of different RL algorithms. We designed and implemented four different ensemble methods combining the following five different RL algorithms: Q-learning, Sarsa, actor-critic (AC), QV-learning, and AC learning automaton. The intuitively designed ensemble methods, namely, majority voting (MV), rank voting, Boltzmann multiplication (BM), and Boltzmann addition, combine the policies derived from the value functions of the different RL algorithms, in contrast to previous work where ensemble methods have been used in RL for representing and learning a single value function. We show experiments on five maze problems of varying complexity; the first problem is simple, but the other four maze tasks are of a dynamic or partially observable nature. The results indicate that the BM and MV ensembles significantly outperform the single RL algorithms.

  15. On Constructing Ensembles for Combinatorial Optimisation.

    Science.gov (United States)

    Hart, Emma; Sim, Kevin

    2017-01-10

    Although the use of ensemble methods in machine-learning is ubiquitous due to their proven ability to outperform their constituent algorithms, ensembles of optimisation algorithms have received relatively little attention. Existing approaches lag behind machine-learning in both theory and practice, with no principled design guidelines available. In this article, we address fundamental questions regarding ensemble composition in optimisation using the domain of bin-packing as an example. In particular, we investigate the trade-off between accuracy and diversity, and whether diversity metrics can be used as a proxy for constructing an ensemble, proposing a number of novel metrics for comparing algorithm diversity. We find that randomly composed ensembles can outperform ensembles of high-performing algorithms under certain conditions and that judicious choice of diversity metric is required to construct good ensembles. The method and findings can be generalised to any metaheuristic ensemble, and lead to better understanding of how to undertake principled ensemble design.

  16. ARM Cloud Retrieval Ensemble Data Set (ACRED)

    Energy Technology Data Exchange (ETDEWEB)

    Zhao, C; Xie, S; Klein, SA; McCoy, R; Comstock, JM; Delanoë, J; Deng, M; Dunn, M; Hogan, RJ; Jensen, MP; Mace, GG; McFarlane, SA; O’Connor, EJ; Protat, A; Shupe, MD; Turner, D; Wang, Z

    2011-09-12

    This document describes a new Atmospheric Radiation Measurement (ARM) data set, the ARM Cloud Retrieval Ensemble Data Set (ACRED), which is created by assembling nine existing ground-based cloud retrievals of ARM measurements from different cloud retrieval algorithms. The current version of ACRED includes an hourly average of nine ground-based retrievals with vertical resolution of 45 m for 512 layers. The techniques used for the nine cloud retrievals are briefly described in this document. This document also outlines the ACRED data availability, variables, and the nine retrieval products. Technical details about the generation of ACRED, such as the methods used for time average and vertical re-grid, are also provided.

  17. Similarity measures for protein ensembles

    DEFF Research Database (Denmark)

    Lindorff-Larsen, Kresten; Ferkinghoff-Borg, Jesper

    2009-01-01

    a synthetic example from molecular dynamics simulations. We then apply the algorithms to revisit the problem of ensemble averaging during structure determination of proteins, and find that an ensemble refinement method is able to recover the correct distribution of conformations better than standard single....... However, instead of examining individual conformations it is in many cases more relevant to analyse ensembles of conformations that have been obtained either through experiments or from methods such as molecular dynamics simulations. We here present three approaches that can be used to compare......Analyses of similarities and changes in protein conformation can provide important information regarding protein function and evolution. Many scores, including the commonly used root mean square deviation, have therefore been developed to quantify the similarities of different protein conformations...

  18. Trends in the predictive performance of raw ensemble weather forecasts

    Science.gov (United States)

    Hemri, Stephan; Scheuerer, Michael; Pappenberger, Florian; Bogner, Konrad; Haiden, Thomas

    2015-04-01

    Over the last two decades the paradigm in weather forecasting has shifted from being deterministic to probabilistic. Accordingly, numerical weather prediction (NWP) models have been run increasingly as ensemble forecasting systems. The goal of such ensemble forecasts is to approximate the forecast probability distribution by a finite sample of scenarios. Global ensemble forecast systems, like the European Centre for Medium-Range Weather Forecasts (ECMWF) ensemble, are prone to probabilistic biases, and are therefore not reliable. They particularly tend to be underdispersive for surface weather parameters. Hence, statistical post-processing is required in order to obtain reliable and sharp forecasts. In this study we apply statistical post-processing to ensemble forecasts of near-surface temperature, 24-hour precipitation totals, and near-surface wind speed from the global ECMWF model. Our main objective is to evaluate the evolution of the difference in skill between the raw ensemble and the post-processed forecasts. The ECMWF ensemble is under continuous development, and hence its forecast skill improves over time. Parts of these improvements may be due to a reduction of probabilistic bias. Thus, we first hypothesize that the gain by post-processing decreases over time. Based on ECMWF forecasts from January 2002 to March 2014 and corresponding observations from globally distributed stations we generate post-processed forecasts by ensemble model output statistics (EMOS) for each station and variable. Parameter estimates are obtained by minimizing the Continuous Ranked Probability Score (CRPS) over rolling training periods that consist of the n days preceding the initialization dates. Given the higher average skill in terms of CRPS of the post-processed forecasts for all three variables, we analyze the evolution of the difference in skill between raw ensemble and EMOS forecasts. The fact that the gap in skill remains almost constant over time, especially for near

  19. A short-range ensemble prediction system for southern Africa

    CSIR Research Space (South Africa)

    Park, R

    2012-10-01

    Full Text Available numerical weather prediction system over southern Africa using the Conformal- Cubic Atmospheric Model (CCAM). An ensemble prediction system (EPS) combines several individual weather model setups into an average forecast system where each member... contributes to the final weather forecast (Atger, 1999). Four different EPSs were configured using lagged-average forecasting techniques (Kalnay, 2003) and two different cloud parameterisation schemes (McGregor, 2003; Rotstayn, 1997). Rainfall forecasts...

  20. Ensemble method for dengue prediction.

    Science.gov (United States)

    Buczak, Anna L; Baugher, Benjamin; Moniz, Linda J; Bagley, Thomas; Babin, Steven M; Guven, Erhan

    2018-01-01

    In the 2015 NOAA Dengue Challenge, participants made three dengue target predictions for two locations (Iquitos, Peru, and San Juan, Puerto Rico) during four dengue seasons: 1) peak height (i.e., maximum weekly number of cases during a transmission season; 2) peak week (i.e., week in which the maximum weekly number of cases occurred); and 3) total number of cases reported during a transmission season. A dengue transmission season is the 12-month period commencing with the location-specific, historical week with the lowest number of cases. At the beginning of the Dengue Challenge, participants were provided with the same input data for developing the models, with the prediction testing data provided at a later date. Our approach used ensemble models created by combining three disparate types of component models: 1) two-dimensional Method of Analogues models incorporating both dengue and climate data; 2) additive seasonal Holt-Winters models with and without wavelet smoothing; and 3) simple historical models. Of the individual component models created, those with the best performance on the prior four years of data were incorporated into the ensemble models. There were separate ensembles for predicting each of the three targets at each of the two locations. Our ensemble models scored higher for peak height and total dengue case counts reported in a transmission season for Iquitos than all other models submitted to the Dengue Challenge. However, the ensemble models did not do nearly as well when predicting the peak week. The Dengue Challenge organizers scored the dengue predictions of the Challenge participant groups. Our ensemble approach was the best in predicting the total number of dengue cases reported for transmission season and peak height for Iquitos, Peru.

  1. Ensembl Genomes: extending Ensembl across the taxonomic space.

    Science.gov (United States)

    Kersey, P J; Lawson, D; Birney, E; Derwent, P S; Haimel, M; Herrero, J; Keenan, S; Kerhornou, A; Koscielny, G; Kähäri, A; Kinsella, R J; Kulesha, E; Maheswari, U; Megy, K; Nuhn, M; Proctor, G; Staines, D; Valentin, F; Vilella, A J; Yates, A

    2010-01-01

    Ensembl Genomes (http://www.ensemblgenomes.org) is a new portal offering integrated access to genome-scale data from non-vertebrate species of scientific interest, developed using the Ensembl genome annotation and visualisation platform. Ensembl Genomes consists of five sub-portals (for bacteria, protists, fungi, plants and invertebrate metazoa) designed to complement the availability of vertebrate genomes in Ensembl. Many of the databases supporting the portal have been built in close collaboration with the scientific community, which we consider as essential for maintaining the accuracy and usefulness of the resource. A common set of user interfaces (which include a graphical genome browser, FTP, BLAST search, a query optimised data warehouse, programmatic access, and a Perl API) is provided for all domains. Data types incorporated include annotation of (protein and non-protein coding) genes, cross references to external resources, and high throughput experimental data (e.g. data from large scale studies of gene expression and polymorphism visualised in their genomic context). Additionally, extensive comparative analysis has been performed, both within defined clades and across the wider taxonomy, and sequence alignments and gene trees resulting from this can be accessed through the site.

  2. Ensemble algorithms in reinforcement learning

    NARCIS (Netherlands)

    Wiering, Marco A; van Hasselt, Hado

    This paper describes several ensemble methods that combine multiple different reinforcement learning (RL) algorithms in a single agent. The aim is to enhance learning speed and final performance by combining the chosen actions or action probabilities of different RL algorithms. We designed and

  3. Ground-based hyperspectral imaging and analysis of Jupiter’s atmosphere during the Juno era

    Science.gov (United States)

    Dahl, Emma; Chanover, Nancy J.; Voelz, David; Kuehn, David M.; Wijerathna, Erandi; Hull, Robert; Strycker, Paul D.; Baines, Kevin H.

    2017-10-01

    The Juno mission to Jupiter has presented ground-based observers with a unique opportunity to collect data while the spacecraft is simultaneously measuring the planet and its atmosphere. Data collected in conjunction with Juno measurements have the capability to complement and enhance wavelength regimes already covered by Juno instruments.In order to enrich Juno’s scientific returns in the visible regime, we use the New Mexico State University Acousto-optic Imaging Camera (NAIC) to obtain hyperspectral image cubes of Jupiter from 470-950 nm with an average spectral resolution (λ/dλ) of 242. We use NAIC with the Apache Point Observatory 3.5-m telescope to image Jupiter’s atmosphere during Juno’s perijove flybys. With these timely, high spectral resolution measurements, we can derive the properties of cloud and haze particulates and estimate cloud heights. We present geometrically and photometrically calibrated spectra of representative regions of Jupiter’s atmosphere to be compared with previous work and laboratory measurements of candidate chromophore materials. The data we present are from the night of March 26th, 2017, captured during Juno’s 5th perijove flyby. We discuss preliminary analyses of these spectra, including implications for future work regarding atmospheric modeling.For the aforementioned observations, NAIC was equipped with a thinned, back-illuminated CCD. Because of the narrow bandwidths NAIC’s spectral tuning element produces, this chip design resulted in etaloning, or “fringing,” in images at wavelengths longer than ~720 nm. We discuss our methodology for correcting the fringing and the progress of a general-use model for correcting fringing in CCDs. Such a model requires the extraction of chip characteristics from monochromatic flats, which can be then be used to model exactly how the interference of light inside the chip results in the fringing pattern. This artificial fringing image can then be removed from images, thereby

  4. Measuring social interaction in music ensembles

    OpenAIRE

    Volpe, Gualtiero; D’Ausilio, Alessandro; Badino, Leonardo; Camurri, Antonio; Fadiga, Luciano

    2016-01-01

    Music ensembles are an ideal test-bed for quantitative analysis of social interaction. Music is an inherently social activity, and music ensembles offer a broad variety of scenarios which are particularly suitable for investigation. Small ensembles, such as string quartets, are deemed a significant example of self-managed teams, where all musicians contribute equally to a task. In bigger ensembles, such as orchestras, the relationship between a leader (the conductor) and a group of followers ...

  5. Statistical Mechanics of Time Domain Ensemble Learning

    OpenAIRE

    Miyoshi, Seiji; Uezu, Tatsuya; Okada, Masato

    2006-01-01

    Conventional ensemble learning combines students in the space domain. On the other hand, in this paper we combine students in the time domain and call it time domain ensemble learning. In this paper, we analyze the generalization performance of time domain ensemble learning in the framework of online learning using a statistical mechanical method. We treat a model in which both the teacher and the student are linear perceptrons with noises. Time domain ensemble learning is twice as effective ...

  6. ENSEMBLE methods to reconcile disparate national long range dispersion forecasting

    Energy Technology Data Exchange (ETDEWEB)

    Mikkelsen, T.; Galmarini, S.; Bianconi, R.; French, S. (eds.)

    2003-11-01

    ENSEMBLE is a web-based decision support system for real-time exchange and evaluation of national long-range dispersion forecasts of nuclear releases with cross-boundary consequences. The system is developed with the purpose to reconcile among disparate national forecasts for long-range dispersion. ENSEMBLE addresses the problem of achieving a common coherent strategy across European national emergency management when national long-range dispersion forecasts differ from one another during an accidental atmospheric release of radioactive material. A series of new decision-making 'ENSEMBLE' procedures and Web-based software evaluation and exchange tools have been created for real-time reconciliation and harmonisation of real-time dispersion forecasts from meteorological and emergency centres across Europe during an accident. The new ENSEMBLE software tools is available to participating national emergency and meteorological forecasting centres, which may choose to integrate them directly into operational emergency information systems, or possibly use them as a basis for future system development. (au)

  7. A comparison of model ensembles for attributing 2012 West African rainfall

    Science.gov (United States)

    Parker, Hannah R.; Lott, Fraser C.; Cornforth, Rosalind J.; Mitchell, Daniel M.; Sparrow, Sarah; Wallom, David

    2017-01-01

    In 2012, heavy rainfall resulted in flooding and devastating impacts across West Africa. With many people highly vulnerable to such events in this region, this study investigates whether anthropogenic climate change has influenced such heavy precipitation events. We use a probabilistic event attribution approach to assess the contribution of anthropogenic greenhouse gas emissions, by comparing the probability of such an event occurring in climate model simulations with all known climate forcings to those where natural forcings only are simulated. An ensemble of simulations from 10 models from the Coupled Model Intercomparison Project Phase 5 (CMIP5) is compared to two much larger ensembles of atmosphere-only simulations, from the Met Office model HadGEM3-A and from weather@home with a regional version of HadAM3P. These are used to assess whether the choice of model ensemble influences the attribution statement that can be made. Results show that anthropogenic greenhouse gas emissions have decreased the probability of high precipitation across most of the model ensembles. However, the magnitude and confidence intervals of the decrease depend on the ensemble used, with more certainty in the magnitude in the atmosphere-only model ensembles due to larger ensemble sizes from single models with more constrained simulations. Certainty is greatly decreased when considering a CMIP5 ensemble that can represent the relevant teleconnections due to a decrease in ensemble members. An increase in probability of high precipitation in HadGEM3-A using the observed trend in sea surface temperatures (SSTs) for natural simulations highlights the need to ensure that estimates of natural SSTs are consistent with observed trends in order for results to be robust. Further work is needed to establish how anthropogenic forcings are affecting the rainfall processes in these simulations in order to better understand the differences in the overall effect.

  8. Ensemble hydromoeteorological forecasting in Denmark

    DEFF Research Database (Denmark)

    Lucatero Villasenor, Diana

    of the main sources of uncertainty in hydrological forecasts. This is the reason why substantiated efforts to include information from Numerical Weather Predictors (NWP) or General Circulation Models (GCM) have been made over the last couple of decades. The present thesis expects to advance the field......teorological extremes such as flood and droughts cause economical and live losses that could be, if not prevented, at least dampened if sufficient time is given to respond to potential threats. This is the ultimate purpose of forecasting which then translates into making reliable predictions...... of ensemble hydrometeorological forecasting by evaluating the added value of NWP and GCM ensemble prediction systems (EPS) for hydrological purposes. The use of NWP EPS that differ in both spatial and temporal resolution to feed a hydrological model for discharge forecasts at specific points, revealed two...

  9. Symanzik flow on HISQ ensembles

    CERN Document Server

    Bazavov, A; Brown, N; DeTar, C; Foley, J; Gottlieb, Steven; Heller, U M; Hetrick, J E; Laiho, J; Levkova, L; Oktay, M; Sugar, R L; Toussaint, D; Van de Water, R S; Zhou, R

    2013-01-01

    We report on a scale determination with gradient-flow techniques on the $N_f = 2 + 1 + 1$ HISQ ensembles generated by the MILC collaboration. The lattice scale $w_0/a$, originally proposed by the BMW collaboration, is computed using Symanzik flow at four lattice spacings ranging from 0.15 to 0.06 fm. With a Taylor series ansatz, the results are simultaneously extrapolated to the continuum and interpolated to physical quark masses. We give a preliminary determination of the scale $w_0$ in physical units, along with associated systematic errors, and compare with results from other groups. We also present a first estimate of autocorrelation lengths as a function of flowtime for these ensembles.

  10. Nonequilibrium statistical mechanics ensemble method

    CERN Document Server

    Eu, Byung Chan

    1998-01-01

    In this monograph, nonequilibrium statistical mechanics is developed by means of ensemble methods on the basis of the Boltzmann equation, the generic Boltzmann equations for classical and quantum dilute gases, and a generalised Boltzmann equation for dense simple fluids The theories are developed in forms parallel with the equilibrium Gibbs ensemble theory in a way fully consistent with the laws of thermodynamics The generalised hydrodynamics equations are the integral part of the theory and describe the evolution of macroscopic processes in accordance with the laws of thermodynamics of systems far removed from equilibrium Audience This book will be of interest to researchers in the fields of statistical mechanics, condensed matter physics, gas dynamics, fluid dynamics, rheology, irreversible thermodynamics and nonequilibrium phenomena

  11. Multimodel ensembles of wheat growth

    DEFF Research Database (Denmark)

    Martre, Pierre; Wallach, Daniel; Asseng, Senthold

    2015-01-01

    , but such studies are difficult to organize and have only recently begun. We report on the largest ensemble study to date, of 27 wheat models tested in four contrasting locations for their accuracy in simulating multiple crop growth and yield variables. The relative error averaged over models was 24...... are applicable to other crop species, and hypothesize that they apply more generally to ecological system models....

  12. Dimensionality Reduction Through Classifier Ensembles

    Science.gov (United States)

    Oza, Nikunj C.; Tumer, Kagan; Norwig, Peter (Technical Monitor)

    1999-01-01

    In data mining, one often needs to analyze datasets with a very large number of attributes. Performing machine learning directly on such data sets is often impractical because of extensive run times, excessive complexity of the fitted model (often leading to overfitting), and the well-known "curse of dimensionality." In practice, to avoid such problems, feature selection and/or extraction are often used to reduce data dimensionality prior to the learning step. However, existing feature selection/extraction algorithms either evaluate features by their effectiveness across the entire data set or simply disregard class information altogether (e.g., principal component analysis). Furthermore, feature extraction algorithms such as principal components analysis create new features that are often meaningless to human users. In this article, we present input decimation, a method that provides "feature subsets" that are selected for their ability to discriminate among the classes. These features are subsequently used in ensembles of classifiers, yielding results superior to single classifiers, ensembles that use the full set of features, and ensembles based on principal component analysis on both real and synthetic datasets.

  13. Ensemble learning incorporating uncertain registration.

    Science.gov (United States)

    Simpson, Ivor J A; Woolrich, Mark W; Andersson, Jesper L R; Groves, Adrian R; Schnabel, Julia A

    2013-04-01

    This paper proposes a novel approach for improving the accuracy of statistical prediction methods in spatially normalized analysis. This is achieved by incorporating registration uncertainty into an ensemble learning scheme. A probabilistic registration method is used to estimate a distribution of probable mappings between subject and atlas space. This allows the estimation of the distribution of spatially normalized feature data, e.g., grey matter probability maps. From this distribution, samples are drawn for use as training examples. This allows the creation of multiple predictors, which are subsequently combined using an ensemble learning approach. Furthermore, extra testing samples can be generated to measure the uncertainty of prediction. This is applied to separating subjects with Alzheimer's disease from normal controls using a linear support vector machine on a region of interest in magnetic resonance images of the brain. We show that our proposed method leads to an improvement in discrimination using voxel-based morphometry and deformation tensor-based morphometry over bootstrap aggregating, a common ensemble learning framework. The proposed approach also generates more reasonable soft-classification predictions than bootstrap aggregating. We expect that this approach could be applied to other statistical prediction tasks where registration is important.

  14. Ensemble dispersion forecasting - Part 1. Concept, approach and indicators

    DEFF Research Database (Denmark)

    Galmarini, S.; Bianconi, R.; Klug, W.

    2004-01-01

    The paper presents an approach to the treatment and analysis of long-range transport and dispersion model forecasts. Long-range is intended here as the space scale of the order of few thousands of kilometers known also as continental scale. The method is called multi-model ensemble dispersion...... and is based on the simultaneous analysis of several model simulations by means of ad-hoc statistical treatments and parameters. The models considered in this study are operational long-range transport and dispersion models used to support decision making in various countries in case of accidental releases...... of harmful volatile substances, in particular radionuclides to the atmosphere. The ensemble dispersion approach and indicators provide a way to reduce several model results to few concise representations that include an estimate of the models' agreement in predicting a specific scenario. The parameters...

  15. An estimate of the inflation factor and analysis sensitivity in the ensemble Kalman filter

    Directory of Open Access Journals (Sweden)

    G. Wu

    2017-07-01

    Full Text Available The ensemble Kalman filter (EnKF is a widely used ensemble-based assimilation method, which estimates the forecast error covariance matrix using a Monte Carlo approach that involves an ensemble of short-term forecasts. While the accuracy of the forecast error covariance matrix is crucial for achieving accurate forecasts, the estimate given by the EnKF needs to be improved using inflation techniques. Otherwise, the sampling covariance matrix of perturbed forecast states will underestimate the true forecast error covariance matrix because of the limited ensemble size and large model errors, which may eventually result in the divergence of the filter. In this study, the forecast error covariance inflation factor is estimated using a generalized cross-validation technique. The improved EnKF assimilation scheme is tested on the atmosphere-like Lorenz-96 model with spatially correlated observations, and is shown to reduce the analysis error and increase its sensitivity to the observations.

  16. On the incidence of meteorological and hydrological processors: Effect of resolution, sharpness and reliability of hydrological ensemble forecasts

    Science.gov (United States)

    Abaza, Mabrouk; Anctil, François; Fortin, Vincent; Perreault, Luc

    2017-12-01

    Meteorological and hydrological ensemble prediction systems are imperfect. Their outputs could often be improved through the use of a statistical processor, opening up the question of the necessity of using both processors (meteorological and hydrological), only one of them, or none. This experiment compares the predictive distributions from four hydrological ensemble prediction systems (H-EPS) utilising the Ensemble Kalman filter (EnKF) probabilistic sequential data assimilation scheme. They differ in the inclusion or not of the Distribution Based Scaling (DBS) method for post-processing meteorological forecasts and the ensemble Bayesian Model Averaging (ensemble BMA) method for hydrological forecast post-processing. The experiment is implemented on three large watersheds and relies on the combination of two meteorological reforecast products: the 4-member Canadian reforecasts from the Canadian Centre for Meteorological and Environmental Prediction (CCMEP) and the 10-member American reforecasts from the National Oceanic and Atmospheric Administration (NOAA), leading to 14 members at each time step. Results show that all four tested H-EPS lead to resolution and sharpness values that are quite similar, with an advantage to DBS + EnKF. The ensemble BMA is unable to compensate for any bias left in the precipitation ensemble forecasts. On the other hand, it succeeds in calibrating ensemble members that are otherwise under-dispersed. If reliability is preferred over resolution and sharpness, DBS + EnKF + ensemble BMA performs best, making use of both processors in the H-EPS system. Conversely, for enhanced resolution and sharpness, DBS is the preferred method.

  17. Comparison of extended medium-range forecast skill between KMA ensemble, ocean coupled ensemble, and GloSea5

    Science.gov (United States)

    Park, Sangwook; Kim, Dong-Joon; Lee, Seung-Woo; Lee, Kie-Woung; Kim, Jongkhun; Song, Eun-Ji; Seo, Kyong-Hwan

    2017-08-01

    This article describes a three way inter-comparison of forecast skill on an extended medium-range time scale using the Korea Meteorological Administration (KMA) operational ensemble numerical weather prediction (NWP) systems (i.e., atmosphere-only global ensemble prediction system (EPSG) and ocean-atmosphere coupledEPSG) and KMA operational seasonal prediction system, the Global Seasonal forecast system version 5 (GloSea5). The main motivation is to investigate whether the ensemble NWP system can provide advantage over the existing seasonal prediction system for the extended medium-range forecast (30 days) even with putting extra resources in extended integration or coupling with ocean with NWP system. Two types of evaluation statistics are examined: the basic verification statistics - the anomaly correlation and RMSE of 500-hPa geopotential height and 1.5-meter surface temperature for the global and East Asia area, and the other is the Real-time Multivariate Madden and Julian Oscillation (MJO) indices (RMM1 and RMM2) - which is used to examine the MJO prediction skill. The MJO is regarded as a main source of forecast skill in the tropics linked to the mid-latitude weather on monthly time scale. Under limited number of experiment cases, the coupled NWP extends the forecast skill of the NWP by a few more days, and thereafter such forecast skill is overtaken by that of the seasonal prediction system. At present stage, it seems there is little gain from the coupled NWP even though more resources are put into it. Considering this, the best combination of numerical product guidance for operational forecasters for an extended medium-range is extension of the forecast lead time of the current ensemble NWP (EPSG) up to 20 days and use of the seasonal prediction system (GloSea5) forecast thereafter, though there exists a matter of consistency between the two systems.

  18. Measuring social interaction in music ensembles.

    Science.gov (United States)

    Volpe, Gualtiero; D'Ausilio, Alessandro; Badino, Leonardo; Camurri, Antonio; Fadiga, Luciano

    2016-05-05

    Music ensembles are an ideal test-bed for quantitative analysis of social interaction. Music is an inherently social activity, and music ensembles offer a broad variety of scenarios which are particularly suitable for investigation. Small ensembles, such as string quartets, are deemed a significant example of self-managed teams, where all musicians contribute equally to a task. In bigger ensembles, such as orchestras, the relationship between a leader (the conductor) and a group of followers (the musicians) clearly emerges. This paper presents an overview of recent research on social interaction in music ensembles with a particular focus on (i) studies from cognitive neuroscience; and (ii) studies adopting a computational approach for carrying out automatic quantitative analysis of ensemble music performances. © 2016 The Author(s).

  19. Analysis of mesoscale forecasts using ensemble methods

    CERN Document Server

    Gross, Markus

    2016-01-01

    Mesoscale forecasts are now routinely performed as elements of operational forecasts and their outputs do appear convincing. However, despite their realistic appearance at times the comparison to observations is less favorable. At the grid scale these forecasts often do not compare well with observations. This is partly due to the chaotic system underlying the weather. Another key problem is that it is impossible to evaluate the risk of making decisions based on these forecasts because they do not provide a measure of confidence. Ensembles provide this information in the ensemble spread and quartiles. However, running global ensembles at the meso or sub mesoscale involves substantial computational resources. National centers do run such ensembles, but the subject of this publication is a method which requires significantly less computation. The ensemble enhanced mesoscale system presented here aims not at the creation of an improved mesoscale forecast model. Also it is not to create an improved ensemble syste...

  20. A Localized Ensemble Kalman Smoother

    Science.gov (United States)

    Butala, Mark D.

    2012-01-01

    Numerous geophysical inverse problems prove difficult because the available measurements are indirectly related to the underlying unknown dynamic state and the physics governing the system may involve imperfect models or unobserved parameters. Data assimilation addresses these difficulties by combining the measurements and physical knowledge. The main challenge in such problems usually involves their high dimensionality and the standard statistical methods prove computationally intractable. This paper develops and addresses the theoretical convergence of a new high-dimensional Monte-Carlo approach called the localized ensemble Kalman smoother.

  1. Wind Power Prediction using Ensembles

    DEFF Research Database (Denmark)

    Giebel, Gregor; Badger, Jake; Landberg, Lars

    2005-01-01

    offshore wind farm and the whole Jutland/Funen area. The utilities used these forecasts for maintenance planning, fuel consumption estimates and over-the-weekend trading on the Leipzig power exchange. Othernotable scientific results include the better accuracy of forecasts made up from a simple...... superposition of two NWP provider (in our case, DMI and DWD), an investigation of the merits of a parameterisation of the turbulent kinetic energy within thedelivered wind speed forecasts, and the finding that a “naïve” downscaling of each of the coarse ECMWF ensemble members with higher resolution HIRLAM did...

  2. A Gaussian mixture ensemble transform filter

    OpenAIRE

    Reich, Sebastian

    2011-01-01

    We generalize the popular ensemble Kalman filter to an ensemble transform filter where the prior distribution can take the form of a Gaussian mixture or a Gaussian kernel density estimator. The design of the filter is based on a continuous formulation of the Bayesian filter analysis step. We call the new filter algorithm the ensemble Gaussian mixture filter (EGMF). The EGMF is implemented for three simple test problems (Brownian dynamics in one dimension, Langevin dynamics in two dimensions, ...

  3. Multi-Model Ensemble Wake Vortex Prediction

    Science.gov (United States)

    Koerner, Stephan; Holzaepfel, Frank; Ahmad, Nash'at N.

    2015-01-01

    Several multi-model ensemble methods are investigated for predicting wake vortex transport and decay. This study is a joint effort between National Aeronautics and Space Administration and Deutsches Zentrum fuer Luft- und Raumfahrt to develop a multi-model ensemble capability using their wake models. An overview of different multi-model ensemble methods and their feasibility for wake applications is presented. The methods include Reliability Ensemble Averaging, Bayesian Model Averaging, and Monte Carlo Simulations. The methodologies are evaluated using data from wake vortex field experiments.

  4. Ensemble methods for handwritten digit recognition

    DEFF Research Database (Denmark)

    Hansen, Lars Kai; Liisberg, Christian; Salamon, P.

    1992-01-01

    by optimizing the receptive fields. It is concluded that it is possible to improve performance significantly by introducing moderate-size ensembles; in particular, a 20-25% improvement has been found. The ensemble random LUTs, when trained on a medium-size database, reach a performance (without rejects) of 94....... It is further shown that it is possible to estimate the ensemble performance as well as the learning curve on a medium-size database. In addition the authors present preliminary analysis of experiments on a large database and show that state-of-the-art performance can be obtained using the ensemble approach...

  5. Joys of Community Ensemble Playing: The Case of the Happy Roll Elastic Ensemble in Taiwan

    Science.gov (United States)

    Hsieh, Yuan-Mei; Kao, Kai-Chi

    2012-01-01

    The Happy Roll Elastic Ensemble (HREE) is a community music ensemble supported by Tainan Culture Centre in Taiwan. With enjoyment and friendship as its primary goals, it aims to facilitate the joys of ensemble playing and the spirit of social networking. This article highlights the key aspects of HREE's development in its first two years…

  6. A short-range multi-model ensemble weather prediction system for South Africa

    CSIR Research Space (South Africa)

    Landman, S

    2010-09-01

    Full Text Available prediction system (EPS) at the South African Weather Service (SAWS) are examined. The ensemble consists of different forecasts from the 12-km LAM of the UK Met Office Unified Model (UM) and the Conformal-Cubic Atmospheric Model (CCAM) covering the South...

  7. ENSO-conditioned weather resampling method for seasonal ensemble streamflow prediction

    NARCIS (Netherlands)

    Beckers, Joost V.L.; Weerts, Albrecht H.; Tijdeman, Erik; Welles, Edwin

    2016-01-01

    Oceanic-atmospheric climate modes, such as El Niño-Southern Oscillation (ENSO), are known to affect the local streamflow regime in many rivers around the world. A new method is proposed to incorporate climate mode information into the well-known ensemble streamflow prediction (ESP) method for

  8. A statistical analysis of three ensembles of crop model responses to temperature and CO2 concentration

    Science.gov (United States)

    Ensembles of process-based crop models are now commonly used to simulate crop growth and development for climate scenarios of temperature and/or precipitation changes corresponding to different projections of atmospheric CO2 concentrations. This approach generates large datasets with thousands of de...

  9. Development of Ensemble Neural Network Convection Parameterizations for Climate Models

    Energy Technology Data Exchange (ETDEWEB)

    Fox-Rabinovitz, M. S.; Krasnopolsky, V. M.

    2012-05-02

    The novel neural network (NN) approach has been formulated and used for development of a NN ensemble stochastic convection parametrization for climate models. This fast parametrization is built based on data from Cloud Resolving Model (CRM) simulations initialized with and forced by TOGA-COARE data. The SAM (System for Atmospheric Modeling), developed by D. Randall, M. Khairoutdinov, and their collaborators, has been used for CRM simulations. The observational data are also used for validation of model simulations. The SAM-simulated data have been averaged and projected onto the GCM space of atmospheric states to implicitly define a stochastic convection parametrization. This parametrization is emulated using an ensemble of NNs. An ensemble of NNs with different NN parameters has been trained and tested. The inherent uncertainty of the stochastic convection parametrization derived in such a way is estimated. Due to these inherent uncertainties, NN ensemble is used to constitute a stochastic NN convection parametrization. The developed NN convection parametrization have been validated in a diagnostic CAM (CAM-NN) run vs. the control CAM run. Actually, CAM inputs have been used, at every time step of the control/original CAM integration, for parallel calculations of the NN convection parametrization (CAM-NN) to produce its outputs as a diagnostic byproduct. Total precipitation (P) and cloudiness (CLD) time series, diurnal cycles, and P and CLD distributions for the large Tropical Pacific Ocean for the parallel CAM-NN and CAM runs show similarity and consistency with the NCEP reanalysis. The P and CLD distributions for the tropical area for the parallel runs have been analyzed first for the TOGA-COARE boreal winter season (November 1992 through February 1993) and then for the winter seasons of the follow-up parallel decadal simulations. The obtained results are encouraging and practically meaningful. They show the validity of the NN approach. This constitutes an

  10. Transition from Poisson to circular unitary ensemble

    Indian Academy of Sciences (India)

    Transitions to universality classes of random matrix ensembles have been useful in the study of weakly-broken symmetries in quantum chaotic systems. Transitions involving Poisson as the initial ensemble have been particularly interesting. The exact two-point correlation function was derived by one of the present authors ...

  11. Improved customer choice predictions using ensemble methods

    NARCIS (Netherlands)

    M.C. van Wezel (Michiel); R. Potharst (Rob)

    2005-01-01

    textabstractIn this paper various ensemble learning methods from machine learning and statistics are considered and applied to the customer choice modeling problem. The application of ensemble learning usually improves the prediction quality of flexible models like decision trees and thus leads to

  12. Urban runoff forecasting with ensemble weather predictions

    DEFF Research Database (Denmark)

    Pedersen, Jonas Wied; Courdent, Vianney Augustin Thomas; Vezzaro, Luca

    This research shows how ensemble weather forecasts can be used to generate urban runoff forecasts up to 53 hours into the future. The results highlight systematic differences between ensemble members that needs to be accounted for when these forecasts are used in practice....

  13. Transition from Poisson to circular unitary ensemble

    Indian Academy of Sciences (India)

    Abstract. Transitions to universality classes of random matrix ensembles have been useful in the study of weakly-broken symmetries in quantum chaotic systems. Transitions involving Poisson as the initial ensemble have been particularly interesting. The exact two-point correlation function was derived by one of the present ...

  14. Perception of ensemble statistics requires attention.

    Science.gov (United States)

    Jackson-Nielsen, Molly; Cohen, Michael A; Pitts, Michael A

    2017-02-01

    To overcome inherent limitations in perceptual bandwidth, many aspects of the visual world are represented as summary statistics (e.g., average size, orientation, or density of objects). Here, we investigated the relationship between summary (ensemble) statistics and visual attention. Recently, it was claimed that one ensemble statistic in particular, color diversity, can be perceived without focal attention. However, a broader debate exists over the attentional requirements of conscious perception, and it is possible that some form of attention is necessary for ensemble perception. To test this idea, we employed a modified inattentional blindness paradigm and found that multiple types of summary statistics (color and size) often go unnoticed without attention. In addition, we found attentional costs in dual-task situations, further implicating a role for attention in statistical perception. Overall, we conclude that while visual ensembles may be processed efficiently, some amount of attention is necessary for conscious perception of ensemble statistics. Copyright © 2016 Elsevier Inc. All rights reserved.

  15. Experimental Hydrologic Ensemble Forecast System for Collaborative R&D and Research-to-Operations Transition in NWS

    Science.gov (United States)

    Seo, D.; Liu, Y.; Herr, H.

    2008-12-01

    Providing uncertainty information is one of the most pressing needs of operational hydrologic forecasting in the National Oceanic and Atmospheric Administration's (NOAA) National Weather Service (NWS) today. To address this need, the NWS Office of Hydrologic Development (OHD) is developing the EXperimental Ensemble Forecast System (XEFS), an integrated short- to long-range hydrologic ensemble prediction system to be implemented at the NWS River Forecast Centers for experimental operation within the next two years. The baseline system includes the Ensemble Pre-Processor (EPP3), Ensemble Streamflow Prediction (ESP2) subsystem, Hydrologic Model Output Statistics (HMOS) streamflow ensemble processor, Ensemble Post-Processor (EnsPost) and the Ensemble Product Generator (EPG), and will use the service-oriented architecture of the Early Flood Warning System (FEWS) of Deltares. To support in-house and collaborative research and development of hydrologic ensemble prediction and data assimilation capabilities, NWS/OHD is developing a research and development version of XEFS, or R&D XEFS, of which the above baseline system is a subset. In this presentation, we describe the overall framework and major components of the R&D XEFS, progress to date, plans, challenges and opportunities for collaborative R&D and research-to-operations transition of the research outcome into NWS hydrologic operations and services.

  16. Generation of scenarios from calibrated ensemble forecasts with a dynamic ensemble copula coupling approach

    CERN Document Server

    Bouallegue, Zied Ben; Theis, Susanne E; Pinson, Pierre

    2015-01-01

    Probabilistic forecasts in the form of ensemble of scenarios are required for complex decision making processes. Ensemble forecasting systems provide such products but the spatio-temporal structures of the forecast uncertainty is lost when statistical calibration of the ensemble forecasts is applied for each lead time and location independently. Non-parametric approaches allow the reconstruction of spatio-temporal joint probability distributions at a low computational cost.For example, the ensemble copula coupling (ECC) method consists in rebuilding the multivariate aspect of the forecast from the original ensemble forecasts. Based on the assumption of error stationarity, parametric methods aim to fully describe the forecast dependence structures. In this study, the concept of ECC is combined with past data statistics in order to account for the autocorrelation of the forecast error. The new approach which preserves the dynamical development of the ensemble members is called dynamic ensemble copula coupling (...

  17. IASI Radiance Data Assimilation in Local Ensemble Transform Kalman Filter

    Science.gov (United States)

    Cho, K.; Hyoung-Wook, C.; Jo, Y.

    2016-12-01

    Korea institute of Atmospheric Prediction Systems (KIAPS) is developing NWP model with data assimilation systems. Local Ensemble Transform Kalman Filter (LETKF) system, one of the data assimilation systems, has been developed for KIAPS Integrated Model (KIM) based on cubed-sphere grid and has successfully assimilated real data. LETKF data assimilation system has been extended to 4D- LETKF which considers time-evolving error covariance within assimilation window and IASI radiance data assimilation using KPOP (KIAPS package for observation processing) with RTTOV (Radiative Transfer for TOVS). The LETKF system is implementing semi operational prediction including conventional (sonde, aircraft) observation and AMSU-A (Advanced Microwave Sounding Unit-A) radiance data from April. Recently, the semi operational prediction system updated radiance observations including GPS-RO, AMV, IASI (Infrared Atmospheric Sounding Interferometer) data at July. A set of simulation of KIM with ne30np4 and 50 vertical levels (of top 0.3hPa) were carried out for short range forecast (10days) within semi operation prediction LETKF system with ensemble forecast 50 members. In order to only IASI impact, our experiments used only conventional and IAIS radiance data to same semi operational prediction set. We carried out sensitivity test for IAIS thinning method (3D and 4D). IASI observation number was increased by temporal (4D) thinning and the improvement of IASI radiance data impact on the forecast skill of model will expect.

  18. Ensemble Kalman filter data assimilation for the MPAS system

    Science.gov (United States)

    Ha, Soyoung; Snyder, Chris

    2015-04-01

    The Model for Prediction Across Scales (MPAS; http://mpas-dev.github.io/) is a global non-hydrostatic numerical atmospheric model based on unstructured centroidal Voronoi meshes that allow both uniform and variable resolutions. The variable resolution allows locally high-resolution meshes that transition smoothly to coarser resolution over the rest of the globe, avoiding the need to drive a limited-area model with lateral boundary conditions from a separate global model. The nonhydrostatic MPAS solver (for both atmospheric and oceanic components) is now coupled to the Data Assimilation Research Testbed (DART; http://www.image.ucar.edu/ DAReS/DART) system with a full capability of ensemble Kalman filter data assimilation. The analysis/forecast cycling experiments using MPAS/DART is successfully tested with real observations for different retrospective cases. Assimilated observations are all conventional data as well as satellite winds and GPS radio occultation refractivity data. Testing on different grid mesh, we examine issues specific to the MPAS grid, such as smoothing in the interpolation and the update of horizontal wind fields, and show their impact on the Ensemble Kalman Filter (EnKF) analysis and the following short-range forecast. Up to 5-day forecasts for a month-long cycle period are verified against observations and compared to the NCEP GFS (Global Forecast System) forecasts.

  19. Physiological evaluation of air-fed ensembles.

    Science.gov (United States)

    Turner, Nina L; Powell, Jeffrey B; Sinkule, Edward J; Novak, Debra A

    2014-03-01

    The goal of this study was to evaluate the respiratory and metabolic stresses of air-fed ensembles used by workers in the nuclear, chemical, and pharmaceutical industries during rest, low-, and moderate-intensity treadmill exercise. Fourteen men and six women wore two different air-fed ensembles (AFE-1 and AFE-2) and one two-piece supplied-air respirator (SA) at rest (REST) and while walking for 6min at oxygen consumption (V.O2) rates of 1.0 (LOW) and 2.0 l min(-1) (MOD). Inhaled CO2 (FICO2), inhaled O2 (FIO2), pressure, and temperature were measured continuously breath-by-breath. For both LOW and MOD, FICO2 was significantly lower (P ensembles than SA (P REST, LOW, and MOD. Inhaled gas temperature was significantly lower in SA than in either air-fed ensemble (P ensembles in both men and women, an observation that has implications for the design of emergency escape protocols for air-fed ensemble wearers. Results show that inhaled gas concentrations may reach physiologically stressful levels in air-fed ensembles during moderate-intensity treadmill walking.

  20. Comparing the Degree of Land-Atmosphere Interaction in Four Atmospheric General Circulation Models

    Science.gov (United States)

    Koster, Randal D.; Dirmeyer, Paul A.; Hahmann, Andrea N.; Ijpelaar, Ruben; Tyahla, Lori; Cox, Peter; Suarez, Max J.; Houser, Paul R. (Technical Monitor)

    2001-01-01

    Land-atmosphere feedback, by which (for example) precipitation-induced moisture anomalies at the land surface affect the overlying atmosphere and thereby the subsequent generation of precipitation, has been examined and quantified with many atmospheric general circulation models (AGCMs). Generally missing from such studies, however, is an indication of the extent to which the simulated feedback strength is model dependent. Four modeling groups have recently performed a highly controlled numerical experiment that allows an objective inter-model comparison of land-atmosphere feedback strength. The experiment essentially consists of an ensemble of simulations in which each member simulation artificially maintains the same time series of surface prognostic variables. Differences in atmospheric behavior between the ensemble members then indicates the degree to which the state of the land surface controls atmospheric processes in that model. A comparison of the four sets of experimental results shows that feedback strength does indeed vary significantly between the AGCMs.

  1. Staging atmospheres

    DEFF Research Database (Denmark)

    Bille, Mikkel; Bjerregaard, Peter; Sørensen, Tim Flohr

    2015-01-01

    The article introduces the special issue on staging atmospheres by surveying the philosophical, political and anthropological literature on atmosphere, and explores the relationship between atmosphere, material culture, subjectivity and affect. Atmosphere seems to occupy one of the classic...

  2. Atomic clock ensemble in space

    Science.gov (United States)

    Cacciapuoti, L.; Salomon, C.

    2011-12-01

    Atomic Clock Ensemble in Space (ACES) is a mission using high-performance clocks and links to test fundamental laws of physics in space. Operated in the microgravity environment of the International Space Station, the ACES clocks, PHARAO and SHM, will generate a frequency reference reaching instability and inaccuracy at the 1 · 10-16 level. A link in the microwave domain (MWL) and an optical link (ELT) will make the ACES clock signal available to ground laboratories equipped with atomic clocks. Space-to-ground and ground-to-ground comparisons of atomic frequency standards will be used to test Einstein's theory of general relativity including a precision measurement of the gravitational red-shift, a search for time variations of fundamental constants, and Lorentz Invariance tests. Applications in geodesy, optical time transfer, and ranging will also be supported. ACES has now reached an advanced technology maturity, with engineering models completed and successfully tested and flight hardware under development. This paper presents the ACES mission concept and the status of its main instruments.

  3. Ensemble Machine Learning Methods and Applications

    CERN Document Server

    Ma, Yunqian

    2012-01-01

    It is common wisdom that gathering a variety of views and inputs improves the process of decision making, and, indeed, underpins a democratic society. Dubbed “ensemble learning” by researchers in computational intelligence and machine learning, it is known to improve a decision system’s robustness and accuracy. Now, fresh developments are allowing researchers to unleash the power of ensemble learning in an increasing range of real-world applications. Ensemble learning algorithms such as “boosting” and “random forest” facilitate solutions to key computational issues such as face detection and are now being applied in areas as diverse as object trackingand bioinformatics.   Responding to a shortage of literature dedicated to the topic, this volume offers comprehensive coverage of state-of-the-art ensemble learning techniques, including various contributions from researchers in leading industrial research labs. At once a solid theoretical study and a practical guide, the volume is a windfall for r...

  4. Ensemble Weight Enumerators for Protograph LDPC Codes

    Science.gov (United States)

    Divsalar, Dariush

    2006-01-01

    Recently LDPC codes with projected graph, or protograph structures have been proposed. In this paper, finite length ensemble weight enumerators for LDPC codes with protograph structures are obtained. Asymptotic results are derived as the block size goes to infinity. In particular we are interested in obtaining ensemble average weight enumerators for protograph LDPC codes which have minimum distance that grows linearly with block size. As with irregular ensembles, linear minimum distance property is sensitive to the proportion of degree-2 variable nodes. In this paper the derived results on ensemble weight enumerators show that linear minimum distance condition on degree distribution of unstructured irregular LDPC codes is a sufficient but not a necessary condition for protograph LDPC codes.

  5. Genetic Algorithm Optimized Neural Networks Ensemble as ...

    African Journals Online (AJOL)

    NJD

    Genetic Algorithm Optimized Neural Networks Ensemble as. Calibration Model for Simultaneous Spectrophotometric. Estimation of Atenolol and Losartan Potassium in Tablets. Dondeti Satyanarayana*, Kamarajan Kannan and Rajappan Manavalan. Department of Pharmacy, Annamalai University, Annamalainagar, Tamil ...

  6. Impacts of calibration strategies and ensemble methods on ensemble flood forecasting over Lanjiang basin, Southeast China

    Science.gov (United States)

    Liu, Li; Xu, Yue-Ping

    2017-04-01

    Ensemble flood forecasting driven by numerical weather prediction products is becoming more commonly used in operational flood forecasting applications.In this study, a hydrological ensemble flood forecasting system based on Variable Infiltration Capacity (VIC) model and quantitative precipitation forecasts from TIGGE dataset is constructed for Lanjiang Basin, Southeast China. The impacts of calibration strategies and ensemble methods on the performance of the system are then evaluated.The hydrological model is optimized by parallel programmed ɛ-NSGAII multi-objective algorithm and two respectively parameterized models are determined to simulate daily flows and peak flows coupled with a modular approach.The results indicatethat the ɛ-NSGAII algorithm permits more efficient optimization and rational determination on parameter setting.It is demonstrated that the multimodel ensemble streamflow mean have better skills than the best singlemodel ensemble mean (ECMWF) and the multimodel ensembles weighted on members and skill scores outperform other multimodel ensembles. For typical flood event, it is proved that the flood can be predicted 3-4 days in advance, but the flows in rising limb can be captured with only 1-2 days ahead due to the flash feature. With respect to peak flows selected by Peaks Over Threshold approach, the ensemble means from either singlemodel or multimodels are generally underestimated as the extreme values are smoothed out by ensemble process.

  7. Conductor gestures influence evaluations of ensemble performance.

    Science.gov (United States)

    Morrison, Steven J; Price, Harry E; Smedley, Eric M; Meals, Cory D

    2014-01-01

    Previous research has found that listener evaluations of ensemble performances vary depending on the expressivity of the conductor's gestures, even when performances are otherwise identical. It was the purpose of the present study to test whether this effect of visual information was evident in the evaluation of specific aspects of ensemble performance: articulation and dynamics. We constructed a set of 32 music performances that combined auditory and visual information and were designed to feature a high degree of contrast along one of two target characteristics: articulation and dynamics. We paired each of four music excerpts recorded by a chamber ensemble in both a high- and low-contrast condition with video of four conductors demonstrating high- and low-contrast gesture specifically appropriate to either articulation or dynamics. Using one of two equivalent test forms, college music majors and non-majors (N = 285) viewed sixteen 30 s performances and evaluated the quality of the ensemble's articulation, dynamics, technique, and tempo along with overall expressivity. Results showed significantly higher evaluations for performances featuring high rather than low conducting expressivity regardless of the ensemble's performance quality. Evaluations for both articulation and dynamics were strongly and positively correlated with evaluations of overall ensemble expressivity.

  8. Ensemble forecasts of road surface temperatures

    Science.gov (United States)

    Sokol, Zbyněk; Bližňák, Vojtěch; Sedlák, Pavel; Zacharov, Petr; Pešice, Petr; Škuthan, Miroslav

    2017-05-01

    This paper describes a new ensemble technique for road surface temperature (RST) forecasting using an energy balance and heat conduction model. Compared to currently used deterministic forecasts, the proposed technique allows the estimation of forecast uncertainty and probabilistic forecasts. The ensemble technique is applied to the METRo-CZ model and stems from error covariance analyses of the forecasted air temperature and humidity 2 m above the ground, wind speed at 10 m and total cloud cover N in octas by the numerical weather prediction (NWP) model. N is used to estimate the shortwave and longwave radiation fluxes. These variables are used to calculate the boundary conditions in the METRo-CZ model. We found that the variable N is crucial for generating the ensembles. Nevertheless, the ensemble spread is too small and underestimates the uncertainty in the RST forecast. One of the reasons is not considering errors in the rain and snow forecast by the NWP model when generating ensembles. Technical issues, such as incorrect sky view factors and the current state of road surface conditions also contribute to errors. Although the ensemble technique underestimates the uncertainty in the RST forecasts, it provides additional information to road authorities who provide winter road maintenance.

  9. Limited-area short-range ensemble predictions targeted for heavy rain in Europe

    Directory of Open Access Journals (Sweden)

    K. Sattler

    2005-01-01

    Full Text Available Inherent uncertainties in short-range quantitative precipitation forecasts (QPF from the high-resolution, limited-area numerical weather prediction model DMI-HIRLAM (LAM are addressed using two different approaches to creating a small ensemble of LAM simulations, with focus on prediction of extreme rainfall events over European river basins. The first ensemble type is designed to represent uncertainty in the atmospheric state of the initial condition and at the lateral LAM boundaries. The global ensemble prediction system (EPS from ECMWF serves as host model to the LAM and provides the state perturbations, from which a small set of significant members is selected. The significance is estimated on the basis of accumulated precipitation over a target area of interest, which contains the river basin(s under consideration. The selected members provide the initial and boundary data for the ensemble integration in the LAM. A second ensemble approach tries to address a portion of the model-inherent uncertainty responsible for errors in the forecasted precipitation field by utilising different parameterisation schemes for condensation and convection in the LAM. Three periods around historical heavy rain events that caused or contributed to disastrous river flooding in Europe are used to study the performance of the LAM ensemble designs. The three cases exhibit different dynamic and synoptic characteristics and provide an indication of the ensemble qualities in different weather situations. Precipitation analyses from the Deutsche Wetterdienst (DWD are used as the verifying reference and a comparison of daily rainfall amounts is referred to the respective river basins of the historical cases.

  10. Potentialities of ensemble strategies for flood forecasting over the Milano urban area

    Science.gov (United States)

    Ravazzani, Giovanni; Amengual, Arnau; Ceppi, Alessandro; Homar, Víctor; Romero, Romu; Lombardi, Gabriele; Mancini, Marco

    2016-08-01

    Analysis of ensemble forecasting strategies, which can provide a tangible backing for flood early warning procedures and mitigation measures over the Mediterranean region, is one of the fundamental motivations of the international HyMeX programme. Here, we examine two severe hydrometeorological episodes that affected the Milano urban area and for which the complex flood protection system of the city did not completely succeed. Indeed, flood damage have exponentially increased during the last 60 years, due to industrial and urban developments. Thus, the improvement of the Milano flood control system needs a synergism between structural and non-structural approaches. First, we examine how land-use changes due to urban development have altered the hydrological response to intense rainfalls. Second, we test a flood forecasting system which comprises the Flash-flood Event-based Spatially distributed rainfall-runoff Transformation, including Water Balance (FEST-WB) and the Weather Research and Forecasting (WRF) models. Accurate forecasts of deep moist convection and extreme precipitation are difficult to be predicted due to uncertainties arising from the numeric weather prediction (NWP) physical parameterizations and high sensitivity to misrepresentation of the atmospheric state; however, two hydrological ensemble prediction systems (HEPS) have been designed to explicitly cope with uncertainties in the initial and lateral boundary conditions (IC/LBCs) and physical parameterizations of the NWP model. No substantial differences in skill have been found between both ensemble strategies when considering an enhanced diversity of IC/LBCs for the perturbed initial conditions ensemble. Furthermore, no additional benefits have been found by considering more frequent LBCs in a mixed physics ensemble, as ensemble spread seems to be reduced. These findings could help to design the most appropriate ensemble strategies before these hydrometeorological extremes, given the computational

  11. Addressing model uncertainty through stochastic parameter perturbations within the High Resolution Rapid Refresh (HRRR) ensemble

    Science.gov (United States)

    Wolff, J.; Jankov, I.; Beck, J.; Carson, L.; Frimel, J.; Harrold, M.; Jiang, H.

    2016-12-01

    It is well known that global and regional numerical weather prediction ensemble systems are under-dispersive, producing unreliable and overconfident ensemble forecasts. Typical approaches to alleviate this problem include the use of multiple dynamic cores, multiple physics suite configurations, or a combination of the two. While these approaches may produce desirable results, they have practical and theoretical deficiencies and are more difficult and costly to maintain. An active area of research that promotes a more unified and sustainable system for addressing the deficiencies in ensemble modeling is the use of stochastic physics to represent model-related uncertainty. Stochastic approaches include Stochastic Parameter Perturbations (SPP), Stochastic Kinetic Energy Backscatter (SKEB), Stochastic Perturbation of Physics Tendencies (SPPT), or some combination of all three. The focus of this study is to assess the model performance within a convection-permitting ensemble at 3-km grid spacing across the Contiguous United States (CONUS) when using stochastic approaches. For this purpose, the test utilized a single physics suite configuration based on the operational High-Resolution Rapid Refresh (HRRR) model, with ensemble members produced by employing stochastic methods. Parameter perturbations were employed in the Rapid Update Cycle (RUC) land surface model and Mellor-Yamada-Nakanishi-Niino (MYNN) planetary boundary layer scheme. Results will be presented in terms of bias, error, spread, skill, accuracy, reliability, and sharpness using the Model Evaluation Tools (MET) verification package. Due to the high level of complexity of running a frequently updating (hourly), high spatial resolution (3 km), large domain (CONUS) ensemble system, extensive high performance computing (HPC) resources were needed to meet this objective. Supercomputing resources were provided through the National Center for Atmospheric Research (NCAR) Strategic Capability (NSC) project support

  12. Response of ENSO amplitude to global warming in CESM large ensemble: uncertainty due to internal variability

    Science.gov (United States)

    Zheng, Xiao-Tong; Hui, Chang; Yeh, Sang-Wook

    2017-08-01

    El Niño-Southern Oscillation (ENSO) is the dominant mode of variability in the coupled ocean-atmospheric system. Future projections of ENSO change under global warming are highly uncertain among models. In this study, the effect of internal variability on ENSO amplitude change in future climate projections is investigated based on a 40-member ensemble from the Community Earth System Model Large Ensemble (CESM-LE) project. A large uncertainty is identified among ensemble members due to internal variability. The inter-member diversity is associated with a zonal dipole pattern of sea surface temperature (SST) change in the mean along the equator, which is similar to the second empirical orthogonal function (EOF) mode of tropical Pacific decadal variability (TPDV) in the unforced control simulation. The uncertainty in CESM-LE is comparable in magnitude to that among models of the Coupled Model Intercomparison Project phase 5 (CMIP5), suggesting the contribution of internal variability to the intermodel uncertainty in ENSO amplitude change. However, the causations between changes in ENSO amplitude and the mean state are distinct between CESM-LE and CMIP5 ensemble. The CESM-LE results indicate that a large ensemble of 15 members is needed to separate the relative contributions to ENSO amplitude change over the twenty-first century between forced response and internal variability.

  13. The role of model dynamics in ensemble Kalman filter performance for chaotic systems

    Science.gov (United States)

    Ng, G.-H.C.; McLaughlin, D.; Entekhabi, D.; Ahanin, A.

    2011-01-01

    The ensemble Kalman filter (EnKF) is susceptible to losing track of observations, or 'diverging', when applied to large chaotic systems such as atmospheric and ocean models. Past studies have demonstrated the adverse impact of sampling error during the filter's update step. We examine how system dynamics affect EnKF performance, and whether the absence of certain dynamic features in the ensemble may lead to divergence. The EnKF is applied to a simple chaotic model, and ensembles are checked against singular vectors of the tangent linear model, corresponding to short-term growth and Lyapunov vectors, corresponding to long-term growth. Results show that the ensemble strongly aligns itself with the subspace spanned by unstable Lyapunov vectors. Furthermore, the filter avoids divergence only if the full linearized long-term unstable subspace is spanned. However, short-term dynamics also become important as non-linearity in the system increases. Non-linear movement prevents errors in the long-term stable subspace from decaying indefinitely. If these errors then undergo linear intermittent growth, a small ensemble may fail to properly represent all important modes, causing filter divergence. A combination of long and short-term growth dynamics are thus critical to EnKF performance. These findings can help in developing practical robust filters based on model dynamics. ?? 2011 The Authors Tellus A ?? 2011 John Wiley & Sons A/S.

  14. Climate model forecast biases assessed with a perturbed physics ensemble

    Science.gov (United States)

    Mulholland, David P.; Haines, Keith; Sparrow, Sarah N.; Wallom, David

    2017-09-01

    Perturbed physics ensembles have often been used to analyse long-timescale climate model behaviour, but have been used less often to study model processes on shorter timescales. We combine a transient perturbed physics ensemble with a set of initialised forecasts to deduce regional process errors present in the standard HadCM3 model, which cause the model to drift in the early stages of the forecast. First, it is shown that the transient drifts in the perturbed physics ensembles can be used to recover quantitatively the parameters that were perturbed. The parameters which exert most influence on the drifts vary regionally, but upper ocean mixing and atmospheric convective processes are particularly important on the 1-month timescale. Drifts in the initialised forecasts are then used to recover the `equivalent parameter perturbations', which allow identification of the physical processes that may be at fault in the HadCM3 representation of the real world. Most parameters show positive and negative adjustments in different regions, indicating that standard HadCM3 values represent a global compromise. The method is verified by correcting an unusually widespread positive bias in the strength of wind-driven ocean mixing, with forecast drifts reduced in a large number of areas as a result. This method could therefore be used to improve the skill of initialised climate model forecasts by reducing model biases through regional adjustments to physical processes, either by tuning or targeted parametrisation refinement. Further, such regionally tuned models might also significantly outperform standard climate models, with global parameter configurations, in longer-term climate studies.

  15. Benchmarking Ensemble Streamflow Prediction Skill in the UK

    Science.gov (United States)

    Harrigan, Shaun; Smith, Katie; Parry, Simon; Tanguy, Maliko; Prudhomme, Christel

    2017-04-01

    Skilful hydrological forecasts at weekly to seasonal lead times would be extremely beneficial for decision-making in operational water management, especially during drought conditions. Hydro-meteorological ensemble forecasting systems are an attractive approach as they use two sources of streamflow predictability: (i) initial hydrologic conditions (IHCs), where soil moisture, groundwater and snow storage states can provide an estimate of future streamflow situations, and (ii) atmospheric predictability, where skilful forecasts of weather and climate variables can be used to force hydrological models. In the UK, prediction of rainfall at long lead times and for summer months in particular is notoriously difficult given the large degree of natural climate variability in ocean influenced mid-latitude regions, but recent research has uncovered exciting prospects for improved rainfall skill at seasonal lead times due to improved prediction of the North Atlantic Oscillation. However, before we fully understand what this improved atmospheric predictability might mean in terms of improved hydrological forecasts, we must first evaluate how much skill can be gained from IHCs alone. Ensemble Streamflow Prediction (ESP) is a well-established method for generating an ensemble of streamflow forecasts in the absence of skilful future meteorological predictions. The aim of this study is therefore to benchmark when (lead time/forecast initialisation month) and where (spatial pattern/catchment characteristics) ESP is skilful across a diverse set of catchments in the UK. Forecast skill was evaluated seamlessly from lead times of 1-day to 12-months and forecasts were initialised at the first of each month over the 1965-2015 hindcast period. This ESP output also provides a robust benchmark against which to assess how much improvement in skill can be achieved when meteorological forecasts are incorporated (next steps). To provide a 'tough to beat' benchmark, several variants of ESP with

  16. Generation of scenarios from calibrated ensemble forecasts with a dual ensemble copula coupling approach

    DEFF Research Database (Denmark)

    Ben Bouallègue, Zied; Heppelmann, Tobias; Theis, Susanne E.

    2016-01-01

    Probabilistic forecasts in the form of ensemble of scenarios are required for complex decision making processes. Ensemble forecasting systems provide such products but the spatio-temporal structures of the forecast uncertainty is lost when statistical calibration of the ensemble forecasts...... is applied for each lead time and location independently. Non-parametric approaches allow the reconstruction of spatio-temporal joint probability distributions at a low computational cost. For example, the ensemble copula coupling (ECC) method rebuilds the multivariate aspect of the forecast from...... the original ensemble forecasts. Based on the assumption of error stationarity, parametric methods aim to fully describe the forecast dependence structures. In this study, the concept of ECC is combined with past data statistics in order to account for the autocorrelation of the forecast error. The new...

  17. Generation of scenarios from calibrated ensemble forecasts with a dynamic ensemble copula coupling approach

    DEFF Research Database (Denmark)

    Ben Bouallègue, Zied; Heppelmann, Tobias; Theis, Susanne E.

    2015-01-01

    Probabilistic forecasts in the form of ensemble of scenarios are required for complex decision making processes. Ensemble forecasting systems provide such products but the spatio-temporal structures of the forecast uncertainty is lost when statistical calibration of the ensemble forecasts...... is applied for each lead time and location independently. Non-parametric approaches allow the reconstruction of spatio-temporal joint probability distributions at a low computational cost.For example, the ensemble copula coupling (ECC) method consists in rebuilding the multivariate aspect of the forecast...... from the original ensemble forecasts. Based on the assumption of error stationarity, parametric methods aim to fully describe the forecast dependence structures. In this study, the concept of ECC is combined with past data statistics in order to account for the autocorrelation of the forecast error...

  18. Meteorological Uncertainty of atmospheric Dispersion model results (MUD)

    DEFF Research Database (Denmark)

    Havskov Sørensen, Jens; Amstrup, Bjarne; Feddersen, Henrik

    The MUD project addresses assessment of uncertainties of atmospheric dispersion model predictions, as well as optimum presentation to decision makers. Previously, it has not been possible to estimate such uncertainties quantitatively, but merely to calculate the 'most likely' dispersion scenario....... In MUD, corresponding ensembles of atmospheric dispersion are computed from which uncertainties of predicted radionuclide concentration and deposition patterns are derived....

  19. Multiscale macromolecular simulation: role of evolving ensembles.

    Science.gov (United States)

    Singharoy, A; Joshi, H; Ortoleva, P J

    2012-10-22

    Multiscale analysis provides an algorithm for the efficient simulation of macromolecular assemblies. This algorithm involves the coevolution of a quasiequilibrium probability density of atomic configurations and the Langevin dynamics of spatial coarse-grained variables denoted order parameters (OPs) characterizing nanoscale system features. In practice, implementation of the probability density involves the generation of constant OP ensembles of atomic configurations. Such ensembles are used to construct thermal forces and diffusion factors that mediate the stochastic OP dynamics. Generation of all-atom ensembles at every Langevin time step is computationally expensive. Here, multiscale computation for macromolecular systems is made more efficient by a method that self-consistently folds in ensembles of all-atom configurations constructed in an earlier step, history, of the Langevin evolution. This procedure accounts for the temporal evolution of these ensembles, accurately providing thermal forces and diffusions. It is shown that efficiency and accuracy of the OP-based simulations is increased via the integration of this historical information. Accuracy improves with the square root of the number of historical timesteps included in the calculation. As a result, CPU usage can be decreased by a factor of 3-8 without loss of accuracy. The algorithm is implemented into our existing force-field based multiscale simulation platform and demonstrated via the structural dynamics of viral capsomers.

  20. The Hydrologic Ensemble Prediction Experiment (HEPEX)

    Science.gov (United States)

    Wood, A. W.; Thielen, J.; Pappenberger, F.; Schaake, J. C.; Hartman, R. K.

    2012-12-01

    The Hydrologic Ensemble Prediction Experiment was established in March, 2004, at a workshop hosted by the European Center for Medium Range Weather Forecasting (ECMWF). With support from the US National Weather Service (NWS) and the European Commission (EC), the HEPEX goal was to bring the international hydrological and meteorological communities together to advance the understanding and adoption of hydrological ensemble forecasts for decision support in emergency management and water resources sectors. The strategy to meet this goal includes meetings that connect the user, forecast producer and research communities to exchange ideas, data and methods; the coordination of experiments to address specific challenges; and the formation of testbeds to facilitate shared experimentation. HEPEX has organized about a dozen international workshops, as well as sessions at scientific meetings (including AMS, AGU and EGU) and special issues of scientific journals where workshop results have been published. Today, the HEPEX mission is to demonstrate the added value of hydrological ensemble prediction systems (HEPS) for emergency management and water resources sectors to make decisions that have important consequences for economy, public health, safety, and the environment. HEPEX is now organised around six major themes that represent core elements of a hydrologic ensemble prediction enterprise: input and pre-processing, ensemble techniques, data assimilation, post-processing, verification, and communication and use in decision making. This poster presents an overview of recent and planned HEPEX activities, highlighting case studies that exemplify the focus and objectives of HEPEX.

  1. An ensemble framework for identifying essential proteins.

    Science.gov (United States)

    Zhang, Xue; Xiao, Wangxin; Acencio, Marcio Luis; Lemke, Ney; Wang, Xujing

    2016-08-25

    Many centrality measures have been proposed to mine and characterize the correlations between network topological properties and protein essentiality. However, most of them show limited prediction accuracy, and the number of common predicted essential proteins by different methods is very small. In this paper, an ensemble framework is proposed which integrates gene expression data and protein-protein interaction networks (PINs). It aims to improve the prediction accuracy of basic centrality measures. The idea behind this ensemble framework is that different protein-protein interactions (PPIs) may show different contributions to protein essentiality. Five standard centrality measures (degree centrality, betweenness centrality, closeness centrality, eigenvector centrality, and subgraph centrality) are integrated into the ensemble framework respectively. We evaluated the performance of the proposed ensemble framework using yeast PINs and gene expression data. The results show that it can considerably improve the prediction accuracy of the five centrality measures individually. It can also remarkably increase the number of common predicted essential proteins among those predicted by each centrality measure individually and enable each centrality measure to find more low-degree essential proteins. This paper demonstrates that it is valuable to differentiate the contributions of different PPIs for identifying essential proteins based on network topological characteristics. The proposed ensemble framework is a successful paradigm to this end.

  2. Constraining regional extreme temperature projections of the CMIP5 ensemble

    Science.gov (United States)

    Vogel, Martha Marie; Seneviratne, Sonia Isabelle

    2017-04-01

    Temperature extremes are expected to increase in frequency and intensity in the future under enhanced global warming. Associated with the future projections of hot extremes are large uncertainties in different regions such as Central Europe. Given the severe impacts it is important to understand physical mechanisms leading to the projected amplified warming of regional extremes. Soil moisture- temperature feedbacks are strongly relevant for these projections as they are a key contributor to the development of regional hot extremes. Since soil moisture itself and soil moisture-temperature feedbacks are subject to change in future they likely contribute to the uncertainties of extreme temperature projections. In this work we link projections of changes in extreme temperatures to changes in land-atmosphere interactions with a particular focus on Central Europe. For this purpose, we employ observational data sets to constrain the model ensemble in the current climate, and consequently the extreme temperature projections.

  3. Embedded random matrix ensembles in quantum physics

    CERN Document Server

    Kota, V K B

    2014-01-01

    Although used with increasing frequency in many branches of physics, random matrix ensembles are not always sufficiently specific to account for important features of the physical system at hand. One refinement which retains the basic stochastic approach but allows for such features consists in the use of embedded ensembles.  The present text is an exhaustive introduction to and survey of this important field. Starting with an easy-to-read introduction to general random matrix theory, the text then develops the necessary concepts from the beginning, accompanying the reader to the frontiers of present-day research. With some notable exceptions, to date these ensembles have primarily been applied in nuclear spectroscopy. A characteristic example is the use of a random two-body interaction in the framework of the nuclear shell model. Yet, topics in atomic physics, mesoscopic physics, quantum information science and statistical mechanics of isolated finite quantum systems can also be addressed using these ensemb...

  4. Statistical ensembles and fragmentation of finite nuclei

    Science.gov (United States)

    Das, P.; Mallik, S.; Chaudhuri, G.

    2017-09-01

    Statistical models based on different ensembles are very commonly used to describe the nuclear multifragmentation reaction in heavy ion collisions at intermediate energies. Canonical model results are more appropriate for finite nuclei calculations while those obtained from the grand canonical ones are more easily calculable. A transformation relation has been worked out for converting results of finite nuclei from grand canonical to canonical and vice versa. The formula shows that, irrespective of the particle number fluctuation in the grand canonical ensemble, exact canonical results can be recovered for observables varying linearly or quadratically with the number of particles. This result is of great significance since the baryon and charge conservation constraints can make the exact canonical calculations extremely difficult in general. This concept developed in this work can be extended in future for transformation to ensembles where analytical solutions do not exist. The applicability of certain equations (isoscaling, etc.) in the regime of finite nuclei can also be tested using this transformation relation.

  5. Total probabilities of ensemble runoff forecasts

    Science.gov (United States)

    Olav Skøien, Jon; Bogner, Konrad; Salamon, Peter; Smith, Paul; Pappenberger, Florian

    2017-04-01

    Ensemble forecasting has a long history from meteorological modelling, as an indication of the uncertainty of the forecasts. However, it is necessary to calibrate and post-process the ensembles as the they often exhibit both bias and dispersion errors. Two of the most common methods for this are Bayesian Model Averaging (Raftery et al., 2005) and Ensemble Model Output Statistics (EMOS) (Gneiting et al., 2005). There are also methods for regionalizing these methods (Berrocal et al., 2007) and for incorporating the correlation between lead times (Hemri et al., 2013). Engeland and Steinsland Engeland and Steinsland (2014) developed a framework which can estimate post-processing parameters varying in space and time, while giving a spatially and temporally consistent output. However, their method is computationally complex for our larger number of stations, which makes it unsuitable for our purpose. Our post-processing method of the ensembles is developed in the framework of the European Flood Awareness System (EFAS - http://www.efas.eu), where we are making forecasts for whole Europe, and based on observations from around 700 catchments. As the target is flood forecasting, we are also more interested in improving the forecast skill for high-flows rather than in a good prediction of the entire flow regime. EFAS uses a combination of ensemble forecasts and deterministic forecasts from different meteorological forecasters to force a distributed hydrologic model and to compute runoff ensembles for each river pixel within the model domain. Instead of showing the mean and the variability of each forecast ensemble individually, we will now post-process all model outputs to estimate the total probability, the post-processed mean and uncertainty of all ensembles. The post-processing parameters are first calibrated for each calibration location, but we are adding a spatial penalty in the calibration process to force a spatial correlation of the parameters. The penalty takes

  6. Matrix averages relating to Ginibre ensembles

    Energy Technology Data Exchange (ETDEWEB)

    Forrester, Peter J [Department of Mathematics and Statistics, University of Melbourne, Victoria 3010 (Australia); Rains, Eric M [Department of Mathematics, California Institute of Technology, Pasadena, CA 91125 (United States)], E-mail: p.forrester@ms.unimelb.edu.au

    2009-09-25

    The theory of zonal polynomials is used to compute the average of a Schur polynomial of argument AX, where A is a fixed matrix and X is from the real Ginibre ensemble. This generalizes a recent result of Sommers and Khoruzhenko (2009 J. Phys. A: Math. Theor. 42 222002), and furthermore allows analogous results to be obtained for the complex and real quaternion Ginibre ensembles. As applications, the positive integer moments of the general variance Ginibre ensembles are computed in terms of generalized hypergeometric functions; these are written in terms of averages over matrices of the same size as the moment to give duality formulas, and the averages of the power sums of the eigenvalues are expressed as finite sums of zonal polynomials.

  7. Ensemble approach for differentiation of malignant melanoma

    Science.gov (United States)

    Rastgoo, Mojdeh; Morel, Olivier; Marzani, Franck; Garcia, Rafael

    2015-04-01

    Melanoma is the deadliest type of skin cancer, yet it is the most treatable kind depending on its early diagnosis. The early prognosis of melanoma is a challenging task for both clinicians and dermatologists. Due to the importance of early diagnosis and in order to assist the dermatologists, we propose an automated framework based on ensemble learning methods and dermoscopy images to differentiate melanoma from dysplastic and benign lesions. The evaluation of our framework on the recent and public dermoscopy benchmark (PH2 dataset) indicates the potential of proposed method. Our evaluation, using only global features, revealed that ensembles such as random forest perform better than single learner. Using random forest ensemble and combination of color and texture features, our framework achieved the highest sensitivity of 94% and specificity of 92%.

  8. Ensemble simulations with discrete classical dynamics

    DEFF Research Database (Denmark)

    Toxværd, Søren

    2013-01-01

    {E}(h)$ is employed to determine the relation with the corresponding energy, $E$ for the analytic dynamics with $h=0$ and the zero-order estimate $E_0(h)$ of the energy for discrete dynamics, appearing in the literature for MD with VA. We derive a corresponding time reversible VA algorithm for canonical dynamics...... for the $(NV\\tilde{T}(h))$ ensemble and determine the relations between the energies and temperatures for the different ensembles, including the $(NVE_0(h))$ and $(NVT_0(h))$ ensembles. The differences in the energies and temperatures are proportional with $h^2$ and they are of the order a few tenths...... of a percent for a traditional value of $h$. The relations between $(NV\\tilde{E}(h))$ and $(NVE)$, and $(NV\\tilde{T}(h))$ and $(NVT)$ are easily determined for a given density and temperature, and allows for using larger time increments in MD. The accurate determinations of the energies are used to determine...

  9. Interannual Variability and Predictability in an Ensemble of Climate Simulations with the MRI-JMA AGCM

    OpenAIRE

    Xiaogu, ZHENG; Masato, Sugi; Carsten S., FREDERIKSEN; National Institute of Water and Atmospheric Research; Meteorological Research Institute; Bureau of Meteorology Research Centre

    2004-01-01

    An analysis of variance approach, for systematically studying and evaluating the nterannual variability and predictability of seasonal mean fields, is demonstrated using an ensemble of six 50-year simulations of the 500 hPa geopotential height field from the Meteorological Research Institute-Japan Meteorological Agency (MRI-JMA) global atmosphere model forced by observed sea surface temperatures (SSTs). The model performance is analysed, for the seasons June-July-August (JJA) and December-Jan...

  10. Estimating model evidence using ensemble-based data assimilation with localization - The model selection problem

    OpenAIRE

    Metref, Sammy; Hannart, Alexis; Ruiz, Juan; Bocquet, Marc; Carrassi, Alberto; Ghil, Michael

    2017-01-01

    In recent years, there has been a growing interest in applying data assimilation (DA) methods, originally designed for state estimation, to the model selection problem. Along these lines, Carrassi et al. (2017) introduced the contextual formulation of model evidence (CME) and showed that CME can be efficiently computed using a hierarchy of ensemble-based DA procedures. Although Carrassi et al. (2017) analyzed the DA methods most commonly used for operational atmospheric and oceanic prediction...

  11. A statistical analysis of three ensembles of crop model responses totemperature and CO2concentration

    DEFF Research Database (Denmark)

    Makowski, D; Asseng, S; Ewert, F.

    2015-01-01

    Ensembles of process-based crop models are increasingly used to simulate crop growth for scenarios of temperature and/or precipitation changes corresponding to different projections of atmospheric CO2 concentrations. This approach generates large datasets with thousands of simulated crop yield da...

  12. Cluster ensembles, quantization and the dilogarithm

    DEFF Research Database (Denmark)

    Fock, Vladimir; Goncharov, Alexander B.

    2009-01-01

    A cluster ensemble is a pair of positive spaces (i.e. varieties equipped with positive atlases), coming with an action of a symmetry group . The space is closely related to the spectrum of a cluster algebra [ 12 ]. The two spaces are related by a morphism . The space is equipped with a closed -form......, possibly degenerate, and the space has a Poisson structure. The map is compatible with these structures. The dilogarithm together with its motivic and quantum avatars plays a central role in the cluster ensemble structure. We define a non-commutative -deformation of the -space. When is a root of unity...

  13. Precipitation Ensembles from Single-Value Forecasts for Hydrological Ensemble Forecasting

    Science.gov (United States)

    Demargne, J.; Schaake, J.; Wu, L.; Welles, E.; Herr, H.; Seo, D.

    2005-05-01

    An ensemble pre-processor was developed to produce short-term precipitation ensembles using operational single-value forecasts. The methodology attempts to quantify the uncertainty in the single-value forecast and to capture the skill therein. These precipitation ensemble forecasts could be then ingested in the NOAA/National Weather Service (NWS) Ensemble Streamflow Prediction (ESP) system to produce probabilistic hydrological forecasts that reflect the uncertainty in forecast precipitation. The procedure constructs the joint distribution of forecast and observed precipitation from historical pairs of forecast and observed values. The probability distribution function of the future events that may occur given a particular single-value forecast is then the conditional distribution of observed precipitation given the forecast. To generate individual ensemble members for each lead time and each location, the historical observed values are replaced with values sampled from the conditional distribution given the single-value forecast. The replacement procedure matches the ranks of historical and rescaled values to preserve the space-time properties of observed precipitation in the ensemble traces. Currently, the ensemble pre-processor is being tested and evaluated at four NOAA/NWS River Forecast Centers (RFCs) in the U.S.A. In this contribution, we present the results thus far from the field and retrospective evaluations, and key science issues that must be addressed toward national operational implementation.

  14. Evaluation of an ensemble precipitation prediction system over the Western Mediterranean area

    Science.gov (United States)

    Martin, M. L.; Santos-Muñoz, D.; Valero, F.; Morata, A.

    2010-10-01

    A short-range ensemble precipitation forecast system has been constructed to be applied to over the Mediterranean area down to a 25-km grid spacing using ten members from the fifth-generation Pennsylvania State University-National Center for Atmospheric Research Model (MM5). The ensemble system consists of ten members, each run with a different combination of two different initial conditions from global models and five different subgrid-scale physics configurations for a 2-weeks period of October 2006. The mesoscale verification is made by using observational precipitation data of Spanish Climatic Network. In terms of the total daily precipitation, spatially averaged over the region, time evolution of bias and root mean square error have shown larger departures in the first and final part of the selected period, maintaining quasi-zero values in the middle of the period. Although the created short-range ensemble shows high-spread-skill correlation values for daily precipitation, the asymmetric shape of the rank histogram shows some underdispersion, suggesting a bias behaviour. Confidence intervals have been built to show observational errors, showing more errors in the bins that suggest bias and underdispersive behaviour of the EPS and less errors in the first bins. Additionally, mean spatial forecast rainfall distributions have shown similar patterns to the observed mean precipitation ones, matching the largest accumulated rainfall values and settling down over the same areas. Relative Operative Characteristics diagrams, showing very outstanding areas, and the attribute diagram are indicative of usefulness of the forecasting system, depicting in general a good agreement between forecast probability and the mean observed frequency. Epsgrams of the spatially averaged total daily precipitation for each ensemble member and for the ensemble system illustrate, in general, good agreement among the different members, showing the spread of the ensemble system with

  15. Links between circulation indices and precipitation in the Mediterranean in an ensemble of regional climate models

    Czech Academy of Sciences Publication Activity Database

    Beranová, Romana; Kyselý, Jan

    2016-01-01

    Roč. 123, č. 3 (2016), s. 693-701 ISSN 0177-798X R&D Projects: GA ČR GAP209/10/2265 EU Projects: European Commission(XE) 505539 - ENSEMBLES Program:FP6 Institutional support: RVO:68378289 Keywords : atmospheric sciences climatology * atmospheric protection * air quality control * air pollution * waste water technology * water pollution control * water management * aquatic pollution Subject RIV: DG - Athmosphere Sciences, Meteorology Impact factor: 2.640, year: 2016 http://link.springer.com/article/10.1007%2Fs00704-015-1381-6

  16. NMR studies of dynamic biomolecular conformational ensembles.

    Science.gov (United States)

    Torchia, Dennis A

    2015-02-01

    Multidimensional heteronuclear NMR approaches can provide nearly complete sequential signal assignments of isotopically enriched biomolecules. The availability of assignments together with measurements of spin relaxation rates, residual spin interactions, J-couplings and chemical shifts provides information at atomic resolution about internal dynamics on timescales ranging from ps to ms, both in solution and in the solid state. However, due to the complexity of biomolecules, it is not possible to extract a unique atomic-resolution description of biomolecular motions even from extensive NMR data when many conformations are sampled on multiple timescales. For this reason, powerful computational approaches are increasingly applied to large NMR data sets to elucidate conformational ensembles sampled by biomolecules. In the past decade, considerable attention has been directed at an important class of biomolecules that function by binding to a wide variety of target molecules. Questions of current interest are: "Does the free biomolecule sample a conformational ensemble that encompasses the conformations found when it binds to various targets; and if so, on what time scale is the ensemble sampled?" This article reviews recent efforts to answer these questions, with a focus on comparing ensembles obtained for the same biomolecules by different investigators. A detailed comparison of results obtained is provided for three biomolecules: ubiquitin, calmodulin and the HIV-1 trans-activation response RNA. Published by Elsevier B.V.

  17. NYYD Ensemble ja Riho Sibul / Anneli Remme

    Index Scriptorium Estoniae

    Remme, Anneli, 1968-

    2001-01-01

    Gavin Bryarsi teos "Jesus' Blood Never Failed Me Yet" NYYD Ensemble'i ja Riho Sibula esituses 27. detsembril Pauluse kirikus Tartus ja 28. detsembril Rootsi- Mihkli kirikus Tallinnas. Kaastegevad Tartu Ülikooli Kammerkoor (Tartus) ja kammerkoor Voces Musicales (Tallinnas). Kunstiline juht Olari Elts

  18. Genetic Algorithm Optimized Neural Networks Ensemble as ...

    African Journals Online (AJOL)

    Improvements in neural network calibration models by a novel approach using neural network ensemble (NNE) for the simultaneous spectrophotometric multicomponent analysis are suggested, with a study on the estimation of the components of an antihypertensive combination, namely, atenolol and losartan potassium.

  19. Canonical Ensemble Model for Black Hole Radiation

    Indian Academy of Sciences (India)

    In this paper, a canonical ensemble model for the black hole quantum tunnelling radiation is introduced. In this model the probability distribution function corresponding to the emission shell is calculated to second order. The formula of pressure and internal energy of the thermal system is modified, and the fundamental ...

  20. Squeezing of Collective Excitations in Spin Ensembles

    DEFF Research Database (Denmark)

    Kraglund Andersen, Christian; Mølmer, Klaus

    2012-01-01

    and analytic calculations, and we obtain squeezing for a wide range of parameters. We also investigate the transfer of the squeezing properties to the cavity field and to an output mode from the cavity. Finally, we investigate how the squeezing is affected by effects of inhomogeneities which would be present...... in solid state implementations of the spin ensembles....

  1. Quantifying Uncertainty Through Global and Mesoscale Ensembles

    Science.gov (United States)

    2009-09-30

    transition to operations is in progress, with porting to the FNMOC large operational LINUX cluster ( OPAL A2) complete. The mesoscale system has been...ensembles to understand model behavior within parameter space, and the NOAA-funded Hurricane Forecast Improvement Project (HFIP). For the mesoscale

  2. The Hydrologic Ensemble Prediction Experiment (HEPEX)

    Science.gov (United States)

    Wood, Andy; Wetterhall, Fredrik; Ramos, Maria-Helena

    2015-04-01

    The Hydrologic Ensemble Prediction Experiment was established in March, 2004, at a workshop hosted by the European Center for Medium Range Weather Forecasting (ECMWF), and co-sponsored by the US National Weather Service (NWS) and the European Commission (EC). The HEPEX goal was to bring the international hydrological and meteorological communities together to advance the understanding and adoption of hydrological ensemble forecasts for decision support. HEPEX pursues this goal through research efforts and practical implementations involving six core elements of a hydrologic ensemble prediction enterprise: input and pre-processing, ensemble techniques, data assimilation, post-processing, verification, and communication and use in decision making. HEPEX has grown through meetings that connect the user, forecast producer and research communities to exchange ideas, data and methods; the coordination of experiments to address specific challenges; and the formation of testbeds to facilitate shared experimentation. In the last decade, HEPEX has organized over a dozen international workshops, as well as sessions at scientific meetings (including AMS, AGU and EGU) and special issues of scientific journals where workshop results have been published. Through these interactions and an active online blog (www.hepex.org), HEPEX has built a strong and active community of nearly 400 researchers & practitioners around the world. This poster presents an overview of recent and planned HEPEX activities, highlighting case studies that exemplify the focus and objectives of HEPEX.

  3. A method for ensemble wildland fire simulation

    Science.gov (United States)

    Mark A. Finney; Isaac C. Grenfell; Charles W. McHugh; Robert C. Seli; Diane Trethewey; Richard D. Stratton; Stuart Brittain

    2011-01-01

    An ensemble simulation system that accounts for uncertainty in long-range weather conditions and two-dimensional wildland fire spread is described. Fuel moisture is expressed based on the energy release component, a US fire danger rating index, and its variation throughout the fire season is modeled using time series analysis of historical weather data. This analysis...

  4. Nonlocal inhomogeneous broadening in plasmonic nanoparticle ensembles

    DEFF Research Database (Denmark)

    Tserkezis, Christos; Maack, Johan Rosenkrantz; Liu, Z.

    )] or realistic noble metals, following sharp or smooth size distribution functions around an ensemble mean value, and identify examples where inhomogeneous broadening can be significant. We also explain how its role becomes less important in most experimentally accessible situations, provided that all...

  5. Investigation of short-term effective radiative forcing of fire aerosols over North America using nudged hindcast ensembles

    Energy Technology Data Exchange (ETDEWEB)

    Liu, Yawen; Zhang, Kai; Qian, Yun; Wang, Yuhang; Zou, Yufei; Song, Yongjia; Wan, Hui; Liu, Xiaohong; Yang, Xiuqun

    2018-01-03

    Aerosols from fire emissions can potentially have large impact on clouds and radiation. However, fire aerosol sources are often intermittent, and their effect on weather and climate is difficult to quantify. Here we investigated the short-term effective radiative forcing of fire aerosols using the global aerosol–climate model Community Atmosphere Model version 5 (CAM5). Different from previous studies, we used nudged hindcast ensembles to quantify the forcing uncertainty due to the chaotic response to small perturbations in the atmosphere state. Daily mean emissions from three fire inventories were used to consider the uncertainty in emission strength and injection heights. The simulated aerosol optical depth (AOD) and mass concentrations were evaluated against in situ measurements and reanalysis data. Overall, the results show the model has reasonably good predicting skills. Short (10-day) nudged ensemble simulations were then performed with and without fire emissions to estimate the effective radiative forcing. Results show fire aerosols have large effects on both liquid and ice clouds over the two selected regions in April 2009. Ensemble mean results show strong negative shortwave cloud radiative effect (SCRE) over almost the entirety of southern Mexico, with a 10-day regional mean value of -3.0Wm-2. Over the central US, the SCRE is positive in the north but negative in the south, and the regional mean SCRE is small (-0.56Wm-2). For the 10-day average, we found a large ensemble spread of regional mean shortwave cloud radiative effect over southern Mexico (15.6% of the corresponding ensemble mean) and the central US (64.3 %), despite the regional mean AOD time series being almost indistinguishable during the 10-day period. Moreover, the ensemble spread is much larger when using daily averages instead of 10-day averages. This demonstrates the importance of using a large ensemble of simulations to estimate the short-term aerosol effective radiative forcing.

  6. A Theoretical Analysis of Why Hybrid Ensembles Work

    Directory of Open Access Journals (Sweden)

    Kuo-Wei Hsu

    2017-01-01

    Full Text Available Inspired by the group decision making process, ensembles or combinations of classifiers have been found favorable in a wide variety of application domains. Some researchers propose to use the mixture of two different types of classification algorithms to create a hybrid ensemble. Why does such an ensemble work? The question remains. Following the concept of diversity, which is one of the fundamental elements of the success of ensembles, we conduct a theoretical analysis of why hybrid ensembles work, connecting using different algorithms to accuracy gain. We also conduct experiments on classification performance of hybrid ensembles of classifiers created by decision tree and naïve Bayes classification algorithms, each of which is a top data mining algorithm and often used to create non-hybrid ensembles. Therefore, through this paper, we provide a complement to the theoretical foundation of creating and using hybrid ensembles.

  7. Impact of hybrid GSI analysis using ETR ensembles

    Indian Academy of Sciences (India)

    NCMRWF Global ForecastSystem) with ETR (Ensemble Transform with Rescaling) based Global Ensemble Forecast (GEFS) ofresolution T-190L28 is investigated. The experiment is conducted for a period of one week in June 2013and forecast ...

  8. An educational model for ensemble streamflow simulation and uncertainty analysis

    National Research Council Canada - National Science Library

    AghaKouchak, A; Nakhjiri, N; Habib, E

    2013-01-01

    ...) are interconnected. The educational toolbox includes a MATLAB Graphical User Interface (GUI) and an ensemble simulation scheme that can be used for teaching uncertainty analysis, parameter estimation, ensemble simulation and model sensitivity...

  9. Space Applications for Ensemble Detection and Analysis Project

    Data.gov (United States)

    National Aeronautics and Space Administration — Ensemble Detection is both a measurement technique and analysis tool. Like a prism that separates light into spectral bands, an ensemble detector mixes a signal with...

  10. Using ensemble forecasting for wind power

    Energy Technology Data Exchange (ETDEWEB)

    Giebel, G.; Landberg, L.; Badger, J. [Risoe National Lab., Roskilde (Denmark); Sattler, K.

    2003-07-01

    Short-term prediction of wind power has a long tradition in Denmark. It is an essential tool for the operators to keep the grid from becoming unstable in a region like Jutland, where more than 27% of the electricity consumption comes from wind power. This means that the minimum load is already lower than the maximum production from wind energy alone. Danish utilities have therefore used short-term prediction of wind energy since the mid-90ies. However, the accuracy is still far from being sufficient in the eyes of the utilities (used to have load forecasts accurate to within 5% on a one-week horizon). The Ensemble project tries to alleviate the dependency of the forecast quality on one model by using multiple models, and also will investigate the possibilities of using the model spread of multiple models or of dedicated ensemble runs for a prediction of the uncertainty of the forecast. Usually, short-term forecasting works (especially for the horizon beyond 6 hours) by gathering input from a Numerical Weather Prediction (NWP) model. This input data is used together with online data in statistical models (this is the case eg in Zephyr/WPPT) to yield the output of the wind farms or of a whole region for the next 48 hours (only limited by the NWP model horizon). For the accuracy of the final production forecast, the accuracy of the NWP prediction is paramount. While many efforts are underway to increase the accuracy of the NWP forecasts themselves (which ultimately are limited by the amount of computing power available, the lack of a tight observational network on the Atlantic and limited physics modelling), another approach is to use ensembles of different models or different model runs. This can be either an ensemble of different models output for the same area, using different data assimilation schemes and different model physics, or a dedicated ensemble run by a large institution, where the same model is run with slight variations in initial conditions and

  11. Optimal initial perturbations for El Nino ensemble prediction with ensemble Kalman filter

    Energy Technology Data Exchange (ETDEWEB)

    Ham, Yoo-Geun; Kang, In-Sik [Seoul National University, School of Earth and Environment Sciences, Seoul (Korea); Kug, Jong-Seong [Korea Ocean Research and Development Institute, Ansan (Korea)

    2009-12-15

    A method for selecting optimal initial perturbations is developed within the framework of an ensemble Kalman filter (EnKF). Among the initial conditions generated by EnKF, ensemble members with fast growing perturbations are selected to optimize the ENSO seasonal forecast skills. Seasonal forecast experiments show that the forecast skills with the selected ensemble members are significantly improved compared with other ensemble members for up to 1-year lead forecasts. In addition, it is found that there is a strong relationship between the forecast skill improvements and flow-dependent instability. That is, correlation skills are significantly improved over the region where the predictable signal is relatively small (i.e. an inverse relationship). It is also shown that forecast skills are significantly improved during ENSO onset and decay phases, which are the most unpredictable periods among the ENSO events. (orig.)

  12. Quantum canonical ensemble: A projection operator approach

    Science.gov (United States)

    Magnus, Wim; Lemmens, Lucien; Brosens, Fons

    2017-09-01

    Knowing the exact number of particles N, and taking this knowledge into account, the quantum canonical ensemble imposes a constraint on the occupation number operators. The constraint particularly hampers the systematic calculation of the partition function and any relevant thermodynamic expectation value for arbitrary but fixed N. On the other hand, fixing only the average number of particles, one may remove the above constraint and simply factorize the traces in Fock space into traces over single-particle states. As is well known, that would be the strategy of the grand-canonical ensemble which, however, comes with an additional Lagrange multiplier to impose the average number of particles. The appearance of this multiplier can be avoided by invoking a projection operator that enables a constraint-free computation of the partition function and its derived quantities in the canonical ensemble, at the price of an angular or contour integration. Introduced in the recent past to handle various issues related to particle-number projected statistics, the projection operator approach proves beneficial to a wide variety of problems in condensed matter physics for which the canonical ensemble offers a natural and appropriate environment. In this light, we present a systematic treatment of the canonical ensemble that embeds the projection operator into the formalism of second quantization while explicitly fixing N, the very number of particles rather than the average. Being applicable to both bosonic and fermionic systems in arbitrary dimensions, transparent integral representations are provided for the partition function ZN and the Helmholtz free energy FN as well as for two- and four-point correlation functions. The chemical potential is not a Lagrange multiplier regulating the average particle number but can be extracted from FN+1 -FN, as illustrated for a two-dimensional fermion gas.

  13. A Review of Mine Rescue Ensembles for Underground Coal Mining in the United States.

    Science.gov (United States)

    Kilinc, F Selcen; Monaghan, William D; Powell, Jeffrey B

    The mining industry is among the top ten industries nationwide with high occupational injury and fatality rates, and mine rescue response may be considered one of the most hazardous activities in mining operations. In the aftermath of an underground mine fire, explosion or water inundation, specially equipped and trained teams have been sent underground to fight fires, rescue entrapped miners, test atmospheric conditions, investigate the causes of the disaster, or recover the dead. Special personal protective ensembles are used by the team members to improve the protection of rescuers against the hazards of mine rescue and recovery. Personal protective ensembles used by mine rescue teams consist of helmet, cap lamp, hood, gloves, protective clothing, boots, kneepads, facemask, breathing apparatus, belt, and suspenders. While improved technology such as wireless warning and communication systems, lifeline pulleys, and lighted vests have been developed for mine rescuers over the last 100 years, recent research in this area of personal protective ensembles has been minimal due to the trending of reduced exposure of rescue workers. In recent years, the exposure of mine rescue teams to hazardous situations has been changing. However, it is vital that members of the teams have the capability and proper protection to immediately respond to a wide range of hazardous situations. Currently, there are no minimum requirements, best practice documents, or nationally recognized consensus standards for protective clothing used by mine rescue teams in the United States (U.S.). The following review provides a summary of potential issues that can be addressed by rescue teams and industry to improve potential exposures to rescue team members should a disaster situation occur. However, the continued trending in the mining industry toward non-exposure to potential hazards for rescue workers should continue to be the primary goal. To assist in continuing this trend, the mining industry

  14. An efficient method to generate a perturbed parameter ensemble of a fully coupled AOGCM without flux-adjustment

    Directory of Open Access Journals (Sweden)

    P. J. Irvine

    2013-09-01

    Full Text Available We present a simple method to generate a perturbed parameter ensemble (PPE of a fully-coupled atmosphere-ocean general circulation model (AOGCM, HadCM3, without requiring flux-adjustment. The aim was to produce an ensemble that samples parametric uncertainty in some key variables and gives a plausible representation of the climate. Six atmospheric parameters, a sea-ice parameter and an ocean parameter were jointly perturbed within a reasonable range to generate an initial group of 200 members. To screen out implausible ensemble members, 20 yr pre-industrial control simulations were run and members whose temperature responses to the parameter perturbations were projected to be outside the range of 13.6 ± 2 °C, i.e. near to the observed pre-industrial global mean, were discarded. Twenty-one members, including the standard unperturbed model, were accepted, covering almost the entire span of the eight parameters, challenging the argument that without flux-adjustment parameter ranges would be unduly restricted. This ensemble was used in 2 experiments; an 800 yr pre-industrial and a 150 yr quadrupled CO2 simulation. The behaviour of the PPE for the pre-industrial control compared well to ERA-40 reanalysis data and the CMIP3 ensemble for a number of surface and atmospheric column variables with the exception of a few members in the Tropics. However, we find that members of the PPE with low values of the entrainment rate coefficient show very large increases in upper tropospheric and stratospheric water vapour concentrations in response to elevated CO2 and one member showed an implausible nonlinear climate response, and as such will be excluded from future experiments with this ensemble. The outcome of this study is a PPE of a fully-coupled AOGCM which samples parametric uncertainty and a simple methodology which would be applicable to other GCMs.

  15. Development of the Ensemble Navy Aerosol Analysis Prediction System (ENAAPS and its application of the Data Assimilation Research Testbed (DART in support of aerosol forecasting

    Directory of Open Access Journals (Sweden)

    J. I. Rubin

    2016-03-01

    through the ensemble data assimilation. The optimized ensemble system was compared to the Navy's current operational aerosol forecasting system, which makes use of NAVDAS-AOD (NRL Atmospheric Variational Data Assimilation System for aerosol optical depth, a 2-D variational data assimilation system. Overall, the two systems had statistically insignificant differences in root-mean-squared error (RMSE, bias, and correlation relative to AERONET-observed AOT. However, the ensemble system is able to better capture sharp gradients in aerosol features compared to the 2DVar system, which has a tendency to smooth out aerosol events. Such skill is not easily observable in bulk metrics. Further, the ENAAPS-DART system will allow for new avenues of model development, such as more efficient lidar and surface station assimilation as well as adaptive source functions. At this early stage of development, the parity with the current variational system is encouraging.

  16. Exploring and Listening to Chinese Classical Ensembles in General Music

    Science.gov (United States)

    Zhang, Wenzhuo

    2017-01-01

    Music diversity is valued in theory, but the extent to which it is efficiently presented in music class remains limited. Within this article, I aim to bridge this gap by introducing four genres of Chinese classical ensembles--Qin and Xiao duets, Jiang Nan bamboo and silk ensembles, Cantonese ensembles, and contemporary Chinese orchestras--into the…

  17. Demonstrating the value of larger ensembles in forecasting physical systems

    Directory of Open Access Journals (Sweden)

    Reason L. Machete

    2016-12-01

    Full Text Available Ensemble simulation propagates a collection of initial states forward in time in a Monte Carlo fashion. Depending on the fidelity of the model and the properties of the initial ensemble, the goal of ensemble simulation can range from merely quantifying variations in the sensitivity of the model all the way to providing actionable probability forecasts of the future. Whatever the goal is, success depends on the properties of the ensemble, and there is a longstanding discussion in meteorology as to the size of initial condition ensemble most appropriate for Numerical Weather Prediction. In terms of resource allocation: how is one to divide finite computing resources between model complexity, ensemble size, data assimilation and other components of the forecast system. One wishes to avoid undersampling information available from the model's dynamics, yet one also wishes to use the highest fidelity model available. Arguably, a higher fidelity model can better exploit a larger ensemble; nevertheless it is often suggested that a relatively small ensemble, say ~16 members, is sufficient and that larger ensembles are not an effective investment of resources. This claim is shown to be dubious when the goal is probabilistic forecasting, even in settings where the forecast model is informative but imperfect. Probability forecasts for a ‘simple’ physical system are evaluated at different lead times; ensembles of up to 256 members are considered. The pure density estimation context (where ensemble members are drawn from the same underlying distribution as the target differs from the forecasting context, where one is given a high fidelity (but imperfect model. In the forecasting context, the information provided by additional members depends also on the fidelity of the model, the ensemble formation scheme (data assimilation, the ensemble interpretation and the nature of the observational noise. The effect of increasing the ensemble size is quantified by

  18. Atmospheric Dispositifs

    DEFF Research Database (Denmark)

    Wieczorek, Izabela

    2015-01-01

    Through the coupling of dispositif with atmosphere this paper engages in a discussion of the atmospherics as both a form of knowledge and a material practice. In doing so the objective is to provide an inventory of tools and methodologies deployed in the construction of atmosphere understood......, the conceptual foundations and protocols for the production of atmosphere in architecture might be found beneath the surface of contemporary debates. In this context, the notion of atmospheric dispositif – illustrated through an oeuvre of the German architect Werner Ruhnau and its theoretical and historical...

  19. Modelling land surface - atmosphere interactions

    DEFF Research Database (Denmark)

    Rasmussen, Søren Højmark

    related to inaccurate land surface modelling, e.g. enhanced warm bias in warm dry summer months. Coupling the regional climate model to a hydrological model shows the potential of improving the surface flux simulations in dry periods and the 2 m air temperature in general. In the dry periods......The study is investigates modelling of land surface – atmosphere interactions in context of fully coupled climatehydrological model. With a special focus of under what condition a fully coupled model system is needed. Regional climate model inter-comparison projects as ENSEMBLES have shown bias...

  20. DART: New Research Using Ensemble Data Assimilation in Geophysical Models

    Science.gov (United States)

    Hoar, T. J.; Raeder, K.

    2015-12-01

    The Data Assimilation Research Testbed (DART) is a community facilityfor ensemble data assimilation developed and supported by the NationalCenter for Atmospheric Research. DART provides a comprehensive suite of software, documentation, and tutorials that can be used for ensemble data assimilation research, operations, and education. Scientists and software engineers at NCAR are available to support DART users who want to use existing DART products or develop their own applications. Current DART users range from university professors teaching data assimilation, to individual graduate students working with simple models, through national laboratories doing operational prediction with large state-of-the-art models. DART runs efficiently on many computational platforms ranging from laptops through thousands of cores on the newest supercomputers.This poster focuses on several recent research activities using DART with geophysical models.Using CAM/DART to understand whether OCO-2 Total Precipitable Water observations can be useful in numerical weather prediction.Impacts of the synergistic use of Infra-red CO retrievals (MOPITT, IASI) in CAM-CHEM/DART assimilations.Assimilation and Analysis of Observations of Amazonian Biomass Burning Emissions by MOPITT (aerosol optical depth), MODIS (carbon monoxide) and MISR (plume height).Long term evaluation of the chemical response of MOPITT-CO assimilation in CAM-CHEM/DART OSSEs for satellite planning and emission inversion capabilities.Improved forward observation operators for land models that have multiple land use/land cover segments in a single grid cell,Simulating mesoscale convective systems (MCSs) using a variable resolution, unstructured grid in the Model for Prediction Across Scales (MPAS) and DART.The mesoscale WRF+DART system generated an ensemble of year-long, real-time initializations of a convection allowing model over the United States.Constraining WACCM with observations in the tropical band (30S-30N) using DART

  1. Exploring Water Level Sensitivity for Metropolitan New York during Sandy (2012 Using Ensemble Storm Surge Simulations

    Directory of Open Access Journals (Sweden)

    Brian A. Colle

    2015-06-01

    Full Text Available This paper describes storm surge simulations made for Sandy (2012 for the Metropolitan New York (NYC area using the Advanced Circulation (ADCIRC model forced by the Weather Research and Forecasting (WRF model. The atmospheric forecast uncertainty was quantified using 11-members from an atmospheric Ensemble Kalman Filter (EnKF system. A control WRF member re-initialized every 24 h demonstrated the capability of the WRF-ADCIRC models to realistically simulate the 2.83 m surge and 4.40 m storm tide (surge + astronomical tide above mean lower low water (MLLW for NYC. Starting about four days before landfall, an ensemble of model runs based on the 11 “best” meteorological predictions illustrate how modest changes in the track (20–100 km and winds (3–5 m s−1 of Sandy approaching the New Jersey coast and NYC can lead to relatively large (0.50–1.50 m storm surge variations. The ensemble also illustrates the extreme importance of the timing of landfall relative to local high tide. The observed coastal flooding was not the worst case for this particular event. Had Sandy made landfall at differing times, locations and stages of the tide, peak water levels could have been up to 0.5 m higher than experienced.

  2. ABCD of Beta Ensembles and Topological Strings

    CERN Document Server

    Krefl, Daniel

    2012-01-01

    We study beta-ensembles with Bn, Cn, and Dn eigenvalue measure and their relation with refined topological strings. Our results generalize the familiar connections between local topological strings and matrix models leading to An measure, and illustrate that all those classical eigenvalue ensembles, and their topological string counterparts, are related one to another via various deformations and specializations, quantum shifts and discrete quotients. We review the solution of the Gaussian models via Macdonald identities, and interpret them as conifold theories. The interpolation between the various models is plainly apparent in this case. For general polynomial potential, we calculate the partition function in the multi-cut phase in a perturbative fashion, beyond tree-level in the large-N limit. The relation to refined topological string orientifolds on the corresponding local geometry is discussed along the way.

  3. Ensemble Forecasting of Major Solar Flares

    CERN Document Server

    Guerra, J A; Uritsky, V M

    2015-01-01

    We present the results from the first ensemble prediction model for major solar flares (M and X classes). Using the probabilistic forecasts from three models hosted at the Community Coordinated Modeling Center (NASA-GSFC) and the NOAA forecasts, we developed an ensemble forecast by linearly combining the flaring probabilities from all four methods. Performance-based combination weights were calculated using a Monte Carlo-type algorithm by applying a decision threshold $P_{th}$ to the combined probabilities and maximizing the Heidke Skill Score (HSS). Using the probabilities and events time series from 13 recent solar active regions (2012 - 2014), we found that a linear combination of probabilities can improve both probabilistic and categorical forecasts. Combination weights vary with the applied threshold and none of the tested individual forecasting models seem to provide more accurate predictions than the others for all values of $P_{th}$. According to the maximum values of HSS, a performance-based weights ...

  4. Rotationally invariant ensembles of integrable matrices.

    Science.gov (United States)

    Yuzbashyan, Emil A; Shastry, B Sriram; Scaramazza, Jasen A

    2016-05-01

    We construct ensembles of random integrable matrices with any prescribed number of nontrivial integrals and formulate integrable matrix theory (IMT)-a counterpart of random matrix theory (RMT) for quantum integrable models. A type-M family of integrable matrices consists of exactly N-M independent commuting N×N matrices linear in a real parameter. We first develop a rotationally invariant parametrization of such matrices, previously only constructed in a preferred basis. For example, an arbitrary choice of a vector and two commuting Hermitian matrices defines a type-1 family and vice versa. Higher types similarly involve a random vector and two matrices. The basis-independent formulation allows us to derive the joint probability density for integrable matrices, similar to the construction of Gaussian ensembles in the RMT.

  5. Various multistage ensembles for prediction of heating energy consumption

    Directory of Open Access Journals (Sweden)

    Radisa Jovanovic

    2015-04-01

    Full Text Available Feedforward neural network models are created for prediction of daily heating energy consumption of a NTNU university campus Gloshaugen using actual measured data for training and testing. Improvement of prediction accuracy is proposed by using neural network ensemble. Previously trained feed-forward neural networks are first separated into clusters, using k-means algorithm, and then the best network of each cluster is chosen as member of an ensemble. Two conventional averaging methods for obtaining ensemble output are applied; simple and weighted. In order to achieve better prediction results, multistage ensemble is investigated. As second level, adaptive neuro-fuzzy inference system with various clustering and membership functions are used to aggregate the selected ensemble members. Feedforward neural network in second stage is also analyzed. It is shown that using ensemble of neural networks can predict heating energy consumption with better accuracy than the best trained single neural network, while the best results are achieved with multistage ensemble.

  6. Creating Diverse Ensemble Classifiers to Reduce Supervision

    Science.gov (United States)

    2005-12-01

    disagree on some inputs (Hansen & Salamon, 1990; Tumer & Ghosh, 1996). We refer to the measure of disagreement as the diversity/ambiguity of the ensemble...they use an objective function that incorporates both an accuracy and diversity term. Tumer and Ghosh (1996) reduce the correlation between...controlled by the amount of features that are eliminated. This method, called input decimation, has been further explored by Tumer and Oza (1999). Zenobi and

  7. Ensemble methods for Etna volcano warning system

    Science.gov (United States)

    Scandura, Danila; Cannavò, Flavio; Aliotta, Marco; Cassisi, Carmelo; Montalto, Placido

    2017-04-01

    The large amount of signals used for volcanic monitoring allow detecting volcano criticalities with unprecedented reliability. At the same time, the use of different monitoring networks makes essential the development of a system that synthesizes into a single information the overall state of the volcano. In this context, the ensemble learning techniques can play a useful role accepting different nature inputs and synthesizing the information in a single output. In broad terms, these techniques use many weak learning algorithms to achieve the best predictive performance compared to any obtained from classical learning algorithms. By averaging the results of each weak learner, the ensemble algorithms reduce the risk of using a single non-discriminative weak learning algorithm and allows for a more accurate classification. Here we used the ensemble techniques to classify three different states of Etna volcano: 1) Quiet; 2) Strombolian activity; 3) Lava fountain. We carried out several simulations using a large data set spanning the 2011-2015 time interval, including the records of most part of monitored geophysical parameters and the corresponding volcanic state. Simulations were performed subdividing the available data into training and test sets. We checked the ability of the proposed method to recognize automatically the lava fountain episodes. To this purpose, we tested different ensemble techniques changing associated parameters and weak learners. The found system was able to identify the lava fountain episode with a reliability over 70% and to detect the beginning of lava fountain episode in the totality of the test cases. Results suggest that the proposed system can be seen as a promising tool for civil protection purposes.

  8. Ensemble Approaches to Facial Action Unit Classification

    OpenAIRE

    Windeatt, T; Dias, K.

    2008-01-01

    Facial action unit (au) classification is an approach to face expression recognition that decouples the recognition of expression from individual actions. In this paper, upper face aus are classified using an ensemble of MLP (Multi-layer perceptron) base classifiers with feature ranking based on PCA components. This approach is compared experimentally with other popular feature-ranking methods applied to Gabor features. Experimental results on Cohn-Kanade database demonstrate that the MLP ens...

  9. Kernel Factory: An Ensemble of Kernel Machines

    OpenAIRE

    M. BALLINGS; D. VAN DEN POEL

    2012-01-01

    We propose an ensemble method for kernel machines. The training data is randomly split into a number of mutually exclusive partitions defined by a row and column parameter. Each partition forms an input space and is transformed by a kernel function into a kernel matrix K. Subsequently, each K is used as training data for a base binary classifier (Random Forest). This results in a number of predictions equal to the number of partitions. A weighted average combines the predictions into one fina...

  10. Ensemble Kalman filter with the unscented transform

    Science.gov (United States)

    Luo, X.; Moroz, I. M.

    2009-03-01

    A modification scheme to the ensemble Kalman filter (EnKF) is introduced based on the concept of the unscented transform [S. Julier, J. Uhlmann, H. Durrant-Whyte, A new method for the nonlinear transformation of means and covariances in filters and estimators, IEEE Trans. Automat. Control. 45 (2000) 477-482; S.J. Julier, J.K. Uhlmann, Unscented filtering and nonlinear estimation, Proc. IEEE 92 (2004) 401-422], which therefore will be called the ensemble unscented Kalman filter (EnUKF) in this work. When the error distribution of the analysis is symmetric (not necessarily Gaussian), it can be shown that, compared with the ordinary EnKF, the EnUKF has more accurate estimations of the ensemble mean and covariance of the background by examining the multidimensional Taylor series expansion term by term. This implies that, the EnUKF may have better performance in state estimation than the ordinary EnKF in the sense that the deviations from the true states are smaller. For verification, some numerical experiments are conducted on a 40-dimensional system due to Lorenz and Emanuel [E.N. Lorenz, K.A. Emanuel, Optimal sites for supplementary weather observations: Simulation with a small model, J. Atmos. Sci. 55 (1998) 399-414]. Simulation results support our argument.

  11. Gradient Flow Analysis on MILC HISQ Ensembles

    CERN Document Server

    Bazavov, A; Brown, N; DeTar, C; Foley, J; Gottlieb, Steven; Heller, U M; Hetrick, J E; Komijani, J; Laiho, J; Levkova, L; Oktay, M; Sugar, R L; Toussaint, D; Van de Water, R S; Zhou, R

    2014-01-01

    We report on a preliminary scale determination with gradient-flow techniques on the $N_f = 2 + 1 + 1$ HISQ ensembles generated by the MILC collaboration. The ensembles include four lattice spacings, ranging from 0.15 to 0.06 fm, and both physical and unphysical values of the quark masses. The scales $\\sqrt{t_0}/a$ and $w_0/a$ are computed using Symanzik flow and the cloverleaf definition of $\\langle E \\rangle$ on each ensemble. Then both scales and the meson masses $aM_\\pi$ and $aM_K$ are adjusted for mistunings in the charm mass. Using a combination of continuum chiral perturbation theory and a Taylor series ansatz in the lattice spacing, the results are simultaneously extrapolated to the continuum and interpolated to physical quark masses. Our preliminary results are $\\sqrt{t_0} = 0.1422(7)$fm and $w_0 = 0.1732(10)$fm. We also find the continuum mass-dependence of $w_0$.

  12. Ensemble transfer learning for Alzheimer's disease diagnosis.

    Science.gov (United States)

    Colbaugh, Rich; Glass, Kristin; Gallegos, Gil

    2017-07-01

    There is considerable interest in developing inexpensive, nonintrusive diagnostic tests for Alzheimer's disease (AD), in the hope these tests will facilitate early diagnosis and support design of effective treatments. While tests based on blood-borne biomarkers such as microRNAs have shown promise, work to date indicates these tests often do not generalize well: diagnostic models derived for one patient group are not accurate when applied to a new cohort. This paper presents a novel ensemble-based transfer learning methodology which induces models that provide accurate diagnoses across distinct patient groups without retraining. The algorithm combines information from supervised ensemble learning and unsupervised ensemble clustering to enable robust transfer learning. The efficacy of the approach is illustrated through a case study involving microRNA-based AD diagnosis. In this test, we use our algorithm to learn a diagnostic model on data from one patient group and then apply the model to a different target group, obtaining diagnostic accuracy which actually exceeds that of a state-of-the-art model trained directly on the target group.

  13. Gradient Flow Analysis on MILC HISQ Ensembles

    Energy Technology Data Exchange (ETDEWEB)

    Brown, Nathan [Washington U., St. Louis; Bazavov, Alexei [Brookhaven; Bernard, Claude [Washington U., St. Louis; DeTar, Carleton [Utah U.; Foley, Justin [Utah U.; Gottlieb, Steven [Indiana U.; Heller, Urs M. [APS, New York; Hetrick, J. E. [U. Pacific, Stockton; Komijani, Javad [Washington U., St. Louis; Laiho, Jack [Syracuse U.; Levkova, Ludmila [Utah U.; Oktay, M. B. [Utah U.; Sugar, Robert [UC, Santa Barbara; Toussaint, Doug [Arizona U.; Van de Water, Ruth S. [Fermilab; Zhou, Ran [Fermilab

    2014-11-14

    We report on a preliminary scale determination with gradient-flow techniques on the $N_f = 2 + 1 + 1$ HISQ ensembles generated by the MILC collaboration. The ensembles include four lattice spacings, ranging from 0.15 to 0.06 fm, and both physical and unphysical values of the quark masses. The scales $\\sqrt{t_0}/a$ and $w_0/a$ are computed using Symanzik flow and the cloverleaf definition of $\\langle E \\rangle$ on each ensemble. Then both scales and the meson masses $aM_\\pi$ and $aM_K$ are adjusted for mistunings in the charm mass. Using a combination of continuum chiral perturbation theory and a Taylor series ansatz in the lattice spacing, the results are simultaneously extrapolated to the continuum and interpolated to physical quark masses. Our preliminary results are $\\sqrt{t_0} = 0.1422(7)$fm and $w_0 = 0.1732(10)$fm. We also find the continuum mass-dependence of $w_0$.

  14. Multivariate localization methods for ensemble Kalman filtering

    KAUST Repository

    Roh, S.

    2015-12-03

    In ensemble Kalman filtering (EnKF), the small number of ensemble members that is feasible to use in a practical data assimilation application leads to sampling variability of the estimates of the background error covariances. The standard approach to reducing the effects of this sampling variability, which has also been found to be highly efficient in improving the performance of EnKF, is the localization of the estimates of the covariances. One family of localization techniques is based on taking the Schur (element-wise) product of the ensemble-based sample covariance matrix and a correlation matrix whose entries are obtained by the discretization of a distance-dependent correlation function. While the proper definition of the localization function for a single state variable has been extensively investigated, a rigorous definition of the localization function for multiple state variables that exist at the same locations has been seldom considered. This paper introduces two strategies for the construction of localization functions for multiple state variables. The proposed localization functions are tested by assimilating simulated observations experiments into the bivariate Lorenz 95 model with their help.

  15. Multivariate localization methods for ensemble Kalman filtering

    KAUST Repository

    Roh, S.

    2015-05-08

    In ensemble Kalman filtering (EnKF), the small number of ensemble members that is feasible to use in a practical data assimilation application leads to sampling variability of the estimates of the background error covariances. The standard approach to reducing the effects of this sampling variability, which has also been found to be highly efficient in improving the performance of EnKF, is the localization of the estimates of the covariances. One family of localization techniques is based on taking the Schur (entry-wise) product of the ensemble-based sample covariance matrix and a correlation matrix whose entries are obtained by the discretization of a distance-dependent correlation function. While the proper definition of the localization function for a single state variable has been extensively investigated, a rigorous definition of the localization function for multiple state variables has been seldom considered. This paper introduces two strategies for the construction of localization functions for multiple state variables. The proposed localization functions are tested by assimilating simulated observations experiments into the bivariate Lorenz 95 model with their help.

  16. Bioprocess optimization under uncertainty using ensemble modeling.

    Science.gov (United States)

    Liu, Yang; Gunawan, Rudiyanto

    2017-02-20

    The performance of model-based bioprocess optimizations depends on the accuracy of the mathematical model. However, models of bioprocesses often have large uncertainty due to the lack of model identifiability. In the presence of such uncertainty, process optimizations that rely on the predictions of a single "best fit" model, e.g. the model resulting from a maximum likelihood parameter estimation using the available process data, may perform poorly in real life. In this study, we employed ensemble modeling to account for model uncertainty in bioprocess optimization. More specifically, we adopted a Bayesian approach to define the posterior distribution of the model parameters, based on which we generated an ensemble of model parameters using a uniformly distributed sampling of the parameter confidence region. The ensemble-based process optimization involved maximizing the lower confidence bound of the desired bioprocess objective (e.g. yield or product titer), using a mean-standard deviation utility function. We demonstrated the performance and robustness of the proposed strategy in an application to a monoclonal antibody batch production by mammalian hybridoma cell culture. Copyright © 2017 The Author(s). Published by Elsevier B.V. All rights reserved.

  17. EnsembleGraph: Interactive Visual Analysis of Spatial-Temporal Behavior for Ensemble Simulation Data

    Energy Technology Data Exchange (ETDEWEB)

    Shu, Qingya; Guo, Hanqi; Che, Limei; Yuan, Xiaoru; Liu, Junfeng; Liang, Jie

    2016-04-19

    We present a novel visualization framework—EnsembleGraph— for analyzing ensemble simulation data, in order to help scientists understand behavior similarities between ensemble members over space and time. A graph-based representation is used to visualize individual spatiotemporal regions with similar behaviors, which are extracted by hierarchical clustering algorithms. A user interface with multiple-linked views is provided, which enables users to explore, locate, and compare regions that have similar behaviors between and then users can investigate and analyze the selected regions in detail. The driving application of this paper is the studies on regional emission influences over tropospheric ozone, which is based on ensemble simulations conducted with different anthropogenic emission absences using the MOZART-4 (model of ozone and related tracers, version 4) model. We demonstrate the effectiveness of our method by visualizing the MOZART-4 ensemble simulation data and evaluating the relative regional emission influences on tropospheric ozone concentrations. Positive feedbacks from domain experts and two case studies prove efficiency of our method.

  18. Convective Scale Ensemble Prediction System in KMA for Early Warning of High Impact Weather

    Science.gov (United States)

    Lee, S.

    2016-12-01

    Economic and societal damages due to severe weather events have been increasing in association with the concentration of human and economic resources such as in metropolitan areas. Severe weather events are often associated with rapidly developing convective scale systems, which are strongly influenced by topography, land use, and urbanization, etc. In the forecast those kinds of severe weather events, the high resolution numerical model is crucial to predict these convective scale severe weather events. However, the NWP models have an inherent limitation in the predictability of atmospheric phenomena, especially in predicting the severe weather. It is partly due to the poor resolution and model physics and partly due to the uncertainty of meteorological events. Nowadays most operational centers are being asked to develop more effective early detection systems that can be used to reduce the risk associated with severe weather events. Furthermore, forecasters need to assess and quantify the risk of occurrence of rare but destructive events. Thus, a policy in KMA to provide the probabilistic information of the severe events in limited area using ensemble method was adapted to meets these needs of the effective early warning systems. Ensemble forecasting using finite members is one of the feasible methods to quantify possibilities of extreme severe weather events. The ensemble forecasting has proved to be a successful way of dealing with that kind of inherent uncertainty of weather and climate forecasts. In this study, the limited area ensemble prediction system (LENS) using the Unified Model (UM) in KMA was developed and evaluated for the warm season of 2015. The model domain covers the limited area over the Korean Peninsula. The high resolution(3-km) limited area ensemble prediction system showed beneficial probabilistic forecast skill in predicting the heavy precipitation events. The sensitive experiment to evaluate the impact of uncertainty in model physics on the

  19. Considerations for Using Hybrid Ensemble-Variational Data Assimilation in NASA-GMAO's Next Reanalysis System

    Science.gov (United States)

    El Akkraoui, Amal; Todling, Ricardo

    2017-01-01

    The Modern-Era Retrospective Analysis for Research and Applications, version 2 (MERRA-2) is the latest reanalysis produced by GMAO, and provides global data spanning the period 1980-present. The atmospheric data assimilation component of MERRA-2 used a 3D-Var scheme, which was operational at the time of its design. Since then, a Hybrid 3D-Var, then a Hybrid 4D-EnVar were implemented, adding an ensemble component to the data assimilation scheme. In this work, we will be examining the benefits of using hybrid ensemble flow-dependent covariances to represent errors and uncertainties in historic periods. Specifically, periods of pre- and post-satellites, as well as periods of active tropical cyclone seasons. Finally, we will also be exploring the use of adaptive localization scales.

  20. Future Changes in Tropical Cyclone Activity in High-Resolution Large-Ensemble Simulations

    Science.gov (United States)

    Yoshida, Kohei; Sugi, Masato; Mizuta, Ryo; Murakami, Hiroyuki; Ishii, Masayoshi

    2017-10-01

    Projected future changes in global tropical cyclone (TC) activity are assessed using 5,000 year scale ensemble simulations for both current and 4 K surface warming climates with a 60 km global atmospheric model. The global number of TCs decreases by 33% in the future projection. Although geographical TC occurrences decrease generally, they increase in the central and eastern parts of the extra tropical North Pacific. Meanwhile, very intense (category 4 and 5) TC occurrences increase over a broader area including the south of Japan and south of Madagascar. The global number of category 4 and 5 TCs significantly decreases, contrary to the increase seen in several previous studies. Lifetime maximum surface wind speeds and precipitation rate are amplified globally. Regional TC activity changes have large uncertainty corresponding to sea surface temperature warming patterns. TC-resolving large-ensemble simulations provide useful information, especially for policy making related to future climate change.

  1. DART: Tools and Support for Ensemble Data Assimilation Research, Operations, and Education

    Science.gov (United States)

    Anderson, J. L.; Raeder, K.; Hoar, T. J.; Collins, N.; Kershaw, H.; Romine, G. S.; Liu, H.; Mizzi, A. P.; Lei, L.; Chatterjee, A.; Karspeck, A. R.; Pedatella, N. M.

    2013-12-01

    The Data Assimilation Research Testbed (DART) is a community facility for ensemble data assimilation developed and supported by the National Center for Atmospheric Research. DART provides a comprehensive suite of software, documentation, examples and tutorials that can be used for ensemble data assimilation research, operations, and education. Scientists and software engineers from the Data Assimilation Research Section at NCAR are available to actively support DART users who want to use existing DART products or develop their own new applications. Current DART users range from university professors teaching data assimilation, to individual graduate students working with simple models, through national laboratories doing operational prediction with large state-of-the-art models. DART runs efficiently on many computational platforms ranging from laptops through thousands of cores on the newest supercomputers. This poster focuses on recent developments for coupled data assimilation with DART and NCAR's Community Earth System Model. DART interfaces to the Community Atmosphere Model (CAM), the Parallel Ocean Program (POP) and the Community Land Model (CLM) can now be used to do multiple component data assimilation with the fully-coupled CESM prediction model. The software innovations required to enable this are described. The latest results for ensemble assimilation experiments with each of the component models are presented along with initial comparisons to corresponding assimilations with the coupled model. A newly developed DART interface to the Whole Atmosphere Community Climate Model (WACCM) is now available. An overview of results of the relative value of assimilating tropospheric and middle atmosphere observations in WACCM is presented. DART is also used with many other types of geophysical models. Highlights of the latest results using DART with the Weather Research and Forecasting (WRF) model for springtime weather over the central United States are also

  2. Propellant Handler?s Ensemble, aka Self-Contained Atmospheric Protective Ensemble (SCAPE), Ventilator Improvement Study Project

    Data.gov (United States)

    National Aeronautics and Space Administration — The Biomedical Engineering and Research Laboratory is working with the NASA Life Support group and BCS Life Support to improve the safety of the environmental...

  3. Articulating Atmospheres

    DEFF Research Database (Denmark)

    Kinch, Sofie

    2011-01-01

    This paper presents an architectural approach to designing computational interfaces by articulating the notion of atmosphere in the field of interaction design. It draws upon the concept of kinesthetic interaction and a philosophical notion on atmosphere emphasizing the importance of bodily exper......” implications and qualities of the approach are identified through concrete examples of a design case, which also investigates the qualities and implications of addressing atmospheres both as design concern and user experience.......This paper presents an architectural approach to designing computational interfaces by articulating the notion of atmosphere in the field of interaction design. It draws upon the concept of kinesthetic interaction and a philosophical notion on atmosphere emphasizing the importance of bodily...... experience in space, presented as middle ground experience. In the field of HCI, middle ground experiences complete the unarticulated spectrum between designing for foreground of attention or background awareness. When “Articulating Atmospheres through Middle Ground Experiences in Interaction Design...

  4. Evolutionary Cluster-Based Synthetic Oversampling Ensemble (ECO-Ensemble) for Imbalance Learning.

    Science.gov (United States)

    Lim, Pin; Goh, Chi Keong; Tan, Kay Chen

    2017-09-01

    Class imbalance problems, where the number of samples in each class is unequal, is prevalent in numerous real world machine learning applications. Traditional methods which are biased toward the majority class are ineffective due to the relative severity of misclassifying rare events. This paper proposes a novel evolutionary cluster-based oversampling ensemble framework, which combines a novel cluster-based synthetic data generation method with an evolutionary algorithm (EA) to create an ensemble. The proposed synthetic data generation method is based on contemporary ideas of identifying oversampling regions using clusters. The novel use of EA serves a twofold purpose of optimizing the parameters of the data generation method while generating diverse examples leveraging on the characteristics of EAs, reducing overall computational cost. The proposed method is evaluated on a set of 40 imbalance datasets obtained from the University of California, Irvine, database, and outperforms current state-of-the-art ensemble algorithms tackling class imbalance problems.

  5. Atmospheric electricity

    CERN Document Server

    Chalmers, J Alan

    1957-01-01

    Atmospheric Electricity brings together numerous studies on various aspects of atmospheric electricity. This book is composed of 13 chapters that cover the main problems in the field, including the maintenance of the negative charge on the earth and the origin of the charges in thunderstorms. After a brief overview of the historical developments of atmospheric electricity, this book goes on dealing with the general principles, results, methods, and the MKS system of the field. The succeeding chapters are devoted to some aspects of electricity in the atmosphere, such as the occurrence and d

  6. Atmospheric Neutrinos

    Directory of Open Access Journals (Sweden)

    Takaaki Kajita

    2012-01-01

    Full Text Available Atmospheric neutrinos are produced as decay products in hadronic showers resulting from collisions of cosmic rays with nuclei in the atmosphere. Electron-neutrinos and muon-neutrinos are produced mainly by the decay chain of charged pions to muons to electrons. Atmospheric neutrino experiments observed zenith angle and energy-dependent deficit of muon-neutrino events. It was found that neutrino oscillations between muon-neutrinos and tau-neutrinos explain these data well. This paper discusses atmospheric neutrino experiments and the neutrino oscillation studies with these neutrinos.

  7. Structure of the transport uncertainty in mesoscale inversions of CO2 sources and sinks using ensemble model simulations

    Directory of Open Access Journals (Sweden)

    J. Noilhan

    2009-06-01

    Full Text Available We study the characteristics of a statistical ensemble of mesoscale simulations in order to estimate the model error in the simulation of CO2 concentrations. The ensemble consists of ten members and the reference simulation using the operationnal short range forecast PEARP, perturbed using the Singular Vector technique. We then used this ensemble of simulations as the initial and boundary conditions for the meso scale model (Méso-NH simulations, which uses CO2 fluxes from the ISBA-A-gs land surface model. The final ensemble represents the model dependence to the boundary conditions, conserving the physical properties of the dynamical schemes, but excluding the intrinsic error of the model. First, the variance of our ensemble is estimated over the domain, with associated spatial and temporal correlations. Second, we extract the signal from noisy horizontal correlations, due to the limited size ensemble, using diffusion equation modelling. The computational cost of such ensemble limits the number of members (simulations especially when running online the carbon flux and the atmospheric models. In the theory, 50 to 100 members would be required to explore the overall sensitivity of the ensemble. The present diffusion model allows us to extract a significant part of the noisy error, and makes this study feasable with a limited number of simulations. Finally, we compute the diagonal and non-diagonal terms of the observation error covariance matrix and introduced it into our CO2 flux matrix inversion for 18 days of the 2005 intensive campaign CERES over the South West of France. Variances are based on model-data mismatch to ensure we treat model bias as well as ensemble dispersion, whereas spatial and temporal covariances are estimated with our method. The horizontal structure of the ensemble variance manifests the discontinuities of the mesoscale structures during the day, but remains locally driven during the night. On the vertical, surface layer

  8. Arctic sea ice area changes in CMIP3 and CMIP5 climate models’ ensembles

    Directory of Open Access Journals (Sweden)

    V. A. Semenov

    2017-01-01

    Full Text Available The shrinking Arctic sea ice cover observed during the last decades is probably the clearest manifestation of ongoing climate change. While climate models in general reproduce the sea ice retreat in the Arctic during the 20th century and simulate further sea ice area loss during the 21st century in response to anthropogenic forcing, the models suffer from large biases and the results exhibit considerable spread. Here, we compare results from the two last generations of climate models, CMIP3 and CMIP5, with respect to total and regional Arctic sea ice change. Different characteristics of sea ice area (SIA in March and September have been analysed for the Entire Arctic, Central Arctic and Barents Sea. Further, the sensitivity of SIA to changes in Northern Hemisphere (NH temperature is investigated and dynamical links between SIA and some atmospheric variability modes are assessed.CMIP3 (SRES A1B and CMIP5 (RCP8.5 models not only simulate a coherent decline of the Arctic SIA but also depict consistent changes in the SIA seasonal cycle. The spatial patterns of SIC variability improve in CMIP5 ensemble, most noticeably in summer when compared to HadISST1 data. A better simulation of summer SIA in the Entire Arctic by CMIP5 models is accompanied by a slightly increased bias for winter season in comparison to CMIP3 ensemble. SIA in the Barents Sea is strongly overestimated by the majority of CMIP3 and CMIP5 models, and projected SIA changes are characterized by a high uncertainty. Both CMIP ensembles depict a significant link between the SIA and NH temperature changes indicating that a part of inter-ensemble SIA spread comes from different temperature sensitivity to anthropogenic forcing. The results suggest that, in general, a sensitivity of SIA to external forcing is enhanced in CMIP5 models. Arctic SIA interannual variability in the end of the 20th century is on average well simulated by both ensembles. To the end of the 21st century, September

  9. The effect of sampling noise in ensemble-based Kalman filters

    Science.gov (United States)

    Sacher, William

    Ensemble-based Kalman filters have drawn a lot of attention in the atmospheric and ocean scientific community because of their potential to be used as a data assimilation tool for numerical prediction in a strongly nonlinear context at an affordable cost. However, many studies have noted practical problems in their implementation. Indeed, being Monte-Carlo methods, the useful parameters are estimated from a sample of limited size of independent realizations of the process. As a consequence, the unavoidable sampling noise impacts the quality of the analysis. An idealized perfect model context is considered in which the analytical expression for the analysis accuracy and reliability as a function of the ensemble size is established, from a second-order moment perspective. It is proved that one can analytically explain the general tendency for ensemble-based Kalman filters to underestimate, on average, the analysis variance and therefore the likeliness for these filters to diverge. Performance of alternative methods, designed to reduce or eliminate sampling error effects, such as the double ensemble Kalman filter or covariance inflation are also analytically explored. For methods using perturbed observations, it is shown that the covariance inflation is the easiest and least expensive method to obtain the most accurate and reliable analysis. These analytical results agreed well with means over a large number of experiments using a perfect, low-resolution, and quasi-geostrophic barotropic model, in a series of observation system simulation experiments of single analysis cycles as well as in a simulated forecast system. In one-analysis cycle experiments with rank histograms, non-perturbed-observation methods show a lack of reliability regardless of the number of members. For small ensemble sizes, sampling error effects are dominant but have a smaller impact than in the perturbed observation method, making non-perturbed-observation method filters much less subject to

  10. Spatial clustering of summer temperature maxima from the CNRM-CM5 climate model ensembles & E-OBS over Europe

    Directory of Open Access Journals (Sweden)

    Margot Bador

    2015-09-01

    Full Text Available Reducing the dimensionality of the complex spatio-temporal variables associated with climate modeling, especially ensembles of climate models, is a challenging and important objective. For studies of detection and attribution, it is especially important to maintain information related to the extreme values of the atmospheric processes. Typical methods for data reduction involve summarizing climate model output information through means and variances, which does not preserve any information about the extremes. In order to help solve this challenge, a dependence summary measure appropriate for extreme values must be inferred. Here, we adapt one such measure from a recent study to a larger domain with a different variable and gridded data from observations and climate model ensembles, i.e. E-OBS observations and the CNRM-CM5 model. The handling of such ensembles of data is proposed, as well as a comparison of the spatial clusterings between two different ensembles, here a present-day and a future ensemble of climate simulations. This method yields valid information concerning extremes, while greatly reducing the data set.

  11. Statistical Mechanics of On-line Ensemble Teacher Learning through a Novel Perceptron Learning Rule

    OpenAIRE

    Hara, Kazuyuki; Miyoshi, Seiji

    2016-01-01

    In ensemble teacher learning, ensemble teachers have only uncertain information about the true teacher, and this information is given by an ensemble consisting of an infinite number of ensemble teachers whose variety is sufficiently rich. In this learning, a student learns from an ensemble teacher that is iteratively selected randomly from a pool of many ensemble teachers. An interesting point of ensemble teacher learning is the asymptotic behavior of the student to approach the true teacher ...

  12. Urban atmospheres.

    Science.gov (United States)

    Gandy, Matthew

    2017-07-01

    What is an urban atmosphere? How can we differentiate an 'atmosphere' from other facets of urban consciousness and experience? This essay explores some of the wider cultural, political, and philosophical connotations of atmospheres as a focal point for critical reflections on space and subjectivity. The idea of an 'affective atmosphere' as a distinctive kind of mood or shared corporeal phenomenon is considered in relation to recent developments in phenomenology, extended conceptions of agency, and new understandings of materialism. The essay draws in particular on the changing characteristics of air and light to reflect on different forms of sensory experience and their wider cultural and political connotations. The argument highlights some of the tensions and anomalies that permeate contemporary understandings of urban atmospheres.

  13. Data assimilation in integrated hydrological modeling using ensemble Kalman filtering

    DEFF Research Database (Denmark)

    Rasmussen, Jørn; Madsen, H.; Jensen, Karsten Høgh

    2015-01-01

    Groundwater head and stream discharge is assimilated using the ensemble transform Kalman filter in an integrated hydrological model with the aim of studying the relationship between the filter performance and the ensemble size. In an attempt to reduce the required number of ensemble members...... numbers and spatial distributions of groundwater head observations and with or without discharge assimilation and parameter estimation. The study shows that (1) more ensemble members are needed when fewer groundwater head observations are assimilated, and (2) assimilating discharge observations...... and estimating parameters requires a much larger ensemble size than just assimilating groundwater head observations. However, the required ensemble size can be greatly reduced with the use of adaptive localization, which by far outperforms distance-based localization. The study is conducted using synthetic data...

  14. Argumentation based joint learning: a novel ensemble learning approach.

    Directory of Open Access Journals (Sweden)

    Junyi Xu

    Full Text Available Recently, ensemble learning methods have been widely used to improve classification performance in machine learning. In this paper, we present a novel ensemble learning method: argumentation based multi-agent joint learning (AMAJL, which integrates ideas from multi-agent argumentation, ensemble learning, and association rule mining. In AMAJL, argumentation technology is introduced as an ensemble strategy to integrate multiple base classifiers and generate a high performance ensemble classifier. We design an argumentation framework named Arena as a communication platform for knowledge integration. Through argumentation based joint learning, high quality individual knowledge can be extracted, and thus a refined global knowledge base can be generated and used independently for classification. We perform numerous experiments on multiple public datasets using AMAJL and other benchmark methods. The results demonstrate that our method can effectively extract high quality knowledge for ensemble classifier and improve the performance of classification.

  15. Argumentation based joint learning: a novel ensemble learning approach.

    Science.gov (United States)

    Xu, Junyi; Yao, Li; Li, Le

    2015-01-01

    Recently, ensemble learning methods have been widely used to improve classification performance in machine learning. In this paper, we present a novel ensemble learning method: argumentation based multi-agent joint learning (AMAJL), which integrates ideas from multi-agent argumentation, ensemble learning, and association rule mining. In AMAJL, argumentation technology is introduced as an ensemble strategy to integrate multiple base classifiers and generate a high performance ensemble classifier. We design an argumentation framework named Arena as a communication platform for knowledge integration. Through argumentation based joint learning, high quality individual knowledge can be extracted, and thus a refined global knowledge base can be generated and used independently for classification. We perform numerous experiments on multiple public datasets using AMAJL and other benchmark methods. The results demonstrate that our method can effectively extract high quality knowledge for ensemble classifier and improve the performance of classification.

  16. Ensemble-Based Data Assimilation in Reservoir Characterization: A Review

    Directory of Open Access Journals (Sweden)

    Seungpil Jung

    2018-02-01

    Full Text Available This paper presents a review of ensemble-based data assimilation for strongly nonlinear problems on the characterization of heterogeneous reservoirs with different production histories. It concentrates on ensemble Kalman filter (EnKF and ensemble smoother (ES as representative frameworks, discusses their pros and cons, and investigates recent progress to overcome their drawbacks. The typical weaknesses of ensemble-based methods are non-Gaussian parameters, improper prior ensembles and finite population size. Three categorized approaches, to mitigate these limitations, are reviewed with recent accomplishments; improvement of Kalman gains, add-on of transformation functions, and independent evaluation of observed data. The data assimilation in heterogeneous reservoirs, applying the improved ensemble methods, is discussed on predicting unknown dynamic data in reservoir characterization.

  17. Probabilistic Flash Flood Forecasting using Stormscale Ensembles

    Science.gov (United States)

    Hardy, J.; Gourley, J. J.; Kain, J. S.; Clark, A.; Novak, D.; Hong, Y.

    2013-12-01

    Flash flooding is one of the most costly and deadly natural hazards in the US and across the globe. The loss of life and property from flash floods could be mitigated with better guidance from hydrological models, but these models have limitations. For example, they are commonly initialized using rainfall estimates derived from weather radars, but the time interval between observations of heavy rainfall and a flash flood can be on the order of minutes, particularly for small basins in urban settings. Increasing the lead time for these events is critical for protecting life and property. Therefore, this study advances the use of quantitative precipitation forecasts (QPFs) from a stormscale NWP ensemble system into a distributed hydrological model setting to yield basin-specific, probabilistic flash flood forecasts (PFFFs). Rainfall error characteristics of the individual members are first diagnosed and quantified in terms of structure, amplitude, and location (SAL; Wernli et al., 2008). Amplitude and structure errors are readily correctable due to their diurnal nature, and the fine scales represented by the CAPS QPF members are consistent with radar-observed rainfall, mainly showing larger errors with afternoon convection. To account for the spatial uncertainty of the QPFs, we use an elliptic smoother, as in Marsh et al. (2012), to produce probabilistic QPFs (PQPFs). The elliptic smoother takes into consideration underdispersion, which is notoriously associated with stormscale ensembles, and thus, is good for targeting the approximate regions that may receive heavy rainfall. However, stormscale details contained in individual members are still needed to yield reasonable flash flood simulations. Therefore, on a case study basis, QPFs from individual members are then run through the hydrological model with their predicted structure and corrected amplitudes, but the locations of individual rainfall elements are perturbed within the PQPF elliptical regions using Monte

  18. Modeling Coordination Problems in a Music Ensemble

    DEFF Research Database (Denmark)

    Frimodt-Møller, Søren R.

    2008-01-01

    This paper considers in general terms, how musicians are able to coordinate through rational choices in a situation of (temporary) doubt in an ensemble performance. A fictitious example involving a 5-bar development in an unknown piece of music is analyzed in terms of epistemic logic, more...... specifically a multi-agent system, where it is shown that perfect coordination can only be certain to take place if the musicians have common knowledge of certain rules of the composition. We subsequently argue, however, that the musicians need not agree on the central features of the piece of music in order...

  19. On Extending Neural Networks with Loss Ensembles for Text Classification

    OpenAIRE

    Hajiabadi, Hamideh; Molla-Aliod, Diego; Monsefi, Reza

    2017-01-01

    Ensemble techniques are powerful approaches that combine several weak learners to build a stronger one. As a meta learning framework, ensemble techniques can easily be applied to many machine learning techniques. In this paper we propose a neural network extended with an ensemble loss function for text classification. The weight of each weak loss function is tuned within the training phase through the gradient propagation optimization method of the neural network. The approach is evaluated on...

  20. Finite n Largest Eigenvalue Probability Distribution Function of Gaussian Ensembles

    OpenAIRE

    Choup, Leonard N.

    2011-01-01

    In this paper we focus on the finite n probability distribution function of the largest eigenvalue in the classical Gaussian Ensemble of n by n matrices (GEn). We derive the finite n largest eigenvalue probability distribution function for the Gaussian Orthogonal and Symplectic Ensembles and also prove an Edgeworth type Theorem for the largest eigenvalue probability distribution function of Gaussian Symplectic Ensemble. The correction terms to the limiting probability distribution are express...

  1. A retrospective streamflow ensemble forecast for an extreme hydrologic event: a case study of Hurricane Irene and on the Hudson River basin

    Science.gov (United States)

    Saleh, Firas; Ramaswamy, Venkatsundar; Georgas, Nickitas; Blumberg, Alan F.; Pullen, Julie

    2016-07-01

    This paper investigates the uncertainties in hourly streamflow ensemble forecasts for an extreme hydrological event using a hydrological model forced with short-range ensemble weather prediction models. A state-of-the art, automated, short-term hydrologic prediction framework was implemented using GIS and a regional scale hydrological model (HEC-HMS). The hydrologic framework was applied to the Hudson River basin ( ˜ 36 000 km2) in the United States using gridded precipitation data from the National Centers for Environmental Prediction (NCEP) North American Regional Reanalysis (NARR) and was validated against streamflow observations from the United States Geologic Survey (USGS). Finally, 21 precipitation ensemble members of the latest Global Ensemble Forecast System (GEFS/R) were forced into HEC-HMS to generate a retrospective streamflow ensemble forecast for an extreme hydrological event, Hurricane Irene. The work shows that ensemble stream discharge forecasts provide improved predictions and useful information about associated uncertainties, thus improving the assessment of risks when compared with deterministic forecasts. The uncertainties in weather inputs may result in false warnings and missed river flooding events, reducing the potential to effectively mitigate flood damage. The findings demonstrate how errors in the ensemble median streamflow forecast and time of peak, as well as the ensemble spread (uncertainty) are reduced 48 h pre-event by utilizing the ensemble framework. The methodology and implications of this work benefit efforts of short-term streamflow forecasts at regional scales, notably regarding the peak timing of an extreme hydrologic event when combined with a flood threshold exceedance diagram. Although the modeling framework was implemented on the Hudson River basin, it is flexible and applicable in other parts of the world where atmospheric reanalysis products and streamflow data are available.

  2. Sequential ensemble-based optimal design for parameter estimation: SEQUENTIAL ENSEMBLE-BASED OPTIMAL DESIGN

    Energy Technology Data Exchange (ETDEWEB)

    Man, Jun [Zhejiang Provincial Key Laboratory of Agricultural Resources and Environment, Institute of Soil and Water Resources and Environmental Science, College of Environmental and Resource Sciences, Zhejiang University, Hangzhou China; Zhang, Jiangjiang [Zhejiang Provincial Key Laboratory of Agricultural Resources and Environment, Institute of Soil and Water Resources and Environmental Science, College of Environmental and Resource Sciences, Zhejiang University, Hangzhou China; Li, Weixuan [Pacific Northwest National Laboratory, Richland Washington USA; Zeng, Lingzao [Zhejiang Provincial Key Laboratory of Agricultural Resources and Environment, Institute of Soil and Water Resources and Environmental Science, College of Environmental and Resource Sciences, Zhejiang University, Hangzhou China; Wu, Laosheng [Department of Environmental Sciences, University of California, Riverside California USA

    2016-10-01

    The ensemble Kalman filter (EnKF) has been widely used in parameter estimation for hydrological models. The focus of most previous studies was to develop more efficient analysis (estimation) algorithms. On the other hand, it is intuitively understandable that a well-designed sampling (data-collection) strategy should provide more informative measurements and subsequently improve the parameter estimation. In this work, a Sequential Ensemble-based Optimal Design (SEOD) method, coupled with EnKF, information theory and sequential optimal design, is proposed to improve the performance of parameter estimation. Based on the first-order and second-order statistics, different information metrics including the Shannon entropy difference (SD), degrees of freedom for signal (DFS) and relative entropy (RE) are used to design the optimal sampling strategy, respectively. The effectiveness of the proposed method is illustrated by synthetic one-dimensional and two-dimensional unsaturated flow case studies. It is shown that the designed sampling strategies can provide more accurate parameter estimation and state prediction compared with conventional sampling strategies. Optimal sampling designs based on various information metrics perform similarly in our cases. The effect of ensemble size on the optimal design is also investigated. Overall, larger ensemble size improves the parameter estimation and convergence of optimal sampling strategy. Although the proposed method is applied to unsaturated flow problems in this study, it can be equally applied in any other hydrological problems.

  3. Combining Spatial Statistical and Ensemble Information in Probabilistic Weather Forecasts

    National Research Council Canada - National Science Library

    Berrocal, Veronica J; Raftery, Adrian E; Gneiting, Tilmann

    2006-01-01

    .... Bayesian model averaging (BMA) is a statistical postprocessing method for forecast ensembles that generates calibrated probabilistic forecast products for weather quantities at individual sites...

  4. Examining Chaotic Convection with Super-Parameterization Ensembles

    Science.gov (United States)

    Jones, Todd R.

    This study investigates a variety of features present in a new configuration of the Community Atmosphere Model (CAM) variant, SP-CAM 2.0. The new configuration (multiple-parameterization-CAM, MP-CAM) changes the manner in which the super-parameterization (SP) concept represents physical tendency feedbacks to the large-scale by using the mean of 10 independent two-dimensional cloud-permitting model (CPM) curtains in each global model column instead of the conventional single CPM curtain. The climates of the SP and MP configurations are examined to investigate any significant differences caused by the application of convective physical tendencies that are more deterministic in nature, paying particular attention to extreme precipitation events and large-scale weather systems, such as the Madden-Julian Oscillation (MJO). A number of small but significant changes in the mean state climate are uncovered, and it is found that the new formulation degrades MJO performance. Despite these deficiencies, the ensemble of possible realizations of convective states in the MP configuration allows for analysis of uncertainty in the small-scale solution, lending to examination of those weather regimes and physical mechanisms associated with strong, chaotic convection. Methods of quantifying precipitation predictability are explored, and use of the most reliable of these leads to the conclusion that poor precipitation predictability is most directly related to the proximity of the global climate model column state to atmospheric critical points. Secondarily, the predictability is tied to the availability of potential convective energy, the presence of mesoscale convective organization on the CPM grid, and the directive power of the large-scale.

  5. DART: A Community Facility for Ensemble Data Assimilation

    Science.gov (United States)

    Hoar, T. J.; Raeder, K.; Anderson, J. L.; Collins, N.; Liu, H.; Romine, G.; Arellano, A. F.; Lawson, G.

    2009-12-01

    The Data Assimilation Research Testbed (DART) is a mature community software facility providing researchers access to state-of-the-art ensemble data assimilation tools. The freely-available DART distribution includes fully functional low-order and high-order models, support for commonly available observations, hooks to easily add both new models and observation types, diagnostic programs to interpret the results, and a full tutorial suitable for self-study or teaching data assimilation concepts, including exercises using the models distributed with DART. DART is used regularly with a number of geophysical models including NCAR's WRF and CAM atmospheric models. DART/WRF is being used for tropical storm analysis and prediction in the Pacific and Atlantic and was used to produce real-time predictions during the 2009 Atlantic hurricane season. DART/CAM has played an integral part in the development of the new CAM version 4 that will be used for NCAR's contribution to the next IPCC. DART/CAM has been run for many model configurations to evaluate CAM systematic errors and parameterization options. DART is also in use for chemical assimilation in the WRF-CHEM and CAM-CHEM versions of these models. New models, both small and large continue to be added to the set compatible with DART. During 2009, DART assimilation was developed for the POP (Parallel Ocean Program) ocean general circulation model that is being used for decadal coupled atmosphere/ocean predictions at NCAR. The newest version of the Planet WRF model, configured for Martian data assimilation, is also now in use with DART. Novel observation types also continue to be added to DART. For instance, assimilation capabilities for radiance observations from the COSMIC and MOPITT instruments on earth and from TES on Mars have been added in 2009.

  6. Atmospheric Infancy

    DEFF Research Database (Denmark)

    Roald, Tone; Pedersen, Ida Egmose; Levin, Kasper

    2017-01-01

    In this article we establish intersubjective meaning-making in infancy as atmospheric. Through qualitative descriptions of five mother–infant dyads in a video-recorded, experimental setting when the infant is 4, 7, 10, and 13 months, we discovered atmospheric appearances with a developmental...... pattern of atmospheric variations. These appearances, we argue, are contextual and intersubjective monologues. The monologues are similar to what Daniel Stern describes with his concept of “vitality affects,” but they arise as a unified force that envelops the mother and child. As such, we present a new...

  7. Ensemble pharmacophore meets ensemble docking: a novel screening strategy for the identification of RIPK1 inhibitors.

    Science.gov (United States)

    Fayaz, S M; Rajanikant, G K

    2014-07-01

    Programmed cell death has been a fascinating area of research since it throws new challenges and questions in spite of the tremendous ongoing research in this field. Recently, necroptosis, a programmed form of necrotic cell death, has been implicated in many diseases including neurological disorders. Receptor interacting serine/threonine protein kinase 1 (RIPK1) is an important regulatory protein involved in the necroptosis and inhibition of this protein is essential to stop necroptotic process and eventually cell death. Current structure-based virtual screening methods involve a wide range of strategies and recently, considering the multiple protein structures for pharmacophore extraction has been emphasized as a way to improve the outcome. However, using the pharmacophoric information completely during docking is very important. Further, in such methods, using the appropriate protein structures for docking is desirable. If not, potential compound hits, obtained through pharmacophore-based screening, may not have correct ranks and scores after docking. Therefore, a comprehensive integration of different ensemble methods is essential, which may provide better virtual screening results. In this study, dual ensemble screening, a novel computational strategy was used to identify diverse and potent inhibitors against RIPK1. All the pharmacophore features present in the binding site were captured using both the apo and holo protein structures and an ensemble pharmacophore was built by combining these features. This ensemble pharmacophore was employed in pharmacophore-based screening of ZINC database. The compound hits, thus obtained, were subjected to ensemble docking. The leads acquired through docking were further validated through feature evaluation and molecular dynamics simulation.

  8. Cluster Ensemble-Based Image Segmentation

    Directory of Open Access Journals (Sweden)

    Xiaoru Wang

    2013-07-01

    Full Text Available Image segmentation is the foundation of computer vision applications. In this paper, we propose a new cluster ensemble-based image segmentation algorithm, which overcomes several problems of traditional methods. We make two main contributions in this paper. First, we introduce the cluster ensemble concept to fuse the segmentation results from different types of visual features effectively, which can deliver a better final result and achieve a much more stable performance for broad categories of images. Second, we exploit the PageRank idea from Internet applications and apply it to the image segmentation task. This can improve the final segmentation results by combining the spatial information of the image and the semantic similarity of regions. Our experiments on four public image databases validate the superiority of our algorithm over conventional single type of feature or multiple types of features-based algorithms, since our algorithm can fuse multiple types of features effectively for better segmentation results. Moreover, our method is also proved to be very competitive in comparison with other state-of-the-art segmentation algorithms.

  9. Ensemble Kalman filtering with residual nudging

    KAUST Repository

    Luo, X.

    2012-10-03

    Covariance inflation and localisation are two important techniques that are used to improve the performance of the ensemble Kalman filter (EnKF) by (in effect) adjusting the sample covariances of the estimates in the state space. In this work, an additional auxiliary technique, called residual nudging, is proposed to monitor and, if necessary, adjust the residual norms of state estimates in the observation space. In an EnKF with residual nudging, if the residual norm of an analysis is larger than a pre-specified value, then the analysis is replaced by a new one whose residual norm is no larger than a pre-specified value. Otherwise, the analysis is considered as a reasonable estimate and no change is made. A rule for choosing the pre-specified value is suggested. Based on this rule, the corresponding new state estimates are explicitly derived in case of linear observations. Numerical experiments in the 40-dimensional Lorenz 96 model show that introducing residual nudging to an EnKF may improve its accuracy and/or enhance its stability against filter divergence, especially in the small ensemble scenario.

  10. Deterministic Mean-Field Ensemble Kalman Filtering

    KAUST Repository

    Law, Kody

    2016-05-03

    The proof of convergence of the standard ensemble Kalman filter (EnKF) from Le Gland, Monbet, and Tran [Large sample asymptotics for the ensemble Kalman filter, in The Oxford Handbook of Nonlinear Filtering, Oxford University Press, Oxford, UK, 2011, pp. 598--631] is extended to non-Gaussian state-space models. A density-based deterministic approximation of the mean-field limit EnKF (DMFEnKF) is proposed, consisting of a PDE solver and a quadrature rule. Given a certain minimal order of convergence k between the two, this extends to the deterministic filter approximation, which is therefore asymptotically superior to standard EnKF for dimension d<2k. The fidelity of approximation of the true distribution is also established using an extension of the total variation metric to random measures. This is limited by a Gaussian bias term arising from nonlinearity/non-Gaussianity of the model, which arises in both deterministic and standard EnKF. Numerical results support and extend the theory.

  11. Online cross-validation-based ensemble learning.

    Science.gov (United States)

    Benkeser, David; Ju, Cheng; Lendle, Sam; van der Laan, Mark

    2018-01-30

    Online estimators update a current estimate with a new incoming batch of data without having to revisit past data thereby providing streaming estimates that are scalable to big data. We develop flexible, ensemble-based online estimators of an infinite-dimensional target parameter, such as a regression function, in the setting where data are generated sequentially by a common conditional data distribution given summary measures of the past. This setting encompasses a wide range of time-series models and, as special case, models for independent and identically distributed data. Our estimator considers a large library of candidate online estimators and uses online cross-validation to identify the algorithm with the best performance. We show that by basing estimates on the cross-validation-selected algorithm, we are asymptotically guaranteed to perform as well as the true, unknown best-performing algorithm. We provide extensions of this approach including online estimation of the optimal ensemble of candidate online estimators. We illustrate excellent performance of our methods using simulations and a real data example where we make streaming predictions of infectious disease incidence using data from a large database. Copyright © 2017 John Wiley & Sons, Ltd. Copyright © 2017 John Wiley & Sons, Ltd.

  12. Group theory for embedded random matrix ensembles

    Science.gov (United States)

    Kota, V. K. B.

    2015-04-01

    Embedded random matrix ensembles are generic models for describing statistical properties of finite isolated quantum many-particle systems. For the simplest spinless fermion (or boson) systems with say m fermions (or bosons) in N single particle states and interacting with say k-body interactions, we have EGUE(k) [embedded GUE of k-body interactions) with GUE embedding and the embedding algebra is U(N). In this paper, using EGUE(k) representation for a Hamiltonian that is fc-body and an independent EGUE(t) representation for a transition operator that is t-body and employing the embedding U(N) algebra, finite-N formulas for moments up to order four are derived, for the first time, for the transition strength densities (transition strengths multiplied by the density of states at the initial and final energies). In the asymptotic limit, these formulas reduce to those derived for the EGOE version and establish that in general bivariate transition strength densities take bivariate Gaussian form for isolated finite quantum systems. Extension of these results for other types of transition operators and EGUE ensembles with further symmetries are discussed.

  13. Protein function prediction using multilabel ensemble classification.

    Science.gov (United States)

    Yu, Guoxian; Rangwala, Huzefa; Domeniconi, Carlotta; Zhang, Guoji; Yu, Zhiwen

    2013-01-01

    High-throughput experimental techniques produce several kinds of heterogeneous proteomic and genomic data sets. To computationally annotate proteins, it is necessary and promising to integrate these heterogeneous data sources. Some methods transform these data sources into different kernels or feature representations. Next, these kernels are linearly (or nonlinearly) combined into a composite kernel. The composite kernel is utilized to develop a predictive model to infer the function of proteins. A protein can have multiple roles and functions (or labels). Therefore, multilabel learning methods are also adapted for protein function prediction. We develop a transductive multilabel classifier (TMC) to predict multiple functions of proteins using several unlabeled proteins. We also propose a method called transductive multilabel ensemble classifier (TMEC) for integrating the different data sources using an ensemble approach. The TMEC trains a graph-based multilabel classifier on each single data source, and then combines the predictions of the individual classifiers. We use a directed birelational graph to capture the relationships between pairs of proteins, between pairs of functions, and between proteins and functions. We evaluate the effectiveness of the TMC and TMEC to predict the functions of proteins on three benchmarks. We show that our approaches perform better than recently proposed protein function prediction methods on composite and multiple kernels. The code, data sets used in this paper and supplemental material are available at https://sites.google.com/site/guoxian85/tmec.

  14. Learning ensemble classifiers for diabetic retinopathy assessment.

    Science.gov (United States)

    Saleh, Emran; Błaszczyński, Jerzy; Moreno, Antonio; Valls, Aida; Romero-Aroca, Pedro; de la Riva-Fernández, Sofia; Słowiński, Roman

    2017-10-06

    Diabetic retinopathy is one of the most common comorbidities of diabetes. Unfortunately, the recommended annual screening of the eye fundus of diabetic patients is too resource-consuming. Therefore, it is necessary to develop tools that may help doctors to determine the risk of each patient to attain this condition, so that patients with a low risk may be screened less frequently and the use of resources can be improved. This paper explores the use of two kinds of ensemble classifiers learned from data: fuzzy random forest and dominance-based rough set balanced rule ensemble. These classifiers use a small set of attributes which represent main risk factors to determine whether a patient is in risk of developing diabetic retinopathy. The levels of specificity and sensitivity obtained in the presented study are over 80%. This study is thus a first successful step towards the construction of a personalized decision support system that could help physicians in daily clinical practice. Copyright © 2017 Elsevier B.V. All rights reserved.

  15. Activity recall in a visual cortical ensemble.

    Science.gov (United States)

    Xu, Shengjin; Jiang, Wanchen; Poo, Mu-Ming; Dan, Yang

    2012-01-22

    Cue-triggered recall of learned temporal sequences is an important cognitive function that has been attributed to higher brain areas. Here recordings in both anesthetized and awake rats demonstrate that after repeated stimulation with a moving spot that evoked sequential firing of an ensemble of primary visual cortex (V1) neurons, just a brief flash at the starting point of the motion path was sufficient to evoke a sequential firing pattern that reproduced the activation order evoked by the moving spot. The speed of recalled spike sequences may reflect the internal dynamics of the network rather than the motion speed. In awake rats, such recall was observed during a synchronized ('quiet wakeful') brain state having large-amplitude, low-frequency local field potential (LFP) but not in a desynchronized ('active') state having low-amplitude, high-frequency LFP. Such conditioning-enhanced, cue-evoked sequential spiking of a V1 ensemble may contribute to experience-based perceptual inference in a brain state-dependent manner.

  16. Nanobiosensing with Arrays and Ensembles of Nanoelectrodes

    Directory of Open Access Journals (Sweden)

    Najmeh Karimian

    2016-12-01

    Full Text Available Since the first reports dating back to the mid-1990s, ensembles and arrays of nanoelectrodes (NEEs and NEAs, respectively have gained an important role as advanced electroanalytical tools thank to their unique characteristics which include, among others, dramatically improved signal/noise ratios, enhanced mass transport and suitability for extreme miniaturization. From the year 2000 onward, these properties have been exploited to develop electrochemical biosensors in which the surfaces of NEEs/NEAs have been functionalized with biorecognition layers using immobilization modes able to take the maximum advantage from the special morphology and composite nature of their surface. This paper presents an updated overview of this field. It consists of two parts. In the first, we discuss nanofabrication methods and the principles of functioning of NEEs/NEAs, focusing, in particular, on those features which are important for the development of highly sensitive and miniaturized biosensors. In the second part, we review literature references dealing the bioanalytical and biosensing applications of sensors based on biofunctionalized arrays/ensembles of nanoelectrodes, focusing our attention on the most recent advances, published in the last five years. The goal of this review is both to furnish fundamental knowledge to researchers starting their activity in this field and provide critical information on recent achievements which can stimulate new ideas for future developments to experienced scientists.

  17. Ensemble Kalman filtering with residual nudging

    Directory of Open Access Journals (Sweden)

    Xiaodong Luo

    2012-10-01

    Full Text Available Covariance inflation and localisation are two important techniques that are used to improve the performance of the ensemble Kalman filter (EnKF by (in effect adjusting the sample covariances of the estimates in the state space. In this work, an additional auxiliary technique, called residual nudging, is proposed to monitor and, if necessary, adjust the residual norms of state estimates in the observation space. In an EnKF with residual nudging, if the residual norm of an analysis is larger than a pre-specified value, then the analysis is replaced by a new one whose residual norm is no larger than a pre-specified value. Otherwise, the analysis is considered as a reasonable estimate and no change is made. A rule for choosing the pre-specified value is suggested. Based on this rule, the corresponding new state estimates are explicitly derived in case of linear observations. Numerical experiments in the 40-dimensional Lorenz 96 model show that introducing residual nudging to an EnKF may improve its accuracy and/or enhance its stability against filter divergence, especially in the small ensemble scenario.

  18. Tropical cyclones in the UPSCALE ensemble of high resolution global climate models

    OpenAIRE

    Roberts, Malcolm J.; Vidale, Pier Luigi; Mizielinski, Matthew S.; Demory, Marie-Estelle; Schiemann, Reinhard; Strachan, Jane; Hodges, Kevin; Bel, Ray; Camp, Joanne

    2015-01-01

    The UPSCALE (UK on PRACE: weather-resolving Simulations of Climate for globAL Environmental risk) project, using PRACE (Partnership for Advanced Computing in Europe) resources, constructed and ran an ensemble of atmosphere-only global climate model simulations, using the Met Office Unified Model GA3 configuration. Each simulation is 27 years in length for both the present climate and an end-of-century future climate, at resolutions of N96 (130 km), N216 (60 km) and N512 (25 km), in order to s...

  19. Analog-Based Postprocessing of Navigation-Related Hydrological Ensemble Forecasts

    Science.gov (United States)

    Hemri, S.; Klein, B.

    2017-11-01

    Inland waterway transport benefits from probabilistic forecasts of water levels as they allow to optimize the ship load and, hence, to minimize the transport costs. Probabilistic state-of-the-art hydrologic ensemble forecasts inherit biases and dispersion errors from the atmospheric ensemble forecasts they are driven with. The use of statistical postprocessing techniques like ensemble model output statistics (EMOS) allows for a reduction of these systematic errors by fitting a statistical model based on training data. In this study, training periods for EMOS are selected based on forecast analogs, i.e., historical forecasts that are similar to the forecast to be verified. Due to the strong autocorrelation of water levels, forecast analogs have to be selected based on entire forecast hydrographs in order to guarantee similar hydrograph shapes. Custom-tailored measures of similarity for forecast hydrographs comprise hydrological series distance (SD), the hydrological matching algorithm (HMA), and dynamic time warping (DTW). Verification against observations reveals that EMOS forecasts for water level at three gauges along the river Rhine with training periods selected based on SD, HMA, and DTW compare favorably with reference EMOS forecasts, which are based on either seasonal training periods or on training periods obtained by dividing the hydrological forecast trajectories into runoff regimes.

  20. Development of an ensemble-adjoint optimization approach to derive uncertainties in net carbon fluxes

    Directory of Open Access Journals (Sweden)

    T. Ziehn

    2011-11-01

    Full Text Available Accurate modelling of the carbon cycle strongly depends on the parametrization of its underlying processes. The Carbon Cycle Data Assimilation System (CCDAS can be used as an estimator algorithm to derive posterior parameter values and uncertainties for the Biosphere Energy Transfer and Hydrology scheme (BETHY. However, the simultaneous optimization of all process parameters can be challenging, due to the complexity and non-linearity of the BETHY model. Therefore, we propose a new concept that uses ensemble runs and the adjoint optimization approach of CCDAS to derive the full probability density function (PDF for posterior soil carbon parameters and the net carbon flux at the global scale. This method allows us to optimize only those parameters that can be constrained best by atmospheric carbon dioxide (CO2 data. The prior uncertainties of the remaining parameters are included in a consistent way through ensemble runs, but are not constrained by data. The final PDF for the optimized parameters and the net carbon flux are then derived by superimposing the individual PDFs for each ensemble member. We find that the optimization with CCDAS converges much faster, due to the smaller number of processes involved. Faster convergence also gives us much increased confidence that we find the global minimum in the reduced parameter space.

  1. NCAR's Experimental Real-time Convection-allowing Ensemble Prediction System

    Science.gov (United States)

    Schwartz, C. S.; Romine, G. S.; Sobash, R.; Fossell, K.

    2016-12-01

    Since April 2015, the National Center for Atmospheric Research's (NCAR's) Mesoscale and Microscale Meteorology (MMM) Laboratory, in collaboration with NCAR's Computational Information Systems Laboratory (CISL), has been producing daily, real-time, 10-member, 48-hr ensemble forecasts with 3-km horizontal grid spacing over the conterminous United States (http://ensemble.ucar.edu). These computationally-intensive, next-generation forecasts are produced on the Yellowstone supercomputer, have been embraced by both amateur and professional weather forecasters, are widely used by NCAR and university researchers, and receive considerable attention on social media. Initial conditions are supplied by NCAR's Data Assimilation Research Testbed (DART) software and the forecast model is NCAR's Weather Research and Forecasting (WRF) model; both WRF and DART are community tools. This presentation will focus on cutting-edge research results leveraging the ensemble dataset, including winter weather predictability, severe weather forecasting, and power outage modeling. Additionally, the unique design of the real-time analysis and forecast system and computational challenges and solutions will be described.

  2. Validation of a short-range ensemble precipitation prediction system over the Iberian Peninsula and Balearics

    Science.gov (United States)

    Santos-Muñoz, D.; Martin, M. L.; Valero, F.; Luna, M. Y.; Morata, A.; Sebastian, L. I.

    2009-09-01

    A short-range ensemble precipitation forecast system has been constructed over the Iberian Peninsula and Balearics by means of the fifth-generation Pennsylvania State University-National Centre for Atmospheric Research Model (MM5). The ensemble system consists of ten members, each run with a different combination of two different initial conditions from global models, IFS-ECMWF and GFS-NCEP, and five different subgrid-scale physics configurations for three months period of 2006. The mesoscale verification is made by using observational precipitation data of the Spanish Climatic Network. To ensure the quality individual members the forecast distribution of each member has been studied, showing good agreement with the observed precipitation distribution. The created short-range ensemble shows high spread-skill correlation values for daily precipitation. However, the asymmetric shape of the rank histogram indicates some underdispersion, suggesting an under-forecasting behaviour. Talagrand diagrams have been created for different precipitation thresholds to analyze more deeply the system calibration, showing a nearly flat shape on the lower bins and a clear accumulation on the higher interval, indicating that the system is well bias affected. The Relative Operating Characteristic curves show a very outstanding area indicating the good discrimination capacity for each of the thresholds. The reliability diagrams are also indicative of the good reliability of the forecasting system, depicting in general good agreement between forecast probability and the mean observed frequency. Because of that, the verification proves the usefulness of the forecasting system over the study area.

  3. Particle Kalman Filtering: A Nonlinear Bayesian Framework for Ensemble Kalman Filters*

    KAUST Repository

    Hoteit, Ibrahim

    2012-02-01

    This paper investigates an approximation scheme of the optimal nonlinear Bayesian filter based on the Gaussian mixture representation of the state probability distribution function. The resulting filter is similar to the particle filter, but is different from it in that the standard weight-type correction in the particle filter is complemented by the Kalman-type correction with the associated covariance matrices in the Gaussian mixture. The authors show that this filter is an algorithm in between the Kalman filter and the particle filter, and therefore is referred to as the particle Kalman filter (PKF). In the PKF, the solution of a nonlinear filtering problem is expressed as the weighted average of an “ensemble of Kalman filters” operating in parallel. Running an ensemble of Kalman filters is, however, computationally prohibitive for realistic atmospheric and oceanic data assimilation problems. For this reason, the authors consider the construction of the PKF through an “ensemble” of ensemble Kalman filters (EnKFs) instead, and call the implementation the particle EnKF (PEnKF). It is shown that different types of the EnKFs can be considered as special cases of the PEnKF. Similar to the situation in the particle filter, the authors also introduce a resampling step to the PEnKF in order to reduce the risk of weights collapse and improve the performance of the filter. Numerical experiments with the strongly nonlinear Lorenz-96 model are presented and discussed.

  4. Ensemble Classification for Anomalous Propagation Echo Detection with Clustering-Based Subset-Selection Method

    Directory of Open Access Journals (Sweden)

    Hansoo Lee

    2017-01-01

    Full Text Available Several types of non-precipitation echoes appear in radar images and disrupt the weather forecasting process. An anomalous propagation echo is an unwanted observation result similar to a precipitation echo. It occurs through radar-beam ducting because of the temperature, humidity distribution, and other complicated atmospheric conditions. Anomalous propagation echoes should be removed because they make weather forecasting difficult. In this paper, we suggest an ensemble classification method based on an artificial neural network and a clustering-based subset-selection method. This method allows us to implement an efficient classification method when a feature space has complicated distributions. By separating the input data into atomic and non-atomic clusters, each derived cluster will receive its own base classifier. In the experiments, we compared our method with a standalone artificial neural network classifier. The suggested ensemble classifier showed 84.14% performance, which was about 2% higher than that of the k-means clustering-based ensemble classifier and about 4% higher than the standalone artificial neural network classifier.

  5. Extended-Range Ensemble Predictions of Convection in the North Australian Monsoon Region

    Science.gov (United States)

    Drosdowsky, Wasyl; Wheeler, Matthew C.

    2017-04-01

    Extended-range (Bureau of Meteorology’s Predictive Ocean-Atmosphere Model for Australia (POAMA). Hindcasts from 1980-2011 are used, initialised on the 1st, 11th and 21st of each month, with a 33-member ensemble. The measure of convection is outgoing longwave radiation (OLR) averaged over the box 120ºE-150ºE, 5ºS-17.5ºS. This averaging serves to focus on the intraseasonal and longer time scales, and is an area of interest to users. The raw hindcasts of daily OLR show a strong systematic adjustment away from their initial values during the first week, and then converge to a mean seasonal cycle of similar amplitude and phase to observations. Hence, forecast OLR anomalies are formed by removing the model’s own seasonal cycle of OLR, which is a function of start time and lead time, a usual practice for dynamical seasonal prediction. Over all hindcasts, the model forecast root-mean-square (RMS) error is smaller than the RMS error of persistence and climatological reference forecasts for leads 3-35 days. Ensemble spread is less than the forecast RMS error (i.e. under-spread) for days 1-12, but slightly greater than the RMS error for longer leads. Binning the individual forecasts based on ensemble spread shows a generally positive relationship between spread and error. Therefore, greater certainty can be given for forecasts with smaller spread.

  6. A review of issues in ensemble-based Kalman filtering

    Energy Technology Data Exchange (ETDEWEB)

    Ehrendorfer, M. [Dept. of Meteorology and Geophysics, The Univ. of Reading (United Kingdom)

    2007-12-15

    Ensemble-based data assimilation methods related to the fundamental theory of Kalman filtering have been explored in a variety of mostly non-operational data assimilation contexts over the past decade with increasing intensity. While promising properties have been reported, a number of issues that arise in the development and application of ensemble-based data assimilation techniques, such as in the basic form of the ensemble Kalman filter (EnKF), still deserve particular attention. The necessity of employing an ensemble of small size represents a fundamental issue which in turn leads to several related points that must be carefully considered. In particular, the need to correct for sampling noise in the covariance structure estimated from the finite ensemble must be mentioned. Covariance inflation, localization through a Schur/Hadamard product, preventing the occurrence of filter divergence and inbreeding, as well as the loss of dynamical balances, are all issues directly related to the use of small ensemble sizes. Attempts to reduce effectively the sampling error due to small ensembles and at the same time maintaining an ensemble spread that realistically describes error structures have given rise to the development of variants of the basic form of the EnKF. These include, for example, the Ensemble Adjustment Kalman Filter (EAKF), the Ensemble Transform Kalman Filter (ETKF), the Ensemble Square-Root Filter (EnSRF), and the Local Ensemble Kalman Filter (LEKF). Further important considerations within ensemble-based Kalman filtering concern issues such as the treatment of model error, stochastic versus deterministic updating algorithms, the case of implementation and computational cost, serial processing of observations, avoiding the appearance of undesired dynamic imbalances, and the treatment of non-Gaussianity and nonlinearity. The discussion of the above issues within ensemble-based Kalman filtering forms the central topic of this article, that starts out with a

  7. High resolution ensemble forecasting for the Gulf of Mexico eddies and fronts

    Science.gov (United States)

    Counillon, F.; Bertino, L.

    2007-05-01

    As oil production moves further into deeper waters, the costs related to strong current hazards are increasing accordingly, and accurate three-dimensional forecasts of currents are urgently needed. To be useful, models have to locate eddies and fronts to an accuracy of 30 km at a nowcast stage, which is almost impossible to accomplish with the use of satellite data of the same accuracy. The use of stochastic forecast allows us to give confidence of our prediction. We are using a nested configuration of the Hybrid coordinate ocean model (HYCOM), where the TOPAZ system, which covers the Atlantic and the Artic, gives lateral boundary condition to a high-resolution (5km) model of the Gulf of Mexico (GOM). TOPAZ is a real-time forecasting coupled ocean-ice model, which assimilates sea level anomaly (SLA), sea surface temperature, and sea ice concentration, with the ensemble Kalman filter. The high- resolution model assimilates SLA using the ensemble optimal interpolation, which updates accordingly the currents, salinity, temperature, and layer interface at all depths. Here, we evaluate the ensemble forecast capabilities of our high-resolution model, for eddy Extreme that has been observed from altimeters around the 15th of July. We run 6 successive ensemble runs composed of 10 members of equal likelihood. Members differ by perturbations of the initial state, of the lateral boundary conditions, and of the atmospheric boundary conditions. We have started the experiment 1 month prior to the shedding event, because it was the time necessary for perturbation of boundary conditions to spread uniformly and reach a significant level across the GOM. The ensemble reproduces well the dynamics of the eddy shedding and produces a significant spread at the boundary of the eddy, but underestimates the RMS error of the SLA. Prior to the shedding time, the error growth increase, induced by the highly non-linear growth of cyclonic eddies at the boundary of the Loop Current. Additionally

  8. Decadal prediction skill in a multi-model ensemble

    Energy Technology Data Exchange (ETDEWEB)

    Oldenborgh, Geert Jan van; Wouters, Bert; Hazeleger, Wilco [Royal Netherlands Meteorological Institute (KNMI), P.O. Box 201, De Bilt (Netherlands); Doblas-Reyes, Francisco J. [Institucio Catalana de Recerca i Estudis Avancats (ICREA) and Institut Catala de Ciencies del Clima (IC3), Barcelona (Spain)

    2012-04-15

    Decadal climate predictions may have skill due to predictable components in boundary conditions (mainly greenhouse gas concentrations but also tropospheric and stratospheric aerosol distributions) and initial conditions (mainly the ocean state). We investigate the skill of temperature and precipitation hindcasts from a multi-model ensemble of four climate forecast systems based on coupled ocean-atmosphere models. Regional variations in skill with and without trend are compared with similarly analysed uninitialised experiments to separate the trend due to monotonically increasing forcings from fluctuations around the trend due to the ocean initial state and aerosol forcings. In temperature most of the skill in both multi-model ensembles comes from the externally forced trends. The rise of the global mean temperature is represented well in the initialised hindcasts, but variations around the trend show little skill beyond the first year due to the absence of volcanic aerosols in the hindcasts and the unpredictability of ENSO. The models have non-trivial skill in hindcasts of North Atlantic sea surface temperature beyond the trend. This skill is highest in the northern North Atlantic in initialised experiments and in the subtropical North Atlantic in uninitialised simulations. A similar result is found in the Pacific Ocean, although the signal is less clear. The uninitialised simulations have good skill beyond the trend in the western North Pacific. The initialised experiments show some skill in the decadal ENSO region in the eastern Pacific, in agreement with previous studies. However, the results in this study are not statistically significant (p {approx} 0.1) by themselves. The initialised models also show some skill in forecasting 4-year mean Sahel rainfall at lead times of 1 and 5 years, in agreement with the observed teleconnection from the Atlantic Ocean. Again, the skill is not statistically significant (p {approx} 0.2). Furthermore, uninitialised simulations

  9. Assessment of an ensemble seasonal streamflow forecasting system for Australia

    Science.gov (United States)

    Bennett, James C.; Wang, Quan J.; Robertson, David E.; Schepen, Andrew; Li, Ming; Michael, Kelvin

    2017-11-01

    Despite an increasing availability of skilful long-range streamflow forecasts, many water agencies still rely on simple resampled historical inflow sequences (stochastic scenarios) to plan operations over the coming year. We assess a recently developed forecasting system called forecast guided stochastic scenarios (FoGSS) as a skilful alternative to standard stochastic scenarios for the Australian continent. FoGSS uses climate forecasts from a coupled ocean-land-atmosphere prediction system, post-processed with the method of calibration, bridging and merging. Ensemble rainfall forecasts force a monthly rainfall-runoff model, while a staged hydrological error model quantifies and propagates hydrological forecast uncertainty through forecast lead times. FoGSS is able to generate ensemble streamflow forecasts in the form of monthly time series to a 12-month forecast horizon. FoGSS is tested on 63 Australian catchments that cover a wide range of climates, including 21 ephemeral rivers. In all perennial and many ephemeral catchments, FoGSS provides an effective alternative to resampled historical inflow sequences. FoGSS generally produces skilful forecasts at shorter lead times ( < 4 months), and transits to climatology-like forecasts at longer lead times. Forecasts are generally reliable and unbiased. However, FoGSS does not perform well in very dry catchments (catchments that experience zero flows more than half the time in some months), sometimes producing strongly negative forecast skill and poor reliability. We attempt to improve forecasts through the use of (i) ESP rainfall forcings, (ii) different rainfall-runoff models, and (iii) a Bayesian prior to encourage the error model to return climatology forecasts in months when the rainfall-runoff model performs poorly. Of these, the use of the prior offers the clearest benefit in very dry catchments, where it moderates strongly negative forecast skill and reduces bias in some instances. However, the prior does not

  10. Ensemble simulations of the decline and recovery of polar ozone

    Science.gov (United States)

    Wilson, R. J.; Austin, J.

    2005-12-01

    An ensemble of simulations of a coupled chemistry-climate model is completed for the period 1960 to 2100. The simulations are divided into two periods, 1960-2005 and 1990-2100. For the past, the model is forced with observed sea surface temperatures, the observed concentrations of chlorofluorocarbons and greenhouse gases, observed aerosol amounts and the observed solar cycle. For the future, the model is forced with the sea surface temperatures taken from a coupled atmosphere-ocean climate simulation for the same model, while the other forcings are taken from a variety of sources. The 15-year overlap between the `past' runs and the `future' runs allowed the results to be tested for the sensitivity to the forcing data. The Antarctic ozone hole developed rapidly in the model from about the late 1970s and the minimum ozone agrees well with observations, albeit with a slight low bias. The ozone hole area is smaller than observed and scales approximately linearly with minimum ozone. Absolute Minimum ozone is attained during the period 2000-2015, but the ozone hole disappears somewhat more slowly than it initially took to form. The ozone hole does not generally dissappear until about 2065, almost two decades later than current expectations. However, depending on the precise definition chosen, and depending on interannual variability and ensemble member, some results as early as 2050 may be devoid of the Antarctic ozone hole, while other results beyond 2075 may have a small ozone hole. In contrast the Arctic recovered to 1980 conditions by about 2040, but with an even larger range of possible dates. No equivalent of the Antarctic ozone hole was simulated in the Arctic. Allowing for model bias, the minimum ozone was about 200 DU, with bias-corrected values below 225DU attained during the period 1995-2020. The depth of the Antarctic ozone hole is driven primarily by changes in halogen amounts, whereas in the Arctic halogen amounts, although important, have a weaker impact

  11. Would be the Atmosphere Chaotic?

    Directory of Open Access Journals (Sweden)

    Isimar de Azevedo Santos

    2013-07-01

    Full Text Available The atmosphere has often been considered “chaotic” when in fact the “chaos” is a manifestation of the models that simulate it, which do not include all the physical mechanisms that exist within it. A weather prediction cannot be perfectly verified after a few days of integration due to the inherent nonlinearity of the equations of the hydrodynamic models. The innovative ideas of Lorenz led to the use of the ensemble forecast, with clear improvements in the quality of the numerical weather prediction. The present study addresses the statement that “even with perfect models and perfect observations, the ‘chaotic’ nature of the atmosphere would impose a finite limit of about two weeks to the predictability of the weather” as the atmosphere is not necessarily “chaotic”, but the models used in the simulation of atmospheric processes are. We conclude, therefore, that potential exists for developments to increase the horizon of numerical weather prediction, starting with better models and observations.

  12. Modality-Driven Classification and Visualization of Ensemble Variance

    Energy Technology Data Exchange (ETDEWEB)

    Bensema, Kevin; Gosink, Luke; Obermaier, Harald; Joy, Kenneth I.

    2016-10-01

    Advances in computational power now enable domain scientists to address conceptual and parametric uncertainty by running simulations multiple times in order to sufficiently sample the uncertain input space. While this approach helps address conceptual and parametric uncertainties, the ensemble datasets produced by this technique present a special challenge to visualization researchers as the ensemble dataset records a distribution of possible values for each location in the domain. Contemporary visualization approaches that rely solely on summary statistics (e.g., mean and variance) cannot convey the detailed information encoded in ensemble distributions that are paramount to ensemble analysis; summary statistics provide no information about modality classification and modality persistence. To address this problem, we propose a novel technique that classifies high-variance locations based on the modality of the distribution of ensemble predictions. Additionally, we develop a set of confidence metrics to inform the end-user of the quality of fit between the distribution at a given location and its assigned class. We apply a similar method to time-varying ensembles to illustrate the relationship between peak variance and bimodal or multimodal behavior. These classification schemes enable a deeper understanding of the behavior of the ensemble members by distinguishing between distributions that can be described by a single tendency and distributions which reflect divergent trends in the ensemble.

  13. Impact of hybrid GSI analysis using ETR ensembles

    Indian Academy of Sciences (India)

    System) with ETR (Ensemble Transform with Rescaling) based Global Ensemble Forecast (GEFS) of resolution T-190L28 is investigated. The experiment is conducted for a period of one week in June 2013 and forecast skills over different spatial domains are compared with respect to mean analysis state. Rainfall forecast is ...

  14. Collaborative Composing in High School String Chamber Music Ensembles

    Science.gov (United States)

    Hopkins, Michael T.

    2015-01-01

    The purpose of this study was to examine collaborative composing in high school string chamber music ensembles. Research questions included the following: (a) How do high school string instrumentalists in chamber music ensembles use verbal and musical forms of communication to collaboratively compose a piece of music? (b) How do selected variables…

  15. Competitive Learning Neural Network Ensemble Weighted by Predicted Performance

    Science.gov (United States)

    Ye, Qiang

    2010-01-01

    Ensemble approaches have been shown to enhance classification by combining the outputs from a set of voting classifiers. Diversity in error patterns among base classifiers promotes ensemble performance. Multi-task learning is an important characteristic for Neural Network classifiers. Introducing a secondary output unit that receives different…

  16. A Comparison of Ensemble Kalman Filters for Storm Surge Assimilation

    KAUST Repository

    Altaf, Muhammad

    2014-08-01

    This study evaluates and compares the performances of several variants of the popular ensembleKalman filter for the assimilation of storm surge data with the advanced circulation (ADCIRC) model. Using meteorological data from Hurricane Ike to force the ADCIRC model on a domain including the Gulf ofMexico coastline, the authors implement and compare the standard stochastic ensembleKalman filter (EnKF) and three deterministic square root EnKFs: the singular evolutive interpolated Kalman (SEIK) filter, the ensemble transform Kalman filter (ETKF), and the ensemble adjustment Kalman filter (EAKF). Covariance inflation and localization are implemented in all of these filters. The results from twin experiments suggest that the square root ensemble filters could lead to very comparable performances with appropriate tuning of inflation and localization, suggesting that practical implementation details are at least as important as the choice of the square root ensemble filter itself. These filters also perform reasonably well with a relatively small ensemble size, whereas the stochastic EnKF requires larger ensemble sizes to provide similar accuracy for forecasts of storm surge.

  17. Ensemble Learning for Multi-Source Neural Machine Translation

    NARCIS (Netherlands)

    Garmash, E.; Monz, C.

    2016-01-01

    In this paper we describe and evaluate methods to perform ensemble prediction in neural machine translation (NMT). We compare two methods of ensemble set induction: sampling parameter initializations for an NMT system, which is a relatively established method in NMT (Sutskever et al., 2014), and NMT

  18. Ensemble Methods for Classification of Physical Activities from Wrist Accelerometry.

    Science.gov (United States)

    Chowdhury, Alok Kumar; Tjondronegoro, Dian; Chandran, Vinod; Trost, Stewart G

    2017-09-01

    To investigate whether the use of ensemble learning algorithms improve physical activity recognition accuracy compared to the single classifier algorithms, and to compare the classification accuracy achieved by three conventional ensemble machine learning methods (bagging, boosting, random forest) and a custom ensemble model comprising four algorithms commonly used for activity recognition (binary decision tree, k nearest neighbor, support vector machine, and neural network). The study used three independent data sets that included wrist-worn accelerometer data. For each data set, a four-step classification framework consisting of data preprocessing, feature extraction, normalization and feature selection, and classifier training and testing was implemented. For the custom ensemble, decisions from the single classifiers were aggregated using three decision fusion methods: weighted majority vote, naïve Bayes combination, and behavior knowledge space combination. Classifiers were cross-validated using leave-one subject out cross-validation and compared on the basis of average F1 scores. In all three data sets, ensemble learning methods consistently outperformed the individual classifiers. Among the conventional ensemble methods, random forest models provided consistently high activity recognition; however, the custom ensemble model using weighted majority voting demonstrated the highest classification accuracy in two of the three data sets. Combining multiple individual classifiers using conventional or custom ensemble learning methods can improve activity recognition accuracy from wrist-worn accelerometer data.

  19. Adaptive calibration of (u,v)‐wind ensemble forecasts

    DEFF Research Database (Denmark)

    Pinson, Pierre

    2012-01-01

    of sufficient reliability. The original framework introduced here allows for an adaptive bivariate calibration of these ensemble forecasts. The originality of this methodology lies in the fact that calibrated ensembles still consist of a set of (space–time) trajectories, after translation and dilation...... of translation and dilation factors are discussed. Copyright © 2012 Royal Meteorological Society...

  20. Preferences of and Attitudes toward Treble Choral Ensembles

    Science.gov (United States)

    Wilson, Jill M.

    2012-01-01

    In choral ensembles, a pursuit where females far outnumber males, concern exists that females are being devalued. Attitudes of female choral singers may be negatively affected by the gender imbalance that exists in mixed choirs and by the placement of the mixed choir as the most select ensemble in a program. The purpose of this research was to…

  1. Carbon flux bias estimation employing Maximum Likelihood Ensemble Filter (MLEF)

    NARCIS (Netherlands)

    Zupanski, Dusanka; Denning, A. Scott; Uliasz, Marek; Zupanski, Milija; Schuh, Andrew E.; Rayner, Peter J.; Peters, Wouter; Corbin, Katherine D.

    2007-01-01

    We evaluate the capability of an ensemble based data assimilation approach, referred to as Maximum Likelihood Ensemble Filter (MLEF), to estimate biases in the CO2 photosynthesis and respiration fluxes. We employ an off-line Lagrangian Particle Dispersion Model (LPDM), which is driven by the carbon

  2. An iterative ensemble Kalman filter for reservoir engineering applications

    NARCIS (Netherlands)

    Krymskaya, M.V.; Hanea, R.G.; Verlaan, M.

    2009-01-01

    The study has been focused on examining the usage and the applicability of ensemble Kalman filtering techniques to the history matching procedures. The ensemble Kalman filter (EnKF) is often applied nowadays to solving such a problem. Meanwhile, traditional EnKF requires assumption of the

  3. Probabilistic Determination of Native State Ensembles of Proteins

    DEFF Research Database (Denmark)

    Olsson, Simon; Vögeli, Beat Rolf; Cavalli, Andrea

    2014-01-01

    ensembles of proteins by the combination of physical force fields and experimental data through modern statistical methodology. As an example, we use NMR residual dipolar couplings to determine a native state ensemble of the extensively studied third immunoglobulin binding domain of protein G (GB3...

  4. Effect of lateral boundary perturbations on the breeding method and the local ensemble transform Kalman filter for mesoscale ensemble prediction

    Directory of Open Access Journals (Sweden)

    Kazuo Saito

    2012-01-01

    Full Text Available The effect of lateral boundary perturbations (LBPs on the mesoscale breeding (MBD method and the local ensemble transform Kalman filter (LETKF as the initial perturbations generators for mesoscale ensemble prediction systems (EPSs was examined. A LBPs method using the Japan Meteorological Agency's (JMA's operational one-week global ensemble prediction was developed and applied to the mesoscale EPS of the Meteorological Research Institute for the World Weather Research Programme, Beijing 2008 Olympics Research and Development Project. The amplitude of the LBPs was adjusted based on the ensemble spread statistics considering the difference of the forecast times of the JMA's one-week EPS and the associated breeding/ensemble Kalman filter (EnKF cycles. LBPs in the ensemble forecast increase the ensemble spread and improve the accuracy of the ensemble mean forecast. In the MBD method, if LBPs were introduced in its breeding cycles, the growth rate of the generated bred vectors is increased, and the ensemble spread and the root mean square errors (RMSEs of the ensemble mean are further improved in the ensemble forecast. With LBPs in the breeding cycles, positional correspondences to the meteorological disturbances and the orthogonality of the bred vectors are improved. Brier Skill Scores (BSSs also showed a remarkable effect of LBPs in the breeding cycles. LBPs showed a similar effect with the LETKF. If LBPs were introduced in the EnKF data assimilation cycles, the ensemble spread, ensemble mean accuracy, and BSSs for precipitation were improved, although the relative advantage of LETKF as the initial perturbations generator against MDB was not necessarily clear. LBPs in the EnKF cycles contribute not to the orthogonalisation but to prevent the underestimation of the forecast error near the lateral boundary.The accuracy of the LETKF analyses was compared with that of the mesoscale 4D-VAR analyses. With LBPs in the LETKF cycles, the RMSEs of the

  5. Certainty and Uncertainty in Convection Permitting Modelling: Downscaling the CESM Large Ensemble

    Science.gov (United States)

    Gutmann, E. D.; Ikeda, K.; Clark, M. P.; Rasmussen, R.; Arnold, J.; Deser, C.

    2016-12-01

    Uncertainty may be uncomfortable, but certainty is absurd. This is particularly true for climate projections because we cannot hope to perform the sort of verification that is performed for other uncertain predictions such as weather forecasting. Here we approach the problem by examining the fundamental uncertainty in the climate system driven by chaotic internal variability. To do this, we downscaled six members of the Community Earth System Model Large Ensemble (CESM-LE) using the Weather Research and Forecasting model (WRF) run at convection permitting (4km grid) scales. These simulations are performed for the period Oct. 1990 - Oct. 1999 and Oct. 2071 - Oct. 2080 over the Colorado Headwaters region, and comparisons in current climate are made to observations from the SNOTEL network. Despite differences in the CESM ensemble members, the WRF simulations show consistent seasonal trends in elevation dependent warming; however the trends in elevation dependent precipitation show no consistent patterns. In addition, one ensemble member, which had a large increase in precipitation, showed a much weaker warming response because the snow albedo feedback permitted this member to stay cooler near the surface in the mountains. This suggests that consistent land surface temperature feedbacks in WRF provide some increases in certainty that are missing for precipitation. We then examine changes in extreme precipitation events in the WRF simulations. Finally, we have also downscaled all 40 members of CESM-LE using the Intermediate Complexity Atmospheric Research model (ICAR). ICAR is used to examine changes in cool-season orographic precipitation using two different microphysics schemes. ICAR and WRF provide similar results for the six ensemble members downscaled with WRF, while ICAR provides the capability to examine the full range of uncertainty, including some uncertainty due to the choice of model physics. We compute both the mean change in WRF, CESM and ICAR, as well as

  6. A Kalman-filter bias correction of ozone deterministic, ensemble-averaged, and probabilistic forecasts

    Energy Technology Data Exchange (ETDEWEB)

    Monache, L D; Grell, G A; McKeen, S; Wilczak, J; Pagowski, M O; Peckham, S; Stull, R; McHenry, J; McQueen, J

    2006-03-20

    Kalman filtering (KF) is used to postprocess numerical-model output to estimate systematic errors in surface ozone forecasts. It is implemented with a recursive algorithm that updates its estimate of future ozone-concentration bias by using past forecasts and observations. KF performance is tested for three types of ozone forecasts: deterministic, ensemble-averaged, and probabilistic forecasts. Eight photochemical models were run for 56 days during summer 2004 over northeastern USA and southern Canada as part of the International Consortium for Atmospheric Research on Transport and Transformation New England Air Quality (AQ) Study. The raw and KF-corrected predictions are compared with ozone measurements from the Aerometric Information Retrieval Now data set, which includes roughly 360 surface stations. The completeness of the data set allowed a thorough sensitivity test of key KF parameters. It is found that the KF improves forecasts of ozone-concentration magnitude and the ability to predict rare events, both for deterministic and ensemble-averaged forecasts. It also improves the ability to predict the daily maximum ozone concentration, and reduces the time lag between the forecast and observed maxima. For this case study, KF considerably improves the predictive skill of probabilistic forecasts of ozone concentration greater than thresholds of 10 to 50 ppbv, but it degrades it for thresholds of 70 to 90 ppbv. Moreover, KF considerably reduces probabilistic forecast bias. The significance of KF postprocessing and ensemble-averaging is that they are both effective for real-time AQ forecasting. KF reduces systematic errors, whereas ensemble-averaging reduces random errors. When combined they produce the best overall forecast.

  7. Ensemble sea ice forecast for predicting compressive situations in the Baltic Sea

    Science.gov (United States)

    Lehtiranta, Jonni; Lensu, Mikko; Kokkonen, Iiro; Haapala, Jari

    2017-04-01

    Forecasting of sea ice hazards is important for winter shipping in the Baltic Sea. In current numerical models the ice thickness distribution and drift are captured well, but compressive situations are often missing from forecast products. Its inclusion is requested by the shipping community, as compression poses a threat to ship operations. As compressing ice is capable of stopping ships for days and even damaging them, its inclusion in ice forecasts is vital. However, we have found that compression can not be predicted well in a deterministic forecast, since it can be a local and a quickly changing phenomenon. It is also very sensitive to small changes in the wind speed and direction, the prevailing ice conditions, and the model parameters. Thus, a probabilistic ensemble simulation is needed to produce a meaningful compression forecast. An ensemble model setup was developed in the SafeWIN project for this purpose. It uses the HELMI multicategory ice model, which was amended for making simulations in parallel. The ensemble was built by perturbing the atmospheric forcing and the physical parameters of the ice pack. The model setup will provide probabilistic forecasts for the compression in the Baltic sea ice. Additionally the model setup provides insight into the uncertainties related to different model parameters and their impact on the model results. We have completed several hindcast simulations for the Baltic Sea for verification purposes. These results are shown to match compression reports gathered from ships. In addition, an ensemble forecast is in preoperational testing phase and its first evaluation will be presented in this work.

  8. Data assimilation the ensemble Kalman filter

    CERN Document Server

    Evensen, Geir

    2007-01-01

    Data Assimilation comprehensively covers data assimilation and inverse methods, including both traditional state estimation and parameter estimation. This text and reference focuses on various popular data assimilation methods, such as weak and strong constraint variational methods and ensemble filters and smoothers. It is demonstrated how the different methods can be derived from a common theoretical basis, as well as how they differ and/or are related to each other, and which properties characterize them, using several examples. Rather than emphasize a particular discipline such as oceanography or meteorology, it presents the mathematical framework and derivations in a way which is common for any discipline where dynamics is merged with measurements. The mathematics level is modest, although it requires knowledge of basic spatial statistics, Bayesian statistics, and calculus of variations. Readers will also appreciate the introduction to the mathematical methods used and detailed derivations, which should b...

  9. Global Optimization Ensemble Model for Classification Methods

    Directory of Open Access Journals (Sweden)

    Hina Anwar

    2014-01-01

    Full Text Available Supervised learning is the process of data mining for deducing rules from training datasets. A broad array of supervised learning algorithms exists, every one of them with its own advantages and drawbacks. There are some basic issues that affect the accuracy of classifier while solving a supervised learning problem, like bias-variance tradeoff, dimensionality of input space, and noise in the input data space. All these problems affect the accuracy of classifier and are the reason that there is no global optimal method for classification. There is not any generalized improvement method that can increase the accuracy of any classifier while addressing all the problems stated above. This paper proposes a global optimization ensemble model for classification methods (GMC that can improve the overall accuracy for supervised learning problems. The experimental results on various public datasets showed that the proposed model improved the accuracy of the classification models from 1% to 30% depending upon the algorithm complexity.

  10. Dynamical Engineering of Interactions in Qudit Ensembles

    Science.gov (United States)

    Choi, Soonwon; Yao, Norman Y.; Lukin, Mikhail D.

    2017-11-01

    We propose and analyze a method to engineer effective interactions in an ensemble of d -level systems (qudits) driven by global control fields. In particular, we present (i) a necessary and sufficient condition under which a given interaction can be decoupled, (ii) the existence of a universal sequence that decouples any (cancelable) interaction, and (iii) an efficient algorithm to engineer a target Hamiltonian from an initial Hamiltonian (if possible). We illustrate the potential of this method with two examples. Specifically, we present a 6-pulse sequence that decouples effective spin-1 dipolar interactions and demonstrate that a spin-1 Ising chain can be engineered to study transitions among three distinct symmetry protected topological phases. Our work enables new approaches for the realization of both many-body quantum memories and programmable analog quantum simulators using existing experimental platforms.

  11. Dynamic principle for ensemble control tools

    Science.gov (United States)

    Samoletov, A.; Vasiev, B.

    2017-11-01

    Dynamical equations describing physical systems in contact with a thermal bath are commonly extended by mathematical tools called "thermostats." These tools are designed for sampling ensembles in statistical mechanics. Here we propose a dynamic principle underlying a range of thermostats which is derived using fundamental laws of statistical physics and ensures invariance of the canonical measure. The principle covers both stochastic and deterministic thermostat schemes. Our method has a clear advantage over a range of proposed and widely used thermostat schemes that are based on formal mathematical reasoning. Following the derivation of the proposed principle, we show its generality and illustrate its applications including design of temperature control tools that differ from the Nosé-Hoover-Langevin scheme.

  12. Atmospheric neutrinos

    Energy Technology Data Exchange (ETDEWEB)

    Kajita, Takaaki [Research Center for Cosmic Neutrinos, Institute for Cosmic Ray Research, University of Tokyo, Kashiwa-no-ha 5-1-5, Kashiwa, Chiba 277-8582 (Japan)

    2004-12-01

    Neutrino oscillation was discovered through the study of atmospheric neutrinos. Atmospheric neutrinos are produced as decay products in hadronic showers resulting from collisions of cosmic rays with nuclei in the atmosphere. Electron neutrinos and muon neutrinos are produced mainly by the decay chain of charged pions to muons and electrons. Depending on the energy of the neutrinos, atmospheric neutrinos are observed as fully contained events, partially contained events and upward-going muon events. The energy range covered by these events is from a few hundred MeV to >1 TeV. Data from various experiments showed zenith angle- and energy-dependent deficit of {nu}{sub {mu}} events, while {nu}{sub e} events did not show any such effect. It was also shown that the {nu}{sub {mu}} survival probability obeys the sinusoidal function as predicted by neutrino oscillations. Two-flavour {nu}{sub {mu}} {r_reversible} {nu}{sub {tau}} oscillations, with sin{sup 2} 2{theta} > 0.90 and {delta}m{sup 2} in the region of 1.9 x 10{sup -3} to 3.0 x 10{sup -3} eV{sup 2}, explain all these data. Various detailed studies using high statistics atmospheric neutrino data excluded the alternative hypotheses that were proposed to explain the {nu}{sub {mu}} deficit.

  13. The Road to DLCZ Protocol in Rubidium Ensemble

    Science.gov (United States)

    Li, Chang; Pu, Yunfei; Jiang, Nan; Chang, Wei; Zhang, Sheng; Center for Quantum Information, Institute for Interdisciplinary Information Sciences, Tsinghua Univ Team

    2017-04-01

    Quantum communication is the powerful approach achieving a fully secure information transferal. The DLCZ protocol ensures that photon linearly decays with transferring distance increasing, which improves the success potential and shortens the time to build up an entangled channel. Apart from that, it provides an advanced idea that building up a quantum internet based on different nodes connected to different sites and themselves. In our laboratory, three sets of laser-cooled Rubidium 87 ensemble have been built. Two of them serve as the single photon emitter, which generate the entanglement between ensemble and photon. What's more, crossed AODs are equipped to multiplex and demultiplex optical circuit so that ensemble is divided into 2 hundred of 2D sub-memory cells. And the third ensemble is used as quantum telecommunication, which converts 780nm photon into telecom-wavelength one. And we have been building double-MOT system, which provides more atoms in ensemble and larger optical density.

  14. An educational model for ensemble streamflow simulation and uncertainty analysis

    Directory of Open Access Journals (Sweden)

    A. AghaKouchak

    2013-02-01

    Full Text Available This paper presents the hands-on modeling toolbox, HBV-Ensemble, designed as a complement to theoretical hydrology lectures, to teach hydrological processes and their uncertainties. The HBV-Ensemble can be used for in-class lab practices and homework assignments, and assessment of students' understanding of hydrological processes. Using this modeling toolbox, students can gain more insights into how hydrological processes (e.g., precipitation, snowmelt and snow accumulation, soil moisture, evapotranspiration and runoff generation are interconnected. The educational toolbox includes a MATLAB Graphical User Interface (GUI and an ensemble simulation scheme that can be used for teaching uncertainty analysis, parameter estimation, ensemble simulation and model sensitivity. HBV-Ensemble was administered in a class for both in-class instruction and a final project, and students submitted their feedback about the toolbox. The results indicate that this educational software had a positive impact on students understanding and knowledge of uncertainty in hydrological modeling.

  15. Multicomponent ensemble models to forecast induced seismicity

    Science.gov (United States)

    Király-Proag, E.; Gischig, V.; Zechar, J. D.; Wiemer, S.

    2018-01-01

    In recent years, human-induced seismicity has become a more and more relevant topic due to its economic and social implications. Several models and approaches have been developed to explain underlying physical processes or forecast induced seismicity. They range from simple statistical models to coupled numerical models incorporating complex physics. We advocate the need for forecast testing as currently the best method for ascertaining if models are capable to reasonably accounting for key physical governing processes—or not. Moreover, operational forecast models are of great interest to help on-site decision-making in projects entailing induced earthquakes. We previously introduced a standardized framework following the guidelines of the Collaboratory for the Study of Earthquake Predictability, the Induced Seismicity Test Bench, to test, validate, and rank induced seismicity models. In this study, we describe how to construct multicomponent ensemble models based on Bayesian weightings that deliver more accurate forecasts than individual models in the case of Basel 2006 and Soultz-sous-Forêts 2004 enhanced geothermal stimulation projects. For this, we examine five calibrated variants of two significantly different model groups: (1) Shapiro and Smoothed Seismicity based on the seismogenic index, simple modified Omori-law-type seismicity decay, and temporally weighted smoothed seismicity; (2) Hydraulics and Seismicity based on numerically modelled pore pressure evolution that triggers seismicity using the Mohr-Coulomb failure criterion. We also demonstrate how the individual and ensemble models would perform as part of an operational Adaptive Traffic Light System. Investigating seismicity forecasts based on a range of potential injection scenarios, we use forecast periods of different durations to compute the occurrence probabilities of seismic events M ≥ 3. We show that in the case of the Basel 2006 geothermal stimulation the models forecast hazardous levels

  16. Atmospheric thermodynamics

    CERN Document Server

    Iribarne, J V

    1973-01-01

    The thermodynamics of the atmosphere is the subject of several chapters in most textbooks on dynamic meteorology, but there is no work in English to give the subject a specific and more extensive treatment. In writing the present textbook, we have tried to fill this rather remarkable gap in the literature related to atmospheric sciences. Our aim has been to provide students of meteorology with a book that can playa role similar to the textbooks on chemical thermodynamics for the chemists. This implies a previous knowledge of general thermodynamics, such as students acquire in general physics courses; therefore, although the basic principles are reviewed (in the first four chapters), they are only briefly discussed, and emphasis is laid on those topics that will be useful in later chapters, through their application to atmospheric problems. No attempt has been made to introduce the thermodynamics of irreversible processes; on the other hand, consideration of heterogeneous and open homogeneous systems permits a...

  17. Robust Ensemble Filtering and Its Relation to Covariance Inflation in the Ensemble Kalman Filter

    KAUST Repository

    Luo, Xiaodong

    2011-12-01

    A robust ensemble filtering scheme based on the H∞ filtering theory is proposed. The optimal H∞ filter is derived by minimizing the supremum (or maximum) of a predefined cost function, a criterion different from the minimum variance used in the Kalman filter. By design, the H∞ filter is more robust than the Kalman filter, in the sense that the estimation error in the H∞ filter in general has a finite growth rate with respect to the uncertainties in assimilation, except for a special case that corresponds to the Kalman filter. The original form of the H∞ filter contains global constraints in time, which may be inconvenient for sequential data assimilation problems. Therefore a variant is introduced that solves some time-local constraints instead, and hence it is called the time-local H∞ filter (TLHF). By analogy to the ensemble Kalman filter (EnKF), the concept of ensemble time-local H∞ filter (EnTLHF) is also proposed. The general form of the EnTLHF is outlined, and some of its special cases are discussed. In particular, it is shown that an EnKF with certain covariance inflation is essentially an EnTLHF. In this sense, the EnTLHF provides a general framework for conducting covariance inflation in the EnKF-based methods. Some numerical examples are used to assess the relative robustness of the TLHF–EnTLHF in comparison with the corresponding KF–EnKF method.

  18. Assessing the impact of land use change on hydrology by ensemble modelling (LUCHEM) II: Ensemble combinations and predictions

    Science.gov (United States)

    Viney, N.R.; Bormann, H.; Breuer, L.; Bronstert, A.; Croke, B.F.W.; Frede, H.; Graff, T.; Hubrechts, L.; Huisman, J.A.; Jakeman, A.J.; Kite, G.W.; Lanini, J.; Leavesley, G.; Lettenmaier, D.P.; Lindstrom, G.; Seibert, J.; Sivapalan, M.; Willems, P.

    2009-01-01

    This paper reports on a project to compare predictions from a range of catchment models applied to a mesoscale river basin in central Germany and to assess various ensemble predictions of catchment streamflow. The models encompass a large range in inherent complexity and input requirements. In approximate order of decreasing complexity, they are DHSVM, MIKE-SHE, TOPLATS, WASIM-ETH, SWAT, PRMS, SLURP, HBV, LASCAM and IHACRES. The models are calibrated twice using different sets of input data. The two predictions from each model are then combined by simple averaging to produce a single-model ensemble. The 10 resulting single-model ensembles are combined in various ways to produce multi-model ensemble predictions. Both the single-model ensembles and the multi-model ensembles are shown to give predictions that are generally superior to those of their respective constituent models, both during a 7-year calibration period and a 9-year validation period. This occurs despite a considerable disparity in performance of the individual models. Even the weakest of models is shown to contribute useful information to the ensembles they are part of. The best model combination methods are a trimmed mean (constructed using the central four or six predictions each day) and a weighted mean ensemble (with weights calculated from calibration performance) that places relatively large weights on the better performing models. Conditional ensembles, in which separate model weights are used in different system states (e.g. summer and winter, high and low flows) generally yield little improvement over the weighted mean ensemble. However a conditional ensemble that discriminates between rising and receding flows shows moderate improvement. An analysis of ensemble predictions shows that the best ensembles are not necessarily those containing the best individual models. Conversely, it appears that some models that predict well individually do not necessarily combine well with other models in

  19. Investigation of short-term effective radiative forcing of fire aerosols over North America using nudged hindcast ensembles

    Directory of Open Access Journals (Sweden)

    Y. Liu

    2018-01-01

    Full Text Available Aerosols from fire emissions can potentially have large impact on clouds and radiation. However, fire aerosol sources are often intermittent, and their effect on weather and climate is difficult to quantify. Here we investigated the short-term effective radiative forcing of fire aerosols using the global aerosol–climate model Community Atmosphere Model version 5 (CAM5. Different from previous studies, we used nudged hindcast ensembles to quantify the forcing uncertainty due to the chaotic response to small perturbations in the atmosphere state. Daily mean emissions from three fire inventories were used to consider the uncertainty in emission strength and injection heights. The simulated aerosol optical depth (AOD and mass concentrations were evaluated against in situ measurements and reanalysis data. Overall, the results show the model has reasonably good predicting skills. Short (10-day nudged ensemble simulations were then performed with and without fire emissions to estimate the effective radiative forcing. Results show fire aerosols have large effects on both liquid and ice clouds over the two selected regions in April 2009. Ensemble mean results show strong negative shortwave cloud radiative effect (SCRE over almost the entirety of southern Mexico, with a 10-day regional mean value of −3.0 W m−2. Over the central US, the SCRE is positive in the north but negative in the south, and the regional mean SCRE is small (−0.56 W m−2. For the 10-day average, we found a large ensemble spread of regional mean shortwave cloud radiative effect over southern Mexico (15.6 % of the corresponding ensemble mean and the central US (64.3 %, despite the regional mean AOD time series being almost indistinguishable during the 10-day period. Moreover, the ensemble spread is much larger when using daily averages instead of 10-day averages. This demonstrates the importance of using a large ensemble of simulations to estimate the short

  20. Ensemble Kalman filtering without the intrinsic need for inflation

    Directory of Open Access Journals (Sweden)

    M. Bocquet

    2011-10-01

    Full Text Available The main intrinsic source of error in the ensemble Kalman filter (EnKF is sampling error. External sources of error, such as model error or deviations from Gaussianity, depend on the dynamical properties of the model. Sampling errors can lead to instability of the filter which, as a consequence, often requires inflation and localization. The goal of this article is to derive an ensemble Kalman filter which is less sensitive to sampling errors. A prior probability density function conditional on the forecast ensemble is derived using Bayesian principles. Even though this prior is built upon the assumption that the ensemble is Gaussian-distributed, it is different from the Gaussian probability density function defined by the empirical mean and the empirical error covariance matrix of the ensemble, which is implicitly used in traditional EnKFs. This new prior generates a new class of ensemble Kalman filters, called finite-size ensemble Kalman filter (EnKF-N. One deterministic variant, the finite-size ensemble transform Kalman filter (ETKF-N, is derived. It is tested on the Lorenz '63 and Lorenz '95 models. In this context, ETKF-N is shown to be stable without inflation for ensemble size greater than the model unstable subspace dimension, at the same numerical cost as the ensemble transform Kalman filter (ETKF. One variant of ETKF-N seems to systematically outperform the ETKF with optimally tuned inflation. However it is shown that ETKF-N does not account for all sampling errors, and necessitates localization like any EnKF, whenever the ensemble size is too small. In order to explore the need for inflation in this small ensemble size regime, a local version of the new class of filters is defined (LETKF-N and tested on the Lorenz '95 toy model. Whatever the size of the ensemble, the filter is stable. Its performance without inflation is slightly inferior to that of LETKF with optimally tuned inflation for small interval between updates, and

  1. Assessing the variation in the predictability of South African seasonal climate with HadRM3P ensemble spreads

    Science.gov (United States)

    Abiodun Lawal, Kamoru; Stone, Daithi A.; Abiodun, Babatunde J.

    2013-04-01

    This study assesses the existence of trends and inter-annual variations in the predictability of the South African seasonal climate using a quasi-stochastic atmospheric system. The paper analyze the ensemble spread of a 50-year HadRM3P model simulations (ranging from 30 to 1000 simulations per month) with different initial conditions for monthly near surface air temperature and rainfall. It quantifies the ensemble spreads of the simulations using the standard deviation (sensitive to outliers) and the distance between 90th and 10th percentiles (Range of Possibility, less sensitive to outliers). Inter-annual variability and trends of standard deviation and range of possibility are then investigated and compared. Spatial-temporal variabilities of standard deviation and range of possibility indicate significantly negative trends in the ensemble spreads for precipitation majorly over inland provinces, from late austral spring to mid austral summer. Temperature exhibits coastal-inland provinces dichotomy, as both metrics of scale show negative trends over the coastal provinces in austral spring and early periods of austral summer. Negative trends of the spatial-temporal variations of the ensemble width implies narrowing of the range of possible predicted values, reduction in uncertainties within the prediction, and the existence of convergent systems in two or more similar states which may become progressively more similar. There exist some agreements between the standard deviation and range of possibility as 0.55 Africa.

  2. A study on reducing update frequency of the forecast samples in the ensemble-based 4DVar data assimilation method

    Directory of Open Access Journals (Sweden)

    Aimei Shao

    2013-02-01

    Full Text Available In the ensemble-based four dimensional variational assimilation method (SVD-En4DVar, a singular value decomposition (SVD technique is used to select the leading eigenvectors and the analysis variables are expressed as the orthogonal bases expansion of the eigenvectors. The experiments with a two-dimensional shallow-water equation model and simulated observations show that the truncation error and rejection of observed signals due to the reduced-dimensional reconstruction of the analysis variable are the major factors that damage the analysis when the ensemble size is not large enough. However, a larger-sized ensemble is daunting computational burden. Experiments with a shallow-water equation model also show that the forecast error covariances remain relatively constant over time. For that reason, we propose an approach that increases the members of the forecast ensemble while reducing the update frequency of the forecast error covariance in order to increase analysis accuracy and to reduce the computational cost. A series of experiments were conducted with the shallow-water equation model to test the efficiency of this approach. The experimental results indicate that this approach is promising. Further experiments with the WRF model show that this approach is also suitable for the real atmospheric data assimilation problem, but the update frequency of the forecast error covariances should not be too low.

  3. Alarming atmospheres

    DEFF Research Database (Denmark)

    Højlund, Marie; Kinch, Sofie

    2014-01-01

    Nurses working in the Neuro-Intensive Care Unit at Aarhus University Hospital lack the tools to prepare children for the alarming atmosphere they will enter when visiting a hospitalised relative. The complex soundscape dominated by alarms and sounds from equipment is mentioned as the main stressor...

  4. Atmospheric humidity

    Science.gov (United States)

    Water vapor plays a critical role in earth's atmosphere. It helps to maintain a habitable surface temperature through absorption of outgoing longwave radiation, and it transfers trmendous amounts of energy from the tropics toward the poles by absorbing latent heat during evaporation and subsequently...

  5. Ensemble prediction of air quality using the WRF/CMAQ model system for health effect studies in China

    Directory of Open Access Journals (Sweden)

    J. Hu

    2017-11-01

    Full Text Available Accurate exposure estimates are required for health effect analyses of severe air pollution in China. Chemical transport models (CTMs are widely used to provide spatial distribution, chemical composition, particle size fractions, and source origins of air pollutants. The accuracy of air quality predictions in China is greatly affected by the uncertainties of emission inventories. The Community Multiscale Air Quality (CMAQ model with meteorological inputs from the Weather Research and Forecasting (WRF model were used in this study to simulate air pollutants in China in 2013. Four simulations were conducted with four different anthropogenic emission inventories, including the Multi-resolution Emission Inventory for China (MEIC, the Emission Inventory for China by School of Environment at Tsinghua University (SOE, the Emissions Database for Global Atmospheric Research (EDGAR, and the Regional Emission inventory in Asia version 2 (REAS2. Model performance of each simulation was evaluated against available observation data from 422 sites in 60 cities across China. Model predictions of O3 and PM2.5 generally meet the model performance criteria, but performance differences exist in different regions, for different pollutants, and among inventories. Ensemble predictions were calculated by linearly combining the results from different inventories to minimize the sum of the squared errors between the ensemble results and the observations in all cities. The ensemble concentrations show improved agreement with observations in most cities. The mean fractional bias (MFB and mean fractional errors (MFEs of the ensemble annual PM2.5 in the 60 cities are −0.11 and 0.24, respectively, which are better than the MFB (−0.25 to −0.16 and MFE (0.26–0.31 of individual simulations. The ensemble annual daily maximum 1 h O3 (O3-1h concentrations are also improved, with mean normalized bias (MNB of 0.03 and mean normalized errors (MNE of 0.14, compared to MNB

  6. Ensemble prediction of air quality using the WRF/CMAQ model system for health effect studies in China

    Science.gov (United States)

    Hu, Jianlin; Li, Xun; Huang, Lin; Ying, Qi; Zhang, Qiang; Zhao, Bin; Wang, Shuxiao; Zhang, Hongliang

    2017-11-01

    Accurate exposure estimates are required for health effect analyses of severe air pollution in China. Chemical transport models (CTMs) are widely used to provide spatial distribution, chemical composition, particle size fractions, and source origins of air pollutants. The accuracy of air quality predictions in China is greatly affected by the uncertainties of emission inventories. The Community Multiscale Air Quality (CMAQ) model with meteorological inputs from the Weather Research and Forecasting (WRF) model were used in this study to simulate air pollutants in China in 2013. Four simulations were conducted with four different anthropogenic emission inventories, including the Multi-resolution Emission Inventory for China (MEIC), the Emission Inventory for China by School of Environment at Tsinghua University (SOE), the Emissions Database for Global Atmospheric Research (EDGAR), and the Regional Emission inventory in Asia version 2 (REAS2). Model performance of each simulation was evaluated against available observation data from 422 sites in 60 cities across China. Model predictions of O3 and PM2.5 generally meet the model performance criteria, but performance differences exist in different regions, for different pollutants, and among inventories. Ensemble predictions were calculated by linearly combining the results from different inventories to minimize the sum of the squared errors between the ensemble results and the observations in all cities. The ensemble concentrations show improved agreement with observations in most cities. The mean fractional bias (MFB) and mean fractional errors (MFEs) of the ensemble annual PM2.5 in the 60 cities are -0.11 and 0.24, respectively, which are better than the MFB (-0.25 to -0.16) and MFE (0.26-0.31) of individual simulations. The ensemble annual daily maximum 1 h O3 (O3-1h) concentrations are also improved, with mean normalized bias (MNB) of 0.03 and mean normalized errors (MNE) of 0.14, compared to MNB of 0.06-0.19 and

  7. A modified iterative ensemble Kalman filter data assimilation method

    Science.gov (United States)

    Xu, Baoxiong; Bai, Yulong; Wang, Yizhao; Li, Zhe; Ma, Boyang

    2017-08-01

    High nonlinearity is a typical characteristic associated with data assimilation systems. Additionally, iterative ensemble based methods have attracted a large amount of research attention, which has been focused on dealing with nonlinearity problems. To solve the local convergence problem of the iterative ensemble Kalman filter, a modified iterative ensemble Kalman filter algorithm was put forward, which was based on a global convergence strategy from the perspective of a Gauss-Newton iteration. Through self-adaption, the step factor was adjusted to enable every iteration to approach expected values during the process of the data assimilation. A sensitivity experiment was carried out in a low dimensional Lorenz-63 chaotic system, as well as a Lorenz-96 model. The new method was tested via ensemble size, observation variance, and inflation factor changes, along with other aspects. Meanwhile, comparative research was conducted with both a traditional ensemble Kalman filter and an iterative ensemble Kalman filter. The results showed that the modified iterative ensemble Kalman filter algorithm was a data assimilation method that was able to effectively estimate a strongly nonlinear system state.

  8. Mixed learning algorithms and features ensemble in hepatotoxicity prediction.

    Science.gov (United States)

    Liew, Chin Yee; Lim, Yen Ching; Yap, Chun Wei

    2011-09-01

    Drug-induced liver injury, although infrequent, is an important safety concern that can lead to fatality in patients and failure in drug developments. In this study, we have used an ensemble of mixed learning algorithms and mixed features for the development of a model to predict hepatic effects. This robust method is based on the premise that no single learning algorithm is optimum for all modelling problems. An ensemble model of 617 base classifiers was built from a diverse set of 1,087 compounds. The ensemble model was validated internally with five-fold cross-validation and 25 rounds of y-randomization. In the external validation of 120 compounds, the ensemble model had achieved an accuracy of 75.0%, sensitivity of 81.9% and specificity of 64.6%. The model was also able to identify 22 of 23 withdrawn drugs or drugs with black box warning against hepatotoxicity. Dronedarone which is associated with severe liver injuries, announced in a recent FDA drug safety communication, was predicted as hepatotoxic by the ensemble model. It was found that the ensemble model was capable of classifying positive compounds (with hepatic effects) well, but less so on negatives compounds when they were structurally similar. The ensemble model built in this study is made available for public use. © Springer Science+Business Media B.V. 2011

  9. An Efficient Ensemble Learning Method for Gene Microarray Classification

    Directory of Open Access Journals (Sweden)

    Alireza Osareh

    2013-01-01

    Full Text Available The gene microarray analysis and classification have demonstrated an effective way for the effective diagnosis of diseases and cancers. However, it has been also revealed that the basic classification techniques have intrinsic drawbacks in achieving accurate gene classification and cancer diagnosis. On the other hand, classifier ensembles have received increasing attention in various applications. Here, we address the gene classification issue using RotBoost ensemble methodology. This method is a combination of Rotation Forest and AdaBoost techniques which in turn preserve both desirable features of an ensemble architecture, that is, accuracy and diversity. To select a concise subset of informative genes, 5 different feature selection algorithms are considered. To assess the efficiency of the RotBoost, other nonensemble/ensemble techniques including Decision Trees, Support Vector Machines, Rotation Forest, AdaBoost, and Bagging are also deployed. Experimental results have revealed that the combination of the fast correlation-based feature selection method with ICA-based RotBoost ensemble is highly effective for gene classification. In fact, the proposed method can create ensemble classifiers which outperform not only the classifiers produced by the conventional machine learning but also the classifiers generated by two widely used conventional ensemble learning methods, that is, Bagging and AdaBoost.

  10. An efficient ensemble learning method for gene microarray classification.

    Science.gov (United States)

    Osareh, Alireza; Shadgar, Bita

    2013-01-01

    The gene microarray analysis and classification have demonstrated an effective way for the effective diagnosis of diseases and cancers. However, it has been also revealed that the basic classification techniques have intrinsic drawbacks in achieving accurate gene classification and cancer diagnosis. On the other hand, classifier ensembles have received increasing attention in various applications. Here, we address the gene classification issue using RotBoost ensemble methodology. This method is a combination of Rotation Forest and AdaBoost techniques which in turn preserve both desirable features of an ensemble architecture, that is, accuracy and diversity. To select a concise subset of informative genes, 5 different feature selection algorithms are considered. To assess the efficiency of the RotBoost, other nonensemble/ensemble techniques including Decision Trees, Support Vector Machines, Rotation Forest, AdaBoost, and Bagging are also deployed. Experimental results have revealed that the combination of the fast correlation-based feature selection method with ICA-based RotBoost ensemble is highly effective for gene classification. In fact, the proposed method can create ensemble classifiers which outperform not only the classifiers produced by the conventional machine learning but also the classifiers generated by two widely used conventional ensemble learning methods, that is, Bagging and AdaBoost.

  11. A multiphysical ensemble system of numerical snow modelling

    Science.gov (United States)

    Lafaysse, Matthieu; Cluzet, Bertrand; Dumont, Marie; Lejeune, Yves; Vionnet, Vincent; Morin, Samuel

    2017-05-01

    Physically based multilayer snowpack models suffer from various modelling errors. To represent these errors, we built the new multiphysical ensemble system ESCROC (Ensemble System Crocus) by implementing new representations of different physical processes in the deterministic coupled multilayer ground/snowpack model SURFEX/ISBA/Crocus. This ensemble was driven and evaluated at Col de Porte (1325 m a.s.l., French alps) over 18 years with a high-quality meteorological and snow data set. A total number of 7776 simulations were evaluated separately, accounting for the uncertainties of evaluation data. The ability of the ensemble to capture the uncertainty associated to modelling errors is assessed for snow depth, snow water equivalent, bulk density, albedo and surface temperature. Different sub-ensembles of the ESCROC system were studied with probabilistic tools to compare their performance. Results show that optimal members of the ESCROC system are able to explain more than half of the total simulation errors. Integrating members with biases exceeding the range corresponding to observational uncertainty is necessary to obtain an optimal dispersion, but this issue can also be a consequence of the fact that meteorological forcing uncertainties were not accounted for. The ESCROC system promises the integration of numerical snow-modelling errors in ensemble forecasting and ensemble assimilation systems in support of avalanche hazard forecasting and other snowpack-modelling applications.

  12. Progressive freezing of interacting spins in isolated finite magnetic ensembles

    Science.gov (United States)

    Bhattacharya, Kakoli; Dupuis, Veronique; Le-Roy, Damien; Deb, Pritam

    2017-02-01

    Self-organization of magnetic nanoparticles into secondary nanostructures provides an innovative way for designing functional nanomaterials with novel properties, different from the constituent primary nanoparticles as well as their bulk counterparts. Collective magnetic properties of such complex closed packing of magnetic nanoparticles makes them more appealing than the individual magnetic nanoparticles in many technological applications. This work reports the collective magnetic behaviour of magnetic ensembles comprising of single domain Fe3O4 nanoparticles. The present work reveals that the ensemble formation is based on the re-orientation and attachment of the nanoparticles in an iso-oriented fashion at the mesoscale regime. Comprehensive dc magnetic measurements show the prevalence of strong interparticle interactions in the ensembles. Due to the close range organization of primary Fe3O4 nanoparticles in the ensemble, the spins of the individual nanoparticles interact through dipolar interactions as realized from remnant magnetization measurements. Signature of super spin glass like behaviour in the ensembles is observed in the memory studies carried out in field cooled conditions. Progressive freezing of spins in the ensembles is corroborated from the Vogel-Fulcher fit of the susceptibility data. Dynamic scaling of relaxation reasserted slow spin dynamics substantiating cluster spin glass like behaviour in the ensembles.

  13. Selecting a climate model subset to optimise key ensemble properties

    Directory of Open Access Journals (Sweden)

    N. Herger

    2018-02-01

    Full Text Available End users studying impacts and risks caused by human-induced climate change are often presented with large multi-model ensembles of climate projections whose composition and size are arbitrarily determined. An efficient and versatile method that finds a subset which maintains certain key properties from the full ensemble is needed, but very little work has been done in this area. Therefore, users typically make their own somewhat subjective subset choices and commonly use the equally weighted model mean as a best estimate. However, different climate model simulations cannot necessarily be regarded as independent estimates due to the presence of duplicated code and shared development history. Here, we present an efficient and flexible tool that makes better use of the ensemble as a whole by finding a subset with improved mean performance compared to the multi-model mean while at the same time maintaining the spread and addressing the problem of model interdependence. Out-of-sample skill and reliability are demonstrated using model-as-truth experiments. This approach is illustrated with one set of optimisation criteria but we also highlight the flexibility of cost functions, depending on the focus of different users. The technique is useful for a range of applications that, for example, minimise present-day bias to obtain an accurate ensemble mean, reduce dependence in ensemble spread, maximise future spread, ensure good performance of individual models in an ensemble, reduce the ensemble size while maintaining important ensemble characteristics, or optimise several of these at the same time. As in any calibration exercise, the final ensemble is sensitive to the metric, observational product, and pre-processing steps used.

  14. Ensembles modeling approach to study Climate Change impacts on Wheat

    Science.gov (United States)

    Ahmed, Mukhtar; Claudio, Stöckle O.; Nelson, Roger; Higgins, Stewart

    2017-04-01

    Simulations of crop yield under climate variability are subject to uncertainties, and quantification of such uncertainties is essential for effective use of projected results in adaptation and mitigation strategies. In this study we evaluated the uncertainties related to crop-climate models using five crop growth simulation models (CropSyst, APSIM, DSSAT, STICS and EPIC) and 14 general circulation models (GCMs) for 2 representative concentration pathways (RCP) of atmospheric CO2 (4.5 and 8.5 W m-2) in the Pacific Northwest (PNW), USA. The aim was to assess how different process-based crop models could be used accurately for estimation of winter wheat growth, development and yield. Firstly, all models were calibrated for high rainfall, medium rainfall, low rainfall and irrigated sites in the PNW using 1979-2010 as the baseline period. Response variables were related to farm management and soil properties, and included crop phenology, leaf area index (LAI), biomass and grain yield of winter wheat. All five models were run from 2000 to 2100 using the 14 GCMs and 2 RCPs to evaluate the effect of future climate (rainfall, temperature and CO2) on winter wheat phenology, LAI, biomass, grain yield and harvest index. Simulated time to flowering and maturity was reduced in all models except EPIC with some level of uncertainty. All models generally predicted an increase in biomass and grain yield under elevated CO2 but this effect was more prominent under rainfed conditions than irrigation. However, there was uncertainty in the simulation of crop phenology, biomass and grain yield under 14 GCMs during three prediction periods (2030, 2050 and 2070). We concluded that to improve accuracy and consistency in simulating wheat growth dynamics and yield under a changing climate, a multimodel ensemble approach should be used.

  15. Atomic clock ensemble in space (ACES) data analysis

    Science.gov (United States)

    Meynadier, F.; Delva, P.; le Poncin-Lafitte, C.; Guerlin, C.; Wolf, P.

    2018-02-01

    The Atomic Clocks Ensemble in Space (ACES/PHARAO mission, ESA & CNES) will be installed on board the International Space Station (ISS) next year. A crucial part of this experiment is its two-way microwave link (MWL), which will compare the timescale generated on board with those provided by several ground stations disseminated on the Earth. A dedicated data analysis center is being implemented at SYRTE—Observatoire de Paris, where our team currently develops theoretical modelling, numerical simulations and the data analysis software itself. In this paper, we present some key aspects of the MWL measurement method and the associated algorithms for simulations and data analysis. We show the results of tests using simulated data with fully realistic effects such as fundamental measurement noise, Doppler, atmospheric delays, or cycle ambiguities. We demonstrate satisfactory performance of the software with respect to the specifications of the ACES mission. The main scientific product of our analysis is the clock desynchronisation between ground and space clocks, i.e. the difference of proper times between the space clocks and ground clocks at participating institutes. While in flight, this measurement will allow for tests of general relativity and Lorentz invariance at unprecedented levels, e.g. measurement of the gravitational redshift at the 3×10-6 level. As a specific example, we use real ISS orbit data with estimated errors at the 10 m level to study the effect of such errors on the clock desynchronisation obtained from MWL data. We demonstrate that the resulting effects are totally negligible.

  16. Ensemble Deep Learning for Biomedical Time Series Classification.

    Science.gov (United States)

    Jin, Lin-Peng; Dong, Jun

    2016-01-01

    Ensemble learning has been proved to improve the generalization ability effectively in both theory and practice. In this paper, we briefly outline the current status of research on it first. Then, a new deep neural network-based ensemble method that integrates filtering views, local views, distorted views, explicit training, implicit training, subview prediction, and Simple Average is proposed for biomedical time series classification. Finally, we validate its effectiveness on the Chinese Cardiovascular Disease Database containing a large number of electrocardiogram recordings. The experimental results show that the proposed method has certain advantages compared to some well-known ensemble methods, such as Bagging and AdaBoost.

  17. Ensemble Deep Learning for Biomedical Time Series Classification

    Directory of Open Access Journals (Sweden)

    Lin-peng Jin

    2016-01-01

    Full Text Available Ensemble learning has been proved to improve the generalization ability effectively in both theory and practice. In this paper, we briefly outline the current status of research on it first. Then, a new deep neural network-based ensemble method that integrates filtering views, local views, distorted views, explicit training, implicit training, subview prediction, and Simple Average is proposed for biomedical time series classification. Finally, we validate its effectiveness on the Chinese Cardiovascular Disease Database containing a large number of electrocardiogram recordings. The experimental results show that the proposed method has certain advantages compared to some well-known ensemble methods, such as Bagging and AdaBoost.

  18. Atmospheric materiality

    DEFF Research Database (Denmark)

    Wieczorek, Izabela

    2016-01-01

    A disjunction between the material and the immaterial has been at the heart of the architectural debate for decades. In this dialectic tension, the notion of atmosphere which increasingly claims attention in architectural discourse seems to be parallactic, leading to the re-evaluation of perceptual...... experience and, consequently, to the conceptual and methodological shifts in the production of space, and hence in the way we think about materiality. In this context, architectural space is understood as a contingent construction – a space of engagement that appears to us as a result of continuous...... and complex interferences revealed through our perception; ‘the atmospheric’ is explored as a spatial and affective quality as well as a sensory background, and materiality as a powerful and almost magical agency in shaping of atmosphere. Challenging existing dichotomies and unraveling intrinsic...

  19. An ensemble of eddy-permitting global ocean reanalyses from the MyOcean project

    Science.gov (United States)

    Masina, Simona; Storto, Andrea; Ferry, Nicolas; Valdivieso, Maria; Haines, Keith; Balmaseda, Magdalena; Zuo, Hao; Drevillon, Marie; Parent, Laurent

    2017-08-01

    A set of four eddy-permitting global ocean reanalyses produced in the framework of the MyOcean project have been compared over the altimetry period 1993-2011. The main differences among the reanalyses used here come from the data assimilation scheme implemented to control the ocean state by inserting reprocessed observations of sea surface temperature (SST), in situ temperature and salinity profiles, sea level anomaly and sea-ice concentration. A first objective of this work includes assessing the interannual variability and trends for a series of parameters, usually considered in the community as essential ocean variables: SST, sea surface salinity, temperature and salinity averaged over meaningful layers of the water column, sea level, transports across pre-defined sections, and sea ice parameters. The eddy-permitting nature of the global reanalyses allows also to estimate eddy kinetic energy. The results show that in general there is a good consistency between the different reanalyses. An intercomparison against experiments without data assimilation was done during the MyOcean project and we conclude that data assimilation is crucial for correctly simulating some quantities such as regional trends of sea level as well as the eddy kinetic energy. A second objective is to show that the ensemble mean of reanalyses can be evaluated as one single system regarding its reliability in reproducing the climate signals, where both variability and uncertainties are assessed through the ensemble spread and signal-to-noise ratio. The main advantage of having access to several reanalyses differing in the way data assimilation is performed is that it becomes possible to assess part of the total uncertainty. Given the fact that we use very similar ocean models and atmospheric forcing, we can conclude that the spread of the ensemble of reanalyses is mainly representative of our ability to gauge uncertainty in the assimilation methods. This uncertainty changes a lot from one ocean

  20. Evaluation of medium-range ensemble flood forecasting based on calibration strategies and ensemble methods in Lanjiang Basin, Southeast China

    Science.gov (United States)

    Liu, Li; Gao, Chao; Xuan, Weidong; Xu, Yue-Ping

    2017-11-01

    Ensemble flood forecasts by hydrological models using numerical weather prediction products as forcing data are becoming more commonly used in operational flood forecasting applications. In this study, a hydrological ensemble flood forecasting system comprised of an automatically calibrated Variable Infiltration Capacity model and quantitative precipitation forecasts from TIGGE dataset is constructed for Lanjiang Basin, Southeast China. The impacts of calibration strategies and ensemble methods on the performance of the system are then evaluated. The hydrological model is optimized by the parallel programmed ε-NSGA II multi-objective algorithm. According to the solutions by ε-NSGA II, two differently parameterized models are determined to simulate daily flows and peak flows at each of the three hydrological stations. Then a simple yet effective modular approach is proposed to combine these daily and peak flows at the same station into one composite series. Five ensemble methods and various evaluation metrics are adopted. The results show that ε-NSGA II can provide an objective determination on parameter estimation, and the parallel program permits a more efficient simulation. It is also demonstrated that the forecasts from ECMWF have more favorable skill scores than other Ensemble Prediction Systems. The multimodel ensembles have advantages over all the single model ensembles and the multimodel methods weighted on members and skill scores outperform other methods. Furthermore, the overall performance at three stations can be satisfactory up to ten days, however the hydrological errors can degrade the skill score by approximately 2 days, and the influence persists until a lead time of 10 days with a weakening trend. With respect to peak flows selected by the Peaks Over Threshold approach, the ensemble means from single models or multimodels are generally underestimated, indicating that the ensemble mean can bring overall improvement in forecasting of flows. For

  1. Ensemble estimators for multivariate entropy estimation.

    Science.gov (United States)

    Sricharan, Kumar; Wei, Dennis; Hero, Alfred O

    2013-07-01

    The problem of estimation of density functionals like entropy and mutual information has received much attention in the statistics and information theory communities. A large class of estimators of functionals of the probability density suffer from the curse of dimensionality, wherein the mean squared error (MSE) decays increasingly slowly as a function of the sample size T as the dimension d of the samples increases. In particular, the rate is often glacially slow of order O(T(-)(γ)(/)(d) ), where γ > 0 is a rate parameter. Examples of such estimators include kernel density estimators, k-nearest neighbor (k-NN) density estimators, k-NN entropy estimators, intrinsic dimension estimators and other examples. In this paper, we propose a weighted affine combination of an ensemble of such estimators, where optimal weights can be chosen such that the weighted estimator converges at a much faster dimension invariant rate of O(T(-1)). Furthermore, we show that these optimal weights can be determined by solving a convex optimization problem which can be performed offline and does not require training data. We illustrate the superior performance of our weighted estimator for two important applications: (i) estimating the Panter-Dite distortion-rate factor and (ii) estimating the Shannon entropy for testing the probability distribution of a random sample.

  2. Model error estimation in ensemble data assimilation

    Directory of Open Access Journals (Sweden)

    S. Gillijns

    2007-01-01

    Full Text Available A new methodology is proposed to estimate and account for systematic model error in linear filtering as well as in nonlinear ensemble based filtering. Our results extend the work of Dee and Todling (2000 on constant bias errors to time-varying model errors. In contrast to existing methodologies, the new filter can also deal with the case where no dynamical model for the systematic error is available. In the latter case, the applicability is limited by a matrix rank condition which has to be satisfied in order for the filter to exist. The performance of the filter developed in this paper is limited by the availability and the accuracy of observations and by the variance of the stochastic model error component. The effect of these aspects on the estimation accuracy is investigated in several numerical experiments using the Lorenz (1996 model. Experimental results indicate that the availability of a dynamical model for the systematic error significantly reduces the variance of the model error estimates, but has only minor effect on the estimates of the system state. The filter is able to estimate additive model error of any type, provided that the rank condition is satisfied and that the stochastic errors and measurement errors are significantly smaller than the systematic errors. The results of this study are encouraging. However, it remains to be seen how the filter performs in more realistic applications.

  3. Identifying complete RNA structural ensembles including pseudoknots.

    Science.gov (United States)

    Gupta, Aditi; Rahman, Reazur; Li, Kejie; Gribskov, Michael

    2012-02-01

    The close relationship between RNA structure and function underlines the significance of accurately predicting RNA structures from sequence information. Structural topologies such as pseudoknots are of particular interest due to their ubiquity and direct involvement in RNA function, but identifying pseudoknots is a computationally challenging problem and existing heuristic approaches usually perform poorly for RNA sequences of even a few hundred bases. We survey the performance of pseudoknot prediction methods on a data set of full-length RNA sequences representing varied sequence lengths, and biological RNA classes such as RNase P RNA, Group I Intron, tmRNA and tRNA. Pseudoknot prediction methods are compared with minimum free energy and suboptimal secondary structure prediction methods in terms of correct base-pairs, stems and pseudoknots and we find that the ensemble of suboptimal structure predictions succeeds in identifying correct structural elements in RNA that are usually missed in MFE and pseudoknot predictions. We propose a strategy to identify a comprehensive set of non-redundant stems in the suboptimal structure space of a RNA molecule by applying heuristics that reduce the structural redundancy of the predicted suboptimal structures by merging slightly varying stems that are predicted to form in local sequence regions. This reduced-redundancy set of structural elements consistently outperforms more specialized approaches.in data sets. Thus, the suboptimal folding space can be used to represent the structural diversity of an RNA molecule more comprehensively than optimal structure prediction approaches alone.

  4. Orchestrating Distributed Resource Ensembles for Petascale Science

    Energy Technology Data Exchange (ETDEWEB)

    Baldin, Ilya; Mandal, Anirban; Ruth, Paul; Yufeng, Xin

    2014-04-24

    Distributed, data-intensive computational science applications of interest to DOE scientific com- munities move large amounts of data for experiment data management, distributed analysis steps, remote visualization, and accessing scientific instruments. These applications need to orchestrate ensembles of resources from multiple resource pools and interconnect them with high-capacity multi- layered networks across multiple domains. It is highly desirable that mechanisms are designed that provide this type of resource provisioning capability to a broad class of applications. It is also important to have coherent monitoring capabilities for such complex distributed environments. In this project, we addressed these problems by designing an abstract API, enabled by novel semantic resource descriptions, for provisioning complex and heterogeneous resources from multiple providers using their native provisioning mechanisms and control planes: computational, storage, and multi-layered high-speed network domains. We used an extensible resource representation based on semantic web technologies to afford maximum flexibility to applications in specifying their needs. We evaluated the effectiveness of provisioning using representative data-intensive ap- plications. We also developed mechanisms for providing feedback about resource performance to the application, to enable closed-loop feedback control and dynamic adjustments to resource allo- cations (elasticity). This was enabled through development of a novel persistent query framework that consumes disparate sources of monitoring data, including perfSONAR, and provides scalable distribution of asynchronous notifications.

  5. Spontaneous avalanche dephasing in large Rydberg ensembles

    Science.gov (United States)

    Boulier, T.; Magnan, E.; Bracamontes, C.; Maslek, J.; Goldschmidt, E. A.; Young, J. T.; Gorshkov, A. V.; Rolston, S. L.; Porto, J. V.

    2017-11-01

    Strong dipole-exchange interactions due to spontaneously produced contaminant states can trigger rapid dephasing in many-body Rydberg ensembles [E. A. Goldschmidt et al., Phys. Rev. Lett. 116, 113001 (2016), 10.1103/PhysRevLett.116.113001]. Such broadening has serious implications for many proposals to coherently use Rydberg interactions, particularly Rydberg dressing proposals. The dephasing arises as a runaway process where the production of the first contaminant atoms facilitates the creation of more contaminant atoms. Here we study the time dependence of this process with stroboscopic approaches. Using a pump-probe technique, we create an excess "pump" Rydberg population and probe its effect with a different "probe" Rydberg transition. We observe a reduced resonant pumping rate and an enhancement of the excitation on both sides of the transition as atoms are added to the pump state. We also observe a time scale for population growth that is significantly shorter than predicted by homogeneous mean-field models, as expected from a clustered growth mechanism where high-order correlations dominate the dynamics. These results support earlier works and confirm that the time scale for the onset of dephasing is reduced by a factor which scales as the inverse of the atom number. In addition, we discuss several approaches to minimize these effects of spontaneous broadening, including stroboscopic techniques and operating at cryogenic temperatures. It is challenging to avoid the unwanted broadening effects, but under some conditions they can be mitigated.

  6. Asymptotic gap probability distributions of the Gaussian unitary ensembles and Jacobi unitary ensembles

    Directory of Open Access Journals (Sweden)

    Shulin Lyu

    2018-01-01

    The σ function, namely, the derivative of the log of the smallest eigenvalue distributions of the finite-n LUE or the JUE, satisfies the Jimbo–Miwa–Okamoto σ form of PV and PVI, although in the shift Jacobi case, with the weight xα(1−xβ, the β parameter does not show up in the equation. We also obtain the asymptotic expansions for the smallest eigenvalue distributions of the Laguerre unitary and Jacobi unitary ensembles after appropriate double scalings, and obtained the constants in the asymptotic expansion of the gap probabilities, expressed in term of the Barnes G-function valuated at special point.

  7. Ensemble-based forecasting at Horns Rev: Ensemble conversion and kernel dressing

    DEFF Research Database (Denmark)

    Pinson, Pierre; Madsen, Henrik

    For management and trading purposes, information on short-term wind generation (from few hours to few days ahead) is even more crucial at large offshore wind farms, since they concentrate a large capacity at a single location. The most complete information that can be provided today consists....... The obtained ensemble forecasts of wind power are then converted into predictive distributions with an original adaptive kernel dressing method. The shape of the kernels is driven by a mean-variance model, the parameters of which are recursively estimated in order to maximize the overall skill of obtained...

  8. Ensembles on configuration space classical, quantum, and beyond

    CERN Document Server

    Hall, Michael J W

    2016-01-01

    This book describes a promising approach to problems in the foundations of quantum mechanics, including the measurement problem. The dynamics of ensembles on configuration space is shown here to be a valuable tool for unifying the formalisms of classical and quantum mechanics, for deriving and extending the latter in various ways, and for addressing the quantum measurement problem. A description of physical systems by means of ensembles on configuration space can be introduced at a very fundamental level: the basic building blocks are a configuration space, probabilities, and Hamiltonian equations of motion for the probabilities. The formalism can describe both classical and quantum systems, and their thermodynamics, with the main difference being the choice of ensemble Hamiltonian. Furthermore, there is a natural way of introducing ensemble Hamiltonians that describe the evolution of hybrid systems; i.e., interacting systems that have distinct classical and quantum sectors, allowing for consistent descriptio...

  9. Representing multiple objects as an ensemble enhances visual cognition.

    Science.gov (United States)

    Alvarez, George A

    2011-03-01

    The visual system can only accurately represent a handful of objects at once. How do we cope with this severe capacity limitation? One possibility is to use selective attention to process only the most relevant incoming information. A complementary strategy is to represent sets of objects as a group or ensemble (e.g. represent the average size of items). Recent studies have established that the visual system computes accurate ensemble representations across a variety of feature domains and current research aims to determine how these representations are computed, why they are computed and where they are coded in the brain. Ensemble representations enhance visual cognition in many ways, making ensemble coding a crucial mechanism for coping with the limitations on visual processing. Copyright © 2011 Elsevier Ltd. All rights reserved.

  10. Quantum Ensemble Classification: A Sampling-Based Learning Control Approach.

    Science.gov (United States)

    Chen, Chunlin; Dong, Daoyi; Qi, Bo; Petersen, Ian R; Rabitz, Herschel

    2017-06-01

    Quantum ensemble classification (QEC) has significant applications in discrimination of atoms (or molecules), separation of isotopes, and quantum information extraction. However, quantum mechanics forbids deterministic discrimination among nonorthogonal states. The classification of inhomogeneous quantum ensembles is very challenging, since there exist variations in the parameters characterizing the members within different classes. In this paper, we recast QEC as a supervised quantum learning problem. A systematic classification methodology is presented by using a sampling-based learning control (SLC) approach for quantum discrimination. The classification task is accomplished via simultaneously steering members belonging to different classes to their corresponding target states (e.g., mutually orthogonal states). First, a new discrimination method is proposed for two similar quantum systems. Then, an SLC method is presented for QEC. Numerical results demonstrate the effectiveness of the proposed approach for the binary classification of two-level quantum ensembles and the multiclass classification of multilevel quantum ensembles.

  11. Protein Remote Homology Detection Based on an Ensemble Learning Approach.

    Science.gov (United States)

    Chen, Junjie; Liu, Bingquan; Huang, Dong

    2016-01-01

    Protein remote homology detection is one of the central problems in bioinformatics. Although some computational methods have been proposed, the problem is still far from being solved. In this paper, an ensemble classifier for protein remote homology detection, called SVM-Ensemble, was proposed with a weighted voting strategy. SVM-Ensemble combined three basic classifiers based on different feature spaces, including Kmer, ACC, and SC-PseAAC. These features consider the characteristics of proteins from various perspectives, incorporating both the sequence composition and the sequence-order information along the protein sequences. Experimental results on a widely used benchmark dataset showed that the proposed SVM-Ensemble can obviously improve the predictive performance for the protein remote homology detection. Moreover, it achieved the best performance and outperformed other state-of-the-art methods.

  12. Prediction of Weather Impacted Airport Capacity using Ensemble Learning

    Science.gov (United States)

    Wang, Yao Xun

    2011-01-01

    Ensemble learning with the Bagging Decision Tree (BDT) model was used to assess the impact of weather on airport capacities at selected high-demand airports in the United States. The ensemble bagging decision tree models were developed and validated using the Federal Aviation Administration (FAA) Aviation System Performance Metrics (ASPM) data and weather forecast at these airports. The study examines the performance of BDT, along with traditional single Support Vector Machines (SVM), for airport runway configuration selection and airport arrival rates (AAR) prediction during weather impacts. Testing of these models was accomplished using observed weather, weather forecast, and airport operation information at the chosen airports. The experimental results show that ensemble methods are more accurate than a single SVM classifier. The airport capacity ensemble method presented here can be used as a decision support model that supports air traffic flow management to meet the weather impacted airport capacity in order to reduce costs and increase safety.

  13. Influence of horizontal resolution and ensemble size on model performance

    CSIR Research Space (South Africa)

    Dalton, A

    2014-10-01

    Full Text Available model forecast fields onto observed gridded mid-summer rainfall over South Africa. Spearman rank correlations are initially used to compare the performance of models with varying horizontal resolution as well as ensemble size. Further verification...

  14. Nitrogen-vacancy ensemble magnetometry based on pump absorption

    DEFF Research Database (Denmark)

    Ahmadi, Sepehr; El-Ella, Haitham A.R.; Hansen, Jørn B.

    2017-01-01

    We demonstrate magnetic field sensing by recording the variation in the pump light absorption with nitrogen-vacancy center ensemble. At a frequency of 10 mHz we obtain a noise floor of ~30 nT/√Hz....

  15. Multiphysics ensemble-based modelling of an alpine snowpack

    Science.gov (United States)

    Lafaysse, Matthieu; Cluzet, Bertrand; Dumont, Marie; Lejeune, Yves; Vionnet, Vincent; Morin, Samuel

    2017-04-01

    Physically based multilayer snowpack models suffer from various modelling errors. It is necessary to quantify these errors in various applications including ensemble forecasting of snowpack conditions and ensemble assimilation of snowpack observations. We present here the new multi-physical ensemble system ESCROC (Ensemble System Crocus) which describes the uncertainties of snowpack modelling by new representations of different physical processes in the deterministic coupled multi-layer ground/snowpack model SURFEX/ISBA/Crocus, including 3 different options for snow metamorphism among others. This ensemble was driven and evaluated at Col de Porte (1325 m a.s.l., French alps) over 18 years with a high quality meteorological and snow dataset. 7776 simulations were evaluated separately accounting for the uncertainties of evaluation data. The ability of the ensemble to capture the uncertainty associated to modelling errors is assessed with probabilistic tools for snow depth, snow water equivalent, bulk density, albedo and surface temperature. Results show that optimal members of the ESCROC system are able to explain about 2/3 of the total simulation errors. The 3 different options of snow metamorphism can exhibit a similar skill for the evaluated variables, with a high dependency of results on the options chosen for the other physical processes (compaction, liquid water percolation, solar radiation absorption, turbulent fluxes, etc.). ESCROC is a promising system to integrate numerical snow modelling errors in ensemble forecasting and ensemble assimilation systems in support of avalanche hazard forecasting and other snowpack modelling applications. It may benefit of any future improvement in the uncertainty quantification about modelling of each specific physical process, such as snow metamorphism modelling.

  16. New vigour involving statisticians to overcome ensemble fatigue

    DEFF Research Database (Denmark)

    Benestad, Rasmus; Sillmann, Jana; Thorarinsdottir, Thordis Linda

    2017-01-01

    Climate simulation data comprise a range of different phenomena with complex and interacting processes. Yet our understanding of the climate is incomplete despite the huge volumes of data, of which only a small fraction has been explored, and many questions remain, particularly those on the chara...... contribute substantially to designing 'smarter' ensemble experiments, improving the distillation of information from ensembles, and helping interpret the relative merits of additional simulations. Future progress may be enhanced by increasing collaborations with statisticians....

  17. Dipolar atomic spin ensembles in a double-well potential

    OpenAIRE

    de Paz, A.; Naylor, B.; Carrance, J. Huckans A.; Gorceix, O.; Marechal, E.; Pedri, P.; Laburthe-Tolra, B.; Vernac, L.

    2014-01-01

    We experimentally study the spin dynamics of mesoscopic ensembles of ultracold magnetic spin-3 atoms located in two separated wells of an optical dipole trap. We use a radio-frequency sweep to selectively flip the spin of the atoms in one of the wells, which produces two separated spin domains of opposite polarization. We observe that these engineered spin domains are metastable with respect to the long-range magnetic dipolar interactions between the two ensembles. The absence of inter-cloud ...

  18. Ensemble Pruning for Glaucoma Detection in an Unbalanced Data Set.

    Science.gov (United States)

    Adler, Werner; Gefeller, Olaf; Gul, Asma; Horn, Folkert K; Khan, Zardad; Lausen, Berthold

    2016-12-07

    Random forests are successful classifier ensemble methods consisting of typically 100 to 1000 classification trees. Ensemble pruning techniques reduce the computational cost, especially the memory demand, of random forests by reducing the number of trees without relevant loss of performance or even with increased performance of the sub-ensemble. The application to the problem of an early detection of glaucoma, a severe eye disease with low prevalence, based on topographical measurements of the eye background faces specific challenges. We examine the performance of ensemble pruning strategies for glaucoma detection in an unbalanced data situation. The data set consists of 102 topographical features of the eye background of 254 healthy controls and 55 glaucoma patients. We compare the area under the receiver operating characteristic curve (AUC), and the Brier score on the total data set, in the majority class, and in the minority class of pruned random forest ensembles obtained with strategies based on the prediction accuracy of greedily grown sub-ensembles, the uncertainty weighted accuracy, and the similarity between single trees. To validate the findings and to examine the influence of the prevalence of glaucoma in the data set, we additionally perform a simulation study with lower prevalences of glaucoma. In glaucoma classification all three pruning strategies lead to improved AUC and smaller Brier scores on the total data set with sub-ensembles as small as 30 to 80 trees compared to the classification results obtained with the full ensemble consisting of 1000 trees. In the simulation study, we were able to show that the prevalence of glaucoma is a critical factor and lower prevalence decreases the performance of our pruning strategies. The memory demand for glaucoma classification in an unbalanced data situation based on random forests could effectively be reduced by the application of pruning strategies without loss of performance in a population with increased

  19. The influence of the new ECMWF Ensemble Prediction System resolution on wind power forecast accuracy and uncertainty estimation

    DEFF Research Database (Denmark)

    Alessandrini, S.; Pinson, Pierre; Sperati, S.

    2011-01-01

    Prediction System (EPS) can be used as indicator of a three-hourly, three days ahead, wind power forecast’s accuracy. In particular it has been noticed that to extract usable information from data the Ensemble members needed to be statistically calibrated, since the rank histograms for the three-day period......The importance of wind power forecasting (WPF) is nowadays commonly recognized because it represents a useful tool to reduce problems of grid integration and to facilitate energy trading. If on one side the prediction accuracy is fundamental to these scopes, on the other it has become also clear...... that a reliable estimation of their uncertainty could be a useful information too. In fact the prediction accuracy is unfortunately not constant and can depend on the location of a particular wind farm, on the forecast time and on the atmospheric situation. Previous studies indicated that the ECMWF Ensemble...

  20. Genetic programming based ensemble system for microarray data classification.

    Science.gov (United States)

    Liu, Kun-Hong; Tong, Muchenxuan; Xie, Shu-Tong; Yee Ng, Vincent To

    2015-01-01

    Recently, more and more machine learning techniques have been applied to microarray data analysis. The aim of this study is to propose a genetic programming (GP) based new ensemble system (named GPES), which can be used to effectively classify different types of cancers. Decision trees are deployed as base classifiers in this ensemble framework with three operators: Min, Max, and Average. Each individual of the GP is an ensemble system, and they become more and more accurate in the evolutionary process. The feature selection technique and balanced subsampling technique are applied to increase the diversity in each ensemble system. The final ensemble committee is selected by a forward search algorithm, which is shown to be capable of fitting data automatically. The performance of GPES is evaluated using five binary class and six multiclass microarray datasets, and results show that the algorithm can achieve better results in most cases compared with some other ensemble systems. By using elaborate base classifiers or applying other sampling techniques, the performance of GPES may be further improved.

  1. A new method for determining the optimal lagged ensemble

    Science.gov (United States)

    DelSole, T.; Tippett, M. K.; Pegion, K.

    2017-01-01

    Abstract We propose a general methodology for determining the lagged ensemble that minimizes the mean square forecast error. The MSE of a lagged ensemble is shown to depend only on a quantity called the cross‐lead error covariance matrix, which can be estimated from a short hindcast data set and parameterized in terms of analytic functions of time. The resulting parameterization allows the skill of forecasts to be evaluated for an arbitrary ensemble size and initialization frequency. Remarkably, the parameterization also can estimate the MSE of a burst ensemble simply by taking the limit of an infinitely small interval between initialization times. This methodology is applied to forecasts of the Madden Julian Oscillation (MJO) from version 2 of the Climate Forecast System version 2 (CFSv2). For leads greater than a week, little improvement is found in the MJO forecast skill when ensembles larger than 5 days are used or initializations greater than 4 times per day. We find that if the initialization frequency is too infrequent, important structures of the lagged error covariance matrix are lost. Lastly, we demonstrate that the forecast error at leads ≥10 days can be reduced by optimally weighting the lagged ensemble members. The weights are shown to depend only on the cross‐lead error covariance matrix. While the methodology developed here is applied to CFSv2, the technique can be easily adapted to other forecast systems. PMID:28580050

  2. On the forecast skill of a convection-permitting ensemble

    Science.gov (United States)

    Schellander-Gorgas, Theresa; Wang, Yong; Meier, Florian; Weidle, Florian; Wittmann, Christoph; Kann, Alexander

    2017-01-01

    The 2.5 km convection-permitting (CP) ensemble AROME-EPS (Applications of Research to Operations at Mesoscale - Ensemble Prediction System) is evaluated by comparison with the regional 11 km ensemble ALADIN-LAEF (Aire Limitée Adaption dynamique Développement InterNational - Limited Area Ensemble Forecasting) to show whether a benefit is provided by a CP EPS. The evaluation focuses on the abilities of the ensembles to quantitatively predict precipitation during a 3-month convective summer period over areas consisting of mountains and lowlands. The statistical verification uses surface observations and 1 km × 1 km precipitation analyses, and the verification scores involve state-of-the-art statistical measures for deterministic and probabilistic forecasts as well as novel spatial verification methods. The results show that the convection-permitting ensemble with higher-resolution AROME-EPS outperforms its mesoscale counterpart ALADIN-LAEF for precipitation forecasts. The positive impact is larger for the mountainous areas than for the lowlands. In particular, the diurnal precipitation cycle is improved in AROME-EPS, which leads to a significant improvement of scores at the concerned times of day (up to approximately one-third of the scored verification measure). Moreover, there are advantages for higher precipitation thresholds at small spatial scales, which are due to the improved simulation of the spatial structure of precipitation.

  3. Impact of ensemble learning in the assessment of skeletal maturity.

    Science.gov (United States)

    Cunha, Pedro; Moura, Daniel C; Guevara López, Miguel Angel; Guerra, Conceição; Pinto, Daniela; Ramos, Isabel

    2014-09-01

    The assessment of the bone age, or skeletal maturity, is an important task in pediatrics that measures the degree of maturation of children's bones. Nowadays, there is no standard clinical procedure for assessing bone age and the most widely used approaches are the Greulich and Pyle and the Tanner and Whitehouse methods. Computer methods have been proposed to automatize the process; however, there is a lack of exploration about how to combine the features of the different parts of the hand, and how to take advantage of ensemble techniques for this purpose. This paper presents a study where the use of ensemble techniques for improving bone age assessment is evaluated. A new computer method was developed that extracts descriptors for each joint of each finger, which are then combined using different ensemble schemes for obtaining a final bone age value. Three popular ensemble schemes are explored in this study: bagging, stacking and voting. Best results were achieved by bagging with a rule-based regression (M5P), scoring a mean absolute error of 10.16 months. Results show that ensemble techniques improve the prediction performance of most of the evaluated regression algorithms, always achieving best or comparable to best results. Therefore, the success of the ensemble methods allow us to conclude that their use may improve computer-based bone age assessment, offering a scalable option for utilizing multiple regions of interest and combining their output.

  4. Effects of ensembles on methane hydrate nucleation kinetics.

    Science.gov (United States)

    Zhang, Zhengcai; Liu, Chan-Juan; Walsh, Matthew R; Guo, Guang-Jun

    2016-06-21

    By performing molecular dynamics simulations to form a hydrate with a methane nano-bubble in liquid water at 250 K and 50 MPa, we report how different ensembles, such as the NPT, NVT, and NVE ensembles, affect the nucleation kinetics of the methane hydrate. The nucleation trajectories are monitored using the face-saturated incomplete cage analysis (FSICA) and the mutually coordinated guest (MCG) order parameter (OP). The nucleation rate and the critical nucleus are obtained using the mean first-passage time (MFPT) method based on the FS cages and the MCG-1 OPs, respectively. The fitting results of MFPT show that hydrate nucleation and growth are coupled together, consistent with the cage adsorption hypothesis which emphasizes that the cage adsorption of methane is a mechanism for both hydrate nucleation and growth. For the three different ensembles, the hydrate nucleation rate is quantitatively ordered as follows: NPT > NVT > NVE, while the sequence of hydrate crystallinity is exactly reversed. However, the largest size of the critical nucleus appears in the NVT ensemble, rather than in the NVE ensemble. These results are helpful for choosing a suitable ensemble when to study hydrate formation via computer simulations, and emphasize the importance of the order degree of the critical nucleus.

  5. Monthly Ensembles in Algal Bloom Predictions on the Baltic Sea

    Science.gov (United States)

    Roiha, Petra; Westerlund, Antti; Stipa, Tapani

    2010-05-01

    In this work we explore the statistical features of monthly ensembles and their capability to predict biogeochemical conditions in the Baltic Sea. Operational marine environmental modelling has been considered hard, and consequently there are very few operational ecological models. Operational modelling of harmful algal blooms is harder still, since it is difficult to separate the algal species in models, and in general, very little is known of HAB properties. We present results of an ensemble approach to HAB forecasting in the Baltic, and discuss the applicability of the forecasting method to biochemical modelling. It turns out that HABs are indeed possible to forecast with useful accuracy. For modelling the algal blooms in Baltic Sea we used FMI operational 3-dimensional biogeochemical model to produce seasonal ensemble forecasts for different physical, chemical and biological variables. The modelled variables were temperature, salinity, velocity, silicate, phosphate, nitrate, diatoms, flagellates and two species of potentially toxic filamentous cyanobacteria nodularia spumigena and aphanizomenon flos-aquae. In this work we concentrate to the latter two. Ensembles were produced by running the biogeochemical model several times and forcing it on every run with different set of seasonal weather parameters from ECMWF's mathematically perturbed ensemble prediction forecasts. The ensembles were then analysed by statistical methods and the median, quartiles, minimum and maximum values were calculated for estimating the probable amounts of algae. Validation for the forecast method was made by comparing the final results against available and valid in-situ HAB data.

  6. Evolutionary Ensemble for In Silico Prediction of Ames Test Mutagenicity

    Science.gov (United States)

    Chen, Huanhuan; Yao, Xin

    Driven by new regulations and animal welfare, the need to develop in silico models has increased recently as alternative approaches to safety assessment of chemicals without animal testing. This paper describes a novel machine learning ensemble approach to building an in silico model for the prediction of the Ames test mutagenicity, one of a battery of the most commonly used experimental in vitro and in vivo genotoxicity tests for safety evaluation of chemicals. Evolutionary random neural ensemble with negative correlation learning (ERNE) [1] was developed based on neural networks and evolutionary algorithms. ERNE combines the method of bootstrap sampling on training data with the method of random subspace feature selection to ensure diversity in creating individuals within an initial ensemble. Furthermore, while evolving individuals within the ensemble, it makes use of the negative correlation learning, enabling individual NNs to be trained as accurate as possible while still manage to maintain them as diverse as possible. Therefore, the resulting individuals in the final ensemble are capable of cooperating collectively to achieve better generalization of prediction. The empirical experiment suggest that ERNE is an effective ensemble approach for predicting the Ames test mutagenicity of chemicals.

  7. Genetic Programming Based Ensemble System for Microarray Data Classification

    Directory of Open Access Journals (Sweden)

    Kun-Hong Liu

    2015-01-01

    Full Text Available Recently, more and more machine learning techniques have been applied to microarray data analysis. The aim of this study is to propose a genetic programming (GP based new ensemble system (named GPES, which can be used to effectively classify different types of cancers. Decision trees are deployed as base classifiers in this ensemble framework with three operators: Min, Max, and Average. Each individual of the GP is an ensemble system, and they become more and more accurate in the evolutionary process. The feature selection technique and balanced subsampling technique are applied to increase the diversity in each ensemble system. The final ensemble committee is selected by a forward search algorithm, which is shown to be capable of fitting data automatically. The performance of GPES is evaluated using five binary class and six multiclass microarray datasets, and results show that the algorithm can achieve better results in most cases compared with some other ensemble systems. By using elaborate base classifiers or applying other sampling techniques, the performance of GPES may be further improved.

  8. Ocean heat content variability in an ensemble of twentieth century ocean reanalyses

    Science.gov (United States)

    de Boisséson, Eric; Balmaseda, Magdalena Alonso; Mayer, Michael

    2017-08-01

    This paper presents a ten-member ensemble of twentieth century Ocean ReAnalyses called ORA-20C. ORA-20C assimilates temperature and salinity profiles and is forced by the ECMWF twentieth century atmospheric reanalysis (ERA-20C) over the 1900-2010 period. This study attempts to identify robust signals of ocean heat content change in ORA-20C and detect contamination by model errors, initial condition uncertainty, surface fluxes and observing system changes. It is shown that ORA-20C trends and variability in the first part of the century result from the surface fluxes and model drift towards a warmer mean state and weak meridional overturning circulation. The impact of the observing system in correcting the mean state causes the deceleration of the warming trend and alters the long-term climate signal. The ensemble spread reflects the long-lasting memory of the initial conditions and the convergence of the system to a solution compatible with surface fluxes, the ocean model and observational constraints. Observations constrain the ocean heat uptake trend in the last decades of the twentieth century, which is similar to trend estimations from the post-satellite era. An ocean heat budget analysis attributes ORA-20C heat content changes to surface fluxes in the first part of the century. The heat flux variability reflects spurious signals stemming from ERA-20C surface fields, which in return result from changes in the atmospheric observing system. The influence of the temperature assimilation increments on the heat budget is growing with time. Increments control the most recent ocean heat uptake signals, highlighting imbalances in forced reanalysis systems in the ocean as well as in the atmosphere.

  9. EnsembleGASVR: A novel ensemble method for classifying missense single nucleotide polymorphisms

    KAUST Repository

    Rapakoulia, Trisevgeni

    2014-04-26

    Motivation: Single nucleotide polymorphisms (SNPs) are considered the most frequently occurring DNA sequence variations. Several computational methods have been proposed for the classification of missense SNPs to neutral and disease associated. However, existing computational approaches fail to select relevant features by choosing them arbitrarily without sufficient documentation. Moreover, they are limited to the problem ofmissing values, imbalance between the learning datasets and most of them do not support their predictions with confidence scores. Results: To overcome these limitations, a novel ensemble computational methodology is proposed. EnsembleGASVR facilitates a twostep algorithm, which in its first step applies a novel evolutionary embedded algorithm to locate close to optimal Support Vector Regression models. In its second step, these models are combined to extract a universal predictor, which is less prone to overfitting issues, systematizes the rebalancing of the learning sets and uses an internal approach for solving the missing values problem without loss of information. Confidence scores support all the predictions and the model becomes tunable by modifying the classification thresholds. An extensive study was performed for collecting the most relevant features for the problem of classifying SNPs, and a superset of 88 features was constructed. Experimental results show that the proposed framework outperforms well-known algorithms in terms of classification performance in the examined datasets. Finally, the proposed algorithmic framework was able to uncover the significant role of certain features such as the solvent accessibility feature, and the top-scored predictions were further validated by linking them with disease phenotypes. © The Author 2014.

  10. Crossover between the Gaussian orthogonal ensemble, the Gaussian unitary ensemble, and Poissonian statistics.

    Science.gov (United States)

    Schweiner, Frank; Laturner, Jeanine; Main, Jörg; Wunner, Günter

    2017-11-01

    Until now only for specific crossovers between Poissonian statistics (P), the statistics of a Gaussian orthogonal ensemble (GOE), or the statistics of a Gaussian unitary ensemble (GUE) have analytical formulas for the level spacing distribution function been derived within random matrix theory. We investigate arbitrary crossovers in the triangle between all three statistics. To this aim we propose an according formula for the level spacing distribution function depending on two parameters. Comparing the behavior of our formula for the special cases of P→GUE, P→GOE, and GOE→GUE with the results from random matrix theory, we prove that these crossovers are described reasonably. Recent investigations by F. Schweiner et al. [Phys. Rev. E 95, 062205 (2017)2470-004510.1103/PhysRevE.95.062205] have shown that the Hamiltonian of magnetoexcitons in cubic semiconductors can exhibit all three statistics in dependence on the system parameters. Evaluating the numerical results for magnetoexcitons in dependence on the excitation energy and on a parameter connected with the cubic valence band structure and comparing the results with the formula proposed allows us to distinguish between regular and chaotic behavior as well as between existent or broken antiunitary symmetries. Increasing one of the two parameters, transitions between different crossovers, e.g., from the P→GOE to the P→GUE crossover, are observed and discussed.

  11. Ensemble Kalman filter versus ensemble smoother for assessing hydraulic conductivity via tracer test data assimilation

    Directory of Open Access Journals (Sweden)

    E. Crestani

    2013-04-01

    Full Text Available Estimating the spatial variability of hydraulic conductivity K in natural aquifers is important for predicting the transport of dissolved compounds. Especially in the nonreactive case, the plume evolution is mainly controlled by the heterogeneity of K. At the local scale, the spatial distribution of K can be inferred by combining the Lagrangian formulation of the transport with a Kalman-filter-based technique and assimilating a sequence of time-lapse concentration C measurements, which, for example, can be evaluated on site through the application of a geophysical method. The objective of this work is to compare the ensemble Kalman filter (EnKF and the ensemble smoother (ES capabilities to retrieve the hydraulic conductivity spatial distribution in a groundwater flow and transport modeling framework. The application refers to a two-dimensional synthetic aquifer in which a tracer test is simulated. Moreover, since Kalman-filter-based methods are optimal only if each of the involved variables fit to a Gaussian probability density function (pdf and since this condition may not be met by some of the flow and transport state variables, issues related to the non-Gaussianity of the variables are analyzed and different transformation of the pdfs are considered in order to evaluate their influence on the performance of the methods. The results show that the EnKF reproduces with good accuracy the hydraulic conductivity field, outperforming the ES regardless of the pdf of the concentrations.

  12. Application of ensemble H-infinity filter in aquifer characterization and comparison to ensemble Kalman filter

    Directory of Open Access Journals (Sweden)

    Tong-chao Nan

    2017-01-01

    Full Text Available Though the ensemble Kalman filter (EnKF has been successfully applied in many areas, it requires explicit and accurate model and measurement error information, leading to difficulties in practice when only limited information on error mechanisms of observational instruments for subsurface systems is accessible. To handle the uncertain errors, we applied a robust data assimilation algorithm, the ensemble H-infinity filter (EnHF, to estimation of aquifer hydraulic heads and conductivities in a flow model with uncertain/correlated observational errors. The impacts of spatial and temporal correlations in measurements were analyzed, and the performance of EnHF was compared with that of the EnKF. The results show that both EnHF and EnKF are able to estimate hydraulic conductivities properly when observations are free of error; EnHF can provide robust estimates of hydraulic conductivities even when no observational error information is provided. In contrast, the estimates of EnKF seem noticeably undermined because of correlated errors and inaccurate error statistics, and filter divergence was observed. It is concluded that EnHF is an efficient assimilation algorithm when observational errors are unknown or error statistics are inaccurate.

  13. Probabilistic versus deterministic skill in predicting the western North Pacific-East Asian summer monsoon variability with multimodel ensembles

    Science.gov (United States)

    Yang, Xiu-Qun; Yang, Dejian; Xie, Qian; Zhang, Yaocun; Ren, Xuejuan; Tang, Youmin

    2017-04-01

    Based on historical forecasts of three quasi-operational multi-model ensemble (MME) systems, this study assesses the superiority of coupled MME over contributing single-model ensembles (SMEs) and over uncoupled atmospheric MME in predicting the Western North Pacific-East Asian summer monsoon variability. The probabilistic and deterministic forecast skills are measured by Brier skill score (BSS) and anomaly correlation (AC), respectively. A forecast-format dependent MME superiority over SMEs is found. The probabilistic forecast skill of the MME is always significantly better than that of each SME, while the deterministic forecast skill of the MME can be lower than that of some SMEs. The MME superiority arises from both the model diversity and the ensemble size increase in the tropics, and primarily from the ensemble size increase in the subtropics. The BSS is composed of reliability and resolution, two attributes characterizing probabilistic forecast skill. The probabilistic skill increase of the MME is dominated by the dramatic improvement in reliability, while resolution is not always improved, similar to AC. A monotonic resolution-AC relationship is further found and qualitatively explained, whereas little relationship can be identified between reliability and AC. It is argued that the MME's success in improving the reliability arises from an effective reduction of the overconfidence in forecast distributions. Moreover, it is examined that the seasonal predictions with coupled MME are more skillful than those with the uncoupled atmospheric MME forced by persisting sea surface temperature (SST) anomalies, since the coupled MME has better predicted the SST anomaly evolution in three key regions.

  14. A real-time evaluation and demonstration of strategies for 'Over-The-Loop' ensemble streamflow forecasting in US watersheds

    Science.gov (United States)

    Wood, Andy; Clark, Elizabeth; Mendoza, Pablo; Nijssen, Bart; Newman, Andy; Clark, Martyn; Nowak, Kenneth; Arnold, Jeffrey

    2017-04-01

    Many if not most national operational streamflow prediction systems rely on a forecaster-in-the-loop approach that require the hands-on-effort of an experienced human forecaster. This approach evolved from the need to correct for long-standing deficiencies in the models and datasets used in forecasting, and the practice often leads to skillful flow predictions despite the use of relatively simple, conceptual models. Yet the 'in-the-loop' forecast process is not reproducible, which limits opportunities to assess and incorporate new techniques systematically, and the effort required to make forecasts in this way is an obstacle to expanding forecast services - e.g., though adding new forecast locations or more frequent forecast updates, running more complex models, or producing forecast and hindcasts that can support verification. In the last decade, the hydrologic forecasting community has begun develop more centralized, 'over-the-loop' systems. The quality of these new forecast products will depend on their ability to leverage research in areas including earth system modeling, parameter estimation, data assimilation, statistical post-processing, weather and climate prediction, verification, and uncertainty estimation through the use of ensembles. Currently, many national operational streamflow forecasting and water management communities have little experience with the strengths and weaknesses of over-the-loop approaches, even as such systems are beginning to be deployed operationally in centers such as ECMWF. There is thus a need both to evaluate these forecasting advances and to demonstrate their potential in a public arena, raising awareness in forecast user communities and development programs alike. To address this need, the US National Center for Atmospheric Research is collaborating with the University of Washington, the Bureau of Reclamation and the US Army Corps of Engineers, using the NCAR 'System for Hydromet Analysis Research and Prediction Applications

  15. Three-dimensional visualization of ensemble weather forecasts – Part 2: Forecasting warm conveyor belt situations for aircraft-based field campaigns

    Directory of Open Access Journals (Sweden)

    M. Rautenhaus

    2015-07-01

    Full Text Available We present the application of interactive three-dimensional (3-D visualization of ensemble weather predictions to forecasting warm conveyor belt situations during aircraft-based atmospheric research campaigns. Motivated by forecast requirements of the T-NAWDEX-Falcon 2012 (THORPEX – North Atlantic Waveguide and Downstream Impact Experiment campaign, a method to predict 3-D probabilities of the spatial occurrence of warm conveyor belts (WCBs has been developed. Probabilities are derived from Lagrangian particle trajectories computed on the forecast wind fields of the European Centre for Medium Range Weather Forecasts (ECMWF ensemble prediction system. Integration of the method into the 3-D ensemble visualization tool Met.3D, introduced in the first part of this study, facilitates interactive visualization of WCB features and derived probabilities in the context of the ECMWF ensemble forecast. We investigate the sensitivity of the method with respect to trajectory seeding and grid spacing of the forecast wind field. Furthermore, we propose a visual analysis method to quantitatively analyse the contribution of ensemble members to a probability region and, thus, to assist the forecaster in interpreting the obtained probabilities. A case study, revisiting a forecast case from T-NAWDEX-Falcon, illustrates the practical application of Met.3D and demonstrates the use of 3-D and uncertainty visualization for weather forecasting and for planning flight routes in the medium forecast range (3 to 7 days before take-off.

  16. Combining 2-m temperature nowcasting and short range ensemble forecasting

    Directory of Open Access Journals (Sweden)

    A. Kann

    2011-12-01

    Full Text Available During recent years, numerical ensemble prediction systems have become an important tool for estimating the uncertainties of dynamical and physical processes as represented in numerical weather models. The latest generation of limited area ensemble prediction systems (LAM-EPSs allows for probabilistic forecasts at high resolution in both space and time. However, these systems still suffer from systematic deficiencies. Especially for nowcasting (0–6 h applications the ensemble spread is smaller than the actual forecast error. This paper tries to generate probabilistic short range 2-m temperature forecasts by combining a state-of-the-art nowcasting method and a limited area ensemble system, and compares the results with statistical methods. The Integrated Nowcasting Through Comprehensive Analysis (INCA system, which has been in operation at the Central Institute for Meteorology and Geodynamics (ZAMG since 2006 (Haiden et al., 2011, provides short range deterministic forecasts at high temporal (15 min–60 min and spatial (1 km resolution. An INCA Ensemble (INCA-EPS of 2-m temperature forecasts is constructed by applying a dynamical approach, a statistical approach, and a combined dynamic-statistical method. The dynamical method takes uncertainty information (i.e. ensemble variance from the operational limited area ensemble system ALADIN-LAEF (Aire Limitée Adaptation Dynamique Développement InterNational Limited Area Ensemble Forecasting which is running operationally at ZAMG (Wang et al., 2011. The purely statistical method assumes a well-calibrated spread-skill relation and applies ensemble spread according to the skill of the INCA forecast of the most recent past. The combined dynamic-statistical approach adapts the ensemble variance gained from ALADIN-LAEF with non-homogeneous Gaussian regression (NGR which yields a statistical mbox{correction} of the first and second moment (mean bias and dispersion for Gaussian distributed continuous

  17. LEARNING PARSIMONIOUS ENSEMBLES FOR UNBALANCED COMPUTATIONAL GENOMICS PROBLEMS

    Science.gov (United States)

    STANESCU, ANA; PANDEY, GAURAV

    2016-01-01

    Prediction problems in biomedical sciences are generally quite difficult, partially due to incomplete knowledge of how the phenomenon of interest is influenced by the variables and measurements used for prediction, as well as a lack of consensus regarding the ideal predictor(s) for specific problems. In these situations, a powerful approach to improving prediction performance is to construct ensembles that combine the outputs of many individual base predictors, which have been successful for many biomedical prediction tasks. Moreover, selecting a parsimonious ensemble can be of even greater value for biomedical sciences, where it is not only important to learn an accurate predictor, but also to interpret what novel knowledge it can provide about the target problem. Ensemble selection is a promising approach for this task because of its ability to select a collectively predictive subset, often a relatively small one, of all input base predictors. One of the most well-known algorithms for ensemble selection, CES (Caruana et al.’s Ensemble Selection), generally performs well in practice, but faces several challenges due to the difficulty of choosing the right values of its various parameters. Since the choices made for these parameters are usually ad-hoc, good performance of CES is difficult to guarantee for a variety of problems or datasets. To address these challenges with CES and other such algorithms, we propose a novel heterogeneous ensemble selection approach based on the paradigm of reinforcement learning (RL), which offers a more systematic and mathematically sound methodology for exploring the many possible combinations of base predictors that can be selected into an ensemble. We develop three RL-based strategies for constructing ensembles and analyze their results on two unbalanced computational genomics problems, namely the prediction of protein function and splice sites in eukaryotic genomes. We show that the resultant ensembles are indeed substantially

  18. Developing an Ensemble Prediction System based on COSMO-DE

    Science.gov (United States)

    Theis, S.; Gebhardt, C.; Buchhold, M.; Ben Bouallègue, Z.; Ohl, R.; Paulat, M.; Peralta, C.

    2010-09-01

    The numerical weather prediction model COSMO-DE is a configuration of the COSMO model with a horizontal grid size of 2.8 km. It has been running operationally at DWD since 2007, it covers the area of Germany and produces forecasts with a lead time of 0-21 hours. The model COSMO-DE is convection-permitting, which means that it does without a parametrisation of deep convection and simulates deep convection explicitly. One aim is an improved forecast of convective heavy rain events. Convection-permitting models are in operational use at several weather services, but currently not in ensemble mode. It is expected that an ensemble system could reveal the advantages of a convection-permitting model even better. The probabilistic approach is necessary, because the explicit simulation of convective processes for more than a few hours cannot be viewed as a deterministic forecast anymore. This is due to the chaotic behaviour and short life cycle of the processes which are simulated explicitly now. In the framework of the project COSMO-DE-EPS, DWD is developing and implementing an ensemble prediction system (EPS) for the model COSMO-DE. The project COSMO-DE-EPS comprises the generation of ensemble members, as well as the verification and visualization of the ensemble forecasts and also statistical postprocessing. A pre-operational mode of the EPS with 20 ensemble members is foreseen to start in 2010. Operational use is envisaged to start in 2012, after an upgrade to 40 members and inclusion of statistical postprocessing. The presentation introduces the project COSMO-DE-EPS and describes the design of the ensemble as it is planned for the pre-operational mode. In particular, the currently implemented method for the generation of ensemble members will be explained and discussed. The method includes variations of initial conditions, lateral boundary conditions, and model physics. At present, pragmatic methods are applied which resemble the basic ideas of a multi-model approach

  19. Particle Swarm Optimization Based Selective Ensemble of Online Sequential Extreme Learning Machine

    OpenAIRE

    Yang Liu; Bo He; Diya Dong; Yue Shen,; Tianhong Yan; Rui Nian; Amaury Lendasse

    2015-01-01

    A novel particle swarm optimization based selective ensemble (PSOSEN) of online sequential extreme learning machine (OS-ELM) is proposed. It is based on the original OS-ELM with an adaptive selective ensemble framework. Two novel insights are proposed in this paper. First, a novel selective ensemble algorithm referred to as particle swarm optimization selective ensemble is proposed, noting that PSOSEN is a general selective ensemble method which is applicable to any learning algorithms, inclu...

  20. Climate change projections of medicanes with a large multi-model ensemble of regional climate models

    Science.gov (United States)

    Romera, Raquel; Gaertner, Miguel Ángel; Sánchez, Enrique; Domínguez, Marta; González-Alemán, Juan Jesús; Miglietta, Mario Marcello

    2017-04-01

    Cyclones with tropical characteristics, usually called medicanes, occasionally develop over the Mediterranean Sea. Possible future changes of medicanes are a matter of concern due to their large damage potential. Here we analyse a large set of climate change projections with regional climate models (RCMs) from the ENSEMBLES project. The aim is to increase our knowledge about the future evolution of medicanes, advancing previous studies along several important lines: use of a large ensemble of RCMs, nested in many different GCMs, and covering a long continuous time period (up to 150 years). The main overall results are a future reduction in the number of medicanes and an increase in the intensity of the strongest medicanes, in agreement with other studies. But the large size of the ensemble reveals some important model-related uncertainties. The frequency decrease is not statistically significant in many of the subset of simulations that extend to 2100, with two simulations even showing no frequency decrease at all. Large decadal changes affect the frequency of medicanes, emphasizing the need for long period simulations. The increase in extreme intensity shows a clear dependence on the GCM driving the simulations. In contrast to the overall results, a few simulations also show changes in the monthly distribution of medicanes, with less winter cases and more autumn and late summer cases. Some environmental variables have been explored in an attempt to offer physical explanations for these results. A plausible reason for the overall decrease of the frequency of medicanes is the projected increase in vertical static stability of the atmosphere. A relevant result is that the general and clear increase in average static stability is unable to stop several simulations projecting higher maximum winds in the future. This could indicate that the increased SST and latent heat fluxes may overcome the limitation of a higher overall static stability, if favourable conditions for

  1. On the proper use of Ensembles for Predictive Uncertainty assessment

    Science.gov (United States)

    Todini, Ezio; Coccia, Gabriele; Ortiz, Enrique

    2015-04-01

    Probabilistic forecasting has become popular in the last decades. Hydrological probabilistic forecasts have been based either on uncertainty processors (Krzysztofowic, 1999; Todini, 2004; Todini, 2008) or on ensembles, following meteorological traditional approaches and the establishment of the HEPEX program (http://hepex.irstea.fr. Unfortunately, the direct use of ensembles as a measure of the predictive density is an incorrect practice, because the ensemble measures the spread of the forecast instead of, following the definition of predictive uncertainty, the conditional probability of the future outcome conditional on the forecast. Only few correct approaches are reported in the literature, which correctly use the ensemble to estimate an expected conditional predictive density (Reggiani et al., 2009), similarly to what is done when several predictive models are available as in the BMA (Raftery et al., 2005) or MCP(Todini, 2008; Coccia and Todini, 2011) approaches. A major problem, limiting the correct use of ensembles, is in fact the difficulty of defining the time dependence of the ensemble members, due to the lack of a consistent ranking: in other words, when dealing with multiple models, the ith model remains the ith model regardless to the time of forecast, while this does not happen when dealing with ensemble members, since there is no definition for the ith member of an ensemble. Nonetheless, the MCP approach (Todini, 2008; Coccia and Todini, 2011), essentially based on a multiple regression in the Normal space, can be easily extended to use ensembles to represent the local (in time) smaller or larger conditional predictive uncertainty, as a function of the ensemble spread. This is done by modifying the classical linear regression equations, impliying perfectly observed predictors, to alternative regression equations similar to the Kalman filter ones, allowing for uncertain predictors. In this way, each prediction in time accounts for both the predictive

  2. Three-dimensional visualization of ensemble weather forecasts – Part 1: The visualization tool Met.3D (version 1.0

    Directory of Open Access Journals (Sweden)

    M. Rautenhaus

    2015-07-01

    Full Text Available We present "Met.3D", a new open-source tool for the interactive three-dimensional (3-D visualization of numerical ensemble weather predictions. The tool has been developed to support weather forecasting during aircraft-based atmospheric field campaigns; however, it is applicable to further forecasting, research and teaching activities. Our work approaches challenging topics related to the visual analysis of numerical atmospheric model output – 3-D visualization, ensemble visualization and how both can be used in a meaningful way suited to weather forecasting. Met.3D builds a bridge from proven 2-D visualization methods commonly used in meteorology to 3-D visualization by combining both visualization types in a 3-D context. We address the issue of spatial perception in the 3-D view and present approaches to using the ensemble to allow the user to assess forecast uncertainty. Interactivity is key to our approach. Met.3D uses modern graphics technology to achieve interactive visualization on standard consumer hardware. The tool supports forecast data from the European Centre for Medium Range Weather Forecasts (ECMWF and can operate directly on ECMWF hybrid sigma-pressure level grids. We describe the employed visualization algorithms, and analyse the impact of the ECMWF grid topology on computing 3-D ensemble statistical quantities. Our techniques are demonstrated with examples from the T-NAWDEX-Falcon 2012 (THORPEX – North Atlantic Waveguide and Downstream Impact Experiment campaign.

  3. Regionalization of post-processed ensemble runoff forecasts

    Directory of Open Access Journals (Sweden)

    J. O. Skøien

    2016-05-01

    Full Text Available For many years, meteorological models have been run with perturbated initial conditions or parameters to produce ensemble forecasts that are used as a proxy of the uncertainty of the forecasts. However, the ensembles are usually both biased (the mean is systematically too high or too low, compared with the observed weather, and has dispersion errors (the ensemble variance indicates a too low or too high confidence in the forecast, compared with the observed weather. The ensembles are therefore commonly post-processed to correct for these shortcomings. Here we look at one of these techniques, referred to as Ensemble Model Output Statistics (EMOS (Gneiting et al., 2005. Originally, the post-processing parameters were identified as a fixed set of parameters for a region. The application of our work is the European Flood Awareness System (http://www.efas.eu, where a distributed model is run with meteorological ensembles as input. We are therefore dealing with a considerably larger data set than previous analyses. We also want to regionalize the parameters themselves for other locations than the calibration gauges. The post-processing parameters are therefore estimated for each calibration station, but with a spatial penalty for deviations from neighbouring stations, depending on the expected semivariance between the calibration catchment and these stations. The estimated post-processed parameters can then be used for regionalization of the postprocessing parameters also for uncalibrated locations using top-kriging in the rtop-package (Skøien et al., 2006, 2014. We will show results from cross-validation of the methodology and although our interest is mainly in identifying exceedance probabilities for certain return levels, we will also show how the rtop package can be used for creating a set of post-processed ensembles through simulations.

  4. Quantifying Monte Carlo uncertainty in ensemble Kalman filter

    Energy Technology Data Exchange (ETDEWEB)

    Thulin, Kristian; Naevdal, Geir; Skaug, Hans Julius; Aanonsen, Sigurd Ivar

    2009-01-15

    This report is presenting results obtained during Kristian Thulin PhD study, and is a slightly modified form of a paper submitted to SPE Journal. Kristian Thulin did most of his portion of the work while being a PhD student at CIPR, University of Bergen. The ensemble Kalman filter (EnKF) is currently considered one of the most promising methods for conditioning reservoir simulation models to production data. The EnKF is a sequential Monte Carlo method based on a low rank approximation of the system covariance matrix. The posterior probability distribution of model variables may be estimated fram the updated ensemble, but because of the low rank covariance approximation, the updated ensemble members become correlated samples from the posterior distribution. We suggest using multiple EnKF runs, each with smaller ensemble size to obtain truly independent samples from the posterior distribution. This allows a point-wise confidence interval for the posterior cumulative distribution function (CDF) to be constructed. We present a methodology for finding an optimal combination of ensemble batch size (n) and number of EnKF runs (m) while keeping the total number of ensemble members ( m x n) constant. The optimal combination of n and m is found through minimizing the integrated mean square error (MSE) for the CDFs and we choose to define an EnKF run with 10.000 ensemble members as having zero Monte Carlo error. The methodology is tested on a simplistic, synthetic 2D model, but should be applicable also to larger, more realistic models. (author). 12 refs., figs.,tabs

  5. Temperature fluctuations in a changing climate: an ensemble-based experimental approach.

    Science.gov (United States)

    Vincze, Miklós; Borcia, Ion Dan; Harlander, Uwe

    2017-03-21

    There is an ongoing debate in the literature about whether the present global warming is increasing local and global temperature variability. The central methodological issues of this debate relate to the proper treatment of normalised temperature anomalies and trends in the studied time series which may be difficult to separate from time-evolving fluctuations. Some argue that temperature variability is indeed increasing globally, whereas others conclude it is decreasing or remains practically unchanged. Meanwhile, a consensus appears to emerge that local variability in certain regions (e.g. Western Europe and North America) has indeed been increasing in the past 40 years. Here we investigate the nature of connections between external forcing and climate variability conceptually by using a laboratory-scale minimal model of mid-latitude atmospheric thermal convection subject to continuously decreasing 'equator-to-pole' temperature contrast ΔT, mimicking climate change. The analysis of temperature records from an ensemble of experimental runs ('realisations') all driven by identical time-dependent external forcing reveals that the collective variability of the ensemble and that of individual realisations may be markedly different - a property to be considered when interpreting climate records.

  6. Meteorological Uncertainty of atmospheric Dispersion model results (MUD)

    DEFF Research Database (Denmark)

    Havskov Sørensen, Jens; Amstrup, Bjarne; Feddersen, Henrik

    uncertainties of the meteorological model results. These uncertainties stem from e.g. limits in meteorological observations used to initialise meteorological forecast series. By perturbing e.g. the initial state of an NWP model run in agreement with the available observational data, an ensemble......The MUD project addresses assessment of uncertainties of atmospheric dispersion model predictions, as well as possibilities for optimum presentation to decision makers. Previously, it has not been possible to estimate such uncertainties quantitatively, but merely to calculate the ‘most likely...... of meteorological forecasts is produced from which uncertainties in the various meteorological parameters are estimated, e.g. probabilities for rain. Corresponding ensembles of atmospheric dispersion can now be computed from which uncertainties of predicted radionuclide concentration and deposition patterns can...

  7. Seamless Hourly Rainfall Ensemble Forecasts for 0 - 10 days

    Science.gov (United States)

    Cooper, Shaun; Seed, Alan

    2014-05-01

    The Australian Bureau of Meteorology uses a number of Numerical Weather Prediction (NWP) models to generate deterministic rainfall forecasts over a range of lead-times, each with a different resolution in space and time and with different forecast domains. High resolution regional NWP models are used to generate forecasts for the first three days, and are typically more accurate than lower resolution Global NWP models that produce forecasts for longer lead times. Consequently, there is a requirement for a seamless forecast system that is able to blend the various NWP forecasts into a single forecast with a uniform resolution over the entire forecast period. NWP rainfall forecasts contain errors at scales that are significant for even large river basins, and ensemble hydrological prediction systems require ensembles of the order of 100 members, which is well beyond the size that can be generated by NWP ensemble systems. The idea, therefore, is to blend the NWP models in such a way that recognises the skill of the NWP at a particular scale and lead time and to use a stochastic model of forecast errors to perturb the blended deterministic forecast to generate a large ensemble. NWP uncertainties are scale and forecast lead time dependent, especially at long forecast lead times, and are characteristic to each model. By blending the models scale by scale it is possible to recognise the increased skill of the models at larger spatial scales and shorter lead times. The stochastic model is applied at each scale, adding increasingly more variability at smaller spatial scales, while preserving the space-time structure of rain. This process allows an ensemble to be generated by blending deterministic forecasts. Two NWP models from the Bureau, ACCESS-G (Global) (~40 km by ~40 km, 3 hourly out to 10 days) and ACCESS-R (Regional) (~12 km by ~12 km, 1 hourly out to 3 days), are downscaled and blended with the stochastic model to produce an ensemble of hourly forecasts out to 10

  8. Application of evolutionary computation on ensemble forecast of quantitative precipitation

    Science.gov (United States)

    Dufek, Amanda S.; Augusto, Douglas A.; Dias, Pedro L. S.; Barbosa, Helio J. C.

    2017-09-01

    An evolutionary computation algorithm known as genetic programming (GP) has been explored as an alternative tool for improving the ensemble forecast of 24-h accumulated precipitation. Three GP versions and six ensembles' languages were applied to several real-world datasets over southern, southeastern and central Brazil during the rainy period from October to February of 2008-2013. According to the results, the GP algorithms performed better than two traditional statistical techniques, with errors 27-57% lower than simple ensemble mean and the MASTER super model ensemble system. In addition, the results revealed that GP algorithms outperformed the best individual forecasts, reaching an improvement of 34-42%. On the other hand, the GP algorithms had a similar performance with respect to each other and to the Bayesian model averaging, but the former are far more versatile techniques. Although the results for the six ensembles' languages are almost indistinguishable, our most complex linear language turned out to be the best overall proposal. Moreover, some meteorological attributes, including the weather patterns over Brazil, seem to play an important role in the prediction of daily rainfall amount.

  9. Curve Boxplot: Generalization of Boxplot for Ensembles of Curves.

    Science.gov (United States)

    Mirzargar, Mahsa; Whitaker, Ross T; Kirby, Robert M

    2014-12-01

    In simulation science, computational scientists often study the behavior of their simulations by repeated solutions with variations in parameters and/or boundary values or initial conditions. Through such simulation ensembles, one can try to understand or quantify the variability or uncertainty in a solution as a function of the various inputs or model assumptions. In response to a growing interest in simulation ensembles, the visualization community has developed a suite of methods for allowing users to observe and understand the properties of these ensembles in an efficient and effective manner. An important aspect of visualizing simulations is the analysis of derived features, often represented as points, surfaces, or curves. In this paper, we present a novel, nonparametric method for summarizing ensembles of 2D and 3D curves. We propose an extension of a method from descriptive statistics, data depth, to curves. We also demonstrate a set of rendering and visualization strategies for showing rank statistics of an ensemble of curves, which is a generalization of traditional whisker plots or boxplots to multidimensional curves. Results are presented for applications in neuroimaging, hurricane forecasting and fluid dynamics.

  10. Visualizing Confidence in Cluster-Based Ensemble Weather Forecast Analyses.

    Science.gov (United States)

    Kumpf, Alexander; Tost, Bianca; Baumgart, Marlene; Riemer, Michael; Westermann, Rudiger; Rautenhaus, Marc

    2018-01-01

    In meteorology, cluster analysis is frequently used to determine representative trends in ensemble weather predictions in a selected spatio-temporal region, e.g., to reduce a set of ensemble members to simplify and improve their analysis. Identified clusters (i.e., groups of similar members), however, can be very sensitive to small changes of the selected region, so that clustering results can be misleading and bias subsequent analyses. In this article, we - a team of visualization scientists and meteorologists-deliver visual analytics solutions to analyze the sensitivity of clustering results with respect to changes of a selected region. We propose an interactive visual interface that enables simultaneous visualization of a) the variation in composition of identified clusters (i.e., their robustness), b) the variability in cluster membership for individual ensemble members, and c) the uncertainty in the spatial locations of identified trends. We demonstrate that our solution shows meteorologists how representative a clustering result is, and with respect to which changes in the selected region it becomes unstable. Furthermore, our solution helps to identify those ensemble members which stably belong to a given cluster and can thus be considered similar. In a real-world application case we show how our approach is used to analyze the clustering behavior of different regions in a forecast of "Tropical Cyclone Karl", guiding the user towards the cluster robustness information required for subsequent ensemble analysis.

  11. Regularized negative correlation learning for neural network ensembles.

    Science.gov (United States)

    Chen, Huanhuan; Yao, Xin

    2009-12-01

    Negative correlation learning (NCL) is a neural network ensemble learning algorithm that introduces a correlation penalty term to the cost function of each individual network so that each neural network minimizes its mean square error (MSE) together with the correlation of the ensemble. This paper analyzes NCL and reveals that the training of NCL (when lambda = 1) corresponds to training the entire ensemble as a single learning machine that only minimizes the MSE without regularization. This analysis explains the reason why NCL is prone to overfitting the noise in the training set. This paper also demonstrates that tuning the correlation parameter lambda in NCL by cross validation cannot overcome the overfitting problem. The paper analyzes this problem and proposes the regularized negative correlation learning (RNCL) algorithm which incorporates an additional regularization term for the whole ensemble. RNCL decomposes the ensemble's training objectives, including MSE and regularization, into a set of sub-objectives, and each sub-objective is implemented by an individual neural network. In this paper, we also provide a Bayesian interpretation for RNCL and provide an automatic algorithm to optimize regularization parameters based on Bayesian inference. The RNCL formulation is applicable to any nonlinear estimator minimizing the MSE. The experiments on synthetic as well as real-world data sets demonstrate that RNCL achieves better performance than NCL, especially when the noise level is nontrivial in the data set.

  12. Ensemble Kalman filtering with one-step-ahead smoothing

    KAUST Repository

    Raboudi, Naila F.

    2018-01-11

    The ensemble Kalman filter (EnKF) is widely used for sequential data assimilation. It operates as a succession of forecast and analysis steps. In realistic large-scale applications, EnKFs are implemented with small ensembles and poorly known model error statistics. This limits their representativeness of the background error covariances and, thus, their performance. This work explores the efficiency of the one-step-ahead (OSA) smoothing formulation of the Bayesian filtering problem to enhance the data assimilation performance of EnKFs. Filtering with OSA smoothing introduces an updated step with future observations, conditioning the ensemble sampling with more information. This should provide an improved background ensemble in the analysis step, which may help to mitigate the suboptimal character of EnKF-based methods. Here, the authors demonstrate the efficiency of a stochastic EnKF with OSA smoothing for state estimation. They then introduce a deterministic-like EnKF-OSA based on the singular evolutive interpolated ensemble Kalman (SEIK) filter. The authors show that the proposed SEIK-OSA outperforms both SEIK, as it efficiently exploits the data twice, and the stochastic EnKF-OSA, as it avoids observational error undersampling. They present extensive assimilation results from numerical experiments conducted with the Lorenz-96 model to demonstrate SEIK-OSA’s capabilities.

  13. Neural ensemble dynamics underlying a long-term associative memory

    Science.gov (United States)

    Grewe, Benjamin F.; Gründemann, Jan; Kitch, Lacey J.; Lecoq, Jerome A.; Parker, Jones G.; Marshall, Jesse D.; Larkin, Margaret C.; Jercog, Pablo E.; Grenier, Francois; Li, Jin Zhong; Lüthi, Andreas; Schnitzer, Mark J.

    2017-01-01

    The brain’s ability to associate different stimuli is vital to long-term memory, but how neural ensembles encode associative memories is unknown. Here we studied how cell ensembles in the basal and lateral amygdala (BLA) encode associations between conditioned and unconditioned stimuli (CS, US). Using a miniature fluorescence microscope, we tracked BLA ensemble neural Ca2+ dynamics during fear learning and extinction over six days in behaving mice. Fear conditioning induced both up- and down-regulation of individual cells’ CS-evoked responses. This bi-directional plasticity mainly occurred after conditioning and reshaped the CS ensemble neural representation to gain similarity to the US-representation. During extinction training with repetitive CS presentations, the CS-representation became more distinctive without reverting to its original form. Throughout, the strength of the ensemble-encoded CS-US association predicted each mouse’s level of behavioral conditioning. These findings support a supervised learning model in which activation of the US-representation guides the transformation of the CS-representation. PMID:28329757

  14. Ensemble size impact on the decadal predictive skill assessment

    Directory of Open Access Journals (Sweden)

    Frank Sienz

    2016-12-01

    Full Text Available Retrospective prediction experiments have to be performed to estimate the skill of decadal prediction systems. These are necessarily restricted in the number due to the computational constraints. From weather and seasonal prediction it is known that the ensemble size is crucial to yield reliable predictions. Differences are expected for decadal predictions due to the differing time-scales of the involved processes and the longer prediction horizon. A conceptual model is applied that enables the systematic analysis of ensemble size dependencies in a framework close to that of decadal predictions. Differences are quantified in terms of the confidence intervals coverage and the power of statistical tests for prediction scores. In addition, the concepts are applied to decadal predicitions of the MiKlip Baseline1 system. It is shown that small ensemble, as well as hindcast sample sizes lead to biased test performances in a way that the detection of a present prediction skill is hampered. Experiments with ensemble sizes smaller than 10 are not recommended to evaluate decadal prediction skill or as basis for the prediction system developement. For regions with low signal-to-noise ratios much larger ensembles are required and it is shown that in this case successful decadal predictions are possible for the Central European summer temperatures.

  15. Universal critical wrapping probabilities in the canonical ensemble

    Directory of Open Access Journals (Sweden)

    Hao Hu

    2015-09-01

    Full Text Available Universal dimensionless quantities, such as Binder ratios and wrapping probabilities, play an important role in the study of critical phenomena. We study the finite-size scaling behavior of the wrapping probability for the Potts model in the random-cluster representation, under the constraint that the total number of occupied bonds is fixed, so that the canonical ensemble applies. We derive that, in the limit L→∞, the critical values of the wrapping probability are different from those of the unconstrained model, i.e. the model in the grand-canonical ensemble, but still universal, for systems with 2yt−d>0 where yt=1/ν is the thermal renormalization exponent and d is the spatial dimension. Similar modifications apply to other dimensionless quantities, such as Binder ratios. For systems with 2yt−d≤0, these quantities share same critical universal values in the two ensembles. It is also derived that new finite-size corrections are induced. These findings apply more generally to systems in the canonical ensemble, e.g. the dilute Potts model with a fixed total number of vacancies. Finally, we formulate an efficient cluster-type algorithm for the canonical ensemble, and confirm these predictions by extensive simulations.

  16. Adiabatically deformed ensemble: Engineering nonthermal states of matter

    Science.gov (United States)

    Kennes, D. M.

    2017-07-01

    We propose a route towards engineering nonthermal states of matter, which show largely unexplored physics. The main idea relies on the adiabatic passage of a thermal ensemble under slow variations of the system Hamiltonian. If the temperature of the initial thermal ensemble is either zero or infinite, the ensemble after the passage is a simple thermal one with the same vanishing or infinite temperature. However, for any finite nonzero temperature, intriguing nonthermal ensembles can be achieved. We exemplify this in (a) a single oscillator, (b) a dimerized interacting one-dimensional chain of spinless fermions, (c) a BCS-type superconductor, and (d) the topological Kitaev chain. We solve these models with a combination of methods: either exactly, numerically using the density matrix renormalization group, or within an approximate functional renormalization group scheme. The designed states show strongly nonthermal behavior in each of the considered models. For example, for the chain of spinless fermions we exemplify how long-ranged nonthermal power-law correlations can be stabilized, and for the Kitaev chain we elucidate how the nonthermal ensemble can largely alter the transition temperature separating topological and trivial phases.

  17. Ensemble of Thermostatically Controlled Loads: Statistical Physics Approach

    Energy Technology Data Exchange (ETDEWEB)

    Chertkov, Michael [Los Alamos National Lab. (LANL), Los Alamos, NM (United States); Skolkovo Inst. of Science and Technology, Moscow (Russia); Chernyak, Vladimir [Wayne State Univ., Detroit, MI (United States). Dept. of Chemistry

    2017-01-17

    Thermostatically Controlled Loads (TCL), e.g. air-conditioners and heaters, are by far the most wide-spread consumers of electricity. Normally the devices are calibrated to provide the so-called bang-bang control of temperature - changing from on to off , and vice versa, depending on temperature. Aggregation of a large group of similar devices into a statistical ensemble is considered, where the devices operate following the same dynamics subject to stochastic perturbations and randomized, Poisson on/off switching policy. We analyze, using theoretical and computational tools of statistical physics, how the ensemble relaxes to a stationary distribution and establish relation between the re- laxation and statistics of the probability flux, associated with devices' cycling in the mixed (discrete, switch on/off , and continuous, temperature) phase space. This allowed us to derive and analyze spec- trum of the non-equilibrium (detailed balance broken) statistical system. and uncover how switching policy affects oscillatory trend and speed of the relaxation. Relaxation of the ensemble is of a practical interest because it describes how the ensemble recovers from significant perturbations, e.g. forceful temporary switching o aimed at utilizing flexibility of the ensemble in providing "demand response" services relieving consumption temporarily to balance larger power grid. We discuss how the statistical analysis can guide further development of the emerging demand response technology.

  18. Validation of the German Mid-Range Climate Prediction (MiKlip) decadal ensemble prediction system using radiosonde observations.

    Science.gov (United States)

    Pattantyús-Ábrahám, Margit; Steinbrecht, Wolfgang

    2015-04-01

    We report validation results for three simulations by the Max-Planck-Institute's Earth System Model (MPI-ESM) ensemble prediction system (EPS), which is used for hindcasts and predictions of global-scale decadal climate variability in the framework of the German MiKlip project. Three experiments were analyzed: Baseline0 simulations with ocean anomaly field initialization, Baseline1 simulations with additional atmospheric full field initialization, and Prototype simulations with full field initialization for both ocean and atmosphere. Our validation compares homogenized radiosonde observations to the decadal ensemble hindcast projections. So far, we focused on the European region. Comparison of observed and simulated European temperature profiles showed noticeable model cold bias, about 1 K near the surface, increasing to about 3 K near the tropopause. This has implications for atmospheric stability, which is underestimated in the simulations compared to the radiosonde profiles. The simulations also tend to overestimate humidity throughout the troposphere. Both biases combine to give, e.g., much larger simulated values for standard severe weather indices, which might be interpreted as higher severe weather probability in the simulations. While Baseline 0 and Baseline 1 simulations showed no dependence of temperature bias on simulation lead years, the temperature bias of Prototype simulations increased systematically with increasing lead years. Apart from bias and probability density functions, the predictive skills of the Ensemble Prediction System were also analyzed with respect to the radiosonde observations. Here the Mean Square Error Skill Score (MSESS) and Continuously Ranked Probability Skill Scores (CRPSS) were used. For tropospheric temperatures, the full-field initialized Prototype experiments showed the best skills (MSESS and CRPSS), compared to the Baseline 0 and 1 experiments. In the stratosphere predictive skills were comparable for all experiments.

  19. Smart Voyage Planning Model Sensitivity Analysis Using Ocean and Atmospheric Models Including Ensemble Methods

    Science.gov (United States)

    2012-09-01

    exceeding wind and sea limits specified by competent authority. STARS utilizes a Dijkstra exhaustive search algorithm for minimum cost as follows... Navigation Toolkit CONOPS Concept of Operations COTS Commercial Off-the-Shelf DBDB-V Digital Bathymetric Data Base Variable Resolution DCAP...platform hydrodynamic models and environmental forecasts combined with improved algorithms enable fuel savings in addition to aiding in heavy weather

  20. Ensemble simulations with perturbed physical parametrisations: Pre-HyMeX case studies

    Science.gov (United States)

    Hally, A.; Fresnay, S.; Richard, E.; Lambert, D.

    2012-04-01

    During the last 15 or 20 years, ensemble prediction systems have made substantial progress and have become an indispensable tool in forecasting the probability of certain weather events. Recent improvements in computer-modelling have also allowed convection and other physically complex processes to be well represented within atmospheric models. This however has also led to more questions, such as the representation of uncertainties in the initial field, the definition of boundary conditions, along with other areas of incertitude in the model. The relative importance of each field remains to be investigated more vigorously in order to allow for further improvements in forecasting, especially for heavy rainfall episodes, which can have large social and economic consequences. This study focuses on model uncertainties related to cloud physics and turbulence parametrisations, which have been so far less studied than other areas such as boundary or initial state uncertainties. The sensitivity of the precipitation forecasts to the details of the parametrisation are assessed by (a) varying the tunable parameters of micro-physical and turbulence schemes of the model within their range of allowed values and (b) by introducing random perturbations on the factors which govern hydro-meteor and/or turbulent kinetic energy evolution. An ensemble of simulations was performed in order to find the optimum setting of the model in relation to the forecasting of precipitation events. Two different sets of initial and boundary conditions were used during the ensembles, one from the French forecasting model AROME and the other from the ECMWF analyses. This methodology was applied in particular to the cases of the 1st to the 5th of November 2011 and the 6th - 7th of September 2010 which prefigure the heavy precipitation events expected to occur during the HyMeX field campaign of Autumn 2012. The simulations were performed with the French research model MESO-NH. The results show that the

  1. European storminess and associated circulation weather types: future changes deduced from a multi-model ensemble of GCM simulations

    OpenAIRE

    Donat, M. G.; G. C. Leckebusch; Pinto, J. G.; Ulbrich, U.

    2010-01-01

    A range of possible changes in the frequency and characteristics of European wind storms under future climate conditions was investigated on the basis of a multi-model ensemble of 9 coupled global climate model (GCM) simulations for the 20th and 21st centuries following the IPCC SRES A1B scenario. A multi-model approach allowed an estimation of the (un)certainties of the climate change signals. General changes in large-scale atmospheric flow were analysed, the occurrence of wind storms was qu...

  2. Ultrafast Rabi oscillation of a Gaussian atom ensemble

    CERN Document Server

    Lee, Han-gyeol; Ahn, Jaewook

    2014-01-01

    We investigate Rabi oscillation of an atom ensemble in Gaussian spatial distribution. By using the ultrafast laser interaction with the cold atomic rubidium vapor spatially confined in a magneto-optical trap, the oscillatory behavior of the atom excitation is probed as a function of the laser pulse power. Theoretical model calculation predicts that the oscillation peaks of the ensemble-atom Rabi flopping fall on the simple Rabi oscillation curve of a single atom and the experimental result shows good agreement with the prediction. We also test the the three-pulse composite interaction $R_x(\\pi/2)R_y(\\pi)R_x(\\pi/2)$ to develop a robust method to achieve a higher fidelity population inversion of the atom ensemble.

  3. Optical properties of indium phosphide nanowire ensembles at various temperatures

    Energy Technology Data Exchange (ETDEWEB)

    Lohn, Andrew J; Onishi, Takehiro; Kobayashi, Nobuhiko P [Baskin School of Engineering, University of California Santa Cruz, Santa Cruz, CA 95064 (United States); Nanostructured Energy Conversion Technology and Research (NECTAR), Advanced Studies Laboratories, University of California Santa Cruz-NASA Ames Research Center, Moffett Field, CA 94035 (United States)

    2010-09-03

    Ensembles that contain two types (zincblende and wurtzite) of indium phosphide nanowires grown on non-single crystalline surfaces were studied by micro-photoluminescence and micro-Raman spectroscopy at various low temperatures. The obtained spectra are discussed with the emphasis on the effects of differing lattice types, geometries, and crystallographic orientations present within an ensemble of nanowires grown on non-single crystalline surfaces. In the photoluminescence spectra, a typical Varshni dependence of band gap energy on temperature was observed for emissions from zincblende nanowires and in the high temperature regime energy transfer from excitonic transitions and band-edge transitions was identified. In contrast, the photoluminescence emissions associated with wurtzite nanowires were rather insensitive to temperature. Raman spectra were collected simultaneously from zincblende and wurtzite nanowires coexisting in an ensemble. Raman peaks of the wurtzite nanowires are interpreted as those related to the zincblende nanowires by a folding of the phonon dispersion.

  4. Generation of Exotic Quantum States of a Cold Atomic Ensemble

    DEFF Research Database (Denmark)

    Christensen, Stefan Lund

    Over the last decades quantum effects have become more and more controllable, leading to the implementations of various quantum information protocols. These protocols are all based on utilizing quantum correlation. In this thesis we consider how states of an atomic ensemble with such correlations...... can be created and characterized. First we consider a spin-squeezed state. This state is generated by performing quantum non-demolition measurements of the atomic population difference. We show a spectroscopically relevant noise reduction of -1.7dB, the ensemble is in a many-body entangled state...... — a nanofiber based light-atom interface. Using a dual-frequency probing method we measure and prepare an ensemble with a sub-Poissonian atom number distribution. This is a first step towards the implementation of more exotic quantum states....

  5. Ensemble streamflow predictions: from climate scenarios to probabilistic weather predictions

    Science.gov (United States)

    Fortin, V.; Evora, N.; Perreault, L.; Trinh, N.; Favre, A.; Benoit, H.

    2004-05-01

    Ensemble streamflow predictions (ESP) are obtained by processing an ensemble of meteorological scenarios through a rainfall-runoff hydrological model to obtain hydrological scenarios. Until recently, these scenarios were typically taken from the climatology. Now that more accurate medium- and long-term numerical weather predictions (NWP) are available, it is tempting to replace climatology by numerical weather forecasts. At least two approaches are possible to take into account the uncerta1000 inty on the meteorological forecast: (1) let a meteorologist propose a subjective probabilistic forecast based on one or more deterministic NWPs, or (2) take advantage of ensemble meteorological forecasts, which are built precisely to assess the level of uncertainty on the deterministic forecast. Practical solutions to problems encountered with both types of meteorological forecasts are discussed, and the methodology used by Hydro-Québec to score the resulting streamflow forecasts is presented.

  6. A Flexible Approach for the Statistical Visualization of Ensemble Data

    Energy Technology Data Exchange (ETDEWEB)

    Potter, K. [Univ. of Utah, Salt Lake City, UT (United States). SCI Institute; Wilson, A. [Sandia National Lab. (SNL-NM), Albuquerque, NM (United States); Bremer, P. [Lawrence Livermore National Lab. (LLNL), Livermore, CA (United States); Williams, Dean N. [Lawrence Livermore National Lab. (LLNL), Livermore, CA (United States); Pascucci, V. [Univ. of Utah, Salt Lake City, UT (United States). SCI Institute; Johnson, C. [Univ. of Utah, Salt Lake City, UT (United States). SCI Institute

    2009-09-29

    Scientists are increasingly moving towards ensemble data sets to explore relationships present in dynamic systems. Ensemble data sets combine spatio-temporal simulation results generated using multiple numerical models, sampled input conditions and perturbed parameters. While ensemble data sets are a powerful tool for mitigating uncertainty, they pose significant visualization and analysis challenges due to their complexity. We present a collection of overview and statistical displays linked through a high level of interactivity to provide a framework for gaining key scientific insight into the distribution of the simulation results as well as the uncertainty associated with the data. In contrast to methods that present large amounts of diverse information in a single display, we argue that combining multiple linked statistical displays yields a clearer presentation of the data and facilitates a greater level of visual data analysis. We demonstrate this approach using driving problems from climate modeling and meteorology and discuss generalizations to other fields.

  7. ENSEMBLE methods to reconcile disparate national long range dispersion forecasts

    DEFF Research Database (Denmark)

    Mikkelsen, Torben; Galmarini, S.; Bianconi, R.

    2003-01-01

    ENSEMBLE is a web-based decision support system for real-time exchange and evaluation of national long-range dispersion forecasts of nuclear releases with cross-boundary consequences. The system is developed with the purpose to reconcile among disparatenational forecasts for long-range dispersion....... ENSEMBLE addresses the problem of achieving a common coherent strategy across European national emergency management when national long-range dispersion forecasts differ from one another during an accidentalatmospheric release of radioactive material. A series of new decision-making “ENSEMBLE” procedures...... and Web-based software evaluation and exchange tools have been created for real-time reconciliation and harmonisation of real-time dispersion forecastsfrom meteorological and emergency centres across Europe during an accident. The new ENSEMBLE software tools is available to participating national...

  8. An ensemble rank learning approach for gene prioritization.

    Science.gov (United States)

    Lee, Po-Feng; Soo, Von-Wun

    2013-01-01

    Several different computational approaches have been developed to solve the gene prioritization problem. We intend to use the ensemble boosting learning techniques to combine variant computational approaches for gene prioritization in order to improve the overall performance. In particular we add a heuristic weighting function to the Rankboost algorithm according to: 1) the absolute ranks generated by the adopted methods for a certain gene, and 2) the ranking relationship between all gene-pairs from each prioritization result. We select 13 known prostate cancer genes in OMIM database as training set and protein coding gene data in HGNC database as test set. We adopt the leave-one-out strategy for the ensemble rank boosting learning. The experimental results show that our ensemble learning approach outperforms the four gene-prioritization methods in ToppGene suite in the ranking results of the 13 known genes in terms of mean average precision, ROC and AUC measures.

  9. New vigour involving statisticians to overcome ensemble fatigue

    Science.gov (United States)

    Benestad, Rasmus; Sillmann, Jana; Thorarinsdottir, Thordis Linda; Guttorp, Peter; Mesquita, Michel D. S.; Tye, Mari R.; Uotila, Petteri; Maule, Cathrine Fox; Thejll, Peter; Drews, Martin; Parding, Kajsa M.

    2017-10-01

    Climate simulation data comprise a range of different phenomena with complex and interacting processes. Yet our understanding of the climate is incomplete despite the huge volumes of data, of which only a small fraction has been explored, and many questions remain, particularly those on the character and origin of uncertainties associated with model simulations and how further modelling efforts can improve understanding. Here, we question whether climate model information could be used more effectively and how so-called 'ensembles of opportunity' should be interpreted. Statisticians can contribute substantially to designing 'smarter' ensemble experiments, improving the distillation of information from ensembles, and helping interpret the relative merits of additional simulations. Future progress may be enhanced by increasing collaborations with statisticians.

  10. Purification of an unpolarized spin ensemble into entangled singlet pairs.

    Science.gov (United States)

    Greiner, Johannes N; Dasari, Durga Bhaktavatsala Rao; Wrachtrup, Jörg

    2017-04-03

    Dynamical polarization of nuclear spin ensembles is of central importance for magnetic resonance studies, precision sensing and for applications in quantum information theory. Here we propose a scheme to generate long-lived singlet pairs in an unpolarized nuclear spin ensemble which is dipolar coupled to the electron spins of a Nitrogen Vacancy center in diamond. The quantum mechanical back-action induced by frequent spin-selective readout of the NV centers allows the nuclear spins to pair up into maximally entangled singlet pairs. Counterintuitively, the robustness of the pair formation to dephasing noise improves with increasing size of the spin ensemble. We also show how the paired nuclear spin state allows for enhanced sensing capabilities of NV centers in diamond.

  11. Describing sequence-ensemble relationships for intrinsically disordered proteins.

    Science.gov (United States)

    Mao, Albert H; Lyle, Nicholas; Pappu, Rohit V

    2013-01-15

    Intrinsically disordered proteins participate in important protein-protein and protein-nucleic acid interactions and control cellular phenotypes through their prominence as dynamic organizers of transcriptional, post-transcriptional and signalling networks. These proteins challenge the tenets of the structure-function paradigm and their functional mechanisms remain a mystery given that they fail to fold autonomously into specific structures. Solving this mystery requires a first principles understanding of the quantitative relationships between information encoded in the sequences of disordered proteins and the ensemble of conformations they sample. Advances in quantifying sequence-ensemble relationships have been facilitated through a four-way synergy between bioinformatics, biophysical experiments, computer simulations and polymer physics theories. In the present review we evaluate these advances and the resultant insights that allow us to develop a concise quantitative framework for describing the sequence-ensemble relationships of intrinsically disordered proteins.

  12. Cavity quantum electrodynamics with a Rydberg-blocked atomic ensemble

    DEFF Research Database (Denmark)

    Guerlin, Christine; Brion, Etienne; Esslinger, Tilman

    2010-01-01

    The realization of a Jaynes-Cummings model in the optical domain is proposed for an atomic ensemble. The scheme exploits the collective coupling of the atoms to a quantized cavity mode and the nonlinearity introduced by coupling to high-lying Rydberg states. A two-photon transition resonantly...... effective Jaynes-Cummings model. We use numerical simulations to show that the cavity transmission can be used to reveal detailed properties of the Jaynes-Cummings ladder of excited states and that the atomic nonlinearity gives rise to highly nontrivial photon emission from the cavity. Finally, we suggest...... couples the single-atom ground state |g> to a Rydberg state |e>via a nonresonant intermediate state |i>, but due to the interaction between Rydberg atoms only a single atom can be resonantly excited in the ensemble. This restricts the state space of the ensemble to the collective ground state |G...

  13. Predicting and attributing recent East African Spring droughts with dynamical-statistical climate model ensembles

    Science.gov (United States)

    Funk, C. C.; Shukla, S.; Hoerling, M. P.; Robertson, F. R.; Hoell, A.; Liebmann, B.

    2013-12-01

    During boreal spring, eastern portions of Kenya and Somalia have experienced more frequent droughts since 1999. Given the region's high levels of food insecurity, better predictions of these droughts could provide substantial humanitarian benefits. We show that dynamical-statistical seasonal climate forecasts, based on the latest generation of coupled atmosphere-ocean and uncoupled atmospheric models, effectively predict boreal spring rainfall in this area. Skill sources are assessed by comparing ensembles driven with full-ocean forcing with ensembles driven with ENSO-only sea surface temperatures (SSTs). Our analysis suggests that both ENSO and non-ENSO Indo-Pacific SST forcing have played an important role in the increase in drought frequencies. Over the past 30 years, La Niña drought teleconnections have strengthened, while non-ENSO Indo-Pacific convection patterns have also supported increased (decreased) Western Pacific (East African) rainfall. To further examine the relative contribution of ENSO, low frequency warming and the Pacific Decadal Oscillation, we present decompositions of ECHAM5, GFS, CAM4 and GMAO AMIP simulations. These decompositions suggest that rapid warming in the western Pacific and steeper western-to-central Pacific SST gradients have likely played an important role in the recent intensification of the Walker circulation, and the associated increase in East African aridity. A linear combination of time series describing the Pacific Decadal Oscillation and the strength of Indo-Pacific warming are shown to track East African rainfall reasonably well. The talk concludes with a few thoughts linking the potentially important interplay of attribution and prediction. At least for recent East African droughts, it appears that a characteristic Indo-Pacific SST and precipitation anomaly pattern can be linked statistically to support forecasts and attribution analyses. The combination of traditional AGCM attribution analyses with simple yet

  14. Ensemble ROCK Methods and Ensemble SWFM Methods for Clustering of Cross Citrus Accessions Based on Mixed Numerical and Categorical Dataset

    Science.gov (United States)

    Alvionita; Sutikno; Suharsono, A.

    2017-03-01

    Cluster analysis is a technique in multivariate analysis methods that reduces (classifying) data. This analysis has the main purpose to classify the objects of observation into groups based on characteristics. In the process, a cluster analysis is not only used for numerical data or categorical data but also developed for mixed data. There are several methods in analyzing the mixed data as ensemble methods and methods Similarity Weight and Filter Methods (SWFM). There is a lot of research on these methods, but the study did not compare the performance given by both of these methods. Therefore, this paper will be compared the performance between the clustering ensemble ROCK methods and ensemble SWFM methods. These methods will be used in clustering cross citrus accessions based on the characteristics of fruit and leaves that involve variables that are a mixture of numerical and categorical. Clustering methods with the best performance determined by looking at the ratio of standard deviation values within groups (SW) with a standard deviation between groups (SB). Methods with the best performance has the smallest ratio. From the result, we get that the performance of ensemble ROCK methods is better than ensemble SWFM methods.

  15. Impact of an extremely large magnitude volcanic eruption on the global climate and carbon cycle estimated from ensemble Earth System Model simulations

    OpenAIRE

    Segschneider, J.; A. Beitsch; Timmreck, C.; V. Brovkin; Ilyina, T.; J. Jungclaus; Lorenz, S. J.; Six, K.D.; Zanchettin, D. (Davide)

    2013-01-01

    The response of the global climate-carbon cycle system to an extremely large Northern Hemisphere mid-latitude volcanic eruption is investigated using ensemble integrations with the comprehensive Earth System Model MPI-ESM. The model includes dynamical compartments of the atmosphere and ocean and interactive modules of the terrestrial biosphere as well as ocean biogeochemistry. The MPI-ESM was forced with anomalies of aerosol optical depth and effective radius of aerosol particles correspondin...

  16. Impact of an extremely large magnitude volcanic eruption on the global climate and carbon cycle estimated from ensemble Earth System Model simulations

    OpenAIRE

    Segschneider, J.; A. Beitsch; Timmreck, C.; V. Brovkin; Ilyina, T.; J. Jungclaus; Lorenz, S. J.; Six, K.D.; Zanchettin, D. (Davide)

    2012-01-01

    The response of the global climate-carbon cycle system to an extremely large Northern Hemisphere mid latitude volcanic eruption is investigated using ensemble integrations with the comprehensive Earth System Model MPI-ESM. The model includes dynamical compartments of the atmosphere and ocean and interactive modules of the terrestrial biosphere as well as ocean biogeochemistry. The MPI-ESM was forced with anomalies of aerosol optical depth and effective radius of aerosol particles corre...

  17. Efficient Kernel-Based Ensemble Gaussian Mixture Filtering

    KAUST Repository

    Liu, Bo

    2015-11-11

    We consider the Bayesian filtering problem for data assimilation following the kernel-based ensemble Gaussian-mixture filtering (EnGMF) approach introduced by Anderson and Anderson (1999). In this approach, the posterior distribution of the system state is propagated with the model using the ensemble Monte Carlo method, providing a forecast ensemble that is then used to construct a prior Gaussian-mixture (GM) based on the kernel density estimator. This results in two update steps: a Kalman filter (KF)-like update of the ensemble members and a particle filter (PF)-like update of the weights, followed by a resampling step to start a new forecast cycle. After formulating EnGMF for any observational operator, we analyze the influence of the bandwidth parameter of the kernel function on the covariance of the posterior distribution. We then focus on two aspects: i) the efficient implementation of EnGMF with (relatively) small ensembles, where we propose a new deterministic resampling strategy preserving the first two moments of the posterior GM to limit the sampling error; and ii) the analysis of the effect of the bandwidth parameter on contributions of KF and PF updates and on the weights variance. Numerical results using the Lorenz-96 model are presented to assess the behavior of EnGMF with deterministic resampling, study its sensitivity to different parameters and settings, and evaluate its performance against ensemble KFs. The proposed EnGMF approach with deterministic resampling suggests improved estimates in all tested scenarios, and is shown to require less localization and to be less sensitive to the choice of filtering parameters.

  18. Understanding the structural ensembles of a highly extended disordered protein.

    Science.gov (United States)

    Daughdrill, Gary W; Kashtanov, Stepan; Stancik, Amber; Hill, Shannon E; Helms, Gregory; Muschol, Martin; Receveur-Bréchot, Véronique; Ytreberg, F Marty

    2012-01-01

    Developing a comprehensive description of the equilibrium structural ensembles for intrinsically disordered proteins (IDPs) is essential to understanding their function. The p53 transactivation domain (p53TAD) is an IDP that interacts with multiple protein partners and contains numerous phosphorylation sites. Multiple techniques were used to investigate the equilibrium structural ensemble of p53TAD in its native and chemically unfolded states. The results from these experiments show that the native state of p53TAD has dimensions similar to a classical random coil while the chemically unfolded state is more extended. To investigate the molecular properties responsible for this behavior, a novel algorithm that generates diverse and unbiased structural ensembles of IDPs was developed. This algorithm was used to generate a large pool of plausible p53TAD structures that were reweighted to identify a subset of structures with the best fit to small angle X-ray scattering data. High weight structures in the native state ensemble show features that are localized to protein binding sites and regions with high proline content. The features localized to the protein binding sites are mostly eliminated in the chemically unfolded ensemble; while, the regions with high proline content remain relatively unaffected. Data from NMR experiments support these results, showing that residues from the protein binding sites experience larger environmental changes upon unfolding by urea than regions with high proline content. This behavior is consistent with the urea-induced exposure of nonpolar and aromatic side-chains in the protein binding sites that are partially excluded from solvent in the native state ensemble.

  19. Ensemble data assimilation for the reconstruction of mantle circulation

    Science.gov (United States)

    Bocher, Marie; Coltice, Nicolas; Fournier, Alexandre; Tackley, Paul

    2016-04-01

    The surface tectonics of the Earth is the result of mantle dynamics. This link between internal and surface dynamics can be used to reconstruct the evolution of mantle circulation. This is classically done by imposing plate tectonics reconstructions as boundary conditions on numerical models of mantle convection. However, this technique does not account for uncertainties in plate tectonics reconstructions and does not allow any dynamical feedback of mantle dynamics on surface tectonics to develop. Mantle convection models are now able to produce surface tectonics comparable to that of the Earth to first order. We capitalize on these convection models to propose a more consistent integration of plate tectonics reconstructions into mantle convection models. For this purpose, we use the ensemble Kalman filter. This method has been developed and successfully applied to meteorology, oceanography and even more recently outer core dynamics. It consists in integrating sequentially a time series of data into a numerical model, starting from an ensemble of possible initial states. The initial ensemble of states is designed to represent an approximation of the probability density function (pdf) of the a priori state of the system. Whenever new observations are available, each member of the ensemble states is corrected considering both the approximated pdf of the state, and the pdf of the new data. Between two observation times, each ensemble member evolution is computed independently, using the convection model. This technique provides at each time an approximation of the pdf of the state of the system, in the form of a finite ensemble of states. We perform synthetic experiments to assess the efficiency of this method for the reconstruction of mantle circulation.

  20. EnsCat: clustering of categorical data via ensembling.

    Science.gov (United States)

    Clarke, Bertrand S; Amiri, Saeid; Clarke, Jennifer L

    2016-09-15

    Clustering is a widely used collection of unsupervised learning techniques for identifying natural classes within a data set. It is often used in bioinformatics to infer population substructure. Genomic data are often categorical and high dimensional, e.g., long sequences of nucleotides. This makes inference challenging: The distance metric is often not well-defined on categorical data; running time for computations using high dimensional data can be considerable; and the Curse of Dimensionality often impedes the interpretation of the results. Up to the present, however, the literature and software addressing clustering for categorical data has not yet led to a standard approach. We present software for an ensemble method that performs well in comparison with other methods regardless of the dimensionality of the data. In an ensemble method a variety of instantiations of a statistical object are found and then combined into a consensus value. It has been known for decades that ensembling generally outperforms the components that comprise it in many settings. Here, we apply this ensembling principle to clustering. We begin by generating many hierarchical clusterings with different clustering sizes. When the dimension of the data is high, we also randomly select subspaces also of variable size, to generate clusterings. Then, we combine these clusterings into a single membership matrix and use this to obtain a new, ensembled dissimilarity matrix using Hamming distance. Ensemble clustering, as implemented in R and called EnsCat, gives more clearly separated clusters than other clustering techniques for categorical data. The latest version with manual and examples is available at https://github.com/jlp2duke/EnsCat .

  1. Oceanic ensemble forecasting in the Gulf of Mexico: An application to the case of the Deep Water Horizon oil spill

    Science.gov (United States)

    Khade, Vikram; Kurian, Jaison; Chang, Ping; Szunyogh, Istvan; Thyng, Kristen; Montuoro, Raffaele

    2017-05-01

    This paper demonstrates the potential of ocean ensemble forecasting in the Gulf of Mexico (GoM). The Bred Vector (BV) technique with one week rescaling frequency is implemented on a 9 km resolution version of the Regional Ocean Modelling System (ROMS). Numerical experiments are carried out by using the HYCOM analysis products to define the initial conditions and the lateral boundary conditions. The growth rates of the forecast uncertainty are estimated to be about 10% of initial amplitude per week. By carrying out ensemble forecast experiments with and without perturbed surface forcing, it is demonstrated that in the coastal regions accounting for uncertainties in the atmospheric forcing is more important than accounting for uncertainties in the ocean initial conditions. In the Loop Current region, the initial condition uncertainties, are the dominant source of the forecast uncertainty. The root-mean-square error of the Lagrangian track forecasts at the 15-day forecast lead time can be reduced by about 10 - 50 km using the ensemble mean Eulerian forecast of the oceanic flow for the computation of the tracks, instead of the single-initial-condition Eulerian forecast.

  2. Potential predictability sources of the 2012 U.S. drought in observations and a regional model ensemble

    Science.gov (United States)

    PaiMazumder, Debasish; Done, James M.

    2016-11-01

    The 2012 drought was the most severe and extensive summertime U.S. drought in half a century with substantial economic loss and impacts on food security and commodity prices. A unique aspect of the 2012 drought was its rapid onset and intensification over the Southern Rockies, extending to the Great Plains during late spring and early summer, and the absence of known precursor large-scale patterns. Drought prediction therefore remains a major challenge. This study evaluates relationships among snow, soil moisture, and precipitation to identify sources of potential predictability of the 2012 summer drought using observations and a Weather Research and Forecasting model multiphysics ensemble experiment. Although underestimated in intensity, the drought signal is robust to the way atmospheric physical processes are represented in the model. For the Southern Rockies, soil moisture exhibits stronger persistence than precipitation in observations and the ensemble experiment. Correlations between winter/spring snowmelt and concurrent and following season soil moisture, and between soil moisture and concurrent and following season precipitation, in both observations and the model ensemble, suggest potential predictability beyond 1 and 2 month lead-time reside in the land surface conditions for apparent flash droughts such as the 2012 drought.

  3. Fuzzy ARTMAP Ensemble Based Decision Making and Application

    Directory of Open Access Journals (Sweden)

    Min Jin

    2013-01-01

    Full Text Available Because the performance of single FAM is affected by the sequence of sample presentation for the offline mode of training, a fuzzy ARTMAP (FAM ensemble approach based on the improved Bayesian belief method is supposed to improve the classification accuracy. The training samples are input into a committee of FAMs in different sequence, the output from these FAMs is combined, and the final decision is derived by the improved Bayesian belief method. The experiment results show that the proposed FAMs’ ensemble can classify the different category reliably and has a better classification performance compared with single FAM.

  4. Spectral statistics of the uni-modular ensemble

    Science.gov (United States)

    Joyner, Christopher H.; Smilansky, Uzy; Weidenmüller, Hans A.

    2017-09-01

    We investigate the spectral statistics of Hermitian matrices in which the elements are chosen uniformly from U(1) , called the uni-modular ensemble (UME), in the limit of large matrix size. Using three complimentary methods; a supersymmetric integration method, a combinatorial graph-theoretical analysis and a Brownian motion approach, we are able to derive expressions for 1 / N corrections to the mean spectral moments and also analyse the fluctuations about this mean. By addressing the same ensemble from three different point of view, we can critically compare their relative advantages and derive some new results.

  5. Reservoir History Matching Using Ensemble Kalman Filters with Anamorphosis Transforms

    KAUST Repository

    Aman, Beshir M.

    2012-12-01

    This work aims to enhance the Ensemble Kalman Filter performance by transforming the non-Gaussian state variables into Gaussian variables to be a step closer to optimality. This is done by using univariate and multivariate Box-Cox transformation. Some History matching methods such as Kalman filter, particle filter and the ensemble Kalman filter are reviewed and applied to a test case in the reservoir application. The key idea is to apply the transformation before the update step and then transform back after applying the Kalman correction. In general, the results of the multivariate method was promising, despite the fact it over-estimated some variables.

  6. Application Bayesian Model Averaging method for ensemble system for Poland

    Science.gov (United States)

    Guzikowski, Jakub; Czerwinska, Agnieszka

    2014-05-01

    The aim of the project is to evaluate methods for generating numerical ensemble weather prediction using a meteorological data from The Weather Research & Forecasting Model and calibrating this data by means of Bayesian Model Averaging (WRF BMA) approach. We are constructing height resolution short range ensemble forecasts using meteorological data (temperature) generated by nine WRF's models. WRF models have 35 vertical levels and 2.5 km x 2.5 km horizontal resolution. The main emphasis is that the used ensemble members has a different parameterization of the physical phenomena occurring in the boundary layer. To calibrate an ensemble forecast we use Bayesian Model Averaging (BMA) approach. The BMA predictive Probability Density Function (PDF) is a weighted average of predictive PDFs associated with each individual ensemble member, with weights that reflect the member's relative skill. For test we chose a case with heat wave and convective weather conditions in Poland area from 23th July to 1st August 2013. From 23th July to 29th July 2013 temperature oscillated below or above 30 Celsius degree in many meteorology stations and new temperature records were added. During this time the growth of the hospitalized patients with cardiovascular system problems was registered. On 29th July 2013 an advection of moist tropical air masses was recorded in the area of Poland causes strong convection event with mesoscale convection system (MCS). MCS caused local flooding, damage to the transport infrastructure, destroyed buildings, trees and injuries and direct threat of life. Comparison of the meteorological data from ensemble system with the data recorded on 74 weather stations localized in Poland is made. We prepare a set of the model - observations pairs. Then, the obtained data from single ensemble members and median from WRF BMA system are evaluated on the basis of the deterministic statistical error Root Mean Square Error (RMSE), Mean Absolute Error (MAE). To evaluation

  7. Ensemble measurement of diffusion: novel beauty and evidence.

    Science.gov (United States)

    Chmelik, Christian; Heinke, Lars; Kortunov, Pavel; Li, Jing; Olson, David; Tzoulaki, Despina; Weitkamp, Jens; Kärger, Jörg

    2009-10-19

    Recording the evolution of concentration profiles in nanoporous materials opens a new field of diffusion research with particle ensembles. The technique is based on the complementary application of interference microscopy and IR micro-imaging. Combining the virtues of diffusion measurements with solids and fluids, it provides information of unprecedented wealth and visual power on transport phenomena in molecular ensembles. These phenomena include the diverging uptake and release patterns for concentration-dependent diffusivities, the mechanisms of mass transfer at the fluid-solid interface and opposing tendencies in local and global concentration evolution.

  8. Heralded atomic-ensemble quantum memory for photon polarization states

    Energy Technology Data Exchange (ETDEWEB)

    Tanji, Haruka; Simon, Jonathan [Department of Physics, Harvard University, Cambridge, MA 02138 (United States); Ghosh, Saikat; Bloom, Benjamin; Vuletic, Vladan [Department of Physics, MIT-Harvard Center for Ultracold Atoms, and Research Laboratory of Electronics, Massachusetts Institute of Technology, Cambridge, MA 02139 (United States)], E-mail: vuletic@mit.edu

    2009-07-15

    We describe the mapping of quantum states between single photons and an atomic ensemble. In particular, we demonstrate a heralded quantum memory based on the mapping of a photon polarization state onto a single collective-spin excitation (magnon) shared between two atomic ensembles. The polarization fidelity above 90(2)% for any input polarization far exceeds the classical limit of 2/3. The process also constitutes a quantum non-destructive probe that detects and regenerates a photon without measuring its polarization.

  9. Universality of Mesoscopic Fluctuations for Orthogonal Polynomial Ensembles

    Science.gov (United States)

    Breuer, Jonathan; Duits, Maurice

    2016-03-01

    We prove that the fluctuations of mesoscopic linear statistics for orthogonal polynomial ensembles are universal in the sense that two measures with asymptotic recurrence coefficients have the same asymptotic mesoscopic fluctuations (under an additional assumption on the local regularity of one of the measures). The convergence rate of the recurrence coefficients determines the range of scales on which the limiting fluctuations are identical. Our main tool is an analysis of the Green's function for the associated Jacobi matrices.As a particular consequencewe obtain a central limit theorem for the modified Jacobi Unitary Ensembles on all mesoscopic scales.

  10. Impact of an extremely large magnitude volcanic eruption on the global climate and carbon cycle estimated from ensemble Earth System Model simulations

    Science.gov (United States)

    Segschneider, J.; Beitsch, A.; Timmreck, C.; Brovkin, V.; Ilyina, T.; Jungclaus, J.; Lorenz, S. J.; Six, K. D.; Zanchettin, D.

    2013-02-01

    The response of the global climate-carbon cycle system to an extremely large Northern Hemisphere mid-latitude volcanic eruption is investigated using ensemble integrations with the comprehensive Earth System Model MPI-ESM. The model includes dynamical compartments of the atmosphere and ocean and interactive modules of the terrestrial biosphere as well as ocean biogeochemistry. The MPI-ESM was forced with anomalies of aerosol optical depth and effective radius of aerosol particles corresponding to a super eruption of the Yellowstone volcanic system. The model experiment consists of an ensemble of fifteen model integrations that are started at different pre-ENSO states of a control experiment and run for 200 years after the volcanic eruption. The climate response to the volcanic eruption is a maximum global monthly mean surface air temperature cooling of 3.8 K for the ensemble mean and from 3.3 K to 4.3 K for individual ensemble members. Atmospheric pCO2 decreases by a maximum of 5 ppm for the ensemble mean and by 3 ppm to 7 ppm for individual ensemble members approximately 6 years after the eruption. The atmospheric carbon content only very slowly returns to near pre-eruption level at year 200 after the eruption. The ocean takes up carbon shortly after the eruption in response to the cooling, changed wind fields and ice cover. This physics-driven uptake is weakly counteracted by a reduction of the biological export production mainly in the tropical Pacific. The land vegetation pool shows a decrease by 4 GtC due to reduced short-wave radiation that has not been present in a smaller scale eruption. The gain of the soil carbon pool determines the amplitude of the CO2 perturbation and the long-term behaviour of the overall system: an initial gain caused by reduced soil respiration is followed by a rather slow return towards pre-eruption levels. During this phase, the ocean compensates partly for the reduced atmospheric carbon content in response to the land's gain. In

  11. Impact of an extremely large magnitude volcanic eruption on the global climate and carbon cycle estimated from ensemble Earth System Model simulations

    Directory of Open Access Journals (Sweden)

    J. Segschneider

    2013-02-01

    Full Text Available The response of the global climate-carbon cycle system to an extremely large Northern Hemisphere mid-latitude volcanic eruption is investigated using ensemble integrations with the comprehensive Earth System Model MPI-ESM. The model includes dynamical compartments of the atmosphere and ocean and interactive modules of the terrestrial biosphere as well as ocean biogeochemistry. The MPI-ESM was forced with anomalies of aerosol optical depth and effective radius of aerosol particles corresponding to a super eruption of the Yellowstone volcanic system. The model experiment consists of an ensemble of fifteen model integrations that are started at different pre-ENSO states of a control experiment and run for 200 years after the volcanic eruption. The climate response to the volcanic eruption is a maximum global monthly mean surface air temperature cooling of 3.8 K for the ensemble mean and from 3.3 K to 4.3 K for individual ensemble members. Atmospheric pCO2 decreases by a maximum of 5 ppm for the ensemble mean and by 3 ppm to 7 ppm for individual ensemble members approximately 6 years after the eruption. The atmospheric carbon content only very slowly returns to near pre-eruption level at year 200 after the eruption. The ocean takes up carbon shortly after the eruption in response to the cooling, changed wind fields and ice cover. This physics-driven uptake is weakly counteracted by a reduction of the biological export production mainly in the tropical Pacific. The land vegetation pool shows a decrease by 4 GtC due to reduced short-wave radiation that has not been present in a smaller scale eruption. The gain of the soil carbon pool determines the amplitude of the CO2 perturbation and the long-term behaviour of the overall system: an initial gain caused by reduced soil respiration is followed by a rather slow return towards pre-eruption levels. During this phase, the ocean compensates partly for the reduced atmospheric

  12. Seasonal streamflow prediction using ensemble streamflow prediction technique for the Rangitata and Waitaki River basins on the South Island of New Zealand

    Science.gov (United States)

    Singh, Shailesh Kumar

    2014-05-01

    Streamflow forecasts are essential for making critical decision for optimal allocation of water supplies for various demands that include irrigation for agriculture, habitat for fisheries, hydropower production and flood warning. The major objective of this study is to explore the Ensemble Streamflow Prediction (ESP) based forecast in New Zealand catchments and to highlights the present capability of seasonal flow forecasting of National Institute of Water and Atmospheric Research (NIWA). In this study a probabilistic forecast framework for ESP is presented. The basic assumption in ESP is that future weather pattern were experienced historically. Hence, past forcing data can be used with current initial condition to generate an ensemble of prediction. Small differences in initial conditions can result in large difference in the forecast. The initial state of catchment can be obtained by continuously running the model till current time and use this initial state with past forcing data to generate ensemble of flow for future. The approach taken here is to run TopNet hydrological models with a range of past forcing data (precipitation, temperature etc.) with current initial conditions. The collection of runs is called the ensemble. ESP give probabilistic forecasts for flow. From ensemble members the probability distributions can be derived. The probability distributions capture part of the intrinsic uncertainty in weather or climate. An ensemble stream flow prediction which provide probabilistic hydrological forecast with lead time up to 3 months is presented for Rangitata, Ahuriri, and Hooker and Jollie rivers in South Island of New Zealand. ESP based seasonal forecast have better skill than climatology. This system can provide better over all information for holistic water resource management.

  13. Multi-model ensemble simulations of olive pollen distribution in Europe in 2014: current status and outlook

    Science.gov (United States)

    Sofiev, Mikhail; Ritenberga, Olga; Albertini, Roberto; Arteta, Joaquim; Belmonte, Jordina; Geller Bernstein, Carmi; Bonini, Maira; Celenk, Sevcan; Damialis, Athanasios; Douros, John; Elbern, Hendrik; Friese, Elmar; Galan, Carmen; Oliver, Gilles; Hrga, Ivana; Kouznetsov, Rostislav; Krajsek, Kai; Magyar, Donat; Parmentier, Jonathan; Plu, Matthieu; Prank, Marje; Robertson, Lennart; Steensen, Birthe Marie; Thibaudon, Michel; Segers, Arjo; Stepanovich, Barbara; Valdebenito, Alvaro M.; Vira, Julius; Vokou, Despoina

    2017-10-01

    The paper presents the first modelling experiment of the European-scale olive pollen dispersion, analyses the quality of the predictions, and outlines the research needs. A 6-model strong ensemble of Copernicus Atmospheric Monitoring Service (CAMS) was run throughout the olive season of 2014, computing the olive pollen distribution. The simulations have been compared with observations in eight countries, which are members of the European Aeroallergen Network (EAN). Analysis was performed for individual models, the ensemble mean and median, and for a dynamically optimised combination of the ensemble members obtained via fusion of the model predictions with observations. The models, generally reproducing the olive season of 2014, showed noticeable deviations from both observations and each other. In particular, the season was reported to start too early by 8 days, but for some models the error mounted to almost 2 weeks. For the end of the season, the disagreement between the models and the observations varied from a nearly perfect match up to 2 weeks too late. A series of sensitivity studies carried out to understand the origin of the disagreements revealed the crucial role of ambient temperature and consistency of its representation by the meteorological models and heat-sum-based phenological model. In particular, a simple correction to the heat-sum threshold eliminated the shift of the start of the season but its validity in other years remains to be checked. The short-term features of the concentration time series were reproduced better, suggesting that the precipitation events and cold/warm spells, as well as the large-scale transport, were represented rather well. Ensemble averaging led to more robust results. The best skill scores were obtained with data fusion, which used the previous days' observations to identify the optimal weighting coefficients of the individual model forecasts. Such combinations were tested for the forecasting period up to 4 days and

  14. Group exhibition: Ensemble sin órganos (Ensemble without organs) at Centro de Arte Contemporáneo Wifredo Lam

    OpenAIRE

    Schwindt, Grace

    2016-01-01

    Ensemble sin órganos \\ud (Ensemble without organs)\\ud July 7–24, 2016 \\ud \\ud Centro de Arte Contemporáneo Wifredo Lam, Havana \\ud San Ignacio No.22, Esquina a Empedrado\\ud Plaza de la Catedral\\ud Havana\\ud Cuba \\ud \\ud T +53 7 8646284 \\ud \\ud \\ud www.wlam.cult.cu \\ud \\ud Curated by Blanca Victoria López and Victor Wang\\ud \\ud Participating artists include: \\ud Ana Mendieta (Cuba) / Anne Teresa De Keersmaeker (Belgium) / Carlos Martiel (Cuba) / Cibelle Cavalli Bastos ...

  15. Statistical prediction of far-field wind-turbine noise, with probabilistic characterization of atmospheric stability

    DEFF Research Database (Denmark)

    Kelly, Mark C.; Barlas, Emre; Sogachev, Andrey

    2018-01-01

    Here we provide statistical low-order characterization of noise propagation from a single wind turbine, as affected by mutually interacting turbine wake and environmental conditions. This is accomplished via a probabilistic model, applied to an ensemble of atmospheric conditions based upon atmosp...

  16. Parameter Estimation for Observation Bias with an Ensemble Kalman Filter

    Science.gov (United States)

    Lorente-Plazas, R.; Hacker, J.

    2015-12-01

    In this work we evaluate a method to estimate systematic errors of individual in-situ observations. The approach is based on parameter estimation using an augmented state in an ensemble adjustment Kalman filter. Biased observations are assimilated in the highly chaotic Lorenz (2005) model that combines small and large scales. For this purpose, synthetic observations are created by introducing a grid-point dependent bias and random errors to a nature run (truth). The sensitivity of the methodology to model error, the number of ensemble members, the number of parameters, and the parameter variance is evaluated. Results demonstrate that the methodology is able to estimate observation bias for a both a perfect model and an imperfect model if the model error is estimated. The parameter estimation and the rms errors are significantly deteriorated if model error is ignored. Errors are qualitatively independent of model forcing when model error is estimated. By contrast, parameter estimation depends on model error when model error is not estimated, especially if observation biases are estimated. Errors increase with the number of estimated parameters, but they are independent of ensemble size as long as the number of ensembles members is large enough. There is an optimum value of parameter variance that minimizes the errors and improves the parameter estimation, but this value depends on observation bias and model error. Overall, the results suggest more accuracy in observation bias estimates than model error estimates.

  17. Short-term ensemble radar rainfall forecasts for hydrological applications

    Science.gov (United States)

    Codo de Oliveira, M.; Rico-Ramirez, M. A.

    2016-12-01

    Flooding is a very common natural disaster around the world, putting local population and economy at risk. Forecasting floods several hours ahead and issuing warnings are of main importance to permit proper response in emergency situations. However, it is important to know the uncertainties related to the rainfall forecasting in order to produce more reliable forecasts. Nowcasting models (short-term rainfall forecasts) are able to produce high spatial and temporal resolution predictions that are useful in hydrological applications. Nonetheless, they are subject to uncertainties mainly due to the nowcasting model used, errors in radar rainfall estimation, temporal development of the velocity field and to the fact that precipitation processes such as growth and decay are not taken into account. In this study an ensemble generation scheme using rain gauge data as a reference to estimate radars errors is used to produce forecasts with up to 3h lead-time. The ensembles try to assess in a realistic way the residual uncertainties that remain even after correction algorithms are applied in the radar data. The ensembles produced are compered to a stochastic ensemble generator. Furthermore, the rainfall forecast output was used as an input in a hydrodynamic sewer network model and also in hydrological model for catchments of different sizes in north England. A comparative analysis was carried of how was carried out to assess how the radar uncertainties propagate into these models. The first named author is grateful to CAPES - Ciencia sem Fronteiras for funding this PhD research.

  18. Control of inhomogeneous atomic ensembles of hyperfine qudits

    DEFF Research Database (Denmark)

    Mischuck, Brian Edward; Merkel, Seth T.; Deutsch, Ivan H.

    2012-01-01

    to the design of robust pulses that mitigate the effects of experimental uncertainty and also for application to tomographic addressing of particular members of an extended ensemble. We study the problem of preparing an arbitrary state in the Hilbert space from an initial fiducial state. We prove...

  19. Random matrix ensembles with random interactions: Results for ...

    Indian Academy of Sciences (India)

    quadrupole. (Q · Q) operator over the EGOE(1+2)-SU(4) ensemble. Finally, going beyond. EGUE(2)-SU(4), it is both interesting and possible (by extending and applying the SU(4) ⊃ SUS(2) ⊗ SUT (2) Wigner–Racah algebra developed by Hecht and.

  20. Tweet-based Target Market Classification Using Ensemble Method

    Directory of Open Access Journals (Sweden)

    Muhammad Adi Khairul Anshary

    2016-09-01

    Full Text Available Target market classification is aimed at focusing marketing activities on the right targets. Classification of target markets can be done through data mining and by utilizing data from social media, e.g. Twitter. The end result of data mining are learning models that can classify new data. Ensemble methods can improve the accuracy of the models and therefore provide better results. In this study, classification of target markets was conducted on a dataset of 3000 tweets in order to extract features. Classification models were constructed to manipulate the training data using two ensemble methods (bagging and boosting. To investigate the effectiveness of the ensemble methods, this study used the CART (classification and regression tree algorithm for comparison. Three categories of consumer goods (computers, mobile phones and cameras and three categories of sentiments (positive, negative and neutral were classified towards three target-market categories. Machine learning was performed using Weka 3.6.9. The results of the test data showed that the bagging method improved the accuracy of CART with 1.9% (to 85.20%. On the other hand, for sentiment classification, the ensemble methods were not successful in increasing the accuracy of CART. The results of this study may be taken into consideration by companies who approach their customers through social media, especially Twitter.

  1. Canonical Ensemble Model for Black Hole Radiation Jingyi Zhang

    Indian Academy of Sciences (India)

    Abstract. In this paper, a canonical ensemble model for the black hole quantum tunnelling radiation is introduced. In this model the probability distribution function corresponding to the emission shell is calculated to second order. The formula of pressure and internal energy of the thermal system is modified, and the ...

  2. Canonical Ensemble Model for Black Hole Horizon of Schwarzschild ...

    Indian Academy of Sciences (India)

    In this paper, we use the canonical ensemble model to discuss the radiation of a Schwarzschild–de Sitter black hole on the black hole horizon. Using this model, we calculate the probability distribution from function of the emission shell. And the statistical meaning which compare with the distribution function is used to ...

  3. Canonical Ensemble Model for Black Hole Horizon of Schwarzschild ...

    Indian Academy of Sciences (India)

    Abstract. In this paper, we use the canonical ensemble model to discuss the radiation of a Schwarzschild–de Sitter black hole on the black hole horizon. Using this model, we calculate the probability distribution from function of the emission shell. And the statistical meaning which compare with the distribution function is ...

  4. Conceptualizing Conceptual Teaching: Practical Strategies for Large Instrumental Ensembles

    Science.gov (United States)

    Tan, Leonard

    2016-01-01

    Half a century ago, calls had already been made for instrumental ensemble directors to move beyond performance to include the teaching of musical concepts in the rehearsal hall. Relatively recent research, however, suggests that conceptual teaching remains relatively infrequent during rehearsals. Given the importance of teaching for long-term…

  5. Modelling of drug release from ensembles of aspirin microcapsules ...

    African Journals Online (AJOL)

    Purpose: In order to determine the drug release profile of an ensemble of aspirin crystals or microcapsules from its particle distribution a mathematical model that considered the individual release characteristics of the component single particles was developed. The model assumed that under sink conditions the release ...

  6. Bayesian model ensembling using meta-trained recurrent neural networks

    NARCIS (Netherlands)

    Ambrogioni, L.; Berezutskaya, Y.; Gü ç lü , U.; Borne, E.W.P. van den; Gü ç lü tü rk, Y.; Gerven, M.A.J. van; Maris, E.G.G.

    2017-01-01

    In this paper we demonstrate that a recurrent neural network meta-trained on an ensemble of arbitrary classification tasks can be used as an approximation of the Bayes optimal classifier. This result is obtained by relying on the framework of e-free approximate Bayesian inference, where the Bayesian

  7. Exploiting ensemble learning for automatic cataract detection and grading.

    Science.gov (United States)

    Yang, Ji-Jiang; Li, Jianqiang; Shen, Ruifang; Zeng, Yang; He, Jian; Bi, Jing; Li, Yong; Zhang, Qinyan; Peng, Lihui; Wang, Qing

    2016-02-01

    Cataract is defined as a lenticular opacity presenting usually with poor visual acuity. It is one of the most common causes of visual impairment worldwide. Early diagnosis demands the expertise of trained healthcare professionals, which may present a barrier to early intervention due to underlying costs. To date, studies reported in the literature utilize a single learning model for retinal image classification in grading cataract severity. We present an ensemble learning based approach as a means to improving diagnostic accuracy. Three independent feature sets, i.e., wavelet-, sketch-, and texture-based features, are extracted from each fundus image. For each feature set, two base learning models, i.e., Support Vector Machine and Back Propagation Neural Network, are built. Then, the ensemble methods, majority voting and stacking, are investigated to combine the multiple base learning models for final fundus image classification. Empirical experiments are conducted for cataract detection (two-class task, i.e., cataract or non-cataractous) and cataract grading (four-class task, i.e., non-cataractous, mild, moderate or severe) tasks. The best performance of the ensemble classifier is 93.2% and 84.5% in terms of the correct classification rates for cataract detection and grading tasks, respectively. The results demonstrate that the ensemble classifier outperforms the single learning model significantly, which also illustrates the effectiveness of the proposed approach. Copyright © 2015 Elsevier Ireland Ltd. All rights reserved.

  8. Path planning in uncertain flow fields using ensemble method

    Science.gov (United States)

    Wang, Tong; Le Maître, Olivier P.; Hoteit, Ibrahim; Knio, Omar M.

    2016-10-01

    An ensemble-based approach is developed to conduct optimal path planning in unsteady ocean currents under uncertainty. We focus our attention on two-dimensional steady and unsteady uncertain flows, and adopt a sampling methodology that is well suited to operational forecasts, where an ensemble of deterministic predictions is used to model and quantify uncertainty. In an operational setting, much about dynamics, topography, and forcing of the ocean environment is uncertain. To address this uncertainty, the flow field is parametrized using a finite number of independent canonical random variables with known densities, and the ensemble is generated by sampling these variables. For each of the resulting realizations of the uncertain current field, we predict the path that minimizes the travel time by solving a boundary value problem (BVP), based on the Pontryagin maximum principle. A family of backward-in-time trajectories starting at the end position is used to generate suitable initial values for the BVP solver. This allows us to examine and analyze the performance of the sampling strategy and to develop insight into extensions dealing with general circulation ocean models. In particular, the ensemble method enables us to perform a statistical analysis of travel times and consequently develop a path planning approach that accounts for these statistics. The proposed methodology is tested for a number of scenarios. We first validate our algorithms by reproducing simple canonical solutions, and then demonstrate our approach in more complex flow fields, including idealized, steady and unsteady double-gyre flows.

  9. Nitrogen-vacancy ensemble magnetometry based on pump absorption

    DEFF Research Database (Denmark)

    Ahmadi, Sepehr; El-Ella, Haitham A. R.; Wojciechowski, Adam M.

    2018-01-01

    We demonstrate magnetic-field sensing using an ensemble of nitrogen-vacancy centers by recording the variation in the pump-light absorption due to the spin-polarization dependence of the total ground-state population. Using a 532 nm pump laser, we measure the absorption of native nitrogen-vacancy...... the nitrogen-vacancy concentration and the detection method....

  10. Malignancy and Abnormality Detection of Mammograms using Classifier Ensembling

    Directory of Open Access Journals (Sweden)

    Nawazish Naveed

    2011-07-01

    Full Text Available The breast cancer detection and diagnosis is a critical and complex procedure that demands high degree of accuracy. In computer aided diagnostic systems, the breast cancer detection is a two stage procedure. First, to classify the malignant and benign mammograms, while in second stage, the type of abnormality is detected. In this paper, we have developed a novel architecture to enhance the classification of malignant and benign mammograms using multi-classification of malignant mammograms into six abnormality classes. DWT (Discrete Wavelet Transformation features are extracted from preprocessed images and passed through different classifiers. To improve accuracy, results generated by various classifiers are ensembled. The genetic algorithm is used to find optimal weights rather than assigning weights to the results of classifiers on the basis of heuristics. The mammograms declared as malignant by ensemble classifiers are divided into six classes. The ensemble classifiers are further used for multiclassification using one-against-all technique for classification. The output of all ensemble classifiers is combined by product, median and mean rule. It has been observed that the accuracy of classification of abnormalities is more than 97% in case of mean rule. The Mammographic Image Analysis Society dataset is used for experimentation.

  11. Development of multimodel ensemble based district level medium ...

    Indian Academy of Sciences (India)

    In this paper, we describe the development strategy of the technique and performance skill of the system during summer monsoon 2009. The study demonstrates the potential of the system for improving rainfall forecasts at five days time scale over Indian region. Districtwise performance of the ensemble rainfall forecast ...

  12. Local ensemble assimilation scheme with global constraints and conservation

    Science.gov (United States)

    Barth, Alexander; Yan, Yajing; Alvera-Azcárate, Aida; Beckers, Jean-Marie

    2016-12-01

    Ensemble assimilation schemes applied in their original, global formulation respect linear conservation properties if the ensemble perturbations are set up accordingly. For realistic ocean systems, only a relatively small number of ensemble members can be calculated. A localization of the ensemble increment is therefore necessary to filter out spurious long-range correlations. The conservation of the global properties will be lost if the assimilation is performed locally, since the conservation requires a coupling between all model grid points which is removed by the localization. The distribution of ocean observations is often highly inhomogeneous. Systematic errors of the observed parts of the ocean state can lead to spurious adjustment of the non-observed parts via data assimilation and thus to a spurious increase or decrease in long-term simulations of global properties which should be conserved. In this paper, we propose a local assimilation scheme (with different variants and assumptions) which can satisfy global conservation properties. The proposed scheme can also be used for non-local observation operators. Different variants of the proposed scheme are tested in an idealized model and compared to the traditional covariance localization with an ad-hoc step enforcing conservation. It is shown that the inclusion of the conservation property reduces the total RMS error and that the presented stochastic and deterministic schemes avoiding error space rotation provide better results than the traditional covariance localization.

  13. Social behaviour shapes hypothalamic neural ensemble representations of conspecific sex

    Science.gov (United States)

    Remedios, Ryan; Kennedy, Ann; Zelikowsky, Moriel; Grewe, Benjamin F.; Schnitzer, Mark J.; Anderson, David J.

    2017-10-01

    All animals possess a repertoire of innate (or instinctive) behaviours, which can be performed without training. Whether such behaviours are mediated by anatomically distinct and/or genetically specified neural pathways remains unknown. Here we report that neural representations within the mouse hypothalamus, that underlie innate social behaviours, are shaped by social experience. Oestrogen receptor 1-expressing (Esr1+) neurons in the ventrolateral subdivision of the ventromedial hypothalamus (VMHvl) control mating and fighting in rodents. We used microendoscopy to image Esr1+ neuronal activity in the VMHvl of male mice engaged in these social behaviours. In sexually and socially experienced adult males, divergent and characteristic neural ensembles represented male versus female conspecifics. However, in inexperienced adult males, male and female intruders activated overlapping neuronal populations. Sex-specific neuronal ensembles gradually separated as the mice acquired social and sexual experience. In mice permitted to investigate but not to mount or attack conspecifics, ensemble divergence did not occur. However, 30 minutes of sexual experience with a female was sufficient to promote the separation of male and female ensembles and to induce an attack response 24 h later. These observations uncover an unexpected social experience-dependent component to the formation of hypothalamic neural assemblies controlling innate social behaviours. More generally, they reveal plasticity and dynamic coding in an evolutionarily ancient deep subcortical structure that is traditionally viewed as a ‘hard-wired’ system.

  14. Ensemble: an Architecture for Mission-Operations Software

    Science.gov (United States)

    Norris, Jeffrey; Powell, Mark; Fox, Jason; Rabe, Kenneth; Shu, IHsiang; McCurdy, Michael; Vera, Alonso

    2008-01-01

    Ensemble is the name of an open architecture for, and a methodology for the development of, spacecraft mission operations software. Ensemble is also potentially applicable to the development of non-spacecraft mission-operations- type software. Ensemble capitalizes on the strengths of the open-source Eclipse software and its architecture to address several issues that have arisen repeatedly in the development of mission-operations software: Heretofore, mission-operations application programs have been developed in disparate programming environments and integrated during the final stages of development of missions. The programs have been poorly integrated, and it has been costly to develop, test, and deploy them. Users of each program have been forced to interact with several different graphical user interfaces (GUIs). Also, the strategy typically used in integrating the programs has yielded serial chains of operational software tools of such a nature that during use of a given tool, it has not been possible to gain access to the capabilities afforded by other tools. In contrast, the Ensemble approach offers a low-risk path towards tighter integration of mission-operations software tools.

  15. Ensemble learning as approach for pipeline condition assessment

    Science.gov (United States)

    Camacho-Navarro, Jhonatan; Ruiz, Magda; Villamizar, Rodolfo; Mujica, Luis; Moreno-Beltrán, Gustavo

    2017-05-01

    The algorithms commonly used for damage condition monitoring present several drawbacks related to unbalanced data, optimal training requirements, low capability to manage feature diversity and low tolerance to errors. In this work, an approach based on ensemble learning is discussed as alternative to obtain more efficient diagnosis. The main advantage of ensemble learning is the use of several algorithms at the same time for a better proficiency. Thereby, combining simplest tree decision algorithms in bagging scheme, the accuracy of damage detection is improved. It takes advantage by combining prediction of preliminary algorithms based on regression models. The methodology is experimentally validated on a carbon steel pipe section, where mass adding conditions are studied as possible failures. Data from an active system based on piezoelectric sensors are stored and characterized through the T2 and Q statistical indexes. Then, they are the inputs to the ensemble learning. The proposed methodology allows determining the condition assessment and damage localizations in the structure. The results of the studied cases show the feasibility of ensemble learning for detecting occurrence of structural damages with successful results.

  16. Orchestrating Literacies: Print Literacy Learning Opportunities within Multimodal Intergenerational Ensembles

    Science.gov (United States)

    McKee, Lori L.; Heydon, Rachel M.

    2015-01-01

    This exploratory case study considered the opportunities for print literacy learning within multimodal ensembles that featured art, singing and digital media within the context of an intergenerational programme that brought together 13 kindergarten children (4 and 5 years) with seven elder companions. Study questions concerned how reading and…

  17. Ensemble modeling for aromatic production in Escherichia coli.

    Directory of Open Access Journals (Sweden)

    Matthew L Rizk

    2009-09-01

    Full Text Available Ensemble Modeling (EM is a recently developed method for metabolic modeling, particularly for utilizing the effect of enzyme tuning data on the production of a specific compound to refine the model. This approach is used here to investigate the production of aromatic products in Escherichia coli. Instead of using dynamic metabolite data to fit a model, the EM approach uses phenotypic data (effects of enzyme overexpression or knockouts on the steady state production rate to screen possible models. These data are routinely generated during strain design. An ensemble of models is constructed that all reach the same steady state and are based on the same mechanistic framework at the elementary reaction level. The behavior of the models spans the kinetics allowable by thermodynamics. Then by using existing data from the literature for the overexpression of genes coding for transketolase (Tkt, transaldolase (Tal, and phosphoenolpyruvate synthase (Pps to screen the ensemble, we arrive at a set of models that properly describes the known enzyme overexpression phenotypes. This subset of models becomes more predictive as additional data are used to refine the models. The final ensemble of models demonstrates the characteristic of the cell that Tkt is the first rate controlling step, and correctly predicts that only after Tkt is overexpressed does an increase in Pps increase the production rate of aromatics. This work demonstrates that EM is able to capture the result of enzyme overexpression on aromatic producing bacteria by successfully utilizing routinely generated enzyme tuning data to guide model learning.

  18. Hierarchical Learning Ensembles: Team Building for Undergraduate Scientists and Engineers

    Science.gov (United States)

    DiLisi, Gregory A.; Eppell, Steven J.; Upton, Jan

    2006-01-01

    We describe the design and implementation of our Hierarchical Learning Ensemble (HLE) model, a pedagogy that assembles interdisciplinary teams of graduate, undergraduate, and secondary-level students to solve science and engineering problems. Our goal is to sensitize undergraduates to working in heterogeneous groups and thus better prepare them…

  19. Middle School Drum Ensemble: An Unlikely Experience in Classroom Democracy

    Science.gov (United States)

    Barbre, James

    2013-01-01

    Though music has a long and successful history within education, it is often one of the first sacrificial lambs when school budgets tighten. Over the course of an academic year, a documentary film sought to tell the story of an American middle school drum ensemble. The context of this group provided an ideal way to examine the nature of student…

  20. Bayesian ensemble approach to error estimation of interatomic potentials

    DEFF Research Database (Denmark)

    Frederiksen, Søren Lund; Jacobsen, Karsten Wedel; Brown, K.S.

    2004-01-01

    Using a Bayesian approach a general method is developed to assess error bars on predictions made by models fitted to data. The error bars are estimated from fluctuations in ensembles of models sampling the model-parameter space with a probability density set by the minimum cost. The method is app...

  1. Music Ensemble Participation: Personality Traits and Music Experience

    Science.gov (United States)

    Torrance, Tracy A.; Bugos, Jennifer A.

    2017-01-01

    The purpose of this study was two-fold: (1) to examine the relationship between personality type and ensemble choice and (2) to examine the differences in personality across age and music experience in young adults. Participants (N = 137; 68 instrumentalists, 69 vocalists) completed a demographic survey and the Big Five Personality Inventory.…

  2. Peer-Teaching in the Secondary Music Ensemble

    Science.gov (United States)

    Johnson, Erik

    2015-01-01

    Peer-teaching is an instructional technique that has been used by teachers world-wide to successfully engage, exercise and deepen student learning. Yet, in some instances, teachers find the application of peer-teaching in large music ensembles at the secondary level to be daunting. This article is meant to be a practical resource for secondary…

  3. The Art of Compiling Protein Binding Site Ensembles.

    Science.gov (United States)

    Bietz, Stefan; Fährrolfes, Rainer; Rarey, Matthias

    2016-12-01

    Structure-based drug design starts with the collection, preparation, and initial analysis of protein structures. With more than 115,000 structures publically available in the Protein Data Bank (PDB), fully automated processes reliably performing these important preprocessing steps are needed. Several tools are available for these tasks, however, most of them do not address the special needs of scientists interested in protein-ligand interactions. In this paper, we summarize our research activities towards an automated processing pipeline from raw PDB data towards ready-to-use protein binding site ensembles. Starting from a single protein structure, the pipeline covers the following phases: Extracting structurally related binding sites from the PDB, aligning disconnected binding site sequences, resolving tautomeric forms and protonation, orienting hydrogens and flippable side-chains, structurally aligning the multitude of binding sites, and performing a reasonable reduction of ensemble structures. The pipeline, named SIENA, creates protein-structural ensembles for the analysis of protein flexibility, molecular design efforts like docking or de novo design within seconds. For the first time, we are able to process the whole PDB in order to create a large collection of protein binding site ensembles. SIENA is available as part of the ZBH ProteinsPlus webserver under http://proteinsplus.zbh.uni-hamburg.de. © 2016 Wiley-VCH Verlag GmbH & Co. KGaA, Weinheim.

  4. An Ensemble Approach in Converging Contents of LMS and KMS

    Science.gov (United States)

    Sabitha, A. Sai; Mehrotra, Deepti; Bansal, Abhay

    2017-01-01

    Currently the challenges in e-Learning are converging the learning content from various sources and managing them within e-learning practices. Data mining learning algorithms can be used and the contents can be converged based on the Metadata of the objects. Ensemble methods use multiple learning algorithms and it can be used to converge the…

  5. Coherent control of mesoscopic atomic ensembles for quantum information

    OpenAIRE

    Beterov, I. I.; Saffman, M.; Zhukov, V. P.; Tretyakov, D. B.; Entin, V. M.; Yakshina, E. A.; Ryabtsev, I. I.; Mansell, C. W.; MacCormick, C.; Bergamini, S.; Fedoruk, M. P.

    2013-01-01

    We discuss methods for coherently controlling mesoscopic atomic ensembles where the number of atoms varies randomly from one experimental run to the next. The proposed schemes are based on adiabatic passage and Rydberg blockade and can be used for implementation of a scalable quantum register formed by an array of randomly loaded optical dipole traps.

  6. A Robust Multimodel Framework for Ensemble Seasonal Hydroclimatic Forecasts

    Science.gov (United States)

    Mendoza, P. A.; Rajagopalan, B.; Clark, M. P.; Cortés, G.; McPhee, J. P.

    2014-12-01

    We provide a framework for careful analysis of the different methodological choices we make when constructing multimodel ensemble seasonal forecasts of hydroclimatic variables. Specifically, we focus on three common modeling decisions: (i) number of models, (ii) multimodel combination approach, and (iii) lead time for prediction. The analysis scheme includes a multimodel ensemble forecasting algorithm based on nonparametric regression, a set of alternatives for the options previously pointed, and a selection of probabilistic verification methods for ensemble forecast evaluation. The usefulness of this framework is tested through an example application aimed to generate spring/summer streamflow forecasts at multiple locations in Central Chile. Results demonstrate the high impact that subjectivity in decision-making may have on the quality of ensemble seasonal hydroclimatic forecasts. In particular, we note that the probabilistic verification criteria may lead to different choices regarding the number of models or the multimodel combination method. We also illustrate how this objective analysis scheme may lead to results that are extremely relevant for the case study presented here, such as skillful seasonal streamflow predictions for very dry conditions.

  7. Applications of Random Matrix Ensembles in Nuclear Systems

    OpenAIRE

    Duras, Maciej M.

    2003-01-01

    The random matrix ensembles (RME), especially Gaussian RME and Ginibre RME, are applied to nuclear systems, molecular systems, and two-dimensional electron systems (Wigner-Dyson electrostatic analogy). Measures of quantum chaos and quantum integrability with respect to eigenergies of quantum systems are defined and calculated.

  8. Spatio-Temporal Ensemble Prediction on Mobile Broadband Network Data

    DEFF Research Database (Denmark)

    Samulevicius, Saulius; Pitarch, Yoann; Pedersen, Torben Bach

    2013-01-01

    network nodes, fully or partly, in low traffic loads. To accomplish such a dynamic network optimization, it is crucial to predict very accurately low traffic periods. In this paper, we tackle this problem using data mining and propose Spatio-Temporal Ensemble Prediction (STEP). In a nutshell, STEP...

  9. Large-scale ensemble averaging of ambulatory impedance cardiograms

    NARCIS (Netherlands)

    Riese, H; Groot, PFC; Van Den Berg, M; Kupper, NHM; Magnee, EHB; Rohaan, EJ; Vrijkotte, TGM; Willemsen, G; De Geus, EJC

    Impedance cardiography has been used increasingly to measure human physiological responses to emotional and mentally engaging stimuli. The validity of large-scale ensemble averaging of ambulatory impedance cardiograms was evaluated for preejection period (PEP), interbeat interval, and dZ/dt((min))

  10. Korean Percussion Ensemble ("Samulnori") in the General Music Classroom

    Science.gov (United States)

    Kang, Sangmi; Yoo, Hyesoo

    2016-01-01

    This article introduces "samulnori" (Korean percussion ensemble), its cultural background, and instructional methods as parts of a classroom approach to teaching upper-level general music. We introduce five of eight sections from "youngnam nong-ak" (a style of samulnori) as a repertoire for teaching Korean percussion music to…

  11. Enhancing COSMO-DE ensemble forecasts by inexpensive techniques

    Directory of Open Access Journals (Sweden)

    Zied Ben Bouallègue

    2013-02-01

    Full Text Available COSMO-DE-EPS, a convection-permitting ensemble prediction system based on the high-resolution numerical weather prediction model COSMO-DE, is pre-operational since December 2010, providing probabilistic forecasts which cover Germany. This ensemble system comprises 20 members based on variations of the lateral boundary conditions, the physics parameterizations and the initial conditions. In order to increase the sample size in a computationally inexpensive way, COSMO-DE-EPS is combined with alternative ensemble techniques: the neighborhood method and the time-lagged approach. Their impact on the quality of the resulting probabilistic forecasts is assessed. Objective verification is performed over a six months period, scores based on the Brier score and its decomposition are shown for June 2011. The combination of the ensemble system with the alternative approaches improves probabilistic forecasts of precipitation in particular for high precipitation thresholds. Moreover, combining COSMO-DE-EPS with only the time-lagged approach improves the skill of area probabilities for precipitation and does not deteriorate the skill of 2 m-temperature and wind gusts forecasts.

  12. Inhomogeneous ensembles of radical pairs in chemical compasses

    Science.gov (United States)

    Procopio, Maria; Ritz, Thorsten

    2016-11-01

    The biophysical basis for the ability of animals to detect the geomagnetic field and to use it for finding directions remains a mystery of sensory biology. One much debated hypothesis suggests that an ensemble of specialized light-induced radical pair reactions can provide the primary signal for a magnetic compass sensor. The question arises what features of such a radical pair ensemble could be optimized by evolution so as to improve the detection of the direction of weak magnetic fields. Here, we focus on the overlooked aspect of the noise arising from inhomogeneity of copies of biomolecules in a realistic biological environment. Such inhomogeneity leads to variations of the radical pair parameters, thereby deteriorating the signal arising from an ensemble and providing a source of noise. We investigate the effect of variations in hyperfine interactions between different copies of simple radical pairs on the directional response of a compass system. We find that the choice of radical pair parameters greatly influences how strongly the directional response of an ensemble is affected by inhomogeneity.

  13. Inhomogeneous broadening in non-interacting nonlocal plasmonic ensembles

    DEFF Research Database (Denmark)

    Tserkezis, Christos; Maack, Johan Rosenkrantz; Liu, Z.

    2016-01-01

    The importance of inhomogeneous broadening due to the size dependence of plasmon resonances in few-nm metallic particle ensembles is investigated through different models describing the nonlocal optical response of plasmonic nanospheres. Modal shifts and plasmon line broadening are shown to becom...

  14. The egg model - A geological ensemble for reservoir simulation

    NARCIS (Netherlands)

    Jansen, J.D.; Fonseca, R.M.; Kahrobaei, S.; Siraj, M.M.; Van Essen, G.M.; Van den Hof, P.M.J.

    2014-01-01

    The ‘Egg Model’ is a synthetic reservoir model consisting of an ensemble of 101 relatively small three-dimensional realizations of a channelized oil reservoir produced under water flooding conditions with eight water injectors and four oil producers. It has been used in numerous publications to

  15. Path planning in uncertain flow fields using ensemble method

    KAUST Repository

    Wang, Tong

    2016-08-20

    An ensemble-based approach is developed to conduct optimal path planning in unsteady ocean currents under uncertainty. We focus our attention on two-dimensional steady and unsteady uncertain flows, and adopt a sampling methodology that is well suited to operational forecasts, where an ensemble of deterministic predictions is used to model and quantify uncertainty. In an operational setting, much about dynamics, topography, and forcing of the ocean environment is uncertain. To address this uncertainty, the flow field is parametrized using a finite number of independent canonical random variables with known densities, and the ensemble is generated by sampling these variables. For each of the resulting realizations of the uncertain current field, we predict the path that minimizes the travel time by solving a boundary value problem (BVP), based on the Pontryagin maximum principle. A family of backward-in-time trajectories starting at the end position is used to generate suitable initial values for the BVP solver. This allows us to examine and analyze the performance of the sampling strategy and to develop insight into extensions dealing with general circulation ocean models. In particular, the ensemble method enables us to perform a statistical analysis of travel times and consequently develop a path planning approach that accounts for these statistics. The proposed methodology is tested for a number of scenarios. We first validate our algorithms by reproducing simple canonical solutions, and then demonstrate our approach in more complex flow fields, including idealized, steady and unsteady double-gyre flows.

  16. Chandrayaan-1 sketch, with its full ensemble of payloads and ...

    Indian Academy of Sciences (India)

    which will be released to land on the Moon during the mission. Salient features of the mission are described here. The ensemble of .... Water (ice or H2O in any form) is an important resource on the Moon, not only for establishing ... ice deposits in the lunar polar regions. Because of severe temperature differences between ...

  17. Experimental real-time multi-model ensemble (MME) prediction of ...

    Indian Academy of Sciences (India)

    NCMRWF), India. Simple ensemble mean (EMN) giving equal weight to member models, bias-corrected ensemble mean (BCEMn) and MME forecast, where different weights are given to member models, are the products of the algorithm tested here.

  18. Extended Gaussian ensemble or q-statistics in hadronic production processes?

    OpenAIRE

    Osada, T.; Utyuzh, O. V.; Wilk, G.; Wlodarczyk, Z

    2005-01-01

    The extended Gaussian ensemble introduced recently as a generalization of the canonical ensemble, which allows to treat energy fluctuations present in the system, is used to analyze the inelasticity distributions in high energy multiparticle production processes.

  19. Skill prediction of local weather forecasts based on the ECMWF ensemble

    Directory of Open Access Journals (Sweden)

    C. Ziehmann

    2001-01-01

    Full Text Available Ensemble Prediction has become an essential part of numerical weather forecasting. In this paper we investigate the ability of ensemble forecasts to provide an a priori estimate of the expected forecast skill. Several quantities derived from the local ensemble distribution are investigated for a two year data set of European Centre for Medium-Range Weather Forecasts (ECMWF temperature and wind speed ensemble forecasts at 30 German stations. The results indicate that the population of the ensemble mode provides useful information for the uncertainty in temperature forecasts. The ensemble entropy is a similar good measure. This is not true for the spread if it is simply calculated as the variance of the ensemble members with respect to the ensemble mean. The number of clusters in the C regions is almost unrelated to the local skill. For wind forecasts, the results are less promising.

  20. A sub-ensemble theory of ideal quantum measurement processes

    Science.gov (United States)

    Allahverdyan, Armen E.; Balian, Roger; Nieuwenhuizen, Theo M.

    2017-01-01

    In order to elucidate the properties currently attributed to ideal measurements, one must explain how the concept of an individual event with a well-defined outcome may emerge from quantum theory which deals with statistical ensembles, and how different runs issued from the same initial state may end up with different final states. This so-called "measurement problem" is tackled with two guidelines. On the one hand, the dynamics of the macroscopic apparatus A coupled to the tested system S is described mathematically within a standard quantum formalism, where " q-probabilities" remain devoid of interpretation. On the other hand, interpretative principles, aimed to be minimal, are introduced to account for the expected features of ideal measurements. Most of the five principles stated here, which relate the quantum formalism to physical reality, are straightforward and refer to macroscopic variables. The process can be identified with a relaxation of S + A to thermodynamic equilibrium, not only for a large ensemble E of runs but even for its sub-ensembles. The different mechanisms of quantum statistical dynamics that ensure these types of relaxation are exhibited, and the required properties of the Hamiltonian of S + A are indicated. The additional theoretical information provided by the study of sub-ensembles remove Schrödinger's quantum ambiguity of the final density operator for E which hinders its direct interpretation, and bring out a commutative behaviour of the pointer observable at the final time. The latter property supports the introduction of a last interpretative principle, needed to switch from the statistical ensembles and sub-ensembles described by quantum theory to individual experimental events. It amounts to identify some formal " q-probabilities" with ordinary frequencies, but only those which refer to the final indications of the pointer. The desired properties of ideal measurements, in particular the uniqueness of the result for each individual

  1. A genetic ensemble approach for gene-gene interaction identification

    Directory of Open Access Journals (Sweden)

    Ho Joshua WK

    2010-10-01

    Full Text Available Abstract Background It has now become clear that gene-gene interactions and gene-environment interactions are ubiquitous and fundamental mechanisms for the development of complex diseases. Though a considerable effort has been put into developing statistical models and algorithmic strategies for identifying such interactions, the accurate identification of those genetic interactions has been proven to be very challenging. Methods In this paper, we propose a new approach for identifying such gene-gene and gene-environment interactions underlying complex diseases. This is a hybrid algorithm and it combines genetic algorithm (GA and an ensemble of classifiers (called genetic ensemble. Using this approach, the original problem of SNP interaction identification is converted into a data mining problem of combinatorial feature selection. By collecting various single nucleotide polymorphisms (SNP subsets as well as environmental factors generated in multiple GA runs, patterns of gene-gene and gene-environment interactions can be extracted using a simple combinatorial ranking method. Also considered in this study is the idea of combining identification results obtained from multiple algorithms. A novel formula based on pairwise double fault is designed to quantify the degree of complementarity. Conclusions Our simulation study demonstrates that the proposed genetic ensemble algorithm has comparable identification power to Multifactor Dimensionality Reduction (MDR and is slightly better than Polymorphism Interaction Analysis (PIA, which are the two most popular methods for gene-gene interaction identification. More importantly, the identification results generated by using our genetic ensemble algorithm are highly complementary to those obtained by PIA and MDR. Experimental results from our simulation studies and real world data application also confirm the effectiveness of the proposed genetic ensemble algorithm, as well as the potential benefits of

  2. Ensemble models of neutrophil trafficking in severe sepsis.

    Directory of Open Access Journals (Sweden)

    Sang Ok Song

    Full Text Available A hallmark of severe sepsis is systemic inflammation which activates leukocytes and can result in their misdirection. This leads to both impaired migration to the locus of infection and increased infiltration into healthy tissues. In order to better understand the pathophysiologic mechanisms involved, we developed a coarse-grained phenomenological model of the acute inflammatory response in CLP (cecal ligation and puncture-induced sepsis in rats. This model incorporates distinct neutrophil kinetic responses to the inflammatory stimulus and the dynamic interactions between components of a compartmentalized inflammatory response. Ensembles of model parameter sets consistent with experimental observations were statistically generated using a Markov-Chain Monte Carlo sampling. Prediction uncertainty in the model states was quantified over the resulting ensemble parameter sets. Forward simulation of the parameter ensembles successfully captured experimental features and predicted that systemically activated circulating neutrophils display impaired migration to the tissue and neutrophil sequestration in the lung, consequently contributing to tissue damage and mortality. Principal component and multiple regression analyses of the parameter ensembles estimated from survivor and non-survivor cohorts provide insight into pathologic mechanisms dictating outcome in sepsis. Furthermore, the model was extended to incorporate hypothetical mechanisms by which immune modulation using extracorporeal blood purification results in improved outcome in septic rats. Simulations identified a sub-population (about 18% of the treated population that benefited from blood purification. Survivors displayed enhanced neutrophil migration to tissue and reduced sequestration of lung neutrophils, contributing to improved outcome. The model ensemble presented herein provides a platform for generating and testing hypotheses in silico, as well as motivating further experimental

  3. Reduction of predictive uncertainty in estimating irrigation water requirement through multi-model ensembles and ensemble averaging

    Science.gov (United States)

    Multsch, S.; Exbrayat, J.-F.; Kirby, M.; Viney, N. R.; Frede, H.-G.; Breuer, L.

    2015-04-01

    Irrigation agriculture plays an increasingly important role in food supply. Many evapotranspiration models are used today to estimate the water demand for irrigation. They consider different stages of crop growth by empirical crop coefficients to adapt evapotranspiration throughout the vegetation period. We investigate the importance of the model structural versus model parametric uncertainty for irrigation simulations by considering six evapotranspiration models and five crop coefficient sets to estimate irrigation water requirements for growing wheat in the Murray-Darling Basin, Australia. The study is carried out using the spatial decision support system SPARE:WATER. We find that structural model uncertainty among reference ET is far more important than model parametric uncertainty introduced by crop coefficients. These crop coefficients are used to estimate irrigation water requirement following the single crop coefficient approach. Using the reliability ensemble averaging (REA) technique, we are able to reduce the overall predictive model uncertainty by more than 10%. The exceedance probability curve of irrigation water requirements shows that a certain threshold, e.g. an irrigation water limit due to water right of 400 mm, would be less frequently exceeded in case of the REA ensemble average (45%) in comparison to the equally weighted ensemble average (66%). We conclude that multi-model ensemble predictions and sophisticated model averaging techniques are helpful in predicting irrigation demand and provide relevant information for decision making.

  4. Making Music or Gaining Grades? Assessment Practices in Tertiary Music Ensembles

    Science.gov (United States)

    Harrison, Scott D.; Lebler, Don; Carey, Gemma; Hitchcock, Matt; O'Bryan, Jessica

    2013-01-01

    Participation in an ensemble is a significant aspect of tertiary music experience. Learning and assessment practices within ensembles have rarely been investigated in Australia and the perceptions of staff and students as to how they learn and are assessed within ensembles remain largely unexplored. This paper reports on part of a larger project…

  5. Uncertainty assessment via Bayesian revision of ensemble streamflow predictions in the operational river Rhine forecasting system

    NARCIS (Netherlands)

    Reggiani, P.; Renner, M.; Weerts, A.H.; Van Gelder, P.A.H.J.M.

    2009-01-01

    Ensemble streamflow forecasts obtained by using hydrological models with ensemble weather products are becoming more frequent in operational flow forecasting. The uncertainty of the ensemble forecast needs to be assessed for these products to become useful in forecasting operations. A comprehensive

  6. Improving the ensemble optimization method through covariance matrix adaptation (CMA-EnOpt)

    NARCIS (Netherlands)

    Fonseca, R.M.; Leeuwenburgh, O.; Hof, P.M.J. van den; Jansen, J.D.

    2013-01-01

    Ensemble Optimization (EnOpt) is a rapidly emerging method for reservoir model based production optimization. EnOpt uses an ensemble of controls to approximate the gradient of the objective function with respect to the controls. Current implementations of EnOpt use a Gaussian ensemble with a

  7. The Oral Tradition in the Sankofa Drum and Dance Ensemble: Student Perceptions

    Science.gov (United States)

    Hess, Juliet

    2009-01-01

    The Sankofa Drum and Dance Ensemble is a Ghanaian drum and dance ensemble that focusses on music in the Ewe tradition. It is based in an elementary school in the Greater Toronto Area and consists of students in Grade 4 through Grade 8. Students in the ensemble study Ghanaian traditional Ewe drumming and dancing in the oral tradition. Nine students…

  8. Atmospheric Solar Heating in Minor Absorption Bands

    Science.gov (United States)

    Chou, Ming-Dah

    1998-01-01

    Solar radiation is the primary source of energy driving atmospheric and oceanic circulations. Concerned with the huge computing time required for computing radiative transfer in weather and climate models, solar heating in minor absorption bands has often been neglected. The individual contributions of these minor bands to the atmospheric heating is small, but collectively they are not negligible. The solar heating in minor bands includes the absorption due to water vapor in the photosynthetically active radiation (PAR) spectral region from 14284/cm to 25000/cm, the ozone absorption and Rayleigh scattering in the near infrared, as well as the O2 and CO2 absorption in a number of weak bands. Detailed high spectral- and angular-resolution calculations show that the total effect of these minor absorption is to enhance the atmospheric solar heating by approximately 10%. Depending upon the strength of the absorption and the overlapping among gaseous absorption, different approaches are applied to parameterize these minor absorption. The parameterizations are accurate and require little extra time for computing radiative fluxes. They have been efficiently implemented in the various atmospheric models at NASA/Goddard Space Flight Center, including cloud ensemble, mesoscale, and climate models.

  9. Ensemble QSAR: a QSAR method based on conformational ensembles and metric descriptors.

    Science.gov (United States)

    Pissurlenkar, Raghuvir R S; Khedkar, Vijay M; Iyer, Radhakrishnan P; Coutinho, Evans C

    2011-07-30

    Quantitative structure-activity relationship (QSAR) is the most versatile tool in computer-assisted molecular design. One conceptual drawback seen in QSAR approaches is the "one chemical-one structure-one parameter value" dogma where the model development is based on physicochemical description for a single molecular conformation, while ignoring the rest of the conformational space. It is well known that molecules have several low-energy conformations populated at physiological temperature, and each conformer makes a significant impact on associated properties such as biological activity. At the level of molecular interaction, the dynamics around the molecular structure is of prime essence rather than the average structure. As a step toward understanding the role of these discrete microscopic states in biological activity, we have put together a theoretically rigorous and computationally tractable formalism coined as eQSAR. In this approach, the biological activity is modeled as a function of physicochemical description for a selected set of low-energy conformers, rather than that's for a single lowest energy conformation. Eigenvalues derived from the "Physicochemical property integrated distance matrices" (PD-matrices) that encompass both 3D structure and physicochemical properties, have been used as descriptors; is a novel addition. eQSAR is validated on three peptide datasets and explicitly elaborated for bradykinin-potentiating peptides. The conformational ensembles were generated by a simple molecular dynamics and consensus dynamics approaches. The eQSAR models are statistically significant and possess the ability to select the most biologically relevant conformation(s) with the relevant physicochemical attributes that have the greatest meaning for description of the biological activity. Copyright © 2011 Wiley Periodicals, Inc.

  10. Generating extreme weather event sets from very large ensembles of regional climate models

    Science.gov (United States)

    Massey, Neil; Guillod, Benoit; Otto, Friederike; Allen, Myles; Jones, Richard; Hall, Jim

    2015-04-01

    Generating extreme weather event sets from very large ensembles of regional climate models Neil Massey, Benoit P. Guillod, Friederike E. L. Otto, Myles R. Allen, Richard Jones, Jim W. Hall Environmental Change Institute, University of Oxford, Oxford, UK Extreme events can have large impacts on societies and are therefore being increasingly studied. In particular, climate change is expected to impact the frequency and intensity of these events. However, a major limitation when investigating extreme weather events is that, by definition, only few events are present in observations. A way to overcome this issue it to use large ensembles of model simulations. Using the volunteer distributed computing (VDC) infrastructure of weather@home [1], we run a very large number (10'000s) of RCM simulations over the European domain at a resolution of 25km, with an improved land-surface scheme, nested within a free-running GCM. Using VDC allows many thousands of climate model runs to be computed. Using observations for the GCM boundary forcings we can run historical "hindcast" simulations over the past 100 to 150 years. This allows us, due to the chaotic variability of the atmosphere, to ascertain how likely an extreme event was, given the boundary forcings, and to derive synthetic event sets. The events in these sets did not actually occur in the observed record but could have occurred given the boundary forcings, with an associated probability. The event sets contain time-series of fields of meteorological variables that allow impact modellers to assess the loss the event would incur. Projections of events into the future are achieved by modelling projections of the sea-surface temperature (SST) and sea-ice boundary forcings, by combining the variability of the SST in the observed record with a range of warming signals derived from the varying responses of SSTs in the CMIP5 ensemble to elevated greenhouse gas (GHG) emissions in three RCP scenarios. Simulating the future with a

  11. The state of the art of flood forecasting - Hydrological Ensemble Prediction Systems

    Science.gov (United States)

    Thielen-Del Pozo, J.; Pappenberger, F.; Salamon, P.; Bogner, K.; Burek, P.; de Roo, A.

    2010-09-01

    Flood forecasting systems form a key part of ‘preparedness' strategies for disastrous floods and provide hydrological services, civil protection authorities and the public with information of upcoming events. Provided the warning leadtime is sufficiently long, adequate preparatory actions can be taken to efficiently reduce the impacts of the flooding. Because of the specific characteristics of each catchment, varying data availability and end-user demands, the design of the best flood forecasting system may differ from catchment to catchment. However, despite the differences in concept and data needs, there is one underlying issue that spans across all systems. There has been an growing awareness and acceptance that uncertainty is a fundamental issue of flood forecasting and needs to be dealt with at the different spatial and temporal scales as well as the different stages of the flood generating processes. Today, operational flood forecasting centres change increasingly from single deterministic forecasts to probabilistic forecasts with various representations of the different contributions of uncertainty. The move towards these so-called Hydrological Ensemble Prediction Systems (HEPS) in flood forecasting represents the state of the art in forecasting science, following on the success of the use of ensembles for weather forecasting (Buizza et al., 2005) and paralleling the move towards ensemble forecasting in other related disciplines such as climate change predictions. The use of HEPS has been internationally fostered by initiatives such as "The Hydrologic Ensemble Prediction Experiment" (HEPEX), created with the aim to investigate how best to produce, communicate and use hydrologic ensemble forecasts in hydrological short-, medium- und long term prediction of hydrological processes. The advantages of quantifying the different contributions of uncertainty as well as the overall uncertainty to obtain reliable and useful flood forecasts also for extreme events

  12. Super-ensemble techniques: Application to surface drift prediction

    Science.gov (United States)

    Vandenbulcke, L.; Beckers, J.-M.; Lenartz, F.; Barth, A.; Poulain, P.-M.; Aidonidis, M.; Meyrat, J.; Ardhuin, F.; Tonani, M.; Fratianni, C.; Torrisi, L.; Pallela, D.; Chiggiato, J.; Tudor, M.; Book, J. W.; Martin, P.; Peggion, G.; Rixen, M.

    2009-09-01

    The prediction of surface drift of floating objects is an important task, with applications such as marine transport, pollutant dispersion, and search-and-rescue activities. But forecasting even the drift of surface waters is very challenging, because it depends on complex interactions of currents driven by the wind, the wave field and the general prevailing circulation. Furthermore, although each of those can be forecasted by deterministic models, the latter all suffer from limitations, resulting in imperfect predictions. In the present study, we try and predict the drift of two buoys launched during the DART06 (Dynamics of the Adriatic sea in Real-Time 2006) and MREA07 (Maritime Rapid Environmental Assessment 2007) sea trials, using the so-called hyper-ensemble technique: different models are combined in order to minimize departure from independent observations during a training period; the obtained combination is then used in forecasting mode. We review and try out different hyper-ensemble techniques, such as the simple ensemble mean, least-squares weighted linear combinations, and techniques based on data assimilation, which dynamically update the model’s weights in the combination when new observations become available. We show that the latter methods alleviate the need of fixing the training length a priori, as older information is automatically discarded. When the forecast period is relatively short (12 h), the discussed methods lead to much smaller forecasting errors compared with individual models (at least three times smaller), with the dynamic methods leading to the best results. When many models are available, errors can be further reduced by removing colinearities between them by performing a principal component analysis. At the same time, this reduces the amount of weights to be determined. In complex environments when meso- and smaller scale eddy activity is strong, such as the Ligurian Sea, the skill of individual models may vary over time periods

  13. Quasiequivalence of multiscale coevolution and ensemble MD simulations: A demonstration with lactoferrin

    Science.gov (United States)

    Yang, J.; Singharoy, A.; Sereda, Yu. V.; Ortoleva, P. J.

    2014-11-01

    Ensemble molecular dynamics computations are performed because a single MD simulation may not represent all the possible scenarios. Ensemble MD consumes a great amount of resources. Here, the similarity of ensemble MD and coupled all-atom, coarse-grained simulation (the multiscale coevolution method) is assessed. Quasiequivalence of two simulations is defined and shown to provide a similarity measure. Quasiequivalence compares trajectories on coarse and relatively finer scales. Good agreement between multiscale coevolution and ensemble MD simulations is demonstrated for lactoferrin. Quasiequivalence along with greater CPU efficiencies of multiscale coevolution relative to ensemble MD, underscore the advantages of multiscale coevolution for nanosystem modeling.

  14. Linking glacial and future climates through an ensemble of GCM simulations

    Directory of Open Access Journals (Sweden)

    J. C. Hargreaves

    2007-01-01

    Full Text Available In this paper we explore the relationships between the modelled climate of the Last Glacial Maximum (LGM and that for doubled atmospheric carbon dioxide compared to the pre-industrial climate by analysing the output from an ensemble of runs from the MIROC3.2 GCM. Our results lend support to the idea in other recent work that the Antarctic is a useful place to look for historical data which can be used to validate models used for climate forecasting of future greenhouse gas induced climate changes, at local, regional and global scales. Good results may also be obtainable using tropical temperatures, particularly those over the ocean. While the greater area in the tropics makes them an attractive area for seeking data, polar amplification of temperature changes may mean that the Antarctic provides a clearer signal relative to the uncertainties in data and model results. Our result for Greenland is not so strong, possibly due to difficulties in accurately modelling the sea ice extent. The MIROC3.2 model shows an asymmetry in climate sensitivity calculated by decreasing rather than increasing the greenhouse gases, with 80% of the ensemble having a weaker cooling than warming. This asymmetry, if confirmed by other studies would mean that direct estimates of climate sensitivity from the LGM are likely to be underestimated by the order of half a degree. Our suspicion is, however, that this result may be highly model dependent. Analysis of the parameters varied in the model suggest the asymmetrical response may be linked to the ice in the clouds, which is therefore indicated as an important area for future research.

  15. Investigating the zonal wind response to SST warming using transient ensemble AGCM experiments

    Energy Technology Data Exchange (ETDEWEB)

    Palipane, Erool; Lu, Jian; Staten, Paul; Chen, Gang; Schneider, Edwin K.

    2016-04-13

    The response of the atmospheric circulation to greenhouse gas-induced SST warming is investigated using large ensemble experiments with two AGCMs, with a focus on the robust feature of the poleward shift of the eddy driven jet. In these experiments, large ensembles of simulations are conducted by abruptly switching the SST forcing on from January 1st to focus on the wintertime circulation adjustment. A hybrid, nite amplitude wave activity budget analysis is performed to elucidate the nonlinear and irreversible aspects of the eddy-mean ow interaction during the adjustment of the zonal wind towards a poleward shifted state. The results conrm the results from earlier more idealized studies, particularly the importance of reduced dissipation of wave activity and the dominant role of the decrease of elective diffusivity in the midlatitudes. Some quantitative discrences do exist between the wave activity budgets of our more realistic experiments and the earlier idealized ones, including larger wave activity tendency and diabatic wave source, and a somewhat greater role of the changing PV gradient in the total reduction of the wave activity dissipation. The relative importance of wave breaking-induced PV mixing versus diabatic PV source in the evolution of the Lagrangian PV gradient is also investigated. The former plays the dominant role in the PV gradient formation during the initial phase of the jet shift, while the latter even opposes the evolution of the Lagrangian PV gradient at times. The possible involvement of the wave reflection level at the poleward flank of the mean jet is also investigated.

  16. Robust intensification of hydroclimatic intensity over East Asia from multi-model ensemble regional projections

    Science.gov (United States)

    Im, Eun-Soon; Choi, Yeon-Woo; Ahn, Joong-Bae

    2017-08-01

    This study assesses the hydroclimatic response to global warming over East Asia from multi-model ensemble regional projections. Four different regional climate models (RCMs), namely, WRF, HadGEM3-RA, RegCM4, and GRIMs, are used for dynamical downscaling of the Hadley Centre Global Environmental Model version 2-Atmosphere and Ocean (HadGEM2-AO) global projections forced by the representative concentration pathway (RCP4.5 and RCP8.5) scenarios. Annual mean precipitation, hydroclimatic intensity index (HY-INT), and wet and dry extreme indices are analyzed to identify the robust behavior of hydroclimatic change in response to enhanced emission scenarios using high-resolution (12.5 km) and long-term (1981-2100) daily precipitation. Ensemble projections exhibit increased hydroclimatic intensity across the entire domain and under both the RCP scenarios. However, a geographical pattern with predominantly intensified HY-INT does not fully emerge in the mean precipitation change because HY-INT is tied to the changes in the precipitation characteristics rather than to those in the precipitation amount. All projections show an enhancement of high intensity precipitation and a reduction of weak intensity precipitation, which lead to a possible shift in hydroclimatic regime prone to an increase of both wet and dry extremes. In general, projections forced by the RCP8.5 scenario tend to produce a much stronger response than do those by the RCP4.5 scenario. However, the temperature increase under the RCP4.5 scenario is sufficiently large to induce significant changes in hydroclimatic intensity, despite the relatively uncertain change in mean precipitation. Likewise, the forced responses of HY-INT and the two extreme indices are more robust than that of mean precipitation, in terms of the statistical significance and model agreement.

  17. A web-application for visualizing uncertainty in numerical ensemble models

    Science.gov (United States)

    Alberti, Koko; Hiemstra, Paul; de Jong, Kor; Karssenberg, Derek

    2013-04-01

    Numerical ensemble models are used in the analysis and forecasting of a wide range of environmental processes. Common use cases include assessing the consequences of nuclear accidents, pollution releases into the ocean or atmosphere, forest fires, volcanic eruptions, or identifying areas at risk from such hazards. In addition to the increased use of scenario analyses and model forecasts, the availability of supplementary data describing errors and model uncertainties is increasingly commonplace. Unfortunately most current visualization routines are not capable of properly representing uncertain information. As a result, uncertainty information is not provided at all, not readily accessible, or it is not communicated effectively to model users such as domain experts, decision makers, policy makers, or even novice users. In an attempt to address these issues a lightweight and interactive web-application has been developed. It makes clear and concise uncertainty visualizations available in a web-based mapping and visualization environment, incorporating aggregation (upscaling) techniques to adjust uncertainty information to the zooming level. The application has been built on a web mapping stack of open source software, and can quantify and visualize uncertainties in numerical ensemble models in such a way that both expert and novice users can investigate uncertainties present in a simple ensemble dataset. As a test case, a dataset was used which forecasts the spread of an airborne tracer across Western Europe. Extrinsic uncertainty representations are used in which dynamic circular glyphs are overlaid on model attribute maps to convey various uncertainty concepts. It supports both basic uncertainty metrics such as standard deviation, standard error, width of the 95% confidence interval and interquartile range, as well as more experimental ones aimed at novice users. Ranges of attribute values can be specified, and the circular glyphs dynamically change size to

  18. Atmosphere: Power, Critique, Politics

    DEFF Research Database (Denmark)

    Albertsen, Niels

    2016-01-01

    This paper hans three interrelated parts. First, atmosphere is approached through the concept of power. Atmospheres 'grip' us directly or mediate power indirectly by manipulating moods and evoking emotions. How does atmosphere relate to different conceptions of power? Second, atmospheric powers may...

  19. Using Ensemble of Neural Networks to Learn Stochastic Convection Parameterizations for Climate and Numerical Weather Prediction Models from Data Simulated by a Cloud Resolving Model

    Directory of Open Access Journals (Sweden)

    Vladimir M. Krasnopolsky

    2013-01-01

    Full Text Available A novel approach based on the neural network (NN ensemble technique is formulated and used for development of a NN stochastic convection parameterization for climate and numerical weather prediction (NWP models. This fast parameterization is built based on learning from data simulated by a cloud-resolving model (CRM initialized with and forced by the observed meteorological data available for 4-month boreal winter from November 1992 to February 1993. CRM-simulated data were averaged and processed to implicitly define a stochastic convection parameterization. This parameterization is learned from the data using an ensemble of NNs. The NN ensemble members are trained and tested. The inherent uncertainty of the stochastic convection parameterization derived following this approach is estimated. The newly developed NN convection parameterization has been tested in National Center of Atmospheric Research (NCAR Community Atmospheric Model (CAM. It produced reasonable and promising decadal climate simulations for a large tropical Pacific region. The extent of the adaptive ability of the developed NN parameterization to the changes in the model environment is briefly discussed. This paper is devoted to a proof of concept and discusses methodology, initial results, and the major challenges of using the NN technique for developing convection parameterizations for climate and NWP models.

  20. The Use of Artificial-Intelligence-Based Ensembles for Intrusion Detection: A Review

    Directory of Open Access Journals (Sweden)

    Gulshan Kumar

    2012-01-01

    Full Text Available In supervised learning-based classification, ensembles have been successfully employed to different application domains. In the literature, many researchers have proposed different ensembles by considering different combination methods, training datasets, base classifiers, and many other factors. Artificial-intelligence-(AI- based techniques play prominent role in development of ensemble for intrusion detection (ID and have many benefits over other techniques. However, there is no comprehensive review of ensembles in general and AI-based ensembles for ID to examine and understand their current research status to solve the ID problem. Here, an updated review of ensembles and their taxonomies has been presented in general. The paper also presents the updated review of various AI-based ensembles for ID (in particular during last decade. The related studies of AI-based ensembles are compared by set of evaluation metrics driven from (1 architecture & approach followed; (2 different methods utilized in different phases of ensemble learning; (3 other measures used to evaluate classification performance of the ensembles. The paper also provides the future directions of the research in this area. The paper will help the better understanding of different directions in which research of ensembles has been done in general and specifically: field of intrusion detection systems (IDSs.

  1. Ensemble-Based Assimilation of Aerosol Observations in GEOS-5

    Science.gov (United States)

    Buchard, V.; Da Silva, A.

    2016-01-01

    MERRA-2 is the latest Aerosol Reanalysis produced at NASA's Global Modeling Assimilation Office (GMAO) from 1979 to present. This reanalysis is based on a version of the GEOS-5 model radiatively coupled to GOCART aerosols and includes assimilation of bias corrected Aerosol Optical Depth (AOD) from AVHRR over ocean, MODIS sensors on both Terra and Aqua satellites, MISR over bright surfaces and AERONET data. In order to assimilate lidar profiles of aerosols, we are updating the aerosol component of our assimilation system to an Ensemble Kalman Filter (EnKF) type of scheme using ensembles generated routinely by the meteorological assimilation. Following the work performed with the first NASA's aerosol reanalysis (MERRAero), we first validate the vertical structure of MERRA-2 aerosol assimilated fields using CALIOP data over regions of particular interest during 2008.

  2. Human resource recommendation algorithm based on ensemble learning and Spark

    Science.gov (United States)

    Cong, Zihan; Zhang, Xingming; Wang, Haoxiang; Xu, Hongjie

    2017-08-01

    Aiming at the problem of “information overload” in the human resources industry, this paper proposes a human resource recommendation algorithm based on Ensemble Learning. The algorithm considers the characteristics and behaviours of both job seeker and job features in the real business circumstance. Firstly, the algorithm uses two ensemble learning methods-Bagging and Boosting. The outputs from both learning methods are then merged to form user interest model. Based on user interest model, job recommendation can be extracted for users. The algorithm is implemented as a parallelized recommendation system on Spark. A set of experiments have been done and analysed. The proposed algorithm achieves significant improvement in accuracy, recall rate and coverage, compared with recommendation algorithms such as UserCF and ItemCF.

  3. Weighted ensemble transform Kalman filter for image assimilation

    Directory of Open Access Journals (Sweden)

    Sebastien Beyou

    2013-01-01

    Full Text Available This study proposes an extension of the Weighted Ensemble Kalman filter (WEnKF proposed by Papadakis et al. (2010 for the assimilation of image observations. The main focus of this study is on a novel formulation of the Weighted filter with the Ensemble Transform Kalman filter (WETKF, incorporating directly as a measurement model a non-linear image reconstruction criterion. This technique has been compared to the original WEnKF on numerical and real world data of 2-D turbulence observed through the transport of a passive scalar. In particular, it has been applied for the reconstruction of oceanic surface current vorticity fields from sea surface temperature (SST satellite data. This latter technique enables a consistent recovery along time of oceanic surface currents and vorticity maps in presence of large missing data areas and strong noise.

  4. Phenotype Recognition with Combined Features and Random Subspace Classifier Ensemble

    Directory of Open Access Journals (Sweden)

    Pham Tuan D

    2011-04-01

    Full Text Available Abstract Background Automated, image based high-content screening is a fundamental tool for discovery in biological science. Modern robotic fluorescence microscopes are able to capture thousands of images from massively parallel experiments such as RNA interference (RNAi or small-molecule screens. As such, efficient computational methods are required for automatic cellular phenotype identification capable of dealing with large image data sets. In this paper we investigated an efficient method for the extraction of quantitative features from images by combining second order statistics, or Haralick features, with curvelet transform. A random subspace based classifier ensemble with multiple layer perceptron (MLP as the base classifier was then exploited for classification. Haralick features estimate image properties related to second-order statistics based on the grey level co-occurrence matrix (GLCM, which has been extensively used for various image processing applications. The curvelet transform has a more sparse representation of the image than wavelet, thus offering a description with higher time frequency resolution and high degree of directionality and anisotropy, which is particularly appropriate for many images rich with edges and curves. A combined feature description from Haralick feature and curvelet transform can further increase the accuracy of classification by taking their complementary information. We then investigate the applicability of the random subspace (RS ensemble method for phenotype classification based on microscopy images. A base classifier is trained with a RS sampled subset of the original feature set and the ensemble assigns a class label by majority voting. Results Experimental results on the phenotype recognition from three benchmarking image sets including HeLa, CHO and RNAi show the effectiveness of the proposed approach. The combined feature is better than any individual one in the classification accuracy. The

  5. One-class kernel subspace ensemble for medical image classification

    Science.gov (United States)

    Zhang, Yungang; Zhang, Bailing; Coenen, Frans; Xiao, Jimin; Lu, Wenjin

    2014-12-01

    Classification of medical images is an important issue in computer-assisted diagnosis. In this paper, a classification scheme based on a one-class kernel principle component analysis (KPCA) model ensemble has been proposed for the classification of medical images. The ensemble consists of one-class KPCA models trained using different image features from each image class, and a proposed product combining rule was used for combining the KPCA models to produce classification confidence scores for assigning an image to each class. The effectiveness of the proposed classification scheme was verified using a breast cancer biopsy image dataset and a 3D optical coherence tomography (OCT) retinal image set. The combination of different image features exploits the complementary strengths of these different feature extractors. The proposed classification scheme obtained promising results on the two medical image sets. The proposed method was also evaluated on the UCI breast cancer dataset (diagnostic), and a competitive result was obtained.

  6. Collective Rabi dynamics of electromagnetically coupled quantum-dot ensembles

    Science.gov (United States)

    Glosser, Connor; Shanker, B.; Piermarocchi, Carlo

    2017-09-01

    Rabi oscillations typify the inherent nonlinearity of optical excitations in quantum dots. Using an integral kernel formulation to solve the three-dimensional Maxwell-Bloch equations in ensembles of up to 104 quantum dots, we observe features in Rabi oscillations due to the interplay of nonlinearity, nonequilibrium excitation, and electromagnetic coupling between the dots. This approach allows us to observe the dynamics of each dot in the ensemble without resorting to spatial averages. Our simulations predict synchronized multiplets of dots that exchange energy, dots that dynamically couple to screen the effect of incident external radiation, localization of the polarization due to randomness and interactions, as well as wavelength-scale regions of enhanced and suppressed polarization.

  7. Two-point Correlator Fits on HISQ Ensembles

    CERN Document Server

    Bazavov, A; Bouchard, C; DeTar, C; Du, D; El-Khadra, A X; Foley, J; Freeland, E D; Gamiz, E; Gottlieb, Steven; Heller, U M; Hetrick, J E; Kim, J; Kronfeld, A S; Laiho, J; Levkova, L; Lightman, M; Mackenzie, P B; Neil, E T; Oktay, M; Simone, J N; Sugar, R L; Toussaint, D; Van de Water, R S; Zhou, R

    2012-01-01

    We present our methods to fit the two point correlators for light, strange, and charmed pseudoscalar meson physics with the highly improved staggered quark (HISQ) action. We make use of the least-squares fit including the full covariance matrix of the correlators and including Gaussian constraints on some parameters. We fit the correlators on a variety of the HISQ ensembles. The lattice spacing ranges from 0.15 fm down to 0.06 fm. The light sea quark mass ranges from 0.2 times the strange quark mass down to the physical light quark mass. The HISQ ensembles also include lattices with different volumes and with unphysical values of the strange quark mass. We use the results from this work to obtain our preliminary results of $f_D$, $f_{D_s}$, $f_{D_s}/f_{D}$, and ratios of quark masses presented in another talk [1].

  8. Inner structure of vehicular ensembles and random matrix theory

    Energy Technology Data Exchange (ETDEWEB)

    Krbálek, Milan, E-mail: milan.krbalek@fjfi.cvut.cz; Hobza, Tomáš

    2016-05-06

    Highlights: • New class of random matrices (DUE) is proposed and analyzed in detail. • Approximation formula for level spacing distribution in DUE ensembles is analytically derived. • Connection between DUE and vehicular systems (analogical to a well-known link between GUE and Mexico buses) is presented. • It is shown that LS distribution of DUE matrices is the same as clearance distribution measured on expressways. - Abstract: We introduce a special class of random matrices (DUE) whose spectral statistics corresponds to statistics of microscopical quantities detected in vehicular flows. Comparing the level spacing distribution (for ordered eigenvalues in unfolded spectra of DUE matrices) with the time-clearance distribution extracted from various areas of the flux-density diagram (evaluated from original traffic data measured on Czech expressways with high occupancies) we demonstrate that the set of classical systems showing an universality associated with Random Matrix Ensembles can be extended by traffic systems.

  9. Harnessing Disordered-Ensemble Quantum Dynamics for Machine Learning

    Science.gov (United States)

    Fujii, Keisuke; Nakajima, Kohei

    2017-08-01

    The quantum computer has an amazing potential of fast information processing. However, the realization of a digital quantum computer is still a challenging problem requiring highly accurate controls and key application strategies. Here we propose a platform, quantum reservoir computing, to solve these issues successfully by exploiting the natural quantum dynamics of ensemble systems, which are ubiquitous in laboratories nowadays, for machine learning. This framework enables ensemble quantum systems to universally emulate nonlinear dynamical systems including classical chaos. A number of numerical experiments show that quantum systems consisting of 5-7 qubits possess computational capabilities comparable to conventional recurrent neural networks of 100-500 nodes. This discovery opens up a paradigm for information processing with artificial intelligence powered by quantum physics.

  10. Coupling of Electron Spin Ensembles to Superconducting Transmission Line Resonators

    Science.gov (United States)

    Sears, Adam; Schuster, David; Dicarlo, Leo; Bishop, Lev; Ginossar, Eran; Frunzio, Luigi; Wesenberg, Janus; Ardavan, Arzhang; Briggs, Andrew; Moelmer, Klauss; Morton, John; Schoelkopf, Robert

    2010-03-01

    Recent proposals have suggested using a mesoscopic ensemble of electron spins to create a quantum memory for superconducting qubits in solid state systems[1]. Such ensembles can have large cavity couplings (˜MHz) and should have long coherence times. Here we show the measurement and coupling of electron spins in ruby and diamond to multiplexed superconducting coplanar waveguide (CPW) cavities, as well as broadband spectroscopy of ruby using a CPW transmission line. We discuss the application of these techniques to electron spin resonance at low power, millikelvin temperatures, and over many gigahertz and evaluate the suitability of our materials for quantum computing. [4pt] [1] Wesenberg J et al 2009 Phys. Rev. Lett. 103 070502

  11. Ensemble coding in amygdala circuits for associative learning.

    Science.gov (United States)

    Gründemann, Jan; Lüthi, Andreas

    2015-12-01

    Associative fear learning in the basolateral amygdala (BLA) is crucial for an animal's survival upon environmental threats. BLA neurons are defined on the basis of their projection target, genetic markers, and associated function. BLA principal neuron responses to threat signaling stimuli are potentiated upon associative fear learning, which is tightly controlled by defined interneuron subpopulations. In addition, BLA population activity correlates with behavioral states and threat or safety signals. BLA neuronal ensembles activated by different behavioral signals can be identified using immediate early gene markers. The next challenge will be to determine the activity patterns and coding properties of defined BLA ensembles in relation to the whole neuronal population. Copyright © 2015 Elsevier Ltd. All rights reserved.

  12. Ensemble learning approaches to predicting complications of blood transfusion.

    Science.gov (United States)

    Murphree, Dennis; Ngufor, Che; Upadhyaya, Sudhindra; Madde, Nagesh; Clifford, Leanne; Kor, Daryl J; Pathak, Jyotishman

    2015-08-01

    Of the 21 million blood components transfused in the United States during 2011, approximately 1 in 414 resulted in complication [1]. Two complications in particular, transfusion-related acute lung injury (TRALI) and transfusion-associated circulatory overload (TACO), are especially concerning. These two alone accounted for 62% of reported transfusion-related fatalities in 2013 [2]. We have previously developed a set of machine learning base models for predicting the likelihood of these adverse reactions, with a goal towards better informing the clinician prior to a transfusion decision. Here we describe recent work incorporating ensemble learning approaches to predicting TACO/TRALI. In particular we describe combining base models via majority voting, stacking of model sets with varying diversity, as well as a resampling/boosting combination algorithm called RUSBoost. We find that while the performance of many models is very good, the ensemble models do not yield significantly better performance in terms of AUC.

  13. Security Enrichment in Intrusion Detection System Using Classifier Ensemble

    Directory of Open Access Journals (Sweden)

    Uma R. Salunkhe

    2017-01-01

    Full Text Available In the era of Internet and with increasing number of people as its end users, a large number of attack categories are introduced daily. Hence, effective detection of various attacks with the help of Intrusion Detection Systems is an emerging trend in research these days. Existing studies show effectiveness of machine learning approaches in handling Intrusion Detection Systems. In this work, we aim to enhance detection rate of Intrusion Detection System by using machine learning technique. We propose a novel classifier ensemble based IDS that is constructed using hybrid approach which combines data level and feature level approach. Classifier ensembles combine the opinions of different experts and improve the intrusion detection rate. Experimental results show the improved detection rates of our system compared to reference technique.

  14. Generalized ensemble theory with non-extensive statistics

    Science.gov (United States)

    Shen, Ke-Ming; Zhang, Ben-Wei; Wang, En-Ke

    2017-12-01

    The non-extensive canonical ensemble theory is reconsidered with the method of Lagrange multipliers by maximizing Tsallis entropy, with the constraint that the normalized term of Tsallis' q -average of physical quantities, the sum ∑ pjq, is independent of the probability pi for Tsallis parameter q. The self-referential problem in the deduced probability and thermal quantities in non-extensive statistics is thus avoided, and thermodynamical relationships are obtained in a consistent and natural way. We also extend the study to the non-extensive grand canonical ensemble theory and obtain the q-deformed Bose-Einstein distribution as well as the q-deformed Fermi-Dirac distribution. The theory is further applied to the generalized Planck law to demonstrate the distinct behaviors of the various generalized q-distribution functions discussed in literature.

  15. Observation Quality Control with a Robust Ensemble Kalman Filter

    KAUST Repository

    Roh, Soojin

    2013-12-01

    Current ensemble-based Kalman filter (EnKF) algorithms are not robust to gross observation errors caused by technical or human errors during the data collection process. In this paper, the authors consider two types of gross observational errors, additive statistical outliers and innovation outliers, and introduce a method to make EnKF robust to gross observation errors. Using both a one-dimensional linear system of dynamics and a 40-variable Lorenz model, the performance of the proposed robust ensemble Kalman filter (REnKF) was tested and it was found that the new approach greatly improves the performance of the filter in the presence of gross observation errors and leads to only a modest loss of accuracy with clean, outlier-free, observations.

  16. Skill forecasting from ensemble predictions of wind power

    DEFF Research Database (Denmark)

    Pinson, Pierre; Nielsen, Henrik Aalborg; Madsen, Henrik

    2009-01-01

    Optimal management and trading of wind generation calls for the providing of uncertainty estimates along with the commonly provided short-term wind power point predictions. Alternative approaches for the use of probabilistic forecasting are introduced. More precisely, focus is given to prediction...... risk indices aiming to give a comprehensive signal on the expected level of forecast uncertainty. Ensemble predictions of wind generation are used as input. A proposal for the definition of prediction risk indices is given. Such skill forecasts are based on the spread of ensemble forecasts (i.e. a set...... of alternative scenarios for the coming period) for a single prediction horizon or over a took-ahead period. It is shown on the test case of a Danish offshore wind farm how these prediction risk indices may be related to several levels of forecast uncertainty (and potential energy imbalances). Wind power...

  17. Estimation of Human Heart Activity Using Ensemble Kalman Filter

    Directory of Open Access Journals (Sweden)

    Pradhnya Arun Priyadarshi

    2017-02-01

    Full Text Available Heart beat measurement techniques come across various challenges. Electrocardiogram (ECG obtained sometimes does not reveal complete information about electrochemical activity of human heart, because of which functioning of heart cannot be studied properly. In this paper Ensemble Kalman Filter (EnKF is used to generate ECG signal efficiently with better accuracy such that the drawbacks of current techniques are eliminated. Here EnKF is applied to second order mathematical model of human heart, input applied to this mathematical model is a pacemaker signal. The initial values of heart muscle movements and electrochemical activity as a discrete data set are used and prediction steps are commenced. EnKF uses ensemble integration technique to model error statistics which helps obtaining more precise output. The results are obtained with negligible sum squared error, therefore the ECG obtained using EnKF can diagnose the disease related to heart with better accuracy.

  18. The limit shape problem for ensembles of Young diagrams

    CERN Document Server

    Hora, Akihito

    2016-01-01

    This book treats ensembles of Young diagrams originating from group-theoretical contexts and investigates what statistical properties are observed there in a large-scale limit. The focus is mainly on analyzing the interesting phenomenon that specific curves appear in the appropriate scaling limit for the profiles of Young diagrams. This problem is regarded as an important origin of recent vital studies on harmonic analysis of huge symmetry structures. As mathematics, an asymptotic theory of representations is developed of the symmetric groups of degree n as n goes to infinity. The framework of rigorous limit theorems (especially the law of large numbers) in probability theory is employed as well as combinatorial analysis of group characters of symmetric groups and applications of Voiculescu's free probability. The central destination here is a clear description of the asymptotic behavior of rescaled profiles of Young diagrams in the Plancherel ensemble from both static and dynamic points of view.

  19. Dipolar atomic spin ensembles in a double-well potential

    Science.gov (United States)

    de Paz, A.; Naylor, B.; Huckans, J.; Carrance, A.; Gorceix, O.; Maréchal, E.; Pedri, P.; Laburthe-Tolra, B.; Vernac, L.

    2014-10-01

    We experimentally study the spin dynamics of mesoscopic ensembles of ultracold magnetic spin-3 atoms located in two separated wells of an optical dipole trap. We use a radio-frequency sweep to selectively flip the spin of the atoms in one of the wells, which produces two separated spin domains of opposite polarization. We observe that these engineered spin domains are metastable with respect to the long-range magnetic dipolar interactions between the two ensembles. The absence of intercloud dipolar spin-exchange processes reveals a classical behavior, in contrast to previous results with atoms loaded in an optical lattice. When we merge the two subsystems, we observe spin-exchange dynamics due to contact interactions which enable the determination of the s-wave scattering length of Cr52 atoms in the S =0 molecular channel a0=13.5-10.5+11aB (where aB is the Bohr radius).

  20. ENSEMBLE DESIGN OF MASQUERADER DETECTION SYSTEMS FOR INFORMATION SECURITY

    Directory of Open Access Journals (Sweden)

    T. Subbulakshmi

    2011-01-01

    Full Text Available Masqueraders are a category of intruders who impersonate other people on a computer system and use this entry point to use the information stored in the systems or throw other attacks into the network. This paper focuses on Ensemble Design of a Masquerader Detection System using Decision trees and Support Vector Machines for classification with two kernel functions linear and linear BSpline. The key idea is to find out specific patterns of command sequence that tells about user behaviour on a system, and use them to build classifiers that can perfectly recognize anomalous and normal behaviour. Real time truncated command line data set collected from a debian Linux server is used for performance comparison of the developed classifiers with the standard truncated command line data set of Schonlau[4]. The results show that Ensemble Design of Masquerader Detection Systems is much faster than individual Decision trees or Support Vector Machines.

  1. Probabilistic aspects of meteorological and ozone regional ensemble forecasts

    Energy Technology Data Exchange (ETDEWEB)

    Monache, L D; Hacker, J; Zhou, Y; Deng, X; Stull, R

    2006-03-20

    This study investigates whether probabilistic ozone forecasts from an ensemble can be made with skill; i.e., high verification resolution and reliability. Twenty-eight ozone forecasts were generated over the Lower Fraser Valley, British Columbia, Canada, for the 5-day period 11-15 August 2004, and compared with 1-hour averaged measurements of ozone concentrations at five stations. The forecasts were obtained by driving the CMAQ model with four meteorological forecasts and seven emission scenarios: a control run, {+-} 50% NO{sub x}, {+-} 50% VOC, and {+-} 50% NO{sub x} combined with VOC. Probabilistic forecast quality is verified using relative operating characteristic curves, Talagrand diagrams, and a new reliability index. Results show that both meteorology and emission perturbations are needed to have a skillful probabilistic forecast system--the meteorology perturbation is important to capture the ozone temporal and spatial distribution, and the emission perturbation is needed to span the range of ozone-concentration magnitudes. Emission perturbations are more important than meteorology perturbations for capturing the likelihood of high ozone concentrations. Perturbations involving NO{sub x} resulted in a more skillful probabilistic forecast for the episode analyzed, and therefore the 50% perturbation values appears to span much of the emission uncertainty for this case. All of the ensembles analyzed show a high ozone concentration bias in the Talagrand diagrams, even when the biases from the unperturbed emissions forecasts are removed from all ensemble members. This result indicates nonlinearity in the ensemble, which arises from both ozone chemistry and its interaction with input from particular meteorological models.

  2. Validation of the Air Force Weather Agency Ensemble Prediction Systems

    Science.gov (United States)

    2014-03-27

    Other than amalgamating the members to create the super ensemble, no further physics configuration changes are made outside of what is done at each...skill trends, the BSS trend is smoothed by averaging with the two closest BSS values to its left and right taking into account five BSS values...typically, the hours surrounding these positive BSS have larger negative values and when using the smoothing technique already mentioned, these averaged

  3. Harmonic superposition method for grand-canonical ensembles

    Science.gov (United States)

    Calvo, F.; Wales, D. J.

    2015-03-01

    The harmonic superposition method provides a unified framework to the equilibrium and relaxation kinetics on complex potential energy landscapes. Here we extend it to grand-canonical statistical ensembles governed by chemical potentials or chemical potential differences, by sampling energy minima corresponding to the various relevant sizes or compositions. The method is applied and validated against conventional Monte Carlo simulations for the problems of chemical equilibrium in nanoalloys and hydrogen absorption in bulk and nanoscale palladium.

  4. An Organic Computing Approach to Self-organising Robot Ensembles

    Directory of Open Access Journals (Sweden)

    Sebastian Albrecht von Mammen

    2016-11-01

    Full Text Available Similar to the Autonomous Computing initiative, that has mainly been advancing techniques for self-optimisation focussing on computing systems and infrastructures, Organic Computing (OC has been driving the development of system design concepts and algorithms for self-adaptive systems at large. Examples of application domains include, for instance, traffic management and control, cloud services, communication protocols, and robotic systems. Such an OC system typically consists of a potentially large set of autonomous and self-managed entities, where each entity acts with a local decision horizon. By means of cooperation of the individual entities, the behaviour of the entire ensemble system is derived. In this article, we present our work on how autonomous, adaptive robot ensembles can benefit from OC technology. Our elaborations are aligned with the different layers of an observer/controller framework which provides the foundation for the individuals' adaptivity at system design-level. Relying on an extended Learning Classifier System (XCS in combination with adequate simulation techniques, this basic system design empowers robot individuals to improve their individual and collaborative performances, e.g. by means of adapting to changing goals and conditions.Not only for the sake of generalisability, but also because of its enormous transformative potential, we stage our research in the domain of robot ensembles that are typically comprised of several quad-rotors and that organise themselves to fulfil spatial tasks such as maintenance of building facades or the collaborative search for mobile targets. Our elaborations detail the architectural concept, provide examples of individual self-optimisation as well as of the optimisation of collaborative efforts, and we show how the user can control the ensembles at multiple levels of abstraction. We conclude with a summary of our approach and an outlook on possible future steps.

  5. Marginalized Particle Filtering Framework for Tuning of Ensemble Filters

    Czech Academy of Sciences Publication Activity Database

    Šmídl, Václav; Hofman, Radek

    2011-01-01

    Roč. 139, č. 11 (2011), s. 3589-3599 ISSN 0027-0644 R&D Projects: GA MV VG20102013018; GA ČR GP102/08/P250 Institutional research plan: CEZ:AV0Z10750506 Keywords : ensemble finter * marginalized particle filter * data assimilation Subject RIV: BB - Applied Statistics, Operational Research Impact factor: 2.688, year: 2011 http://library.utia.cas.cz/separaty/2011/AS/smidl-0367533.pdf

  6. Solutions of matrix models in the DIII generator ensemble

    OpenAIRE

    Roussel, Harold

    1994-01-01

    In this paper we solve two matrix models, using standard and new techniques. The two models are represented by special form of antisymmetric matrices and are classified in the DIII generator ensemble. It is shown that, in the double scaling limit, their free energy has the same behavior as previous models describing oriented and unoriented surfaces. We also found an additional solution for the first model.

  7. Stochastic ensembles, conformationally adaptive teamwork, and enzymatic detoxification.

    Science.gov (United States)

    Atkins, William M; Qian, Hong

    2011-05-17

    It has been appreciated for a long time that enzymes exist as conformational ensembles throughout multiple stages of the reactions they catalyze, but there is renewed interest in the functional implications. The energy landscape that results from conformationlly diverse poteins is a complex surface with an energetic topography in multiple dimensions, even at the transition state(s) leading to product formation, and this represents a new paradigm. At the same time there has been renewed interest in conformational ensembles, a new paradigm concerning enzyme function has emerged, wherein catalytic promiscuity has clear biological advantages in some cases. "Useful", or biologically functional, promiscuity or the related behavior of "multifunctionality" can be found in the immune system, enzymatic detoxification, signal transduction, and the evolution of new function from an existing pool of folded protein scaffolds. Experimental evidence supports the widely held assumption that conformational heterogeneity promotes functional promiscuity. The common link between these coevolving paradigms is the inherent structural plasticity and conformational dynamics of proteins that, on one hand, lead to complex but evolutionarily selected energy landscapes and, on the other hand, promote functional promiscuity. Here we consider a logical extension of the overlap between these two nascent paradigms: functionally promiscuous and multifunctional enzymes such as detoxification enzymes are expected to have an ensemble landscape with more states accessible on multiple time scales than substrate specific enzymes. Two attributes of detoxification enzymes become important in the context of conformational ensembles: these enzymes metabolize multiple substrates, often in substrate mixtures, and they can form multiple products from a single substrate. These properties, combined with complex conformational landscapes, lead to the possibility of interesting time-dependent, or emergent

  8. Ensemble Feature Learning of Genomic Data Using Support Vector Machine.

    Directory of Open Access Journals (Sweden)

    Ali Anaissi

    Full Text Available The identification of a subset of genes having the ability to capture the necessary information to distinguish classes of patients is crucial in bioinformatics applications. Ensemble and bagging methods have been shown to work effectively in the process of gene selection and classification. Testament to that is random forest which combines random decision trees with bagging to improve overall feature selection and classification accuracy. Surprisingly, the adoption of these methods in support vector machines has only recently received attention but mostly on classification not gene selection. This paper introduces an ensemble SVM-Recursive Feature Elimination (ESVM-RFE for gene selection that follows the concepts of ensemble and bagging used in random forest but adopts the backward elimination strategy which is the rationale of RFE algorithm. The rationale behind this is, building ensemble SVM models using randomly drawn bootstrap samples from the training set, will produce different feature rankings which will be subsequently aggregated as one feature ranking. As a result, the decision for elimination of features is based upon the ranking of multiple SVM models instead of choosing one particular model. Moreover, this approach will address the problem of imbalanced datasets by constructing a nearly balanced bootstrap sample. Our experiments show that ESVM-RFE for gene selection substantially increased the classification performance on five microarray datasets compared to state-of-the-art methods. Experiments on the childhood leukaemia dataset show that an average 9% better accuracy is achieved by ESVM-RFE over SVM-RFE, and 5% over random forest based approach. The selected genes by the ESVM-RFE algorithm were further explored with Singular Value Decomposition (SVD which reveals significant clusters with the selected data.

  9. Ensemble positive unlabeled learning for disease gene identification.

    Science.gov (United States)

    Yang, Peng; Li, Xiaoli; Chua, Hon-Nian; Kwoh, Chee-Keong; Ng, See-Kiong

    2014-01-01

    An increasing number of genes have been experimentally confirmed in recent years as causative genes to various human diseases. The newly available knowledge can be exploited by machine learning methods to discover additional unknown genes that are likely to be associated with diseases. In particular, positive unlabeled learning (PU learning) methods, which require only a positive training set P (confirmed disease genes) and an unlabeled set U (the unknown candidate genes) instead of a negative training set N, have been shown to be effective in uncovering new disease genes in the current scenario. Using only a single source of data for prediction can be susceptible to bias due to incompleteness and noise in the genomic data and a single machine learning predictor prone to bias caused by inherent limitations of individual methods. In this paper, we propose an effective PU learning framework that integrates multiple biological data sources and an ensemble of powerful machine learning classifiers for disease gene identification. Our proposed method integrates data from multiple biological sources for training PU learning classifiers. A novel ensemble-based PU learning method EPU is then used to integrate multiple PU learning classifiers to achieve accurate and robust disease gene predictions. Our evaluation experiments across six disease groups showed that EPU achieved significantly better results compared with various state-of-the-art prediction methods as well as ensemble learning classifiers. Through integrating multiple biological data sources for training and the outputs of an ensemble of PU learning classifiers for prediction, we are able to minimize the potential bias and errors in individual data sources and machine learning algorithms to achieve more accurate and robust disease gene predictions. In the future, our EPU method provides an effective framework to integrate the additional biological and computational resources for better disease gene predictions.

  10. Ensemble Feature Learning of Genomic Data Using Support Vector Machine.

    Science.gov (United States)

    Anaissi, Ali; Goyal, Madhu; Catchpoole, Daniel R; Braytee, Ali; Kennedy, Paul J

    2016-01-01

    The identification of a subset of genes having the ability to capture the necessary information to distinguish classes of patients is crucial in bioinformatics applications. Ensemble and bagging methods have been shown to work effectively in the process of gene selection and classification. Testament to that is random forest which combines random decision trees with bagging to improve overall feature selection and classification accuracy. Surprisingly, the adoption of these methods in support vector machines has only recently received attention but mostly on classification not gene selection. This paper introduces an ensemble SVM-Recursive Feature Elimination (ESVM-RFE) for gene selection that follows the concepts of ensemble and bagging used in random forest but adopts the backward elimination strategy which is the rationale of RFE algorithm. The rationale behind this is, building ensemble SVM models using randomly drawn bootstrap samples from the training set, will produce different feature rankings which will be subsequently aggregated as one feature ranking. As a result, the decision for elimination of features is based upon the ranking of multiple SVM models instead of choosing one particular model. Moreover, this approach will address the problem of imbalanced datasets by constructing a nearly balanced bootstrap sample. Our experiments show that ESVM-RFE for gene selection substantially increased the classification performance on five microarray datasets compared to state-of-the-art methods. Experiments on the childhood leukaemia dataset show that an average 9% better accuracy is achieved by ESVM-RFE over SVM-RFE, and 5% over random forest based approach. The selected genes by the ESVM-RFE algorithm were further explored with Singular Value Decomposition (SVD) which reveals significant clusters with the selected data.

  11. Tweet-based Target Market Classification Using Ensemble Method

    OpenAIRE

    Muhammad Adi Khairul Anshary; Bambang Riyanto Trilaksono

    2016-01-01

    Target market classification is aimed at focusing marketing activities on the right targets. Classification of target markets can be done through data mining and by utilizing data from social media, e.g. Twitter. The end result of data mining are learning models that can classify new data. Ensemble methods can improve the accuracy of the models and therefore provide better results. In this study, classification of target markets was conducted on a dataset of 3000 tweets in order to extract fe...

  12. Data Assimilation using an Ensemble of Models: A hierarchical approach

    OpenAIRE

    Rayner, Peter

    2017-01-01

    One characteristic of biogeochemical models is uncertainty about their formulation. Data assimilation should take this uncertainty into account. A common approach is to use an ensemble of models. We must assign probabilities not only to the parameters of the models but the models themselves. The method of hierarchical modelling allows us to calculate these probabilities. This paper describes the approach, develops the algebra for the most common case then applies it to the TRANSCO...

  13. Dynamic ensemble selection methods for heterogeneous data mining

    OpenAIRE

    Ballard, Chris; Wang, Wenjia

    2016-01-01

    Big data is often collected from multiple sources with possibly different features, representations and granularity and hence is defined as heterogeneous data. Such multiple datasets need to be fused together in some ways for further analysis. Data fusion at feature level requires domain knowledge and can be time-consuming and ineffective, but it could be avoided if decision-level fusion is applied properly. Ensemble methods appear to be an appropriate paradigm to do just that as each subset ...

  14. Automatic Music Genre Classification Using Ensemble of Classifiers

    OpenAIRE

    Silla Jr, Carlos N.; Kaestner, Celso A. A.; Koerich, Alessandro L.

    2007-01-01

    This paper presents a novel approach to the task of automatic music genre classification which is based on multiple feature vectors and ensemble of classifiers. Multiple feature vectors are extracted from a single music piece. First, three 30-second music segments, one from the beginning, one from the middle and one from end part of a music piece are selected and feature vectors are extracted from each segment. Individual classifiers are trained to account for each feature vector extracted fr...

  15. Dynamic Metabolic Model Building Based on the Ensemble Modeling Approach

    Energy Technology Data Exchange (ETDEWEB)

    Liao, James C. [Univ. of California, Los Angeles, CA (United States)

    2016-10-01

    Ensemble modeling of kinetic systems addresses the challenges of kinetic model construction, with respect to parameter value selection, and still allows for the rich insights possible from kinetic models. This project aimed to show that constructing, implementing, and analyzing such models is a useful tool for the metabolic engineering toolkit, and that they can result in actionable insights from models. Key concepts are developed and deliverable publications and results are presented.

  16. Fluctuations in a quasi-stationary shallow cumulus cloud ensemble

    Directory of Open Access Journals (Sweden)

    M. Sakradzija

    2015-01-01

    Full Text Available We propose an approach to stochastic parameterisation of shallow cumulus clouds to represent the convective variability and its dependence on the model resolution. To collect information about the individual cloud lifecycles and the cloud ensemble as a whole, we employ a large eddy simulation (LES model and a cloud tracking algorithm, followed by conditional sampling of clouds at the cloud-base level. In the case of a shallow cumulus ensemble, the cloud-base mass flux distribution is bimodal, due to the different shallow cloud subtypes, active and passive clouds. Each distribution mode can be approximated using a Weibull distribution, which is a generalisation of exponential distribution by accounting for the change in distribution shape due to the diversity of cloud lifecycles. The exponential distribution of cloud mass flux previously suggested for deep convection parameterisation is a special case of the Weibull distribution, which opens a way towards unification of the statistical convective ensemble formalism of shallow and deep cumulus clouds. Based on the empirical and theoretical findings, a stochastic model has been developed to simulate a shallow convective cloud ensemble. It is formulated as a compound random process, with the number of convective elements drawn from a Poisson distribution, and the cloud mass flux sampled from a mixed Weibull distribution. Convective memory is accounted for through the explicit cloud lifecycles, making the model formulation consistent with the choice of the Weibull cloud mass flux distribution function. The memory of individual shallow clouds is required to capture the correct convective variability. The resulting distribution of the subgrid convective states in the considered shallow cumulus case is scale-adaptive – the smaller the grid size, the broader the distribution.

  17. The egg model - A geological ensemble for reservoir simulation

    OpenAIRE

    Jansen, J.D.; Fonseca, R.M.; Kahrobaei, S.; Siraj, M.M.; Van Essen, G.M.; Van den Hof, P.M.J.

    2014-01-01

    The ‘Egg Model’ is a synthetic reservoir model consisting of an ensemble of 101 relatively small three-dimensional realizations of a channelized oil reservoir produced under water flooding conditions with eight water injectors and four oil producers. It has been used in numerous publications to demonstrate a variety of aspects related to computer-assisted flooding optimization and history matching. Unfortunately the details of the parameter settings are not always identical and not always ful...

  18. Disease-associated mutations that alter the RNA structural ensemble.

    Directory of Open Access Journals (Sweden)

    Matthew Halvorsen

    2010-08-01

    Full Text Available Genome-wide association studies (GWAS often identify disease-associated mutations in intergenic and non-coding regions of the genome. Given the high percentage of the human genome that is transcribed, we postulate that for some observed associations the disease phenotype is caused by a structural rearrangement in a regulatory region of the RNA transcript. To identify such mutations, we have performed a genome-wide analysis of all known disease-associated Single Nucleotide Polymorphisms (SNPs from the Human Gene Mutation Database (HGMD that map to the untranslated regions (UTRs of a gene. Rather than using minimum free energy approaches (e.g. mFold, we use a partition function calculation that takes into consideration the ensemble of possible RNA conformations for a given sequence. We identified in the human genome disease-associated SNPs that significantly alter the global conformation of the UTR to which they map. For six disease-states (Hyperferritinemia Cataract Syndrome, beta-Thalassemia, Cartilage-Hair Hypoplasia, Retinoblastoma, Chronic Obstructive Pulmonary Disease (COPD, and Hypertension, we identified multiple SNPs in UTRs that alter the mRNA structural ensemble of the associated genes. Using a Boltzmann sampling procedure for sub-optimal RNA structures, we are able to characterize and visualize the nature of the conformational changes induced by the disease-associated mutations in the structural ensemble. We observe in several cases (specifically the 5' UTRs of FTL and RB1 SNP-induced conformational changes analogous to those observed in bacterial regulatory Riboswitches when specific ligands bind. We propose that the UTR and SNP combinations we identify constitute a "RiboSNitch," that is a regulatory RNA in which a specific SNP has a structural consequence that results in a disease phenotype. Our SNPfold algorithm can help identify RiboSNitches by leveraging GWAS data and an analysis of the mRNA structural ensemble.

  19. Polynomial Chaos Based Acoustic Uncertainty Predictions from Ocean Forecast Ensembles

    Science.gov (United States)

    Dennis, S.

    2016-02-01

    Most significant ocean acoustic propagation occurs at tens of kilometers, at scales small compared basin and to most fine scale ocean modeling. To address the increased emphasis on uncertainty quantification, for example transmission loss (TL) probability density functions (PDF) within some radius, a polynomial chaos (PC) based method is utilized. In order to capture uncertainty in ocean modeling, Navy Coastal Ocean Model (NCOM) now includes ensembles distributed to reflect the ocean analysis statistics. Since the ensembles are included in the data assimilation for the new forecast ensembles, the acoustic modeling uses the ensemble predictions in a similar fashion for creating sound speed distribution over an acoustically relevant domain. Within an acoustic domain, singular value decomposition over the combined time-space structure of the sound speeds can be used to create Karhunen-Loève expansions of sound speed, subject to multivariate normality testing. These sound speed expansions serve as a basis for Hermite polynomial chaos expansions of derived quantities, in particular TL. The PC expansion coefficients result from so-called non-intrusive methods, involving evaluation of TL at multi-dimensional Gauss-Hermite quadrature collocation points. Traditional TL calculation from standard acoustic propagation modeling could be prohibitively time consuming at all multi-dimensional collocation points. This method employs Smolyak order and gridding methods to allow adaptive sub-sampling of the collocation points to determine only the most significant PC expansion coefficients to within a preset tolerance. Practically, the Smolyak order and grid sizes grow only polynomially in the number of Karhunen-Loève terms, alleviating the curse of dimensionality. The resulting TL PC coefficients allow the determination of TL PDF normality and its mean and standard deviation. In the non-normal case, PC Monte Carlo methods are used to rapidly establish the PDF. This work was

  20. Probing RNA native conformational ensembles with structural constraints

    DEFF Research Database (Denmark)

    Fonseca, Rasmus; van den Bedem, Henry; Bernauer, Julie

    2016-01-01

    substates, which are difficult to characterize experimentally and computationally. Here, we present an innovative, entirely kinematic computational procedure to efficiently explore the native ensemble of RNA molecules. Our procedure projects degrees of freedom onto a subspace of conformation space defined...... by distance constraints in the tertiary structure. The dimensionality reduction enables efficient exploration of conformational space. We show that the conformational distributions obtained with our method broadly sample the conformational landscape observed in NMR experiments. Compared to normal mode...