WorldWideScience

Sample records for cumulus ensemble model

  1. Parallelization of the NASA Goddard Cumulus Ensemble Model for Massively Parallel Computing

    Directory of Open Access Journals (Sweden)

    Hann-Ming Henry Juang

    2007-01-01

    Full Text Available Massively parallel computing, using a message passing interface (MPI, has been implemented into a three-dimensional version of the Goddard Cumulus Ensemble (GCE model. The implementation uses the domainresemble concept to design a code structure for both the whole domain and sub-domains after decomposition. Instead of inserting a group of MPI related statements into the model routine, these statements are packed into a single routine. In other words, only a single call statement to the model code is utilized once in a place, thus there is minimal impact on the original code. Therefore, the model is easily modified and/or managed by the model developers and/or users, who have little knowledge of massively parallel computing.

  2. Integrated cumulus ensemble and turbulence (ICET): An integrated parameterization system for general circulation models (GCMs)

    Energy Technology Data Exchange (ETDEWEB)

    Evans, J.L.; Frank, W.M.; Young, G.S. [Pennsylvania State Univ., University Park, PA (United States)

    1996-04-01

    Successful simulations of the global circulation and climate require accurate representation of the properties of shallow and deep convective clouds, stable-layer clouds, and the interactions between various cloud types, the boundary layer, and the radiative fluxes. Each of these phenomena play an important role in the global energy balance, and each must be parameterized in a global climate model. These processes are highly interactive. One major problem limiting the accuracy of parameterizations of clouds and other processes in general circulation models (GCMs) is that most of the parameterization packages are not linked with a common physical basis. Further, these schemes have not, in general, been rigorously verified against observations adequate to the task of resolving subgrid-scale effects. To address these problems, we are designing a new Integrated Cumulus Ensemble and Turbulence (ICET) parameterization scheme, installing it in a climate model (CCM2), and evaluating the performance of the new scheme using data from Atmospheric Radiation Measurement (ARM) Program Cloud and Radiation Testbed (CART) sites.

  3. The Goddard Cumulus Ensemble Model (GCE): Improvements and Applications for Studying Precipitation Processes

    Science.gov (United States)

    Tao, Wei-Kuo; Lang, Stephen E.; Zeng, Xiping; Li, Xiaowen; Matsui, Toshi; Mohr, Karen; Posselt, Derek; Chern, Jiundar; Peters-Lidard, Christa; Norris, Peter M.; Kang, In-Sik; Choi, Ildae; Hou, Arthur; Lau, K.-M.; Yang, Young-Min

    2014-01-01

    Convection is the primary transport process in the Earth's atmosphere. About two-thirds of the Earth's rainfall and severe floods derive from convection. In addition, two-thirds of the global rain falls in the tropics, while the associated latent heat release accounts for three-fourths of the total heat energy for the Earth's atmosphere. Cloud-resolving models (CRMs) have been used to improve our understanding of cloud and precipitation processes and phenomena from micro-scale to cloud-scale and mesoscale as well as their interactions with radiation and surface processes. CRMs use sophisticated and realistic representations of cloud microphysical processes and can reasonably well resolve the time evolution, structure, and life cycles of clouds and cloud systems. CRMs also allow for explicit interaction between clouds, outgoing longwave (cooling) and incoming solar (heating) radiation, and ocean and land surface processes. Observations are required to initialize CRMs and to validate their results. The Goddard Cumulus Ensemble model (GCE) has been developed and improved at NASA/Goddard Space Flight Center over the past three decades. It is amulti-dimensional non-hydrostatic CRM that can simulate clouds and cloud systems in different environments. Early improvements and testing were presented in Tao and Simpson (1993) and Tao et al. (2003a). A review on the application of the GCE to the understanding of precipitation processes can be found in Simpson and Tao (1993) and Tao (2003). In this paper, recent model improvements (microphysics, radiation and land surface processes) are described along with their impact and performance on cloud and precipitation events in different geographic locations via comparisons with observations. In addition, recent advanced applications of the GCE are presented that include understanding the physical processes responsible for diurnal variation, examining the impact of aerosols (cloud condensation nuclei or CCN and ice nuclei or IN) on

  4. A Regulation of Tropical Climate by Radiative Cooling as Simulated in a Cumulus Ensemble Model

    Science.gov (United States)

    Sui, Chung-Hsiung; Lau, K.-M.; Li, X.; Chou, M.-D.; Einaudi, Franco (Technical Monitor)

    2000-01-01

    Responses of tropical atmosphere to low-boundary forcing are investigated in a 2-D cumulus ensemble model (CEM) with an imposed warm-pool and cold-pool SST contrast (deltaSST). The domain-mean vertical motion is constrained to produce heat sink and moisture source as in the observed tropical climate. In a series of experiments, the warm pool SST is specified at different values while the cold pool SST is specified at 26 C. The strength of the circulation increases with increasing deltaSST until deltaSST reaches 3.5 C, and remains unchanged as deltaSST exceeds 3.5 C. The regulation of tropical convection by zonal SST gradient is constrained by the radiative cooling over the cold pool. For deltaSST less than 3.5 C, an enhanced subsidence warming is balanced by a reduced condensation heating over the cold pool. For deltaSST greater than 3.5 C, the subsidence regime expands over the entire cold pool where no condensation heating exist so that a further enhanced subsidence warming can no longer be sustained. The above regulation mechanism is also evident in the change of energy at the top of the atmosphere (TOA) that is dominated by cloud and water vapor greenhouse effect (c (sub LW)) and G (sub clear). The change in shortwave radiation at TOA is largely cancelled between the warm pool and cold pool, likely due to the same imposed vertical motion in our experiments. For deltaSST less than 3.5 C, an increase of deltaSST is associated with a large increase in c (sub Lw) due to increased total clouds in response to enhanced SST-induced circulation. For deltaSST greater than 3.5 C, clouds over the warm pool decrease with increasing SST, and the change in c (sub LW) is much smaller. In both dSST regimes, the change in CLW is larger than the change in G(sub clear) which is slightly negative. However, in the case of uniform warming (deltaSST=0), DeltaG(sub clear), is positive, approximately 5 W per square meters per degree change of SST.

  5. Fluctuations in a quasi-stationary shallow cumulus cloud ensemble

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    M. Sakradzija

    2014-08-01

    Full Text Available We propose an approach to stochastic parameterization of shallow cumulus clouds to represent the convective variability and its dependence on the model resolution. To collect the information about the individual cloud lifecycles and the cloud ensemble as a whole, we employ a Large-Eddy Simulation model (LES 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. Each distribution mode can be approximated with a Weibull distribution, explaining the deviation from a single-parameter exponential shape through the diversity in cloud lifecycles. The exponential distribution of cloud mass flux previously suggested for deep convection parameterization 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.

  6. 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.

  7. Sensitivities of Cumulus-Ensemble Rainfall in a Cloud-Resolving Model with Parameterized Large-Scale Dynamics.

    Science.gov (United States)

    Mapes, Brian E.

    2004-09-01

    The problem of closure in cumulus parameterization requires an understanding of the sensitivities of convective cloud systems to their large-scale setting. As a step toward such an understanding, this study probes some sensitivities of a simulated ensemble of convective clouds in a two-dimensional cloud-resolving model (CRM). The ensemble is initially in statistical equilibrium with a steady imposed background forcing (cooling and moistening). Large-scale stimuli are imposed as horizontally uniform perturbations nudged into the model fields over 10 min, and the rainfall response of the model clouds is monitored.In order to reduce a major source of artificial insensitivity in the CRM, a simple parameterization scheme is devised to account for heating-induced large-scale (i.e., domain averaged) vertical motions that would develop in nature but are forbidden by the periodic boundary conditions. The effects of this large-scale vertical motion are parameterized as advective tendency terms that are applied as a uniform forcing throughout the domain, just like the background forcing. This parameterized advection is assumed to lag rainfall (used as a proxy for heating) by a specified time scale. The time scale determines (via a gravity wave space time conversion factor) the size of the large-scale region represented by the periodic CRM domain, which can be of arbitrary size or dimensionality.The sensitivity of rain rate to deep cooling and moistening, representing an upward displacement by a large-scale wave of first baroclinic mode structure, is positive. Near linearity is found for ±1 K perturbations, and the sensitivity is about equally divided between temperature and moisture effects. For a second baroclinic mode (vertical dipole) displacement, the sign of the perturbation in the lower troposphere dominates the convective response. In this dipole case, the initial sensitivity is very large, but quantitative results are distorted by the oversimplified large

  8. Interaction of a cumulus cloud ensemble with the large-scale environment. III - Semi-prognostic test of the Arakawa-Schubert cumulus parameterization

    Science.gov (United States)

    Lord, S. J.

    1982-01-01

    The verification of the Arakawa and Schubert (1974) cumulus parameterization is continued using a semiprognostic approach. Observed data from Phase III of GATE are used to provide estimates of the large-scale forcing of a cumulus ensemble at each observation time. Instantaneous values of the precipitation and the warming and drying due to cumulus convection are calculated using the parameterization. The results show that the calculated precipitation agrees very well with estimates from the observed large-scale moisture budget and from radar observations. The calculated vertical profiles of cumulus warming and drying also are quite similar to the observed. It is shown that the closure assumption adopted in the parameterization (the cloud-work function quasi-equilibrium) results in errors of generally less than 10% in the calculated precipitation. The sensitivity of the parameterization to some assumptions of the cloud ensemble model and the solution method for the cloud-base mass flux is investigated.

  9. China summer precipitation simulations using an optimal ensemble of cumulus schemes

    Institute of Scientific and Technical Information of China (English)

    Shuyan LIU; Wei GAO; Min XU; Xueyuan WANG; Xin-Zhong LIANG

    2009-01-01

    RegCM3 (REGional Climate Model) simulations of precipitation in China in 1991 and 1998 are very sensitive to the cumulus parameterization. Among the four schemes available, none has superior skills over the whole of China, but each captures certain observed signals in distinct regions. The Grell scheme with the FritschChappell closure produces the smallest biases over the North; the Grell scheme with the Arakawa-Schubert closure performs the best over the southeast of 100°E;the Anthes-Kuo scheme is superior over the northeast; and the Emanuel scheme is more realistic over the southwest of 100~E and along the Yangtze River Basin. These differences indicate a strong degree of independence and complementarity between the parameterizations. As such,an ensemble is developed from the four schemes, whose relative contributions or weights are optimized locally to yield overall minimum root-mean-square errors from observed daily precipitation. The skill gain is evaluated by applying the identical distribution of the weights in a different period. It is shown that the ensemble always produces gross biases that are smaller than the individual schemes in both 1991 and 1998. The ensemble, however,cannot eliminate the large rainfall deficits over the southwest of 100°E and along the Yangtze River Basin that are systematic across all schemes. Further improvements can be made by a super-ensemble based on more cumulus schemes and/or multiple models.

  10. Advancing Models and Evaluation of Cumulus, Climate and Aerosol Interactions

    Energy Technology Data Exchange (ETDEWEB)

    Gettelman, Andrew [University Corporation for Atmospheric Research (NCAR), Boulder, CO (United States)

    2015-10-27

    This project was successfully able to meet its’ goals, but faced some serious challenges due to personnel issues. Nonetheless, it was largely successful. The Project Objectives were as follows: 1. Develop a unified representation of stratifom and cumulus cloud microphysics for NCAR/DOE global community models. 2. Examine the effects of aerosols on clouds and their impact on precipitation in stratiform and cumulus clouds. We will also explore the effects of clouds and precipitation on aerosols. 3. Test these new formulations using advanced evaluation techniques and observations and release

  11. Cumulus-specific genes are transcriptionally silent following somatic cell nuclear transfer in a mouse model

    Institute of Scientific and Technical Information of China (English)

    2007-01-01

    This study investigated whether four cumulus-specific genes: follicular stimulating hormone receptor (FSHr), hyaluronan synthase 2 (Has2), prostaglandin synthase 2 (Ptgs2) and steroidogenic acute regulator protein (Star), were correctly reprogrammed to be transcriptionally silent following somatic cell nuclear transfer (SCNT) in a murine model. Cumulus cells of C57×CBA F1 female mouse were injected into enucleated oocytes, followed by activation in 10 μmol/L strontium chloride for 5 h and subsequent in vitro culture up to the blastocyst stage. Expression of cumulus-specific genes in SCNT-derived embryos at 2-cell, 4-cell and day 4.5 blastocyst stages was compared with corresponding in vivo fertilized embryos by real-time PCR. It was demonstrated that immediately after the first cell cycle, SCNT-derived 2-cell stage embryos did not express all four cumulus-specific genes, which continually remained silent at the 4-cell and blastocyst stages. It is therefore concluded that all four cumulus-specific genes were correctly reprogrammed to be silent following nuclear transfer with cumulus donor cells in the mouse model. This would imply that the poor preimplantation developmental competence of SCNT embryos derived from cumulus cells is due to incomplete reprogramming of other embryonic genes, rather than cumulus-specific genes.

  12. Semiprognostic tests of the Arakawa-Schubert cumulus parameterization using simulated data

    Science.gov (United States)

    Xu, Kuan-Man; Arakawa, Akio

    1992-01-01

    Semiprognostic tests are performed against data simulated by a cumulus ensemble model to evaluate the Arakawa-Schubert (A-S) cumulus parametrization. It is found that the A-S cumulus parametrization is generally valid despite the existence of mesoscale organization in cumulus convection. The nondiagnostic and nondeterministic aspects of the A-S cumulus parametrization are examined by testing the sensitivity of the parametrization to the horizontal grid resolution. It is also shown that the inclusion of convective-scale downdrafts improves the results of semiprognostic tests.

  13. Investigation of Aerosol Indirect Effects using a Cumulus Microphysics Parameterization in a Regional Climate Model

    Energy Technology Data Exchange (ETDEWEB)

    Lim, Kyo-Sun; Fan, Jiwen; Leung, Lai-Yung R.; Ma, Po-Lun; Singh, Balwinder; Zhao, Chun; Zhang, Yang; Zhang, Guang; Song, Xiaoliang

    2014-01-29

    A new Zhang and McFarlane (ZM) cumulus scheme includes a two-moment cloud microphysics parameterization for convective clouds. This allows aerosol effects to be investigated more comprehensively by linking aerosols with microphysical processes in both stratiform clouds that are explicitly resolved and convective clouds that are parameterized in climate models. This new scheme is implemented in the Weather Research and Forecasting (WRF) model, which is coupled with the physics and aerosol packages from the Community Atmospheric Model version 5 (CAM5). A test case of July 2008 during the East Asian summer monsoon is selected to evaluate the performance of the new ZM scheme and to investigate aerosol effects on monsoon precipitation. The precipitation and radiative fluxes simulated by the new ZM scheme show a better agreement with observations compared to simulations with the original ZM scheme that does not include convective cloud microphysics and aerosol convective cloud interactions. Detailed analysis suggests that an increase in detrained cloud water and ice mass by the new ZM scheme is responsible for this improvement. To investigate precipitation response to increased anthropogenic aerosols, a sensitivity experiment is performed that mimics a clean environment by reducing the primary aerosols and anthropogenic emissions to 30% of that used in the control simulation of a polluted environment. The simulated surface precipitation is reduced by 9.8% from clean to polluted environment and the reduction is less significant when microphysics processes are excluded from the cumulus clouds. Ensemble experiments with ten members under each condition (i.e., clean and polluted) indicate similar response of the monsoon precipitation to increasing aerosols.

  14. Embryo quality predictive models based on cumulus cells gene expression

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    Devjak R

    2016-06-01

    Full Text Available Since the introduction of in vitro fertilization (IVF in clinical practice of infertility treatment, the indicators for high quality embryos were investigated. Cumulus cells (CC have a specific gene expression profile according to the developmental potential of the oocyte they are surrounding, and therefore, specific gene expression could be used as a biomarker. The aim of our study was to combine more than one biomarker to observe improvement in prediction value of embryo development. In this study, 58 CC samples from 17 IVF patients were analyzed. This study was approved by the Republic of Slovenia National Medical Ethics Committee. Gene expression analysis [quantitative real time polymerase chain reaction (qPCR] for five genes, analyzed according to embryo quality level, was performed. Two prediction models were tested for embryo quality prediction: a binary logistic and a decision tree model. As the main outcome, gene expression levels for five genes were taken and the area under the curve (AUC for two prediction models were calculated. Among tested genes, AMHR2 and LIF showed significant expression difference between high quality and low quality embryos. These two genes were used for the construction of two prediction models: the binary logistic model yielded an AUC of 0.72 ± 0.08 and the decision tree model yielded an AUC of 0.73 ± 0.03. Two different prediction models yielded similar predictive power to differentiate high and low quality embryos. In terms of eventual clinical decision making, the decision tree model resulted in easy-to-interpret rules that are highly applicable in clinical practice.

  15. Proteomics-based systems biology modeling of bovine germinal vesicle stage oocyte and cumulus cell interaction.

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    Divyaswetha Peddinti

    Full Text Available BACKGROUND: Oocytes are the female gametes which establish the program of life after fertilization. Interactions between oocyte and the surrounding cumulus cells at germinal vesicle (GV stage are considered essential for proper maturation or 'programming' of oocytes, which is crucial for normal fertilization and embryonic development. However, despite its importance, little is known about the molecular events and pathways involved in this bidirectional communication. METHODOLOGY/PRINCIPAL FINDINGS: We used differential detergent fractionation multidimensional protein identification technology (DDF-Mud PIT on bovine GV oocyte and cumulus cells and identified 811 and 1247 proteins in GV oocyte and cumulus cells, respectively; 371 proteins were significantly differentially expressed between each cell type. Systems biology modeling, which included Gene Ontology (GO and canonical genetic pathway analysis, showed that cumulus cells have higher expression of proteins involved in cell communication, generation of precursor metabolites and energy, as well as transport than GV oocytes. Our data also suggests a hypothesis that oocytes may depend on the presence of cumulus cells to generate specific cellular signals to coordinate their growth and maturation. CONCLUSIONS/SIGNIFICANCE: Systems biology modeling of bovine oocytes and cumulus cells in the context of GO and protein interaction networks identified the signaling pathways associated with the proteins involved in cell-to-cell signaling biological process that may have implications in oocyte competence and maturation. This first comprehensive systems biology modeling of bovine oocytes and cumulus cell proteomes not only provides a foundation for signaling and cell physiology at the GV stage of oocyte development, but are also valuable for comparative studies of other stages of oocyte development at the molecular level.

  16. Mechanisms and Model Diversity of Trade-Wind Shallow Cumulus Cloud Feedbacks: A Review

    Science.gov (United States)

    Vial, Jessica; Bony, Sandrine; Stevens, Bjorn; Vogel, Raphaela

    2017-07-01

    Shallow cumulus clouds in the trade-wind regions are at the heart of the long standing uncertainty in climate sensitivity estimates. In current climate models, cloud feedbacks are strongly influenced by cloud-base cloud amount in the trades. Therefore, understanding the key factors controlling cloudiness near cloud-base in shallow convective regimes has emerged as an important topic of investigation. We review physical understanding of these key controlling factors and discuss the value of the different approaches that have been developed so far, based on global and high-resolution model experimentations and process-oriented analyses across a range of models and for observations. The trade-wind cloud feedbacks appear to depend on two important aspects: (1) how cloudiness near cloud-base is controlled by the local interplay between turbulent, convective and radiative processes; (2) how these processes interact with their surrounding environment and are influenced by mesoscale organization. Our synthesis of studies that have explored these aspects suggests that the large diversity of model responses is related to fundamental differences in how the processes controlling trade cumulus operate in models, notably, whether they are parameterized or resolved. In models with parameterized convection, cloudiness near cloud-base is very sensitive to the vigor of convective mixing in response to changes in environmental conditions. This is in contrast with results from high-resolution models, which suggest that cloudiness near cloud-base is nearly invariant with warming and independent of large-scale environmental changes. Uncertainties are difficult to narrow using current observations, as the trade cumulus variability and its relation to large-scale environmental factors strongly depend on the time and/or spatial scales at which the mechanisms are evaluated. New opportunities for testing physical understanding of the factors controlling shallow cumulus cloud responses using

  17. Shallow-cumulus cloud feedback: model uncertainties and perspectives of observational constraint

    Science.gov (United States)

    Bony, Sandrine

    2017-04-01

    Shallow-cumulus clouds constitute the most prominent cloud type on Earth, and their response to changing environmental conditions is critical for climate sensitivity. Research over the last decade has pointed out the importance of the interplay between clouds, convection, turbulence and circulation in controlling this response. Unfortunately, numerical models represent this interplay in diverse ways, which translates into different shallow-cumulus cloud feedbacks in climate change. Climate models predict that the cloud-base cloud fraction is very sensitive to changes in environmental conditions, while process models suggest that it is very resilient to such changes. To understand and solve this contradiction, a field campaign named EUREC4A (Elucidating the role of clouds-circulation coupling in climate) will be organized in the lower Atlantic trades in Jan-Fev 2020. The scientific objectives of this campaign will be presented, and the experimental strategy envisioned to reach these objectives will be discussed.

  18. Effects of cumulus entrainment and multiple cloud types on a January global climate model simulation

    Science.gov (United States)

    Yao, Mao-Sung; Del Genio, Anthony D.

    1989-01-01

    An improved version of the GISS Model II cumulus parameterization designed for long-term climate integrations is used to study the effects of entrainment and multiple cloud types on the January climate simulation. Instead of prescribing convective mass as a fixed fraction of the cloud base grid-box mass, it is calculated based on the closure assumption that the cumulus convection restores the atmosphere to a neutral moist convective state at cloud base. This change alone significantly improves the distribution of precipitation, convective mass exchanges, and frequencies in the January climate. The vertical structure of the tropical atmosphere exhibits quasi-equilibrium behavior when this closure is used, even though there is no explicit constraint applied above cloud base.

  19. Numerical Simulation of Chennai Heavy Rainfall Using MM5 Mesoscale Model with Different Cumulus Parameterization Schemes

    Science.gov (United States)

    Litta, A. J.; Chakrapani, B.; Mohankumar, K.

    2007-07-01

    Heavy rainfall events become significant in human affairs when they are combined with hydrological elements. The problem of forecasting heavy precipitation is especially difficult since it involves making a quantitative precipitation forecast, a problem well recognized as challenging. Chennai (13.04°N and 80.17°E) faced incessant and heavy rain about 27 cm in 24 hours up to 8.30 a.m on 27th October 2005 completely threw life out of gear. This torrential rain caused by deep depression which lay 150km east of Chennai city in Bay of Bengal intensified and moved west north-west direction and crossed north Tamil Nadu and south Andhra Pradesh coast on 28th morning. In the present study, we investigate the predictability of the MM5 mesoscale model using different cumulus parameterization schemes for the heavy rainfall event over Chennai. MM5 Version 3.7 (PSU/NCAR) is run with two-way triply nested grids using Lambert Conformal Coordinates (LCC) with a nest ratio of 3:1 and 23 vertical layers. Grid sizes of 45, 15 and 5 km are used for domains 1, 2 and 3 respectively. The cumulus parameterization schemes used in this study are Anthes-Kuo scheme (AK), the Betts-Miller scheme (BM), the Grell scheme (GR) and the Kain-Fritsch scheme (KF). The present study shows that the prediction of heavy rainfall is sensitive to cumulus parameterization schemes. In the time series of rainfall, Grell scheme is in good agreement with observation. The ideal combination of the nesting domains, horizontal resolution and cloud parameterization is able to simulate the heavy rainfall event both qualitatively and quantitatively.

  20. Improvement and implementation of a parameterization for shallow cumulus in the global climate model ECHAM5-HAM

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    Isotta, Francesco; Spichtinger, Peter; Lohmann, Ulrike; von Salzen, Knut

    2010-05-01

    Convection is a crucial component of weather and climate. Its parameterization in General Circulation Models (GCMs) is one of the largest sources of uncertainty. Convection redistributes moisture and heat, affects the radiation budget and transports tracers from the PBL to higher levels. Shallow convection is very common over the globe, in particular over the oceans in the trade wind regions. A recently developed shallow convection scheme by von Salzen and McFarlane (2002) is implemented in the ECHAM5-HAM GCM instead of the standard convection scheme by Tiedtke (1989). The scheme of von Salzen and McFarlane (2002) is a bulk parameterization for an ensemble of transient shallow cumuli. A life cycle is considered, as well as inhomogeneities in the horizontal distribution of in-cloud properties due to mixing. The shallow convection scheme is further developed to take the ice phase and precipitation in form of rain and snow into account. The double moment microphysics scheme for cloud droplets and ice crystals implemented is consistent with the stratiform scheme and with the other types of convective clouds. The ice phase permits to alter the criterion to distinguish between shallow convection and the other two types of convection, namely deep and mid-level, which are still calculated by the Tiedtke (1989) scheme. The lunching layer of the test parcel in the shallow convection scheme is chosen as the one with maximum moist static energy in the three lowest levels. The latter is modified to the ``frozen moist static energy'' to account for the ice phase. Moreover, tracers (e.g. aerosols) are transported in the updraft and scavenged in and below clouds. As a first test of the performance of the new scheme and the interaction with the rest of the model, the Barbados Oceanographic and Meteorological EXperiment (BOMEX) and the Rain In Cumulus over the Ocean experiment (RICO) case are simulated with the single column model (SCM) and the results are compared with large eddy

  1. Canonical Ensemble Model for Black Hole Radiation

    Indian Academy of Sciences (India)

    Jingyi Zhang

    2014-09-01

    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 equation of thermodynamics is also discussed.

  2. 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...

  3. 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...

  4. 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...

  5. Ensembles of signal transduction models using Pareto Optimal Ensemble Techniques (POETs).

    Science.gov (United States)

    Song, Sang Ok; Chakrabarti, Anirikh; Varner, Jeffrey D

    2010-07-01

    Mathematical modeling of complex gene expression programs is an emerging tool for understanding disease mechanisms. However, identification of large models sometimes requires training using qualitative, conflicting or even contradictory data sets. One strategy to address this challenge is to estimate experimentally constrained model ensembles using multiobjective optimization. In this study, we used Pareto Optimal Ensemble Techniques (POETs) to identify a family of proof-of-concept signal transduction models. POETs integrate Simulated Annealing (SA) with Pareto optimality to identify models near the optimal tradeoff surface between competing training objectives. We modeled a prototypical-signaling network using mass-action kinetics within an ordinary differential equation (ODE) framework (64 ODEs in total). The true model was used to generate synthetic immunoblots from which the POET algorithm identified the 117 unknown model parameters. POET generated an ensemble of signaling models, which collectively exhibited population-like behavior. For example, scaled gene expression levels were approximately normally distributed over the ensemble following the addition of extracellular ligand. Also, the ensemble recovered robust and fragile features of the true model, despite significant parameter uncertainty. Taken together, these results suggest that experimentally constrained model ensembles could capture qualitatively important network features without exact parameter information.

  6. De praeceptis ferendis: good practice in multi-model ensembles

    Directory of Open Access Journals (Sweden)

    I. Kioutsioukis

    2014-06-01

    Full Text Available Ensembles of air quality models have been formally and empirically shown to outperform single models in many cases. Evidence suggests that ensemble error is reduced when the members form a diverse and accurate ensemble. Diversity and accuracy are hence two factors that should be taken care of while designing ensembles in order for them to provide better predictions. There exists a trade-off between diversity and accuracy for which one cannot be gained without expenses of the other. Theoretical aspects like the bias-variance-covariance decomposition and the accuracy-diversity decomposition are linked together and support the importance of creating ensemble that incorporates both the elements. Hence, the common practice of unconditional averaging of models without prior manipulation limits the advantages of ensemble averaging. We demonstrate the importance of ensemble accuracy and diversity through an inter-comparison of ensemble products for which a sound mathematical framework exists, and provide specific recommendations for model selection and weighting for multi model ensembles. To this end we have devised statistical tools that can be used for diagnostic evaluation of ensemble modelling products, complementing existing operational methods.

  7. Numerical Simulations of the 1 May 2012 Deep Convection Event over Cuba: Sensitivity to Cumulus and Microphysical Schemes in a High-Resolution Model

    Directory of Open Access Journals (Sweden)

    Yandy G. Mayor

    2015-01-01

    Full Text Available This paper evaluates the sensitivity to cumulus and microphysics schemes, as represented in numerical simulations of the Weather Research and Forecasting model, in characterizing a deep convection event over the Cuban island on 1 May 2012. To this end, 30 experiments combining five cumulus and six microphysics schemes, in addition to two experiments in which the cumulus parameterization was turned off, are tested in order to choose the combination that represents the event precipitation more accurately. ERA Interim is used as lateral boundary condition data for the downscaling procedure. Results show that convective schemes are more important than microphysics schemes for determining the precipitation areas within a high-resolution domain simulation. Also, while one cumulus scheme captures the overall spatial convective structure of the event more accurately than others, it fails to capture the precipitation intensity. This apparent discrepancy leads to sensitivity related to the verification method used to rank the scheme combinations. This sensitivity is also observed in a comparison between parameterized and explicit cumulus formation when the Kain-Fritsch scheme was used. A loss of added value is also found when the Grell-Freitas cumulus scheme was activated at 1 km grid spacing.

  8. 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.

  9. Ensemble Bayesian model averaging using Markov Chain Monte Carlo sampling

    NARCIS (Netherlands)

    Vrugt, J.A.; Diks, C.G.H.; Clark, M.

    2008-01-01

    Bayesian model averaging (BMA) has recently been proposed as a statistical method to calibrate forecast ensembles from numerical weather models. Successful implementation of BMA however, requires accurate estimates of the weights and variances of the individual competing models in the ensemble. In t

  10. Soil texture reclassification by an ensemble model

    Science.gov (United States)

    Cisty, Milan; Hlavcova, Kamila

    2015-04-01

    a prerequisite for solving some subsequent task, this bias is propagated to the subsequent modelling or other work. Therefore, for the sake of achieving more general and precise outputs while solving such tasks, the authors of the present paper are proposing a hybrid approach, which has the potential for obtaining improved results. Although the authors continue recommending the use of the mentioned parametric PSD models in the proposed methodology, the final prediction is made by an ensemble machine learning algorithm based on regression trees, the so-called Random Forest algorithm, which is built on top of the outputs of such models, which serves as an ensemble members. An improvement in precision was proved, and it is documented in the paper that the ensemble model worked better than any of its constituents. References Nemes, A., Wosten, J.H.M., Lilly, A., Voshaar, J.H.O.: Evaluation of different procedures to interpolate particle-size distributions to achieve compatibility within soil databases. Geoderma 90, 187- 202 (1999) Hwang, S.: Effect of texture on the performance of soil particle-size distribution models. Geoderma 123, 363-371 (2004) Botula, Y.D., Cornelis, W.M., Baert, G., Mafuka, P., Van Ranst, E.: Particle size distribution models for soils of the humid tropics. J Soils Sediments. 13, 686-698 (2013)

  11. The role of ensemble post-processing for modeling the ensemble tail

    Science.gov (United States)

    Van De Vyver, Hans; Van Schaeybroeck, Bert; Vannitsem, Stéphane

    2016-04-01

    The past decades the numerical weather prediction community has witnessed a paradigm shift from deterministic to probabilistic forecast and state estimation (Buizza and Leutbecher, 2015; Buizza et al., 2008), in an attempt to quantify the uncertainties associated with initial-condition and model errors. An important benefit of a probabilistic framework is the improved prediction of extreme events. However, one may ask to what extent such model estimates contain information on the occurrence probability of extreme events and how this information can be optimally extracted. Different approaches have been proposed and applied on real-world systems which, based on extreme value theory, allow the estimation of extreme-event probabilities conditional on forecasts and state estimates (Ferro, 2007; Friederichs, 2010). Using ensemble predictions generated with a model of low dimensionality, a thorough investigation is presented quantifying the change of predictability of extreme events associated with ensemble post-processing and other influencing factors including the finite ensemble size, lead time and model assumption and the use of different covariates (ensemble mean, maximum, spread...) for modeling the tail distribution. Tail modeling is performed by deriving extreme-quantile estimates using peak-over-threshold representation (generalized Pareto distribution) or quantile regression. Common ensemble post-processing methods aim to improve mostly the ensemble mean and spread of a raw forecast (Van Schaeybroeck and Vannitsem, 2015). Conditional tail modeling, on the other hand, is a post-processing in itself, focusing on the tails only. Therefore, it is unclear how applying ensemble post-processing prior to conditional tail modeling impacts the skill of extreme-event predictions. This work is investigating this question in details. Buizza, Leutbecher, and Isaksen, 2008: Potential use of an ensemble of analyses in the ECMWF Ensemble Prediction System, Q. J. R. Meteorol

  12. Ice formation and development in aged, wintertime cumulus over the UK: observations and modelling

    Science.gov (United States)

    Crawford, I.; Bower, K. N.; Choularton, T. W.; Dearden, C.; Crosier, J.; Westbrook, C.; Capes, G.; Coe, H.; Connolly, P. J.; Dorsey, J. R.; Gallagher, M. W.; Williams, P.; Trembath, J.; Cui, Z.; Blyth, A.

    2012-06-01

    In situ high resolution aircraft measurements of cloud microphysical properties were made in coordination with ground based remote sensing observations of a line of small cumulus clouds, using Radar and Lidar, as part of the Aerosol Properties, PRocesses And InfluenceS on the Earth's climate (APPRAISE) project. A narrow but extensive line (~100 km long) of shallow convective clouds over the southern UK was studied. Cloud top temperatures were observed to be higher than -8 °C, but the clouds were seen to consist of supercooled droplets and varying concentrations of ice particles. No ice particles were observed to be falling into the cloud tops from above. Current parameterisations of ice nuclei (IN) numbers predict too few particles will be active as ice nuclei to account for ice particle concentrations at the observed, near cloud top, temperatures (-7.5 °C). The role of mineral dust particles, consistent with concentrations observed near the surface, acting as high temperature IN is considered important in this case. It was found that very high concentrations of ice particles (up to 100 L-1) could be produced by secondary ice particle production providing the observed small amount of primary ice (about 0.01 L-1) was present to initiate it. This emphasises the need to understand primary ice formation in slightly supercooled clouds. It is shown using simple calculations that the Hallett-Mossop process (HM) is the likely source of the secondary ice. Model simulations of the case study were performed with the Aerosol Cloud and Precipitation Interactions Model (ACPIM). These parcel model investigations confirmed the HM process to be a very important mechanism for producing the observed high ice concentrations. A key step in generating the high concentrations was the process of collision and coalescence of rain drops, which once formed fell rapidly through the cloud, collecting ice particles which caused them to freeze and form instant large riming particles. The

  13. Deformed Gaussian Orthogonal Ensemble Analysis of the Interacting Boson Model

    CERN Document Server

    Pato, M P; Lima, C L; Hussein, M S; Alhassid, Y

    1994-01-01

    A Deformed Gaussian Orthogonal Ensemble (DGOE) which interpolates between the Gaussian Orthogonal Ensemble and a Poissonian Ensemble is constructed. This new ensemble is then applied to the analysis of the chaotic properties of the low lying collective states of nuclei described by the Interacting Boson Model (IBM). This model undergoes a transition order-chaos-order from the $SU(3)$ limit to the $O(6)$ limit. Our analysis shows that the quantum fluctuations of the IBM Hamiltonian, both of the spectrum and the eigenvectors, follow the expected behaviour predicted by the DGOE when one goes from one limit to the other.

  14. Flexible modeling frameworks to replace small ensembles of hydrological models and move toward large ensembles?

    Science.gov (United States)

    Addor, Nans; Clark, Martyn P.; Mizukami, Naoki

    2017-04-01

    Climate change impacts on hydrological processes are typically assessed using small ensembles of hydrological models. That is, a handful of hydrological models are typically driven by a larger number of climate models. Such a setup has several limitations. Because the number of hydrological models is small, only a small proportion of the model space is sampled, likely leading to an underestimation of the uncertainties in the projections. Further, sampling is arbitrary: although hydrological models should be selected to provide a representative sample of existing models (in terms of complexity and governing hypotheses), they are instead usually selected based on legacy reasons. Furthermore, running several hydrological models currently constitutes a practical challenge because each model must be setup and calibrated individually. Finally, and probably most importantly, the differences between the projected impacts cannot be directly related to differences between hydrological models, because the models are different in almost every possible aspect. We are hence in a situation in which different hydrological models deliver different projections, but for reasons that are mostly unclear, and in which the uncertainty in the projections is probably underestimated. To overcome these limitations, we are experimenting with the flexible modeling framework FUSE (Framework for Understanding Model Errors). FUSE enables to construct conceptual models piece by piece (in a "pick and mix" approach), so it can be used to generate a large number of models that mimic existing models and/or models that differ from other models in single targeted respect (e.g. how baseflow is generated). FUSE hence allows for controlled modeling experiments, and for a more systematic and exhaustive sampling of the model space. Here we explore climate change impacts over the contiguous USA on a 12km grid using two groups of three models: the first group involves the commonly used models VIC, PRMS and HEC

  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. 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.

  17. Ensemble inequivalence: Landau theory and the ABC model

    Science.gov (United States)

    Cohen, O.; Mukamel, D.

    2012-12-01

    It is well known that systems with long-range interactions may exhibit different phase diagrams when studied within two different ensembles. In many of the previously studied examples of ensemble inequivalence, the phase diagrams differ only when the transition in one of the ensembles is first order. By contrast, in a recent study of a generalized ABC model, the canonical and grand-canonical ensembles of the model were shown to differ even when they both exhibit a continuous transition. Here we show that the order of the transition where ensemble inequivalence may occur is related to the symmetry properties of the order parameter associated with the transition. This is done by analyzing the Landau expansion of a generic model with long-range interactions. The conclusions drawn from the generic analysis are demonstrated for the ABC model by explicit calculation of its Landau expansion.

  18. Ice formation and development in aged, wintertime cumulus over the UK: observations and modelling

    Directory of Open Access Journals (Sweden)

    I. Crawford

    2012-06-01

    Full Text Available In situ high resolution aircraft measurements of cloud microphysical properties were made in coordination with ground based remote sensing observations of a line of small cumulus clouds, using Radar and Lidar, as part of the Aerosol Properties, PRocesses And InfluenceS on the Earth's climate (APPRAISE project. A narrow but extensive line (~100 km long of shallow convective clouds over the southern UK was studied. Cloud top temperatures were observed to be higher than −8 °C, but the clouds were seen to consist of supercooled droplets and varying concentrations of ice particles. No ice particles were observed to be falling into the cloud tops from above. Current parameterisations of ice nuclei (IN numbers predict too few particles will be active as ice nuclei to account for ice particle concentrations at the observed, near cloud top, temperatures (−7.5 °C.

    The role of mineral dust particles, consistent with concentrations observed near the surface, acting as high temperature IN is considered important in this case. It was found that very high concentrations of ice particles (up to 100 L−1 could be produced by secondary ice particle production providing the observed small amount of primary ice (about 0.01 L−1 was present to initiate it. This emphasises the need to understand primary ice formation in slightly supercooled clouds. It is shown using simple calculations that the Hallett-Mossop process (HM is the likely source of the secondary ice.

    Model simulations of the case study were performed with the Aerosol Cloud and Precipitation Interactions Model (ACPIM. These parcel model investigations confirmed the HM process to be a very important mechanism for producing the observed high ice concentrations. A key step in generating the high concentrations was the process of collision and coalescence of rain drops, which once formed fell rapidly through the cloud, collecting ice particles which caused them

  19. Three-model ensemble wind prediction in southern Italy

    Science.gov (United States)

    Torcasio, Rosa Claudia; Federico, Stefano; Calidonna, Claudia Roberta; Avolio, Elenio; Drofa, Oxana; Landi, Tony Christian; Malguzzi, Piero; Buzzi, Andrea; Bonasoni, Paolo

    2016-03-01

    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.

  20. Improving High-resolution Weather Forecasts using the Weather Research and Forecasting (WRF) Model with Upgraded Kain-Fritsch Cumulus Scheme

    Science.gov (United States)

    High-resolution weather forecasting is affected by many aspects, i.e. model initial conditions, subgrid-scale cumulus convection and cloud microphysics schemes. Recent 12km grid studies using the Weather Research and Forecasting (WRF) model have identified the importance of inco...

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

    Directory of Open Access Journals (Sweden)

    A. AghaKouchak

    2012-06-01

    Full Text Available This paper presents a 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 model, 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 model includes a MATLAB Graphical User Interface (GUI and an ensemble simulation scheme that can be used for not only hydrological processes, but also for teaching uncertainty analysis, parameter estimation, ensemble simulation and model sensitivity.

  2. Impact of subgrid-scale radiative heating variability on the stratocumulus-to-trade cumulus transition in climate models

    Energy Technology Data Exchange (ETDEWEB)

    Xiao, Heng; Gustafson, William I.; Wang, Hailong

    2014-04-29

    Subgrid-scale interactions between turbulence and radiation are potentially important for accurately reproducing marine low clouds in climate models. To better understand the impact of these interactions, the Weather Research and Forecasting (WRF) model is configured for large eddy simulation (LES) to study the stratocumulus-to-trade cumulus (Sc-to-Cu) transition. Using the GEWEX Atmospheric System Studies (GASS) composite Lagrangian transition case and the Atlantic Trade Wind Experiment (ATEX) case, it is shown that the lack of subgrid-scale turbulence-radiation interaction, as is the case in current generation climate models, accelerates the Sc-to-Cu transition. Our analysis suggests that in cloud-topped boundary layers subgrid-scale turbulence-radiation interactions contribute to stronger production of temperature variance, which in turn leads to stronger buoyancy production of turbulent kinetic energy and helps to maintain the Sc cover.

  3. RACORO Continental Boundary Layer Cloud Investigations: 3. Separation of Parameterization Biases in Single-Column Model CAM5 Simulations of Shallow Cumulus

    Science.gov (United States)

    Lin, Wuyin; Liu, Yangang; Vogelmann, Andrew M.; Fridlind, Ann; Endo, Satoshi; Song, Hua; Feng, Sha; Toto, Tami; Li, Zhijin; Zhang, Minghua

    2015-01-01

    Climatically important low-level clouds are commonly misrepresented in climate models. The FAst-physics System TEstbed and Research (FASTER) Project has constructed case studies from the Atmospheric Radiation Measurement Climate Research Facility's Southern Great Plain site during the RACORO aircraft campaign to facilitate research on model representation of boundary-layer clouds. This paper focuses on using the single-column Community Atmosphere Model version 5 (SCAM5) simulations of a multi-day continental shallow cumulus case to identify specific parameterization causes of low-cloud biases. Consistent model biases among the simulations driven by a set of alternative forcings suggest that uncertainty in the forcing plays only a relatively minor role. In-depth analysis reveals that the model's shallow cumulus convection scheme tends to significantly under-produce clouds during the times when shallow cumuli exist in the observations, while the deep convective and stratiform cloud schemes significantly over-produce low-level clouds throughout the day. The links between model biases and the underlying assumptions of the shallow cumulus scheme are further diagnosed with the aid of large-eddy simulations and aircraft measurements, and by suppressing the triggering of the deep convection scheme. It is found that the weak boundary layer turbulence simulated is directly responsible for the weak cumulus activity and the simulated boundary layer stratiform clouds. Increased vertical and temporal resolutions are shown to lead to stronger boundary layer turbulence and reduction of low-cloud biases.

  4. Selecting, weeding, and weighting biased climate model ensembles

    Science.gov (United States)

    Jackson, C. S.; Picton, J.; Huerta, G.; Nosedal Sanchez, A.

    2012-12-01

    In the Bayesian formulation, the "log-likelihood" is a test statistic for selecting, weeding, or weighting climate model ensembles with observational data. This statistic has the potential to synthesize the physical and data constraints on quantities of interest. One of the thorny issues for formulating the log-likelihood is how one should account for biases. While in the past we have included a generic discrepancy term, not all biases affect predictions of quantities of interest. We make use of a 165-member ensemble CAM3.1/slab ocean climate models with different parameter settings to think through the issues that are involved with predicting each model's sensitivity to greenhouse gas forcing given what can be observed from the base state. In particular we use multivariate empirical orthogonal functions to decompose the differences that exist among this ensemble to discover what fields and regions matter to the model's sensitivity. We find that the differences that matter are a small fraction of the total discrepancy. Moreover, weighting members of the ensemble using this knowledge does a relatively poor job of adjusting the ensemble mean toward the known answer. This points out the shortcomings of using weights to correct for biases in climate model ensembles created by a selection process that does not emphasize the priorities of your log-likelihood.

  5. Statistical Ensemble Theory of Gompertz Growth Model

    Directory of Open Access Journals (Sweden)

    Takuya Yamano

    2009-11-01

    Full Text Available An ensemble formulation for the Gompertz growth function within the framework of statistical mechanics is presented, where the two growth parameters are assumed to be statistically distributed. The growth can be viewed as a self-referential process, which enables us to use the Bose-Einstein statistics picture. The analytical entropy expression pertain to the law can be obtained in terms of the growth velocity distribution as well as the Gompertz function itself for the whole process.

  6. Empowering Cloud Resolving Models Through GPU and Asynchronous IO Project

    Data.gov (United States)

    National Aeronautics and Space Administration — Facilitate use of Goddard Cumulus Ensemble (GCE), Weather Research and Forecasting Model (WRF), and Goddard Multi-scale Modeling Framework (MMF) with bin...

  7. 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

  8. 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...

  9. Numerical weather prediction model tuning via ensemble prediction system

    Science.gov (United States)

    Jarvinen, H.; Laine, M.; Ollinaho, P.; Solonen, A.; Haario, H.

    2011-12-01

    This paper discusses a novel approach to tune predictive skill of numerical weather prediction (NWP) models. NWP models contain tunable parameters which appear in parameterizations schemes of sub-grid scale physical processes. Currently, numerical values of these parameters are specified manually. In a recent dual manuscript (QJRMS, revised) we developed a new concept and method for on-line estimation of the NWP model parameters. The EPPES ("Ensemble prediction and parameter estimation system") method requires only minimal changes to the existing operational ensemble prediction infra-structure and it seems very cost-effective because practically no new computations are introduced. The approach provides an algorithmic decision making tool for model parameter optimization in operational NWP. In EPPES, statistical inference about the NWP model tunable parameters is made by (i) generating each member of the ensemble of predictions using different model parameter values, drawn from a proposal distribution, and (ii) feeding-back the relative merits of the parameter values to the proposal distribution, based on evaluation of a suitable likelihood function against verifying observations. In the presentation, the method is first illustrated in low-order numerical tests using a stochastic version of the Lorenz-95 model which effectively emulates the principal features of ensemble prediction systems. The EPPES method correctly detects the unknown and wrongly specified parameters values, and leads to an improved forecast skill. Second, results with an atmospheric general circulation model based ensemble prediction system show that the NWP model tuning capacity of EPPES scales up to realistic models and ensemble prediction systems. Finally, a global top-end NWP model tuning exercise with preliminary results is published.

  10. Development of Ensemble Model Based Water Demand Forecasting Model

    Science.gov (United States)

    Kwon, Hyun-Han; So, Byung-Jin; Kim, Seong-Hyeon; Kim, Byung-Seop

    2014-05-01

    In recent years, Smart Water Grid (SWG) concept has globally emerged over the last decade and also gained significant recognition in South Korea. Especially, there has been growing interest in water demand forecast and optimal pump operation and this has led to various studies regarding energy saving and improvement of water supply reliability. Existing water demand forecasting models are categorized into two groups in view of modeling and predicting their behavior in time series. One is to consider embedded patterns such as seasonality, periodicity and trends, and the other one is an autoregressive model that is using short memory Markovian processes (Emmanuel et al., 2012). The main disadvantage of the abovementioned model is that there is a limit to predictability of water demands of about sub-daily scale because the system is nonlinear. In this regard, this study aims to develop a nonlinear ensemble model for hourly water demand forecasting which allow us to estimate uncertainties across different model classes. The proposed model is consist of two parts. One is a multi-model scheme that is based on combination of independent prediction model. The other one is a cross validation scheme named Bagging approach introduced by Brieman (1996) to derive weighting factors corresponding to individual models. Individual forecasting models that used in this study are linear regression analysis model, polynomial regression, multivariate adaptive regression splines(MARS), SVM(support vector machine). The concepts are demonstrated through application to observed from water plant at several locations in the South Korea. Keywords: water demand, non-linear model, the ensemble forecasting model, uncertainty. Acknowledgements This subject is supported by Korea Ministry of Environment as "Projects for Developing Eco-Innovation Technologies (GT-11-G-02-001-6)

  11. Ensemble modeling for aromatic production in Escherichia coli.

    Directory of Open Access Journals (Sweden)

    Matthew L Rizk

    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.

  12. Probabilistic Quantitative Precipitation Forecasting Using Ensemble Model Output Statistics

    CERN Document Server

    Scheuerer, Michael

    2013-01-01

    Statistical post-processing of dynamical forecast ensembles is an essential component of weather forecasting. In this article, we present a post-processing method that generates full predictive probability distributions for precipitation accumulations based on ensemble model output statistics (EMOS). We model precipitation amounts by a generalized extreme value distribution that is left-censored at zero. This distribution permits modelling precipitation on the original scale without prior transformation of the data. A closed form expression for its continuous rank probability score can be derived and permits computationally efficient model fitting. We discuss an extension of our approach that incorporates further statistics characterizing the spatial variability of precipitation amounts in the vicinity of the location of interest. The proposed EMOS method is applied to daily 18-h forecasts of 6-h accumulated precipitation over Germany in 2011 using the COSMO-DE ensemble prediction system operated by the Germa...

  13. Are paleoclimate model ensembles consistent with the MARGO data synthesis?

    Directory of Open Access Journals (Sweden)

    J. C. Hargreaves

    2011-03-01

    Full Text Available We investigate the consistency of various ensembles of model simulations with the Multiproxy Approach for the Reconstruction of the Glacial Ocean Surface (MARGO sea surface temperature data synthesis. We discover that while two multi-model ensembles, created through the Paleoclimate Model Intercomparison Projects (PMIP and PMIP2, pass our simple tests of reliability, an ensemble based on parameter variation in a single model does not perform so well. We show that accounting for observational uncertainty in the MARGO database is of prime importance for correctly evaluating the ensembles. Perhaps surprisingly, the inclusion of a coupled dynamical ocean (compared to the use of a slab ocean does not appear to cause a wider spread in the sea surface temperature anomalies, but rather causes systematic changes with more heat transported north in the Atlantic. There is weak evidence that the sea surface temperature data may be more consistent with meridional overturning in the North Atlantic being similar for the LGM and the present day, however, the small size of the PMIP2 ensemble prevents any statistically significant results from being obtained.

  14. Are paleoclimate model ensembles consistent with the MARGO data synthesis?

    Directory of Open Access Journals (Sweden)

    J. C. Hargreaves

    2011-08-01

    Full Text Available We investigate the consistency of various ensembles of climate model simulations with the Multiproxy Approach for the Reconstruction of the Glacial Ocean Surface (MARGO sea surface temperature data synthesis. We discover that while two multi-model ensembles, created through the Paleoclimate Model Intercomparison Projects (PMIP and PMIP2, pass our simple tests of reliability, an ensemble based on parameter variation in a single model does not perform so well. We show that accounting for observational uncertainty in the MARGO database is of prime importance for correctly evaluating the ensembles. Perhaps surprisingly, the inclusion of a coupled dynamical ocean (compared to the use of a slab ocean does not appear to cause a wider spread in the sea surface temperature anomalies, but rather causes systematic changes with more heat transported north in the Atlantic. There is weak evidence that the sea surface temperature data may be more consistent with meridional overturning in the North Atlantic being similar for the LGM and the present day. However, the small size of the PMIP2 ensemble prevents any statistically significant results from being obtained.

  15. Impacts of Cumulus Momentum Transport on MJO Simulation

    Institute of Scientific and Technical Information of China (English)

    LING Jian; LI Chongyin; JIA Xiaolong

    2009-01-01

    Vertical cumulus momentum transport is an important physical process in the tropical atmosphere and plays a key role in the evolution of the tropical atmospheric system.This paper focuses on the impact of the vertical cumulus momentum transport on Madden-Julian Oscillation (MJO) simulation in two global climate models (GCMs).The Tiedtke cumulus parameterization scheme is applied to both GCMs [CAM2 and Spectral Atmospheric general circulation Model of LASG/IAP (SAMIL)].It is found that the MJO simulation ability might be influenced by the vertical cumulus momentum transport through the cumulus parameterization scheme.However,the use of vertical momentum transport in different models provides different results.In order to improve model's MJO simulation ability,we must introduce vertical cumulus momentum transport in a more reasonable way into models.Furthermore,the coherence of the parameterization and the underlying model also need to be considered.

  16. Lessons from Climate Modeling on the Design and Use of Ensembles for Crop Modeling

    Science.gov (United States)

    Wallach, Daniel; Mearns, Linda O.; Ruane, Alexander C.; Roetter, Reimund P.; Asseng, Senthold

    2016-01-01

    Working with ensembles of crop models is a recent but important development in crop modeling which promises to lead to better uncertainty estimates for model projections and predictions, better predictions using the ensemble mean or median, and closer collaboration within the modeling community. There are numerous open questions about the best way to create and analyze such ensembles. Much can be learned from the field of climate modeling, given its much longer experience with ensembles. We draw on that experience to identify questions and make propositions that should help make ensemble modeling with crop models more rigorous and informative. The propositions include defining criteria for acceptance of models in a crop MME, exploring criteria for evaluating the degree of relatedness of models in a MME, studying the effect of number of models in the ensemble, development of a statistical model of model sampling, creation of a repository for MME results, studies of possible differential weighting of models in an ensemble, creation of single model ensembles based on sampling from the uncertainty distribution of parameter values or inputs specifically oriented toward uncertainty estimation, the creation of super ensembles that sample more than one source of uncertainty, the analysis of super ensemble results to obtain information on total uncertainty and the separate contributions of different sources of uncertainty and finally further investigation of the use of the multi-model mean or median as a predictor.

  17. Supermodel - Interactive Ensemble of Low-dimensional Models

    Science.gov (United States)

    Basnarkov, Lasko; Duane, Gregory; Kocarev, Ljupco

    2013-04-01

    The accuracy of numerical weather prediction is steadily increasing due to the advances in different scientific disciplines. One of them aims at understanding the physics that underlies the atmospheric dynamics. Although the basic laws are well known there is large room for improvement in modeling various small scale processes. Currently they are generally parametrized and thus we are facing dozens of atmospheric models that are used in different meteorological centers around the world. The models are based on the same fluid dynamics laws, but generally differ in spatial resolution, parametrisation of the unresolved processes and also in the corresponding parameter values. Another key factor that contributes to the prediction improvement is the increase of the available computational power. As one consequence the grid resolution is getting smaller. As another, the contemporary numerical weather prediction schemes consider combinations of the outputs of the ensembles of models -- different perturbations of the same model or even different models. Considering interactive ensembles- with dynamical exchange of information between models that run simultaneously-is a novel approach toward improving the weather forecast or climate projection. Although flux exchange between different ocean and atmospheric models has some history, coupling different atmospheric models is rather new. The coupling schemes can be different and the first approaches are those that combine corresponding dynamical variables or tendency components. In this work we present an example with an artificial toy model- the Lorenz 96 model-that shares some properties with the atmosphere. As reality (the atmosphere) we consider one Lorenz 96 class III system, while as its imperfect models are taken three class II systems that have different forcing terms. The interactive ensemble has tendencies that are weighted combinations of the individual models' tendencies. The weights are obtained with statistical

  18. Matrix models for β-ensembles from Nekrasov partition functions

    NARCIS (Netherlands)

    Sułkowski, P.

    2010-01-01

    We relate Nekrasov partition functions, with arbitrary values of ∊ 1, ∊ 2 parameters, to matrix models for β-ensembles. We find matrix models encoding the instanton part of Nekrasov partition functions, whose measure, to the leading order in ∊ 2 expansion, is given by the Vandermonde determinant to

  19. The interplay between cooperativity and diversity in model threshold ensembles.

    Science.gov (United States)

    Cervera, Javier; Manzanares, José A; Mafe, Salvador

    2014-10-06

    The interplay between cooperativity and diversity is crucial for biological ensembles because single molecule experiments show a significant degree of heterogeneity and also for artificial nanostructures because of the high individual variability characteristic of nanoscale units. We study the cross-effects between cooperativity and diversity in model threshold ensembles composed of individually different units that show a cooperative behaviour. The units are modelled as statistical distributions of parameters (the individual threshold potentials here) characterized by central and width distribution values. The simulations show that the interplay between cooperativity and diversity results in ensemble-averaged responses of interest for the understanding of electrical transduction in cell membranes, the experimental characterization of heterogeneous groups of biomolecules and the development of biologically inspired engineering designs with individually different building blocks. © 2014 The Author(s) Published by the Royal Society. All rights reserved.

  20. Cumulus Parameterization: Those Who Can Remember the Past Are Condemned to Repeat It

    CERN Document Server

    Del Genio, Anthony D

    2016-01-01

    Moist convection plays a leading role in the dynamics and energy budget of Earth's tropics and influences the sensitivity of Earth's climate to greenhouse gas increases. Because individual convective cells are much smaller than the gridboxes of 3-dimensional global climate models (GCMs), these models parameterize the effects of an ensemble of moist convective updrafts and downdrafts on the environment. Cumulus parameterization has been a focus of the terrestrial meteorology community for half a century. Only in past decade, however, have GCMs with moist convective physics been applied to other planets. Given our lack of detailed knowledge about convective clouds except on Earth, planetary GCMs are often designed with very simple approaches to cumulus parameterization, adopted from the earliest generations of terrestrial GCMs. These parameterizations were based on breakthroughs in understanding of convection in their time. However, at the same time that planetary GCMs have begun to emerge, a quiet revolution i...

  1. 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

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

    CSIR Research Space (South Africa)

    Dalton, A

    2014-10-01

    Full Text Available southern Africa. Furthermore a comparison is made between forecast skill of the 850 hPa geopotential heights and raw model rainfall outputs. The determination of skill was done by way of empirical post-processing procedures in order to project ensemble mean...

  3. 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 demo

  4. A new ensemble model for short term wind power prediction

    DEFF Research Database (Denmark)

    Madsen, Henrik; Albu, Razvan-Daniel; Felea, Ioan

    2012-01-01

    As the objective of this study, a non-linear ensemble system is used to develop a new model for predicting wind speed in short-term time scale. Short-term wind power prediction becomes an extremely important field of research for the energy sector. Regardless of the recent advancements in the re-search...

  5. 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

  6. Comparing climate change impacts on crops in Belgium based on CMIP3 and EU-ENSEMBLES multi-model ensembles

    Science.gov (United States)

    Vanuytrecht, E.; Raes, D.; Willems, P.; Semenov, M.

    2012-04-01

    Global Circulation Models (GCMs) are sophisticated tools to study the future evolution of the climate. Yet, the coarse scale of GCMs of hundreds of kilometers raises questions about the suitability for agricultural impact assessments. These assessments are often made at field level and require consideration of interactions at sub-GCM grid scale (e.g., elevation-dependent climatic changes). Regional climate models (RCMs) were developed to provide climate projections at a spatial scale of 25-50 km for limited regions, e.g. Europe (Giorgi and Mearns, 1991). Climate projections from GCMs or RCMs are available as multi-model ensembles. These ensembles are based on large data sets of simulations produced by modelling groups worldwide, who performed a set of coordinated climate experiments in which climate models were run for a common set of experiments and various emissions scenarios (Knutti et al., 2010). The use of multi-model ensembles in climate change studies is an important step in quantifying uncertainty in impact predictions, which will underpin more informed decisions for adaptation and mitigation to changing climate (Semenov and Stratonovitch, 2010). The objective of our study was to evaluate the effect of the spatial scale of climate projections on climate change impacts for cereals in Belgium. Climate scenarios were based on two multi-model ensembles, one comprising 15 GCMs of the Coupled Model Intercomparison Project phase 3 (CMIP3; Meehl et al., 2007) with spatial resolution of 200-300 km, the other comprising 9 RCMs of the EU-ENSEMBLES project (van der Linden and Mitchell, 2009) with spatial resolution of 25 km. To be useful for agricultural impact assessments, the projections of GCMs and RCMs were downscaled to the field level. Long series (240 cropping seasons) of local-scale climate scenarios were generated by the LARS-WG weather generator (Semenov et al., 2010) via statistical inference. Crop growth and development were simulated with the Aqua

  7. Ensemble Kinetic Modeling of Metabolic Networks from Dynamic Metabolic Profiles

    Directory of Open Access Journals (Sweden)

    Gengjie Jia

    2012-11-01

    Full Text Available Kinetic modeling of metabolic pathways has important applications in metabolic engineering, but significant challenges still remain. The difficulties faced vary from finding best-fit parameters in a highly multidimensional search space to incomplete parameter identifiability. To meet some of these challenges, an ensemble modeling method is developed for characterizing a subset of kinetic parameters that give statistically equivalent goodness-of-fit to time series concentration data. The method is based on the incremental identification approach, where the parameter estimation is done in a step-wise manner. Numerical efficacy is achieved by reducing the dimensionality of parameter space and using efficient random parameter exploration algorithms. The shift toward using model ensembles, instead of the traditional “best-fit” models, is necessary to directly account for model uncertainty during the application of such models. The performance of the ensemble modeling approach has been demonstrated in the modeling of a generic branched pathway and the trehalose pathway in Saccharomyces cerevisiae using generalized mass action (GMA kinetics.

  8. Optimization of multi-model ensemble forecasting of typhoon waves

    Directory of Open Access Journals (Sweden)

    Shun-qi Pan

    2016-01-01

    Full Text Available Accurately forecasting ocean waves during typhoon events is extremely important in aiding the mitigation and minimization of their potential damage to the coastal infrastructure, and the protection of coastal communities. However, due to the complex hydrological and meteorological interaction and uncertainties arising from different modeling systems, quantifying the uncertainties and improving the forecasting accuracy of modeled typhoon-induced waves remain challenging. This paper presents a practical approach to optimizing model-ensemble wave heights in an attempt to improve the accuracy of real-time typhoon wave forecasting. A locally weighted learning algorithm is used to obtain the weights for the wave heights computed by the WAVEWATCH III wave model driven by winds from four different weather models (model-ensembles. The optimized weights are subsequently used to calculate the resulting wave heights from the model-ensembles. The results show that the Optimization is capable of capturing the different behavioral effects of the different weather models on wave generation. Comparison with the measurements at the selected wave buoy locations shows that the optimized weights, obtained through a training process, can significantly improve the accuracy of the forecasted wave heights over the standard mean values, particularly for typhoon-induced peak waves. The results also indicate that the algorithm is easy to implement and practical for real-time wave forecasting.

  9. A new ensemble model for short term wind power prediction

    DEFF Research Database (Denmark)

    Madsen, Henrik; Albu, Razvan-Daniel; Felea, Ioan;

    2012-01-01

    As the objective of this study, a non-linear ensemble system is used to develop a new model for predicting wind speed in short-term time scale. Short-term wind power prediction becomes an extremely important field of research for the energy sector. Regardless of the recent advancements in the re......-search of prediction models, it was observed that different models have different capabilities and also no single model is suitable under all situations. The idea behind EPS (ensemble prediction systems) is to take advantage of the unique features of each subsystem to detain diverse patterns that exist in the dataset....... The conferred results show that the prediction errors can be decreased, while the computation time is reduced....

  10. A model for luminescence of localized state ensemble

    OpenAIRE

    Li, Q.; Xu, S. J.; Xie, M H; Tong, S. Y.

    2004-01-01

    A distribution function for localized carriers, $f(E,T)=\\frac{1}{e^{(E-E_a)/k_BT}+\\tau_{tr}/\\tau_r}$, is proposed by solving a rate equation, in which, electrical carriers' generation, thermal escape, recapture and radiative recombination are taken into account. Based on this distribution function, a model is developed for luminescence from localized state ensemble with a Gaussian-type density of states. The model reproduces quantitatively all the anomalous temperature behaviors of localized ...

  11. Forecasting Crude Oil Price with Multiscale Denoising Ensemble Model

    Directory of Open Access Journals (Sweden)

    Xia Li

    2014-01-01

    Full Text Available Crude oil price becomes more volatile and sensitive to increasingly diversified influencing factors with higher level of deregulations worldwide. Current methodologies are being challenged as they have been constrained by traditional approaches assuming homogeneous time horizons and investment strategies. Approximations they provided over the long term time horizon no longer satisfy the accuracy requirement at shorter term and more microlevels. This paper proposes a novel crude oil price forecasting model based on the wavelet denoising ARMA models ensemble by least square support vector regression with the reduced forecasting matrix dimensions by independent component analysis. The proposed methodology combines the multi resolution analysis and nonlinear ensemble framework. The wavelet denoising based algorithm is introduced to separate and extract the underlying data components with distinct features, corresponding to investors with different investment scales, which are modeled with time series models of different specifications and parameters. Then least square support vector regression is introduced to nonlinearly ensemble results based on different wavelet families to further reduce the estimation biases and improve the forecasting generalizability. Empirical studies show the significant performance improvement when the proposed model is tested against the bench-mark models.

  12. Cloud-Aerosol-Radiation (CAR ensemble modeling system

    Directory of Open Access Journals (Sweden)

    X.-Z. Liang

    2013-04-01

    Full Text Available A Cloud-Aerosol-Radiation (CAR ensemble modeling system has been developed to incorporate the largest choices of alternative parameterizations for cloud properties (cover, water, radius, optics, geometry, aerosol properties (type, profile, optics, radiation transfers (solar, infrared, and their interactions. These schemes form the most comprehensive collection currently available in the literature, including those used by the world leading general circulation models (GCMs. The CAR provides a unique framework to determine (via intercomparison across all schemes, reduce (via optimized ensemble simulations, and attribute specific key factors for (via physical process sensitivity analyses the model discrepancies and uncertainties in representing greenhouse gas, aerosol and cloud radiative forcing effects. This study presents a general description of the CAR system and illustrates its capabilities for climate modeling applications, especially in the context of estimating climate sensitivity and uncertainty range caused by cloud-aerosol-radiation interactions. For demonstration purpose, the evaluation is based on several CAR standalone and coupled climate model experiments, each comparing a limited subset of the full system ensemble with up to 896 members. It is shown that the quantification of radiative forcings and climate impacts strongly depends on the choices of the cloud, aerosol and radiation schemes. The prevailing schemes used in current GCMs are likely insufficient in variety and physically biased in a significant way. There exists large room for improvement by optimally combining radiation transfer with cloud property schemes.

  13. Multi-wheat-model ensemble responses to interannual climatic variability

    DEFF Research Database (Denmark)

    Ruane, A C; Hudson, N I; Asseng, S

    2016-01-01

    evaluate results against the interannual variability of growing season temperature, precipitation, and solar radiation. The amount of information used for calibration has only a minor effect on most models' climate response, and even small multi-model ensembles prove beneficial. Wheat model clusters reveal...... common characteristics of yield response to climate; however models rarely share the same cluster at all four sites indicating substantial independence. Only a weak relationship (R2 ≤ 0.24) was found between the models' sensitivities to interannual temperature variability and their response to long...

  14. A model for ensemble NMR quantum computer using antiferromagnetic structure

    CERN Document Server

    Kokin, A A

    2000-01-01

    The one-dimensional homonuclear periodic array of nuclear spins I = 1/2,owing to hyperfine interaction of nuclear spins with electronic magneticmoments in antiferromagnetic structure, is considered. The neighbor nuclearspins in such array are opposite oriented and have resonant frequenciesdetermined by hyperfine interaction constant, applied magnetic field value andinteraction with the left and right nuclear neighbor spins. The resonantfrequencies difference of nuclear spins, when the neighbor spins have differentand the same states, is used to control the spin dynamics by means of selectiveresonant RF-pulses both for single nuclear spins and for ensemble of nuclearspins with the same resonant frequency. A model for the NMR quantum computer of cellular-automata type based on anone-dimensional homonuclear periodic array of spins is proposed. This model maybe generalized to a large ensemble of parallel working one-dimensional arraysand to two-dimensional and three-dimensional structures.

  15. Development and evaluation of novel forecasting adaptive ensemble model

    Directory of Open Access Journals (Sweden)

    C.M. Anish

    2016-09-01

    Full Text Available This paper proposes a new ensemble based adaptive forecasting structure for efficient different interval days' ahead prediction of five different asset values (NAV. In this approach three individual adaptive structures such as adaptive moving average (AMA, adaptive auto regressive moving average (AARMA and feedback radial basis function network (FRBF are employed to first train with conventional LMS, conventional forward-backward LMS and corresponding learning algorithm of FRBF respectively. After successful validation of each model the output obtained by each individual model is optimally weighted using Genetic algorithm (GA as well as particle swarm optimization (PSO based techniques to produce the best possible different days ahead prediction accuracy. Finally the results of prediction obtained of the NAV values are compared with the results obtained by individual predictors as well as by other four existing ensemble schemes. It is in general demonstrated that in all cases the proposed forecasting scheme outperforms other competitive methods.

  16. Multi-model ensemble hydrologic prediction and uncertainties analysis

    Directory of Open Access Journals (Sweden)

    S. Jiang

    2014-09-01

    Full Text Available Modelling uncertainties (i.e. input errors, parameter uncertainties and model structural errors inevitably exist in hydrological prediction. A lot of recent attention has focused on these, of which input error modelling, parameter optimization and multi-model ensemble strategies are the three most popular methods to demonstrate the impacts of modelling uncertainties. In this paper the Xinanjiang model, the Hybrid rainfall–runoff model and the HYMOD model were applied to the Mishui Basin, south China, for daily streamflow ensemble simulation and uncertainty analysis. The three models were first calibrated by two parameter optimization algorithms, namely, the Shuffled Complex Evolution method (SCE-UA and the Shuffled Complex Evolution Metropolis method (SCEM-UA; next, the input uncertainty was accounted for by introducing a normally-distributed error multiplier; then, the simulation sets calculated from the three models were combined by Bayesian model averaging (BMA. The results show that both these parameter optimization algorithms generate good streamflow simulations; specifically the SCEM-UA can imply parameter uncertainty and give the posterior distribution of the parameters. Considering the precipitation input uncertainty, the streamflow simulation precision does not improve very much. While the BMA combination not only improves the streamflow prediction precision, it also gives quantitative uncertainty bounds for the simulation sets. The SCEM-UA calculated prediction interval is better than the SCE-UA calculated one. These results suggest that considering the model parameters' uncertainties and doing multi-model ensemble simulations are very practical for streamflow prediction and flood forecasting, from which more precision prediction and more reliable uncertainty bounds can be generated.

  17. An ensemble model of QSAR tools for regulatory risk assessment.

    Science.gov (United States)

    Pradeep, Prachi; Povinelli, Richard J; White, Shannon; Merrill, Stephen J

    2016-01-01

    Quantitative structure activity relationships (QSARs) are theoretical models that relate a quantitative measure of chemical structure to a physical property or a biological effect. QSAR predictions can be used for chemical risk assessment for protection of human and environmental health, which makes them interesting to regulators, especially in the absence of experimental data. For compatibility with regulatory use, QSAR models should be transparent, reproducible and optimized to minimize the number of false negatives. In silico QSAR tools are gaining wide acceptance as a faster alternative to otherwise time-consuming clinical and animal testing methods. However, different QSAR tools often make conflicting predictions for a given chemical and may also vary in their predictive performance across different chemical datasets. In a regulatory context, conflicting predictions raise interpretation, validation and adequacy concerns. To address these concerns, ensemble learning techniques in the machine learning paradigm can be used to integrate predictions from multiple tools. By leveraging various underlying QSAR algorithms and training datasets, the resulting consensus prediction should yield better overall predictive ability. We present a novel ensemble QSAR model using Bayesian classification. The model allows for varying a cut-off parameter that allows for a selection in the desirable trade-off between model sensitivity and specificity. The predictive performance of the ensemble model is compared with four in silico tools (Toxtree, Lazar, OECD Toolbox, and Danish QSAR) to predict carcinogenicity for a dataset of air toxins (332 chemicals) and a subset of the gold carcinogenic potency database (480 chemicals). Leave-one-out cross validation results show that the ensemble model achieves the best trade-off between sensitivity and specificity (accuracy: 83.8 % and 80.4 %, and balanced accuracy: 80.6 % and 80.8 %) and highest inter-rater agreement [kappa (κ): 0

  18. Improved forecasting of thermospheric densities using multi-model ensembles

    Science.gov (United States)

    Elvidge, Sean; Godinez, Humberto C.; Angling, Matthew J.

    2016-07-01

    This paper presents the first known application of multi-model ensembles to the forecasting of the thermosphere. A multi-model ensemble (MME) is a method for combining different, independent models. The main advantage of using an MME is to reduce the effect of model errors and bias, since it is expected that the model errors will, at least partly, cancel. The MME, with its reduced uncertainties, can then be used as the initial conditions in a physics-based thermosphere model for forecasting. This should increase the forecast skill since a reduction in the errors of the initial conditions of a model generally increases model skill. In this paper the Thermosphere-Ionosphere Electrodynamic General Circulation Model (TIE-GCM), the US Naval Research Laboratory Mass Spectrometer and Incoherent Scatter radar Exosphere 2000 (NRLMSISE-00), and Global Ionosphere-Thermosphere Model (GITM) have been used to construct the MME. As well as comparisons between the MMEs and the "standard" runs of the model, the MME densities have been propagated forward in time using the TIE-GCM. It is shown that thermospheric forecasts of up to 6 h, using the MME, have a reduction in the root mean square error of greater than 60 %. The paper also highlights differences in model performance between times of solar minimum and maximum.

  19. Flood Forecast and Early Warning with High-Resolution Ensemble Rainfall from Numerical Weather Prediction Model

    OpenAIRE

    Yu, Wansik; NAKAKITA, Eiichi; Jung, Kwansue

    2016-01-01

    This paper investigates the applicability of ensemble forecasts of numerical weather prediction (NWP) model for flood forecasting. In this study, 10 km resolution ensemble rainfalls forecast and their downscaled forecasts of 2 km resolution were used in the hydrologic model as input data for flood forecasting and application of flood early warning. Ensemble data consists of 51 members and 48 hr forecast time. Ensemble outputs are verified spatially whether they can produce suitable rainfall p...

  20. 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.

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

    Science.gov (United States)

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

    2016-10-01

    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.

  2. Meteorological Drought Prediction Using a Multi-Model Ensemble Approach

    Science.gov (United States)

    Chen, L.; Mo, K. C.; Zhang, Q.; Huang, J.

    2013-12-01

    In the United States, drought is among the costliest natural hazards, with an annual average of 6 billion dollars in damage. Drought prediction from monthly to seasonal time scales is of critical importance to disaster mitigation, agricultural planning, and multi-purpose reservoir management. Started in December 2012, NOAA Climate Prediction Center (CPC) has been providing operational Standardized Precipitation Index (SPI) Outlooks using the National Multi-Model Ensemble (NMME) forecasts, to support CPC's monthly drought outlooks and briefing activities. The current NMME system consists of six model forecasts from U.S. and Canada modeling centers, including the CFSv2, CM2.1, GEOS-5, CCSM3.0, CanCM3, and CanCM4 models. In this study, we conduct an assessment of the meteorological drought predictability using the retrospective NMME forecasts for the period from 1982 to 2010. Before predicting SPI, monthly-mean precipitation (P) forecasts from each model were bias corrected and spatially downscaled (BCSD) to regional grids of 0.5-degree resolution over the contiguous United States based on the probability distribution functions derived from the hindcasts. The corrected P forecasts were then appended to the CPC Unified Precipitation Analysis to form a P time series for computing 3-month and 6-month SPIs. The ensemble SPI forecasts are the equally weighted mean of the six model forecasts. Two performance measures, the anomaly correlation and root-mean-square errors against the observations, are used to evaluate forecast skill. For P forecasts, errors vary among models and skill generally is low after the second month. All model P forecasts have higher skill in winter and lower skill in summer. In wintertime, BCSD improves both P and SPI forecast skill. Most improvements are over the western mountainous regions and along the Great Lake. Overall, SPI predictive skill is regionally and seasonally dependent. The six-month SPI forecasts are skillful out to four months. For

  3. Ensemble feature selection integrating elitist roles and quantum game model

    Institute of Scientific and Technical Information of China (English)

    Weiping Ding; Jiandong Wang; Zhijin Guan; Quan Shi

    2015-01-01

    To accelerate the selection process of feature subsets in the rough set theory (RST), an ensemble elitist roles based quantum game (EERQG) algorithm is proposed for feature selec-tion. Firstly, the multilevel elitist roles based dynamics equilibrium strategy is established, and both immigration and emigration of elitists are able to be self-adaptive to balance between exploration and exploitation for feature selection. Secondly, the utility matrix of trust margins is introduced to the model of multilevel elitist roles to enhance various elitist roles’ performance of searching the optimal feature subsets, and the win-win utility solutions for feature selec-tion can be attained. Meanwhile, a novel ensemble quantum game strategy is designed as an intriguing exhibiting structure to perfect the dynamics equilibrium of multilevel elitist roles. Final y, the en-semble manner of multilevel elitist roles is employed to achieve the global minimal feature subset, which wil greatly improve the fea-sibility and effectiveness. Experiment results show the proposed EERQG algorithm has superiority compared to the existing feature selection algorithms.

  4. Multi-Wheat-Model Ensemble Responses to Interannual Climate Variability

    Science.gov (United States)

    Ruane, Alex C.; Hudson, Nicholas I.; Asseng, Senthold; Camarrano, Davide; Ewert, Frank; Martre, Pierre; Boote, Kenneth J.; Thorburn, Peter J.; Aggarwal, Pramod K.; Angulo, Carlos

    2016-01-01

    We compare 27 wheat models' yield responses to interannual climate variability, analyzed at locations in Argentina, Australia, India, and The Netherlands as part of the Agricultural Model Intercomparison and Improvement Project (AgMIP) Wheat Pilot. Each model simulated 1981e2010 grain yield, and we evaluate results against the interannual variability of growing season temperature, precipitation, and solar radiation. The amount of information used for calibration has only a minor effect on most models' climate response, and even small multi-model ensembles prove beneficial. Wheat model clusters reveal common characteristics of yield response to climate; however models rarely share the same cluster at all four sites indicating substantial independence. Only a weak relationship (R2 0.24) was found between the models' sensitivities to interannual temperature variability and their response to long-termwarming, suggesting that additional processes differentiate climate change impacts from observed climate variability analogs and motivating continuing analysis and model development efforts.

  5. Ice formation and development in aged, wintertime cumulus over the UK : observations and modelling

    Directory of Open Access Journals (Sweden)

    I. Crawford

    2011-11-01

    Full Text Available In-situ high resolution aircraft measurements of cloud microphysical properties were made in coordination with ground based remote sensing observations of Radar and Lidar as part of the Aerosol Properties, PRocesses And InfluenceS on the Earth's climate (APPRAISE project. A narrow but extensive line (~100 km long of shallow convective clouds over the southern UK was studied. Cloud top temperatures were observed to be higher than ~−8 °C, but the clouds were seen to consist of supercooled droplets and varying concentrations of ice particles. No ice particles were observed to be falling into the cloud tops from above. Current parameterisations of ice nuclei (IN numbers predict too few particles will be active as ice nuclei to account for ice particle concentrations at the observed near cloud top temperatures (~−7 °C. The role of biological particles, consistent with concentrations observed near the surface, acting as potential efficient high temperature IN is considered important in this case. It was found that very high concentrations of ice particles (up to 100 L−1 could be produced by powerful secondary ice particle production emphasising the importance of understanding primary ice formation in slightly supercooled clouds.

    Aircraft penetrations at −3.5 °C, showed peak ice crystal concentrations of up to 100 L−1 which together with the characteristic ice crystal habits observed (generally rimed ice particles and columns suggested secondary ice production had occurred. To investigate whether the Hallett-Mossop (HM secondary ice production process could account for these observations, ice splinter production rates were calculated. These calculated rates and observations could only be reconciled provided the constraint that only droplets >24 μm in diameter could lead to splinter production, was relaxed slightly by 2 μm.

    Model simulations of the case study were also performed with the WRF

  6. A Bayesian ensemble of sensitivity measures for severe accident modeling

    Energy Technology Data Exchange (ETDEWEB)

    Hoseyni, Seyed Mohsen [Department of Basic Sciences, East Tehran Branch, Islamic Azad University, Tehran (Iran, Islamic Republic of); Di Maio, Francesco, E-mail: francesco.dimaio@polimi.it [Energy Department, Politecnico di Milano, Via La Masa 34, 20156 Milano (Italy); Vagnoli, Matteo [Energy Department, Politecnico di Milano, Via La Masa 34, 20156 Milano (Italy); Zio, Enrico [Energy Department, Politecnico di Milano, Via La Masa 34, 20156 Milano (Italy); Chair on System Science and Energetic Challenge, Fondation EDF – Electricite de France Ecole Centrale, Paris, and Supelec, Paris (France); Pourgol-Mohammad, Mohammad [Department of Mechanical Engineering, Sahand University of Technology, Tabriz (Iran, Islamic Republic of)

    2015-12-15

    Highlights: • We propose a sensitivity analysis (SA) method based on a Bayesian updating scheme. • The Bayesian updating schemes adjourns an ensemble of sensitivity measures. • Bootstrap replicates of a severe accident code output are fed to the Bayesian scheme. • The MELCOR code simulates the fission products release of LOFT LP-FP-2 experiment. • Results are compared with those of traditional SA methods. - Abstract: In this work, a sensitivity analysis framework is presented to identify the relevant input variables of a severe accident code, based on an incremental Bayesian ensemble updating method. The proposed methodology entails: (i) the propagation of the uncertainty in the input variables through the severe accident code; (ii) the collection of bootstrap replicates of the input and output of limited number of simulations for building a set of finite mixture models (FMMs) for approximating the probability density function (pdf) of the severe accident code output of the replicates; (iii) for each FMM, the calculation of an ensemble of sensitivity measures (i.e., input saliency, Hellinger distance and Kullback–Leibler divergence) and the updating when a new piece of evidence arrives, by a Bayesian scheme, based on the Bradley–Terry model for ranking the most relevant input model variables. An application is given with respect to a limited number of simulations of a MELCOR severe accident model describing the fission products release in the LP-FP-2 experiment of the loss of fluid test (LOFT) facility, which is a scaled-down facility of a pressurized water reactor (PWR).

  7. Predicting artificailly drained areas by means of selective model ensemble

    DEFF Research Database (Denmark)

    Møller, Anders Bjørn; Beucher, Amélie; Iversen, Bo Vangsø

    . The approaches employed include decision trees, discriminant analysis, regression models, neural networks and support vector machines amongst others. Several models are trained with each method, using variously the original soil covariates and principal components of the covariates. With a large ensemble...... out since the mid-19th century, and it has been estimated that half of the cultivated area is artificially drained (Olesen, 2009). A number of machine learning approaches can be used to predict artificially drained areas in geographic space. However, instead of choosing the most accurate model....... The study aims firstly to train a large number of models to predict the extent of artificially drained areas using various machine learning approaches. Secondly, the study will develop a method for selecting the models, which give a good prediction of artificially drained areas, when used in conjunction...

  8. Modeling Dynamic Systems with Efficient Ensembles of Process-Based Models.

    Science.gov (United States)

    Simidjievski, Nikola; Todorovski, Ljupčo; Džeroski, Sašo

    2016-01-01

    Ensembles are a well established machine learning paradigm, leading to accurate and robust models, predominantly applied to predictive modeling tasks. Ensemble models comprise a finite set of diverse predictive models whose combined output is expected to yield an improved predictive performance as compared to an individual model. In this paper, we propose a new method for learning ensembles of process-based models of dynamic systems. The process-based modeling paradigm employs domain-specific knowledge to automatically learn models of dynamic systems from time-series observational data. Previous work has shown that ensembles based on sampling observational data (i.e., bagging and boosting), significantly improve predictive performance of process-based models. However, this improvement comes at the cost of a substantial increase of the computational time needed for learning. To address this problem, the paper proposes a method that aims at efficiently learning ensembles of process-based models, while maintaining their accurate long-term predictive performance. This is achieved by constructing ensembles with sampling domain-specific knowledge instead of sampling data. We apply the proposed method to and evaluate its performance on a set of problems of automated predictive modeling in three lake ecosystems using a library of process-based knowledge for modeling population dynamics. The experimental results identify the optimal design decisions regarding the learning algorithm. The results also show that the proposed ensembles yield significantly more accurate predictions of population dynamics as compared to individual process-based models. Finally, while their predictive performance is comparable to the one of ensembles obtained with the state-of-the-art methods of bagging and boosting, they are substantially more efficient.

  9. Ensemble single column model validation in the tropical western Pacific

    Science.gov (United States)

    Hume, Timothy; Jakob, Christian

    2007-05-01

    Single column models (SCMs) are useful tools for the evaluation of parameterizations of radiative and moist processes used in general circulation models (GCMs). SCM applications have usually been limited to regions where high-quality observations are available to derive the necessary boundary condition or forcing data. Recently, researchers have developed techniques for deriving SCM forcing data from other data sets, such as NWP (numerical weather prediction) analyses. The uncertainties inherent in these forcing data products have an unknown and possibly significant effect on SCM runs. This paper shows how an ensemble SCM (ESCM) approach can be used to minimize the uncertainty in SCM simulations resulting from uncertainties in the forcing data. Some innovative evaluation techniques have been applied to ESCM runs at the tropical western Pacific Atmospheric Radiation Measurement (ARM) program sites at Manus Island and Nauru. These techniques, making use of traditional ensemble verification methods and objectively determined cloud regimes, are shown to be able to highlight parameterization deficiencies and provide a useful tool for testing new or improved model parameterizations.

  10. Extreme winds over Europe in the ENSEMBLES regional climate models

    Directory of Open Access Journals (Sweden)

    S. D. Outten

    2013-01-01

    Full Text Available Extreme winds cause vast amounts of damage every year and represent a major concern for numerous industries including construction, afforestation, wind energy and many others. Under a changing climate, the intensity and frequency of extreme events are expected to change, and accurate predictions of these changes will be invaluable to decision makers and society as a whole. This work examines four regional climate model downscalings over Europe from the "ENSEMBLE-based Predictions of Climate Changes and their Impacts" project (ENSEMBLES, and investigates the predicted changes in the 50 yr return wind speeds and the associated uncertainties. This is accomplished by employing the peaks-over-threshold method with the use of the Generalised Pareto Distribution. The models show that for much of Europe the 50 yr return wind is projected to change by less than 2 m s−1, while the uncertainties associated with the statistical estimates are larger than this. In keeping with previous works in this field, the largest source of uncertainty is found to be the inter-model spread, with some locations showing differences in the 50 yr return wind of over 20 m s−1 between two different downscalings.

  11. Extreme winds over Europe in the ENSEMBLES regional climate models

    Directory of Open Access Journals (Sweden)

    S. D. Outten

    2013-05-01

    Full Text Available Extreme winds cause vast amounts of damage every year and represent a major concern for numerous industries including construction, afforestation, wind energy and many others. Under a changing climate, the intensity and frequency of extreme events are expected to change, and accurate projections of these changes will be invaluable to decision makers and society as a whole. This work examines four regional climate model downscalings over Europe following the SRES A1B scenario from the "ENSEMBLE-based Predictions of Climate Changes and their Impacts" project (ENSEMBLES. It investigates the projected changes in the 50 yr return wind speeds and the associated uncertainties. This is accomplished by employing the peaks-over-threshold method with the use of the generalised Pareto distribution. The models show that, for much of Europe, the 50 yr return wind is projected to change by less than 2 m s−1, while the uncertainties associated with the statistical estimates are larger than this. In keeping with previous works in this field, the largest source of uncertainty is found to be the inter-model spread, with some locations showing differences in the 50 yr return wind of over 20 m s−1 between two different downscalings.

  12. Altered Cortical Ensembles in Mouse Models of Schizophrenia.

    Science.gov (United States)

    Hamm, Jordan P; Peterka, Darcy S; Gogos, Joseph A; Yuste, Rafael

    2017-04-05

    In schizophrenia, brain-wide alterations have been identified at the molecular and cellular levels, yet how these phenomena affect cortical circuit activity remains unclear. We studied two mouse models of schizophrenia-relevant disease processes: chronic ketamine (KET) administration and Df(16)A(+/-), modeling 22q11.2 microdeletions, a genetic variant highly penetrant for schizophrenia. Local field potential recordings in visual cortex confirmed gamma-band abnormalities similar to patient studies. Two-photon calcium imaging of local cortical populations revealed in both models a deficit in the reliability of neuronal coactivity patterns (ensembles), which was not a simple consequence of altered single-neuron activity. This effect was present in ongoing and sensory-evoked activity and was not replicated by acute ketamine administration or pharmacogenetic parvalbumin-interneuron suppression. These results are consistent with the hypothesis that schizophrenia is an "attractor" disease and demonstrate that degraded neuronal ensembles are a common consequence of diverse genetic, cellular, and synaptic alterations seen in chronic schizophrenia. Published by Elsevier Inc.

  13. Thermodynamic state ensemble models of cis-regulation.

    Directory of Open Access Journals (Sweden)

    Marc S Sherman

    Full Text Available A major goal in computational biology is to develop models that accurately predict a gene's expression from its surrounding regulatory DNA. Here we present one class of such models, thermodynamic state ensemble models. We describe the biochemical derivation of the thermodynamic framework in simple terms, and lay out the mathematical components that comprise each model. These components include (1 the possible states of a promoter, where a state is defined as a particular arrangement of transcription factors bound to a DNA promoter, (2 the binding constants that describe the affinity of the protein-protein and protein-DNA interactions that occur in each state, and (3 whether each state is capable of transcribing. Using these components, we demonstrate how to compute a cis-regulatory function that encodes the probability of a promoter being active. Our intention is to provide enough detail so that readers with little background in thermodynamics can compose their own cis-regulatory functions. To facilitate this goal, we also describe a matrix form of the model that can be easily coded in any programming language. This formalism has great flexibility, which we show by illustrating how phenomena such as competition between transcription factors and cooperativity are readily incorporated into these models. Using this framework, we also demonstrate that Michaelis-like functions, another class of cis-regulatory models, are a subset of the thermodynamic framework with specific assumptions. By recasting Michaelis-like functions as thermodynamic functions, we emphasize the relationship between these models and delineate the specific circumstances representable by each approach. Application of thermodynamic state ensemble models is likely to be an important tool in unraveling the physical basis of combinatorial cis-regulation and in generating formalisms that accurately predict gene expression from DNA sequence.

  14. Ensemble hidden Markov models with application to landmine detection

    Science.gov (United States)

    Hamdi, Anis; Frigui, Hichem

    2015-12-01

    We introduce an ensemble learning method for temporal data that uses a mixture of hidden Markov models (HMM). We hypothesize that the data are generated by K models, each of which reflects a particular trend in the data. The proposed approach, called ensemble HMM (eHMM), is based on clustering within the log-likelihood space and has two main steps. First, one HMM is fit to each of the N individual training sequences. For each fitted model, we evaluate the log-likelihood of each sequence. This results in an N-by-N log-likelihood distance matrix that will be partitioned into K groups using a relational clustering algorithm. In the second step, we learn the parameters of one HMM per cluster. We propose using and optimizing various training approaches for the different K groups depending on their size and homogeneity. In particular, we investigate the maximum likelihood (ML), the minimum classification error (MCE), and the variational Bayesian (VB) training approaches. Finally, to test a new sequence, its likelihood is computed in all the models and a final confidence value is assigned by combining the models' outputs using an artificial neural network. We propose both discrete and continuous versions of the eHMM. Our approach was evaluated on a real-world application for landmine detection using ground-penetrating radar (GPR). Results show that both the continuous and discrete eHMM can identify meaningful and coherent HMM mixture components that describe different properties of the data. Each HMM mixture component models a group of data that share common attributes. These attributes are reflected in the mixture model's parameters. The results indicate that the proposed method outperforms the baseline HMM that uses one model for each class in the data.

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

    Science.gov (United States)

    Anderson, Jeffrey; Raeder, Kevin; Hoar, Tim; Collins, Nancy; Romine, Glen; Barre, Jerome; Gaubert, Benjamin; Arellano, Ave; Wuerth, Stephanie

    2016-04-01

    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 several recent research activities using DART with geophysical models: 1). Using CAM/DART to understand whether OCO-2 Total Precipitable Water observations can be useful in numerical weather prediction. 2). Impacts of the synergistic use of Infra-red CO retrievals (MOPITT, IASI) in CAMCHEM/DART assimilations. 3). Assimilation and Analysis of Observations of Amazonian Biomass Burning Emissions by MOPITT (aerosol optical depth), MODIS (carbon monoxide) and MISR (plume height). 4). Long term evaluation of the chemical response of MOPITT-CO assimilation in CAM-CHEM/DART OSSEs for satellite planning and emission inversion capabilities. 5). Improved forward observation operators for land models that have multiple land use/land cover segments in a single grid cell, enabling studies of the inherent variability in a single gridcell. Future enhancements are also discussed: 1). The CICE component of the Community Earth System Model will be added to the existing suite of components, which can be used for data assimilation. 2). Fully coupled

  16. Ensemble Forecasting of Tropical Cyclone Motion Using a Baroclinic Model

    Institute of Scientific and Technical Information of China (English)

    Xiaqiong ZHOU; Johnny C.L.CHEN

    2006-01-01

    The purpose of this study is to investigate the effectiveness of two different ensemble forecasting (EF) techniques-the lagged-averaged forecast (LAF) and the breeding of growing modes (BGM). In the BGM experiments, the vortex and the environment are perturbed separately (named BGMV and BGME).Tropical cyclone (TC) motions in two difficult situations are studied: a large vortex interacting with its environment, and an apparent binary interaction. The former is Typhoon Yancy and the latter involves Typhoon Ed and super Typhoon Flo, all occurring during the Tropical Cyclone Motion Experiment TCM-90. The model used is the baroclinic model of the University of New South Wales. The lateral boundary tendencies are computed from atmospheric analysis data. Only the relative skill of the ensemble forecast mean over the control run is used to evaluate the effectiveness of the EF methods, although the EF technique is also used to quantify forecast uncertainty in some studies. In the case of Yancy, the ensemble mean forecasts of each of the three methodologies are better than that of the control, with LAF being the best. The mean track of the LAF is close to the best track, and it predicts landfall over Taiwan. The improvements in LAF and the full BGM where both the environment and vortex are perturbed suggest the importance of combining the perturbation of the vortex and environment when the interaction between the two is appreciable. In the binary interaction case of Ed and Flo, the forecasts of Ed appear to be insensitive to perturbations of the environment and/or the vortex, which apparently results from erroneous forecasts by the model of the interaction between the subtropical ridge and Ed, as well as from the interaction between the two typhoons, thus reducing the effectiveness of the EF technique. This conclusion is reached through sensitivity experiments on the domain of the model and by adding or eliminating certain features in the model atmosphere. Nevertheless, the

  17. Using HYSPLIT Generated Ensembles to Improve the Simulation of Plume Dispersion and Assess Model Uncertainty.

    Science.gov (United States)

    Chai, T.; Stein, A. F.; Ngan, F.

    2016-12-01

    Over the last few years, the use of dispersion model ensembles has become an increasingly attractive approach to study atmospheric transport in the lower troposphere. The HYSPLIT modeling system has a built-in capability to produce three different simulation ensembles. These ensembles have been constructed based on applied case studies using different sets of initial conditions and internal model physical parameters. They are not meant to be comprehensive and only account for some of the components of the concentration uncertainty. The first one, called "Meteorological Grid" ensemble, is created by slightly offsetting the meteorological data to test the sensitivity of the advection calculation to the gradients in the meteorological data fields. The rationale for the shifting is to assess the effect that a limited spatial and temporal resolution meteorological data field has on the output concentration. The second, called the "Turbulence" ensemble, represents the uncertainty in the concentration calculation arising from the model's discrete characterization of the turbulent random motions of its lagrangian particles. In this ensemble approach, the number of particles released is reduced and multiple simulations are run, each with a different random number seed. The third, the "Physics" ensemble, is constructed by varying key physical model parameters and model options such as the Lagrangian representation of the particles/puffs, Lagrangian timescales, and vertical and horizontal dispersion parameterizations. One of the biggest challenges in creating dispersion ensembles is developing the appropriate member selection process to get the most accurate results, quantify ensemble uncertainty, and use computing resources more efficiently by avoiding the use of redundant model information. In this work, we use the HYSPLIT modeling system to generate ensembles and evaluate them against the Cross-Appalachian Tracer Experiment (CAPTEX). Furthermore, we apply a reduction

  18. Ensemble ecosystem modeling for predicting ecosystem response to predator reintroduction.

    Science.gov (United States)

    Baker, Christopher M; Gordon, Ascelin; Bode, Michael

    2017-04-01

    Introducing a new or extirpated species to an ecosystem is risky, and managers need quantitative methods that can predict the consequences for the recipient ecosystem. Proponents of keystone predator reintroductions commonly argue that the presence of the predator will restore ecosystem function, but this has not always been the case, and mathematical modeling has an important role to play in predicting how reintroductions will likely play out. We devised an ensemble modeling method that integrates species interaction networks and dynamic community simulations and used it to describe the range of plausible consequences of 2 keystone-predator reintroductions: wolves (Canis lupus) to Yellowstone National Park and dingoes (Canis dingo) to a national park in Australia. Although previous methods for predicting ecosystem responses to such interventions focused on predicting changes around a given equilibrium, we used Lotka-Volterra equations to predict changing abundances through time. We applied our method to interaction networks for wolves in Yellowstone National Park and for dingoes in Australia. Our model replicated the observed dynamics in Yellowstone National Park and produced a larger range of potential outcomes for the dingo network. However, we also found that changes in small vertebrates or invertebrates gave a good indication about the potential future state of the system. Our method allowed us to predict when the systems were far from equilibrium. Our results showed that the method can also be used to predict which species may increase or decrease following a reintroduction and can identify species that are important to monitor (i.e., species whose changes in abundance give extra insight into broad changes in the system). Ensemble ecosystem modeling can also be applied to assess the ecosystem-wide implications of other types of interventions including assisted migration, biocontrol, and invasive species eradication. © 2016 Society for Conservation Biology.

  19. Higher precision estimates of regional polar warming by ensemble regression of climate model projections

    Energy Technology Data Exchange (ETDEWEB)

    Bracegirdle, Thomas J. [British Antarctic Survey, Cambridge (United Kingdom); Stephenson, David B. [University of Exeter, Mathematics Research Institute, Exeter (United Kingdom); NCAS-Climate, Reading (United Kingdom)

    2012-12-15

    This study presents projections of twenty-first century wintertime surface temperature changes over the high-latitude regions based on the third Coupled Model Inter-comparison Project (CMIP3) multi-model ensemble. The state-dependence of the climate change response on the present day mean state is captured using a simple yet robust ensemble linear regression model. The ensemble regression approach gives different and more precise estimated mean responses compared to the ensemble mean approach. Over the Arctic in January, ensemble regression gives less warming than the ensemble mean along the boundary between sea ice and open ocean (sea ice edge). Most notably, the results show 3 C less warming over the Barents Sea ({proportional_to} 7 C compared to {proportional_to} 10 C). In addition, the ensemble regression method gives projections that are 30 % more precise over the Sea of Okhostk, Bering Sea and Labrador Sea. For the Antarctic in winter (July) the ensemble regression method gives 2 C more warming over the Southern Ocean close to the Greenwich Meridian ({proportional_to} 7 C compared to {proportional_to} 5 C). Projection uncertainty was almost half that of the ensemble mean uncertainty over the Southern Ocean between 30 W to 90 E and 30 % less over the northern Antarctic Peninsula. The ensemble regression model avoids the need for explicit ad hoc weighting of models and exploits the whole ensemble to objectively identify overly influential outlier models. Bootstrap resampling shows that maximum precision over the Southern Ocean can be obtained with ensembles having as few as only six climate models. (orig.)

  20. Intercomparison of prediction skills of ensemble methods using monthly mean temperature simulated by CMIP5 models

    Science.gov (United States)

    Seong, Min-Gyu; Suh, Myoung-Seok; Kim, Chansoo

    2017-08-01

    This study focuses on an objective comparison of eight ensemble methods using the same data, training period, training method, and validation period. The eight ensemble methods are: BMA (Bayesian Model Averaging), HMR (Homogeneous Multiple Regression), EMOS (Ensemble Model Output Statistics), HMR+ with positive coefficients, EMOS+ with positive coefficients, PEA_ROC (Performance-based Ensemble Averaging using ROot mean square error and temporal Correlation coefficient), WEA_Tay (Weighted Ensemble Averaging based on Taylor's skill score), and MME (Multi-Model Ensemble). Forty-five years (1961-2005) of data from 14 CMIP5 models and APHRODITE (Asian Precipitation- Highly-Resolved Observational Data Integration Towards Evaluation of Water Resources) data were used to compare the performance of the eight ensemble methods. Although some models underestimated the variability of monthly mean temperature (MMT), most of the models effectively simulated the spatial distribution of MMT. Regardless of training periods and the number of ensemble members, the prediction skills of BMA and the four multiple linear regressions (MLR) were superior to the other ensemble methods (PEA_ROC, WEA_Tay, MME) in terms of deterministic prediction. In terms of probabilistic prediction, the four MLRs showed better prediction skills than BMA. However, the differences among the four MLRs and BMA were not significant. This resulted from the similarity of BMA weights and regression coefficients. Furthermore, prediction skills of the four MLRs were very similar. Overall, the four MLRs showed the best prediction skills among the eight ensemble methods. However, more comprehensive work is needed to select the best ensemble method among the numerous ensemble methods.

  1. Simulation of solar radiative transfer in cumulus clouds

    Energy Technology Data Exchange (ETDEWEB)

    Zuev, V.E.; Titov, G.A. [Institute of Atmospheric Optics, Tomsk (Russian Federation)

    1996-04-01

    This work presents a 3-D model of radiative transfer which is used to study the relationship between the spatial distribution of cumulus clouds and fluxes (albedo and transmittance) of visible solar radiation.

  2. Ensemble of regional climate model projections for Ireland

    Science.gov (United States)

    Nolan, Paul; McGrath, Ray

    2016-04-01

    The method of Regional Climate Modelling (RCM) was employed to assess the impacts of a warming climate on the mid-21st-century climate of Ireland. The RCM simulations were run at high spatial resolution, up to 4 km, thus allowing a better evaluation of the local effects of climate change. Simulations were run for a reference period 1981-2000 and future period 2041-2060. Differences between the two periods provide a measure of climate change. To address the issue of uncertainty, a multi-model ensemble approach was employed. Specifically, the future climate of Ireland was simulated using three different RCMs, driven by four Global Climate Models (GCMs). To account for the uncertainty in future emissions, a number of SRES (B1, A1B, A2) and RCP (4.5, 8.5) emission scenarios were used to simulate the future climate. Through the ensemble approach, the uncertainty in the RCM projections can be partially quantified, thus providing a measure of confidence in the predictions. In addition, likelihood values can be assigned to the projections. The RCMs used in this work are the COnsortium for Small-scale MOdeling-Climate Limited-area Modelling (COSMO-CLM, versions 3 and 4) model and the Weather Research and Forecasting (WRF) model. The GCMs used are the Max Planck Institute's ECHAM5, the UK Met Office's HadGEM2-ES, the CGCM3.1 model from the Canadian Centre for Climate Modelling and the EC-Earth consortium GCM. The projections for mid-century indicate an increase of 1-1.6°C in mean annual temperatures, with the largest increases seen in the east of the country. Warming is enhanced for the extremes (i.e. hot or cold days), with the warmest 5% of daily maximum summer temperatures projected to increase by 0.7-2.6°C. The coldest 5% of night-time temperatures in winter are projected to rise by 1.1-3.1°C. Averaged over the whole country, the number of frost days is projected to decrease by over 50%. The projections indicate an average increase in the length of the growing season

  3. Ensemble Modeling of the 23 July 2012 Coronal Mass Ejection

    Science.gov (United States)

    Cash, M. D.; Biesecker, D. A.; Pizzo, V.; Koning, C. A.; Millward, G.; Arge, C. N.; Henney, C. J.; Odstrcil, D.

    2015-10-01

    On 23 July 2012 a significant and rapid coronal mass ejection (CME) was detected in situ by the Solar Terrestrial Relations Observatory (STEREO) A. This CME was unusual due to its extremely brief Sun-to-1 AU transit time of less than 21 h and its exceptionally high impact speed of 2246 km/s. If this CME had been Earth directed, it would have produced a significant geomagnetic storm with potentially serious consequences. To protect our ground- and space-based assets, there is a clear need to accurately forecast the arrival times of such events using realistic input parameters and models run in near real time. Using Wang-Sheely-Arge (WSA)-Enlil, the operational model currently employed at the NOAA Space Weather Prediction Center, we investigate the sensitivity of the 23 July CME event to model input parameters. Variations in the initial CME speed, angular width, and direction, as well as the ambient solar wind background, are investigated using an ensemble approach to study the effect on the predicted arrival time of the CME at STEREO A. Factors involved in the fast transit time of this large CME are discussed, and potential improvements to modeling such events with the WSA-Enlil model are presented.

  4. Hybrid ensemble 4DVar assimilation of stratospheric ozone using a global shallow water model

    Science.gov (United States)

    Allen, Douglas R.; Hoppel, Karl W.; Kuhl, David D.

    2016-07-01

    Wind extraction from stratospheric ozone (O3) assimilation is examined using a hybrid ensemble 4-D variational assimilation (4DVar) shallow water model (SWM) system coupled to the tracer advection equation. Stratospheric radiance observations are simulated using global observations of the SWM fluid height (Z), while O3 observations represent sampling by a typical polar-orbiting satellite. Four ensemble sizes were examined (25, 50, 100, and 1518 members), with the largest ensemble equal to the number of dynamical state variables. The optimal length scale for ensemble localization was found by tuning an ensemble Kalman filter (EnKF). This scale was then used for localizing the ensemble covariances that were blended with conventional covariances in the hybrid 4DVar experiments. Both optimal length scale and optimal blending coefficient increase with ensemble size, with optimal blending coefficients varying from 0.2-0.5 for small ensembles to 0.5-1.0 for large ensembles. The hybrid system outperforms conventional 4DVar for all ensemble sizes, while for large ensembles the hybrid produces similar results to the offline EnKF. Assimilating O3 in addition to Z benefits the winds in the hybrid system, with the fractional improvement in global vector wind increasing from ˜ 35 % with 25 and 50 members to ˜ 50 % with 1518 members. For the smallest ensembles (25 and 50 members), the hybrid 4DVar assimilation improves the zonal wind analysis over conventional 4DVar in the Northern Hemisphere (winter-like) region and also at the Equator, where Z observations alone have difficulty constraining winds due to lack of geostrophy. For larger ensembles (100 and 1518 members), the hybrid system results in both zonal and meridional wind error reductions, relative to 4DVar, across the globe.

  5. Dynamically downscaled multi-model ensemble seasonal forecasts over Ethiopia

    Science.gov (United States)

    Asharaf, Shakeel; Fröhlich, Kristina; Fernandez, Jesus; Cardoso, Rita; Nikulin, Grigory; Früh, Barbara

    2016-04-01

    Truthful and reliable seasonal rainfall predictions have an important social and economic value for the east African countries as their economy is highly dependent on rain-fed agriculture and pastoral systems. Only June to September (JJAS) seasonal rainfall accounts to more than 80% crop production in Ethiopia. Hence, seasonal foresting is a crucial concern for the region. The European Provision of Regional Impact Assessment on a seasonal to decadal timescale (EUPORIAS) project offers a common framework to understand hindcast uncertainties through the use of multi-model and multi-member simulations over east Africa. Under this program, the participating regional climate models (RCMs) were driven by the atmospheric-only version of the ECEARTH global climate model, which provides hindcasts of a five-months period (May to September) from 1991-2012. In this study the RCMs downscaled rainfall is evaluated with respect to the observed JJAS rainfall over Ethiopia. Both deterministic and probabilistic based forecast skills are assessed. Our preliminary results show the potential usefulness of multi-model ensemble simulations in forecasting the seasonal rainfall over the region.

  6. Identification of hydrological model parameter variation using ensemble Kalman filter

    Science.gov (United States)

    Deng, Chao; Liu, Pan; Guo, Shenglian; Li, Zejun; Wang, Dingbao

    2016-12-01

    Hydrological model parameters play an important role in the ability of model prediction. In a stationary context, parameters of hydrological models are treated as constants; however, model parameters may vary with time under climate change and anthropogenic activities. The technique of ensemble Kalman filter (EnKF) is proposed to identify the temporal variation of parameters for a two-parameter monthly water balance model (TWBM) by assimilating the runoff observations. Through a synthetic experiment, the proposed method is evaluated with time-invariant (i.e., constant) parameters and different types of parameter variations, including trend, abrupt change and periodicity. Various levels of observation uncertainty are designed to examine the performance of the EnKF. The results show that the EnKF can successfully capture the temporal variations of the model parameters. The application to the Wudinghe basin shows that the water storage capacity (SC) of the TWBM model has an apparent increasing trend during the period from 1958 to 2000. The identified temporal variation of SC is explained by land use and land cover changes due to soil and water conservation measures. In contrast, the application to the Tongtianhe basin shows that the estimated SC has no significant variation during the simulation period of 1982-2013, corresponding to the relatively stationary catchment properties. The evapotranspiration parameter (C) has temporal variations while no obvious change patterns exist. The proposed method provides an effective tool for quantifying the temporal variations of the model parameters, thereby improving the accuracy and reliability of model simulations and forecasts.

  7. THE ENSEMBLE FORECASTING OF TROPICAL CYCLONE MOTION I:USING A PRIMITIVE EQUATION BAROTROPIC MODEL

    Institute of Scientific and Technical Information of China (English)

    周霞琼; 端义宏; 朱永禔

    2003-01-01

    Ensemble forecasting of tropical cyclone (TC) motion was studied usinga primitive equation barotropic model by perturbing initial position and structure for 1979 - 1993 TC. The results show that TC initial position perturbation affects its track, but the ensemble mean is close to control forecast. Experiments was also performed by perturbing TC initial parameters which were used to generate TC initial field, and more improvement can be obtained by taking ensemble mean of selective member than selecting members randomly. The skill of 60 % - 70 % of all cases is improved in selective ensemble mean. When the ambient steeringcurrent is weak, more improvement can be obtained over the control forecast.

  8. Usage of ensemble geothermal models to consider geological uncertainties

    Science.gov (United States)

    Rühaak, Wolfram; Steiner, Sarah; Welsch, Bastian; Sass, Ingo

    2015-04-01

    The usage of geothermal energy for instance by borehole heat exchangers (BHE) is a promising concept for a sustainable supply of heat for buildings. BHE are closed pipe systems, in which a fluid is circulating. Heat from the surrounding rocks is transferred to the fluid purely by conduction. The fluid carries the heat to the surface, where it can be utilized. Larger arrays of BHE require typically previous numerical models. Motivations are the design of the system (number and depth of the required BHE) but also regulatory reasons. Especially such regulatory operating permissions often require maximum realistic models. Although such realistic models are possible in many cases with today's codes and computer resources, they are often expensive in terms of time and effort. A particular problem is the knowledge about the accuracy of the achieved results. An issue, which is often neglected while dealing with highly complex models, is the quantification of parameter uncertainties as a consequence of the natural heterogeneity of the geological subsurface. Experience has shown, that these heterogeneities can lead to wrong forecasts. But also variations in the technical realization and especially of the operational parameters (which are mainly a consequence of the regional climate) can lead to strong variations in the simulation results. Instead of one very detailed single forecast model, it should be considered, to model numerous more simple models. By varying parameters, the presumed subsurface uncertainties, but also the uncertainties in the presumed operational parameters can be reflected. Finally not only one single result should be reported, but instead the range of possible solutions and their respective probabilities. In meteorology such an approach is well known as ensemble-modeling. The concept is demonstrated at a real world data set and discussed.

  9. Ensemble models on palaeoclimate to predict India's groundwater challenge

    Directory of Open Access Journals (Sweden)

    Partha Sarathi Datta

    2013-09-01

    Full Text Available In many parts of the world, freshwater crisis is largely due to increasing water consumption and pollution by rapidly growing population and aspirations for economic development, but, ascribed usually to the climate. However, limited understanding and knowledge gaps in the factors controlling climate and uncertainties in the climate models are unable to assess the probable impacts on water availability in tropical regions. In this context, review of ensemble models on δ18O and δD in rainfall and groundwater, 3H- and 14C- ages of groundwater and 14C- age of lakes sediments helped to reconstruct palaeoclimate and long-term recharge in the North-west India; and predict future groundwater challenge. The annual mean temperature trend indicates both warming/cooling in different parts of India in the past and during 1901–2010. Neither the GCMs (Global Climate Models nor the observational record indicates any significant change/increase in temperature and rainfall over the last century, and climate change during the last 1200 yrs BP. In much of the North-West region, deep groundwater renewal occurred from past humid climate, and shallow groundwater renewal from limited modern recharge over the past decades. To make water management to be more responsive to climate change, the gaps in the science of climate change need to be bridged.

  10. Crop model improvement reduces the uncertainty of the response to temperature of multi-model ensembles

    DEFF Research Database (Denmark)

    Maiorano, Andrea; Martre, Pierre; Asseng, Senthold

    2017-01-01

    To improve climate change impact estimates and to quantify their uncertainty, multi-model ensembles (MMEs) have been suggested. Model improvements can improve the accuracy of simulations and reduce the uncertainty of climate change impact assessments. Furthermore, they can reduce the number of mo...

  11. Characterizing and visualizing predictive uncertainty in numerical ensembles through Bayesian model averaging.

    Science.gov (United States)

    Gosink, Luke; Bensema, Kevin; Pulsipher, Trenton; Obermaier, Harald; Henry, Michael; Childs, Hank; Joy, Kenneth I

    2013-12-01

    Numerical ensemble forecasting is a powerful tool that drives many risk analysis efforts and decision making tasks. These ensembles are composed of individual simulations that each uniquely model a possible outcome for a common event of interest: e.g., the direction and force of a hurricane, or the path of travel and mortality rate of a pandemic. This paper presents a new visual strategy to help quantify and characterize a numerical ensemble's predictive uncertainty: i.e., the ability for ensemble constituents to accurately and consistently predict an event of interest based on ground truth observations. Our strategy employs a Bayesian framework to first construct a statistical aggregate from the ensemble. We extend the information obtained from the aggregate with a visualization strategy that characterizes predictive uncertainty at two levels: at a global level, which assesses the ensemble as a whole, as well as a local level, which examines each of the ensemble's constituents. Through this approach, modelers are able to better assess the predictive strengths and weaknesses of the ensemble as a whole, as well as individual models. We apply our method to two datasets to demonstrate its broad applicability.

  12. Bayesian Model Averaging for Ensemble-Based Estimates of Solvation Free Energies

    CERN Document Server

    Gosink, Luke J; Reehl, Sarah M; Whitney, Paul D; Mobley, David L; Baker, Nathan A

    2016-01-01

    This paper applies the Bayesian Model Averaging (BMA) statistical ensemble technique to estimate small molecule solvation free energies. There is a wide range methods for predicting solvation free energies, ranging from empirical statistical models to ab initio quantum mechanical approaches. Each of these methods are based on a set of conceptual assumptions that can affect a method's predictive accuracy and transferability. Using an iterative statistical process, we have selected and combined solvation energy estimates using an ensemble of 17 diverse methods from the SAMPL4 blind prediction study to form a single, aggregated solvation energy estimate. The ensemble design process evaluates the statistical information in each individual method as well as the performance of the aggregate estimate obtained from the ensemble as a whole. Methods that possess minimal or redundant information are pruned from the ensemble and the evaluation process repeats until aggregate predictive performance can no longer be improv...

  13. 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

  14. A Single Column Model Ensemble Approach Applied to the TWP-ICE Experiment

    Energy Technology Data Exchange (ETDEWEB)

    Davies, Laura; Jakob, Christian; Cheung, K.; Del Genio, Anthony D.; Hill, Adrian; Hume, Timothy; Keane, R. J.; Komori, T.; Larson, Vincent E.; Lin, Yanluan; Liu, Xiaohong; Nielsen, Brandon J.; Petch, Jon C.; Plant, R. S.; Singh, M. S.; Shi, Xiangjun; Song, X.; Wang, Weiguo; Whitall, M. A.; Wolf, A.; Xie, Shaocheng; Zhang, Guang J.

    2013-06-27

    Single column models (SCM) are useful testbeds for investigating the parameterisation schemes of numerical weather prediction and climate models. The usefulness of SCM simulations are limited, however, by the accuracy of the best-estimate large-scale data prescribed. One method to address this uncertainty is to perform ensemble simulations of the SCM. This study first derives an ensemble of large-scale data for the Tropical Warm Pool International Cloud Experiment (TWP-ICE) based on an estimate of a possible source of error in the best-estimate product. This data is then used to carry out simulations with 11 SCM and 2 cloud-resolving models (CRM). Best-estimate simulations are also performed. All models show that moisture related variables are close to observations and there are limited differences between the best-estimate and ensemble mean values. The models, however, show different sensitivities to changes in the forcing particularly when weakly forced. The ensemble simulations highlight important differences in the moisture budget between the SCM and CRM. Systematic differences are also apparent in the ensemble mean vertical structure of cloud variables. The ensemble is further used to investigate relations between cloud variables and precipitation identifying large differences between CRM and SCM. This study highlights that additional information can be gained by performing ensemble simulations enhancing the information derived from models using the more traditional single best-estimate simulation.

  15. 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.

  16. Sky cover from MFRSR observations: cumulus clouds

    Directory of Open Access Journals (Sweden)

    E. Kassianov

    2011-01-01

    Full Text Available The diffuse all-sky surface irradiances measured at two nearby wavelengths in the visible spectral range and their model clear-sky counterparts are two main components of a new method for estimating the fractional sky cover of different cloud types, including cumulus clouds. The performance of this method is illustrated using 1-min resolution data from ground-based Multi-Filter Rotating Shadowband Radiometer (MFRSR. The MFRSR data are collected at the US Department of Energy Atmospheric Radiation Measurement (ARM Climate Research Facility (ACRF Southern Great Plains (SGP site during the summer of 2007 and represent 13 days with cumulus clouds. Good agreement is obtained between estimated values of the fractional sky cover and those provided by a well-established independent method based on broadband observations.

  17. Characterizing Drought Events from a Hydrological Model Ensemble

    Science.gov (United States)

    Smith, Katie; Parry, Simon; Prudhomme, Christel; Hannaford, Jamie; Tanguy, Maliko; Barker, Lucy; Svensson, Cecilia

    2017-04-01

    Hydrological droughts are a slow onset natural hazard that can affect large areas. Within the United Kingdom there have been eight major drought events over the last 50 years, with several events acting at the continental scale, and covering the entire nation. Many of these events have lasted several years and had significant impacts on agriculture, the environment and the economy. Generally in the UK, due to a northwest-southeast gradient in rainfall and relief, as well as varying underlying geology, droughts tend to be most severe in the southeast, which can threaten water supplies to the capital in London. With the impacts of climate change likely to increase the severity and duration of drought events worldwide, it is crucial that we gain an understanding of the characteristics of some of the longer and more extreme droughts of the 19th and 20th centuries, so we may utilize this information in planning for the future. Hydrological models are essential both for reconstructing such events that predate streamflow records, and for use in drought forecasting. However, whilst the uncertainties involved in modelling hydrological extremes on the flooding end of the flow regime have been studied in depth over the past few decades, the uncertainties in simulating droughts and low flow events have not yet received such rigorous academic attention. The "Cascade of Uncertainty" approach has been applied to explore uncertainty and coherence across simulations of notable drought events from the past 50 years using the airGR family of daily lumped catchment models. Parameter uncertainty has been addressed using a Latin Hypercube sampled experiment of 500,000 parameter sets per model (GR4J, GR5J and GR6J), over more than 200 catchments across the UK. The best performing model parameterisations, determined using a multi-objective function approach, have then been taken forward for use in the assessment of the impact of model parameters and model structure on drought event

  18. A "Dressed" Ensemble Kalman Filter Using the Hybrid Coordinate Ocean Model in the Pacific

    Institute of Scientific and Technical Information of China (English)

    WAN Liying; ZHU Jiang; WANG Hui; YAN Changxiang; Laurent BERTINO

    2009-01-01

    The computational cost required by the Ensemble Kalman Filter (EnKF) is much larger than that of some simpler assimilation schemes,such as Optimal Interpolation (OI) or three-dimension variational assimilation (3DVAR).Ensemble optimal interpolation (EnOI),a crudely simplified implementation of EnKF,is sometimes used as a substitute in some oceanic applications and requires much less computational time than EnKF.In this paper,to compromise between computational cost and dynamic covaxiance,we use the idea of "dressing" a small size dynamical ensemble with a larger number of static ensembles in order to form an approximate dynamic covaxiance.The term "dressing" means that a dynamical ensemble seed from model runs is perturbed by adding the anomalies of some static ensembles.This dressing EnKF (DrEnKF for short) scheme is tested in assimilation of real altimetry data in the Pacific using the HYbrid Coordinate Ocean Model (HYCOM) over a four-year period.Ten dynamical ensemble seeds are each dressed by 10 static ensemble members selected from a 100-member static ensemble.Results are compared to two EnKF assimilation runs that use 10 and 100 dynamical ensemble members.Both temperature and salinity fields from the DrEnKF and the EnKF are compared to observations from Argo floats and an OI SST dataset.The results show that the DrEnKF and the 100-member EnKF yield similar root mean square errors (RMSE)at every model level. Error covariance matrices from the DrEnKF and the 100-member EnKF are also compared and show good agreement.

  19. Ensemble flood forecasting to support dam water release operation using 10 and 2 km-resolution JMA Nonhydrostatic Model ensemble rainfalls

    Directory of Open Access Journals (Sweden)

    K. Kobayashi

    2015-12-01

    Full Text Available This paper presents a study on short-term ensemble flood forecasting specifically for small dam catchments in Japan. Numerical ensemble simulations of rainfall from the Japan Meteorological Agency Nonhydrostatic Model are used as the input data to a rainfall–runoff model for predicting river discharge into a dam. The ensemble weather simulations use a conventional 10 km and a high-resolution 2 km spatial resolution. A distributed rainfall–runoff model is constructed for the Kasahori dam catchment (approx. 70 km2 and applied with the ensemble rainfalls. The results show that the hourly maximum and cumulative catchment-average rainfalls of the 2 km-resolution JMA-NHM ensemble simulation are more appropriate than the 10 km-resolution rainfalls. All the simulated inflows based on the 2 and 10 km rainfalls become larger than the flood discharge of 140 m3 s−1; a threshold value for flood control. The inflows with the 10 km-resolution ensemble rainfall are all considerably smaller than the observations, while, at least one simulated discharge out of 11 ensemble members with the 2 km-resolution rainfalls reproduces the first peak of the inflow at the Kasahori dam with similar amplitude to observations, although there are spatiotemporal lags between simulation and observation. To take positional lags into account of the ensemble discharge simulation, the rainfall distribution in each ensemble member is shifted so that the catchment-averaged cumulative rainfall of the Kasahori dam maximizes. The runoff simulation with the position-shifted rainfalls show much better results than the original ensemble discharge simulations.

  20. Ensemble modeling of CMEs using the WSA-ENLIL+Cone model

    CERN Document Server

    Mays, M L; Pulkkinen, A A; Odstrcil, D; MacNeice, P J; Rastaetter, L; LaSota, J A; Zheng, Y; Kuznetsova, M M

    2015-01-01

    Ensemble modeling of CMEs provides a probabilistic forecast of CME arrival time which includes an estimation of arrival time uncertainty from the spread and distribution of predictions and forecast confidence in the likelihood of CME arrival. The real-time ensemble modeling of CME propagation uses the WSA-ENLIL+Cone model installed at the CCMC and executed in real-time. The current implementation evaluates the sensitivity of WSA-ENLIL+Cone model simulations of CME propagation to initial CME parameters. We discuss the results of real-time ensemble simulations for a total of 35 CME events between January 2013 - July 2014. For the 17 events where the CME was predicted to arrive at Earth, the mean absolute arrival time prediction error was 12.3 hours, which is comparable to the errors reported in other studies. For predictions of CME arrival at Earth the correct rejection rate is 62% and the false-alarm rate is 38%. The arrival time was within the range of the ensemble arrival predictions for 8 out of 17 events. ...

  1. The assisted prediction modelling frame with hybridisation and ensemble for business risk forecasting and an implementation

    Science.gov (United States)

    Li, Hui; Hong, Lu-Yao; Zhou, Qing; Yu, Hai-Jie

    2015-08-01

    The business failure of numerous companies results in financial crises. The high social costs associated with such crises have made people to search for effective tools for business risk prediction, among which, support vector machine is very effective. Several modelling means, including single-technique modelling, hybrid modelling, and ensemble modelling, have been suggested in forecasting business risk with support vector machine. However, existing literature seldom focuses on the general modelling frame for business risk prediction, and seldom investigates performance differences among different modelling means. We reviewed researches on forecasting business risk with support vector machine, proposed the general assisted prediction modelling frame with hybridisation and ensemble (APMF-WHAE), and finally, investigated the use of principal components analysis, support vector machine, random sampling, and group decision, under the general frame in forecasting business risk. Under the APMF-WHAE frame with support vector machine as the base predictive model, four specific predictive models were produced, namely, pure support vector machine, a hybrid support vector machine involved with principal components analysis, a support vector machine ensemble involved with random sampling and group decision, and an ensemble of hybrid support vector machine using group decision to integrate various hybrid support vector machines on variables produced from principle components analysis and samples from random sampling. The experimental results indicate that hybrid support vector machine and ensemble of hybrid support vector machines were able to produce dominating performance than pure support vector machine and support vector machine ensemble.

  2. A flexible additive inflation scheme for treating model error in ensemble Kalman Filters

    Science.gov (United States)

    Sommer, Matthias; Janjic, Tijana

    2017-04-01

    Data assimilation algorithms require an accurate estimate of the uncertainty of the prior, background, field. However, the background error covariance derived from the ensemble of numerical model simulations does not adequately represent the uncertainty of it. This is partially due to the sampling error that arises from the use of a small number of ensemble members to represent the background error covariance. It is also partially a consequence of the fact that the model does not represent its own error. Several mechanisms have been introduced so far aiming at alleviating the detrimental e ffects of misrepresented ensemble covariances, allowing for the successful implementation of ensemble data assimilation techniques for atmospheric dynamics. One of the established approaches in ensemble data assimilation is additive inflation which perturbs each ensemble member with a sample from a given distribution. This results in a fixed rank of the model error covariance matrix. Here, a more flexible approach is suggested where the model error samples are treated as additional synthetic ensemble members which are used in the update step of data assimilation but are not forecast. In this way, the rank of the model error covariance matrix can be chosen independently of the ensemble. The eff ect of this altered additive inflation method on the performance of the filter is analyzed here in an idealised experiment. It is shown that the additional synthetic ensemble members can make it feasible to achieve convergence in an otherwise divergent setting of data assimilation. The use of this method also allows for a less stringent localization radius.

  3. Creating Discriminative Models for Time Series Classification and Clustering by HMM Ensembles.

    Science.gov (United States)

    Asadi, Nazanin; Mirzaei, Abdolreza; Haghshenas, Ehsan

    2016-12-01

    Classification of temporal data sequences is a fundamental branch of machine learning with a broad range of real world applications. Since the dimensionality of temporal data is significantly larger than static data, and its modeling and interpreting is more complicated, performing classification and clustering on temporal data is more complex as well. Hidden Markov models (HMMs) are well-known statistical models for modeling and analysis of sequence data. Besides, ensemble methods, which employ multiple models to obtain the target model, revealed good performances in the conducted experiments. All these facts are a high level of motivation to employ HMM ensembles in the task of classification and clustering of time series data. So far, no effective classification and clustering method based on HMM ensembles has been proposed. Moreover, employing the limited existing HMM ensemble methods has trouble separating models of distinct classes as a vital task. In this paper, according to previous points a new framework based on HMM ensembles for classification and clustering is proposed. In addition to its strong theoretical background by employing the Rényi entropy for ensemble learning procedure, the main contribution of the proposed method is addressing HMM-based methods problem in separating models of distinct classes by considering the inverse emission matrix of the opposite class to build an opposite model. The proposed algorithms perform more effectively compared to other methods especially other HMM ensemble-based methods. Moreover, the proposed clustering framework, which derives benefits from both similarity-based and model-based methods, together with the Rényi-based ensemble method revealed its superiority in several measurements.

  4. A single-column model ensemble approach applied to the TWP-ICE experiment

    Science.gov (United States)

    Davies, L.; Jakob, C.; Cheung, K.; Genio, A. Del; Hill, A.; Hume, T.; Keane, R. J.; Komori, T.; Larson, V. E.; Lin, Y.; Liu, X.; Nielsen, B. J.; Petch, J.; Plant, R. S.; Singh, M. S.; Shi, X.; Song, X.; Wang, W.; Whitall, M. A.; Wolf, A.; Xie, S.; Zhang, G.

    2013-06-01

    Single-column models (SCM) are useful test beds for investigating the parameterization schemes of numerical weather prediction and climate models. The usefulness of SCM simulations are limited, however, by the accuracy of the best estimate large-scale observations prescribed. Errors estimating the observations will result in uncertainty in modeled simulations. One method to address the modeled uncertainty is to simulate an ensemble where the ensemble members span observational uncertainty. This study first derives an ensemble of large-scale data for the Tropical Warm Pool International Cloud Experiment (TWP-ICE) based on an estimate of a possible source of error in the best estimate product. These data are then used to carry out simulations with 11 SCM and two cloud-resolving models (CRM). Best estimate simulations are also performed. All models show that moisture-related variables are close to observations and there are limited differences between the best estimate and ensemble mean values. The models, however, show different sensitivities to changes in the forcing particularly when weakly forced. The ensemble simulations highlight important differences in the surface evaporation term of the moisture budget between the SCM and CRM. Differences are also apparent between the models in the ensemble mean vertical structure of cloud variables, while for each model, cloud properties are relatively insensitive to forcing. The ensemble is further used to investigate cloud variables and precipitation and identifies differences between CRM and SCM particularly for relationships involving ice. This study highlights the additional analysis that can be performed using ensemble simulations and hence enables a more complete model investigation compared to using the more traditional single best estimate simulation only.

  5. Characterizing RNA ensembles from NMR data with kinematic models

    DEFF Research Database (Denmark)

    Fonseca, Rasmus; Pachov, Dimitar V.; Bernauer, Julie;

    2014-01-01

    the conformational landscapes of 3D RNA encoded by NMR proton chemical shifts. KGSrna resolves motionally averaged NMR data into structural contributions; when coupled with residual dipolar coupling data, a KGSrna ensemble revealed a previously uncharacterized transient excited state of the HIV-1 trans...

  6. Effects of cumulus parameterization closures on simulations of summer precipitation over the continental United States

    Science.gov (United States)

    Qiao, Fengxue; Liang, Xin-Zhong

    2016-09-01

    This study examines the effects of five cumulus closure assumptions on simulations of summer precipitation in the continental U.S. by utilizing an ensemble cumulus parameterization (ECP) that incorporates multiple alternate closure schemes into a single cloud model formulation. Results demonstrate that closure algorithms significantly affect the summer mean, daily frequency and intensity, and diurnal variation of precipitation, with strong regional dependence. Overall, the vertical velocity (W) closure produces the smallest summer mean biases, while the moisture convergence (MC) closure most realistically reproduces daily variability. Both closures have advantages over others in simulating U.S. daily rainfall frequency distribution, though both slightly overestimate intense rain events. The MC closure is superior at capturing summer rainfall amount, daily variability, and heavy rainfall frequency over the Central U.S., but systematically produces wet biases over the North American Monsoon (NAM) region and Southeast U.S., which can be reduced by using the W closure. The instability tendency (TD) and the total instability adjustment (KF) closures are better at capturing observed diurnal signals over the Central U.S. and the NAM, respectively. The results reasonably explain the systematic behaviors of several major cumulus parameterizations. A preliminary experiment combining two optimal closures (averaged moisture convergence and vertical velocity) in the ECP scheme significantly reduced the wet (dry) biases over the Southeast U.S. in the summer of 1993 (2003), and greatly improved daily rainfall correlations over the NAM. Further improved model simulation skills may be achieved in the future if optimal closures and their appropriate weights can be derived at different time scales based on specific climate regimes.

  7. Effects of cumulus parameterization closures on simulations of summer precipitation over the continental United States

    Science.gov (United States)

    Qiao, Fengxue; Liang, Xin-Zhong

    2017-07-01

    This study examines the effects of five cumulus closure assumptions on simulations of summer precipitation in the continental U.S. by utilizing an ensemble cumulus parameterization (ECP) that incorporates multiple alternate closure schemes into a single cloud model formulation. Results demonstrate that closure algorithms significantly affect the summer mean, daily frequency and intensity, and diurnal variation of precipitation, with strong regional dependence. Overall, the vertical velocity (W) closure produces the smallest summer mean biases, while the moisture convergence (MC) closure most realistically reproduces daily variability. Both closures have advantages over others in simulating U.S. daily rainfall frequency distribution, though both slightly overestimate intense rain events. The MC closure is superior at capturing summer rainfall amount, daily variability, and heavy rainfall frequency over the Central U.S., but systematically produces wet biases over the North American Monsoon (NAM) region and Southeast U.S., which can be reduced by using the W closure. The instability tendency (TD) and the total instability adjustment (KF) closures are better at capturing observed diurnal signals over the Central U.S. and the NAM, respectively. The results reasonably explain the systematic behaviors of several major cumulus parameterizations. A preliminary experiment combining two optimal closures (averaged moisture convergence and vertical velocity) in the ECP scheme significantly reduced the wet (dry) biases over the Southeast U.S. in the summer of 1993 (2003), and greatly improved daily rainfall correlations over the NAM. Further improved model simulation skills may be achieved in the future if optimal closures and their appropriate weights can be derived at different time scales based on specific climate regimes.

  8. With or without a conductor: Comparative analysis of leadership models in the musical ensemble

    Directory of Open Access Journals (Sweden)

    Kovačević Mia

    2016-01-01

    Full Text Available In search of innovative models of work organization and therefore the artistic process of one musical ensemble, in the last ten years musical ensembles have developed examples of non-traditional artistic-performing decisions and organizational practice. The paper is conceived as a research and analysis of the dominant models of leadership (i.e. organizing, conducting business applicable on the music ensembles and experiences of the musicians. The aim is to recognize and define leadership styles that encourage the increase of motivation and productivity of musicians within the musical ensemble. The paper will specifically investigate the relationship and differences between the two dominant models of leadership, leadership of conductor and collaborative leadership. At the same time, the paper describes and analyses an experiment that was conducted by the Ensemble Metamorphosis, which applied into their work two dominant models of leadership. In an effort to increase the motivation and productivity of musicians, Ensemble Metamorphosis also searched for a new management model of work organization and a new model of leadership. The aim of this paper was therefore to investigate the effects of leadership models that improve the artistic quality, motivation of the musicians, psychological climate and overall increase productivity of musical organization.

  9. Comparing model ensembles in an event attribution study of 2012 West African rainfall

    Science.gov (United States)

    Parker, Hannah; Lott, Fraser C.; Cornforth, Rosalind J.

    2016-04-01

    In 2012, heavy rainfall resulted in flooding and devastating impacts across West Africa. With many people highly vulnerable to such events in this region, here we investigate 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 climateprediction.net (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, although the magnitude and confidence intervals of the decrease depend on the model ensemble used. The influences of significant teleconnections are then removed from the CMIP5 ensemble to see how this influences the results and compares with the atmosphere-only ensembles.

  10. Hybrid Modeling of Flotation Height in Air Flotation Oven Based on Selective Bagging Ensemble Method

    Directory of Open Access Journals (Sweden)

    Shuai Hou

    2013-01-01

    Full Text Available The accurate prediction of the flotation height is very necessary for the precise control of the air flotation oven process, therefore, avoiding the scratch and improving production quality. In this paper, a hybrid flotation height prediction model is developed. Firstly, a simplified mechanism model is introduced for capturing the main dynamic behavior of the process. Thereafter, for compensation of the modeling errors existing between actual system and mechanism model, an error compensation model which is established based on the proposed selective bagging ensemble method is proposed for boosting prediction accuracy. In the framework of the selective bagging ensemble method, negative correlation learning and genetic algorithm are imposed on bagging ensemble method for promoting cooperation property between based learners. As a result, a subset of base learners can be selected from the original bagging ensemble for composing a selective bagging ensemble which can outperform the original one in prediction accuracy with a compact ensemble size. Simulation results indicate that the proposed hybrid model has a better prediction performance in flotation height than other algorithms’ performance.

  11. Post Processing Numerical Weather Prediction Model Rainfall Forecasts for Use in Ensemble Streamflow Forecasting in Australia

    Science.gov (United States)

    Shrestha, D. L.; Robertson, D.; Bennett, J.; Ward, P.; Wang, Q. J.

    2012-12-01

    Through the water information research and development alliance (WIRADA) project, CSIRO is conducting research to improve flood and short-term streamflow forecasting services delivered by the Australian Bureau of Meteorology. WIRADA aims to build and test systems to generate ensemble flood and short-term streamflow forecasts with lead times of up to 10 days by integrating rainfall forecasts from Numerical Weather Prediction (NWP) models and hydrological modelling. Here we present an overview of the latest progress towards developing this system. Rainfall during the forecast period is a major source of uncertainty in streamflow forecasting. Ensemble rainfall forecasts are used in streamflow forecasting to characterise the rainfall uncertainty. In Australia, NWP models provide forecasts of rainfall and other weather conditions for lead times of up to 10 days. However, rainfall forecasts from Australian NWP models are deterministic and often contain systematic errors. We use a simplified Bayesian joint probability (BJP) method to post-process rainfall forecasts from the latest generation of Australian NWP models. The BJP method generates reliable and skilful ensemble rainfall forecasts. The post-processed rainfall ensembles are then used to force a semi-distributed conceptual rainfall runoff model to produce ensemble streamflow forecasts. The performance of the ensemble streamflow forecasts is evaluated on a number of Australian catchments and the benefits of using post processed rainfall forecasts are demonstrated.

  12. The Effect of Cumulus Cloud Field Anisotropy on Domain-Averaged Solar Fluxes and Atmospheric Heating Rates

    Science.gov (United States)

    Hinkelman, Laura M.; Evans, K. Franklin; Clothiaux, Eugene E.; Ackerman, Thomas P.; Stackhouse, Paul W., Jr.

    2006-01-01

    Cumulus clouds can become tilted or elongated in the presence of wind shear. Nevertheless, most studies of the interaction of cumulus clouds and radiation have assumed these clouds to be isotropic. This paper describes an investigation of the effect of fair-weather cumulus cloud field anisotropy on domain-averaged solar fluxes and atmospheric heating rate profiles. A stochastic field generation algorithm was used to produce twenty three-dimensional liquid water content fields based on the statistical properties of cloud scenes from a large eddy simulation. Progressively greater degrees of x-z plane tilting and horizontal stretching were imposed on each of these scenes, so that an ensemble of scenes was produced for each level of distortion. The resulting scenes were used as input to a three-dimensional Monte Carlo radiative transfer model. Domain-average transmission, reflection, and absorption of broadband solar radiation were computed for each scene along with the average heating rate profile. Both tilt and horizontal stretching were found to significantly affect calculated fluxes, with the amount and sign of flux differences depending strongly on sun position relative to cloud distortion geometry. The mechanisms by which anisotropy interacts with solar fluxes were investigated by comparisons to independent pixel approximation and tilted independent pixel approximation computations for the same scenes. Cumulus anisotropy was found to most strongly impact solar radiative transfer by changing the effective cloud fraction, i.e., the cloud fraction when the field is projected on a surface perpendicular to the direction of the incident solar beam.

  13. Characterizing climate predictability and model response variability from multiple initial condition and multi-model ensembles

    CERN Document Server

    Kumar, Devashish

    2016-01-01

    Climate models are thought to solve boundary value problems unlike numerical weather prediction, which is an initial value problem. However, climate internal variability (CIV) is thought to be relatively important at near-term (0-30 year) prediction horizons, especially at higher resolutions. The recent availability of significant numbers of multi-model (MME) and multi-initial condition (MICE) ensembles allows for the first time a direct sensitivity analysis of CIV versus model response variability (MRV). Understanding the relative agreement and variability of MME and MICE ensembles for multiple regions, resolutions, and projection horizons is critical for focusing model improvements, diagnostics, and prognosis, as well as impacts, adaptation, and vulnerability studies. Here we find that CIV (MICE agreement) is lower (higher) than MRV (MME agreement) across all spatial resolutions and projection time horizons for both temperature and precipitation. However, CIV dominates MRV over higher latitudes generally an...

  14. Bivariate ensemble model output statistics approach for joint forecasting of wind speed and temperature

    Science.gov (United States)

    Baran, Sándor; Möller, Annette

    2017-02-01

    Forecast ensembles are typically employed to account for prediction uncertainties in numerical weather prediction models. However, ensembles often exhibit biases and dispersion errors, thus they require statistical post-processing to improve their predictive performance. Two popular univariate post-processing models are the Bayesian model averaging (BMA) and the ensemble model output statistics (EMOS). In the last few years, increased interest has emerged in developing multivariate post-processing models, incorporating dependencies between weather quantities, such as for example a bivariate distribution for wind vectors or even a more general setting allowing to combine any types of weather variables. In line with a recently proposed approach to model temperature and wind speed jointly by a bivariate BMA model, this paper introduces an EMOS model for these weather quantities based on a bivariate truncated normal distribution. The bivariate EMOS model is applied to temperature and wind speed forecasts of the 8-member University of Washington mesoscale ensemble and the 11-member ALADIN-HUNEPS ensemble of the Hungarian Meteorological Service and its predictive performance is compared to the performance of the bivariate BMA model and a multivariate Gaussian copula approach, post-processing the margins with univariate EMOS. While the predictive skills of the compared methods are similar, the bivariate EMOS model requires considerably lower computation times than the bivariate BMA method.

  15. Clustered iterative stochastic ensemble method for multi-modal calibration of subsurface flow models

    KAUST Repository

    Elsheikh, Ahmed H.

    2013-05-01

    A novel multi-modal parameter estimation algorithm is introduced. Parameter estimation is an ill-posed inverse problem that might admit many different solutions. This is attributed to the limited amount of measured data used to constrain the inverse problem. The proposed multi-modal model calibration algorithm uses an iterative stochastic ensemble method (ISEM) for parameter estimation. ISEM employs an ensemble of directional derivatives within a Gauss-Newton iteration for nonlinear parameter estimation. ISEM is augmented with a clustering step based on k-means algorithm to form sub-ensembles. These sub-ensembles are used to explore different parts of the search space. Clusters are updated at regular intervals of the algorithm to allow merging of close clusters approaching the same local minima. Numerical testing demonstrates the potential of the proposed algorithm in dealing with multi-modal nonlinear parameter estimation for subsurface flow models. © 2013 Elsevier B.V.

  16. Data assimilation in integrated hydrological modeling using ensemble Kalman filtering: evaluating the effect of ensemble size and localization on filter performance

    Directory of Open Access Journals (Sweden)

    J. Rasmussen

    2015-02-01

    Full Text Available 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, an adaptive localization method is used. The performance of the adaptive localization method is compared to the more common local analysis localization. The relationship between filter performance in terms of hydraulic head and discharge error and the number of ensemble members is investigated for varying 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 local analysis localization.

  17. One-Step Dynamic Classifier Ensemble Model for Customer Value Segmentation with Missing Values

    Directory of Open Access Journals (Sweden)

    Jin Xiao

    2014-01-01

    Full Text Available Scientific customer value segmentation (CVS is the base of efficient customer relationship management, and customer credit scoring, fraud detection, and churn prediction all belong to CVS. In real CVS, the customer data usually include lots of missing values, which may affect the performance of CVS model greatly. This study proposes a one-step dynamic classifier ensemble model for missing values (ODCEM model. On the one hand, ODCEM integrates the preprocess of missing values and the classification modeling into one step; on the other hand, it utilizes multiple classifiers ensemble technology in constructing the classification models. The empirical results in credit scoring dataset “German” from UCI and the real customer churn prediction dataset “China churn” show that the ODCEM outperforms four commonly used “two-step” models and the ensemble based model LMF and can provide better decision support for market managers.

  18. An Intelligent Ensemble Neural Network Model for Wind Speed Prediction in Renewable Energy Systems.

    Science.gov (United States)

    Ranganayaki, V; Deepa, S N

    2016-01-01

    Various criteria are proposed to select the number of hidden neurons in artificial neural network (ANN) models and based on the criterion evolved an intelligent ensemble neural network model is proposed to predict wind speed in renewable energy applications. The intelligent ensemble neural model based wind speed forecasting is designed by averaging the forecasted values from multiple neural network models which includes multilayer perceptron (MLP), multilayer adaptive linear neuron (Madaline), back propagation neural network (BPN), and probabilistic neural network (PNN) so as to obtain better accuracy in wind speed prediction with minimum error. The random selection of hidden neurons numbers in artificial neural network results in overfitting or underfitting problem. This paper aims to avoid the occurrence of overfitting and underfitting problems. The selection of number of hidden neurons is done in this paper employing 102 criteria; these evolved criteria are verified by the computed various error values. The proposed criteria for fixing hidden neurons are validated employing the convergence theorem. The proposed intelligent ensemble neural model is applied for wind speed prediction application considering the real time wind data collected from the nearby locations. The obtained simulation results substantiate that the proposed ensemble model reduces the error value to minimum and enhances the accuracy. The computed results prove the effectiveness of the proposed ensemble neural network (ENN) model with respect to the considered error factors in comparison with that of the earlier models available in the literature.

  19. SVR-Boosting ensemble model for electricity price forecasting in electric power market

    Institute of Scientific and Technical Information of China (English)

    ZHOU Dian-min; GAO Lin; GUAN Xiao-hong; GAO Feng

    2008-01-01

    A revised support vector regression (SVR) ensemble model based on boosting algorithm (SVR-Boos-ting) is presented in this paper for electricity price forecasting in electric power market. In the light of charac-the forecasting model to inhibit the learning from abnormal data in electricity price sequence. The results from actual data indicate that, compared with the single support vector regression model, the proposed SVR-Boosting ensemble model is able to enhance the stability of the model output remarkably, acquire higher predicting accu-racy, and possess comparatively satisfactory generalization capability.

  20. IMPACTS OF CUMULUS PARAMETERIZATION AND RESOLUTION ON THE MJO SIMULATION

    Institute of Scientific and Technical Information of China (English)

    JIA Xiao-long; LI Chong-yin; LING Jian

    2009-01-01

    Madden-Julian Oscillations (MJO) in six integrations using an AGCM with different cumulus parameterization schemes and resolutions are examined to investigate their impacts on the MJO simulation.Results suggest that the MJO simulation can be affected by both resolution and cumulus parameterization,though the latter,which determines the fundamental ability of the AGCM in simulating the MJO and the characteristics of the simulated MJO,is more crucial than the former. Model resolution can substantially affect the simulated MJO in certain aspects. Increasing resolution cannot improve the simulated MJO substantially,but can significantly modulate the detailed character of the simulated MJO; meanwhile,the impacts of resolution are dependent on the cumulus parameterization,determining the basic features of the MJO. Changes in the resolution do not alter the nature of the simulated MJO but rather regulate the simulation itself,which is constrained by cumulus parameterization schemes. Theretbre,the vertical resolution needs to be increased simultaneously. The vertical profile of diabatic heating may be a crucial factor that is responsible for these different modeling results. To a large extent,it is determined by the cumulus parameterization scheme used.

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

    Science.gov (United States)

    2012-09-01

    ATMOSPHERIC MODELS INCLUDING ENSEMBLE METHODS Scott E. Miller Lieutenant Commander, United States Navy B.S., University of South Carolina, 2000 B.S...Typical gas turbine fuel consumption curve and relationship to sea state .......51  Figure 16.  DDG 58 speed reduction curves for bow seas...Day Time Group ECDIS-N Electronic Chart Display and Information System – Navy ECMWF European Center for Medium Range Weather Forecasts EFAS

  2. Force sensor based tool condition monitoring using a heterogeneous ensemble learning model.

    Science.gov (United States)

    Wang, Guofeng; Yang, Yinwei; Li, Zhimeng

    2014-11-14

    Tool condition monitoring (TCM) plays an important role in improving machining efficiency and guaranteeing workpiece quality. In order to realize reliable recognition of the tool condition, a robust classifier needs to be constructed to depict the relationship between tool wear states and sensory information. However, because of the complexity of the machining process and the uncertainty of the tool wear evolution, it is hard for a single classifier to fit all the collected samples without sacrificing generalization ability. In this paper, heterogeneous ensemble learning is proposed to realize tool condition monitoring in which the support vector machine (SVM), hidden Markov model (HMM) and radius basis function (RBF) are selected as base classifiers and a stacking ensemble strategy is further used to reflect the relationship between the outputs of these base classifiers and tool wear states. Based on the heterogeneous ensemble learning classifier, an online monitoring system is constructed in which the harmonic features are extracted from force signals and a minimal redundancy and maximal relevance (mRMR) algorithm is utilized to select the most prominent features. To verify the effectiveness of the proposed method, a titanium alloy milling experiment was carried out and samples with different tool wear states were collected to build the proposed heterogeneous ensemble learning classifier. Moreover, the homogeneous ensemble learning model and majority voting strategy are also adopted to make a comparison. The analysis and comparison results show that the proposed heterogeneous ensemble learning classifier performs better in both classification accuracy and stability.

  3. Using meteorological ensembles for atmospheric dispersion modelling of the Fukushima nuclear accident

    Science.gov (United States)

    Périllat, Raphaël; Korsakissok, Irène; Mallet, Vivien; Mathieu, Anne; Sekiyama, Thomas; Didier, Damien; Kajino, Mizuo; Igarashi, Yasuhito; Adachi, Kouji

    2016-04-01

    Dispersion models are used in response to an accidental release of radionuclides of the atmosphere, to infer mitigation actions, and complement field measurements for the assessment of short and long term environmental and sanitary impacts. However, the predictions of these models are subject to important uncertainties, especially due to input data, such as meteorological fields or source term. This is still the case more than four years after the Fukushima disaster (Korsakissok et al., 2012, Girard et al., 2014). In the framework of the SAKURA project, an MRI-IRSN collaboration, a meteorological ensemble of 20 members designed by MRI (Sekiyama et al. 2013) was used with IRSN's atmospheric dispersion models. Another ensemble, retrieved from ECMWF and comprising 50 members, was also used for comparison. The MRI ensemble is 3-hour assimilated, with a 3-kilometers resolution, designed to reduce the meteorological uncertainty in the Fukushima case. The ECMWF is a 24-hour forecast with a coarser grid, representative of the uncertainty of the data available in a crisis context. First, it was necessary to assess the quality of the ensembles for our purpose, to ensure that their spread was representative of the uncertainty of meteorological fields. Using meteorological observations allowed characterizing the ensembles' spread, with tools such as Talagrand diagrams. Then, the uncertainty was propagated through atmospheric dispersion models. The underlying question is whether the output spread is larger than the input spread, that is, whether small uncertainties in meteorological fields can produce large differences in atmospheric dispersion results. Here again, the use of field observations was crucial, in order to characterize the spread of the ensemble of atmospheric dispersion simulations. In the case of the Fukushima accident, gamma dose rates, air activities and deposition data were available. Based on these data, selection criteria for the ensemble members were

  4. How historic simulation-observation discrepancy affects future warming projections in a very large model ensemble

    Science.gov (United States)

    Goodwin, Philip

    2016-10-01

    Projections of future climate made by model-ensembles have credibility because the historic simulations by these models are consistent with, or near-consistent with, historic observations. However, it is not known how small inconsistencies between the ranges of observed and simulated historic climate change affects the future projections made by a model ensemble. Here, the impact of historical simulation-observation inconsistencies on future warming projections is quantified in a 4-million member Monte Carlo ensemble from a new efficient Earth System Model (ESM). Of the 4-million ensemble members, a subset of 182,500 are consistent with historic ranges of warming, heat uptake and carbon uptake simulated by the Climate Model Intercomparison Project 5 (CMIP5) ensemble. This simulation-consistent subset projects similar future warming ranges to the CMIP5 ensemble for all four RCP scenarios, indicating the new ESM represents an efficient tool to explore parameter space for future warming projections based on historic performance. A second subset of 14,500 ensemble members are consistent with historic observations for warming, heat uptake and carbon uptake. This observation-consistent subset projects a narrower range for future warming, with the lower bounds of projected warming still similar to CMIP5, but the upper warming bounds reduced by 20-35 %. These findings suggest that part of the upper range of twenty-first century CMIP5 warming projections may reflect historical simulation-observation inconsistencies. However, the agreement of lower bounds for projected warming implies that the likelihood of warming exceeding dangerous levels over the twenty-first century is unaffected by small discrepancies between CMIP5 models and observations.

  5. 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

  6. Improving a Deep Learning based RGB-D Object Recognition Model by Ensemble Learning

    DEFF Research Database (Denmark)

    Aakerberg, Andreas; Nasrollahi, Kamal; Heder, Thomas

    2017-01-01

    Augmenting RGB images with depth information is a well-known method to significantly improve the recognition accuracy of object recognition models. Another method to im- prove the performance of visual recognition models is ensemble learning. However, this method has not been widely explored...... in combination with deep convolutional neural network based RGB-D object recognition models. Hence, in this paper, we form different ensembles of complementary deep convolutional neural network models, and show that this can be used to increase the recognition performance beyond existing limits. Experiments...

  7. Skin lesion computational diagnosis of dermoscopic images: Ensemble models based on input feature manipulation.

    Science.gov (United States)

    Oliveira, Roberta B; Pereira, Aledir S; Tavares, João Manuel R S

    2017-10-01

    The number of deaths worldwide due to melanoma has risen in recent times, in part because melanoma is the most aggressive type of skin cancer. Computational systems have been developed to assist dermatologists in early diagnosis of skin cancer, or even to monitor skin lesions. However, there still remains a challenge to improve classifiers for the diagnosis of such skin lesions. The main objective of this article is to evaluate different ensemble classification models based on input feature manipulation to diagnose skin lesions. Input feature manipulation processes are based on feature subset selections from shape properties, colour variation and texture analysis to generate diversity for the ensemble models. Three subset selection models are presented here: (1) a subset selection model based on specific feature groups, (2) a correlation-based subset selection model, and (3) a subset selection model based on feature selection algorithms. Each ensemble classification model is generated using an optimum-path forest classifier and integrated with a majority voting strategy. The proposed models were applied on a set of 1104 dermoscopic images using a cross-validation procedure. The best results were obtained by the first ensemble classification model that generates a feature subset ensemble based on specific feature groups. The skin lesion diagnosis computational system achieved 94.3% accuracy, 91.8% sensitivity and 96.7% specificity. The input feature manipulation process based on specific feature subsets generated the greatest diversity for the ensemble classification model with very promising results. Copyright © 2017 Elsevier B.V. All rights reserved.

  8. Advanced ensemble modelling of flexible macromolecules using X-ray solution scattering.

    Science.gov (United States)

    Tria, Giancarlo; Mertens, Haydyn D T; Kachala, Michael; Svergun, Dmitri I

    2015-03-01

    Dynamic ensembles of macromolecules mediate essential processes in biology. Understanding the mechanisms driving the function and molecular interactions of 'unstructured' and flexible molecules requires alternative approaches to those traditionally employed in structural biology. Small-angle X-ray scattering (SAXS) is an established method for structural characterization of biological macromolecules in solution, and is directly applicable to the study of flexible systems such as intrinsically disordered proteins and multi-domain proteins with unstructured regions. The Ensemble Optimization Method (EOM) [Bernadó et al. (2007 ▶). J. Am. Chem. Soc. 129, 5656-5664] was the first approach introducing the concept of ensemble fitting of the SAXS data from flexible systems. In this approach, a large pool of macromolecules covering the available conformational space is generated and a sub-ensemble of conformers coexisting in solution is selected guided by the fit to the experimental SAXS data. This paper presents a series of new developments and advancements to the method, including significantly enhanced functionality and also quantitative metrics for the characterization of the results. Building on the original concept of ensemble optimization, the algorithms for pool generation have been redesigned to allow for the construction of partially or completely symmetric oligomeric models, and the selection procedure was improved to refine the size of the ensemble. Quantitative measures of the flexibility of the system studied, based on the characteristic integral parameters of the selected ensemble, are introduced. These improvements are implemented in the new EOM version 2.0, and the capabilities as well as inherent limitations of the ensemble approach in SAXS, and of EOM 2.0 in particular, are discussed.

  9. Advanced ensemble modelling of flexible macromolecules using X-ray solution scattering

    Directory of Open Access Journals (Sweden)

    Giancarlo Tria

    2015-03-01

    Full Text Available Dynamic ensembles of macromolecules mediate essential processes in biology. Understanding the mechanisms driving the function and molecular interactions of `unstructured' and flexible molecules requires alternative approaches to those traditionally employed in structural biology. Small-angle X-ray scattering (SAXS is an established method for structural characterization of biological macromolecules in solution, and is directly applicable to the study of flexible systems such as intrinsically disordered proteins and multi-domain proteins with unstructured regions. The Ensemble Optimization Method (EOM [Bernadó et al. (2007. J. Am. Chem. Soc. 129, 5656–5664] was the first approach introducing the concept of ensemble fitting of the SAXS data from flexible systems. In this approach, a large pool of macromolecules covering the available conformational space is generated and a sub-ensemble of conformers coexisting in solution is selected guided by the fit to the experimental SAXS data. This paper presents a series of new developments and advancements to the method, including significantly enhanced functionality and also quantitative metrics for the characterization of the results. Building on the original concept of ensemble optimization, the algorithms for pool generation have been redesigned to allow for the construction of partially or completely symmetric oligomeric models, and the selection procedure was improved to refine the size of the ensemble. Quantitative measures of the flexibility of the system studied, based on the characteristic integral parameters of the selected ensemble, are introduced. These improvements are implemented in the new EOM version 2.0, and the capabilities as well as inherent limitations of the ensemble approach in SAXS, and of EOM 2.0 in particular, are discussed.

  10. Uncertainty analysis of hydrological ensemble forecasts in a distributed model utilising short-range rainfall prediction

    Directory of Open Access Journals (Sweden)

    Y. Xuan

    2009-03-01

    Full Text Available Advances in mesoscale numerical weather predication make it possible to provide rainfall forecasts along with many other data fields at increasingly higher spatial resolutions. It is currently possible to incorporate high-resolution NWPs directly into flood forecasting systems in order to obtain an extended lead time. It is recognised, however, that direct application of rainfall outputs from the NWP model can contribute considerable uncertainty to the final river flow forecasts as the uncertainties inherent in the NWP are propagated into hydrological domains and can also be magnified by the scaling process. As the ensemble weather forecast has become operationally available, it is of particular interest to the hydrologist to investigate both the potential and implication of ensemble rainfall inputs to the hydrological modelling systems in terms of uncertainty propagation. In this paper, we employ a distributed hydrological model to analyse the performance of the ensemble flow forecasts based on the ensemble rainfall inputs from a short-range high-resolution mesoscale weather model. The results show that: (1 The hydrological model driven by QPF can produce forecasts comparable with those from a raingauge-driven one; (2 The ensemble hydrological forecast is able to disseminate abundant information with regard to the nature of the weather system and the confidence of the forecast itself; and (3 the uncertainties as well as systematic biases are sometimes significant and, as such, extra effort needs to be made to improve the quality of such a system.

  11. Performance of multi-physics ensembles in convective precipitation events over northeastern Spain

    Science.gov (United States)

    García-Ortega, E.; Lorenzana, J.; Merino, A.; Fernández-González, S.; López, L.; Sánchez, J. L.

    2017-07-01

    Convective precipitation with hail greatly affects southwestern Europe, causing major economic losses. The local character of this meteorological phenomenon is a serious obstacle to forecasting. Therefore, the development of reliable short-term forecasts constitutes an essential challenge to minimizing and managing risks. However, deterministic outcomes are affected by different uncertainty sources, such as physics parameterizations. This study examines the performance of different combinations of physics schemes of the Weather Research and Forecasting model to describe the spatial distribution of precipitation in convective environments with hail falls. Two 30-member multi-physics ensembles, with two and three domains of maximum resolution 9 and 3km each, were designed using various combinations of cumulus, microphysics and radiation schemes. The experiment was evaluated for 10 convective precipitation days with hail over 2005-2010 in northeastern Spain. Different indexes were used to evaluate the ability of each ensemble member to capture the precipitation patterns, which were compared with observations of a rain-gauge network. A standardized metric was constructed to identify optimal performers. Results show interesting differences between the two ensembles. In two domain simulations, the selection of cumulus parameterizations was crucial, with the Betts-Miller-Janjic scheme the best. In contrast, the Kain-Fristch cumulus scheme gave the poorest results, suggesting that it should not be used in the study area. Nevertheless, in three domain simulations, the cumulus schemes used in coarser domains were not critical and the best results depended mainly on microphysics schemes. The best performance was shown by Morrison, New Thomson and Goddard microphysics.

  12. The ensemble particle filter (EnPF) in rainfall-runoff models

    NARCIS (Netherlands)

    Van Delft, G.; El Serafy, G.Y.; Heemink, A.W.

    2009-01-01

    Rainfall-runoff models play a very important role in flood forecasting. However, these models contain large uncertainties caused by errors in both the model itself and the input data. Data assimilation techniques are being used to reduce these uncertainties. The ensemble Kalman filter (EnKF) and the

  13. Data Assimilation for Wildland Fires: Ensemble Kalman filters in coupled atmosphere-surface models

    OpenAIRE

    Mandel, Jan; Beezley, Jonathan D.; Coen, Janice L.; Kim, Minjeong

    2007-01-01

    Two wildland fire models are described, one based on reaction-diffusion-convection partial differential equations, and one based on semi-empirical fire spread by the level let method. The level set method model is coupled with the Weather Research and Forecasting (WRF) atmospheric model. The regularized and the morphing ensemble Kalman filter are used for data assimilation.

  14. Data Assimilation for Wildland Fires: Ensemble Kalman filters in coupled atmosphere-surface models

    CERN Document Server

    Mandel, Jan; Coen, Janice L; Kim, Minjeong

    2007-01-01

    Two wildland fire models are described, one based on reaction-diffusion-convection partial differential equations, and one based on empirical fire spread by the level let method. The level set method model is coupled with the Weather Research and Forecasting (WRF) atmospheric model. The regularized and the morphing ensemble Kalman filter are used for data assimilation.

  15. The ensemble particle filter (EnPF) in rainfall-runoff models

    NARCIS (Netherlands)

    Van Delft, G.; El Serafy, G.Y.; Heemink, A.W.

    2009-01-01

    Rainfall-runoff models play a very important role in flood forecasting. However, these models contain large uncertainties caused by errors in both the model itself and the input data. Data assimilation techniques are being used to reduce these uncertainties. The ensemble Kalman filter (EnKF) and the

  16. Multimodel ensembles of wheat growth: Many models are better than one

    NARCIS (Netherlands)

    Martre, P.; Wallach, D.; Asseng, S.; Ewert, F.; Jones, J.W.; Rötter, R.P.; Boote, K.J.; Ruane, A.C.; Thorburn, P.; Cammarano, D.; Hatfield, J.L.; Rosenzweig, C.; Aggarwal, P.K.; Angula, C.; Basso, B.; Bertuzzi, P.; Biernath, C.; Brisson, N.; Challinor, A.; Doltra, J.; Gayler, S.; Goldberg, R.A.; Grant, R.F.; Heng, L.; Hooker, J.; Hunt, L.A.; Ingwersen, J.; Izaurralde, C.; Kersebaum, K.C.; Mueller, C.; Kumar, S.; Nendel, C.; O'Leary, G.J.; Olesen, J.E.; Osborne, T.M.; Palosuo, T.; Priesack, E.; Ripoche, D.; Semenov, M.A.; Shcherbak, I.; Steduto, P.; Stöckle, C.O.; Stratonovitch, P.; Streck, T.; Supit, I.; Tao, Fulu; Travasso, M.; Waha, K.; White, J.W.; Wolf, J.

    2015-01-01

    Crop models of crop growth are increasingly used to quantify the impact of global changes due to climate or crop management. Therefore, accuracy of simulation results is a major concern. Studies with ensembles of crop models can give valuable information about model accuracy and uncertainty, but suc

  17. 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.

  18. Stochastic dynamics of small ensembles of non-processive molecular motors: the parallel cluster model.

    Science.gov (United States)

    Erdmann, Thorsten; Albert, Philipp J; Schwarz, Ulrich S

    2013-11-07

    Non-processive molecular motors have to work together in ensembles in order to generate appreciable levels of force or movement. In skeletal muscle, for example, hundreds of myosin II molecules cooperate in thick filaments. In non-muscle cells, by contrast, small groups with few tens of non-muscle myosin II motors contribute to essential cellular processes such as transport, shape changes, or mechanosensing. Here we introduce a detailed and analytically tractable model for this important situation. Using a three-state crossbridge model for the myosin II motor cycle and exploiting the assumptions of fast power stroke kinetics and equal load sharing between motors in equivalent states, we reduce the stochastic reaction network to a one-step master equation for the binding and unbinding dynamics (parallel cluster model) and derive the rules for ensemble movement. We find that for constant external load, ensemble dynamics is strongly shaped by the catch bond character of myosin II, which leads to an increase of the fraction of bound motors under load and thus to firm attachment even for small ensembles. This adaptation to load results in a concave force-velocity relation described by a Hill relation. For external load provided by a linear spring, myosin II ensembles dynamically adjust themselves towards an isometric state with constant average position and load. The dynamics of the ensembles is now determined mainly by the distribution of motors over the different kinds of bound states. For increasing stiffness of the external spring, there is a sharp transition beyond which myosin II can no longer perform the power stroke. Slow unbinding from the pre-power-stroke state protects the ensembles against detachment.

  19. Stochastic dynamics of small ensembles of non-processive molecular motors: The parallel cluster model

    Science.gov (United States)

    Erdmann, Thorsten; Albert, Philipp J.; Schwarz, Ulrich S.

    2013-11-01

    Non-processive molecular motors have to work together in ensembles in order to generate appreciable levels of force or movement. In skeletal muscle, for example, hundreds of myosin II molecules cooperate in thick filaments. In non-muscle cells, by contrast, small groups with few tens of non-muscle myosin II motors contribute to essential cellular processes such as transport, shape changes, or mechanosensing. Here we introduce a detailed and analytically tractable model for this important situation. Using a three-state crossbridge model for the myosin II motor cycle and exploiting the assumptions of fast power stroke kinetics and equal load sharing between motors in equivalent states, we reduce the stochastic reaction network to a one-step master equation for the binding and unbinding dynamics (parallel cluster model) and derive the rules for ensemble movement. We find that for constant external load, ensemble dynamics is strongly shaped by the catch bond character of myosin II, which leads to an increase of the fraction of bound motors under load and thus to firm attachment even for small ensembles. This adaptation to load results in a concave force-velocity relation described by a Hill relation. For external load provided by a linear spring, myosin II ensembles dynamically adjust themselves towards an isometric state with constant average position and load. The dynamics of the ensembles is now determined mainly by the distribution of motors over the different kinds of bound states. For increasing stiffness of the external spring, there is a sharp transition beyond which myosin II can no longer perform the power stroke. Slow unbinding from the pre-power-stroke state protects the ensembles against detachment.

  20. Multi-model ensemble analysis of Pacific and Atlantic SST variability in unperturbed climate simulations

    Science.gov (United States)

    Zanchettin, D.; Bothe, O.; Rubino, A.; Jungclaus, J. H.

    2016-08-01

    We assess internally-generated climate variability expressed by a multi-model ensemble of unperturbed climate simulations. We focus on basin-scale annual-average sea surface temperatures (SSTs) from twenty multicentennial pre-industrial control simulations contributing to the fifth phase of the Coupled Model Intercomparison Project. Ensemble spatial patterns of regional modes of variability and ensemble (cross-)wavelet-based phase-frequency diagrams of corresponding paired indices summarize the ensemble characteristics of inter-basin and regional-to-global SST interactions on a broad range of timescales. Results reveal that tropical and North Pacific SSTs are a source of simulated interannual global SST variability. The North Atlantic-average SST fluctuates in rough co-phase with the global-average SST on multidecadal timescales, which makes it difficult to discern the Atlantic Multidecadal Variability (AMV) signal from the global signal. The two leading modes of tropical and North Pacific SST variability converge towards co-phase in the multi-model ensemble, indicating that the Pacific Decadal Oscillation (PDO) results from a combination of tropical and extra-tropical processes. No robust inter- or multi-decadal inter-basin SST interaction arises from our ensemble analysis between the Pacific and Atlantic oceans, though specific phase-locked fluctuations occur between Pacific and Atlantic modes of SST variability in individual simulations and/or periods within individual simulations. The multidecadal modulation of PDO by the AMV identified in observations appears to be a recurrent but not typical feature of ensemble-simulated internal variability. Understanding the mechanism(s) and circumstances favoring such inter-basin SST phasing and related uncertainties in their simulated representation could help constraining uncertainty in decadal climate predictions.

  1. Predicting lymphatic filariasis transmission and elimination dynamics using a multi-model ensemble framework

    Directory of Open Access Journals (Sweden)

    Morgan E. Smith

    2017-03-01

    Full Text Available Mathematical models of parasite transmission provide powerful tools for assessing the impacts of interventions. Owing to complexity and uncertainty, no single model may capture all features of transmission and elimination dynamics. Multi-model ensemble modelling offers a framework to help overcome biases of single models. We report on the development of a first multi-model ensemble of three lymphatic filariasis (LF models (EPIFIL, LYMFASIM, and TRANSFIL, and evaluate its predictive performance in comparison with that of the constituents using calibration and validation data from three case study sites, one each from the three major LF endemic regions: Africa, Southeast Asia and Papua New Guinea (PNG. We assessed the performance of the respective models for predicting the outcomes of annual MDA strategies for various baseline scenarios thought to exemplify the current endemic conditions in the three regions. The results show that the constructed multi-model ensemble outperformed the single models when evaluated across all sites. Single models that best fitted calibration data tended to do less well in simulating the out-of-sample, or validation, intervention data. Scenario modelling results demonstrate that the multi-model ensemble is able to compensate for variance between single models in order to produce more plausible predictions of intervention impacts. Our results highlight the value of an ensemble approach to modelling parasite control dynamics. However, its optimal use will require further methodological improvements as well as consideration of the organizational mechanisms required to ensure that modelling results and data are shared effectively between all stakeholders.

  2. A flood episode in northern Italy: multi-model and single-model mesoscale meteorological ensembles for hydrological predictions

    Directory of Open Access Journals (Sweden)

    S. Davolio

    2013-06-01

    Full Text Available Numerical weather prediction models can be coupled with hydrological models to generate streamflow forecasts. Several ensemble approaches have been recently developed in order to take into account the different sources of errors and provide probabilistic forecasts feeding a flood forecasting system. Within this framework, the present study aims at comparing two high-resolution limited-area meteorological ensembles, covering short and medium range, obtained via different methodologies, but implemented with similar number of members, horizontal resolution (about 7 km, and driving global ensemble prediction system. The former is a multi-model ensemble, based on three mesoscale models (BOLAM, COSMO, and WRF, while the latter, following a single-model approach, is the operational ensemble forecasting system developed within the COSMO consortium, COSMO-LEPS (limited-area ensemble prediction system. The meteorological models are coupled with a distributed rainfall-runoff model (TOPKAPI to simulate the discharge of the Reno River (northern Italy, for a recent severe weather episode affecting northern Apennines. The evaluation of the ensemble systems is performed both from a meteorological perspective over northern Italy and in terms of discharge prediction over the Reno River basin during two periods of heavy precipitation between 29 November and 2 December 2008. For each period, ensemble performance has been compared at two different forecast ranges. It is found that, for the intercomparison undertaken in this specific study, both mesoscale model ensembles outperform the global ensemble for application at basin scale. Horizontal resolution is found to play a relevant role in modulating the precipitation distribution. Moreover, the multi-model ensemble provides a better indication concerning the occurrence, intensity and timing of the two observed discharge peaks, with respect to COSMO-LEPS. This seems to be ascribable to the different behaviour of the

  3. Ensemble engineering and statistical modeling for parameter calibration towards optimal design of microbial fuel cells

    Science.gov (United States)

    Sun, Hongyue; Luo, Shuai; Jin, Ran; He, Zhen

    2017-07-01

    Mathematical modeling is an important tool to investigate the performance of microbial fuel cell (MFC) towards its optimized design. To overcome the shortcoming of traditional MFC models, an ensemble model is developed through integrating both engineering model and statistical analytics for the extrapolation scenarios in this study. Such an ensemble model can reduce laboring effort in parameter calibration and require fewer measurement data to achieve comparable accuracy to traditional statistical model under both the normal and extreme operation regions. Based on different weight between current generation and organic removal efficiency, the ensemble model can give recommended input factor settings to achieve the best current generation and organic removal efficiency. The model predicts a set of optimal design factors for the present tubular MFCs including the anode flow rate of 3.47 mL min-1, organic concentration of 0.71 g L-1, and catholyte pumping flow rate of 14.74 mL min-1 to achieve the peak current at 39.2 mA. To maintain 100% organic removal efficiency, the anode flow rate and organic concentration should be controlled lower than 1.04 mL min-1 and 0.22 g L-1, respectively. The developed ensemble model can be potentially modified to model other types of MFCs or bioelectrochemical systems.

  4. The Local Ensemble Transform Kalman Filter (LETKF) with a Global NWP Model on the Cubed Sphere

    Science.gov (United States)

    Shin, Seoleun; Kang, Ji-Sun; Jo, Youngsoon

    2016-07-01

    We develop an ensemble data assimilation system using the four-dimensional local ensemble transform kalman filter (LEKTF) for a global hydrostatic numerical weather prediction (NWP) model formulated on the cubed sphere. Forecast-analysis cycles run stably and thus provide newly updated initial states for the model to produce ensemble forecasts every 6 h. Performance of LETKF implemented to the global NWP model is verified using the ECMWF reanalysis data and conventional observations. Global mean values of bias and root mean square difference are significantly reduced by the data assimilation. Besides, statistics of forecast and analysis converge well as the forecast-analysis cycles are repeated. These results suggest that the combined system of LETKF and the global NWP formulated on the cubed sphere shows a promising performance for operational uses.

  5. Muscle activation described with a differential equation model for large ensembles of locally coupled molecular motors.

    Science.gov (United States)

    Walcott, Sam

    2014-10-01

    Molecular motors, by turning chemical energy into mechanical work, are responsible for active cellular processes. Often groups of these motors work together to perform their biological role. Motors in an ensemble are coupled and exhibit complex emergent behavior. Although large motor ensembles can be modeled with partial differential equations (PDEs) by assuming that molecules function independently of their neighbors, this assumption is violated when motors are coupled locally. It is therefore unclear how to describe the ensemble behavior of the locally coupled motors responsible for biological processes such as calcium-dependent skeletal muscle activation. Here we develop a theory to describe locally coupled motor ensembles and apply the theory to skeletal muscle activation. The central idea is that a muscle filament can be divided into two phases: an active and an inactive phase. Dynamic changes in the relative size of these phases are described by a set of linear ordinary differential equations (ODEs). As the dynamics of the active phase are described by PDEs, muscle activation is governed by a set of coupled ODEs and PDEs, building on previous PDE models. With comparison to Monte Carlo simulations, we demonstrate that the theory captures the behavior of locally coupled ensembles. The theory also plausibly describes and predicts muscle experiments from molecular to whole muscle scales, suggesting that a micro- to macroscale muscle model is within reach.

  6. Ensemble forecasting of sub-seasonal to seasonal streamflow by a Bayesian joint probability modelling approach

    Science.gov (United States)

    Zhao, Tongtiegang; Schepen, Andrew; Wang, Q. J.

    2016-10-01

    The Bayesian joint probability (BJP) modelling approach is used operationally to produce seasonal (three-month-total) ensemble streamflow forecasts in Australia. However, water resource managers are calling for more informative sub-seasonal forecasts. Taking advantage of BJP's capability of handling multiple predictands, ensemble forecasting of sub-seasonal to seasonal streamflows is investigated for 23 catchments around Australia. Using antecedent streamflow and climate indices as predictors, monthly forecasts are developed for the three-month period ahead. Forecast reliability and skill are evaluated for the period 1982-2011 using a rigorous leave-five-years-out cross validation strategy. BJP ensemble forecasts of monthly streamflow volumes are generally reliable in ensemble spread. Forecast skill, relative to climatology, is positive in 74% of cases in the first month, decreasing to 57% and 46% respectively for streamflow forecasts for the final two months of the season. As forecast skill diminishes with increasing lead time, the monthly forecasts approach climatology. Seasonal forecasts accumulated from monthly forecasts are found to be similarly skilful to forecasts from BJP models based on seasonal totals directly. The BJP modelling approach is demonstrated to be a viable option for producing ensemble time-series sub-seasonal to seasonal streamflow forecasts.

  7. Electrical coupling in ensembles of nonexcitable cells: modeling the spatial map of single cell potentials.

    Science.gov (United States)

    Cervera, Javier; Manzanares, Jose Antonio; Mafe, Salvador

    2015-02-19

    We analyze the coupling of model nonexcitable (non-neural) cells assuming that the cell membrane potential is the basic individual property. We obtain this potential on the basis of the inward and outward rectifying voltage-gated channels characteristic of cell membranes. We concentrate on the electrical coupling of a cell ensemble rather than on the biochemical and mechanical characteristics of the individual cells, obtain the map of single cell potentials using simple assumptions, and suggest procedures to collectively modify this spatial map. The response of the cell ensemble to an external perturbation and the consequences of cell isolation, heterogeneity, and ensemble size are also analyzed. The results suggest that simple coupling mechanisms can be significant for the biophysical chemistry of model biomolecular ensembles. In particular, the spatiotemporal map of single cell potentials should be relevant for the uptake and distribution of charged nanoparticles over model cell ensembles and the collective properties of droplet networks incorporating protein ion channels inserted in lipid bilayers.

  8. Modeling hypoxia in the Chesapeake Bay: Ensemble estimation using a Bayesian hierarchical model

    Science.gov (United States)

    Stow, Craig A.; Scavia, Donald

    2009-02-01

    Quantifying parameter and prediction uncertainty in a rigorous framework can be an important component of model skill assessment. Generally, models with lower uncertainty will be more useful for prediction and inference than models with higher uncertainty. Ensemble estimation, an idea with deep roots in the Bayesian literature, can be useful to reduce model uncertainty. It is based on the idea that simultaneously estimating common or similar parameters among models can result in more precise estimates. We demonstrate this approach using the Streeter-Phelps dissolved oxygen sag model fit to 29 years of data from Chesapeake Bay. Chesapeake Bay has a long history of bottom water hypoxia and several models are being used to assist management decision-making in this system. The Bayesian framework is particularly useful in a decision context because it can combine both expert-judgment and rigorous parameter estimation to yield model forecasts and a probabilistic estimate of the forecast uncertainty.

  9. An ensemble formulation of PBL fluxes in a GCM

    Science.gov (United States)

    Sud, Y. C.; Smith, W. E.

    1984-01-01

    An ensemble approach is applied to Planetary Boundary Layer (PBL) calculations with the bulk Richardson number identified as the key parameter. An ensemble averaging calculation was carried out to rederive the bulk friction and heat transport coefficients for the mean condition. Two simulations are carried out and compared. Significant differences in PBL fluxes low level cloudiness, land surface roughness heights, and surface evaporation are noted between the modified and unmodified simulations. Modifications to the model were: (1) the relationship between actual and potential Effective Temperature (ET) to accord with Sud and Fennessy (1982); (2) maximum permissible instantaneous ET at any time is 1.5 mm per hr; (3) moisture distribution in low level cumulus convection to be consistent with no precipitation; (4) appearance of supersaturation clouds to be consistent with supersaturation condition at that level; (5) invoking a simple function for stomatal diffusion effect in the ET calculation.

  10. Ensemble regression model-based anomaly detection for cyber-physical intrusion detection in smart grids

    DEFF Research Database (Denmark)

    Kosek, Anna Magdalena; Gehrke, Oliver

    2016-01-01

    on an ensemble of non-linear artificial neural network DER models which detect and evaluate anomalies in DER operation. The proposed method is validated against measurement data which yields a precision of 0.947 and an accuracy of 0.976. This improves the precision and accuracy of a classic model-based anomaly...

  11. 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...

  12. Improved sub-seasonal meteorological forecast skill using weighted multi-model ensemble simulations

    NARCIS (Netherlands)

    Wanders, Niko|info:eu-repo/dai/nl/364253940; Wood, Eric F.

    2016-01-01

    Sub-seasonal to seasonal weather and hydrological forecasts have the potential to provide vital information for a variety of water-related decision makers. Here, we investigate the skill of four sub-seasonal forecast models from phase-2 of the North American Multi-Model Ensemble using reforecasts

  13. Multi-objective calibration of forecast ensembles using Bayesian model averaging

    NARCIS (Netherlands)

    Vrugt, J.A.; Clark, M.P.; Diks, C.G.H.; Duan, Q.; Robinson, B.A.

    2006-01-01

    Bayesian Model Averaging (BMA) has recently been proposed as a method for statistical postprocessing of forecast ensembles from numerical weather prediction models. The BMA predictive probability density function (PDF) of any weather quantity of interest is a weighted average of PDFs centered on the

  14. Multi-criterion model ensemble of CMIP5 surface air temperature over China

    Science.gov (United States)

    Yang, Tiantian; Tao, Yumeng; Li, Jingjing; Zhu, Qian; Su, Lu; He, Xiaojia; Zhang, Xiaoming

    2017-05-01

    The global circulation models (GCMs) are useful tools for simulating climate change, projecting future temperature changes, and therefore, supporting the preparation of national climate adaptation plans. However, different GCMs are not always in agreement with each other over various regions. The reason is that GCMs' configurations, module characteristics, and dynamic forcings vary from one to another. Model ensemble techniques are extensively used to post-process the outputs from GCMs and improve the variability of model outputs. Root-mean-square error (RMSE), correlation coefficient (CC, or R) and uncertainty are commonly used statistics for evaluating the performances of GCMs. However, the simultaneous achievements of all satisfactory statistics cannot be guaranteed in using many model ensemble techniques. In this paper, we propose a multi-model ensemble framework, using a state-of-art evolutionary multi-objective optimization algorithm (termed MOSPD), to evaluate different characteristics of ensemble candidates and to provide comprehensive trade-off information for different model ensemble solutions. A case study of optimizing the surface air temperature (SAT) ensemble solutions over different geographical regions of China is carried out. The data covers from the period of 1900 to 2100, and the projections of SAT are analyzed with regard to three different statistical indices (i.e., RMSE, CC, and uncertainty). Among the derived ensemble solutions, the trade-off information is further analyzed with a robust Pareto front with respect to different statistics. The comparison results over historical period (1900-2005) show that the optimized solutions are superior over that obtained simple model average, as well as any single GCM output. The improvements of statistics are varying for different climatic regions over China. Future projection (2006-2100) with the proposed ensemble method identifies that the largest (smallest) temperature changes will happen in the

  15. Multimodel Ensembles of Wheat Growth: More Models are Better than One

    Science.gov (United States)

    Martre, Pierre; Wallach, Daniel; Asseng, Senthold; Ewert, Frank; Jones, James W.; Rotter, Reimund P.; Boote, Kenneth J.; Ruane, Alex C.; Thorburn, Peter J.; Cammarano, Davide; Hatfield, Jerry L.; Rosenzweig, Cynthia; Aggarwal, Pramod K.; Angulo, Carlos; Basso, Bruno; Bertuzzi, Patrick; Biernath, Christian; Brisson, Nadine; Challinor, Andrew J.; Doltra, Jordi; Gayler, Sebastian; Goldberg, Richie; Grant, Robert F.; Heng, Lee; Hooker, Josh

    2015-01-01

    Crop models of crop growth are increasingly used to quantify the impact of global changes due to climate or crop management. Therefore, accuracy of simulation results is a major concern. Studies with ensembles of crop models can give valuable information about model accuracy and uncertainty, 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 24-38% for the different end-of-season variables including grain yield (GY) and grain protein concentration (GPC). There was little relation between error of a model for GY or GPC and error for in-season variables. Thus, most models did not arrive at accurate simulations of GY and GPC by accurately simulating preceding growth dynamics. Ensemble simulations, taking either the mean (e-mean) or median (e-median) of simulated values, gave better estimates than any individual model when all variables were considered. Compared to individual models, e-median ranked first in simulating measured GY and third in GPC. The error of e-mean and e-median declined with an increasing number of ensemble members, with little decrease beyond 10 models. We conclude that multimodel ensembles can be used to create new estimators with improved accuracy and consistency in simulating growth dynamics. We argue that these results are applicable to other crop species, and hypothesize that they apply more generally to ecological system models.

  16. Multimodel Ensembles of Wheat Growth: Many Models are Better than One

    Science.gov (United States)

    Martre, Pierre; Wallach, Daniel; Asseng, Senthold; Ewert, Frank; Jones, James W.; Rotter, Reimund P.; Boote, Kenneth J.; Ruane, Alexander C.; Thorburn, Peter J.; Cammarano, Davide; Hatfield, Jerry L.; Rosenzweig, Cynthia; Aggarwal, Pramod K.; Angulo, Carlos; Basso, Bruno; Bertuzzi, Patrick; Biernath, Christian; Brisson, Nadine; Challinor, Andrew J.; Doltra, Jordi; Gayler, Sebastian; Goldberg, Richie; Grant, Robert F.; Heng, Lee; Hooker, Josh; Hunt, Leslie A.; Ingwersen, Joachim; Izaurralde, Roberto C.; Kersebaum, Kurt Christian; Kumar, Soora Naresh; Nendel, Claas; O'Leary, Garry; Olesen, Jorgen E; Osborne, Tom M.; Palosuo, Taru; Priesack, Eckart

    2015-01-01

    Crop models of crop growth are increasingly used to quantify the impact of global changes due to climate or crop management. Therefore, accuracy of simulation results is a major concern. Studies with ensembles of crop model scan give valuable information about model accuracy and uncertainty, 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 2438 for the different end-of-season variables including grain yield (GY) and grain protein concentration (GPC). There was little relation between error of a model for GY or GPC and error for in-season variables. Thus, most models did not arrive at accurate simulations of GY and GPC by accurately simulating preceding growth dynamics. Ensemble simulations, taking either the mean (e-mean) or median (e-median) of simulated values, gave better estimates than any individual model when all variables were considered. Compared to individual models, e-median ranked first in simulating measured GY and third in GPC. The error of e-mean and e-median declined with an increasing number of ensemble members, with little decrease beyond 10 models. We conclude that multimodel ensembles can be used to create new estimators with improved accuracy and consistency in simulating growth dynamics. We argue that these results are applicable to other crop species, and hypothesize that they apply more generally to ecological system models.

  17. Improving sub-pixel imperviousness change prediction by ensembling heterogeneous non-linear regression models

    Science.gov (United States)

    Drzewiecki, Wojciech

    2016-12-01

    In this work nine non-linear regression models were compared for sub-pixel impervious surface area mapping from Landsat images. The comparison was done in three study areas both for accuracy of imperviousness coverage evaluation in individual points in time and accuracy of imperviousness change assessment. The performance of individual machine learning algorithms (Cubist, Random Forest, stochastic gradient boosting of regression trees, k-nearest neighbors regression, random k-nearest neighbors regression, Multivariate Adaptive Regression Splines, averaged neural networks, and support vector machines with polynomial and radial kernels) was also compared with the performance of heterogeneous model ensembles constructed from the best models trained using particular techniques. The results proved that in case of sub-pixel evaluation the most accurate prediction of change may not necessarily be based on the most accurate individual assessments. When single methods are considered, based on obtained results Cubist algorithm may be advised for Landsat based mapping of imperviousness for single dates. However, Random Forest may be endorsed when the most reliable evaluation of imperviousness change is the primary goal. It gave lower accuracies for individual assessments, but better prediction of change due to more correlated errors of individual predictions. Heterogeneous model ensembles performed for individual time points assessments at least as well as the best individual models. In case of imperviousness change assessment the ensembles always outperformed single model approaches. It means that it is possible to improve the accuracy of sub-pixel imperviousness change assessment using ensembles of heterogeneous non-linear regression models.

  18. A user credit assessment model based on clustering ensemble for broadband network new media service supervision

    Science.gov (United States)

    Liu, Fang; Cao, San-xing; Lu, Rui

    2012-04-01

    This paper proposes a user credit assessment model based on clustering ensemble aiming to solve the problem that users illegally spread pirated and pornographic media contents within the user self-service oriented broadband network new media platforms. Its idea is to do the new media user credit assessment by establishing indices system based on user credit behaviors, and the illegal users could be found according to the credit assessment results, thus to curb the bad videos and audios transmitted on the network. The user credit assessment model based on clustering ensemble proposed by this paper which integrates the advantages that swarm intelligence clustering is suitable for user credit behavior analysis and K-means clustering could eliminate the scattered users existed in the result of swarm intelligence clustering, thus to realize all the users' credit classification automatically. The model's effective verification experiments are accomplished which are based on standard credit application dataset in UCI machine learning repository, and the statistical results of a comparative experiment with a single model of swarm intelligence clustering indicates this clustering ensemble model has a stronger creditworthiness distinguishing ability, especially in the aspect of predicting to find user clusters with the best credit and worst credit, which will facilitate the operators to take incentive measures or punitive measures accurately. Besides, compared with the experimental results of Logistic regression based model under the same conditions, this clustering ensemble model is robustness and has better prediction accuracy.

  19. Data assimilation with the ensemble Kalman filter in a numerical model of the North Sea

    Science.gov (United States)

    Ponsar, Stéphanie; Luyten, Patrick; Dulière, Valérie

    2016-08-01

    Coastal management and maritime safety strongly rely on accurate representations of the sea state. Both dynamical models and observations provide abundant pieces of information. However, none of them provides the complete picture. The assimilation of observations into models is one way to improve our knowledge of the ocean state. Its application in coastal models remains challenging because of the wide range of temporal and spatial variabilities of the processes involved. This study investigates the assimilation of temperature profiles with the ensemble Kalman filter in 3-D North Sea simulations. The model error is represented by the standard deviation of an ensemble of model states. Parameters' values for the ensemble generation are first computed from the misfit between the data and the model results without assimilation. Then, two square root algorithms are applied to assimilate the data. The impact of data assimilation on the simulated temperature is assessed. Results show that the ensemble Kalman filter is adequate for improving temperature forecasts in coastal areas, under adequate model error specification.

  20. Monitoring seasonal influenza epidemics by using internet search data with an ensemble penalized regression model

    Science.gov (United States)

    Guo, Pi; Zhang, Jianjun; Wang, Li; Yang, Shaoyi; Luo, Ganfeng; Deng, Changyu; Wen, Ye; Zhang, Qingying

    2017-01-01

    Seasonal influenza epidemics cause serious public health problems in China. Search queries-based surveillance was recently proposed to complement traditional monitoring approaches of influenza epidemics. However, developing robust techniques of search query selection and enhancing predictability for influenza epidemics remains a challenge. This study aimed to develop a novel ensemble framework to improve penalized regression models for detecting influenza epidemics by using Baidu search engine query data from China. The ensemble framework applied a combination of bootstrap aggregating (bagging) and rank aggregation method to optimize penalized regression models. Different algorithms including lasso, ridge, elastic net and the algorithms in the proposed ensemble framework were compared by using Baidu search engine queries. Most of the selected search terms captured the peaks and troughs of the time series curves of influenza cases. The predictability of the conventional penalized regression models were improved by the proposed ensemble framework. The elastic net regression model outperformed the compared models, with the minimum prediction errors. We established a Baidu search engine queries-based surveillance model for monitoring influenza epidemics, and the proposed model provides a useful tool to support the public health response to influenza and other infectious diseases. PMID:28422149

  1. MCMC for non-linear state space models using ensembles of latent sequences

    OpenAIRE

    2013-01-01

    Non-linear state space models are a widely-used class of models for biological, economic, and physical processes. Fitting these models to observed data is a difficult inference problem that has no straightforward solution. We take a Bayesian approach to the inference of unknown parameters of a non-linear state model; this, in turn, requires the availability of efficient Markov Chain Monte Carlo (MCMC) sampling methods for the latent (hidden) variables and model parameters. Using the ensemble ...

  2. Advancing monthly streamflow prediction accuracy of CART models using ensemble learning paradigms

    Science.gov (United States)

    Erdal, Halil Ibrahim; Karakurt, Onur

    2013-01-01

    SummaryStreamflow forecasting is one of the most important steps in the water resources planning and management. Ensemble techniques such as bagging, boosting and stacking have gained popularity in hydrological forecasting in the recent years. The study investigates the potential usage of two ensemble learning paradigms (i.e., bagging; stochastic gradient boosting) in building classification and regression trees (CARTs) ensembles to advance the streamflow prediction accuracy. The study, initially, investigates the use of classification and regression trees for monthly streamflow forecasting and employs a support vector regression (SVR) model as the benchmark model. The analytic results indicate that CART outperforms SVR in both training and testing phases. Although the obtained results of CART model in training phase are considerable, it is not in testing phase. Thus, to optimize the prediction accuracy of CART for monthly streamflow forecasting, we incorporate bagging and stochastic gradient boosting which are rooted in same philosophy, advancing the prediction accuracy of weak learners. Comparing with the results of bagged regression trees (BRTs) and stochastic gradient boosted regression trees (GBRTs) models possess satisfactory monthly streamflow forecasting performance than CART and SVR models. Overall, it is found that ensemble learning paradigms can remarkably advance the prediction accuracy of CART models in monthly streamflow forecasting.

  3. Ensemble distribution models in conservation prioritization: from consensus predictions to consensus reserve networks

    Science.gov (United States)

    Meller, Laura; Cabeza, Mar; Pironon, Samuel; Barbet-Massin, Morgane; Maiorano, Luigi; Georges, Damien; Thuiller, Wilfried

    2014-01-01

    Aim Conservation planning exercises increasingly rely on species distributions predicted either from one particular statistical model or, more recently, from an ensemble of models (i.e. ensemble forecasting). However, it has not yet been explored how different ways of summarizing ensemble predictions affect conservation planning outcomes. We evaluate these effects and compare commonplace consensus methods, applied before the conservation prioritization phase, to a novel method that applies consensus after reserve selection. Location Europe. Methods We used an ensemble of predicted distributions of 146 Western Palaearctic bird species in alternative ways: four different consensus methods, as well as distributions discounted with variability, were used to produce inputs for spatial conservation prioritization. In addition, we developed and tested a novel method, in which we built 100 datasets by sampling the ensemble of predicted distributions, ran a conservation prioritization analysis on each of them and averaged the resulting priority ranks. We evaluated the conservation outcome against three controls: (i) a null control, based on random ranking of cells; (2) the reference solution, based on an expert-refined dataset; and (3) the independent solution, based on an independent dataset. Results Networks based on predicted distributions were more representative of rare species than randomly selected networks. Alternative methods to summarize ensemble predictions differed in representativeness of resulting reserve networks. Our novel method resulted in better representation of rare species than pre-selection consensus methods. Main conclusions Retaining information about the variation in the predicted distributions throughout the conservation prioritization seems to provide better results than summarizing the predictions before conservation prioritization. Our results highlight the need to understand and consider model-based uncertainty when using predicted

  4. Transferability of hydrological models and ensemble averaging methods between contrasting climatic periods

    Science.gov (United States)

    Broderick, Ciaran; Matthews, Tom; Wilby, Robert L.; Bastola, Satish; Murphy, Conor

    2016-10-01

    Understanding hydrological model predictive capabilities under contrasting climate conditions enables more robust decision making. Using Differential Split Sample Testing (DSST), we analyze the performance of six hydrological models for 37 Irish catchments under climate conditions unlike those used for model training. Additionally, we consider four ensemble averaging techniques when examining interperiod transferability. DSST is conducted using 2/3 year noncontinuous blocks of (i) the wettest/driest years on record based on precipitation totals and (ii) years with a more/less pronounced seasonal precipitation regime. Model transferability between contrasting regimes was found to vary depending on the testing scenario, catchment, and evaluation criteria considered. As expected, the ensemble average outperformed most individual ensemble members. However, averaging techniques differed considerably in the number of times they surpassed the best individual model member. Bayesian Model Averaging (BMA) and the Granger-Ramanathan Averaging (GRA) method were found to outperform the simple arithmetic mean (SAM) and Akaike Information Criteria Averaging (AICA). Here GRA performed better than the best individual model in 51%-86% of cases (according to the Nash-Sutcliffe criterion). When assessing model predictive skill under climate change conditions we recommend (i) setting up DSST to select the best available analogues of expected annual mean and seasonal climate conditions; (ii) applying multiple performance criteria; (iii) testing transferability using a diverse set of catchments; and (iv) using a multimodel ensemble in conjunction with an appropriate averaging technique. Given the computational efficiency and performance of GRA relative to BMA, the former is recommended as the preferred ensemble averaging technique for climate assessment.

  5. 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

    levels, and can thus be used to calculate temperature and [CO2] thresholds leading to yield loss or yield gain, without re-running the original complex crop models. Our approach is illustrated with three yield datasets simulated by 19 maize models, 26 wheat models, and 13 rice models. Several statistical......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 data...... in the simulation protocols. Here we demonstrate that statistical models based on random-coefficient regressions are able to emulate ensembles of process-based crop models. An important advantage of the proposed statistical models is that they can interpolate between temperature levels and between CO2 concentration...

  6. Matrix Models and Eigenvalue Statistics for Truncations of Classical Ensembles of Random Unitary Matrices

    Science.gov (United States)

    Killip, Rowan; Kozhan, Rostyslav

    2017-02-01

    We consider random non-normal matrices constructed by removing one row and column from samples from Dyson's circular ensembles or samples from the classical compact groups. We develop sparse matrix models whose spectral measures match these ensembles. This allows us to compute the joint law of the eigenvalues, which have a natural interpretation as resonances for open quantum systems or as electrostatic charges located in a dielectric medium. Our methods allow us to consider all values of {β > 0}, not merely {β=1,2,4}.

  7. Multi-Model Long-Range Ensemble Forecast for Decision Support in Hydroelectric Operations

    Science.gov (United States)

    Kunkel, M. L.; Parkinson, S.; Blestrud, D.; Holbrook, V. P.

    2014-12-01

    Idaho Power Company (IPC) is a hydroelectric based utility serving over a million customers in southern Idaho and eastern Oregon. Hydropower makes up ~50% of our power generation and accurate predictions of streamflow and precipitation drive our long-term planning and decision support for operations. We investigate the use of a multi-model ensemble approach for mid and long-range streamflow and precipitation forecasts throughout the Snake River Basin. Forecast are prepared using an Idaho Power developed ensemble forecasting technique for 89 locations throughout the Snake River Basin for periods of 3 to 18 months in advance. A series of multivariable linear regression, multivariable non-linear regression and multivariable Kalman filter techniques are combined in an ensemble forecast based upon two data types, historical data (streamflow, precipitation, climate indices [i.e. PDO, ENSO, AO, etc…]) and single value decomposition derived values based upon atmospheric heights and sea surface temperatures.

  8. Maximization of seasonal forecasts performance combining Grand Multi-Model Ensembles

    Science.gov (United States)

    Alessandri, Andrea; De Felice, Matteo; Catalano, Franco; Lee, Doo Young; Yoo, Jin Ho; Lee, June-Yi; Wang, Bin

    2014-05-01

    Multi-Model Ensembles (MMEs) are powerful tools in dynamical climate prediction as they account for the overconfidence and the uncertainties related to single-model errors. Previous works suggested that the potential benefit that can be expected by using a MME amplify with the increase of the independence of the contributing Seasonal Prediction Systems. In this work we combine the two Multi Model Ensemble (MME) Seasonal Prediction Systems (SPSs) independently developed by the European (ENSEMBLES) and by the Asian-Pacific (CliPAS/APCC) communities. To this aim, all the possible multi-model combinations obtained by putting together the 5 models from ENSEMBLES and the 11 models from CliPAS/APCC have been evaluated. The grand ENSEMBLES-CliPAS/APCC Multi-Model enhances significantly the skill compared to previous estimates from the contributing MMEs. The combinations of SPSs maximizing the skill that is currently attainable for specific predictands/phenomena is evaluated. Our results show that, in general, the better combinations of SPSs are obtained by mixing ENSEMBLES and CliPAS/APCC models and that only a limited number of SPSs is required to obtain the maximum performance. The number and selection of models that perform better is usually different depending on the region/phenomenon under consideration. As an example for the tropical Pacific, the maximum performance is obtained with only the combination of 5-to-6 SPSs from the grand ENSEMBLES-CliPAS/APCC MME. With particular focus over Tropical Pacific, the relationship between performance and bias of the grand-MME combinations is evaluated. The skill of the grand-MME combinations over Euro-Mediterranean and East-Asia regions is further evaluated as a function of the capability of the selected contributing SPSs to forecast anomalies of the Polar/Siberian highs during winter and of the Asian summer monsoon precipitation during summer. Our results indicate that, combining SPSs from independent MME sources is a good

  9. Crop Model Improvement Reduces the Uncertainty of the Response to Temperature of Multi-Model Ensembles

    Science.gov (United States)

    Maiorano, Andrea; Martre, Pierre; Asseng, Senthold; Ewert, Frank; Mueller, Christoph; Roetter, Reimund P.; Ruane, Alex C.; Semenov, Mikhail A.; Wallach, Daniel; Wang, Enli

    2016-01-01

    To improve climate change impact estimates and to quantify their uncertainty, multi-model ensembles (MMEs) have been suggested. Model improvements can improve the accuracy of simulations and reduce the uncertainty of climate change impact assessments. Furthermore, they can reduce the number of models needed in a MME. Herein, 15 wheat growth models of a larger MME were improved through re-parameterization and/or incorporating or modifying heat stress effects on phenology, leaf growth and senescence, biomass growth, and grain number and size using detailed field experimental data from the USDA Hot Serial Cereal experiment (calibration data set). Simulation results from before and after model improvement were then evaluated with independent field experiments from a CIMMYT worldwide field trial network (evaluation data set). Model improvements decreased the variation (10th to 90th model ensemble percentile range) of grain yields simulated by the MME on average by 39% in the calibration data set and by 26% in the independent evaluation data set for crops grown in mean seasonal temperatures greater than 24 C. MME mean squared error in simulating grain yield decreased by 37%. A reduction in MME uncertainty range by 27% increased MME prediction skills by 47%. Results suggest that the mean level of variation observed in field experiments and used as a benchmark can be reached with half the number of models in the MME. Improving crop models is therefore important to increase the certainty of model-based impact assessments and allow more practical, i.e. smaller MMEs to be used effectively.

  10. Multi-Model Grand Ensemble Hydrologic Forecasting in the Fu River Basin Using Bayesian Model Averaging

    Directory of Open Access Journals (Sweden)

    Bo Qu

    2017-01-01

    Full Text Available Statistical post-processing for multi-model grand ensemble (GE hydrologic predictions is necessary, in order to achieve more accurate and reliable probabilistic forecasts. This paper presents a case study which applies Bayesian model averaging (BMA to statistically post-process raw GE runoff forecasts in the Fu River basin in China, at lead times ranging from 6 to 120 h. The raw forecasts were generated by running the Xinanjiang hydrologic model with ensemble forecasts (164 forecast members, using seven different “THORPEX Interactive Grand Global Ensemble” (TIGGE weather centres as forcing inputs. Some measures, such as data transformation and high-dimensional optimization, were included in the experiment after considering the practical water regime and data conditions. The results indicate that the BMA post-processing method is capable of improving the performance of raw GE runoff forecasts, yielding more calibrated and sharp predictive probability density functions (PDFs, over a range of lead times from 24 to 120 h. The analysis of percentile forecasts in two different flood events illustrates the great potential and prospects of BMA GE probabilistic river discharge forecasts, for taking precautions against severe flooding events.

  11. Application of an Ensemble Kalman filter to a 1-D coupled hydrodynamic-ecosystem model of the Ligurian Sea

    NARCIS (Netherlands)

    Lenartz, F.; Raick, C.; Soetaert, K.E.R.; Grégoire, M.

    2007-01-01

    The Ensemble Kalman filter (EnKF) has been applied to a 1-D complex ecosystem model coupled with a hydrodynamic model of the Ligurian Sea. In order to improve the performance of the EnKF, an ensemble subsampling strategy has been used to better represent the covariance matrices and a pre-analysis st

  12. Comparison of mean properties of simulated convection in a cloud-resolving model with those produced by cumulus parameterization

    Energy Technology Data Exchange (ETDEWEB)

    Dudhia, J.; Parsons, D.B. [National Center for Atmospheric Research, Boulder, CO (United States)

    1996-04-01

    An Intensive Observation Period (IOP) of the Atmospheric Radiation Measurement (ARM) Program took place at the Southern Great Plains (SGP) Cloud and Radiation Testbed (CART) site from June 16-26, 1993. The National Center for Atmospheric Research (NCAR)/Penn State Mesoscale Model (MM5) has been used to simulate this period on a 60-km domain with 20- and 6.67-km nests centered on Lamont, Oklahoma. Simulations are being run with data assimilation by the nudging technique to incorporate upper-air and surface data from a variety of platforms. The model maintains dynamical consistency between the fields, while the data correct for model biases that may occur during long-term simulations and provide boundary conditions. For the work reported here the Mesoscale Atmospheric Prediction System (MAPS) of the National Ocean and Atmospheric Administration (NOAA) 3-hourly analyses were used to drive the 60-km domain while the inner domains were unforced. A continuous 10-day period was simulated.

  13. Broad range of 2050 warming from an observationally constrained large climate model ensemble

    Science.gov (United States)

    Rowlands, Daniel J.; Frame, David J.; Ackerley, Duncan; Aina, Tolu; Booth, Ben B. B.; Christensen, Carl; Collins, Matthew; Faull, Nicholas; Forest, Chris E.; Grandey, Benjamin S.; Gryspeerdt, Edward; Highwood, Eleanor J.; Ingram, William J.; Knight, Sylvia; Lopez, Ana; Massey, Neil; McNamara, Frances; Meinshausen, Nicolai; Piani, Claudio; Rosier, Suzanne M.; Sanderson, Benjamin M.; Smith, Leonard A.; Stone, Dáithí A.; Thurston, Milo; Yamazaki, Kuniko; Hiro Yamazaki, Y.; Allen, Myles R.

    2012-04-01

    Incomplete understanding of three aspects of the climate system--equilibrium climate sensitivity, rate of ocean heat uptake and historical aerosol forcing--and the physical processes underlying them lead to uncertainties in our assessment of the global-mean temperature evolution in the twenty-first century. Explorations of these uncertainties have so far relied on scaling approaches, large ensembles of simplified climate models, or small ensembles of complex coupled atmosphere-ocean general circulation models which under-represent uncertainties in key climate system properties derived from independent sources. Here we present results from a multi-thousand-member perturbed-physics ensemble of transient coupled atmosphere-ocean general circulation model simulations. We find that model versions that reproduce observed surface temperature changes over the past 50 years show global-mean temperature increases of 1.4-3K by 2050, relative to 1961-1990, under a mid-range forcing scenario. This range of warming is broadly consistent with the expert assessment provided by the Intergovernmental Panel on Climate Change Fourth Assessment Report, but extends towards larger warming than observed in ensembles-of-opportunity typically used for climate impact assessments. From our simulations, we conclude that warming by the middle of the twenty-first century that is stronger than earlier estimates is consistent with recent observed temperature changes and a mid-range `no mitigation' scenario for greenhouse-gas emissions.

  14. Compressing an Ensemble with Statistical Models: An Algorithm for Global 3D Spatio-Temporal Temperature

    KAUST Repository

    Castruccio, Stefano

    2015-04-02

    One of the main challenges when working with modern climate model ensembles is the increasingly larger size of the data produced, and the consequent difficulty in storing large amounts of spatio-temporally resolved information. Many compression algorithms can be used to mitigate this problem, but since they are designed to compress generic scientific data sets, they do not account for the nature of climate model output and they compress only individual simulations. In this work, we propose a different, statistics-based approach that explicitly accounts for the space-time dependence of the data for annual global three-dimensional temperature fields in an initial condition ensemble. The set of estimated parameters is small (compared to the data size) and can be regarded as a summary of the essential structure of the ensemble output; therefore, it can be used to instantaneously reproduce the temperature fields in an ensemble with a substantial saving in storage and time. The statistical model exploits the gridded geometry of the data and parallelization across processors. It is therefore computationally convenient and allows to fit a non-trivial model to a data set of one billion data points with a covariance matrix comprising of 10^18 entries.

  15. Crop model data assimilation with the Ensemble Kalman filter for improving regional crop yield forecasts

    NARCIS (Netherlands)

    Wit, de A.J.W.; Diepen, van C.A.

    2007-01-01

    Uncertainty in spatial and temporal distribution of rainfall in regional crop yield simulations comprises a major fraction of the error on crop model simulation results. In this paper we used an Ensemble Kalman filter (EnKF) to assimilate coarse resolution satellite microwave sensor derived soil

  16. Ensemble modelling and structured decision-making to support Emergency Disease Management.

    Science.gov (United States)

    Webb, Colleen T; Ferrari, Matthew; Lindström, Tom; Carpenter, Tim; Dürr, Salome; Garner, Graeme; Jewell, Chris; Stevenson, Mark; Ward, Michael P; Werkman, Marleen; Backer, Jantien; Tildesley, Michael

    2017-03-01

    Epidemiological models in animal health are commonly used as decision-support tools to understand the impact of various control actions on infection spread in susceptible populations. Different models contain different assumptions and parameterizations, and policy decisions might be improved by considering outputs from multiple models. However, a transparent decision-support framework to integrate outputs from multiple models is nascent in epidemiology. Ensemble modelling and structured decision-making integrate the outputs of multiple models, compare policy actions and support policy decision-making. We briefly review the epidemiological application of ensemble modelling and structured decision-making and illustrate the potential of these methods using foot and mouth disease (FMD) models. In case study one, we apply structured decision-making to compare five possible control actions across three FMD models and show which control actions and outbreak costs are robustly supported and which are impacted by model uncertainty. In case study two, we develop a methodology for weighting the outputs of different models and show how different weighting schemes may impact the choice of control action. Using these case studies, we broadly illustrate the potential of ensemble modelling and structured decision-making in epidemiology to provide better information for decision-making and outline necessary development of these methods for their further application. Crown Copyright © 2017. Published by Elsevier B.V. All rights reserved.

  17. Combining multi-objective optimization and bayesian model averaging to calibrate forecast ensembles of soil hydraulic models

    Energy Technology Data Exchange (ETDEWEB)

    Vrugt, Jasper A [Los Alamos National Laboratory; Wohling, Thomas [NON LANL

    2008-01-01

    Most studies in vadose zone hydrology use a single conceptual model for predictive inference and analysis. Focusing on the outcome of a single model is prone to statistical bias and underestimation of uncertainty. In this study, we combine multi-objective optimization and Bayesian Model Averaging (BMA) to generate forecast ensembles of soil hydraulic models. To illustrate our method, we use observed tensiometric pressure head data at three different depths in a layered vadose zone of volcanic origin in New Zealand. A set of seven different soil hydraulic models is calibrated using a multi-objective formulation with three different objective functions that each measure the mismatch between observed and predicted soil water pressure head at one specific depth. The Pareto solution space corresponding to these three objectives is estimated with AMALGAM, and used to generate four different model ensembles. These ensembles are post-processed with BMA and used for predictive analysis and uncertainty estimation. Our most important conclusions for the vadose zone under consideration are: (1) the mean BMA forecast exhibits similar predictive capabilities as the best individual performing soil hydraulic model, (2) the size of the BMA uncertainty ranges increase with increasing depth and dryness in the soil profile, (3) the best performing ensemble corresponds to the compromise (or balanced) solution of the three-objective Pareto surface, and (4) the combined multi-objective optimization and BMA framework proposed in this paper is very useful to generate forecast ensembles of soil hydraulic models.

  18. Visualizing projected Climate Changes - the CMIP5 Multi-Model Ensemble

    Science.gov (United States)

    Böttinger, Michael; Eyring, Veronika; Lauer, Axel; Meier-Fleischer, Karin

    2017-04-01

    Large ensembles add an additional dimension to climate model simulations. Internal variability of the climate system can be assessed for example by multiple climate model simulations with small variations in the initial conditions or by analyzing the spread in large ensembles made by multiple climate models under common protocols. This spread is often used as a measure of uncertainty in climate projections. In the context of the fifth phase of the WCRP's Coupled Model Intercomparison Project (CMIP5), more than 40 different coupled climate models were employed to carry out a coordinated set of experiments. Time series of the development of integral quantities such as the global mean temperature change for all models visualize the spread in the multi-model ensemble. A similar approach can be applied to 2D-visualizations of projected climate changes such as latitude-longitude maps showing the multi-model mean of the ensemble by adding a graphical representation of the uncertainty information. This has been demonstrated for example with static figures in chapter 12 of the last IPCC report (AR5) using different so-called stippling and hatching techniques. In this work, we focus on animated visualizations of multi-model ensemble climate projections carried out within CMIP5 as a way of communicating climate change results to the scientific community as well as to the public. We take a closer look at measures of robustness or uncertainty used in recent publications suitable for animated visualizations. Specifically, we use the ESMValTool [1] to process and prepare the CMIP5 multi-model data in combination with standard visualization tools such as NCL and the commercial 3D visualization software Avizo to create the animations. We compare different visualization techniques such as height fields or shading with transparency for creating animated visualization of ensemble mean changes in temperature and precipitation including corresponding robustness measures. [1] Eyring, V

  19. One-Step Dynamic Classifier Ensemble Model for Customer Value Segmentation with Missing Values

    OpenAIRE

    Jin Xiao; Bing Zhu; Geer Teng; Changzheng He; Dunhu Liu

    2014-01-01

    Scientific customer value segmentation (CVS) is the base of efficient customer relationship management, and customer credit scoring, fraud detection, and churn prediction all belong to CVS. In real CVS, the customer data usually include lots of missing values, which may affect the performance of CVS model greatly. This study proposes a one-step dynamic classifier ensemble model for missing values (ODCEM) model. On the one hand, ODCEM integrates the preprocess of missing values and the classif...

  20. Sand-Dust Storm Ensemble Forecast Model Based on Rough Set

    Institute of Scientific and Technical Information of China (English)

    LU Zhiying; YANG Le; LI Yanying; ZHAO Zhichao

    2007-01-01

    To improve the accuracy of sand-dust storm forecast system, a sand-dust storm ensemble forecast model based on rough set (RS) is proposed. The feature data are extracted from the historical data sets using the self-organization map (SOM) clustering network and single fields forecast to form the feature values with low dimensions. Then, the unwanted attributes are reduced according to RS to discretize the continuous feature values. Lastly, the minimum decision rules are constructed according to the remainder attributes, namely sand-dust storm ensemble forecast model based on RS is constructed. Results comparison between the proposed model and the back propagation neural network model show that the sand-storm forecast model based on RS has better stability, faster running speed, and its forecasting accuracy ratio is increased from 17.1% to 86.21%.

  1. An iterative stochastic ensemble method for parameter estimation of subsurface flow models

    KAUST Repository

    Elsheikh, Ahmed H.

    2013-06-01

    Parameter estimation for subsurface flow models is an essential step for maximizing the value of numerical simulations for future prediction and the development of effective control strategies. We propose the iterative stochastic ensemble method (ISEM) as a general method for parameter estimation based on stochastic estimation of gradients using an ensemble of directional derivatives. ISEM eliminates the need for adjoint coding and deals with the numerical simulator as a blackbox. The proposed method employs directional derivatives within a Gauss-Newton iteration. The update equation in ISEM resembles the update step in ensemble Kalman filter, however the inverse of the output covariance matrix in ISEM is regularized using standard truncated singular value decomposition or Tikhonov regularization. We also investigate the performance of a set of shrinkage based covariance estimators within ISEM. The proposed method is successfully applied on several nonlinear parameter estimation problems for subsurface flow models. The efficiency of the proposed algorithm is demonstrated by the small size of utilized ensembles and in terms of error convergence rates. © 2013 Elsevier Inc.

  2. Multi-wheat-model ensemble responses to interannual climate variability

    NARCIS (Netherlands)

    Ruane, Alex C.; Hudson, Nicholas I.; Asseng, Senthold; Camarrano, Davide; Ewert, Frank; Martre, Pierre; Boote, Kenneth J.; Thorburn, Peter J.; Aggarwal, Pramod K.; Angulo, Carlos; Basso, Bruno; Bertuzzi, Patrick; Biernath, Christian; Brisson, Nadine; Challinor, Andrew J.; Doltra, Jordi; Gayler, Sebastian; Goldberg, Richard; Grant, Robert F.; Heng, Lee; Hooker, Josh; Hunt, Leslie A.; Ingwersen, Joachim; Izaurralde, Roberto C.; Kersebaum, Kurt Christian; Kumar, Soora Naresh; Müller, Christoph; Nendel, Claas; O'Leary, Garry; Olesen, Jørgen E.; Osborne, Tom M.; Palosuo, Taru; Priesack, Eckart; Ripoche, Dominique; Rötter, Reimund P.; Semenov, Mikhail A.; Shcherbak, Iurii; Steduto, Pasquale; Stöckle, Claudio O.; Stratonovitch, Pierre; Streck, Thilo; Supit, Iwan; Tao, Fulu; Travasso, Maria; Waha, Katharina; Wallach, Daniel; White, Jeffrey W.; Wolf, Joost

    2016-01-01

    We compare 27 wheat models' yield responses to interannual climate variability, analyzed at locations in Argentina, Australia, India, and The Netherlands as part of the Agricultural Model Intercomparison and Improvement Project (AgMIP) Wheat Pilot. Each model simulated 1981-2010 grain yield, and

  3. The spread amongst ENSEMBLES regional scenarios: regional climate models, driving general circulation models and interannual variability

    Energy Technology Data Exchange (ETDEWEB)

    Deque, M.; Somot, S. [Meteo-France, Centre National de Recherches Meteorologiques, CNRS/GAME, Toulouse Cedex 01 (France); Sanchez-Gomez, E. [Cerfacs/CNRS, SUC URA1875, Toulouse Cedex 01 (France); Goodess, C.M. [University of East Anglia, Climatic Research Unit, Norwich (United Kingdom); Jacob, D. [Max Planck Institute for Meteorology, Hamburg (Germany); Lenderink, G. [KNMI, Postbus 201, De Bilt (Netherlands); Christensen, O.B. [Danish Meteorological Institute, Copenhagen Oe (Denmark)

    2012-03-15

    Various combinations of thirteen regional climate models (RCM) and six general circulation models (GCM) were used in FP6-ENSEMBLES. The response to the SRES-A1B greenhouse gas concentration scenario over Europe, calculated as the difference between the 2021-2050 and the 1961-1990 means can be viewed as an expected value about which various uncertainties exist. Uncertainties are measured here by variance explained for temperature and precipitation changes over eight European sub-areas. Three sources of uncertainty can be evaluated from the ENSEMBLES database. Sampling uncertainty is due to the fact that the model climate is estimated as an average over a finite number of years (30) despite a non-negligible interannual variability. Regional model uncertainty is due to the fact that the RCMs use different techniques to discretize the equations and to represent sub-grid effects. Global model uncertainty is due to the fact that the RCMs have been driven by different GCMs. Two methods are presented to fill the many empty cells of the ENSEMBLES RCM x GCM matrix. The first one is based on the same approach as in FP5-PRUDENCE. The second one uses the concept of weather regimes to attempt to separate the contribution of the GCM and the RCM. The variance of the climate response is analyzed with respect to the contribution of the GCM and the RCM. The two filling methods agree that the main contributor to the spread is the choice of the GCM, except for summer precipitation where the choice of the RCM dominates the uncertainty. Of course the implication of the GCM to the spread varies with the region, being maximum in the South-western part of Europe, whereas the continental parts are more sensitive to the choice of the RCM. The third cause of spread is systematically the interannual variability. The total uncertainty about temperature is not large enough to mask the 2021-2050 response which shows a similar pattern to the one obtained for 2071-2100 in PRUDENCE. The uncertainty

  4. Ensemble superparameterization versus stochastic parameterization: A comparison of model uncertainty representation in tropical weather prediction

    Science.gov (United States)

    Subramanian, Aneesh C.; Palmer, Tim N.

    2017-06-01

    Stochastic schemes to represent model uncertainty in the European Centre for Medium-Range Weather Forecasts (ECMWF) ensemble prediction system has helped improve its probabilistic forecast skill over the past decade by both improving its reliability and reducing the ensemble mean error. The largest uncertainties in the model arise from the model physics parameterizations. In the tropics, the parameterization of moist convection presents a major challenge for the accurate prediction of weather and climate. Superparameterization is a promising alternative strategy for including the effects of moist convection through explicit turbulent fluxes calculated from a cloud-resolving model (CRM) embedded within a global climate model (GCM). In this paper, we compare the impact of initial random perturbations in embedded CRMs, within the ECMWF ensemble prediction system, with stochastically perturbed physical tendency (SPPT) scheme as a way to represent model uncertainty in medium-range tropical weather forecasts. We especially focus on forecasts of tropical convection and dynamics during MJO events in October-November 2011. These are well-studied events for MJO dynamics as they were also heavily observed during the DYNAMO field campaign. We show that a multiscale ensemble modeling approach helps improve forecasts of certain aspects of tropical convection during the MJO events, while it also tends to deteriorate certain large-scale dynamic fields with respect to stochastically perturbed physical tendencies approach that is used operationally at ECMWF.type="synopsis">type="main">Plain Language SummaryProbabilistic weather forecasts, especially for tropical weather, is still a significant challenge for global weather forecasting systems. Expressing uncertainty along with weather forecasts is important for informed decision making. Hence, we explore the use of a relatively new approach in using super-parameterization, where a cloud resolving model is embedded within a global

  5. Ensemble prediction model of solar proton events associated with solar flares and coronal mass ejections

    Institute of Scientific and Technical Information of China (English)

    Xin Huang; Hua-Ning Wang; Le-Ping Li

    2012-01-01

    An ensemble prediction model of solar proton events (SPEs),combining the information of solar flares and coronal mass ejections (CMEs),is built.In this model,solar flares are parameterized by the peak flux,the duration and the longitude.In addition,CMEs are parameterized by the width,the speed and the measurement position angle.The importance of each parameter for the occurrence of SPEs is estimated by the information gain ratio.We find that the CME width and speed are more informative than the flare's peak flux and duration.As the physical mechanism of SPEs is not very clear,a hidden naive Bayes approach,which is a probability-based calculation method from the field of machine learning,is used to build the prediction model from the observational data.As is known,SPEs originate from solar flares and/or shock waves associated with CMEs.Hence,we first build two base prediction models using the properties of solar flares and CMEs,respectively.Then the outputs of these models are combined to generate the ensemble prediction model of SPEs.The ensemble prediction model incorporating the complementary information of solar flares and CMEs achieves better performance than each base prediction model taken separately.

  6. Temperature and precipitation effects on wheat yield across a European transect: a crop model ensemble analysis using impact response surfaces

    NARCIS (Netherlands)

    Pirttioja, N.; Carter, T.R.; Fronzek, S.; Bindi, M.; Hoffmann, H.; Palosuo, T.; Ruiz-Ramos, M.; Tao, F.; Trnka, M.; Acutis, M.; Supit, I.

    2015-01-01

    This study explored the utility of the impact response surface (IRS) approach for investigating model ensemble crop yield responses under a large range of changes in climate. IRSs of spring and winter wheat Triticum aestivum yields were constructed from a 26-member ensemble of process-based crop sim

  7. Ensemble statistical and subspace clustering model for analysis of autism spectrum disorder phenotypes.

    Science.gov (United States)

    Al-Jabery, Khalid; Obafemi-Ajayi, Tayo; Olbricht, Gayla R; Takahashi, T Nicole; Kanne, Stephen; Wunsch, Donald

    2016-08-01

    Heterogeneity in Autism Spectrum Disorder (ASD) is complex including variability in behavioral phenotype as well as clinical, physiologic, and pathologic parameters. The fifth edition of the Diagnostic and Statistical Manual of Mental Disorders (DSM-5) now diagnoses ASD using a 2-dimensional model based social communication deficits and fixated interests and repetitive behaviors. Sorting out heterogeneity is crucial for study of etiology, diagnosis, treatment and prognosis. In this paper, we present an ensemble model for analyzing ASD phenotypes using several machine learning techniques and a k-dimensional subspace clustering algorithm. Our ensemble also incorporates statistical methods at several stages of analysis. We apply this model to a sample of 208 probands drawn from the Simon Simplex Collection Missouri Site patients. The results provide useful evidence that is helpful in elucidating the phenotype complexity within ASD. Our model can be extended to other disorders that exhibit a diverse range of heterogeneity.

  8. Biological ensemble modeling to evaluate potential futures of living marine resources

    DEFF Research Database (Denmark)

    Gårdmark, Anna; Lindegren, Martin; Neuenfeldt, Stefan

    2013-01-01

    in all models, intense fishing prevented recovery, and climate change further decreased the cod population. Our study demonstrates how the biological ensemble modeling approach makes it possible to evaluate the relative importance of different sources of uncertainty in future species responses, as well......) as an example. The core of the approach is to expose an ensemble of models with different ecological assumptions to climate forcing, using multiple realizations of each climate scenario. We simulated the long-term response of cod to future fishing and climate change in seven ecological models ranging from...... as to seek scientific conclusions and sustainable management solutions robust to uncertainty of food web processes in the face of climate change...

  9. Visualizing Cumulus Clouds in Virtual Reality

    NARCIS (Netherlands)

    Griffith, E.J.

    2010-01-01

    This thesis focuses on interactively visualizing, and ultimately simulating, cumulus clouds both in virtual reality (VR) and with a standard desktop computer. The cumulus clouds in question are found in data sets generated by Large-Eddy Simulations (LES), which are used to simulate a small section

  10. Visualizing Cumulus Clouds in Virtual Reality

    NARCIS (Netherlands)

    Griffith, E.J.

    2010-01-01

    This thesis focuses on interactively visualizing, and ultimately simulating, cumulus clouds both in virtual reality (VR) and with a standard desktop computer. The cumulus clouds in question are found in data sets generated by Large-Eddy Simulations (LES), which are used to simulate a small section o

  11. Bovine cumulus-oocyte disconnection in vitro

    DEFF Research Database (Denmark)

    Maddox-Hyttel, Poul

    1987-01-01

    Cumulus-oocyte complexes were obtained from cows by aspiration of small (1-6 mm in diameter) antral follicles after slaughter. Complexes with a compact multilayered cumulus investment were cultured and processed for transmission electron microscopy after different periods of culture including a 0...

  12. From deep TLS validation to ensembles of atomic models built from elemental motions. Addenda and corrigendum.

    Science.gov (United States)

    Urzhumtsev, Alexandre; Afonine, Pavel V; Van Benschoten, Andrew H; Fraser, James S; Adams, Paul D

    2016-09-01

    Researcher feedback has indicated that in Urzhumtsev et al. [(2015) Acta Cryst. D71, 1668-1683] clarification of key parts of the algorithm for interpretation of TLS matrices in terms of elemental atomic motions and corresponding ensembles of atomic models is required. Also, it has been brought to the attention of the authors that the incorrect PDB code was reported for one of test models. These issues are addressed in this article.

  13. Emulation of an ensemble Kalman filter algorithm on a flood wave propagation model

    OpenAIRE

    Barthélémy, S.; Ricci, S.; Pannekoucke, O.; Thual, O.; Malaterre, P.O.

    2013-01-01

    This study describes the emulation of an Ensemble Kalman Filter (EnKF) algorithm on a 1-D flood wave propagation model. This model is forced at the upstream boundary with a random variable with gaussian statistics and a correlation function in time with gaussian shape. This allows for, in the case without assimilation, the analytical study of the covariance functions of the propagated signal anomaly. This study is validated numerically wit...

  14. Ensemble modelling of nitrogen fluxes: data fusion for a Swedish meso-scale catchment

    Science.gov (United States)

    Exbrayat, J.-F.; Viney, N. R.; Seibert, J.; Wrede, S.; Frede, H.-G.; Breuer, L.

    2010-12-01

    Model predictions of biogeochemical fluxes at the landscape scale are highly uncertain, both with respect to stochastic (parameter) and structural uncertainty. In this study 5 different models (LASCAM, LASCAM-S, a self-developed tool, SWAT and HBV-N-D) designed to simulate hydrological fluxes as well as mobilisation and transport of one or several nitrogen species were applied to the mesoscale River Fyris catchment in mid-eastern Sweden. Hydrological calibration against 5 years of recorded daily discharge at two stations gave highly variable results with Nash-Sutcliffe Efficiency (NSE) ranging between 0.48 and 0.83. Using the calibrated hydrological parameter sets, the parameter uncertainty linked to the nitrogen parameters was explored in order to cover the range of possible predictions of exported loads for 3 nitrogen species: nitrate (NO3), ammonium (NH4) and total nitrogen (Tot-N). For each model and each nitrogen species, predictions were ranked in two different ways according to the performance indicated by two different goodness-of-fit measures: the coefficient of determination R2 and the root mean square error RMSE. A total of 2160 deterministic Single Model Ensembles (SME) was generated using an increasing number of members (from the 2 best to the 10 best single predictions). Finally the best SME for each model, nitrogen species and discharge station were selected and merged into 330 different Multi-Model Ensembles (MME). The evolution of changes in R2 and RMSE was used as a performance descriptor of the ensemble procedure. In each studied case, numerous ensemble merging schemes were identified which outperformed any of their members. Improvement rates were generally higher when worse members were introduced. The highest improvements were achieved for the nitrogen SMEs compiled with multiple linear regression models with R2 selected members, which resulted in the RMSE decreasing by up to 90%.

  15. Coastal aquifer management under parameter uncertainty: Ensemble surrogate modeling based simulation-optimization

    Science.gov (United States)

    Janardhanan, S.; Datta, B.

    2011-12-01

    Surrogate models are widely used to develop computationally efficient simulation-optimization models to solve complex groundwater management problems. Artificial intelligence based models are most often used for this purpose where they are trained using predictor-predictand data obtained from a numerical simulation model. Most often this is implemented with the assumption that the parameters and boundary conditions used in the numerical simulation model are perfectly known. However, in most practical situations these values are uncertain. Under these circumstances the application of such approximation surrogates becomes limited. In our study we develop a surrogate model based coupled simulation optimization methodology for determining optimal pumping strategies for coastal aquifers considering parameter uncertainty. An ensemble surrogate modeling approach is used along with multiple realization optimization. The methodology is used to solve a multi-objective coastal aquifer management problem considering two conflicting objectives. Hydraulic conductivity and the aquifer recharge are considered as uncertain values. Three dimensional coupled flow and transport simulation model FEMWATER is used to simulate the aquifer responses for a number of scenarios corresponding to Latin hypercube samples of pumping and uncertain parameters to generate input-output patterns for training the surrogate models. Non-parametric bootstrap sampling of this original data set is used to generate multiple data sets which belong to different regions in the multi-dimensional decision and parameter space. These data sets are used to train and test multiple surrogate models based on genetic programming. The ensemble of surrogate models is then linked to a multi-objective genetic algorithm to solve the pumping optimization problem. Two conflicting objectives, viz, maximizing total pumping from beneficial wells and minimizing the total pumping from barrier wells for hydraulic control of

  16. A Bayesian posterior predictive framework for weighting ensemble regional climate models

    Science.gov (United States)

    Fan, Yanan; Olson, Roman; Evans, Jason P.

    2017-06-01

    We present a novel Bayesian statistical approach to computing model weights in climate change projection ensembles in order to create probabilistic projections. The weight of each climate model is obtained by weighting the current day observed data under the posterior distribution admitted under competing climate models. We use a linear model to describe the model output and observations. The approach accounts for uncertainty in model bias, trend and internal variability, including error in the observations used. Our framework is general, requires very little problem-specific input, and works well with default priors. We carry out cross-validation checks that confirm that the method produces the correct coverage.

  17. Ensemble renormalization group for the random-field hierarchical model.

    Science.gov (United States)

    Decelle, Aurélien; Parisi, Giorgio; Rocchi, Jacopo

    2014-03-01

    The renormalization group (RG) methods are still far from being completely understood in quenched disordered systems. In order to gain insight into the nature of the phase transition of these systems, it is common to investigate simple models. In this work we study a real-space RG transformation on the Dyson hierarchical lattice with a random field, which leads to a reconstruction of the RG flow and to an evaluation of the critical exponents of the model at T=0. We show that this method gives very accurate estimations of the critical exponents by comparing our results with those obtained by some of us using an independent method.

  18. Quantifying Uncertainty in Flood Inundation Mapping Using Streamflow Ensembles and Multiple Hydraulic Modeling Techniques

    Science.gov (United States)

    Hosseiny, S. M. H.; Zarzar, C.; Gomez, M.; Siddique, R.; Smith, V.; Mejia, A.; Demir, I.

    2016-12-01

    The National Water Model (NWM) provides a platform for operationalize nationwide flood inundation forecasting and mapping. The ability to model flood inundation on a national scale will provide invaluable information to decision makers and local emergency officials. Often, forecast products use deterministic model output to provide a visual representation of a single inundation scenario, which is subject to uncertainty from various sources. While this provides a straightforward representation of the potential inundation, the inherent uncertainty associated with the model output should be considered to optimize this tool for decision making support. The goal of this study is to produce ensembles of future flood inundation conditions (i.e. extent, depth, and velocity) to spatially quantify and visually assess uncertainties associated with the predicted flood inundation maps. The setting for this study is located in a highly urbanized watershed along the Darby Creek in Pennsylvania. A forecasting framework coupling the NWM with multiple hydraulic models was developed to produce a suite ensembles of future flood inundation predictions. Time lagged ensembles from the NWM short range forecasts were used to account for uncertainty associated with the hydrologic forecasts. The forecasts from the NWM were input to iRIC and HEC-RAS two-dimensional software packages, from which water extent, depth, and flow velocity were output. Quantifying the agreement between output ensembles for each forecast grid provided the uncertainty metrics for predicted flood water inundation extent, depth, and flow velocity. For visualization, a series of flood maps that display flood extent, water depth, and flow velocity along with the underlying uncertainty associated with each of the forecasted variables were produced. The results from this study demonstrate the potential to incorporate and visualize model uncertainties in flood inundation maps in order to identify the high flood risk zones.

  19. Ensemble-based conditioning of reservoir models to seismic data

    NARCIS (Netherlands)

    Leeuwenburgh, O.; Brouwer, J.; Trani, M.

    2011-01-01

    While 3D seismic has been the basis for geological model building for a long time, time-lapse seismic has primarily been used in a qualitative manner to assist in monitoring reservoir behavior. With the growing acceptance of assisted history matching methods has come an equally rising interest in in

  20. Ensemble-based conditioning of reservoir models to seismic data

    NARCIS (Netherlands)

    Leeuwenburgh, O.; Brouwer, J.; Trani, M.

    2010-01-01

    While 3D seismic has been the basis for geological model building for a long time, time-lapse seismic has primarily been used in a qualitative manner to assist in monitoring reservoir behavior. With the growing acceptance of assisted history matching methods has come an equally rising interest in in

  1. Short ensembles: an efficient method for discerning climate-relevant sensitivities in atmospheric general circulation models

    Directory of Open Access Journals (Sweden)

    H. Wan

    2014-09-01

    Full Text Available This paper explores the feasibility of an experimentation strategy for investigating sensitivities in fast components of atmospheric general circulation models. The basic idea is to replace the traditional serial-in-time long-term climate integrations by representative ensembles of shorter simulations. The key advantage of the proposed method lies in its efficiency: since fewer days of simulation are needed, the computational cost is less, and because individual realizations are independent and can be integrated simultaneously, the new dimension of parallelism can dramatically reduce the turnaround time in benchmark tests, sensitivities studies, and model tuning exercises. The strategy is not appropriate for exploring sensitivity of all model features, but it is very effective in many situations. Two examples are presented using the Community Atmosphere Model, version 5. In the first example, the method is used to characterize sensitivities of the simulated clouds to time-step length. Results show that 3-day ensembles of 20 to 50 members are sufficient to reproduce the main signals revealed by traditional 5-year simulations. A nudging technique is applied to an additional set of simulations to help understand the contribution of physics–dynamics interaction to the detected time-step sensitivity. In the second example, multiple empirical parameters related to cloud microphysics and aerosol life cycle are perturbed simultaneously in order to find out which parameters have the largest impact on the simulated global mean top-of-atmosphere radiation balance. It turns out that 12-member ensembles of 10-day simulations are able to reveal the same sensitivities as seen in 4-year simulations performed in a previous study. In both cases, the ensemble method reduces the total computational time by a factor of about 15, and the turnaround time by a factor of several hundred. The efficiency of the method makes it particularly useful for the development of

  2. Regional climate models downscaling in the Alpine area with Multimodel SuperEnsemble

    Directory of Open Access Journals (Sweden)

    D. Cane

    2012-08-01

    Full Text Available The climatic scenarios show a strong signal of warming in the Alpine area already for the mid XXI century. The climate simulations, however, even when obtained with Regional Climate Models (RCMs, are affected by strong errors where compared with observations, due to their difficulties in representing the complex orography of the Alps and limitations in their physical parametrization.

    Therefore the aim of this work is reducing these model biases using a specific post processing statistic technique to obtain a more suitable projection of climate change scenarios in the Alpine area.

    For our purposes we use a selection of RCMs runs from the ENSEMBLES project, carefully chosen in order to maximise the variety of leading Global Climate Models and of the RCMs themselves, calculated on the SRES scenario A1B. The reference observation for the Greater Alpine Area are extracted from the European dataset E-OBS produced by the project ENSEMBLES with an available resolution of 25 km. For the study area of Piedmont daily temperature and precipitation observations (1957–present were carefully gridded on a 14-km grid over Piedmont Region with an Optimal Interpolation technique.

    Hence, we applied the Multimodel SuperEnsemble technique to temperature fields, reducing the high biases of RCMs temperature field compared to observations in the control period.

    We propose also the first application to RCMS of a brand new probabilistic Multimodel SuperEnsemble Dressing technique to estimate precipitation fields, already applied successfully to weather forecast models, with careful description of precipitation Probability Density Functions conditioned to the model outputs. This technique reduces the strong precipitation overestimation by RCMs over the alpine chain and reproduces well the monthly behaviour of precipitation in the control period.

  3. Short ensembles: an efficient method for discerning climate-relevant sensitivities in atmospheric general circulation models

    Science.gov (United States)

    Wan, H.; Rasch, P. J.; Zhang, K.; Qian, Y.; Yan, H.; Zhao, C.

    2014-09-01

    This paper explores the feasibility of an experimentation strategy for investigating sensitivities in fast components of atmospheric general circulation models. The basic idea is to replace the traditional serial-in-time long-term climate integrations by representative ensembles of shorter simulations. The key advantage of the proposed method lies in its efficiency: since fewer days of simulation are needed, the computational cost is less, and because individual realizations are independent and can be integrated simultaneously, the new dimension of parallelism can dramatically reduce the turnaround time in benchmark tests, sensitivities studies, and model tuning exercises. The strategy is not appropriate for exploring sensitivity of all model features, but it is very effective in many situations. Two examples are presented using the Community Atmosphere Model, version 5. In the first example, the method is used to characterize sensitivities of the simulated clouds to time-step length. Results show that 3-day ensembles of 20 to 50 members are sufficient to reproduce the main signals revealed by traditional 5-year simulations. A nudging technique is applied to an additional set of simulations to help understand the contribution of physics-dynamics interaction to the detected time-step sensitivity. In the second example, multiple empirical parameters related to cloud microphysics and aerosol life cycle are perturbed simultaneously in order to find out which parameters have the largest impact on the simulated global mean top-of-atmosphere radiation balance. It turns out that 12-member ensembles of 10-day simulations are able to reveal the same sensitivities as seen in 4-year simulations performed in a previous study. In both cases, the ensemble method reduces the total computational time by a factor of about 15, and the turnaround time by a factor of several hundred. The efficiency of the method makes it particularly useful for the development of high

  4. From deep TLS validation to ensembles of atomic models built from elemental motions

    Energy Technology Data Exchange (ETDEWEB)

    Urzhumtsev, Alexandre, E-mail: sacha@igbmc.fr [Centre for Integrative Biology, Institut de Génétique et de Biologie Moléculaire et Cellulaire, CNRS–INSERM–UdS, 1 Rue Laurent Fries, BP 10142, 67404 Illkirch (France); Université de Lorraine, BP 239, 54506 Vandoeuvre-les-Nancy (France); Afonine, Pavel V. [Lawrence Berkeley National Laboratory, Berkeley, California (United States); Van Benschoten, Andrew H.; Fraser, James S. [University of California, San Francisco, San Francisco, CA 94158 (United States); Adams, Paul D. [Lawrence Berkeley National Laboratory, Berkeley, California (United States); University of California Berkeley, Berkeley, CA 94720 (United States); Centre for Integrative Biology, Institut de Génétique et de Biologie Moléculaire et Cellulaire, CNRS–INSERM–UdS, 1 Rue Laurent Fries, BP 10142, 67404 Illkirch (France)

    2015-07-28

    Procedures are described for extracting the vibration and libration parameters corresponding to a given set of TLS matrices and their simultaneous validation. Knowledge of these parameters allows the generation of structural ensembles corresponding to these matrices. The translation–libration–screw model first introduced by Cruickshank, Schomaker and Trueblood describes the concerted motions of atomic groups. Using TLS models can improve the agreement between calculated and experimental diffraction data. Because the T, L and S matrices describe a combination of atomic vibrations and librations, TLS models can also potentially shed light on molecular mechanisms involving correlated motions. However, this use of TLS models in mechanistic studies is hampered by the difficulties in translating the results of refinement into molecular movement or a structural ensemble. To convert the matrices into a constituent molecular movement, the matrix elements must satisfy several conditions. Refining the T, L and S matrix elements as independent parameters without taking these conditions into account may result in matrices that do not represent concerted molecular movements. Here, a mathematical framework and the computational tools to analyze TLS matrices, resulting in either explicit decomposition into descriptions of the underlying motions or a report of broken conditions, are described. The description of valid underlying motions can then be output as a structural ensemble. All methods are implemented as part of the PHENIX project.

  5. Generalized network structures: The configuration model and the canonical ensemble of simplicial complexes

    Science.gov (United States)

    Courtney, Owen T.; Bianconi, Ginestra

    2016-06-01

    Simplicial complexes are generalized network structures able to encode interactions occurring between more than two nodes. Simplicial complexes describe a large variety of complex interacting systems ranging from brain networks to social and collaboration networks. Here we characterize the structure of simplicial complexes using their generalized degrees that capture fundamental properties of one, two, three, or more linked nodes. Moreover, we introduce the configuration model and the canonical ensemble of simplicial complexes, enforcing, respectively, the sequence of generalized degrees of the nodes and the sequence of the expected generalized degrees of the nodes. We evaluate the entropy of these ensembles, finding the asymptotic expression for the number of simplicial complexes in the configuration model. We provide the algorithms for the construction of simplicial complexes belonging to the configuration model and the canonical ensemble of simplicial complexes. We give an expression for the structural cutoff of simplicial complexes that for simplicial complexes of dimension d =1 reduces to the structural cutoff of simple networks. Finally, we provide a numerical analysis of the natural correlations emerging in the configuration model of simplicial complexes without structural cutoff.

  6. Determining Key Model Parameters of Rapidly Intensifying Hurricane Guillermo(1997) using the Ensemble Kalman Filter

    CERN Document Server

    Godinez, Humberto C; Fierro, Alexandre O; Guimond, Stephen R; Kao, Jim

    2011-01-01

    In this work we present the assimilation of dual-Doppler radar observations for rapidly intensifying hurricane Guillermo (1997) using the Ensemble Kalman Filter (EnKF) to determine key model parameters. A unique aspect of Guillermo was that during the period of radar observations strong convective bursts, attributable to wind shear, formed primarily within the eastern semicircle of the eyewall. To reproduce this observed structure within a hurricane model, background wind shear of some magnitude must be specified; as well as turbulence and surface parameters appropriately specified so that the impact of the shear on the simulated hurricane vortex can be realized. To first illustrate the complex nonlinear interactions induced by changes in these parameters, an ensemble of 120 simulations have been conducted in which individual members were formulated by sampling the parameters within a certain range via a Latin hypercube approach. Next, data from the 120 simulations and two distinct derived fields of observati...

  7. Ensemble flood simulation for a small dam catchment in Japan using 10 and 2 km resolution nonhydrostatic model rainfalls

    Science.gov (United States)

    Kobayashi, Kenichiro; Otsuka, Shigenori; Apip; Saito, Kazuo

    2016-08-01

    This paper presents a study on short-term ensemble flood forecasting specifically for small dam catchments in Japan. Numerical ensemble simulations of rainfall from the Japan Meteorological Agency nonhydrostatic model (JMA-NHM) are used as the input data to a rainfall-runoff model for predicting river discharge into a dam. The ensemble weather simulations use a conventional 10 km and a high-resolution 2 km spatial resolutions. A distributed rainfall-runoff model is constructed for the Kasahori dam catchment (approx. 70 km2) and applied with the ensemble rainfalls. The results show that the hourly maximum and cumulative catchment-average rainfalls of the 2 km resolution JMA-NHM ensemble simulation are more appropriate than the 10 km resolution rainfalls. All the simulated inflows based on the 2 and 10 km rainfalls become larger than the flood discharge of 140 m3 s-1, a threshold value for flood control. The inflows with the 10 km resolution ensemble rainfall are all considerably smaller than the observations, while at least one simulated discharge out of 11 ensemble members with the 2 km resolution rainfalls reproduces the first peak of the inflow at the Kasahori dam with similar amplitude to observations, although there are spatiotemporal lags between simulation and observation. To take positional lags into account of the ensemble discharge simulation, the rainfall distribution in each ensemble member is shifted so that the catchment-averaged cumulative rainfall of the Kasahori dam maximizes. The runoff simulation with the position-shifted rainfalls shows much better results than the original ensemble discharge simulations.

  8. Climate change hotspots in the CMIP5 global climate model ensemble

    OpenAIRE

    Diffenbaugh, Noah S; Giorgi, Filippo

    2012-01-01

    We use a statistical metric of multi-dimensional climate change to quantify the emergence of global climate change hotspots in the CMIP5 climate model ensemble. Our hotspot metric extends previous work through the inclusion of extreme seasonal temperature and precipitation, which exert critical influence on climate change impacts. The results identify areas of the Amazon, the Sahel and tropical West Africa, Indonesia, and the Tibetan Plateau as persistent regional climate change hotspots thro...

  9. Stochastic dynamics of small ensembles of non-processive molecular motors: the parallel cluster model

    CERN Document Server

    Erdmann, Thorsten; Schwarz, Ulrich S

    2013-01-01

    Non-processive molecular motors have to work together in ensembles in order to generate appreciable levels of force or movement. In skeletal muscle, for example, hundreds of myosin II molecules cooperate in thick filaments. In non-muscle cells, by contrast, small groups with few tens of non-muscle myosin II motors contribute to essential cellular processes such as transport, shape changes or mechanosensing. Here we introduce a detailed and analytically tractable model for this important situation. Using a three-state crossbridge model for the myosin II motor cycle and exploiting the assumptions of fast power stroke kinetics and equal load sharing between motors in equivalent states, we reduce the stochastic reaction network to a one-step master equation for the binding and unbinding dynamics (parallel cluster model) and derive the rules for ensemble movement. We find that for constant external load, ensemble dynamics is strongly shaped by the catch bond character of myosin II, which leads to an increase of th...

  10. Prediction of Coal Face Gas Concentration by Multi-Scale Selective Ensemble Hybrid Modeling

    Directory of Open Access Journals (Sweden)

    WU Xiang

    2014-06-01

    Full Text Available A selective ensemble hybrid modeling prediction method based on wavelet transformation is proposed to improve the fitting and generalization capability of the existing prediction models of the coal face gas concentration, which has a strong stochastic volatility. Mallat algorithm was employed for the multi-scale decomposition and single-scale reconstruction of the gas concentration time series. Then, it predicted every subsequence by sparsely weighted multi unstable ELM(extreme learning machine predictor within method SERELM(sparse ensemble regressors of ELM. At last, it superimposed the predicted values of these models to obtain the predicted values of the original sequence. The proposed method takes advantage of characteristics of multi scale analysis of wavelet transformation, accuracy and fast characteristics of ELM prediction and the generalization ability of L1 regularized selective ensemble learning method. The results show that the forecast accuracy has large increase by using the proposed method. The average relative error is 0.65%, the maximum relative error is 4.16% and the probability of relative error less than 1% reaches 0.785.

  11. Ensemble predictive model for more accurate soil organic carbon spectroscopic estimation

    Science.gov (United States)

    Vašát, Radim; Kodešová, Radka; Borůvka, Luboš

    2017-07-01

    A myriad of signal pre-processing strategies and multivariate calibration techniques has been explored in attempt to improve the spectroscopic prediction of soil organic carbon (SOC) over the last few decades. Therefore, to come up with a novel, more powerful, and accurate predictive approach to beat the rank becomes a challenging task. However, there may be a way, so that combine several individual predictions into a single final one (according to ensemble learning theory). As this approach performs best when combining in nature different predictive algorithms that are calibrated with structurally different predictor variables, we tested predictors of two different kinds: 1) reflectance values (or transforms) at each wavelength and 2) absorption feature parameters. Consequently we applied four different calibration techniques, two per each type of predictors: a) partial least squares regression and support vector machines for type 1, and b) multiple linear regression and random forest for type 2. The weights to be assigned to individual predictions within the ensemble model (constructed as a weighted average) were determined by an automated procedure that ensured the best solution among all possible was selected. The approach was tested at soil samples taken from surface horizon of four sites differing in the prevailing soil units. By employing the ensemble predictive model the prediction accuracy of SOC improved at all four sites. The coefficient of determination in cross-validation (R2cv) increased from 0.849, 0.611, 0.811 and 0.644 (the best individual predictions) to 0.864, 0.650, 0.824 and 0.698 for Site 1, 2, 3 and 4, respectively. Generally, the ensemble model affected the final prediction so that the maximal deviations of predicted vs. observed values of the individual predictions were reduced, and thus the correlation cloud became thinner as desired.

  12. Cloud-Aerosol-Radiation (CAR) Ensemble Modeling System:Overall Accuracy and Efficiency

    Institute of Scientific and Technical Information of China (English)

    Feng ZHANG; Xin-Zhong LIANG; ZENG Qingcun; Yu GU; Shenjian SU

    2013-01-01

    The Cloud-Aerosol-Radiation (CAR) ensemble modeling system has recently been built to better understand cloud/aerosol/radiation processes and determine the uncertainties caused by different treatments of cloud/aerosol/radiation in climate models.The CAR system comprises a large scheme collection of cloud,aerosol,and radiation processes available in the literature,including those commonly used by the world's leading GCMs.In this study,detailed analyses of the overall accuracy and efficiency of the CAR system were performed.Despite the different observations used,the overall accuracies of the CAR ensemble means were found to be very good for both shortwave (SW) and longwave (LW) radiation calculations.Taking the percentage errors for July 2004 compared to ISCCP (International Satellite Cloud Climatology Project)data over (60°N,60°S) as an example,even among the 448 CAR members selected here,those errors of the CAR ensemble means were only about-0.67% (-0.6 W m-2) and-0.82% (-2.0 W m-2) for SW and LW upward fluxes at the top of atmosphere,and 0.06% (0.1 W m-2) and-2.12% (-7.8 W m-2) for SW and LW downward fluxes at the surface,respectively.Furthermore,model SW frequency distributions in July 2004 covered the observational ranges entirely,with ensemble means located in the middle of the ranges.Moreover,it was found that the accuracy of radiative transfer calculations can be significantly enhanced by using certain combinations of cloud schemes for the cloud cover fraction,particle effective size,water path,and optical properties,along with better explicit treatments for unresolved cloud structures.

  13. Making Tree Ensembles Interpretable

    OpenAIRE

    Hara, Satoshi; Hayashi, Kohei

    2016-01-01

    Tree ensembles, such as random forest and boosted trees, are renowned for their high prediction performance, whereas their interpretability is critically limited. In this paper, we propose a post processing method that improves the model interpretability of tree ensembles. After learning a complex tree ensembles in a standard way, we approximate it by a simpler model that is interpretable for human. To obtain the simpler model, we derive the EM algorithm minimizing the KL divergence from the ...

  14. An assessment of a multi-model ensemble of decadal climate predictions

    Science.gov (United States)

    Bellucci, A.; Haarsma, R.; Gualdi, S.; Athanasiadis, P. J.; Caian, M.; Cassou, C.; Fernandez, E.; Germe, A.; Jungclaus, J.; Kröger, J.; Matei, D.; Müller, W.; Pohlmann, H.; Salas y Melia, D.; Sanchez, E.; Smith, D.; Terray, L.; Wyser, K.; Yang, S.

    2015-05-01

    A multi-model ensemble of decadal prediction experiments, performed in the framework of the EU-funded COMBINE (Comprehensive Modelling of the Earth System for Better Climate Prediction and Projection) Project following the 5th Coupled Model Intercomparison Project protocol is examined. The ensemble combines a variety of dynamical models, initialization and perturbation strategies, as well as data assimilation products employed to constrain the initial state of the system. Taking advantage of the multi-model approach, several aspects of decadal climate predictions are assessed, including predictive skill, impact of the initialization strategy and the level of uncertainty characterizing the predicted fluctuations of key climate variables. The present analysis adds to the growing evidence that the current generation of climate models adequately initialized have significant skill in predicting years ahead not only the anthropogenic warming but also part of the internal variability of the climate system. An important finding is that the multi-model ensemble mean does generally outperform the individual forecasts, a well-documented result for seasonal forecasting, supporting the need to extend the multi-model framework to real-time decadal predictions in order to maximize the predictive capabilities of currently available decadal forecast systems. The multi-model perspective did also allow a more robust assessment of the impact of the initialization strategy on the quality of decadal predictions, providing hints of an improved forecast skill under full-value (with respect to anomaly) initialization in the near-term range, over the Indo-Pacific equatorial region. Finally, the consistency across the different model predictions was assessed. Specifically, different systems reveal a general agreement in predicting the near-term evolution of surface temperatures, displaying positive correlations between different decadal hindcasts over most of the global domain.

  15. Improved sub-seasonal meteorological forecast skill using weighted multi-model ensemble simulations

    Science.gov (United States)

    Wanders, Niko; Wood, Eric F.

    2016-09-01

    Sub-seasonal to seasonal weather and hydrological forecasts have the potential to provide vital information for a variety of water-related decision makers. Here, we investigate the skill of four sub-seasonal forecast models from phase-2 of the North American Multi-Model Ensemble using reforecasts for the period 1982-2012. Two weighted multi-model ensemble means from the models have been developed for predictions of both sub-seasonal precipitation and temperature. By combining models through optimal weights, the multi-model forecast skill is significantly improved compared to a ‘standard’ equally weighted multi-model forecast mean. We show that optimal model weights are robust and the forecast skill is maintained for increased length of time and regions with a low initial forecast skill show significant skill after optimal weighting of the individual model forecast. The sub-seasonal model forecasts models show high skill over the tropics, approximating their skill at monthly resolution. Using the weighted approach, a significant increase is found in the forecast skill for dry, wet, cold and warm extreme events. The weighted mean approach brings significant advances to sub-seasonal forecasting due to its reduced uncertainty in the forecasts with a gain in forecast skill. This significantly improves their value for end-user applications and our ability to use them to prepare for upcoming extreme conditions, like floods and droughts.

  16. An ensemble based nonlinear orthogonal matching pursuit algorithm for sparse history matching of reservoir models

    KAUST Repository

    Fsheikh, Ahmed H.

    2013-01-01

    A nonlinear orthogonal matching pursuit (NOMP) for sparse calibration of reservoir models is presented. Sparse calibration is a challenging problem as the unknowns are both the non-zero components of the solution and their associated weights. NOMP is a greedy algorithm that discovers at each iteration the most correlated components of the basis functions with the residual. The discovered basis (aka support) is augmented across the nonlinear iterations. Once the basis functions are selected from the dictionary, the solution is obtained by applying Tikhonov regularization. The proposed algorithm relies on approximate gradient estimation using an iterative stochastic ensemble method (ISEM). ISEM utilizes an ensemble of directional derivatives to efficiently approximate gradients. In the current study, the search space is parameterized using an overcomplete dictionary of basis functions built using the K-SVD algorithm.

  17. Modelling Laser Milling of Microcavities for the Manufacturing of DES with Ensembles

    Directory of Open Access Journals (Sweden)

    Pedro Santos

    2014-01-01

    Full Text Available A set of designed experiments, involving the use of a pulsed Nd:YAG laser system milling 316L Stainless Steel, serve to study the laser-milling process of microcavities in the manufacture of drug-eluting stents (DES. Diameter, depth, and volume error are considered to be optimized as functions of the process parameters, which include laser intensity, pulse frequency, and scanning speed. Two different DES shapes are studied that combine semispheres and cylinders. Process inputs and outputs are defined by considering the process parameters that can be changed under industrial conditions and the industrial requirements of this manufacturing process. In total, 162 different conditions are tested in a process that is modeled with the following state-of-the-art data-mining regression techniques: Support Vector Regression, Ensembles, Artificial Neural Networks, Linear Regression, and Nearest Neighbor Regression. Ensemble regression emerged as the most suitable technique for studying this industrial problem. Specifically, Iterated Bagging ensembles with unpruned model trees outperformed the other methods in the tests. This method can predict the geometrical dimensions of the machined microcavities with relative errors related to the main average value in the range of 3 to 23%, which are considered very accurate predictions, in view of the characteristics of this innovative industrial task.

  18. Limit order book and its modeling in terms of Gibbs Grand-Canonical Ensemble

    Science.gov (United States)

    Bicci, Alberto

    2016-12-01

    In the domain of so called Econophysics some attempts have been already made for applying the theory of thermodynamics and statistical mechanics to economics and financial markets. In this paper a similar approach is made from a different perspective, trying to model the limit order book and price formation process of a given stock by the Grand-Canonical Gibbs Ensemble for the bid and ask orders. The application of the Bose-Einstein statistics to this ensemble allows then to derive the distribution of the sell and buy orders as a function of price. As a consequence we can define in a meaningful way expressions for the temperatures of the ensembles of bid orders and of ask orders, which are a function of minimum bid, maximum ask and closure prices of the stock as well as of the exchanged volume of shares. It is demonstrated that the difference between the ask and bid orders temperatures can be related to the VAO (Volume Accumulation Oscillator), an indicator empirically defined in Technical Analysis of stock markets. Furthermore the derived distributions for aggregate bid and ask orders can be subject to well defined validations against real data, giving a falsifiable character to the model.

  19. On the (in)consistency of a multi-model ensemble of the past 30 years land surface state.

    Science.gov (United States)

    Dutra, Emanuel; Schellekens, Jaap; Beck, Hylke; Balsamo, Gianpaolo

    2016-04-01

    Global land-surface and hydrological models are a fundamental tool in understanding the land-surface state and evolution either coupled to atmospheric models for climate and weather predictions or in stand-alone mode. In this study we take a recently developed dataset consisting in stand-alone simulations by 10 global hydrological and land surface models sharing the same atmospheric forcing for the period 1979-2012 (the eart2Observe dataset). This multi-model ensemble provides the first freely available dataset with such a spatial/temporal scale that allows for a characterization of the multi-model characteristics such as inter-model consistency and error-spread relationship. We will present a metric for the ensemble consistency using the concept of potential predictability, that can be interpreted as a proxy for the multi-model agreement. Initial results point to regions of low inter-model agreement in the polar and tropical regions, the latter also present when comparing globally available precipitation datasets. In addition to this, the discharge ensemble spread around the ensemble mean was compared to the error of the ensemble mean for several large-scale and small scale basins. This showed a general under-estimation of the ensemble spread, particularly in tropical basins, suggesting that the current dataset lacks the representation of the precipitation uncertainty in the input meteorological data.

  20. Uncertainties in Ensemble Predictions of Future Antarctic Mass Loss with the fETISh Model

    Science.gov (United States)

    Pattyn, F.

    2015-12-01

    Marine ice sheet models should be capable of handling complex feedbacks between ice and ocean, such as marine ice sheet instability, and the atmosphere, such as the elevation-mass balance feedback, operating at different time scales. Recent model intercomparisons (e.g., SeaRISE, MISMIP) have shown that the complexity of many ice sheet models is focused on processes that are either not well captured numerically (spatial resolution issue) or are of secondary importance compared to the essential features of marine ice sheet dynamics. Here, we propose a new and fast computing ice sheet model, devoid of most complexity, but capturing the essential feedbacks when coupled to ocean or atmospheric models. Its computational efficiency guarantees to easily tests its advantages as well as limits through ensemble modelling. The fETISh (fast Elementary Thermomechanical (marine) Ice Sheet) model is a vertically integrated hybrid (SSA/SIA) ice sheet model. Although vertically integrated, thermomechanical coupling is ensured through a simplified representation of ice sheet thermodynamics based on an analytical solution of the vertical temperature profile, including strain heating and horizontal advection. The marine boundary is represented by a flux condition similar to Pollard & Deconto (2012), based on Schoof (2007). Buttressing of ice shelves is taken into account via the Shallow-Shelf Approximation (SSA). The ice sheet model is solved on four staggered finite difference grids for numerical efficiency/stability. Numerical tests following EISMINT, ISMIP and MISMIP are performed as a prerequisite. The fETISh model is forced with different ice-shelf melt rates and basal sliding perturbations to allow comparison with recent model intercomparisons of the Antarctic ice sheet (e.g., SeaRISE, Favier et al. (2013)). These forcings are further completed with a set of scenarios involving ice-shelf buttressing and unbuttressing. All experiments are carried out on different spatial

  1. Biological ensemble modeling to evaluate potential futures of living marine resources

    DEFF Research Database (Denmark)

    Gårdmark, Anna; Lindegren, Martin; Neuenfeldt, Stefan

    2013-01-01

    trajectories carried through to uncertainty of cod responses. Models ignoring the feedback from prey on cod showed large interannual fluctuations in cod dynamics and were more sensitive to the underlying uncertainty of climate forcing than models accounting for such stabilizing predator–prey feedbacks. Yet......Natural resource management requires approaches to understand and handle sources of uncertainty in future responses of complex systems to human activities. Here we present one such approach, the “biological ensemble modeling approach,” using the Eastern Baltic cod (Gadus morhua callarias...

  2. Improved confidence in regional climate model simulations of precipitation evaluated using drought statistics from the ENSEMBLES models

    Science.gov (United States)

    Maule, Cathrine Fox; Thejll, Peter; Christensen, Jens H.; Svendsen, Synne H.; Hannaford, Jamie

    2013-01-01

    An ensemble of regional climate model simulations from the European framework project ENSEMBLES is compared with observations of low precipitation events across a number of European regions. We characterize precipitation deficits in terms of two drought indices, the Standardized Precipitation Index and the self-calibrated Palmer Drought Severity Index. Models that robustly describe the observations for the period 1961-2000 in given regions are identified and an assessment of the overall performance of the ensemble is provided. The results show that in general, models capture the most severe drought events and that the ensemble mean model also performs well. Some regions that appear to be more problematic to simulate well are also identified. These are relatively small regions and have rather complex topographical features. The analysis suggests that assessment of future drought occurrence based on climate change experiments in general would appear to be robust. But due to the heterogeneous and often fine-scaled structure of drought occurrence, quantitative results should be used with great care, particularly in regions with complex terrain and limited information about past drought occurrence.

  3. Heat and salt redistribution within the Mediterranean Sea in the Med-CORDEX model ensemble

    Science.gov (United States)

    Llasses, J.; Jordà, G.; Gomis, D.; Adloff, F.; Macías, D.; Harzallah, A.; Arsouze, T.; Akthar, N.; Li, L.; Elizalde, A.; Sannino, G.

    2016-06-01

    Characterizing and understanding the basic functioning of the Mediterranean Sea in terms of heat and salt redistribution within the basin is a crucial issue to predict its evolution. Here we quantify and analyze the heat and salt transfers using a simple box model consisting of four layers in the vertical for each of the two (western and eastern) basins. Namely, we box-average 14 regional simulations of the Med-CORDEX ensemble plus a regional and a global reanalysis, computing for each of them the heat and salt exchanges between layers. First, we analyze in detail the mechanisms behind heat and salt redistribution at different time scales from the outputs of a single simulation (NEMOMED8). We show that in the western basin the transfer between layer 1 (0-150 m) and layer 2 (150-600 m) is upwards for most models both for heat and salt, while in the eastern basin both transfers are downwards. A feature common to both basins is that the transports are smaller in summer than in winter due to the enhanced stratification, which dampen the mixing between layers. From the comparison of the 16 simulations we observe that the spread between models is much larger than the ensemble average for the salt transfer and for the heat transfer between layer 1 and layer 2. At lower layers (below 600 m) there is a set of models showing a good agreement between them, while others are not correlated with any other. The mechanisms behind the ensemble spread are not straightforward. First, to have a coarse resolution prevents the model to correctly represent the heat and salt redistribution in the basin. Second, those models with a very different initial stratification also show a very different redistribution, especially at intermediate and deep layers. Finally, the assimilation of data seems to perturb the heat and salt redistribution. Besides this, the differences among regional models that share similar spatial resolution and initial conditions are induced by more subtle mechanisms

  4. Multi-model ensemble-based probabilistic prediction of tropical cyclogenesis using TIGGE model forecasts

    Science.gov (United States)

    Jaiswal, Neeru; Kishtawal, C. M.; Bhomia, Swati; Pal, P. K.

    2016-10-01

    An extended range tropical cyclogenesis forecast model has been developed using the forecasts of global models available from TIGGE portal. A scheme has been developed to detect the signatures of cyclogenesis in the global model forecast fields [i.e., the mean sea level pressure and surface winds (10 m horizontal winds)]. For this, a wind matching index was determined between the synthetic cyclonic wind fields and the forecast wind fields. The thresholds of 0.4 for wind matching index and 1005 hpa for pressure were determined to detect the cyclonic systems. These detected cyclonic systems in the study region are classified into different cyclone categories based on their intensity (maximum wind speed). The forecasts of up to 15 days from three global models viz., ECMWF, NCEP and UKMO have been used to predict cyclogenesis based on multi-model ensemble approach. The occurrence of cyclonic events of different categories in all the forecast steps in the grided region (10 × 10 km2) was used to estimate the probability of the formation of cyclogenesis. The probability of cyclogenesis was estimated by computing the grid score using the wind matching index by each model and at each forecast step and convolving it with Gaussian filter. The proposed method is used to predict the cyclogenesis of five named tropical cyclones formed during the year 2013 in the north Indian Ocean. The 6-8 days advance cyclogenesis of theses systems were predicted using the above approach. The mean lead prediction time for the cyclogenesis event of the proposed model has been found as 7 days.

  5. Data Assimilation for Vadose Zone Flow Modeling Using the Ensemble Kalman Filter

    Science.gov (United States)

    Zhang, Y.; Schaap, M. G.; Zha, Y.; Xue, L.

    2015-12-01

    The natural system is open and complex and the hydraulic parameters needed for describing flow and transport in the vadose zone are often poorly known, making it prone to multiple interpretations, mathematical descriptions and uncertainty. Quite often a reasonable "handle" on a sites flow characteristics can be gained only through direct observation of the flow processes itself, determination of the spatial- and probability distributions of material properties combined with computationally expensive inversions of the Richards equation. In groundwater systems, the ensemble Kalman filter (EnKF) has proven to be an effective alternative to model inversions by assimilating observations directly into an ensemble of groundwater models from which time and/or space-variable variable probabilistic quantities of the flow process can be derived. Application of EnKF to Richards equation-type unsaturated flow problems, however, is more challenging than in groundwater systems because the relation of state and model parameters is strongly nonlinear. In addition, the type of functional dependence of moisture content and hydraulic conductivity on matric potential leads to high-dimensional (in the parameter space) problems even under conditions where closed-form expressions of these models such as van Genuchten-Mualem formulations are used. In this study, we updated soil water retention parameters and hydraulic conductivity together and used Restart EnKF, which rerun the nonlinear model from the initial time to obtain the updated state variables, in synthetic cases to explore the factors that may influence estimation results, including the initial estimate, the ensemble size, the observation error, and the assimilation interval. We embedded the EnKF into the Bayesian model averaging framework to enhance the model reliability and reduce predictive uncertainties. This approach is evaluated from a 15 m deep semi-arid highly heterogeneous and anisotropic vadose zone site at the

  6. Heat and salt redistribution within the Mediterranean basin in the Med-CORDEX model ensemble

    Science.gov (United States)

    Llasses, Josep; Jordà, Gabriel; Gomis, Damià; Adloff, Fanny; Macías, Diego; Harzallah, Ali; Arsouze, Thomas; Akthar, Naveed; Li, Laurent; Elizalde, Alberto; Sannino, Gianmaria

    2016-04-01

    Characterizing and understanding the basic functioning of the Mediterranean Sea in terms of heat and salt redistribution within the basin is a crucial issue to predict its evolution. Here we quantify and analyze the heat and salt transfers using a simple box model consisting of 4 layers in the vertical for each of the two (western and eastern) sub-basins. Namely, we box-average 14 regional simulations of the MedCORDEX ensemble plus a regional and a global reanalysis, computing for each of them the heat and salt exchanges between layers. First, we analyze in detail the heat and salt redistribution at different time scales from the outputs of a single simulation (NEMOMED8). We show that in the western basin the transfer between the surface (0-150m) and intermediate (150-600 m) layers is upwards for both heat and salt, while in the eastern basin both transfers are downwards. A feature common to both sub-basins is that the transports are smaller in summer than in winter due to the enhanced stratification, which dampen the mixing between layers. From the comparison of the 16 simulations we observe that the spread between models is much larger than the ensemble average for the salt transfer and for the heat transfer between the surface and intermediate layers. At lower layers there is a set of models showing a good agreement between them, while others are not correlated with any other. The mechanisms behind the ensemble spread are not straightforward. First, to have a coarse resolution prevents the model to correctly represent the heat and salt redistribution in the basin. Second, those models with a very different initial stratification also show a very different redistribution, especially at intermediate and deep layers. Finally, the assimilation of data seems to perturb the heat and salt redistribution. Besides this, the differences among regional models that share similar spatial resolution and initial conditions are induced by more subtle mechanisms which depend on

  7. A glacial systems model configured for large ensemble analysis of Antarctic deglaciation

    Directory of Open Access Journals (Sweden)

    R. Briggs

    2013-04-01

    Full Text Available This article describes the Memorial University of Newfoundland/Penn State University (MUN/PSU glacial systems model (GSM that has been developed specifically for large-ensemble data-constrained analysis of past Antarctic Ice Sheet evolution. Our approach emphasizes the introduction of a large set of model parameters to explicitly account for the uncertainties inherent in the modelling of such a complex system. At the core of the GSM is a 3-D thermo-mechanically coupled ice sheet model that solves both the shallow ice and shallow shelf approximations. This enables the different stress regimes of ice sheet, ice shelves, and ice streams to be represented. The grounding line is modelled through an analytical sub-grid flux parametrization. To this dynamical core the following have been added: a heavily parametrized basal drag component; a visco-elastic isostatic adjustment solver; a diverse set of climate forcings (to remove any reliance on any single method; tidewater and ice shelf calving functionality; and a new physically-motivated empirically-derived sub-shelf melt (SSM component. To assess the accuracy of the latter, we compare predicted SSM values against a compilation of published observations. Within parametric and observational uncertainties, computed SSM for the present day ice sheet is in accord with observations for all but the Filchner ice shelf. The GSM has 31 ensemble parameters that are varied to account (in part for the uncertainty in the ice-physics, the climate forcing, and the ice-ocean interaction. We document the parameters and parametric sensitivity of the model to motivate the choice of ensemble parameters in a quest to approximately bound reality (within the limits of 31 parameters.

  8. Ensemble Forecasting of Coronal Mass Ejections Using the WSA-ENLIL with CONED Model

    Science.gov (United States)

    Emmons, D.; Acebal, A.; Pulkkinen, A.; Taktakishvili, A.; MacNeice, P.; Odstricil, D.

    2013-01-01

    The combination of the Wang-Sheeley-Arge (WSA) coronal model, ENLIL heliospherical model version 2.7, and CONED Model version 1.3 (WSA-ENLIL with CONED Model) was employed to form ensemble forecasts for 15 halo coronal mass ejections (halo CMEs). The input parameter distributions were formed from 100 sets of CME cone parameters derived from the CONED Model. The CONED Model used image processing along with the bootstrap approach to automatically calculate cone parameter distributions from SOHO/LASCO imagery based on techniques described by Pulkkinen et al. (2010). The input parameter distributions were used as input to WSA-ENLIL to calculate the temporal evolution of the CMEs, which were analyzed to determine the propagation times to the L1 Lagrangian point and the maximum Kp indices due to the impact of the CMEs on the Earth's magnetosphere. The Newell et al. (2007) Kp index formula was employed to calculate the maximum Kp indices based on the predicted solar wind parameters near Earth assuming two magnetic field orientations: a completely southward magnetic field and a uniformly distributed clock-angle in the Newell et al. (2007) Kp index formula. The forecasts for 5 of the 15 events had accuracy such that the actual propagation time was within the ensemble average plus or minus one standard deviation. Using the completely southward magnetic field assumption, 10 of the 15 events contained the actual maximum Kp index within the range of the ensemble forecast, compared to 9 of the 15 events when using a uniformly distributed clock angle.

  9. Evaporation-condensation transition of the two-dimensional Potts model in the microcanonical ensemble

    KAUST Repository

    Nogawa, Tomoaki

    2011-12-05

    The evaporation-condensation transition of the Potts model on a square lattice is numerically investigated by the Wang-Landau sampling method. An intrinsically system-size-dependent discrete transition between supersaturation state and phase-separation state is observed in the microcanonical ensemble by changing constrained internal energy. We calculate the microcanonical temperature, as a derivative of microcanonical entropy, and condensation ratio, and perform a finite-size scaling of them to indicate the clear tendency of numerical data to converge to the infinite-size limit predicted by phenomenological theory for the isotherm lattice gas model. © 2011 American Physical Society.

  10. Evaporation-condensation transition of the two-dimensional Potts model in the microcanonical ensemble.

    Science.gov (United States)

    Nogawa, Tomoaki; Ito, Nobuyasu; Watanabe, Hiroshi

    2011-12-01

    The evaporation-condensation transition of the Potts model on a square lattice is numerically investigated by the Wang-Landau sampling method. An intrinsically system-size-dependent discrete transition between supersaturation state and phase-separation state is observed in the microcanonical ensemble by changing constrained internal energy. We calculate the microcanonical temperature, as a derivative of microcanonical entropy, and condensation ratio, and perform a finite-size scaling of them to indicate the clear tendency of numerical data to converge to the infinite-size limit predicted by phenomenological theory for the isotherm lattice gas model.

  11. Accounting for model error due to unresolved scales within ensemble Kalman filtering

    CERN Document Server

    Mitchell, Lewis

    2014-01-01

    We propose a method to account for model error due to unresolved scales in the context of the ensemble transform Kalman filter (ETKF). The approach extends to this class of algorithms the deterministic model error formulation recently explored for variational schemes and extended Kalman filter. The model error statistic required in the analysis update is estimated using historical reanalysis increments and a suitable model error evolution law. Two different versions of the method are described; a time-constant model error treatment where the same model error statistical description is time-invariant, and a time-varying treatment where the assumed model error statistics is randomly sampled at each analysis step. We compare both methods with the standard method of dealing with model error through inflation and localization, and illustrate our results with numerical simulations on a low order nonlinear system exhibiting chaotic dynamics. The results show that the filter skill is significantly improved through th...

  12. Bovine cumulus-oocyte disconnection in vitro

    DEFF Research Database (Denmark)

    Maddox-Hyttel, Poul

    1987-01-01

    Cumulus-oocyte complexes were obtained from cows by aspiration of small (1-6 mm in diameter) antral follicles after slaughter. Complexes with a compact multilayered cumulus investment were cultured and processed for transmission electron microscopy after different periods of culture including a 0...... frequency of gap junctions was maintained until 12-18 h of culture where the junctional contact was completely disrupted. This decrease in intercellular communication was parallelled by resumption of oocyte meiosis....

  13. 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

  14. Ensemble modelling of nitrogen fluxes: data fusion for a Swedish meso-scale catchment

    Directory of Open Access Journals (Sweden)

    J.-F. Exbrayat

    2010-08-01

    Full Text Available Model predictions of biogeochemical fluxes at the landscape scale are highly uncertain, both with respect to stochastic (parameter and structural uncertainty. In this study 5 different models (LASCAM, LASCAM-S, a self-developed tool, SWAT and HBV-N-D designed to simulate hydrological fluxes as well as mobilisation and transport of one or several nitrogen species were applied to the mesoscale River Fyris catchment in mid-eastern Sweden.

    Hydrological calibration against 5 years of recorded daily discharge at two stations gave highly variable results with Nash-Sutcliffe Efficiency (NSE ranging between 0.48 and 0.83. Using the calibrated hydrological parameter sets, the parameter uncertainty linked to the nitrogen parameters was explored in order to cover the range of possible predictions of exported loads for 3 nitrogen species: nitrate (NO3, ammonium (NH4 and total nitrogen (Tot-N. For each model and each nitrogen species, predictions were ranked in two different ways according to the performance indicated by two different goodness-of-fit measures: the coefficient of determination R2 and the root mean square error RMSE. A total of 2160 deterministic Single Model Ensembles (SME was generated using an increasing number of members (from the 2 best to the 10 best single predictions. Finally, the best SME for each model, nitrogen species and discharge station were selected and merged into 330 different Multi-Model Ensembles (MME. The evolution of changes in R2 and RMSE was used as a performance descriptor of the ensemble procedure.

    In each studied case, numerous ensemble merging schemes were identified which outperformed any of their members. Improvement rates were generally higher when worse members were introduced. The highest improvements were achieved for the nitrogen SMEs compiled with multiple linear regression models with R2 selected members, which

  15. Non-intrusive Ensemble Kalman filtering for large scale geophysical models

    Science.gov (United States)

    Amour, Idrissa; Kauranne, Tuomo

    2016-04-01

    Advanced data assimilation techniques, such as variational assimilation methods, present often challenging implementation issues for large-scale models, both because of computational complexity and because of complexity of implementation. We present a non-intrusive wrapper library that addresses this problem by isolating the direct model and the linear algebra employed in data assimilation from each other completely. In this approach we have adopted a hybrid Variational Ensemble Kalman filter that combines Ensemble propagation with a 3DVAR analysis stage. The inverse problem of state and covariance propagation from prior to posterior estimates is thereby turned into a time-independent problem. This feature allows the linear algebra and minimization steps required in the variational step to be conducted outside the direct model and no tangent linear or adjoint codes are required. Communication between the model and the assimilation module is conducted exclusively via standard input and output files of the model. This non-intrusive approach is tested with the comprehensive 3D lake and shallow sea model COHERENS that is used to forecast and assimilate turbidity in lake Säkylän Pyhäjärvi in Finland, using both sparse satellite images and continuous real-time point measurements as observations.

  16. Improved fitting of solution X-ray scattering data to macromolecular structures and structural ensembles by explicit water modeling.

    Science.gov (United States)

    Grishaev, Alexander; Guo, Liang; Irving, Thomas; Bax, Ad

    2010-11-10

    A new procedure, AXES, is introduced for fitting small-angle X-ray scattering (SAXS) data to macromolecular structures and ensembles of structures. By using explicit water models to account for the effect of solvent, and by restricting the adjustable fitting parameters to those that dominate experimental uncertainties, including sample/buffer rescaling, detector dark current, and, within a narrow range, hydration layer density, superior fits between experimental high resolution structures and SAXS data are obtained. AXES results are found to be more discriminating than standard Crysol fitting of SAXS data when evaluating poorly or incorrectly modeled protein structures. AXES results for ensembles of structures previously generated for ubiquitin show improved fits over fitting of the individual members of these ensembles, indicating these ensembles capture the dynamic behavior of proteins in solution.

  17. Local Ensemble Kalman Particle Filters for efficient data assimilation

    CERN Document Server

    Robert, Sylvain

    2016-01-01

    Ensemble methods such as the Ensemble Kalman Filter (EnKF) are widely used for data assimilation in large-scale geophysical applications, as for example in numerical weather prediction (NWP). There is a growing interest for physical models with higher and higher resolution, which brings new challenges for data assimilation techniques because of the presence of non-linear and non-Gaussian features that are not adequately treated by the EnKF. We propose two new localized algorithms based on the Ensemble Kalman Particle Filter (EnKPF), a hybrid method combining the EnKF and the Particle Filter (PF) in a way that maintains scalability and sample diversity. Localization is a key element of the success of EnKFs in practice, but it is much more challenging to apply to PFs. The algorithms that we introduce in the present paper provide a compromise between the EnKF and the PF while avoiding some of the problems of localization for pure PFs. Numerical experiments with a simplified model of cumulus convection based on a...

  18. WE-E-BRE-05: Ensemble of Graphical Models for Predicting Radiation Pneumontis Risk

    Energy Technology Data Exchange (ETDEWEB)

    Lee, S; Ybarra, N; Jeyaseelan, K; El Naqa, I [McGill University, Montreal, Quebec (Canada); Faria, S; Kopek, N [Montreal General Hospital, Montreal, Quebec (Canada)

    2014-06-15

    Purpose: We propose a prior knowledge-based approach to construct an interaction graph of biological and dosimetric radiation pneumontis (RP) covariates for the purpose of developing a RP risk classifier. Methods: We recruited 59 NSCLC patients who received curative radiotherapy with minimum 6 month follow-up. 16 RP events was observed (CTCAE grade ≥2). Blood serum was collected from every patient before (pre-RT) and during RT (mid-RT). From each sample the concentration of the following five candidate biomarkers were taken as covariates: alpha-2-macroglobulin (α2M), angiotensin converting enzyme (ACE), transforming growth factor β (TGF-β), interleukin-6 (IL-6), and osteopontin (OPN). Dose-volumetric parameters were also included as covariates. The number of biological and dosimetric covariates was reduced by a variable selection scheme implemented by L1-regularized logistic regression (LASSO). Posterior probability distribution of interaction graphs between the selected variables was estimated from the data under the literature-based prior knowledge to weight more heavily the graphs that contain the expected associations. A graph ensemble was formed by averaging the most probable graphs weighted by their posterior, creating a Bayesian Network (BN)-based RP risk classifier. Results: The LASSO selected the following 7 RP covariates: (1) pre-RT concentration level of α2M, (2) α2M level mid- RT/pre-RT, (3) pre-RT IL6 level, (4) IL6 level mid-RT/pre-RT, (5) ACE mid-RT/pre-RT, (6) PTV volume, and (7) mean lung dose (MLD). The ensemble BN model achieved the maximum sensitivity/specificity of 81%/84% and outperformed univariate dosimetric predictors as shown by larger AUC values (0.78∼0.81) compared with MLD (0.61), V20 (0.65) and V30 (0.70). The ensembles obtained by incorporating the prior knowledge improved classification performance for the ensemble size 5∼50. Conclusion: We demonstrated a probabilistic ensemble method to detect robust associations between

  19. A top-down model to generate ensembles of runoff from a large number of hillslopes

    Directory of Open Access Journals (Sweden)

    P. R. Furey

    2013-09-01

    Full Text Available We hypothesize that total hillslope water loss for a rainfall–runoff event is inversely related to a function of a lognormal random variable, based on basin- and point-scale observations taken from the 21 km2 Goodwin Creek Experimental Watershed (GCEW in Mississippi, USA. A top-down approach is used to develop a new runoff generation model both to test our physical-statistical hypothesis and to provide a method of generating ensembles of runoff from a large number of hillslopes in a basin. The model is based on the assumption that the probability distributions of a runoff/loss ratio have a space–time rescaling property. We test this assumption using streamflow and rainfall data from GCEW. For over 100 rainfall–runoff events, we find that the spatial probability distributions of a runoff/loss ratio can be rescaled to a new distribution that is common to all events. We interpret random within-event differences in runoff/loss ratios in the model to arise from soil moisture spatial variability. Observations of water loss during events in GCEW support this interpretation. Our model preserves water balance in a mean statistical sense and supports our hypothesis. As an example, we use the model to generate ensembles of runoff at a large number of hillslopes for a rainfall–runoff event in GCEW.

  20. A top-down model to generate ensembles of runoff from a large number of hillslopes

    Science.gov (United States)

    Furey, P. R.; Gupta, V. K.; Troutman, B. M.

    2013-09-01

    We hypothesize that total hillslope water loss for a rainfall-runoff event is inversely related to a function of a lognormal random variable, based on basin- and point-scale observations taken from the 21 km2 Goodwin Creek Experimental Watershed (GCEW) in Mississippi, USA. A top-down approach is used to develop a new runoff generation model both to test our physical-statistical hypothesis and to provide a method of generating ensembles of runoff from a large number of hillslopes in a basin. The model is based on the assumption that the probability distributions of a runoff/loss ratio have a space-time rescaling property. We test this assumption using streamflow and rainfall data from GCEW. For over 100 rainfall-runoff events, we find that the spatial probability distributions of a runoff/loss ratio can be rescaled to a new distribution that is common to all events. We interpret random within-event differences in runoff/loss ratios in the model to arise from soil moisture spatial variability. Observations of water loss during events in GCEW support this interpretation. Our model preserves water balance in a mean statistical sense and supports our hypothesis. As an example, we use the model to generate ensembles of runoff at a large number of hillslopes for a rainfall-runoff event in GCEW.

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

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    J. Noilhan

    2008-12-01

    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 by Singular Vector (SV technic. We then used this ensemble of simulations as the initial and boundary conditions for the meso scale model simulations, here the atmospheric transport model Méso-NH, transporting 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. 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. Finally, we compute the diagonal and non-diagonal terms of the observation error covariance matrix and introduced it into our CO2 flux matrix inversion over 18 days of the 2005 intensive campaign CERES over the South West of France. On the horizontal plane, variance of the ensemble follows the discontinuities of the mesoscale structures during the day, but remain locally driven during the night. On the vertical, surface layer variance shows large correlations with the upper levels in the boundary layer (>0.6, down to 0.4 with the low free troposphere. Large temporal correlations were found during the afternoon (>0.5 for several hours, reduced during the night. Diffusion equation model extracted relevant error covariance signals on the horizontal space, and shows reduced correlations over mountain area and during the night over the continent. The posterior error reduction on the inverted CO2 fluxes accounting for the model error correlations illustrates finally the predominance of the temporal over the spatial correlations

  2. Arctic sea ice area in CMIP3 and CMIP5 climate model ensembles - variability and change

    Science.gov (United States)

    Semenov, V. A.; Martin, T.; Behrens, L. K.; Latif, M.

    2015-02-01

    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 model results exhibit considerable spread. The last generation of climate models from World Climate Research Programme Coupled Model Intercomparison Project Phase 5 (CMIP5), when compared to the previous CMIP3 model ensemble and considering the whole Arctic, were found to be more consistent with the observed changes in sea ice extent during the recent decades. Some CMIP5 models project strongly accelerated (non-linear) sea ice loss during the first half of the 21st century. Here, complementary to previous studies, we compare results from CMIP3 and CMIP5 with respect to regional Arctic sea ice change. We focus on September and March sea ice. Sea ice area (SIA) variability, sea ice concentration (SIC) variability, and characteristics of the SIA seasonal cycle and interannual variability have been analysed for the whole Arctic, termed Entire Arctic, Central Arctic and Barents Sea. Further, the sensitivity of SIA changes to changes in Northern Hemisphere (NH) averaged temperature is investigated and several important dynamical links between SIA and natural climate variability involving the Atlantic Meridional Overturning Circulation (AMOC), North Atlantic Oscillation (NAO) and sea level pressure gradient (SLPG) in the western Barents Sea opening serving as an index of oceanic inflow to the Barents Sea are studied. The CMIP3 and CMIP5 models not only simulate a coherent decline of the Arctic SIA but also depict consistent changes in the SIA seasonal cycle and in the aforementioned dynamical links. The spatial patterns of SIC variability improve in the CMIP5 ensemble, particularly in summer. Both

  3. Using qflux to constrain modeled Congo Basin rainfall in the CMIP5 ensemble

    Science.gov (United States)

    Creese, A.; Washington, R.

    2016-11-01

    Coupled models are the tools by which we diagnose and project future climate, yet in certain regions they are critically underevaluated. The Congo Basin is one such region which has received limited scientific attention, due to the severe scarcity of observational data. There is a large difference in the climatology of rainfall in global coupled climate models over the basin. This study attempts to address this research gap by evaluating modeled rainfall magnitude and distribution amongst global coupled models in the Coupled Model Intercomparison Project 5 (CMIP5) ensemble. Mean monthly rainfall between models varies by up to a factor of 5 in some months, and models disagree on the location of maximum rainfall. The ensemble mean, which is usually considered a "best estimate" of coupled model output, does not agree with any single model, and as such is unlikely to present a possible rainfall state. Moisture flux (qflux) convergence (which is assumed to be better constrained than parameterized rainfall) is found to have a strong relationship with rainfall; strongest correlations occur at 700 hPa in March-May (r = 0.70) and 850 hPa in June-August, September-November, and December-February (r = 0.66, r = 0.71, and r = 0.81). In the absence of observations, this relationship could be used to constrain the wide spectrum of modeled rainfall and give a better understanding of Congo rainfall climatology. Analysis of moisture transport pathways indicates that modeled rainfall is sensitive to the amount of moisture entering the basin. A targeted observation campaign at key Congo Basin boundaries could therefore help to constrain model rainfall.

  4. An ensemble Kalman filter for statistical estimation of physics constrained nonlinear regression models

    Energy Technology Data Exchange (ETDEWEB)

    Harlim, John, E-mail: jharlim@psu.edu [Department of Mathematics and Department of Meteorology, the Pennsylvania State University, University Park, PA 16802, Unites States (United States); Mahdi, Adam, E-mail: amahdi@ncsu.edu [Department of Mathematics, North Carolina State University, Raleigh, NC 27695 (United States); Majda, Andrew J., E-mail: jonjon@cims.nyu.edu [Department of Mathematics and Center for Atmosphere and Ocean Science, Courant Institute of Mathematical Sciences, New York University, New York, NY 10012 (United States)

    2014-01-15

    A central issue in contemporary science is the development of nonlinear data driven statistical–dynamical models for time series of noisy partial observations from nature or a complex model. It has been established recently that ad-hoc quadratic multi-level regression models can have finite-time blow-up of statistical solutions and/or pathological behavior of their invariant measure. Recently, a new class of physics constrained nonlinear regression models were developed to ameliorate this pathological behavior. Here a new finite ensemble Kalman filtering algorithm is developed for estimating the state, the linear and nonlinear model coefficients, the model and the observation noise covariances from available partial noisy observations of the state. Several stringent tests and applications of the method are developed here. In the most complex application, the perfect model has 57 degrees of freedom involving a zonal (east–west) jet, two topographic Rossby waves, and 54 nonlinearly interacting Rossby waves; the perfect model has significant non-Gaussian statistics in the zonal jet with blocked and unblocked regimes and a non-Gaussian skewed distribution due to interaction with the other 56 modes. We only observe the zonal jet contaminated by noise and apply the ensemble filter algorithm for estimation. Numerically, we find that a three dimensional nonlinear stochastic model with one level of memory mimics the statistical effect of the other 56 modes on the zonal jet in an accurate fashion, including the skew non-Gaussian distribution and autocorrelation decay. On the other hand, a similar stochastic model with zero memory levels fails to capture the crucial non-Gaussian behavior of the zonal jet from the perfect 57-mode model.

  5. Nonlocal continuous models for forced vibration analysis of two- and three-dimensional ensembles of single-walled carbon nanotubes

    Science.gov (United States)

    Kiani, Keivan

    2014-06-01

    Novel nonlocal discrete and continuous models are proposed for dynamic analysis of two- and three-dimensional ensembles of single-walled carbon nanotubes (SWCNTs). The generated extra van der Waals forces between adjacent SWCNTs due to their lateral motions are evaluated via Lennard-Jones potential function. Using a nonlocal Rayleigh beam model, the discrete and continuous models are developed for both two- and three-dimensional ensembles of SWCNTs acted upon by transverse dynamic loads. The capabilities of the proposed continuous models in capturing the vibration behavior of SWCNTs ensembles are then examined through various numerical simulations. A reasonably good agreement between the results of the continuous models and those of the discrete ones is also reported. The effects of the applied load frequency, intertube spaces, and small-scale parameter on the transverse dynamic responses of both two- and three-dimensional ensembles of SWCNTs are explained. The proposed continuous models would be very useful for dynamic analyses of large populated ensembles of SWCNTs whose discrete models suffer from both computational efforts and labor costs.

  6. Optimal drug cocktail design: methods for targeting molecular ensembles and insights from theoretical model systems.

    Science.gov (United States)

    Radhakrishnan, Mala L; Tidor, Bruce

    2008-05-01

    Drug resistance is a significant obstacle in the effective treatment of diseases with rapidly mutating targets, such as AIDS, malaria, and certain forms of cancer. Such targets are remarkably efficient at exploring the space of functional mutants and at evolving to evade drug binding while still maintaining their biological role. To overcome this challenge, drug regimens must be active against potential target variants. Such a goal may be accomplished by one drug molecule that recognizes multiple variants or by a drug "cocktail"--a small collection of drug molecules that collectively binds all desired variants. Ideally, one wants the smallest cocktail possible due to the potential for increased toxicity with each additional drug. Therefore, the task of designing a regimen for multiple target variants can be framed as an optimization problem--find the smallest collection of molecules that together "covers" the relevant target variants. In this work, we formulate and apply this optimization framework to theoretical model target ensembles. These results are analyzed to develop an understanding of how the physical properties of a target ensemble relate to the properties of the optimal cocktail. We focus on electrostatic variation within target ensembles, as it is one important mechanism by which drug resistance is achieved. Using integer programming, we systematically designed optimal cocktails to cover model target ensembles. We found that certain drug molecules covered much larger regions of target space than others, a phenomenon explained by theory grounded in continuum electrostatics. Molecules within optimal cocktails were often dissimilar, such that each drug was responsible for binding variants with a certain electrostatic property in common. On average, the number of molecules in the optimal cocktails correlated with the number of variants, the differences in the variants' electrostatic properties at the binding interface, and the level of binding affinity

  7. Skill and reliability of climate model ensembles at the Last Glacial Maximum and mid-Holocene

    Directory of Open Access Journals (Sweden)

    J. C. Hargreaves

    2013-03-01

    Full Text Available Paleoclimate simulations provide us with an opportunity to critically confront and evaluate the performance of climate models in simulating the response of the climate system to changes in radiative forcing and other boundary conditions. Hargreaves et al. (2011 analysed the reliability of the Paleoclimate Modelling Intercomparison Project, PMIP2 model ensemble with respect to the MARGO sea surface temperature data synthesis (MARGO Project Members, 2009 for the Last Glacial Maximum (LGM, 21 ka BP. Here we extend that work to include a new comprehensive collection of land surface data (Bartlein et al., 2011, and introduce a novel analysis of the predictive skill of the models. We include output from the PMIP3 experiments, from the two models for which suitable data are currently available. We also perform the same analyses for the PMIP2 mid-Holocene (6 ka BP ensembles and available proxy data sets. Our results are predominantly positive for the LGM, suggesting that as well as the global mean change, the models can reproduce the observed pattern of change on the broadest scales, such as the overall land–sea contrast and polar amplification, although the more detailed sub-continental scale patterns of change remains elusive. In contrast, our results for the mid-Holocene are substantially negative, with the models failing to reproduce the observed changes with any degree of skill. One cause of this problem could be that the globally- and annually-averaged forcing anomaly is very weak at the mid-Holocene, and so the results are dominated by the more localised regional patterns in the parts of globe for which data are available. The root cause of the model-data mismatch at these scales is unclear. If the proxy calibration is itself reliable, then representativity error in the data-model comparison, and missing climate feedbacks in the models are other possible sources of error.

  8. ENSO Forecasts in the North American Multi-Model Ensemble: Composite Analysis and Verification

    Science.gov (United States)

    Chen, L. C.

    2015-12-01

    In this study, we examine precipitation and temperature forecasts during El Nino/Southern Oscillation (ENSO) events in six models in the North American Multi-Model Ensemble (NMME), including the CFSv2, CanCM3, CanCM4, FLOR, GEOS5, and CCSM4 models, by comparing the model-based ENSO composites to the observed. The composite analysis is conducted using the 1982-2010 hindcasts for each of the six models with selected ENSO episodes based on the seasonal Ocean Nino Index (ONI) just prior to the date the forecasts were initiated. Two sets of composites are constructed over the North American continent: one based on precipitation and temperature anomalies, the other based on their probability of occurrence in a tercile-based system. The composites apply to monthly mean conditions in November, December, January, February, and March, respectively, as well as to the five-month aggregates representing the winter conditions. For the anomaly composites, we use the anomaly correlation coefficient and root-mean-square error against the observed composites for evaluation. For the probability composites, unlike conventional probabilistic forecast verification assuming binary outcomes to the observations, both model and observed composites are expressed in probability terms. Performance metrics for such validation are limited. Therefore, we develop a probability anomaly correlation measure and a probability score for assessment, so the results are comparable to the anomaly composite evaluation. We found that all NMME models predict ENSO precipitation patterns well during wintertime; however, some models have large discrepancies between the model temperature composites and the observed. The skill is higher for the multi-model ensemble, as well as the five-month aggregates. Comparing to the anomaly composites, the probability composites have superior skill in predicting ENSO temperature patterns and are less sensitive to the sample used to construct the composites, suggesting that

  9. Extended regional climate model projections for Europe until the mid-twentyfirst century: combining ENSEMBLES and CMIP3

    Science.gov (United States)

    Heinrich, Georg; Gobiet, Andreas; Mendlik, Thomas

    2014-01-01

    This study aims at sharpening the existing knowledge of expected seasonal mean climate change and its uncertainty over Europe for the two key climate variables air temperature and precipitation amount until the mid-twentyfirst century. For this purpose, we assess and compensate the global climate model (GCM) sampling bias of the ENSEMBLES regional climate model (RCM) projections by combining them with the full set of the CMIP3 GCM ensemble. We first apply a cross-validation in order to assess the skill of different statistical data reconstruction methods in reproducing ensemble mean and standard deviation. We then select the most appropriate reconstruction method in order to fill the missing values of the ENSEMBLES simulation matrix and further extend the matrix by all available CMIP3 GCM simulations forced by the A1B emission scenario. Cross-validation identifies a randomized scaling approach as superior in reconstructing the ensemble spread. Errors in ensemble mean and standard deviation are mostly less than 0.1 K and 1.0 % for air temperature and precipitation amount, respectively. Reconstruction of the missing values reveals that expected seasonal mean climate change of the ENSEMBLES RCM projections is not significantly biased and that the associated uncertainty is not underestimated due to sampling of only a few driving GCMs. In contrast, the spread of the extended simulation matrix is partly significantly lower, sharpening our knowledge about future climate change over Europe by reducing uncertainty in some regions. Furthermore, this study gives substantial weight to recent climate change impact studies based on the ENSEMBLES projections, since it confirms the robustness of the climate forcing of these studies concerning GCM sampling.

  10. Future changes in South American temperature and precipitation in an ensemble of CORDEX regional climate model simulations

    Science.gov (United States)

    Kjellström, Erik; Nikulin, Grigory; Rana, Arun; Fuentes Franco, Ramón

    2017-04-01

    In this study we investigate possible changes in temperature and precipitation on a regional scale over South America from 1961 to 2100. We use data from two ensembles of climate simulations, one global and one regional, over the South America CORDEX domain. The global ensemble includes ten coupled atmosphere ocean general circulation models (AOGCMs) from the CMIP5 project with horizontal resolution varying from about 1° to 3°, namely CanESM2, CSIRO-Mk3, CNRM-CM5, HadGEM2-ES, NorESM1-M, EC-EARTH, MIROC5, GFDL-ESM2M, MPI-ESM-LR and NorESM1-M. In the regional ensemble all 10 AOGCMs are downscaled at the Rossby Centre (SMHI) by a regional climate model - RCA4 at 0.44° resolution. Three forcing scenarios are considered: RCP2.6 (five out of ten AOGCMs); RCP4.5 and RCP8.5. The experimental setup allows us to illustrate how uncertainties in future climate change are related to forcing scenario and to forcing AOGCM at different time periods. Further, taking both AOGCM and RCM ensembles and focusing on seasonal mean temperature and precipitation over South America we i) evaluate the ability of the ensembles and their individual members to simulate the observed climatology in South America, ii) analyse similarities and differences in future climate projections between the two ensembles and iii) assess how both ensembles capture the spread of the grand CMIP5 ensemble. We also address higher-order variability by showing results for changes in temperature extremes and for changes in intensity and frequency of extreme precipitation.

  11. Connecting a connectome to behavior: an ensemble of neuroanatomical models of C. elegans klinotaxis.

    Directory of Open Access Journals (Sweden)

    Eduardo J Izquierdo

    Full Text Available Increased efforts in the assembly and analysis of connectome data are providing new insights into the principles underlying the connectivity of neural circuits. However, despite these considerable advances in connectomics, neuroanatomical data must be integrated with neurophysiological and behavioral data in order to obtain a complete picture of neural function. Due to its nearly complete wiring diagram and large behavioral repertoire, the nematode worm Caenorhaditis elegans is an ideal organism in which to explore in detail this link between neural connectivity and behavior. In this paper, we develop a neuroanatomically-grounded model of salt klinotaxis, a form of chemotaxis in which changes in orientation are directed towards the source through gradual continual adjustments. We identify a minimal klinotaxis circuit by systematically searching the C. elegans connectome for pathways linking chemosensory neurons to neck motor neurons, and prune the resulting network based on both experimental considerations and several simplifying assumptions. We then use an evolutionary algorithm to find possible values for the unknown electrophsyiological parameters in the network such that the behavioral performance of the entire model is optimized to match that of the animal. Multiple runs of the evolutionary algorithm produce an ensemble of such models. We analyze in some detail the mechanisms by which one of the best evolved circuits operates and characterize the similarities and differences between this mechanism and other solutions in the ensemble. Finally, we propose a series of experiments to determine which of these alternatives the worm may be using.

  12. Assessing the predictive capability of randomized tree-based ensembles in streamflow modelling

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    S. Galelli

    2013-02-01

    Full Text Available Combining randomization methods with ensemble prediction is emerging as an effective option to balance accuracy and computational efficiency in data-driven modeling. In this paper we investigate the prediction capability of extremely randomized trees (Extra-Trees, in terms of accuracy, explanation ability and computational efficiency, in a streamflow modeling exercise. Extra-Trees are a totally randomized tree-based ensemble method that (i alleviates the poor generalization property and tendency to overfitting of traditional standalone decision trees (e.g. CART; (ii is computationally very efficient; and, (iii allows to infer the relative importance of the input variables, which might help in the ex-post physical interpretation of the model. The Extra-Trees potential is analyzed on two real-world case studies (Marina catchment (Singapore and Canning River (Western Australia representing two different morphoclimatic contexts comparatively with other tree-based methods (CART and M5 and parametric data-driven approaches (ANNs and multiple linear regression. Results show that Extra-Trees perform comparatively well to the best of the benchmarks (i.e. M5 in both the watersheds, while outperforming the other approaches in terms of computational requirement when adopted on large datasets. In addition, the ranking of the input variable provided can be given a physically meaningful interpretation.

  13. Accounting for Epistemic Uncertainty in PSHA: Logic Tree and Ensemble Model

    Science.gov (United States)

    Taroni, M.; Marzocchi, W.; Selva, J.

    2014-12-01

    The logic tree scheme is the probabilistic framework that has been widely used in the last decades to take into account epistemic uncertainties in probabilistic seismic hazard analysis (PSHA). Notwithstanding the vital importance for PSHA to incorporate properly the epistemic uncertainties, we argue that the use of the logic tree in a PSHA context has conceptual and practical drawbacks. Despite some of these drawbacks have been reported in the past, a careful evaluation of their impact on PSHA is still lacking. This is the goal of the present work. In brief, we show that i) PSHA practice does not meet the assumptions that stand behind the logic tree scheme; ii) the output of a logic tree is often misinterpreted and/or misleading, e.g., the use of percentiles (median included) in a logic tree scheme raises theoretical difficulties from a probabilistic point of view; iii) in case the assumptions that stand behind a logic tree are actually met, this leads to several problems in testing any PSHA model. We suggest a different strategy - based on ensemble modeling - to account for epistemic uncertainties in a more proper probabilistic framework. Finally, we show that in many PSHA practical applications, the logic tree is de facto loosely applied to build sound ensemble models.

  14. Separation of the bioclimatic spaces of Himalayan tree rhododendron species predicted by ensemble suitability models

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    Sailesh Ranjitkar

    2014-08-01

    Full Text Available The tree rhododendrons include the most widely distributed Himalayan Rhododendron species belonging to the subsection Arborea. Distributions of two members of this sub-species were modelled using bioclimatic data for current conditions (1950–2000. A subset of the least correlated bioclimatic variables was used for ecological niche modelling (ENM. We used an ENM ensemble method in the BiodiversityR R-package to map the suitable climatic space for tree rhododendrons based on 217 point location records. Ensemble bioclimatic models for tree rhododendrons had high predictive power with bioclimatic variables, which also separated the climatic spaces for the two species. Tree rhododendrons were found occurring in a wide range of climate and the distributional limits were associated with isothermality, temperature ranges, temperature of the wettest quarter, and precipitation of the warmest quarter of the year. The most suitable climatic space for tree rhododendrons was predicted to be in western Yunnan, China, with suitability declining towards the west and east. Its occurrence in a wide range of climatic settings with highly dissected habitats speaks to the adaptive capacity of the species, which might open up future options for their conservation planning in regions where they are listed as threatened.

  15. Assessing the predictive capability of randomized tree-based ensembles in streamflow modelling

    Science.gov (United States)

    Galelli, S.; Castelletti, A.

    2013-07-01

    Combining randomization methods with ensemble prediction is emerging as an effective option to balance accuracy and computational efficiency in data-driven modelling. In this paper, we investigate the prediction capability of extremely randomized trees (Extra-Trees), in terms of accuracy, explanation ability and computational efficiency, in a streamflow modelling exercise. Extra-Trees are a totally randomized tree-based ensemble method that (i) alleviates the poor generalisation property and tendency to overfitting of traditional standalone decision trees (e.g. CART); (ii) is computationally efficient; and, (iii) allows to infer the relative importance of the input variables, which might help in the ex-post physical interpretation of the model. The Extra-Trees potential is analysed on two real-world case studies - Marina catchment (Singapore) and Canning River (Western Australia) - representing two different morphoclimatic contexts. The evaluation is performed against other tree-based methods (CART and M5) and parametric data-driven approaches (ANNs and multiple linear regression). Results show that Extra-Trees perform comparatively well to the best of the benchmarks (i.e. M5) in both the watersheds, while outperforming the other approaches in terms of computational requirement when adopted on large datasets. In addition, the ranking of the input variable provided can be given a physically meaningful interpretation.

  16. 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

  17. Partitioning internal variability and model uncertainty components in a multireplicate multimodel ensemble of hydrometeorological future projections

    Science.gov (United States)

    Hingray, Benoit; Saïd, Mériem; Lafaysse, Matthieu; Gailhlard, Joël; Mezghani, Abdelkader

    2014-05-01

    A simple and robust framework was proposed by Hingray and Mériem (2013) for the partitioning of the different components of internal variability and model uncertainty in a multireplicate multimodel ensemble (MRMME) of climate projections obtained for a suite of statistical downscaling models (SDMs) and global climate models (GCMs). It is based on the quasi-ergodic assumption for transient climate simulations. Model uncertainty components are estimated from the noise-free signals of each modeling chain using a two-way ANOVA framework. The residuals from the noise-free signal are used to estimate the large and small scale internal variability (IV) components associated with each considered GCM/SDM configuration. This framework makes it possible to take into account all runs and replicates available from any climate ensemble of opportunity. This quasi-ergodic ANOVA framework was applied to the MRMME of hydrometeorological simulations produced for the Upper Durance River basin (French Alps) over the 1860-2100 period within the RIWER2030 research project (http://www.lthe.fr/RIWER2030/). The different uncertainty sources were quantified as a function of lead time for projected changes in temperature, precipitation, evaporation losses, snow cover and discharges (Lafaysse et al., 2013). For temperature, GCM uncertainty prevails and, as opposed to IV, SDM uncertainty is non-negligible. Significant warming and in turn significant changes are predicted for evaporation, snow cover and seasonality of discharges. For precipitation, GCM and SDM uncertainty components are of the same order. Despite high model uncertainty, the non-zero climate change response of simulation chains is significant and annual precipitation is expected to decrease. However, high values are obtained for the large and small scale components of IV, inherited respectively from the GCMs and the different replicates of a given SDM. The same applies for annual discharge. The uncertainty in values that could

  18. LGM permafrost distribution: how well can the latest PMIP multi-model ensembles perform reconstruction?

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    K. Saito

    2013-08-01

    Full Text Available Here, global-scale frozen ground distribution from the Last Glacial Maximum (LGM has been reconstructed using multi-model ensembles of global climate models, and then compared with evidence-based knowledge and earlier numerical results. Modeled soil temperatures, taken from Paleoclimate Modelling Intercomparison Project phase III (PMIP3 simulations, were used to diagnose the subsurface thermal regime and determine underlying frozen ground types for the present day (pre-industrial; 0 kya and the LGM (21 kya. This direct method was then compared to an earlier indirect method, which categorizes underlying frozen ground type from surface air temperature, applying to both the PMIP2 (phase II and PMIP3 products. Both direct and indirect diagnoses for 0 kya showed strong agreement with the present-day observation-based map. The soil temperature ensemble showed a higher diversity around the border between permafrost and seasonally frozen ground among the models, partly due to varying subsurface processes, implementation, and settings. The area of continuous permafrost estimated by the PMIP3 multi-model analysis through the direct (indirect method was 26.0 (17.7 million km2 for LGM, in contrast to 15.1 (11.2 million km2 for the pre-industrial control, whereas seasonally frozen ground decreased from 34.5 (26.6 million km2 to 18.1 (16.0 million km2. These changes in area resulted mainly from a cooler climate at LGM, but from other factors as well, such as the presence of huge land ice sheets and the consequent expansion of total land area due to sea-level change. LGM permafrost boundaries modeled by the PMIP3 ensemble – improved over those of the PMIP2 due to higher spatial resolutions and improved climatology – also compared better to previous knowledge derived from geomorphological and geocryological evidence. Combinatorial applications of coupled climate models and detailed stand-alone physical-ecological models for the cold-region terrestrial

  19. NCAR Contribution to A U.S. National Multi-Model Ensemble (NMME) ISI Prediction System

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    Tribbia, Joseph [Univ. Corporation for Atmospheric Research (UCAR), Boulder, CO (United States)

    2015-11-25

    NCAR brought the latest version of the Community Earth System Model (version 1, CESM1) into the mix of models in the NMME effort. This new version uses our newest atmospheric model CAM5 and produces a coupled climate and ENSO that are generally as good or better than those of the Community Climate System Model version 4 (CCSM4). Compared to CCSM4, the new coupled model has a superior climate response with respect to low clouds in both the subtropical stratus regimes and the Arctic. However, CESM1 has been run to date using a prognostic aerosol model that more than doubles its computational cost. We are currently evaluating a version of the new model using prescribed aerosols and expect it will be ready for integrations in summer 2012. Because of this NCAR has not been able to complete the hindcast integrations using the NCAR loosely-coupled ensemble Kalman filter assimilation method nor has it contributed to the current (Stage I) NMME operational utilization. The expectation is that this model will be included in the NMME in late 2012 or early 2013. The initialization method will utilize the Ensemble Kalman Filter Assimilation methods developed at NCAR using the Data Assimilation Research Testbed (DART) in conjunction with Jeff Anderson’s team in CISL. This methodology has been used in our decadal prediction contributions to CMIP5. During the course of this project, NCAR has setup and performed all the needed hindcast and forecast simulations and provide the requested fields to our collaborators. In addition, NCAR researchers have participated fully in research themes (i) and (ii). Specifically, i) we have begun to evaluate and optimize our system in hindcast mode, focusing on the optimal number of ensemble members, methodologies to recalibrate individual dynamical models, and accessing our forecasts across multiple time scales, i.e., beyond two weeks, and ii) we have begun investigation of the role of different ocean initial conditions in seasonal forecasts. The

  20. Soft sensor modeling based on variable partition ensemble method for nonlinear batch processes

    Science.gov (United States)

    Wang, Li; Chen, Xiangguang; Yang, Kai; Jin, Huaiping

    2017-01-01

    Batch processes are always characterized by nonlinear and system uncertain properties, therefore, the conventional single model may be ill-suited. A local learning strategy soft sensor based on variable partition ensemble method is developed for the quality prediction of nonlinear and non-Gaussian batch processes. A set of input variable sets are obtained by bootstrapping and PMI criterion. Then, multiple local GPR models are developed based on each local input variable set. When a new test data is coming, the posterior probability of each best performance local model is estimated based on Bayesian inference and used to combine these local GPR models to get the final prediction result. The proposed soft sensor is demonstrated by applying to an industrial fed-batch chlortetracycline fermentation process.

  1. Diagnosing the possible dynamics controlling Sahel precipitation in the short-range ensemble community atmospheric model hindcasts

    Science.gov (United States)

    Tseng, Yu-heng; Lin, Yen-heng; Lo, Min-hui; Yang, Shu-chih

    2016-11-01

    The actual dynamics and physical mechanisms affecting the Sahel precipitation pattern and amplitude in the climate models remain under debate due to the inconsistent drying and rainfall variability/pattern among them. We diagnose the boreal summer rainfall pattern in the Sahel and its possible causes using short-range ensemble hindcasts based on NCAR community atmospheric model with the local ensemble transform Kalman filter (CAM-LETKF) data assimilation. The CAM-LETKF assimilation was conducted using 64 ensemble members with an assimilation cycle of 6-h. By comparing the superior and inferior groups within these 64 ensembles, we confirmed the influence of the Atlantic in the West Sahel rainfall (a robust feature in the ensembles) and a severe model bias resulting from erroneously modeled locations and magnitudes of low-level Sahara heat low (SHL) and African easterly jet (AEJ). This bias is highly related to atmospheric jet dynamics as shown in recent studies and local wave instability triggered mainly by the boundary-layer temperature gradient and amplified by land-atmosphere interactions. In particular, our results demonstrated that more accurate divergence and convergence fields resulting from improved SHL and AEJ in the superior groups enabled more accurate rainbelt patterns to be discerned, thus improving the ensemble mean model hindcast prediction by more than 25 % in precipitation and 16 % in temperature. We concluded that the use of low-resolution climate models to project future rainfall in the Sahel requires caution because the model hindcasts may quickly diverge even the same boundary conditions and forcings are applied. The model bias may easily grow up within a few months in the short-range CAM-LETKF hindcast, let along the free model centennial simulations. Unconstrained future climate model projections for the Sahel must more effectively capture the short-term key boundary-layer dynamics in the boreal summer to be credible regardless model dynamics

  2. Diagnosing the possible dynamics controlling Sahel precipitation in the short-range ensemble community atmospheric model hindcasts

    Science.gov (United States)

    Tseng, Yu-heng; Lin, Yen-heng; Lo, Min-hui; Yang, Shu-chih

    2016-01-01

    The actual dynamics and physical mechanisms affecting the Sahel precipitation pattern and amplitude in the climate models remain under debate due to the inconsistent drying and rainfall variability/pattern among them. We diagnose the boreal summer rainfall pattern in the Sahel and its possible causes using short-range ensemble hindcasts based on NCAR community atmospheric model with the local ensemble transform Kalman filter (CAM-LETKF) data assimilation. The CAM-LETKF assimilation was conducted using 64 ensemble members with an assimilation cycle of 6-h. By comparing the superior and inferior groups within these 64 ensembles, we confirmed the influence of the Atlantic in the West Sahel rainfall (a robust feature in the ensembles) and a severe model bias resulting from erroneously modeled locations and magnitudes of low-level Sahara heat low (SHL) and African easterly jet (AEJ). This bias is highly related to atmospheric jet dynamics as shown in recent studies and local wave instability triggered mainly by the boundary-layer temperature gradient and amplified by land-atmosphere interactions. In particular, our results demonstrated that more accurate divergence and convergence fields resulting from improved SHL and AEJ in the superior groups enabled more accurate rainbelt patterns to be discerned, thus improving the ensemble mean model hindcast prediction by more than 25 % in precipitation and 16 % in temperature. We concluded that the use of low-resolution climate models to project future rainfall in the Sahel requires caution because the model hindcasts may quickly diverge even the same boundary conditions and forcings are applied. The model bias may easily grow up within a few months in the short-range CAM-LETKF hindcast, let along the free model centennial simulations. Unconstrained future climate model projections for the Sahel must more effectively capture the short-term key boundary-layer dynamics in the boreal summer to be credible regardless model dynamics

  3. Improving predictive mapping of deep-water habitats: Considering multiple model outputs and ensemble techniques

    Science.gov (United States)

    Robert, Katleen; Jones, Daniel O. B.; Roberts, J. Murray; Huvenne, Veerle A. I.

    2016-07-01

    In the deep sea, biological data are often sparse; hence models capturing relationships between observed fauna and environmental variables (acquired via acoustic mapping techniques) are often used to produce full coverage species assemblage maps. Many statistical modelling techniques are being developed, but there remains a need to determine the most appropriate mapping techniques. Predictive habitat modelling approaches (redundancy analysis, maximum entropy and random forest) were applied to a heterogeneous section of seabed on Rockall Bank, NE Atlantic, for which landscape indices describing the spatial arrangement of habitat patches were calculated. The predictive maps were based on remotely operated vehicle (ROV) imagery transects high-resolution autonomous underwater vehicle (AUV) sidescan backscatter maps. Area under the curve (AUC) and accuracy indicated similar performances for the three models tested, but performance varied by species assemblage, with the transitional species assemblage showing the weakest predictive performances. Spatial predictions of habitat suitability differed between statistical approaches, but niche similarity metrics showed redundancy analysis and random forest predictions to be most similar. As one statistical technique could not be found to outperform the others when all assemblages were considered, ensemble mapping techniques, where the outputs of many models are combined, were applied. They showed higher accuracy than any single model. Different statistical approaches for predictive habitat modelling possess varied strengths and weaknesses and by examining the outputs of a range of modelling techniques and their differences, more robust predictions, with better described variation and areas of uncertainties, can be achieved. As improvements to prediction outputs can be achieved without additional costly data collection, ensemble mapping approaches have clear value for spatial management.

  4. Using an ensemble smoother to evaluate parameter uncertainty of an integrated hydrological model of Yanqi basin

    Science.gov (United States)

    Li, Ning; McLaughlin, Dennis; Kinzelbach, Wolfgang; Li, WenPeng; Dong, XinGuang

    2015-10-01

    Model uncertainty needs to be quantified to provide objective assessments of the reliability of model predictions and of the risk associated with management decisions that rely on these predictions. This is particularly true in water resource studies that depend on model-based assessments of alternative management strategies. In recent decades, Bayesian data assimilation methods have been widely used in hydrology to assess uncertain model parameters and predictions. In this case study, a particular data assimilation algorithm, the Ensemble Smoother with Multiple Data Assimilation (ESMDA) (Emerick and Reynolds, 2012), is used to derive posterior samples of uncertain model parameters and forecasts for a distributed hydrological model of Yanqi basin, China. This model is constructed using MIKESHE/MIKE11software, which provides for coupling between surface and subsurface processes (DHI, 2011a-d). The random samples in the posterior parameter ensemble are obtained by using measurements to update 50 prior parameter samples generated with a Latin Hypercube Sampling (LHS) procedure. The posterior forecast samples are obtained from model runs that use the corresponding posterior parameter samples. Two iterative sample update methods are considered: one based on an a perturbed observation Kalman filter update and one based on a square root Kalman filter update. These alternatives give nearly the same results and converge in only two iterations. The uncertain parameters considered include hydraulic conductivities, drainage and river leakage factors, van Genuchten soil property parameters, and dispersion coefficients. The results show that the uncertainty in many of the parameters is reduced during the smoother updating process, reflecting information obtained from the observations. Some of the parameters are insensitive and do not benefit from measurement information. The correlation coefficients among certain parameters increase in each iteration, although they generally

  5. Quantum quench in matrix models: Dynamical phase transitions, Selective equilibration and the Generalized Gibbs Ensemble

    CERN Document Server

    Mandal, Gautam

    2013-01-01

    Quantum quench dynamics is considered in a one dimensional unitary matrix model with a single trace potential. This model is integrable and has been studied in the context of non-critical string theory. We find dynamical phase transitions, and study the role of the quantum critical point. In course of the time evolutions, we find evidence of selective equilibration for a certain class of observables. The equilibrium is governed by the Generalized Gibbs Ensemble (GGE) and differs from the standard Gibbs ensemble. We compute the production of entropy which is O(N) for large N matrices. An important feature of the equilibration is the appearance of an energy cascade, reminiscent of the Richardson cascade in turbulence, where we find flow of energy from initial long wavelength modes to progressively shorter wavelength excitations. We discuss possible implication of the equilibration and of GGE in string theories and higher spin theories. In another related study, we compute time evolutions in a double trace unita...

  6. Development of statistical prediction models for Changma precipitation: An ensemble approach

    Science.gov (United States)

    Kim, Jin-Yong; Seo, Kyong-Hwan; Son, Jun-Hyeok; Ha, Kyung-Ja

    2017-05-01

    An ensemble statistical forecast scheme with a one-month lead is developed to predict year-to-year variations of Changma rainfall over the Korean peninsula. Spring sea surface temperature (SST) anomalies over the North Atlantic, the North Pacific and the tropical Pacific Ocean have been proposed as useful predictors in a previous study. Through a forward-stepwise regression method, four additional springtime predictors are selected: the northern Indian Ocean (NIO) SST, the North Atlantic SST change (NAC), the snow cover anomaly over the Eurasian continent (EUSC), and the western North Pacific outgoing longwave radiation anomaly (WNP (OLR)). Using these, three new prediction models are developed. A simple arithmetic ensemble mean produces much improved forecast skills compared to the original prediction model of Lee and Seo (2013). Skill scores measured by temporal correlation and MSSS (mean square error skill score) are improved by about 9% and 17%, respectively. The GMSS (Gerrity skill score) and hit rate based on a tercile prediction validation scheme are also enhanced by about 19% and 13%, respectively. The reversed NIO, reversed WNP (OLR), and reversed NAC are all related to the enhancement of a cyclonic circulation anomaly to the south or southwest of the Korean peninsula, which induces southeasterly moisture flux into the peninsula and increasing Changma precipitation. The EUSC predictor induces an enhancement of the Okhotsk Sea high downstream and thus strengthening of Changma front.

  7. Modeling the inherent optical properties of aquatic particles using an irregular hexahedral ensemble

    Science.gov (United States)

    Xu, Guanglang; Sun, Bingqiang; Brooks, Sarah D.; Yang, Ping; Kattawar, George W.; Zhang, Xiaodong

    2017-04-01

    A statistical approach in defining particle morphology in terms of an ensemble of hexahedra of distorted shapes is employed for modeling the Inherent Optical Properties (IOPs) of aquatic particles. The approach is inspired by the rich variability in shapes of real aquatic particles that cannot be represented by one particular shape. Two methods, the Invariant Imbedding T-matrix (II-TM) and Physical Geometric Optics Hybrid (PGOH) method, are combined to simulate the IOPs for aquatic particles of sizes ranging from the Rayleigh scattering to geometric optics regimes. Nonspherical effects on the IOPs are examined by comparing the results with predictions based on the Lorenz-Mie theory to explore the limitations of assuming the particles to be spherical. We pay special attention to backscattering-related and polarimetric scattering properties, particularly the backscattering ratio, Gordon parameter, backscattering volume scattering function and the degree of linear polarization. The simulated IOPs are compared with the in-situ measurements to assess the feasibility of using a hexahedral ensemble in modeling the IOPs of the aquatic particles.

  8. Recognition of emotions using multimodal physiological signals and an ensemble deep learning model.

    Science.gov (United States)

    Yin, Zhong; Zhao, Mengyuan; Wang, Yongxiong; Yang, Jingdong; Zhang, Jianhua

    2017-03-01

    Using deep-learning methodologies to analyze multimodal physiological signals becomes increasingly attractive for recognizing human emotions. However, the conventional deep emotion classifiers may suffer from the drawback of the lack of the expertise for determining model structure and the oversimplification of combining multimodal feature abstractions. In this study, a multiple-fusion-layer based ensemble classifier of stacked autoencoder (MESAE) is proposed for recognizing emotions, in which the deep structure is identified based on a physiological-data-driven approach. Each SAE consists of three hidden layers to filter the unwanted noise in the physiological features and derives the stable feature representations. An additional deep model is used to achieve the SAE ensembles. The physiological features are split into several subsets according to different feature extraction approaches with each subset separately encoded by a SAE. The derived SAE abstractions are combined according to the physiological modality to create six sets of encodings, which are then fed to a three-layer, adjacent-graph-based network for feature fusion. The fused features are used to recognize binary arousal or valence states. DEAP multimodal database was employed to validate the performance of the MESAE. By comparing with the best existing emotion classifier, the mean of classification rate and F-score improves by 5.26%. The superiority of the MESAE against the state-of-the-art shallow and deep emotion classifiers has been demonstrated under different sizes of the available physiological instances. Copyright © 2016 Elsevier Ireland Ltd. All rights reserved.

  9. Development and Evaluation of Storm Surge Ensemble Forecasting for the Philippines Using JMA Storm Surge Model

    Science.gov (United States)

    Lapidez, J. P. B.; Tablazon, J. P.; Lagmay, A. M. F. A.; Suarez, J. K. B.; Santiago, J. T.

    2014-12-01

    The Philippines is one of the countries most vulnerable to storm surge. It is located in the North-western Pacific basin which is the most active basin in the planet. An average of 20 tropical cyclones enters the Philippine area of responsibility (PAR) every year. The archipelagic nature of the country with regions having gently sloping coasts and shallow bays also contribute to the formation of extreme surges. Last November 2013, storm surge brought by super typhoon Haiyan severely damaged several coastal regions in the Visayan Islands. Haiyan left more than 6 300 casualties and damages amounting to more than $ 2 billion. Extreme storm surge events such as this highlight the need to establish a storm surge early warning system for the country. This study explores the development and evaluation of storm surge ensemble forecasting for the Philippines using the Japan Meteorological Agency (JMA) storm surge model. 36-hour, 24-hour, and 12-hour tropical cyclone forecasts are used to generate an ensemble storm surge forecast to give the most probable storm surge height at a specific point brought by an incoming tropical cyclone. The result of the storm surge forecast is compared to tide gauge record to evaluate the accuracy. The total time of computation and dissemination of forecast result is also examined to assess the feasibility of using the JMA storm surge model for operational purposes.

  10. Structural ensemble dynamics based closure model for wall-bounded turbulent flow

    Institute of Scientific and Technical Information of China (English)

    Zhen-Su She; Ning Hu; You Wu

    2009-01-01

    Wall-bounded turbulent flow involves the development of multi-scale turbulent eddies, as well as a sharply varying boundary layer. Its theoretical descriptions are yet phenomenological. We present here a new framework called structural ensemble dynamics (SED), which aims at using systematically all relevant statistical properties of turbulent structures for a quantitative description of ensemble means. A new set of closure equations based on the SED approach for a turbulent channel flow is presented. SED order functions are defined, and numerically determined from data of direct numerical simulations (DNS). Computational results show that the new closure model reproduces accurately the solution of the original Navier-Stokes simulation, including the mean velocity profile, the kinetic energy of the stream-wise velocity component, and every term in the energy budget equation. It is suggested that the SED-based studies of turbulent structure builds a bridge between the studies of physical mechanisms of turbulence and the development of accurate model equations for engineering predictions.

  11. 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.

  12. Evaluation of Local Ensemble Transform Kalman Filter System for the Global FSU Atmospheric Model

    Science.gov (United States)

    Cintra, R. S.; Cocke, S.

    2014-12-01

    This paper shows the results of a implementation of the data assimilation system to obtain the initial condition to the atmospheric general circulation model (AGCM) for the Florida State University/USA. The better quality of forecasts is given the more accurate the estimate of the initial conditions. The process of combining observations and short-range forecast to obtain an analysis is called data assimilation. The data assimilation system called "Local ensemble transform Kalman filter (LETKF) is implemented. A prediction estimates ensemble in state space represents the model errors in that scheme. The LETKF is tested with the AGCM Florida State University Global Spectral Model (FSUGSM). The model is a multilevel (27 vertical levels) spectral primitive equation model with a vertical σ-coordinate. All variables are expanded horizontally in a truncated series of spherical harmonic functions (at resolution T63) and a transform technique is applied to calculate the physical processes in real space. The LETKF data assimilation experiments are based in two types of the synthetic observations data (surface pressure, absolute temperature, zonal component wind, meridional component wind and humidity) to evaluate the LETKF system for FSUGSM. The data assimilation experiments are based on observational systems simulation experiments where the "nature" is assumed to be known, and adding random noise to the nature run. The first experiment, the "nature" fields are the FSUGSM forecasts without data assimilation, afterwards, we use the "National Centers for Environment Prediction" reanalysis to obtain the "nature" fields. The observations are localized at every other grid point of the model. The forecast ensemble size is 20 members. The numerical experiments have a one-month assimilation cycle, for the period 01/01/2001 to 31/01/2001 at (00, 06, 12 and 18 GMT) for each day. We compare the behavior of the model by comparing with its forecast, observations and nature fields. A

  13. Improvement of disease prediction and modeling through the use of meteorological ensembles: human plague in Uganda.

    Directory of Open Access Journals (Sweden)

    Sean M Moore

    Full Text Available Climate and weather influence the occurrence, distribution, and incidence of infectious diseases, particularly those caused by vector-borne or zoonotic pathogens. Thus, models based on meteorological data have helped predict when and where human cases are most likely to occur. Such knowledge aids in targeting limited prevention and control resources and may ultimately reduce the burden of diseases. Paradoxically, localities where such models could yield the greatest benefits, such as tropical regions where morbidity and mortality caused by vector-borne diseases is greatest, often lack high-quality in situ local meteorological data. Satellite- and model-based gridded climate datasets can be used to approximate local meteorological conditions in data-sparse regions, however their accuracy varies. Here we investigate how the selection of a particular dataset can influence the outcomes of disease forecasting models. Our model system focuses on plague (Yersinia pestis infection in the West Nile region of Uganda. The majority of recent human cases have been reported from East Africa and Madagascar, where meteorological observations are sparse and topography yields complex weather patterns. Using an ensemble of meteorological datasets and model-averaging techniques we find that the number of suspected cases in the West Nile region was negatively associated with dry season rainfall (December-February and positively with rainfall prior to the plague season. We demonstrate that ensembles of available meteorological datasets can be used to quantify climatic uncertainty and minimize its impacts on infectious disease models. These methods are particularly valuable in regions with sparse observational networks and high morbidity and mortality from vector-borne diseases.

  14. Improvement of disease prediction and modeling through the use of meteorological ensembles: human plague in Uganda.

    Science.gov (United States)

    Moore, Sean M; Monaghan, Andrew; Griffith, Kevin S; Apangu, Titus; Mead, Paul S; Eisen, Rebecca J

    2012-01-01

    Climate and weather influence the occurrence, distribution, and incidence of infectious diseases, particularly those caused by vector-borne or zoonotic pathogens. Thus, models based on meteorological data have helped predict when and where human cases are most likely to occur. Such knowledge aids in targeting limited prevention and control resources and may ultimately reduce the burden of diseases. Paradoxically, localities where such models could yield the greatest benefits, such as tropical regions where morbidity and mortality caused by vector-borne diseases is greatest, often lack high-quality in situ local meteorological data. Satellite- and model-based gridded climate datasets can be used to approximate local meteorological conditions in data-sparse regions, however their accuracy varies. Here we investigate how the selection of a particular dataset can influence the outcomes of disease forecasting models. Our model system focuses on plague (Yersinia pestis infection) in the West Nile region of Uganda. The majority of recent human cases have been reported from East Africa and Madagascar, where meteorological observations are sparse and topography yields complex weather patterns. Using an ensemble of meteorological datasets and model-averaging techniques we find that the number of suspected cases in the West Nile region was negatively associated with dry season rainfall (December-February) and positively with rainfall prior to the plague season. We demonstrate that ensembles of available meteorological datasets can be used to quantify climatic uncertainty and minimize its impacts on infectious disease models. These methods are particularly valuable in regions with sparse observational networks and high morbidity and mortality from vector-borne diseases.

  15. Improvement of Disease Prediction and Modeling through the Use of Meteorological Ensembles: Human Plague in Uganda

    Science.gov (United States)

    Moore, Sean M.; Monaghan, Andrew; Griffith, Kevin S.; Apangu, Titus; Mead, Paul S.; Eisen, Rebecca J.

    2012-01-01

    Climate and weather influence the occurrence, distribution, and incidence of infectious diseases, particularly those caused by vector-borne or zoonotic pathogens. Thus, models based on meteorological data have helped predict when and where human cases are most likely to occur. Such knowledge aids in targeting limited prevention and control resources and may ultimately reduce the burden of diseases. Paradoxically, localities where such models could yield the greatest benefits, such as tropical regions where morbidity and mortality caused by vector-borne diseases is greatest, often lack high-quality in situ local meteorological data. Satellite- and model-based gridded climate datasets can be used to approximate local meteorological conditions in data-sparse regions, however their accuracy varies. Here we investigate how the selection of a particular dataset can influence the outcomes of disease forecasting models. Our model system focuses on plague (Yersinia pestis infection) in the West Nile region of Uganda. The majority of recent human cases have been reported from East Africa and Madagascar, where meteorological observations are sparse and topography yields complex weather patterns. Using an ensemble of meteorological datasets and model-averaging techniques we find that the number of suspected cases in the West Nile region was negatively associated with dry season rainfall (December-February) and positively with rainfall prior to the plague season. We demonstrate that ensembles of available meteorological datasets can be used to quantify climatic uncertainty and minimize its impacts on infectious disease models. These methods are particularly valuable in regions with sparse observational networks and high morbidity and mortality from vector-borne diseases. PMID:23024750

  16. Towards global empirical upscaling of FLUXNET eddy covariance observations: validation of a model tree ensemble approach using a biosphere model

    Directory of Open Access Journals (Sweden)

    M. Jung

    2009-05-01

    Full Text Available Global, spatially and temporally explicit estimates of carbon and water fluxes derived from empirical up-scaling eddy covariance measurements would constitute a new and possibly powerful data stream to study the variability of the global terrestrial carbon and water cycle. This paper introduces and validates a machine learning approach dedicated to the upscaling of observations from the current global network of eddy covariance towers (FLUXNET. We present a new model TRee Induction ALgorithm (TRIAL that performs hierarchical stratification of the data set into units where particular multiple regressions for a target variable hold. We propose an ensemble approach (Evolving tRees with RandOm gRowth, ERROR where the base learning algorithm is perturbed in order to gain a diverse sequence of different model trees which evolves over time.

    We evaluate the efficiency of the model tree ensemble approach using an artificial data set derived from the the Lund-Potsdam-Jena managed Land (LPJmL biosphere model. We aim at reproducing global monthly gross primary production as simulated by LPJmL from 1998–2005 using only locations and months where high quality FLUXNET data exist for the training of the model trees. The model trees are trained with the LPJmL land cover and meteorological input data, climate data, and the fraction of absorbed photosynthetic active radiation simulated by LPJmL. Given that we know the "true result" in the form of global LPJmL simulations we can effectively study the performance of the model tree ensemble upscaling and associated problems of extrapolation capacity.

    We show that the model tree ensemble is able to explain 92% of the variability of the global LPJmL GPP simulations. The mean spatial pattern and the seasonal variability of GPP that constitute the largest sources of variance are very well reproduced (96% and 94% of variance explained respectively while the monthly interannual anomalies which occupy

  17. A two-model hydrologic ensemble prediction of hydrograph: case study from the upper Nysa Klodzka river basin (SW Poland)

    Science.gov (United States)

    Niedzielski, Tomasz; Mizinski, Bartlomiej

    2016-04-01

    The HydroProg system has been elaborated in frame of the research project no. 2011/01/D/ST10/04171 of the National Science Centre of Poland and is steadily producing multimodel ensemble predictions of hydrograph in real time. Although there are six ensemble members available at present, the longest record of predictions and their statistics is available for two data-based models (uni- and multivariate autoregressive models). Thus, we consider 3-hour predictions of water levels, with lead times ranging from 15 to 180 minutes, computed every 15 minutes since August 2013 for the Nysa Klodzka basin (SW Poland) using the two approaches and their two-model ensemble. Since the launch of the HydroProg system there have been 12 high flow episodes, and the objective of this work is to present the performance of the two-model ensemble in the process of forecasting these events. For a sake of brevity, we limit our investigation to a single gauge located at the Nysa Klodzka river in the town of Klodzko, which is centrally located in the studied basin. We identified certain regular scenarios of how the models perform in predicting the high flows in Klodzko. At the initial phase of the high flow, well before the rising limb of hydrograph, the two-model ensemble is found to provide the most skilful prognoses of water levels. However, while forecasting the rising limb of hydrograph, either the two-model solution or the vector autoregressive model offers the best predictive performance. In addition, it is hypothesized that along with the development of the rising limb phase, the vector autoregression becomes the most skilful approach amongst the scrutinized ones. Our simple two-model exercise confirms that multimodel hydrologic ensemble predictions cannot be treated as universal solutions suitable for forecasting the entire high flow event, but their superior performance may hold only for certain phases of a high flow.

  18. 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

  19. An Ensemble Recentering Kalman Filter with an Application to Argo Temperature Data Assimilation into the NASA GEOS-5 Coupled Model

    Science.gov (United States)

    Keppenne, Christian L.

    2013-01-01

    A two-step ensemble recentering Kalman filter (ERKF) analysis scheme is introduced. The algorithm consists of a recentering step followed by an ensemble Kalman filter (EnKF) analysis step. The recentering step is formulated such as to adjust the prior distribution of an ensemble of model states so that the deviations of individual samples from the sample mean are unchanged but the original sample mean is shifted to the prior position of the most likely particle, where the likelihood of each particle is measured in terms of closeness to a chosen subset of the observations. The computational cost of the ERKF is essentially the same as that of a same size EnKF. The ERKF is applied to the assimilation of Argo temperature profiles into the OGCM component of an ensemble of NASA GEOS-5 coupled models. Unassimilated Argo salt data are used for validation. A surprisingly small number (16) of model trajectories is sufficient to significantly improve model estimates of salinity over estimates from an ensemble run without assimilation. The two-step algorithm also performs better than the EnKF although its performance is degraded in poorly observed regions.

  20. The sea-level fingerprint of the Antarctic ice sheet: an ensemble GIA modelling approach

    Science.gov (United States)

    Spada, Giorgio; Galassi, Gaia; Melini, Daniele

    2017-04-01

    During the last decade, Glacial Isostatic Adjustment (GIA) modelling has seen a considerable development, stimulated by the increasing number and quality of sea-level observations and of various geodetic constraints. The fundamental equation of GIA (the Sea Level Equation) accounts for a number of physical ingredients that make GIA modelling quite realistic, such as rotational effects on sea-level change, the migration of the shorelines, and the time-evolving topography in the presence of marine based ice. However, concerning the spatiotemporal distribution of the late-Pleistocene ice sheets, the GIA models published in the literature by different groups are characterised by significantly different features. These are the volumes of the ice sheets at the Last Glacial Maximum, the presence and the duration of abrupt melting episodes (meltwater pulses) and the timing of the end of deglaciation. These differences can be mainly attributed to the different sets of proxies employed to constrain the melting chronology and, sometimes, to different assumptions about the Earth's viscosity profile. One of most important sources of uncertainty is the melting chronology of the Antarctic ice sheet, which is poorly constrained by the limited amount of relative sea-level data available in the near field of the ice sheet. To test whether the GIA models developed so far for the deglaciation of Antarctic ice sheet are converging or not towards a unique solution, here we collectively consider the models of the melting history of Antarctica published in the literature so far and for each of them we solve the Sea Level Equation. Following a multi-model ensemble approach, we estimate the ensemble mean and its uncertainty, in terms of the geometry and of the time history of the sea-level fingerprints.

  1. Archive Access to the THORPEX Interactive Grand Global Ensemble (TIGGE) Suite of Model Output

    Science.gov (United States)

    Rutledge, G. K.; Schuster, D.; Worley, S.; Stepaniak, D.; Toth, Z.; Zhu, Y.; Bougeault, P.; Anthony, S.

    2008-05-01

    The World Meteorological Organization (WMO) Observing System Research and Predictability EXperiment (THORPEX) Programme (THORPEX) Interactive Grand Global Ensemble (TIGGE), is a key component of the World Weather Research Programme intended to accelerate improvements in 1-day to 2-week weather forecasts. Centralized archives of ensemble model forecast data, from many international centers, are being used to enable extensive data sharing and research during Phase I of the project. The designated TIGGE archive centers include the Chinese Meteorological Administration (CMA), The European Center for Medium-Range Weather Forecasts (ECMWF), and The National Center for Atmospheric Research (NCAR). Scientific data requirements and archive planning solidified in late 2005, and archive collection was initiated in October 2006 with receipt of partial sets of parameters from multiple data providers. Ten operational weather forecasting centers producing daily global ensemble forecasts to 1-2 weeks ahead have agreed to deliver in near-real-time a selection of forecast data to the TIGGE data archives at CMA, ECMWF and NCAR. The objective of TIGGE (GEO task WE-06-03) is to establish closer cooperation between the academic and operational community by encouraging use of operational products for research, and to explore actively the concept and benefits of multi- model probabilistic weather forecasts, with a particular focus on severe weather prediction. The future operational use of the TIGGE infrastructure as part of a "Global Interactive Forecasting System" will be considered, subject to positive results from research undertaken with the TIGGE data archives. The Unidata Internet Data Distribution (IDD) system is the primary mode used to transport ensemble model data from the data providers to the archive centers. ECMWF acts as one initial collection point to collect model output from the Japanese Meteorological Agency (JMA), Korea Meteorological Administration (KMA), Meteo

  2. The diagnostics of diabetes mellitus based on ensemble modeling and hair/urine element level analysis.

    Science.gov (United States)

    Chen, Hui; Tan, Chao; Lin, Zan; Wu, Tong

    2014-07-01

    The aim of the present work focuses on exploring the feasibility of analyzing the relationship between diabetes mellitus and several element levels in hair/urine specimens by chemometrics. A dataset involving 211 specimens and eight element concentrations was used. The control group was divided into three age subsets in order to analyze the influence of age. It was found that the most obvious difference was the effect of age on the level of zinc and iron. The decline of iron concentration with age in hair was exactly consistent with the opposite trend in urine. Principal component analysis (PCA) was used as a tool for a preliminary evaluation of the data. Both ensemble and single support vector machine (SVM) algorithms were used as the classification tools. On average, the accuracy, sensitivity and specificity of ensemble SVM models were 99%, 100%, 99% and 97%, 89%, 99% for hair and urine samples, respectively. The findings indicate that hair samples are superior to urine samples. Even so, it can provide more valuable information for prevention, diagnostics, treatment and research of diabetes by simultaneously analyzing the hair and urine samples.

  3. Elastic network models capture the motions apparent within ensembles of RNA structures.

    Science.gov (United States)

    Zimmermann, Michael T; Jernigan, Robert L

    2014-06-01

    The role of structure and dynamics in mechanisms for RNA becomes increasingly important. Computational approaches using simple dynamics models have been successful at predicting the motions of proteins and are often applied to ribonucleo-protein complexes but have not been thoroughly tested for well-packed nucleic acid structures. In order to characterize a true set of motions, we investigate the apparent motions from 16 ensembles of experimentally determined RNA structures. These indicate a relatively limited set of motions that are captured by a small set of principal components (PCs). These limited motions closely resemble the motions computed from low frequency normal modes from elastic network models (ENMs), either at atomic or coarse-grained resolution. Various ENM model types, parameters, and structure representations are tested here against the experimental RNA structural ensembles, exposing differences between models for proteins and for folded RNAs. Differences in performance are seen, depending on the structure alignment algorithm used to generate PCs, modulating the apparent utility of ENMs but not significantly impacting their ability to generate functional motions. The loss of dynamical information upon coarse-graining is somewhat larger for RNAs than for globular proteins, indicating, perhaps, the lower cooperativity of the less densely packed RNA. However, the RNA structures show less sensitivity to the elastic network model parameters than do proteins. These findings further demonstrate the utility of ENMs and the appropriateness of their application to well-packed RNA-only structures, justifying their use for studying the dynamics of ribonucleo-proteins, such as the ribosome and regulatory RNAs.

  4. 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...

  5. Ensemble downscaling in coupled solar wind-magnetosphere modeling for space weather forecasting.

    Science.gov (United States)

    Owens, M J; Horbury, T S; Wicks, R T; McGregor, S L; Savani, N P; Xiong, M

    2014-06-01

    Advanced forecasting of space weather requires simulation of the whole Sun-to-Earth system, which necessitates driving magnetospheric models with the outputs from solar wind models. This presents a fundamental difficulty, as the magnetosphere is sensitive to both large-scale solar wind structures, which can be captured by solar wind models, and small-scale solar wind "noise," which is far below typical solar wind model resolution and results primarily from stochastic processes. Following similar approaches in terrestrial climate modeling, we propose statistical "downscaling" of solar wind model results prior to their use as input to a magnetospheric model. As magnetospheric response can be highly nonlinear, this is preferable to downscaling the results of magnetospheric modeling. To demonstrate the benefit of this approach, we first approximate solar wind model output by smoothing solar wind observations with an 8 h filter, then add small-scale structure back in through the addition of random noise with the observed spectral characteristics. Here we use a very simple parameterization of noise based upon the observed probability distribution functions of solar wind parameters, but more sophisticated methods will be developed in the future. An ensemble of results from the simple downscaling scheme are tested using a model-independent method and shown to add value to the magnetospheric forecast, both improving the best estimate and quantifying the uncertainty. We suggest a number of features desirable in an operational solar wind downscaling scheme.

  6. Landmine detection using ensemble discrete hidden Markov models with context dependent training methods

    Science.gov (United States)

    Hamdi, Anis; Missaoui, Oualid; Frigui, Hichem; Gader, Paul

    2010-04-01

    We propose a landmine detection algorithm that uses ensemble discrete hidden Markov models with context dependent training schemes. We hypothesize that the data are generated by K models. These different models reflect the fact that mines and clutter objects have different characteristics depending on the mine type, soil and weather conditions, and burial depth. Model identification is based on clustering in the log-likelihood space. First, one HMM is fit to each of the N individual sequence. For each fitted model, we evaluate the log-likelihood of each sequence. This will result in an N x N log-likelihood distance matrix that will be partitioned into K groups. In the second step, we learn the parameters of one discrete HMM per group. We propose using and optimizing various training approaches for the different K groups depending on their size and homogeneity. In particular, we will investigate the maximum likelihood, and the MCE-based discriminative training approaches. Results on large and diverse Ground Penetrating Radar data collections show that the proposed method can identify meaningful and coherent HMM models that describe different properties of the data. Each HMM models a group of alarm signatures that share common attributes such as clutter, mine type, and burial depth. Our initial experiments have also indicated that the proposed mixture model outperform the baseline HMM that uses one model for the mine and one model for the background.

  7. Surrogate model based iterative ensemble smoother for subsurface flow data assimilation

    Science.gov (United States)

    Chang, Haibin; Liao, Qinzhuo; Zhang, Dongxiao

    2017-02-01

    Subsurface geological formation properties often involve some degree of uncertainty. Thus, for most conditions, uncertainty quantification and data assimilation are necessary for predicting subsurface flow. The surrogate model based method is one common type of uncertainty quantification method, in which a surrogate model is constructed for approximating the relationship between model output and model input. Based on the prediction ability, the constructed surrogate model can be utilized for performing data assimilation. In this work, we develop an algorithm for implementing an iterative ensemble smoother (ES) using the surrogate model. We first derive an iterative ES scheme using a regular routine. In order to utilize surrogate models, we then borrow the idea of Chen and Oliver (2013) to modify the Hessian, and further develop an independent parameter based iterative ES formula. Finally, we establish the algorithm for the implementation of iterative ES using surrogate models. Two surrogate models, the PCE surrogate and the interpolation surrogate, are introduced for illustration. The performances of the proposed algorithm are tested by synthetic cases. The results show that satisfactory data assimilation results can be obtained by using surrogate models that have sufficient accuracy.

  8. 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.

  9. 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

    2016-06-01

    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.

  10. Arctic climate changes in the 21st century: Ensemble model estimates accounting for realism in present-day climate simulation

    Science.gov (United States)

    Eliseev, A. V.; Semenov, V. A.

    2016-11-01

    In the course of forecasting future climate changes in the Arctic Region based on calculations and an ensemble of the state-of-the-art global climate models, the results depend on the method of construction the statistics from the models.

  11. Comparison of ensemble post-processing approaches, based on empirical and dynamical error modelisation of rainfall-runoff model forecasts

    Science.gov (United States)

    Chardon, J.; Mathevet, T.; Le Lay, M.; Gailhard, J.

    2012-04-01

    In the context of a national energy company (EDF : Electricité de France), hydro-meteorological forecasts are necessary to ensure safety and security of installations, meet environmental standards and improve water ressources management and decision making. Hydrological ensemble forecasts allow a better representation of meteorological and hydrological forecasts uncertainties and improve human expertise of hydrological forecasts, which is essential to synthesize available informations, coming from different meteorological and hydrological models and human experience. An operational hydrological ensemble forecasting chain has been developed at EDF since 2008 and is being used since 2010 on more than 30 watersheds in France. This ensemble forecasting chain is characterized ensemble pre-processing (rainfall and temperature) and post-processing (streamflow), where a large human expertise is solicited. The aim of this paper is to compare 2 hydrological ensemble post-processing methods developed at EDF in order improve ensemble forecasts reliability (similar to Monatanari &Brath, 2004; Schaefli et al., 2007). The aim of the post-processing methods is to dress hydrological ensemble forecasts with hydrological model uncertainties, based on perfect forecasts. The first method (called empirical approach) is based on a statistical modelisation of empirical error of perfect forecasts, by streamflow sub-samples of quantile class and lead-time. The second method (called dynamical approach) is based on streamflow sub-samples of quantile class and streamflow variation, and lead-time. On a set of 20 watersheds used for operational forecasts, results show that both approaches are necessary to ensure a good post-processing of hydrological ensemble, allowing a good improvement of reliability, skill and sharpness of ensemble forecasts. The comparison of the empirical and dynamical approaches shows the limits of the empirical approach which is not able to take into account hydrological

  12. Seasonal Drought Prediction in East Africa: Can National Multi-Model Ensemble Forecasts Help?

    Science.gov (United States)

    Shukla, Shraddhanand; Roberts, J. B.; Funk, Christopher; Robertson, F. R.; Hoell, Andrew

    2015-01-01

    The increasing food and water demands of East Africa's growing population are stressing the region's inconsistent water resources and rain-fed agriculture. As recently as in 2011 part of this region underwent one of the worst famine events in its history. Timely and skillful drought forecasts at seasonal scale for this region can inform better water and agro-pastoral management decisions, support optimal allocation of the region's water resources, and mitigate socio-economic losses incurred by droughts. However seasonal drought prediction in this region faces several challenges. Lack of skillful seasonal rainfall forecasts; the focus of this presentation, is one of those major challenges. In the past few decades, major strides have been taken towards improvement of seasonal scale dynamical climate forecasts. The National Centers for Environmental Prediction's (NCEP) National Multi-model Ensemble (NMME) is one such state-of-the-art dynamical climate forecast system. The NMME incorporates climate forecasts from 6+ fully coupled dynamical models resulting in 100+ ensemble member forecasts. Recent studies have indicated that in general NMME offers improvement over forecasts from any single model. However thus far the skill of NMME for forecasting rainfall in a vulnerable region like the East Africa has been unexplored. In this presentation we report findings of a comprehensive analysis that examines the strength and weakness of NMME in forecasting rainfall at seasonal scale in East Africa for all three of the prominent seasons for the region. (i.e. March-April-May, July-August-September and October-November- December). Simultaneously we also describe hybrid approaches; that combine statistical approaches with NMME forecasts; to improve rainfall forecast skill in the region when raw NMME forecasts lack in skill.

  13. Validation of precipitation over Japan during 1985-2004 simulated by three regional climate models and two multi-model ensemble means

    Energy Technology Data Exchange (ETDEWEB)

    Ishizaki, Yasuhiro [Meteorological Research Institute, Tsukuba (Japan); National Institute for Environmental Studies, Tsukuba (Japan); Nakaegawa, Toshiyuki; Takayabu, Izuru [Meteorological Research Institute, Tsukuba (Japan)

    2012-07-15

    We dynamically downscaled Japanese reanalysis data (JRA-25) for 60 regions of Japan using three regional climate models (RCMs): the Non-Hydrostatic Regional Climate Model (NHRCM), modified RAMS version 4.3 (NRAMS), and modified Weather Research and Forecasting model (TWRF). We validated their simulations of the precipitation climatology and interannual variations of summer and winter precipitation. We also validated precipitation for two multi-model ensemble means: the arithmetic ensemble mean (AEM) and an ensemble mean weighted according to model reliability. In the 60 regions NRAMS simulated both the winter and summer climatological precipitation better than JRA-25, and NHRCM simulated the wintertime precipitation better than JRA-25. TWRF, however, overestimated precipitation in the 60 regions in both the winter and summer, and NHRCM overestimated precipitation in the summer. The three RCMs simulated interannual variations, particularly summer precipitation, better than JRA-25. AEM simulated both climatological precipitation and interannual variations during the two seasons more realistically than JRA-25 and the three RCMs overall, but the best RCM was often superior to the AEM result. In contrast, the weighted ensemble mean skills were usually superior to those of the best RCM. Thus, both RCMs and multi-model ensemble means, especially multi-model ensemble means weighted according to model reliability, are powerful tools for simulating seasonal and interannual variability of precipitation in Japan under the current climate. (orig.)

  14. Future precipitation in Portugal: high-resolution projections using WRF model and EURO-CORDEX multi-model ensembles

    Science.gov (United States)

    Soares, Pedro M. M.; Cardoso, Rita M.; Lima, Daniela C. A.; Miranda, Pedro M. A.

    2016-11-01

    Portugal, which is located in the west limit of the Mediterranean subtropics, is a small region with a complex orography with large precipitation gradients and interannual variability. In this study, the newer and higher resolution regional climate simulations, covering Portugal, are evaluated in present climate and used to investigate the rainfall projections for the end of the twenty-first century, following the RCP4.5 and RCP8.5 emission scenarios. The EURO-CORDEX historical simulations, at 0.11° and at 0.44° resolution, are evaluated against gridded observations of precipitation, which allows the assembly of four multi-model ensembles. An extra simulation, at even higher resolution (9 km) with WRF is also analysed. In present climate, the models are able to describe the precipitation temporal and spatial patterns as well its distributions, although there is a large spread and an overestimation of larger rainfall quantiles. The multi-model ensembles show that selecting the best performing models adds quality to the overall representation of rainfall. The high-resolution simulations augment the spatial details of precipitation, but objectively do not seem to add value with respect to the coarse resolution. Regarding the RCP8.5 scenario, WRF and the multi-model ensembles consistently predict important losses of precipitation in Portugal in spring, summer and autumn, ranging from -10% and -50%. For all seasons, the changes are more severe in the southern basins. The precipitation distributions show, for all models, important reductions of the contribution from low to moderate/high precipitation bins and augments of days with strong rainfall. Furthermore, a prominent growth of high-ranking percentiles is predicted reaching values over 70% in some regions. Generally, the changes associated with the RCP4.5 scenario have the same signal and features, but with smaller magnitudes.

  15. Data Base Extension for the Ensemble Model Using a Flexible Implementation

    CERN Document Server

    Ackermann, Wolfgang

    2005-01-01

    To guarantee an adequate design and a proper functionality of various machine components it is of great importance to perform detailed studies of charged particle transport. However, it is often not necessary to initiate individual kinetic simulations. When the evolution of integral quantities is of research interest, it is worth treating an investigated particle ensemble as a whole and applying a macroscopic formulation. Using a collision-less kinetic approach, the simplified model is derived from the well-known Vlasov equation. Instead of solving directly this equation, one can use moments of the density function obtained by means of an averaging process. This formalism had been implemented into the beam dynamics simulation program V-Code and a fundamental database of various beam line elements like cavities, drift spaces, solenoids, quadrupoles and steerers was set up. A flexible realization of the C++ code representing the cavities and the drift spaces can be automatically used for an arbitrary order of m...

  16. Using Ensemble Models to Classify the Sentiment Expressed in Suicide Notes

    Science.gov (United States)

    McCart, James A.; Finch, Dezon K.; Jarman, Jay; Hickling, Edward; Lind, Jason D.; Richardson, Matthew R.; Berndt, Donald J.; Luther, Stephen L.

    2012-01-01

    In 2007, suicide was the tenth leading cause of death in the U.S. Given the significance of this problem, suicide was the focus of the 2011 Informatics for Integrating Biology and the Bedside (i2b2) Natural Language Processing (NLP) shared task competition (track two). Specifically, the challenge concentrated on sentiment analysis, predicting the presence or absence of 15 emotions (labels) simultaneously in a collection of suicide notes spanning over 70 years. Our team explored multiple approaches combining regular expression-based rules, statistical text mining (STM), and an approach that applies weights to text while accounting for multiple labels. Our best submission used an ensemble of both rules and STM models to achieve a micro-averaged F1 score of 0.5023, slightly above the mean from the 26 teams that competed (0.4875). PMID:22879763

  17. Exact and approximate ensemble treatments of thermal pairing in a multilevel model

    Science.gov (United States)

    Hung, N. Quang; Dang, N. Dinh

    2009-05-01

    A systematic comparison is conducted for pairing properties of finite systems at nonzero temperature as predicted by the exact solutions of the pairing problem embedded in three principal statistical ensembles, as well as the unprojected (FTBCS1+SCQRPA) and Lipkin+Nogami projected (FTLN1+SCQRPA) theories that include the quasiparticle number fluctuation and coupling to pair vibrations within the self-consistent quasiparticle random-phase approximation. The numerical calculations are performed for the pairing gap, total energy, heat capacity, entropy, and microcanonical temperature within the doubly folded equidistant multilevel pairing model. The FTLN1+SCQRPA predictions agree best with the exact grand-canonical results. In general, all approaches clearly show that the superfluid-normal phase transition is smoothed out in finite systems. A novel formula is suggested for extracting the empirical pairing gap in reasonable agreement with the exact canonical results.

  18. Exact and approximate ensemble treatments of thermal pairing in a multilevel model

    CERN Document Server

    Hung, N Quang

    2009-01-01

    A systematic comparison is conducted for pairing properties of finite systems at nonzero temperature as predicted by the exact solutions of the pairing problem embedded in three principal statistical ensembles, as well as the unprojected (FTBCS1+SCQRPA) and Lipkin-Nogami projected (FTLN1+SCQRPA) theories that include the quasiparticle number fluctuation and coupling to pair vibrations within the self-consistent quasiparticle random-phase approximation. The numerical calculations are performed for the pairing gap, total energy, heat capacity, entropy, and microcanonical temperature within the doubly-folded equidistant multilevel pairing model. The FTLN1+SCQRPA predictions agree best with the exact grand-canonical results. In general, all approaches clearly show that the superfluid-normal phase transition is smoothed out in finite systems. A novel formula is suggested for extracting the empirical pairing gap in reasonable agreement with the exact canonical results.

  19. Using ensemble models to classify the sentiment expressed in suicide notes.

    Science.gov (United States)

    McCart, James A; Finch, Dezon K; Jarman, Jay; Hickling, Edward; Lind, Jason D; Richardson, Matthew R; Berndt, Donald J; Luther, Stephen L

    2012-01-01

    In 2007, suicide was the tenth leading cause of death in the U.S. Given the significance of this problem, suicide was the focus of the 2011 Informatics for Integrating Biology and the Bedside (i2b2) Natural Language Processing (NLP) shared task competition (track two). Specifically, the challenge concentrated on sentiment analysis, predicting the presence or absence of 15 emotions (labels) simultaneously in a collection of suicide notes spanning over 70 years. Our team explored multiple approaches combining regular expression-based rules, statistical text mining (STM), and an approach that applies weights to text while accounting for multiple labels. Our best submission used an ensemble of both rules and STM models to achieve a micro-averaged F(1) score of 0.5023, slightly above the mean from the 26 teams that competed (0.4875).

  20. Ensemble Methods

    Science.gov (United States)

    Re, Matteo; Valentini, Giorgio

    2012-03-01

    Ensemble methods are statistical and computational learning procedures reminiscent of the human social learning behavior of seeking several opinions before making any crucial decision. The idea of combining the opinions of different "experts" to obtain an overall “ensemble” decision is rooted in our culture at least from the classical age of ancient Greece, and it has been formalized during the Enlightenment with the Condorcet Jury Theorem[45]), which proved that the judgment of a committee is superior to those of individuals, provided the individuals have reasonable competence. Ensembles are sets of learning machines that combine in some way their decisions, or their learning algorithms, or different views of data, or other specific characteristics to obtain more reliable and more accurate predictions in supervised and unsupervised learning problems [48,116]. A simple example is represented by the majority vote ensemble, by which the decisions of different learning machines are combined, and the class that receives the majority of “votes” (i.e., the class predicted by the majority of the learning machines) is the class predicted by the overall ensemble [158]. In the literature, a plethora of terms other than ensembles has been used, such as fusion, combination, aggregation, and committee, to indicate sets of learning machines that work together to solve a machine learning problem [19,40,56,66,99,108,123], but in this chapter we maintain the term ensemble in its widest meaning, in order to include the whole range of combination methods. Nowadays, ensemble methods represent one of the main current research lines in machine learning [48,116], and the interest of the research community on ensemble methods is witnessed by conferences and workshops specifically devoted to ensembles, first of all the multiple classifier systems (MCS) conference organized by Roli, Kittler, Windeatt, and other researchers of this area [14,62,85,149,173]. Several theories have been

  1. Assessing Uncertainties of Water Footprints Using an Ensemble of Crop Growth Models on Winter Wheat

    Directory of Open Access Journals (Sweden)

    Kurt Christian Kersebaum

    2016-12-01

    Full Text Available Crop productivity and water consumption form the basis to calculate the water footprint (WF of a specific crop. Under current climate conditions, calculated evapotranspiration is related to observed crop yields to calculate WF. The assessment of WF under future climate conditions requires the simulation of crop yields adding further uncertainty. To assess the uncertainty of model based assessments of WF, an ensemble of crop models was applied to data from five field experiments across Europe. Only limited data were provided for a rough calibration, which corresponds to a typical situation for regional assessments, where data availability is limited. Up to eight models were applied for wheat. The coefficient of variation for the simulated actual evapotranspiration between models was in the range of 13%–19%, which was higher than the inter-annual variability. Simulated yields showed a higher variability between models in the range of 17%–39%. Models responded differently to elevated CO2 in a FACE (Free-Air Carbon Dioxide Enrichment experiment, especially regarding the reduction of water consumption. The variability of calculated WF between models was in the range of 15%–49%. Yield predictions contributed more to this variance than the estimation of water consumption. Transpiration accounts on average for 51%–68% of the total actual evapotranspiration.

  2. Multi-model ensemble schemes for predicting northeast monsoon rainfall over peninsular India

    Indian Academy of Sciences (India)

    Nachiketa Acharya; S C Kar; Makarand A Kulkarni; U C Mohanty; L N Sahoo

    2011-10-01

    The northeast (NE) monsoon season (October, November and December) is the major period of rainfall activity over south peninsular India. This study is mainly focused on the prediction of northeast monsoon rainfall using lead-1 products (forecasts for the season issued in beginning of September) of seven general circulation models (GCMs). An examination of the performances of these GCMs during hindcast runs (1982–2008) indicates that these models are not able to simulate the observed interannual variability of rainfall. Inaccurate response of the models to sea surface temperatures may be one of the probable reasons for the poor performance of these models to predict seasonal mean rainfall anomalies over the study domain. An attempt has been made to improve the accuracy of predicted rainfall using three different multi-model ensemble (MME) schemes, viz., simple arithmetic mean of models (EM), principal component regression (PCR) and singular value decomposition based multiple linear regressions (SVD). It is found out that among these three schemes, SVD based MME has more skill than other MME schemes as well as member models.

  3. Error reduction and representation in stages (ERRIS) in hydrological modelling for ensemble streamflow forecasting

    Science.gov (United States)

    Li, Ming; Wang, Q. J.; Bennett, James C.; Robertson, David E.

    2016-09-01

    This study develops a new error modelling method for ensemble short-term and real-time streamflow forecasting, called error reduction and representation in stages (ERRIS). The novelty of ERRIS is that it does not rely on a single complex error model but runs a sequence of simple error models through four stages. At each stage, an error model attempts to incrementally improve over the previous stage. Stage 1 establishes parameters of a hydrological model and parameters of a transformation function for data normalization, Stage 2 applies a bias correction, Stage 3 applies autoregressive (AR) updating, and Stage 4 applies a Gaussian mixture distribution to represent model residuals. In a case study, we apply ERRIS for one-step-ahead forecasting at a range of catchments. The forecasts at the end of Stage 4 are shown to be much more accurate than at Stage 1 and to be highly reliable in representing forecast uncertainty. Specifically, the forecasts become more accurate by applying the AR updating at Stage 3, and more reliable in uncertainty spread by using a mixture of two Gaussian distributions to represent the residuals at Stage 4. ERRIS can be applied to any existing calibrated hydrological models, including those calibrated to deterministic (e.g. least-squares) objectives.

  4. Drought Prediction over the United States Using the North American Multi Model Ensemble

    Science.gov (United States)

    Mo, K. C.; Lettenmaier, D. P.

    2014-12-01

    We analyzed the skill of drought forecasts over the United States based on drought indices derived from the hydroclimate forecasts from the North American Multi model ensemble (NMME). The test period is from 1982-2010 and forecasts are initialized from the beginning of January, April, July and October. We analyzed the forecast skill of drought indices such as the 6-month standardized precipitation index (SPI6), monthly mean soil moisture percentiles (SMP) and the 3-month standardized runoff index (SRI3). The soil moisture and runoff were obtained by drive the Variable Infiltration Capacity land model with forcing derived from the NMME members (NMME_VIC). Drought indices from each member were computed and they were put into percentiles determined from all members in the training period at a given lead. We then formed an ensemble grand mean by averaging all indices together and determined the concurrence measure which is the extent to which all different members agree. We find that : 1) The grand mean has higher skill than individual member; 2) During winter, forecasts are skillful in the regimes where the initial conditions dominant contributions to skill, the agreement between the grand mean and members are above 70-80% . At high leads, the concurrence measure drops to 50-60%, even when forecasts are unskillful. 3) During summer, forecast skill is low and concurrence measure drops to 10-30%, 4). The skill of drought forecasts is regionally and seasonally dependent. The NMME_VIC forecasts tend to over forecast drought events with large false alarm rate. After lead-1, the low thread score indicates no skill. The forecast errors will be analyzed to determine the origin of forecast skill.

  5. A global water resources ensemble of hydrological models: the eartH2Observe Tier-1 dataset

    Directory of Open Access Journals (Sweden)

    J. Schellekens

    2017-07-01

    Full Text Available The dataset presented here consists of an ensemble of 10 global hydrological and land surface models for the period 1979–2012 using a reanalysis-based meteorological forcing dataset (0.5° resolution. The current dataset serves as a state of the art in current global hydrological modelling and as a benchmark for further improvements in the coming years. A signal-to-noise ratio analysis revealed low inter-model agreement over (i snow-dominated regions and (ii tropical rainforest and monsoon areas. The large uncertainty of precipitation in the tropics is not reflected in the ensemble runoff. Verification of the results against benchmark datasets for evapotranspiration, snow cover, snow water equivalent, soil moisture anomaly and total water storage anomaly using the tools from The International Land Model Benchmarking Project (ILAMB showed overall useful model performance, while the ensemble mean generally outperformed the single model estimates. The results also show that there is currently no single best model for all variables and that model performance is spatially variable. In our unconstrained model runs the ensemble mean of total runoff into the ocean was 46 268 km3 yr−1 (334 kg m−2 yr−1, while the ensemble mean of total evaporation was 537 kg m−2 yr−1. All data are made available openly through a Water Cycle Integrator portal (WCI, wci.earth2observe.eu, and via a direct http and ftp download. The portal follows the protocols of the open geospatial consortium such as OPeNDAP, WCS and WMS. The DOI for the data is https://doi.org/10.1016/10.5281/zenodo.167070.

  6. A global water resources ensemble of hydrological models: the eartH2Observe Tier-1 dataset

    Science.gov (United States)

    Schellekens, Jaap; Dutra, Emanuel; Martínez-de la Torre, Alberto; Balsamo, Gianpaolo; van Dijk, Albert; Sperna Weiland, Frederiek; Minvielle, Marie; Calvet, Jean-Christophe; Decharme, Bertrand; Eisner, Stephanie; Fink, Gabriel; Flörke, Martina; Peßenteiner, Stefanie; van Beek, Rens; Polcher, Jan; Beck, Hylke; Orth, René; Calton, Ben; Burke, Sophia; Dorigo, Wouter; Weedon, Graham P.

    2017-07-01

    The dataset presented here consists of an ensemble of 10 global hydrological and land surface models for the period 1979-2012 using a reanalysis-based meteorological forcing dataset (0.5° resolution). The current dataset serves as a state of the art in current global hydrological modelling and as a benchmark for further improvements in the coming years. A signal-to-noise ratio analysis revealed low inter-model agreement over (i) snow-dominated regions and (ii) tropical rainforest and monsoon areas. The large uncertainty of precipitation in the tropics is not reflected in the ensemble runoff. Verification of the results against benchmark datasets for evapotranspiration, snow cover, snow water equivalent, soil moisture anomaly and total water storage anomaly using the tools from The International Land Model Benchmarking Project (ILAMB) showed overall useful model performance, while the ensemble mean generally outperformed the single model estimates. The results also show that there is currently no single best model for all variables and that model performance is spatially variable. In our unconstrained model runs the ensemble mean of total runoff into the ocean was 46 268 km3 yr-1 (334 kg m-2 yr-1), while the ensemble mean of total evaporation was 537 kg m-2 yr-1. All data are made available openly through a Water Cycle Integrator portal (WCI, wci.earth2observe.eu), and via a direct http and ftp download. The portal follows the protocols of the open geospatial consortium such as OPeNDAP, WCS and WMS. The DOI for the data is https://doi.org/10.1016/10.5281/zenodo.167070.

  7. Cumulus convection and the terrestrial water-vapor distribution

    Science.gov (United States)

    Donner, Leo J.

    1988-01-01

    Cumulus convection plays a significant role in determining the structure of the terrestrial water vapor field. Cumulus convection acts directly on the moisture field by condensing and precipitating water vapor and by redistributing water vapor through cumulus induced eddy circulations. The mechanisms by which cumulus convection influences the terrestrial water vapor distribution is outlined. Calculations using a theory due to Kuo is used to illustrate the mechanisms by which cumulus convection works. Understanding of these processes greatly aids the ability of researchers to interpret the seasonal and spatial distribution of atmospheric water vapor by providing information on the nature of sources and sinks and the global circulation.

  8. A CN-Based Ensembled Hydrological Model for Enhanced Watershed Runoff Prediction

    Directory of Open Access Journals (Sweden)

    Muhammad Ajmal

    2016-01-01

    Full Text Available A major structural inconsistency of the traditional curve number (CN model is its dependence on an unstable fixed initial abstraction, which normally results in sudden jumps in runoff estimation. Likewise, the lack of pre-storm soil moisture accounting (PSMA procedure is another inherent limitation of the model. To circumvent those problems, we used a variable initial abstraction after ensembling the traditional CN model and a French four-parameter (GR4J model to better quantify direct runoff from ungauged watersheds. To mimic the natural rainfall-runoff transformation at the watershed scale, our new parameterization designates intrinsic parameters and uses a simple structure. It exhibited more accurate and consistent results than earlier methods in evaluating data from 39 forest-dominated watersheds, both for small and large watersheds. In addition, based on different performance evaluation indicators, the runoff reproduction results show that the proposed model produced more consistent results for dry, normal, and wet watershed conditions than the other models used in this study.

  9. What can we gain by using Bayesian methods to combine information from a multi-model ensemble?

    Science.gov (United States)

    Jonko, A. K.; Urban, N. M.

    2016-12-01

    Multi-model ensembles are used extensively to study both future climate projections and properties of the climate system such as climate sensitivity and feedbacks. Individual climate model projections generally disagree with one another, can be biased and are not independent. How to combine results from various models to assess their projections and the uncertainties associated with them is a difficult, but important question. Many different approaches, ranging from giving each model one vote, to model weighting and Bayesian methods, have been used to date. Here we evaluate the utility of a Bayesian reduced model framework relative to a simple pooling of global climate model (GCM) projections. Rather than focusing on the discrete projections made by individual GCMs, this approach allows us to generate probabilistic projections that smoothly interpolate between the dynamics of the multi-model ensemble. The simple model is an idealized ocean atmosphere energy balance model (EBM), fit to surface temperatures of GCMs participating in the Coupled Model Intercomparison Project version 5 (CMIP5) by tuning several parameters, including equilibrium climate sensitivity, forcing and feedback. We derive probability distributions of the reduced model parameters for each GCM individually as well as jointly for all GCMs in a Bayesian hierarchical modeling framework, using CMIP5 abrupt CO2 quadrupling simulations. We then compare climate sensitivity and feedback estimates as well as temperature projections for historical and RCP8.5 scenarios generated using these two approaches to results obtained from the multi-model ensemble alone.

  10. 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

  11. Ensemble modeling of the likely public health impact of a pre-erythrocytic malaria vaccine.

    Directory of Open Access Journals (Sweden)

    Thomas Smith

    2012-01-01

    Full Text Available BACKGROUND: The RTS,S malaria vaccine may soon be licensed. Models of impact of such vaccines have mainly considered deployment via the World Health Organization's Expanded Programme on Immunization (EPI in areas of stable endemic transmission of Plasmodium falciparum, and have been calibrated for such settings. Their applicability to low transmission settings is unclear. Evaluations of the efficiency of different deployment strategies in diverse settings should consider uncertainties in model structure. METHODS AND FINDINGS: An ensemble of 14 individual-based stochastic simulation models of P. falciparum dynamics, with differing assumptions about immune decay, transmission heterogeneity, and treatment access, was constructed. After fitting to an extensive library of field data, each model was used to predict the likely health benefits of RTS,S deployment, via EPI (with or without catch-up vaccinations, supplementary vaccination of school-age children, or mass vaccination every 5 y. Settings with seasonally varying transmission, with overall pre-intervention entomological inoculation rates (EIRs of two, 11, and 20 infectious bites per person per annum, were considered. Predicted benefits of EPI vaccination programs over the simulated 14-y time horizon were dependent on duration of protection. Nevertheless, EPI strategies (with an initial catch-up phase averted the most deaths per dose at the higher EIRs, although model uncertainty increased with EIR. At two infectious bites per person per annum, mass vaccination strategies substantially reduced transmission, leading to much greater health effects per dose, even at modest coverage. CONCLUSIONS: In higher transmission settings, EPI strategies will be most efficient, but vaccination additional to the EPI in targeted low transmission settings, even at modest coverage, might be more efficient than national-level vaccination of infants. The feasibility and economics of mass vaccination, and the

  12. Large Deviations and Ensembles of Trajectories in Stochastic Models(Frontiers in Nonequilibrium Physics-Fundamental Theory, Glassy & Granular Materials, and Computational Physics-)

    OpenAIRE

    Robert L., JACK; Peter, SOLLICH; Department of Physics, University of Bath; King's College London, Department of Mathematics

    2010-01-01

    We consider ensembles of trajectories associated with large deviations of time-integrated quantities in stochastic models. Motivated by proposals that these ensembles are relevant for physical processes such as shearing and glassy relaxation, we show how they can be generated directly using auxiliary stochastic processes. We illustrate our results using the Glauber-Ising chain, for which biased ensembles of trajectories can exhibit ferromagnetic ordering. We discuss the relation between such ...

  13. Ensemble Data Mining Methods

    Science.gov (United States)

    Oza, Nikunj C.

    2004-01-01

    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 better prediction accuracy than any of the individual models could on their own. The basic goal when designing an ensemble is the same as when establishing a committee of people: each member of the committee should be as competent as possible, but the members should be complementary to one another. If the members are not complementary, Le., if they always agree, then the committee is unnecessary---any one member is sufficient. If the members are complementary, then when one or a few members make an error, the probability is high that the remaining members can correct this error. Research in ensemble methods has largely revolved around designing ensembles consisting of competent yet complementary models.

  14. A comparative assessment of GIS-based data mining models and a novel ensemble model in groundwater well potential mapping

    Science.gov (United States)

    Naghibi, Seyed Amir; Moghaddam, Davood Davoodi; Kalantar, Bahareh; Pradhan, Biswajeet; Kisi, Ozgur

    2017-05-01

    In recent years, application of ensemble models has been increased tremendously in various types of natural hazard assessment such as landslides and floods. However, application of this kind of robust models in groundwater potential mapping is relatively new. This study applied four data mining algorithms including AdaBoost, Bagging, generalized additive model (GAM), and Naive Bayes (NB) models to map groundwater potential. Then, a novel frequency ratio data mining ensemble model (FREM) was introduced and evaluated. For this purpose, eleven groundwater conditioning factors (GCFs), including altitude, slope aspect, slope angle, plan curvature, stream power index (SPI), river density, distance from rivers, topographic wetness index (TWI), land use, normalized difference vegetation index (NDVI), and lithology were mapped. About 281 well locations with high potential were selected. Wells were randomly partitioned into two classes for training the models (70% or 197) and validating them (30% or 84). AdaBoost, Bagging, GAM, and NB algorithms were employed to get groundwater potential maps (GPMs). The GPMs were categorized into potential classes using natural break method of classification scheme. In the next stage, frequency ratio (FR) value was calculated for the output of the four aforementioned models and were summed, and finally a GPM was produced using FREM. For validating the models, area under receiver operating characteristics (ROC) curve was calculated. The ROC curve for prediction dataset was 94.8, 93.5, 92.6, 92.0, and 84.4% for FREM, Bagging, AdaBoost, GAM, and NB models, respectively. The results indicated that FREM had the best performance among all the models. The better performance of the FREM model could be related to reduction of over fitting and possible errors. Other models such as AdaBoost, Bagging, GAM, and NB also produced acceptable performance in groundwater modelling. The GPMs produced in the current study may facilitate groundwater exploitation

  15. Supplementary Material for: Compressing an Ensemble With Statistical Models: An Algorithm for Global 3D Spatio-Temporal Temperature

    KAUST Repository

    Castruccio, Stefano

    2016-01-01

    One of the main challenges when working with modern climate model ensembles is the increasingly larger size of the data produced, and the consequent difficulty in storing large amounts of spatio-temporally resolved information. Many compression algorithms can be used to mitigate this problem, but since they are designed to compress generic scientific datasets, they do not account for the nature of climate model output and they compress only individual simulations. In this work, we propose a different, statistics-based approach that explicitly accounts for the space-time dependence of the data for annual global three-dimensional temperature fields in an initial condition ensemble. The set of estimated parameters is small (compared to the data size) and can be regarded as a summary of the essential structure of the ensemble output; therefore, it can be used to instantaneously reproduce the temperature fields in an ensemble with a substantial saving in storage and time. The statistical model exploits the gridded geometry of the data and parallelization across processors. It is therefore computationally convenient and allows to fit a nontrivial model to a dataset of 1 billion data points with a covariance matrix comprising of 1018 entries. Supplementary materials for this article are available online.

  16. A multi-model ensemble of downscaled spatial climate change scenarios for the Dommel catchment, Western Europe

    NARCIS (Netherlands)

    Vliet, van M.T.H.; Blenkinsop, S.; Burton, A.; Harpman, C.; Broers, H.P.; Fowler, H.J.

    2012-01-01

    Regional or local scale hydrological impact studies require high resolution climate change scenarios which should incorporate some assessment of uncertainties in future climate projections. This paper describes a method used to produce a multi-model ensemble of multivariate weather simulations inclu

  17. An Ensemble Nonlinear Model Predictive Control Algorithm in an Artificial Pancreas for People with Type 1 Diabetes

    DEFF Research Database (Denmark)

    Boiroux, Dimitri; Hagdrup, Morten; Mahmoudi, Zeinab

    2016-01-01

    This paper presents a novel ensemble nonlinear model predictive control (NMPC) algorithm for glucose regulation in type 1 diabetes. In this approach, we consider a number of scenarios describing different uncertainties, for instance meals or metabolic variations. We simulate a population of 9 pat...

  18. Assessing the future change of precipitation and reference evapotranspiration over Florida using ranked CMIP5 model ensemble

    Science.gov (United States)

    Hwang, S.; Chang, S. J.; Graham, W. D.

    2014-12-01

    The ultimate goal of this study is to assess future water vulnerability over Florida, based on the change in precipitation and evapotranspiration estimated using the most advanced Global Climate Model (GCM) ensemble. We evaluated the skills of CMIP5 (Climate Model Inter-comparison project, phase 5) climate models in reproducing retrospective climatology over the state of Florida for the key climate variables important from the hydrological and agricultural perspectives (i.e., precipitation (Precp), maximum and minimum temperature (Tmax and Tmin), and wind speed (Ws)). The biases of raw CMIP5 were estimated using two different grid-based observational datasets as references. Based on the accuracy of various predictors such as mean climatology, temporal variability, extreme frequency, etc., the GCMs were ranked for each of the different reference datasets, climate variables, and predictors. The variation of the ranks was examined and rank-based GCM weights were assigned. The weights were then used to develop future ensembles (for 4 different RCP gas-emission scenarios) for the annual cycle of monthly mean and variance of precipitation and reference evapotranspiration (ETo). Finally the differences between the retrospective and future ensembles were investigated to assess future climate change impacts on water vulnerability using simple indices (e.g., ETo/Precp., drought index, and Standardized Precp. index). The uncertainties of the assessment were quantified by the spread range of ensembles and a reliability factor for the GCMs estimated using a measure of model biases and convergence criterion.

  19. Simulation of the climatic effects of land use/land cover changes in eastern China using multi-model ensembles

    Science.gov (United States)

    Zhang, Xianliang; Xiong, Zhe; Zhang, Xuezhen; Shi, Ying; Liu, Jiyuan; Shao, Quanqin; Yan, Xiaodong

    2017-07-01

    Human activities have caused substantial land use/cover change (LUCC) in China, especially in northeast China, the Loess Plateau and southern China. Three high-resolution regional climate models were used to simulate the impacts of LUCC on climate through one control experiment and three land use change experiments from 1980 to 2000. The results showed that multi-regional climate model ensemble simulations (the arithmetic ensemble mean (AEM) and Bayesian model averaging (BMA)) provide more accurate results than a single model in over 70% grid cells of study regions. Uncertainty was reduced when using the two ensemble methods. The results of the AEM and BMA ensembles showed that the temperatures decreased by 0.2-0.4 °C in northeast China, the Yangtze river valley and the north of the Loess Plateau, and by 0.6-1.0 °C in the south of the Loess Plateau in spring, autumn and winter. The AEM precipitations changed by - 40-40 mm in in spring and winter, and by - 100-100 mm in summer and autumn, while the BMA precipitations changed by - 20-20 mm in spring, autumn and winter, and by - 50-50 mm in summer. The seasonal precipitation decreased in northeast China and the Yangtze river valley, and increased in the Loess Plateau in most grid cells of study regions. Winter and spring precipitation decreased more in the Yangtze river valley and the Loess Plateau than in northeast China.

  20. Seeking for the rational basis of the Median Model: the optimal combination of multi-model ensemble results

    Directory of Open Access Journals (Sweden)

    A. Riccio

    2007-12-01

    Full Text Available In this paper we present an approach for the statistical analysis of multi-model ensemble results. The models considered here are operational long-range transport and dispersion models, also used for the real-time simulation of pollutant dispersion or the accidental release of radioactive nuclides.

    We first introduce the theoretical basis (with its roots sinking into the Bayes theorem and then apply this approach to the analysis of model results obtained during the ETEX-1 exercise. We recover some interesting results, supporting the heuristic approach called "median model", originally introduced in Galmarini et al. (2004a, b.

    This approach also provides a way to systematically reduce (and quantify model uncertainties, thus supporting the decision-making process and/or regulatory-purpose activities in a very effective manner.

  1. Seeking for the rational basis of the median model: the optimal combination of multi-model ensemble results

    Directory of Open Access Journals (Sweden)

    A. Riccio

    2007-04-01

    Full Text Available In this paper we present an approach for the statistical analysis of multi-model ensemble results. The models considered here are operational long-range transport and dispersion models, also used for the real-time simulation of pollutant dispersion or the accidental release of radioactive nuclides.

    We first introduce the theoretical basis (with its roots sinking into the Bayes theorem and then apply this approach to the analysis of model results obtained during the ETEX-1 exercise. We recover some interesting results, supporting the heuristic approach called "median model", originally introduced in Galmarini et al. (2004a, b.

    This approach also provides a way to systematically reduce (and quantify model uncertainties, thus supporting the decision-making process and/or regulatory-purpose activities in a very effective manner.

  2. Shallow Cumulus Variability at the ARM Eastern North Atlantic Site

    Science.gov (United States)

    Lamer, K.; Kollias, P.; Ghate, V. P.; Luke, E. P.

    2016-12-01

    Cumulus clouds play a critical role in modulating the radiative and hydrological budget of the lower troposphere. These clouds, which are ubiquitous in regions of large-scale subsidence over the oceans, tend to be misrepresented in global climate models. Island-based, long-term, high-resolution ground-based observations can provide valuable insights on the factors controlling their macroscopic and microphysical properties and subsequenlty assist in model evaluation and guidance. Previous studies, limited to fair-weather cumuli over land, revealed that their fractional coverage is only weakly correlated with several parameters; the best ones being complex dynamical characteristics of the subcloud layer (vertical velocity skewness and eddy coherence). Other studies noted a relationship between cumuli depth and their propensity to precipitate. The current study will expand on such analysis by performing detail characterization of the full spectrum of shallow cumulus fields from non-precipitating to precipitating in the context of the large-scale forcing (i.e. thermodynamic structure and subsidence rates). Two years of ground-based remote sensing observations collected at the Atmospheric Radiation Measurement (ARM) Eastern North Atlantic (ENA) site are used to document macroscopic (cloud depth, cord length, cover), microphysical (liquid water path, cloud base rain rate) and dynamical (cloud base mass flux, eddy dissipation rate) cumuli properties. The observed variability in shallow cumulus is examined in relation to the variability of the large-scale environment as captured by the humidity profile, the magnitude of the low-level horizontal winds and near-surface aerosol conditions.

  3. The atmospheric component of the Mediterranean Sea water budget in a WRF multi-physics ensemble and observations

    Science.gov (United States)

    Di Luca, Alejandro; Flaounas, Emmanouil; Drobinski, Philippe; Brossier, Cindy Lebeaupin

    2014-11-01

    The use of high resolution atmosphere-ocean coupled regional climate models to study possible future climate changes in the Mediterranean Sea requires an accurate simulation of the atmospheric component of the water budget (i.e., evaporation, precipitation and runoff). A specific configuration of the version 3.1 of the weather research and forecasting (WRF) regional climate model was shown to systematically overestimate the Mediterranean Sea water budget mainly due to an excess of evaporation (~1,450 mm yr-1) compared with observed estimations (~1,150 mm yr-1). In this article, a 70-member multi-physics ensemble is used to try to understand the relative importance of various sub-grid scale processes in the Mediterranean Sea water budget and to evaluate its representation by comparing simulated results with observed-based estimates. The physics ensemble was constructed by performing 70 1-year long simulations using version 3.3 of the WRF model by combining six cumulus, four surface/planetary boundary layer and three radiation schemes. Results show that evaporation variability across the multi-physics ensemble (˜10 % of the mean evaporation) is dominated by the choice of the surface layer scheme that explains more than ˜70 % of the total variance and that the overestimation of evaporation in WRF simulations is generally related with an overestimation of surface exchange coefficients due to too large values of the surface roughness parameter and/or the simulation of too unstable surface conditions. Although the influence of radiation schemes on evaporation variability is small (˜13 % of the total variance), radiation schemes strongly influence exchange coefficients and vertical humidity gradients near the surface due to modifications of temperature lapse rates. The precipitation variability across the physics ensemble (˜35 % of the mean precipitation) is dominated by the choice of both cumulus (˜55 % of the total variance) and planetary boundary layer (˜32 % of

  4. Emulation of an ensemble Kalman filter algorithm on a flood wave propagation model

    Science.gov (United States)

    Barthélémy, S.; Ricci, S.; Pannekoucke, O.; Thual, O.; Malaterre, P. O.

    2013-06-01

    This study describes the emulation of an Ensemble Kalman Filter (EnKF) algorithm on a 1-D flood wave propagation model. This model is forced at the upstream boundary with a random variable with gaussian statistics and a correlation function in time with gaussian shape. This allows for, in the case without assimilation, the analytical study of the covariance functions of the propagated signal anomaly. This study is validated numerically with an ensemble method. In the case with assimilation with one observation point, where synthetical observations are generated by adding an error to a true state, the dynamic of the background error covariance functions is not straightforward and a numerical approach using an EnKF algorithm is prefered. First, those numerical experiments show that both background error variance and correlation length scale are reduced at the observation point. This reduction of variance and correlation length scale is propagated downstream by the dynamics of the model. Then, it is shown that the application of a Best Linear Unbiased Estimator (BLUE) algorithm using the background error covariance matrix converged from the EnKF algorithm, provides the same results as the EnKF but with a cheaper computational cost, thus allowing for the use of data assimilation in the context of real time flood forecasting. Moreover it was demonstrated that the reduction of background error correlation length scale and variance at the observation point depends on the error observation statistics. This feature is quantified by abacus built from linear regressions over a limited set of EnKF experiments. These abacus that describe the background error variance and the correlation length scale in the neighboring of the observation point combined with analytical expressions that describe the background error variance and the correlation length scale away from the observation point provide parametrized models for the variance and the correlation length scale. Using this

  5. Emulation of an ensemble Kalman filter algorithm on a flood wave propagation model

    Directory of Open Access Journals (Sweden)

    S. Barthélémy

    2013-06-01

    Full Text Available This study describes the emulation of an Ensemble Kalman Filter (EnKF algorithm on a 1-D flood wave propagation model. This model is forced at the upstream boundary with a random variable with gaussian statistics and a correlation function in time with gaussian shape. This allows for, in the case without assimilation, the analytical study of the covariance functions of the propagated signal anomaly. This study is validated numerically with an ensemble method. In the case with assimilation with one observation point, where synthetical observations are generated by adding an error to a true state, the dynamic of the background error covariance functions is not straightforward and a numerical approach using an EnKF algorithm is prefered. First, those numerical experiments show that both background error variance and correlation length scale are reduced at the observation point. This reduction of variance and correlation length scale is propagated downstream by the dynamics of the model. Then, it is shown that the application of a Best Linear Unbiased Estimator (BLUE algorithm using the background error covariance matrix converged from the EnKF algorithm, provides the same results as the EnKF but with a cheaper computational cost, thus allowing for the use of data assimilation in the context of real time flood forecasting. Moreover it was demonstrated that the reduction of background error correlation length scale and variance at the observation point depends on the error observation statistics. This feature is quantified by abacus built from linear regressions over a limited set of EnKF experiments. These abacus that describe the background error variance and the correlation length scale in the neighboring of the observation point combined with analytical expressions that describe the background error variance and the correlation length scale away from the observation point provide parametrized models for the variance and the correlation length

  6. Seasonal drought ensemble predictions based on multiple climate models in the upper Han River Basin, China

    Science.gov (United States)

    Ma, Feng; Ye, Aizhong; Duan, Qingyun

    2017-03-01

    An experimental seasonal drought forecasting system is developed based on 29-year (1982-2010) seasonal meteorological hindcasts generated by the climate models from the North American Multi-Model Ensemble (NMME) project. This system made use of a bias correction and spatial downscaling method, and a distributed time-variant gain model (DTVGM) hydrologic model. DTVGM was calibrated using observed daily hydrological data and its streamflow simulations achieved Nash-Sutcliffe efficiency values of 0.727 and 0.724 during calibration (1978-1995) and validation (1996-2005) periods, respectively, at the Danjiangkou reservoir station. The experimental seasonal drought forecasting system (known as NMME-DTVGM) is used to generate seasonal drought forecasts. The forecasts were evaluated against the reference forecasts (i.e., persistence forecast and climatological forecast). The NMME-DTVGM drought forecasts have higher detectability and accuracy and lower false alarm rate than the reference forecasts at different lead times (from 1 to 4 months) during the cold-dry season. No apparent advantage is shown in drought predictions during spring and summer seasons because of a long memory of the initial conditions in spring and a lower predictive skill for precipitation in summer. Overall, the NMME-based seasonal drought forecasting system has meaningful skill in predicting drought several months in advance, which can provide critical information for drought preparedness and response planning as well as the sustainable practice of water resource conservation over the basin.

  7. Hybrid variational-ensemble assimilation of lightning observations in a mesoscale model

    Directory of Open Access Journals (Sweden)

    K. Apodaca

    2014-05-01

    Full Text Available Lightning measurements from the Geostationary Lightning Mapper (GLM that will be aboard the Goestationary Operational Environmental Satellite – R Series will bring new information that can have the potential for improving the initialization of numerical weather prediction models by assisting in the detection of clouds and convection through data assimilation. In this study we focus on investigating the utility of lightning observations in mesoscale and regional applications suitable for current operational environments, in which convection cannot be explicitly resolved. Therefore, we examine the impact of lightning observations on storm environment. Preliminary steps in developing a lightning data assimilation capability suitable for mesoscale modeling are presented in this paper. World Wide Lightning Location Network (WWLLN data was utilized as a proxy for GLM measurements and was assimilated with the Maximum Likelihood Ensemble Filter, interfaced with the Nonhydrostatic Mesoscale Model core of the Weather Research and Forecasting system (WRF-NMM. In order to test this methodology, regional data assimilation experiments were conducted. Results indicate that lightning data assimilation had a positive impact on the following: information content, influencing several dynamical variables in the model (e.g., moisture, temperature, and winds, improving initial conditions, and partially improving WRF-NMM forecasts during several data assimilation cycles.

  8. Sensitivity of the Simulated Tropical Intraseasonal Oscillation to Cumulus Parameterizations

    Institute of Scientific and Technical Information of China (English)

    JIA Xiaolong; LI Chongyin

    2008-01-01

    The sensitivity of the simulated tropical intraseasonal oscillation or MJO (Madden and Julian oscilla tion)to different cumulus parameterizations is studied by using an atmospheric general circulation model (GCM)-SAMIL(Spectral Atmospheric Model of IAP LASG).Results show that performance of the model in simulating the MJO alters widely when using two different cumulus parameterization schemes-the moist convective adjustment scheme(MCA)and the Zhang-McFarlane(ZM)scheme.MJO simulated by the MCA scheme was found to be more realistic than that simulated by the ZM scheme.MJO produced by the ZM scheme is too weak and shows little propagation characteristics.Weak moisture convergence at low levels simulated by the ZM scheme is not enough to maintain the structure and the eastward propagation of the oscillation.These two cumulus schemes produced different vertical structures of the heating profile.The heating profile produced by the ZM scheme is nearly uniform with height and the heating is too weak compared to that produced by the MCA,which maybe contributes greatly to the failure of simulating a reasonable MJO.Comparing the simulated MJO by these two schemes indicate that the MJO simulated by the GCM is highly sensitive to cumulus parameterizations implanted in.The diabatic heating profile plays an important role in the performance of the GCM.Three sensitivity experiments with different heating profiles are designed in which modified heating profiles peak respectively in the upper troposphere(UH), middle troposphere(MH),and lower troposphere(LH).Both the LH run and the MH run produce eastward propagating signals on the intraseasonal timescale,while it is interesting that the intraseasonal timescale signals produced by the UH run propagate westward.It indicates that a realistic intraseasonal oscillation is more prone to be excited when the maximum heating concentrates in the middle-low levels,especially in the middle levels,while westward propagating disturbances axe more

  9. Implementation of Localized Ensemble Assimilation for a Three-Dimensional Radiation Belt Model (Invited)

    Science.gov (United States)

    Godinez, H. C.; Chen, Y.; Kellerman, A. C.; Subbotin, D.; Shprits, Y.

    2013-12-01

    Earth's outer radiation belt is very dynamic and energetic electrons therein undergo constant changes due to acceleration, loss, and trans- port processes. In this work we improve the accuracy of simulated electron phase space density (PSD) of the Versatile Electron Radiation Belt (VERB) code, a three-dimensional radiation belt model, by implementing the localized ensemble transform Kalman filter (LETKF) assimilation method. Assimilation methods based on Kalman filtering have been successfully applied to one-dimensional radial diffusion radiation belt models, where it has been shown to greatly improve the model estimation of electron phase space density (PSD). This work expands upon previous research by implementing the LETKF method to assimilate observed electron density into VERB, a three-dimensional radiation belt model. In particular, the LETKF will perform the assimilation locally, where the size of the local region is defined by the diffusion of electrons in the model. This will enable the optimal assimilation of data throughout the model consistently with the flow of electrons. Two sets of assimilation experiments are presented. The first is an identical-twin experiment, where artificial data is generated from the same model, with the purpose of verifying the assimilation method. In the second set of experiments, real PSD observational data from missions such as CRRES and/or the Van Allen Probes are assimilated into VERB. The results show that data assimilation significantly improves the accuracy of the VERB model by efficiently including the available observations at the appropriate pitch angles, energy levels, and L-shell regions throughout the model.

  10. Evaluation of drought propagation in an ensemble mean of large-scale hydrological models

    Directory of Open Access Journals (Sweden)

    A. F. Van Loon

    2012-11-01

    Full Text Available Hydrological drought is increasingly studied using large-scale models. It is, however, not sure whether large-scale models reproduce the development of hydrological drought correctly. The pressing question is how well do large-scale models simulate the propagation from meteorological to hydrological drought? To answer this question, we evaluated the simulation of drought propagation in an ensemble mean of ten large-scale models, both land-surface models and global hydrological models, that participated in the model intercomparison project of WATCH (WaterMIP. For a selection of case study areas, we studied drought characteristics (number of droughts, duration, severity, drought propagation features (pooling, attenuation, lag, lengthening, and hydrological drought typology (classical rainfall deficit drought, rain-to-snow-season drought, wet-to-dry-season drought, cold snow season drought, warm snow season drought, composite drought.

    Drought characteristics simulated by large-scale models clearly reflected drought propagation; i.e. drought events became fewer and longer when moving through the hydrological cycle. However, more differentiation was expected between fast and slowly responding systems, with slowly responding systems having fewer and longer droughts in runoff than fast responding systems. This was not found using large-scale models. Drought propagation features were poorly reproduced by the large-scale models, because runoff reacted immediately to precipitation, in all case study areas. This fast reaction to precipitation, even in cold climates in winter and in semi-arid climates in summer, also greatly influenced the hydrological drought typology as identified by the large-scale models. In general, the large-scale models had the correct representation of drought types, but the percentages of occurrence had some important mismatches, e.g. an overestimation of classical rainfall deficit droughts, and an

  11. Exact matrix treatment of an osmotic ensemble model of adsorption and pressure induced structural transitions in metal organic frameworks.

    Science.gov (United States)

    Dunne, Lawrence J; Manos, George

    2016-03-14

    Here we present an exactly treated quasi-one dimensional statistical mechanical osmotic ensemble model of pressure and adsorption induced breathing structural transformations of metal-organic frameworks (MOFs). The treatment uses a transfer matrix method. The model successfully reproduces the gas and pressure induced structural changes which are observed experimentally in MOFs. The model treatment presented here is a significant step towards analytical statistical mechanical treatments of flexible metal-organic frameworks.

  12. Evaluation of drought propagation in an ensemble mean of large-scale hydrological models

    Directory of Open Access Journals (Sweden)

    A. F. Van Loon

    2012-07-01

    Full Text Available Hydrological drought is increasingly studied using large-scale models. It is, however, not sure whether large-scale models reproduce the development of hydrological drought correctly. The pressing question is: how well do large-scale models simulate the propagation from meteorological to hydrological drought? To answer this question, we evaluated the simulation of drought propagation in an ensemble mean of ten large-scale models, both land-surface models and global hydrological models, that were part of the model intercomparison project of WATCH (WaterMIP. For a selection of case study areas, we studied drought characteristics (number of droughts, duration, severity, drought propagation features (pooling, attenuation, lag, lengthening, and hydrological drought typology (classical rainfall deficit drought, rain-to-snow-season drought, wet-to-dry-season drought, cold snow season drought, warm snow season drought, composite drought.

    Drought characteristics simulated by large-scale models clearly reflected drought propagation, i.e. drought events became less and longer when moving through the hydrological cycle. However, more differentiation was expected between fast and slowly responding systems, with slowly responding systems having less and longer droughts in runoff than fast responding systems. This was not found using large-scale models. Drought propagation features were poorly reproduced by the large-scale models, because runoff reacted immediately to precipitation, in all case study areas. This fast reaction to precipitation, even in cold climates in winter and in semi-arid climates in summer, also greatly influenced the hydrological drought typology as identified by the large-scale models. In general, the large-scale models had the correct representation of drought types, but the percentages of occurrence had some important mismatches, e.g. an overestimation of classical rainfall deficit droughts, and an

  13. Ensemble single column modeling (ESCM) in the tropical western Pacific: Forcing data sets and uncertainty analysis

    Science.gov (United States)

    Hume, Timothy; Jakob, Christian

    2005-07-01

    Single column models (SCMs) are useful tools for the evaluation of parameterisations of radiative and moist processes used in general circulation models (GCMs). Most SCM studies to date have concentrated on regions where there is a sufficiently dense observational network to derive the required forcing data. This paper describes an ensemble single column modeling (ESCM) approach where the forcing data are derived from numerical weather prediction (NWP) analysis products. To highlight the benefits of the ESCM approach, four forcing data sets were derived for a two year period at the Tropical Western Pacific ARM (Atmospheric Radiation Measurement Program) sites at Manus Island and Nauru. In the first section of the study, the NWP derived forcing data are validated against a range of observations at the tropical sites. In the second section, the sensitivity of two different SCMs to uncertainties in the forcing data sets are analysed. It is shown that despite the inherent uncertainties in the NWP derived forcing data, an ESCM approach is able to identify errors in the SCM physics. This suggests the ESCM approach is useful for testing parameterisations in relatively observation sparse regions, such as the TWP.

  14. Binary Classifier Calibration using an Ensemble of Near Isotonic Regression Models

    Science.gov (United States)

    Naeini, Mahdi Pakdaman; Cooper, Gregory F.

    2017-01-01

    Learning accurate probabilistic models from data is crucial in many practical tasks in data mining. In this paper we present a new non-parametric calibration method called ensemble of near isotonic regression (ENIR). The method can be considered as an extension of BBQ [20], a recently proposed calibration method, as well as the commonly used calibration method based on isotonic regression (IsoRegC) [27]. ENIR is designed to address the key limitation of IsoRegC which is the monotonicity assumption of the predictions. Similar to BBQ, the method post-processes the output of a binary classifier to obtain calibrated probabilities. Thus it can be used with many existing classification models to generate accurate probabilistic predictions. We demonstrate the performance of ENIR on synthetic and real datasets for commonly applied binary classification models. Experimental results show that the method outperforms several common binary classifier calibration methods. In particular on the real data, ENIR commonly performs statistically significantly better than the other methods, and never worse. It is able to improve the calibration power of classifiers, while retaining their discrimination power. The method is also computationally tractable for large scale datasets, as it is O(N log N) time, where N is the number of samples.

  15. Simulating Quantum Spin Models using Rydberg-Excited Atomic Ensembles in Magnetic Microtrap Arrays

    CERN Document Server

    Whitlock, Shannon; Hannaford, Peter

    2016-01-01

    We propose a scheme to simulate lattice spin models based on strong and long-range interacting Rydberg atoms stored in a large-spacing array of magnetic microtraps. Each spin is encoded in a collective spin state involving a single $nP$ Rydberg atom excited from an ensemble of ground-state alkali atoms prepared via Rydberg blockade. After the excitation laser is switched off the Rydberg spin states on neighbouring lattice sites interact via general isotropic or anisotropic spin-spin interactions. To read out the collective spin states we propose a single Rydberg atom triggered avalanche scheme in which the presence of a single Rydberg atom conditionally transfers a large number of ground-state atoms in the trap to an untrapped state which can be readily detected by site-resolved absorption imaging. Such a quantum simulator should allow the study of quantum spin systems in almost arbitrary two-dimensional configurations. This paves the way towards engineering exotic spin models, such as spin models based on tr...

  16. Comparison Experiments of Different Model Error Schemes in Ensemble Kalman Filter Soil Moisture Assimilation

    Science.gov (United States)

    Nie, Suping; Zhu, Jiang; Luo, Yong

    2010-05-01

    The purpose of this study is to explore the performances of different model error scheme in soil moisture data assimilation. Based on the ensemble Kalman filter (EnKF) and the atmosphere-vegetation interaction model (AVIM), point-scale analysis results for three schemes, 1) covariance inflation (CI), 2) direct random disturbance (DRD), and 3) error source random disturbance (ESRD), are combined under conditions of different observational error estimations, different observation layers, and different observation intervals using a series of idealized experiments. The results shows that all these schemes obtain good assimilation results when the assumed observational error is an accurate statistical representation of the actual error used to perturb the original truth value, and the ESRD scheme has the least root mean square error (RMSE). Overestimation or underestimation of the observational errors can affect the assimilation results of CI and DRD schemes sensitively. The performances of these two schemes deteriorate obviously while the ESRD scheme keeps its capability well. When the observation layers or observation interval increase, the performances of both CI and DRD schemes decline evidently. But for the ESRD scheme, as it can assimilate multi-layer observations coordinately, the increased observations improve the assimilation results further. Moreover, as the ESRD scheme contains a certain amount of model error estimation functions in its assimilation process, it also has a good performance in assimilating sparse-time observations.

  17. Climate Change Hotspots Identification in China through the CMIP5 Global Climate Model Ensemble

    Directory of Open Access Journals (Sweden)

    Huanghe Gu

    2014-01-01

    Full Text Available China is one of the countries vulnerable to adverse climate changes. The potential climate change hotspots in China throughout the 21st century are identified in this study by using a multimodel, multiscenario climate model ensemble that includes Phase Five of the Coupled Model Intercomparison Project (CMIP5 atmosphere-ocean general circulation models. Both high (RCP8.5 and low (RCP4.5 greenhouse gas emission trajectories are tested, and both the mean and extreme seasonal temperature and precipitation are considered in identifying regional climate change hotspots. Tarim basin and Tibetan Plateau in West China are identified as persistent regional climate change hotspots in both the RCP4.5 and RCP8.5 scenarios. The aggregate impacts of climate change increase throughout the 21st century and are more significant in RCP8.5 than in RCP4.5. Extreme hot event and mean temperature are two climate variables that greatly contribute to the hotspots calculation in all regions. The contribution of other climate variables exhibits a notable subregional variability. South China is identified as another hotspot based on the change of extreme dry event, especially in SON and DJF, which indicates that such event will frequently occur in the future. Our results can contribute to the designing of national and cross-national adaptation and mitigation policies.

  18. Twenty-first century changes in snowfall climate in Northern Europe in ENSEMBLES regional climate models

    Science.gov (United States)

    Räisänen, Jouni

    2016-01-01

    Changes in snowfall in northern Europe (55-71°N, 5-35°E) are analysed from 12 regional model simulations of twenty-first century climate under the Special Report on Emissions Scenarios A1B scenario. As an ensemble mean, the models suggest a decrease in the winter total snowfall in nearly all of northern Europe. In the middle of the winter, however, snowfall generally increases in the coldest areas. The borderline between increasing and decreasing snowfall broadly coincides with the -11 °C isotherm in baseline (1980-2010) monthly mean temperature, although with variation between models and grid boxes. High extremes of daily snowfall remain nearly unchanged, except for decreases in the mildest areas, where snowfall as a whole becomes much less common. A smaller fraction of the snow in the simulated late twenty-first century climate falls on severely cold days and a larger fraction on days with near-zero temperatures. Not only do days with low temperatures become less common, but they also typically have more positive anomalies of sea level pressure and less snowfall for the same temperature than in the present-day climate.

  19. Multi-model ensemble forecasting of North Atlantic tropical cyclone activity

    Science.gov (United States)

    Villarini, Gabriele; Luitel, Beda; Vecchi, Gabriel A.; Ghosh, Joyee

    2016-09-01

    North Atlantic tropical cyclones (TCs) and hurricanes are responsible for a large number of fatalities and economic damage. Skillful seasonal predictions of the North Atlantic TC activity can provide basic information critical to our improved preparedness. This study focuses on the development of statistical-dynamical seasonal forecasting systems for different quantities related to the frequency and intensity of North Atlantic TCs. These models use only tropical Atlantic and tropical mean sea surface temperatures (SSTs) to describe the variability exhibited by the observational records because they reflect the importance of both local and non-local effects on the genesis and development of TCs in the North Atlantic basin. A set of retrospective forecasts of SSTs by six experimental seasonal-to-interannual prediction systems from the North American Multi-Model Ensemble are used as covariates. The retrospective forecasts are performed over the period 1982-2015. The skill of these statistical-dynamical models is quantified for different quantities (basin-wide number of tropical storms and hurricanes, power dissipation index and accumulated cyclone energy) for forecasts initialized as early as November of the year prior to the season to forecast. The results of this work show that it is possible to obtain skillful retrospective forecasts of North Atlantic TC activity with a long lead time. Moreover, probabilistic forecasts of North Atlantic TC activity for the 2016 season are provided.

  20. Sensitivity of land surface and Cumulus schemes for Thunderstorm prediction

    Science.gov (United States)

    Kumar, Dinesh; Mohanty, U. C.; Kumar, Krishan

    2016-06-01

    The cloud processes play an important role in all forms of precipitation. Its proper representation is one of the challenging tasks in mesoscale numerical simulation. Studies have revealed that mesoscale feature require proper initialization which may likely to improve the convective system rainfall forecasts. Understanding the precipitation process, model initial condition accuracy and resolved/sub grid-scale precipitation processes representation, are the important areas which needed to improve in order to represent the mesoscale features properly. Various attempts have been done in order to improve the model performance through grid resolution, physical parameterizations, etc. But it is the physical parameterizations which provide a convective atmosphere for the development and intensification of convective events. Further, physical parameterizations consist of cumulus convection, surface fluxes of heat, moisture, momentum, and vertical mixing in the planetary boundary layer (PBL). How PBL and Cumulus schemes capture the evolution of thunderstorm have been analysed by taking thunderstorm cases occurred over Kolkata, India in the year 2011. PBL and cumulus schemes were customized for WSM-6 microphysics because WSM series has been widely used in operational forecast. Results have shown that KF (PBL scheme) and WSM-6 (Cumulus Scheme) have reproduced the evolution of surface variable such as CAPE, temperature and rainfall very much like observation. Further, KF and WSM-6 scheme also provided the increased moisture availability in the lower atmosphere which was taken to higher level by strong vertical velocities providing a platform to initiate a thunderstorm much better. Overestimation of rain in WSM-6 occurs primarily because of occurrence of melting and freezing process within a deeper layer in WSM-6 scheme. These Schemes have reproduced the spatial pattern and peak rainfall coverage closer to TRMM observation. It is the the combination of WSM-6, and KF schemes

  1. Sensitivity of land surface and Cumulus schemes for Thunderstorm prediction

    Directory of Open Access Journals (Sweden)

    D. Kumar

    2016-06-01

    Full Text Available The cloud processes play an important role in all forms of precipitation. Its proper representation is one of the challenging tasks in mesoscale numerical simulation. Studies have revealed that mesoscale feature require proper initialization which may likely to improve the convective system rainfall forecasts. Understanding the precipitation process, model initial condition accuracy and resolved/sub grid-scale precipitation processes representation, are the important areas which needed to improve in order to represent the mesoscale features properly. Various attempts have been done in order to improve the model performance through grid resolution, physical parameterizations, etc. But it is the physical parameterizations which provide a convective atmosphere for the development and intensification of convective events. Further, physical parameterizations consist of cumulus convection, surface fluxes of heat, moisture, momentum, and vertical mixing in the planetary boundary layer (PBL. How PBL and Cumulus schemes capture the evolution of thunderstorm have been analysed by taking thunderstorm cases occurred over Kolkata, India in the year 2011. PBL and cumulus schemes were customized for WSM-6 microphysics because WSM series has been widely used in operational forecast. Results have shown that KF (PBL scheme and WSM-6 (Cumulus Scheme have reproduced the evolution of surface variable such as CAPE, temperature and rainfall very much like observation. Further, KF and WSM-6 scheme also provided the increased moisture availability in the lower atmosphere which was taken to higher level by strong vertical velocities providing a platform to initiate a thunderstorm much better. Overestimation of rain in WSM-6 occurs primarily because of occurrence of melting and freezing process within a deeper layer in WSM-6 scheme. These Schemes have reproduced the spatial pattern and peak rainfall coverage closer to TRMM observation. It is the the combination of WSM-6

  2. Data assimilation with the Ensemble Kalman Filter in simple forced and coupled models of the equatorial Pacific Ocean

    Science.gov (United States)

    Leeuwenburgh, O.; Burgers, G.

    2003-04-01

    An ocean data assimilation and forecast system for the Equatorial Pacific is presented. The Ensemble Kalman Filter is used to combine several types of real data with a reduced-gravity shallow-water model containing a simplified SST equation. A preliminary version of this assimilation system has been found in the past to produce skillful forecasts of Nino 3 and Nino 4 SST anomalies when artifical data obtained from model runs are used. The small size and simplicity of the model now allows us to experiment with different types of real data, ensemble sizes, assimilation frequency, etc. Forecasts are made by coupling a statistical atmosphere to the ocean model. We make a comparison between assimilation of subsurface temperature information and sea surface temperature and height into a model forced by observed winds, and assimilation of both ocean data and observed winds into the coupled model. The influence of model error can be studied by introducing changes to the model parameterizations or by comparing the difference in skill between the real data case and a twin experiment setup. The results are compared with the historical record of SST anomalies and will serve as a benchmark for the implementation of the Ensemble Kalman Filter with more elaborate models.

  3. 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.

  4. Ensemble Kalman Filter Data Assimilation with the ParFlow Hydrologic Model

    Science.gov (United States)

    Williams, J. L., III

    2015-12-01

    Hydrometeorological research has shown that simulations of atmospheric processes benefit from sophisticated land surface formulations. Moisture and energy fluxes between the land surface and lower atmosphere are influenced strongly not only by atmospheric conditions, but by terrestrial hydrologic processes, soil moisture distribution in particular. By improving the representation of hydrologic processes, better predictive skill can be achieved in a fully-coupled weather forcasting model. Further improvements in the model can be realized by incorporating observed data values into the hydrologic model. This work applies the Ensemble Kalman Filter functionality included in the Data Assimilation Assimilation Research Testbed (DART), a collection of data assimilation tools maintained at the National Center for Atmospheric Research, to the ParFlow hydrologic model—the hydrologic component of the TerrSysMP fully coupled hydrologic - land surface - atmospheric model system. This generalized data assimilation tool allows observations of variables in the hydrologic component of the system to be incorporated into the overall error covariance matrix thus guiding the development of quantities that define the model state. Single dimension column tests, and a three-dimensional idealized catchment drainage and dry-out test were performed with the ParFlow-DART system to evaluate the effects of assimilating pressure head, soil moisture, and outflow observations on the development of the model through time. The data assimilation system was then applied to the hydrologic portion a fully-coupled (subsurface, land surface, and atmosphere) simulation over the North Rhine-Westphalia region in western Germany to demonstrate the utility of this system in a non-idealized and realistic forecasting situation. The success of these tests will allow the ParFlow-DART system to be developed into a complete data assimilation package for the TerrSysMP fully-coupled modeling system.

  5. Comparison of OMI NO2 tropospheric columns with an ensemble of global and European regional air quality models

    Directory of Open Access Journals (Sweden)

    D. Zyryanov

    2010-04-01

    Full Text Available We present a comparison of tropospheric NO2 from OMI measurements to the median of an ensemble of Regional Air Quality (RAQ models, and an intercomparison of the contributing RAQ models and two global models for the period July 2008–June 2009 over Europe. The model forecasts were produced routinely on a daily basis in the context of the European GEMS ("Global and regional Earth-system (atmosphere Monitoring using Satellite and in-situ data" project. The tropospheric vertical column of the RAQ ensemble median shows a spatial distribution which agrees well with the OMI NO2 observations, with a correlation r=0.8. This is higher than the correlations from any one of the individual RAQ models, which supports the use of a model ensemble approach for regional air pollution forecasting. The global models show high correlations compared to OMI, but with significantly less spatial detail, due to their coarser resolution. Deviations in the tropospheric NO2 columns of individual RAQ models from the mean were in the range of 20–34% in winter and 40–62% in summer, suggesting that the RAQ ensemble prediction is relatively more uncertain in the summer months. The ensemble median shows a stronger seasonal cycle of NO2 columns than OMI, and the ensemble is on average 50% below the OMI observations in summer, whereas in winter the bias is small. On the other hand the ensemble median shows a somewhat weaker seasonal cycle than NO2 surface observations from the Dutch Air Quality Network, and on average a negative bias of 14%. Full profile information was available for two RAQ models and for the global models. For these models the retrieval averaging kernel was applied. Minor differences are found for area-averaged model columns with and without applying the kernel, which shows that the impact of replacing the a priori profiles by the RAQ model profiles is on average small. However, the contrast between major hotspots and rural areas is stronger for the direct

  6. Algorithms for the analysis of ensemble neural spiking activity using simultaneous-event multivariate point-process models.

    Science.gov (United States)

    Ba, Demba; Temereanca, Simona; Brown, Emery N

    2014-01-01

    Understanding how ensembles of neurons represent and transmit information in the patterns of their joint spiking activity is a fundamental question in computational neuroscience. At present, analyses of spiking activity from neuronal ensembles are limited because multivariate point process (MPP) models cannot represent simultaneous occurrences of spike events at an arbitrarily small time resolution. Solo recently reported a simultaneous-event multivariate point process (SEMPP) model to correct this key limitation. In this paper, we show how Solo's discrete-time formulation of the SEMPP model can be efficiently fit to ensemble neural spiking activity using a multinomial generalized linear model (mGLM). Unlike existing approximate procedures for fitting the discrete-time SEMPP model, the mGLM is an exact algorithm. The MPP time-rescaling theorem can be used to assess model goodness-of-fit. We also derive a new marked point-process (MkPP) representation of the SEMPP model that leads to new thinning and time-rescaling algorithms for simulating an SEMPP stochastic process. These algorithms are much simpler than multivariate extensions of algorithms for simulating a univariate point process, and could not be arrived at without the MkPP representation. We illustrate the versatility of the SEMPP model by analyzing neural spiking activity from pairs of simultaneously-recorded rat thalamic neurons stimulated by periodic whisker deflections, and by simulating SEMPP data. In the data analysis example, the SEMPP model demonstrates that whisker motion significantly modulates simultaneous spiking activity at the 1 ms time scale and that the stimulus effect is more than one order of magnitude greater for simultaneous activity compared with non-simultaneous activity. Together, the mGLM, the MPP time-rescaling theorem and the MkPP representation of the SEMPP model offer a theoretically sound, practical tool for measuring joint spiking propensity in a neuronal ensemble.

  7. Comparison of statistical and theoretical habitat models for conservation planning: the benefit of ensemble prediction

    Science.gov (United States)

    Jones-Farrand, D. Todd; Fearer, Todd M.; Thogmartin, Wayne E.; Thompson, Frank R.; Nelson, Mark D.; Tirpak, John M.

    2011-01-01

    (conservation planning for a particular species in a particular geography) yield different answers and thus different conservation strategies. We assert that using only one habitat model (even if validated) as the foundation of a conservation plan is risky. Using multiple models (i.e., ensemble prediction) can reduce uncertainty and increase efficacy of conservation action when models corroborate one another and increase understanding of the system when they do not.

  8. Evaluation of a CMIP5 derived dynamical global wind wave climate model ensemble

    Science.gov (United States)

    Hemer, Mark A.; Trenham, Claire E.

    2016-07-01

    Much effort has gone into evaluating the skill of General Circulation Models (GCMs) for 'standard' climate variables such as surface (air and/or sea) temperature, or precipitation. Whether climate model skill to simulate standard variables translates to the performance of dynamical GCM forced wind-wave simulations is yet to be established. We assess an ensemble of historical dynamical wave climate simulations whereby surface winds taken from GCMs participating in the Coupled Model Intercomparison Project (CMIP) are used to force a spectral wave model. The GCMs used include 8 CMIP5 models and two dynamically downscaled CMIP3 models. The climatological properties of key integrated wave parameters (significant wave height, maximum wave height, mean wave period and direction) are evaluated, using two independent methods, relative to three historical wave hindcast/reanalysis datasets over 13 areas of the global ocean. We identify that high performance of GCMs for 'standard' climate variables does not imply high performance for GCM forced wave simulations. We also identify there is little to no benefit in choosing a higher resolution CMIP5 GCM (with resolution of ∼1.4°) over a lower resolution GCM (∼2.8°) to improve skill of GCM forced dynamical wave simulations. With the conscious push towards developing projections of waves and storm surges to aid assessments of possible climate driven impacts to coastal communities, we stress the need to evaluate the performance of a GCM for the marine meteorological climate independently of the performance of the GCM for the 'standard' climate variables.

  9. 3-D evaluation of tropospheric ozone simulations by an ensemble of regional Chemistry Transport Model

    Directory of Open Access Journals (Sweden)

    D. Zyryanov

    2011-10-01

    Full Text Available A detailed 3-D evaluation of an ensemble of five regional CTM's and one global CTM with focus on free tropospheric ozone over Europe is presented. It is performed over a summer period (June to August 2008 in the context of the GEMS-RAQ project. A data set of about 400 vertical ozone profiles from balloon soundings and commercial aircraft at 11 different locations is used for model evaluation, in addition to satellite measurements with the infrared nadir IASI sounder showing largest sensitivity to free tropospheric ozone. In the free troposphere, models using the same top and boundary conditions from MOZART-IFS exhibit a systematic negative bias with respect to observed profiles of about −20%. RMSE values are constantly growing with altitude, from 22% to 32% to 53%, respectively for 0–2 km, 2–8 km and 8–10 km height ranges. Lowest correlation is found in the free troposphere, with minimum coefficients (R between 0.2 to 0.45 near 8 km, as compared to 0.7 near the surface and similar values around 10 km. Use of hourly instead of monthly chemical boundary conditions generally improves the model skill. Lower tropospheric 0–6 km partial ozone columns derived from IASI show a clear North-South gradient over Europe, which is qualitatively reproduced by the models. Also the temporal variability showing decreasing ozone concentrations in the lower troposphere (0–6 km columns during summer is well catched by models even if systematic bias remains (the value of the bias being also controlled by the type of BC used. A multi-day case study of a through with low tropopause was conducted and showed that both IASI and models were able to resolve strong horizontal gradients of middle and upper tropospheric ozone occurring in the vicinity of an upper tropospheric frontal zone.

  10. Characterizing uncertainties in recent trends of global terrestrial net primary production through ensemble modeling

    Science.gov (United States)

    Wang, W.; Hashimoto, H.; Ganguly, S.; Votava, P.; Nemani, R. R.; Myneni, R. B.

    2010-12-01

    Large uncertainties exist in our understanding of the trends and variability in global net primary production (NPP) and its controls. This study attempts to address this question through a multi-model ensemble experiment. In particular, we drive ecosystem models including CASA, LPJ, Biome-BGC, TOPS-BGC, and BEAMS with a long-term climate dataset (i.e., CRU-NCEP) to estimate global NPP from 1901 to 2009 at a spatial resolution of 0.5 x 0.5 degree. We calculate the trends of simulated NPP during different time periods and test their sensitivities to climate variables of solar radiation, air temperature, precipitation, vapor pressure deficit (VPD), and atmospheric CO2 levels. The results indicate a large diversity among the simulated NPP trends over the past 50 years, ranging from nearly no trend to an increasing trend of ~0.1 PgC/yr. Spatial patterns of the NPP generally show positive trends in boreal forests, induced mainly by increasing temperatures in these regions; they also show negative trends in the tropics, although the spatial patterns are more diverse. These diverse trends result from different climatic sensitivities of NPP among the tested models. Depending the ecological processes (e.g., photosynthesis or respiration) a model emphasizes, it can be more or less responsive to changes in solar radiation, temperatures, water, or atmospheric CO2 levels. Overall, these results highlight the limit of current ecosystem models in simulating NPP, which cannot be easily observed. They suggest that the traditional single-model approach is not ideal for characterizing trends and variability in global carbon cycling.

  11. Heat strain imposed by personal protective ensembles: quantitative analysis using a thermoregulation model

    Science.gov (United States)

    Xu, Xiaojiang; Gonzalez, Julio A.; Santee, William R.; Blanchard, Laurie A.; Hoyt, Reed W.

    2016-07-01

    The objective of this paper is to study the effects of personal protective equipment (PPE) and specific PPE layers, defined as thermal/evaporative resistances and the mass, on heat strain during physical activity. A stepwise thermal manikin testing and modeling approach was used to analyze a PPE ensemble with four layers: uniform, ballistic protection, chemical protective clothing, and mask and gloves. The PPE was tested on a thermal manikin, starting with the uniform, then adding an additional layer in each step. Wearing PPE increases the metabolic rates (dot{M}) , thus dot{M} were adjusted according to the mass of each of four configurations. A human thermoregulatory model was used to predict endurance time for each configuration at fixed dot{M} and at its mass adjusted dot{M} . Reductions in endurance time due to resistances, and due to mass, were separately determined using predicted results. Fractional contributions of PPE's thermal/evaporative resistances by layer show that the ballistic protection and the chemical protective clothing layers contribute about 20 %, respectively. Wearing the ballistic protection over the uniform reduced endurance time from 146 to 75 min, with 31 min of the decrement due to the additional resistances of the ballistic protection, and 40 min due to increased dot{M} associated with the additional mass. Effects of mass on heat strain are of a similar magnitude relative to effects of increased resistances. Reducing resistances and mass can both significantly alleviate heat strain.

  12. A local ensemble transform Kalman filter data assimilation system for the NCEP global model

    Science.gov (United States)

    Szunyogh, Istvan; Kostelich, Eric J.; Gyarmati, Gyorgyi; Kalnay, Eugenia; Hunt, Brian R.; Ott, Edward; Satterfield, Elizabeth; Yorke, James A.

    2008-01-01

    The accuracy and computational efficiency of a parallel computer implementation of the Local Ensemble Transform Kalman Filter (LETKF) data assimilation scheme on the model component of the 2004 version of the Global Forecast System (GFS) of the National Centers for Environmental Prediction (NCEP) is investigated. Numerical experiments are carried out at model resolution T62L28. All atmospheric observations that were operationally assimilated by NCEP in 2004, except for satellite radiances, are assimilated with the LETKF. The accuracy of the LETKF analyses is evaluated by comparing it to that of the Spectral Statistical Interpolation (SSI), which was the operational global data assimilation scheme of NCEP in 2004. For the selected set of observations, the LETKF analyses are more accurate than the SSI analyses in the Southern Hemisphere extratropics and are comparably accurate in the Northern Hemisphere extratropics and in the Tropics. The computational wall-clock times achieved on a Beowulf cluster of 3.6 GHz Xeon processors make our implementation of the LETKF on the NCEP GFS a widely applicable analysis-forecast system, especially for research purposes. For instance, the generation of four daily analyses at the resolution of the NCAR-NCEP reanalysis (T62L28) for a full season (90 d), using 40 processors, takes less than 4 d of wall-clock time.

  13. A minimal model of peptide binding predicts ensemble properties of serum antibodies

    Directory of Open Access Journals (Sweden)

    Greiff Victor

    2012-02-01

    Full Text Available Background The importance of peptide microarrays as a tool for serological diagnostics has strongly increased over the last decade. However, interpretation of the binding signals is still hampered by our limited understanding of the technology. This is in particular true for arrays probed with antibody mixtures of unknown complexity, such as sera. To gain insight into how signals depend on peptide amino acid sequences, we probed random-sequence peptide microarrays with sera of healthy and infected mice. We analyzed the resulting antibody binding profiles with regression methods and formulated a minimal model to explain our findings. Results Multivariate regression analysis relating peptide sequence to measured signals led to the definition of amino acid-associated weights. Although these weights do not contain information on amino acid position, they predict up to 40-50% of the binding profiles' variation. Mathematical modeling shows that this position-independent ansatz is only adequate for highly diverse random antibody mixtures which are not dominated by a few antibodies. Experimental results suggest that sera from healthy individuals correspond to that case, in contrast to sera of infected ones. Conclusions Our results indicate that position-independent amino acid-associated weights predict linear epitope binding of antibody mixtures only if the mixture is random, highly diverse, and contains no dominant antibodies. The discovered ensemble property is an important step towards an understanding of peptide-array serum-antibody binding profiles. It has implications for both serological diagnostics and B cell epitope mapping.

  14. Using the MESH modelling system for hydrological ensemble forecasting of the Laurentian Great Lakes at the regional scale

    Directory of Open Access Journals (Sweden)

    A. Pietroniro

    2006-08-01

    Full Text Available Environment Canada has been developing a community environmental modelling system (Modélisation Environmentale Communautaire – MEC, which is designed to facilitate coupling between models focusing on different components of the earth system. The ultimate objective of MEC is to use the coupled models to produce operational forecasts. MESH (MEC – Surface and Hydrology, a configuration of MEC currently under development, is specialized for coupled land-surface and hydrological models. To determine the specific requirements for MESH, its different components were implemented on the Laurentian Great Lakes watershed, situated on the Canada–U.S. border. This experiment showed that MESH can help us better understand the behaviour of different land-surface models, test different schemes for producing ensemble streamflow forecasts, and provide a means of sharing the data, the models and the results with collaborators and end-users. This modelling framework is at the heart of a testbed proposal for the Hydrologic Ensemble Prediction Experiment (HEPEX which should allow us to make use of the North American Ensemble Forecasting System (NAEFS to improve streamflow forecasts of the Great Lakes tributaries, and demonstrate how MESH can contribute to a Community Hydrologic Prediction System (CHPS.

  15. Deep ensemble learning of sparse regression models for brain disease diagnosis.

    Science.gov (United States)

    Suk, Heung-Il; Lee, Seong-Whan; Shen, Dinggang

    2017-04-01

    Recent studies on brain imaging analysis witnessed the core roles of machine learning techniques in computer-assisted intervention for brain disease diagnosis. Of various machine-learning techniques, sparse regression models have proved their effectiveness in handling high-dimensional data but with a small number of training samples, especially in medical problems. In the meantime, deep learning methods have been making great successes by outperforming the state-of-the-art performances in various applications. In this paper, we propose a novel framework that combines the two conceptually different methods of sparse regression and deep learning for Alzheimer's disease/mild cognitive impairment diagnosis and prognosis. Specifically, we first train multiple sparse regression models, each of which is trained with different values of a regularization control parameter. Thus, our multiple sparse regression models potentially select different feature subsets from the original feature set; thereby they have different powers to predict the response values, i.e., clinical label and clinical scores in our work. By regarding the response values from our sparse regression models as target-level representations, we then build a deep convolutional neural network for clinical decision making, which thus we call 'Deep Ensemble Sparse Regression Network.' To our best knowledge, this is the first work that combines sparse regression models with deep neural network. In our experiments with the ADNI cohort, we validated the effectiveness of the proposed method by achieving the highest diagnostic accuracies in three classification tasks. We also rigorously analyzed our results and compared with the previous studies on the ADNI cohort in the literature.

  16. Inverse transport modeling of volcanic sulfur dioxide emissions using large-scale ensemble simulations

    Science.gov (United States)

    Heng, Y.; Hoffmann, L.; Griessbach, S.; Rößler, T.; Stein, O.

    2015-10-01

    An inverse transport modeling approach based on the concepts of sequential importance resampling and parallel computing is presented to reconstruct altitude-resolved time series of volcanic emissions, which often can not be obtained directly with current measurement techniques. A new inverse modeling and simulation system, which implements the inversion approach with the Lagrangian transport model Massive-Parallel Trajectory Calculations (MPTRAC) is developed to provide reliable transport simulations of volcanic sulfur dioxide (SO2). In the inverse modeling system MPTRAC is used to perform two types of simulations, i. e., large-scale ensemble simulations for the reconstruction of volcanic emissions and final transport simulations. The transport simulations are based on wind fields of the ERA-Interim meteorological reanalysis of the European Centre for Medium Range Weather Forecasts. The reconstruction of altitude-dependent SO2 emission time series is also based on Atmospheric Infrared Sounder (AIRS) satellite observations. A case study for the eruption of the Nabro volcano, Eritrea, in June 2011, with complex emission patterns, is considered for method validation. Meteosat Visible and InfraRed Imager (MVIRI) near-real-time imagery data are used to validate the temporal development of the reconstructed emissions. Furthermore, the altitude distributions of the emission time series are compared with top and bottom altitude measurements of aerosol layers obtained by the Cloud-Aerosol Lidar with Orthogonal Polarization (CALIOP) and the Michelson Interferometer for Passive Atmospheric Sounding (MIPAS) satellite instruments. The final transport simulations provide detailed spatial and temporal information on the SO2 distributions of the Nabro eruption. The SO2 column densities from the simulations are in good qualitative agreement with the AIRS observations. Our new inverse modeling and simulation system is expected to become a useful tool to also study other volcanic

  17. Inverse transport modeling of volcanic sulfur dioxide emissions using large-scale ensemble simulations

    Directory of Open Access Journals (Sweden)

    Y. Heng

    2015-10-01

    Full Text Available An inverse transport modeling approach based on the concepts of sequential importance resampling and parallel computing is presented to reconstruct altitude-resolved time series of volcanic emissions, which often can not be obtained directly with current measurement techniques. A new inverse modeling and simulation system, which implements the inversion approach with the Lagrangian transport model Massive-Parallel Trajectory Calculations (MPTRAC is developed to provide reliable transport simulations of volcanic sulfur dioxide (SO2. In the inverse modeling system MPTRAC is used to perform two types of simulations, i. e., large-scale ensemble simulations for the reconstruction of volcanic emissions and final transport simulations. The transport simulations are based on wind fields of the ERA-Interim meteorological reanalysis of the European Centre for Medium Range Weather Forecasts. The reconstruction of altitude-dependent SO2 emission time series is also based on Atmospheric Infrared Sounder (AIRS satellite observations. A case study for the eruption of the Nabro volcano, Eritrea, in June 2011, with complex emission patterns, is considered for method validation. Meteosat Visible and InfraRed Imager (MVIRI near-real-time imagery data are used to validate the temporal development of the reconstructed emissions. Furthermore, the altitude distributions of the emission time series are compared with top and bottom altitude measurements of aerosol layers obtained by the Cloud–Aerosol Lidar with Orthogonal Polarization (CALIOP and the Michelson Interferometer for Passive Atmospheric Sounding (MIPAS satellite instruments. The final transport simulations provide detailed spatial and temporal information on the SO2 distributions of the Nabro eruption. The SO2 column densities from the simulations are in good qualitative agreement with the AIRS observations. Our new inverse modeling and simulation system is expected to become a useful tool to also study

  18. The Arctic Sea ice in the CMIP3 climate model ensemble – variability and anthropogenic change

    Directory of Open Access Journals (Sweden)

    L. K. Behrens

    2012-12-01

    Full Text Available The strongest manifestation of global warming is observed in the Arctic. The warming in the Arctic during the recent decades is about twice as strong as in the global average and has been accompanied by a summer sea ice decline that is very likely unprecedented during the last millennium. Here, Arctic sea ice variability is analyzed in the ensemble of CMIP3 models. Complementary to several previous studies, we focus on regional aspects, in particular on the Barents Sea. We also investigate the changes in the seasonal cycle and interannual variability. In all regions, the models predict a reduction in sea ice area and sea ice volume during 1900–2100. Toward the end of the 21st century, the models simulate higher sea ice area variability in September than in March, whereas the variability in the preindustrial control runs is higher in March. Furthermore, the amplitude and phase of the sea ice seasonal cycle change in response to enhanced greenhouse warming. The amplitude of the sea ice area seasonal cycle increases due to the very strong sea ice area decline in September. The seasonal cycle amplitude of the sea ice volume decreases due to the stronger reduction of sea ice volume in March.

    Multi-model mean estimates for the late 20th century are comparable with observational data only for the entire Arctic and the Central Arctic. In the Barents Sea, differences between the multi-model mean and the observational data are more pronounced. Regional sea ice sensitivity to Northern Hemisphere average surface warming has been investigated.

  19. Streamflow hindcasting in European river basins via multi-parametric ensemble of the mesoscale hydrologic model (mHM)

    Science.gov (United States)

    Noh, Seong Jin; Rakovec, Oldrich; Kumar, Rohini; Samaniego, Luis

    2016-04-01

    There have been tremendous improvements in distributed hydrologic modeling (DHM) which made a process-based simulation with a high spatiotemporal resolution applicable on a large spatial scale. Despite of increasing information on heterogeneous property of a catchment, DHM is still subject to uncertainties inherently coming from model structure, parameters and input forcing. Sequential data assimilation (DA) may facilitate improved streamflow prediction via DHM using real-time observations to correct internal model states. In conventional DA methods such as state updating, parametric uncertainty is, however, often ignored mainly due to practical limitations of methodology to specify modeling uncertainty with limited ensemble members. If parametric uncertainty related with routing and runoff components is not incorporated properly, predictive uncertainty by DHM may be insufficient to capture dynamics of observations, which may deteriorate predictability. Recently, a multi-scale parameter regionalization (MPR) method was proposed to make hydrologic predictions at different scales using a same set of model parameters without losing much of the model performance. The MPR method incorporated within the mesoscale hydrologic model (mHM, http://www.ufz.de/mhm) could effectively represent and control uncertainty of high-dimensional parameters in a distributed model using global parameters. In this study, we present a global multi-parametric ensemble approach to incorporate parametric uncertainty of DHM in DA to improve streamflow predictions. To effectively represent and control uncertainty of high-dimensional parameters with limited number of ensemble, MPR method is incorporated with DA. Lagged particle filtering is utilized to consider the response times and non-Gaussian characteristics of internal hydrologic processes. The hindcasting experiments are implemented to evaluate impacts of the proposed DA method on streamflow predictions in multiple European river basins

  20. Canonical Ensemble Model for Black Hole Horizon of Schwarzschild–de Sitter Black Holes Quantum Tunnelling Radiation

    Indian Academy of Sciences (India)

    W. X. Zhong

    2014-09-01

    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 investigate the black hole tunnelling radiation spectrum.We also discuss the mechanism of information flowing from the black hole.

  1. Improved ensemble-mean forecasting of ENSO events by a zero-mean stochastic error model of an intermediate coupled model

    Science.gov (United States)

    Zheng, Fei; Zhu, Jiang

    2016-12-01

    How to design a reliable ensemble prediction strategy with considering the major uncertainties of a forecasting system is a crucial issue for performing an ensemble forecast. In this study, a new stochastic perturbation technique is developed to improve the prediction skills of El Niño-Southern Oscillation (ENSO) through using an intermediate coupled model. We first estimate and analyze the model uncertainties from the ensemble Kalman filter analysis results through assimilating the observed sea surface temperatures. Then, based on the pre-analyzed properties of model errors, we develop a zero-mean stochastic model-error model to characterize the model uncertainties mainly induced by the missed physical processes of the original model (e.g., stochastic atmospheric forcing, extra-tropical effects, Indian Ocean Dipole). Finally, we perturb each member of an ensemble forecast at each step by the developed stochastic model-error model during the 12-month forecasting process, and add the zero-mean perturbations into the physical fields to mimic the presence of missing processes and high-frequency stochastic noises. The impacts of stochastic model-error perturbations on ENSO deterministic predictions are examined by performing two sets of 21-year hindcast experiments, which are initialized from the same initial conditions and differentiated by whether they consider the stochastic perturbations. The comparison results show that the stochastic perturbations have a significant effect on improving the ensemble-mean prediction skills during the entire 12-month forecasting process. This improvement occurs mainly because the nonlinear terms in the model can form a positive ensemble-mean from a series of zero-mean perturbations, which reduces the forecasting biases and then corrects the forecast through this nonlinear heating mechanism.

  2. Multi-model ensemble projections of future extreme heat stress on rice across southern China

    Science.gov (United States)

    He, Liang; Cleverly, James; Wang, Bin; Jin, Ning; Mi, Chunrong; Liu, De Li; Yu, Qiang

    2017-08-01

    Extreme heat events have become more frequent and intense with climate warming, and these heatwaves are a threat to rice production in southern China. Projected changes in heat stress in rice provide an assessment of the potential impact on crop production and can direct measures for adaptation to climate change. In this study, we calculated heat stress indices using statistical scaling techniques, which can efficiently downscale output from general circulation models (GCMs). Data across the rice belt in southern China were obtained from 28 GCMs in the Coupled Model Intercomparison Project phase 5 (CMIP5) with two emissions scenarios (RCP4.5 for current emissions and RCP8.5 for increasing emissions). Multi-model ensemble projections over the historical period (1960-2010) reproduced the trend of observations in heat stress indices (root-mean-square error RMSE = 6.5 days) better than multi-model arithmetic mean (RMSE 8.9 days) and any individual GCM (RMSE 11.4 days). The frequency of heat stress events was projected to increase by 2061-2100 in both scenarios (up to 185 and 319% for RCP4.5 and RCP8.5, respectively), especially in the middle and lower reaches of the Yangtze River. This increasing risk of exposure to heat stress above 30 °C during flowering and grain filling is predicted to impact rice production. The results of our study suggest the importance of specific adaption or mitigation strategies, such as selection of heat-tolerant cultivars and adjustment of planting date in a warmer future world.

  3. Self-adaptive prediction of cloud resource demands using ensemble model and subtractive-fuzzy clustering based fuzzy neural network.

    Science.gov (United States)

    Chen, Zhijia; Zhu, Yuanchang; Di, Yanqiang; Feng, Shaochong

    2015-01-01

    In IaaS (infrastructure as a service) cloud environment, users are provisioned with virtual machines (VMs). To allocate resources for users dynamically and effectively, accurate resource demands predicting is essential. For this purpose, this paper proposes a self-adaptive prediction method using ensemble model and subtractive-fuzzy clustering based fuzzy neural network (ESFCFNN). We analyze the characters of user preferences and demands. Then the architecture of the prediction model is constructed. We adopt some base predictors to compose the ensemble model. Then the structure and learning algorithm of fuzzy neural network is researched. To obtain the number of fuzzy rules and the initial value of the premise and consequent parameters, this paper proposes the fuzzy c-means combined with subtractive clustering algorithm, that is, the subtractive-fuzzy clustering. Finally, we adopt different criteria to evaluate the proposed method. The experiment results show that the method is accurate and effective in predicting the resource demands.

  4. Self-Adaptive Prediction of Cloud Resource Demands Using Ensemble Model and Subtractive-Fuzzy Clustering Based Fuzzy Neural Network

    Science.gov (United States)

    Chen, Zhijia; Zhu, Yuanchang; Di, Yanqiang; Feng, Shaochong

    2015-01-01

    In IaaS (infrastructure as a service) cloud environment, users are provisioned with virtual machines (VMs). To allocate resources for users dynamically and effectively, accurate resource demands predicting is essential. For this purpose, this paper proposes a self-adaptive prediction method using ensemble model and subtractive-fuzzy clustering based fuzzy neural network (ESFCFNN). We analyze the characters of user preferences and demands. Then the architecture of the prediction model is constructed. We adopt some base predictors to compose the ensemble model. Then the structure and learning algorithm of fuzzy neural network is researched. To obtain the number of fuzzy rules and the initial value of the premise and consequent parameters, this paper proposes the fuzzy c-means combined with subtractive clustering algorithm, that is, the subtractive-fuzzy clustering. Finally, we adopt different criteria to evaluate the proposed method. The experiment results show that the method is accurate and effective in predicting the resource demands. PMID:25691896

  5. Modeling conformational ensembles of slow functional motions in Pin1-WW.

    Directory of Open Access Journals (Sweden)

    Faruck Morcos

    Full Text Available Protein-protein interactions are often mediated by flexible loops that experience conformational dynamics on the microsecond to millisecond time scales. NMR relaxation studies can map these dynamics. However, defining the network of inter-converting conformers that underlie the relaxation data remains generally challenging. Here, we combine NMR relaxation experiments with simulation to visualize networks of inter-converting conformers. We demonstrate our approach with the apo Pin1-WW domain, for which NMR has revealed conformational dynamics of a flexible loop in the millisecond range. We sample and cluster the free energy landscape using Markov State Models (MSM with major and minor exchange states with high correlation with the NMR relaxation data and low NOE violations. These MSM are hierarchical ensembles of slowly interconverting, metastable macrostates and rapidly interconverting microstates. We found a low population state that consists primarily of holo-like conformations and is a "hub" visited by most pathways between macrostates. These results suggest that conformational equilibria between holo-like and alternative conformers pre-exist in the intrinsic dynamics of apo Pin1-WW. Analysis using MutInf, a mutual information method for quantifying correlated motions, reveals that WW dynamics not only play a role in substrate recognition, but also may help couple the substrate binding site on the WW domain to the one on the catalytic domain. Our work represents an important step towards building networks of inter-converting conformational states and is generally applicable.

  6. Climate change under aggressive mitigation: the ENSEMBLES multi-model experiment

    Science.gov (United States)

    Johns, T. C.; Royer, J.-F.; Höschel, I.; Huebener, H.; Roeckner, E.; Manzini, E.; May, W.; Dufresne, J.-L.; Otterå, O. H.; van Vuuren, D. P.; Salas Y Melia, D.; Giorgetta, M. A.; Denvil, S.; Yang, S.; Fogli, P. G.; Körper, J.; Tjiputra, J. F.; Stehfest, E.; Hewitt, C. D.

    2011-11-01

    to 1990, with further large reductions needed beyond that to achieve the E1 concentrations pathway. Negative allowable anthropogenic carbon emissions at and beyond 2100 cannot be ruled out for the E1 scenario. There is self-consistency between the multi-model ensemble of allowable anthropogenic carbon emissions and the E1 scenario emissions from IMAGE 2.4.

  7. Climate change under aggressive mitigation: the ENSEMBLES multi-model experiment

    Energy Technology Data Exchange (ETDEWEB)

    Johns, T.C.; Hewitt, C.D. [Met Office, Hadley Centre, Exeter (United Kingdom); Royer, J.F.; Salas y. Melia, D. [Centre National de Recherches Meteorologiques-Groupe d' Etude de l' Atmosphere Meteorologique (CNRM-GAME Meteo-France CNRS), Toulouse (France); Hoeschel, I.; Koerper, J. [Freie Universitaet Berlin, Institute for Meteorology, Berlin (Germany); Huebener, H. [Hessian Agency for the Environment and Geology, Wiesbaden (Germany); Roeckner, E.; Giorgetta, M.A. [Max Planck Institute for Meteorology, Hamburg (Germany); Manzini, E. [Max Planck Institute for Meteorology, Hamburg (Germany); Istituto Nazionale di Geofisica e Vulcanologia, Bologna (Italy); Centro Euro-Mediterraneo per i Cambiamenti Climatici (CMCC), Bologna (Italy); May, W.; Yang, S. [Danish Meteorological Institute, Danish Climate Centre, Copenhagen (Denmark); Dufresne, J.L. [Laboratoire de Meteorologie Dynamique (LMD/IPSL), UMR 8539 CNRS, ENS, UPMC, Ecole Polytechnique, Paris Cedex 05 (France); Otteraa, O.H. [Nansen Environmental and Remote Sensing Center, Bergen (Norway); Bjerknes Centre for Climate Research, Bergen (Norway); Uni. Bjerknes Centre, Bergen (Norway); Vuuren, D.P. van [Utrecht University, Utrecht (Netherlands); Planbureau voor de Leefomgeving (PBL), Bilthoven (Netherlands); Denvil, S. [Institut Pierre Simon Laplace (IPSL), FR 636 CNRS, UVSQ, UPMC, Paris Cedex 05 (France); Fogli, P.G. [Centro Euro-Mediterraneo per i Cambiamenti Climatici (CMCC), Bologna (Italy); Tjiputra, J.F. [University of Bergen, Department of Geophysics, Bergen (Norway); Bjerknes Centre for Climate Research, Bergen (Norway); Stehfest, E. [Planbureau voor de Leefomgeving (PBL), Bilthoven (Netherlands)

    2011-11-15

    to 1990, with further large reductions needed beyond that to achieve the E1 concentrations pathway. Negative allowable anthropogenic carbon emissions at and beyond 2100 cannot be ruled out for the E1 scenario. There is self-consistency between the multi-model ensemble of allowable anthropogenic carbon emissions and the E1 scenario emissions from IMAGE 2.4. (orig.)

  8. Dual states estimation of a subsurface flow-transport coupled model using ensemble Kalman filtering

    KAUST Repository

    El Gharamti, Mohamad

    2013-10-01

    Modeling the spread of subsurface contaminants requires coupling a groundwater flow model with a contaminant transport model. Such coupling may provide accurate estimates of future subsurface hydrologic states if essential flow and contaminant data are assimilated in the model. Assuming perfect flow, an ensemble Kalman filter (EnKF) can be used for direct data assimilation into the transport model. This is, however, a crude assumption as flow models can be subject to many sources of uncertainty. If the flow is not accurately simulated, contaminant predictions will likely be inaccurate even after successive Kalman updates of the contaminant model with the data. The problem is better handled when both flow and contaminant states are concurrently estimated using the traditional joint state augmentation approach. In this paper, we introduce a dual estimation strategy for data assimilation into a one-way coupled system by treating the flow and the contaminant models separately while intertwining a pair of distinct EnKFs, one for each model. The presented strategy only deals with the estimation of state variables but it can also be used for state and parameter estimation problems. This EnKF-based dual state-state estimation procedure presents a number of novel features: (i) it allows for simultaneous estimation of both flow and contaminant states in parallel; (ii) it provides a time consistent sequential updating scheme between the two models (first flow, then transport); (iii) it simplifies the implementation of the filtering system; and (iv) it yields more stable and accurate solutions than does the standard joint approach. We conducted synthetic numerical experiments based on various time stepping and observation strategies to evaluate the dual EnKF approach and compare its performance with the joint state augmentation approach. Experimental results show that on average, the dual strategy could reduce the estimation error of the coupled states by 15% compared with the

  9. Lessons Learned from Assimilating Altimeter Data into a Coupled General Circulation Model with the GMAO Augmented Ensemble Kalman Filter

    Science.gov (United States)

    Keppenne, Christian; Vernieres, Guillaume; Rienecker, Michele; Jacob, Jossy; Kovach, Robin

    2011-01-01

    Satellite altimetry measurements have provided global, evenly distributed observations of the ocean surface since 1993. However, the difficulties introduced by the presence of model biases and the requirement that data assimilation systems extrapolate the sea surface height (SSH) information to the subsurface in order to estimate the temperature, salinity and currents make it difficult to optimally exploit these measurements. This talk investigates the potential of the altimetry data assimilation once the biases are accounted for with an ad hoc bias estimation scheme. Either steady-state or state-dependent multivariate background-error covariances from an ensemble of model integrations are used to address the problem of extrapolating the information to the sub-surface. The GMAO ocean data assimilation system applied to an ensemble of coupled model instances using the GEOS-5 AGCM coupled to MOM4 is used in the investigation. To model the background error covariances, the system relies on a hybrid ensemble approach in which a small number of dynamically evolved model trajectories is augmented on the one hand with past instances of the state vector along each trajectory and, on the other, with a steady state ensemble of error estimates from a time series of short-term model forecasts. A state-dependent adaptive error-covariance localization and inflation algorithm controls how the SSH information is extrapolated to the sub-surface. A two-step predictor corrector approach is used to assimilate future information. Independent (not-assimilated) temperature and salinity observations from Argo floats are used to validate the assimilation. A two-step projection method in which the system first calculates a SSH increment and then projects this increment vertically onto the temperature, salt and current fields is found to be most effective in reconstructing the sub-surface information. The performance of the system in reconstructing the sub-surface fields is particularly

  10. Constraining a compositional flow model with flow-chemical data using an ensemble-based Kalman filter

    KAUST Repository

    Gharamti, M. E.

    2014-03-01

    Isothermal compositional flow models require coupling transient compressible flows and advective transport systems of various chemical species in subsurface porous media. Building such numerical models is quite challenging and may be subject to many sources of uncertainties because of possible incomplete representation of some geological parameters that characterize the system\\'s processes. Advanced data assimilation methods, such as the ensemble Kalman filter (EnKF), can be used to calibrate these models by incorporating available data. In this work, we consider the problem of estimating reservoir permeability using information about phase pressure as well as the chemical properties of fluid components. We carry out state-parameter estimation experiments using joint and dual updating schemes in the context of the EnKF with a two-dimensional single-phase compositional flow model (CFM). Quantitative and statistical analyses are performed to evaluate and compare the performance of the assimilation schemes. Our results indicate that including chemical composition data significantly enhances the accuracy of the permeability estimates. In addition, composition data provide more information to estimate system states and parameters than do standard pressure data. The dual state-parameter estimation scheme provides about 10% more accurate permeability estimates on average than the joint scheme when implemented with the same ensemble members, at the cost of twice more forward model integrations. At similar computational cost, the dual approach becomes only beneficial after using large enough ensembles.

  11. Constraining a compositional flow model with flow-chemical data using an ensemble-based Kalman filter

    Science.gov (United States)

    Gharamti, M. E.; Kadoura, A.; Valstar, J.; Sun, S.; Hoteit, I.

    2014-03-01

    Isothermal compositional flow models require coupling transient compressible flows and advective transport systems of various chemical species in subsurface porous media. Building such numerical models is quite challenging and may be subject to many sources of uncertainties because of possible incomplete representation of some geological parameters that characterize the system's processes. Advanced data assimilation methods, such as the ensemble Kalman filter (EnKF), can be used to calibrate these models by incorporating available data. In this work, we consider the problem of estimating reservoir permeability using information about phase pressure as well as the chemical properties of fluid components. We carry out state-parameter estimation experiments using joint and dual updating schemes in the context of the EnKF with a two-dimensional single-phase compositional flow model (CFM). Quantitative and statistical analyses are performed to evaluate and compare the performance of the assimilation schemes. Our results indicate that including chemical composition data significantly enhances the accuracy of the permeability estimates. In addition, composition data provide more information to estimate system states and parameters than do standard pressure data. The dual state-parameter estimation scheme provides about 10% more accurate permeability estimates on average than the joint scheme when implemented with the same ensemble members, at the cost of twice more forward model integrations. At similar computational cost, the dual approach becomes only beneficial after using large enough ensembles.

  12. The mechanisms of North Atlantic CO2 uptake in a large Earth System Model ensemble

    Directory of Open Access Journals (Sweden)

    P. R. Halloran

    2014-10-01

    vary rapidly. Given the importance of this sink and its apparent variability, it is critical that we understand the mechanisms behind its operation. Here we explore subpolar North Atlantic CO2 uptake across a large ensemble of Earth System Model simulations, and find that models show a peak in sink strength around the middle of the century after which CO2 uptake begins to decline. We identify different drivers of change on interannual and multidecadal timescales. Short-term variability appears to be driven by fluctuations in regional seawater temperature and alkalinity, whereas the longer-term evolution throughout the coming century is largely occurring through a counterintuitive response to rising atmospheric CO2 concentrations. At high atmospheric CO2 concentrations the contrasting Ravelle factors between the subtropical and subpolar gyres, combined with the transport of surface waters from the subtropical to subpolar gyre, means that the subpolar CO2 uptake capacity is largely satisfied from its southern boundary rather than through air–sea CO2 flux. Our findings indicate that: (i we can explain the mechanisms of subpolar North Atlantic CO2 uptake variability across a broad range of Earth System Models, (ii a focus on understanding the mechanisms behind contemporary variability may not directly tell us about how the sink will change in the future, (iii to identify long-term change in the North Atlantic CO2 sink we should focus observational resources on monitoring subtropical as well as the subpolar seawater CO2, (iv recent observations of a weakening subpolar North Atlantic CO2 sink suggests that the sink strength is already in long-term decline.

  13. The application of Nonlinear Local Lyapunov Vectors to the Zebiak-Cane Model and their performance in the Ensemble Prediction

    Science.gov (United States)

    Hou, Zhaolu; Li, Jianping; Ding, Ruiqiang; Feng, Jie

    2017-04-01

    Nonlinear local Lyapunov vectors (NLLVs) have been developed to indicate orthogonal directions in phase space with different error growth rates. Comparing to the breeding vectors (BVs), NLLVs can span the fast-growing perturbation subspace efficiently and may gasp more components in analysis errors than the BVs in the nonlinear dynamical system. Here, NLLVs are employed in the Zebiak-Cane (ZC) atmosphere-ocean coupled model and represent a nonlinear, finite-time extension of the local Lyapunov vectors of the ZC model. The statistical properties of NLLVs is not very sensitive to the choice of the breeding parameter. However, the non-leading NLLVs have some randomness, which increase the diversity of NLLVs. Not only the leading NLLV but also the non-leading NLLVs are flow-dependent and related to the background ENSO evolution of the ZC model in the aspect of spatial structure and error growth rate. the non-leading NLLVs also are the instability direction related to the ENSO process in the ZC model. Due to the non-leading NLLVs, the subspace of the first few NLLVs can describe better the analysis error than that of the same number BVs in the ZC model. NLLVs as initial ensemble perturbations are applied to the ensemble prediction of ENSO and the performance are systematically compared to those of the random perturbation (RP) technique, and the BV method in the prefect environment. The results demonstrate that the RP technique has the worst performance and the NLLVs method is the best in the ensemble forecasts. In particular, the NLLV technique can reduce the "spring barrier" for ENSO prediction further than the other ensemble method.

  14. Constraints on the coupling between tectonics and landform evolution from numerical modelling, thermochronology and ensemble inference

    Science.gov (United States)

    Braun, J.

    2003-04-01

    of sites by using a simple two dimensional model of soil production and transport. Finally I will show how the predictions of complex, forward numerical models can be properly tested against observations by using ensemble inference methods. I will illustrate this point by demonstrating how the geometry of the Alpine Fault as well as the rate of oblique convergence between the Australian and Pacific plates in the South Island of New Zealand can be accurately constrained by inverting a wide range of thermochronological data.

  15. "A space-time ensemble Kalman filter for state and parameter estimation of groundwater transport models"

    Science.gov (United States)

    Briseño, Jessica; Herrera, Graciela S.

    2010-05-01

    Herrera (1998) proposed a method for the optimal design of groundwater quality monitoring networks that involves space and time in a combined form. The method was applied later by Herrera et al (2001) and by Herrera and Pinder (2005). To get the estimates of the contaminant concentration being analyzed, this method uses a space-time ensemble Kalman filter, based on a stochastic flow and transport model. When the method is applied, it is important that the characteristics of the stochastic model be congruent with field data, but, in general, it is laborious to manually achieve a good match between them. For this reason, the main objective of this work is to extend the space-time ensemble Kalman filter proposed by Herrera, to estimate the hydraulic conductivity, together with hydraulic head and contaminant concentration, and its application in a synthetic example. The method has three steps: 1) Given the mean and the semivariogram of the natural logarithm of hydraulic conductivity (ln K), random realizations of this parameter are obtained through two alternatives: Gaussian simulation (SGSim) and Latin Hypercube Sampling method (LHC). 2) The stochastic model is used to produce hydraulic head (h) and contaminant (C) realizations, for each one of the conductivity realizations. With these realization the mean of ln K, h and C are obtained, for h and C, the mean is calculated in space and time, and also the cross covariance matrix h-ln K-C in space and time. The covariance matrix is obtained averaging products of the ln K, h and C realizations on the estimation points and times, and the positions and times with data of the analyzed variables. The estimation points are the positions at which estimates of ln K, h or C are gathered. In an analogous way, the estimation times are those at which estimates of any of the three variables are gathered. 3) Finally the ln K, h and C estimate are obtained using the space-time ensemble Kalman filter. The realization mean for each one

  16. Performance comparison of several response surface surrogate models and ensemble methods for water injection optimization under uncertainty

    Science.gov (United States)

    Babaei, Masoud; Pan, Indranil

    2016-06-01

    In this paper we defined a relatively complex reservoir engineering optimization problem of maximizing the net present value of the hydrocarbon production in a water flooding process by controlling the water injection rates in multiple control periods. We assessed the performance of a number of response surface surrogate models and their ensembles which are combined by Dempster-Shafer theory and Weighted Averaged Surrogates as found in contemporary literature works. Most of these ensemble methods are based on the philosophy that multiple weak learners can be leveraged to obtain one strong learner which is better than the individual weak ones. Even though these techniques have been shown to work well for test bench functions, we found them not offering a considerable improvement compared to an individually used cubic radial basis function surrogate model. Our simulations on two and three dimensional cases, with varying number of optimization variables suggest that cubic radial basis functions-based surrogate model is reliable, outperforms Kriging surrogates and multivariate adaptive regression splines, and if it does not outperform, it is rarely outperformed by the ensemble surrogate models.

  17. Using ensemble models to identify and apportion heavy metal pollution sources in agricultural soils on a local scale.

    Science.gov (United States)

    Wang, Qi; Xie, Zhiyi; Li, Fangbai

    2015-11-01

    This study aims to identify and apportion multi-source and multi-phase heavy metal pollution from natural and anthropogenic inputs using ensemble models that include stochastic gradient boosting (SGB) and random forest (RF) in agricultural soils on the local scale. The heavy metal pollution sources were quantitatively assessed, and the results illustrated the suitability of the ensemble models for the assessment of multi-source and multi-phase heavy metal pollution in agricultural soils on the local scale. The results of SGB and RF consistently demonstrated that anthropogenic sources contributed the most to the concentrations of Pb and Cd in agricultural soils in the study region and that SGB performed better than RF.

  18. 3-D evaluation of tropospheric ozone simulations by an ensemble of regional Chemistry Transport Model

    Directory of Open Access Journals (Sweden)

    D. Zyryanov

    2012-04-01

    Full Text Available A detailed 3-D evaluation of an ensemble of five regional Chemistry Transport Models (RCTM and one global CTM with focus on free tropospheric ozone over Europe is presented. It is performed over a summer period (June to August 2008 in the context of the GEMS-RAQ project. A data set of about 400 vertical ozone profiles from balloon soundings and commercial aircraft at 11 different locations is used for model evaluation, in addition to satellite measurements with the infrared nadir sounder (IASI showing largest sensitivity to free tropospheric ozone. In the middle troposphere, the four regional models using the same top and boundary conditions from IFS-MOZART exhibit a systematic negative bias with respect to observed profiles of about −20%. Root Mean Square Error (RMSE values are constantly growing with altitude, from 22% to 32% to 53%, respectively for 0–2 km, 2–8 km and 8–10 km height ranges. Lowest correlation is found in the middle troposphere, with minimum coefficients (R between 0.2 to 0.45 near 8 km, as compared to 0.7 near the surface and similar values around 10 km. A sensitivity test made with the CHIMERE mode also shows that using hourly instead of monthly chemical boundary conditions generally improves the model skill (i.e. improve RMSE and correlation. Lower tropospheric 0–6 km partial ozone columns derived from IASI show a clear North-South gradient over Europe, which is qualitatively reproduced by the models. Also the temporal variability showing decreasing ozone concentrations in the lower troposphere (0–6 km columns during summer is well reproduced by models even if systematic bias remains (the value of the bias being also controlled by the type of used boundary conditions. A multi-day case study of a trough with low tropopause was conducted and showed that both IASI and models were able to resolve strong horizontal gradients of middle and upper tropospheric ozone occurring in the vicinity of an upper

  19. Gold price analysis based on ensemble empirical model decomposition and independent component analysis

    Science.gov (United States)

    Xian, Lu; He, Kaijian; Lai, Kin Keung

    2016-07-01

    In recent years, the increasing level of volatility of the gold price has received the increasing level of attention from the academia and industry alike. Due to the complexity and significant fluctuations observed in the gold market, however, most of current approaches have failed to produce robust and consistent modeling and forecasting results. Ensemble Empirical Model Decomposition (EEMD) and Independent Component Analysis (ICA) are novel data analysis methods that can deal with nonlinear and non-stationary time series. This study introduces a new methodology which combines the two methods and applies it to gold price analysis. This includes three steps: firstly, the original gold price series is decomposed into several Intrinsic Mode Functions (IMFs) by EEMD. Secondly, IMFs are further processed with unimportant ones re-grouped. Then a new set of data called Virtual Intrinsic Mode Functions (VIMFs) is reconstructed. Finally, ICA is used to decompose VIMFs into statistically Independent Components (ICs). The decomposition results reveal that the gold price series can be represented by the linear combination of ICs. Furthermore, the economic meanings of ICs are analyzed and discussed in detail, according to the change trend and ICs' transformation coefficients. The analyses not only explain the inner driving factors and their impacts but also conduct in-depth analysis on how these factors affect gold price. At the same time, regression analysis has been conducted to verify our analysis. Results from the empirical studies in the gold markets show that the EEMD-ICA serve as an effective technique for gold price analysis from a new perspective.

  20. Projection of temperature and heat waves for Africa with an ensemble of CORDEX Regional Climate Models

    Science.gov (United States)

    Dosio, Alessandro

    2017-07-01

    The most severe effects of global warning will be related to the frequency and severity of extreme events. We provide an analysis of projections of temperature and related extreme events for Africa based on a large ensemble of Regional Climate Models from the COordinated Regional climate Downscaling EXperiment (CORDEX). Results are presented not only by means of widely used indices but also with a recently developed Heat Wave Magnitude Index-daily (HWMId), which takes into account both heat wave duration and intensity. Results show that under RCP8.5, warming of more than 3.5 °C is projected in JFM over most of the continent, whereas in JAS temperatures over large part of Northern Africa, the Sahara and the Arabian peninsula are projected to increase up to 6 °C. Large increase in in the number of warm days (Tx90p) is found over sub equatorial Africa, with values up to more than 90 % in JAS, and more than 80 % in JFM over e.g., the gulf of Guinea, Central African Republic, South Sudan and Ethiopia. Changes in Tn90p (warm nights) are usually larger, with some models projecting Tn90p reaching 95 % starting from around 2060 even under RCP4.5 over the Gulf of Guinea and the Sahel. Results also show that the total length of heat spells projected to occur normally (i.e. once every 2 years) under RCP8.5 may be longer than those occurring once every 30 years under the lower emission scenario. By employing the recently developed HWMId index, it is possible to investigate the relationship between heat wave length ad intensity; in particular it is shown that very intense heat waves such as that occurring over the Horn of Africa may have values of HWMId larger than that of longer, but relatively weak, heat waves over West Africa.

  1. Assessment of seasonal prediction of South Pacific Convergence Zone using APCC multi-model ensembles

    Science.gov (United States)

    Kim, Ok-Yeon

    2017-07-01

    We have quantified and examined the South Pacific convergence zone (SPCZ) characteristics for the purpose of its seasonal prediction, by defining two orientation indices, strength and area. The multi-model ensemble (MME) tends to simulate the ENSO-associated shift of SPCZ orientation, especially for the 1-month forecast lead. The migration of the SPCZ orientation indices associated with ENSO phases is clear in the observation and the MME. The variation of the SPCZ strength and area associated with ENSO phases is not as clear as in the SPCZ orientation. In spite of marginal changes in the SPCZ strength and area related to ENSO phases, the SPCZ strength becomes a bit stronger during El Niño and weaker during La Niña, which is represented in individual models and MME. The performance of the MME in simulating the variability of the SPCZ orientation, strength and area is also examined. We found that the MME reasonably predicts the observed interannual variability of the western portion of the SPCZ, with systematic and marginal shift southward. Compared to the western part of the SPCZ, the MME seems to have a limitation in predicting the variability of the eastern part. In comparison to the SPCZ orientation, the MME is not capable of predicting the strength and area of the SPCZ. The interannual variability of the SPCZ strength in the MME is systematically weaker compared to that in the analysis. By comparison with SPCZ orientation and strength, the SPCZ area is not resolved in the MME. The SPCZ is a main source of precipitation in the South Pacific, and the SPCZ predictability also influences high impact weather prediction such as tropical cyclones. Therefore, skillful predictions of seasonal variability of the SPCZ could benefit users who utilize the seasonal forecasting information for their decision making in many applicable sectors.

  2. Projection of temperature and heat waves for Africa with an ensemble of CORDEX Regional Climate Models

    Science.gov (United States)

    Dosio, Alessandro

    2016-09-01

    The most severe effects of global warning will be related to the frequency and severity of extreme events. We provide an analysis of projections of temperature and related extreme events for Africa based on a large ensemble of Regional Climate Models from the COordinated Regional climate Downscaling EXperiment (CORDEX). Results are presented not only by means of widely used indices but also with a recently developed Heat Wave Magnitude Index-daily (HWMId), which takes into account both heat wave duration and intensity. Results show that under RCP8.5, warming of more than 3.5 °C is projected in JFM over most of the continent, whereas in JAS temperatures over large part of Northern Africa, the Sahara and the Arabian peninsula are projected to increase up to 6 °C. Large increase in in the number of warm days (Tx90p) is found over sub equatorial Africa, with values up to more than 90 % in JAS, and more than 80 % in JFM over e.g., the gulf of Guinea, Central African Republic, South Sudan and Ethiopia. Changes in Tn90p (warm nights) are usually larger, with some models projecting Tn90p reaching 95 % starting from around 2060 even under RCP4.5 over the Gulf of Guinea and the Sahel. Results also show that the total length of heat spells projected to occur normally (i.e. once every 2 years) under RCP8.5 may be longer than those occurring once every 30 years under the lower emission scenario. By employing the recently developed HWMId index, it is possible to investigate the relationship between heat wave length ad intensity; in particular it is shown that very intense heat waves such as that occurring over the Horn of Africa may have values of HWMId larger than that of longer, but relatively weak, heat waves over West Africa.

  3. Ensemble models of proteins and protein domains based on distance distribution restraints.

    Science.gov (United States)

    Jeschke, Gunnar

    2016-04-01

    Conformational ensembles of intrinsically disordered peptide chains are not fully determined by experimental observations. Uncertainty due to lack of experimental restraints and due to intrinsic disorder can be distinguished if distance distributions restraints are available. Such restraints can be obtained from pulsed dipolar electron paramagnetic resonance (EPR) spectroscopy applied to pairs of spin labels. Here, we introduce a Monte Carlo approach for generating conformational ensembles that are consistent with a set of distance distribution restraints, backbone dihedral angle statistics in known protein structures, and optionally, secondary structure propensities or membrane immersion depths. The approach is tested with simulated restraints for a terminal and an internal loop and for a protein with 69 residues by using sets of sparse restraints for underlying well-defined conformations and for published ensembles of a premolten globule-like and a coil-like intrinsically disordered protein.

  4. Design and Implementation of a Parallel Multivariate Ensemble Kalman Filter for the Poseidon Ocean General Circulation Model

    Science.gov (United States)

    Keppenne, Christian L.; Rienecker, Michele M.; Koblinsky, Chester (Technical Monitor)

    2001-01-01

    A multivariate ensemble Kalman filter (MvEnKF) implemented on a massively parallel computer architecture has been implemented for the Poseidon ocean circulation model and tested with a Pacific Basin model configuration. There are about two million prognostic state-vector variables. Parallelism for the data assimilation step is achieved by regionalization of the background-error covariances that are calculated from the phase-space distribution of the ensemble. Each processing element (PE) collects elements of a matrix measurement functional from nearby PEs. To avoid the introduction of spurious long-range covariances associated with finite ensemble sizes, the background-error covariances are given compact support by means of a Hadamard (element by element) product with a three-dimensional canonical correlation function. The methodology and the MvEnKF configuration are discussed. It is shown that the regionalization of the background covariances; has a negligible impact on the quality of the analyses. The parallel algorithm is very efficient for large numbers of observations but does not scale well beyond 100 PEs at the current model resolution. On a platform with distributed memory, memory rather than speed is the limiting factor.

  5. 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.

  6. A one-way coupled atmospheric-hydrological modeling system with combination of high-resolution and ensemble precipitation forecasting

    Science.gov (United States)

    Wu, Zhiyong; Wu, Juan; Lu, Guihua

    2016-09-01

    Coupled hydrological and atmospheric modeling is an effective tool for providing advanced flood forecasting. However, the uncertainties in precipitation forecasts are still considerable. To address uncertainties, a one-way coupled atmospheric-hydrological modeling system, with a combination of high-resolution and ensemble precipitation forecasting, has been developed. It consists of three high-resolution single models and four sets of ensemble forecasts from the THORPEX Interactive Grande Global Ensemble database. The former provides higher forecasting accuracy, while the latter provides the range of forecasts. The combined precipitation forecasting was then implemented to drive the Chinese National Flood Forecasting System in the 2007 and 2008 Huai River flood hindcast analysis. The encouraging results demonstrated that the system can clearly give a set of forecasting hydrographs for a flood event and has a promising relative stability in discharge peaks and timing for warning purposes. It not only gives a deterministic prediction, but also generates probability forecasts. Even though the signal was not persistent until four days before the peak discharge was observed in the 2007 flood event, the visualization based on threshold exceedance provided clear and concise essential warning information at an early stage. Forecasters could better prepare for the possibility of a flood at an early stage, and then issue an actual warning if the signal strengthened. This process may provide decision support for civil protection authorities. In future studies, different weather forecasts will be assigned various weight coefficients to represent the covariance of predictors and the extremes of distributions.

  7. A novel hybrid decomposition-and-ensemble model based on CEEMD and GWO for short-term PM2.5 concentration forecasting

    Science.gov (United States)

    Niu, Mingfei; Wang, Yufang; Sun, Shaolong; Li, Yongwu

    2016-06-01

    To enhance prediction reliability and accuracy, a hybrid model based on the promising principle of "decomposition and ensemble" and a recently proposed meta-heuristic called grey wolf optimizer (GWO) is introduced for daily PM2.5 concentration forecasting. Compared with existing PM2.5 forecasting methods, this proposed model has improved the prediction accuracy and hit rates of directional prediction. The proposed model involves three main steps, i.e., decomposing the original PM2.5 series into several intrinsic mode functions (IMFs) via complementary ensemble empirical mode decomposition (CEEMD) for simplifying the complex data; individually predicting each IMF with support vector regression (SVR) optimized by GWO; integrating all predicted IMFs for the ensemble result as the final prediction by another SVR optimized by GWO. Seven benchmark models, including single artificial intelligence (AI) models, other decomposition-ensemble models with different decomposition methods and models with the same decomposition-ensemble method but optimized by different algorithms, are considered to verify the superiority of the proposed hybrid model. The empirical study indicates that the proposed hybrid decomposition-ensemble model is remarkably superior to all considered benchmark models for its higher prediction accuracy and hit rates of directional prediction.

  8. Effective boson-spin model for nuclei ensemble based universal quantum memory

    CERN Document Server

    Song, Z; Shi, T; Sun, C P

    2004-01-01

    We study the collective excitation of a macroscopic ensemble of polarized nuclei fixed in a quantum dot. Under the approximately homogeneous condition that we explicitly present in this paper, this many-particle system behaves as a single mode boson interacting with the spin of a single conduction band electron confined in this quantum dot. Within this effective spin-boson system, the quantum information carried by the electronic spin can be coherently transferred into the collective bosonic mode of excitation in the ensemble of nuclei. In this sense, the collective bosonic excitation can serve as a stable quantum memory to store the quantum information of spin state of electron.

  9. Twenty-first century probabilistic projections of precipitation over Ontario, Canada through a regional climate model ensemble

    Science.gov (United States)

    Wang, Xiuquan; Huang, Guohe; Liu, Jinliang

    2016-06-01

    In this study, probabilistic projections of precipitation for the Province of Ontario are developed through a regional climate model ensemble to help investigate how global warming would affect its local climate. The PRECIS regional climate modeling system is employed to perform ensemble simulations, driven by a set of boundary conditions from a HadCM3-based perturbed-physics ensemble. The PRECIS ensemble simulations are fed into a Bayesian hierarchical model to quantify uncertain factors affecting the resulting projections of precipitation and thus generate probabilistic precipitation changes at grid point scales. Following that, reliable precipitation projections throughout the twenty-first century are developed for the entire province by applying the probabilistic changes to the observed precipitation. The results show that the vast majority of cities in Ontario are likely to suffer positive changes in annual precipitation in 2030, 2050, and 2080 s in comparison to the baseline observations. This may suggest that the whole province is likely to gain more precipitation throughout the twenty-first century in response to global warming. The analyses on the projections of seasonal precipitation further demonstrate that the entire province is likely to receive more precipitation in winter, spring, and autumn throughout this century while summer precipitation is only likely to increase slightly in 2030 s and would decrease gradually afterwards. However, because the magnitude of projected decrease in summer precipitation is relatively small in comparison with the anticipated increases in other three seasons, the annual precipitation over Ontario is likely to suffer a progressive increase throughout the twenty-first century (by 7.0 % in 2030 s, 9.5 % in 2050 s, and 12.6 % in 2080 s). Besides, the degree of uncertainty for precipitation projections is analyzed. The results suggest that future changes in spring precipitation show higher degree of uncertainty than other

  10. Climate change hotspots in the CMIP5 global climate model ensemble.

    Science.gov (United States)

    Diffenbaugh, Noah S; Giorgi, Filippo

    2012-01-10

    We use a statistical metric of multi-dimensional climate change to quantify the emergence of global climate change hotspots in the CMIP5 climate model ensemble. Our hotspot metric extends previous work through the inclusion of extreme seasonal temperature and precipitation, which exert critical influence on climate change impacts. The results identify areas of the Amazon, the Sahel and tropical West Africa, Indonesia, and the Tibetan Plateau as persistent regional climate change hotspots throughout the 21(st) century of the RCP8.5 and RCP4.5 forcing pathways. In addition, areas of southern Africa, the Mediterranean, the Arctic, and Central America/western North America also emerge as prominent regional climate change hotspots in response to intermediate and high levels of forcing. Comparisons of different periods of the two forcing pathways suggest that the pattern of aggregate change is fairly robust to the level of global warming below approximately 2°C of global warming (relative to the late-20(th)-century baseline), but not at the higher levels of global warming that occur in the late-21(st)-century period of the RCP8.5 pathway, with areas of southern Africa, the Mediterranean, and the Arctic exhibiting particular intensification of relative aggregate climate change in response to high levels of forcing. Although specific impacts will clearly be shaped by the interaction of climate change with human and biological vulnerabilities, our identification of climate change hotspots can help to inform mitigation and adaptation decisions by quantifying the rate, magnitude and causes of the aggregate climate response in different parts of the world.

  11. Emergence of multiple ocean ecosystem drivers in a large ensemble suite with an earth system model

    Directory of Open Access Journals (Sweden)

    K. B. Rodgers

    2014-12-01

    Full Text Available Marine ecosystems are increasingly impacted by human-induced changes. Ocean ecosystem drivers – including warming, acidification, deoxygenation and perturbations to biological productivity – can co-occur in space and time, but detecting their trends is complicated by the presence of noise associated with natural variability in the climate system. Here we use Large Initial-Condition Ensemble Simulations with a comprehensive Earth System Model under a historical/RCP8.5 pathway over 1950–2100 to consider emergence characteristics for the four individual and combined drivers. Using a one-standard deviation (67% confidence threshold of signal-to-noise to define emergence with a 30 yr trend window, we show that ocean acidification emerges much earlier than other drivers, namely during the 20th century over most of the global ocean. For biological productivity, the anthropogenic signal does not emerge from the noise over most of the global ocean before the end of the 21st century. The early emergence pattern for sea surface temperature in low latitudes is reversed from that of subsurface oxygen inventories, where emergence occurs earlier in the Southern Ocean. For the combined multiple-driver field, 41% of the global ocean exhibits emergence for the 2005–2014 period, and 63% for the 2075–2084 period. The combined multiple-driver field reveals emergence patterns by the end of this century that are relatively high over much of the Southern Ocean, North Pacific, and Atlantic, but relatively low over the tropics and the South Pacific. In regions with pronounced emergence characteristics, marine ecosystems can be expected to be pushed outside of their comfort zone determined by the degree of natural background variability to which they are adapted. The results here thus have implications not only for optimization of the ocean observing system, but also for risk assessment and mitigation strategies.

  12. Assessment of climate change impacts on climate variables using probabilistic ensemble modeling and trend analysis

    Science.gov (United States)

    Safavi, Hamid R.; Sajjadi, Sayed Mahdi; Raghibi, Vahid

    2016-08-01

    Water resources in snow-dependent regions have undergone significant changes due to climate change. Snow measurements in these regions have revealed alarming declines in snowfall over the past few years. The Zayandeh-Rud River in central Iran chiefly depends on winter falls as snow for supplying water from wet regions in high Zagrous Mountains to the downstream, (semi-)arid, low-lying lands. In this study, the historical records (baseline: 1971-2000) of climate variables (temperature and precipitation) in the wet region were chosen to construct a probabilistic ensemble model using 15 GCMs in order to forecast future trends and changes while the Long Ashton Research Station Weather Generator (LARS-WG) was utilized to project climate variables under two A2 and B1 scenarios to a future period (2015-2044). Since future snow water equivalent (SWE) forecasts by GCMs were not available for the study area, an artificial neural network (ANN) was implemented to build a relationship between climate variables and snow water equivalent for the baseline period to estimate future snowfall amounts. As a last step, homogeneity and trend tests were performed to evaluate the robustness of the data series and changes were examined to detect past and future variations. Results indicate different characteristics of the climate variables at upstream stations. A shift is observed in the type of precipitation from snow to rain as well as in its quantities across the subregions. The key role in these shifts and the subsequent side effects such as water losses is played by temperature.

  13. Towards global empirical upscaling of FLUXNET eddy covariance observations: validation of a model tree ensemble approach using a biosphere model

    Science.gov (United States)

    Jung, M.; Reichstein, M.; Bondeau, A.

    2009-10-01

    Global, spatially and temporally explicit estimates of carbon and water fluxes derived from empirical up-scaling eddy covariance measurements would constitute a new and possibly powerful data stream to study the variability of the global terrestrial carbon and water cycle. This paper introduces and validates a machine learning approach dedicated to the upscaling of observations from the current global network of eddy covariance towers (FLUXNET). We present a new model TRee Induction ALgorithm (TRIAL) that performs hierarchical stratification of the data set into units where particular multiple regressions for a target variable hold. We propose an ensemble approach (Evolving tRees with RandOm gRowth, ERROR) where the base learning algorithm is perturbed in order to gain a diverse sequence of different model trees which evolves over time. We evaluate the efficiency of the model tree ensemble (MTE) approach using an artificial data set derived from the Lund-Potsdam-Jena managed Land (LPJmL) biosphere model. We aim at reproducing global monthly gross primary production as simulated by LPJmL from 1998-2005 using only locations and months where high quality FLUXNET data exist for the training of the model trees. The model trees are trained with the LPJmL land cover and meteorological input data, climate data, and the fraction of absorbed photosynthetic active radiation simulated by LPJmL. Given that we know the "true result" in the form of global LPJmL simulations we can effectively study the performance of the MTE upscaling and associated problems of extrapolation capacity. We show that MTE is able to explain 92% of the variability of the global LPJmL GPP simulations. The mean spatial pattern and the seasonal variability of GPP that constitute the largest sources of variance are very well reproduced (96% and 94% of variance explained respectively) while the monthly interannual anomalies which occupy much less variance are less well matched (41% of variance explained

  14. The use of multi-model ensembles from global climate models for impact assessment of climate change

    Science.gov (United States)

    Semenov, M. A.

    2009-04-01

    The IPCC 4th Assessment Report was based on large datasets of projections of future climate produced by eighteen modelling groups worldwide who performed a set of coordinated climate experiments in which numerous global climate models (GCMs) have been run for a common set of experiments and various emission scenarios. These datasets are freely available form the IPCC Data Distribution Centre (www.ipcc-data.org) and can be used by the research community to assess the impact of changing climate on various systems of interest including impacts on agricultural crops and natural ecosystems, biodiversity and plant diseases. Multi-model ensembles (MME) emphasize the uncertainty in climate predictions resulting from structural differences in the global climate model design as well as uncertainty to variations of initial conditions or model parameters. This paper describes a methodology based on a stochastic weather generator for linking MME of predictions from GCMs with process-based impact models to assess impacts of climate change on biological or ecological systems. The latest version of the LARS-WG weather generator is described which allows seamlessly generating daily site-specific climate scenarios worldwide by utilising local daily weather and MME from GCMs. Examples of impacts on wheat in Europe, based on MME, are discussed, including changes in severity of drought and heat stress around flowering.

  15. STUDY ON THE METEOROLOGICAL PREDICTION MODEL USING THE LEARNING ALGORITHM OF NEURAL ENSEMBLE BASED ON PSO ALGORITHMS

    Institute of Scientific and Technical Information of China (English)

    WU Jian-sheng; JIN Long

    2009-01-01

    Because of the difficulty in deciding on the structure of BP neural network in operational meteorological application and the tendency tbr the network to transform to an issue of local solution,a hybrid Particle Swarm Optimization Algorithm based on Artificial Neural Network (PSO-BP) model is proposed for monthly mean rainfall of the whole area of Guangxi. It combines Particle Swarm Optimization (PSO) with BP,that is,the number of hidden nodes and connection weights are optimized by the implementation of PSO operation. The method produces a better network architecture and initial connection weights,trains the traditional backward propagation again by training samples. The ensemble strategy is carried out for the linear programming to calculate the best weights based on the "east sum of the error absolute value" as the optimal rule. The weighted coefficient of each ensemble individual is obtained. The results show that the method can effectively improve learning and generalization ability of the neural network.

  16. Sparse calibration of subsurface flow models using nonlinear orthogonal matching pursuit and an iterative stochastic ensemble method

    KAUST Repository

    Elsheikh, Ahmed H.

    2013-06-01

    We introduce a nonlinear orthogonal matching pursuit (NOMP) for sparse calibration of subsurface flow models. Sparse calibration is a challenging problem as the unknowns are both the non-zero components of the solution and their associated weights. NOMP is a greedy algorithm that discovers at each iteration the most correlated basis function with the residual from a large pool of basis functions. The discovered basis (aka support) is augmented across the nonlinear iterations. Once a set of basis functions are selected, the solution is obtained by applying Tikhonov regularization. The proposed algorithm relies on stochastically approximated gradient using an iterative stochastic ensemble method (ISEM). In the current study, the search space is parameterized using an overcomplete dictionary of basis functions built using the K-SVD algorithm. The proposed algorithm is the first ensemble based algorithm that tackels the sparse nonlinear parameter estimation problem. © 2013 Elsevier Ltd.

  17. A hybrid variational-ensemble data assimilation scheme with systematic error correction for limited-area ocean models

    Science.gov (United States)

    Oddo, Paolo; Storto, Andrea; Dobricic, Srdjan; Russo, Aniello; Lewis, Craig; Onken, Reiner; Coelho, Emanuel

    2016-10-01

    A hybrid variational-ensemble data assimilation scheme to estimate the vertical and horizontal parts of the background error covariance matrix for an ocean variational data assimilation system is presented and tested in a limited-area ocean model implemented in the western Mediterranean Sea. An extensive data set collected during the Recognized Environmental Picture Experiments conducted in June 2014 by the Centre for Maritime Research and Experimentation has been used for assimilation and validation. The hybrid scheme is used to both correct the systematic error introduced in the system from the external forcing (initialisation, lateral and surface open boundary conditions) and model parameterisation, and improve the representation of small-scale errors in the background error covariance matrix. An ensemble system is run offline for further use in the hybrid scheme, generated through perturbation of assimilated observations. Results of four different experiments have been compared. The reference experiment uses the classical stationary formulation of the background error covariance matrix and has no systematic error correction. The other three experiments account for, or not, systematic error correction and hybrid background error covariance matrix combining the static and the ensemble-derived errors of the day. Results show that the hybrid scheme when used in conjunction with the systematic error correction reduces the mean absolute error of temperature and salinity misfit by 55 and 42 % respectively, versus statistics arising from standard climatological covariances without systematic error correction.

  18. Combining large model ensembles with extreme value statistics to improve attribution statements of rare events

    Directory of Open Access Journals (Sweden)

    Sebastian Sippel

    2015-09-01

    In conclusion, our study shows that EVT and empirical estimates based on numerical simulations can indeed be used to productively inform each other, for instance to derive appropriate EVT parameters for short observational time series. Further, the combination of ensemble simulations with EVT allows us to significantly reduce the number of simulations needed for statements about the tails.

  19. An ensemble approach to assess hydrological models' contribution to uncertainties in the analysis of climate change impact on water resources

    Directory of Open Access Journals (Sweden)

    J. A. Velázquez

    2013-02-01

    Full Text Available Over the recent years, several research efforts investigated the impact of climate change on water resources for different regions of the world. The projection of future river flows is affected by different sources of uncertainty in the hydro-climatic modelling chain. One of the aims of the QBic3 project (Québec-Bavarian International Collaboration on Climate Change is to assess the contribution to uncertainty of hydrological models by using an ensemble of hydrological models presenting a diversity of structural complexity (i.e., lumped, semi distributed and distributed models. The study investigates two humid, mid-latitude catchments with natural flow conditions; one located in Southern Québec (Canada and one in Southern Bavaria (Germany. Daily flow is simulated with four different hydrological models, forced by outputs from regional climate models driven by global climate models over a reference (1971–2000 and a future (2041–2070 period. The results show that, for our hydrological model ensemble, the choice of model strongly affects the climate change response of selected hydrological indicators, especially those related to low flows. Indicators related to high flows seem less sensitive on the choice of the hydrological model.

  20. A hybrid ensemble-OI Kalman filter for efficient data assimilation into a 3-D biogeochemical model of the Mediterranean

    KAUST Repository

    Tsiaras, Kostas P.

    2017-04-20

    A hybrid ensemble data assimilation scheme (HYBRID), combining a flow-dependent with a static background covariance, was developed and implemented for assimilating satellite (SeaWiFS) Chl-a data into a marine ecosystem model of the Mediterranean. The performance of HYBRID was assessed against a model free-run, the ensemble-based singular evolutive interpolated Kalman (SEIK) and its variant with static covariance (SFEK), with regard to the assimilated variable (Chl-a) and non-assimilated variables (dissolved inorganic nutrients). HYBRID was found more efficient than both SEIK and SFEK, reducing the Chl-a error by more than 40% in most areas, as compared to the free-run. Data assimilation had a positive overall impact on nutrients, except for a deterioration of nitrates simulation by SEIK in the most productive area (Adriatic). This was related to SEIK pronounced update in this area and the phytoplankton limitation on phosphate that lead to a built up of excess nitrates. SEIK was found more efficient in productive and variable areas, where its ensemble exhibited important spread. SFEK had an effect mostly on Chl-a, performing better than SEIK in less dynamic areas, adequately described by the dominant modes of its static covariance. HYBRID performed well in all areas, due to its “blended” covariance. Its flow-dependent component appears to track changes in the system dynamics, while its static covariance helps maintaining sufficient spread in the forecast. HYBRID sensitivity experiments showed that an increased contribution from the flow-dependent covariance results in a deterioration of nitrates, similar to SEIK, while the improvement of HYBRID with increasing flow-dependent ensemble size quickly levels off.

  1. Dynamical downscaling of regional climate over eastern China using RSM with multiple physics scheme ensembles

    Science.gov (United States)

    Peishu, Zong; Jianping, Tang; Shuyu, Wang; Lingyun, Xie; Jianwei, Yu; Yunqian, Zhu; Xiaorui, Niu; Chao, Li

    2017-08-01

    The parameterization of physical processes is one of the critical elements to properly simulate the regional climate over eastern China. It is essential to conduct detailed analyses on the effect of physical parameterization schemes on regional climate simulation, to provide more reliable regional climate change information. In this paper, we evaluate the 25-year (1983-2007) summer monsoon climate characteristics of precipitation and surface air temperature by using the regional spectral model (RSM) with different physical schemes. The ensemble results using the reliability ensemble averaging (REA) method are also assessed. The result shows that the RSM model has the capacity to reproduce the spatial patterns, the variations, and the temporal tendency of surface air temperature and precipitation over eastern China. And it tends to predict better climatology characteristics over the Yangtze River basin and the South China. The impact of different physical schemes on RSM simulations is also investigated. Generally, the CLD3 cloud water prediction scheme tends to produce larger precipitation because of its overestimation of the low-level moisture. The systematic biases derived from the KF2 cumulus scheme are larger than those from the RAS scheme. The scale-selective bias correction (SSBC) method improves the simulation of the temporal and spatial characteristics of surface air temperature and precipitation and advances the circulation simulation capacity. The REA ensemble results show significant improvement in simulating temperature and precipitation distribution, which have much higher correlation coefficient and lower root mean square error. The REA result of selected experiments is better than that of nonselected experiments, indicating the necessity of choosing better ensemble samples for ensemble.

  2. Myers-Pospelov model as an ensemble of Pais-Uhlenbeck oscillators: unitarity and Lorentz invariance violation

    Science.gov (United States)

    Lopez-Sarrion, Justo; Reyes, Carlos M.

    2013-04-01

    We study a generalization of the Pais-Uhlenbeck oscillator for fermionic variables. Next, we consider an ensemble of these oscillators and we identify a particular case of the Myers-Pospelov model which is relevant for effective theories of quantum gravity. Finally, by taking advantage of this connection, we analyze, for this model, unitarity at one loop order in the low energy regime where no ghost states can be created on-shell. This energy regime is the relevant one when we consider the Myers-Pospelov model as a true effective theory coming from a new space-time structure.

  3. Myers-Pospelov Model as an Ensemble of Pais-Uhlenbeck Oscillators: Unitarity and Lorentz Invariance Violation

    CERN Document Server

    Lopez-Sarrion, Justo

    2013-01-01

    We study a generalization of a Pais-Uhlenbeck oscillator for fermionic variables. Next, we consider an ensemble of these oscillators and we identify a particular case of the Myers-Pospelov model which is relevant for effective theories of quantum gravity. Finally, by taking the advantage of this connection, we analyze, for this model, the unitarity at one loop order in the low energy regime where no ghost states can be created on-shell. This energy regime is the relevant one when we consider the Myers-Pospelov model as a true effective theory coming from new space-time structure.

  4. Impacts of different cumulus physics over south Asia region with case study tropical cyclone Viyaru

    CERN Document Server

    Fahad, Abdullah Al

    2015-01-01

    Tropical Cyclone Viyaru, formerly known as Cyclonic Storm Mahasen was a rapidly intensifying, category 01B storm that made landfall in Chittagong, Bangladesh on the 16th of May, 2013. In this study, the sensitivity of numerical simulations of tropical cyclone to cumulus physics parametrization is carried out with a view to determine the best cumulus physics option for prediction of the cyclones track, timing, and central pressure evolution in the Bay of Bengal. For this purpose, the tropical cyclone Viyaru has been simulated by WRF ARW in a nested domain with NCEP Global Final Analysis(FNL) data as initial and boundary conditions. The model domain consists of one parent domain and one nested domain. The resolution of the parent domain is 36 km while the nested domain has a resolution of 12 km. Five numerical simulations have been done with the same micro-physics scheme (WSM3), planetary boundary layer scheme,NOAH land surface scheme but different Cumulus Parametrization scheme. Four cumulus Parametrization sc...

  5. A Hybrid Model Based on Ensemble Empirical Mode Decomposition and Fruit Fly Optimization Algorithm for Wind Speed Forecasting

    Directory of Open Access Journals (Sweden)

    Zongxi Qu

    2016-01-01

    Full Text Available As a type of clean and renewable energy, the superiority of wind power has increasingly captured the world’s attention. Reliable and precise wind speed prediction is vital for wind power generation systems. Thus, a more effective and precise prediction model is essentially needed in the field of wind speed forecasting. Most previous forecasting models could adapt to various wind speed series data; however, these models ignored the importance of the data preprocessing and model parameter optimization. In view of its importance, a novel hybrid ensemble learning paradigm is proposed. In this model, the original wind speed data is firstly divided into a finite set of signal components by ensemble empirical mode decomposition, and then each signal is predicted by several artificial intelligence models with optimized parameters by using the fruit fly optimization algorithm and the final prediction values were obtained by reconstructing the refined series. To estimate the forecasting ability of the proposed model, 15 min wind speed data for wind farms in the coastal areas of China was performed to forecast as a case study. The empirical results show that the proposed hybrid model is superior to some existing traditional forecasting models regarding forecast performance.

  6. Evaluation of conditional non-linear optimal perturbation obtained by an ensemble-based approach using the Lorenz-63 model

    Directory of Open Access Journals (Sweden)

    Xudong Yin

    2014-02-01

    Full Text Available The authors propose to implement conditional non-linear optimal perturbation related to model parameters (CNOP-P through an ensemble-based approach. The approach was first used in our earlier study and is improved to be suitable for calculating CNOP-P. Idealised experiments using the Lorenz-63 model are conducted to evaluate the performance of the improved ensemble-based approach. The results show that the maximum prediction error after optimisation has been multiplied manifold compared with the initial-guess prediction error, and is extremely close to, or greater than, the maximum value of the exhaustive attack method (a million random samples. The calculation of CNOP-P by the ensemble-based approach is capable of maintaining a high accuracy over a long prediction time under different constraints and initial conditions. Further, the CNOP-P obtained by the approach is applied to sensitivity analysis of the Lorenz-63 model. The sensitivity analysis indicates that when the prediction time is set to 0.2 time units, the Lorenz-63 model becomes extremely insensitive to one parameter, which leaves the other two parameters to affect the uncertainty of the model. Finally, a serial of parameter estimation experiments are performed to verify sensitivity analysis. It is found that when the three parameters are estimated simultaneously, the insensitive parameter is estimated much worse, but the Lorenz-63 model can still generate a very good simulation thanks to the relatively accurate values of the other two parameters. When only two sensitive parameters are estimated simultaneously and the insensitive parameter is left to be non-optimised, the outcome is better than the case when the three parameters are estimated simultaneously. With the increase of prediction time and observation, however, the model sensitivity to the insensitive parameter increases accordingly and the insensitive parameter can also be estimated successfully.

  7. Critical behavior in topological ensembles

    CERN Document Server

    Bulycheva, K; Nechaev, S

    2014-01-01

    We consider the relation between three physical problems: 2D directed lattice random walks in an external magnetic field, ensembles of torus knots, and 5d Abelian SUSY gauge theory with massless hypermultiplet in $\\Omega$ background. All these systems exhibit the critical behavior typical for the "area+length" statistics of grand ensembles of 2D directed paths. In particular, using the combinatorial description, we have found the new critical behavior in the ensembles of the torus knots and in the instanton ensemble in 5d gauge theory. The relation with the integrable model is discussed.

  8. Anvil Glaciation in a Deep Cumulus Updraught over Florida Simulated with the Explicit Microphysics Model. I: Impact of Various Nucleation Processes

    Science.gov (United States)

    Phillips, Vaughan T. J.; Andronache, Constantin; Sherwood, Steven C.; Bansemer, Aaron; Conant, William C.; Demott, Paul J.; Flagan, Richard C.; Heymsfield, Andy; Jonsson, Haflidi; Poellot, Micheal; Rissman, Tracey A.; Seinfeld, John H.; Vanreken, Tim; Varutbangkul, Varuntida; Wilson, James C.

    2005-01-01

    Simulations of a cumulonimbus cloud observed in the Cirrus regional Study of Tropical Anvils and Cirrus Layers-Florida Area Cirrus Experiment (CRYSTAL-FACE) with an advanced version of the Explicit Microphysics Model (EMM) are presented. The EMM has size-resolved aerosols and predicts the time evolution of sizes, bulk densities and axial ratios of ice particles. Observations by multiple aircraft in the troposphere provide inputs to the model, including observations of the ice nuclei and of the entire size distribution of condensation nuclei. Homogeneous droplet freezing is found to be the source of almost all of the ice crystals in the anvil updraught of this particular model cloud. Most of the simulated droplets that freeze to form anvil crystals appear to be nucleated by activation of aerosols far above cloud base in the interior of the cloud ("secondary" or "in cloud" droplet nucleation). This is partly because primary droplets formed at cloud base are invariably depleted by accretion before they can reach the anvil base in the updraught, which promotes an increase with height of the average supersaturation in the updraught aloft. More than half of these aerosols, activated far above cloud base, are entrained into the updraught of this model cloud from the lateral environment above about 5 km above mean sea level. This confirms the importance of remote sources of atmospheric aerosol for anvil glaciation. Other nucleation processes impinge indirectly upon the anvil glaciation by modifying the concentration of supercooled droplets in the upper levels of the mixed-phase region. For instance, the warm-rain process produces a massive indirect impact on the anvil crystal concentration, because it determines the mass of precipitation forming in the updraught. It competes with homogeneous freezing as a sink for cloud droplets. The effects from turbulent enhancement of the warm-rain process and from the nucleation processes on the anvil ice properties are assessed.

  9. Motility contrast imaging of live porcine cumulus-oocyte complexes

    Science.gov (United States)

    An, Ran; Turek, John; Machaty, Zoltan; Nolte, David

    2013-02-01

    Freshly-harvested porcine oocytes are invested with cumulus granulosa cells in cumulus-oocyte complexes (COCs). The cumulus cell layer is usually too thick to image the living oocyte under a conventional microscope. Therefore, it is difficult to assess the oocyte viability. The low success rate of implantation is the main problem for in vitro fertilization. In this paper, we demonstrate our dynamic imaging technique called motility contrast imaging (MCI) that provides a non-invasive way to monitor the COCs before and after maturation. MCI shows a change of intracellular activity during oocyte maturation, and a measures dynamic contrast between the cumulus granulosa shell and the oocytes. MCI also shows difference in the spectral response between oocytes that were graded into quality classes. MCI is based on shortcoherence digital holography. It uses intracellular motility as the endogenous imaging contrast of living tissue. MCI presents a new approach for cumulus-oocyte complex assessment.

  10. Sensitivity of hurricane track to cumulus parameterization schemes in the WRF model for three intense tropical cyclones: impact of convective asymmetry

    Science.gov (United States)

    Shepherd, Tristan J.; Walsh, Kevin J.

    2017-08-01

    This study investigates the effect of the choice of convective parameterization (CP) scheme on the simulated tracks of three intense tropical cyclones (TCs), using the Weather Research and Forecasting (WRF) model. We focus on diagnosing the competing influences of large-scale steering flow, beta drift and convectively induced changes in track, as represented by four different CP schemes (Kain-Fritsch (KF), Betts-Miller-Janjic (BMJ), Grell-3D (G-3), and the Tiedtke (TD) scheme). The sensitivity of the results to initial conditions, model domain size and shallow convection is also tested. We employ a diagnostic technique by Chan et al. (J Atmos Sci 59:1317-1336, 2002) that separates the influence of the large-scale steering flow, beta drift and the modifications of the steering flow by the storm-scale convection. The combined effect of the steering flow and the beta drift causes TCs typically to move in the direction of the wavenumber-1 (WN-1) cyclonic potential vorticity tendency (PVT). In instances of asymmetrical TCs, the simulated TC motion does not necessarily match the motion expected from the WN-1 PVT due to changes in the convective pattern. In the present study, we test this concept in the WRF simulations and investigate whether if the diagnosed motion from the WN-1 PVT and the TC motion do not match, this can be related to the emerging evolution of changes in convective structure. Several systematic results are found across the three cyclone cases. The sensitivity of TC track to initial conditions (the initialisation time and model domain size) is less than the sensitivity of TC track to changing the CP scheme. The simulated track is not overly sensitive to shallow convection in the KF, BMJ, and TD schemes, compared to the track difference between CP schemes. The G3 scheme, however, is highly sensitive to shallow convection being used. Furthermore, while agreement between the simulated TC track direction and the WN-1 diagnostic is usually good, there are

  11. Sensitivity of hurricane track to cumulus parameterization schemes in the WRF model for three intense tropical cyclones: impact of convective asymmetry

    Science.gov (United States)

    Shepherd, Tristan J.; Walsh, Kevin J.

    2016-08-01

    This study investigates the effect of the choice of convective parameterization (CP) scheme on the simulated tracks of three intense tropical cyclones (TCs), using the Weather Research and Forecasting (WRF) model. We focus on diagnosing the competing influences of large-scale steering flow, beta drift and convectively induced changes in track, as represented by four different CP schemes (Kain-Fritsch (KF), Betts-Miller-Janjic (BMJ), Grell-3D (G-3), and the Tiedtke (TD) scheme). The sensitivity of the results to initial conditions, model domain size and shallow convection is also tested. We employ a diagnostic technique by Chan et al. (J Atmos Sci 59:1317-1336, 2002) that separates the influence of the large-scale steering flow, beta drift and the modifications of the steering flow by the storm-scale convection. The combined effect of the steering flow and the beta drift causes TCs typically to move in the direction of the wavenumber-1 (WN-1) cyclonic potential vorticity tendency (PVT). In instances of asymmetrical TCs, the simulated TC motion does not necessarily match the motion expected from the WN-1 PVT due to changes in the convective pattern. In the present study, we test this concept in the WRF simulations and investigate whether if the diagnosed motion from the WN-1 PVT and the TC motion do not match, this can be related to the emerging evolution of changes in convective structure. Several systematic results are found across the three cyclone cases. The sensitivity of TC track to initial conditions (the initialisation time and model domain size) is less than the sensitivity of TC track to changing the CP scheme. The simulated track is not overly sensitive to shallow convection in the KF, BMJ, and TD schemes, compared to the track difference between CP schemes. The G3 scheme, however, is highly sensitive to shallow convection being used. Furthermore, while agreement between the simulated TC track direction and the WN-1 diagnostic is usually good, there are

  12. Coupled atmosphere and land-surface assimilation of surface observations with a single column model and ensemble data assimilation

    Science.gov (United States)

    Rostkier-Edelstein, Dorita; Hacker, Joshua P.; Snyder, Chris

    2014-05-01

    Numerical weather prediction and data assimilation models are composed of coupled atmosphere and land-surface (LS) components. If possible, the assimilation procedure should be coupled so that observed information in one module is used to correct fields in the coupled module. There have been some attempts in this direction using optimal interpolation, nudging and 2/3DVAR data assimilation techniques. Aside from satellite remote sensed observations, reference height in-situ observations of temperature and moisture have been used in these studies. Among other problems, difficulties in coupled atmosphere and LS assimilation arise as a result of the different time scales characteristic of each component and the unsteady correlation between these components under varying flow conditions. Ensemble data-assimilation techniques rely on flow dependent observations-model covariances. Provided that correlations and covariances between land and atmosphere can be adequately simulated and sampled, ensemble data assimilation should enable appropriate assimilation of observations simultaneously into the atmospheric and LS states. Our aim is to explore assimilation of reference height in-situ temperature and moisture observations into the coupled atmosphere-LS modules(simultaneously) in NCAR's WRF-ARW model using the NCAR's DART ensemble data-assimilation system. Observing system simulation experiments (OSSEs) are performed using the single column model (SCM) version of WRF. Numerical experiments during a warm season are centered on an atmospheric and soil column in the South Great Plains. Synthetic observations are derived from "truth" WRF-SCM runs for a given date,initialized and forced using North American Regional Reanalyses (NARR). WRF-SCM atmospheric and LS ensembles are created by mixing the atmospheric and soil NARR profile centered on a given date with that from another day (randomly chosen from the same season) with weights drawn from a logit-normal distribution. Three

  13. A One-Step-Ahead Smoothing-Based Joint Ensemble Kalman Filter for State-Parameter Estimation of Hydrological Models

    KAUST Repository

    El Gharamti, Mohamad

    2015-11-26

    The ensemble Kalman filter (EnKF) recursively integrates field data into simulation models to obtain a better characterization of the model’s state and parameters. These are generally estimated following a state-parameters joint augmentation strategy. In this study, we introduce a new smoothing-based joint EnKF scheme, in which we introduce a one-step-ahead smoothing of the state before updating the parameters. Numerical experiments are performed with a two-dimensional synthetic subsurface contaminant transport model. The improved performance of the proposed joint EnKF scheme compared to the standard joint EnKF compensates for the modest increase in the computational cost.

  14. DART: A Community Facility Providing State-of-the-Art, Efficient Ensemble Data Assimilation for Large (Coupled) Geophysical Models

    Science.gov (United States)

    Hoar, T. J.; Anderson, J. L.; Collins, N.; Kershaw, H.; Hendricks, J.; Raeder, K.; Mizzi, A. P.; Barré, J.; Gaubert, B.; Madaus, L. E.; Aydogdu, A.; Raeder, J.; Arango, H.; Moore, A. M.; Edwards, C. A.; Curchitser, E. N.; Escudier, R.; Dussin, R.; Bitz, C. M.; Zhang, Y. F.; Shrestha, P.; Rosolem, R.; Rahman, M.

    2016-12-01

    Strongly-coupled ensemble data assimilation with multiple high-resolution model components requires massive state vectors which need to be efficiently stored and accessed throughout the assimilation process. Supercomputer architectures are tending towards increasing the number of cores per node but have the same or less memory per node. Recent advances in the Data Assimilation Research Testbed (DART), a freely-available community ensemble data assimilation facility that works with dozens of large geophysical models, have addressed the need to run with a smaller memory footprint on a higher node count by utilizing MPI-2 one-sided communication to do non-blocking asynchronous access of distributed data. DART runs efficiently on many computational platforms ranging from laptops through thousands of cores on the newest supercomputers. Benefits of the new DART implementation will be shown. In addition, overviews of the most recently supported models will be presented: CAM-CHEM, WRF-CHEM, CM1, OpenGGCM, FESOM, ROMS, CICE5, TerrSysMP (COSMO, CLM, ParFlow), JULES, and CABLE. 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 and state agencies doing operational prediction with large state-of-the-art models.

  15. Analyzing Tropical Waves Using the Parallel Ensemble Empirical Model Decomposition Method: Preliminary Results from Hurricane Sandy

    Science.gov (United States)

    Shen, Bo-Wen; Cheung, Samson; Li, Jui-Lin F.; Wu, Yu-ling

    2013-01-01

    In this study, we discuss the performance of the parallel ensemble empirical mode decomposition (EMD) in the analysis of tropical waves that are associated with tropical cyclone (TC) formation. To efficiently analyze high-resolution, global, multiple-dimensional data sets, we first implement multilevel parallelism into the ensemble EMD (EEMD) and obtain a parallel speedup of 720 using 200 eight-core processors. We then apply the parallel EEMD (PEEMD) to extract the intrinsic mode functions (IMFs) from preselected data sets that represent (1) idealized tropical waves and (2) large-scale environmental flows associated with Hurricane Sandy (2012). Results indicate that the PEEMD is efficient and effective in revealing the major wave characteristics of the data, such as wavelengths and periods, by sifting out the dominant (wave) components. This approach has a potential for hurricane climate study by examining the statistical relationship between tropical waves and TC formation.

  16. Dispersion of ensembles of non-interacting particles. [stellar motion model

    Science.gov (United States)

    Heard, W. B.

    1976-01-01

    The dynamics of an ensemble of noninteracting particles dispersing from a common origin and moving in a common force field with an initial distribution of momenta is analyzed using an approach where the particles are considered as a continuum described by a phase-space distribution function. General solutions are obtained for both the distribution function and the associated spatial density function. The linear case of small departures from circular orbits in an axisymmetric gravitational field is treated along with the specific case of particle dispersion from an object in a circular orbit in the same type of field. Numerical results are presented for the latter case, and consideration is given to the inverse problem of determining the initial time and velocity distribution from knowledge of the ensemble structure at a later time. Explicit results are provided for the case of an ellipsoidal distribution of initial momenta, and a numerical procedure is indicated for treating more general cases.

  17. Multi-ensemble regional simulation of Indian monsoon during contrasting rainfall years: role of convective schemes and nested domain

    Science.gov (United States)

    Devanand, Anjana; Ghosh, Subimal; Paul, Supantha; Karmakar, Subhankar; Niyogi, Dev

    2017-08-01

    Regional simulations of the seasonal Indian summer monsoon rainfall (ISMR) require an understanding of the model sensitivities to physics and resolution, and its effect on the model uncertainties. It is also important to quantify the added value in the simulated sub-regional precipitation characteristics by a regional climate model (RCM), when compared to coarse resolution rainfall products. This study presents regional model simulations of ISMR at seasonal scale using the Weather Research and Forecasting (WRF) model with the synoptic scale forcing from ERA-interim reanalysis, for three contrasting monsoon seasons, 1994 (excess), 2002 (deficit) and 2010 (normal). Impact of four cumulus schemes, viz., Kain-Fritsch (KF), Betts-Janjić-Miller, Grell 3D and modified Kain-Fritsch (KFm), and two micro physical parameterization schemes, viz., WRF Single Moment Class 5 scheme and Lin et al. scheme (LIN), with eight different possible combinations are analyzed. The impact of spectral nudging on model sensitivity is also studied. In WRF simulations using spectral nudging, improvement in model rainfall appears to be consistent in regions with topographic variability such as Central Northeast and Konkan Western Ghat sub-regions. However the results are also dependent on choice of cumulus scheme used, with KF and KFm providing relatively good performance and the eight member ensemble mean showing better results for these sub-regions. There is no consistent improvement noted in Northeast and Peninsular Indian monsoon regions. Results indicate that the regional simulations using nested domains can provide some improvements on ISMR simulations. Spectral nudging is found to improve upon the model simulations in terms of reducing the intra ensemble spread and hence the uncertainty in the model simulated precipitation. The results provide important insights regarding the need for further improvements in the regional climate simulations of ISMR for various sub-regions and contribute

  18. Future changes in drought characteristics over Southern South America projected by a CMIP5 multi-model ensemble

    Science.gov (United States)

    Rivera, J. A.; Penalba, O. C.

    2013-05-01

    The impact of climate change on drought main characteristics (frequency, duration and severity) was assessed over Southern South America through the precipitation outputs from a multi-model ensemble of 15 climate models of the Coupled Model Intercomparison Project Phase 5 (CMIP5). The Standardized Precipitation Index was used as a drought indicator, given its temporal flexibility and simplicity. Changes in drought characteristics were identified by the difference for early (2011-2040) and late (2071-2100) 21st century values with respect to the 1979-2008 baseline. In order to evaluate the multi-model outputs, model biases where identified through a comparison with the drought characteristics from the Global Precipitation Climatology Centre database for the baseline period. Future climate projections under moderate- and high-emission scenarios showed that the occurrence of short-term and long-term droughts will be more frequent in the 21st century, with shorter durations and greater severities over much of the study area. This result is independent on the scenario considered, since no significant differences were observed on drought changes. Taking into account that in most of the region the multi-model ensemble tends to produce less number of droughts, with higher duration and lower severity, the future changes scenario might be even more dramatic. Therefore, Southern South America could experience more frequent water shortages with significant economic losses if proper adaptation measures are not proposed timely.

  19. 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...

  20. Ensembl 2017

    Science.gov (United States)

    Aken, Bronwen L.; Achuthan, Premanand; Akanni, Wasiu; Amode, M. Ridwan; Bernsdorff, Friederike; Bhai, Jyothish; Billis, Konstantinos; Carvalho-Silva, Denise; Cummins, Carla; Clapham, Peter; Gil, Laurent; Girón, Carlos García; Gordon, Leo; Hourlier, Thibaut; Hunt, Sarah E.; Janacek, Sophie H.; Juettemann, Thomas; Keenan, Stephen; Laird, Matthew R.; Lavidas, Ilias; Maurel, Thomas; McLaren, William; Moore, Benjamin; Murphy, Daniel N.; Nag, Rishi; Newman, Victoria; Nuhn, Michael; Ong, Chuang Kee; Parker, Anne; Patricio, Mateus; Riat, Harpreet Singh; Sheppard, Daniel; Sparrow, Helen; Taylor, Kieron; Thormann, Anja; Vullo, Alessandro; Walts, Brandon; Wilder, Steven P.; Zadissa, Amonida; Kostadima, Myrto; Martin, Fergal J.; Muffato, Matthieu; Perry, Emily; Ruffier, Magali; Staines, Daniel M.; Trevanion, Stephen J.; Cunningham, Fiona; Yates, Andrew; Zerbino, Daniel R.; Flicek, Paul

    2017-01-01

    Ensembl (www.ensembl.org) is a database and genome browser for enabling research on vertebrate genomes. We import, analyse, curate and integrate a diverse collection of large-scale reference data to create a more comprehensive view of genome biology than would be possible from any individual dataset. Our extensive data resources include evidence-based gene and regulatory region annotation, genome variation and gene trees. An accompanying suite of tools, infrastructure and programmatic access methods ensure uniform data analysis and distribution for all supported species. Together, these provide a comprehensive solution for large-scale and targeted genomics applications alike. Among many other developments over the past year, we have improved our resources for gene regulation and comparative genomics, and added CRISPR/Cas9 target sites. We released new browser functionality and tools, including improved filtering and prioritization of genome variation, Manhattan plot visualization for linkage disequilibrium and eQTL data, and an ontology search for phenotypes, traits and dise