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

NASA Technical Reports Server (NTRS)

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.

Uncertainty in tsunami sediment transport modeling

USGS Publications Warehouse

Jaffe, Bruce E.; Goto, Kazuhisa; Sugawara, Daisuke; Gelfenbaum, Guy R.; La Selle, SeanPaul M.

2016-01-01

Erosion and deposition from tsunamis record information about tsunami hydrodynamics and size that can be interpreted to improve tsunami hazard assessment. We explore sources and methods for quantifying uncertainty in tsunami sediment transport modeling. Uncertainty varies with tsunami, study site, available input data, sediment grain size, and model. Although uncertainty has the potential to be large, published case studies indicate that both forward and inverse tsunami sediment transport models perform well enough to be useful for deciphering tsunami characteristics, including size, from deposits. New techniques for quantifying uncertainty, such as Ensemble Kalman Filtering inversion, and more rigorous reporting of uncertainties will advance the science of tsunami sediment transport modeling. Uncertainty may be decreased with additional laboratory studies that increase our understanding of the semi-empirical parameters and physics of tsunami sediment transport, standardized benchmark tests to assess model performance, and development of hybrid modeling approaches to exploit the strengths of forward and inverse models.

Reduction of uncertainty in global black carbon direct radiative forcing constrained by observations

NASA Astrophysics Data System (ADS)

Wang, R.; Balkanski, Y.; Boucher, O.; Ciais, P.; Schuster, G. L.; Chevallier, F.; Samset, B. H.; Valari, M.; Liu, J.; Tao, S.

2017-12-01

Black carbon (BC) absorbs sunlight and contributes to global warming. However, the size of this effect, namely the direct radiative forcing (DRF), ranges from +0.1 to +1.0 W m-2, largely due to discrepancies between modeled and observed BC radiation absorption. Studies that adjusted emissions to correct biases of models resulted in a revised upward estimate of the BC DRF. However, the observation-based BC RF was not optimized against observations in a rigorous mathematical manner, because uncertainties in emissions and the representativeness errors due to use of coarse-resolution models were not fully assessed. Here we simulated the absorption of solar radiation by BC from all sources at the 10-km resolution by combining a nested aerosol model with a downscaling method. The normalized mean bias in BC radiation absorption was reduced from -51% to -24% in Asia and from -57% to -50% elsewhere. We applied a Bayesian method that account for model, representativeness and observational uncertainties to estimate the BC RF and its uncertainty. Using the high-resolution model reduces uncertainty in BC DRF from -101%/+152% to -70%/+71% over Asia and from -83%/+108% to -64%/+68% over other continental regions. We derived an observation-based BC DRF of 0.61 Wm-2 (0.16 to 1.40 as 90% confidence) as our best estimate.

Modeling of 2D diffusion processes based on microscopy data: parameter estimation and practical identifiability analysis.

PubMed

Hock, Sabrina; Hasenauer, Jan; Theis, Fabian J

2013-01-01

Diffusion is a key component of many biological processes such as chemotaxis, developmental differentiation and tissue morphogenesis. Since recently, the spatial gradients caused by diffusion can be assessed in-vitro and in-vivo using microscopy based imaging techniques. The resulting time-series of two dimensional, high-resolutions images in combination with mechanistic models enable the quantitative analysis of the underlying mechanisms. However, such a model-based analysis is still challenging due to measurement noise and sparse observations, which result in uncertainties of the model parameters. We introduce a likelihood function for image-based measurements with log-normal distributed noise. Based upon this likelihood function we formulate the maximum likelihood estimation problem, which is solved using PDE-constrained optimization methods. To assess the uncertainty and practical identifiability of the parameters we introduce profile likelihoods for diffusion processes. As proof of concept, we model certain aspects of the guidance of dendritic cells towards lymphatic vessels, an example for haptotaxis. Using a realistic set of artificial measurement data, we estimate the five kinetic parameters of this model and compute profile likelihoods. Our novel approach for the estimation of model parameters from image data as well as the proposed identifiability analysis approach is widely applicable to diffusion processes. The profile likelihood based method provides more rigorous uncertainty bounds in contrast to local approximation methods.

A Multi-Band Uncertainty Set Based Robust SCUC With Spatial and Temporal Budget Constraints

DOE Office of Scientific and Technical Information (OSTI.GOV)

Dai, Chenxi; Wu, Lei; Wu, Hongyu

2016-11-01

The dramatic increase of renewable energy resources in recent years, together with the long-existing load forecast errors and increasingly involved price sensitive demands, has introduced significant uncertainties into power systems operation. In order to guarantee the operational security of power systems with such uncertainties, robust optimization has been extensively studied in security-constrained unit commitment (SCUC) problems, for immunizing the system against worst uncertainty realizations. However, traditional robust SCUC models with single-band uncertainty sets may yield over-conservative solutions in most cases. This paper proposes a multi-band robust model to accurately formulate various uncertainties with higher resolution. By properly tuning band intervalsmore » and weight coefficients of individual bands, the proposed multi-band robust model can rigorously and realistically reflect spatial/temporal relationships and asymmetric characteristics of various uncertainties, and in turn could effectively leverage the tradeoff between robustness and economics of robust SCUC solutions. The proposed multi-band robust SCUC model is solved by Benders decomposition (BD) and outer approximation (OA), while taking the advantage of integral property of the proposed multi-band uncertainty set. In addition, several accelerating techniques are developed for enhancing the computational performance and the convergence speed. Numerical studies on a 6-bus system and the modified IEEE 118-bus system verify the effectiveness of the proposed robust SCUC approach for enhancing uncertainty modeling capabilities and mitigating conservativeness of the robust SCUC solution.« less

Definition and solution of a stochastic inverse problem for the Manning's n parameter field in hydrodynamic models.

PubMed

Butler, T; Graham, L; Estep, D; Dawson, C; Westerink, J J

2015-04-01

The uncertainty in spatially heterogeneous Manning's n fields is quantified using a novel formulation and numerical solution of stochastic inverse problems for physics-based models. The uncertainty is quantified in terms of a probability measure and the physics-based model considered here is the state-of-the-art ADCIRC model although the presented methodology applies to other hydrodynamic models. An accessible overview of the formulation and solution of the stochastic inverse problem in a mathematically rigorous framework based on measure theory is presented. Technical details that arise in practice by applying the framework to determine the Manning's n parameter field in a shallow water equation model used for coastal hydrodynamics are presented and an efficient computational algorithm and open source software package are developed. A new notion of "condition" for the stochastic inverse problem is defined and analyzed as it relates to the computation of probabilities. This notion of condition is investigated to determine effective output quantities of interest of maximum water elevations to use for the inverse problem for the Manning's n parameter and the effect on model predictions is analyzed.

Definition and solution of a stochastic inverse problem for the Manning's n parameter field in hydrodynamic models

NASA Astrophysics Data System (ADS)

Butler, T.; Graham, L.; Estep, D.; Dawson, C.; Westerink, J. J.

2015-04-01

The uncertainty in spatially heterogeneous Manning's n fields is quantified using a novel formulation and numerical solution of stochastic inverse problems for physics-based models. The uncertainty is quantified in terms of a probability measure and the physics-based model considered here is the state-of-the-art ADCIRC model although the presented methodology applies to other hydrodynamic models. An accessible overview of the formulation and solution of the stochastic inverse problem in a mathematically rigorous framework based on measure theory is presented. Technical details that arise in practice by applying the framework to determine the Manning's n parameter field in a shallow water equation model used for coastal hydrodynamics are presented and an efficient computational algorithm and open source software package are developed. A new notion of "condition" for the stochastic inverse problem is defined and analyzed as it relates to the computation of probabilities. This notion of condition is investigated to determine effective output quantities of interest of maximum water elevations to use for the inverse problem for the Manning's n parameter and the effect on model predictions is analyzed.

Rigorous Photogrammetric Processing of CHANG'E-1 and CHANG'E-2 Stereo Imagery for Lunar Topographic Mapping

NASA Astrophysics Data System (ADS)

Di, K.; Liu, Y.; Liu, B.; Peng, M.

2012-07-01

Chang'E-1(CE-1) and Chang'E-2(CE-2) are the two lunar orbiters of China's lunar exploration program. Topographic mapping using CE-1 and CE-2 images is of great importance for scientific research as well as for preparation of landing and surface operation of Chang'E-3 lunar rover. In this research, we developed rigorous sensor models of CE-1 and CE-2 CCD cameras based on push-broom imaging principle with interior and exterior orientation parameters. Based on the rigorous sensor model, the 3D coordinate of a ground point in lunar body-fixed (LBF) coordinate system can be calculated by space intersection from the image coordinates of con-jugate points in stereo images, and the image coordinates can be calculated from 3D coordinates by back-projection. Due to uncer-tainties of the orbit and the camera, the back-projected image points are different from the measured points. In order to reduce these inconsistencies and improve precision, we proposed two methods to refine the rigorous sensor model: 1) refining EOPs by correcting the attitude angle bias, 2) refining the interior orientation model by calibration of the relative position of the two linear CCD arrays. Experimental results show that the mean back-projection residuals of CE-1 images are reduced to better than 1/100 pixel by method 1 and the mean back-projection residuals of CE-2 images are reduced from over 20 pixels to 0.02 pixel by method 2. Consequently, high precision DEM (Digital Elevation Model) and DOM (Digital Ortho Map) are automatically generated.

Using expert knowledge to incorporate uncertainty in cause-of-death assignments for modeling of cause-specific mortality

USGS Publications Warehouse

Walsh, Daniel P.; Norton, Andrew S.; Storm, Daniel J.; Van Deelen, Timothy R.; Heisy, Dennis M.

2018-01-01

Implicit and explicit use of expert knowledge to inform ecological analyses is becoming increasingly common because it often represents the sole source of information in many circumstances. Thus, there is a need to develop statistical methods that explicitly incorporate expert knowledge, and can successfully leverage this information while properly accounting for associated uncertainty during analysis. Studies of cause-specific mortality provide an example of implicit use of expert knowledge when causes-of-death are uncertain and assigned based on the observer's knowledge of the most likely cause. To explicitly incorporate this use of expert knowledge and the associated uncertainty, we developed a statistical model for estimating cause-specific mortality using a data augmentation approach within a Bayesian hierarchical framework. Specifically, for each mortality event, we elicited the observer's belief of cause-of-death by having them specify the probability that the death was due to each potential cause. These probabilities were then used as prior predictive values within our framework. This hierarchical framework permitted a simple and rigorous estimation method that was easily modified to include covariate effects and regularizing terms. Although applied to survival analysis, this method can be extended to any event-time analysis with multiple event types, for which there is uncertainty regarding the true outcome. We conducted simulations to determine how our framework compared to traditional approaches that use expert knowledge implicitly and assume that cause-of-death is specified accurately. Simulation results supported the inclusion of observer uncertainty in cause-of-death assignment in modeling of cause-specific mortality to improve model performance and inference. Finally, we applied the statistical model we developed and a traditional method to cause-specific survival data for white-tailed deer, and compared results. We demonstrate that model selection results changed between the two approaches, and incorporating observer knowledge in cause-of-death increased the variability associated with parameter estimates when compared to the traditional approach. These differences between the two approaches can impact reported results, and therefore, it is critical to explicitly incorporate expert knowledge in statistical methods to ensure rigorous inference.

Uncertainty information in climate data records from Earth observation

NASA Astrophysics Data System (ADS)

Merchant, Christopher J.; Paul, Frank; Popp, Thomas; Ablain, Michael; Bontemps, Sophie; Defourny, Pierre; Hollmann, Rainer; Lavergne, Thomas; Laeng, Alexandra; de Leeuw, Gerrit; Mittaz, Jonathan; Poulsen, Caroline; Povey, Adam C.; Reuter, Max; Sathyendranath, Shubha; Sandven, Stein; Sofieva, Viktoria F.; Wagner, Wolfgang

2017-07-01

The question of how to derive and present uncertainty information in climate data records (CDRs) has received sustained attention within the European Space Agency Climate Change Initiative (CCI), a programme to generate CDRs addressing a range of essential climate variables (ECVs) from satellite data. Here, we review the nature, mathematics, practicalities, and communication of uncertainty information in CDRs from Earth observations. This review paper argues that CDRs derived from satellite-based Earth observation (EO) should include rigorous uncertainty information to support the application of the data in contexts such as policy, climate modelling, and numerical weather prediction reanalysis. Uncertainty, error, and quality are distinct concepts, and the case is made that CDR products should follow international metrological norms for presenting quantified uncertainty. As a baseline for good practice, total standard uncertainty should be quantified per datum in a CDR, meaning that uncertainty estimates should clearly discriminate more and less certain data. In this case, flags for data quality should not duplicate uncertainty information, but instead describe complementary information (such as the confidence in the uncertainty estimate provided or indicators of conditions violating the retrieval assumptions). The paper discusses the many sources of error in CDRs, noting that different errors may be correlated across a wide range of timescales and space scales. Error effects that contribute negligibly to the total uncertainty in a single-satellite measurement can be the dominant sources of uncertainty in a CDR on the large space scales and long timescales that are highly relevant for some climate applications. For this reason, identifying and characterizing the relevant sources of uncertainty for CDRs is particularly challenging. The characterization of uncertainty caused by a given error effect involves assessing the magnitude of the effect, the shape of the error distribution, and the propagation of the uncertainty to the geophysical variable in the CDR accounting for its error correlation properties. Uncertainty estimates can and should be validated as part of CDR validation when possible. These principles are quite general, but the approach to providing uncertainty information appropriate to different ECVs is varied, as confirmed by a brief review across different ECVs in the CCI. User requirements for uncertainty information can conflict with each other, and a variety of solutions and compromises are possible. The concept of an ensemble CDR as a simple means of communicating rigorous uncertainty information to users is discussed. Our review concludes by providing eight concrete recommendations for good practice in providing and communicating uncertainty in EO-based climate data records.

The impact of personalized probabilistic wall thickness models on peak wall stress in abdominal aortic aneurysms.

PubMed

Biehler, J; Wall, W A

2018-02-01

If computational models are ever to be used in high-stakes decision making in clinical practice, the use of personalized models and predictive simulation techniques is a must. This entails rigorous quantification of uncertainties as well as harnessing available patient-specific data to the greatest extent possible. Although researchers are beginning to realize that taking uncertainty in model input parameters into account is a necessity, the predominantly used probabilistic description for these uncertain parameters is based on elementary random variable models. In this work, we set out for a comparison of different probabilistic models for uncertain input parameters using the example of an uncertain wall thickness in finite element models of abdominal aortic aneurysms. We provide the first comparison between a random variable and a random field model for the aortic wall and investigate the impact on the probability distribution of the computed peak wall stress. Moreover, we show that the uncertainty about the prevailing peak wall stress can be reduced if noninvasively available, patient-specific data are harnessed for the construction of the probabilistic wall thickness model. Copyright © 2017 John Wiley & Sons, Ltd.

Modeling and simulation of high dimensional stochastic multiscale PDE systems at the exascale

DOE Office of Scientific and Technical Information (OSTI.GOV)

Zabaras, Nicolas J.

2016-11-08

Predictive Modeling of multiscale and Multiphysics systems requires accurate data driven characterization of the input uncertainties, and understanding of how they propagate across scales and alter the final solution. This project develops a rigorous mathematical framework and scalable uncertainty quantification algorithms to efficiently construct realistic low dimensional input models, and surrogate low complexity systems for the analysis, design, and control of physical systems represented by multiscale stochastic PDEs. The work can be applied to many areas including physical and biological processes, from climate modeling to systems biology.

Definition and solution of a stochastic inverse problem for the Manning’s n parameter field in hydrodynamic models

DOE PAGES

Butler, Troy; Graham, L.; Estep, D.; ...

2015-02-03

The uncertainty in spatially heterogeneous Manning’s n fields is quantified using a novel formulation and numerical solution of stochastic inverse problems for physics-based models. The uncertainty is quantified in terms of a probability measure and the physics-based model considered here is the state-of-the-art ADCIRC model although the presented methodology applies to other hydrodynamic models. An accessible overview of the formulation and solution of the stochastic inverse problem in a mathematically rigorous framework based on measure theory is presented in this paper. Technical details that arise in practice by applying the framework to determine the Manning’s n parameter field in amore » shallow water equation model used for coastal hydrodynamics are presented and an efficient computational algorithm and open source software package are developed. A new notion of “condition” for the stochastic inverse problem is defined and analyzed as it relates to the computation of probabilities. Finally, this notion of condition is investigated to determine effective output quantities of interest of maximum water elevations to use for the inverse problem for the Manning’s n parameter and the effect on model predictions is analyzed.« less

Interval Predictor Models with a Formal Characterization of Uncertainty and Reliability

NASA Technical Reports Server (NTRS)

Crespo, Luis G.; Giesy, Daniel P.; Kenny, Sean P.

2014-01-01

This paper develops techniques for constructing empirical predictor models based on observations. By contrast to standard models, which yield a single predicted output at each value of the model's inputs, Interval Predictors Models (IPM) yield an interval into which the unobserved output is predicted to fall. The IPMs proposed prescribe the output as an interval valued function of the model's inputs, render a formal description of both the uncertainty in the model's parameters and of the spread in the predicted output. Uncertainty is prescribed as a hyper-rectangular set in the space of model's parameters. The propagation of this set through the empirical model yields a range of outputs of minimal spread containing all (or, depending on the formulation, most) of the observations. Optimization-based strategies for calculating IPMs and eliminating the effects of outliers are proposed. Outliers are identified by evaluating the extent by which they degrade the tightness of the prediction. This evaluation can be carried out while the IPM is calculated. When the data satisfies mild stochastic assumptions, and the optimization program used for calculating the IPM is convex (or, when its solution coincides with the solution to an auxiliary convex program), the model's reliability (that is, the probability that a future observation would be within the predicted range of outputs) can be bounded rigorously by a non-asymptotic formula.

Generalized uncertainty principle and quantum gravity phenomenology

NASA Astrophysics Data System (ADS)

Bosso, Pasquale

The fundamental physical description of Nature is based on two mutually incompatible theories: Quantum Mechanics and General Relativity. Their unification in a theory of Quantum Gravity (QG) remains one of the main challenges of theoretical physics. Quantum Gravity Phenomenology (QGP) studies QG effects in low-energy systems. The basis of one such phenomenological model is the Generalized Uncertainty Principle (GUP), which is a modified Heisenberg uncertainty relation and predicts a deformed canonical commutator. In this thesis, we compute Planck-scale corrections to angular momentum eigenvalues, the hydrogen atom spectrum, the Stern-Gerlach experiment, and the Clebsch-Gordan coefficients. We then rigorously analyze the GUP-perturbed harmonic oscillator and study new coherent and squeezed states. Furthermore, we introduce a scheme for increasing the sensitivity of optomechanical experiments for testing QG effects. Finally, we suggest future projects that may potentially test QG effects in the laboratory.

Quantifying structural uncertainty on fault networks using a marked point process within a Bayesian framework

NASA Astrophysics Data System (ADS)

Aydin, Orhun; Caers, Jef Karel

2017-08-01

Faults are one of the building-blocks for subsurface modeling studies. Incomplete observations of subsurface fault networks lead to uncertainty pertaining to location, geometry and existence of faults. In practice, gaps in incomplete fault network observations are filled based on tectonic knowledge and interpreter's intuition pertaining to fault relationships. Modeling fault network uncertainty with realistic models that represent tectonic knowledge is still a challenge. Although methods that address specific sources of fault network uncertainty and complexities of fault modeling exists, a unifying framework is still lacking. In this paper, we propose a rigorous approach to quantify fault network uncertainty. Fault pattern and intensity information are expressed by means of a marked point process, marked Strauss point process. Fault network information is constrained to fault surface observations (complete or partial) within a Bayesian framework. A structural prior model is defined to quantitatively express fault patterns, geometries and relationships within the Bayesian framework. Structural relationships between faults, in particular fault abutting relations, are represented with a level-set based approach. A Markov Chain Monte Carlo sampler is used to sample posterior fault network realizations that reflect tectonic knowledge and honor fault observations. We apply the methodology to a field study from Nankai Trough & Kumano Basin. The target for uncertainty quantification is a deep site with attenuated seismic data with only partially visible faults and many faults missing from the survey or interpretation. A structural prior model is built from shallow analog sites that are believed to have undergone similar tectonics compared to the site of study. Fault network uncertainty for the field is quantified with fault network realizations that are conditioned to structural rules, tectonic information and partially observed fault surfaces. We show the proposed methodology generates realistic fault network models conditioned to data and a conceptual model of the underlying tectonics.

Characterization of the energy-dependent uncertainty and correlation in silicon neutron displacement damage metrics

NASA Astrophysics Data System (ADS)

Griffin, Patrick; Rochman, Dimitri; Koning, Arjan

2017-09-01

A rigorous treatment of the uncertainty in the underlying nuclear data on silicon displacement damage metrics is presented. The uncertainty in the cross sections and recoil atom spectra are propagated into the energy-dependent uncertainty contribution in the silicon displacement kerma and damage energy using a Total Monte Carlo treatment. An energy-dependent covariance matrix is used to characterize the resulting uncertainty. A strong correlation between different reaction channels is observed in the high energy neutron contributions to the displacement damage metrics which supports the necessity of using a Monte Carlo based method to address the nonlinear nature of the uncertainty propagation.

Verifying and Validating Simulation Models

DOE Office of Scientific and Technical Information (OSTI.GOV)

Hemez, Francois M.

2015-02-23

This presentation is a high-level discussion of the Verification and Validation (V&V) of computational models. Definitions of V&V are given to emphasize that “validation” is never performed in a vacuum; it accounts, instead, for the current state-of-knowledge in the discipline considered. In particular comparisons between physical measurements and numerical predictions should account for their respective sources of uncertainty. The differences between error (bias), aleatoric uncertainty (randomness) and epistemic uncertainty (ignorance, lack-of- knowledge) are briefly discussed. Four types of uncertainty in physics and engineering are discussed: 1) experimental variability, 2) variability and randomness, 3) numerical uncertainty and 4) model-form uncertainty. Statisticalmore » sampling methods are available to propagate, and analyze, variability and randomness. Numerical uncertainty originates from the truncation error introduced by the discretization of partial differential equations in time and space. Model-form uncertainty is introduced by assumptions often formulated to render a complex problem more tractable and amenable to modeling and simulation. The discussion concludes with high-level guidance to assess the “credibility” of numerical simulations, which stems from the level of rigor with which these various sources of uncertainty are assessed and quantified.« less

Data Assimilation - Advances and Applications

DOE Office of Scientific and Technical Information (OSTI.GOV)

Williams, Brian J.

2014-07-30

This presentation provides an overview of data assimilation (model calibration) for complex computer experiments. Calibration refers to the process of probabilistically constraining uncertain physics/engineering model inputs to be consistent with observed experimental data. An initial probability distribution for these parameters is updated using the experimental information. Utilization of surrogate models and empirical adjustment for model form error in code calibration form the basis for the statistical methodology considered. The role of probabilistic code calibration in supporting code validation is discussed. Incorporation of model form uncertainty in rigorous uncertainty quantification (UQ) analyses is also addressed. Design criteria used within a batchmore » sequential design algorithm are introduced for efficiently achieving predictive maturity and improved code calibration. Predictive maturity refers to obtaining stable predictive inference with calibrated computer codes. These approaches allow for augmentation of initial experiment designs for collecting new physical data. A standard framework for data assimilation is presented and techniques for updating the posterior distribution of the state variables based on particle filtering and the ensemble Kalman filter are introduced.« less

A Bayesian Approach for Measurements of Stray Neutrons at Proton Therapy Facilities: Quantifying Neutron Dose Uncertainty.

PubMed

Dommert, M; Reginatto, M; Zboril, M; Fiedler, F; Helmbrecht, S; Enghardt, W; Lutz, B

2017-11-28

Bonner sphere measurements are typically analyzed using unfolding codes. It is well known that it is difficult to get reliable estimates of uncertainties for standard unfolding procedures. An alternative approach is to analyze the data using Bayesian parameter estimation. This method provides reliable estimates of the uncertainties of neutron spectra leading to rigorous estimates of uncertainties of the dose. We extend previous Bayesian approaches and apply the method to stray neutrons in proton therapy environments by introducing a new parameterized model which describes the main features of the expected neutron spectra. The parameterization is based on information that is available from measurements and detailed Monte Carlo simulations. The validity of this approach has been validated with results of an experiment using Bonner spheres carried out at the experimental hall of the OncoRay proton therapy facility in Dresden. © The Author 2017. Published by Oxford University Press. All rights reserved. For Permissions, please email: journals.permissions@oup.com.

Using Uncertainty Quantification to Guide Development and Improvements of a Regional-Scale Model of the Coastal Lowlands Aquifer System Spanning Texas, Louisiana, Mississippi, Alabama and Florida

NASA Astrophysics Data System (ADS)

Foster, L. K.; Clark, B. R.; Duncan, L. L.; Tebo, D. T.; White, J.

2017-12-01

Several historical groundwater models exist within the Coastal Lowlands Aquifer System (CLAS), which spans the Gulf Coastal Plain in Texas, Louisiana, Mississippi, Alabama, and Florida. The largest of these models, called the Gulf Coast Regional Aquifer System Analysis (RASA) model, has been brought into a new framework using the Newton formulation for MODFLOW-2005 (MODFLOW-NWT) and serves as the starting point of a new investigation underway by the U.S. Geological Survey to improve understanding of the CLAS and provide predictions of future groundwater availability within an uncertainty quantification (UQ) framework. The use of an UQ framework will not only provide estimates of water-level observation worth, hydraulic parameter uncertainty, boundary-condition uncertainty, and uncertainty of future potential predictions, but it will also guide the model development process. Traditionally, model development proceeds from dataset construction to the process of deterministic history matching, followed by deterministic predictions using the model. This investigation will combine the use of UQ with existing historical models of the study area to assess in a quantitative framework the effect model package and property improvements have on the ability to represent past-system states, as well as the effect on the model's ability to make certain predictions of water levels, water budgets, and base-flow estimates. Estimates of hydraulic property information and boundary conditions from the existing models and literature, forming the prior, will be used to make initial estimates of model forecasts and their corresponding uncertainty, along with an uncalibrated groundwater model run within an unconstrained Monte Carlo analysis. First-Order Second-Moment (FOSM) analysis will also be used to investigate parameter and predictive uncertainty, and guide next steps in model development prior to rigorous history matching by using PEST++ parameter estimation code.

Uncertainty Propagation in OMFIT

NASA Astrophysics Data System (ADS)

Smith, Sterling; Meneghini, Orso; Sung, Choongki

2017-10-01

A rigorous comparison of power balance fluxes and turbulent model fluxes requires the propagation of uncertainties in the kinetic profiles and their derivatives. Making extensive use of the python uncertainties package, the OMFIT framework has been used to propagate covariant uncertainties to provide an uncertainty in the power balance calculation from the ONETWO code, as well as through the turbulent fluxes calculated by the TGLF code. The covariant uncertainties arise from fitting 1D (constant on flux surface) density and temperature profiles and associated random errors with parameterized functions such as a modified tanh. The power balance and model fluxes can then be compared with quantification of the uncertainties. No effort is made at propagating systematic errors. A case study will be shown for the effects of resonant magnetic perturbations on the kinetic profiles and fluxes at the top of the pedestal. A separate attempt at modeling the random errors with Monte Carlo sampling will be compared to the method of propagating the fitting function parameter covariant uncertainties. Work supported by US DOE under DE-FC02-04ER54698, DE-FG2-95ER-54309, DE-SC 0012656.

Ground-based research with heavy ions for space radiation protection

NASA Astrophysics Data System (ADS)

Durante, M.; Kronenberg, A.

Human exposure to ionizing radiation is one of the acknowledged potential showstoppers for long duration manned interplanetary missions. Human exploratory missions cannot be safely performed without a substantial reduction of the uncertainties associated with different space radiation health risks, and the development of effective countermeasures. Most of our knowledge of the biological effects of heavy charged particles comes from accelerator-based experiments. During the 35th COSPAR meeting, recent ground-based experiments with high-energy iron ions were discussed, and these results are briefly summarised in this paper. High-quality accelerator-based research with heavy ions will continue to be the main source of knowledge of space radiation health effects and will lead to reductions of the uncertainties in predictions of human health risks. Efforts in materials science, nutrition and pharmaceutical sciences and their rigorous evaluation with biological model systems in ground-based accelerator experiments will lead to the development of safe and effective countermeasures to permit human exploration of the Solar System.

User Guidelines and Best Practices for CASL VUQ Analysis Using Dakota

DOE Office of Scientific and Technical Information (OSTI.GOV)

Adams, Brian M.; Coleman, Kayla; Gilkey, Lindsay N.

Sandia’s Dakota software (available at http://dakota.sandia.gov) supports science and engineering transformation through advanced exploration of simulations. Specifically it manages and analyzes ensembles of simulations to provide broader and deeper perspective for analysts and decision makers. This enables them to enhance understanding of risk, improve products, and assess simulation credibility. In its simplest mode, Dakota can automate typical parameter variation studies through a generic interface to a physics-based computational model. This can lend efficiency and rigor to manual parameter perturbation studies already being conducted by analysts. However, Dakota also delivers advanced parametric analysis techniques enabling design exploration, optimization, model calibration, riskmore » analysis, and quantification of margins and uncertainty with such models. It directly supports verification and validation activities. Dakota algorithms enrich complex science and engineering models, enabling an analyst to answer crucial questions of - Sensitivity: Which are the most important input factors or parameters entering the simulation, and how do they influence key outputs?; Uncertainty: What is the uncertainty or variability in simulation output, given uncertainties in input parameters? How safe, reliable, robust, or variable is my system? (Quantification of margins and uncertainty, QMU); Optimization: What parameter values yield the best performing design or operating condition, given constraints? Calibration: What models and/or parameters best match experimental data? In general, Dakota is the Consortium for Advanced Simulation of Light Water Reactors (CASL) delivery vehicle for verification, validation, and uncertainty quantification (VUQ) algorithms. It permits ready application of the VUQ methods described above to simulation codes by CASL researchers, code developers, and application engineers.« less

Uncertainty in the Modeling of Tsunami Sediment Transport

NASA Astrophysics Data System (ADS)

Jaffe, B. E.; Sugawara, D.; Goto, K.; Gelfenbaum, G. R.; La Selle, S.

2016-12-01

Erosion and deposition from tsunamis record information about tsunami hydrodynamics and size that can be interpreted to improve tsunami hazard assessment. A recent study (Jaffe et al., 2016) explores sources and methods for quantifying uncertainty in tsunami sediment transport modeling. Uncertainty varies with tsunami properties, study site characteristics, available input data, sediment grain size, and the model used. Although uncertainty has the potential to be large, case studies for both forward and inverse models have shown that sediment transport modeling provides useful information on tsunami inundation and hydrodynamics that can be used to improve tsunami hazard assessment. New techniques for quantifying uncertainty, such as Ensemble Kalman Filtering inversion, and more rigorous reporting of uncertainties will advance the science of tsunami sediment transport modeling. Uncertainty may be decreased with additional laboratory studies that increase our understanding of the semi-empirical parameters and physics of tsunami sediment transport, standardized benchmark tests to assess model performance, and the development of hybrid modeling approaches to exploit the strengths of forward and inverse models. As uncertainty in tsunami sediment transport modeling is reduced, and with increased ability to quantify uncertainty, the geologic record of tsunamis will become more valuable in the assessment of tsunami hazard. Jaffe, B., Goto, K., Sugawara, D., Gelfenbaum, G., and La Selle, S., "Uncertainty in Tsunami Sediment Transport Modeling", Journal of Disaster Research Vol. 11 No. 4, pp. 647-661, 2016, doi: 10.20965/jdr.2016.p0647 https://www.fujipress.jp/jdr/dr/dsstr001100040647/

Uncertainty quantification for nuclear density functional theory and information content of new measurements.

PubMed

McDonnell, J D; Schunck, N; Higdon, D; Sarich, J; Wild, S M; Nazarewicz, W

2015-03-27

Statistical tools of uncertainty quantification can be used to assess the information content of measured observables with respect to present-day theoretical models, to estimate model errors and thereby improve predictive capability, to extrapolate beyond the regions reached by experiment, and to provide meaningful input to applications and planned measurements. To showcase new opportunities offered by such tools, we make a rigorous analysis of theoretical statistical uncertainties in nuclear density functional theory using Bayesian inference methods. By considering the recent mass measurements from the Canadian Penning Trap at Argonne National Laboratory, we demonstrate how the Bayesian analysis and a direct least-squares optimization, combined with high-performance computing, can be used to assess the information content of the new data with respect to a model based on the Skyrme energy density functional approach. Employing the posterior probability distribution computed with a Gaussian process emulator, we apply the Bayesian framework to propagate theoretical statistical uncertainties in predictions of nuclear masses, two-neutron dripline, and fission barriers. Overall, we find that the new mass measurements do not impose a constraint that is strong enough to lead to significant changes in the model parameters. The example discussed in this study sets the stage for quantifying and maximizing the impact of new measurements with respect to current modeling and guiding future experimental efforts, thus enhancing the experiment-theory cycle in the scientific method.

Increased scientific rigor will improve reliability of research and effectiveness of management

USGS Publications Warehouse

Sells, Sarah N.; Bassing, Sarah B.; Barker, Kristin J.; Forshee, Shannon C.; Keever, Allison; Goerz, James W.; Mitchell, Michael S.

2018-01-01

Rigorous science that produces reliable knowledge is critical to wildlife management because it increases accurate understanding of the natural world and informs management decisions effectively. Application of a rigorous scientific method based on hypothesis testing minimizes unreliable knowledge produced by research. To evaluate the prevalence of scientific rigor in wildlife research, we examined 24 issues of the Journal of Wildlife Management from August 2013 through July 2016. We found 43.9% of studies did not state or imply a priori hypotheses, which are necessary to produce reliable knowledge. We posit that this is due, at least in part, to a lack of common understanding of what rigorous science entails, how it produces more reliable knowledge than other forms of interpreting observations, and how research should be designed to maximize inferential strength and usefulness of application. Current primary literature does not provide succinct explanations of the logic behind a rigorous scientific method or readily applicable guidance for employing it, particularly in wildlife biology; we therefore synthesized an overview of the history, philosophy, and logic that define scientific rigor for biological studies. A rigorous scientific method includes 1) generating a research question from theory and prior observations, 2) developing hypotheses (i.e., plausible biological answers to the question), 3) formulating predictions (i.e., facts that must be true if the hypothesis is true), 4) designing and implementing research to collect data potentially consistent with predictions, 5) evaluating whether predictions are consistent with collected data, and 6) drawing inferences based on the evaluation. Explicitly testing a priori hypotheses reduces overall uncertainty by reducing the number of plausible biological explanations to only those that are logically well supported. Such research also draws inferences that are robust to idiosyncratic observations and unavoidable human biases. Offering only post hoc interpretations of statistical patterns (i.e., a posteriorihypotheses) adds to uncertainty because it increases the number of plausible biological explanations without determining which have the greatest support. Further, post hocinterpretations are strongly subject to human biases. Testing hypotheses maximizes the credibility of research findings, makes the strongest contributions to theory and management, and improves reproducibility of research. Management decisions based on rigorous research are most likely to result in effective conservation of wildlife resources. 

Hard Constraints in Optimization Under Uncertainty

NASA Technical Reports Server (NTRS)

Crespo, Luis G.; Giesy, Daniel P.; Kenny, Sean P.

2008-01-01

This paper proposes a methodology for the analysis and design of systems subject to parametric uncertainty where design requirements are specified via hard inequality constraints. Hard constraints are those that must be satisfied for all parameter realizations within a given uncertainty model. Uncertainty models given by norm-bounded perturbations from a nominal parameter value, i.e., hyper-spheres, and by sets of independently bounded uncertain variables, i.e., hyper-rectangles, are the focus of this paper. These models, which are also quite practical, allow for a rigorous mathematical treatment within the proposed framework. Hard constraint feasibility is determined by sizing the largest uncertainty set for which the design requirements are satisfied. Analytically verifiable assessments of robustness are attained by comparing this set with the actual uncertainty model. Strategies that enable the comparison of the robustness characteristics of competing design alternatives, the description and approximation of the robust design space, and the systematic search for designs with improved robustness are also proposed. Since the problem formulation is generic and the tools derived only require standard optimization algorithms for their implementation, this methodology is applicable to a broad range of engineering problems.

Model-based assessment of estuary ecosystem health using the latent health factor index, with application to the richibucto estuary.

PubMed

Chiu, Grace S; Wu, Margaret A; Lu, Lin

2013-01-01

The ability to quantitatively assess ecological health is of great interest to those tasked with monitoring and conserving ecosystems. For decades, biomonitoring research and policies have relied on multimetric health indices of various forms. Although indices are numbers, many are constructed based on qualitative procedures, thus limiting the quantitative rigor of the practical interpretations of such indices. The statistical modeling approach to construct the latent health factor index (LHFI) was recently developed. With ecological data that otherwise are used to construct conventional multimetric indices, the LHFI framework expresses such data in a rigorous quantitative model, integrating qualitative features of ecosystem health and preconceived ecological relationships among such features. This hierarchical modeling approach allows unified statistical inference of health for observed sites (along with prediction of health for partially observed sites, if desired) and of the relevance of ecological drivers, all accompanied by formal uncertainty statements from a single, integrated analysis. Thus far, the LHFI approach has been demonstrated and validated in a freshwater context. We adapt this approach to modeling estuarine health, and illustrate it on the previously unassessed system in Richibucto in New Brunswick, Canada, where active oyster farming is a potential stressor through its effects on sediment properties. Field data correspond to health metrics that constitute the popular AZTI marine biotic index and the infaunal trophic index, as well as abiotic predictors preconceived to influence biota. Our paper is the first to construct a scientifically sensible model that rigorously identifies the collective explanatory capacity of salinity, distance downstream, channel depth, and silt-clay content-all regarded a priori as qualitatively important abiotic drivers-towards site health in the Richibucto ecosystem. This suggests the potential effectiveness of the LHFI approach for assessing not only freshwater systems but aquatic ecosystems in general.

Development of a Risk-Based Comparison Methodology of Carbon Capture Technologies

DOE Office of Scientific and Technical Information (OSTI.GOV)

Engel, David W.; Dalton, Angela C.; Dale, Crystal

2014-06-01

Given the varying degrees of maturity among existing carbon capture (CC) technology alternatives, an understanding of the inherent technical and financial risk and uncertainty associated with these competing technologies is requisite to the success of carbon capture as a viable solution to the greenhouse gas emission challenge. The availability of tools and capabilities to conduct rigorous, risk–based technology comparisons is thus highly desirable for directing valuable resources toward the technology option(s) with a high return on investment, superior carbon capture performance, and minimum risk. To address this research need, we introduce a novel risk-based technology comparison method supported by anmore » integrated multi-domain risk model set to estimate risks related to technological maturity, technical performance, and profitability. Through a comparison between solid sorbent and liquid solvent systems, we illustrate the feasibility of estimating risk and quantifying uncertainty in a single domain (modular analytical capability) as well as across multiple risk dimensions (coupled analytical capability) for comparison. This method brings technological maturity and performance to bear on profitability projections, and carries risk and uncertainty modeling across domains via inter-model sharing of parameters, distributions, and input/output. The integration of the models facilitates multidimensional technology comparisons within a common probabilistic risk analysis framework. This approach and model set can equip potential technology adopters with the necessary computational capabilities to make risk-informed decisions about CC technology investment. The method and modeling effort can also be extended to other industries where robust tools and analytical capabilities are currently lacking for evaluating nascent technologies.« less

Uncertainty Reduction using Bayesian Inference and Sensitivity Analysis: A Sequential Approach to the NASA Langley Uncertainty Quantification Challenge

NASA Technical Reports Server (NTRS)

Sankararaman, Shankar

2016-01-01

This paper presents a computational framework for uncertainty characterization and propagation, and sensitivity analysis under the presence of aleatory and epistemic un- certainty, and develops a rigorous methodology for efficient refinement of epistemic un- certainty by identifying important epistemic variables that significantly affect the overall performance of an engineering system. The proposed methodology is illustrated using the NASA Langley Uncertainty Quantification Challenge (NASA-LUQC) problem that deals with uncertainty analysis of a generic transport model (GTM). First, Bayesian inference is used to infer subsystem-level epistemic quantities using the subsystem-level model and corresponding data. Second, tools of variance-based global sensitivity analysis are used to identify four important epistemic variables (this limitation specified in the NASA-LUQC is reflective of practical engineering situations where not all epistemic variables can be refined due to time/budget constraints) that significantly affect system-level performance. The most significant contribution of this paper is the development of the sequential refine- ment methodology, where epistemic variables for refinement are not identified all-at-once. Instead, only one variable is first identified, and then, Bayesian inference and global sensi- tivity calculations are repeated to identify the next important variable. This procedure is continued until all 4 variables are identified and the refinement in the system-level perfor- mance is computed. The advantages of the proposed sequential refinement methodology over the all-at-once uncertainty refinement approach are explained, and then applied to the NASA Langley Uncertainty Quantification Challenge problem.

An approach based on Hierarchical Bayesian Graphical Models for measurement interpretation under uncertainty

NASA Astrophysics Data System (ADS)

Skataric, Maja; Bose, Sandip; Zeroug, Smaine; Tilke, Peter

2017-02-01

It is not uncommon in the field of non-destructive evaluation that multiple measurements encompassing a variety of modalities are available for analysis and interpretation for determining the underlying states of nature of the materials or parts being tested. Despite and sometimes due to the richness of data, significant challenges arise in the interpretation manifested as ambiguities and inconsistencies due to various uncertain factors in the physical properties (inputs), environment, measurement device properties, human errors, and the measurement data (outputs). Most of these uncertainties cannot be described by any rigorous mathematical means, and modeling of all possibilities is usually infeasible for many real time applications. In this work, we will discuss an approach based on Hierarchical Bayesian Graphical Models (HBGM) for the improved interpretation of complex (multi-dimensional) problems with parametric uncertainties that lack usable physical models. In this setting, the input space of the physical properties is specified through prior distributions based on domain knowledge and expertise, which are represented as Gaussian mixtures to model the various possible scenarios of interest for non-destructive testing applications. Forward models are then used offline to generate the expected distribution of the proposed measurements which are used to train a hierarchical Bayesian network. In Bayesian analysis, all model parameters are treated as random variables, and inference of the parameters is made on the basis of posterior distribution given the observed data. Learned parameters of the posterior distribution obtained after the training can therefore be used to build an efficient classifier for differentiating new observed data in real time on the basis of pre-trained models. We will illustrate the implementation of the HBGM approach to ultrasonic measurements used for cement evaluation of cased wells in the oil industry.

Uncertainty quantification for nuclear density functional theory and information content of new measurements

DOE PAGES

McDonnell, J. D.; Schunck, N.; Higdon, D.; ...

2015-03-24

Statistical tools of uncertainty quantification can be used to assess the information content of measured observables with respect to present-day theoretical models, to estimate model errors and thereby improve predictive capability, to extrapolate beyond the regions reached by experiment, and to provide meaningful input to applications and planned measurements. To showcase new opportunities offered by such tools, we make a rigorous analysis of theoretical statistical uncertainties in nuclear density functional theory using Bayesian inference methods. By considering the recent mass measurements from the Canadian Penning Trap at Argonne National Laboratory, we demonstrate how the Bayesian analysis and a direct least-squaresmore » optimization, combined with high-performance computing, can be used to assess the information content of the new data with respect to a model based on the Skyrme energy density functional approach. Employing the posterior probability distribution computed with a Gaussian process emulator, we apply the Bayesian framework to propagate theoretical statistical uncertainties in predictions of nuclear masses, two-neutron dripline, and fission barriers. Overall, we find that the new mass measurements do not impose a constraint that is strong enough to lead to significant changes in the model parameters. In addition, the example discussed in this study sets the stage for quantifying and maximizing the impact of new measurements with respect to current modeling and guiding future experimental efforts, thus enhancing the experiment-theory cycle in the scientific method.« less

Uncertainty quantification for nuclear density functional theory and information content of new measurements

DOE Office of Scientific and Technical Information (OSTI.GOV)

McDonnell, J. D.; Schunck, N.; Higdon, D.

2015-03-24

Statistical tools of uncertainty quantification can be used to assess the information content of measured observables with respect to present-day theoretical models, to estimate model errors and thereby improve predictive capability, to extrapolate beyond the regions reached by experiment, and to provide meaningful input to applications and planned measurements. To showcase new opportunities offered by such tools, we make a rigorous analysis of theoretical statistical uncertainties in nuclear density functional theory using Bayesian inference methods. By considering the recent mass measurements from the Canadian Penning Trap at Argonne National Laboratory, we demonstrate how the Bayesian analysis and a direct least-squaresmore » optimization, combined with high-performance computing, can be used to assess the information content of the new data with respect to a model based on the Skyrme energy density functional approach. Employing the posterior probability distribution computed with a Gaussian process emulator, we apply the Bayesian framework to propagate theoretical statistical uncertainties in predictions of nuclear masses, two-neutron dripline, and fission barriers. Overall, we find that the new mass measurements do not impose a constraint that is strong enough to lead to significant changes in the model parameters. As a result, the example discussed in this study sets the stage for quantifying and maximizing the impact of new measurements with respect to current modeling and guiding future experimental efforts, thus enhancing the experiment-theory cycle in the scientific method.« less

Constraining a complex biogeochemical model for CO2 and N2O emission simulations from various land uses by model-data fusion

NASA Astrophysics Data System (ADS)

Houska, Tobias; Kraus, David; Kiese, Ralf; Breuer, Lutz

2017-07-01

This study presents the results of a combined measurement and modelling strategy to analyse N2O and CO2 emissions from adjacent arable land, forest and grassland sites in Hesse, Germany. The measured emissions reveal seasonal patterns and management effects, including fertilizer application, tillage, harvest and grazing. The measured annual N2O fluxes are 4.5, 0.4 and 0.1 kg N ha-1 a-1, and the CO2 fluxes are 20.0, 12.2 and 3.0 t C ha-1 a-1 for the arable land, grassland and forest sites, respectively. An innovative model-data fusion concept based on a multicriteria evaluation (soil moisture at different depths, yield, CO2 and N2O emissions) is used to rigorously test the LandscapeDNDC biogeochemical model. The model is run in a Latin-hypercube-based uncertainty analysis framework to constrain model parameter uncertainty and derive behavioural model runs. The results indicate that the model is generally capable of predicting trace gas emissions, as evaluated with RMSE as the objective function. The model shows a reasonable performance in simulating the ecosystem C and N balances. The model-data fusion concept helps to detect remaining model errors, such as missing (e.g. freeze-thaw cycling) or incomplete model processes (e.g. respiration rates after harvest). This concept further elucidates the identification of missing model input sources (e.g. the uptake of N through shallow groundwater on grassland during the vegetation period) and uncertainty in the measured validation data (e.g. forest N2O emissions in winter months). Guidance is provided to improve the model structure and field measurements to further advance landscape-scale model predictions.

Quantifying and reducing model-form uncertainties in Reynolds-averaged Navier–Stokes simulations: A data-driven, physics-informed Bayesian approach

DOE Office of Scientific and Technical Information (OSTI.GOV)

Xiao, H., E-mail: hengxiao@vt.edu; Wu, J.-L.; Wang, J.-X.

Despite their well-known limitations, Reynolds-Averaged Navier–Stokes (RANS) models are still the workhorse tools for turbulent flow simulations in today's engineering analysis, design and optimization. While the predictive capability of RANS models depends on many factors, for many practical flows the turbulence models are by far the largest source of uncertainty. As RANS models are used in the design and safety evaluation of many mission-critical systems such as airplanes and nuclear power plants, quantifying their model-form uncertainties has significant implications in enabling risk-informed decision-making. In this work we develop a data-driven, physics-informed Bayesian framework for quantifying model-form uncertainties in RANS simulations.more » Uncertainties are introduced directly to the Reynolds stresses and are represented with compact parameterization accounting for empirical prior knowledge and physical constraints (e.g., realizability, smoothness, and symmetry). An iterative ensemble Kalman method is used to assimilate the prior knowledge and observation data in a Bayesian framework, and to propagate them to posterior distributions of velocities and other Quantities of Interest (QoIs). We use two representative cases, the flow over periodic hills and the flow in a square duct, to evaluate the performance of the proposed framework. Both cases are challenging for standard RANS turbulence models. Simulation results suggest that, even with very sparse observations, the obtained posterior mean velocities and other QoIs have significantly better agreement with the benchmark data compared to the baseline results. At most locations the posterior distribution adequately captures the true model error within the developed model form uncertainty bounds. The framework is a major improvement over existing black-box, physics-neutral methods for model-form uncertainty quantification, where prior knowledge and details of the models are not exploited. This approach has potential implications in many fields in which the governing equations are well understood but the model uncertainty comes from unresolved physical processes. - Highlights: • Proposed a physics–informed framework to quantify uncertainty in RANS simulations. • Framework incorporates physical prior knowledge and observation data. • Based on a rigorous Bayesian framework yet fully utilizes physical model. • Applicable for many complex physical systems beyond turbulent flows.« less

Branch-and-Bound algorithm applied to uncertainty quantification of a Boiling Water Reactor Station Blackout

DOE PAGES

Nielsen, Joseph; Tokuhiro, Akira; Hiromoto, Robert; ...

2015-11-13

Evaluation of the impacts of uncertainty and sensitivity in modeling presents a significant set of challenges in particular to high fidelity modeling. Computational costs and validation of models creates a need for cost effective decision making with regards to experiment design. Experiments designed to validate computation models can be used to reduce uncertainty in the physical model. In some cases, large uncertainty in a particular aspect of the model may or may not have a large impact on the final results. For example, modeling of a relief valve may result in large uncertainty, however, the actual effects on final peakmore » clad temperature in a reactor transient may be small and the large uncertainty with respect to valve modeling may be considered acceptable. Additionally, the ability to determine the adequacy of a model and the validation supporting it should be considered within a risk informed framework. Low fidelity modeling with large uncertainty may be considered adequate if the uncertainty is considered acceptable with respect to risk. In other words, models that are used to evaluate the probability of failure should be evaluated more rigorously with the intent of increasing safety margin. Probabilistic risk assessment (PRA) techniques have traditionally been used to identify accident conditions and transients. Traditional classical event tree methods utilize analysts’ knowledge and experience to identify the important timing of events in coordination with thermal-hydraulic modeling. These methods lack the capability to evaluate complex dynamic systems. In these systems, time and energy scales associated with transient events may vary as a function of transition times and energies to arrive at a different physical state. Dynamic PRA (DPRA) methods provide a more rigorous analysis of complex dynamic systems. Unfortunately DPRA methods introduce issues associated with combinatorial explosion of states. This study presents a methodology to address combinatorial explosion using a Branch-and-Bound algorithm applied to Dynamic Event Trees (DET), which utilize LENDIT (L – Length, E – Energy, N – Number, D – Distribution, I – Information, and T – Time) as well as a set theory to describe system, state, resource, and response (S2R2) sets to create bounding functions for the DET. The optimization of the DET in identifying high probability failure branches is extended to create a Phenomenological Identification and Ranking Table (PIRT) methodology to evaluate modeling parameters important to safety of those failure branches that have a high probability of failure. The PIRT can then be used as a tool to identify and evaluate the need for experimental validation of models that have the potential to reduce risk. Finally, in order to demonstrate this methodology, a Boiling Water Reactor (BWR) Station Blackout (SBO) case study is presented.« less

Order Under Uncertainty: Robust Differential Expression Analysis Using Probabilistic Models for Pseudotime Inference

PubMed Central

Campbell, Kieran R.

2016-01-01

Single cell gene expression profiling can be used to quantify transcriptional dynamics in temporal processes, such as cell differentiation, using computational methods to label each cell with a ‘pseudotime’ where true time series experimentation is too difficult to perform. However, owing to the high variability in gene expression between individual cells, there is an inherent uncertainty in the precise temporal ordering of the cells. Pre-existing methods for pseudotime estimation have predominantly given point estimates precluding a rigorous analysis of the implications of uncertainty. We use probabilistic modelling techniques to quantify pseudotime uncertainty and propagate this into downstream differential expression analysis. We demonstrate that reliance on a point estimate of pseudotime can lead to inflated false discovery rates and that probabilistic approaches provide greater robustness and measures of the temporal resolution that can be obtained from pseudotime inference. PMID:27870852

Microscopic optical model potential based on a Dirac Brueckner Hartree Fock approach and the relevant uncertainty analysis

NASA Astrophysics Data System (ADS)

Xu, Ruirui; Ma, Zhongyu; Muether, Herbert; van Dalen, E. N. E.; Liu, Tinjin; Zhang, Yue; Zhang, Zhi; Tian, Yuan

2017-09-01

A relativistic microscopic optical model potential, named CTOM, for nucleon-nucleus scattering is investigated in the framework of Dirac-Brueckner-Hartree-Fock approach. The microscopic feature of CTOM is guaranteed through rigorously adopting the isospin dependent DBHF calculation within the subtracted T matrix scheme. In order to verify its prediction power, a global study n, p+ A scattering are carried out. The predicted scattering observables coincide with experimental data within a good accuracy over a broad range of targets and a large region of energies only with two free items, namely the free-range factor t in the applied improved local density approximation and minor adjustments of the scalar and vector potentials in the low-density region. In addition, to estimate the uncertainty of the theoretical results, the deterministic simple least square approach is preliminarily employed to derive the covariance of predicted angular distributions, which is also briefly contained in this paper.

Final Report, DOE Early Career Award: Predictive modeling of complex physical systems: new tools for statistical inference, uncertainty quantification, and experimental design

DOE Office of Scientific and Technical Information (OSTI.GOV)

Marzouk, Youssef

Predictive simulation of complex physical systems increasingly rests on the interplay of experimental observations with computational models. Key inputs, parameters, or structural aspects of models may be incomplete or unknown, and must be developed from indirect and limited observations. At the same time, quantified uncertainties are needed to qualify computational predictions in the support of design and decision-making. In this context, Bayesian statistics provides a foundation for inference from noisy and limited data, but at prohibitive computional expense. This project intends to make rigorous predictive modeling *feasible* in complex physical systems, via accelerated and scalable tools for uncertainty quantification, Bayesianmore » inference, and experimental design. Specific objectives are as follows: 1. Develop adaptive posterior approximations and dimensionality reduction approaches for Bayesian inference in high-dimensional nonlinear systems. 2. Extend accelerated Bayesian methodologies to large-scale {\\em sequential} data assimilation, fully treating nonlinear models and non-Gaussian state and parameter distributions. 3. Devise efficient surrogate-based methods for Bayesian model selection and the learning of model structure. 4. Develop scalable simulation/optimization approaches to nonlinear Bayesian experimental design, for both parameter inference and model selection. 5. Demonstrate these inferential tools on chemical kinetic models in reacting flow, constructing and refining thermochemical and electrochemical models from limited data. Demonstrate Bayesian filtering on canonical stochastic PDEs and in the dynamic estimation of inhomogeneous subsurface properties and flow fields.« less

Random Predictor Models for Rigorous Uncertainty Quantification: Part 2

NASA Technical Reports Server (NTRS)

Crespo, Luis G.; Kenny, Sean P.; Giesy, Daniel P.

2015-01-01

This and a companion paper propose techniques for constructing parametric mathematical models describing key features of the distribution of an output variable given input-output data. By contrast to standard models, which yield a single output value at each value of the input, Random Predictors Models (RPMs) yield a random variable at each value of the input. Optimization-based strategies for calculating RPMs having a polynomial dependency on the input and a linear dependency on the parameters are proposed. These formulations yield RPMs having various levels of fidelity in which the mean, the variance, and the range of the model's parameter, thus of the output, are prescribed. As such they encompass all RPMs conforming to these prescriptions. The RPMs are optimal in the sense that they yield the tightest predictions for which all (or, depending on the formulation, most) of the observations are less than a fixed number of standard deviations from the mean prediction. When the data satisfies mild stochastic assumptions, and the optimization problem(s) used to calculate the RPM is convex (or, when its solution coincides with the solution to an auxiliary convex problem), the model's reliability, which is the probability that a future observation would be within the predicted ranges, is bounded rigorously.

Random Predictor Models for Rigorous Uncertainty Quantification: Part 1

NASA Technical Reports Server (NTRS)

Crespo, Luis G.; Kenny, Sean P.; Giesy, Daniel P.

2015-01-01

This and a companion paper propose techniques for constructing parametric mathematical models describing key features of the distribution of an output variable given input-output data. By contrast to standard models, which yield a single output value at each value of the input, Random Predictors Models (RPMs) yield a random variable at each value of the input. Optimization-based strategies for calculating RPMs having a polynomial dependency on the input and a linear dependency on the parameters are proposed. These formulations yield RPMs having various levels of fidelity in which the mean and the variance of the model's parameters, thus of the predicted output, are prescribed. As such they encompass all RPMs conforming to these prescriptions. The RPMs are optimal in the sense that they yield the tightest predictions for which all (or, depending on the formulation, most) of the observations are less than a fixed number of standard deviations from the mean prediction. When the data satisfies mild stochastic assumptions, and the optimization problem(s) used to calculate the RPM is convex (or, when its solution coincides with the solution to an auxiliary convex problem), the model's reliability, which is the probability that a future observation would be within the predicted ranges, can be bounded tightly and rigorously.

What Do We Mean By Sensitivity Analysis? The Need For A Comprehensive Characterization Of Sensitivity In Earth System Models

NASA Astrophysics Data System (ADS)

Razavi, S.; Gupta, H. V.

2014-12-01

Sensitivity analysis (SA) is an important paradigm in the context of Earth System model development and application, and provides a powerful tool that serves several essential functions in modelling practice, including 1) Uncertainty Apportionment - attribution of total uncertainty to different uncertainty sources, 2) Assessment of Similarity - diagnostic testing and evaluation of similarities between the functioning of the model and the real system, 3) Factor and Model Reduction - identification of non-influential factors and/or insensitive components of model structure, and 4) Factor Interdependence - investigation of the nature and strength of interactions between the factors, and the degree to which factors intensify, cancel, or compensate for the effects of each other. A variety of sensitivity analysis approaches have been proposed, each of which formally characterizes a different "intuitive" understanding of what is meant by the "sensitivity" of one or more model responses to its dependent factors (such as model parameters or forcings). These approaches are based on different philosophies and theoretical definitions of sensitivity, and range from simple local derivatives and one-factor-at-a-time procedures to rigorous variance-based (Sobol-type) approaches. In general, each approach focuses on, and identifies, different features and properties of the model response and may therefore lead to different (even conflicting) conclusions about the underlying sensitivity. This presentation revisits the theoretical basis for sensitivity analysis, and critically evaluates existing approaches so as to demonstrate their flaws and shortcomings. With this background, we discuss several important properties of response surfaces that are associated with the understanding and interpretation of sensitivity. Finally, a new approach towards global sensitivity assessment is developed that is consistent with important properties of Earth System model response surfaces.

Bayesian evidence computation for model selection in non-linear geoacoustic inference problems.

PubMed

Dettmer, Jan; Dosso, Stan E; Osler, John C

2010-12-01

This paper applies a general Bayesian inference approach, based on Bayesian evidence computation, to geoacoustic inversion of interface-wave dispersion data. Quantitative model selection is carried out by computing the evidence (normalizing constants) for several model parameterizations using annealed importance sampling. The resulting posterior probability density estimate is compared to estimates obtained from Metropolis-Hastings sampling to ensure consistent results. The approach is applied to invert interface-wave dispersion data collected on the Scotian Shelf, off the east coast of Canada for the sediment shear-wave velocity profile. Results are consistent with previous work on these data but extend the analysis to a rigorous approach including model selection and uncertainty analysis. The results are also consistent with core samples and seismic reflection measurements carried out in the area.

Towards extensive spatio-temporal reconstructions of North American land cover: a comparison of state-of-the-art pollen-vegetation models

NASA Astrophysics Data System (ADS)

Dawson, A.; Trachsel, M.; Goring, S. J.; Paciorek, C. J.; McLachlan, J. S.; Jackson, S. T.; Williams, J. W.

2017-12-01

Pollen records have been extensively used to reconstruct past changes in vegetation and study the underlying processes. However, developing the statistical techniques needed to accurately represent both data and process uncertainties is a formidable challenge. Recent advances in paleoecoinformatics (e.g. the Neotoma Paleoecology Database and the European Pollen Database), Bayesian age-depth models, and process-based pollen-vegetation models, and Bayesian hierarchical modeling have pushed paleovegetation reconstructions forward to a point where multiple sources of uncertainty can be incorporated into reconstructions, which in turn enables new hypotheses to be asked and more rigorous integration of paleovegetation data with earth system models and terrestrial ecosystem models. Several kinds of pollen-vegetation models have been developed, notably LOVE/REVEALS, STEPPS, and classical transfer functions such as the modern analog technique. LOVE/REVEALS has been adopted as the standard method for the LandCover6k effort to develop quantitative reconstructions of land cover for the Holocene, while STEPPS has been developed recently as part of the PalEON project and applied to reconstruct with uncertainty shifts in forest composition in New England and the upper Midwest during the late Holocene. Each PVM has different assumptions and structure and uses different input data, but few comparisons among approaches yet exist. Here, we present new reconstructions of land cover change in northern North America during the Holocene based on LOVE/REVEALS and data drawn from the Neotoma database and compare STEPPS-based reconstructions to those from LOVE/REVEALS. These parallel developments with LOVE/REVEALS provide an opportunity to compare and contrast models, and to begin to generate continental scale reconstructions, with explicit uncertainties, that can provide a base for interdisciplinary research within the biogeosciences. We show how STEPPS provides an important benchmark for past land-cover reconstruction, and how the LandCover 6k effort in North America advances our understanding of the past by allowing cross-continent comparisons using standardized methods and quantifying the impact of humans in the early Anthropocene.

Predictability of the geospace variations and measuring the capability to model the state of the system

NASA Astrophysics Data System (ADS)

Pulkkinen, A.

2012-12-01

Empirical modeling has been the workhorse of the past decades in predicting the state of the geospace. For example, numerous empirical studies have shown that global geoeffectiveness indices such as Kp and Dst are generally well predictable from the solar wind input. These successes have been facilitated partly by the strongly externally driven nature of the system. Although characterizing the general state of the system is valuable and empirical modeling will continue playing an important role, refined physics-based quantification of the state of the system has been the obvious next step in moving toward more mature science. Importantly, more refined and localized products are needed also for space weather purposes. Predictions of local physical quantities are necessary to make physics-based links to the impacts on specific systems. As we have introduced more localized predictions of the geospace state one central question is how predictable these local quantities are? This complex question can be addressed by rigorously measuring the model performance against the observed data. Space sciences community has made great advanced on this topic over the past few years and there are ongoing efforts in SHINE, CEDAR and GEM to carry out community-wide evaluations of the state-of-the-art solar and heliospheric, ionosphere-thermosphere and geospace models, respectively. These efforts will help establish benchmarks and thus provide means to measure the progress in the field analogous to monitoring of the improvement in lower atmospheric weather predictions carried out rigorously since 1980s. In this paper we will discuss some of the latest advancements in predicting the local geospace parameters and give an overview of some of the community efforts to rigorously measure the model performances. We will also briefly discuss some of the future opportunities for advancing the geospace modeling capability. These will include further development in data assimilation and ensemble modeling (e.g. taking into account uncertainty in the inflow boundary conditions).

Uncertainty evaluation of thickness and warp of a silicon wafer measured by a spectrally resolved interferometer

NASA Astrophysics Data System (ADS)

Praba Drijarkara, Agustinus; Gergiso Gebrie, Tadesse; Lee, Jae Yong; Kang, Chu-Shik

2018-06-01

Evaluation of uncertainty of thickness and gravity-compensated warp of a silicon wafer measured by a spectrally resolved interferometer is presented. The evaluation is performed in a rigorous manner, by analysing the propagation of uncertainty from the input quantities through all the steps of measurement functions, in accordance with the ISO Guide to the Expression of Uncertainty in Measurement. In the evaluation, correlation between input quantities as well as uncertainty attributed to thermal effect, which were not included in earlier publications, are taken into account. The temperature dependence of the group refractive index of silicon was found to be nonlinear and varies widely within a wafer and also between different wafers. The uncertainty evaluation described here can be applied to other spectral interferometry applications based on similar principles.

The epistemological status of general circulation models

NASA Astrophysics Data System (ADS)

Loehle, Craig

2018-03-01

Forecasts of both likely anthropogenic effects on climate and consequent effects on nature and society are based on large, complex software tools called general circulation models (GCMs). Forecasts generated by GCMs have been used extensively in policy decisions related to climate change. However, the relation between underlying physical theories and results produced by GCMs is unclear. In the case of GCMs, many discretizations and approximations are made, and simulating Earth system processes is far from simple and currently leads to some results with unknown energy balance implications. Statistical testing of GCM forecasts for degree of agreement with data would facilitate assessment of fitness for use. If model results need to be put on an anomaly basis due to model bias, then both visual and quantitative measures of model fit depend strongly on the reference period used for normalization, making testing problematic. Epistemology is here applied to problems of statistical inference during testing, the relationship between the underlying physics and the models, the epistemic meaning of ensemble statistics, problems of spatial and temporal scale, the existence or not of an unforced null for climate fluctuations, the meaning of existing uncertainty estimates, and other issues. Rigorous reasoning entails carefully quantifying levels of uncertainty.

Inferring the source of evaporated waters using stable H and O isotopes

NASA Astrophysics Data System (ADS)

Bowen, G. J.; Putman, A.; Brooks, J. R.; Bowling, D. R.; Oerter, E.; Good, S. P.

2017-12-01

Stable isotope ratios of H and O are widely used identify the source of water, e.g., in aquifers, river runoff, soils, plant xylem, and plant-based beverages. In situations where the sampled water is partially evaporated, its isotope values will have evolved along an evaporation line (EL) in δ2H/δ18O space, and back-correction along the EL to its intersection with a meteoric water line (MWL) has been used to estimate the source water's isotope ratios. Several challenges and potential pitfalls exist with traditional approaches to this problem, including potential for bias from a commonly used regression-based approach for EL slope estimation and incomplete estimation of uncertainty in most studies. We suggest the value of a model-based approach to EL estimation, and introduce a mathematical framework that eliminates the need to explicitly estimate the EL-MWL intersection, simplifying analysis and facilitating more rigorous uncertainty estimation. We apply this analysis framework to data from 1,000 lakes sampled in EPA's 2007 National Lakes Assessment. We find that data for most lakes is consistent with a water source similar to annual runoff, estimated from monthly precipitation and evaporation within the lake basin. Strong evidence for both summer- and winter-biased sources exists, however, with winter bias pervasive in most snow-prone regions. The new analytical framework should improve the rigor of source-water inference from evaporated samples in ecohydrology and related sciences, and our initial results from U.S. lakes suggest that previous interpretations of lakes as unbiased isotope integrators may only be valid in certain climate regimes.

Uncertainty Analysis of OC5-DeepCwind Floating Semisubmersible Offshore Wind Test Campaign

DOE Office of Scientific and Technical Information (OSTI.GOV)

Robertson, Amy N

This paper examines how to assess the uncertainty levels for test measurements of the Offshore Code Comparison, Continued, with Correlation (OC5)-DeepCwind floating offshore wind system, examined within the OC5 project. The goal of the OC5 project was to validate the accuracy of ultimate and fatigue load estimates from a numerical model of the floating semisubmersible using data measured during scaled tank testing of the system under wind and wave loading. The examination of uncertainty was done after the test, and it was found that the limited amount of data available did not allow for an acceptable uncertainty assessment. Therefore, thismore » paper instead qualitatively examines the sources of uncertainty associated with this test to start a discussion of how to assess uncertainty for these types of experiments and to summarize what should be done during future testing to acquire the information needed for a proper uncertainty assessment. Foremost, future validation campaigns should initiate numerical modeling before testing to guide the test campaign, which should include a rigorous assessment of uncertainty, and perform validation during testing to ensure that the tests address all of the validation needs.« less

Uncertainty Analysis of OC5-DeepCwind Floating Semisubmersible Offshore Wind Test Campaign: Preprint

DOE Office of Scientific and Technical Information (OSTI.GOV)

Robertson, Amy N

This paper examines how to assess the uncertainty levels for test measurements of the Offshore Code Comparison, Continued, with Correlation (OC5)-DeepCwind floating offshore wind system, examined within the OC5 project. The goal of the OC5 project was to validate the accuracy of ultimate and fatigue load estimates from a numerical model of the floating semisubmersible using data measured during scaled tank testing of the system under wind and wave loading. The examination of uncertainty was done after the test, and it was found that the limited amount of data available did not allow for an acceptable uncertainty assessment. Therefore, thismore » paper instead qualitatively examines the sources of uncertainty associated with this test to start a discussion of how to assess uncertainty for these types of experiments and to summarize what should be done during future testing to acquire the information needed for a proper uncertainty assessment. Foremost, future validation campaigns should initiate numerical modeling before testing to guide the test campaign, which should include a rigorous assessment of uncertainty, and perform validation during testing to ensure that the tests address all of the validation needs.« less

How Reliable is Bayesian Model Averaging Under Noisy Data? Statistical Assessment and Implications for Robust Model Selection

NASA Astrophysics Data System (ADS)

Schöniger, Anneli; Wöhling, Thomas; Nowak, Wolfgang

2014-05-01

Bayesian model averaging ranks the predictive capabilities of alternative conceptual models based on Bayes' theorem. The individual models are weighted with their posterior probability to be the best one in the considered set of models. Finally, their predictions are combined into a robust weighted average and the predictive uncertainty can be quantified. This rigorous procedure does, however, not yet account for possible instabilities due to measurement noise in the calibration data set. This is a major drawback, since posterior model weights may suffer a lack of robustness related to the uncertainty in noisy data, which may compromise the reliability of model ranking. We present a new statistical concept to account for measurement noise as source of uncertainty for the weights in Bayesian model averaging. Our suggested upgrade reflects the limited information content of data for the purpose of model selection. It allows us to assess the significance of the determined posterior model weights, the confidence in model selection, and the accuracy of the quantified predictive uncertainty. Our approach rests on a brute-force Monte Carlo framework. We determine the robustness of model weights against measurement noise by repeatedly perturbing the observed data with random realizations of measurement error. Then, we analyze the induced variability in posterior model weights and introduce this "weighting variance" as an additional term into the overall prediction uncertainty analysis scheme. We further determine the theoretical upper limit in performance of the model set which is imposed by measurement noise. As an extension to the merely relative model ranking, this analysis provides a measure of absolute model performance. To finally decide, whether better data or longer time series are needed to ensure a robust basis for model selection, we resample the measurement time series and assess the convergence of model weights for increasing time series length. We illustrate our suggested approach with an application to model selection between different soil-plant models following up on a study by Wöhling et al. (2013). Results show that measurement noise compromises the reliability of model ranking and causes a significant amount of weighting uncertainty, if the calibration data time series is not long enough to compensate for its noisiness. This additional contribution to the overall predictive uncertainty is neglected without our approach. Thus, we strongly advertise to include our suggested upgrade in the Bayesian model averaging routine.

GIA Model Statistics for GRACE Hydrology, Cryosphere, and Ocean Science

NASA Astrophysics Data System (ADS)

Caron, L.; Ivins, E. R.; Larour, E.; Adhikari, S.; Nilsson, J.; Blewitt, G.

2018-03-01

We provide a new analysis of glacial isostatic adjustment (GIA) with the goal of assembling the model uncertainty statistics required for rigorously extracting trends in surface mass from the Gravity Recovery and Climate Experiment (GRACE) mission. Such statistics are essential for deciphering sea level, ocean mass, and hydrological changes because the latter signals can be relatively small (≤2 mm/yr water height equivalent) over very large regions, such as major ocean basins and watersheds. With abundant new >7 year continuous measurements of vertical land motion (VLM) reported by Global Positioning System stations on bedrock and new relative sea level records, our new statistical evaluation of GIA uncertainties incorporates Bayesian methodologies. A unique aspect of the method is that both the ice history and 1-D Earth structure vary through a total of 128,000 forward models. We find that best fit models poorly capture the statistical inferences needed to correctly invert for lower mantle viscosity and that GIA uncertainty exceeds the uncertainty ascribed to trends from 14 years of GRACE data in polar regions.

Bayesian calibration of mechanistic aquatic biogeochemical models and benefits for environmental management

NASA Astrophysics Data System (ADS)

Arhonditsis, George B.; Papantou, Dimitra; Zhang, Weitao; Perhar, Gurbir; Massos, Evangelia; Shi, Molu

2008-09-01

Aquatic biogeochemical models have been an indispensable tool for addressing pressing environmental issues, e.g., understanding oceanic response to climate change, elucidation of the interplay between plankton dynamics and atmospheric CO 2 levels, and examination of alternative management schemes for eutrophication control. Their ability to form the scientific basis for environmental management decisions can be undermined by the underlying structural and parametric uncertainty. In this study, we outline how we can attain realistic predictive links between management actions and ecosystem response through a probabilistic framework that accommodates rigorous uncertainty analysis of a variety of error sources, i.e., measurement error, parameter uncertainty, discrepancy between model and natural system. Because model uncertainty analysis essentially aims to quantify the joint probability distribution of model parameters and to make inference about this distribution, we believe that the iterative nature of Bayes' Theorem is a logical means to incorporate existing knowledge and update the joint distribution as new information becomes available. The statistical methodology begins with the characterization of parameter uncertainty in the form of probability distributions, then water quality data are used to update the distributions, and yield posterior parameter estimates along with predictive uncertainty bounds. Our illustration is based on a six state variable (nitrate, ammonium, dissolved organic nitrogen, phytoplankton, zooplankton, and bacteria) ecological model developed for gaining insight into the mechanisms that drive plankton dynamics in a coastal embayment; the Gulf of Gera, Island of Lesvos, Greece. The lack of analytical expressions for the posterior parameter distributions was overcome using Markov chain Monte Carlo simulations; a convenient way to obtain representative samples of parameter values. The Bayesian calibration resulted in realistic reproduction of the key temporal patterns of the system, offered insights into the degree of information the data contain about model inputs, and also allowed the quantification of the dependence structure among the parameter estimates. Finally, our study uses two synthetic datasets to examine the ability of the updated model to provide estimates of predictive uncertainty for water quality variables of environmental management interest.

Cost-effective water quality assessment through the integration of monitoring data and modeling results

NASA Astrophysics Data System (ADS)

Lobuglio, Joseph N.; Characklis, Gregory W.; Serre, Marc L.

2007-03-01

Sparse monitoring data and error inherent in water quality models make the identification of waters not meeting regulatory standards uncertain. Additional monitoring can be implemented to reduce this uncertainty, but it is often expensive. These costs are currently a major concern, since developing total maximum daily loads, as mandated by the Clean Water Act, will require assessing tens of thousands of water bodies across the United States. This work uses the Bayesian maximum entropy (BME) method of modern geostatistics to integrate water quality monitoring data together with model predictions to provide improved estimates of water quality in a cost-effective manner. This information includes estimates of uncertainty and can be used to aid probabilistic-based decisions concerning the status of a water (i.e., impaired or not impaired) and the level of monitoring needed to characterize the water for regulatory purposes. This approach is applied to the Catawba River reservoir system in western North Carolina as a means of estimating seasonal chlorophyll a concentration. Mean concentration and confidence intervals for chlorophyll a are estimated for 66 reservoir segments over an 11-year period (726 values) based on 219 measured seasonal averages and 54 model predictions. Although the model predictions had a high degree of uncertainty, integration of modeling results via BME methods reduced the uncertainty associated with chlorophyll estimates compared with estimates made solely with information from monitoring efforts. Probabilistic predictions of future chlorophyll levels on one reservoir are used to illustrate the cost savings that can be achieved by less extensive and rigorous monitoring methods within the BME framework. While BME methods have been applied in several environmental contexts, employing these methods as a means of integrating monitoring and modeling results, as well as application of this approach to the assessment of surface water monitoring networks, represent unexplored areas of research.

Prognostics Uncertainty Management with Application to Government and Industry

NASA Technical Reports Server (NTRS)

Celaya, Jose; Sankararaman, Shankar; Daigle, Matthew; Saxena, Abhinav; Goebel, Kai

2014-01-01

Predictions about the future are contingent on future usage, but also on the quality of the models employed and the assessment of the current health state. These factors, amongst others, need to be considered to arrive at a prediction that is conducted through a rigorous method but where the confidence bounds are not prohibitively large.

Model-Based Assessment of Estuary Ecosystem Health Using the Latent Health Factor Index, with Application to the Richibucto Estuary

PubMed Central

Chiu, Grace S.; Wu, Margaret A.; Lu, Lin

2013-01-01

The ability to quantitatively assess ecological health is of great interest to those tasked with monitoring and conserving ecosystems. For decades, biomonitoring research and policies have relied on multimetric health indices of various forms. Although indices are numbers, many are constructed based on qualitative procedures, thus limiting the quantitative rigor of the practical interpretations of such indices. The statistical modeling approach to construct the latent health factor index (LHFI) was recently developed. With ecological data that otherwise are used to construct conventional multimetric indices, the LHFI framework expresses such data in a rigorous quantitative model, integrating qualitative features of ecosystem health and preconceived ecological relationships among such features. This hierarchical modeling approach allows unified statistical inference of health for observed sites (along with prediction of health for partially observed sites, if desired) and of the relevance of ecological drivers, all accompanied by formal uncertainty statements from a single, integrated analysis. Thus far, the LHFI approach has been demonstrated and validated in a freshwater context. We adapt this approach to modeling estuarine health, and illustrate it on the previously unassessed system in Richibucto in New Brunswick, Canada, where active oyster farming is a potential stressor through its effects on sediment properties. Field data correspond to health metrics that constitute the popular AZTI marine biotic index and the infaunal trophic index, as well as abiotic predictors preconceived to influence biota. Our paper is the first to construct a scientifically sensible model that rigorously identifies the collective explanatory capacity of salinity, distance downstream, channel depth, and silt–clay content–all regarded a priori as qualitatively important abiotic drivers–towards site health in the Richibucto ecosystem. This suggests the potential effectiveness of the LHFI approach for assessing not only freshwater systems but aquatic ecosystems in general. PMID:23785443

Linked Sensitivity Analysis, Calibration, and Uncertainty Analysis Using a System Dynamics Model for Stroke Comparative Effectiveness Research.

PubMed

Tian, Yuan; Hassmiller Lich, Kristen; Osgood, Nathaniel D; Eom, Kirsten; Matchar, David B

2016-11-01

As health services researchers and decision makers tackle more difficult problems using simulation models, the number of parameters and the corresponding degree of uncertainty have increased. This often results in reduced confidence in such complex models to guide decision making. To demonstrate a systematic approach of linked sensitivity analysis, calibration, and uncertainty analysis to improve confidence in complex models. Four techniques were integrated and applied to a System Dynamics stroke model of US veterans, which was developed to inform systemwide intervention and research planning: Morris method (sensitivity analysis), multistart Powell hill-climbing algorithm and generalized likelihood uncertainty estimation (calibration), and Monte Carlo simulation (uncertainty analysis). Of 60 uncertain parameters, sensitivity analysis identified 29 needing calibration, 7 that did not need calibration but significantly influenced key stroke outcomes, and 24 not influential to calibration or stroke outcomes that were fixed at their best guess values. One thousand alternative well-calibrated baselines were obtained to reflect calibration uncertainty and brought into uncertainty analysis. The initial stroke incidence rate among veterans was identified as the most influential uncertain parameter, for which further data should be collected. That said, accounting for current uncertainty, the analysis of 15 distinct prevention and treatment interventions provided a robust conclusion that hypertension control for all veterans would yield the largest gain in quality-adjusted life years. For complex health care models, a mixed approach was applied to examine the uncertainty surrounding key stroke outcomes and the robustness of conclusions. We demonstrate that this rigorous approach can be practical and advocate for such analysis to promote understanding of the limits of certainty in applying models to current decisions and to guide future data collection. © The Author(s) 2016.

Estimating ecosystem carbon change in the Conterminous United States based on 40 years of land-use change and disturbance

NASA Astrophysics Data System (ADS)

Sleeter, B. M.; Rayfield, B.; Liu, J.; Sherba, J.; Daniel, C.; Frid, L.; Wilson, T. S.; Zhu, Z.

2016-12-01

Since 1970, the combined changes in land use, land management, climate, and natural disturbances have dramatically altered land cover in the United States, resulting in the potential for significant changes in terrestrial carbon storage and flux between ecosystems and the atmosphere. Processes including urbanization, agricultural expansion and contraction, and forest management have had impacts - both positive and negative - on the amount of natural vegetation, the age structure of forests, and the amount of impervious cover. Anthropogenic change coupled with climate-driven changes in natural disturbance regimes, particularly the frequency and severity of wildfire, together determine the spatio-temporal patterns of land change and contribute to changing ecosystem carbon dynamics. Quantifying this effect and its associated uncertainties is fundamental to developing a rigorous and transparent carbon monitoring and assessment programs. However, large-scale systematic inventories of historical land change and their associated uncertainties are sparse. To address this need, we present a newly developed modeling framework, the Land Use and Carbon Scenario Simulator (LUCAS). The LUCAS model integrates readily available high quality, empirical land-change data into a stochastic space-time simulation model representing land change feedbacks on carbon cycling in terrestrial ecosystems. We applied the LUCAS model to estimate regional scale changes in carbon storage, atmospheric flux, and net biome production in 84 ecological regions of the conterminous United States for the period 1970-2015. The model was parameterized using a newly available set of high resolution (30 m) land-change data, compiled from Landsat remote sensing imagery, including estimates of uncertainty. Carbon flux parameters for each ecological region were derived from the IBIS dynamic global vegetation model with full carbon cycle accounting. This paper presents our initial findings describing regional and temporal changes and variability in carbon storage and flux resulting from land use change and disturbance between 1973 and 2015. Additionally, based on stochastic simulations we quantify and present key sources of uncertainty in the estimation of terrestrial ecosystem carbon dynamics.

Uncertainty quantification in nanomechanical measurements using the atomic force microscope

Treesearch

Ryan Wagner; Robert Moon; Jon Pratt; Gordon Shaw; Arvind Raman

2011-01-01

Quantifying uncertainty in measured properties of nanomaterials is a prerequisite for the manufacture of reliable nanoengineered materials and products. Yet, rigorous uncertainty quantification (UQ) is rarely applied for material property measurements with the atomic force microscope (AFM), a widely used instrument that can measure properties at nanometer scale...

Quantification of uncertainty in aerosol optical thickness retrieval arising from aerosol microphysical model and other sources, applied to Ozone Monitoring Instrument (OMI) measurements

NASA Astrophysics Data System (ADS)

Määttä, A.; Laine, M.; Tamminen, J.; Veefkind, J. P.

2014-05-01

Satellite instruments are nowadays successfully utilised for measuring atmospheric aerosol in many applications as well as in research. Therefore, there is a growing need for rigorous error characterisation of the measurements. Here, we introduce a methodology for quantifying the uncertainty in the retrieval of aerosol optical thickness (AOT). In particular, we concentrate on two aspects: uncertainty due to aerosol microphysical model selection and uncertainty due to imperfect forward modelling. We apply the introduced methodology for aerosol optical thickness retrieval of the Ozone Monitoring Instrument (OMI) on board NASA's Earth Observing System (EOS) Aura satellite, launched in 2004. We apply statistical methodologies that improve the uncertainty estimates of the aerosol optical thickness retrieval by propagating aerosol microphysical model selection and forward model error more realistically. For the microphysical model selection problem, we utilise Bayesian model selection and model averaging methods. Gaussian processes are utilised to characterise the smooth systematic discrepancies between the measured and modelled reflectances (i.e. residuals). The spectral correlation is composed empirically by exploring a set of residuals. The operational OMI multi-wavelength aerosol retrieval algorithm OMAERO is used for cloud-free, over-land pixels of the OMI instrument with the additional Bayesian model selection and model discrepancy techniques introduced here. The method and improved uncertainty characterisation is demonstrated by several examples with different aerosol properties: weakly absorbing aerosols, forest fires over Greece and Russia, and Sahara desert dust. The statistical methodology presented is general; it is not restricted to this particular satellite retrieval application.

Addressing the impact of environmental uncertainty in plankton model calibration with a dedicated software system: the Marine Model Optimization Testbed (MarMOT 1.1 alpha)

NASA Astrophysics Data System (ADS)

Hemmings, J. C. P.; Challenor, P. G.

2012-04-01

A wide variety of different plankton system models have been coupled with ocean circulation models, with the aim of understanding and predicting aspects of environmental change. However, an ability to make reliable inferences about real-world processes from the model behaviour demands a quantitative understanding of model error that remains elusive. Assessment of coupled model output is inhibited by relatively limited observing system coverage of biogeochemical components. Any direct assessment of the plankton model is further inhibited by uncertainty in the physical state. Furthermore, comparative evaluation of plankton models on the basis of their design is inhibited by the sensitivity of their dynamics to many adjustable parameters. Parameter uncertainty has been widely addressed by calibrating models at data-rich ocean sites. However, relatively little attention has been given to quantifying uncertainty in the physical fields required by the plankton models at these sites, and tendencies in the biogeochemical properties due to the effects of horizontal processes are often neglected. Here we use model twin experiments, in which synthetic data are assimilated to estimate a system's known "true" parameters, to investigate the impact of error in a plankton model's environmental input data. The experiments are supported by a new software tool, the Marine Model Optimization Testbed, designed for rigorous analysis of plankton models in a multi-site 1-D framework. Simulated errors are derived from statistical characterizations of the mixed layer depth, the horizontal flux divergence tendencies of the biogeochemical tracers and the initial state. Plausible patterns of uncertainty in these data are shown to produce strong temporal and spatial variability in the expected simulation error variance over an annual cycle, indicating variation in the significance attributable to individual model-data differences. An inverse scheme using ensemble-based estimates of the simulation error variance to allow for this environment error performs well compared with weighting schemes used in previous calibration studies, giving improved estimates of the known parameters. The efficacy of the new scheme in real-world applications will depend on the quality of statistical characterizations of the input data. Practical approaches towards developing reliable characterizations are discussed.

Population growth rates of reef sharks with and without fishing on the great barrier reef: robust estimation with multiple models.

PubMed

Hisano, Mizue; Connolly, Sean R; Robbins, William D

2011-01-01

Overfishing of sharks is a global concern, with increasing numbers of species threatened by overfishing. For many sharks, both catch rates and underwater visual surveys have been criticized as indices of abundance. In this context, estimation of population trends using individual demographic rates provides an important alternative means of assessing population status. However, such estimates involve uncertainties that must be appropriately characterized to credibly and effectively inform conservation efforts and management. Incorporating uncertainties into population assessment is especially important when key demographic rates are obtained via indirect methods, as is often the case for mortality rates of marine organisms subject to fishing. Here, focusing on two reef shark species on the Great Barrier Reef, Australia, we estimated natural and total mortality rates using several indirect methods, and determined the population growth rates resulting from each. We used bootstrapping to quantify the uncertainty associated with each estimate, and to evaluate the extent of agreement between estimates. Multiple models produced highly concordant natural and total mortality rates, and associated population growth rates, once the uncertainties associated with the individual estimates were taken into account. Consensus estimates of natural and total population growth across multiple models support the hypothesis that these species are declining rapidly due to fishing, in contrast to conclusions previously drawn from catch rate trends. Moreover, quantitative projections of abundance differences on fished versus unfished reefs, based on the population growth rate estimates, are comparable to those found in previous studies using underwater visual surveys. These findings appear to justify management actions to substantially reduce the fishing mortality of reef sharks. They also highlight the potential utility of rigorously characterizing uncertainty, and applying multiple assessment methods, to obtain robust estimates of population trends in species threatened by overfishing.

Population Growth Rates of Reef Sharks with and without Fishing on the Great Barrier Reef: Robust Estimation with Multiple Models

PubMed Central

Hisano, Mizue; Connolly, Sean R.; Robbins, William D.

2011-01-01

Overfishing of sharks is a global concern, with increasing numbers of species threatened by overfishing. For many sharks, both catch rates and underwater visual surveys have been criticized as indices of abundance. In this context, estimation of population trends using individual demographic rates provides an important alternative means of assessing population status. However, such estimates involve uncertainties that must be appropriately characterized to credibly and effectively inform conservation efforts and management. Incorporating uncertainties into population assessment is especially important when key demographic rates are obtained via indirect methods, as is often the case for mortality rates of marine organisms subject to fishing. Here, focusing on two reef shark species on the Great Barrier Reef, Australia, we estimated natural and total mortality rates using several indirect methods, and determined the population growth rates resulting from each. We used bootstrapping to quantify the uncertainty associated with each estimate, and to evaluate the extent of agreement between estimates. Multiple models produced highly concordant natural and total mortality rates, and associated population growth rates, once the uncertainties associated with the individual estimates were taken into account. Consensus estimates of natural and total population growth across multiple models support the hypothesis that these species are declining rapidly due to fishing, in contrast to conclusions previously drawn from catch rate trends. Moreover, quantitative projections of abundance differences on fished versus unfished reefs, based on the population growth rate estimates, are comparable to those found in previous studies using underwater visual surveys. These findings appear to justify management actions to substantially reduce the fishing mortality of reef sharks. They also highlight the potential utility of rigorously characterizing uncertainty, and applying multiple assessment methods, to obtain robust estimates of population trends in species threatened by overfishing. PMID:21966402

Orbital State Uncertainty Realism

NASA Astrophysics Data System (ADS)

Horwood, J.; Poore, A. B.

2012-09-01

Fundamental to the success of the space situational awareness (SSA) mission is the rigorous inclusion of uncertainty in the space surveillance network. The *proper characterization of uncertainty* in the orbital state of a space object is a common requirement to many SSA functions including tracking and data association, resolution of uncorrelated tracks (UCTs), conjunction analysis and probability of collision, sensor resource management, and anomaly detection. While tracking environments, such as air and missile defense, make extensive use of Gaussian and local linearity assumptions within algorithms for uncertainty management, space surveillance is inherently different due to long time gaps between updates, high misdetection rates, nonlinear and non-conservative dynamics, and non-Gaussian phenomena. The latter implies that "covariance realism" is not always sufficient. SSA also requires "uncertainty realism"; the proper characterization of both the state and covariance and all non-zero higher-order cumulants. In other words, a proper characterization of a space object's full state *probability density function (PDF)* is required. In order to provide a more statistically rigorous treatment of uncertainty in the space surveillance tracking environment and to better support the aforementioned SSA functions, a new class of multivariate PDFs are formulated which more accurately characterize the uncertainty of a space object's state or orbit. The new distribution contains a parameter set controlling the higher-order cumulants which gives the level sets a distinctive "banana" or "boomerang" shape and degenerates to a Gaussian in a suitable limit. Using the new class of PDFs within the general Bayesian nonlinear filter, the resulting filter prediction step (i.e., uncertainty propagation) is shown to have the *same computational cost as the traditional unscented Kalman filter* with the former able to maintain a proper characterization of the uncertainty for up to *ten times as long* as the latter. The filter correction step also furnishes a statistically rigorous *prediction error* which appears in the likelihood ratios for scoring the association of one report or observation to another. Thus, the new filter can be used to support multi-target tracking within a general multiple hypothesis tracking framework. Additionally, the new distribution admits a distance metric which extends the classical Mahalanobis distance (chi^2 statistic). This metric provides a test for statistical significance and facilitates single-frame data association methods with the potential to easily extend the covariance-based track association algorithm of Hill, Sabol, and Alfriend. The filtering, data fusion, and association methods using the new class of orbital state PDFs are shown to be mathematically tractable and operationally viable.

Reliability model for ductile hybrid FRP rebar using randomly dispersed chopped fibers

NASA Astrophysics Data System (ADS)

Behnam, Bashar Ramzi

Fiber reinforced polymer composites or simply FRP composites have become more attractive to civil engineers in the last two decades due to their unique mechanical properties. However, there are many obstacles such as low elasticity modulus, non-ductile behavior, high cost of the fibers, high manufacturing costs, and absence of rigorous characterization of the uncertainties of the mechanical properties that restrict the use of these composites. However, when FRP composites are used to develop reinforcing rebars in concrete structural members to replace the conventional steel, a huge benefit can be achieved since FRP materials don't corrode. Two FRP rebar models are proposed that make use of multiple types of fibers to achieve ductility, and chopped fibers are used to reduce the manufacturing costs. In order to reach the most optimum fractional volume of each type of fiber, to minimize the cost of the proposed rebars, and to achieve a safe design by considering uncertainties in the materials and geometry of sections, appropriate material resistance factors have been developed, and a Reliability Based Design Optimization (RBDO), has been conducted for the proposed schemes.

A Fatigue Crack Size Evaluation Method Based on Lamb Wave Simulation and Limited Experimental Data

PubMed Central

He, Jingjing; Ran, Yunmeng; Liu, Bin; Yang, Jinsong; Guan, Xuefei

2017-01-01

This paper presents a systematic and general method for Lamb wave-based crack size quantification using finite element simulations and Bayesian updating. The method consists of construction of a baseline quantification model using finite element simulation data and Bayesian updating with limited Lamb wave data from target structure. The baseline model correlates two proposed damage sensitive features, namely the normalized amplitude and phase change, with the crack length through a response surface model. The two damage sensitive features are extracted from the first received S0 mode wave package. The model parameters of the baseline model are estimated using finite element simulation data. To account for uncertainties from numerical modeling, geometry, material and manufacturing between the baseline model and the target model, Bayesian method is employed to update the baseline model with a few measurements acquired from the actual target structure. A rigorous validation is made using in-situ fatigue testing and Lamb wave data from coupon specimens and realistic lap-joint components. The effectiveness and accuracy of the proposed method is demonstrated under different loading and damage conditions. PMID:28902148

Development of a fourth generation predictive capability maturity model.

DOE Office of Scientific and Technical Information (OSTI.GOV)

Hills, Richard Guy; Witkowski, Walter R.; Urbina, Angel

2013-09-01

The Predictive Capability Maturity Model (PCMM) is an expert elicitation tool designed to characterize and communicate completeness of the approaches used for computational model definition, verification, validation, and uncertainty quantification associated for an intended application. The primary application of this tool at Sandia National Laboratories (SNL) has been for physics-based computational simulations in support of nuclear weapons applications. The two main goals of a PCMM evaluation are 1) the communication of computational simulation capability, accurately and transparently, and 2) the development of input for effective planning. As a result of the increasing importance of computational simulation to SNLs mission, themore » PCMM has evolved through multiple generations with the goal to provide more clarity, rigor, and completeness in its application. This report describes the approach used to develop the fourth generation of the PCMM.« less

Response to Burgman and Regan: the elephant in the rhetoric on info-gap decision theory.

PubMed

Sniedovich, Moshe

2014-01-01

The formal, rigorous assessment of IGDT in Sniedovich (2012) reveals that this theory's central pillar, namely its robustness model, is a reinvention of a well-established model of local robustness, known universally as radius of stability (circa 1960). As a matter of fact, this robustness model is a simple model derived from Wald's famous maximin paradigm (circa 1940). This means that had there been any gap in the state of the art that IGDT could have possibly presumed to fill, this gap had already been filled decades ago, well before IGDT was even contemplated. The conclusion therefore is that there is no gap in the state of the art that IGDT does fill, or can possibly fill, or is called upon to fill. Also, since IGDT is based on a definition of local robustness, the theory is unsuitable for the treatment of a severe uncertainty of the type that this theory claims to address. Therefore, since the theory claims to be particularly suitable for the treatment of a severe, unbounded uncertainty, the inevitable conclusion is that this theory constitutes a voodoo decision theory par excellence. Fig. 1 speaks for itself so that no amount of rhetoric can explain this fact away. The Letter's attempt to brush off valid, rigorous, well-documented criticism of IGDT as "... haggling over terminology ..." is yet another attempt to avoid dealing with the elephant in the IGDT room. Nothing will be gained from the use of misleading rhetorics to argue that ideas, models, techniques, approaches, etc., that go back to the 1940s and 1960s, are IGDT innovations. But more than this, what good can come of misapplications of these ideas in applied ecology and conservation biology? In the Appendix, I address a more intriguing question, namely: QUESTION 2: What could possibly be the rationale that motivated a search for a (nonexistent) gap in the state of the art for IGDT to fill?

Impact of AMS-02 Measurements on Reducing GCR Model Uncertainties

NASA Technical Reports Server (NTRS)

Slaba, T. C.; O'Neill, P. M.; Golge, S.; Norbury, J. W.

2015-01-01

For vehicle design, shield optimization, mission planning, and astronaut risk assessment, the exposure from galactic cosmic rays (GCR) poses a significant and complex problem both in low Earth orbit and in deep space. To address this problem, various computational tools have been developed to quantify the exposure and risk in a wide range of scenarios. Generally, the tool used to describe the ambient GCR environment provides the input into subsequent computational tools and is therefore a critical component of end-to-end procedures. Over the past few years, several researchers have independently and very carefully compared some of the widely used GCR models to more rigorously characterize model differences and quantify uncertainties. All of the GCR models studied rely heavily on calibrating to available near-Earth measurements of GCR particle energy spectra, typically over restricted energy regions and short time periods. In this work, we first review recent sensitivity studies quantifying the ions and energies in the ambient GCR environment of greatest importance to exposure quantities behind shielding. Currently available measurements used to calibrate and validate GCR models are also summarized within this context. It is shown that the AMS-II measurements will fill a critically important gap in the measurement database. The emergence of AMS-II measurements also provides a unique opportunity to validate existing models against measurements that were not used to calibrate free parameters in the empirical descriptions. Discussion is given regarding rigorous approaches to implement the independent validation efforts, followed by recalibration of empirical parameters.

Physics Verification Overview

DOE Office of Scientific and Technical Information (OSTI.GOV)

Doebling, Scott William

The purpose of the verification project is to establish, through rigorous convergence analysis, that each ASC computational physics code correctly implements a set of physics models and algorithms (code verification); Evaluate and analyze the uncertainties of code outputs associated with the choice of temporal and spatial discretization (solution or calculation verification); and Develop and maintain the capability to expand and update these analyses on demand. This presentation describes project milestones.

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

DTIC Science & Technology

2011-06-28

wind, temperature, and moisture variables, while the oceanographic ET is derived from ocean current, temperature, and salinity variables. Estimates of...wind, temperature, and moisture variables while the oceanographic ET is derived from ocean current temperature, and salinity variables. Estimates of...uncertainty in the model. Rigorously accurate ensemble methods for describing the distribution of future states given past information include particle

How measurement science can improve confidence in research results.

PubMed

Plant, Anne L; Becker, Chandler A; Hanisch, Robert J; Boisvert, Ronald F; Possolo, Antonio M; Elliott, John T

2018-04-01

The current push for rigor and reproducibility is driven by a desire for confidence in research results. Here, we suggest a framework for a systematic process, based on consensus principles of measurement science, to guide researchers and reviewers in assessing, documenting, and mitigating the sources of uncertainty in a study. All study results have associated ambiguities that are not always clarified by simply establishing reproducibility. By explicitly considering sources of uncertainty, noting aspects of the experimental system that are difficult to characterize quantitatively, and proposing alternative interpretations, the researcher provides information that enhances comparability and reproducibility.

Adequacy of TRMM satellite rainfall data in driving the SWAT modeling of Tiaoxi catchment (Taihu lake basin, China)

NASA Astrophysics Data System (ADS)

Li, Dan; Christakos, George; Ding, Xinxin; Wu, Jiaping

2018-01-01

Spatial rainfall data is an essential input to Distributed Hydrological Models (DHM), and a significant contributor to hydrological model uncertainty. Model uncertainty is higher when rain gauges are sparse, as is often the case in practice. Currently, satellite-based precipitation products increasingly provide an alternative means to ground-based rainfall estimates, in which case a rigorous product assessment is required before implementation. Accordingly, the twofold objective of this work paper was the real-world assessment of both (a) the Tropical Rainfall Measuring Mission (TRMM) rainfall product using gauge data, and (b) the TRMM product's role in forcing data for hydrologic simulations in the area of the Tiaoxi catchment (Taihu lake basin, China). The TRMM rainfall products used in this study are the Version-7 real-time 3B42RT and the post-real-time 3B42. It was found that the TRMM rainfall data showed a superior performance at the monthly and annual scales, fitting well with surface observation-based frequency rainfall distributions. The Nash-Sutcliffe Coefficient of Efficiency (NSCE) and the relative bias ratio (BIAS) were used to evaluate hydrologic model performance. The satisfactory performance of the monthly runoff simulations in the Tiaoxi study supports the view that the implementation of real-time 3B42RT allows considerable room for improvement. At the same time, post-real-time 3B42 can be a valuable tool of hydrologic modeling, water balance analysis, and basin water resource management, especially in developing countries or at remote locations in which rainfall gauges are scarce.

How Confident can we be in Flood Risk Assessments?

NASA Astrophysics Data System (ADS)

Merz, B.

2017-12-01

Flood risk management should be based on risk analyses quantifying the risk and its reduction for different risk reduction strategies. However, validating risk estimates by comparing model simulations with past observations is hardly possible, since the assessment typically encompasses extreme events and their impacts that have not been observed before. Hence, risk analyses are strongly based on assumptions and expert judgement. This situation opens the door for cognitive biases, such as `illusion of certainty', `overconfidence' or `recency bias'. Such biases operate specifically in complex situations with many factors involved, when uncertainty is high and events are probabilistic, or when close learning feedback loops are missing - aspects that all apply to risk analyses. This contribution discusses how confident we can be in flood risk assessments, and reflects about more rigorous approaches towards their validation.

A statistical kinematic source inversion approach based on the QUESO library for uncertainty quantification and prediction

NASA Astrophysics Data System (ADS)

Zielke, Olaf; McDougall, Damon; Mai, Martin; Babuska, Ivo

2014-05-01

Seismic, often augmented with geodetic data, are frequently used to invert for the spatio-temporal evolution of slip along a rupture plane. The resulting images of the slip evolution for a single event, inferred by different research teams, often vary distinctly, depending on the adopted inversion approach and rupture model parameterization. This observation raises the question, which of the provided kinematic source inversion solutions is most reliable and most robust, and — more generally — how accurate are fault parameterization and solution predictions? These issues are not included in "standard" source inversion approaches. Here, we present a statistical inversion approach to constrain kinematic rupture parameters from teleseismic body waves. The approach is based a) on a forward-modeling scheme that computes synthetic (body-)waves for a given kinematic rupture model, and b) on the QUESO (Quantification of Uncertainty for Estimation, Simulation, and Optimization) library that uses MCMC algorithms and Bayes theorem for sample selection. We present Bayesian inversions for rupture parameters in synthetic earthquakes (i.e. for which the exact rupture history is known) in an attempt to identify the cross-over at which further model discretization (spatial and temporal resolution of the parameter space) is no longer attributed to a decreasing misfit. Identification of this cross-over is of importance as it reveals the resolution power of the studied data set (i.e. teleseismic body waves), enabling one to constrain kinematic earthquake rupture histories of real earthquakes at a resolution that is supported by data. In addition, the Bayesian approach allows for mapping complete posterior probability density functions of the desired kinematic source parameters, thus enabling us to rigorously assess the uncertainties in earthquake source inversions.

Assessment of SFR Wire Wrap Simulation Uncertainties

DOE Office of Scientific and Technical Information (OSTI.GOV)

Delchini, Marc-Olivier G.; Popov, Emilian L.; Pointer, William David

Predictive modeling and simulation of nuclear reactor performance and fuel are challenging due to the large number of coupled physical phenomena that must be addressed. Models that will be used for design or operational decisions must be analyzed for uncertainty to ascertain impacts to safety or performance. Rigorous, structured uncertainty analyses are performed by characterizing the model’s input uncertainties and then propagating the uncertainties through the model to estimate output uncertainty. This project is part of the ongoing effort to assess modeling uncertainty in Nek5000 simulations of flow configurations relevant to the advanced reactor applications of the Nuclear Energy Advancedmore » Modeling and Simulation (NEAMS) program. Three geometries are under investigation in these preliminary assessments: a 3-D pipe, a 3-D 7-pin bundle, and a single pin from the Thermal-Hydraulic Out-of-Reactor Safety (THORS) facility. Initial efforts have focused on gaining an understanding of Nek5000 modeling options and integrating Nek5000 with Dakota. These tasks are being accomplished by demonstrating the use of Dakota to assess parametric uncertainties in a simple pipe flow problem. This problem is used to optimize performance of the uncertainty quantification strategy and to estimate computational requirements for assessments of complex geometries. A sensitivity analysis to three turbulent models was conducted for a turbulent flow in a single wire wrapped pin (THOR) geometry. Section 2 briefly describes the software tools used in this study and provides appropriate references. Section 3 presents the coupling interface between Dakota and a computational fluid dynamic (CFD) code (Nek5000 or STARCCM+), with details on the workflow, the scripts used for setting up the run, and the scripts used for post-processing the output files. In Section 4, the meshing methods used to generate the THORS and 7-pin bundle meshes are explained. Sections 5, 6 and 7 present numerical results for the 3-D pipe, the single pin THORS mesh, and the 7-pin bundle mesh, respectively.« less

Antineutrino analysis for continuous monitoring of nuclear reactors: Sensitivity study

DOE Office of Scientific and Technical Information (OSTI.GOV)

Stewart, Christopher; Erickson, Anna

This paper explores the various contributors to uncertainty on predictions of the antineutrino source term which is used for reactor antineutrino experiments and is proposed as a safeguard mechanism for future reactor installations. The errors introduced during simulation of the reactor burnup cycle from variation in nuclear reaction cross sections, operating power, and other factors are combined with those from experimental and predicted antineutrino yields, resulting from fissions, evaluated, and compared. The most significant contributor to uncertainty on the reactor antineutrino source term when the reactor was modeled in 3D fidelity with assembly-level heterogeneity was found to be the uncertaintymore » on the antineutrino yields. Using the reactor simulation uncertainty data, the dedicated observation of a rigorously modeled small, fast reactor by a few-ton near-field detector was estimated to offer reduction of uncertainty on antineutrino yields in the 3.0–6.5 MeV range to a few percent for the primary power-producing fuel isotopes, even with zero prior knowledge of the yields.« less

Assessment of uncertainty in the numerical simulation of solar irradiance over inclined PV panels: New algorithms using measurements and modeling tools

DOE Office of Scientific and Technical Information (OSTI.GOV)

Xie, Yu; Sengupta, Manajit; Dooraghi, Mike

Development of accurate transposition models to simulate plane-of-array (POA) irradiance from horizontal measurements or simulations is a complex process mainly because of the anisotropic distribution of diffuse solar radiation in the atmosphere. The limited availability of reliable POA measurements at large temporal and spatial scales leads to difficulties in the comprehensive evaluation of transposition models. This paper proposes new algorithms to assess the uncertainty of transposition models using both surface-based observations and modeling tools. We reviewed the analytical derivation of POA irradiance and the approximation of isotropic diffuse radiation that simplifies the computation. Two transposition models are evaluated against themore » computation by the rigorous analytical solution. We proposed a new algorithm to evaluate transposition models using the clear-sky measurements at the National Renewable Energy Laboratory's (NREL's) Solar Radiation Research Laboratory (SRRL) and a radiative transfer model that integrates diffuse radiances of various sky-viewing angles. We found that the radiative transfer model and a transposition model based on empirical regressions are superior to the isotropic models when compared to measurements. We further compared the radiative transfer model to the transposition models under an extensive range of idealized conditions. Our results suggest that the empirical transposition model has slightly higher cloudy-sky POA irradiance than the radiative transfer model, but performs better than the isotropic models under clear-sky conditions. Significantly smaller POA irradiances computed by the transposition models are observed when the photovoltaics (PV) panel deviates from the azimuthal direction of the sun. The new algorithms developed in the current study have opened the door to a more comprehensive evaluation of transposition models for various atmospheric conditions and solar and PV orientations.« less

Assessment of uncertainty in the numerical simulation of solar irradiance over inclined PV panels: New algorithms using measurements and modeling tools

DOE PAGES

Xie, Yu; Sengupta, Manajit; Dooraghi, Mike

2018-03-20

Development of accurate transposition models to simulate plane-of-array (POA) irradiance from horizontal measurements or simulations is a complex process mainly because of the anisotropic distribution of diffuse solar radiation in the atmosphere. The limited availability of reliable POA measurements at large temporal and spatial scales leads to difficulties in the comprehensive evaluation of transposition models. This paper proposes new algorithms to assess the uncertainty of transposition models using both surface-based observations and modeling tools. We reviewed the analytical derivation of POA irradiance and the approximation of isotropic diffuse radiation that simplifies the computation. Two transposition models are evaluated against themore » computation by the rigorous analytical solution. We proposed a new algorithm to evaluate transposition models using the clear-sky measurements at the National Renewable Energy Laboratory's (NREL's) Solar Radiation Research Laboratory (SRRL) and a radiative transfer model that integrates diffuse radiances of various sky-viewing angles. We found that the radiative transfer model and a transposition model based on empirical regressions are superior to the isotropic models when compared to measurements. We further compared the radiative transfer model to the transposition models under an extensive range of idealized conditions. Our results suggest that the empirical transposition model has slightly higher cloudy-sky POA irradiance than the radiative transfer model, but performs better than the isotropic models under clear-sky conditions. Significantly smaller POA irradiances computed by the transposition models are observed when the photovoltaics (PV) panel deviates from the azimuthal direction of the sun. The new algorithms developed in the current study have opened the door to a more comprehensive evaluation of transposition models for various atmospheric conditions and solar and PV orientations.« less

Evaluation of a hierarchy of models reveals importance of substrate limitation for predicting carbon dioxide and methane exchange in restored wetlands

NASA Astrophysics Data System (ADS)

Oikawa, P. Y.; Jenerette, G. D.; Knox, S. H.; Sturtevant, C.; Verfaillie, J.; Dronova, I.; Poindexter, C. M.; Eichelmann, E.; Baldocchi, D. D.

2017-01-01

Wetlands and flooded peatlands can sequester large amounts of carbon (C) and have high greenhouse gas mitigation potential. There is growing interest in financing wetland restoration using C markets; however, this requires careful accounting of both CO2 and CH4 exchange at the ecosystem scale. Here we present a new model, the PEPRMT model (Peatland Ecosystem Photosynthesis Respiration and Methane Transport), which consists of a hierarchy of biogeochemical models designed to estimate CO2 and CH4 exchange in restored managed wetlands. Empirical models using temperature and/or photosynthesis to predict respiration and CH4 production were contrasted with a more process-based model that simulated substrate-limited respiration and CH4 production using multiple carbon pools. Models were parameterized by using a model-data fusion approach with multiple years of eddy covariance data collected in a recently restored wetland and a mature restored wetland. A third recently restored wetland site was used for model validation. During model validation, the process-based model explained 70% of the variance in net ecosystem exchange of CO2 (NEE) and 50% of the variance in CH4 exchange. Not accounting for high respiration following restoration led to empirical models overestimating annual NEE by 33-51%. By employing a model-data fusion approach we provide rigorous estimates of uncertainty in model predictions, accounting for uncertainty in data, model parameters, and model structure. The PEPRMT model is a valuable tool for understanding carbon cycling in restored wetlands and for application in carbon market-funded wetland restoration, thereby advancing opportunity to counteract the vast degradation of wetlands and flooded peatlands.

Robust control of nonlinear MAGLEV suspension system with mismatched uncertainties via DOBC approach.

PubMed

Yang, Jun; Zolotas, Argyrios; Chen, Wen-Hua; Michail, Konstantinos; Li, Shihua

2011-07-01

Robust control of a class of uncertain systems that have disturbances and uncertainties not satisfying "matching" condition is investigated in this paper via a disturbance observer based control (DOBC) approach. In the context of this paper, "matched" disturbances/uncertainties stand for the disturbances/uncertainties entering the system through the same channels as control inputs. By properly designing a disturbance compensation gain, a novel composite controller is proposed to counteract the "mismatched" lumped disturbances from the output channels. The proposed method significantly extends the applicability of the DOBC methods. Rigorous stability analysis of the closed-loop system with the proposed method is established under mild assumptions. The proposed method is applied to a nonlinear MAGnetic LEViation (MAGLEV) suspension system. Simulation shows that compared to the widely used integral control method, the proposed method provides significantly improved disturbance rejection and robustness against load variation. Copyright © 2011 ISA. Published by Elsevier Ltd. All rights reserved.

Uncertainty estimation of a complex water quality model: The influence of Box-Cox transformation on Bayesian approaches and comparison with a non-Bayesian method

NASA Astrophysics Data System (ADS)

Freni, Gabriele; Mannina, Giorgio

In urban drainage modelling, uncertainty analysis is of undoubted necessity. However, uncertainty analysis in urban water-quality modelling is still in its infancy and only few studies have been carried out. Therefore, several methodological aspects still need to be experienced and clarified especially regarding water quality modelling. The use of the Bayesian approach for uncertainty analysis has been stimulated by its rigorous theoretical framework and by the possibility of evaluating the impact of new knowledge on the modelling predictions. Nevertheless, the Bayesian approach relies on some restrictive hypotheses that are not present in less formal methods like the Generalised Likelihood Uncertainty Estimation (GLUE). One crucial point in the application of Bayesian method is the formulation of a likelihood function that is conditioned by the hypotheses made regarding model residuals. Statistical transformations, such as the use of Box-Cox equation, are generally used to ensure the homoscedasticity of residuals. However, this practice may affect the reliability of the analysis leading to a wrong uncertainty estimation. The present paper aims to explore the influence of the Box-Cox equation for environmental water quality models. To this end, five cases were considered one of which was the “real” residuals distributions (i.e. drawn from available data). The analysis was applied to the Nocella experimental catchment (Italy) which is an agricultural and semi-urbanised basin where two sewer systems, two wastewater treatment plants and a river reach were monitored during both dry and wet weather periods. The results show that the uncertainty estimation is greatly affected by residual transformation and a wrong assumption may also affect the evaluation of model uncertainty. The use of less formal methods always provide an overestimation of modelling uncertainty with respect to Bayesian method but such effect is reduced if a wrong assumption is made regarding the residuals distribution. If residuals are not normally distributed, the uncertainty is over-estimated if Box-Cox transformation is not applied or non-calibrated parameter is used.

A Peep into the Uncertainty-Complexity-Relevance Modeling Trilemma through Global Sensitivity and Uncertainty Analysis

NASA Astrophysics Data System (ADS)

Munoz-Carpena, R.; Muller, S. J.; Chu, M.; Kiker, G. A.; Perz, S. G.

2014-12-01

Model Model complexity resulting from the need to integrate environmental system components cannot be understated. In particular, additional emphasis is urgently needed on rational approaches to guide decision making through uncertainties surrounding the integrated system across decision-relevant scales. However, in spite of the difficulties that the consideration of modeling uncertainty represent for the decision process, it should not be avoided or the value and science behind the models will be undermined. These two issues; i.e., the need for coupled models that can answer the pertinent questions and the need for models that do so with sufficient certainty, are the key indicators of a model's relevance. Model relevance is inextricably linked with model complexity. Although model complexity has advanced greatly in recent years there has been little work to rigorously characterize the threshold of relevance in integrated and complex models. Formally assessing the relevance of the model in the face of increasing complexity would be valuable because there is growing unease among developers and users of complex models about the cumulative effects of various sources of uncertainty on model outputs. In particular, this issue has prompted doubt over whether the considerable effort going into further elaborating complex models will in fact yield the expected payback. New approaches have been proposed recently to evaluate the uncertainty-complexity-relevance modeling trilemma (Muller, Muñoz-Carpena and Kiker, 2011) by incorporating state-of-the-art global sensitivity and uncertainty analysis (GSA/UA) in every step of the model development so as to quantify not only the uncertainty introduced by the addition of new environmental components, but the effect that these new components have over existing components (interactions, non-linear responses). Outputs from the analysis can also be used to quantify system resilience (stability, alternative states, thresholds or tipping points) in the face of environmental and anthropogenic change (Perz, Muñoz-Carpena, Kiker and Holt, 2013), and through MonteCarlo mapping potential management activities over the most important factors or processes to influence the system towards behavioral (desirable) outcomes (Chu-Agor, Muñoz-Carpena et al., 2012).

Uncertainties in Earthquake Loss Analysis: A Case Study From Southern California

NASA Astrophysics Data System (ADS)

Mahdyiar, M.; Guin, J.

2005-12-01

Probabilistic earthquake hazard and loss analyses play important roles in many areas of risk management, including earthquake related public policy and insurance ratemaking. Rigorous loss estimation for portfolios of properties is difficult since there are various types of uncertainties in all aspects of modeling and analysis. It is the objective of this study to investigate the sensitivity of earthquake loss estimation to uncertainties in regional seismicity, earthquake source parameters, ground motions, and sites' spatial correlation on typical property portfolios in Southern California. Southern California is an attractive region for such a study because it has a large population concentration exposed to significant levels of seismic hazard. During the last decade, there have been several comprehensive studies of most regional faults and seismogenic sources. There have also been detailed studies on regional ground motion attenuations and regional and local site responses to ground motions. This information has been used by engineering seismologists to conduct regional seismic hazard and risk analysis on a routine basis. However, one of the more difficult tasks in such studies is the proper incorporation of uncertainties in the analysis. From the hazard side, there are uncertainties in the magnitudes, rates and mechanisms of the seismic sources and local site conditions and ground motion site amplifications. From the vulnerability side, there are considerable uncertainties in estimating the state of damage of buildings under different earthquake ground motions. From an analytical side, there are challenges in capturing the spatial correlation of ground motions and building damage, and integrating thousands of loss distribution curves with different degrees of correlation. In this paper we propose to address some of these issues by conducting loss analyses of a typical small portfolio in southern California, taking into consideration various source and ground motion uncertainties. The approach is designed to integrate loss distribution functions with different degrees of correlation for portfolio analysis. The analysis is based on USGS 2002 regional seismicity model.

Modeling with uncertain science: estimating mitigation credits from abating lead poisoning in Golden Eagles.

PubMed

Fitts Cochrane, Jean; Lonsdorf, Eric; Allison, Taber D; Sanders-Reed, Carol A

2015-09-01

Challenges arise when renewable energy development triggers "no net loss" policies for protected species, such as where wind energy facilities affect Golden Eagles in the western United States. When established mitigation approaches are insufficient to fully avoid or offset losses, conservation goals may still be achievable through experimental implementation of unproven mitigation methods provided they are analyzed within a framework that deals transparently and rigorously with uncertainty. We developed an approach to quantify and analyze compensatory mitigation that (1) relies on expert opinion elicited in a thoughtful and structured process to design the analysis (models) and supplement available data, (2) builds computational models as hypotheses about cause-effect relationships, (3) represents scientific uncertainty in stochastic model simulations, (4) provides probabilistic predictions of "relative" mortality with and without mitigation, (5) presents results in clear formats useful to applying risk management preferences (regulatory standards) and selecting strategies and levels of mitigation for immediate action, and (6) defines predictive parameters in units that could be monitored effectively, to support experimental adaptive management and reduction in uncertainty. We illustrate the approach with a case study characterized by high uncertainty about underlying biological processes and high conservation interest: estimating the quantitative effects of voluntary strategies to abate lead poisoning in Golden Eagles in Wyoming due to ingestion of spent game hunting ammunition.

Characterising bias in regulatory risk and decision analysis: An analysis of heuristics applied in health technology appraisal, chemicals regulation, and climate change governance.

PubMed

MacGillivray, Brian H

2017-08-01

In many environmental and public health domains, heuristic methods of risk and decision analysis must be relied upon, either because problem structures are ambiguous, reliable data is lacking, or decisions are urgent. This introduces an additional source of uncertainty beyond model and measurement error - uncertainty stemming from relying on inexact inference rules. Here we identify and analyse heuristics used to prioritise risk objects, to discriminate between signal and noise, to weight evidence, to construct models, to extrapolate beyond datasets, and to make policy. Some of these heuristics are based on causal generalisations, yet can misfire when these relationships are presumed rather than tested (e.g. surrogates in clinical trials). Others are conventions designed to confer stability to decision analysis, yet which may introduce serious error when applied ritualistically (e.g. significance testing). Some heuristics can be traced back to formal justifications, but only subject to strong assumptions that are often violated in practical applications. Heuristic decision rules (e.g. feasibility rules) in principle act as surrogates for utility maximisation or distributional concerns, yet in practice may neglect costs and benefits, be based on arbitrary thresholds, and be prone to gaming. We highlight the problem of rule-entrenchment, where analytical choices that are in principle contestable are arbitrarily fixed in practice, masking uncertainty and potentially introducing bias. Strategies for making risk and decision analysis more rigorous include: formalising the assumptions and scope conditions under which heuristics should be applied; testing rather than presuming their underlying empirical or theoretical justifications; using sensitivity analysis, simulations, multiple bias analysis, and deductive systems of inference (e.g. directed acyclic graphs) to characterise rule uncertainty and refine heuristics; adopting "recovery schemes" to correct for known biases; and basing decision rules on clearly articulated values and evidence, rather than convention. Copyright © 2017. Published by Elsevier Ltd.

Tight finite-key analysis for quantum cryptography

PubMed Central

Tomamichel, Marco; Lim, Charles Ci Wen; Gisin, Nicolas; Renner, Renato

2012-01-01

Despite enormous theoretical and experimental progress in quantum cryptography, the security of most current implementations of quantum key distribution is still not rigorously established. One significant problem is that the security of the final key strongly depends on the number, M, of signals exchanged between the legitimate parties. Yet, existing security proofs are often only valid asymptotically, for unrealistically large values of M. Another challenge is that most security proofs are very sensitive to small differences between the physical devices used by the protocol and the theoretical model used to describe them. Here we show that these gaps between theory and experiment can be simultaneously overcome by using a recently developed proof technique based on the uncertainty relation for smooth entropies. PMID:22252558

Tight finite-key analysis for quantum cryptography.

PubMed

Tomamichel, Marco; Lim, Charles Ci Wen; Gisin, Nicolas; Renner, Renato

2012-01-17

Despite enormous theoretical and experimental progress in quantum cryptography, the security of most current implementations of quantum key distribution is still not rigorously established. One significant problem is that the security of the final key strongly depends on the number, M, of signals exchanged between the legitimate parties. Yet, existing security proofs are often only valid asymptotically, for unrealistically large values of M. Another challenge is that most security proofs are very sensitive to small differences between the physical devices used by the protocol and the theoretical model used to describe them. Here we show that these gaps between theory and experiment can be simultaneously overcome by using a recently developed proof technique based on the uncertainty relation for smooth entropies.

Estimating the Health Effects of Greenhouse Gas Mitigation Strategies: Addressing Parametric, Model, and Valuation Challenges

PubMed Central

Hess, Jeremy J.; Ebi, Kristie L.; Markandya, Anil; Balbus, John M.; Wilkinson, Paul; Haines, Andy; Chalabi, Zaid

2014-01-01

Background: Policy decisions regarding climate change mitigation are increasingly incorporating the beneficial and adverse health impacts of greenhouse gas emission reduction strategies. Studies of such co-benefits and co-harms involve modeling approaches requiring a range of analytic decisions that affect the model output. Objective: Our objective was to assess analytic decisions regarding model framework, structure, choice of parameters, and handling of uncertainty when modeling health co-benefits, and to make recommendations for improvements that could increase policy uptake. Methods: We describe the assumptions and analytic decisions underlying models of mitigation co-benefits, examining their effects on modeling outputs, and consider tools for quantifying uncertainty. Discussion: There is considerable variation in approaches to valuation metrics, discounting methods, uncertainty characterization and propagation, and assessment of low-probability/high-impact events. There is also variable inclusion of adverse impacts of mitigation policies, and limited extension of modeling domains to include implementation considerations. Going forward, co-benefits modeling efforts should be carried out in collaboration with policy makers; these efforts should include the full range of positive and negative impacts and critical uncertainties, as well as a range of discount rates, and should explicitly characterize uncertainty. We make recommendations to improve the rigor and consistency of modeling of health co-benefits. Conclusion: Modeling health co-benefits requires systematic consideration of the suitability of model assumptions, of what should be included and excluded from the model framework, and how uncertainty should be treated. Increased attention to these and other analytic decisions has the potential to increase the policy relevance and application of co-benefits modeling studies, potentially helping policy makers to maximize mitigation potential while simultaneously improving health. Citation: Remais JV, Hess JJ, Ebi KL, Markandya A, Balbus JM, Wilkinson P, Haines A, Chalabi Z. 2014. Estimating the health effects of greenhouse gas mitigation strategies: addressing parametric, model, and valuation challenges. Environ Health Perspect 122:447–455; http://dx.doi.org/10.1289/ehp.1306744 PMID:24583270

Evaluating Organic Aerosol Model Performance: Impact of two Embedded Assumptions

NASA Astrophysics Data System (ADS)

Jiang, W.; Giroux, E.; Roth, H.; Yin, D.

2004-05-01

Organic aerosols are important due to their abundance in the polluted lower atmosphere and their impact on human health and vegetation. However, modeling organic aerosols is a very challenging task because of the complexity of aerosol composition, structure, and formation processes. Assumptions and their associated uncertainties in both models and measurement data make model performance evaluation a truly demanding job. Although some assumptions are obvious, others are hidden and embedded, and can significantly impact modeling results, possibly even changing conclusions about model performance. This paper focuses on analyzing the impact of two embedded assumptions on evaluation of organic aerosol model performance. One assumption is about the enthalpy of vaporization widely used in various secondary organic aerosol (SOA) algorithms. The other is about the conversion factor used to obtain ambient organic aerosol concentrations from measured organic carbon. These two assumptions reflect uncertainties in the model and in the ambient measurement data, respectively. For illustration purposes, various choices of the assumed values are implemented in the evaluation process for an air quality model based on CMAQ (the Community Multiscale Air Quality Model). Model simulations are conducted for the Lower Fraser Valley covering Southwest British Columbia, Canada, and Northwest Washington, United States, for a historical pollution episode in 1993. To understand the impact of the assumed enthalpy of vaporization on modeling results, its impact on instantaneous organic aerosol yields (IAY) through partitioning coefficients is analysed first. The analysis shows that utilizing different enthalpy of vaporization values causes changes in the shapes of IAY curves and in the response of SOA formation capability of reactive organic gases to temperature variations. These changes are then carried into the air quality model and cause substantial changes in the organic aerosol modeling results. In another aspect, using different assumed factors to convert measured organic carbon to organic aerosol concentrations cause substantial variations in the processed ambient data themselves, which are normally used as performance targets for model evaluations. The combination of uncertainties in the modeling results and in the moving performance targets causes major uncertainties in the final conclusion about the model performance. Without further information, the best thing that a modeler can do is to choose a combination of the assumed values from the sensible parameter ranges available in the literature, based on the best match of the modeling results with the processed measurement data. However, the best match of the modeling results with the processed measurement data may not necessarily guarantee that the model itself is rigorous and the model performance is robust. Conclusions on the model performance can only be reached with sufficient understanding of the uncertainties and their impact.

Trans-Dimensional Bayesian Imaging of 3-D Crustal and Upper Mantle Structure in Northeast Asia

NASA Astrophysics Data System (ADS)

Kim, S.; Tkalcic, H.; Rhie, J.; Chen, Y.

2016-12-01

Imaging 3-D structures using stepwise inversions of ambient noise and receiver function data is now a routine work. Here, we carry out the inversion in the trans-dimensional and hierarchical extension of the Bayesian framework to obtain rigorous estimates of uncertainty and high-resolution images of crustal and upper mantle structures beneath Northeast (NE) Asia. The methods inherently account for data sensitivities by means of using adaptive parameterizations and treating data noise as free parameters. Therefore, parsimonious results from the methods are balanced out between model complexity and data fitting. This allows fully exploiting data information, preventing from over- or under-estimation of the data fit, and increases model resolution. In addition, the reliability of results is more rigorously checked through the use of Bayesian uncertainties. It is shown by various synthetic recovery tests that complex and spatially variable features are well resolved in our resulting images of NE Asia. Rayleigh wave phase and group velocity tomograms (8-70 s), a 3-D shear-wave velocity model from depth inversions of the estimated dispersion maps, and regional 3-D models (NE China, the Korean Peninsula, and the Japanese islands) from joint inversions with receiver function data of dense networks are presented. High-resolution models are characterized by a number of tectonically meaningful features. We focus our interpretation on complex patterns of sub-lithospheric low velocity structures that extend from back-arc regions to continental margins. We interpret the anomalies in conjunction with distal and distributed intraplate volcanoes in NE Asia. Further discussion on other imaged features will be presented.

Monte Carlo simulation for uncertainty estimation on structural data in implicit 3-D geological modeling, a guide for disturbance distribution selection and parameterization

NASA Astrophysics Data System (ADS)

Pakyuz-Charrier, Evren; Lindsay, Mark; Ogarko, Vitaliy; Giraud, Jeremie; Jessell, Mark

2018-04-01

Three-dimensional (3-D) geological structural modeling aims to determine geological information in a 3-D space using structural data (foliations and interfaces) and topological rules as inputs. This is necessary in any project in which the properties of the subsurface matters; they express our understanding of geometries in depth. For that reason, 3-D geological models have a wide range of practical applications including but not restricted to civil engineering, the oil and gas industry, the mining industry, and water management. These models, however, are fraught with uncertainties originating from the inherent flaws of the modeling engines (working hypotheses, interpolator's parameterization) and the inherent lack of knowledge in areas where there are no observations combined with input uncertainty (observational, conceptual and technical errors). Because 3-D geological models are often used for impactful decision-making it is critical that all 3-D geological models provide accurate estimates of uncertainty. This paper's focus is set on the effect of structural input data measurement uncertainty propagation in implicit 3-D geological modeling. This aim is achieved using Monte Carlo simulation for uncertainty estimation (MCUE), a stochastic method which samples from predefined disturbance probability distributions that represent the uncertainty of the original input data set. MCUE is used to produce hundreds to thousands of altered unique data sets. The altered data sets are used as inputs to produce a range of plausible 3-D models. The plausible models are then combined into a single probabilistic model as a means to propagate uncertainty from the input data to the final model. In this paper, several improved methods for MCUE are proposed. The methods pertain to distribution selection for input uncertainty, sample analysis and statistical consistency of the sampled distribution. Pole vector sampling is proposed as a more rigorous alternative than dip vector sampling for planar features and the use of a Bayesian approach to disturbance distribution parameterization is suggested. The influence of incorrect disturbance distributions is discussed and propositions are made and evaluated on synthetic and realistic cases to address the sighted issues. The distribution of the errors of the observed data (i.e., scedasticity) is shown to affect the quality of prior distributions for MCUE. Results demonstrate that the proposed workflows improve the reliability of uncertainty estimation and diminish the occurrence of artifacts.

A fully Bayesian method for jointly fitting instrumental calibration and X-ray spectral models

DOE Office of Scientific and Technical Information (OSTI.GOV)

Xu, Jin; Yu, Yaming; Van Dyk, David A.

2014-10-20

Owing to a lack of robust principled methods, systematic instrumental uncertainties have generally been ignored in astrophysical data analysis despite wide recognition of the importance of including them. Ignoring calibration uncertainty can cause bias in the estimation of source model parameters and can lead to underestimation of the variance of these estimates. We previously introduced a pragmatic Bayesian method to address this problem. The method is 'pragmatic' in that it introduced an ad hoc technique that simplified computation by neglecting the potential information in the data for narrowing the uncertainty for the calibration product. Following that work, we use amore » principal component analysis to efficiently represent the uncertainty of the effective area of an X-ray (or γ-ray) telescope. Here, however, we leverage this representation to enable a principled, fully Bayesian method that coherently accounts for the calibration uncertainty in high-energy spectral analysis. In this setting, the method is compared with standard analysis techniques and the pragmatic Bayesian method. The advantage of the fully Bayesian method is that it allows the data to provide information not only for estimation of the source parameters but also for the calibration product—here the effective area, conditional on the adopted spectral model. In this way, it can yield more accurate and efficient estimates of the source parameters along with valid estimates of their uncertainty. Provided that the source spectrum can be accurately described by a parameterized model, this method allows rigorous inference about the effective area by quantifying which possible curves are most consistent with the data.« less

Spatial scaling and multi-model inference in landscape genetics: Martes americana in northern Idaho

Treesearch

Tzeidle N. Wasserman; Samuel A. Cushman; Michael K. Schwartz; David O. Wallin

2010-01-01

Individual-based analyses relating landscape structure to genetic distances across complex landscapes enable rigorous evaluation of multiple alternative hypotheses linking landscape structure to gene flow. We utilize two extensions to increase the rigor of the individual-based causal modeling approach to inferring relationships between landscape patterns and gene flow...

The Source Inversion Validation (SIV) Initiative: A Collaborative Study on Uncertainty Quantification in Earthquake Source Inversions

NASA Astrophysics Data System (ADS)

Mai, P. M.; Schorlemmer, D.; Page, M.

2012-04-01

Earthquake source inversions image the spatio-temporal rupture evolution on one or more fault planes using seismic and/or geodetic data. Such studies are critically important for earthquake seismology in general, and for advancing seismic hazard analysis in particular, as they reveal earthquake source complexity and help (i) to investigate earthquake mechanics; (ii) to develop spontaneous dynamic rupture models; (iii) to build models for generating rupture realizations for ground-motion simulations. In applications (i - iii), the underlying finite-fault source models are regarded as "data" (input information), but their uncertainties are essentially unknown. After all, source models are obtained from solving an inherently ill-posed inverse problem to which many a priori assumptions and uncertain observations are applied. The Source Inversion Validation (SIV) project is a collaborative effort to better understand the variability between rupture models for a single earthquake (as manifested in the finite-source rupture model database) and to develop robust uncertainty quantification for earthquake source inversions. The SIV project highlights the need to develop a long-standing and rigorous testing platform to examine the current state-of-the-art in earthquake source inversion, and to develop and test novel source inversion approaches. We will review the current status of the SIV project, and report the findings and conclusions of the recent workshops. We will briefly discuss several source-inversion methods, how they treat uncertainties in data, and assess the posterior model uncertainty. Case studies include initial forward-modeling tests on Green's function calculations, and inversion results for synthetic data from spontaneous dynamic crack-like strike-slip earthquake on steeply dipping fault, embedded in a layered crustal velocity-density structure.

Space-Time Data fusion for Remote Sensing Applications

NASA Technical Reports Server (NTRS)

Braverman, Amy; Nguyen, H.; Cressie, N.

2011-01-01

NASA has been collecting massive amounts of remote sensing data about Earth's systems for more than a decade. Missions are selected to be complementary in quantities measured, retrieval techniques, and sampling characteristics, so these datasets are highly synergistic. To fully exploit this, a rigorous methodology for combining data with heterogeneous sampling characteristics is required. For scientific purposes, the methodology must also provide quantitative measures of uncertainty that propagate input-data uncertainty appropriately. We view this as a statistical inference problem. The true but notdirectly- observed quantities form a vector-valued field continuous in space and time. Our goal is to infer those true values or some function of them, and provide to uncertainty quantification for those inferences. We use a spatiotemporal statistical model that relates the unobserved quantities of interest at point-level to the spatially aggregated, observed data. We describe and illustrate our method using CO2 data from two NASA data sets.

Scientifically defensible fish conservation and recovery plans: Addressing diffuse threats and developing rigorous adaptive management plans

USGS Publications Warehouse

Maas-Hebner, Kathleen G.; Schreck, Carl B.; Hughes, Robert M.; Yeakley, Alan; Molina, Nancy

2016-01-01

We discuss the importance of addressing diffuse threats to long-term species and habitat viability in fish conservation and recovery planning. In the Pacific Northwest, USA, salmonid management plans have typically focused on degraded freshwater habitat, dams, fish passage, harvest rates, and hatchery releases. However, such plans inadequately address threats related to human population and economic growth, intra- and interspecific competition, and changes in climate, ocean, and estuarine conditions. Based on reviews conducted on eight conservation and/or recovery plans, we found that though threats resulting from such changes are difficult to model and/or predict, they are especially important for wide-ranging diadromous species. Adaptive management is also a critical but often inadequately constructed component of those plans. Adaptive management should be designed to respond to evolving knowledge about the fish and their supporting ecosystems; if done properly, it should help improve conservation efforts by decreasing uncertainty regarding known and diffuse threats. We conclude with a general call for environmental managers and planners to reinvigorate the adaptive management process in future management plans, including more explicitly identifying critical uncertainties, implementing monitoring programs to reduce those uncertainties, and explicitly stating what management actions will occur when pre-identified trigger points are reached.

Uncertainty and variability in computational and mathematical models of cardiac physiology.

PubMed

Mirams, Gary R; Pathmanathan, Pras; Gray, Richard A; Challenor, Peter; Clayton, Richard H

2016-12-01

Mathematical and computational models of cardiac physiology have been an integral component of cardiac electrophysiology since its inception, and are collectively known as the Cardiac Physiome. We identify and classify the numerous sources of variability and uncertainty in model formulation, parameters and other inputs that arise from both natural variation in experimental data and lack of knowledge. The impact of uncertainty on the outputs of Cardiac Physiome models is not well understood, and this limits their utility as clinical tools. We argue that incorporating variability and uncertainty should be a high priority for the future of the Cardiac Physiome. We suggest investigating the adoption of approaches developed in other areas of science and engineering while recognising unique challenges for the Cardiac Physiome; it is likely that novel methods will be necessary that require engagement with the mathematics and statistics community. The Cardiac Physiome effort is one of the most mature and successful applications of mathematical and computational modelling for describing and advancing the understanding of physiology. After five decades of development, physiological cardiac models are poised to realise the promise of translational research via clinical applications such as drug development and patient-specific approaches as well as ablation, cardiac resynchronisation and contractility modulation therapies. For models to be included as a vital component of the decision process in safety-critical applications, rigorous assessment of model credibility will be required. This White Paper describes one aspect of this process by identifying and classifying sources of variability and uncertainty in models as well as their implications for the application and development of cardiac models. We stress the need to understand and quantify the sources of variability and uncertainty in model inputs, and the impact of model structure and complexity and their consequences for predictive model outputs. We propose that the future of the Cardiac Physiome should include a probabilistic approach to quantify the relationship of variability and uncertainty of model inputs and outputs. © 2016 The Authors. The Journal of Physiology published by John Wiley & Sons Ltd on behalf of The Physiological Society.

Multiscale Informatics for Low-Temperature Propane Oxidation: Further Complexities in Studies of Complex Reactions

DOE Office of Scientific and Technical Information (OSTI.GOV)

Burke, Michael P.; Goldsmith, C. Franklin; Klippenstein, Stephen J.

2015-07-16

We have developed a multi-scale approach (Burke, M. P.; Klippenstein, S. J.; Harding, L. B. Proc. Combust. Inst. 2013, 34, 547–555.) to kinetic model formulation that directly incorporates elementary kinetic theories as a means to provide reliable, physics-based extrapolation to unexplored conditions. Here, we extend and generalize the multi-scale modeling strategy to treat systems of considerable complexity – involving multi-well reactions, potentially missing reactions, non-statistical product branching ratios, and non-Boltzmann (i.e. non-thermal) reactant distributions. The methodology is demonstrated here for a subsystem of low-temperature propane oxidation, as a representative system for low-temperature fuel oxidation. A multi-scale model is assembled andmore » informed by a wide variety of targets that include ab initio calculations of molecular properties, rate constant measurements of isolated reactions, and complex systems measurements. Active model parameters are chosen to accommodate both “parametric” and “structural” uncertainties. Theoretical parameters (e.g. barrier heights) are included as active model parameters to account for parametric uncertainties in the theoretical treatment; experimental parameters (e.g. initial temperatures) are included to account for parametric uncertainties in the physical models of the experiments. RMG software is used to assess potential structural uncertainties due to missing reactions. Additionally, branching ratios among product channels are included as active model parameters to account for structural uncertainties related to difficulties in modeling sequences of multiple chemically activated steps. The approach is demonstrated here for interpreting time-resolved measurements of OH, HO2, n-propyl, i-propyl, propene, oxetane, and methyloxirane from photolysis-initiated low-temperature oxidation of propane at pressures from 4 to 60 Torr and temperatures from 300 to 700 K. In particular, the multi-scale informed model provides a consistent quantitative explanation of both ab initio calculations and time-resolved species measurements. The present results show that interpretations of OH measurements are significantly more complicated than previously thought – in addition to barrier heights for key transition states considered previously, OH profiles also depend on additional theoretical parameters for R + O2 reactions, secondary reactions, QOOH + O2 reactions, and treatment of non-Boltzmann reaction sequences. Extraction of physically rigorous information from those measurements may require more sophisticated treatment of all of those model aspects, as well as additional experimental data under more conditions, to discriminate among possible interpretations and ensure model reliability. Keywords: Optimization, Uncertainty quantification, Chemical mechanism, Low-Temperature Oxidation, Non-Boltzmann« less

Sodium Bearing Waste Processing Alternatives Analysis

DOE Office of Scientific and Technical Information (OSTI.GOV)

Murphy, James Anthony; Palmer, Brent J; Perry, Keith Joseph

2003-12-01

A multidisciplinary team gathered to develop a BBWI recommendation to DOE-ID on the processing alternatives for the sodium bearing waste in the INTEC Tank Farm. Numerous alternatives were analyzed using a rigorous, systematic approach. The data gathered were evaluated through internal and external peer reviews for consistency and validity. Three alternatives were identified to be top performers: Risk-based Calcination, MACT to WIPP Calcination and Cesium Ion Exchange. A dual-path through early Conceptual design is recommended for MACT to WIPP Calcination and Cesium Ion Exchange since Risk-based Calcination does not require design. If calcination alternatives are not considered based on givingmore » Type of Processing criteria significantly greater weight, the CsIX/TRUEX alternative follows CsIX in ranking. However, since CsIX/TRUEX shares common uncertainties with CsIX, reasonable backups, which follow in ranking, are the TRUEX and UNEX alternatives. Key uncertainties must be evaluated by the decision-makers to choose one final alternative. Those key uncertainties and a path forward for the technology roadmapping of these alternatives is provided.« less

Reconstruction of Long-Term Discharge Data in a Snow Dominant Region considering Uncertainty in Snow Measurement

NASA Astrophysics Data System (ADS)

Kim, S.

2016-12-01

This study to improve the accuracy of discharge simulation at the head water of the Tone River Basin (Yagisawa Dam Basin; 167 km2 and Naramata Dam Basin; 67 km2), Japan, where the river discharge is governed by the snowmelt and thus much uncertainty was originated in our previous study (Kim et al, 2011). To decrease the uncertainty in our hydrological modeling and simulation, snowmelt amounts are estimated rigorously using an improved degree-day method. The degree-day method, which is the simplest method to estimate snowmelt, is adopted with an improved degree-day factor estimation method. The degree-day factor for the target area is estimated using the observed temperature and the observed river discharge of the snowmelt season. Using long-term observed data, the unique relationship between the degree-day factor and temperature are extracted, and the estimated degree-day factor as a function of temperature is applied for the winter season discharge simulation. Rainfall-runoff simulation for the rest of season is done by the kinematic wave model based on the stage-discharge relationship, considering surface-subsurface flow generation. Finally, long-term (1979-2008) simulation output for the dam inflow is reconstructed and compared with the observed one. ( Kim, S., Tachikawa, Y., Nakakita, E., Yorozu, K. and Shiiba, M. 2011. Climate change impact on river flow of the Tone river basin, Japan, Annual Journal of Hydraulic Engneering, JSCE, 55:S_85-S_90.)

Accurate measurements of solar spectral irradiance between 4000-10000 cm-1

NASA Astrophysics Data System (ADS)

Elsey, J.; Coleman, M. D.; Gardiner, T.; Shine, K. P.

2017-12-01

The near-infrared solar spectral irradiance (SSI) is an important input into simulations of weather and climate; the distribution of energy throughout this region of the spectrum influences atmospheric heating rates and the global hydrological cycle through absorption and scattering by water vapour. Current measurements by a mixture of ground-based and space-based instruments show differences of around 10% in the 4000-7000 cm-1 region, with no resolution to this controversy in sight. This work presents observations of SSI taken using a ground-based Fourier Transform spectrometer between 4000-10000 cm-1 at a field site in Camborne, UK, with particular focus on a rigorously defined uncertainty budget. While there is good agreement between this work and the commonly-used ATLAS3 spectrum between 7000-10000 cm-1, the SSI is systematically lower by 10% than ATLAS3 between 4000-7000 cm-1, with no overlap within the k = 2 measurement uncertainties.

Remote auditing of radiotherapy facilities using optically stimulated luminescence dosimeters

DOE Office of Scientific and Technical Information (OSTI.GOV)

Lye, Jessica, E-mail: jessica.lye@arpansa.gov.au; Dunn, Leon; Kenny, John

Purpose: On 1 July 2012, the Australian Clinical Dosimetry Service (ACDS) released its Optically Stimulated Luminescent Dosimeter (OSLD) Level I audit, replacing the previous TLD based audit. The aim of this work is to present the results from this new service and the complete uncertainty analysis on which the audit tolerances are based. Methods: The audit release was preceded by a rigorous evaluation of the InLight® nanoDot OSLD system from Landauer (Landauer, Inc., Glenwood, IL). Energy dependence, signal fading from multiple irradiations, batch variation, reader variation, and dose response factors were identified and quantified for each individual OSLD. The detectorsmore » are mailed to the facility in small PMMA blocks, based on the design of the existing Radiological Physics Centre audit. Modeling and measurement were used to determine a factor that could convert the dose measured in the PMMA block, to dose in water for the facility's reference conditions. This factor is dependent on the beam spectrum. The TPR{sub 20,10} was used as the beam quality index to determine the specific block factor for a beam being audited. The audit tolerance was defined using a rigorous uncertainty calculation. The audit outcome is then determined using a scientifically based two tiered action level approach. Audit outcomes within two standard deviations were defined as Pass (Optimal Level), within three standard deviations as Pass (Action Level), and outside of three standard deviations the outcome is Fail (Out of Tolerance). Results: To-date the ACDS has audited 108 photon beams with TLD and 162 photon beams with OSLD. The TLD audit results had an average deviation from ACDS of 0.0% and a standard deviation of 1.8%. The OSLD audit results had an average deviation of −0.2% and a standard deviation of 1.4%. The relative combined standard uncertainty was calculated to be 1.3% (1σ). Pass (Optimal Level) was reduced to ≤2.6% (2σ), and Fail (Out of Tolerance) was reduced to >3.9% (3σ) for the new OSLD audit. Previously with the TLD audit the Pass (Optimal Level) and Fail (Out of Tolerance) were set at ≤4.0% (2σ) and >6.0% (3σ). Conclusions: The calculated standard uncertainty of 1.3% at one standard deviation is consistent with the measured standard deviation of 1.4% from the audits and confirming the suitability of the uncertainty budget derived audit tolerances. The OSLD audit shows greater accuracy than the previous TLD audit, justifying the reduction in audit tolerances. In the TLD audit, all outcomes were Pass (Optimal Level) suggesting that the tolerances were too conservative. In the OSLD audit 94% of the audits have resulted in Pass (Optimal level) and 6% of the audits have resulted in Pass (Action Level). All Pass (Action level) results have been resolved with a repeat OSLD audit, or an on-site ion chamber measurement.« less

IMPACT: Integrated Modeling of Perturbations in Atmospheres for Conjunction Tracking

NASA Astrophysics Data System (ADS)

Koller, J.; Brennan, S.; Godinez, H. C.; Higdon, D. M.; Klimenko, A.; Larsen, B.; Lawrence, E.; Linares, R.; McLaughlin, C. A.; Mehta, P. M.; Palmer, D.; Ridley, A. J.; Shoemaker, M.; Sutton, E.; Thompson, D.; Walker, A.; Wohlberg, B.

2013-12-01

Low-Earth orbiting satellites suffer from atmospheric drag due to thermospheric density which changes on the order of several magnitudes especially during space weather events. Solar flares, precipitating particles and ionospheric currents cause the upper atmosphere to heat up, redistribute, and cool again. These processes are intrinsically included in empirical models, e.g. MSIS and Jacchia-Bowman type models. However, sensitivity analysis has shown that atmospheric drag has the highest influence on satellite conjunction analysis and empirical model still do not adequately represent a desired accuracy. Space debris and collision avoidance have become an increasingly operational reality. It is paramount to accurately predict satellite orbits and include drag effect driven by space weather. The IMPACT project (Integrated Modeling of Perturbations in Atmospheres for Conjunction Tracking), funded with over $5 Million by the Los Alamos Laboratory Directed Research and Development office, has the goal to develop an integrated system of atmospheric drag modeling, orbit propagation, and conjunction analysis with detailed uncertainty quantification to address the space debris and collision avoidance problem. Now with over two years into the project, we have developed an integrated solution combining physics-based density modeling of the upper atmosphere between 120-700 km altitude, satellite drag forecasting for quiet and disturbed geomagnetic conditions, and conjunction analysis with non-Gaussian uncertainty quantification. We are employing several novel approaches including a unique observational sensor developed at Los Alamos; machine learning with a support-vector machine approach of the coupling between solar drivers of the upper atmosphere and satellite drag; rigorous data assimilative modeling using a physics-based approach instead of empirical modeling of the thermosphere; and a computed-tomography method for extracting temporal maps of thermospheric densities using ground based observations. The developed IMPACT framework is an open research framework enabling the exchange and testing of a variety of atmospheric density models, orbital propagators, drag coefficient models, ground based observations, etc. and study their effect on conjunctions and uncertainty predictions. The framework is based on a modern service-oriented architecture controlled by a web interface and providing 3D visualizations. The goal of this project is to revolutionize the ability to monitor and track space objects during highly disturbed space weather conditions, provide suitable forecasts for satellite drag conditions and conjunction analysis, and enable the exchange of models, codes, and data in an open research environment. We will present capabilities and results of the IMPACT framework including a demo of the control interface and visualizations.

Matter Gravitates, but Does Gravity Matter?

ERIC Educational Resources Information Center

Groetsch, C. W.

2011-01-01

The interplay of physical intuition, computational evidence, and mathematical rigor in a simple trajectory model is explored. A thought experiment based on the model is used to elicit student conjectures on the influence of a physical parameter; a mathematical model suggests a computational investigation of the conjectures, and rigorous analysis…

Hierarchical Bayesian Model Averaging for Chance Constrained Remediation Designs

NASA Astrophysics Data System (ADS)

Chitsazan, N.; Tsai, F. T.

2012-12-01

Groundwater remediation designs are heavily relying on simulation models which are subjected to various sources of uncertainty in their predictions. To develop a robust remediation design, it is crucial to understand the effect of uncertainty sources. In this research, we introduce a hierarchical Bayesian model averaging (HBMA) framework to segregate and prioritize sources of uncertainty in a multi-layer frame, where each layer targets a source of uncertainty. The HBMA framework provides an insight to uncertainty priorities and propagation. In addition, HBMA allows evaluating model weights in different hierarchy levels and assessing the relative importance of models in each level. To account for uncertainty, we employ a chance constrained (CC) programming for stochastic remediation design. Chance constrained programming was implemented traditionally to account for parameter uncertainty. Recently, many studies suggested that model structure uncertainty is not negligible compared to parameter uncertainty. Using chance constrained programming along with HBMA can provide a rigorous tool for groundwater remediation designs under uncertainty. In this research, the HBMA-CC was applied to a remediation design in a synthetic aquifer. The design was to develop a scavenger well approach to mitigate saltwater intrusion toward production wells. HBMA was employed to assess uncertainties from model structure, parameter estimation and kriging interpolation. An improved harmony search optimization method was used to find the optimal location of the scavenger well. We evaluated prediction variances of chloride concentration at the production wells through the HBMA framework. The results showed that choosing the single best model may lead to a significant error in evaluating prediction variances for two reasons. First, considering the single best model, variances that stem from uncertainty in the model structure will be ignored. Second, considering the best model with non-dominant model weight may underestimate or overestimate prediction variances by ignoring other plausible propositions. Chance constraints allow developing a remediation design with a desirable reliability. However, considering the single best model, the calculated reliability will be different from the desirable reliability. We calculated the reliability of the design for the models at different levels of HBMA. The results showed that by moving toward the top layers of HBMA, the calculated reliability converges to the chosen reliability. We employed the chance constrained optimization along with the HBMA framework to find the optimal location and pumpage for the scavenger well. The results showed that using models at different levels in the HBMA framework, the optimal location of the scavenger well remained the same, but the optimal extraction rate was altered. Thus, we concluded that the optimal pumping rate was sensitive to the prediction variance. Also, the prediction variance was changed by using different extraction rate. Using very high extraction rate will cause prediction variances of chloride concentration at the production wells to approach zero regardless of which HBMA models used.

Bayesian Inference of High-Dimensional Dynamical Ocean Models

NASA Astrophysics Data System (ADS)

Lin, J.; Lermusiaux, P. F. J.; Lolla, S. V. T.; Gupta, A.; Haley, P. J., Jr.

2015-12-01

This presentation addresses a holistic set of challenges in high-dimension ocean Bayesian nonlinear estimation: i) predict the probability distribution functions (pdfs) of large nonlinear dynamical systems using stochastic partial differential equations (PDEs); ii) assimilate data using Bayes' law with these pdfs; iii) predict the future data that optimally reduce uncertainties; and (iv) rank the known and learn the new model formulations themselves. Overall, we allow the joint inference of the state, equations, geometry, boundary conditions and initial conditions of dynamical models. Examples are provided for time-dependent fluid and ocean flows, including cavity, double-gyre and Strait flows with jets and eddies. The Bayesian model inference, based on limited observations, is illustrated first by the estimation of obstacle shapes and positions in fluid flows. Next, the Bayesian inference of biogeochemical reaction equations and of their states and parameters is presented, illustrating how PDE-based machine learning can rigorously guide the selection and discovery of complex ecosystem models. Finally, the inference of multiscale bottom gravity current dynamics is illustrated, motivated in part by classic overflows and dense water formation sites and their relevance to climate monitoring and dynamics. This is joint work with our MSEAS group at MIT.

Late Quaternary sedimentological and climate changes at Lake Bosumtwi Ghana: new constraints from laminae analysis and radiocarbon age modeling

USGS Publications Warehouse

Shanahan, Timothy M.; Beck, J. Warren; Overpeck, Jonathan T.; McKay, Nicholas P.; Pigati, Jeffrey S.; Peck, John A.; Scholz, Christopher A.; Heil, Clifford W.; King, John W.

2012-01-01

The Lake Bosumtwi sediment record represents one of the longest and highest-resolution terrestrial records of paleoclimate change available from sub-Saharan Africa. Here we report a new sediment age model framework for the last ~ 45 cal kyr of sedimentation using a combination of high-resolution radiocarbon dating, Bayesian age-depth modeling and lamination counting. Our results highlight the practical limits of these methods for reducing age model uncertainties and suggest that even with very high sampling densities, radiocarbon uncertainties of at least a few hundred years are unavoidable. Age model uncertainties are smallest during the Holocene (205 yr) and the glacial (360 yr) but are large at the base of the record (1660 yr), due to a combination of decreasing sample density, larger calibration uncertainties and increases in radiocarbon age scatter. For portions of the chronology older than ~ 35 cal kyr, additional considerations, such as the use of a low-blank graphitization system and more rigorous sample pretreatment were necessary to generate a reliable age depth model because of the incorporation of small amounts of younger carbon. A comparison of radiocarbon age model results and lamination counts over the time interval ~ 15–30 cal kyr agree with an overall discrepancy of ~ 10% and display similar changes in sedimentation rate, supporting the annual nature of sediment laminations in the early part of the record. Changes in sedimentation rates reconstructed from the age-depth model indicate that intervals of enhanced sediment delivery occurred at 16–19, 24 and 29–31 cal kyr, broadly synchronous with reconstructed drought episodes elsewhere in northern West Africa and potentially, with changes in Atlantic meridional heat transport during North Atlantic Heinrich events. These data suggest that millennial-scale drought events in the West African monsoon region were latitudinally extensive, reaching within several hundred kilometers of the Guinea coast. This is inconsistent with a simple southward shift in the mean position of the monsoon rainbelt, and requires changes in moisture convergence as a result of either a reduction in the moisture content of the tropical rainbelt, decreased convection, or both.

Respectful Modeling: Addressing Uncertainty in Dynamic System Models for Molecular Biology.

PubMed

Tsigkinopoulou, Areti; Baker, Syed Murtuza; Breitling, Rainer

2017-06-01

Although there is still some skepticism in the biological community regarding the value and significance of quantitative computational modeling, important steps are continually being taken to enhance its accessibility and predictive power. We view these developments as essential components of an emerging 'respectful modeling' framework which has two key aims: (i) respecting the models themselves and facilitating the reproduction and update of modeling results by other scientists, and (ii) respecting the predictions of the models and rigorously quantifying the confidence associated with the modeling results. This respectful attitude will guide the design of higher-quality models and facilitate the use of models in modern applications such as engineering and manipulating microbial metabolism by synthetic biology. Copyright © 2016 Elsevier Ltd. All rights reserved.

Linking trading ratio with TMDL (total maximum daily load) allocation matrix and uncertainty analysis.

PubMed

Zhang, H X

2008-01-01

An innovative approach for total maximum daily load (TMDL) allocation and implementation is the watershed-based pollutant trading. Given the inherent scientific uncertainty for the tradeoffs between point and nonpoint sources, setting of trading ratios can be a contentious issue and was already listed as an obstacle by several pollutant trading programs. One of the fundamental reasons that a trading ratio is often set higher (e.g. greater than 2) is to allow for uncertainty in the level of control needed to attain water quality standards, and to provide a buffer in case traded reductions are less effective than expected. However, most of the available studies did not provide an approach to explicitly address the determination of trading ratio. Uncertainty analysis has rarely been linked to determination of trading ratio.This paper presents a practical methodology in estimating "equivalent trading ratio (ETR)" and links uncertainty analysis with trading ratio determination from TMDL allocation process. Determination of ETR can provide a preliminary evaluation of "tradeoffs" between various combination of point and nonpoint source control strategies on ambient water quality improvement. A greater portion of NPS load reduction in overall TMDL load reduction generally correlates with greater uncertainty and thus requires greater trading ratio. The rigorous quantification of trading ratio will enhance the scientific basis and thus public perception for more informed decision in overall watershed-based pollutant trading program. (c) IWA Publishing 2008.

Expert elicitation of population-level effects of disturbance

USGS Publications Warehouse

Fleishman, Erica; Burgman, Mark; Runge, Michael C.; Schick, Robert S; Krauss, Scott; Popper, Arthur N.; Hawkins, Anthony

2016-01-01

Expert elicitation is a rigorous method for synthesizing expert knowledge to inform decision making and is reliable and practical when field data are limited. We evaluated the feasibility of applying expert elicitation to estimate population-level effects of disturbance on marine mammals. Diverse experts estimated parameters related to mortality and sublethal injury of North Atlantic right whales (Eubalaena glacialis). We are now eliciting expert knowledge on the movement of right whales among geographic regions to parameterize a spatial model of health. Expert elicitation complements methods such as simulation models or extrapolations from other species, sometimes with greater accuracy and less uncertainty.

An agent-based approach to financial stylized facts

NASA Astrophysics Data System (ADS)

Shimokawa, Tetsuya; Suzuki, Kyoko; Misawa, Tadanobu

2007-06-01

An important challenge of the financial theory in recent years is to construct more sophisticated models which have consistencies with as many financial stylized facts that cannot be explained by traditional models. Recently, psychological studies on decision making under uncertainty which originate in Kahneman and Tversky's research attract a lot of interest as key factors which figure out the financial stylized facts. These psychological results have been applied to the theory of investor's decision making and financial equilibrium modeling. This paper, following these behavioral financial studies, would like to propose an agent-based equilibrium model with prospect theoretical features of investors. Our goal is to point out a possibility that loss-averse feature of investors explains vast number of financial stylized facts and plays a crucial role in price formations of financial markets. Price process which is endogenously generated through our model has consistencies with, not only the equity premium puzzle and the volatility puzzle, but great kurtosis, asymmetry of return distribution, auto-correlation of return volatility, cross-correlation between return volatility and trading volume. Moreover, by using agent-based simulations, the paper also provides a rigorous explanation from the viewpoint of a lack of market liquidity to the size effect, which means that small-sized stocks enjoy excess returns compared to large-sized stocks.

A hybrid finite element - statistical energy analysis approach to robust sound transmission modeling

NASA Astrophysics Data System (ADS)

Reynders, Edwin; Langley, Robin S.; Dijckmans, Arne; Vermeir, Gerrit

2014-09-01

When considering the sound transmission through a wall in between two rooms, in an important part of the audio frequency range, the local response of the rooms is highly sensitive to uncertainty in spatial variations in geometry, material properties and boundary conditions, which have a wave scattering effect, while the local response of the wall is rather insensitive to such uncertainty. For this mid-frequency range, a computationally efficient modeling strategy is adopted that accounts for this uncertainty. The partitioning wall is modeled deterministically, e.g. with finite elements. The rooms are modeled in a very efficient, nonparametric stochastic way, as in statistical energy analysis. All components are coupled by means of a rigorous power balance. This hybrid strategy is extended so that the mean and variance of the sound transmission loss can be computed as well as the transition frequency that loosely marks the boundary between low- and high-frequency behavior of a vibro-acoustic component. The method is first validated in a simulation study, and then applied for predicting the airborne sound insulation of a series of partition walls of increasing complexity: a thin plastic plate, a wall consisting of gypsum blocks, a thicker masonry wall and a double glazing. It is found that the uncertainty caused by random scattering is important except at very high frequencies, where the modal overlap of the rooms is very high. The results are compared with laboratory measurements, and both are found to agree within the prediction uncertainty in the considered frequency range.

Assessing climate change and socio-economic uncertainties in long term management of water resources

NASA Astrophysics Data System (ADS)

Jahanshahi, Golnaz; Dawson, Richard; Walsh, Claire; Birkinshaw, Stephen; Glenis, Vassilis

2015-04-01

Long term management of water resources is challenging for decision makers given the range of uncertainties that exist. Such uncertainties are a function of long term drivers of change, such as climate, environmental loadings, demography, land use and other socio economic drivers. Impacts of climate change on frequency of extreme events such as drought make it a serious threat to water resources and water security. The release of probabilistic climate information, such as the UKCP09 scenarios, provides improved understanding of some uncertainties in climate models. This has motivated a more rigorous approach to dealing with other uncertainties in order to understand the sensitivity of investment decisions to future uncertainty and identify adaptation options that are as far as possible robust. We have developed and coupled a system of models that includes a weather generator, simulations of catchment hydrology, demand for water and the water resource system. This integrated model has been applied in the Thames catchment which supplies the city of London, UK. This region is one of the driest in the UK and hence sensitive to water availability. In addition, it is one of the fastest growing parts of the UK and plays an important economic role. Key uncertainties in long term water resources in the Thames catchment, many of which result from earth system processes, are identified and quantified. The implications of these uncertainties are explored using a combination of uncertainty analysis and sensitivity testing. The analysis shows considerable uncertainty in future rainfall, river flow and consequently water resource. For example, results indicate that by the 2050s, low flow (Q95) in the Thames catchment will range from -44 to +9% compared with the control scenario (1970s). Consequently, by the 2050s the average number of drought days are expected to increase 4-6 times relative to the 1970s. Uncertainties associated with urban growth increase these risks further. Adaptation measures, such as new reservoirs can manage these risks to a certain extent, but our sensitivity testing demonstrates that they are less robust to future uncertainties than measures taken to reduce water demand. Keywords: Climate change, Uncertainty, Decision making, Drought, Risk, Water resources management.

Underwater passive acoustic localization of Pacific walruses in the northeastern Chukchi Sea.

PubMed

Rideout, Brendan P; Dosso, Stan E; Hannay, David E

2013-09-01

This paper develops and applies a linearized Bayesian localization algorithm based on acoustic arrival times of marine mammal vocalizations at spatially-separated receivers which provides three-dimensional (3D) location estimates with rigorous uncertainty analysis. To properly account for uncertainty in receiver parameters (3D hydrophone locations and synchronization times) and environmental parameters (water depth and sound-speed correction), these quantities are treated as unknowns constrained by prior estimates and prior uncertainties. Unknown scaling factors on both the prior and arrival-time uncertainties are estimated by minimizing Akaike's Bayesian information criterion (a maximum entropy condition). Maximum a posteriori estimates for sound source locations and times, receiver parameters, and environmental parameters are calculated simultaneously using measurements of arrival times for direct and interface-reflected acoustic paths. Posterior uncertainties for all unknowns incorporate both arrival time and prior uncertainties. Monte Carlo simulation results demonstrate that, for the cases considered here, linearization errors are small and the lack of an accurate sound-speed profile does not cause significant biases in the estimated locations. A sequence of Pacific walrus vocalizations, recorded in the Chukchi Sea northwest of Alaska, is localized using this technique, yielding a track estimate and uncertainties with an estimated speed comparable to normal walrus swim speeds.

Incorporating uncertainty into mercury-offset decisions with a probabilistic network for National Pollutant Discharge Elimination System permit holders: an interim report

USGS Publications Warehouse

Wood, Alexander

2004-01-01

This interim report describes an alternative approach for evaluating the efficacy of using mercury (Hg) offsets to improve water quality. Hg-offset programs may allow dischargers facing higher-pollution control costs to meet their regulatory obligations by making more cost effective pollutant-reduction decisions. Efficient Hg management requires methods to translate that science and economics into a regulatory decision framework. This report documents the work in progress by the U.S. Geological Surveys Western Geographic Science Center in collaboration with Stanford University toward developing this decision framework to help managers, regulators, and other stakeholders decide whether offsets can cost effectively meet the Hg total maximum daily load (TMDL) requirements in the Sacramento River watershed. Two key approaches being considered are: (1) a probabilistic approach that explicitly incorporates scientific uncertainty, cost information, and value judgments; and (2) a quantitative approach that captures uncertainty in testing the feasibility of Hg offsets. Current fate and transport-process models commonly attempt to predict chemical transformations and transport pathways deterministically. However, the physical, chemical, and biologic processes controlling the fate and transport of Hg in aquatic environments are complex and poorly understood. Deterministic models of Hg environmental behavior contain large uncertainties, reflecting this lack of understanding. The uncertainty in these underlying physical processes may produce similarly large uncertainties in the decisionmaking process. However, decisions about control strategies are still being made despite the large uncertainties in current Hg loadings, the relations between total Hg (HgT) loading and methylmercury (MeHg) formation, and the relations between control efforts and Hg content in fish. The research presented here focuses on an alternative analytical approach to the current use of safety factors and deterministic methods for Hg TMDL decision support, one that is fully compatible with an adaptive management approach. This alternative approach uses empirical data and informed judgment to provide a scientific and technical basis for helping National Pollutant Discharge Elimination System (NPDES) permit holders make management decisions. An Hg-offset system would be an option if a wastewater-treatment plant could not achieve NPDES permit requirements for HgT reduction. We develop a probabilistic decision-analytical model consisting of three submodels for HgT loading, MeHg, and cost mitigation within a Bayesian network that integrates information of varying rigor and detail into a simple model of a complex system. Hg processes are identified and quantified by using a combination of historical data, statistical models, and expert judgment. Such an integrated approach to uncertainty analysis allows easy updating of prediction and inference when observations of model variables are made. We demonstrate our approach with data from the Cache Creek watershed (a subbasin of the Sacramento River watershed). The empirical models used to generate the needed probability distributions are based on the same empirical models currently being used by the Central Valley Regional Water Quality Control Cache Creek Hg TMDL working group. The significant difference is that input uncertainty and error are explicitly included in the model and propagated throughout its algorithms. This work demonstrates how to integrate uncertainty into the complex and highly uncertain Hg TMDL decisionmaking process. The various sources of uncertainty are propagated as decision risk that allows decisionmakers to simultaneously consider uncertainties in remediation/implementation costs while attempting to meet environmental/ecologic targets. We must note that this research is on going. As more data are collected, the HgT and cost-mitigation submodels are updated and the uncer

A multi-fidelity analysis selection method using a constrained discrete optimization formulation

NASA Astrophysics Data System (ADS)

Stults, Ian C.

The purpose of this research is to develop a method for selecting the fidelity of contributing analyses in computer simulations. Model uncertainty is a significant component of result validity, yet it is neglected in most conceptual design studies. When it is considered, it is done so in only a limited fashion, and therefore brings the validity of selections made based on these results into question. Neglecting model uncertainty can potentially cause costly redesigns of concepts later in the design process or can even cause program cancellation. Rather than neglecting it, if one were to instead not only realize the model uncertainty in tools being used but also use this information to select the tools for a contributing analysis, studies could be conducted more efficiently and trust in results could be quantified. Methods for performing this are generally not rigorous or traceable, and in many cases the improvement and additional time spent performing enhanced calculations are washed out by less accurate calculations performed downstream. The intent of this research is to resolve this issue by providing a method which will minimize the amount of time spent conducting computer simulations while meeting accuracy and concept resolution requirements for results. In many conceptual design programs, only limited data is available for quantifying model uncertainty. Because of this data sparsity, traditional probabilistic means for quantifying uncertainty should be reconsidered. This research proposes to instead quantify model uncertainty using an evidence theory formulation (also referred to as Dempster-Shafer theory) in lieu of the traditional probabilistic approach. Specific weaknesses in using evidence theory for quantifying model uncertainty are identified and addressed for the purposes of the Fidelity Selection Problem. A series of experiments was conducted to address these weaknesses using n-dimensional optimization test functions. These experiments found that model uncertainty present in analyses with 4 or fewer input variables could be effectively quantified using a strategic distribution creation method; if more than 4 input variables exist, a Frontier Finding Particle Swarm Optimization should instead be used. Once model uncertainty in contributing analysis code choices has been quantified, a selection method is required to determine which of these choices should be used in simulations. Because much of the selection done for engineering problems is driven by the physics of the problem, these are poor candidate problems for testing the true fitness of a candidate selection method. Specifically moderate and high dimensional problems' variability can often be reduced to only a few dimensions and scalability often cannot be easily addressed. For these reasons a simple academic function was created for the uncertainty quantification, and a canonical form of the Fidelity Selection Problem (FSP) was created. Fifteen best- and worst-case scenarios were identified in an effort to challenge the candidate selection methods both with respect to the characteristics of the tradeoff between time cost and model uncertainty and with respect to the stringency of the constraints and problem dimensionality. The results from this experiment show that a Genetic Algorithm (GA) was able to consistently find the correct answer, but under certain circumstances, a discrete form of Particle Swarm Optimization (PSO) was able to find the correct answer more quickly. To better illustrate how the uncertainty quantification and discrete optimization might be conducted for a "real world" problem, an illustrative example was conducted using gas turbine engines.

Stochastic Ocean Predictions with Dynamically-Orthogonal Primitive Equations

NASA Astrophysics Data System (ADS)

Subramani, D. N.; Haley, P., Jr.; Lermusiaux, P. F. J.

2017-12-01

The coastal ocean is a prime example of multiscale nonlinear fluid dynamics. Ocean fields in such regions are complex and intermittent with unstationary heterogeneous statistics. Due to the limited measurements, there are multiple sources of uncertainties, including the initial conditions, boundary conditions, forcing, parameters, and even the model parameterizations and equations themselves. For efficient and rigorous quantification and prediction of these uncertainities, the stochastic Dynamically Orthogonal (DO) PDEs for a primitive equation ocean modeling system with a nonlinear free-surface are derived and numerical schemes for their space-time integration are obtained. Detailed numerical studies with idealized-to-realistic regional ocean dynamics are completed. These include consistency checks for the numerical schemes and comparisons with ensemble realizations. As an illustrative example, we simulate the 4-d multiscale uncertainty in the Middle Atlantic/New York Bight region during the months of Jan to Mar 2017. To provide intitial conditions for the uncertainty subspace, uncertainties in the region were objectively analyzed using historical data. The DO primitive equations were subsequently integrated in space and time. The probability distribution function (pdf) of the ocean fields is compared to in-situ, remote sensing, and opportunity data collected during the coincident POSYDON experiment. Results show that our probabilistic predictions had skill and are 3- to 4- orders of magnitude faster than classic ensemble schemes.

Optimal resource allocation for defense of targets based on differing measures of attractiveness.

PubMed

Bier, Vicki M; Haphuriwat, Naraphorn; Menoyo, Jaime; Zimmerman, Rae; Culpen, Alison M

2008-06-01

This article describes the results of applying a rigorous computational model to the problem of the optimal defensive resource allocation among potential terrorist targets. In particular, our study explores how the optimal budget allocation depends on the cost effectiveness of security investments, the defender's valuations of the various targets, and the extent of the defender's uncertainty about the attacker's target valuations. We use expected property damage, expected fatalities, and two metrics of critical infrastructure (airports and bridges) as our measures of target attractiveness. Our results show that the cost effectiveness of security investment has a large impact on the optimal budget allocation. Also, different measures of target attractiveness yield different optimal budget allocations, emphasizing the importance of developing more realistic terrorist objective functions for use in budget allocation decisions for homeland security.

Towards a supported common NEAMS software stack

DOE Office of Scientific and Technical Information (OSTI.GOV)

Cormac Garvey

2012-04-01

The NEAMS IPSC's are developing multidimensional, multiphysics, multiscale simulation codes based on first principles that will be capable of predicting all aspects of current and future nuclear reactor systems. These new breeds of simulation codes will include rigorous verification, validation and uncertainty quantification checks to quantify the accuracy and quality of the simulation results. The resulting NEAMS IPSC simulation codes will be an invaluable tool in designing the next generation of Nuclear Reactors and also contribute to a more speedy process in the acquisition of licenses from the NRC for new Reactor designs. Due to the high resolution of themore » models, the complexity of the physics and the added computational resources to quantify the accuracy/quality of the results, the NEAMS IPSC codes will require large HPC resources to carry out the production simulation runs.« less

Covariance propagation in spectral indices

DOE PAGES

Griffin, P. J.

2015-01-09

In this study, the dosimetry community has a history of using spectral indices to support neutron spectrum characterization and cross section validation efforts. An important aspect to this type of analysis is the proper consideration of the contribution of the spectrum uncertainty to the total uncertainty in calculated spectral indices (SIs). This study identifies deficiencies in the traditional treatment of the SI uncertainty, provides simple bounds to the spectral component in the SI uncertainty estimates, verifies that these estimates are reflected in actual applications, details a methodology that rigorously captures the spectral contribution to the uncertainty in the SI, andmore » provides quantified examples that demonstrate the importance of the proper treatment the spectral contribution to the uncertainty in the SI.« less

New Insights into the Estimation of Extreme Geomagnetic Storm Occurrences

NASA Astrophysics Data System (ADS)

Ruffenach, Alexis; Winter, Hugo; Lavraud, Benoit; Bernardara, Pietro

2017-04-01

Space weather events such as intense geomagnetic storms are major disturbances of the near-Earth environment that may lead to serious impacts on our modern society. As such, it is of great importance to estimate their probability, and in particular that of extreme events. One approach largely used in statistical sciences for extreme events probability estimates is Extreme Value Analysis (EVA). Using this rigorous statistical framework, estimations of the occurrence of extreme geomagnetic storms are performed here based on the most relevant global parameters related to geomagnetic storms, such as ground parameters (e.g. geomagnetic Dst and aa indexes), and space parameters related to the characteristics of Coronal Mass Ejections (CME) (velocity, southward magnetic field component, electric field). Using our fitted model, we estimate the annual probability of a Carrington-type event (Dst = -850nT) to be on the order of 10-3, with a lower limit of the uncertainties on the return period of ˜500 years. Our estimate is significantly higher than that of most past studies, which typically had a return period of a few 100 years at maximum. Thus precautions are required when extrapolating intense values. Currently, the complexity of the processes and the length of available data inevitably leads to significant uncertainties in return period estimates for the occurrence of extreme geomagnetic storms. However, our application of extreme value models for extrapolating into the tail of the distribution provides a mathematically justified framework for the estimation of extreme return periods, thereby enabling the determination of more accurate estimates and reduced associated uncertainties.

Uncertainty Modeling for Structural Control Analysis and Synthesis

NASA Technical Reports Server (NTRS)

Campbell, Mark E.; Crawley, Edward F.

1996-01-01

The development of an accurate model of uncertainties for the control of structures that undergo a change in operational environment, based solely on modeling and experimentation in the original environment is studied. The application used throughout this work is the development of an on-orbit uncertainty model based on ground modeling and experimentation. A ground based uncertainty model consisting of mean errors and bounds on critical structural parameters is developed. The uncertainty model is created using multiple data sets to observe all relevant uncertainties in the system. The Discrete Extended Kalman Filter is used as an identification/parameter estimation method for each data set, in addition to providing a covariance matrix which aids in the development of the uncertainty model. Once ground based modal uncertainties have been developed, they are localized to specific degrees of freedom in the form of mass and stiffness uncertainties. Two techniques are presented: a matrix method which develops the mass and stiffness uncertainties in a mathematical manner; and a sensitivity method which assumes a form for the mass and stiffness uncertainties in macroelements and scaling factors. This form allows the derivation of mass and stiffness uncertainties in a more physical manner. The mass and stiffness uncertainties of the ground based system are then mapped onto the on-orbit system, and projected to create an analogous on-orbit uncertainty model in the form of mean errors and bounds on critical parameters. The Middeck Active Control Experiment is introduced as experimental verification for the localization and projection methods developed. In addition, closed loop results from on-orbit operations of the experiment verify the use of the uncertainty model for control analysis and synthesis in space.

The impact of uncertainty on optimal emission policies

NASA Astrophysics Data System (ADS)

Botta, Nicola; Jansson, Patrik; Ionescu, Cezar

2018-05-01

We apply a computational framework for specifying and solving sequential decision problems to study the impact of three kinds of uncertainties on optimal emission policies in a stylized sequential emission problem.We find that uncertainties about the implementability of decisions on emission reductions (or increases) have a greater impact on optimal policies than uncertainties about the availability of effective emission reduction technologies and uncertainties about the implications of trespassing critical cumulated emission thresholds. The results show that uncertainties about the implementability of decisions on emission reductions (or increases) call for more precautionary policies. In other words, delaying emission reductions to the point in time when effective technologies will become available is suboptimal when these uncertainties are accounted for rigorously. By contrast, uncertainties about the implications of exceeding critical cumulated emission thresholds tend to make early emission reductions less rewarding.

Acting on uncertainty in landscape management—options forestry.

Treesearch

Jonathan Thompson

2005-01-01

In response to the highly uncertain outcomes inherent in forest management, “options forestry” has been introduced as a novel approach that includes an honest appraisal of uncertainties and learning as a specific objective. The strategy is unique in that it uses a variety of management pathways, all designed to reach the same goal, and structures them in a rigorous...

Microbicide safety/efficacy studies in animals: macaques and small animal models.

PubMed

Veazey, Ronald S

2008-09-01

A number of microbicide candidates have failed to prevent HIV transmission in human clinical trials, and there is uncertainty as to how many additional trials can be supported by the field. Regardless, there are far too many microbicide candidates in development, and a logical and consistent method for screening and selecting candidates for human clinical trials is desperately needed. The unique host and cell specificity of HIV, however, provides challenges for microbicide safety and efficacy screening, that can only be addressed by rigorous testing in relevant laboratory animal models. A number of laboratory animal model systems ranging from rodents to nonhuman primates, and single versus multiple dose challenges have recently been developed to test microbicide candidates. These models have shed light on both the safety and efficacy of candidate microbicides as well as the early mechanisms involved in transmission. This article summarizes the major advantages and disadvantages of the relevant animal models for microbicide safety and efficacy testing. Currently, nonhuman primates are the only relevant and effective laboratory model for screening microbicide candidates. Given the consistent failures of prior strategies, it is now clear that rigorous safety and efficacy testing in nonhuman primates should be a prerequisite for advancing additional microbicide candidates to human clinical trials.

Microbicide Safety/Efficacy studies in animals -macaques and small animal models

PubMed Central

Veazey, Ronald S.

2009-01-01

Purpose of review A number of microbicide candidates have failed to prevent HIV transmission in human clinical trials, and there is uncertainty as to how many additional trials can be supported by the field. Regardless, there are far too many microbicide candidates in development, and a logical and consistent method for screening and selecting candidates for human clinical trials is desperately needed. However, the unique host and cell specificity of HIV provides challenges for microbicide safety and efficacy screening, that can only be addressed by rigorous testing in relevant laboratory animal models. Recent findings A number of laboratory animal model systems ranging from rodents to nonhuman primates, and single versus multiple dose challenges have recently been developed to test microbicide candidates. These models have shed light on both the safety and efficacy of candidate microbicides as well as the early mechanisms involved in transmission. This article summarizes the major advantages and disadvantages of the relevant animal models for microbicide safety and efficacy testing. Summary Currently, nonhuman primates are the only relevant and effective laboratory model for screening microbicide candidates. Given the consistent failures of prior strategies, it is now clear that rigorous safety and efficacy testing in nonhuman primates should be a pre-requisite for advancing additional microbicide candidates to human clinical trials. PMID:19373023

Next-Generation Satellite Precipitation Products for Understanding Global and Regional Water Variability

NASA Technical Reports Server (NTRS)

Hou, Arthur Y.

2011-01-01

A major challenge in understanding the space-time variability of continental water fluxes is the lack of accurate precipitation estimates over complex terrains. While satellite precipitation observations can be used to complement ground-based data to obtain improved estimates, space-based and ground-based estimates come with their own sets of uncertainties, which must be understood and characterized. Quantitative estimation of uncertainties in these products also provides a necessary foundation for merging satellite and ground-based precipitation measurements within a rigorous statistical framework. Global Precipitation Measurement (GPM) is an international satellite mission that will provide next-generation global precipitation data products for research and applications. It consists of a constellation of microwave sensors provided by NASA, JAXA, CNES, ISRO, EUMETSAT, DOD, NOAA, NPP, and JPSS. At the heart of the mission is the GPM Core Observatory provided by NASA and JAXA to be launched in 2013. The GPM Core, which will carry the first space-borne dual-frequency radar and a state-of-the-art multi-frequency radiometer, is designed to set new reference standards for precipitation measurements from space, which can then be used to unify and refine precipitation retrievals from all constellation sensors. The next-generation constellation-based satellite precipitation estimates will be characterized by intercalibrated radiometric measurements and physical-based retrievals using a common observation-derived hydrometeor database. For pre-launch algorithm development and post-launch product evaluation, NASA supports an extensive ground validation (GV) program in cooperation with domestic and international partners to improve (1) physics of remote-sensing algorithms through a series of focused field campaigns, (2) characterization of uncertainties in satellite and ground-based precipitation products over selected GV testbeds, and (3) modeling of atmospheric processes and land surface hydrology through simulation, downscaling, and data assimilation. An overview of the GPM mission, science status, and synergies with HyMex activities will be presented

Downscaling future climate scenarios to fine scales for hydrologic and ecological modeling and analysis

USGS Publications Warehouse

Flint, Lorraine E.; Flint, Alan L.

2012-01-01

The methodology, which includes a sequence of rigorous analyses and calculations, is intended to reduce the addition of uncertainty to the climate data as a result of the downscaling while providing the fine-scale climate information necessary for ecological analyses. It results in new but consistent data sets for the US at 4 km, the southwest US at 270 m, and California at 90 m and illustrates the utility of fine-scale downscaling to analyses of ecological processes influenced by topographic complexity.

Advances in global sensitivity analyses of demographic-based species distribution models to address uncertainties in dynamic landscapes.

PubMed

Naujokaitis-Lewis, Ilona; Curtis, Janelle M R

2016-01-01

Developing a rigorous understanding of multiple global threats to species persistence requires the use of integrated modeling methods that capture processes which influence species distributions. Species distribution models (SDMs) coupled with population dynamics models can incorporate relationships between changing environments and demographics and are increasingly used to quantify relative extinction risks associated with climate and land-use changes. Despite their appeal, uncertainties associated with complex models can undermine their usefulness for advancing predictive ecology and informing conservation management decisions. We developed a computationally-efficient and freely available tool (GRIP 2.0) that implements and automates a global sensitivity analysis of coupled SDM-population dynamics models for comparing the relative influence of demographic parameters and habitat attributes on predicted extinction risk. Advances over previous global sensitivity analyses include the ability to vary habitat suitability across gradients, as well as habitat amount and configuration of spatially-explicit suitability maps of real and simulated landscapes. Using GRIP 2.0, we carried out a multi-model global sensitivity analysis of a coupled SDM-population dynamics model of whitebark pine (Pinus albicaulis) in Mount Rainier National Park as a case study and quantified the relative influence of input parameters and their interactions on model predictions. Our results differed from the one-at-time analyses used in the original study, and we found that the most influential parameters included the total amount of suitable habitat within the landscape, survival rates, and effects of a prevalent disease, white pine blister rust. Strong interactions between habitat amount and survival rates of older trees suggests the importance of habitat in mediating the negative influences of white pine blister rust. Our results underscore the importance of considering habitat attributes along with demographic parameters in sensitivity routines. GRIP 2.0 is an important decision-support tool that can be used to prioritize research, identify habitat-based thresholds and management intervention points to improve probability of species persistence, and evaluate trade-offs of alternative management options.

Advances in global sensitivity analyses of demographic-based species distribution models to address uncertainties in dynamic landscapes

PubMed Central

Curtis, Janelle M.R.

2016-01-01

Developing a rigorous understanding of multiple global threats to species persistence requires the use of integrated modeling methods that capture processes which influence species distributions. Species distribution models (SDMs) coupled with population dynamics models can incorporate relationships between changing environments and demographics and are increasingly used to quantify relative extinction risks associated with climate and land-use changes. Despite their appeal, uncertainties associated with complex models can undermine their usefulness for advancing predictive ecology and informing conservation management decisions. We developed a computationally-efficient and freely available tool (GRIP 2.0) that implements and automates a global sensitivity analysis of coupled SDM-population dynamics models for comparing the relative influence of demographic parameters and habitat attributes on predicted extinction risk. Advances over previous global sensitivity analyses include the ability to vary habitat suitability across gradients, as well as habitat amount and configuration of spatially-explicit suitability maps of real and simulated landscapes. Using GRIP 2.0, we carried out a multi-model global sensitivity analysis of a coupled SDM-population dynamics model of whitebark pine (Pinus albicaulis) in Mount Rainier National Park as a case study and quantified the relative influence of input parameters and their interactions on model predictions. Our results differed from the one-at-time analyses used in the original study, and we found that the most influential parameters included the total amount of suitable habitat within the landscape, survival rates, and effects of a prevalent disease, white pine blister rust. Strong interactions between habitat amount and survival rates of older trees suggests the importance of habitat in mediating the negative influences of white pine blister rust. Our results underscore the importance of considering habitat attributes along with demographic parameters in sensitivity routines. GRIP 2.0 is an important decision-support tool that can be used to prioritize research, identify habitat-based thresholds and management intervention points to improve probability of species persistence, and evaluate trade-offs of alternative management options. PMID:27547529

A Bayesian Network Based Global Sensitivity Analysis Method for Identifying Dominant Processes in a Multi-physics Model

NASA Astrophysics Data System (ADS)

Dai, H.; Chen, X.; Ye, M.; Song, X.; Zachara, J. M.

2016-12-01

Sensitivity analysis has been an important tool in groundwater modeling to identify the influential parameters. Among various sensitivity analysis methods, the variance-based global sensitivity analysis has gained popularity for its model independence characteristic and capability of providing accurate sensitivity measurements. However, the conventional variance-based method only considers uncertainty contribution of single model parameters. In this research, we extended the variance-based method to consider more uncertainty sources and developed a new framework to allow flexible combinations of different uncertainty components. We decompose the uncertainty sources into a hierarchical three-layer structure: scenario, model and parametric. Furthermore, each layer of uncertainty source is capable of containing multiple components. An uncertainty and sensitivity analysis framework was then constructed following this three-layer structure using Bayesian network. Different uncertainty components are represented as uncertain nodes in this network. Through the framework, variance-based sensitivity analysis can be implemented with great flexibility of using different grouping strategies for uncertainty components. The variance-based sensitivity analysis thus is improved to be able to investigate the importance of an extended range of uncertainty sources: scenario, model, and other different combinations of uncertainty components which can represent certain key model system processes (e.g., groundwater recharge process, flow reactive transport process). For test and demonstration purposes, the developed methodology was implemented into a test case of real-world groundwater reactive transport modeling with various uncertainty sources. The results demonstrate that the new sensitivity analysis method is able to estimate accurate importance measurements for any uncertainty sources which were formed by different combinations of uncertainty components. The new methodology can provide useful information for environmental management and decision-makers to formulate policies and strategies.

A complete representation of uncertainties in layer-counted paleoclimatic archives

NASA Astrophysics Data System (ADS)

Boers, Niklas; Goswami, Bedartha; Ghil, Michael

2017-09-01

Accurate time series representation of paleoclimatic proxy records is challenging because such records involve dating errors in addition to proxy measurement errors. Rigorous attention is rarely given to age uncertainties in paleoclimatic research, although the latter can severely bias the results of proxy record analysis. Here, we introduce a Bayesian approach to represent layer-counted proxy records - such as ice cores, sediments, corals, or tree rings - as sequences of probability distributions on absolute, error-free time axes. The method accounts for both proxy measurement errors and uncertainties arising from layer-counting-based dating of the records. An application to oxygen isotope ratios from the North Greenland Ice Core Project (NGRIP) record reveals that the counting errors, although seemingly small, lead to substantial uncertainties in the final representation of the oxygen isotope ratios. In particular, for the older parts of the NGRIP record, our results show that the total uncertainty originating from dating errors has been seriously underestimated. Our method is next applied to deriving the overall uncertainties of the Suigetsu radiocarbon comparison curve, which was recently obtained from varved sediment cores at Lake Suigetsu, Japan. This curve provides the only terrestrial radiocarbon comparison for the time interval 12.5-52.8 kyr BP. The uncertainties derived here can be readily employed to obtain complete error estimates for arbitrary radiometrically dated proxy records of this recent part of the last glacial interval.

Photovoltaic Calibrations at the National Renewable Energy Laboratory and Uncertainty Analysis Following the ISO 17025 Guidelines

DOE Office of Scientific and Technical Information (OSTI.GOV)

Emery, Keith

The measurement of photovoltaic (PV) performance with respect to reference conditions requires measuring current versus voltage for a given tabular reference spectrum, junction temperature, and total irradiance. This report presents the procedures implemented by the PV Cell and Module Performance Characterization Group at the National Renewable Energy Laboratory (NREL) to achieve the lowest practical uncertainty. A rigorous uncertainty analysis of these procedures is presented, which follows the International Organization for Standardization (ISO) Guide to the Expression of Uncertainty in Measurement. This uncertainty analysis is required for the team’s laboratory accreditation under ISO standard 17025, “General Requirements for the Competence ofmore » Testing and Calibration Laboratories.” The report also discusses additional areas where the uncertainty can be reduced.« less

Calcification, Storm Damage and Population Resilience of Tabular Corals under Climate Change

PubMed Central

Madin, Joshua S.; Hughes, Terry P.; Connolly, Sean R.

2012-01-01

Two facets of climate change–increased tropical storm intensity and ocean acidification–are expected to detrimentally affect reef-building organisms by increasing their mortality rates and decreasing their calcification rates. Our current understanding of these effects is largely based on individual organisms’ short-term responses to experimental manipulations. However, predicting the ecologically-relevant effects of climate change requires understanding the long-term demographic implications of these organism-level responses. In this study, we investigate how storm intensity and calcification rate interact to affect population dynamics of the table coral Acropora hyacinthus, a dominant and geographically widespread ecosystem engineer on wave-exposed Indo-Pacific reefs. We develop a mechanistic framework based on the responses of individual-level demographic rates to changes in the physical and chemical environment, using a size-structured population model that enables us to rigorously incorporate uncertainty. We find that table coral populations are vulnerable to future collapse, placing in jeopardy many other reef organisms that are dependent upon them for shelter and food. Resistance to collapse is largely insensitive to predicted changes in storm intensity, but is highly dependent on the extent to which calcification influences both the mechanical properties of reef substrate and the colony-level trade-off between growth rate and skeletal strength. This study provides the first rigorous quantitative accounting of the demographic implications of the effects of ocean acidification and changes in storm intensity, and provides a template for further studies of climate-induced shifts in ecosystems, including coral reefs. PMID:23056379

Trans-dimensional ambient noise tomography of the northeast Asia

NASA Astrophysics Data System (ADS)

Kim, Seongryong; Tkalčić, Hrvoje; Rhie, Junkee; Chen, Youlin

2016-04-01

A trans-dimensional and hierarchical Bayesian tomography is performed to estimate spatial variations of shear wave velocity and provide the uncertainty in the northeast Asia region from the ambient noise data. The method accounts for irregular data distribution and sensitivity using adaptive partition property of Voronoi cells. Importantly, the number of basis functions used to parameterise the Earth model in the inversion and the level of data noise are implicitly balanced by the information contained in the data (and treated as free parameters in the inversions). Thereby more reliable models and their rigorous uncertainties are estimated by avoiding over- or under-estimation and explicit regularisation. We measure Rayleigh wave phase and group velocity (8-70 s) for available inter-station paths between more than 300 broadband stations. The obtained group and phase velocity maps reveal characteristic features beneath the former (East Sea also known as Japan Sea) and the current back-arc (Okinawa trough) regions, where relatively high and low velocities are estimated at intermediate (20-40 s) and longer periods (50-60 s), respectively. We observe that the low velocity anomalies extend to beneath intraplate volcanoes in the northeast China and the Korean Peninsula. Based on the depth sensitivity of surface wave dispersions and previous geological evidences, we argue that the intraplate volcanism in this region might be influenced by sub-lithospheric processes related to the subduction of the Pacific and Philippine Sea plates.

A Greenhouse-Gas Information System: Monitoring and Validating Emissions Reporting and Mitigation

DOE Office of Scientific and Technical Information (OSTI.GOV)

Jonietz, Karl K.; Dimotakis, Paul E.; Rotman, Douglas A.

2011-09-26

This study and report focus on attributes of a greenhouse-gas information system (GHGIS) needed to support MRV&V needs. These needs set the function of such a system apart from scientific/research monitoring of GHGs and carbon-cycle systems, and include (not exclusively): the need for a GHGIS that is operational, as required for decision-support; the need for a system that meets specifications derived from imposed requirements; the need for rigorous calibration, verification, and validation (CV&V) standards, processes, and records for all measurement and modeling/data-inversion data; the need to develop and adopt an uncertainty-quantification (UQ) regimen for all measurement and modeling data; andmore » the requirement that GHGIS products can be subjected to third-party questioning and scientific scrutiny. This report examines and assesses presently available capabilities that could contribute to a future GHGIS. These capabilities include sensors and measurement technologies; data analysis and data uncertainty quantification (UQ) practices and methods; and model-based data-inversion practices, methods, and their associated UQ. The report further examines the need for traceable calibration, verification, and validation processes and attached metadata; differences between present science-/research-oriented needs and those that would be required for an operational GHGIS; the development, operation, and maintenance of a GHGIS missions-operations center (GMOC); and the complex systems engineering and integration that would be required to develop, operate, and evolve a future GHGIS.« less

Predictive uncertainty analysis of a saltwater intrusion model using null-space Monte Carlo

USGS Publications Warehouse

Herckenrath, Daan; Langevin, Christian D.; Doherty, John

2011-01-01

Because of the extensive computational burden and perhaps a lack of awareness of existing methods, rigorous uncertainty analyses are rarely conducted for variable-density flow and transport models. For this reason, a recently developed null-space Monte Carlo (NSMC) method for quantifying prediction uncertainty was tested for a synthetic saltwater intrusion model patterned after the Henry problem. Saltwater intrusion caused by a reduction in fresh groundwater discharge was simulated for 1000 randomly generated hydraulic conductivity distributions, representing a mildly heterogeneous aquifer. From these 1000 simulations, the hydraulic conductivity distribution giving rise to the most extreme case of saltwater intrusion was selected and was assumed to represent the "true" system. Head and salinity values from this true model were then extracted and used as observations for subsequent model calibration. Random noise was added to the observations to approximate realistic field conditions. The NSMC method was used to calculate 1000 calibration-constrained parameter fields. If the dimensionality of the solution space was set appropriately, the estimated uncertainty range from the NSMC analysis encompassed the truth. Several variants of the method were implemented to investigate their effect on the efficiency of the NSMC method. Reducing the dimensionality of the null-space for the processing of the random parameter sets did not result in any significant gains in efficiency and compromised the ability of the NSMC method to encompass the true prediction value. The addition of intrapilot point heterogeneity to the NSMC process was also tested. According to a variogram comparison, this provided the same scale of heterogeneity that was used to generate the truth. However, incorporation of intrapilot point variability did not make a noticeable difference to the uncertainty of the prediction. With this higher level of heterogeneity, however, the computational burden of generating calibration-constrained parameter fields approximately doubled. Predictive uncertainty variance computed through the NSMC method was compared with that computed through linear analysis. The results were in good agreement, with the NSMC method estimate showing a slightly smaller range of prediction uncertainty than was calculated by the linear method. Copyright 2011 by the American Geophysical Union.

On combination of strict Bayesian principles with model reduction technique or how stochastic model calibration can become feasible for large-scale applications

NASA Astrophysics Data System (ADS)

Oladyshkin, S.; Schroeder, P.; Class, H.; Nowak, W.

2013-12-01

Predicting underground carbon dioxide (CO2) storage represents a challenging problem in a complex dynamic system. Due to lacking information about reservoir parameters, quantification of uncertainties may become the dominant question in risk assessment. Calibration on past observed data from pilot-scale test injection can improve the predictive power of the involved geological, flow, and transport models. The current work performs history matching to pressure time series from a pilot storage site operated in Europe, maintained during an injection period. Simulation of compressible two-phase flow and transport (CO2/brine) in the considered site is computationally very demanding, requiring about 12 days of CPU time for an individual model run. For that reason, brute-force approaches for calibration are not feasible. In the current work, we explore an advanced framework for history matching based on the arbitrary polynomial chaos expansion (aPC) and strict Bayesian principles. The aPC [1] offers a drastic but accurate stochastic model reduction. Unlike many previous chaos expansions, it can handle arbitrary probability distribution shapes of uncertain parameters, and can therefore handle directly the statistical information appearing during the matching procedure. We capture the dependence of model output on these multipliers with the expansion-based reduced model. In our study we keep the spatial heterogeneity suggested by geophysical methods, but consider uncertainty in the magnitude of permeability trough zone-wise permeability multipliers. Next combined the aPC with Bootstrap filtering (a brute-force but fully accurate Bayesian updating mechanism) in order to perform the matching. In comparison to (Ensemble) Kalman Filters, our method accounts for higher-order statistical moments and for the non-linearity of both the forward model and the inversion, and thus allows a rigorous quantification of calibrated model uncertainty. The usually high computational costs of accurate filtering become very feasible for our suggested aPC-based calibration framework. However, the power of aPC-based Bayesian updating strongly depends on the accuracy of prior information. In the current study, the prior assumptions on the model parameters were not satisfactory and strongly underestimate the reservoir pressure. Thus, the aPC-based response surface used in Bootstrap filtering is fitted to a distant and poorly chosen region within the parameter space. Thanks to the iterative procedure suggested in [2] we overcome this drawback with small computational costs. The iteration successively improves the accuracy of the expansion around the current estimation of the posterior distribution. The final result is a calibrated model of the site that can be used for further studies, with an excellent match to the data. References [1] Oladyshkin S. and Nowak W. Data-driven uncertainty quantification using the arbitrary polynomial chaos expansion. Reliability Engineering and System Safety, 106:179-190, 2012. [2] Oladyshkin S., Class H., Nowak W. Bayesian updating via Bootstrap filtering combined with data-driven polynomial chaos expansions: methodology and application to history matching for carbon dioxide storage in geological formations. Computational Geosciences, 17 (4), 671-687, 2013.

A sampling-based computational strategy for the representation of epistemic uncertainty in model predictions with evidence theory.

DOE Office of Scientific and Technical Information (OSTI.GOV)

Johnson, J. D.; Oberkampf, William Louis; Helton, Jon Craig

2006-10-01

Evidence theory provides an alternative to probability theory for the representation of epistemic uncertainty in model predictions that derives from epistemic uncertainty in model inputs, where the descriptor epistemic is used to indicate uncertainty that derives from a lack of knowledge with respect to the appropriate values to use for various inputs to the model. The potential benefit, and hence appeal, of evidence theory is that it allows a less restrictive specification of uncertainty than is possible within the axiomatic structure on which probability theory is based. Unfortunately, the propagation of an evidence theory representation for uncertainty through a modelmore » is more computationally demanding than the propagation of a probabilistic representation for uncertainty, with this difficulty constituting a serious obstacle to the use of evidence theory in the representation of uncertainty in predictions obtained from computationally intensive models. This presentation describes and illustrates a sampling-based computational strategy for the representation of epistemic uncertainty in model predictions with evidence theory. Preliminary trials indicate that the presented strategy can be used to propagate uncertainty representations based on evidence theory in analysis situations where naive sampling-based (i.e., unsophisticated Monte Carlo) procedures are impracticable due to computational cost.« less

Climate Benchmark Missions: CLARREO

NASA Technical Reports Server (NTRS)

Wielicki, Bruce A.; Young, David F.

2010-01-01

CLARREO (Climate Absolute Radiance and Refractivity Observatory) is one of the four Tier 1 missions recommended by the recent NRC decadal survey report on Earth Science and Applications from Space (NRC, 2007). The CLARREO mission addresses the need to rigorously observe climate change on decade time scales and to use decadal change observations as the most critical method to determine the accuracy of climate change projections such as those used in the Fourth Assessment Report of the Intergovernmental Panel on Climate Change (IPCC AR4). A rigorously known accuracy of both decadal change observations as well as climate projections is critical in order to enable sound policy decisions. The CLARREO mission accomplishes this critical objective through highly accurate and SI traceable decadal change observations sensitive to many of the key uncertainties in climate radiative forcings, responses, and feedbacks that in turn drive uncertainty in current climate model projections. The same uncertainties also lead to uncertainty in attribution of climate change to anthropogenic forcing. The CLARREO breakthrough in decadal climate change observations is to achieve the required levels of accuracy and traceability to SI standards for a set of observations sensitive to a wide range of key decadal change variables. These accuracy levels are determined both by the projected decadal changes as well as by the background natural variability that such signals must be detected against. The accuracy for decadal change traceability to SI standards includes uncertainties of calibration, sampling, and analysis methods. Unlike most other missions, all of the CLARREO requirements are judged not by instantaneous accuracy, but instead by accuracy in large time/space scale average decadal changes. Given the focus on decadal climate change, the NRC Decadal Survey concluded that the single most critical issue for decadal change observations was their lack of accuracy and low confidence in observing the small but critical climate change signals. CLARREO is the recommended attack on this challenge, and builds on the last decade of climate observation advances in the Earth Observing System as well as metrological advances at NIST (National Institute of Standards and Technology) and other standards laboratories.

Future discharge of the French tributaries of the Rhine: a semi-distributed multi-model approach using CMIP5 projections

NASA Astrophysics Data System (ADS)

Thirel, Guillaume; de Lavenne, Alban; Wagner, Jean-Pierre; Perrin, Charles; Gerlinger, Kai; Drogue, Gilles; Renard, Benjamin

2016-04-01

Several projects studied the impact of climate change on the Rhine basin during the past years, using the CMIP3 projections (see Explore2070, FLOW MS, RheinBlick2050 or VULNAR), either on the French or German sides. These studies showed the likely decrease of low flows and a high uncertainty regarding the evolution of high flows. This may have tremendous impacts on several aspects related to discharge, including pollution, flood protection, irrigation, rivers ecosystems and drinking water. While focusing on the same basin (or part of it), many differences including the climate scenarios and models, the hydrological models and the study periods used for these projects make the outcomes of these projects difficult to compare rigorously. Therefore the MOSARH21 (stands for MOselle-SArre-RHine discharge in the 21st century) was built to update and homogenise discharge projections for the French tributaries of the Rhine basin. Two types of models were used: the physically-oriented LARSIM model, which is widely used in Germany and was used in one of the previous projects (FLOW MS), and the semi-distributed conceptual GRSD model tested on French catchments for various objectives. Through the use of these two hydrological models and multiple sets of parameters obtained by various calibrations runs, the structural and parametric uncertainties in the hydrological projections were quantified, as they tend to be neglected in climate change impact studies. The focus of the impact analysis is put on low flows, high flows and regime. Although this study considers only French tributaries of the Rhine, it will foster further cooperation on transboundary basins across Europe, and should contribute to propose better bases for the future definition of adaptation strategies between riverine countries.

Characterizing Drought Events from a Hydrological Model Ensemble

NASA Astrophysics Data System (ADS)

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 detection and characterization. This ensemble approach allows for uncertainty estimates and confidence intervals to be explored in simulations of drought event characteristics, such as duration and severity, which would not otherwise be available from a deterministic approach. The acquired understanding of uncertainty in drought events may then be applied to historic drought reconstructions, supplying evidence which could prove vital in decision making scenarios.

Rigorous simulations of a helical core fiber by the use of transformation optics formalism.

PubMed

Napiorkowski, Maciej; Urbanczyk, Waclaw

2014-09-22

We report for the first time on rigorous numerical simulations of a helical-core fiber by using a full vectorial method based on the transformation optics formalism. We modeled the dependence of circular birefringence of the fundamental mode on the helix pitch and analyzed the effect of a birefringence increase caused by the mode displacement induced by a core twist. Furthermore, we analyzed the complex field evolution versus the helix pitch in the first order modes, including polarization and intensity distribution. Finally, we show that the use of the rigorous vectorial method allows to better predict the confinement loss of the guided modes compared to approximate methods based on equivalent in-plane bending models.

When Spreadsheets Become Software - Quality Control Challenges and Approaches - 13360

DOE Office of Scientific and Technical Information (OSTI.GOV)

Fountain, Stefanie A.; Chen, Emmie G.; Beech, John F.

2013-07-01

As part of a preliminary waste acceptance criteria (PWAC) development, several commercial models were employed, including the Hydrologic Evaluation of Landfill Performance model (HELP) [1], the Disposal Unit Source Term - Multiple Species model (DUSTMS) [2], and the Analytical Transient One, Two, and Three-Dimensional model (AT123D) [3]. The results of these models were post-processed in MS Excel spreadsheets to convert the model results to alternate units, compare the groundwater concentrations to the groundwater concentration thresholds, and then to adjust the waste contaminant masses (based on average concentration over the waste volume) as needed in an attempt to achieve groundwater concentrationsmore » at the limiting point of assessment that would meet the compliance concentrations while maximizing the potential use of the landfill (i.e., maximizing the volume of projected waste being generated that could be placed in the landfill). During the course of the PWAC calculation development, one of the Microsoft (MS) Excel spreadsheets used to post-process the results of the commercial model packages grew to include more than 575,000 formulas across 18 worksheets. This spreadsheet was used to assess six base scenarios as well as nine uncertainty/sensitivity scenarios. The complexity of the spreadsheet resulted in the need for a rigorous quality control (QC) procedure to verify data entry and confirm the accuracy of formulas. (authors)« less

First Monte Carlo analysis of fragmentation functions from single-inclusive e + e - annihilation

DOE PAGES

Sato, Nobuo; Ethier, J. J.; Melnitchouk, W.; ...

2016-12-02

Here, we perform the first iterative Monte Carlo (IMC) analysis of fragmentation functions constrained by all available data from single-inclusive $e^+ e^-$ annihilation into pions and kaons. The IMC method eliminates potential bias in traditional analyses based on single fits introduced by fixing parameters not well contrained by the data, and provides a statistically rigorous determination of uncertainties. Our analysis reveals specific features of fragmentation functions using the new IMC methodology and those obtained from previous analyses, especially for light quarks and for strange quark fragmentation to kaons.

Consistent Chemical Mechanism from Collaborative Data Processing

DOE PAGES

Slavinskaya, Nadezda; Starcke, Jan-Hendrik; Abbasi, Mehdi; ...

2016-04-01

Numerical tool of Process Informatics Model (PrIMe) is mathematically rigorous and numerically efficient approach for analysis and optimization of chemical systems. It handles heterogeneous data and is scalable to a large number of parameters. The Boundto-Bound Data Collaboration module of the automated data-centric infrastructure of PrIMe was used for the systematic uncertainty and data consistency analyses of the H 2/CO reaction model (73/17) and 94 experimental targets (ignition delay times). The empirical rule for evaluation of the shock tube experimental data is proposed. The initial results demonstrate clear benefits of the PrIMe methods for an evaluation of the kinetic datamore » quality and data consistency and for developing predictive kinetic models.« less

Throwing the Uncertainty Toolbox at Antarctica: Multi-model Ensemble Simulation, Emulation and Bayesian Calibration of Marine Ice Sheet Instability

NASA Astrophysics Data System (ADS)

Edwards, T.

2015-12-01

Modelling Antarctic marine ice sheet instability (MISI) - the potential for sustained grounding line retreat along downsloping bedrock - is very challenging because high resolution at the grounding line is required for reliable simulation. Assessing modelling uncertainties is even more difficult, because such models are very computationally expensive, restricting the number of simulations that can be performed. Quantifying uncertainty in future Antarctic instability has therefore so far been limited. There are several ways to tackle this problem, including: Simulating a small domain, to reduce expense and allow the use of ensemble methods; Parameterising response of the grounding line to the onset of MISI, for the same reasons; Emulating the simulator with a statistical model, to explore the impacts of uncertainties more thoroughly; Substituting physical models with expert-elicited statistical distributions. Methods 2-4 require rigorous testing against observations and high resolution models to have confidence in their results. We use all four to examine the dependence of MISI in the Amundsen Sea Embayment (ASE) on uncertain model inputs, including bedrock topography, ice viscosity, basal friction, model structure (sliding law and treatment of grounding line migration) and MISI triggers (including basal melting and risk of ice shelf collapse). We compare simulations from a 3000 member ensemble with GRISLI (methods 2, 4) with a 284 member ensemble from BISICLES (method 1) and also use emulation (method 3). Results from the two ensembles show similarities, despite very different model structures and ensemble designs. Basal friction and topography have a large effect on the extent of grounding line retreat, and the sliding law strongly modifies sea level contributions through changes in the rate and extent of grounding line retreat and the rate of ice thinning. Over 50 years, MISI in the ASE gives up to 1.1 mm/year (95% quantile) SLE in GRISLI (calibrated with ASE mass losses in a Bayesian framework), and up to 1.2 mm/year SLE (95% quantile) in the 270 completed BISICLES simulations (no calibration). We will show preliminary results emulating the models, calibrating with observations, and comparing them to assess structural uncertainty. We use these to improve MISI projections for the whole continent.

Uncertainty and Variability in Physiologically-Based ...

EPA Pesticide Factsheets

EPA announced the availability of the final report, Uncertainty and Variability in Physiologically-Based Pharmacokinetic (PBPK) Models: Key Issues and Case Studies. This report summarizes some of the recent progress in characterizing uncertainty and variability in physiologically-based pharmacokinetic models and their predictions for use in risk assessment. This report summarizes some of the recent progress in characterizing uncertainty and variability in physiologically-based pharmacokinetic models and their predictions for use in risk assessment.

pplacer: linear time maximum-likelihood and Bayesian phylogenetic placement of sequences onto a fixed reference tree

PubMed Central

2010-01-01

Background Likelihood-based phylogenetic inference is generally considered to be the most reliable classification method for unknown sequences. However, traditional likelihood-based phylogenetic methods cannot be applied to large volumes of short reads from next-generation sequencing due to computational complexity issues and lack of phylogenetic signal. "Phylogenetic placement," where a reference tree is fixed and the unknown query sequences are placed onto the tree via a reference alignment, is a way to bring the inferential power offered by likelihood-based approaches to large data sets. Results This paper introduces pplacer, a software package for phylogenetic placement and subsequent visualization. The algorithm can place twenty thousand short reads on a reference tree of one thousand taxa per hour per processor, has essentially linear time and memory complexity in the number of reference taxa, and is easy to run in parallel. Pplacer features calculation of the posterior probability of a placement on an edge, which is a statistically rigorous way of quantifying uncertainty on an edge-by-edge basis. It also can inform the user of the positional uncertainty for query sequences by calculating expected distance between placement locations, which is crucial in the estimation of uncertainty with a well-sampled reference tree. The software provides visualizations using branch thickness and color to represent number of placements and their uncertainty. A simulation study using reads generated from 631 COG alignments shows a high level of accuracy for phylogenetic placement over a wide range of alignment diversity, and the power of edge uncertainty estimates to measure placement confidence. Conclusions Pplacer enables efficient phylogenetic placement and subsequent visualization, making likelihood-based phylogenetics methodology practical for large collections of reads; it is freely available as source code, binaries, and a web service. PMID:21034504

Examples of Communicating Uncertainty Applied to Earthquake Hazard and Risk Products

NASA Astrophysics Data System (ADS)

Wald, D. J.

2013-12-01

When is communicating scientific modeling uncertainty effective? One viewpoint is that the answer depends on whether one is communicating hazard or risk: hazards have quantifiable uncertainties (which, granted, are often ignored), yet risk uncertainties compound uncertainties inherent in the hazard with those of the risk calculations, and are thus often larger. Larger, yet more meaningful: since risk entails societal impact of some form, consumers of such information tend to have a better grasp of the potential uncertainty ranges for loss information than they do for less-tangible hazard values (like magnitude, peak acceleration, or stream flow). I present two examples that compare and contrast communicating uncertainty for earthquake hazard and risk products. The first example is the U.S. Geological Survey's (USGS) ShakeMap system, which portrays the uncertain, best estimate of the distribution and intensity of shaking over the potentially impacted region. The shaking intensity is well constrained at seismograph locations yet is uncertain elsewhere, so shaking uncertainties are quantified and presented spatially. However, with ShakeMap, it seems that users tend to believe what they see is accurate in part because (1) considering the shaking uncertainty complicates the picture, and (2) it would not necessarily alter their decision-making. In contrast, when it comes to making earthquake-response decisions based on uncertain loss estimates, actions tend to be made only after analysis of the confidence in (or source of) such estimates. Uncertain ranges of loss estimates instill tangible images for users, and when such uncertainties become large, intuitive reality-check alarms go off, for example, when the range of losses presented become too wide to be useful. The USGS Prompt Assessment of Global Earthquakes for Response (PAGER) system, which in near-real time alerts users to the likelihood of ranges of potential fatalities and economic impact, is aimed at facilitating rapid and proportionate earthquake response. For uncertainty representation, PAGER employs an Earthquake Impact Scale (EIS) that provides simple alerting thresholds, derived from systematic analyses of past earthquake impact and response levels. The alert levels are characterized by alerts of green (little or no impact), yellow (regional impact and response), orange (national-scale impact and response), and red (major disaster, necessitating international response). We made a conscious attempt at both simple and intuitive color-coded alerting criterion; yet, we preserve the necessary uncertainty measures (with simple histograms) by which one can gauge the likelihood for the alert to be over- or underestimated. In these hazard and loss modeling examples, both products are widely used across a range of technical as well as general audiences. Ironically, ShakeMap uncertainties--rigorously reported and portrayed for the primarily scientific portion of the audience--are rarely employed and are routinely misunderstood; for PAGER, uncertainties aimed at a wider user audience seem to be more easily digested. We discuss how differences in the way these uncertainties are portrayed may play into their acceptance and uptake, or lack thereof.

Modelling the anaerobic digestion of solid organic waste - Substrate characterisation method for ADM1 using a combined biochemical and kinetic parameter estimation approach.

PubMed

Poggio, D; Walker, M; Nimmo, W; Ma, L; Pourkashanian, M

2016-07-01

This work proposes a novel and rigorous substrate characterisation methodology to be used with ADM1 to simulate the anaerobic digestion of solid organic waste. The proposed method uses data from both direct substrate analysis and the methane production from laboratory scale anaerobic digestion experiments and involves assessment of four substrate fractionation models. The models partition the organic matter into a mixture of particulate and soluble fractions with the decision on the most suitable model being made on quality of fit between experimental and simulated data and the uncertainty of the calibrated parameters. The method was tested using samples of domestic green and food waste and using experimental data from both short batch tests and longer semi-continuous trials. The results showed that in general an increased fractionation model complexity led to better fit but with increased uncertainty. When using batch test data the most suitable model for green waste included one particulate and one soluble fraction, whereas for food waste two particulate fractions were needed. With richer semi-continuous datasets, the parameter estimation resulted in less uncertainty therefore allowing the description of the substrate with a more complex model. The resulting substrate characterisations and fractionation models obtained from batch test data, for both waste samples, were used to validate the method using semi-continuous experimental data and showed good prediction of methane production, biogas composition, total and volatile solids, ammonia and alkalinity. Copyright © 2016 The Authors. Published by Elsevier Ltd.. All rights reserved.

Structured decision making as a framework for large-scale wildlife harvest management decisions

USGS Publications Warehouse

Robinson, Kelly F.; Fuller, Angela K.; Hurst, Jeremy E.; Swift, Bryan L.; Kirsch, Arthur; Farquhar, James F.; Decker, Daniel J.; Siemer, William F.

2016-01-01

Fish and wildlife harvest management at large spatial scales often involves making complex decisions with multiple objectives and difficult tradeoffs, population demographics that vary spatially, competing stakeholder values, and uncertainties that might affect management decisions. Structured decision making (SDM) provides a formal decision analytic framework for evaluating difficult decisions by breaking decisions into component parts and separating the values of stakeholders from the scientific evaluation of management actions and uncertainty. The result is a rigorous, transparent, and values-driven process. This decision-aiding process provides the decision maker with a more complete understanding of the problem and the effects of potential management actions on stakeholder values, as well as how key uncertainties can affect the decision. We use a case study to illustrate how SDM can be used as a decision-aiding tool for management decision making at large scales. We evaluated alternative white-tailed deer (Odocoileus virginianus) buck-harvest regulations in New York designed to reduce harvest of yearling bucks, taking into consideration the values of the state wildlife agency responsible for managing deer, as well as deer hunters. We incorporated tradeoffs about social, ecological, and economic management concerns throughout the state. Based on the outcomes of predictive models, expert elicitation, and hunter surveys, the SDM process identified management alternatives that optimized competing objectives. The SDM process provided biologists and managers insight about aspects of the buck-harvest decision that helped them adopt a management strategy most compatible with diverse hunter values and management concerns.

A hierarchical Bayesian model for understanding the spatiotemporal dynamics of the intestinal epithelium

PubMed Central

Parker, Aimée; Pin, Carmen; Carding, Simon R.; Watson, Alastair J. M.; Byrne, Helen M.

2017-01-01

Our work addresses two key challenges, one biological and one methodological. First, we aim to understand how proliferation and cell migration rates in the intestinal epithelium are related under healthy, damaged (Ara-C treated) and recovering conditions, and how these relations can be used to identify mechanisms of repair and regeneration. We analyse new data, presented in more detail in a companion paper, in which BrdU/IdU cell-labelling experiments were performed under these respective conditions. Second, in considering how to more rigorously process these data and interpret them using mathematical models, we use a probabilistic, hierarchical approach. This provides a best-practice approach for systematically modelling and understanding the uncertainties that can otherwise undermine the generation of reliable conclusions—uncertainties in experimental measurement and treatment, difficult-to-compare mathematical models of underlying mechanisms, and unknown or unobserved parameters. Both spatially discrete and continuous mechanistic models are considered and related via hierarchical conditional probability assumptions. We perform model checks on both in-sample and out-of-sample datasets and use them to show how to test possible model improvements and assess the robustness of our conclusions. We conclude, for the present set of experiments, that a primarily proliferation-driven model suffices to predict labelled cell dynamics over most time-scales. PMID:28753601

A hierarchical Bayesian model for understanding the spatiotemporal dynamics of the intestinal epithelium.

PubMed

Maclaren, Oliver J; Parker, Aimée; Pin, Carmen; Carding, Simon R; Watson, Alastair J M; Fletcher, Alexander G; Byrne, Helen M; Maini, Philip K

2017-07-01

Our work addresses two key challenges, one biological and one methodological. First, we aim to understand how proliferation and cell migration rates in the intestinal epithelium are related under healthy, damaged (Ara-C treated) and recovering conditions, and how these relations can be used to identify mechanisms of repair and regeneration. We analyse new data, presented in more detail in a companion paper, in which BrdU/IdU cell-labelling experiments were performed under these respective conditions. Second, in considering how to more rigorously process these data and interpret them using mathematical models, we use a probabilistic, hierarchical approach. This provides a best-practice approach for systematically modelling and understanding the uncertainties that can otherwise undermine the generation of reliable conclusions-uncertainties in experimental measurement and treatment, difficult-to-compare mathematical models of underlying mechanisms, and unknown or unobserved parameters. Both spatially discrete and continuous mechanistic models are considered and related via hierarchical conditional probability assumptions. We perform model checks on both in-sample and out-of-sample datasets and use them to show how to test possible model improvements and assess the robustness of our conclusions. We conclude, for the present set of experiments, that a primarily proliferation-driven model suffices to predict labelled cell dynamics over most time-scales.

Incorporating uncertainty into medical decision making: an approach to unexpected test results.

PubMed

Bianchi, Matt T; Alexander, Brian M; Cash, Sydney S

2009-01-01

The utility of diagnostic tests derives from the ability to translate the population concepts of sensitivity and specificity into information that will be useful for the individual patient: the predictive value of the result. As the array of available diagnostic testing broadens, there is a temptation to de-emphasize history and physical findings and defer to the objective rigor of technology. However, diagnostic test interpretation is not always straightforward. One significant barrier to routine use of probability-based test interpretation is the uncertainty inherent in pretest probability estimation, the critical first step of Bayesian reasoning. The context in which this uncertainty presents the greatest challenge is when test results oppose clinical judgment. It is this situation when decision support would be most helpful. The authors propose a simple graphical approach that incorporates uncertainty in pretest probability and has specific application to the interpretation of unexpected results. This method quantitatively demonstrates how uncertainty in disease probability may be amplified when test results are unexpected (opposing clinical judgment), even for tests with high sensitivity and specificity. The authors provide a simple nomogram for determining whether an unexpected test result suggests that one should "switch diagnostic sides.'' This graphical framework overcomes the limitation of pretest probability uncertainty in Bayesian analysis and guides decision making when it is most challenging: interpretation of unexpected test results.

Broad-band Lg Attenuation Tomography in Eastern Eurasia and The Resolution, Uncertainty and Data Predication

NASA Astrophysics Data System (ADS)

Chen, Y.; Xu, X.

2017-12-01

The broad band Lg 1/Q tomographic models in eastern Eurasia are inverted from source- and site-corrected path 1/Q data. The path 1/Q are measured between stations (or events) by the two-station (TS), reverse two-station (RTS) and reverse two-event (RTE) methods, respectively. Because path 1/Q are computed using logarithm of the product of observed spectral ratios and simplified 1D geometrical spreading correction, they are subject to "modeling errors" dominated by uncompensated 3D structural effects. We have found in Chen and Xie [2017] that these errors closely follow normal distribution after the long-tailed outliers are screened out (similar to teleseismic travel time residuals). We thus rigorously analyze the statistics of these errors collected from repeated samplings of station (and event) pairs from 1.0 to 10.0Hz and reject about 15% outliers at each frequency band. The resultant variance of Δ/Q decreases with frequency as 1/f2. The 1/Q tomography using screened data is now a stochastic inverse problem with solutions approximate the means of Gaussian random variables and the model covariance matrix is that of Gaussian variables with well-known statistical behavior. We adopt a new SVD based tomographic method to solve for 2D Q image together with its resolution and covariance matrices. The RTS and RTE yield the most reliable 1/Q data free of source and site effects, but the path coverage is rather sparse due to very strict recording geometry. The TS absorbs the effects of non-unit site response ratios into 1/Q data. The RTS also yields site responses, which can then be corrected from the path 1/Q of TS to make them also free of site effect. The site corrected TS data substantially improve path coverage, allowing able to solve for 1/Q tomography up to 6.0Hz. The model resolution and uncertainty are first quantitively accessed by spread functions (fulfilled by resolution matrix) and covariance matrix. The reliably retrieved Q models correlate well with the distinct tectonic blocks featured by the most recent major deformations and vary with frequencies. With the 1/Q tomographic model and its covariance matrix, we can formally estimate the uncertainty of any path-specific Lg 1/Q prediction. This new capability significantly benefits source estimation for which reliable uncertainty estimate is especially important.

Approaching the investigation of plasma turbulence through a rigorous verification and validation procedure: A practical example

DOE Office of Scientific and Technical Information (OSTI.GOV)

Ricci, P., E-mail: paolo.ricci@epfl.ch; Riva, F.; Theiler, C.

In the present work, a Verification and Validation procedure is presented and applied showing, through a practical example, how it can contribute to advancing our physics understanding of plasma turbulence. Bridging the gap between plasma physics and other scientific domains, in particular, the computational fluid dynamics community, a rigorous methodology for the verification of a plasma simulation code is presented, based on the method of manufactured solutions. This methodology assesses that the model equations are correctly solved, within the order of accuracy of the numerical scheme. The technique to carry out a solution verification is described to provide a rigorousmore » estimate of the uncertainty affecting the numerical results. A methodology for plasma turbulence code validation is also discussed, focusing on quantitative assessment of the agreement between experiments and simulations. The Verification and Validation methodology is then applied to the study of plasma turbulence in the basic plasma physics experiment TORPEX [Fasoli et al., Phys. Plasmas 13, 055902 (2006)], considering both two-dimensional and three-dimensional simulations carried out with the GBS code [Ricci et al., Plasma Phys. Controlled Fusion 54, 124047 (2012)]. The validation procedure allows progress in the understanding of the turbulent dynamics in TORPEX, by pinpointing the presence of a turbulent regime transition, due to the competition between the resistive and ideal interchange instabilities.« less

Imprecise Probability Methods for Weapons UQ

DOE Office of Scientific and Technical Information (OSTI.GOV)

Picard, Richard Roy; Vander Wiel, Scott Alan

Building on recent work in uncertainty quanti cation, we examine the use of imprecise probability methods to better characterize expert knowledge and to improve on misleading aspects of Bayesian analysis with informative prior distributions. Quantitative approaches to incorporate uncertainties in weapons certi cation are subject to rigorous external peer review, and in this regard, certain imprecise probability methods are well established in the literature and attractive. These methods are illustrated using experimental data from LANL detonator impact testing.

Stakeholder-Driven Quality Improvement: A Compelling Force for Clinical Practice Guidelines.

PubMed

Rosenfeld, Richard M; Wyer, Peter C

2018-01-01

Clinical practice guideline development should be driven by rigorous methodology, but what is less clear is where quality improvement enters the process: should it be a priority-guiding force, or should it enter only after recommendations are formulated? We argue for a stakeholder-driven approach to guideline development, with an overriding goal of quality improvement based on stakeholder perceptions of needs, uncertainties, and knowledge gaps. In contrast, the widely used topic-driven approach, which often makes recommendations based only on randomized controlled trials, is driven by epidemiologic purity and evidence rigor, with quality improvement a downstream consideration. The advantages of a stakeholder-driven versus a topic-driven approach are highlighted by comparisons of guidelines for otitis media with effusion, thyroid nodules, sepsis, and acute bacterial rhinosinusitis. These comparisons show that stakeholder-driven guidelines are more likely to address the quality improvement needs and pressing concerns of clinicians and patients, including understudied populations and patients with multiple chronic conditions. Conversely, a topic-driven approach often addresses "typical" patients, based on research that may not reflect the needs of high-risk groups excluded from studies because of ethical issues or a desire for purity of research design.

Simulation-Based Probabilistic Tsunami Hazard Analysis: Empirical and Robust Hazard Predictions

NASA Astrophysics Data System (ADS)

De Risi, Raffaele; Goda, Katsuichiro

2017-08-01

Probabilistic tsunami hazard analysis (PTHA) is the prerequisite for rigorous risk assessment and thus for decision-making regarding risk mitigation strategies. This paper proposes a new simulation-based methodology for tsunami hazard assessment for a specific site of an engineering project along the coast, or, more broadly, for a wider tsunami-prone region. The methodology incorporates numerous uncertain parameters that are related to geophysical processes by adopting new scaling relationships for tsunamigenic seismic regions. Through the proposed methodology it is possible to obtain either a tsunami hazard curve for a single location, that is the representation of a tsunami intensity measure (such as inundation depth) versus its mean annual rate of occurrence, or tsunami hazard maps, representing the expected tsunami intensity measures within a geographical area, for a specific probability of occurrence in a given time window. In addition to the conventional tsunami hazard curve that is based on an empirical statistical representation of the simulation-based PTHA results, this study presents a robust tsunami hazard curve, which is based on a Bayesian fitting methodology. The robust approach allows a significant reduction of the number of simulations and, therefore, a reduction of the computational effort. Both methods produce a central estimate of the hazard as well as a confidence interval, facilitating the rigorous quantification of the hazard uncertainties.

The effects of geometric uncertainties on computational modelling of knee biomechanics

NASA Astrophysics Data System (ADS)

Meng, Qingen; Fisher, John; Wilcox, Ruth

2017-08-01

The geometry of the articular components of the knee is an important factor in predicting joint mechanics in computational models. There are a number of uncertainties in the definition of the geometry of cartilage and meniscus, and evaluating the effects of these uncertainties is fundamental to understanding the level of reliability of the models. In this study, the sensitivity of knee mechanics to geometric uncertainties was investigated by comparing polynomial-based and image-based knee models and varying the size of meniscus. The results suggested that the geometric uncertainties in cartilage and meniscus resulting from the resolution of MRI and the accuracy of segmentation caused considerable effects on the predicted knee mechanics. Moreover, even if the mathematical geometric descriptors can be very close to the imaged-based articular surfaces, the detailed contact pressure distribution produced by the mathematical geometric descriptors was not the same as that of the image-based model. However, the trends predicted by the models based on mathematical geometric descriptors were similar to those of the imaged-based models.

What is needed to make low-density lipoprotein transport in human aorta computational models suitable to explore links to atherosclerosis? Impact of initial and inflow boundary conditions.

PubMed

De Nisco, Giuseppe; Zhang, Peng; Calò, Karol; Liu, Xiao; Ponzini, Raffaele; Bignardi, Cristina; Rizzo, Giovanna; Deng, Xiaoyan; Gallo, Diego; Morbiducci, Umberto

2018-02-08

Personalized computational hemodynamics (CH) is a promising tool to clarify/predict the link between low density lipoproteins (LDL) transport in aorta, disturbed shear and atherogenesis. However, CH uses simplifying assumptions that represent sources of uncertainty. In particular, modelling blood-side to wall LDL transfer is challenged by the cumbersomeness of protocols needed to obtain reliable LDL concentration profile estimations. This paucity of data is limiting the establishment of rigorous CH protocols able to balance the trade-offs among the variety of in vivo data to be acquired, and the accuracy required by biological/clinical applications. In this study, we analyze the impact of LDL concentration initialization (initial conditions, ICs) and inflow boundary conditions (BCs) on CH models of LDL blood-to-wall transfer in aorta. Technically, in an image-based model of human aorta, two different inflow BCs are generated imposing subject-specific inflow 3D PC-MRI measured or idealized (flat) velocity profiles. For each simulated BC, four different ICs for LDL concentration are applied, imposing as IC the LDL distribution resulting from steady-state simulations with average conditions, or constant LDL concentration values. Based on CH results, we conclude that: (1) the imposition of realistic 3D velocity profiles as inflow BC reduces the uncertainty affecting the representation of LDL transfer; (2) different LDL concentration ICs lead to markedly different patterns of LDL transfer. Given that it is not possible to verify in vivo the proper LDL concentration initialization to be applied, we suggest to carefully set and unambiguously declare the imposed BCs and LDL concentration IC when modelling LDL transfer in aorta, in order to obtain reproducible and ultimately comparable results among different laboratories. Copyright © 2017 Elsevier Ltd. All rights reserved.

Quantifying pollen-vegetation relationships to reconstruct ancient forests using 19th-century forest composition and pollen data

USGS Publications Warehouse

Dawson, Andria; Paciorek, Christopher J.; McLachlan, Jason S.; Goring, Simon; Williams, John W.; Jackson, Stephen T.

2016-01-01

Mitigation of climate change and adaptation to its effects relies partly on how effectively land-atmosphere interactions can be quantified. Quantifying composition of past forest ecosystems can help understand processes governing forest dynamics in a changing world. Fossil pollen data provide information about past forest composition, but rigorous interpretation requires development of pollen-vegetation models (PVMs) that account for interspecific differences in pollen production and dispersal. Widespread and intensified land-use over the 19th and 20th centuries may have altered pollen-vegetation relationships. Here we use STEPPS, a Bayesian hierarchical spatial PVM, to estimate key process parameters and associated uncertainties in the pollen-vegetation relationship. We apply alternate dispersal kernels, and calibrate STEPPS using a newly developed Euro-American settlement-era calibration data set constructed from Public Land Survey data and fossil pollen samples matched to the settlement-era using expert elicitation. Models based on the inverse power-law dispersal kernel outperformed those based on the Gaussian dispersal kernel, indicating that pollen dispersal kernels are fat tailed. Pine and birch have the highest pollen productivities. Pollen productivity and dispersal estimates are generally consistent with previous understanding from modern data sets, although source area estimates are larger. Tests of model predictions demonstrate the ability of STEPPS to predict regional compositional patterns.

Quantifying pollen-vegetation relationships to reconstruct ancient forests using 19th-century forest composition and pollen data

NASA Astrophysics Data System (ADS)

Dawson, Andria; Paciorek, Christopher J.; McLachlan, Jason S.; Goring, Simon; Williams, John W.; Jackson, Stephen T.

2016-04-01

Mitigation of climate change and adaptation to its effects relies partly on how effectively land-atmosphere interactions can be quantified. Quantifying composition of past forest ecosystems can help understand processes governing forest dynamics in a changing world. Fossil pollen data provide information about past forest composition, but rigorous interpretation requires development of pollen-vegetation models (PVMs) that account for interspecific differences in pollen production and dispersal. Widespread and intensified land-use over the 19th and 20th centuries may have altered pollen-vegetation relationships. Here we use STEPPS, a Bayesian hierarchical spatial PVM, to estimate key process parameters and associated uncertainties in the pollen-vegetation relationship. We apply alternate dispersal kernels, and calibrate STEPPS using a newly developed Euro-American settlement-era calibration data set constructed from Public Land Survey data and fossil pollen samples matched to the settlement-era using expert elicitation. Models based on the inverse power-law dispersal kernel outperformed those based on the Gaussian dispersal kernel, indicating that pollen dispersal kernels are fat tailed. Pine and birch have the highest pollen productivities. Pollen productivity and dispersal estimates are generally consistent with previous understanding from modern data sets, although source area estimates are larger. Tests of model predictions demonstrate the ability of STEPPS to predict regional compositional patterns.

Oncology Modeling for Fun and Profit! Key Steps for Busy Analysts in Health Technology Assessment.

PubMed

Beca, Jaclyn; Husereau, Don; Chan, Kelvin K W; Hawkins, Neil; Hoch, Jeffrey S

2018-01-01

In evaluating new oncology medicines, two common modeling approaches are state transition (e.g., Markov and semi-Markov) and partitioned survival. Partitioned survival models have become more prominent in oncology health technology assessment processes in recent years. Our experience in conducting and evaluating models for economic evaluation has highlighted many important and practical pitfalls. As there is little guidance available on best practices for those who wish to conduct them, we provide guidance in the form of 'Key steps for busy analysts,' who may have very little time and require highly favorable results. Our guidance highlights the continued need for rigorous conduct and transparent reporting of economic evaluations regardless of the modeling approach taken, and the importance of modeling that better reflects reality, which includes better approaches to considering plausibility, estimating relative treatment effects, dealing with post-progression effects, and appropriate characterization of the uncertainty from modeling itself.

MUSiC - Model-independent search for deviations from Standard Model predictions in CMS

NASA Astrophysics Data System (ADS)

Pieta, Holger

2010-02-01

We present an approach for a model independent search in CMS. Systematically scanning the data for deviations from the standard model Monte Carlo expectations, such an analysis can help to understand the detector and tune event generators. By minimizing the theoretical bias the analysis is furthermore sensitive to a wide range of models for new physics, including the uncounted number of models not-yet-thought-of. After sorting the events into classes defined by their particle content (leptons, photons, jets and missing transverse energy), a minimally prejudiced scan is performed on a number of distributions. Advanced statistical methods are used to determine the significance of the deviating regions, rigorously taking systematic uncertainties into account. A number of benchmark scenarios, including common models of new physics and possible detector effects, have been used to gauge the power of such a method. )

Modelling of plasma-based dry reforming: how do uncertainties in the input data affect the calculation results?

NASA Astrophysics Data System (ADS)

Wang, Weizong; Berthelot, Antonin; Zhang, Quanzhi; Bogaerts, Annemie

2018-05-01

One of the main issues in plasma chemistry modeling is that the cross sections and rate coefficients are subject to uncertainties, which yields uncertainties in the modeling results and hence hinders the predictive capabilities. In this paper, we reveal the impact of these uncertainties on the model predictions of plasma-based dry reforming in a dielectric barrier discharge. For this purpose, we performed a detailed uncertainty analysis and sensitivity study. 2000 different combinations of rate coefficients, based on the uncertainty from a log-normal distribution, are used to predict the uncertainties in the model output. The uncertainties in the electron density and electron temperature are around 11% and 8% at the maximum of the power deposition for a 70% confidence level. Still, this can have a major effect on the electron impact rates and hence on the calculated conversions of CO2 and CH4, as well as on the selectivities of CO and H2. For the CO2 and CH4 conversion, we obtain uncertainties of 24% and 33%, respectively. For the CO and H2 selectivity, the corresponding uncertainties are 28% and 14%, respectively. We also identify which reactions contribute most to the uncertainty in the model predictions. In order to improve the accuracy and reliability of plasma chemistry models, we recommend using only verified rate coefficients, and we point out the need for dedicated verification experiments.

Seismic velocity structure of the forearc in northern Cascadia from Bayesian inversion of teleseismic data

NASA Astrophysics Data System (ADS)

Gosselin, J.; Audet, P.; Schaeffer, A. J.

2017-12-01

The seismic velocity structure in the forearc of subduction zones provides important constraints on material properties, with implications for seismogenesis. In Cascadia, previous studies have imaged a downgoing low-velocity zone (LVZ) characterized by an elevated P-to-S velocity ratio (Vp/Vs) down to 45 km depth, near the intersection with the mantle wedge corner, beyond which the signature of the LVZ disappears. These results, combined with the absence of a "normal" continental Moho, indicate that the down-going oceanic crust likely carries large amounts of overpressured free fluids that are released downdip at the onset of crustal eclogitization, and are further stored in the mantle wedge as serpentinite. These overpressured free fluids affect the stability of the plate interface and facilitate slow slip. These results are based on the inversion and migration of scattered teleseismic data for individual layer properties; a methodology which suffers from regularization and smoothing, non-uniqueness, and does not consider model uncertainty. This study instead applies trans-dimensional Bayesian inversion of teleseismic data collected in the forearc of northern Cascadia (the CAFÉ experiment in northern Washington) to provide rigorous, quantitative estimates of local velocity structure, and associated uncertainties (particularly Vp/Vs structure and depth to the plate interface). Trans-dimensional inversion is a generalization of fixed-dimensional inversion that includes the number (and type) of parameters required to describe the velocity model (or data error model) as unknown in the problem. This allows model complexity to be inherently determined by data information content, not by subjective regularization. The inversion is implemented here using the reversible-jump Markov chain Monte Carlo algorithm. The result is an ensemble set of candidate velocity-structure models which approximate the posterior probability density (PPD) of the model parameters. The solution to the inverse problem, and associated uncertainties, are described by properties of the PPD. The results obtained here will eventually be integrated with teleseismic data from OBS stations from the Cascadia Initiative to provide constraints across the entire seismogenic portion of the plate interface.

Uncertainty analysis of an inflow forecasting model: extension of the UNEEC machine learning-based method

NASA Astrophysics Data System (ADS)

Pianosi, Francesca; Lal Shrestha, Durga; Solomatine, Dimitri

2010-05-01

This research presents an extension of UNEEC (Uncertainty Estimation based on Local Errors and Clustering, Shrestha and Solomatine, 2006, 2008 & Solomatine and Shrestha, 2009) method in the direction of explicit inclusion of parameter uncertainty. UNEEC method assumes that there is an optimal model and the residuals of the model can be used to assess the uncertainty of the model prediction. It is assumed that all sources of uncertainty including input, parameter and model structure uncertainty are explicitly manifested in the model residuals. In this research, theses assumptions are relaxed, and the UNEEC method is extended to consider parameter uncertainty as well (abbreviated as UNEEC-P). In UNEEC-P, first we use Monte Carlo (MC) sampling in parameter space to generate N model realizations (each of which is a time series), estimate the prediction quantiles based on the empirical distribution functions of the model residuals considering all the residual realizations, and only then apply the standard UNEEC method that encapsulates the uncertainty of a hydrologic model (expressed by quantiles of the error distribution) in a machine learning model (e.g., ANN). UNEEC-P is applied first to a linear regression model of synthetic data, and then to a real case study of forecasting inflow to lake Lugano in northern Italy. The inflow forecasting model is a stochastic heteroscedastic model (Pianosi and Soncini-Sessa, 2009). The preliminary results show that the UNEEC-P method produces wider uncertainty bounds, which is consistent with the fact that the method considers also parameter uncertainty of the optimal model. In the future UNEEC method will be further extended to consider input and structure uncertainty which will provide more realistic estimation of model predictions.

Shall we upgrade one-dimensional secondary settler models used in WWTP simulators? - An assessment of model structure uncertainty and its propagation.

PubMed

Plósz, Benedek Gy; De Clercq, Jeriffa; Nopens, Ingmar; Benedetti, Lorenzo; Vanrolleghem, Peter A

2011-01-01

In WWTP models, the accurate assessment of solids inventory in bioreactors equipped with solid-liquid separators, mostly described using one-dimensional (1-D) secondary settling tank (SST) models, is the most fundamental requirement of any calibration procedure. Scientific knowledge on characterising particulate organics in wastewater and on bacteria growth is well-established, whereas 1-D SST models and their impact on biomass concentration predictions are still poorly understood. A rigorous assessment of two 1-DSST models is thus presented: one based on hyperbolic (the widely used Takács-model) and one based on parabolic (the more recently presented Plósz-model) partial differential equations. The former model, using numerical approximation to yield realistic behaviour, is currently the most widely used by wastewater treatment process modellers. The latter is a convection-dispersion model that is solved in a numerically sound way. First, the explicit dispersion in the convection-dispersion model and the numerical dispersion for both SST models are calculated. Second, simulation results of effluent suspended solids concentration (XTSS,Eff), sludge recirculation stream (XTSS,RAS) and sludge blanket height (SBH) are used to demonstrate the distinct behaviour of the models. A thorough scenario analysis is carried out using SST feed flow rate, solids concentration, and overflow rate as degrees of freedom, spanning a broad loading spectrum. A comparison between the measurements and the simulation results demonstrates a considerably improved 1-D model realism using the convection-dispersion model in terms of SBH, XTSS,RAS and XTSS,Eff. Third, to assess the propagation of uncertainty derived from settler model structure to the biokinetic model, the impact of the SST model as sub-model in a plant-wide model on the general model performance is evaluated. A long-term simulation of a bulking event is conducted that spans temperature evolution throughout a summer/winter sequence. The model prediction in terms of nitrogen removal, solids inventory in the bioreactors and solids retention time as a function of the solids settling behaviour is investigated. It is found that the settler behaviour, simulated by the hyperbolic model, can introduce significant errors into the approximation of the solids retention time and thus solids inventory of the system. We demonstrate that these impacts can potentially cause deterioration of the predictive power of the biokinetic model, evidenced by an evaluation of the system's nitrogen removal efficiency. The convection-dispersion model exhibits superior behaviour, and the use of this type of model thus is highly recommended, especially bearing in mind future challenges, e.g., the explicit representation of uncertainty in WWTP models.

Uncertainty evaluation of dead zone of diagnostic ultrasound equipment

NASA Astrophysics Data System (ADS)

Souza, R. M.; Alvarenga, A. V.; Braz, D. S.; Petrella, L. I.; Costa-Felix, R. P. B.

2016-07-01

This paper presents a model for evaluating measurement uncertainty of a feature used in the assessment of ultrasound images: dead zone. The dead zone was measured by two technicians of the INMETRO's Laboratory of Ultrasound using a phantom and following the standard IEC/TS 61390. The uncertainty model was proposed based on the Guide to the Expression of Uncertainty in Measurement. For the tested equipment, results indicate a dead zone of 1.01 mm, and based on the proposed model, the expanded uncertainty was 0.17 mm. The proposed uncertainty model contributes as a novel way for metrological evaluation of diagnostic imaging by ultrasound.

Probabilistic estimation of splitting coefficients of normal modes of the Earth, and their uncertainties, using an autoregressive technique

NASA Astrophysics Data System (ADS)

Pachhai, S.; Masters, G.; Laske, G.

2017-12-01

Earth's normal-mode spectra are crucial to studying the long wavelength structure of the Earth. Such observations have been used extensively to estimate "splitting coefficients" which, in turn, can be used to determine the three-dimensional velocity and density structure. Most past studies apply a non-linear iterative inversion to estimate the splitting coefficients which requires that the earthquake source is known. However, it is challenging to know the source details, particularly for big events as used in normal-mode analyses. Additionally, the final solution of the non-linear inversion can depend on the choice of damping parameter and starting model. To circumvent the need to know the source, a two-step linear inversion has been developed and successfully applied to many mantle and core sensitive modes. The first step takes combinations of the data from a single event to produce spectra known as "receiver strips". The autoregressive nature of the receiver strips can then be used to estimate the structure coefficients without the need to know the source. Based on this approach, we recently employed a neighborhood algorithm to measure the splitting coefficients for an isolated inner-core sensitive mode (13S2). This approach explores the parameter space efficiently without any need of regularization and finds the structure coefficients which best fit the observed strips. Here, we implement a Bayesian approach to data collected for earthquakes from early 2000 and more recent. This approach combines the data (through likelihood) and prior information to provide rigorous parameter values and their uncertainties for both isolated and coupled modes. The likelihood function is derived from the inferred errors of the receiver strips which allows us to retrieve proper uncertainties. Finally, we apply model selection criteria that balance the trade-offs between fit (likelihood) and model complexity to investigate the degree and type of structure (elastic and anelastic) required to explain the data.

Controlled source electromagnetic data analysis with seismic constraints and rigorous uncertainty estimation in the Black Sea

NASA Astrophysics Data System (ADS)

Gehrmann, R. A. S.; Schwalenberg, K.; Hölz, S.; Zander, T.; Dettmer, J.; Bialas, J.

2016-12-01

In 2014 an interdisciplinary survey was conducted as part of the German SUGAR project in the Western Black Sea targeting gas hydrate occurrences in the Danube Delta. Marine controlled source electromagnetic (CSEM) data were acquired with an inline seafloor-towed array (BGR), and a two-polarization horizontal ocean-bottom source and receiver configuration (GEOMAR). The CSEM data are co-located with high-resolution 2-D and 3-D seismic reflection data (GEOMAR). We present results from 2-D regularized inversion (MARE2DEM by Kerry Key), which provides a smooth model of the electrical resistivity distribution beneath the source and multiple receivers. The 2-D approach includes seafloor topography and structural constraints from seismic data. We estimate uncertainties from the regularized inversion and compare them to 1-D Bayesian inversion results. The probabilistic inversion for a layered subsurface treats the parameter values and the number of layers as unknown by applying reversible-jump Markov-chain Monte Carlo sampling. A non-diagonal data covariance matrix obtained from residual error analysis accounts for correlated errors. The resulting resistivity models show generally high resistivity values between 3 and 10 Ωm on average which can be partly attributed to depleted pore water salinities due to sea-level low stands in the past, and locally up to 30 Ωm which is likely caused by gas hydrates. At the base of the gas hydrate stability zone resistivities rise up to more than 100 Ωm which could be due to gas hydrate as well as a layer of free gas underneath. However, the deeper parts also show the largest model parameter uncertainties. Archie's Law is used to derive estimates of the gas hydrate saturation, which vary between 30 and 80% within the anomalous layers considering salinity and porosity profiles from a distant DSDP bore hole.

Parametric assessment of climate change impacts of automotive material substitution.

PubMed

Geyer, Roland

2008-09-15

Quantifying the net climate change impact of automotive material substitution is not a trivial task. It requires the assessment of the mass reduction potential of automotive materials, the greenhouse gas (GHG) emissions from their production and recycling, and their impact on GHG emissions from vehicle use. The model presented in this paper is based on life cycle assessment (LCA) and completely parameterized, i.e., its computational structure is separated from the required input data, which is not traditionally done in LCAs. The parameterization increases scientific rigor and transparency of the assessment methodology, facilitates sensitivity and uncertainty analysis of the results, and also makes it possible to compare different studies and explain their disparities. The state of the art of the modeling methodology is reviewed and advanced. Assessment of the GHG emission impacts of material recycling through consequential system expansion shows that our understanding of this issue is still incomplete. This is a critical knowledge gap since a case study shows thatfor materials such as aluminum, the GHG emission impacts of material production and recycling are both of the same size as the use phase savings from vehicle mass reduction.

Estimation of integral curves from high angular resolution diffusion imaging (HARDI) data.

PubMed

Carmichael, Owen; Sakhanenko, Lyudmila

2015-05-15

We develop statistical methodology for a popular brain imaging technique HARDI based on the high order tensor model by Özarslan and Mareci [10]. We investigate how uncertainty in the imaging procedure propagates through all levels of the model: signals, tensor fields, vector fields, and fibers. We construct asymptotically normal estimators of the integral curves or fibers which allow us to trace the fibers together with confidence ellipsoids. The procedure is computationally intense as it blends linear algebra concepts from high order tensors with asymptotical statistical analysis. The theoretical results are illustrated on simulated and real datasets. This work generalizes the statistical methodology proposed for low angular resolution diffusion tensor imaging by Carmichael and Sakhanenko [3], to several fibers per voxel. It is also a pioneering statistical work on tractography from HARDI data. It avoids all the typical limitations of the deterministic tractography methods and it delivers the same information as probabilistic tractography methods. Our method is computationally cheap and it provides well-founded mathematical and statistical framework where diverse functionals on fibers, directions and tensors can be studied in a systematic and rigorous way.

Estimation of integral curves from high angular resolution diffusion imaging (HARDI) data

PubMed Central

Carmichael, Owen; Sakhanenko, Lyudmila

2015-01-01

We develop statistical methodology for a popular brain imaging technique HARDI based on the high order tensor model by Özarslan and Mareci [10]. We investigate how uncertainty in the imaging procedure propagates through all levels of the model: signals, tensor fields, vector fields, and fibers. We construct asymptotically normal estimators of the integral curves or fibers which allow us to trace the fibers together with confidence ellipsoids. The procedure is computationally intense as it blends linear algebra concepts from high order tensors with asymptotical statistical analysis. The theoretical results are illustrated on simulated and real datasets. This work generalizes the statistical methodology proposed for low angular resolution diffusion tensor imaging by Carmichael and Sakhanenko [3], to several fibers per voxel. It is also a pioneering statistical work on tractography from HARDI data. It avoids all the typical limitations of the deterministic tractography methods and it delivers the same information as probabilistic tractography methods. Our method is computationally cheap and it provides well-founded mathematical and statistical framework where diverse functionals on fibers, directions and tensors can be studied in a systematic and rigorous way. PMID:25937674

DOE Office of Scientific and Technical Information (OSTI.GOV)

English, Shawn A.; Briggs, Timothy M.; Nelson, Stacy M.

Simulations of low velocity impact with a flat cylindrical indenter upon a carbon fiber fabric reinforced polymer laminate are rigorously validated. Comparison of the impact energy absorption between the model and experiment is used as the validation metric. Additionally, non-destructive evaluation, including ultrasonic scans and three-dimensional computed tomography, provide qualitative validation of the models. The simulations include delamination, matrix cracks and fiber breaks. An orthotropic damage and failure constitutive model, capable of predicting progressive damage and failure, is developed in conjunction and described. An ensemble of simulations incorporating model parameter uncertainties is used to predict a response distribution which ismore » then compared to experimental output using appropriate statistical methods. Lastly, the model form errors are exposed and corrected for use in an additional blind validation analysis. The result is a quantifiable confidence in material characterization and model physics when simulating low velocity impact in structures of interest.« less

Quantitative validation of carbon-fiber laminate low velocity impact simulations

DOE PAGES

English, Shawn A.; Briggs, Timothy M.; Nelson, Stacy M.

2015-09-26

Simulations of low velocity impact with a flat cylindrical indenter upon a carbon fiber fabric reinforced polymer laminate are rigorously validated. Comparison of the impact energy absorption between the model and experiment is used as the validation metric. Additionally, non-destructive evaluation, including ultrasonic scans and three-dimensional computed tomography, provide qualitative validation of the models. The simulations include delamination, matrix cracks and fiber breaks. An orthotropic damage and failure constitutive model, capable of predicting progressive damage and failure, is developed in conjunction and described. An ensemble of simulations incorporating model parameter uncertainties is used to predict a response distribution which ismore » then compared to experimental output using appropriate statistical methods. Lastly, the model form errors are exposed and corrected for use in an additional blind validation analysis. The result is a quantifiable confidence in material characterization and model physics when simulating low velocity impact in structures of interest.« less

Sliding mode control for Mars entry based on extended state observer

NASA Astrophysics Data System (ADS)

Lu, Kunfeng; Xia, Yuanqing; Shen, Ganghui; Yu, Chunmei; Zhou, Liuyu; Zhang, Lijun

2017-11-01

This paper addresses high-precision Mars entry guidance and control approach via sliding mode control (SMC) and Extended State Observer (ESO). First, differential flatness (DF) approach is applied to the dynamic equations of the entry vehicle to represent the state variables more conveniently. Then, the presented SMC law can guarantee the property of finite-time convergence of tracking error, which requires no information on high uncertainties that are estimated by ESO, and the rigorous proof of tracking error convergence is given. Finally, Monte Carlo simulation results are presented to demonstrate the effectiveness of the suggested approach.

Total focusing method (TFM) robustness to material deviations

NASA Astrophysics Data System (ADS)

Painchaud-April, Guillaume; Badeau, Nicolas; Lepage, Benoit

2018-04-01

The total focusing method (TFM) is becoming an accepted nondestructive evaluation method for industrial inspection. What was a topic of discussion in the applied research community just a few years ago is now being deployed in critical industrial applications, such as inspecting welds in pipelines. However, the method's sensitivity to unexpected parametric changes (material and geometric) has not been rigorously assessed. In this article, we investigate the robustness of TFM in relation to unavoidable deviations from modeled nominal inspection component characteristics, such as sound velocities and uncertainties about the parts' internal and external diameters. We also review TFM's impact on the standard inspection modes often encountered in industrial inspections, and we present a theoretical model supported by empirical observations to illustrate the discussion.

Understanding Coastal Wetland Vulnerability to Sea-Level Rise Enhanced Inundation Using Real-Time Stage Monitoring, LiDAR, and Monte Carlo Simulation in Everglades National Park

NASA Astrophysics Data System (ADS)

Cooper, H.; Zhang, C.

2017-12-01

Coastal wetlands are one of the most productive ecological systems in the world, providing critical habitat area and valuable ecosystem services such as carbon sequestration. However, due to their location in low lying areas, coastal wetlands are particularly vulnerable to sea-level rise (SLR). Everglades National Park (ENP) encompasses the southern-most portion of the Greater Everglades Ecosystem, and is the largest subtropical wetland in the USA. Water depths have shown to have a significant relationship to vegetation community composition and organization while also playing a crucial role in vegetation health throughout the Everglades. Live plants play a vital role in maintaining soil structure (i.e. elevation), and decreases in vegetation health can cause peat collapse or wetland loss resulting in dramatic habitat, organic soil, and elevation loss posing concerns for Everglades management and restoration. One suspected mechanism for peat collapse is enhanced inundation due to SLR, thus mapping and modeling water depths is a critical component to understanding the potential impacts of future SLR. Previous research in the Everglades focused on a conventional Water Depth Model (WDM) approach where a Digital Elevation Model (DEM) is subtracted from a Water Table Elevation Model (WTEM). In this study, the conventional WDM approach is extended to a more rigorous WDM technique so that the accuracy and precision of the underlying data may be considered. Monte Carlo simulation is used to propagate probability distributions through our SLR depth model using our Random Forest-based LiDAR DEM, Empirical Bayesian Kriging-based WTEMs, uncertainties in vertical datums, soil accretion projections, and regional sea-level rise projections. Water depth maps were produced for the wet and dry seasons in April and October, which successfully revealed the potential spatial and temporal water depth variations due to future SLR. It is concluded that a more rigorous WDM technique helps to increase the integrity of derived products used to support and guide coastal restoration managers and planners under the challenge of rising seas.

Spectral optimization and uncertainty quantification in combustion modeling

NASA Astrophysics Data System (ADS)

Sheen, David Allan

Reliable simulations of reacting flow systems require a well-characterized, detailed chemical model as a foundation. Accuracy of such a model can be assured, in principle, by a multi-parameter optimization against a set of experimental data. However, the inherent uncertainties in the rate evaluations and experimental data leave a model still characterized by some finite kinetic rate parameter space. Without a careful analysis of how this uncertainty space propagates into the model's predictions, those predictions can at best be trusted only qualitatively. In this work, the Method of Uncertainty Minimization using Polynomial Chaos Expansions is proposed to quantify these uncertainties. In this method, the uncertainty in the rate parameters of the as-compiled model is quantified. Then, the model is subjected to a rigorous multi-parameter optimization, as well as a consistency-screening process. Lastly, the uncertainty of the optimized model is calculated using an inverse spectral optimization technique, and then propagated into a range of simulation conditions. An as-compiled, detailed H2/CO/C1-C4 kinetic model is combined with a set of ethylene combustion data to serve as an example. The idea that the hydrocarbon oxidation model should be understood and developed in a hierarchical fashion has been a major driving force in kinetics research for decades. How this hierarchical strategy works at a quantitative level, however, has never been addressed. In this work, we use ethylene and propane combustion as examples and explore the question of hierarchical model development quantitatively. The Method of Uncertainty Minimization using Polynomial Chaos Expansions is utilized to quantify the amount of information that a particular combustion experiment, and thereby each data set, contributes to the model. This knowledge is applied to explore the relationships among the combustion chemistry of hydrogen/carbon monoxide, ethylene, and larger alkanes. Frequently, new data will become available, and it will be desirable to know the effect that inclusion of these data has on the optimized model. Two cases are considered here. In the first, a study of H2/CO mass burning rates has recently been published, wherein the experimentally-obtained results could not be reconciled with any extant H2/CO oxidation model. It is shown in that an optimized H2/CO model can be developed that will reproduce the results of the new experimental measurements. In addition, the high precision of the new experiments provide a strong constraint on the reaction rate parameters of the chemistry model, manifested in a significant improvement in the precision of simulations. In the second case, species time histories were measured during n-heptane oxidation behind reflected shock waves. The highly precise nature of these measurements is expected to impose critical constraints on chemical kinetic models of hydrocarbon combustion. The results show that while an as-compiled, prior reaction model of n-alkane combustion can be accurate in its prediction of the detailed species profiles, the kinetic parameter uncertainty in the model remains to be too large to obtain a precise prediction of the data. Constraining the prior model against the species time histories within the measurement uncertainties led to notable improvements in the precision of model predictions against the species data as well as the global combustion properties considered. Lastly, we show that while the capability of the multispecies measurement presents a step-change in our precise knowledge of the chemical processes in hydrocarbon combustion, accurate data of global combustion properties are still necessary to predict fuel combustion.

The effects of geometric uncertainties on computational modelling of knee biomechanics

PubMed Central

Fisher, John; Wilcox, Ruth

2017-01-01

The geometry of the articular components of the knee is an important factor in predicting joint mechanics in computational models. There are a number of uncertainties in the definition of the geometry of cartilage and meniscus, and evaluating the effects of these uncertainties is fundamental to understanding the level of reliability of the models. In this study, the sensitivity of knee mechanics to geometric uncertainties was investigated by comparing polynomial-based and image-based knee models and varying the size of meniscus. The results suggested that the geometric uncertainties in cartilage and meniscus resulting from the resolution of MRI and the accuracy of segmentation caused considerable effects on the predicted knee mechanics. Moreover, even if the mathematical geometric descriptors can be very close to the imaged-based articular surfaces, the detailed contact pressure distribution produced by the mathematical geometric descriptors was not the same as that of the image-based model. However, the trends predicted by the models based on mathematical geometric descriptors were similar to those of the imaged-based models. PMID:28879008

A flexible Bayesian assessment for the expected impact of data on prediction confidence for optimal sampling designs

NASA Astrophysics Data System (ADS)

Leube, Philipp; Geiges, Andreas; Nowak, Wolfgang

2010-05-01

Incorporating hydrogeological data, such as head and tracer data, into stochastic models of subsurface flow and transport helps to reduce prediction uncertainty. Considering limited financial resources available for the data acquisition campaign, information needs towards the prediction goal should be satisfied in a efficient and task-specific manner. For finding the best one among a set of design candidates, an objective function is commonly evaluated, which measures the expected impact of data on prediction confidence, prior to their collection. An appropriate approach to this task should be stochastically rigorous, master non-linear dependencies between data, parameters and model predictions, and allow for a wide variety of different data types. Existing methods fail to fulfill all these requirements simultaneously. For this reason, we introduce a new method, denoted as CLUE (Cross-bred Likelihood Uncertainty Estimator), that derives the essential distributions and measures of data utility within a generalized, flexible and accurate framework. The method makes use of Bayesian GLUE (Generalized Likelihood Uncertainty Estimator) and extends it to an optimal design method by marginalizing over the yet unknown data values. Operating in a purely Bayesian Monte-Carlo framework, CLUE is a strictly formal information processing scheme free of linearizations. It provides full flexibility associated with the type of measurements (linear, non-linear, direct, indirect) and accounts for almost arbitrary sources of uncertainty (e.g. heterogeneity, geostatistical assumptions, boundary conditions, model concepts) via stochastic simulation and Bayesian model averaging. This helps to minimize the strength and impact of possible subjective prior assumptions, that would be hard to defend prior to data collection. Our study focuses on evaluating two different uncertainty measures: (i) expected conditional variance and (ii) expected relative entropy of a given prediction goal. The applicability and advantages are shown in a synthetic example. Therefor, we consider a contaminant source, posing a threat on a drinking water well in an aquifer. Furthermore, we assume uncertainty in geostatistical parameters, boundary conditions and hydraulic gradient. The two mentioned measures evaluate the sensitivity of (1) general prediction confidence and (2) exceedance probability of a legal regulatory threshold value on sampling locations.

Improving Baseline Model Assumptions: Evaluating the Impacts of Typical Methodological Approaches in Watershed Models

NASA Astrophysics Data System (ADS)

Muenich, R. L.; Kalcic, M. M.; Teshager, A. D.; Long, C. M.; Wang, Y. C.; Scavia, D.

2017-12-01

Thanks to the availability of open-source software, online tutorials, and advanced software capabilities, watershed modeling has expanded its user-base and applications significantly in the past thirty years. Even complicated models like the Soil and Water Assessment Tool (SWAT) are being used and documented in hundreds of peer-reviewed publications each year, and likely more applied in practice. These models can help improve our understanding of present, past, and future conditions, or analyze important "what-if" management scenarios. However, baseline data and methods are often adopted and applied without rigorous testing. In multiple collaborative projects, we have evaluated the influence of some of these common approaches on model results. Specifically, we examined impacts of baseline data and assumptions involved in manure application, combined sewer overflows, and climate data incorporation across multiple watersheds in the Western Lake Erie Basin. In these efforts, we seek to understand the impact of using typical modeling data and assumptions, versus using improved data and enhanced assumptions on model outcomes and thus ultimately, study conclusions. We provide guidance for modelers as they adopt and apply data and models for their specific study region. While it is difficult to quantitatively assess the full uncertainty surrounding model input data and assumptions, recognizing the impacts of model input choices is important when considering actions at the both the field and watershed scales.

Impact of input data uncertainty on environmental exposure assessment models: A case study for electromagnetic field modelling from mobile phone base stations.

PubMed

Beekhuizen, Johan; Heuvelink, Gerard B M; Huss, Anke; Bürgi, Alfred; Kromhout, Hans; Vermeulen, Roel

2014-11-01

With the increased availability of spatial data and computing power, spatial prediction approaches have become a standard tool for exposure assessment in environmental epidemiology. However, such models are largely dependent on accurate input data. Uncertainties in the input data can therefore have a large effect on model predictions, but are rarely quantified. With Monte Carlo simulation we assessed the effect of input uncertainty on the prediction of radio-frequency electromagnetic fields (RF-EMF) from mobile phone base stations at 252 receptor sites in Amsterdam, The Netherlands. The impact on ranking and classification was determined by computing the Spearman correlations and weighted Cohen's Kappas (based on tertiles of the RF-EMF exposure distribution) between modelled values and RF-EMF measurements performed at the receptor sites. The uncertainty in modelled RF-EMF levels was large with a median coefficient of variation of 1.5. Uncertainty in receptor site height, building damping and building height contributed most to model output uncertainty. For exposure ranking and classification, the heights of buildings and receptor sites were the most important sources of uncertainty, followed by building damping, antenna- and site location. Uncertainty in antenna power, tilt, height and direction had a smaller impact on model performance. We quantified the effect of input data uncertainty on the prediction accuracy of an RF-EMF environmental exposure model, thereby identifying the most important sources of uncertainty and estimating the total uncertainty stemming from potential errors in the input data. This approach can be used to optimize the model and better interpret model output. Copyright © 2014 Elsevier Inc. All rights reserved.

Model averaging techniques for quantifying conceptual model uncertainty.

PubMed

Singh, Abhishek; Mishra, Srikanta; Ruskauff, Greg

2010-01-01

In recent years a growing understanding has emerged regarding the need to expand the modeling paradigm to include conceptual model uncertainty for groundwater models. Conceptual model uncertainty is typically addressed by formulating alternative model conceptualizations and assessing their relative likelihoods using statistical model averaging approaches. Several model averaging techniques and likelihood measures have been proposed in the recent literature for this purpose with two broad categories--Monte Carlo-based techniques such as Generalized Likelihood Uncertainty Estimation or GLUE (Beven and Binley 1992) and criterion-based techniques that use metrics such as the Bayesian and Kashyap Information Criteria (e.g., the Maximum Likelihood Bayesian Model Averaging or MLBMA approach proposed by Neuman 2003) and Akaike Information Criterion-based model averaging (AICMA) (Poeter and Anderson 2005). These different techniques can often lead to significantly different relative model weights and ranks because of differences in the underlying statistical assumptions about the nature of model uncertainty. This paper provides a comparative assessment of the four model averaging techniques (GLUE, MLBMA with KIC, MLBMA with BIC, and AIC-based model averaging) mentioned above for the purpose of quantifying the impacts of model uncertainty on groundwater model predictions. Pros and cons of each model averaging technique are examined from a practitioner's perspective using two groundwater modeling case studies. Recommendations are provided regarding the use of these techniques in groundwater modeling practice.

Robust Fault Detection Using Robust Z1 Estimation and Fuzzy Logic

NASA Technical Reports Server (NTRS)

Curry, Tramone; Collins, Emmanuel G., Jr.; Selekwa, Majura; Guo, Ten-Huei (Technical Monitor)

2001-01-01

This research considers the application of robust Z(sub 1), estimation in conjunction with fuzzy logic to robust fault detection for an aircraft fight control system. It begins with the development of robust Z(sub 1) estimators based on multiplier theory and then develops a fixed threshold approach to fault detection (FD). It then considers the use of fuzzy logic for robust residual evaluation and FD. Due to modeling errors and unmeasurable disturbances, it is difficult to distinguish between the effects of an actual fault and those caused by uncertainty and disturbance. Hence, it is the aim of a robust FD system to be sensitive to faults while remaining insensitive to uncertainty and disturbances. While fixed thresholds only allow a decision on whether a fault has or has not occurred, it is more valuable to have the residual evaluation lead to a conclusion related to the degree of, or probability of, a fault. Fuzzy logic is a viable means of determining the degree of a fault and allows the introduction of human observations that may not be incorporated in the rigorous threshold theory. Hence, fuzzy logic can provide a more reliable and informative fault detection process. Using an aircraft flight control system, the results of FD using robust Z(sub 1) estimation with a fixed threshold are demonstrated. FD that combines robust Z(sub 1) estimation and fuzzy logic is also demonstrated. It is seen that combining the robust estimator with fuzzy logic proves to be advantageous in increasing the sensitivity to smaller faults while remaining insensitive to uncertainty and disturbances.

Multiscale sagebrush rangeland habitat modeling in southwest Wyoming

USGS Publications Warehouse

Homer, Collin G.; Aldridge, Cameron L.; Meyer, Debra K.; Coan, Michael J.; Bowen, Zachary H.

2009-01-01

Sagebrush-steppe ecosystems in North America have experienced dramatic elimination and degradation since European settlement. As a result, sagebrush-steppe dependent species have experienced drastic range contractions and population declines. Coordinated ecosystem-wide research, integrated with monitoring and management activities, would improve the ability to maintain existing sagebrush habitats. However, current data only identify resource availability locally, with rigorous spatial tools and models that accurately model and map sagebrush habitats over large areas still unavailable. Here we report on an effort to produce a rigorous large-area sagebrush-habitat classification and inventory with statistically validated products and estimates of precision in the State of Wyoming. This research employs a combination of significant new tools, including (1) modeling sagebrush rangeland as a series of independent continuous field components that can be combined and customized by any user at multiple spatial scales; (2) collecting ground-measured plot data on 2.4-meter imagery in the same season the satellite imagery is acquired; (3) effective modeling of ground-measured data on 2.4-meter imagery to maximize subsequent extrapolation; (4) acquiring multiple seasons (spring, summer, and fall) of an additional two spatial scales of imagery (30 meter and 56 meter) for optimal large-area modeling; (5) using regression tree classification technology that optimizes data mining of multiple image dates, ratios, and bands with ancillary data to extrapolate ground training data to coarser resolution sensors; and (6) employing rigorous accuracy assessment of model predictions to enable users to understand the inherent uncertainties. First-phase results modeled eight rangeland components (four primary targets and four secondary targets) as continuous field predictions. The primary targets included percent bare ground, percent herbaceousness, percent shrub, and percent litter. The four secondary targets included percent sagebrush (Artemisia spp.), percent big sagebrush (Artemisia tridentata), percent Wyoming sagebrush (Artemisia tridentata wyomingensis), and sagebrush height (centimeters). Results were validated by an independent accuracy assessment with root mean square error (RMSE) values ranging from 6.38 percent for bare ground to 2.99 percent for sagebrush at the QuickBird scale and RMSE values ranging from 12.07 percent for bare ground to 6.34 percent for sagebrush at the full Landsat scale. Subsequent project phases are now in progress, with plans to deliver products that improve accuracies of existing components, model new components, complete models over larger areas, track changes over time (from 1988 to 2007), and ultimately model wildlife population trends against these changes. We believe these results offer significant improvement in sagebrush rangeland quantification at multiple scales and offer users products that have been rigorously validated.

A generalized mixed effects model of abundance for mark-resight data when sampling is without replacement

USGS Publications Warehouse

McClintock, B.T.; White, Gary C.; Burnham, K.P.; Pryde, M.A.; Thomson, David L.; Cooch, Evan G.; Conroy, Michael J.

2009-01-01

In recent years, the mark-resight method for estimating abundance when the number of marked individuals is known has become increasingly popular. By using field-readable bands that may be resighted from a distance, these techniques can be applied to many species, and are particularly useful for relatively small, closed populations. However, due to the different assumptions and general rigidity of the available estimators, researchers must often commit to a particular model without rigorous quantitative justification for model selection based on the data. Here we introduce a nonlinear logit-normal mixed effects model addressing this need for a more generalized framework. Similar to models available for mark-recapture studies, the estimator allows a wide variety of sampling conditions to be parameterized efficiently under a robust sampling design. Resighting rates may be modeled simply or with more complexity by including fixed temporal and random individual heterogeneity effects. Using information theory, the model(s) best supported by the data may be selected from the candidate models proposed. Under this generalized framework, we hope the uncertainty associated with mark-resight model selection will be reduced substantially. We compare our model to other mark-resight abundance estimators when applied to mainland New Zealand robin (Petroica australis) data recently collected in Eglinton Valley, Fiordland National Park and summarize its performance in simulation experiments.

Statistical ecology comes of age.

PubMed

Gimenez, Olivier; Buckland, Stephen T; Morgan, Byron J T; Bez, Nicolas; Bertrand, Sophie; Choquet, Rémi; Dray, Stéphane; Etienne, Marie-Pierre; Fewster, Rachel; Gosselin, Frédéric; Mérigot, Bastien; Monestiez, Pascal; Morales, Juan M; Mortier, Frédéric; Munoz, François; Ovaskainen, Otso; Pavoine, Sandrine; Pradel, Roger; Schurr, Frank M; Thomas, Len; Thuiller, Wilfried; Trenkel, Verena; de Valpine, Perry; Rexstad, Eric

2014-12-01

The desire to predict the consequences of global environmental change has been the driver towards more realistic models embracing the variability and uncertainties inherent in ecology. Statistical ecology has gelled over the past decade as a discipline that moves away from describing patterns towards modelling the ecological processes that generate these patterns. Following the fourth International Statistical Ecology Conference (1-4 July 2014) in Montpellier, France, we analyse current trends in statistical ecology. Important advances in the analysis of individual movement, and in the modelling of population dynamics and species distributions, are made possible by the increasing use of hierarchical and hidden process models. Exciting research perspectives include the development of methods to interpret citizen science data and of efficient, flexible computational algorithms for model fitting. Statistical ecology has come of age: it now provides a general and mathematically rigorous framework linking ecological theory and empirical data.

Statistical ecology comes of age

PubMed Central

Gimenez, Olivier; Buckland, Stephen T.; Morgan, Byron J. T.; Bez, Nicolas; Bertrand, Sophie; Choquet, Rémi; Dray, Stéphane; Etienne, Marie-Pierre; Fewster, Rachel; Gosselin, Frédéric; Mérigot, Bastien; Monestiez, Pascal; Morales, Juan M.; Mortier, Frédéric; Munoz, François; Ovaskainen, Otso; Pavoine, Sandrine; Pradel, Roger; Schurr, Frank M.; Thomas, Len; Thuiller, Wilfried; Trenkel, Verena; de Valpine, Perry; Rexstad, Eric

2014-01-01

The desire to predict the consequences of global environmental change has been the driver towards more realistic models embracing the variability and uncertainties inherent in ecology. Statistical ecology has gelled over the past decade as a discipline that moves away from describing patterns towards modelling the ecological processes that generate these patterns. Following the fourth International Statistical Ecology Conference (1–4 July 2014) in Montpellier, France, we analyse current trends in statistical ecology. Important advances in the analysis of individual movement, and in the modelling of population dynamics and species distributions, are made possible by the increasing use of hierarchical and hidden process models. Exciting research perspectives include the development of methods to interpret citizen science data and of efficient, flexible computational algorithms for model fitting. Statistical ecology has come of age: it now provides a general and mathematically rigorous framework linking ecological theory and empirical data. PMID:25540151

OR14-V-Uncertainty-PD2La Uncertainty Quantification for Nuclear Safeguards and Nondestructive Assay Final Report

DOE Office of Scientific and Technical Information (OSTI.GOV)

Nicholson, Andrew D.; Croft, Stephen; McElroy, Robert Dennis

2017-08-01

The various methods of nondestructive assay (NDA) of special nuclear material (SNM) have applications in nuclear nonproliferation, including detection and identification of illicit SNM at border crossings and quantifying SNM at nuclear facilities for safeguards. No assay method is complete without “error bars,” which provide one way of expressing confidence in the assay result. Consequently, NDA specialists typically provide error bars and also partition total uncertainty into “random” and “systematic” components so that, for example, an error bar can be developed for the total mass estimate in multiple items. Uncertainty Quantification (UQ) for NDA has always been important, but itmore » is recognized that greater rigor is needed and achievable using modern statistical methods.« less

Climate data induced uncertainty in model-based estimations of terrestrial primary productivity

NASA Astrophysics Data System (ADS)

Wu, Zhendong; Ahlström, Anders; Smith, Benjamin; Ardö, Jonas; Eklundh, Lars; Fensholt, Rasmus; Lehsten, Veiko

2017-06-01

Model-based estimations of historical fluxes and pools of the terrestrial biosphere differ substantially. These differences arise not only from differences between models but also from differences in the environmental and climatic data used as input to the models. Here we investigate the role of uncertainties in historical climate data by performing simulations of terrestrial gross primary productivity (GPP) using a process-based dynamic vegetation model (LPJ-GUESS) forced by six different climate datasets. We find that the climate induced uncertainty, defined as the range among historical simulations in GPP when forcing the model with the different climate datasets, can be as high as 11 Pg C yr-1 globally (9% of mean GPP). We also assessed a hypothetical maximum climate data induced uncertainty by combining climate variables from different datasets, which resulted in significantly larger uncertainties of 41 Pg C yr-1 globally or 32% of mean GPP. The uncertainty is partitioned into components associated to the three main climatic drivers, temperature, precipitation, and shortwave radiation. Additionally, we illustrate how the uncertainty due to a given climate driver depends both on the magnitude of the forcing data uncertainty (climate data range) and the apparent sensitivity of the modeled GPP to the driver (apparent model sensitivity). We find that LPJ-GUESS overestimates GPP compared to empirically based GPP data product in all land cover classes except for tropical forests. Tropical forests emerge as a disproportionate source of uncertainty in GPP estimation both in the simulations and empirical data products. The tropical forest uncertainty is most strongly associated with shortwave radiation and precipitation forcing, of which climate data range contributes higher to overall uncertainty than apparent model sensitivity to forcing. Globally, precipitation dominates the climate induced uncertainty over nearly half of the vegetated land area, which is mainly due to climate data range and less so due to the apparent model sensitivity. Overall, climate data ranges are found to contribute more to the climate induced uncertainty than apparent model sensitivity to forcing. Our study highlights the need to better constrain tropical climate, and demonstrates that uncertainty caused by climatic forcing data must be considered when comparing and evaluating carbon cycle model results and empirical datasets.

Identifying influences on model uncertainty: an application using a forest carbon budget model

Treesearch

James E. Smith; Linda S. Heath

2001-01-01

Uncertainty is an important consideration for both developers and users of environmental simulation models. Establishing quantitative estimates of uncertainty for deterministic models can be difficult when the underlying bases for such information are scarce. We demonstrate an application of probabilistic uncertainty analysis that provides for refinements in...

Full uncertainty quantification of N2O and NO emissions using the biogeochemical model LandscapeDNDC on site and regional scale

NASA Astrophysics Data System (ADS)

Haas, Edwin; Santabarbara, Ignacio; Kiese, Ralf; Butterbach-Bahl, Klaus

2017-04-01

Numerical simulation models are increasingly used to estimate greenhouse gas emissions at site to regional / national scale and are outlined as the most advanced methodology (Tier 3) in the framework of UNFCCC reporting. Process-based models incorporate the major processes of the carbon and nitrogen cycle of terrestrial ecosystems and are thus thought to be widely applicable at various conditions and spatial scales. Process based modelling requires high spatial resolution input data on soil properties, climate drivers and management information. The acceptance of model based inventory calculations depends on the assessment of the inventory's uncertainty (model, input data and parameter induced uncertainties). In this study we fully quantify the uncertainty in modelling soil N2O and NO emissions from arable, grassland and forest soils using the biogeochemical model LandscapeDNDC. We address model induced uncertainty (MU) by contrasting two different soil biogeochemistry modules within LandscapeDNDC. The parameter induced uncertainty (PU) was assessed by using joint parameter distributions for key parameters describing microbial C and N turnover processes as obtained by different Bayesian calibration studies for each model configuration. Input data induced uncertainty (DU) was addressed by Bayesian calibration of soil properties, climate drivers and agricultural management practices data. For the MU, DU and PU we performed several hundred simulations each to contribute to the individual uncertainty assessment. For the overall uncertainty quantification we assessed the model prediction probability, followed by sampled sets of input datasets and parameter distributions. Statistical analysis of the simulation results have been used to quantify the overall full uncertainty of the modelling approach. With this study we can contrast the variation in model results to the different sources of uncertainties for each ecosystem. Further we have been able to perform a fully uncertainty analysis for modelling N2O and NO emissions from arable, grassland and forest soils necessary for the comprehensibility of modelling results. We have applied the methodology to a regional inventory to assess the overall modelling uncertainty for a regional N2O and NO emissions inventory for the state of Saxony, Germany.

Aerosol-type retrieval and uncertainty quantification from OMI data

NASA Astrophysics Data System (ADS)

Kauppi, Anu; Kolmonen, Pekka; Laine, Marko; Tamminen, Johanna

2017-11-01

We discuss uncertainty quantification for aerosol-type selection in satellite-based atmospheric aerosol retrieval. The retrieval procedure uses precalculated aerosol microphysical models stored in look-up tables (LUTs) and top-of-atmosphere (TOA) spectral reflectance measurements to solve the aerosol characteristics. The forward model approximations cause systematic differences between the modelled and observed reflectance. Acknowledging this model discrepancy as a source of uncertainty allows us to produce more realistic uncertainty estimates and assists the selection of the most appropriate LUTs for each individual retrieval.This paper focuses on the aerosol microphysical model selection and characterisation of uncertainty in the retrieved aerosol type and aerosol optical depth (AOD). The concept of model evidence is used as a tool for model comparison. The method is based on Bayesian inference approach, in which all uncertainties are described as a posterior probability distribution. When there is no single best-matching aerosol microphysical model, we use a statistical technique based on Bayesian model averaging to combine AOD posterior probability densities of the best-fitting models to obtain an averaged AOD estimate. We also determine the shared evidence of the best-matching models of a certain main aerosol type in order to quantify how plausible it is that it represents the underlying atmospheric aerosol conditions.The developed method is applied to Ozone Monitoring Instrument (OMI) measurements using a multiwavelength approach for retrieving the aerosol type and AOD estimate with uncertainty quantification for cloud-free over-land pixels. Several larger pixel set areas were studied in order to investigate the robustness of the developed method. We evaluated the retrieved AOD by comparison with ground-based measurements at example sites. We found that the uncertainty of AOD expressed by posterior probability distribution reflects the difficulty in model selection. The posterior probability distribution can provide a comprehensive characterisation of the uncertainty in this kind of problem for aerosol-type selection. As a result, the proposed method can account for the model error and also include the model selection uncertainty in the total uncertainty budget.

Neutronics qualification of the Jules Horowitz reactor fuel by interpretation of the VALMONT experimental program - Transposition of the uncertainties on the reactivity of JHR with JEF2.2 and JEFF3.1.1

DOE Office of Scientific and Technical Information (OSTI.GOV)

Leray, O.; Hudelot, J. P.; Antony, M.

2011-07-01

The new European material testing Jules Horowitz Reactor (JHR), currently under construction in Cadarache center (CEA France), will use LEU (20% enrichment in {sup 235}U) fuels (U{sub 3}Si{sub 2} for the start up and UMoAl in the future) which are quite different from the industrial oxide fuel, for which an extensive neutronics qualification database has been established. The HORUS3D/N neutronics calculation scheme, used for the design and safety studies of the JHR, is being developed within the framework of a rigorous verification-validation-qualification methodology. In this framework, the experimental VALMONT (Validation of Aluminium Molybdenum uranium fuel for Neutronics) program has beenmore » performed in the MINERVE facility of CEA Cadarache (France), in order to qualify the capability of HORUS3D/N to accurately calculate the reactivity of the JHR reactor. The MINERVE facility using the oscillation technique provides accurate measurements of reactivity effect of samples. The VALMONT program includes oscillations of samples of UAl{sub x}/Al and UMo/Al with enrichments ranging from 0.2% to 20% and Uranium densities from 2.2 to 8 g/cm{sup 3}. The geometry of the samples and the pitch of the experimental lattice ensure maximum representativeness with the neutron spectrum expected for JHR. By comparing the effect of the sample with the one of a known fuel specimen, the reactivity effect can be measured in absolute terms and be compared to computational results. Special attention was paid to the rigorous determination and reduction of the experimental uncertainties. The calculational analysis of the VALMONT results was performed with the French deterministic code APOLLO2. A comparison of the impact of the different calculation methods, data libraries and energy meshes that were tested is presented. The interpretation of the VALMONT experimental program allowed the qualification of JHR fuel UMoAl8 (with an enrichment of 19.75% {sup 235}U) by the Minerve-dedicated interpretation tool: PIMS. The effect of energy meshes and evaluations put forward the JEFF3.1.1/SHEM scheme that leads to a better calculation of the reactivity effect of VALMONT samples. Then, in order to quantify the impact of the uncertainties linked to the basic nuclear data, their propagation from the cross section measurement to the final computational result was analysed in a rigorous way by using a nuclear data re-estimation method based on Gauss-Newton iterations. This study concludes that the prior uncertainties due to nuclear data (uranium, aluminium, beryllium and water) on the reactivity of the Begin Of Cycle (BOC) for the JHR core reach 1217 pcm at 2{sigma}. Now, the uppermost uncertainty on the JHR reactivity is due to aluminium. (authors)« less

Reinventing the High School Government Course: Rigor, Simulations, and Learning from Text

ERIC Educational Resources Information Center

Parker, Walter C.; Lo, Jane C.

2016-01-01

The high school government course is arguably the main site of formal civic education in the country today. This article presents the curriculum that resulted from a multiyear study aimed at improving the course. The pedagogic model, called "Knowledge in Action," centers on a rigorous form of project-based learning where the projects are…

Estimating Cosmic-Ray Spectral Parameters from Simulated Detector Responses with Detector Design Implications

NASA Technical Reports Server (NTRS)

Howell, L. W.

2001-01-01

A simple power law model consisting of a single spectral index (alpha-1) is believed to be an adequate description of the galactic cosmic-ray (GCR) proton flux at energies below 10(exp 13) eV, with a transition at knee energy (E(sub k)) to a steeper spectral index alpha-2 > alpha-1 above E(sub k). The maximum likelihood procedure is developed for estimating these three spectral parameters of the broken power law energy spectrum from simulated detector responses. These estimates and their surrounding statistical uncertainty are being used to derive the requirements in energy resolution, calorimeter size, and energy response of a proposed sampling calorimeter for the Advanced Cosmic-ray Composition Experiment for the Space Station (ACCESS). This study thereby permits instrument developers to make important trade studies in design parameters as a function of the science objectives, which is particularly important for space-based detectors where physical parameters, such as dimension and weight, impose rigorous practical limits to the design envelope.

A VLF-based technique in applications to digital control of nonlinear hybrid multirate systems

NASA Astrophysics Data System (ADS)

Vassilyev, Stanislav; Ulyanov, Sergey; Maksimkin, Nikolay

2017-01-01

In this paper, a technique for rigorous analysis and design of nonlinear multirate digital control systems on the basis of the reduction method and sublinear vector Lyapunov functions is proposed. The control system model under consideration incorporates continuous-time dynamics of the plant and discrete-time dynamics of the controller and takes into account uncertainties of the plant, bounded disturbances, nonlinear characteristics of sensors and actuators. We consider a class of multirate systems where the control update rate is slower than the measurement sampling rates and periodic non-uniform sampling is admitted. The proposed technique does not use the preliminary discretization of the system, and, hence, allows one to eliminate the errors associated with the discretization and improve the accuracy of analysis. The technique is applied to synthesis of digital controller for a flexible spacecraft in the fine stabilization mode and decentralized controller for a formation of autonomous underwater vehicles. Simulation results are provided to validate the good performance of the designed controllers.

CFD Validation Experiment of a Mach 2.5 Axisymmetric Shock-Wave/Boundary-Layer Interaction

NASA Technical Reports Server (NTRS)

Davis, David O.

2015-01-01

Experimental investigations of specific flow phenomena, e.g., Shock Wave Boundary-Layer Interactions (SWBLI), provide great insight to the flow behavior but often lack the necessary details to be useful as CFD validation experiments. Reasons include: 1.Undefined boundary conditions Inconsistent results 2.Undocumented 3D effects (CL only measurements) 3.Lack of uncertainty analysis While there are a number of good subsonic experimental investigations that are sufficiently documented to be considered test cases for CFD and turbulence model validation, the number of supersonic and hypersonic cases is much less. This was highlighted by Settles and Dodsons [1] comprehensive review of available supersonic and hypersonic experimental studies. In all, several hundred studies were considered for their database.Of these, over a hundred were subjected to rigorous acceptance criteria. Based on their criteria, only 19 (12 supersonic, 7 hypersonic) were considered of sufficient quality to be used for validation purposes. Aeschliman and Oberkampf [2] recognized the need to develop a specific methodology for experimental studies intended specifically for validation purposes.

Partitioning the Uncertainty in Estimates of Mean Basal Area Obtained from 10-year Diameter Growth Model Predictions

Treesearch

Ronald E. McRoberts

2005-01-01

Uncertainty in model-based predictions of individual tree diameter growth is attributed to three sources: measurement error for predictor variables, residual variability around model predictions, and uncertainty in model parameter estimates. Monte Carlo simulations are used to propagate the uncertainty from the three sources through a set of diameter growth models to...

Multi-objective calibration and uncertainty analysis of hydrologic models; A comparative study between formal and informal methods

NASA Astrophysics Data System (ADS)

Shafii, M.; Tolson, B.; Matott, L. S.

2012-04-01

Hydrologic modeling has benefited from significant developments over the past two decades. This has resulted in building of higher levels of complexity into hydrologic models, which eventually makes the model evaluation process (parameter estimation via calibration and uncertainty analysis) more challenging. In order to avoid unreasonable parameter estimates, many researchers have suggested implementation of multi-criteria calibration schemes. Furthermore, for predictive hydrologic models to be useful, proper consideration of uncertainty is essential. Consequently, recent research has emphasized comprehensive model assessment procedures in which multi-criteria parameter estimation is combined with statistically-based uncertainty analysis routines such as Bayesian inference using Markov Chain Monte Carlo (MCMC) sampling. Such a procedure relies on the use of formal likelihood functions based on statistical assumptions, and moreover, the Bayesian inference structured on MCMC samplers requires a considerably large number of simulations. Due to these issues, especially in complex non-linear hydrological models, a variety of alternative informal approaches have been proposed for uncertainty analysis in the multi-criteria context. This study aims at exploring a number of such informal uncertainty analysis techniques in multi-criteria calibration of hydrological models. The informal methods addressed in this study are (i) Pareto optimality which quantifies the parameter uncertainty using the Pareto solutions, (ii) DDS-AU which uses the weighted sum of objective functions to derive the prediction limits, and (iii) GLUE which describes the total uncertainty through identification of behavioral solutions. The main objective is to compare such methods with MCMC-based Bayesian inference with respect to factors such as computational burden, and predictive capacity, which are evaluated based on multiple comparative measures. The measures for comparison are calculated both for calibration and evaluation periods. The uncertainty analysis methodologies are applied to a simple 5-parameter rainfall-runoff model, called HYMOD.

An uncertainty analysis of wildfire modeling [Chapter 13

Treesearch

Karin Riley; Matthew Thompson

2017-01-01

Before fire models can be understood, evaluated, and effectively applied to support decision making, model-based uncertainties must be analyzed. In this chapter, we identify and classify sources of uncertainty using an established analytical framework, and summarize results graphically in an uncertainty matrix. Our analysis facilitates characterization of the...

Managing Wind Power Uncertainty Through Strategic Reserve Purchasing

DOE Office of Scientific and Technical Information (OSTI.GOV)

Du, Ershun; Zhang, Ning; Kang, Chongqing

With the rapidly increasing penetration of wind power, wind producers are becoming increasingly responsible for the deviation of the wind power output from the forecast. Such uncertainty results in revenue losses to the wind power producers (WPPs) due to penalties in ex-post imbalance settlements. This paper explores the opportunities available for WPPs if they can purchase or schedule some reserves to offset part of their deviation rather than being fully penalized in the real time market. The revenue for WPPs under such mechanism is modeled. The optimal strategy for managing the uncertainty of wind power by purchasing reserves to maximizemore » the WPP's revenue is analytically derived with rigorous optimality conditions. The amount of energy and reserves that should be bid in the market are explicitly quantified by the probabilistic forecast and the prices of the energy and reserves. A case study using the price data from ERCOT and wind power data from NREL is performed to verify the effectiveness of the derived optimal bidding strategy and the benefits of reserve purchasing. Additionally, the proposed bidding strategy can also reduce the risk of variations on WPP's revenue.« less

Statistical Methods Applied to Gamma-ray Spectroscopy Algorithms in Nuclear Security Missions

DOE Office of Scientific and Technical Information (OSTI.GOV)

Fagan, Deborah K.; Robinson, Sean M.; Runkle, Robert C.

2012-10-01

In a wide range of nuclear security missions, gamma-ray spectroscopy is a critical research and development priority. One particularly relevant challenge is the interdiction of special nuclear material for which gamma-ray spectroscopy supports the goals of detecting and identifying gamma-ray sources. This manuscript examines the existing set of spectroscopy methods, attempts to categorize them by the statistical methods on which they rely, and identifies methods that have yet to be considered. Our examination shows that current methods effectively estimate the effect of counting uncertainty but in many cases do not address larger sources of decision uncertainty—ones that are significantly moremore » complex. We thus explore the premise that significantly improving algorithm performance requires greater coupling between the problem physics that drives data acquisition and statistical methods that analyze such data. Untapped statistical methods, such as Bayes Modeling Averaging and hierarchical and empirical Bayes methods have the potential to reduce decision uncertainty by more rigorously and comprehensively incorporating all sources of uncertainty. We expect that application of such methods will demonstrate progress in meeting the needs of nuclear security missions by improving on the existing numerical infrastructure for which these analyses have not been conducted.« less

The critical role of uncertainty in projections of hydrological extremes

NASA Astrophysics Data System (ADS)

Meresa, Hadush K.; Romanowicz, Renata J.

2017-08-01

This paper aims to quantify the uncertainty in projections of future hydrological extremes in the Biala Tarnowska River at Koszyce gauging station, south Poland. The approach followed is based on several climate projections obtained from the EURO-CORDEX initiative, raw and bias-corrected realizations of catchment precipitation, and flow simulations derived using multiple hydrological model parameter sets. The projections cover the 21st century. Three sources of uncertainty are considered: one related to climate projection ensemble spread, the second related to the uncertainty in hydrological model parameters and the third related to the error in fitting theoretical distribution models to annual extreme flow series. The uncertainty of projected extreme indices related to hydrological model parameters was conditioned on flow observations from the reference period using the generalized likelihood uncertainty estimation (GLUE) approach, with separate criteria for high- and low-flow extremes. Extreme (low and high) flow quantiles were estimated using the generalized extreme value (GEV) distribution at different return periods and were based on two different lengths of the flow time series. A sensitivity analysis based on the analysis of variance (ANOVA) shows that the uncertainty introduced by the hydrological model parameters can be larger than the climate model variability and the distribution fit uncertainty for the low-flow extremes whilst for the high-flow extremes higher uncertainty is observed from climate models than from hydrological parameter and distribution fit uncertainties. This implies that ignoring one of the three uncertainty sources may cause great risk to future hydrological extreme adaptations and water resource planning and management.

New activity-based funding model for Australian private sector overnight rehabilitation cases: the rehabilitation Australian National Sub-Acute and Non-Acute Patient (AN-SNAP) model.

PubMed

Hanning, Brian; Predl, Nicolle

2015-09-01

Traditional overnight rehabilitation payment models in the private sector are not based on a rigorous classification system and vary greatly between contracts with no consideration of patient complexity. The payment rates are not based on relative cost and the length-of-stay (LOS) point at which a reduced rate applies (step downs) varies markedly. The rehabilitation Australian National Sub-Acute and Non-Acute Patient (AN-SNAP) model (RAM), which has been in place for over 2 years in some private hospitals, bases payment on a rigorous classification system, relative cost and industry LOS. RAM is in the process of being rolled out more widely. This paper compares and contrasts RAM with traditional overnight rehabilitation payment models. It considers the advantages of RAM for hospitals and Australian Health Service Alliance. It also considers payment model changes in the context of maintaining industry consistency with Electronic Claims Lodgement and Information Processing System Environment (ECLIPSE) and health reform generally.

Constraining geostatistical models with hydrological data to improve prediction realism

NASA Astrophysics Data System (ADS)

Demyanov, V.; Rojas, T.; Christie, M.; Arnold, D.

2012-04-01

Geostatistical models reproduce spatial correlation based on the available on site data and more general concepts about the modelled patters, e.g. training images. One of the problem of modelling natural systems with geostatistics is in maintaining realism spatial features and so they agree with the physical processes in nature. Tuning the model parameters to the data may lead to geostatistical realisations with unrealistic spatial patterns, which would still honour the data. Such model would result in poor predictions, even though although fit the available data well. Conditioning the model to a wider range of relevant data provide a remedy that avoid producing unrealistic features in spatial models. For instance, there are vast amounts of information about the geometries of river channels that can be used in describing fluvial environment. Relations between the geometrical channel characteristics (width, depth, wave length, amplitude, etc.) are complex and non-parametric and are exhibit a great deal of uncertainty, which is important to propagate rigorously into the predictive model. These relations can be described within a Bayesian approach as multi-dimensional prior probability distributions. We propose a way to constrain multi-point statistics models with intelligent priors obtained from analysing a vast collection of contemporary river patterns based on previously published works. We applied machine learning techniques, namely neural networks and support vector machines, to extract multivariate non-parametric relations between geometrical characteristics of fluvial channels from the available data. An example demonstrates how ensuring geological realism helps to deliver more reliable prediction of a subsurface oil reservoir in a fluvial depositional environment.

Validation of a 30 m resolution flood hazard model of the conterminous United States

NASA Astrophysics Data System (ADS)

Wing, Oliver E. J.; Bates, Paul D.; Sampson, Christopher C.; Smith, Andrew M.; Johnson, Kris A.; Erickson, Tyler A.

2017-09-01

This paper reports the development of a ˜30 m resolution two-dimensional hydrodynamic model of the conterminous U.S. using only publicly available data. The model employs a highly efficient numerical solution of the local inertial form of the shallow water equations which simulates fluvial flooding in catchments down to 50 km2 and pluvial flooding in all catchments. Importantly, we use the U.S. Geological Survey (USGS) National Elevation Dataset to determine topography; the U.S. Army Corps of Engineers National Levee Dataset to explicitly represent known flood defenses; and global regionalized flood frequency analysis to characterize return period flows and rainfalls. We validate these simulations against the complete catalogue of Federal Emergency Management Agency (FEMA) Special Flood Hazard Area (SFHA) maps and detailed local hydraulic models developed by the USGS. Where the FEMA SFHAs are based on high-quality local models, the continental-scale model attains a hit rate of 86%. This correspondence improves in temperate areas and for basins above 400 km2. Against the higher quality USGS data, the average hit rate reaches 92% for the 1 in 100 year flood, and 90% for all flood return periods. Given typical hydraulic modeling uncertainties in the FEMA maps and USGS model outputs (e.g., errors in estimating return period flows), it is probable that the continental-scale model can replicate both to within error. The results show that continental-scale models may now offer sufficient rigor to inform some decision-making needs with dramatically lower cost and greater coverage than approaches based on a patchwork of local studies.

Uncertainty and Variability in Physiologically-Based Pharmacokinetic (PBPK) Models: Key Issues and Case Studies (Final Report)

EPA Science Inventory

EPA announced the availability of the final report, Uncertainty and Variability in Physiologically-Based Pharmacokinetic (PBPK) Models: Key Issues and Case Studies. This report summarizes some of the recent progress in characterizing uncertainty and variability in physi...

Uncertainty in the use of MAMA software to measure particle morphological parameters from SEM images

DOE Office of Scientific and Technical Information (OSTI.GOV)

Schwartz, Daniel S.; Tandon, Lav

The MAMA software package developed at LANL is designed to make morphological measurements on a wide variety of digital images of objects. At LANL, we have focused on using MAMA to measure scanning electron microscope (SEM) images of particles, as this is a critical part of our forensic analysis of interdicted radiologic materials. In order to successfully use MAMA to make such measurements, we must understand the level of uncertainty involved in the process, so that we can rigorously support our quantitative conclusions.

MUSiC - A Generic Search for Deviations from Monte Carlo Predictions in CMS

NASA Astrophysics Data System (ADS)

Hof, Carsten

2009-05-01

We present a model independent analysis approach, systematically scanning the data for deviations from the Standard Model Monte Carlo expectation. Such an analysis can contribute to the understanding of the CMS detector and the tuning of the event generators. Furthermore, due to the minimal theoretical bias this approach is sensitive to a variety of models of new physics, including those not yet thought of. Events are classified into event classes according to their particle content (muons, electrons, photons, jets and missing transverse energy). A broad scan of various distributions is performed, identifying significant deviations from the Monte Carlo simulation. We outline the importance of systematic uncertainties, which are taken into account rigorously within the algorithm. Possible detector effects and generator issues, as well as models involving supersymmetry and new heavy gauge bosons have been used as an input to the search algorithm.

Risk assessment of turbine rotor failure using probabilistic ultrasonic non-destructive evaluations

NASA Astrophysics Data System (ADS)

Guan, Xuefei; Zhang, Jingdan; Zhou, S. Kevin; Rasselkorde, El Mahjoub; Abbasi, Waheed A.

2014-02-01

The study presents a method and application of risk assessment methodology for turbine rotor fatigue failure using probabilistic ultrasonic nondestructive evaluations. A rigorous probabilistic modeling for ultrasonic flaw sizing is developed by incorporating the model-assisted probability of detection, and the probability density function (PDF) of the actual flaw size is derived. Two general scenarios, namely the ultrasonic inspection with an identified flaw indication and the ultrasonic inspection without flaw indication, are considered in the derivation. To perform estimations for fatigue reliability and remaining useful life, uncertainties from ultrasonic flaw sizing and fatigue model parameters are systematically included and quantified. The model parameter PDF is estimated using Bayesian parameter estimation and actual fatigue testing data. The overall method is demonstrated using a realistic application of steam turbine rotor, and the risk analysis under given safety criteria is provided to support maintenance planning.

Testing stress shadowing effects at the South American subduction zone

NASA Astrophysics Data System (ADS)

Roth, F.; Dahm, T.; Hainzl, S.

2017-11-01

The seismic gap hypothesis assumes that a characteristic earthquake is followed by a long period with a reduced occurrence probability for the next large event on the same fault segment, as a consequence of the induced stress shadow. The gap model is commonly accepted by geologists and is often used for time-dependent seismic hazard estimations. However, systematic and rigorous tests to verify the seismic gap model have often failed so far, which might be partially related to limited data and too tight model assumptions. In this study, we relax the assumption of a characteristic size and location of repeating earthquakes and analyse one of the best available data sets, namely the historical record of major earthquakes along a 3000 km long linear segment of the South American subduction zone. To test whether a stress shadow effect is observable, we compiled a comprehensive catalogue of mega-thrust earthquakes along this plate boundary from 1520 to 2015 containing 174 earthquakes with Mw > 6.5. In our new testing approach, we analyse the time span between an earthquake and the last event that ruptured the epicentre location, where we consider the impact of the uncertainties of epicentres and rupture extensions. Assuming uniform boundary conditions along the trench, we compare the distribution of these recurrence times with simple recurrence models. We find that the distribution is in all cases almost exponentially distributed corresponding to a random (Poissonian) process; despite some tendencies for clustering of the Mw ≥ 7 events and a weak quasi-periodicity of the Mw ≥ 8 earthquakes, respectively. To verify whether the absence of a clear stress shadow signal is related to physical assumptions or data uncertainties, we perform simulations of a physics-based stochastic earthquake model considering rate and state-dependent earthquake nucleation, which are adapted to the observations with regard to the number of events, spatial extend, size distribution and involved uncertainties. Our simulations show that the catalogue uncertainties lead to a significant blurring of the theoretically peaked distribution, but the distribution would be still distinguishable from the observed one for Mw ≥ 7 events. However, considering the stress transfer to adjacent fault segments and heterogeneous instead of constant stress drop within the rupture zone can explain the observed recurrence time distribution. We conclude that simplified recurrence models, ignoring the complexity of the underlying physical process, cannot be applied for forecasting the Mw ≥ 7 earthquake occurrence at this plate boundary.

Stochastic Inversion of Geomagnetic Observatory Data Including Rigorous Treatment of the Ocean Induction Effect With Implications for Transition Zone Water Content and Thermal Structure

NASA Astrophysics Data System (ADS)

Munch, F. D.; Grayver, A. V.; Kuvshinov, A.; Khan, A.

2018-01-01

In this paper we estimate and invert local electromagnetic (EM) sounding data for 1-D conductivity profiles in the presence of nonuniform oceans and continents to most rigorously account for the ocean induction effect that is known to strongly influence coastal observatories. We consider a new set of high-quality time series of geomagnetic observatory data, including hitherto unused data from island observatories installed over the last decade. The EM sounding data are inverted in the period range 3-85 days using stochastic optimization and model exploration techniques to provide estimates of model range and uncertainty. The inverted conductivity profiles are best constrained in the depth range 400-1,400 km and reveal significant lateral variations between 400 km and 1,000 km depth. To interpret the inverted conductivity anomalies in terms of water content and temperature, we combine laboratory-measured electrical conductivity of mantle minerals with phase equilibrium computations. Based on this procedure, relatively low temperatures (1200-1350°C) are observed in the transition zone (TZ) underneath stations located in Southern Australia, Southern Europe, Northern Africa, and North America. In contrast, higher temperatures (1400-1500°C) are inferred beneath observatories on islands, Northeast Asia, and central Australia. TZ water content beneath European and African stations is ˜0.05-0.1 wt %, whereas higher water contents (˜0.5-1 wt %) are inferred underneath North America, Asia, and Southern Australia. Comparison of the inverted water contents with laboratory-constrained water storage capacities suggests the presence of melt in or around the TZ underneath four geomagnetic observatories in North America and Northeast Asia.

Case studies in Bayesian microbial risk assessments.

PubMed

Kennedy, Marc C; Clough, Helen E; Turner, Joanne

2009-12-21

The quantification of uncertainty and variability is a key component of quantitative risk analysis. Recent advances in Bayesian statistics make it ideal for integrating multiple sources of information, of different types and quality, and providing a realistic estimate of the combined uncertainty in the final risk estimates. We present two case studies related to foodborne microbial risks. In the first, we combine models to describe the sequence of events resulting in illness from consumption of milk contaminated with VTEC O157. We used Monte Carlo simulation to propagate uncertainty in some of the inputs to computer models describing the farm and pasteurisation process. Resulting simulated contamination levels were then assigned to consumption events from a dietary survey. Finally we accounted for uncertainty in the dose-response relationship and uncertainty due to limited incidence data to derive uncertainty about yearly incidences of illness in young children. Options for altering the risk were considered by running the model with different hypothetical policy-driven exposure scenarios. In the second case study we illustrate an efficient Bayesian sensitivity analysis for identifying the most important parameters of a complex computer code that simulated VTEC O157 prevalence within a managed dairy herd. This was carried out in 2 stages, first to screen out the unimportant inputs, then to perform a more detailed analysis on the remaining inputs. The method works by building a Bayesian statistical approximation to the computer code using a number of known code input/output pairs (training runs). We estimated that the expected total number of children aged 1.5-4.5 who become ill due to VTEC O157 in milk is 8.6 per year, with 95% uncertainty interval (0,11.5). The most extreme policy we considered was banning on-farm pasteurisation of milk, which reduced the estimate to 6.4 with 95% interval (0,11). In the second case study the effective number of inputs was reduced from 30 to 7 in the screening stage, and just 2 inputs were found to explain 82.8% of the output variance. A combined total of 500 runs of the computer code were used. These case studies illustrate the use of Bayesian statistics to perform detailed uncertainty and sensitivity analyses, integrating multiple information sources in a way that is both rigorous and efficient.

Do state-of-the-art CMIP5 ESMs accurately represent observed vegetation-rainfall feedbacks? Focus on the Sahel

NASA Astrophysics Data System (ADS)

Notaro, M.; Wang, F.; Yu, Y.; Mao, J.; Shi, X.; Wei, Y.

2017-12-01

The semi-arid Sahel ecoregion is an established hotspot of land-atmosphere coupling. Ocean-land-atmosphere interactions received considerable attention by modeling studies in response to the devastating 1970s-90s Sahel drought, which models suggest was driven by sea-surface temperature (SST) anomalies and amplified by local vegetation-atmosphere feedbacks. Vegetation affects the atmosphere through biophysical feedbacks by altering the albedo, roughness, and transpiration and thereby modifying exchanges of energy, momentum, and moisture with the atmosphere. The current understanding of these potentially competing processes is primarily based on modeling studies, with biophysical feedbacks serving as a key uncertainty source in regional climate change projections among Earth System Models (ESMs). In order to reduce this uncertainty, it is critical to rigorously evaluate the representation of vegetation feedbacks in ESMs against an observational benchmark in order to diagnose systematic biases and their sources. However, it is challenging to successfully isolate vegetation's feedbacks on the atmosphere, since the atmospheric control on vegetation growth dominates the atmospheric feedback response to vegetation anomalies and the atmosphere is simultaneously influenced by oceanic and terrestrial anomalies. In response to this challenge, a model-validated multivariate statistical method, Stepwise Generalized Equilibrium Feedback Assessment (SGEFA), is developed, which extracts the forcing of a slowly-evolving environmental variable [e.g. SST or leaf area index (LAI)] on the rapidly-evolving atmosphere. By applying SGEFA to observational and remotely-sensed data, an observational benchmark is established for Sahel vegetation feedbacks. In this work, the simulated responses in key atmospheric variables, including evapotranspiration, albedo, wind speed, vertical motion, temperature, stability, and rainfall, to Sahel LAI anomalies are statistically assessed in Coupled Model Intercomparison Project Phase 5 (CMIP5) ESMs through SGEFA. The dominant mechanism, such as albedo feedback, moisture recycling, or momentum feedback, in each ESM is evaluated against the observed benchmark. SGEFA facilitates a systematic assessment of model biases in land-atmosphere interactions.

Uncertainty modelling and analysis of volume calculations based on a regular grid digital elevation model (DEM)

NASA Astrophysics Data System (ADS)

Li, Chang; Wang, Qing; Shi, Wenzhong; Zhao, Sisi

2018-05-01

The accuracy of earthwork calculations that compute terrain volume is critical to digital terrain analysis (DTA). The uncertainties in volume calculations (VCs) based on a DEM are primarily related to three factors: 1) model error (ME), which is caused by an adopted algorithm for a VC model, 2) discrete error (DE), which is usually caused by DEM resolution and terrain complexity, and 3) propagation error (PE), which is caused by the variables' error. Based on these factors, the uncertainty modelling and analysis of VCs based on a regular grid DEM are investigated in this paper. Especially, how to quantify the uncertainty of VCs is proposed by a confidence interval based on truncation error (TE). In the experiments, the trapezoidal double rule (TDR) and Simpson's double rule (SDR) were used to calculate volume, where the TE is the major ME, and six simulated regular grid DEMs with different terrain complexity and resolution (i.e. DE) were generated by a Gauss synthetic surface to easily obtain the theoretical true value and eliminate the interference of data errors. For PE, Monte-Carlo simulation techniques and spatial autocorrelation were used to represent DEM uncertainty. This study can enrich uncertainty modelling and analysis-related theories of geographic information science.

Incorporating uncertainty in predictive species distribution modelling.

PubMed

Beale, Colin M; Lennon, Jack J

2012-01-19

Motivated by the need to solve ecological problems (climate change, habitat fragmentation and biological invasions), there has been increasing interest in species distribution models (SDMs). Predictions from these models inform conservation policy, invasive species management and disease-control measures. However, predictions are subject to uncertainty, the degree and source of which is often unrecognized. Here, we review the SDM literature in the context of uncertainty, focusing on three main classes of SDM: niche-based models, demographic models and process-based models. We identify sources of uncertainty for each class and discuss how uncertainty can be minimized or included in the modelling process to give realistic measures of confidence around predictions. Because this has typically not been performed, we conclude that uncertainty in SDMs has often been underestimated and a false precision assigned to predictions of geographical distribution. We identify areas where development of new statistical tools will improve predictions from distribution models, notably the development of hierarchical models that link different types of distribution model and their attendant uncertainties across spatial scales. Finally, we discuss the need to develop more defensible methods for assessing predictive performance, quantifying model goodness-of-fit and for assessing the significance of model covariates.

Holistic uncertainty analysis in river basin modeling for climate vulnerability assessment

NASA Astrophysics Data System (ADS)

Taner, M. U.; Wi, S.; Brown, C.

2017-12-01

The challenges posed by uncertain future climate are a prominent concern for water resources managers. A number of frameworks exist for assessing the impacts of climate-related uncertainty, including internal climate variability and anthropogenic climate change, such as scenario-based approaches and vulnerability-based approaches. While in many cases climate uncertainty may be dominant, other factors such as future evolution of the river basin, hydrologic response and reservoir operations are potentially significant sources of uncertainty. While uncertainty associated with modeling hydrologic response has received attention, very little attention has focused on the range of uncertainty and possible effects of the water resources infrastructure and management. This work presents a holistic framework that allows analysis of climate, hydrologic and water management uncertainty in water resources systems analysis with the aid of a water system model designed to integrate component models for hydrology processes and water management activities. The uncertainties explored include those associated with climate variability and change, hydrologic model parameters, and water system operation rules. A Bayesian framework is used to quantify and model the uncertainties at each modeling steps in integrated fashion, including prior and the likelihood information about model parameters. The framework is demonstrated in a case study for the St. Croix Basin located at border of United States and Canada.

Experimental benchmark of kinetic simulations of capacitively coupled plasmas in molecular gases

NASA Astrophysics Data System (ADS)

Donkó, Z.; Derzsi, A.; Korolov, I.; Hartmann, P.; Brandt, S.; Schulze, J.; Berger, B.; Koepke, M.; Bruneau, B.; Johnson, E.; Lafleur, T.; Booth, J.-P.; Gibson, A. R.; O'Connell, D.; Gans, T.

2018-01-01

We discuss the origin of uncertainties in the results of numerical simulations of low-temperature plasma sources, focusing on capacitively coupled plasmas. These sources can be operated in various gases/gas mixtures, over a wide domain of excitation frequency, voltage, and gas pressure. At low pressures, the non-equilibrium character of the charged particle transport prevails and particle-based simulations become the primary tools for their numerical description. The particle-in-cell method, complemented with Monte Carlo type description of collision processes, is a well-established approach for this purpose. Codes based on this technique have been developed by several authors/groups, and have been benchmarked with each other in some cases. Such benchmarking demonstrates the correctness of the codes, but the underlying physical model remains unvalidated. This is a key point, as this model should ideally account for all important plasma chemical reactions as well as for the plasma-surface interaction via including specific surface reaction coefficients (electron yields, sticking coefficients, etc). In order to test the models rigorously, comparison with experimental ‘benchmark data’ is necessary. Examples will be given regarding the studies of electron power absorption modes in O2, and CF4-Ar discharges, as well as on the effect of modifications of the parameters of certain elementary processes on the computed discharge characteristics in O2 capacitively coupled plasmas.

Parameter-induced uncertainty quantification of crop yields, soil N2O and CO2 emission for 8 arable sites across Europe using the LandscapeDNDC model

NASA Astrophysics Data System (ADS)

Santabarbara, Ignacio; Haas, Edwin; Kraus, David; Herrera, Saul; Klatt, Steffen; Kiese, Ralf

2014-05-01

When using biogeochemical models to estimate greenhouse gas emissions at site to regional/national levels, the assessment and quantification of the uncertainties of simulation results are of significant importance. The uncertainties in simulation results of process-based ecosystem models may result from uncertainties of the process parameters that describe the processes of the model, model structure inadequacy as well as uncertainties in the observations. Data for development and testing of uncertainty analisys were corp yield observations, measurements of soil fluxes of nitrous oxide (N2O) and carbon dioxide (CO2) from 8 arable sites across Europe. Using the process-based biogeochemical model LandscapeDNDC for simulating crop yields, N2O and CO2 emissions, our aim is to assess the simulation uncertainty by setting up a Bayesian framework based on Metropolis-Hastings algorithm. Using Gelman statistics convergence criteria and parallel computing techniques, enable multi Markov Chains to run independently in parallel and create a random walk to estimate the joint model parameter distribution. Through means distribution we limit the parameter space, get probabilities of parameter values and find the complex dependencies among them. With this parameter distribution that determines soil-atmosphere C and N exchange, we are able to obtain the parameter-induced uncertainty of simulation results and compare them with the measurements data.

Accuracy and performance of 3D mask models in optical projection lithography

NASA Astrophysics Data System (ADS)

Agudelo, Viviana; Evanschitzky, Peter; Erdmann, Andreas; Fühner, Tim; Shao, Feng; Limmer, Steffen; Fey, Dietmar

2011-04-01

Different mask models have been compared: rigorous electromagnetic field (EMF) modeling, rigorous EMF modeling with decomposition techniques and the thin mask approach (Kirchhoff approach) to simulate optical diffraction from different mask patterns in projection systems for lithography. In addition, each rigorous model was tested for two different formulations for partially coherent imaging: The Hopkins assumption and rigorous simulation of mask diffraction orders for multiple illumination angles. The aim of this work is to closely approximate results of the rigorous EMF method by the thin mask model enhanced with pupil filtering techniques. The validity of this approach for different feature sizes, shapes and illumination conditions is investigated.

Assessment of SWE data assimilation for ensemble streamflow predictions

NASA Astrophysics Data System (ADS)

Franz, Kristie J.; Hogue, Terri S.; Barik, Muhammad; He, Minxue

2014-11-01

An assessment of data assimilation (DA) for Ensemble Streamflow Prediction (ESP) using seasonal water supply hindcasting in the North Fork of the American River Basin (NFARB) and the National Weather Service (NWS) hydrologic forecast models is undertaken. Two parameter sets, one from the California Nevada River Forecast Center (RFC) and one from the Differential Evolution Adaptive Metropolis (DREAM) algorithm, are tested. For each parameter set, hindcasts are generated using initial conditions derived with and without the inclusion of a DA scheme that integrates snow water equivalent (SWE) observations. The DREAM-DA scenario uses an Integrated Uncertainty and Ensemble-based data Assimilation (ICEA) framework that also considers model and parameter uncertainty. Hindcasts are evaluated using deterministic and probabilistic forecast verification metrics. In general, the impact of DA on the skill of the seasonal water supply predictions is mixed. For deterministic (ensemble mean) predictions, the Percent Bias (PBias) is improved with integration of the DA. DREAM-DA and the RFC-DA have the lowest biases and the RFC-DA has the lowest Root Mean Squared Error (RMSE). However, the RFC and DREAM-DA have similar RMSE scores. For the probabilistic predictions, the RFC and DREAM have the highest Continuous Ranked Probability Skill Scores (CRPSS) and the RFC has the best discrimination for low flows. Reliability results are similar between the non-DA and DA tests and the DREAM and DREAM-DA have better reliability than the RFC and RFC-DA for forecast dates February 1 and later. Despite producing improved streamflow simulations in previous studies, the hindcast analysis suggests that the DA method tested may not result in obvious improvements in streamflow forecasts. We advocate that integration of hindcasting and probabilistic metrics provides more rigorous insight on model performance for forecasting applications, such as in this study.

Uncertainty quantification in application of the enrichment meter principle for nondestructive assay of special nuclear material

DOE PAGES

Burr, Tom; Croft, Stephen; Jarman, Kenneth D.

2015-09-05

The various methods of nondestructive assay (NDA) of special nuclear material (SNM) have applications in nuclear nonproliferation, including detection and identification of illicit SNM at border crossings, and quantifying SNM at nuclear facilities for safeguards. No assay method is complete without “error bars,” which provide one way of expressing confidence in the assay result. Consequently, NDA specialists typically quantify total uncertainty in terms of “random” and “systematic” components, and then specify error bars for the total mass estimate in multiple items. Uncertainty quantification (UQ) for NDA has always been important, but it is recognized that greater rigor is needed andmore » achievable using modern statistical methods. To this end, we describe the extent to which the guideline for expressing uncertainty in measurements (GUM) can be used for NDA. Also, we propose improvements over GUM for NDA by illustrating UQ challenges that it does not address, including calibration with errors in predictors, model error, and item-specific biases. A case study is presented using low-resolution NaI spectra and applying the enrichment meter principle to estimate the U-235 mass in an item. The case study illustrates how to update the current American Society for Testing and Materials guide for application of the enrichment meter principle using gamma spectra from a NaI detector.« less

A New Combined Stepwise-Based High-Order Decoupled Direct and Reduced-Form Method To Improve Uncertainty Analysis in PM2.5 Simulations.

PubMed

Huang, Zhijiong; Hu, Yongtao; Zheng, Junyu; Yuan, Zibing; Russell, Armistead G; Ou, Jiamin; Zhong, Zhuangmin

2017-04-04

The traditional reduced-form model (RFM) based on the high-order decoupled direct method (HDDM), is an efficient uncertainty analysis approach for air quality models, but it has large biases in uncertainty propagation due to the limitation of the HDDM in predicting nonlinear responses to large perturbations of model inputs. To overcome the limitation, a new stepwise-based RFM method that combines several sets of local sensitive coefficients under different conditions is proposed. Evaluations reveal that the new RFM improves the prediction of nonlinear responses. The new method is applied to quantify uncertainties in simulated PM 2.5 concentrations in the Pearl River Delta (PRD) region of China as a case study. Results show that the average uncertainty range of hourly PM 2.5 concentrations is -28% to 57%, which can cover approximately 70% of the observed PM 2.5 concentrations, while the traditional RFM underestimates the upper bound of the uncertainty range by 1-6%. Using a variance-based method, the PM 2.5 boundary conditions and primary PM 2.5 emissions are found to be the two major uncertainty sources in PM 2.5 simulations. The new RFM better quantifies the uncertainty range in model simulations and can be applied to improve applications that rely on uncertainty information.

Parent Management Training-Oregon Model: Adapting Intervention with Rigorous Research.

PubMed

Forgatch, Marion S; Kjøbli, John

2016-09-01

Parent Management Training-Oregon Model (PMTO(®) ) is a set of theory-based parenting programs with status as evidence-based treatments. PMTO has been rigorously tested in efficacy and effectiveness trials in different contexts, cultures, and formats. Parents, the presumed agents of change, learn core parenting practices, specifically skill encouragement, limit setting, monitoring/supervision, interpersonal problem solving, and positive involvement. The intervention effectively prevents and ameliorates children's behavior problems by replacing coercive interactions with positive parenting practices. Delivery format includes sessions with individual families in agencies or families' homes, parent groups, and web-based and telehealth communication. Mediational models have tested parenting practices as mechanisms of change for children's behavior and found support for the theory underlying PMTO programs. Moderating effects include children's age, maternal depression, and social disadvantage. The Norwegian PMTO implementation is presented as an example of how PMTO has been tailored to reach diverse populations as delivered by multiple systems of care throughout the nation. An implementation and research center in Oslo provides infrastructure and promotes collaboration between practitioners and researchers to conduct rigorous intervention research. Although evidence-based and tested within a wide array of contexts and populations, PMTO must continue to adapt to an ever-changing world. © 2016 Family Process Institute.

A Rigorous Temperature-Dependent Stochastic Modelling and Testing for MEMS-Based Inertial Sensor Errors.

PubMed

El-Diasty, Mohammed; Pagiatakis, Spiros

2009-01-01

In this paper, we examine the effect of changing the temperature points on MEMS-based inertial sensor random error. We collect static data under different temperature points using a MEMS-based inertial sensor mounted inside a thermal chamber. Rigorous stochastic models, namely Autoregressive-based Gauss-Markov (AR-based GM) models are developed to describe the random error behaviour. The proposed AR-based GM model is initially applied to short stationary inertial data to develop the stochastic model parameters (correlation times). It is shown that the stochastic model parameters of a MEMS-based inertial unit, namely the ADIS16364, are temperature dependent. In addition, field kinematic test data collected at about 17 °C are used to test the performance of the stochastic models at different temperature points in the filtering stage using Unscented Kalman Filter (UKF). It is shown that the stochastic model developed at 20 °C provides a more accurate inertial navigation solution than the ones obtained from the stochastic models developed at -40 °C, -20 °C, 0 °C, +40 °C, and +60 °C. The temperature dependence of the stochastic model is significant and should be considered at all times to obtain optimal navigation solution for MEMS-based INS/GPS integration.

Resolution, uncertainty and data predictability of tomographic Lg attenuation models—application to Southeastern China

NASA Astrophysics Data System (ADS)

Chen, Youlin; Xie, Jiakang

2017-07-01

We address two fundamental issues that pertain to Q tomography using high-frequency regional waves, particularly the Lg wave. The first issue is that Q tomography uses complex 'reduced amplitude data' as input. These data are generated by taking the logarithm of the product of (1) the observed amplitudes and (2) the simplified 1D geometrical spreading correction. They are thereby subject to 'modeling errors' that are dominated by uncompensated 3D structural effects; however, no knowledge of the statistical behaviour of these errors exists to justify the widely used least-squares methods for solving Q tomography. The second issue is that Q tomography has been solved using various iterative methods such as LSQR (Least-Squares QR, where QR refers to a QR factorization of a matrix into the product of an orthogonal matrix Q and an upper triangular matrix R) and SIRT (Simultaneous Iterative Reconstruction Technique) that do not allow for the quantitative estimation of model resolution and error. In this study, we conduct the first rigorous analysis of the statistics of the reduced amplitude data and find that the data error distribution is predominantly normal, but with long-tailed outliers. This distribution is similar to that of teleseismic traveltime residuals. We develop a screening procedure to remove outliers so that data closely follow a normal distribution. Next, we develop an efficient tomographic method based on the PROPACK software package to perform singular value decomposition on a data kernel matrix, which enables us to solve for the inverse, model resolution and covariance matrices along with the optimal Q model. These matrices permit for various quantitative model appraisals, including the evaluation of the formal resolution and error. Further, they allow formal uncertainty estimates of predicted data (Q) along future paths to be made at any specified confidence level. This new capability significantly benefits the practical missions of source identification and source size estimation, for which reliable uncertainty estimates are especially important. We apply the new methodologies to data from southeastern China to obtain a 1 Hz Lg Q model, which exhibits patterns consistent with what is known about the geology and tectonics of the region. We also solve for the site response model.

The neural representation of unexpected uncertainty during value-based decision making.

PubMed

Payzan-LeNestour, Elise; Dunne, Simon; Bossaerts, Peter; O'Doherty, John P

2013-07-10

Uncertainty is an inherent property of the environment and a central feature of models of decision-making and learning. Theoretical propositions suggest that one form, unexpected uncertainty, may be used to rapidly adapt to changes in the environment, while being influenced by two other forms: risk and estimation uncertainty. While previous studies have reported neural representations of estimation uncertainty and risk, relatively little is known about unexpected uncertainty. Here, participants performed a decision-making task while undergoing functional magnetic resonance imaging (fMRI), which, in combination with a Bayesian model-based analysis, enabled us to separately examine each form of uncertainty examined. We found representations of unexpected uncertainty in multiple cortical areas, as well as the noradrenergic brainstem nucleus locus coeruleus. Other unique cortical regions were found to encode risk, estimation uncertainty, and learning rate. Collectively, these findings support theoretical models in which several formally separable uncertainty computations determine the speed of learning. Copyright © 2013 Elsevier Inc. All rights reserved.

UNCERTAINTY ANALYSIS OF TCE USING THE DOSE EXPOSURE ESTIMATING MODEL (DEEM) IN ACSL

EPA Science Inventory

The ACSL-based Dose Exposure Estimating Model(DEEM) under development by EPA is used to perform art uncertainty analysis of a physiologically based pharmacokinetic (PSPK) model of trichloroethylene (TCE). This model involves several circulating metabolites such as trichloroacet...

75 FR 2523 - Office of Innovation and Improvement; Overview Information; Arts in Education Model Development...

Federal Register 2010, 2011, 2012, 2013, 2014

2010-01-15

... that is based on rigorous scientifically based research methods to assess the effectiveness of a...) Relies on measurements or observational methods that provide reliable and valid data across evaluators... of innovative, cohesive models that are based on research and have demonstrated that they effectively...

Squaring the circle of healthcare supplies.

PubMed

Böhme, Tillmann; Williams, Sharon; Childerhouse, Paul; Deakins, Eric; Towill, Denis

2014-01-01

The purpose of this paper is to use a systems lens to assess the comparative performance of healthcare supply chains and provide guidance for their improvement. A well-established and rigorous multi-method audit methodology, based on the uncertainty circle model, yields an objective assessment of value stream performance in eight Australasian public sector hospitals. Cause-effect analysis identifies the major barriers to achieving smooth, seamless flows. Potentially high-leverage remedial actions identified using systems thinking are examined with the aid of an exemplar case. The majority of the healthcare value streams studied are underperforming compared with those in the European automotive industry. Every public hospital appears to be caught in the grip of vicious circles of system uncertainty, in large part being caused by problems of their own making. The single exception is making good progress towards seamless functional integration, which has been achieved by elevating supply chain management to a core competence; having a clearly articulated supply chain vision; adopting a systems approach; and, managing supplies with accurate information. The small number of cases limits the generalisability of the findings at this time. Hospital supply chain managers endeavouring to achieve smooth and seamless supply flows should attempt to elevate the status of supplies management within their organisation to that of a core competence, and should use accurate information to manage their value streams holistically as a set of interwoven processes. A four-level prism model is proposed as a useful framework for thus improving healthcare supply delivery systems. Material flow concepts originally developed to provide objective assessments of value stream performance in commercial settings are adapted for use in a healthcare setting. The ability to identify exemplar organisations via a context-free uncertainty measure, and to use systems thinking to identify high-leverage solutions, supports the transfer of appropriate best practices even between organisations in dissimilar business and economic settings.

TH-CD-207A-08: Simulated Real-Time Image Guidance for Lung SBRT Patients Using Scatter Imaging

DOE Office of Scientific and Technical Information (OSTI.GOV)

Redler, G; Cifter, G; Templeton, A

2016-06-15

Purpose: To develop a comprehensive Monte Carlo-based model for the acquisition of scatter images of patient anatomy in real-time, during lung SBRT treatment. Methods: During SBRT treatment, images of patient anatomy can be acquired from scattered radiation. To rigorously examine the utility of scatter images for image guidance, a model is developed using MCNP code to simulate scatter images of phantoms and lung cancer patients. The model is validated by comparing experimental and simulated images of phantoms of different complexity. The differentiation between tissue types is investigated by imaging objects of known compositions (water, lung, and bone equivalent). A lungmore » tumor phantom, simulating materials and geometry encountered during lung SBRT treatments, is used to investigate image noise properties for various quantities of delivered radiation (monitor units(MU)). Patient scatter images are simulated using the validated simulation model. 4DCT patient data is converted to an MCNP input geometry accounting for different tissue composition and densities. Lung tumor phantom images acquired with decreasing imaging time (decreasing MU) are used to model the expected noise amplitude in patient scatter images, producing realistic simulated patient scatter images with varying temporal resolution. Results: Image intensity in simulated and experimental scatter images of tissue equivalent objects (water, lung, bone) match within the uncertainty (∼3%). Lung tumor phantom images agree as well. Specifically, tumor-to-lung contrast matches within the uncertainty. The addition of random noise approximating quantum noise in experimental images to simulated patient images shows that scatter images of lung tumors can provide images in as fast as 0.5 seconds with CNR∼2.7. Conclusions: A scatter imaging simulation model is developed and validated using experimental phantom scatter images. Following validation, lung cancer patient scatter images are simulated. These simulated patient images demonstrate the clinical utility of scatter imaging for real-time tumor tracking during lung SBRT.« less

Example of a Bayes network of relations among visual features

NASA Astrophysics Data System (ADS)

Agosta, John M.

1991-10-01

Bayes probability networks, also termed `influence diagrams,' promise to be a versatile, rigorous, and expressive uncertainty reasoning tool. This paper presents an example of how a Bayes network can express constraints among visual hypotheses. An example is presented of a model composed of cylindric primitives, inferred from a line drawing of a plumbing fixture. Conflict between interpretations of candidate cylinders is expressed by two parameters, one for the presence and one for the absence of visual evidence of their intersection. It is shown how `partial exclusion' relations are so generated and how they determine the degree of competition among the set of hypotheses. Solving this network obtains the assemblies of cylinders most likely to form an object.

An Efficient Deterministic Approach to Model-based Prediction Uncertainty Estimation

NASA Technical Reports Server (NTRS)

Daigle, Matthew J.; Saxena, Abhinav; Goebel, Kai

2012-01-01

Prognostics deals with the prediction of the end of life (EOL) of a system. EOL is a random variable, due to the presence of process noise and uncertainty in the future inputs to the system. Prognostics algorithm must account for this inherent uncertainty. In addition, these algorithms never know exactly the state of the system at the desired time of prediction, or the exact model describing the future evolution of the system, accumulating additional uncertainty into the predicted EOL. Prediction algorithms that do not account for these sources of uncertainty are misrepresenting the EOL and can lead to poor decisions based on their results. In this paper, we explore the impact of uncertainty in the prediction problem. We develop a general model-based prediction algorithm that incorporates these sources of uncertainty, and propose a novel approach to efficiently handle uncertainty in the future input trajectories of a system by using the unscented transformation. Using this approach, we are not only able to reduce the computational load but also estimate the bounds of uncertainty in a deterministic manner, which can be useful to consider during decision-making. Using a lithium-ion battery as a case study, we perform several simulation-based experiments to explore these issues, and validate the overall approach using experimental data from a battery testbed.

The Solar X-Ray Limb

NASA Astrophysics Data System (ADS)

Battaglia, Marina; Hudson, Hugh S.; Hurford, Gordon J.; Krucker, Säm; Schwartz, Richard A.

2017-07-01

We describe a new technique to measure the height of the X-ray limb with observations from occulted X-ray flare sources as observed by the RHESSI (the Reuven Ramaty High-Energy Spectroscopic Imager) satellite. This method has model dependencies different from those present in traditional observations at optical wavelengths, which depend upon detailed modeling involving radiative transfer in a medium with complicated geometry and flows. It thus provides an independent and more rigorous measurement of the “true” solar radius, which means that of the mass distribution. RHESSI’s measurement makes use of the flare X-ray source’s spatial Fourier components (the visibilities), which are sensitive to the presence of the sharp edge at the lower boundary of the occulted source. We have found a suitable flare event for analysis, SOL2011-10-20T03:25 (M1.7), and report a first result from this novel technique here. Using a four-minute integration over the 3-25 keV photon energy range, we find {R}{{X} - {ray}}=960.11+/- 0.15+/- 0.29 arcsec, at 1 au, where the uncertainties include statistical uncertainties from the method and a systematic error. The standard VAL-C model predicts a value of 959.94 arcsec, which is about 1σ below our value.

Information content of incubation experiments for inverse estimation of pools in the Rothamsted carbon model: a Bayesian approach

NASA Astrophysics Data System (ADS)

Scharnagl, Benedikt; Vrugt, Jasper A.; Vereecken, Harry; Herbst, Michael

2010-05-01

Turnover of soil organic matter is usually described with multi-compartment models. However, a major drawback of these models is that the conceptually defined compartments (or pools) do not necessarily correspond to measurable soil organic carbon (SOC) fractions in real practice. This not only impairs our ability to rigorously evaluate SOC models but also makes it difficult to derive accurate initial states. In this study, we tested the usefulness and applicability of inverse modeling to derive the various carbon pool sizes in the Rothamsted carbon model (ROTHC) using a synthetic time series of mineralization rates from laboratory incubation. To appropriately account for data and model uncertainty we considered a Bayesian approach using the recently developed DiffeRential Evolution Adaptive Metropolis (DREAM) algorithm. This Markov chain Monte Carlo scheme derives the posterior probability density distribution of the initial pool sizes at the start of incubation from observed mineralization rates. We used the Kullback-Leibler divergence to quantify the information contained in the data and to illustrate the effect of increasing incubation times on the reliability of the pool size estimates. Our results show that measured mineralization rates generally provide sufficient information to reliably estimate the sizes of all active pools in the ROTHC model. However, with about 900 days of incubation, these experiments are excessively long. The use of prior information on microbial biomass provided a way forward to significantly reduce uncertainty and required duration of incubation to about 600 days. Explicit consideration of model parameter uncertainty in the estimation process further impaired the identifiability of initial pools, especially for the more slowly decomposing pools. Our illustrative case studies show how Bayesian inverse modeling can be used to provide important insights into the information content of incubation experiments. Moreover, the outcome of this virtual experiment helps to explain the results of related real-world studies on SOC dynamics.

Uncertainty analysis: an evaluation metric for synthesis science

Treesearch

Mark E. Harmon; Becky Fasth; Charles B. Halpern; James A. Lutz

2015-01-01

The methods for conducting reductionist ecological science are well known and widely used. In contrast, those used in the synthesis of ecological science (i.e., synthesis science) are still being developed, vary widely, and often lack the rigor of reductionist approaches. This is unfortunate because the synthesis of ecological parts into a greater whole is...

Reducing the Risk of Suicide in Patients with Bipolar Disorder: Interventions and Safeguards

ERIC Educational Resources Information Center

Newman, Cory F.

2005-01-01

Bipolar disorder exacts a terrible toll on its sufferers owing to the repeated, severe disruptions in the patients' lives, the discomfort and uncertainties of being on rigorous, ongoing pharmacotherapy regimens, the emotional difficulties inherent in experiencing depression and mania, and the fear of a deteriorating course. Patients with bipolar…

Computer-Based Model Calibration and Uncertainty Analysis: Terms and Concepts

DTIC Science & Technology

2015-07-01

uncertainty analyses throughout the lifecycle of planning, designing, and operating of Civil Works flood risk management projects as described in...value 95% of the time. In the frequentist approach to PE, model parameters area regarded as having true values, and their estimate is based on the...in catchment models. 1. Evaluating parameter uncertainty. Water Resources Research 19(5):1151–1172. Lee, P. M. 2012. Bayesian statistics: An

On the Isothermality of Solar Plasmas

NASA Technical Reports Server (NTRS)

Landi, E.; Klimchuk, J. A.

2010-01-01

Recent measurements have shown that the quiet unstructured solar corona observed at the solar limb is close to isothermal, at a temperature that does not appear to change over wide areas or with time. Some in dividual active loop structures have also been found to be nearly iso thermal both along their axis and across their cross-section. Even a complex active region observed at the solar limb has been found to be composed of three distinct isothermal plasmas. If confirmed, these r esults would pose formidable challenges to the current theoretical understanding of the thermal structure and heating of the solar corona. For example, no current theoretical model can explain the excess dens ities and lifetimes of many observed loops if the loops are in fact i sothermal. All of these measurements are based on the so-called emiss ion measure (EM) diagnostic technique that is applied to a set of opt ically thin lines under the assumption of isothermal plasma. It provi des simultaneous measurement of both the temperature and EM. However, no study has ever been carried out to quantify the uncertainties in the technique and to rigorously assess its ability to discriminate bet ween isothermal and multithermal plasmas. Such a study is the topic o f the present work. We define a formal measure of the uncertainty in the EM diagnostic technique that can easily be applied to real data. We here apply it to synthetic data based on a variety of assumed plas ma thermal distributions, and develop a method to quantitatively asse ss the degree of multithermality of a plasma.

Accuracy assessment for a multi-parameter optical calliper in on line automotive applications

NASA Astrophysics Data System (ADS)

D'Emilia, G.; Di Gasbarro, D.; Gaspari, A.; Natale, E.

2017-08-01

In this work, a methodological approach based on the evaluation of the measurement uncertainty is applied to an experimental test case, related to the automotive sector. The uncertainty model for different measurement procedures of a high-accuracy optical gauge is discussed in order to individuate the best measuring performances of the system for on-line applications and when the measurement requirements are becoming more stringent. In particular, with reference to the industrial production and control strategies of high-performing turbochargers, two uncertainty models are proposed, discussed and compared, to be used by the optical calliper. Models are based on an integrated approach between measurement methods and production best practices to emphasize their mutual coherence. The paper shows the possible advantages deriving from the considerations that the measurement uncertainty modelling provides, in order to keep control of the uncertainty propagation on all the indirect measurements useful for production statistical control, on which basing further improvements.

Multiple Damage Progression Paths in Model-Based Prognostics

NASA Technical Reports Server (NTRS)

Daigle, Matthew; Goebel, Kai Frank

2011-01-01

Model-based prognostics approaches employ domain knowledge about a system, its components, and how they fail through the use of physics-based models. Component wear is driven by several different degradation phenomena, each resulting in their own damage progression path, overlapping to contribute to the overall degradation of the component. We develop a model-based prognostics methodology using particle filters, in which the problem of characterizing multiple damage progression paths is cast as a joint state-parameter estimation problem. The estimate is represented as a probability distribution, allowing the prediction of end of life and remaining useful life within a probabilistic framework that supports uncertainty management. We also develop a novel variance control mechanism that maintains an uncertainty bound around the hidden parameters to limit the amount of estimation uncertainty and, consequently, reduce prediction uncertainty. We construct a detailed physics-based model of a centrifugal pump, to which we apply our model-based prognostics algorithms. We illustrate the operation of the prognostic solution with a number of simulation-based experiments and demonstrate the performance of the chosen approach when multiple damage mechanisms are active

Conservative strategy-based ensemble surrogate model for optimal groundwater remediation design at DNAPLs-contaminated sites

NASA Astrophysics Data System (ADS)

Ouyang, Qi; Lu, Wenxi; Lin, Jin; Deng, Wenbing; Cheng, Weiguo

2017-08-01

The surrogate-based simulation-optimization techniques are frequently used for optimal groundwater remediation design. When this technique is used, surrogate errors caused by surrogate-modeling uncertainty may lead to generation of infeasible designs. In this paper, a conservative strategy that pushes the optimal design into the feasible region was used to address surrogate-modeling uncertainty. In addition, chance-constrained programming (CCP) was adopted to compare with the conservative strategy in addressing this uncertainty. Three methods, multi-gene genetic programming (MGGP), Kriging (KRG) and support vector regression (SVR), were used to construct surrogate models for a time-consuming multi-phase flow model. To improve the performance of the surrogate model, ensemble surrogates were constructed based on combinations of different stand-alone surrogate models. The results show that: (1) the surrogate-modeling uncertainty was successfully addressed by the conservative strategy, which means that this method is promising for addressing surrogate-modeling uncertainty. (2) The ensemble surrogate model that combines MGGP with KRG showed the most favorable performance, which indicates that this ensemble surrogate can utilize both stand-alone surrogate models to improve the performance of the surrogate model.

Optical simulations of organic light-emitting diodes through a combination of rigorous electromagnetic solvers and Monte Carlo ray-tracing methods

NASA Astrophysics Data System (ADS)

Bahl, Mayank; Zhou, Gui-Rong; Heller, Evan; Cassarly, William; Jiang, Mingming; Scarmozzino, Rob; Gregory, G. Groot

2014-09-01

Over the last two decades there has been extensive research done to improve the design of Organic Light Emitting Diodes (OLEDs) so as to enhance light extraction efficiency, improve beam shaping, and allow color tuning through techniques such as the use of patterned substrates, photonic crystal (PCs) gratings, back reflectors, surface texture, and phosphor down-conversion. Computational simulation has been an important tool for examining these increasingly complex designs. It has provided insights for improving OLED performance as a result of its ability to explore limitations, predict solutions, and demonstrate theoretical results. Depending upon the focus of the design and scale of the problem, simulations are carried out using rigorous electromagnetic (EM) wave optics based techniques, such as finite-difference time-domain (FDTD) and rigorous coupled wave analysis (RCWA), or through ray optics based technique such as Monte Carlo ray-tracing. The former are typically used for modeling nanostructures on the OLED die, and the latter for modeling encapsulating structures, die placement, back-reflection, and phosphor down-conversion. This paper presents the use of a mixed-level simulation approach which unifies the use of EM wave-level and ray-level tools. This approach uses rigorous EM wave based tools to characterize the nanostructured die and generate both a Bidirectional Scattering Distribution function (BSDF) and a far-field angular intensity distribution. These characteristics are then incorporated into the ray-tracing simulator to obtain the overall performance. Such mixed-level approach allows for comprehensive modeling of the optical characteristic of OLEDs and can potentially lead to more accurate performance than that from individual modeling tools alone.

Rigor of cell fate decision by variable p53 pulses and roles of cooperative gene expression by p53

PubMed Central

Murakami, Yohei; Takada, Shoji

2012-01-01

Upon DNA damage, the cell fate decision between survival and apoptosis is largely regulated by p53-related networks. Recent experiments found a series of discrete p53 pulses in individual cells, which led to the hypothesis that the cell fate decision upon DNA damage is controlled by counting the number of p53 pulses. Under this hypothesis, Sun et al. (2009) modeled the Bax activation switch in the apoptosis signal transduction pathway that can rigorously “count” the number of uniform p53 pulses. Based on experimental evidence, here we use variable p53 pulses with Sun et al.’s model to investigate how the variability in p53 pulses affects the rigor of the cell fate decision by the pulse number. Our calculations showed that the experimentally anticipated variability in the pulse sizes reduces the rigor of the cell fate decision. In addition, we tested the roles of the cooperativity in PUMA expression by p53, finding that lower cooperativity is plausible for more rigorous cell fate decision. This is because the variability in the p53 pulse height is more amplified in PUMA expressions with more cooperative cases. PMID:27857606

Robust output feedback stabilization for a flexible marine riser system.

PubMed

Zhao, Zhijia; Liu, Yu; Guo, Fang

2017-12-06

The aim of this paper is to develop a boundary control for the vibration reduction of a flexible marine riser system in the presence of parametric uncertainties and system states obtained inaccurately. To this end, an adaptive output feedback boundary control is proposed to suppress the riser's vibration fusing with observer-based backstepping, high-gain observers and robust adaptive control theory. In addition, the parameter adaptive laws are designed to compensate for the system parametric uncertainties, and the disturbance observer is introduced to mitigate the effects of external environmental disturbance. The uniformly bounded stability of the closed-loop system is achieved through rigorous Lyapunov analysis without any discretisation or simplification of the dynamics in the time and space, and the state observer error is ensured to exponentially converge to zero as time grows to infinity. In the end, the simulation and comparison studies are carried out to illustrate the performance of the proposed control under the proper choice of the design parameters. Copyright © 2017 ISA. Published by Elsevier Ltd. All rights reserved.

Optimization of monitoring networks based on uncertainty quantification of model predictions of contaminant transport

NASA Astrophysics Data System (ADS)

Vesselinov, V. V.; Harp, D.

2010-12-01

The process of decision making to protect groundwater resources requires a detailed estimation of uncertainties in model predictions. Various uncertainties associated with modeling a natural system, such as: (1) measurement and computational errors; (2) uncertainties in the conceptual model and model-parameter estimates; (3) simplifications in model setup and numerical representation of governing processes, contribute to the uncertainties in the model predictions. Due to this combination of factors, the sources of predictive uncertainties are generally difficult to quantify individually. Decision support related to optimal design of monitoring networks requires (1) detailed analyses of existing uncertainties related to model predictions of groundwater flow and contaminant transport, (2) optimization of the proposed monitoring network locations in terms of their efficiency to detect contaminants and provide early warning. We apply existing and newly-proposed methods to quantify predictive uncertainties and to optimize well locations. An important aspect of the analysis is the application of newly-developed optimization technique based on coupling of Particle Swarm and Levenberg-Marquardt optimization methods which proved to be robust and computationally efficient. These techniques and algorithms are bundled in a software package called MADS. MADS (Model Analyses for Decision Support) is an object-oriented code that is capable of performing various types of model analyses and supporting model-based decision making. The code can be executed under different computational modes, which include (1) sensitivity analyses (global and local), (2) Monte Carlo analysis, (3) model calibration, (4) parameter estimation, (5) uncertainty quantification, and (6) model selection. The code can be externally coupled with any existing model simulator through integrated modules that read/write input and output files using a set of template and instruction files (consistent with the PEST I/O protocol). MADS can also be internally coupled with a series of built-in analytical simulators. MADS provides functionality to work directly with existing control files developed for the code PEST (Doherty 2009). To perform the computational modes mentioned above, the code utilizes (1) advanced Latin-Hypercube sampling techniques (including Improved Distributed Sampling), (2) various gradient-based Levenberg-Marquardt optimization methods, (3) advanced global optimization methods (including Particle Swarm Optimization), and (4) a selection of alternative objective functions. The code has been successfully applied to perform various model analyses related to environmental management of real contamination sites. Examples include source identification problems, quantification of uncertainty, model calibration, and optimization of monitoring networks. The methodology and software codes are demonstrated using synthetic and real case studies where monitoring networks are optimized taking into account the uncertainty in model predictions of contaminant transport.

MUSiC - A general search for deviations from monte carlo predictions in CMS

NASA Astrophysics Data System (ADS)

Biallass, Philipp A.; CMS Collaboration

2009-06-01

A model independent analysis approach in CMS is presented, systematically scanning the data for deviations from the Monte Carlo expectation. Such an analysis can contribute to the understanding of the detector and the tuning of the event generators. Furthermore, due to the minimal theoretical bias this approach is sensitive to a variety of models of new physics, including those not yet thought of. Events are classified into event classes according to their particle content (muons, electrons, photons, jets and missing transverse energy). A broad scan of various distributions is performed, identifying significant deviations from the Monte Carlo simulation. The importance of systematic uncertainties is outlined, which are taken into account rigorously within the algorithm. Possible detector effects and generator issues, as well as models involving Supersymmetry and new heavy gauge bosons are used as an input to the search algorithm.

MODIS land cover uncertainty in regional climate simulations

NASA Astrophysics Data System (ADS)

Li, Xue; Messina, Joseph P.; Moore, Nathan J.; Fan, Peilei; Shortridge, Ashton M.

2017-12-01

MODIS land cover datasets are used extensively across the climate modeling community, but inherent uncertainties and associated propagating impacts are rarely discussed. This paper modeled uncertainties embedded within the annual MODIS Land Cover Type (MCD12Q1) products and propagated these uncertainties through the Regional Atmospheric Modeling System (RAMS). First, land cover uncertainties were modeled using pixel-based trajectory analyses from a time series of MCD12Q1 for Urumqi, China. Second, alternative land cover maps were produced based on these categorical uncertainties and passed into RAMS. Finally, simulations from RAMS were analyzed temporally and spatially to reveal impacts. Our study found that MCD12Q1 struggles to discriminate between grasslands and croplands or grasslands and barren in this study area. Such categorical uncertainties have significant impacts on regional climate model outputs. All climate variables examined demonstrated impact across the various regions, with latent heat flux affected most with a magnitude of 4.32 W/m2 in domain average. Impacted areas were spatially connected to locations of greater land cover uncertainty. Both biophysical characteristics and soil moisture settings in regard to land cover types contribute to the variations among simulations. These results indicate that formal land cover uncertainty analysis should be included in MCD12Q1-fed climate modeling as a routine procedure.

Robust simulation of buckled structures using reduced order modeling

NASA Astrophysics Data System (ADS)

Wiebe, R.; Perez, R. A.; Spottswood, S. M.

2016-09-01

Lightweight metallic structures are a mainstay in aerospace engineering. For these structures, stability, rather than strength, is often the critical limit state in design. For example, buckling of panels and stiffeners may occur during emergency high-g maneuvers, while in supersonic and hypersonic aircraft, it may be induced by thermal stresses. The longstanding solution to such challenges was to increase the sizing of the structural members, which is counter to the ever present need to minimize weight for reasons of efficiency and performance. In this work we present some recent results in the area of reduced order modeling of post- buckled thin beams. A thorough parametric study of the response of a beam to changing harmonic loading parameters, which is useful in exposing complex phenomena and exercising numerical models, is presented. Two error metrics that use but require no time stepping of a (computationally expensive) truth model are also introduced. The error metrics are applied to several interesting forcing parameter cases identified from the parametric study and are shown to yield useful information about the quality of a candidate reduced order model. Parametric studies, especially when considering forcing and structural geometry parameters, coupled environments, and uncertainties would be computationally intractable with finite element models. The goal is to make rapid simulation of complex nonlinear dynamic behavior possible for distributed systems via fast and accurate reduced order models. This ability is crucial in allowing designers to rigorously probe the robustness of their designs to account for variations in loading, structural imperfections, and other uncertainties.

Effect of Streamflow Forecast Uncertainty on Real-Time Reservoir Operation

NASA Astrophysics Data System (ADS)

Zhao, T.; Cai, X.; Yang, D.

2010-12-01

Various hydrological forecast products have been applied to real-time reservoir operation, including deterministic streamflow forecast (DSF), DSF-based probabilistic streamflow forecast (DPSF), and ensemble streamflow forecast (ESF), which represent forecast uncertainty in the form of deterministic forecast error, deterministic forecast error-based uncertainty distribution, and ensemble forecast errors, respectively. Compared to previous studies that treat these forecast products as ad hoc inputs for reservoir operation models, this paper attempts to model the uncertainties involved in the various forecast products and explores their effect on real-time reservoir operation decisions. In hydrology, there are various indices reflecting the magnitude of streamflow forecast uncertainty; meanwhile, few models illustrate the forecast uncertainty evolution process. This research introduces Martingale Model of Forecast Evolution (MMFE) from supply chain management and justifies its assumptions for quantifying the evolution of uncertainty in streamflow forecast as time progresses. Based on MMFE, this research simulates the evolution of forecast uncertainty in DSF, DPSF, and ESF, and applies the reservoir operation models (dynamic programming, DP; stochastic dynamic programming, SDP; and standard operation policy, SOP) to assess the effect of different forms of forecast uncertainty on real-time reservoir operation. Through a hypothetical single-objective real-time reservoir operation model, the results illustrate that forecast uncertainty exerts significant effects. Reservoir operation efficiency, as measured by a utility function, decreases as the forecast uncertainty increases. Meanwhile, these effects also depend on the type of forecast product being used. In general, the utility of reservoir operation with ESF is nearly as high as the utility obtained with a perfect forecast; the utilities of DSF and DPSF are similar to each other but not as efficient as ESF. Moreover, streamflow variability and reservoir capacity can change the magnitude of the effects of forecast uncertainty, but not the relative merit of DSF, DPSF, and ESF. Schematic diagram of the increase in forecast uncertainty with forecast lead-time and the dynamic updating property of real-time streamflow forecast

Development of a Prototype Model-Form Uncertainty Knowledge Base

NASA Technical Reports Server (NTRS)

Green, Lawrence L.

2016-01-01

Uncertainties are generally classified as either aleatory or epistemic. Aleatory uncertainties are those attributed to random variation, either naturally or through manufacturing processes. Epistemic uncertainties are generally attributed to a lack of knowledge. One type of epistemic uncertainty is called model-form uncertainty. The term model-form means that among the choices to be made during a design process within an analysis, there are different forms of the analysis process, which each give different results for the same configuration at the same flight conditions. Examples of model-form uncertainties include the grid density, grid type, and solver type used within a computational fluid dynamics code, or the choice of the number and type of model elements within a structures analysis. The objectives of this work are to identify and quantify a representative set of model-form uncertainties and to make this information available to designers through an interactive knowledge base (KB). The KB can then be used during probabilistic design sessions, so as to enable the possible reduction of uncertainties in the design process through resource investment. An extensive literature search has been conducted to identify and quantify typical model-form uncertainties present within aerospace design. An initial attempt has been made to assemble the results of this literature search into a searchable KB, usable in real time during probabilistic design sessions. A concept of operations and the basic structure of a model-form uncertainty KB are described. Key operations within the KB are illustrated. Current limitations in the KB, and possible workarounds are explained.

Embedded ensemble propagation for improving performance, portability, and scalability of uncertainty quantification on emerging computational architectures

DOE PAGES

Phipps, Eric T.; D'Elia, Marta; Edwards, Harold C.; ...

2017-04-18

In this study, quantifying simulation uncertainties is a critical component of rigorous predictive simulation. A key component of this is forward propagation of uncertainties in simulation input data to output quantities of interest. Typical approaches involve repeated sampling of the simulation over the uncertain input data, and can require numerous samples when accurately propagating uncertainties from large numbers of sources. Often simulation processes from sample to sample are similar and much of the data generated from each sample evaluation could be reused. We explore a new method for implementing sampling methods that simultaneously propagates groups of samples together in anmore » embedded fashion, which we call embedded ensemble propagation. We show how this approach takes advantage of properties of modern computer architectures to improve performance by enabling reuse between samples, reducing memory bandwidth requirements, improving memory access patterns, improving opportunities for fine-grained parallelization, and reducing communication costs. We describe a software technique for implementing embedded ensemble propagation based on the use of C++ templates and describe its integration with various scientific computing libraries within Trilinos. We demonstrate improved performance, portability and scalability for the approach applied to the simulation of partial differential equations on a variety of CPU, GPU, and accelerator architectures, including up to 131,072 cores on a Cray XK7 (Titan).« less

Impact of observation error structure on satellite soil moisture assimilation into a rainfall-runoff model

USDA-ARS?s Scientific Manuscript database

In Ensemble Kalman Filter (EnKF)-based data assimilation, the background prediction of a model is updated using observations and relative weights based on the model prediction and observation uncertainties. In practice, both model and observation uncertainties are difficult to quantify and they have...

Integrating model behavior, optimization, and sensitivity/uncertainty analysis: overview and application of the MOUSE software toolbox

USDA-ARS?s Scientific Manuscript database

This paper provides an overview of the Model Optimization, Uncertainty, and SEnsitivity Analysis (MOUSE) software application, an open-source, Java-based toolbox of visual and numerical analysis components for the evaluation of environmental models. MOUSE is based on the OPTAS model calibration syst...

ZY3-02 Laser Altimeter Footprint Geolocation Prediction

PubMed Central

Xie, Junfeng; Tang, Xinming; Mo, Fan; Li, Guoyuan; Zhu, Guangbin; Wang, Zhenming; Fu, Xingke; Gao, Xiaoming; Dou, Xianhui

2017-01-01

Successfully launched on 30 May 2016, ZY3-02 is the first Chinese surveying and mapping satellite equipped with a lightweight laser altimeter. Calibration is necessary before the laser altimeter becomes operational. Laser footprint location prediction is the first step in calibration that is based on ground infrared detectors, and it is difficult because the sample frequency of the ZY3-02 laser altimeter is 2 Hz, and the distance between two adjacent laser footprints is about 3.5 km. In this paper, we build an on-orbit rigorous geometric prediction model referenced to the rigorous geometric model of optical remote sensing satellites. The model includes three kinds of data that must be predicted: pointing angle, orbit parameters, and attitude angles. The proposed method is verified by a ZY3-02 laser altimeter on-orbit geometric calibration test. Five laser footprint prediction experiments are conducted based on the model, and the laser footprint prediction accuracy is better than 150 m on the ground. The effectiveness and accuracy of the on-orbit rigorous geometric prediction model are confirmed by the test results. The geolocation is predicted precisely by the proposed method, and this will give a reference to the geolocation prediction of future land laser detectors in other laser altimeter calibration test. PMID:28934160

ZY3-02 Laser Altimeter Footprint Geolocation Prediction.

PubMed

Xie, Junfeng; Tang, Xinming; Mo, Fan; Li, Guoyuan; Zhu, Guangbin; Wang, Zhenming; Fu, Xingke; Gao, Xiaoming; Dou, Xianhui

2017-09-21

Successfully launched on 30 May 2016, ZY3-02 is the first Chinese surveying and mapping satellite equipped with a lightweight laser altimeter. Calibration is necessary before the laser altimeter becomes operational. Laser footprint location prediction is the first step in calibration that is based on ground infrared detectors, and it is difficult because the sample frequency of the ZY3-02 laser altimeter is 2 Hz, and the distance between two adjacent laser footprints is about 3.5 km. In this paper, we build an on-orbit rigorous geometric prediction model referenced to the rigorous geometric model of optical remote sensing satellites. The model includes three kinds of data that must be predicted: pointing angle, orbit parameters, and attitude angles. The proposed method is verified by a ZY3-02 laser altimeter on-orbit geometric calibration test. Five laser footprint prediction experiments are conducted based on the model, and the laser footprint prediction accuracy is better than 150 m on the ground. The effectiveness and accuracy of the on-orbit rigorous geometric prediction model are confirmed by the test results. The geolocation is predicted precisely by the proposed method, and this will give a reference to the geolocation prediction of future land laser detectors in other laser altimeter calibration test.

Uncertainty assessment of a model for biological nitrogen and phosphorus removal: Application to a large wastewater treatment plant

NASA Astrophysics Data System (ADS)

Mannina, Giorgio; Cosenza, Alida; Viviani, Gaspare

In the last few years, the use of mathematical models in WasteWater Treatment Plant (WWTP) processes has become a common way to predict WWTP behaviour. However, mathematical models generally demand advanced input for their implementation that must be evaluated by an extensive data-gathering campaign, which cannot always be carried out. This fact, together with the intrinsic complexity of the model structure, leads to model results that may be very uncertain. Quantification of the uncertainty is imperative. However, despite the importance of uncertainty quantification, only few studies have been carried out in the wastewater treatment field, and those studies only included a few of the sources of model uncertainty. Seeking the development of the area, the paper presents the uncertainty assessment of a mathematical model simulating biological nitrogen and phosphorus removal. The uncertainty assessment was conducted according to the Generalised Likelihood Uncertainty Estimation (GLUE) methodology that has been scarcely applied in wastewater field. The model was based on activated-sludge models 1 (ASM) and 2 (ASM2). Different approaches can be used for uncertainty analysis. The GLUE methodology requires a large number of Monte Carlo simulations in which a random sampling of individual parameters drawn from probability distributions is used to determine a set of parameter values. Using this approach, model reliability was evaluated based on its capacity to globally limit the uncertainty. The method was applied to a large full-scale WWTP for which quantity and quality data was gathered. The analysis enabled to gain useful insights for WWTP modelling identifying the crucial aspects where higher uncertainty rely and where therefore, more efforts should be provided in terms of both data gathering and modelling practises.

Climate data induced uncertainty in model based estimations of terrestrial primary productivity

NASA Astrophysics Data System (ADS)

Wu, Z.; Ahlström, A.; Smith, B.; Ardö, J.; Eklundh, L.; Fensholt, R.; Lehsten, V.

2016-12-01

Models used to project global vegetation and carbon cycle differ in their estimates of historical fluxes and pools. These differences arise not only from differences between models but also from differences in the environmental and climatic data that forces the models. Here we investigate the role of uncertainties in historical climate data, encapsulated by a set of six historical climate datasets. We focus on terrestrial gross primary productivity (GPP) and analyze the results from a dynamic process-based vegetation model (LPJ-GUESS) forced by six different climate datasets and two empirical datasets of GPP (derived from flux towers and remote sensing). We find that the climate induced uncertainty, defined as the difference among historical simulations in GPP when forcing the model with the different climate datasets, can be as high as 33 Pg C yr-1 globally (19% of mean GPP). The uncertainty is partitioned into the three main climatic drivers, temperature, precipitation, and shortwave radiation. Additionally, we illustrate how the uncertainty due to a given climate driver depends both on the magnitude of the forcing data uncertainty (the data range) and the sensitivity of the modeled GPP to the driver (the ecosystem sensitivity). The analysis is performed globally and stratified into five land cover classes. We find that the dynamic vegetation model overestimates GPP, compared to empirically based GPP data over most areas, except for the tropical region. Both the simulations and empirical estimates agree that the tropical region is a disproportionate source of uncertainty in GPP estimation. This is mainly caused by uncertainties in shortwave radiation forcing, of which climate data range contributes slightly higher uncertainty than ecosystem sensitivity to shortwave radiation. We also find that precipitation dominated the climate induced uncertainty over nearly half of terrestrial vegetated surfaces, which is mainly due to large ecosystem sensitivity to precipitation. Overall, climate data ranges are found to contribute more to the climate induced uncertainty than ecosystem sensitivity. Our study highlights the need to better constrain tropical climate and demonstrate that uncertainty caused by climatic forcing data must be considered when comparing and evaluating model results and empirical datasets.

Monte-Carlo-based uncertainty propagation with hierarchical models—a case study in dynamic torque

NASA Astrophysics Data System (ADS)

Klaus, Leonard; Eichstädt, Sascha

2018-04-01

For a dynamic calibration, a torque transducer is described by a mechanical model, and the corresponding model parameters are to be identified from measurement data. A measuring device for the primary calibration of dynamic torque, and a corresponding model-based calibration approach, have recently been developed at PTB. The complete mechanical model of the calibration set-up is very complex, and involves several calibration steps—making a straightforward implementation of a Monte Carlo uncertainty evaluation tedious. With this in mind, we here propose to separate the complete model into sub-models, with each sub-model being treated with individual experiments and analysis. The uncertainty evaluation for the overall model then has to combine the information from the sub-models in line with Supplement 2 of the Guide to the Expression of Uncertainty in Measurement. In this contribution, we demonstrate how to carry this out using the Monte Carlo method. The uncertainty evaluation involves various input quantities of different origin and the solution of a numerical optimisation problem.

Balance Calibration – A Method for Assigning a Direct-Reading Uncertainty to an Electronic Balance.

DOE Office of Scientific and Technical Information (OSTI.GOV)

Mike Stears

2010-07-01

Paper Title: Balance Calibration – A method for assigning a direct-reading uncertainty to an electronic balance. Intended Audience: Those who calibrate or use electronic balances. Abstract: As a calibration facility, we provide on-site (at the customer’s location) calibrations of electronic balances for customers within our company. In our experience, most of our customers are not using their balance as a comparator, but simply putting an unknown quantity on the balance and reading the displayed mass value. Manufacturer’s specifications for balances typically include specifications such as readability, repeatability, linearity, and sensitivity temperature drift, but what does this all mean when themore » balance user simply reads the displayed mass value and accepts the reading as the true value? This paper discusses a method for assigning a direct-reading uncertainty to a balance based upon the observed calibration data and the environment where the balance is being used. The method requires input from the customer regarding the environment where the balance is used and encourages discussion with the customer regarding sources of uncertainty and possible means for improvement; the calibration process becomes an educational opportunity for the balance user as well as calibration personnel. This paper will cover the uncertainty analysis applied to the calibration weights used for the field calibration of balances; the uncertainty is calculated over the range of environmental conditions typically encountered in the field and the resulting range of air density. The temperature stability in the area of the balance is discussed with the customer and the temperature range over which the balance calibration is valid is decided upon; the decision is based upon the uncertainty needs of the customer and the desired rigor in monitoring by the customer. Once the environmental limitations are decided, the calibration is performed and the measurement data is entered into a custom spreadsheet. The spreadsheet uses measurement results, along with the manufacturer’s specifications, to assign a direct-read measurement uncertainty to the balance. The fact that the assigned uncertainty is a best-case uncertainty is discussed with the customer; the assigned uncertainty contains no allowance for contributions associated with the unknown weighing sample, such as density, static charges, magnetism, etc. The attendee will learn uncertainty considerations associated with balance calibrations along with one method for assigning an uncertainty to a balance used for non-comparison measurements.« less

On the Directional Dependence and Null Space Freedom in Uncertainty Bound Identification

NASA Technical Reports Server (NTRS)

Lim, K. B.; Giesy, D. P.

1997-01-01

In previous work, the determination of uncertainty models via minimum norm model validation is based on a single set of input and output measurement data. Since uncertainty bounds at each frequency is directionally dependent for multivariable systems, this will lead to optimistic uncertainty levels. In addition, the design freedom in the uncertainty model has not been utilized to further reduce uncertainty levels. The above issues are addressed by formulating a min- max problem. An analytical solution to the min-max problem is given to within a generalized eigenvalue problem, thus avoiding a direct numerical approach. This result will lead to less conservative and more realistic uncertainty models for use in robust control.

A tool for efficient, model-independent management optimization under uncertainty

USGS Publications Warehouse

White, Jeremy; Fienen, Michael N.; Barlow, Paul M.; Welter, Dave E.

2018-01-01

To fill a need for risk-based environmental management optimization, we have developed PESTPP-OPT, a model-independent tool for resource management optimization under uncertainty. PESTPP-OPT solves a sequential linear programming (SLP) problem and also implements (optional) efficient, “on-the-fly” (without user intervention) first-order, second-moment (FOSM) uncertainty techniques to estimate model-derived constraint uncertainty. Combined with a user-specified risk value, the constraint uncertainty estimates are used to form chance-constraints for the SLP solution process, so that any optimal solution includes contributions from model input and observation uncertainty. In this way, a “single answer” that includes uncertainty is yielded from the modeling analysis. PESTPP-OPT uses the familiar PEST/PEST++ model interface protocols, which makes it widely applicable to many modeling analyses. The use of PESTPP-OPT is demonstrated with a synthetic, integrated surface-water/groundwater model. The function and implications of chance constraints for this synthetic model are discussed.

Uncertainty Representation and Interpretation in Model-Based Prognostics Algorithms Based on Kalman Filter Estimation

NASA Technical Reports Server (NTRS)

Galvan, Jose Ramon; Saxena, Abhinav; Goebel, Kai Frank

2012-01-01

This article discusses several aspects of uncertainty representation and management for model-based prognostics methodologies based on our experience with Kalman Filters when applied to prognostics for electronics components. In particular, it explores the implications of modeling remaining useful life prediction as a stochastic process, and how it relates to uncertainty representation, management and the role of prognostics in decision-making. A distinction between the interpretations of estimated remaining useful life probability density function is explained and a cautionary argument is provided against mixing interpretations for two while considering prognostics in making critical decisions.

MODFLOW-2005, the U.S. Geological Survey modular ground-water model - documentation of shared node local grid refinement (LGR) and the boundary flow and head (BFH) package

USGS Publications Warehouse

Mehl, Steffen W.; Hill, Mary C.

2006-01-01

This report documents the addition of shared node Local Grid Refinement (LGR) to MODFLOW-2005, the U.S. Geological Survey modular, transient, three-dimensional, finite-difference ground-water flow model. LGR provides the capability to simulate ground-water flow using one block-shaped higher-resolution local grid (a child model) within a coarser-grid parent model. LGR accomplishes this by iteratively coupling two separate MODFLOW-2005 models such that heads and fluxes are balanced across the shared interfacing boundary. LGR can be used in two-and three-dimensional, steady-state and transient simulations and for simulations of confined and unconfined ground-water systems. Traditional one-way coupled telescopic mesh refinement (TMR) methods can have large, often undetected, inconsistencies in heads and fluxes across the interface between two model grids. The iteratively coupled shared-node method of LGR provides a more rigorous coupling in which the solution accuracy is controlled by convergence criteria defined by the user. In realistic problems, this can result in substantially more accurate solutions and require an increase in computer processing time. The rigorous coupling enables sensitivity analysis, parameter estimation, and uncertainty analysis that reflects conditions in both model grids. This report describes the method used by LGR, evaluates LGR accuracy and performance for two- and three-dimensional test cases, provides input instructions, and lists selected input and output files for an example problem. It also presents the Boundary Flow and Head (BFH) Package, which allows the child and parent models to be simulated independently using the boundary conditions obtained through the iterative process of LGR.

Gradient-based model calibration with proxy-model assistance

NASA Astrophysics Data System (ADS)

Burrows, Wesley; Doherty, John

2016-02-01

Use of a proxy model in gradient-based calibration and uncertainty analysis of a complex groundwater model with large run times and problematic numerical behaviour is described. The methodology is general, and can be used with models of all types. The proxy model is based on a series of analytical functions that link all model outputs used in the calibration process to all parameters requiring estimation. In enforcing history-matching constraints during the calibration and post-calibration uncertainty analysis processes, the proxy model is run for the purposes of populating the Jacobian matrix, while the original model is run when testing parameter upgrades; the latter process is readily parallelized. Use of a proxy model in this fashion dramatically reduces the computational burden of complex model calibration and uncertainty analysis. At the same time, the effect of model numerical misbehaviour on calculation of local gradients is mitigated, this allowing access to the benefits of gradient-based analysis where lack of integrity in finite-difference derivatives calculation would otherwise have impeded such access. Construction of a proxy model, and its subsequent use in calibration of a complex model, and in analysing the uncertainties of predictions made by that model, is implemented in the PEST suite.

Making Invasion models useful for decision makers; incorporating uncertainty, knowledge gaps, and decision-making preferences

Treesearch

Denys Yemshanov; Frank H Koch; Mark Ducey

2015-01-01

Uncertainty is inherent in model-based forecasts of ecological invasions. In this chapter, we explore how the perceptions of that uncertainty can be incorporated into the pest risk assessment process. Uncertainty changes a decision maker’s perceptions of risk; therefore, the direct incorporation of uncertainty may provide a more appropriate depiction of risk. Our...

Prediction of seismic collapse risk of steel moment frame mid-rise structures by meta-heuristic algorithms

NASA Astrophysics Data System (ADS)

Jough, Fooad Karimi Ghaleh; Şensoy, Serhan

2016-12-01

Different performance levels may be obtained for sideway collapse evaluation of steel moment frames depending on the evaluation procedure used to handle uncertainties. In this article, the process of representing modelling uncertainties, record to record (RTR) variations and cognitive uncertainties for moment resisting steel frames of various heights is discussed in detail. RTR uncertainty is used by incremental dynamic analysis (IDA), modelling uncertainties are considered through backbone curves and hysteresis loops of component, and cognitive uncertainty is presented in three levels of material quality. IDA is used to evaluate RTR uncertainty based on strong ground motion records selected by the k-means algorithm, which is favoured over Monte Carlo selection due to its time saving appeal. Analytical equations of the Response Surface Method are obtained through IDA results by the Cuckoo algorithm, which predicts the mean and standard deviation of the collapse fragility curve. The Takagi-Sugeno-Kang model is used to represent material quality based on the response surface coefficients. Finally, collapse fragility curves with the various sources of uncertainties mentioned are derived through a large number of material quality values and meta variables inferred by the Takagi-Sugeno-Kang fuzzy model based on response surface method coefficients. It is concluded that a better risk management strategy in countries where material quality control is weak, is to account for cognitive uncertainties in fragility curves and the mean annual frequency.

The Western States Water Mission: A Hyper-Resolution Hydrological Model and Data Integration Platform for the Western United States

NASA Astrophysics Data System (ADS)

Famiglietti, J. S.; David, C. H.; Reager, J. T., II; Oaida, C.; Stampoulis, D.; Levoe, S.; Liu, P. W.; Trangsrud, A.; Basilio, R. R.; Allen, G. H.; Crichton, D. J.; Emery, C. M.; Farr, T.; Granger, S. L.; Hobbs, J.; Malhotra, S.; Osterman, G. B.; Rueckert, M.; Turmon, M.

2017-12-01

The Western States Water Mission (WSWM) is a high-resolution (3 km2), hydrological model and data integration platform under development at the Jet Propulsion Laboratory for the last 2 years. Distinctive features of the WSWM are its explicit representations of river networks and deep groundwater, an emphasis on uncertainty quantification, a major visualization and data distribution effort, and its focus on multivariate data assimilation, including GRACE/FO, SMAP, SWOT and MODSCAG fractional snow covered area. Importantly, the WSWM is actively managed as a flight project, i.e. with the rigor of a satellite mission. In this presentation we give an overview of the WSWM, including past accomplishments status, and future plans. In particular, results from recent 30-year simulations with GRACE and MODSCAG assimilation will be presented.

A probabilistic approach for the estimation of earthquake source parameters from spectral inversion

NASA Astrophysics Data System (ADS)

Supino, M.; Festa, G.; Zollo, A.

2017-12-01

The amplitude spectrum of a seismic signal related to an earthquake source carries information about the size of the rupture, moment, stress and energy release. Furthermore, it can be used to characterize the Green's function of the medium crossed by the seismic waves. We describe the earthquake amplitude spectrum assuming a generalized Brune's (1970) source model, and direct P- and S-waves propagating in a layered velocity model, characterized by a frequency-independent Q attenuation factor. The observed displacement spectrum depends indeed on three source parameters, the seismic moment (through the low-frequency spectral level), the corner frequency (that is a proxy of the fault length) and the high-frequency decay parameter. These parameters are strongly correlated each other and with the quality factor Q; a rigorous estimation of the associated uncertainties and parameter resolution is thus needed to obtain reliable estimations.In this work, the uncertainties are characterized adopting a probabilistic approach for the parameter estimation. Assuming an L2-norm based misfit function, we perform a global exploration of the parameter space to find the absolute minimum of the cost function and then we explore the cost-function associated joint a-posteriori probability density function around such a minimum, to extract the correlation matrix of the parameters. The global exploration relies on building a Markov chain in the parameter space and on combining a deterministic minimization with a random exploration of the space (basin-hopping technique). The joint pdf is built from the misfit function using the maximum likelihood principle and assuming a Gaussian-like distribution of the parameters. It is then computed on a grid centered at the global minimum of the cost-function. The numerical integration of the pdf finally provides mean, variance and correlation matrix associated with the set of best-fit parameters describing the model. Synthetic tests are performed to investigate the robustness of the method and uncertainty propagation from the data-space to the parameter space. Finally, the method is applied to characterize the source parameters of the earthquakes occurring during the 2016-2017 Central Italy sequence, with the goal of investigating the source parameter scaling with magnitude.

Quantifying model-structure- and parameter-driven uncertainties in spring wheat phenology prediction with Bayesian analysis

DOE PAGES

Alderman, Phillip D.; Stanfill, Bryan

2016-10-06

Recent international efforts have brought renewed emphasis on the comparison of different agricultural systems models. Thus far, analysis of model-ensemble simulated results has not clearly differentiated between ensemble prediction uncertainties due to model structural differences per se and those due to parameter value uncertainties. Additionally, despite increasing use of Bayesian parameter estimation approaches with field-scale crop models, inadequate attention has been given to the full posterior distributions for estimated parameters. The objectives of this study were to quantify the impact of parameter value uncertainty on prediction uncertainty for modeling spring wheat phenology using Bayesian analysis and to assess the relativemore » contributions of model-structure-driven and parameter-value-driven uncertainty to overall prediction uncertainty. This study used a random walk Metropolis algorithm to estimate parameters for 30 spring wheat genotypes using nine phenology models based on multi-location trial data for days to heading and days to maturity. Across all cases, parameter-driven uncertainty accounted for between 19 and 52% of predictive uncertainty, while model-structure-driven uncertainty accounted for between 12 and 64%. Here, this study demonstrated the importance of quantifying both model-structure- and parameter-value-driven uncertainty when assessing overall prediction uncertainty in modeling spring wheat phenology. More generally, Bayesian parameter estimation provided a useful framework for quantifying and analyzing sources of prediction uncertainty.« less

Accounting for Uncertainty in Decision Analytic Models Using Rank Preserving Structural Failure Time Modeling: Application to Parametric Survival Models.

PubMed

Bennett, Iain; Paracha, Noman; Abrams, Keith; Ray, Joshua

2018-01-01

Rank Preserving Structural Failure Time models are one of the most commonly used statistical methods to adjust for treatment switching in oncology clinical trials. The method is often applied in a decision analytic model without appropriately accounting for additional uncertainty when determining the allocation of health care resources. The aim of the study is to describe novel approaches to adequately account for uncertainty when using a Rank Preserving Structural Failure Time model in a decision analytic model. Using two examples, we tested and compared the performance of the novel Test-based method with the resampling bootstrap method and with the conventional approach of no adjustment. In the first example, we simulated life expectancy using a simple decision analytic model based on a hypothetical oncology trial with treatment switching. In the second example, we applied the adjustment method on published data when no individual patient data were available. Mean estimates of overall and incremental life expectancy were similar across methods. However, the bootstrapped and test-based estimates consistently produced greater estimates of uncertainty compared with the estimate without any adjustment applied. Similar results were observed when using the test based approach on a published data showing that failing to adjust for uncertainty led to smaller confidence intervals. Both the bootstrapping and test-based approaches provide a solution to appropriately incorporate uncertainty, with the benefit that the latter can implemented by researchers in the absence of individual patient data. Copyright © 2018 International Society for Pharmacoeconomics and Outcomes Research (ISPOR). Published by Elsevier Inc. All rights reserved.

Prediction uncertainty and data worth assessment for groundwater transport times in an agricultural catchment

NASA Astrophysics Data System (ADS)

Zell, Wesley O.; Culver, Teresa B.; Sanford, Ward E.

2018-06-01

Uncertainties about the age of base-flow discharge can have serious implications for the management of degraded environmental systems where subsurface pathways, and the ongoing release of pollutants that accumulated in the subsurface during past decades, dominate the water quality signal. Numerical groundwater models may be used to estimate groundwater return times and base-flow ages and thus predict the time required for stakeholders to see the results of improved agricultural management practices. However, the uncertainty inherent in the relationship between (i) the observations of atmospherically-derived tracers that are required to calibrate such models and (ii) the predictions of system age that the observations inform have not been investigated. For example, few if any studies have assessed the uncertainty of numerically-simulated system ages or evaluated the uncertainty reductions that may result from the expense of collecting additional subsurface tracer data. In this study we combine numerical flow and transport modeling of atmospherically-derived tracers with prediction uncertainty methods to accomplish four objectives. First, we show the relative importance of head, discharge, and tracer information for characterizing response times in a uniquely data rich catchment that includes 266 age-tracer measurements (SF6, CFCs, and 3H) in addition to long term monitoring of water levels and stream discharge. Second, we calculate uncertainty intervals for model-simulated base-flow ages using both linear and non-linear methods, and find that the prediction sensitivity vector used by linear first-order second-moment methods results in much larger uncertainties than non-linear Monte Carlo methods operating on the same parameter uncertainty. Third, by combining prediction uncertainty analysis with multiple models of the system, we show that data-worth calculations and monitoring network design are sensitive to variations in the amount of water leaving the system via stream discharge and irrigation withdrawals. Finally, we demonstrate a novel model-averaged computation of potential data worth that can account for these uncertainties in model structure.

Hotspots of uncertainty in land-use and land-cover change projections: a global-scale model comparison.

PubMed

Prestele, Reinhard; Alexander, Peter; Rounsevell, Mark D A; Arneth, Almut; Calvin, Katherine; Doelman, Jonathan; Eitelberg, David A; Engström, Kerstin; Fujimori, Shinichiro; Hasegawa, Tomoko; Havlik, Petr; Humpenöder, Florian; Jain, Atul K; Krisztin, Tamás; Kyle, Page; Meiyappan, Prasanth; Popp, Alexander; Sands, Ronald D; Schaldach, Rüdiger; Schüngel, Jan; Stehfest, Elke; Tabeau, Andrzej; Van Meijl, Hans; Van Vliet, Jasper; Verburg, Peter H

2016-12-01

Model-based global projections of future land-use and land-cover (LULC) change are frequently used in environmental assessments to study the impact of LULC change on environmental services and to provide decision support for policy. These projections are characterized by a high uncertainty in terms of quantity and allocation of projected changes, which can severely impact the results of environmental assessments. In this study, we identify hotspots of uncertainty, based on 43 simulations from 11 global-scale LULC change models representing a wide range of assumptions of future biophysical and socioeconomic conditions. We attribute components of uncertainty to input data, model structure, scenario storyline and a residual term, based on a regression analysis and analysis of variance. From this diverse set of models and scenarios, we find that the uncertainty varies, depending on the region and the LULC type under consideration. Hotspots of uncertainty appear mainly at the edges of globally important biomes (e.g., boreal and tropical forests). Our results indicate that an important source of uncertainty in forest and pasture areas originates from different input data applied in the models. Cropland, in contrast, is more consistent among the starting conditions, while variation in the projections gradually increases over time due to diverse scenario assumptions and different modeling approaches. Comparisons at the grid cell level indicate that disagreement is mainly related to LULC type definitions and the individual model allocation schemes. We conclude that improving the quality and consistency of observational data utilized in the modeling process and improving the allocation mechanisms of LULC change models remain important challenges. Current LULC representation in environmental assessments might miss the uncertainty arising from the diversity of LULC change modeling approaches, and many studies ignore the uncertainty in LULC projections in assessments of LULC change impacts on climate, water resources or biodiversity. © 2016 The Authors. Global Change Biology Published by John Wiley & Sons Ltd.

Multivariate Probabilistic Analysis of an Hydrological Model

NASA Astrophysics Data System (ADS)

Franceschini, Samuela; Marani, Marco

2010-05-01

Model predictions derived based on rainfall measurements and hydrological model results are often limited by the systematic error of measuring instruments, by the intrinsic variability of the natural processes and by the uncertainty of the mathematical representation. We propose a means to identify such sources of uncertainty and to quantify their effects based on point-estimate approaches, as a valid alternative to cumbersome Montecarlo methods. We present uncertainty analyses on the hydrologic response to selected meteorological events, in the mountain streamflow-generating portion of the Brenta basin at Bassano del Grappa, Italy. The Brenta river catchment has a relatively uniform morphology and quite a heterogeneous rainfall-pattern. In the present work, we evaluate two sources of uncertainty: data uncertainty (the uncertainty due to data handling and analysis) and model uncertainty (the uncertainty related to the formulation of the model). We thus evaluate the effects of the measurement error of tipping-bucket rain gauges, the uncertainty in estimating spatially-distributed rainfall through block kriging, and the uncertainty associated with estimated model parameters. To this end, we coupled a deterministic model based on the geomorphological theory of the hydrologic response to probabilistic methods. In particular we compare the results of Monte Carlo Simulations (MCS) to the results obtained, in the same conditions, using Li's Point Estimate Method (LiM). The LiM is a probabilistic technique that approximates the continuous probability distribution function of the considered stochastic variables by means of discrete points and associated weights. This allows to satisfactorily reproduce results with only few evaluations of the model function. The comparison between the LiM and MCS results highlights the pros and cons of using an approximating method. LiM is less computationally demanding than MCS, but has limited applicability especially when the model response is highly nonlinear. Higher-order approximations can provide more accurate estimations, but reduce the numerical advantage of the LiM. The results of the uncertainty analysis identify the main sources of uncertainty in the computation of river discharge. In this particular case the spatial variability of rainfall and the model parameters uncertainty are shown to have the greatest impact on discharge evaluation. This, in turn, highlights the need to support any estimated hydrological response with probability information and risk analysis results in order to provide a robust, systematic framework for decision making.

DOE Office of Scientific and Technical Information (OSTI.GOV)

Burr, Tom; Croft, Stephen; Jarman, Kenneth D.

The various methods of nondestructive assay (NDA) of special nuclear material (SNM) have applications in nuclear nonproliferation, including detection and identification of illicit SNM at border crossings, and quantifying SNM at nuclear facilities for safeguards. No assay method is complete without “error bars,” which provide one way of expressing confidence in the assay result. Consequently, NDA specialists typically quantify total uncertainty in terms of “random” and “systematic” components, and then specify error bars for the total mass estimate in multiple items. Uncertainty quantification (UQ) for NDA has always been important, but it is recognized that greater rigor is needed andmore » achievable using modern statistical methods. To this end, we describe the extent to which the guideline for expressing uncertainty in measurements (GUM) can be used for NDA. Also, we propose improvements over GUM for NDA by illustrating UQ challenges that it does not address, including calibration with errors in predictors, model error, and item-specific biases. A case study is presented using low-resolution NaI spectra and applying the enrichment meter principle to estimate the U-235 mass in an item. The case study illustrates how to update the current American Society for Testing and Materials guide for application of the enrichment meter principle using gamma spectra from a NaI detector.« less

Large-Scale Uncertainty and Error Analysis for Time-dependent Fluid/Structure Interactions in Wind Turbine Applications

DOE Office of Scientific and Technical Information (OSTI.GOV)

Alonso, Juan J.; Iaccarino, Gianluca

2013-08-25

The following is the final report covering the entire period of this aforementioned grant, June 1, 2011 - May 31, 2013 for the portion of the effort corresponding to Stanford University (SU). SU has partnered with Sandia National Laboratories (PI: Mike S. Eldred) and Purdue University (PI: Dongbin Xiu) to complete this research project and this final report includes those contributions made by the members of the team at Stanford. Dr. Eldred is continuing his contributions to this project under a no-cost extension and his contributions to the overall effort will be detailed at a later time (once his effortmore » has concluded) on a separate project submitted by Sandia National Laboratories. At Stanford, the team is made up of Profs. Alonso, Iaccarino, and Duraisamy, post-doctoral researcher Vinod Lakshminarayan, and graduate student Santiago Padron. At Sandia National Laboratories, the team includes Michael Eldred, Matt Barone, John Jakeman, and Stefan Domino, and at Purdue University, we have Prof. Dongbin Xiu as our main collaborator. The overall objective of this project was to develop a novel, comprehensive methodology for uncertainty quantification by combining stochastic expansions (nonintrusive polynomial chaos and stochastic collocation), the adjoint approach, and fusion with experimental data to account for aleatory and epistemic uncertainties from random variable, random field, and model form sources. The expected outcomes of this activity were detailed in the proposal and are repeated here to set the stage for the results that we have generated during the time period of execution of this project: 1. The rigorous determination of an error budget comprising numerical errors in physical space and statistical errors in stochastic space and its use for optimal allocation of resources; 2. A considerable increase in efficiency when performing uncertainty quantification with a large number of uncertain variables in complex non-linear multi-physics problems; 3. A solution to the long-time integration problem of spectral chaos approaches; 4. A rigorous methodology to account for aleatory and epistemic uncertainties, to emphasize the most important variables via dimension reduction and dimension-adaptive refinement, and to support fusion with experimental data using Bayesian inference; 5. The application of novel methodologies to time-dependent reliability studies in wind turbine applications including a number of efforts relating to the uncertainty quantification in vertical-axis wind turbine applications. In this report, we summarize all accomplishments in the project (during the time period specified) focusing on advances in UQ algorithms and deployment efforts to the wind turbine application area. Detailed publications in each of these areas have also been completed and are available from the respective conference proceedings and journals as detailed in a later section.« less

Multifidelity, Multidisciplinary Design Under Uncertainty with Non-Intrusive Polynomial Chaos

NASA Technical Reports Server (NTRS)

West, Thomas K., IV; Gumbert, Clyde

2017-01-01

The primary objective of this work is to develop an approach for multifidelity uncertainty quantification and to lay the framework for future design under uncertainty efforts. In this study, multifidelity is used to describe both the fidelity of the modeling of the physical systems, as well as the difference in the uncertainty in each of the models. For computational efficiency, a multifidelity surrogate modeling approach based on non-intrusive polynomial chaos using the point-collocation technique is developed for the treatment of both multifidelity modeling and multifidelity uncertainty modeling. Two stochastic model problems are used to demonstrate the developed methodologies: a transonic airfoil model and multidisciplinary aircraft analysis model. The results of both showed the multifidelity modeling approach was able to predict the output uncertainty predicted by the high-fidelity model as a significant reduction in computational cost.

A global wetland methane emissions and uncertainty dataset for atmospheric chemical transport models (WetCHARTs version 1.0)

NASA Astrophysics Data System (ADS)

Bloom, A. Anthony; Bowman, Kevin W.; Lee, Meemong; Turner, Alexander J.; Schroeder, Ronny; Worden, John R.; Weidner, Richard; McDonald, Kyle C.; Jacob, Daniel J.

2017-06-01

Wetland emissions remain one of the principal sources of uncertainty in the global atmospheric methane (CH4) budget, largely due to poorly constrained process controls on CH4 production in waterlogged soils. Process-based estimates of global wetland CH4 emissions and their associated uncertainties can provide crucial prior information for model-based top-down CH4 emission estimates. Here we construct a global wetland CH4 emission model ensemble for use in atmospheric chemical transport models (WetCHARTs version 1.0). Our 0.5° × 0.5° resolution model ensemble is based on satellite-derived surface water extent and precipitation reanalyses, nine heterotrophic respiration simulations (eight carbon cycle models and a data-constrained terrestrial carbon cycle analysis) and three temperature dependence parameterizations for the period 2009-2010; an extended ensemble subset based solely on precipitation and the data-constrained terrestrial carbon cycle analysis is derived for the period 2001-2015. We incorporate the mean of the full and extended model ensembles into GEOS-Chem and compare the model against surface measurements of atmospheric CH4; the model performance (site-level and zonal mean anomaly residuals) compares favourably against published wetland CH4 emissions scenarios. We find that uncertainties in carbon decomposition rates and the wetland extent together account for more than 80 % of the dominant uncertainty in the timing, magnitude and seasonal variability in wetland CH4 emissions, although uncertainty in the temperature CH4 : C dependence is a significant contributor to seasonal variations in mid-latitude wetland CH4 emissions. The combination of satellite, carbon cycle models and temperature dependence parameterizations provides a physically informed structural a priori uncertainty that is critical for top-down estimates of wetland CH4 fluxes. Specifically, our ensemble can provide enhanced information on the prior CH4 emission uncertainty and the error covariance structure, as well as a means for using posterior flux estimates and their uncertainties to quantitatively constrain the biogeochemical process controls of global wetland CH4 emissions.

Hotspots of uncertainty in land-use and land-cover change projections: A global-scale model comparison

DOE PAGES

Prestele, Reinhard; Alexander, Peter; Rounsevell, Mark D. A.; ...

2016-05-02

Model-based global projections of future land use and land cover (LULC) change are frequently used in environmental assessments to study the impact of LULC change on environmental services and to provide decision support for policy. These projections are characterized by a high uncertainty in terms of quantity and allocation of projected changes, which can severely impact the results of environmental assessments. In this study, we identify hotspots of uncertainty, based on 43 simulations from 11 global-scale LULC change models representing a wide range of assumptions of future biophysical and socio-economic conditions. We attribute components of uncertainty to input data, modelmore » structure, scenario storyline and a residual term, based on a regression analysis and analysis of variance. From this diverse set of models and scenarios we find that the uncertainty varies, depending on the region and the LULC type under consideration. Hotspots of uncertainty appear mainly at the edges of globally important biomes (e.g. boreal and tropical forests). Our results indicate that an important source of uncertainty in forest and pasture areas originates from different input data applied in the models. Cropland, in contrast, is more consistent among the starting conditions, while variation in the projections gradually increases over time due to diverse scenario assumptions and different modeling approaches. Comparisons at the grid cell level indicate that disagreement is mainly related to LULC type definitions and the individual model allocation schemes. We conclude that improving the quality and consistency of observational data utilized in the modeling process as well as improving the allocation mechanisms of LULC change models remain important challenges. Furthermore, current LULC representation in environmental assessments might miss the uncertainty arising from the diversity of LULC change modeling approaches and many studies ignore the uncertainty in LULC projections in assessments of LULC change impacts on climate, water resources or biodiversity.« less

Hotspots of uncertainty in land-use and land-cover change projections: A global-scale model comparison

DOE Office of Scientific and Technical Information (OSTI.GOV)

Prestele, Reinhard; Alexander, Peter; Rounsevell, Mark D. A.

Model-based global projections of future land use and land cover (LULC) change are frequently used in environmental assessments to study the impact of LULC change on environmental services and to provide decision support for policy. These projections are characterized by a high uncertainty in terms of quantity and allocation of projected changes, which can severely impact the results of environmental assessments. In this study, we identify hotspots of uncertainty, based on 43 simulations from 11 global-scale LULC change models representing a wide range of assumptions of future biophysical and socio-economic conditions. We attribute components of uncertainty to input data, modelmore » structure, scenario storyline and a residual term, based on a regression analysis and analysis of variance. From this diverse set of models and scenarios we find that the uncertainty varies, depending on the region and the LULC type under consideration. Hotspots of uncertainty appear mainly at the edges of globally important biomes (e.g. boreal and tropical forests). Our results indicate that an important source of uncertainty in forest and pasture areas originates from different input data applied in the models. Cropland, in contrast, is more consistent among the starting conditions, while variation in the projections gradually increases over time due to diverse scenario assumptions and different modeling approaches. Comparisons at the grid cell level indicate that disagreement is mainly related to LULC type definitions and the individual model allocation schemes. We conclude that improving the quality and consistency of observational data utilized in the modeling process as well as improving the allocation mechanisms of LULC change models remain important challenges. Furthermore, current LULC representation in environmental assessments might miss the uncertainty arising from the diversity of LULC change modeling approaches and many studies ignore the uncertainty in LULC projections in assessments of LULC change impacts on climate, water resources or biodiversity.« less

MODFLOW–LGR—Documentation of ghost node local grid refinement (LGR2) for multiple areas and the boundary flow and head (BFH2) package

USGS Publications Warehouse

Mehl, Steffen W.; Hill, Mary C.

2013-01-01

This report documents the addition of ghost node Local Grid Refinement (LGR2) to MODFLOW-2005, the U.S. Geological Survey modular, transient, three-dimensional, finite-difference groundwater flow model. LGR2 provides the capability to simulate groundwater flow using multiple block-shaped higher-resolution local grids (a child model) within a coarser-grid parent model. LGR2 accomplishes this by iteratively coupling separate MODFLOW-2005 models such that heads and fluxes are balanced across the grid-refinement interface boundary. LGR2 can be used in two-and three-dimensional, steady-state and transient simulations and for simulations of confined and unconfined groundwater systems. Traditional one-way coupled telescopic mesh refinement methods can have large, often undetected, inconsistencies in heads and fluxes across the interface between two model grids. The iteratively coupled ghost-node method of LGR2 provides a more rigorous coupling in which the solution accuracy is controlled by convergence criteria defined by the user. In realistic problems, this can result in substantially more accurate solutions and require an increase in computer processing time. The rigorous coupling enables sensitivity analysis, parameter estimation, and uncertainty analysis that reflects conditions in both model grids. This report describes the method used by LGR2, evaluates accuracy and performance for two-and three-dimensional test cases, provides input instructions, and lists selected input and output files for an example problem. It also presents the Boundary Flow and Head (BFH2) Package, which allows the child and parent models to be simulated independently using the boundary conditions obtained through the iterative process of LGR2.

Evaluation of cloud-resolving model simulations of midlatitude cirrus with ARM and A-train observations

DOE PAGES

Muhlbauer, A.; Ackerman, T. P.; Lawson, R. P.; ...

2015-07-14

Cirrus clouds are ubiquitous in the upper troposphere and still constitute one of the largest uncertainties in climate predictions. Our paper evaluates cloud-resolving model (CRM) and cloud system-resolving model (CSRM) simulations of a midlatitude cirrus case with comprehensive observations collected under the auspices of the Atmospheric Radiation Measurements (ARM) program and with spaceborne observations from the National Aeronautics and Space Administration A-train satellites. The CRM simulations are driven with periodic boundary conditions and ARM forcing data, whereas the CSRM simulations are driven by the ERA-Interim product. Vertical profiles of temperature, relative humidity, and wind speeds are reasonably well simulated bymore » the CSRM and CRM, but there are remaining biases in the temperature, wind speeds, and relative humidity, which can be mitigated through nudging the model simulations toward the observed radiosonde profiles. Simulated vertical velocities are underestimated in all simulations except in the CRM simulations with grid spacings of 500 m or finer, which suggests that turbulent vertical air motions in cirrus clouds need to be parameterized in general circulation models and in CSRM simulations with horizontal grid spacings on the order of 1 km. The simulated ice water content and ice number concentrations agree with the observations in the CSRM but are underestimated in the CRM simulations. The underestimation of ice number concentrations is consistent with the overestimation of radar reflectivity in the CRM simulations and suggests that the model produces too many large ice particles especially toward the cloud base. Simulated cloud profiles are rather insensitive to perturbations in the initial conditions or the dimensionality of the model domain, but the treatment of the forcing data has a considerable effect on the outcome of the model simulations. Despite considerable progress in observations and microphysical parameterizations, simulating the microphysical, macrophysical, and radiative properties of cirrus remains challenging. Comparing model simulations with observations from multiple instruments and observational platforms is important for revealing model deficiencies and for providing rigorous benchmarks. But, there still is considerable need for reducing observational uncertainties and providing better observations especially for relative humidity and for the size distribution and chemical composition of aerosols in the upper troposphere.« less

Circular instead of hierarchical: methodological principles for the evaluation of complex interventions

PubMed Central

Walach, Harald; Falkenberg, Torkel; Fønnebø, Vinjar; Lewith, George; Jonas, Wayne B

2006-01-01

Background The reasoning behind evaluating medical interventions is that a hierarchy of methods exists which successively produce improved and therefore more rigorous evidence based medicine upon which to make clinical decisions. At the foundation of this hierarchy are case studies, retrospective and prospective case series, followed by cohort studies with historical and concomitant non-randomized controls. Open-label randomized controlled studies (RCTs), and finally blinded, placebo-controlled RCTs, which offer most internal validity are considered the most reliable evidence. Rigorous RCTs remove bias. Evidence from RCTs forms the basis of meta-analyses and systematic reviews. This hierarchy, founded on a pharmacological model of therapy, is generalized to other interventions which may be complex and non-pharmacological (healing, acupuncture and surgery). Discussion The hierarchical model is valid for limited questions of efficacy, for instance for regulatory purposes and newly devised products and pharmacological preparations. It is inadequate for the evaluation of complex interventions such as physiotherapy, surgery and complementary and alternative medicine (CAM). This has to do with the essential tension between internal validity (rigor and the removal of bias) and external validity (generalizability). Summary Instead of an Evidence Hierarchy, we propose a Circular Model. This would imply a multiplicity of methods, using different designs, counterbalancing their individual strengths and weaknesses to arrive at pragmatic but equally rigorous evidence which would provide significant assistance in clinical and health systems innovation. Such evidence would better inform national health care technology assessment agencies and promote evidence based health reform. PMID:16796762

Rational selection of experimental readout and intervention sites for reducing uncertainties in computational model predictions.

PubMed

Flassig, Robert J; Migal, Iryna; der Zalm, Esther van; Rihko-Struckmann, Liisa; Sundmacher, Kai

2015-01-16

Understanding the dynamics of biological processes can substantially be supported by computational models in the form of nonlinear ordinary differential equations (ODE). Typically, this model class contains many unknown parameters, which are estimated from inadequate and noisy data. Depending on the ODE structure, predictions based on unmeasured states and associated parameters are highly uncertain, even undetermined. For given data, profile likelihood analysis has been proven to be one of the most practically relevant approaches for analyzing the identifiability of an ODE structure, and thus model predictions. In case of highly uncertain or non-identifiable parameters, rational experimental design based on various approaches has shown to significantly reduce parameter uncertainties with minimal amount of effort. In this work we illustrate how to use profile likelihood samples for quantifying the individual contribution of parameter uncertainty to prediction uncertainty. For the uncertainty quantification we introduce the profile likelihood sensitivity (PLS) index. Additionally, for the case of several uncertain parameters, we introduce the PLS entropy to quantify individual contributions to the overall prediction uncertainty. We show how to use these two criteria as an experimental design objective for selecting new, informative readouts in combination with intervention site identification. The characteristics of the proposed multi-criterion objective are illustrated with an in silico example. We further illustrate how an existing practically non-identifiable model for the chlorophyll fluorescence induction in a photosynthetic organism, D. salina, can be rendered identifiable by additional experiments with new readouts. Having data and profile likelihood samples at hand, the here proposed uncertainty quantification based on prediction samples from the profile likelihood provides a simple way for determining individual contributions of parameter uncertainties to uncertainties in model predictions. The uncertainty quantification of specific model predictions allows identifying regions, where model predictions have to be considered with care. Such uncertain regions can be used for a rational experimental design to render initially highly uncertain model predictions into certainty. Finally, our uncertainty quantification directly accounts for parameter interdependencies and parameter sensitivities of the specific prediction.

A stochastic optimization model under modeling uncertainty and parameter certainty for groundwater remediation design--part I. Model development.

PubMed

He, L; Huang, G H; Lu, H W

2010-04-15

Solving groundwater remediation optimization problems based on proxy simulators can usually yield optimal solutions differing from the "true" ones of the problem. This study presents a new stochastic optimization model under modeling uncertainty and parameter certainty (SOMUM) and the associated solution method for simultaneously addressing modeling uncertainty associated with simulator residuals and optimizing groundwater remediation processes. This is a new attempt different from the previous modeling efforts. The previous ones focused on addressing uncertainty in physical parameters (i.e. soil porosity) while this one aims to deal with uncertainty in mathematical simulator (arising from model residuals). Compared to the existing modeling approaches (i.e. only parameter uncertainty is considered), the model has the advantages of providing mean-variance analysis for contaminant concentrations, mitigating the effects of modeling uncertainties on optimal remediation strategies, offering confidence level of optimal remediation strategies to system designers, and reducing computational cost in optimization processes. 2009 Elsevier B.V. All rights reserved.

Uncertainty analysis in geospatial merit matrix–based hydropower resource assessment

DOE PAGES

Pasha, M. Fayzul K.; Yeasmin, Dilruba; Saetern, Sen; ...

2016-03-30

Hydraulic head and mean annual streamflow, two main input parameters in hydropower resource assessment, are not measured at every point along the stream. Translation and interpolation are used to derive these parameters, resulting in uncertainties. This study estimates the uncertainties and their effects on model output parameters: the total potential power and the number of potential locations (stream-reach). These parameters are quantified through Monte Carlo Simulation (MCS) linking with a geospatial merit matrix based hydropower resource assessment (GMM-HRA) Model. The methodology is applied to flat, mild, and steep terrains. Results show that the uncertainty associated with the hydraulic head ismore » within 20% for mild and steep terrains, and the uncertainty associated with streamflow is around 16% for all three terrains. Output uncertainty increases as input uncertainty increases. However, output uncertainty is around 10% to 20% of the input uncertainty, demonstrating the robustness of the GMM-HRA model. Hydraulic head is more sensitive to output parameters in steep terrain than in flat and mild terrains. Furthermore, mean annual streamflow is more sensitive to output parameters in flat terrain.« less

Optimal Objective-Based Experimental Design for Uncertain Dynamical Gene Networks with Experimental Error.

PubMed

Mohsenizadeh, Daniel N; Dehghannasiri, Roozbeh; Dougherty, Edward R

2018-01-01

In systems biology, network models are often used to study interactions among cellular components, a salient aim being to develop drugs and therapeutic mechanisms to change the dynamical behavior of the network to avoid undesirable phenotypes. Owing to limited knowledge, model uncertainty is commonplace and network dynamics can be updated in different ways, thereby giving multiple dynamic trajectories, that is, dynamics uncertainty. In this manuscript, we propose an experimental design method that can effectively reduce the dynamics uncertainty and improve performance in an interaction-based network. Both dynamics uncertainty and experimental error are quantified with respect to the modeling objective, herein, therapeutic intervention. The aim of experimental design is to select among a set of candidate experiments the experiment whose outcome, when applied to the network model, maximally reduces the dynamics uncertainty pertinent to the intervention objective.

A computational framework for modeling targets as complex adaptive systems

NASA Astrophysics Data System (ADS)

Santos, Eugene; Santos, Eunice E.; Korah, John; Murugappan, Vairavan; Subramanian, Suresh

2017-05-01

Modeling large military targets is a challenge as they can be complex systems encompassing myriad combinations of human, technological, and social elements that interact, leading to complex behaviors. Moreover, such targets have multiple components and structures, extending across multiple spatial and temporal scales, and are in a state of change, either in response to events in the environment or changes within the system. Complex adaptive system (CAS) theory can help in capturing the dynamism, interactions, and more importantly various emergent behaviors, displayed by the targets. However, a key stumbling block is incorporating information from various intelligence, surveillance and reconnaissance (ISR) sources, while dealing with the inherent uncertainty, incompleteness and time criticality of real world information. To overcome these challenges, we present a probabilistic reasoning network based framework called complex adaptive Bayesian Knowledge Base (caBKB). caBKB is a rigorous, overarching and axiomatic framework that models two key processes, namely information aggregation and information composition. While information aggregation deals with the union, merger and concatenation of information and takes into account issues such as source reliability and information inconsistencies, information composition focuses on combining information components where such components may have well defined operations. Since caBKBs can explicitly model the relationships between information pieces at various scales, it provides unique capabilities such as the ability to de-aggregate and de-compose information for detailed analysis. Using a scenario from the Network Centric Operations (NCO) domain, we will describe how our framework can be used for modeling targets with a focus on methodologies for quantifying NCO performance metrics.

Quantum probabilistic logic programming

NASA Astrophysics Data System (ADS)

Balu, Radhakrishnan

2015-05-01

We describe a quantum mechanics based logic programming language that supports Horn clauses, random variables, and covariance matrices to express and solve problems in probabilistic logic. The Horn clauses of the language wrap random variables, including infinite valued, to express probability distributions and statistical correlations, a powerful feature to capture relationship between distributions that are not independent. The expressive power of the language is based on a mechanism to implement statistical ensembles and to solve the underlying SAT instances using quantum mechanical machinery. We exploit the fact that classical random variables have quantum decompositions to build the Horn clauses. We establish the semantics of the language in a rigorous fashion by considering an existing probabilistic logic language called PRISM with classical probability measures defined on the Herbrand base and extending it to the quantum context. In the classical case H-interpretations form the sample space and probability measures defined on them lead to consistent definition of probabilities for well formed formulae. In the quantum counterpart, we define probability amplitudes on Hinterpretations facilitating the model generations and verifications via quantum mechanical superpositions and entanglements. We cast the well formed formulae of the language as quantum mechanical observables thus providing an elegant interpretation for their probabilities. We discuss several examples to combine statistical ensembles and predicates of first order logic to reason with situations involving uncertainty.

A Statistics-Based Material Property Analysis to Support TPS Characterization

NASA Technical Reports Server (NTRS)

Copeland, Sean R.; Cozmuta, Ioana; Alonso, Juan J.

2012-01-01

Accurate characterization of entry capsule heat shield material properties is a critical component in modeling and simulating Thermal Protection System (TPS) response in a prescribed aerothermal environment. The thermal decomposition of the TPS material during the pyrolysis and charring processes is poorly characterized and typically results in large uncertainties in material properties as inputs for ablation models. These material property uncertainties contribute to large design margins on flight systems and cloud re- construction efforts for data collected during flight and ground testing, making revision to existing models for entry systems more challenging. The analysis presented in this work quantifies how material property uncertainties propagate through an ablation model and guides an experimental test regimen aimed at reducing these uncertainties and characterizing the dependencies between properties in the virgin and charred states for a Phenolic Impregnated Carbon Ablator (PICA) based TPS. A sensitivity analysis identifies how the high-fidelity model behaves in the expected flight environment, while a Monte Carlo based uncertainty propagation strategy is used to quantify the expected spread in the in-depth temperature response of the TPS. An examination of how perturbations to the input probability density functions affect output temperature statistics is accomplished using a Kriging response surface of the high-fidelity model. Simulations are based on capsule configuration and aerothermal environments expected during the Mars Science Laboratory (MSL) entry sequence. We identify and rank primary sources of uncertainty from material properties in a flight-relevant environment, show the dependence on spatial orientation and in-depth location on those uncertainty contributors, and quantify how sensitive the expected results are.

Evaluating uncertainty in environmental life-cycle assessment. A case study comparing two insulation options for a Dutch one-family dwelling.

PubMed

Huijbregts, Mark A J; Gilijamse, Wim; Ragas, Ad M J; Reijnders, Lucas

2003-06-01

The evaluation of uncertainty is relatively new in environmental life-cycle assessment (LCA). It provides useful information to assess the reliability of LCA-based decisions and to guide future research toward reducing uncertainty. Most uncertainty studies in LCA quantify only one type of uncertainty, i.e., uncertainty due to input data (parameter uncertainty). However, LCA outcomes can also be uncertain due to normative choices (scenario uncertainty) and the mathematical models involved (model uncertainty). The present paper outlines a new methodology that quantifies parameter, scenario, and model uncertainty simultaneously in environmental life-cycle assessment. The procedure is illustrated in a case study that compares two insulation options for a Dutch one-family dwelling. Parameter uncertainty was quantified by means of Monte Carlo simulation. Scenario and model uncertainty were quantified by resampling different decision scenarios and model formulations, respectively. Although scenario and model uncertainty were not quantified comprehensively, the results indicate that both types of uncertainty influence the case study outcomes. This stresses the importance of quantifying parameter, scenario, and model uncertainty simultaneously. The two insulation options studied were found to have significantly different impact scores for global warming, stratospheric ozone depletion, and eutrophication. The thickest insulation option has the lowest impact on global warming and eutrophication, and the highest impact on stratospheric ozone depletion.

A rigorous approach to investigating common assumptions about disease transmission: Process algebra as an emerging modelling methodology for epidemiology.

PubMed

McCaig, Chris; Begon, Mike; Norman, Rachel; Shankland, Carron

2011-03-01

Changing scale, for example, the ability to move seamlessly from an individual-based model to a population-based model, is an important problem in many fields. In this paper, we introduce process algebra as a novel solution to this problem in the context of models of infectious disease spread. Process algebra allows us to describe a system in terms of the stochastic behaviour of individuals, and is a technique from computer science. We review the use of process algebra in biological systems, and the variety of quantitative and qualitative analysis techniques available. The analysis illustrated here solves the changing scale problem: from the individual behaviour we can rigorously derive equations to describe the mean behaviour of the system at the level of the population. The biological problem investigated is the transmission of infection, and how this relates to individual interactions.

Impacts of uncertainties in weather and streamflow observations in calibration and evaluation of an elevation distributed HBV-model

NASA Astrophysics Data System (ADS)

Engeland, K.; Steinsland, I.; Petersen-Øverleir, A.; Johansen, S.

2012-04-01

The aim of this study is to assess the uncertainties in streamflow simulations when uncertainties in both observed inputs (precipitation and temperature) and streamflow observations used in the calibration of the hydrological model are explicitly accounted for. To achieve this goal we applied the elevation distributed HBV model operating on daily time steps to a small catchment in high elevation in Southern Norway where the seasonal snow cover is important. The uncertainties in precipitation inputs were quantified using conditional simulation. This procedure accounts for the uncertainty related to the density of the precipitation network, but neglects uncertainties related to measurement bias/errors and eventual elevation gradients in precipitation. The uncertainties in temperature inputs were quantified using a Bayesian temperature interpolation procedure where the temperature lapse rate is re-estimated every day. The uncertainty in the lapse rate was accounted for whereas the sampling uncertainty related to network density was neglected. For every day a random sample of precipitation and temperature inputs were drawn to be applied as inputs to the hydrologic model. The uncertainties in observed streamflow were assessed based on the uncertainties in the rating curve model. A Bayesian procedure was applied to estimate the probability for rating curve models with 1 to 3 segments and the uncertainties in their parameters. This method neglects uncertainties related to errors in observed water levels. Note that one rating curve was drawn to make one realisation of a whole time series of streamflow, thus the rating curve errors lead to a systematic bias in the streamflow observations. All these uncertainty sources were linked together in both calibration and evaluation of the hydrologic model using a DREAM based MCMC routine. Effects of having less information (e.g. missing one streamflow measurement for defining the rating curve or missing one precipitation station) was also investigated.

A rigorous multiple independent binding site model for determining cell-based equilibrium dissociation constants.

PubMed

Drake, Andrew W; Klakamp, Scott L

2007-01-10

A new 4-parameter nonlinear equation based on the standard multiple independent binding site model (MIBS) is presented for fitting cell-based ligand titration data in order to calculate the ligand/cell receptor equilibrium dissociation constant and the number of receptors/cell. The most commonly used linear (Scatchard Plot) or nonlinear 2-parameter model (a single binding site model found in commercial programs like Prism(R)) used for analysis of ligand/receptor binding data assumes only the K(D) influences the shape of the titration curve. We demonstrate using simulated data sets that, depending upon the cell surface receptor expression level, the number of cells titrated, and the magnitude of the K(D) being measured, this assumption of always being under K(D)-controlled conditions can be erroneous and can lead to unreliable estimates for the binding parameters. We also compare and contrast the fitting of simulated data sets to the commonly used cell-based binding equation versus our more rigorous 4-parameter nonlinear MIBS model. It is shown through these simulations that the new 4-parameter MIBS model, when used for cell-based titrations under optimal conditions, yields highly accurate estimates of all binding parameters and hence should be the preferred model to fit cell-based experimental nonlinear titration data.

The ends of uncertainty: Air quality science and planning in Central California

DOE Office of Scientific and Technical Information (OSTI.GOV)

Fine, James

Air quality planning in Central California is complicated and controversial despite millions of dollars invested to improve scientific understanding. This research describes and critiques the use of photochemical air quality simulation modeling studies in planning to attain standards for ground-level ozone in the San Francisco Bay Area and the San Joaquin Valley during the 1990's. Data are gathered through documents and interviews with planners, modelers, and policy-makers at public agencies and with representatives from the regulated and environmental communities. Interactions amongst organizations are diagramed to identify significant nodes of interaction. Dominant policy coalitions are described through narratives distinguished by theirmore » uses of and responses to uncertainty, their exposures to risks, and their responses to the principles of conservatism, civil duty, and caution. Policy narratives are delineated using aggregated respondent statements to describe and understand advocacy coalitions. I found that models impacted the planning process significantly, but were used not purely for their scientific capabilities. Modeling results provided justification for decisions based on other constraints and political considerations. Uncertainties were utilized opportunistically by stakeholders instead of managed explicitly. Ultimately, the process supported the partisan views of those in control of the modeling. Based on these findings, as well as a review of model uncertainty analysis capabilities, I recommend modifying the planning process to allow for the development and incorporation of uncertainty information, while addressing the need for inclusive and meaningful public participation. By documenting an actual air quality planning process these findings provide insights about the potential for using new scientific information and understanding to achieve environmental goals, most notably the analysis of uncertainties in modeling applications. Concurrently, needed uncertainty information is identified and capabilities to produce it are assessed. Practices to facilitate incorporation of uncertainty information are suggested based on research findings, as well as theory from the literatures of the policy sciences, decision sciences, science and technology studies, consensus-based and communicative planning, and modeling.« less

Accounting for parameter uncertainty in the definition of parametric distributions used to describe individual patient variation in health economic models.

PubMed

Degeling, Koen; IJzerman, Maarten J; Koopman, Miriam; Koffijberg, Hendrik

2017-12-15

Parametric distributions based on individual patient data can be used to represent both stochastic and parameter uncertainty. Although general guidance is available on how parameter uncertainty should be accounted for in probabilistic sensitivity analysis, there is no comprehensive guidance on reflecting parameter uncertainty in the (correlated) parameters of distributions used to represent stochastic uncertainty in patient-level models. This study aims to provide this guidance by proposing appropriate methods and illustrating the impact of this uncertainty on modeling outcomes. Two approaches, 1) using non-parametric bootstrapping and 2) using multivariate Normal distributions, were applied in a simulation and case study. The approaches were compared based on point-estimates and distributions of time-to-event and health economic outcomes. To assess sample size impact on the uncertainty in these outcomes, sample size was varied in the simulation study and subgroup analyses were performed for the case-study. Accounting for parameter uncertainty in distributions that reflect stochastic uncertainty substantially increased the uncertainty surrounding health economic outcomes, illustrated by larger confidence ellipses surrounding the cost-effectiveness point-estimates and different cost-effectiveness acceptability curves. Although both approaches performed similar for larger sample sizes (i.e. n = 500), the second approach was more sensitive to extreme values for small sample sizes (i.e. n = 25), yielding infeasible modeling outcomes. Modelers should be aware that parameter uncertainty in distributions used to describe stochastic uncertainty needs to be reflected in probabilistic sensitivity analysis, as it could substantially impact the total amount of uncertainty surrounding health economic outcomes. If feasible, the bootstrap approach is recommended to account for this uncertainty.

A methodology for computing uncertainty bounds of multivariable systems based on sector stability theory concepts

NASA Technical Reports Server (NTRS)

Waszak, Martin R.

1992-01-01

The application of a sector-based stability theory approach to the formulation of useful uncertainty descriptions for linear, time-invariant, multivariable systems is explored. A review of basic sector properties and sector-based approach are presented first. The sector-based approach is then applied to several general forms of parameter uncertainty to investigate its advantages and limitations. The results indicate that the sector uncertainty bound can be used effectively to evaluate the impact of parameter uncertainties on the frequency response of the design model. Inherent conservatism is a potential limitation of the sector-based approach, especially for highly dependent uncertain parameters. In addition, the representation of the system dynamics can affect the amount of conservatism reflected in the sector bound. Careful application of the model can help to reduce this conservatism, however, and the solution approach has some degrees of freedom that may be further exploited to reduce the conservatism.

Diffraction-based overlay measurement on dedicated mark using rigorous modeling method

NASA Astrophysics Data System (ADS)

Lu, Hailiang; Wang, Fan; Zhang, Qingyun; Chen, Yonghui; Zhou, Chang

2012-03-01

Diffraction Based Overlay (DBO) is widely evaluated by numerous authors, results show DBO can provide better performance than Imaging Based Overlay (IBO). However, DBO has its own problems. As well known, Modeling based DBO (mDBO) faces challenges of low measurement sensitivity and crosstalk between various structure parameters, which may result in poor accuracy and precision. Meanwhile, main obstacle encountered by empirical DBO (eDBO) is that a few pads must be employed to gain sufficient information on overlay-induced diffraction signature variations, which consumes more wafer space and costs more measuring time. Also, eDBO may suffer from mark profile asymmetry caused by processes. In this paper, we propose an alternative DBO technology that employs a dedicated overlay mark and takes a rigorous modeling approach. This technology needs only two or three pads for each direction, which is economic and time saving. While overlay measurement error induced by mark profile asymmetry being reduced, this technology is expected to be as accurate and precise as scatterometry technologies.

Velocity variations and uncertainty from transdimensional P-wave tomography of North America

NASA Astrophysics Data System (ADS)

Burdick, Scott; Lekić, Vedran

2017-05-01

High-resolution models of seismic velocity variations constructed using body-wave tomography inform the study of the origin, fate and thermochemical state of mantle domains. In order to reliably relate these variations to material properties including temperature, composition and volatile content, we must accurately retrieve both the patterns and amplitudes of variations and quantify the uncertainty associated with the estimates of each. For these reasons, we image the mantle beneath North America with P-wave traveltimes from USArray using a novel method for 3-D probabilistic body-wave tomography. The method uses a Transdimensional Hierarchical Bayesian framework with a reversible-jump Markov Chain Monte Carlo algorithm in order to generate an ensemble of possible velocity models. We analyse this ensemble solution to obtain the posterior probability distribution of velocities, thereby yielding error bars and enabling rigorous hypothesis testing. Overall, we determine that the average uncertainty (1σ) of compressional wave velocity estimates beneath North America is ∼0.25 per cent dVP/VP, increasing with proximity to complex structure and decreasing with depth. The addition of USArray data reduces the uncertainty beneath the Eastern US by over 50 per cent in the upper mantle and 25-40 per cent below the transition zone and ∼30 per cent throughout the mantle beneath the Western US. In the absence of damping and smoothing, we recover amplitudes of variations 10-80 per cent higher than a standard inversion approach. Accounting for differences in data coverage, we infer that the length scale of heterogeneity is ∼50 per cent longer at shallow depths beneath the continental platform than beneath tectonically active regions. We illustrate the model trade-off analysis for the Cascadia slab and the New Madrid Seismic Zone, where we find that smearing due to the limitations of the illumination is relatively minor.

Measurement system for diffraction efficiency of convex gratings

NASA Astrophysics Data System (ADS)

Liu, Peng; Chen, Xin-hua; Zhou, Jian-kang; Zhao, Zhi-cheng; Liu, Quan; Luo, Chao; Wang, Xiao-feng; Tang, Min-xue; Shen, Wei-min

2017-08-01

A measurement system for diffraction efficiency of convex gratings is designed. The measurement system mainly includes four components as a light source, a front system, a dispersing system that contains a convex grating, and a detector. Based on the definition and measuring principle of diffraction efficiency, the optical scheme of the measurement system is analyzed and the design result is given. Then, in order to validate the feasibility of the designed system, the measurement system is set up and the diffraction efficiency of a convex grating with the aperture of 35 mm, the curvature-radius of 72mm, the blazed angle of 6.4°, the grating period of 2.5μm and the working waveband of 400nm-900nm is tested. Based on GUM (Guide to the Expression of Uncertainty in Measurement), the uncertainties in the measuring results are evaluated. The measured diffraction efficiency data are compared to the theoretical ones, which are calculated based on the grating groove parameters got by an atomic force microscope and Rigorous Couple Wave Analysis, and the reliability of the measurement system is illustrated. Finally, the measurement performance of the system is analyzed and tested. The results show that, the testing accuracy, the testing stability and the testing repeatability are 2.5%, 0.085% and 3.5% , respectively.

Estimating the sources of global sea level rise with data assimilation techniques.

PubMed

Hay, Carling C; Morrow, Eric; Kopp, Robert E; Mitrovica, Jerry X

2013-02-26

A rapidly melting ice sheet produces a distinctive geometry, or fingerprint, of sea level (SL) change. Thus, a network of SL observations may, in principle, be used to infer sources of meltwater flux. We outline a formalism, based on a modified Kalman smoother, for using tide gauge observations to estimate the individual sources of global SL change. We also report on a series of detection experiments based on synthetic SL data that explore the feasibility of extracting source information from SL records. The Kalman smoother technique iteratively calculates the maximum-likelihood estimate of Greenland ice sheet (GIS) and West Antarctic ice sheet (WAIS) melt at each time step, and it accommodates data gaps while also permitting the estimation of nonlinear trends. Our synthetic tests indicate that when all tide gauge records are used in the analysis, it should be possible to estimate GIS and WAIS melt rates greater than ∼0.3 and ∼0.4 mm of equivalent eustatic sea level rise per year, respectively. We have also implemented a multimodel Kalman filter that allows us to account rigorously for additional contributions to SL changes and their associated uncertainty. The multimodel filter uses 72 glacial isostatic adjustment models and 3 ocean dynamic models to estimate the most likely models for these processes given the synthetic observations. We conclude that our modified Kalman smoother procedure provides a powerful method for inferring melt rates in a warming world.

Estimating the sources of global sea level rise with data assimilation techniques

PubMed Central

Hay, Carling C.; Morrow, Eric; Kopp, Robert E.; Mitrovica, Jerry X.

2013-01-01

A rapidly melting ice sheet produces a distinctive geometry, or fingerprint, of sea level (SL) change. Thus, a network of SL observations may, in principle, be used to infer sources of meltwater flux. We outline a formalism, based on a modified Kalman smoother, for using tide gauge observations to estimate the individual sources of global SL change. We also report on a series of detection experiments based on synthetic SL data that explore the feasibility of extracting source information from SL records. The Kalman smoother technique iteratively calculates the maximum-likelihood estimate of Greenland ice sheet (GIS) and West Antarctic ice sheet (WAIS) melt at each time step, and it accommodates data gaps while also permitting the estimation of nonlinear trends. Our synthetic tests indicate that when all tide gauge records are used in the analysis, it should be possible to estimate GIS and WAIS melt rates greater than ∼0.3 and ∼0.4 mm of equivalent eustatic sea level rise per year, respectively. We have also implemented a multimodel Kalman filter that allows us to account rigorously for additional contributions to SL changes and their associated uncertainty. The multimodel filter uses 72 glacial isostatic adjustment models and 3 ocean dynamic models to estimate the most likely models for these processes given the synthetic observations. We conclude that our modified Kalman smoother procedure provides a powerful method for inferring melt rates in a warming world. PMID:22543163

Temporal Cyber Attack Detection.

DOE Office of Scientific and Technical Information (OSTI.GOV)

Ingram, Joey Burton; Draelos, Timothy J.; Galiardi, Meghan

Rigorous characterization of the performance and generalization ability of cyber defense systems is extremely difficult, making it hard to gauge uncertainty, and thus, confidence. This difficulty largely stems from a lack of labeled attack data that fully explores the potential adversarial space. Currently, performance of cyber defense systems is typically evaluated in a qualitative manner by manually inspecting the results of the system on live data and adjusting as needed. Additionally, machine learning has shown promise in deriving models that automatically learn indicators of compromise that are more robust than analyst-derived detectors. However, to generate these models, most algorithms requiremore » large amounts of labeled data (i.e., examples of attacks). Algorithms that do not require annotated data to derive models are similarly at a disadvantage, because labeled data is still necessary when evaluating performance. In this work, we explore the use of temporal generative models to learn cyber attack graph representations and automatically generate data for experimentation and evaluation. Training and evaluating cyber systems and machine learning models requires significant, annotated data, which is typically collected and labeled by hand for one-off experiments. Automatically generating such data helps derive/evaluate detection models and ensures reproducibility of results. Experimentally, we demonstrate the efficacy of generative sequence analysis techniques on learning the structure of attack graphs, based on a realistic example. These derived models can then be used to generate more data. Additionally, we provide a roadmap for future research efforts in this area.« less

Multi-profile Bayesian alignment model for LC-MS data analysis with integration of internal standards

PubMed Central

Tsai, Tsung-Heng; Tadesse, Mahlet G.; Di Poto, Cristina; Pannell, Lewis K.; Mechref, Yehia; Wang, Yue; Ressom, Habtom W.

2013-01-01

Motivation: Liquid chromatography-mass spectrometry (LC-MS) has been widely used for profiling expression levels of biomolecules in various ‘-omic’ studies including proteomics, metabolomics and glycomics. Appropriate LC-MS data preprocessing steps are needed to detect true differences between biological groups. Retention time (RT) alignment, which is required to ensure that ion intensity measurements among multiple LC-MS runs are comparable, is one of the most important yet challenging preprocessing steps. Current alignment approaches estimate RT variability using either single chromatograms or detected peaks, but do not simultaneously take into account the complementary information embedded in the entire LC-MS data. Results: We propose a Bayesian alignment model for LC-MS data analysis. The alignment model provides estimates of the RT variability along with uncertainty measures. The model enables integration of multiple sources of information including internal standards and clustered chromatograms in a mathematically rigorous framework. We apply the model to LC-MS metabolomic, proteomic and glycomic data. The performance of the model is evaluated based on ground-truth data, by measuring correlation of variation, RT difference across runs and peak-matching performance. We demonstrate that Bayesian alignment model improves significantly the RT alignment performance through appropriate integration of relevant information. Availability and implementation: MATLAB code, raw and preprocessed LC-MS data are available at http://omics.georgetown.edu/alignLCMS.html Contact: hwr@georgetown.edu Supplementary information: Supplementary data are available at Bioinformatics online. PMID:24013927

On the uncertainty of phenological responses to climate change and its implication for terrestrial biosphere models

NASA Astrophysics Data System (ADS)

Migliavacca, M.; Sonnentag, O.; Keenan, T. F.; Cescatti, A.; O'Keefe, J.; Richardson, A. D.

2012-01-01

Phenology, the timing of recurring life cycle events, controls numerous land surface feedbacks to the climate systems through the regulation of exchanges of carbon, water and energy between the biosphere and atmosphere. Land surface models, however, are known to have systematic errors in the simulation of spring phenology, which potentially could propagate to uncertainty in modeled responses to future climate change. Here, we analyzed the Harvard Forest phenology record to investigate and characterize the sources of uncertainty in phenological forecasts and the subsequent impacts on model forecasts of carbon and water cycling in the future. Using a model-data fusion approach, we combined information from 20 yr of phenological observations of 11 North American woody species with 12 phenological models of different complexity to predict leaf bud-burst. The evaluation of different phenological models indicated support for spring warming models with photoperiod limitations and, though to a lesser extent, to chilling models based on the alternating model structure. We assessed three different sources of uncertainty in phenological forecasts: parameter uncertainty, model uncertainty, and driver uncertainty. The latter was characterized running the models to 2099 using 2 different IPCC climate scenarios (A1fi vs. B1, i.e. high CO2 emissions vs. low CO2 emissions scenario). Parameter uncertainty was the smallest (average 95% CI: 2.4 day century-1 for scenario B1 and 4.5 day century-1 for A1fi), whereas driver uncertainty was the largest (up to 8.4 day century-1 in the simulated trends). The uncertainty related to model structure is also large and the predicted bud-burst trends as well as the shape of the smoothed projections varied somewhat among models (±7.7 day century-1 for A1fi, ±3.6 day century-1 for B1). The forecast sensitivity of bud-burst to temperature (i.e. days bud-burst advanced per degree of warming) varied between 2.2 day °C-1 and 5.2 day °C-1 depending on model structure. We quantified the impact of uncertainties in bud-burst forecasts on simulated carbon and water fluxes using a process-based terrestrial biosphere model. Uncertainty in phenology model structure led to uncertainty in the description of the seasonality of processes, which accumulated to uncertainty in annual model estimates of gross primary productivity (GPP) and evapotranspiration (ET) of 9.6% and 2.9% respectively. A sensitivity analysis shows that a variation of ±10 days in bud-burst dates led to a variation of ±5.0% for annual GPP and about ±2.0% for ET. For phenology models, differences among future climate scenarios represent the largest source of uncertainty, followed by uncertainties related to model structure, and finally, uncertainties related to model parameterization. The uncertainties we have quantified will affect the description of the seasonality of processes and in particular the simulation of carbon uptake by forest ecosystems, with a larger impact of uncertainties related to phenology model structure, followed by uncertainties related to phenological model parameterization.

DIMM-SC: a Dirichlet mixture model for clustering droplet-based single cell transcriptomic data.

PubMed

Sun, Zhe; Wang, Ting; Deng, Ke; Wang, Xiao-Feng; Lafyatis, Robert; Ding, Ying; Hu, Ming; Chen, Wei

2018-01-01

Single cell transcriptome sequencing (scRNA-Seq) has become a revolutionary tool to study cellular and molecular processes at single cell resolution. Among existing technologies, the recently developed droplet-based platform enables efficient parallel processing of thousands of single cells with direct counting of transcript copies using Unique Molecular Identifier (UMI). Despite the technology advances, statistical methods and computational tools are still lacking for analyzing droplet-based scRNA-Seq data. Particularly, model-based approaches for clustering large-scale single cell transcriptomic data are still under-explored. We developed DIMM-SC, a Dirichlet Mixture Model for clustering droplet-based Single Cell transcriptomic data. This approach explicitly models UMI count data from scRNA-Seq experiments and characterizes variations across different cell clusters via a Dirichlet mixture prior. We performed comprehensive simulations to evaluate DIMM-SC and compared it with existing clustering methods such as K-means, CellTree and Seurat. In addition, we analyzed public scRNA-Seq datasets with known cluster labels and in-house scRNA-Seq datasets from a study of systemic sclerosis with prior biological knowledge to benchmark and validate DIMM-SC. Both simulation studies and real data applications demonstrated that overall, DIMM-SC achieves substantially improved clustering accuracy and much lower clustering variability compared to other existing clustering methods. More importantly, as a model-based approach, DIMM-SC is able to quantify the clustering uncertainty for each single cell, facilitating rigorous statistical inference and biological interpretations, which are typically unavailable from existing clustering methods. DIMM-SC has been implemented in a user-friendly R package with a detailed tutorial available on www.pitt.edu/∼wec47/singlecell.html. wei.chen@chp.edu or hum@ccf.org. Supplementary data are available at Bioinformatics online. © The Author 2017. Published by Oxford University Press. All rights reserved. For Permissions, please e-mail: journals.permissions@oup.com

Uncertainty Aware Structural Topology Optimization Via a Stochastic Reduced Order Model Approach

NASA Technical Reports Server (NTRS)

Aguilo, Miguel A.; Warner, James E.

2017-01-01

This work presents a stochastic reduced order modeling strategy for the quantification and propagation of uncertainties in topology optimization. Uncertainty aware optimization problems can be computationally complex due to the substantial number of model evaluations that are necessary to accurately quantify and propagate uncertainties. This computational complexity is greatly magnified if a high-fidelity, physics-based numerical model is used for the topology optimization calculations. Stochastic reduced order model (SROM) methods are applied here to effectively 1) alleviate the prohibitive computational cost associated with an uncertainty aware topology optimization problem; and 2) quantify and propagate the inherent uncertainties due to design imperfections. A generic SROM framework that transforms the uncertainty aware, stochastic topology optimization problem into a deterministic optimization problem that relies only on independent calls to a deterministic numerical model is presented. This approach facilitates the use of existing optimization and modeling tools to accurately solve the uncertainty aware topology optimization problems in a fraction of the computational demand required by Monte Carlo methods. Finally, an example in structural topology optimization is presented to demonstrate the effectiveness of the proposed uncertainty aware structural topology optimization approach.

a New Model for Fuzzy Personalized Route Planning Using Fuzzy Linguistic Preference Relation

NASA Astrophysics Data System (ADS)

Nadi, S.; Houshyaripour, A. H.

2017-09-01

This paper proposes a new model for personalized route planning under uncertain condition. Personalized routing, involves different sources of uncertainty. These uncertainties can be raised from user's ambiguity about their preferences, imprecise criteria values and modelling process. The proposed model uses Fuzzy Linguistic Preference Relation Analytical Hierarchical Process (FLPRAHP) to analyse user's preferences under uncertainty. Routing is a multi-criteria task especially in transportation networks, where the users wish to optimize their routes based on different criteria. However, due to the lake of knowledge about the preferences of different users and uncertainties available in the criteria values, we propose a new personalized fuzzy routing method based on the fuzzy ranking using center of gravity. The model employed FLPRAHP method to aggregate uncertain criteria values regarding uncertain user's preferences while improve consistency with least possible comparisons. An illustrative example presents the effectiveness and capability of the proposed model to calculate best personalize route under fuzziness and uncertainty.

The development of a 3D risk analysis method.

PubMed

I, Yet-Pole; Cheng, Te-Lung

2008-05-01

Much attention has been paid to the quantitative risk analysis (QRA) research in recent years due to more and more severe disasters that have happened in the process industries. Owing to its calculation complexity, very few software, such as SAFETI, can really make the risk presentation meet the practice requirements. However, the traditional risk presentation method, like the individual risk contour in SAFETI, is mainly based on the consequence analysis results of dispersion modeling, which usually assumes that the vapor cloud disperses over a constant ground roughness on a flat terrain with no obstructions and concentration fluctuations, which is quite different from the real situations of a chemical process plant. All these models usually over-predict the hazardous regions in order to maintain their conservativeness, which also increases the uncertainty of the simulation results. On the other hand, a more rigorous model such as the computational fluid dynamics (CFD) model can resolve the previous limitations; however, it cannot resolve the complexity of risk calculations. In this research, a conceptual three-dimensional (3D) risk calculation method was proposed via the combination of results of a series of CFD simulations with some post-processing procedures to obtain the 3D individual risk iso-surfaces. It is believed that such technique will not only be limited to risk analysis at ground level, but also be extended into aerial, submarine, or space risk analyses in the near future.

Model and parametric uncertainty in source-based kinematic models of earthquake ground motion

USGS Publications Warehouse

Hartzell, Stephen; Frankel, Arthur; Liu, Pengcheng; Zeng, Yuehua; Rahman, Shariftur

2011-01-01

Four independent ground-motion simulation codes are used to model the strong ground motion for three earthquakes: 1994 Mw 6.7 Northridge, 1989 Mw 6.9 Loma Prieta, and 1999 Mw 7.5 Izmit. These 12 sets of synthetics are used to make estimates of the variability in ground-motion predictions. In addition, ground-motion predictions over a grid of sites are used to estimate parametric uncertainty for changes in rupture velocity. We find that the combined model uncertainty and random variability of the simulations is in the same range as the variability of regional empirical ground-motion data sets. The majority of the standard deviations lie between 0.5 and 0.7 natural-log units for response spectra and 0.5 and 0.8 for Fourier spectra. The estimate of model epistemic uncertainty, based on the different model predictions, lies between 0.2 and 0.4, which is about one-half of the estimates for the standard deviation of the combined model uncertainty and random variability. Parametric uncertainty, based on variation of just the average rupture velocity, is shown to be consistent in amplitude with previous estimates, showing percentage changes in ground motion from 50% to 300% when rupture velocity changes from 2.5 to 2.9 km/s. In addition, there is some evidence that mean biases can be reduced by averaging ground-motion estimates from different methods.

From cutting-edge pointwise cross-section to groupwise reaction rate: A primer

NASA Astrophysics Data System (ADS)

Sublet, Jean-Christophe; Fleming, Michael; Gilbert, Mark R.

2017-09-01

The nuclear research and development community has a history of using both integral and differential experiments to support accurate lattice-reactor, nuclear reactor criticality and shielding simulations, as well as verification and validation efforts of cross sections and emitted particle spectra. An important aspect to this type of analysis is the proper consideration of the contribution of the neutron spectrum in its entirety, with correct propagation of uncertainties and standard deviations derived from Monte Carlo simulations, to the local and total uncertainty in the simulated reactions rates (RRs), which usually only apply to one application at a time. This paper identifies deficiencies in the traditional treatment, and discusses correct handling of the RR uncertainty quantification and propagation, including details of the cross section components in the RR uncertainty estimates, which are verified for relevant applications. The methodology that rigorously captures the spectral shift and cross section contributions to the uncertainty in the RR are discussed with quantified examples that demonstrate the importance of the proper treatment of the spectrum profile and cross section contributions to the uncertainty in the RR and subsequent response functions. The recently developed inventory code FISPACT-II, when connected to the processed nuclear data libraries TENDL-2015, ENDF/B-VII.1, JENDL-4.0u or JEFF-3.2, forms an enhanced multi-physics platform providing a wide variety of advanced simulation methods for modelling activation, transmutation, burnup protocols and simulating radiation damage sources terms. The system has extended cutting-edge nuclear data forms, uncertainty quantification and propagation methods, which have been the subject of recent integral and differential, fission, fusion and accelerators validation efforts. The simulation system is used to accurately and predictively probe, understand and underpin a modern and sustainable understanding of the nuclear physics that is so important for many areas of science and technology; advanced fission and fuel systems, magnetic and inertial confinement fusion, high energy, accelerator physics, medical application, isotope production, earth exploration, astrophysics and homeland security.

Quantifying model uncertainty in seasonal Arctic sea-ice forecasts

NASA Astrophysics Data System (ADS)

Blanchard-Wrigglesworth, Edward; Barthélemy, Antoine; Chevallier, Matthieu; Cullather, Richard; Fučkar, Neven; Massonnet, François; Posey, Pamela; Wang, Wanqiu; Zhang, Jinlun; Ardilouze, Constantin; Bitz, Cecilia; Vernieres, Guillaume; Wallcraft, Alan; Wang, Muyin

2017-04-01

Dynamical model forecasts in the Sea Ice Outlook (SIO) of September Arctic sea-ice extent over the last decade have shown lower skill than that found in both idealized model experiments and hindcasts of previous decades. Additionally, it is unclear how different model physics, initial conditions or post-processing techniques contribute to SIO forecast uncertainty. In this work, we have produced a seasonal forecast of 2015 Arctic summer sea ice using SIO dynamical models initialized with identical sea-ice thickness in the central Arctic. Our goals are to calculate the relative contribution of model uncertainty and irreducible error growth to forecast uncertainty and assess the importance of post-processing, and to contrast pan-Arctic forecast uncertainty with regional forecast uncertainty. We find that prior to forecast post-processing, model uncertainty is the main contributor to forecast uncertainty, whereas after forecast post-processing forecast uncertainty is reduced overall, model uncertainty is reduced by an order of magnitude, and irreducible error growth becomes the main contributor to forecast uncertainty. While all models generally agree in their post-processed forecasts of September sea-ice volume and extent, this is not the case for sea-ice concentration. Additionally, forecast uncertainty of sea-ice thickness grows at a much higher rate along Arctic coastlines relative to the central Arctic ocean. Potential ways of offering spatial forecast information based on the timescale over which the forecast signal beats the noise are also explored.

Global Sensitivity Analysis for Identifying Important Parameters of Nitrogen Nitrification and Denitrification under Model and Scenario Uncertainties

NASA Astrophysics Data System (ADS)

Ye, M.; Chen, Z.; Shi, L.; Zhu, Y.; Yang, J.

2017-12-01

Nitrogen reactive transport modeling is subject to uncertainty in model parameters, structures, and scenarios. While global sensitivity analysis is a vital tool for identifying the parameters important to nitrogen reactive transport, conventional global sensitivity analysis only considers parametric uncertainty. This may result in inaccurate selection of important parameters, because parameter importance may vary under different models and modeling scenarios. By using a recently developed variance-based global sensitivity analysis method, this paper identifies important parameters with simultaneous consideration of parametric uncertainty, model uncertainty, and scenario uncertainty. In a numerical example of nitrogen reactive transport modeling, a combination of three scenarios of soil temperature and two scenarios of soil moisture leads to a total of six scenarios. Four alternative models are used to evaluate reduction functions used for calculating actual rates of nitrification and denitrification. The model uncertainty is tangled with scenario uncertainty, as the reduction functions depend on soil temperature and moisture content. The results of sensitivity analysis show that parameter importance varies substantially between different models and modeling scenarios, which may lead to inaccurate selection of important parameters if model and scenario uncertainties are not considered. This problem is avoided by using the new method of sensitivity analysis in the context of model averaging and scenario averaging. The new method of sensitivity analysis can be applied to other problems of contaminant transport modeling when model uncertainty and/or scenario uncertainty are present.

Model parameter uncertainty analysis for an annual field-scale P loss model

NASA Astrophysics Data System (ADS)

Bolster, Carl H.; Vadas, Peter A.; Boykin, Debbie

2016-08-01

Phosphorous (P) fate and transport models are important tools for developing and evaluating conservation practices aimed at reducing P losses from agricultural fields. Because all models are simplifications of complex systems, there will exist an inherent amount of uncertainty associated with their predictions. It is therefore important that efforts be directed at identifying, quantifying, and communicating the different sources of model uncertainties. In this study, we conducted an uncertainty analysis with the Annual P Loss Estimator (APLE) model. Our analysis included calculating parameter uncertainties and confidence and prediction intervals for five internal regression equations in APLE. We also estimated uncertainties of the model input variables based on values reported in the literature. We then predicted P loss for a suite of fields under different management and climatic conditions while accounting for uncertainties in the model parameters and inputs and compared the relative contributions of these two sources of uncertainty to the overall uncertainty associated with predictions of P loss. Both the overall magnitude of the prediction uncertainties and the relative contributions of the two sources of uncertainty varied depending on management practices and field characteristics. This was due to differences in the number of model input variables and the uncertainties in the regression equations associated with each P loss pathway. Inspection of the uncertainties in the five regression equations brought attention to a previously unrecognized limitation with the equation used to partition surface-applied fertilizer P between leaching and runoff losses. As a result, an alternate equation was identified that provided similar predictions with much less uncertainty. Our results demonstrate how a thorough uncertainty and model residual analysis can be used to identify limitations with a model. Such insight can then be used to guide future data collection and model development and evaluation efforts.

Finite state projection based bounds to compare chemical master equation models using single-cell data

DOE Office of Scientific and Technical Information (OSTI.GOV)

Fox, Zachary; Neuert, Gregor; Department of Pharmacology, School of Medicine, Vanderbilt University, Nashville, Tennessee 37232

2016-08-21

Emerging techniques now allow for precise quantification of distributions of biological molecules in single cells. These rapidly advancing experimental methods have created a need for more rigorous and efficient modeling tools. Here, we derive new bounds on the likelihood that observations of single-cell, single-molecule responses come from a discrete stochastic model, posed in the form of the chemical master equation. These strict upper and lower bounds are based on a finite state projection approach, and they converge monotonically to the exact likelihood value. These bounds allow one to discriminate rigorously between models and with a minimum level of computational effort.more » In practice, these bounds can be incorporated into stochastic model identification and parameter inference routines, which improve the accuracy and efficiency of endeavors to analyze and predict single-cell behavior. We demonstrate the applicability of our approach using simulated data for three example models as well as for experimental measurements of a time-varying stochastic transcriptional response in yeast.« less

Uncertainty

USGS Publications Warehouse

Hunt, Randall J.

2012-01-01

Management decisions will often be directly informed by model predictions. However, we now know there can be no expectation of a single ‘true’ model; thus, model results are uncertain. Understandable reporting of underlying uncertainty provides necessary context to decision-makers, as model results are used for management decisions. This, in turn, forms a mechanism by which groundwater models inform a risk-management framework because uncertainty around a prediction provides the basis for estimating the probability or likelihood of some event occurring. Given that the consequences of management decisions vary, it follows that the extent of and resources devoted to an uncertainty analysis may depend on the consequences. For events with low impact, a qualitative, limited uncertainty analysis may be sufficient for informing a decision. For events with a high impact, on the other hand, the risks might be better assessed and associated decisions made using a more robust and comprehensive uncertainty analysis. The purpose of this chapter is to provide guidance on uncertainty analysis through discussion of concepts and approaches, which can vary from heuristic (i.e. the modeller’s assessment of prediction uncertainty based on trial and error and experience) to a comprehensive, sophisticated, statistics-based uncertainty analysis. Most of the material presented here is taken from Doherty et al. (2010) if not otherwise cited. Although the treatment here is necessarily brief, the reader can find citations for the source material and additional references within this chapter.

Spatial Uncertainty Modeling of Fuzzy Information in Images for Pattern Classification

PubMed Central

Pham, Tuan D.

2014-01-01

The modeling of the spatial distribution of image properties is important for many pattern recognition problems in science and engineering. Mathematical methods are needed to quantify the variability of this spatial distribution based on which a decision of classification can be made in an optimal sense. However, image properties are often subject to uncertainty due to both incomplete and imprecise information. This paper presents an integrated approach for estimating the spatial uncertainty of vagueness in images using the theory of geostatistics and the calculus of probability measures of fuzzy events. Such a model for the quantification of spatial uncertainty is utilized as a new image feature extraction method, based on which classifiers can be trained to perform the task of pattern recognition. Applications of the proposed algorithm to the classification of various types of image data suggest the usefulness of the proposed uncertainty modeling technique for texture feature extraction. PMID:25157744

A computational proof of concept of a machine-intelligent artificial pancreas using Lyapunov stability and differential game theory.

PubMed

Greenwood, Nigel J C; Gunton, Jenny E

2014-07-01

This study demonstrated the novel application of a "machine-intelligent" mathematical structure, combining differential game theory and Lyapunov-based control theory, to the artificial pancreas to handle dynamic uncertainties. Realistic type 1 diabetes (T1D) models from the literature were combined into a composite system. Using a mixture of "black box" simulations and actual data from diabetic medical histories, realistic sets of diabetic time series were constructed for blood glucose (BG), interstitial fluid glucose, infused insulin, meal estimates, and sometimes plasma insulin assays. The problem of underdetermined parameters was side stepped by applying a variant of a genetic algorithm to partial information, whereby multiple candidate-personalized models were constructed and then rigorously tested using further data. These formed a "dynamic envelope" of trajectories in state space, where each trajectory was generated by a hypothesis on the hidden T1D system dynamics. This dynamic envelope was then culled to a reduced form to cover observed dynamic behavior. A machine-intelligent autonomous algorithm then implemented game theory to construct real-time insulin infusion strategies, based on the flow of these trajectories through state space and their interactions with hypoglycemic or near-hyperglycemic states. This technique was tested on 2 simulated participants over a total of fifty-five 24-hour days, with no hypoglycemic or hyperglycemic events, despite significant uncertainties from using actual diabetic meal histories with 10-minute warnings. In the main case studies, BG was steered within the desired target set for 99.8% of a 16-hour daily assessment period. Tests confirmed algorithm robustness for ±25% carbohydrate error. For over 99% of the overall 55-day simulation period, either formal controller stability was achieved to the desired target or else the trajectory was within the desired target. These results suggest that this is a stable, high-confidence way to generate closed-loop insulin infusion strategies. © 2014 Diabetes Technology Society.

MODEL UNCERTAINTY ANALYSIS, FIELD DATA COLLECTION AND ANALYSIS OF CONTAMINATED VAPOR INTRUSION INTO BUILDINGS

EPA Science Inventory

To address uncertainty associated with the evaluation of vapor intrusion problems we are working on a three part strategy that includes: evaluation of uncertainty in model-based assessments; collection of field data and assessment of sites using EPA and state protocols.

Effects of uncertain topographic input data on two-dimensional flow modeling in a gravel-bed river

USGS Publications Warehouse

Legleiter, C.J.; Kyriakidis, P.C.; McDonald, R.R.; Nelson, J.M.

2011-01-01

Many applications in river research and management rely upon two-dimensional (2D) numerical models to characterize flow fields, assess habitat conditions, and evaluate channel stability. Predictions from such models are potentially highly uncertain due to the uncertainty associated with the topographic data provided as input. This study used a spatial stochastic simulation strategy to examine the effects of topographic uncertainty on flow modeling. Many, equally likely bed elevation realizations for a simple meander bend were generated and propagated through a typical 2D model to produce distributions of water-surface elevation, depth, velocity, and boundary shear stress at each node of the model's computational grid. Ensemble summary statistics were used to characterize the uncertainty associated with these predictions and to examine the spatial structure of this uncertainty in relation to channel morphology. Simulations conditioned to different data configurations indicated that model predictions became increasingly uncertain as the spacing between surveyed cross sections increased. Model sensitivity to topographic uncertainty was greater for base flow conditions than for a higher, subbankfull flow (75% of bankfull discharge). The degree of sensitivity also varied spatially throughout the bend, with the greatest uncertainty occurring over the point bar where the flow field was influenced by topographic steering effects. Uncertain topography can therefore introduce significant uncertainty to analyses of habitat suitability and bed mobility based on flow model output. In the presence of such uncertainty, the results of these studies are most appropriately represented in probabilistic terms using distributions of model predictions derived from a series of topographic realizations. Copyright 2011 by the American Geophysical Union.

Uncertainty Analysis of Coupled Socioeconomic-Cropping Models: Building Confidence in Climate Change Decision-Support Tools for Local Stakeholders

NASA Astrophysics Data System (ADS)

Malard, J. J.; Rojas, M.; Adamowski, J. F.; Gálvez, J.; Tuy, H. A.; Melgar-Quiñonez, H.

2015-12-01

While cropping models represent the biophysical aspects of agricultural systems, system dynamics modelling offers the possibility of representing the socioeconomic (including social and cultural) aspects of these systems. The two types of models can then be coupled in order to include the socioeconomic dimensions of climate change adaptation in the predictions of cropping models.We develop a dynamically coupled socioeconomic-biophysical model of agricultural production and its repercussions on food security in two case studies from Guatemala (a market-based, intensive agricultural system and a low-input, subsistence crop-based system). Through the specification of the climate inputs to the cropping model, the impacts of climate change on the entire system can be analysed, and the participatory nature of the system dynamics model-building process, in which stakeholders from NGOs to local governmental extension workers were included, helps ensure local trust in and use of the model.However, the analysis of climate variability's impacts on agroecosystems includes uncertainty, especially in the case of joint physical-socioeconomic modelling, and the explicit representation of this uncertainty in the participatory development of the models is important to ensure appropriate use of the models by the end users. In addition, standard model calibration, validation, and uncertainty interval estimation techniques used for physically-based models are impractical in the case of socioeconomic modelling. We present a methodology for the calibration and uncertainty analysis of coupled biophysical (cropping) and system dynamics (socioeconomic) agricultural models, using survey data and expert input to calibrate and evaluate the uncertainty of the system dynamics as well as of the overall coupled model. This approach offers an important tool for local decision makers to evaluate the potential impacts of climate change and their feedbacks through the associated socioeconomic system.

Methodology for Uncertainty Analysis of Dynamic Computational Toxicology Models

EPA Science Inventory

The task of quantifying the uncertainty in both parameter estimates and model predictions has become more important with the increased use of dynamic computational toxicology models by the EPA. Dynamic toxicological models include physiologically-based pharmacokinetic (PBPK) mode...

Kinetics versus thermodynamics in materials modeling: The case of the di-vacancy in iron

NASA Astrophysics Data System (ADS)

Djurabekova, F.; Malerba, L.; Pasianot, R. C.; Olsson, P.; Nordlund, K.

2010-07-01

Monte Carlo models are widely used for the study of microstructural and microchemical evolution of materials under irradiation. However, they often link explicitly the relevant activation energies to the energy difference between local equilibrium states. We provide a simple example (di-vacancy migration in iron) in which a rigorous activation energy calculation, by means of both empirical interatomic potentials and density functional theory methods, clearly shows that such a link is not granted, revealing a migration mechanism that a thermodynamics-linked activation energy model cannot predict. Such a mechanism is, however, fully consistent with thermodynamics. This example emphasizes the importance of basing Monte Carlo methods on models where the activation energies are rigorously calculated, rather than deduced from widespread heuristic equations.

An application of a hydraulic model simulator in flood risk assessment under changing climatic conditions

NASA Astrophysics Data System (ADS)

Doroszkiewicz, J. M.; Romanowicz, R. J.

2016-12-01

The standard procedure of climate change impact assessment on future hydrological extremes consists of a chain of consecutive actions, starting from the choice of GCM driven by an assumed CO2 scenario, through downscaling of climatic forcing to a catchment scale, estimation of hydrological extreme indices using hydrological modelling tools and subsequent derivation of flood risk maps with the help of a hydraulic model. Among many possible sources of uncertainty, the main are the uncertainties related to future climate scenarios, climate models, downscaling techniques and hydrological and hydraulic models. Unfortunately, we cannot directly assess the impact of these different sources of uncertainties on flood risk in future due to lack of observations of future climate realizations. The aim of this study is an assessment of a relative impact of different sources of uncertainty on the uncertainty of flood risk maps. Due to the complexity of the processes involved, an assessment of total uncertainty of maps of inundation probability might be very computer time consuming. As a way forward we present an application of a hydraulic model simulator based on a nonlinear transfer function model for the chosen locations along the river reach. The transfer function model parameters are estimated based on the simulations of the hydraulic model at each of the model cross-sections. The study shows that the application of a simulator substantially reduces the computer requirements related to the derivation of flood risk maps under future climatic conditions. Biala Tarnowska catchment, situated in southern Poland is used as a case study. Future discharges at the input to a hydraulic model are obtained using the HBV model and climate projections obtained from the EUROCORDEX project. The study describes a cascade of uncertainty related to different stages of the process of derivation of flood risk maps under changing climate conditions. In this context it takes into account the uncertainty of future climate projections, an uncertainty of flow routing model, the propagation of that uncertainty through the hydraulic model, and finally, the uncertainty related to the derivation of flood risk maps.

Compositional variations in sands of the Bagnold Dunes, Gale Crater, Mars, from visible-shortwave infrared spectroscopy and comparison with ground truth from the Curiosity Rover

USGS Publications Warehouse

Lapotre, Mathieu G.A.; Ehlmann, B. L.; Minson, Sarah E.; Arvidson, R. E.; Ayoub, F.; Fraeman, A. A.; Ewing, R. C.; Bridges, N. T.

2017-01-01

During its ascent up Mount Sharp, the Mars Science Laboratory Curiosity rover traversed the Bagnold Dune Field. We model sand modal mineralogy and grain size at four locations near the rover traverse, using orbital shortwave infrared single scattering albedo spectra and a Markov-Chain Monte Carlo implementation of Hapke's radiative transfer theory to fully constrain uncertainties and permitted solutions. These predictions, evaluated against in situ measurements at one site from the Curiosity rover, show that XRD-measured mineralogy of the basaltic sands is within the 95% confidence interval of model predictions. However, predictions are relatively insensitive to grain size and are non-unique, especially when modeling the composition of minerals with solid solutions. We find an overall basaltic mineralogy and show subtle spatial variations in composition in and around the Bagnold dunes, consistent with a mafic enrichment of sands with cumulative transport distance by sorting of olivine, pyroxene, and plagioclase grains during aeolian saltation. Furthermore, the large variations in Fe and Mg abundances (~20 wt%) at the Bagnold Dunes suggest that compositional variability induced by wind sorting may be enhanced by local mixing with proximal sand sources. Our estimates demonstrate a method for orbital quantification of composition with rigorous uncertainty determination and provide key constraints for interpreting in situ measurements of compositional variability within martian aeolian sandstones.

Geotechnical parameter spatial distribution stochastic analysis based on multi-precision information assimilation

NASA Astrophysics Data System (ADS)

Wang, C.; Rubin, Y.

2014-12-01

Spatial distribution of important geotechnical parameter named compression modulus Es contributes considerably to the understanding of the underlying geological processes and the adequate assessment of the Es mechanics effects for differential settlement of large continuous structure foundation. These analyses should be derived using an assimilating approach that combines in-situ static cone penetration test (CPT) with borehole experiments. To achieve such a task, the Es distribution of stratum of silty clay in region A of China Expo Center (Shanghai) is studied using the Bayesian-maximum entropy method. This method integrates rigorously and efficiently multi-precision of different geotechnical investigations and sources of uncertainty. Single CPT samplings were modeled as a rational probability density curve by maximum entropy theory. Spatial prior multivariate probability density function (PDF) and likelihood PDF of the CPT positions were built by borehole experiments and the potential value of the prediction point, then, preceding numerical integration on the CPT probability density curves, the posterior probability density curve of the prediction point would be calculated by the Bayesian reverse interpolation framework. The results were compared between Gaussian Sequential Stochastic Simulation and Bayesian methods. The differences were also discussed between single CPT samplings of normal distribution and simulated probability density curve based on maximum entropy theory. It is shown that the study of Es spatial distributions can be improved by properly incorporating CPT sampling variation into interpolation process, whereas more informative estimations are generated by considering CPT Uncertainty for the estimation points. Calculation illustrates the significance of stochastic Es characterization in a stratum, and identifies limitations associated with inadequate geostatistical interpolation techniques. This characterization results will provide a multi-precision information assimilation method of other geotechnical parameters.

Recent advances in evidence-based psychiatry.

PubMed

Geddes, J; Carney, S

2001-06-01

There is increasing interest in the potential contribution of evidence-based medicine to clinical decision making in psychiatry. In this article, we describe some of the recent advances in evidence-based psychiatry and outline future challenges. Narrative review. The successful introduction of evidence-based practice into psychiatry requires the acquisition of new skills by clinicians. It is also important that policy statements that aim to be evidence-based, such as clinical practice guidelines, use rigorous methods to synthesize the primary evidence and do not overlook its limitations. One result of the systematic reviewing of evidence is the identification of important residual clinical uncertainties. Primary research can then be focused on these questions. For questions regarding therapy in psychiatry, it will be necessary to undertake some large, simple randomized trials. Making the best available evidence readily accessible in a clinical setting, however, remains a significant challenge. Collaboration between clinicians, researchers, policy-makers, and those involved in information technology is required to optimize the contribution of evidence-based medicine in psychiatry.

Characterizing Uncertainty and Variability in PBPK Models: State of the Science and Needs for Research and Implementation

EPA Science Inventory

Mode-of-action based risk and safety assessments can rely upon tissue dosimetry estimates in animals and humans obtained from physiologically-based pharmacokinetic (PBPK) modeling. However, risk assessment also increasingly requires characterization of uncertainty and variabilit...

Modelling Future Cardiovascular Disease Mortality in the United States: National Trends and Racial and Ethnic Disparities

PubMed Central

Pearson-Stuttard, Jonathan; Guzman-Castillo, Maria; Penalvo, Jose L.; Rehm, Colin D.; Afshin, Ashkan; Danaei, Goodarz; Kypridemos, Chris; Gaziano, Tom; Mozaffarian, Dariush; Capewell, Simon; O’Flaherty, Martin

2016-01-01

Background Accurate forecasting of cardiovascular disease (CVD) mortality is crucial to guide policy and programming efforts. Prior forecasts have often not incorporated past trends in rates of reduction in CVD mortality. This creates uncertainties about future trends in CVD mortality and disparities. Methods and Results To forecast US CVD mortality and disparities to 2030, we developed a hierarchical Bayesian model to determine and incorporate prior age, period and cohort (APC) effects from 1979–2012, stratified by age, gender and race; which we combined with expected demographic shifts to 2030. Data sources included the National Vital Statistics System, SEER single year population estimates, and US Bureau of Statistics 2012 National Population projections. We projected coronary disease and stroke deaths to 2030, first based on constant APC effects at 2012 values, as most commonly done (conventional); and then using more rigorous projections incorporating expected trends in APC effects (trend-based). We primarily evaluated absolute mortality. The conventional model projected total coronary and stroke deaths by 2030 to increase by approximately 18% (67,000 additional coronary deaths/year) and 50% (64,000 additional stroke deaths/year). Conversely, the trend-based model projected that coronary mortality would fall by 2030 by approximately 27% (79,000 fewer deaths/year); and stroke mortality would remain unchanged (200 fewer deaths/year). Health disparities will be improved in stroke deaths, but not coronary deaths. Conclusions After accounting for prior mortality trends and expected demographic shifts, total US coronary deaths are expected to decline, while stroke mortality will remain relatively constant. Health disparities in stroke, but not coronary, deaths will be improved but not eliminated. These APC approaches offer more plausible predictions than conventional estimates. PMID:26846769

A Discussion on Uncertainty Representation and Interpretation in Model-Based Prognostics Algorithms based on Kalman Filter Estimation Applied to Prognostics of Electronics Components

NASA Technical Reports Server (NTRS)

Celaya, Jose R.; Saxen, Abhinav; Goebel, Kai

2012-01-01

This article discusses several aspects of uncertainty representation and management for model-based prognostics methodologies based on our experience with Kalman Filters when applied to prognostics for electronics components. In particular, it explores the implications of modeling remaining useful life prediction as a stochastic process and how it relates to uncertainty representation, management, and the role of prognostics in decision-making. A distinction between the interpretations of estimated remaining useful life probability density function and the true remaining useful life probability density function is explained and a cautionary argument is provided against mixing interpretations for the two while considering prognostics in making critical decisions.

Simulating carbon flows in Amazonian rainforests: how intensive C-cycle data can help to reduce vegetation model uncertainty

NASA Astrophysics Data System (ADS)

Galbraith, D.; Levine, N. M.; Christoffersen, B. O.; Imbuzeiro, H. A.; Powell, T.; Costa, M. H.; Saleska, S. R.; Moorcroft, P. R.; Malhi, Y.

2014-12-01

The mathematical codes embedded within different vegetation models ultimately represent alternative hypotheses of biosphere functioning. While formulations for some processes (e.g. leaf-level photosynthesis) are often shared across vegetation models, other processes (e.g. carbon allocation) are much more variable in their representation across models. This creates the opportunity for equifinality - models can simulate similar values of key metrics such as NPP or biomass through very different underlying causal pathways. Intensive carbon cycle measurements allow for quantification of a comprehensive suite of carbon fluxes such as the productivity and respiration of leaves, roots and wood, allowing for in-depth assessment of carbon flows within ecosystems. Thus, they provide important information on poorly-constrained C-cycle processes such as allocation. We conducted an in-depth evaluation of the ability of four commonly used dynamic global vegetation models (CLM, ED2, IBIS, JULES) to simulate carbon cycle processes at ten lowland Amazonian rainforest sites where individual C-cycle components have been measured. The rigorous model-data comparison procedure allowed identification of biases which were specific to different models, providing clear avenues for model improvement and allowing determination of internal C-cycling pathways that were better supported by data. Furthermore, the intensive C-cycle data allowed for explicit testing of the validity of a number of assumptions made by specific models in the simulation of carbon allocation and plant respiration. For example, the ED2 model assumes that maintenance respiration of stems is negligible while JULES assumes equivalent allocation of NPP to fine roots and leaves. We argue that field studies focusing on simultaneous measurement of a large number of component fluxes are fundamentally important for reducing uncertainty in vegetation model simulations.

Representing radar rainfall uncertainty with ensembles based on a time-variant geostatistical error modelling approach

NASA Astrophysics Data System (ADS)

Cecinati, Francesca; Rico-Ramirez, Miguel Angel; Heuvelink, Gerard B. M.; Han, Dawei

2017-05-01

The application of radar quantitative precipitation estimation (QPE) to hydrology and water quality models can be preferred to interpolated rainfall point measurements because of the wide coverage that radars can provide, together with a good spatio-temporal resolutions. Nonetheless, it is often limited by the proneness of radar QPE to a multitude of errors. Although radar errors have been widely studied and techniques have been developed to correct most of them, residual errors are still intrinsic in radar QPE. An estimation of uncertainty of radar QPE and an assessment of uncertainty propagation in modelling applications is important to quantify the relative importance of the uncertainty associated to radar rainfall input in the overall modelling uncertainty. A suitable tool for this purpose is the generation of radar rainfall ensembles. An ensemble is the representation of the rainfall field and its uncertainty through a collection of possible alternative rainfall fields, produced according to the observed errors, their spatial characteristics, and their probability distribution. The errors are derived from a comparison between radar QPE and ground point measurements. The novelty of the proposed ensemble generator is that it is based on a geostatistical approach that assures a fast and robust generation of synthetic error fields, based on the time-variant characteristics of errors. The method is developed to meet the requirement of operational applications to large datasets. The method is applied to a case study in Northern England, using the UK Met Office NIMROD radar composites at 1 km resolution and at 1 h accumulation on an area of 180 km by 180 km. The errors are estimated using a network of 199 tipping bucket rain gauges from the Environment Agency. 183 of the rain gauges are used for the error modelling, while 16 are kept apart for validation. The validation is done by comparing the radar rainfall ensemble with the values recorded by the validation rain gauges. The validated ensemble is then tested on a hydrological case study, to show the advantage of probabilistic rainfall for uncertainty propagation. The ensemble spread only partially captures the mismatch between the modelled and the observed flow. The residual uncertainty can be attributed to other sources of uncertainty, in particular to model structural uncertainty, parameter identification uncertainty, uncertainty in other inputs, and uncertainty in the observed flow.

A Review of Hypothesized Determinants Associated with Bighorn Sheep (Ovis canadensis) Die-Offs

PubMed Central

Miller, David S.; Hoberg, Eric; Weiser, Glen; Aune, Keith; Atkinson, Mark; Kimberling, Cleon

2012-01-01

Multiple determinants have been hypothesized to cause or favor disease outbreaks among free-ranging bighorn sheep (Ovis canadensis) populations. This paper considered direct and indirect causes of mortality, as well as potential interactions among proposed environmental, host, and agent determinants of disease. A clear, invariant relationship between a single agent and field outbreaks has not yet been documented, in part due to methodological limitations and practical challenges associated with developing rigorous study designs. Therefore, although there is a need to develop predictive models for outbreaks and validated mitigation strategies, uncertainty remains as to whether outbreaks are due to endemic or recently introduced agents. Consequently, absence of established and universal explanations for outbreaks contributes to conflict among wildlife and livestock stakeholders over land use and management practices. This example illustrates the challenge of developing comprehensive models for understanding and managing wildlife diseases in complex biological and sociological environments. PMID:22567546

Robustness of Flexible Systems With Component-Level Uncertainties

NASA Technical Reports Server (NTRS)

Maghami, Peiman G.

2000-01-01

Robustness of flexible systems in the presence of model uncertainties at the component level is considered. Specifically, an approach for formulating robustness of flexible systems in the presence of frequency and damping uncertainties at the component level is presented. The synthesis of the components is based on a modifications of a controls-based algorithm for component mode synthesis. The formulation deals first with robustness of synthesized flexible systems. It is then extended to deal with global (non-synthesized ) dynamic models with component-level uncertainties by projecting uncertainties from component levels to system level. A numerical example involving a two-dimensional simulated docking problem is worked out to demonstrate the feasibility of the proposed approach.

Quadratic partial eigenvalue assignment in large-scale stochastic dynamic systems for resilient and economic design

DOE Office of Scientific and Technical Information (OSTI.GOV)

Das, Sonjoy; Goswami, Kundan; Datta, Biswa N.

2014-12-10

Failure of structural systems under dynamic loading can be prevented via active vibration control which shifts the damped natural frequencies of the systems away from the dominant range of loading spectrum. The damped natural frequencies and the dynamic load typically show significant variations in practice. A computationally efficient methodology based on quadratic partial eigenvalue assignment technique and optimization under uncertainty has been formulated in the present work that will rigorously account for these variations and result in an economic and resilient design of structures. A novel scheme based on hierarchical clustering and importance sampling is also developed in this workmore » for accurate and efficient estimation of probability of failure to guarantee the desired resilience level of the designed system. Numerical examples are presented to illustrate the proposed methodology.« less

Intensity attenuation for active crustal regions

NASA Astrophysics Data System (ADS)

Allen, Trevor I.; Wald, David J.; Worden, C. Bruce

2012-07-01

We develop globally applicable macroseismic intensity prediction equations (IPEs) for earthquakes of moment magnitude M W 5.0-7.9 and intensities of degree II and greater for distances less than 300 km for active crustal regions. The IPEs are developed for two distance metrics: closest distance to rupture ( R rup) and hypocentral distance ( R hyp). The key objective for developing the model based on hypocentral distance—in addition to more rigorous and standard measure R rup—is to provide an IPE which can be used in near real-time earthquake response systems for earthquakes anywhere in the world, where information regarding the rupture dimensions of a fault may not be known in the immediate aftermath of the event. We observe that our models, particularly the model for the R rup distance metric, generally have low median residuals with magnitude and distance. In particular, we address whether the direct use of IPEs leads to a reduction in overall uncertainties when compared with methods which use a combination of ground-motion prediction equations and ground motion to intensity conversion equations. Finally, using topographic gradient as a proxy and median model predictions, we derive intensity-based site amplification factors. These factors lead to a small reduction of residuals at shallow gradients at strong shaking levels. However, the overall effect on total median residuals is relatively small. This is in part due to the observation that the median site condition for intensity observations used to develop these IPEs is approximately near the National Earthquake Hazard Reduction Program CD site-class boundary.

Testability of evolutionary game dynamics based on experimental economics data

NASA Astrophysics Data System (ADS)

Wang, Yijia; Chen, Xiaojie; Wang, Zhijian

2017-11-01

Understanding the dynamic processes of a real game system requires an appropriate dynamics model, and rigorously testing a dynamics model is nontrivial. In our methodological research, we develop an approach to testing the validity of game dynamics models that considers the dynamic patterns of angular momentum and speed as measurement variables. Using Rock-Paper-Scissors (RPS) games as an example, we illustrate the geometric patterns in the experiment data. We then derive the related theoretical patterns from a series of typical dynamics models. By testing the goodness-of-fit between the experimental and theoretical patterns, we show that the validity of these models can be evaluated quantitatively. Our approach establishes a link between dynamics models and experimental systems, which is, to the best of our knowledge, the most effective and rigorous strategy for ascertaining the testability of evolutionary game dynamics models.

A Mathematical Evaluation of the Core Conductor Model

PubMed Central

Clark, John; Plonsey, Robert

1966-01-01

This paper is a mathematical evaluation of the core conductor model where its three dimensionality is taken into account. The problem considered is that of a single, active, unmyelinated nerve fiber situated in an extensive, homogeneous, conducting medium. Expressions for the various core conductor parameters have been derived in a mathematically rigorous manner according to the principles of electromagnetic theory. The purpose of employing mathematical rigor in this study is to bring to light the inherent assumptions of the one dimensional core conductor model, providing a method of evaluating the accuracy of this linear model. Based on the use of synthetic squid axon data, the conclusion of this study is that the linear core conductor model is a good approximation for internal but not external parameters. PMID:5903155

A methodology for the rigorous verification of plasma simulation codes

NASA Astrophysics Data System (ADS)

Riva, Fabio

2016-10-01

The methodology used to assess the reliability of numerical simulation codes constitutes the Verification and Validation (V&V) procedure. V&V is composed by two separate tasks: the verification, which is a mathematical issue targeted to assess that the physical model is correctly solved, and the validation, which determines the consistency of the code results, and therefore of the physical model, with experimental data. In the present talk we focus our attention on the verification, which in turn is composed by the code verification, targeted to assess that a physical model is correctly implemented in a simulation code, and the solution verification, that quantifies the numerical error affecting a simulation. Bridging the gap between plasma physics and other scientific domains, we introduced for the first time in our domain a rigorous methodology for the code verification, based on the method of manufactured solutions, as well as a solution verification based on the Richardson extrapolation. This methodology was applied to GBS, a three-dimensional fluid code based on a finite difference scheme, used to investigate the plasma turbulence in basic plasma physics experiments and in the tokamak scrape-off layer. Overcoming the difficulty of dealing with a numerical method intrinsically affected by statistical noise, we have now generalized the rigorous verification methodology to simulation codes based on the particle-in-cell algorithm, which are employed to solve Vlasov equation in the investigation of a number of plasma physics phenomena.

WE-D-BRE-07: Variance-Based Sensitivity Analysis to Quantify the Impact of Biological Uncertainties in Particle Therapy

DOE Office of Scientific and Technical Information (OSTI.GOV)

Kamp, F.; Brueningk, S.C.; Wilkens, J.J.

Purpose: In particle therapy, treatment planning and evaluation are frequently based on biological models to estimate the relative biological effectiveness (RBE) or the equivalent dose in 2 Gy fractions (EQD2). In the context of the linear-quadratic model, these quantities depend on biological parameters (α, β) for ions as well as for the reference radiation and on the dose per fraction. The needed biological parameters as well as their dependency on ion species and ion energy typically are subject to large (relative) uncertainties of up to 20–40% or even more. Therefore it is necessary to estimate the resulting uncertainties in e.g.more » RBE or EQD2 caused by the uncertainties of the relevant input parameters. Methods: We use a variance-based sensitivity analysis (SA) approach, in which uncertainties in input parameters are modeled by random number distributions. The evaluated function is executed 10{sup 4} to 10{sup 6} times, each run with a different set of input parameters, randomly varied according to their assigned distribution. The sensitivity S is a variance-based ranking (from S = 0, no impact, to S = 1, only influential part) of the impact of input uncertainties. The SA approach is implemented for carbon ion treatment plans on 3D patient data, providing information about variations (and their origin) in RBE and EQD2. Results: The quantification enables 3D sensitivity maps, showing dependencies of RBE and EQD2 on different input uncertainties. The high number of runs allows displaying the interplay between different input uncertainties. The SA identifies input parameter combinations which result in extreme deviations of the result and the input parameter for which an uncertainty reduction is the most rewarding. Conclusion: The presented variance-based SA provides advantageous properties in terms of visualization and quantification of (biological) uncertainties and their impact. The method is very flexible, model independent, and enables a broad assessment of uncertainties. Supported by DFG grant WI 3745/1-1 and DFG cluster of excellence: Munich-Centre for Advanced Photonics.« less

The Value of Decision Analytical Modeling in Surgical Research: An Example of Laparoscopic Versus Open Distal Pancreatectomy.

PubMed

Tax, Casper; Govaert, Paulien H M; Stommel, Martijn W J; Besselink, Marc G H; Gooszen, Hein G; Rovers, Maroeska M

2017-11-02

To illustrate how decision modeling may identify relevant uncertainty and can preclude or identify areas of future research in surgery. To optimize use of research resources, a tool is needed that assists in identifying relevant uncertainties and the added value of reducing these uncertainties. The clinical pathway for laparoscopic distal pancreatectomy (LDP) versus open (ODP) for nonmalignant lesions was modeled in a decision tree. Cost-effectiveness based on complications, hospital stay, costs, quality of life, and survival was analyzed. The effect of existing uncertainty on the cost-effectiveness was addressed, as well as the expected value of eliminating uncertainties. Based on 29 nonrandomized studies (3.701 patients) the model shows that LDP is more cost-effective compared with ODP. Scenarios in which LDP does not outperform ODP for cost-effectiveness seem unrealistic, e.g., a 30-day mortality rate of 1.79 times higher after LDP as compared with ODP, conversion in 62.2%, surgically repair of incisional hernias in 21% after LDP, or an average 2.3 days longer hospital stay after LDP than after ODP. Taking all uncertainty into account, LDP remained more cost-effective. Minimizing these uncertainties did not change the outcome. The results show how decision analytical modeling can help to identify relevant uncertainty and guide decisions for future research in surgery. Based on the current available evidence, a randomized clinical trial on complications, hospital stay, costs, quality of life, and survival is highly unlikely to change the conclusion that LDP is more cost-effective than ODP.

Can agent based models effectively reduce fisheries management implementation uncertainty?

NASA Astrophysics Data System (ADS)

Drexler, M.

2016-02-01

Uncertainty is an inherent feature of fisheries management. Implementation uncertainty remains a challenge to quantify often due to unintended responses of users to management interventions. This problem will continue to plague both single species and ecosystem based fisheries management advice unless the mechanisms driving these behaviors are properly understood. Equilibrium models, where each actor in the system is treated as uniform and predictable, are not well suited to forecast the unintended behaviors of individual fishers. Alternatively, agent based models (AMBs) can simulate the behaviors of each individual actor driven by differing incentives and constraints. This study evaluated the feasibility of using AMBs to capture macro scale behaviors of the US West Coast Groundfish fleet. Agent behavior was specified at the vessel level. Agents made daily fishing decisions using knowledge of their own cost structure, catch history, and the histories of catch and quota markets. By adding only a relatively small number of incentives, the model was able to reproduce highly realistic macro patterns of expected outcomes in response to management policies (catch restrictions, MPAs, ITQs) while preserving vessel heterogeneity. These simulations indicate that agent based modeling approaches hold much promise for simulating fisher behaviors and reducing implementation uncertainty. Additional processes affecting behavior, informed by surveys, are continually being added to the fisher behavior model. Further coupling of the fisher behavior model to a spatial ecosystem model will provide a fully integrated social, ecological, and economic model capable of performing management strategy evaluations to properly consider implementation uncertainty in fisheries management.

Durability reliability analysis for corroding concrete structures under uncertainty

NASA Astrophysics Data System (ADS)

Zhang, Hao

2018-02-01

This paper presents a durability reliability analysis of reinforced concrete structures subject to the action of marine chloride. The focus is to provide insight into the role of epistemic uncertainties on durability reliability. The corrosion model involves a number of variables whose probabilistic characteristics cannot be fully determined due to the limited availability of supporting data. All sources of uncertainty, both aleatory and epistemic, should be included in the reliability analysis. Two methods are available to formulate the epistemic uncertainty: the imprecise probability-based method and the purely probabilistic method in which the epistemic uncertainties are modeled as random variables. The paper illustrates how the epistemic uncertainties are modeled and propagated in the two methods, and shows how epistemic uncertainties govern the durability reliability.

UQTools: The Uncertainty Quantification Toolbox - Introduction and Tutorial

NASA Technical Reports Server (NTRS)

Kenny, Sean P.; Crespo, Luis G.; Giesy, Daniel P.

2012-01-01

UQTools is the short name for the Uncertainty Quantification Toolbox, a software package designed to efficiently quantify the impact of parametric uncertainty on engineering systems. UQTools is a MATLAB-based software package and was designed to be discipline independent, employing very generic representations of the system models and uncertainty. Specifically, UQTools accepts linear and nonlinear system models and permits arbitrary functional dependencies between the system s measures of interest and the probabilistic or non-probabilistic parametric uncertainty. One of the most significant features incorporated into UQTools is the theoretical development centered on homothetic deformations and their application to set bounding and approximating failure probabilities. Beyond the set bounding technique, UQTools provides a wide range of probabilistic and uncertainty-based tools to solve key problems in science and engineering.

Aeroservoelastic Uncertainty Model Identification from Flight Data

NASA Technical Reports Server (NTRS)

Brenner, Martin J.

2001-01-01

Uncertainty modeling is a critical element in the estimation of robust stability margins for stability boundary prediction and robust flight control system development. There has been a serious deficiency to date in aeroservoelastic data analysis with attention to uncertainty modeling. Uncertainty can be estimated from flight data using both parametric and nonparametric identification techniques. The model validation problem addressed in this paper is to identify aeroservoelastic models with associated uncertainty structures from a limited amount of controlled excitation inputs over an extensive flight envelope. The challenge to this problem is to update analytical models from flight data estimates while also deriving non-conservative uncertainty descriptions consistent with the flight data. Multisine control surface command inputs and control system feedbacks are used as signals in a wavelet-based modal parameter estimation procedure for model updates. Transfer function estimates are incorporated in a robust minimax estimation scheme to get input-output parameters and error bounds consistent with the data and model structure. Uncertainty estimates derived from the data in this manner provide an appropriate and relevant representation for model development and robust stability analysis. This model-plus-uncertainty identification procedure is applied to aeroservoelastic flight data from the NASA Dryden Flight Research Center F-18 Systems Research Aircraft.

Uncertainty in simulating wheat yields under climate change

NASA Astrophysics Data System (ADS)

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

2013-09-01

Projections of climate change impacts on crop yields are inherently uncertain. Uncertainty is often quantified when projecting future greenhouse gas emissions and their influence on climate. However, multi-model uncertainty analysis of crop responses to climate change is rare because systematic and objective comparisons among process-based crop simulation models are difficult. Here we present the largest standardized model intercomparison for climate change impacts so far. We found that individual crop models are able to simulate measured wheat grain yields accurately under a range of environments, particularly if the input information is sufficient. However, simulated climate change impacts vary across models owing to differences in model structures and parameter values. A greater proportion of the uncertainty in climate change impact projections was due to variations among crop models than to variations among downscaled general circulation models. Uncertainties in simulated impacts increased with CO2 concentrations and associated warming. These impact uncertainties can be reduced by improving temperature and CO2 relationships in models and better quantified through use of multi-model ensembles. Less uncertainty in describing how climate change may affect agricultural productivity will aid adaptation strategy development andpolicymaking.

How to `Elk-test' biogeochemical models in a data rich world? (Invited)

NASA Astrophysics Data System (ADS)

Reichstein, M.; Ciais, P.; Seneviratne, S. I.; Carvalhais, N.; Dalmonech, D.; Jung, M.; Luo, Y.; Mahecha, M. D.; Moffat, A. M.; Tomelleri, E.; Zaehle, S.

2010-12-01

Process-oriented biogeochemical models are a primary tool that has been used to project future states of climate and ecosystems in the earth system in response to anthropogenic and other forcing, and receive tremendous attention also in the context us the planned assessment report AR5 by the IPCC. However, model intercomparison and data-model comparison studies indicate large uncertainties regarding predictions of global interactions between atmosphere and biosphere. Rigorous scientific testing of these models is essential but very challenging, largely because neither it is technically and ethically possible to perform global earth-scale experiments, nor do we have replicate Earths for hypothesis testing. Hence, model evaluations have to rely on monitoring data such as ecological observation networks, global remote sensing or short-term and small-scale experiments. Here, we critically examine strategies of how model evaluations have been performed with a particular emphasis on terrestrial ecosystems. Often weak ‘validations’ are being presented which do not take advantage of all the relevant information in the observed data, but also apparent falsifications are made, that are hampered by a confusion of system processes with system behavior. We propose that a stronger integration of recent advances in pattern-oriented and system-oriented methodologies will lead to more satisfying earth system model evaluation and development, and show a few enlightening examples from terrestrial biogeochemical modeling and other disciplines. Moreover it is crucial to take advantage of the multidimensional nature of arising earth observation data sets which should be matched by models simultaneously, instead of relying on univariate simple comparisons. A new critical model evaluation is needed to improve future IPCC assessments in order to reduce uncertainties by distinguishing plausible simulation trajectories from fairy tales.

Articulating uncertainty as part of scientific argumentation during model-based exoplanet detection tasks

NASA Astrophysics Data System (ADS)

Lee, Hee-Sun; Pallant, Amy; Pryputniewicz, Sarah

2015-08-01

Teaching scientific argumentation has emerged as an important goal for K-12 science education. In scientific argumentation, students are actively involved in coordinating evidence with theory based on their understanding of the scientific content and thinking critically about the strengths and weaknesses of the cited evidence in the context of the investigation. We developed a one-week-long online curriculum module called "Is there life in space?" where students conduct a series of four model-based tasks to learn how scientists detect extrasolar planets through the “wobble” and transit methods. The simulation model allows students to manipulate various parameters of an imaginary star and planet system such as planet size, orbit size, planet-orbiting-plane angle, and sensitivity of telescope equipment, and to adjust the display settings for graphs illustrating the relative velocity and light intensity of the star. Students can use model-based evidence to formulate an argument on whether particular signals in the graphs guarantee the presence of a planet. Students' argumentation is facilitated by the four-part prompts consisting of multiple-choice claim, open-ended explanation, Likert-scale uncertainty rating, and open-ended uncertainty rationale. We analyzed 1,013 scientific arguments formulated by 302 high school student groups taught by 7 teachers. We coded these arguments in terms of the accuracy of their claim, the sophistication of explanation connecting evidence to the established knowledge base, the uncertainty rating, and the scientific validity of uncertainty. We found that (1) only 18% of the students' uncertainty rationale involved critical reflection on limitations inherent in data and concepts, (2) 35% of students' uncertainty rationale reflected their assessment of personal ability and knowledge, rather than scientific sources of uncertainty related to the evidence, and (3) the nature of task such as the use of noisy data or the framing of critiquing scientists' discovery encouraged students' articulation of scientific uncertainty sources in different ways.

Assessment of parametric uncertainty for groundwater reactive transport modeling,

USGS Publications Warehouse

Shi, Xiaoqing; Ye, Ming; Curtis, Gary P.; Miller, Geoffery L.; Meyer, Philip D.; Kohler, Matthias; Yabusaki, Steve; Wu, Jichun

2014-01-01

The validity of using Gaussian assumptions for model residuals in uncertainty quantification of a groundwater reactive transport model was evaluated in this study. Least squares regression methods explicitly assume Gaussian residuals, and the assumption leads to Gaussian likelihood functions, model parameters, and model predictions. While the Bayesian methods do not explicitly require the Gaussian assumption, Gaussian residuals are widely used. This paper shows that the residuals of the reactive transport model are non-Gaussian, heteroscedastic, and correlated in time; characterizing them requires using a generalized likelihood function such as the formal generalized likelihood function developed by Schoups and Vrugt (2010). For the surface complexation model considered in this study for simulating uranium reactive transport in groundwater, parametric uncertainty is quantified using the least squares regression methods and Bayesian methods with both Gaussian and formal generalized likelihood functions. While the least squares methods and Bayesian methods with Gaussian likelihood function produce similar Gaussian parameter distributions, the parameter distributions of Bayesian uncertainty quantification using the formal generalized likelihood function are non-Gaussian. In addition, predictive performance of formal generalized likelihood function is superior to that of least squares regression and Bayesian methods with Gaussian likelihood function. The Bayesian uncertainty quantification is conducted using the differential evolution adaptive metropolis (DREAM(zs)) algorithm; as a Markov chain Monte Carlo (MCMC) method, it is a robust tool for quantifying uncertainty in groundwater reactive transport models. For the surface complexation model, the regression-based local sensitivity analysis and Morris- and DREAM(ZS)-based global sensitivity analysis yield almost identical ranking of parameter importance. The uncertainty analysis may help select appropriate likelihood functions, improve model calibration, and reduce predictive uncertainty in other groundwater reactive transport and environmental modeling.

Quantitative body DW-MRI biomarkers uncertainty estimation using unscented wild-bootstrap.

PubMed

Freiman, M; Voss, S D; Mulkern, R V; Perez-Rossello, J M; Warfield, S K

2011-01-01

We present a new method for the uncertainty estimation of diffusion parameters for quantitative body DW-MRI assessment. Diffusion parameters uncertainty estimation from DW-MRI is necessary for clinical applications that use these parameters to assess pathology. However, uncertainty estimation using traditional techniques requires repeated acquisitions, which is undesirable in routine clinical use. Model-based bootstrap techniques, for example, assume an underlying linear model for residuals rescaling and cannot be utilized directly for body diffusion parameters uncertainty estimation due to the non-linearity of the body diffusion model. To offset this limitation, our method uses the Unscented transform to compute the residuals rescaling parameters from the non-linear body diffusion model, and then applies the wild-bootstrap method to infer the body diffusion parameters uncertainty. Validation through phantom and human subject experiments shows that our method identify the regions with higher uncertainty in body DWI-MRI model parameters correctly with realtive error of -36% in the uncertainty values.

Multi-model seasonal forecast of Arctic sea-ice: forecast uncertainty at pan-Arctic and regional scales

NASA Astrophysics Data System (ADS)

Blanchard-Wrigglesworth, E.; Barthélemy, A.; Chevallier, M.; Cullather, R.; Fučkar, N.; Massonnet, F.; Posey, P.; Wang, W.; Zhang, J.; Ardilouze, C.; Bitz, C. M.; Vernieres, G.; Wallcraft, A.; Wang, M.

2017-08-01

Dynamical model forecasts in the Sea Ice Outlook (SIO) of September Arctic sea-ice extent over the last decade have shown lower skill than that found in both idealized model experiments and hindcasts of previous decades. Additionally, it is unclear how different model physics, initial conditions or forecast post-processing (bias correction) techniques contribute to SIO forecast uncertainty. In this work, we have produced a seasonal forecast of 2015 Arctic summer sea ice using SIO dynamical models initialized with identical sea-ice thickness in the central Arctic. Our goals are to calculate the relative contribution of model uncertainty and irreducible error growth to forecast uncertainty and assess the importance of post-processing, and to contrast pan-Arctic forecast uncertainty with regional forecast uncertainty. We find that prior to forecast post-processing, model uncertainty is the main contributor to forecast uncertainty, whereas after forecast post-processing forecast uncertainty is reduced overall, model uncertainty is reduced by an order of magnitude, and irreducible error growth becomes the main contributor to forecast uncertainty. While all models generally agree in their post-processed forecasts of September sea-ice volume and extent, this is not the case for sea-ice concentration. Additionally, forecast uncertainty of sea-ice thickness grows at a much higher rate along Arctic coastlines relative to the central Arctic ocean. Potential ways of offering spatial forecast information based on the timescale over which the forecast signal beats the noise are also explored.

Water Table Uncertainties due to Uncertainties in Structure and Properties of an Unconfined Aquifer.

PubMed

Hauser, Juerg; Wellmann, Florian; Trefry, Mike

2018-03-01

We consider two sources of geology-related uncertainty in making predictions of the steady-state water table elevation for an unconfined aquifer. That is the uncertainty in the depth to base of the aquifer and in the hydraulic conductivity distribution within the aquifer. Stochastic approaches to hydrological modeling commonly use geostatistical techniques to account for hydraulic conductivity uncertainty within the aquifer. In the absence of well data allowing derivation of a relationship between geophysical and hydrological parameters, the use of geophysical data is often limited to constraining the structural boundaries. If we recover the base of an unconfined aquifer from an analysis of geophysical data, then the associated uncertainties are a consequence of the geophysical inversion process. In this study, we illustrate this by quantifying water table uncertainties for the unconfined aquifer formed by the paleochannel network around the Kintyre Uranium deposit in Western Australia. The focus of the Bayesian parametric bootstrap approach employed for the inversion of the available airborne electromagnetic data is the recovery of the base of the paleochannel network and the associated uncertainties. This allows us to then quantify the associated influences on the water table in a conceptualized groundwater usage scenario and compare the resulting uncertainties with uncertainties due to an uncertain hydraulic conductivity distribution within the aquifer. Our modeling shows that neither uncertainties in the depth to the base of the aquifer nor hydraulic conductivity uncertainties alone can capture the patterns of uncertainty in the water table that emerge when the two are combined. © 2017, National Ground Water Association.

A Model-Based Prognostics Approach Applied to Pneumatic Valves

NASA Technical Reports Server (NTRS)

Daigle, Matthew J.; Goebel, Kai

2011-01-01

Within the area of systems health management, the task of prognostics centers on predicting when components will fail. Model-based prognostics exploits domain knowledge of the system, its components, and how they fail by casting the underlying physical phenomena in a physics-based model that is derived from first principles. Uncertainty cannot be avoided in prediction, therefore, algorithms are employed that help in managing these uncertainties. The particle filtering algorithm has become a popular choice for model-based prognostics due to its wide applicability, ease of implementation, and support for uncertainty management. We develop a general model-based prognostics methodology within a robust probabilistic framework using particle filters. As a case study, we consider a pneumatic valve from the Space Shuttle cryogenic refueling system. We develop a detailed physics-based model of the pneumatic valve, and perform comprehensive simulation experiments to illustrate our prognostics approach and evaluate its effectiveness and robustness. The approach is demonstrated using historical pneumatic valve data from the refueling system.

Hierarchical Bayesian Model Averaging for Non-Uniqueness and Uncertainty Analysis of Artificial Neural Networks

NASA Astrophysics Data System (ADS)

Fijani, E.; Chitsazan, N.; Nadiri, A.; Tsai, F. T.; Asghari Moghaddam, A.

2012-12-01

Artificial Neural Networks (ANNs) have been widely used to estimate concentration of chemicals in groundwater systems. However, estimation uncertainty is rarely discussed in the literature. Uncertainty in ANN output stems from three sources: ANN inputs, ANN parameters (weights and biases), and ANN structures. Uncertainty in ANN inputs may come from input data selection and/or input data error. ANN parameters are naturally uncertain because they are maximum-likelihood estimated. ANN structure is also uncertain because there is no unique ANN model given a specific case. Therefore, multiple plausible AI models are generally resulted for a study. One might ask why good models have to be ignored in favor of the best model in traditional estimation. What is the ANN estimation variance? How do the variances from different ANN models accumulate to the total estimation variance? To answer these questions we propose a Hierarchical Bayesian Model Averaging (HBMA) framework. Instead of choosing one ANN model (the best ANN model) for estimation, HBMA averages outputs of all plausible ANN models. The model weights are based on the evidence of data. Therefore, the HBMA avoids overconfidence on the single best ANN model. In addition, HBMA is able to analyze uncertainty propagation through aggregation of ANN models in a hierarchy framework. This method is applied for estimation of fluoride concentration in the Poldasht plain and the Bazargan plain in Iran. Unusually high fluoride concentration in the Poldasht and Bazargan plains has caused negative effects on the public health. Management of this anomaly requires estimation of fluoride concentration distribution in the area. The results show that the HBMA provides a knowledge-decision-based framework that facilitates analyzing and quantifying ANN estimation uncertainties from different sources. In addition HBMA allows comparative evaluation of the realizations for each source of uncertainty by segregating the uncertainty sources in a hierarchical framework. Fluoride concentration estimation using the HBMA method shows better agreement to the observation data in the test step because they are not based on a single model with a non-dominate weights.

Bioeconomic and market models

Treesearch

Richard Haynes; Darius Adams; Peter Ince; John Mills; Ralph Alig

2006-01-01

The United States has a century of experience with the development of models that describe markets for forest products and trends in resource conditions. In the last four decades, increasing rigor in policy debates has stimulated the development of models to support policy analysis. Increasingly, research has evolved (often relying on computer-based models) to increase...

Structured Uncertainty Bound Determination From Data for Control and Performance Validation

NASA Technical Reports Server (NTRS)

Lim, Kyong B.

2003-01-01

This report attempts to document the broad scope of issues that must be satisfactorily resolved before one can expect to methodically obtain, with a reasonable confidence, a near-optimal robust closed loop performance in physical applications. These include elements of signal processing, noise identification, system identification, model validation, and uncertainty modeling. Based on a recently developed methodology involving a parameterization of all model validating uncertainty sets for a given linear fractional transformation (LFT) structure and noise allowance, a new software, Uncertainty Bound Identification (UBID) toolbox, which conveniently executes model validation tests and determine uncertainty bounds from data, has been designed and is currently available. This toolbox also serves to benchmark the current state-of-the-art in uncertainty bound determination and in turn facilitate benchmarking of robust control technology. To help clarify the methodology and use of the new software, two tutorial examples are provided. The first involves the uncertainty characterization of a flexible structure dynamics, and the second example involves a closed loop performance validation of a ducted fan based on an uncertainty bound from data. These examples, along with other simulation and experimental results, also help describe the many factors and assumptions that determine the degree of success in applying robust control theory to practical problems.

Uncertainty analysis of terrestrial net primary productivity and net biome productivity in China during 1901-2005

NASA Astrophysics Data System (ADS)

Shao, Junjiong; Zhou, Xuhui; Luo, Yiqi; Zhang, Guodong; Yan, Wei; Li, Jiaxuan; Li, Bo; Dan, Li; Fisher, Joshua B.; Gao, Zhiqiang; He, Yong; Huntzinger, Deborah; Jain, Atul K.; Mao, Jiafu; Meng, Jihua; Michalak, Anna M.; Parazoo, Nicholas C.; Peng, Changhui; Poulter, Benjamin; Schwalm, Christopher R.; Shi, Xiaoying; Sun, Rui; Tao, Fulu; Tian, Hanqin; Wei, Yaxing; Zeng, Ning; Zhu, Qiuan; Zhu, Wenquan

2016-05-01

Despite the importance of net primary productivity (NPP) and net biome productivity (NBP), estimates of NPP and NBP for China are highly uncertain. To investigate the main sources of uncertainty, we synthesized model estimates of NPP and NBP for China from published literature and the Multi-scale Synthesis and Terrestrial Model Intercomparison Project (MsTMIP). The literature-based results showed that total NPP and NBP in China were 3.35 ± 1.25 and 0.14 ± 0.094 Pg C yr-1, respectively. Classification and regression tree analysis based on literature data showed that model type was the primary source of the uncertainty, explaining 36% and 64% of the variance in NPP and NBP, respectively. Spatiotemporal scales, land cover conditions, inclusion of the N cycle, and effects of N addition also contributed to the overall uncertainty. Results based on the MsTMIP data suggested that model structures were overwhelmingly important (>90%) for the overall uncertainty compared to simulations with different combinations of time-varying global change factors. The interannual pattern of NPP was similar among diverse studies and increased by 0.012 Pg C yr-1 during 1981-2000. In addition, high uncertainty in China's NPP occurred in areas with high productivity, whereas NBP showed the opposite pattern. Our results suggest that to significantly reduce uncertainty in estimated NPP and NBP, model structures should be substantially tested on the basis of empirical results. To this end, coordinated distributed experiments with multiple global change factors might be a practical approach that can validate specific structures of different models.

Integrating research tools to support the management of social-ecological systems under climate change

USGS Publications Warehouse

Miller, Brian W.; Morisette, Jeffrey T.

2014-01-01

Developing resource management strategies in the face of climate change is complicated by the considerable uncertainty associated with projections of climate and its impacts and by the complex interactions between social and ecological variables. The broad, interconnected nature of this challenge has resulted in calls for analytical frameworks that integrate research tools and can support natural resource management decision making in the face of uncertainty and complex interactions. We respond to this call by first reviewing three methods that have proven useful for climate change research, but whose application and development have been largely isolated: species distribution modeling, scenario planning, and simulation modeling. Species distribution models provide data-driven estimates of the future distributions of species of interest, but they face several limitations and their output alone is not sufficient to guide complex decisions for how best to manage resources given social and economic considerations along with dynamic and uncertain future conditions. Researchers and managers are increasingly exploring potential futures of social-ecological systems through scenario planning, but this process often lacks quantitative response modeling and validation procedures. Simulation models are well placed to provide added rigor to scenario planning because of their ability to reproduce complex system dynamics, but the scenarios and management options explored in simulations are often not developed by stakeholders, and there is not a clear consensus on how to include climate model outputs. We see these strengths and weaknesses as complementarities and offer an analytical framework for integrating these three tools. We then describe the ways in which this framework can help shift climate change research from useful to usable.

Assessing and reporting uncertainties in dietary exposure analysis: Mapping of uncertainties in a tiered approach.

PubMed

Kettler, Susanne; Kennedy, Marc; McNamara, Cronan; Oberdörfer, Regina; O'Mahony, Cian; Schnabel, Jürgen; Smith, Benjamin; Sprong, Corinne; Faludi, Roland; Tennant, David

2015-08-01

Uncertainty analysis is an important component of dietary exposure assessments in order to understand correctly the strength and limits of its results. Often, standard screening procedures are applied in a first step which results in conservative estimates. If through those screening procedures a potential exceedance of health-based guidance values is indicated, within the tiered approach more refined models are applied. However, the sources and types of uncertainties in deterministic and probabilistic models can vary or differ. A key objective of this work has been the mapping of different sources and types of uncertainties to better understand how to best use uncertainty analysis to generate more realistic comprehension of dietary exposure. In dietary exposure assessments, uncertainties can be introduced by knowledge gaps about the exposure scenario, parameter and the model itself. With this mapping, general and model-independent uncertainties have been identified and described, as well as those which can be introduced and influenced by the specific model during the tiered approach. This analysis identifies that there are general uncertainties common to point estimates (screening or deterministic methods) and probabilistic exposure assessment methods. To provide further clarity, general sources of uncertainty affecting many dietary exposure assessments should be separated from model-specific uncertainties. Copyright © 2015 The Authors. Published by Elsevier Ltd.. All rights reserved.

Uncertainties in Atomic Data and Their Propagation Through Spectral Models. I.

NASA Technical Reports Server (NTRS)

Bautista, M. A.; Fivet, V.; Quinet, P.; Dunn, J.; Gull, T. R.; Kallman, T. R.; Mendoza, C.

2013-01-01

We present a method for computing uncertainties in spectral models, i.e., level populations, line emissivities, and emission line ratios, based upon the propagation of uncertainties originating from atomic data.We provide analytic expressions, in the form of linear sets of algebraic equations, for the coupled uncertainties among all levels. These equations can be solved efficiently for any set of physical conditions and uncertainties in the atomic data. We illustrate our method applied to spectral models of Oiii and Fe ii and discuss the impact of the uncertainties on atomic systems under different physical conditions. As to intrinsic uncertainties in theoretical atomic data, we propose that these uncertainties can be estimated from the dispersion in the results from various independent calculations. This technique provides excellent results for the uncertainties in A-values of forbidden transitions in [Fe ii]. Key words: atomic data - atomic processes - line: formation - methods: data analysis - molecular data - molecular processes - techniques: spectroscopic

Measurement uncertainty analysis techniques applied to PV performance measurements

NASA Astrophysics Data System (ADS)

Wells, C.

1992-10-01

The purpose of this presentation is to provide a brief introduction to measurement uncertainty analysis, outline how it is done, and illustrate uncertainty analysis with examples drawn from the PV field, with particular emphasis toward its use in PV performance measurements. The uncertainty information we know and state concerning a PV performance measurement or a module test result determines, to a significant extent, the value and quality of that result. What is measurement uncertainty analysis? It is an outgrowth of what has commonly been called error analysis. But uncertainty analysis, a more recent development, gives greater insight into measurement processes and tests, experiments, or calibration results. Uncertainty analysis gives us an estimate of the interval about a measured value or an experiment's final result within which we believe the true value of that quantity will lie. Why should we take the time to perform an uncertainty analysis? A rigorous measurement uncertainty analysis: Increases the credibility and value of research results; allows comparisons of results from different labs; helps improve experiment design and identifies where changes are needed to achieve stated objectives (through use of the pre-test analysis); plays a significant role in validating measurements and experimental results, and in demonstrating (through the post-test analysis) that valid data have been acquired; reduces the risk of making erroneous decisions; demonstrates quality assurance and quality control measures have been accomplished; define Valid Data as data having known and documented paths of: Origin, including theory; measurements; traceability to measurement standards; computations; uncertainty analysis of results.

The uncertainty cascade in flood risk assessment under changing climatic conditions - the Biala Tarnowska case study

NASA Astrophysics Data System (ADS)

Doroszkiewicz, Joanna; Romanowicz, Renata

2016-04-01

Uncertainty in the results of the hydraulic model is not only associated with the limitations of that model and the shortcomings of data. An important factor that has a major impact on the uncertainty of the flood risk assessment in a changing climate conditions is associated with the uncertainty of future climate scenarios (IPCC WG I, 2013). Future climate projections provided by global climate models are used to generate future runoff required as an input to hydraulic models applied in the derivation of flood risk maps. Biala Tarnowska catchment, situated in southern Poland is used as a case study. Future discharges at the input to a hydraulic model are obtained using the HBV model and climate projections obtained from the EUROCORDEX project. The study describes a cascade of uncertainty related to different stages of the process of derivation of flood risk maps under changing climate conditions. In this context it takes into account the uncertainty of future climate projections, an uncertainty of flow routing model, the propagation of that uncertainty through the hydraulic model, and finally, the uncertainty related to the derivation of flood risk maps. One of the aims of this study is an assessment of a relative impact of different sources of uncertainty on the uncertainty of flood risk maps. Due to the complexity of the process, an assessment of total uncertainty of maps of inundation probability might be very computer time consuming. As a way forward we present an application of a hydraulic model simulator based on a nonlinear transfer function model for the chosen locations along the river reach. The transfer function model parameters are estimated based on the simulations of the hydraulic model at each of the model cross-section. The study shows that the application of the simulator substantially reduces the computer requirements related to the derivation of flood risk maps under future climatic conditions. Acknowledgements: This work was supported by the project CHIHE (Climate Change Impact on Hydrological Extremes), carried out in the Institute of Geophysics Polish Academy of Sciences, funded by Norway Grants (contract No. Pol-Nor/196243/80/2013). The hydro-meteorological observations were provided by the Institute of Meteorology and Water Management (IMGW), Poland.

Incorporating Land-Use Mapping Uncertainty in Remote Sensing Based Calibration of Land-Use Change Models

NASA Astrophysics Data System (ADS)

Cockx, K.; Van de Voorde, T.; Canters, F.; Poelmans, L.; Uljee, I.; Engelen, G.; de Jong, K.; Karssenberg, D.; van der Kwast, J.

2013-05-01

Building urban growth models typically involves a process of historic calibration based on historic time series of land-use maps, usually obtained from satellite imagery. Both the remote sensing data analysis to infer land use and the subsequent modelling of land-use change are subject to uncertainties, which may have an impact on the accuracy of future land-use predictions. Our research aims to quantify and reduce these uncertainties by means of a particle filter data assimilation approach that incorporates uncertainty in land-use mapping and land-use model parameter assessment into the calibration process. This paper focuses on part of this work, more in particular the modelling of uncertainties associated with the impervious surface cover estimation and urban land-use classification adopted in the land-use mapping approach. Both stages are submitted to a Monte Carlo simulation to assess their relative contribution to and their combined impact on the uncertainty in the derived land-use maps. The approach was applied on the central part of the Flanders region (Belgium), using a time-series of Landsat/SPOT-HRV data covering the years 1987, 1996, 2005 and 2012. Although the most likely land-use map obtained from the simulation is very similar to the original classification, it is shown that the errors related to the impervious surface sub-pixel fraction estimation have a strong impact on the land-use map's uncertainty. Hence, incorporating uncertainty in the land-use change model calibration through particle filter data assimilation is proposed to address the uncertainty observed in the derived land-use maps and to reduce uncertainty in future land-use predictions.

Lognormal Approximations of Fault Tree Uncertainty Distributions.

PubMed

El-Shanawany, Ashraf Ben; Ardron, Keith H; Walker, Simon P

2018-01-26

Fault trees are used in reliability modeling to create logical models of fault combinations that can lead to undesirable events. The output of a fault tree analysis (the top event probability) is expressed in terms of the failure probabilities of basic events that are input to the model. Typically, the basic event probabilities are not known exactly, but are modeled as probability distributions: therefore, the top event probability is also represented as an uncertainty distribution. Monte Carlo methods are generally used for evaluating the uncertainty distribution, but such calculations are computationally intensive and do not readily reveal the dominant contributors to the uncertainty. In this article, a closed-form approximation for the fault tree top event uncertainty distribution is developed, which is applicable when the uncertainties in the basic events of the model are lognormally distributed. The results of the approximate method are compared with results from two sampling-based methods: namely, the Monte Carlo method and the Wilks method based on order statistics. It is shown that the closed-form expression can provide a reasonable approximation to results obtained by Monte Carlo sampling, without incurring the computational expense. The Wilks method is found to be a useful means of providing an upper bound for the percentiles of the uncertainty distribution while being computationally inexpensive compared with full Monte Carlo sampling. The lognormal approximation method and Wilks's method appear attractive, practical alternatives for the evaluation of uncertainty in the output of fault trees and similar multilinear models. © 2018 Society for Risk Analysis.

Constructing Scientific Arguments Using Evidence from Dynamic Computational Climate Models

NASA Astrophysics Data System (ADS)

Pallant, Amy; Lee, Hee-Sun

2015-04-01

Modeling and argumentation are two important scientific practices students need to develop throughout school years. In this paper, we investigated how middle and high school students ( N = 512) construct a scientific argument based on evidence from computational models with which they simulated climate change. We designed scientific argumentation tasks with three increasingly complex dynamic climate models. Each scientific argumentation task consisted of four parts: multiple-choice claim, openended explanation, five-point Likert scale uncertainty rating, and open-ended uncertainty rationale. We coded 1,294 scientific arguments in terms of a claim's consistency with current scientific consensus, whether explanations were model based or knowledge based and categorized the sources of uncertainty (personal vs. scientific). We used chi-square and ANOVA tests to identify significant patterns. Results indicate that (1) a majority of students incorporated models as evidence to support their claims, (2) most students used model output results shown on graphs to confirm their claim rather than to explain simulated molecular processes, (3) students' dependence on model results and their uncertainty rating diminished as the dynamic climate models became more and more complex, (4) some students' misconceptions interfered with observing and interpreting model results or simulated processes, and (5) students' uncertainty sources reflected more frequently on their assessment of personal knowledge or abilities related to the tasks than on their critical examination of scientific evidence resulting from models. These findings have implications for teaching and research related to the integration of scientific argumentation and modeling practices to address complex Earth systems.

Oil release from Macondo well MC252 following the Deepwater Horizon accident.

PubMed

Griffiths, Stewart K

2012-05-15

Oil flow rates and cumulative discharge from the BP Macondo Prospect well in the Gulf of Mexico are calculated using a physically based model along with wellhead pressures measured at the blowout preventer (BOP) over the 86-day period following the Deepwater Horizon accident. Parameters appearing in the model are determined empirically from pressures measured during well shut-in and from pressures and flow rates measured the preceding day. This methodology rigorously accounts for ill-characterized evolution of the marine riser, installation and removal of collection caps, and any erosion at the wellhead. The calculated initial flow rate is 67,100 stock-tank barrels per day (stbd), which decays to 54,400 stbd just prior to installation of the capping stack and subsequent shut-in. The calculated cumulative discharge is 5.4 million stock-tank barrels, of which 4.6 million barrels entered the Gulf. Quantifiable uncertainties in these values are -9.3% and +7.5%, yielding a likely total discharge in the range from 4.9 to 5.8 million barrels. Minimum and maximum credible values of this discharge are 4.6 and 6.2 million barrels. Alternative calculations using the reservoir and sea-floor pressures indicate that any erosion within the BOP had little affect on cumulative discharge.

Issues in the Pharmacokinetics of Trichloroethylene and Its Metabolites

PubMed Central

Chiu, Weihsueh A.; Okino, Miles S.; Lipscomb, John C.; Evans, Marina V.

2006-01-01

Much progress has been made in understanding the complex pharmacokinetics of trichloroethylene (TCE). Qualitatively, it is clear that TCE is metabolized to multiple metabolites either locally or into systemic circulation. Many of these metabolites are thought to have toxicologic importance. In addition, efforts to develop physiologically based pharmacokinetic (PBPK) models have led to a better quantitative assessment of the dosimetry of TCE and several of its metabolites. As part of a mini-monograph on key issues in the health risk assessment of TCE, this article is a review of a number of the current scientific issues in TCE pharmacokinetics and recent PBPK modeling efforts with a focus on literature published since 2000. Particular attention is paid to factors affecting PBPK modeling for application to risk assessment. Recent TCE PBPK modeling efforts, coupled with methodologic advances in characterizing uncertainty and variability, suggest that rigorous application of PBPK modeling to TCE risk assessment appears feasible at least for TCE and its major oxidative metabolites trichloroacetic acid and trichloroethanol. However, a number of basic structural hypotheses such as enterohepatic recirculation, plasma binding, and flow- or diffusion-limited treatment of tissue distribution require additional evaluation and analysis. Moreover, there are a number of metabolites of potential toxicologic interest, such as chloral, dichloroacetic acid, and those derived from glutathione conjugation, for which reliable pharmacokinetic data is sparse because of analytical difficulties or low concentrations in systemic circulation. It will be a challenge to develop reliable dosimetry for such cases. PMID:16966104

Uncertainty analysis of hydrological modeling in a tropical area using different algorithms

NASA Astrophysics Data System (ADS)

Rafiei Emam, Ammar; Kappas, Martin; Fassnacht, Steven; Linh, Nguyen Hoang Khanh

2018-01-01

Hydrological modeling outputs are subject to uncertainty resulting from different sources of errors (e.g., error in input data, model structure, and model parameters), making quantification of uncertainty in hydrological modeling imperative and meant to improve reliability of modeling results. The uncertainty analysis must solve difficulties in calibration of hydrological models, which further increase in areas with data scarcity. The purpose of this study is to apply four uncertainty analysis algorithms to a semi-distributed hydrological model, quantifying different source of uncertainties (especially parameter uncertainty) and evaluate their performance. In this study, the Soil and Water Assessment Tools (SWAT) eco-hydrological model was implemented for the watershed in the center of Vietnam. The sensitivity of parameters was analyzed, and the model was calibrated. The uncertainty analysis for the hydrological model was conducted based on four algorithms: Generalized Likelihood Uncertainty Estimation (GLUE), Sequential Uncertainty Fitting (SUFI), Parameter Solution method (ParaSol) and Particle Swarm Optimization (PSO). The performance of the algorithms was compared using P-factor and Rfactor, coefficient of determination (R 2), the Nash Sutcliffe coefficient of efficiency (NSE) and Percent Bias (PBIAS). The results showed the high performance of SUFI and PSO with P-factor>0.83, R-factor <0.56 and R 2>0.91, NSE>0.89, and 0.18

Using deep neural networks to augment NIF post-shot analysis

NASA Astrophysics Data System (ADS)

Humbird, Kelli; Peterson, Luc; McClarren, Ryan; Field, John; Gaffney, Jim; Kruse, Michael; Nora, Ryan; Spears, Brian

2017-10-01

Post-shot analysis of National Ignition Facility (NIF) experiments is the process of determining which simulation inputs yield results consistent with experimental observations. This analysis is typically accomplished by running suites of manually adjusted simulations, or Monte Carlo sampling surrogate models that approximate the response surfaces of the physics code. These approaches are expensive and often find simulations that match only a small subset of observables simultaneously. We demonstrate an alternative method for performing post-shot analysis using inverse models, which map directly from experimental observables to simulation inputs with quantified uncertainties. The models are created using a novel machine learning algorithm which automates the construction and initialization of deep neural networks to optimize predictive accuracy. We show how these neural networks, trained on large databases of post-shot simulations, can rigorously quantify the agreement between simulation and experiment. This work performed under the auspices of the U.S. Department of Energy by Lawrence Livermore National Laboratory under Contract DE-AC52-07NA27344.

A rigorous statistical framework for spatio-temporal pollution prediction and estimation of its long-term impact on health.

PubMed

Lee, Duncan; Mukhopadhyay, Sabyasachi; Rushworth, Alastair; Sahu, Sujit K

2017-04-01

In the United Kingdom, air pollution is linked to around 40000 premature deaths each year, but estimating its health effects is challenging in a spatio-temporal study. The challenges include spatial misalignment between the pollution and disease data; uncertainty in the estimated pollution surface; and complex residual spatio-temporal autocorrelation in the disease data. This article develops a two-stage model that addresses these issues. The first stage is a spatio-temporal fusion model linking modeled and measured pollution data, while the second stage links these predictions to the disease data. The methodology is motivated by a new five-year study investigating the effects of multiple pollutants on respiratory hospitalizations in England between 2007 and 2011, using pollution and disease data relating to local and unitary authorities on a monthly time scale. © The Author 2016. Published by Oxford University Press. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com.

Can hydraulic-modelled rating curves reduce uncertainty in high flow data?

NASA Astrophysics Data System (ADS)

Westerberg, Ida; Lam, Norris; Lyon, Steve W.

2017-04-01

Flood risk assessments rely on accurate discharge data records. Establishing a reliable rating curve for calculating discharge from stage at a gauging station normally takes years of data collection efforts. Estimation of high flows is particularly difficult as high flows occur rarely and are often practically difficult to gauge. Hydraulically-modelled rating curves can be derived based on as few as two concurrent stage-discharge and water-surface slope measurements at different flow conditions. This means that a reliable rating curve can, potentially, be derived much faster than a traditional rating curve based on numerous stage-discharge gaugings. In this study we compared the uncertainty in discharge data that resulted from these two rating curve modelling approaches. We applied both methods to a Swedish catchment, accounting for uncertainties in the stage-discharge gauging and water-surface slope data for the hydraulic model and in the stage-discharge gauging data and rating-curve parameters for the traditional method. We focused our analyses on high-flow uncertainty and the factors that could reduce this uncertainty. In particular, we investigated which data uncertainties were most important, and at what flow conditions the gaugings should preferably be taken. First results show that the hydraulically-modelled rating curves were more sensitive to uncertainties in the calibration measurements of discharge than water surface slope. The uncertainty of the hydraulically-modelled rating curves were lowest within the range of the three calibration stage-discharge gaugings (i.e. between median and two-times median flow) whereas uncertainties were higher outside of this range. For instance, at the highest observed stage of the 24-year stage record, the 90% uncertainty band was -15% to +40% of the official rating curve. Additional gaugings at high flows (i.e. four to five times median flow) would likely substantially reduce those uncertainties. These first results show the potential of the hydraulically-modelled curves, particularly where the calibration gaugings are of high quality and cover a wide range of flow conditions.

Uncertainty based modeling of rainfall-runoff: Combined differential evolution adaptive Metropolis (DREAM) and K-means clustering

NASA Astrophysics Data System (ADS)

Zahmatkesh, Zahra; Karamouz, Mohammad; Nazif, Sara

2015-09-01

Simulation of rainfall-runoff process in urban areas is of great importance considering the consequences and damages of extreme runoff events and floods. The first issue in flood hazard analysis is rainfall simulation. Large scale climate signals have been proved to be effective in rainfall simulation and prediction. In this study, an integrated scheme is developed for rainfall-runoff modeling considering different sources of uncertainty. This scheme includes three main steps of rainfall forecasting, rainfall-runoff simulation and future runoff prediction. In the first step, data driven models are developed and used to forecast rainfall using large scale climate signals as rainfall predictors. Due to high effect of different sources of uncertainty on the output of hydrologic models, in the second step uncertainty associated with input data, model parameters and model structure is incorporated in rainfall-runoff modeling and simulation. Three rainfall-runoff simulation models are developed for consideration of model conceptual (structural) uncertainty in real time runoff forecasting. To analyze the uncertainty of the model structure, streamflows generated by alternative rainfall-runoff models are combined, through developing a weighting method based on K-means clustering. Model parameters and input uncertainty are investigated using an adaptive Markov Chain Monte Carlo method. Finally, calibrated rainfall-runoff models are driven using the forecasted rainfall to predict future runoff for the watershed. The proposed scheme is employed in the case study of the Bronx River watershed, New York City. Results of uncertainty analysis of rainfall-runoff modeling reveal that simultaneous estimation of model parameters and input uncertainty significantly changes the probability distribution of the model parameters. It is also observed that by combining the outputs of the hydrological models using the proposed clustering scheme, the accuracy of runoff simulation in the watershed is remarkably improved up to 50% in comparison to the simulations by the individual models. Results indicate that the developed methodology not only provides reliable tools for rainfall and runoff modeling, but also adequate time for incorporating required mitigation measures in dealing with potentially extreme runoff events and flood hazard. Results of this study can be used in identification of the main factors affecting flood hazard analysis.

Communicating uncertainties in earth sciences in view of user needs

NASA Astrophysics Data System (ADS)

de Vries, Wim; Kros, Hans; Heuvelink, Gerard

2014-05-01

Uncertainties are inevitable in all results obtained in the earth sciences, regardless whether these are based on field observations, experimental research or predictive modelling. When informing decision and policy makers or stakeholders, it is important that these uncertainties are also communicated. In communicating results, it important to apply a "Progressive Disclosure of Information (PDI)" from non-technical information through more specialised information, according to the user needs. Generalized information is generally directed towards non-scientific audiences and intended for policy advice. Decision makers have to be aware of the implications of the uncertainty associated with results, so that they can account for it in their decisions. Detailed information on the uncertainties is generally intended for scientific audiences to give insight in underlying approaches and results. When communicating uncertainties, it is important to distinguish between scientific results that allow presentation in terms of probabilistic measures of uncertainty and more intrinsic uncertainties and errors that cannot be expressed in mathematical terms. Examples of earth science research that allow probabilistic measures of uncertainty, involving sophisticated statistical methods, are uncertainties in spatial and/or temporal variations in results of: • Observations, such as soil properties measured at sampling locations. In this case, the interpolation uncertainty, caused by a lack of data collected in space, can be quantified by e.g. kriging standard deviation maps or animations of conditional simulations. • Experimental measurements, comparing impacts of treatments at different sites and/or under different conditions. In this case, an indication of the average and range in measured responses to treatments can be obtained from a meta-analysis, summarizing experimental findings between replicates and across studies, sites, ecosystems, etc. • Model predictions due to uncertain model parameters (parametric variability). These uncertainties can be quantified by uncertainty propagation methods such as Monte Carlo simulation methods. Examples of intrinsic uncertainties that generally cannot be expressed in mathematical terms are errors or biases in: • Results of experiments and observations due to inadequate sampling and errors in analyzing data in the laboratory and even in data reporting. • Results of (laboratory) experiments that are limited to a specific domain or performed under circumstances that differ from field circumstances. • Model structure, due to lack of knowledge of the underlying processes. Structural uncertainty, which may cause model inadequacy/ bias, is inherent in model approaches since models are approximations of reality. Intrinsic uncertainties often occur in an emerging field where ongoing new findings, either experiments or field observations of new model findings, challenge earlier work. In this context, climate scientists working within the IPCC have adopted a lexicon to communicate confidence in their findings, ranging from "very high", "high", "medium", "low" and "very low" confidence. In fact, there are also statistical methods to gain insight in uncertainties in model predictions due to model assumptions (i.e. model structural error). Examples are comparing model results with independent observations or a systematic intercomparison of predictions from multiple models. In the latter case, Bayesian model averaging techniques can be used, in which each model considered gets an assigned prior probability of being the 'true' model. This approach works well with statistical (regression) models, but extension to physically-based models is cumbersome. An alternative is the use of state-space models in which structural errors are represent as (additive) noise terms. In this presentation, we focus on approaches that are relevant at the science - policy interface, including multiple scientific disciplines and policy makers with different subject areas. Approaches to communicate uncertainties in results of observations or model predictions are discussed, distinguishing results that include probabilistic measures of uncertainty and more intrinsic uncertainties. Examples concentrate on uncertainties in nitrogen (N) related environmental issues, including: • Spatio-temporal trends in atmospheric N deposition, in view of the policy question whether there is a declining or increasing trend. • Carbon response to N inputs to terrestrial ecosystems, based on meta-analysis of N addition experiments and other approaches, in view of the policy relevance of N emission control. • Calculated spatial variations in the emissions of nitrous-oxide and ammonia, in view of the need of emission policies at different spatial scales. • Calculated N emissions and losses by model intercomparisons, in view of the policy need to apply no-regret decisions with respect to the control of those emissions.

Quantification of uncertainties for application in detonation simulation

NASA Astrophysics Data System (ADS)

Zheng, Miao; Ma, Zhibo

2016-06-01

Numerical simulation has become an important means in designing detonation systems, and the quantification of its uncertainty is also necessary to reliability certification. As to quantifying the uncertainty, it is the most important to analyze how the uncertainties occur and develop, and how the simulations develop from benchmark models to new models. Based on the practical needs of engineering and the technology of verification & validation, a framework of QU(quantification of uncertainty) is brought forward in the case that simulation is used on detonation system for scientific prediction. An example is offered to describe the general idea of quantification of simulation uncertainties.

Digital morphogenesis via Schelling segregation

NASA Astrophysics Data System (ADS)

Barmpalias, George; Elwes, Richard; Lewis-Pye, Andrew

2018-04-01

Schelling’s model of segregation looks to explain the way in which particles or agents of two types may come to arrange themselves spatially into configurations consisting of large homogeneous clusters, i.e. connected regions consisting of only one type. As one of the earliest agent based models studied by economists and perhaps the most famous model of self-organising behaviour, it also has direct links to areas at the interface between computer science and statistical mechanics, such as the Ising model and the study of contagion and cascading phenomena in networks. While the model has been extensively studied it has largely resisted rigorous analysis, prior results from the literature generally pertaining to variants of the model which are tweaked so as to be amenable to standard techniques from statistical mechanics or stochastic evolutionary game theory. In Brandt et al (2012 Proc. 44th Annual ACM Symp. on Theory of Computing) provided the first rigorous analysis of the unperturbed model, for a specific set of input parameters. Here we provide a rigorous analysis of the model’s behaviour much more generally and establish some surprising forms of threshold behaviour, notably the existence of situations where an increased level of intolerance for neighbouring agents of opposite type leads almost certainly to decreased segregation.

Validation and uncertainty analysis of a pre-treatment 2D dose prediction model

NASA Astrophysics Data System (ADS)

Baeza, Jose A.; Wolfs, Cecile J. A.; Nijsten, Sebastiaan M. J. J. G.; Verhaegen, Frank

2018-02-01

Independent verification of complex treatment delivery with megavolt photon beam radiotherapy (RT) has been effectively used to detect and prevent errors. This work presents the validation and uncertainty analysis of a model that predicts 2D portal dose images (PDIs) without a patient or phantom in the beam. The prediction model is based on an exponential point dose model with separable primary and secondary photon fluence components. The model includes a scatter kernel, off-axis ratio map, transmission values and penumbra kernels for beam-delimiting components. These parameters were derived through a model fitting procedure supplied with point dose and dose profile measurements of radiation fields. The model was validated against a treatment planning system (TPS; Eclipse) and radiochromic film measurements for complex clinical scenarios, including volumetric modulated arc therapy (VMAT). Confidence limits on fitted model parameters were calculated based on simulated measurements. A sensitivity analysis was performed to evaluate the effect of the parameter uncertainties on the model output. For the maximum uncertainty, the maximum deviating measurement sets were propagated through the fitting procedure and the model. The overall uncertainty was assessed using all simulated measurements. The validation of the prediction model against the TPS and the film showed a good agreement, with on average 90.8% and 90.5% of pixels passing a (2%,2 mm) global gamma analysis respectively, with a low dose threshold of 10%. The maximum and overall uncertainty of the model is dependent on the type of clinical plan used as input. The results can be used to study the robustness of the model. A model for predicting accurate 2D pre-treatment PDIs in complex RT scenarios can be used clinically and its uncertainties can be taken into account.

Incorporating parametric uncertainty into population viability analysis models

USGS Publications Warehouse

McGowan, Conor P.; Runge, Michael C.; Larson, Michael A.

2011-01-01

Uncertainty in parameter estimates from sampling variation or expert judgment can introduce substantial uncertainty into ecological predictions based on those estimates. However, in standard population viability analyses, one of the most widely used tools for managing plant, fish and wildlife populations, parametric uncertainty is often ignored in or discarded from model projections. We present a method for explicitly incorporating this source of uncertainty into population models to fully account for risk in management and decision contexts. Our method involves a two-step simulation process where parametric uncertainty is incorporated into the replication loop of the model and temporal variance is incorporated into the loop for time steps in the model. Using the piping plover, a federally threatened shorebird in the USA and Canada, as an example, we compare abundance projections and extinction probabilities from simulations that exclude and include parametric uncertainty. Although final abundance was very low for all sets of simulations, estimated extinction risk was much greater for the simulation that incorporated parametric uncertainty in the replication loop. Decisions about species conservation (e.g., listing, delisting, and jeopardy) might differ greatly depending on the treatment of parametric uncertainty in population models.

How certain is desiccation in west African Sahel rainfall (1930-1990)?

NASA Astrophysics Data System (ADS)

Chappell, Adrian; Agnew, Clive T.

2008-04-01

Hypotheses for the late 1960s to 1990 period of desiccation (secular decrease in rainfall) in the west African Sahel (WAS) are typically tested by comparing empirical evidence or model predictions against "observations" of Sahelian rainfall. The outcomes of those comparisons can have considerable influence on the understanding of regional and global environmental systems. Inverse-distance squared area-weighted (IDW) estimates of WAS rainfall observations are commonly aggregated over space to provide temporal patterns without uncertainty. Spatial uncertainty of WAS rainfall was determined using the median approximation sequential indicator simulation. Every year (1930-1990) 300 equally probable realizations of annual summer rainfall were produced to honor station observations, match percentiles of the observed cumulative distributions and indicator variograms and perform adequately during cross validation. More than 49% of the IDW mean annual rainfall fell outside the 5th and 95th percentiles for annual rainfall realization means. The IDW means represented an extreme realization. Uncertainty in desiccation was determined by repeatedly (100,000) sampling the annual distribution of rainfall realization means and by applying Mann-Kendall nonparametric slope detection and significance testing. All of the negative gradients for the entire period were statistically significant. None of the negative gradients for the expected desiccation period were statistically significant. The results support the presence of a long-term decline in annual rainfall but demonstrate that short-term desiccation (1965-1990) cannot be detected. Estimates of uncertainty for precipitation and other climate variables in this or other regions, or across the globe, are essential for the rigorous detection of spatial patterns and time series trends.

A bi-objective model for robust yard allocation scheduling for outbound containers

NASA Astrophysics Data System (ADS)

Liu, Changchun; Zhang, Canrong; Zheng, Li

2017-01-01

This article examines the yard allocation problem for outbound containers, with consideration of uncertainty factors, mainly including the arrival and operation time of calling vessels. Based on the time buffer inserting method, a bi-objective model is constructed to minimize the total operational cost and to maximize the robustness of fighting against the uncertainty. Due to the NP-hardness of the constructed model, a two-stage heuristic is developed to solve the problem. In the first stage, initial solutions are obtained by a greedy algorithm that looks n-steps ahead with the uncertainty factors set as their respective expected values; in the second stage, based on the solutions obtained in the first stage and with consideration of uncertainty factors, a neighbourhood search heuristic is employed to generate robust solutions that can fight better against the fluctuation of uncertainty factors. Finally, extensive numerical experiments are conducted to test the performance of the proposed method.

Understanding and predicting changing use of groundwater with climate and other uncertainties: a Bayesian approach

NASA Astrophysics Data System (ADS)

Costa, F. A. F.; Keir, G.; McIntyre, N.; Bulovic, N.

2015-12-01

Most groundwater supply bores in Australia do not have flow metering equipment and so regional groundwater abstraction rates are not well known. Past estimates of unmetered abstraction for regional numerical groundwater modelling typically have not attempted to quantify the uncertainty inherent in the estimation process in detail. In particular, the spatial properties of errors in the estimates are almost always neglected. Here, we apply Bayesian spatial models to estimate these abstractions at a regional scale, using the state-of-the-art computationally inexpensive approaches of integrated nested Laplace approximation (INLA) and stochastic partial differential equations (SPDE). We examine a case study in the Condamine Alluvium aquifer in southern Queensland, Australia; even in this comparatively data-rich area with extensive groundwater abstraction for agricultural irrigation, approximately 80% of bores do not have reliable metered flow records. Additionally, the metering data in this area are characterised by complicated statistical features, such as zero-valued observations, non-normality, and non-stationarity. While this precludes the use of many classical spatial estimation techniques, such as kriging, our model (using the R-INLA package) is able to accommodate these features. We use a joint model to predict both probability and magnitude of abstraction from bores in space and time, and examine the effect of a range of high-resolution gridded meteorological covariates upon the predictive ability of the model. Deviance Information Criterion (DIC) scores are used to assess a range of potential models, which reward good model fit while penalising excessive model complexity. We conclude that maximum air temperature (as a reasonably effective surrogate for evapotranspiration) is the most significant single predictor of abstraction rate; and that a significant spatial effect exists (represented by the SPDE approximation of a Gaussian random field with a Matérn covariance function). Our final model adopts air temperature, solar exposure, and normalized difference vegetation index (NDVI) as covariates, shows good agreement with previous estimates at a regional scale, and additionally offers rigorous quantification of uncertainty in the estimate.

Development of a Framework for Model-Based Analysis, Uncertainty Quantification, and Robust Control Design of Nonlinear Smart Composite Systems

DTIC Science & Technology

2015-06-04

control, vibration and noise control, health monitoring, and energy harvesting . However, these advantages come at the cost of rate-dependent hysteresis...configuration used for energy harvesting . Uncertainty Quantification Uncertainty quantification is pursued in two steps: (i) determination of densities...Crews and R.C. Smith, “Quantification of parameter and model uncertainty for shape mem- ory alloy bending actuators,” Journal of Intelligent material

In search of a statistical probability model for petroleum-resource assessment : a critique of the probabilistic significance of certain concepts and methods used in petroleum-resource assessment : to that end, a probabilistic model is sketched

USGS Publications Warehouse

Grossling, Bernardo F.

1975-01-01

Exploratory drilling is still in incipient or youthful stages in those areas of the world where the bulk of the potential petroleum resources is yet to be discovered. Methods of assessing resources from projections based on historical production and reserve data are limited to mature areas. For most of the world's petroleum-prospective areas, a more speculative situation calls for a critical review of resource-assessment methodology. The language of mathematical statistics is required to define more rigorously the appraisal of petroleum resources. Basically, two approaches have been used to appraise the amounts of undiscovered mineral resources in a geologic province: (1) projection models, which use statistical data on the past outcome of exploration and development in the province; and (2) estimation models of the overall resources of the province, which use certain known parameters of the province together with the outcome of exploration and development in analogous provinces. These two approaches often lead to widely different estimates. Some of the controversy that arises results from a confusion of the probabilistic significance of the quantities yielded by each of the two approaches. Also, inherent limitations of analytic projection models-such as those using the logistic and Gomperts functions --have often been ignored. The resource-assessment problem should be recast in terms that provide for consideration of the probability of existence of the resource and of the probability of discovery of a deposit. Then the two above-mentioned models occupy the two ends of the probability range. The new approach accounts for (1) what can be expected with reasonably high certainty by mere projections of what has been accomplished in the past; (2) the inherent biases of decision-makers and resource estimators; (3) upper bounds that can be set up as goals for exploration; and (4) the uncertainties in geologic conditions in a search for minerals. Actual outcomes can then be viewed as phenomena subject to statistical uncertainty and responsive to changes in economic and technologic factors.

Special Issue on Uncertainty Quantification in Multiscale System Design and Simulation

DOE PAGES

Wang, Yan; Swiler, Laura

2017-09-07

The importance of uncertainty has been recognized in various modeling, simulation, and analysis applications, where inherent assumptions and simplifications affect the accuracy of model predictions for physical phenomena. As model predictions are now heavily relied upon for simulation-based system design, which includes new materials, vehicles, mechanical and civil structures, and even new drugs, wrong model predictions could potentially cause catastrophic consequences. Therefore, uncertainty and associated risks due to model errors should be quantified to support robust systems engineering.

Special Issue on Uncertainty Quantification in Multiscale System Design and Simulation

DOE Office of Scientific and Technical Information (OSTI.GOV)

Wang, Yan; Swiler, Laura

The importance of uncertainty has been recognized in various modeling, simulation, and analysis applications, where inherent assumptions and simplifications affect the accuracy of model predictions for physical phenomena. As model predictions are now heavily relied upon for simulation-based system design, which includes new materials, vehicles, mechanical and civil structures, and even new drugs, wrong model predictions could potentially cause catastrophic consequences. Therefore, uncertainty and associated risks due to model errors should be quantified to support robust systems engineering.

Evaluation of global fine-resolution precipitation products and their uncertainty quantification in ensemble discharge simulations

NASA Astrophysics Data System (ADS)

Qi, W.; Zhang, C.; Fu, G.; Sweetapple, C.; Zhou, H.

2016-02-01

The applicability of six fine-resolution precipitation products, including precipitation radar, infrared, microwave and gauge-based products, using different precipitation computation recipes, is evaluated using statistical and hydrological methods in northeastern China. In addition, a framework quantifying uncertainty contributions of precipitation products, hydrological models, and their interactions to uncertainties in ensemble discharges is proposed. The investigated precipitation products are Tropical Rainfall Measuring Mission (TRMM) products (TRMM3B42 and TRMM3B42RT), Global Land Data Assimilation System (GLDAS)/Noah, Asian Precipitation - Highly-Resolved Observational Data Integration Towards Evaluation of Water Resources (APHRODITE), Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks (PERSIANN), and a Global Satellite Mapping of Precipitation (GSMAP-MVK+) product. Two hydrological models of different complexities, i.e. a water and energy budget-based distributed hydrological model and a physically based semi-distributed hydrological model, are employed to investigate the influence of hydrological models on simulated discharges. Results show APHRODITE has high accuracy at a monthly scale compared with other products, and GSMAP-MVK+ shows huge advantage and is better than TRMM3B42 in relative bias (RB), Nash-Sutcliffe coefficient of efficiency (NSE), root mean square error (RMSE), correlation coefficient (CC), false alarm ratio, and critical success index. These findings could be very useful for validation, refinement, and future development of satellite-based products (e.g. NASA Global Precipitation Measurement). Although large uncertainty exists in heavy precipitation, hydrological models contribute most of the uncertainty in extreme discharges. Interactions between precipitation products and hydrological models can have the similar magnitude of contribution to discharge uncertainty as the hydrological models. A better precipitation product does not guarantee a better discharge simulation because of interactions. It is also found that a good discharge simulation depends on a good coalition of a hydrological model and a precipitation product, suggesting that, although the satellite-based precipitation products are not as accurate as the gauge-based products, they could have better performance in discharge simulations when appropriately combined with hydrological models. This information is revealed for the first time and very beneficial for precipitation product applications.

Minimax confidence intervals in geomagnetism

NASA Technical Reports Server (NTRS)

Stark, Philip B.

1992-01-01

The present paper uses theory of Donoho (1989) to find lower bounds on the lengths of optimally short fixed-length confidence intervals (minimax confidence intervals) for Gauss coefficients of the field of degree 1-12 using the heat flow constraint. The bounds on optimal minimax intervals are about 40 percent shorter than Backus' intervals: no procedure for producing fixed-length confidence intervals, linear or nonlinear, can give intervals shorter than about 60 percent the length of Backus' in this problem. While both methods rigorously account for the fact that core field models are infinite-dimensional, the application of the techniques to the geomagnetic problem involves approximations and counterfactual assumptions about the data errors, and so these results are likely to be extremely optimistic estimates of the actual uncertainty in Gauss coefficients.

The distribution of lingering subsurface oil from the Exxon Valdez oil spill

USGS Publications Warehouse

Michel, Jacqueline; Nixon, Zachary; Hayes, Miles O.; Irvine, Gail V.; Short, Jeffrey W.

2011-01-01

This study used field data and a suite of geospatial models to identify areas where subsurface oil is likely to still be present on the shorelines of Prince William Sound (PWS) and the Gulf of Alaska (GOA) affected by the Exxon Valdez oil spill, as well as the factors related to continued presence of such oil. The goal was to identify factors and accompanying models that could serve as screening tools to prioritize shorelines for different remediation methods. The models were based on data collected at 314 shoreline segments surveyed between 2001 and 2007. These field data allowed us to identify a number of geomorphologic and hydrologic factors that have contributed to the persistence of subsurface oil within PWS and GOA two decades after the spill. Because synoptic data layers for describing each of these factors at all locations were not available, the models developed used existing data sets as surrogates to represent these factors, such as distance to a stream mouth or shoreline convexity. While the linkages between the data used and the physical phenomena that drive persistence are not clearly understood in all cases, the performance of these models was remarkably good. The models simultaneously evaluate all identified variables to predict the presence of different types of subsurface oiling in a rigorous, unbiased manner. The refined model results suggest there are a limited but significant number of as-yet unsurveyed locations in the study area that are likely to contain subsurface oil. Furthermore, the model results may be used to quantitatively prioritize shoreline for investigation with known uncertainty.

Evaluating Predictive Uncertainty of Hyporheic Exchange Modelling

NASA Astrophysics Data System (ADS)

Chow, R.; Bennett, J.; Dugge, J.; Wöhling, T.; Nowak, W.

2017-12-01

Hyporheic exchange is the interaction of water between rivers and groundwater, and is difficult to predict. One of the largest contributions to predictive uncertainty for hyporheic fluxes have been attributed to the representation of heterogeneous subsurface properties. This research aims to evaluate which aspect of the subsurface representation - the spatial distribution of hydrofacies or the model for local-scale (within-facies) heterogeneity - most influences the predictive uncertainty. Also, we seek to identify data types that help reduce this uncertainty best. For this investigation, we conduct a modelling study of the Steinlach River meander, in Southwest Germany. The Steinlach River meander is an experimental site established in 2010 to monitor hyporheic exchange at the meander scale. We use HydroGeoSphere, a fully integrated surface water-groundwater model, to model hyporheic exchange and to assess the predictive uncertainty of hyporheic exchange transit times (HETT). A highly parameterized complex model is built and treated as `virtual reality', which is in turn modelled with simpler subsurface parameterization schemes (Figure). Then, we conduct Monte-Carlo simulations with these models to estimate the predictive uncertainty. Results indicate that: Uncertainty in HETT is relatively small for early times and increases with transit times. Uncertainty from local-scale heterogeneity is negligible compared to uncertainty in the hydrofacies distribution. Introducing more data to a poor model structure may reduce predictive variance, but does not reduce predictive bias. Hydraulic head observations alone cannot constrain the uncertainty of HETT, however an estimate of hyporheic exchange flux proves to be more effective at reducing this uncertainty. Figure: Approach for evaluating predictive model uncertainty. A conceptual model is first developed from the field investigations. A complex model (`virtual reality') is then developed based on that conceptual model. This complex model then serves as the basis to compare simpler model structures. Through this approach, predictive uncertainty can be quantified relative to a known reference solution.

`spup' - An R Package for Analysis of Spatial Uncertainty Propagation and Application to Trace Gas Emission Simulations

NASA Astrophysics Data System (ADS)

Sawicka, K.; Breuer, L.; Houska, T.; Santabarbara Ruiz, I.; Heuvelink, G. B. M.

2016-12-01

Computer models have become a crucial tool in engineering and environmental sciences for simulating the behaviour of complex static and dynamic systems. However, while many models are deterministic, the uncertainty in their predictions needs to be estimated before they are used for decision support. Advances in uncertainty propagation analysis and assessment have been paralleled by a growing number of software tools for uncertainty analysis, but none has gained recognition for a universal applicability, including case studies with spatial models and spatial model inputs. Due to the growing popularity and applicability of the open source R programming language we undertook a project to develop an R package that facilitates uncertainty propagation analysis in spatial environmental modelling. In particular, the `spup' package provides functions for examining the uncertainty propagation starting from input data and model parameters, via the environmental model onto model predictions. The functions include uncertainty model specification, stochastic simulation and propagation of uncertainty using Monte Carlo techniques, as well as several uncertainty visualization functions. Here we will demonstrate that the 'spup' package is an effective and easy-to-use tool to be applied even in a very complex study case, and that it can be used in multi-disciplinary research and model-based decision support. As an example, we use the ecological LandscapeDNDC model to analyse propagation of uncertainties associated with spatial variability of the model driving forces such as rainfall, nitrogen deposition and fertilizer inputs. The uncertainty propagation is analysed for the prediction of emissions of N2O and CO2 for a German low mountainous, agriculturally developed catchment. The study tests the effect of spatial correlations on spatially aggregated model outputs, and could serve as an advice for developing best management practices and model improvement strategies.

Resonant tunneling assisted propagation and amplification of plasmons in high electron mobility transistors

DOE Office of Scientific and Technical Information (OSTI.GOV)

Bhardwaj, Shubhendu; Sensale-Rodriguez, Berardi; Xing, Huili Grace

A rigorous theoretical and computational model is developed for the plasma-wave propagation in high electron mobility transistor structures with electron injection from a resonant tunneling diode at the gate. We discuss the conditions in which low-loss and sustainable plasmon modes can be supported in such structures. The developed analytical model is used to derive the dispersion relation for these plasmon-modes. A non-linear full-wave-hydrodynamic numerical solver is also developed using a finite difference time domain algorithm. The developed analytical solutions are validated via the numerical solution. We also verify previous observations that were based on a simplified transmission line model. Itmore » is shown that at high levels of negative differential conductance, plasmon amplification is indeed possible. The proposed rigorous models can enable accurate design and optimization of practical resonant tunnel diode-based plasma-wave devices for terahertz sources, mixers, and detectors, by allowing a precise representation of their coupling when integrated with other electromagnetic structures.« less

Modeling Pilot State in Next Generation Aircraft Alert Systems

NASA Technical Reports Server (NTRS)

Carlin, Alan S.; Alexander, Amy L.; Schurr, Nathan

2011-01-01

The Next Generation Air Transportation System will introduce new, advanced sensor technologies into the cockpit that must convey a large number of potentially complex alerts. Our work focuses on the challenges associated with prioritizing aircraft sensor alerts in a quick and efficient manner, essentially determining when and how to alert the pilot This "alert decision" becomes very difficult in NextGen due to the following challenges: 1) the increasing number of potential hazards, 2) the uncertainty associated with the state of potential hazards as well as pilot slate , and 3) the limited time to make safely-critical decisions. In this paper, we focus on pilot state and present a model for anticipating duration and quality of pilot behavior, for use in a larger system which issues aircraft alerts. We estimate pilot workload, which we model as being dependent on factors including mental effort, task demands. and task performance. We perform a mathematically rigorous analysis of the model and resulting alerting plans. We simulate the model in software and present simulated results with respect to manipulation of the pilot measures.

Monitoring and modeling as a continuing learning process: the use of hydrological models in a general probabilistic framework.

NASA Astrophysics Data System (ADS)

Baroni, G.; Gräff, T.; Reinstorf, F.; Oswald, S. E.

2012-04-01

Nowadays uncertainty and sensitivity analysis are considered basic tools for the assessment of hydrological models and the evaluation of the most important sources of uncertainty. In this context, in the last decades several methods have been developed and applied in different hydrological conditions. However, in most of the cases, the studies have been done by investigating mainly the influence of the parameter uncertainty on the simulated outputs and few approaches tried to consider also other sources of uncertainty i.e. input and model structure. Moreover, several constrains arise when spatially distributed parameters are involved. To overcome these limitations a general probabilistic framework based on Monte Carlo simulations and the Sobol method has been proposed. In this study, the general probabilistic framework was applied at field scale using a 1D physical-based hydrological model (SWAP). Furthermore, the framework was extended at catchment scale in combination with a spatially distributed hydrological model (SHETRAN). The models are applied in two different experimental sites in Germany: a relatively flat cropped field close to Potsdam (Brandenburg) and a small mountainous catchment with agricultural land use (Schaefertal, Harz Mountains). For both cases, input and parameters are considered as major sources of uncertainty. Evaluation of the models was based on soil moisture detected at plot scale in different depths and, for the catchment site, also with daily discharge values. The study shows how the framework can take into account all the various sources of uncertainty i.e. input data, parameters (either in scalar or spatially distributed form) and model structures. The framework can be used in a loop in order to optimize further monitoring activities used to improve the performance of the model. In the particular applications, the results show how the sources of uncertainty are specific for each process considered. The influence of the input data as well as the presence of compensating errors become clear by the different processes simulated.

Uncertainty aggregation and reduction in structure-material performance prediction

NASA Astrophysics Data System (ADS)

Hu, Zhen; Mahadevan, Sankaran; Ao, Dan

2018-02-01

An uncertainty aggregation and reduction framework is presented for structure-material performance prediction. Different types of uncertainty sources, structural analysis model, and material performance prediction model are connected through a Bayesian network for systematic uncertainty aggregation analysis. To reduce the uncertainty in the computational structure-material performance prediction model, Bayesian updating using experimental observation data is investigated based on the Bayesian network. It is observed that the Bayesian updating results will have large error if the model cannot accurately represent the actual physics, and that this error will be propagated to the predicted performance distribution. To address this issue, this paper proposes a novel uncertainty reduction method by integrating Bayesian calibration with model validation adaptively. The observation domain of the quantity of interest is first discretized into multiple segments. An adaptive algorithm is then developed to perform model validation and Bayesian updating over these observation segments sequentially. Only information from observation segments where the model prediction is highly reliable is used for Bayesian updating; this is found to increase the effectiveness and efficiency of uncertainty reduction. A composite rotorcraft hub component fatigue life prediction model, which combines a finite element structural analysis model and a material damage model, is used to demonstrate the proposed method.

Uncertainty in Simulating Wheat Yields Under Climate Change

NASA Technical Reports Server (NTRS)

Asseng, S.; Ewert, F.; Rosenzweig, Cynthia; Jones, J. W.; Hatfield, J. W.; Ruane, A. C.; Boote, K. J.; Thornburn, P. J.; Rotter, R. P.; Cammarano, D.;

A Systematic Review of Cost-Effectiveness Models in Type 1 Diabetes Mellitus.

PubMed

Henriksson, Martin; Jindal, Ramandeep; Sternhufvud, Catarina; Bergenheim, Klas; Sörstadius, Elisabeth; Willis, Michael

2016-06-01

Critiques of cost-effectiveness modelling in type 1 diabetes mellitus (T1DM) are scarce and are often undertaken in combination with type 2 diabetes mellitus (T2DM) models. However, T1DM is a separate disease, and it is therefore important to appraise modelling methods in T1DM. This review identified published economic models in T1DM and provided an overview of the characteristics and capabilities of available models, thus enabling a discussion of best-practice modelling approaches in T1DM. A systematic review of Embase(®), MEDLINE(®), MEDLINE(®) In-Process, and NHS EED was conducted to identify available models in T1DM. Key conferences and health technology assessment (HTA) websites were also reviewed. The characteristics of each model (e.g. model structure, simulation method, handling of uncertainty, incorporation of treatment effect, data for risk equations, and validation procedures, based on information in the primary publication) were extracted, with a focus on model capabilities. We identified 13 unique models. Overall, the included studies varied greatly in scope as well as in the quality and quantity of information reported, but six of the models (Archimedes, CDM [Core Diabetes Model], CRC DES [Cardiff Research Consortium Discrete Event Simulation], DCCT [Diabetes Control and Complications Trial], Sheffield, and EAGLE [Economic Assessment of Glycaemic control and Long-term Effects of diabetes]) were the most rigorous and thoroughly reported. Most models were Markov based, and cohort and microsimulation methods were equally common. All of the more comprehensive models employed microsimulation methods. Model structure varied widely, with the more holistic models providing a comprehensive approach to microvascular and macrovascular events, as well as including adverse events. The majority of studies reported a lifetime horizon, used a payer perspective, and had the capability for sensitivity analysis. Several models have been developed that provide useful insight into T1DM modelling. Based on a review of the models identified in this study, we identified a set of 'best in class' methods for the different technical aspects of T1DM modelling.

On-orbit servicing system assessment and optimization methods based on lifecycle simulation under mixed aleatory and epistemic uncertainties

NASA Astrophysics Data System (ADS)

Yao, Wen; Chen, Xiaoqian; Huang, Yiyong; van Tooren, Michel

2013-06-01

To assess the on-orbit servicing (OOS) paradigm and optimize its utilities by taking advantage of its inherent flexibility and responsiveness, the OOS system assessment and optimization methods based on lifecycle simulation under uncertainties are studied. The uncertainty sources considered in this paper include both the aleatory (random launch/OOS operation failure and on-orbit component failure) and the epistemic (the unknown trend of the end-used market price) types. Firstly, the lifecycle simulation under uncertainties is discussed. The chronological flowchart is presented. The cost and benefit models are established, and the uncertainties thereof are modeled. The dynamic programming method to make optimal decision in face of the uncertain events is introduced. Secondly, the method to analyze the propagation effects of the uncertainties on the OOS utilities is studied. With combined probability and evidence theory, a Monte Carlo lifecycle Simulation based Unified Uncertainty Analysis (MCS-UUA) approach is proposed, based on which the OOS utility assessment tool under mixed uncertainties is developed. Thirdly, to further optimize the OOS system under mixed uncertainties, the reliability-based optimization (RBO) method is studied. To alleviate the computational burden of the traditional RBO method which involves nested optimum search and uncertainty analysis, the framework of Sequential Optimization and Mixed Uncertainty Analysis (SOMUA) is employed to integrate MCS-UUA, and the RBO algorithm SOMUA-MCS is developed. Fourthly, a case study on the OOS system for a hypothetical GEO commercial communication satellite is investigated with the proposed assessment tool. Furthermore, the OOS system is optimized with SOMUA-MCS. Lastly, some conclusions are given and future research prospects are highlighted.

An Uncertainty Quantification Framework for Remote Sensing Retrievals

NASA Astrophysics Data System (ADS)

Braverman, A. J.; Hobbs, J.

2017-12-01

Remote sensing data sets produced by NASA and other space agencies are the result of complex algorithms that infer geophysical state from observed radiances using retrieval algorithms. The processing must keep up with the downlinked data flow, and this necessitates computational compromises that affect the accuracies of retrieved estimates. The algorithms are also limited by imperfect knowledge of physics and of ancillary inputs that are required. All of this contributes to uncertainties that are generally not rigorously quantified by stepping outside the assumptions that underlie the retrieval methodology. In this talk we discuss a practical framework for uncertainty quantification that can be applied to a variety of remote sensing retrieval algorithms. Ours is a statistical approach that uses Monte Carlo simulation to approximate the sampling distribution of the retrieved estimates. We will discuss the strengths and weaknesses of this approach, and provide a case-study example from the Orbiting Carbon Observatory 2 mission.

Information content of incubation experiments for inverse estimation of pools in the Rothamsted carbon model: a Bayesian perspective

NASA Astrophysics Data System (ADS)

Scharnagl, B.; Vrugt, J. A.; Vereecken, H.; Herbst, M.

2010-02-01

A major drawback of current soil organic carbon (SOC) models is that their conceptually defined pools do not necessarily correspond to measurable SOC fractions in real practice. This not only impairs our ability to rigorously evaluate SOC models but also makes it difficult to derive accurate initial states of the individual carbon pools. In this study, we tested the feasibility of inverse modelling for estimating pools in the Rothamsted carbon model (ROTHC) using mineralization rates observed during incubation experiments. This inverse approach may provide an alternative to existing SOC fractionation methods. To illustrate our approach, we used a time series of synthetically generated mineralization rates using the ROTHC model. We adopted a Bayesian approach using the recently developed DiffeRential Evolution Adaptive Metropolis (DREAM) algorithm to infer probability density functions of the various carbon pools at the start of incubation. The Kullback-Leibler divergence was used to quantify the information content of the mineralization rate data. Our results indicate that measured mineralization rates generally provided sufficient information to reliably estimate all carbon pools in the ROTHC model. The incubation time necessary to appropriately constrain all pools was about 900 days. The use of prior information on microbial biomass carbon significantly reduced the uncertainty of the initial carbon pools, decreasing the required incubation time to about 600 days. Simultaneous estimation of initial carbon pools and decomposition rate constants significantly increased the uncertainty of the carbon pools. This effect was most pronounced for the intermediate and slow pools. Altogether, our results demonstrate that it is particularly difficult to derive reasonable estimates of the humified organic matter pool and the inert organic matter pool from inverse modelling of mineralization rates observed during incubation experiments.

Parameter optimization, sensitivity, and uncertainty analysis of an ecosystem model at a forest flux tower site in the United States

USGS Publications Warehouse

Wu, Yiping; Liu, Shuguang; Huang, Zhihong; Yan, Wende

2014-01-01

Ecosystem models are useful tools for understanding ecological processes and for sustainable management of resources. In biogeochemical field, numerical models have been widely used for investigating carbon dynamics under global changes from site to regional and global scales. However, it is still challenging to optimize parameters and estimate parameterization uncertainty for complex process-based models such as the Erosion Deposition Carbon Model (EDCM), a modified version of CENTURY, that consider carbon, water, and nutrient cycles of ecosystems. This study was designed to conduct the parameter identifiability, optimization, sensitivity, and uncertainty analysis of EDCM using our developed EDCM-Auto, which incorporated a comprehensive R package—Flexible Modeling Framework (FME) and the Shuffled Complex Evolution (SCE) algorithm. Using a forest flux tower site as a case study, we implemented a comprehensive modeling analysis involving nine parameters and four target variables (carbon and water fluxes) with their corresponding measurements based on the eddy covariance technique. The local sensitivity analysis shows that the plant production-related parameters (e.g., PPDF1 and PRDX) are most sensitive to the model cost function. Both SCE and FME are comparable and performed well in deriving the optimal parameter set with satisfactory simulations of target variables. Global sensitivity and uncertainty analysis indicate that the parameter uncertainty and the resulting output uncertainty can be quantified, and that the magnitude of parameter-uncertainty effects depends on variables and seasons. This study also demonstrates that using the cutting-edge R functions such as FME can be feasible and attractive for conducting comprehensive parameter analysis for ecosystem modeling.

Ensemble-based flash-flood modelling: Taking into account hydrodynamic parameters and initial soil moisture uncertainties

NASA Astrophysics Data System (ADS)

Edouard, Simon; Vincendon, Béatrice; Ducrocq, Véronique

2018-05-01

Intense precipitation events in the Mediterranean often lead to devastating flash floods (FF). FF modelling is affected by several kinds of uncertainties and Hydrological Ensemble Prediction Systems (HEPS) are designed to take those uncertainties into account. The major source of uncertainty comes from rainfall forcing and convective-scale meteorological ensemble prediction systems can manage it for forecasting purpose. But other sources are related to the hydrological modelling part of the HEPS. This study focuses on the uncertainties arising from the hydrological model parameters and initial soil moisture with aim to design an ensemble-based version of an hydrological model dedicated to Mediterranean fast responding rivers simulations, the ISBA-TOP coupled system. The first step consists in identifying the parameters that have the strongest influence on FF simulations by assuming perfect precipitation. A sensitivity study is carried out first using a synthetic framework and then for several real events and several catchments. Perturbation methods varying the most sensitive parameters as well as initial soil moisture allow designing an ensemble-based version of ISBA-TOP. The first results of this system on some real events are presented. The direct perspective of this work will be to drive this ensemble-based version with the members of a convective-scale meteorological ensemble prediction system to design a complete HEPS for FF forecasting.

PC-BASED SUPERCOMPUTING FOR UNCERTAINTY AND SENSITIVITY ANALYSIS OF MODELS

EPA Science Inventory

Evaluating uncertainty and sensitivity of multimedia environmental models that integrate assessments of air, soil, sediments, groundwater, and surface water is a difficult task. It can be an enormous undertaking even for simple, single-medium models (i.e. groundwater only) descr...

Info-gap management of public health Policy for TB with HIV-prevalence and epidemiological uncertainty

PubMed Central

2012-01-01

Background Formulation and evaluation of public health policy commonly employs science-based mathematical models. For instance, epidemiological dynamics of TB is dominated, in general, by flow between actively and latently infected populations. Thus modelling is central in planning public health intervention. However, models are highly uncertain because they are based on observations that are geographically and temporally distinct from the population to which they are applied. Aims We aim to demonstrate the advantages of info-gap theory, a non-probabilistic approach to severe uncertainty when worst cases cannot be reliably identified and probability distributions are unreliable or unavailable. Info-gap is applied here to mathematical modelling of epidemics and analysis of public health decision-making. Methods Applying info-gap robustness analysis to tuberculosis/HIV (TB/HIV) epidemics, we illustrate the critical role of incorporating uncertainty in formulating recommendations for interventions. Robustness is assessed as the magnitude of uncertainty that can be tolerated by a given intervention. We illustrate the methodology by exploring interventions that alter the rates of diagnosis, cure, relapse and HIV infection. Results We demonstrate several policy implications. Equivalence among alternative rates of diagnosis and relapse are identified. The impact of initial TB and HIV prevalence on the robustness to uncertainty is quantified. In some configurations, increased aggressiveness of intervention improves the predicted outcome but also reduces the robustness to uncertainty. Similarly, predicted outcomes may be better at larger target times, but may also be more vulnerable to model error. Conclusions The info-gap framework is useful for managing model uncertainty and is attractive when uncertainties on model parameters are extreme. When a public health model underlies guidelines, info-gap decision theory provides valuable insight into the confidence of achieving agreed-upon goals. PMID:23249291

Info-gap management of public health Policy for TB with HIV-prevalence and epidemiological uncertainty.

PubMed

Ben-Haim, Yakov; Dacso, Clifford C; Zetola, Nicola M

2012-12-19

Formulation and evaluation of public health policy commonly employs science-based mathematical models. For instance, epidemiological dynamics of TB is dominated, in general, by flow between actively and latently infected populations. Thus modelling is central in planning public health intervention. However, models are highly uncertain because they are based on observations that are geographically and temporally distinct from the population to which they are applied. We aim to demonstrate the advantages of info-gap theory, a non-probabilistic approach to severe uncertainty when worst cases cannot be reliably identified and probability distributions are unreliable or unavailable. Info-gap is applied here to mathematical modelling of epidemics and analysis of public health decision-making. Applying info-gap robustness analysis to tuberculosis/HIV (TB/HIV) epidemics, we illustrate the critical role of incorporating uncertainty in formulating recommendations for interventions. Robustness is assessed as the magnitude of uncertainty that can be tolerated by a given intervention. We illustrate the methodology by exploring interventions that alter the rates of diagnosis, cure, relapse and HIV infection. We demonstrate several policy implications. Equivalence among alternative rates of diagnosis and relapse are identified. The impact of initial TB and HIV prevalence on the robustness to uncertainty is quantified. In some configurations, increased aggressiveness of intervention improves the predicted outcome but also reduces the robustness to uncertainty. Similarly, predicted outcomes may be better at larger target times, but may also be more vulnerable to model error. The info-gap framework is useful for managing model uncertainty and is attractive when uncertainties on model parameters are extreme. When a public health model underlies guidelines, info-gap decision theory provides valuable insight into the confidence of achieving agreed-upon goals.

Using Predictive Uncertainty Analysis to Assess Hydrologic Model Performance for a Watershed in Oregon

NASA Astrophysics Data System (ADS)

Brannan, K. M.; Somor, A.

2016-12-01

A variety of statistics are used to assess watershed model performance but these statistics do not directly answer the question: what is the uncertainty of my prediction. Understanding predictive uncertainty is important when using a watershed model to develop a Total Maximum Daily Load (TMDL). TMDLs are a key component of the US Clean Water Act and specify the amount of a pollutant that can enter a waterbody when the waterbody meets water quality criteria. TMDL developers use watershed models to estimate pollutant loads from nonpoint sources of pollution. We are developing a TMDL for bacteria impairments in a watershed in the Coastal Range of Oregon. We setup an HSPF model of the watershed and used the calibration software PEST to estimate HSPF hydrologic parameters and then perform predictive uncertainty analysis of stream flow. We used Monte-Carlo simulation to run the model with 1,000 different parameter sets and assess predictive uncertainty. In order to reduce the chance of specious parameter sets, we accounted for the relationships among parameter values by using mathematically-based regularization techniques and an estimate of the parameter covariance when generating random parameter sets. We used a novel approach to select flow data for predictive uncertainty analysis. We set aside flow data that occurred on days that bacteria samples were collected. We did not use these flows in the estimation of the model parameters. We calculated a percent uncertainty for each flow observation based 1,000 model runs. We also used several methods to visualize results with an emphasis on making the data accessible to both technical and general audiences. We will use the predictive uncertainty estimates in the next phase of our work, simulating bacteria fate and transport in the watershed.

Uncertainties in tidal theory: Implications for bloated hot Jupiters

NASA Astrophysics Data System (ADS)

Leconte, Jérémy; Chabrier, Gilles; Baraffe, Isabelle

2011-11-01

Thanks to the combination of transit photometry and radial velocity doppler measurements, we are now able to constrain theoretical models of the structure and evolution of objects in the whole mass range between icy giants and stars, including the giant planet/brown dwarf overlapping mass regime (Leconte et al. 2009). In the giant planet mass range, the significant fraction of planets showing a larger radius than predicted by the models suggests that a missing physical mechanism which is either injecting energy in the deep convective zone or reducing the net outward thermal flux is taking place in these objects. Several possibilities have been suggested for such a mechanism:•downward transport of kinetic energy originating from strong winds generated at the planet's surface (Showman & Guillot 2002),•enhanced opacity sources in hot-Jupiter atmospheres (Burrows et al. 2007),•ohmic dissipation in the ionized atmosphere (Batygin & Stevenson 2010),•(inefficient) layered or oscillatory convection in the planet's interior (Chabrier & Baraffe 2007),•Tidal heating due to circularization of the orbit, as originally suggested by Bodenheimer, Lin & Mardling (2001).Here we first review the differences between current models of tidal evolution and their uncertainties. We then revisit the viability of the tidal heating hypothesis using a tidal model which treats properly the highly eccentric and misaligned orbits commonly encountered in exoplanetary systems. We stress again that the low order expansions in eccentricity often used in constant phase lag tidal models (i.e. constant Q) necessarily yields incorrect results as soon as the (present or initial) eccentricity exceeds ~ 0.2, as can be rigorously demonstrated from Kepler's equations.

Underestimation of Variance of Predicted Health Utilities Derived from Multiattribute Utility Instruments.

PubMed

Chan, Kelvin K W; Xie, Feng; Willan, Andrew R; Pullenayegum, Eleanor M

2017-04-01

Parameter uncertainty in value sets of multiattribute utility-based instruments (MAUIs) has received little attention previously. This false precision leads to underestimation of the uncertainty of the results of cost-effectiveness analyses. The aim of this study is to examine the use of multiple imputation as a method to account for this uncertainty of MAUI scoring algorithms. We fitted a Bayesian model with random effects for respondents and health states to the data from the original US EQ-5D-3L valuation study, thereby estimating the uncertainty in the EQ-5D-3L scoring algorithm. We applied these results to EQ-5D-3L data from the Commonwealth Fund (CWF) Survey for Sick Adults ( n = 3958), comparing the standard error of the estimated mean utility in the CWF population using the predictive distribution from the Bayesian mixed-effect model (i.e., incorporating parameter uncertainty in the value set) with the standard error of the estimated mean utilities based on multiple imputation and the standard error using the conventional approach of using MAUI (i.e., ignoring uncertainty in the value set). The mean utility in the CWF population based on the predictive distribution of the Bayesian model was 0.827 with a standard error (SE) of 0.011. When utilities were derived using the conventional approach, the estimated mean utility was 0.827 with an SE of 0.003, which is only 25% of the SE based on the full predictive distribution of the mixed-effect model. Using multiple imputation with 20 imputed sets, the mean utility was 0.828 with an SE of 0.011, which is similar to the SE based on the full predictive distribution. Ignoring uncertainty of the predicted health utilities derived from MAUIs could lead to substantial underestimation of the variance of mean utilities. Multiple imputation corrects for this underestimation so that the results of cost-effectiveness analyses using MAUIs can report the correct degree of uncertainty.

Operationalising uncertainty in data and models for integrated water resources management.

PubMed

Blind, M W; Refsgaard, J C

2007-01-01

Key sources of uncertainty of importance for water resources management are (1) uncertainty in data; (2) uncertainty related to hydrological models (parameter values, model technique, model structure); and (3) uncertainty related to the context and the framing of the decision-making process. The European funded project 'Harmonised techniques and representative river basin data for assessment and use of uncertainty information in integrated water management (HarmoniRiB)' has resulted in a range of tools and methods to assess such uncertainties, focusing on items (1) and (2). The project also engaged in a number of discussions surrounding uncertainty and risk assessment in support of decision-making in water management. Based on the project's results and experiences, and on the subsequent discussions a number of conclusions can be drawn on the future needs for successful adoption of uncertainty analysis in decision support. These conclusions range from additional scientific research on specific uncertainties, dedicated guidelines for operational use to capacity building at all levels. The purpose of this paper is to elaborate on these conclusions and anchoring them in the broad objective of making uncertainty and risk assessment an essential and natural part in future decision-making processes.

Uncertainty and sensitivity analysis of fission gas behavior in engineering-scale fuel modeling

DOE PAGES

Pastore, Giovanni; Swiler, L. P.; Hales, Jason D.; ...

2014-10-12

The role of uncertainties in fission gas behavior calculations as part of engineering-scale nuclear fuel modeling is investigated using the BISON fuel performance code and a recently implemented physics-based model for the coupled fission gas release and swelling. Through the integration of BISON with the DAKOTA software, a sensitivity analysis of the results to selected model parameters is carried out based on UO2 single-pellet simulations covering different power regimes. The parameters are varied within ranges representative of the relative uncertainties and consistent with the information from the open literature. The study leads to an initial quantitative assessment of the uncertaintymore » in fission gas behavior modeling with the parameter characterization presently available. Also, the relative importance of the single parameters is evaluated. Moreover, a sensitivity analysis is carried out based on simulations of a fuel rod irradiation experiment, pointing out a significant impact of the considered uncertainties on the calculated fission gas release and cladding diametral strain. The results of the study indicate that the commonly accepted deviation between calculated and measured fission gas release by a factor of 2 approximately corresponds to the inherent modeling uncertainty at high fission gas release. Nevertheless, higher deviations may be expected for values around 10% and lower. Implications are discussed in terms of directions of research for the improved modeling of fission gas behavior for engineering purposes.« less

Designing an Educational Game with Ten Steps to Complex Learning

ERIC Educational Resources Information Center

Enfield, Jacob

2012-01-01

Few instructional design (ID) models exist which are specific for developing educational games. Moreover, those extant ID models have not been rigorously evaluated. No ID models were found which focus on educational games with complex learning objectives. "Ten Steps to Complex Learning" (TSCL) is based on the four component instructional…

Vaporization and Zonal Mixing in Performance Modeling of Advanced LOX-Methane Rockets

NASA Technical Reports Server (NTRS)

Williams, George J., Jr.; Stiegemeier, Benjamin R.

2013-01-01

Initial modeling of LOX-Methane reaction control (RCE) 100 lbf thrusters and larger, 5500 lbf thrusters with the TDK/VIPER code has shown good agreement with sea-level and altitude test data. However, the vaporization and zonal mixing upstream of the compressible flow stage of the models leveraged empirical trends to match the sea-level data. This was necessary in part because the codes are designed primarily to handle the compressible part of the flow (i.e. contraction through expansion) and in part because there was limited data on the thrusters themselves on which to base a rigorous model. A more rigorous model has been developed which includes detailed vaporization trends based on element type and geometry, radial variations in mixture ratio within each of the "zones" associated with elements and not just between zones of different element types, and, to the extent possible, updated kinetic rates. The Spray Combustion Analysis Program (SCAP) was leveraged to support assumptions in the vaporization trends. Data of both thrusters is revisited and the model maintains a good predictive capability while addressing some of the major limitations of the previous version.

Distributed Soil Moisture Estimation in a Mountainous Semiarid Basin: Constraining Soil Parameter Uncertainty through Field Studies

NASA Astrophysics Data System (ADS)

Yatheendradas, S.; Vivoni, E.

2007-12-01

A common practice in distributed hydrological modeling is to assign soil hydraulic properties based on coarse textural datasets. For semiarid regions with poor soil information, the performance of a model can be severely constrained due to the high model sensitivity to near-surface soil characteristics. Neglecting the uncertainty in soil hydraulic properties, their spatial variation and their naturally-occurring horizonation can potentially affect the modeled hydrological response. In this study, we investigate such effects using the TIN-based Real-time Integrated Basin Simulator (tRIBS) applied to the mid-sized (100 km2) Sierra Los Locos watershed in northern Sonora, Mexico. The Sierra Los Locos basin is characterized by complex mountainous terrain leading to topographic organization of soil characteristics and ecosystem distributions. We focus on simulations during the 2004 North American Monsoon Experiment (NAME) when intensive soil moisture measurements and aircraft- based soil moisture retrievals are available in the basin. Our experiments focus on soil moisture comparisons at the point, topographic transect and basin scales using a range of different soil characterizations. We compare the distributed soil moisture estimates obtained using (1) a deterministic simulation based on soil texture from coarse soil maps, (2) a set of ensemble simulations that capture soil parameter uncertainty and their spatial distribution, and (3) a set of simulations that conditions the ensemble on recent soil profile measurements. Uncertainties considered in near-surface soil characterization provide insights into their influence on the modeled uncertainty, into the value of soil profile observations, and into effective use of on-going field observations for constraining the soil moisture response uncertainty.

Uncertainty analysis of terrestrial net primary productivity and net biome productivity in China during 1901-2005

DOE PAGES

Shao, Junjiong; Zhou, Xuhui; Luo, Yiqi; ...

2016-04-28

Here, despite the importance of net primary productivity (NPP) and net biome productivity (NBP), estimates of NPP and NBP for China are highly uncertain. To investigate the main sources of uncertainty, we synthesized model estimates of NPP and NBP for China from published literature and the Multi-scale Synthesis and Terrestrial Model Intercomparison Project (MsTMIP). The literature-based results showed that total NPP and NBP in China were 3.35 ± 1.25 and 0.14 ± 0.094 Pg C yr –1, respectively. Classification and regression tree analysis based on literature data showed that model type was the primary source of the uncertainty, explaining 36%more » and 64% of the variance in NPP and NBP, respectively. Spatiotemporal scales, land cover conditions, inclusion of the N cycle, and effects of N addition also contributed to the overall uncertainty. Results based on the MsTMIP data suggested that model structures were overwhelmingly important (>90%) for the overall uncertainty compared to simulations with different combinations of time-varying global change factors. The interannual pattern of NPP was similar among diverse studies and increased by 0.012 Pg C yr –1 during 1981–2000. In addition, high uncertainty in China's NPP occurred in areas with high productivity, whereas NBP showed the opposite pattern. Our results suggest that to significantly reduce uncertainty in estimated NPP and NBP, model structures should be substantially tested on the basis of empirical results. To this end, coordinated distributed experiments with multiple global change factors might be a practical approach that can validate specific structures of different models.« less

Uncertainty analysis of terrestrial net primary productivity and net biome productivity in China during 1901-2005

DOE Office of Scientific and Technical Information (OSTI.GOV)

Shao, Junjiong; Zhou, Xuhui; Luo, Yiqi

Here, despite the importance of net primary productivity (NPP) and net biome productivity (NBP), estimates of NPP and NBP for China are highly uncertain. To investigate the main sources of uncertainty, we synthesized model estimates of NPP and NBP for China from published literature and the Multi-scale Synthesis and Terrestrial Model Intercomparison Project (MsTMIP). The literature-based results showed that total NPP and NBP in China were 3.35 ± 1.25 and 0.14 ± 0.094 Pg C yr –1, respectively. Classification and regression tree analysis based on literature data showed that model type was the primary source of the uncertainty, explaining 36%more » and 64% of the variance in NPP and NBP, respectively. Spatiotemporal scales, land cover conditions, inclusion of the N cycle, and effects of N addition also contributed to the overall uncertainty. Results based on the MsTMIP data suggested that model structures were overwhelmingly important (>90%) for the overall uncertainty compared to simulations with different combinations of time-varying global change factors. The interannual pattern of NPP was similar among diverse studies and increased by 0.012 Pg C yr –1 during 1981–2000. In addition, high uncertainty in China's NPP occurred in areas with high productivity, whereas NBP showed the opposite pattern. Our results suggest that to significantly reduce uncertainty in estimated NPP and NBP, model structures should be substantially tested on the basis of empirical results. To this end, coordinated distributed experiments with multiple global change factors might be a practical approach that can validate specific structures of different models.« less

Challenges of Iranian Adolescents for Preventing Dental Caries

PubMed Central

Fallahi, Arezoo; Ghofranipour, Fazlollah; Ahmadi, Fazlollah; Malekafzali, Beheshteh; Hajizadeh, Ebrahim

2014-01-01

Background: Oral health plays a vital role in people’s general health and well-being. With regard to the costly treatments of oral diseases, preventive programs need to be designed for dental caries based on children’s perspectives. Objectives: The purpose of this study was to describe and explore challenges for caring dental health based on children’s perspectives. Patients and Methods: A qualitative design with content analysis approach was applied to collect and analyze the perspectives of students about factors influencing oral and dental care. Eighteen Iranian students in 8 guidance schools were chosen through the purposive sampling. Semi-structured interviews were held for data gathering. In order to support the validity and rigor of the data, different criteria such as acceptability, confirmability, and transferability were utilized. Results: During data analysis, four main themes developed: “barriers to dental health,” “maintaining dental health,” “uncertainty in decision-making” and “supportive factors”. “Uncertainty in decision-making” and “barriers to dental health” were the main challenges for preventing dental caries among adolescents. Conclusions: “Certainty in decision-making” to maintain dental health depends on overcoming the barriers of dental health. Further research is needed to confirm the findings of this study. PMID:25593720

Testing the robustness of management decisions to uncertainty: Everglades restoration scenarios.

PubMed

Fuller, Michael M; Gross, Louis J; Duke-Sylvester, Scott M; Palmer, Mark

2008-04-01

To effectively manage large natural reserves, resource managers must prepare for future contingencies while balancing the often conflicting priorities of different stakeholders. To deal with these issues, managers routinely employ models to project the response of ecosystems to different scenarios that represent alternative management plans or environmental forecasts. Scenario analysis is often used to rank such alternatives to aid the decision making process. However, model projections are subject to uncertainty in assumptions about model structure, parameter values, environmental inputs, and subcomponent interactions. We introduce an approach for testing the robustness of model-based management decisions to the uncertainty inherent in complex ecological models and their inputs. We use relative assessment to quantify the relative impacts of uncertainty on scenario ranking. To illustrate our approach we consider uncertainty in parameter values and uncertainty in input data, with specific examples drawn from the Florida Everglades restoration project. Our examples focus on two alternative 30-year hydrologic management plans that were ranked according to their overall impacts on wildlife habitat potential. We tested the assumption that varying the parameter settings and inputs of habitat index models does not change the rank order of the hydrologic plans. We compared the average projected index of habitat potential for four endemic species and two wading-bird guilds to rank the plans, accounting for variations in parameter settings and water level inputs associated with hypothetical future climates. Indices of habitat potential were based on projections from spatially explicit models that are closely tied to hydrology. For the American alligator, the rank order of the hydrologic plans was unaffected by substantial variation in model parameters. By contrast, simulated major shifts in water levels led to reversals in the ranks of the hydrologic plans in 24.1-30.6% of the projections for the wading bird guilds and several individual species. By exposing the differential effects of uncertainty, relative assessment can help resource managers assess the robustness of scenario choice in model-based policy decisions.

Expert judgement and uncertainty quantification for climate change

NASA Astrophysics Data System (ADS)

Oppenheimer, Michael; Little, Christopher M.; Cooke, Roger M.

2016-05-01

Expert judgement is an unavoidable element of the process-based numerical models used for climate change projections, and the statistical approaches used to characterize uncertainty across model ensembles. Here, we highlight the need for formalized approaches to unifying numerical modelling with expert judgement in order to facilitate characterization of uncertainty in a reproducible, consistent and transparent fashion. As an example, we use probabilistic inversion, a well-established technique used in many other applications outside of climate change, to fuse two recent analyses of twenty-first century Antarctic ice loss. Probabilistic inversion is but one of many possible approaches to formalizing the role of expert judgement, and the Antarctic ice sheet is only one possible climate-related application. We recommend indicators or signposts that characterize successful science-based uncertainty quantification.

Uncertainty prediction for PUB

NASA Astrophysics Data System (ADS)

Mendiondo, E. M.; Tucci, C. M.; Clarke, R. T.; Castro, N. M.; Goldenfum, J. A.; Chevallier, P.

2003-04-01

IAHS’ initiative of Prediction in Ungaged Basins (PUB) attempts to integrate monitoring needs and uncertainty prediction for river basins. This paper outlines alternative ways of uncertainty prediction which could be linked with new blueprints for PUB, thereby showing how equifinality-based models should be grasped using practical strategies of gauging like the Nested Catchment Experiment (NCE). Uncertainty prediction is discussed from observations of Potiribu Project, which is a NCE layout at representative basins of a suptropical biome of 300,000 km2 in South America. Uncertainty prediction is assessed at the microscale (1 m2 plots), at the hillslope (0,125 km2) and at the mesoscale (0,125 - 560 km2). At the microscale, uncertainty-based models are constrained by temporal variations of state variables with changing likelihood surfaces of experiments using Green-Ampt model. Two new blueprints emerged from this NCE for PUB: (1) the Scale Transferability Scheme (STS) at the hillslope scale and the Integrating Process Hypothesis (IPH) at the mesoscale. The STS integrates a multi-dimensional scaling with similarity thresholds, as a generalization of the Representative Elementary Area (REA), using spatial correlation from point (distributed) to area (lumped) process. In this way, STS addresses uncertainty-bounds of model parameters, into an upscaling process at the hillslope. In the other hand, the IPH approach regionalizes synthetic hydrographs, thereby interpreting the uncertainty bounds of streamflow variables. Multiscale evidences from Potiribu NCE layout show novel pathways of uncertainty prediction under a PUB perspective in representative basins of world biomes.

Data and Model Uncertainties associated with Biogeochemical Groundwater Remediation and their impact on Decision Analysis

NASA Astrophysics Data System (ADS)

Pandey, S.; Vesselinov, V. V.; O'Malley, D.; Karra, S.; Hansen, S. K.

2016-12-01

Models and data are used to characterize the extent of contamination and remediation, both of which are dependent upon the complex interplay of processes ranging from geochemical reactions, microbial metabolism, and pore-scale mixing to heterogeneous flow and external forcings. Characterization is wrought with important uncertainties related to the model itself (e.g. conceptualization, model implementation, parameter values) and the data used for model calibration (e.g. sparsity, measurement errors). This research consists of two primary components: (1) Developing numerical models that incorporate the complex hydrogeology and biogeochemistry that drive groundwater contamination and remediation; (2) Utilizing novel techniques for data/model-based analyses (such as parameter calibration and uncertainty quantification) to aid in decision support for optimal uncertainty reduction related to characterization and remediation of contaminated sites. The reactive transport models are developed using PFLOTRAN and are capable of simulating a wide range of biogeochemical and hydrologic conditions that affect the migration and remediation of groundwater contaminants under diverse field conditions. Data/model-based analyses are achieved using MADS, which utilizes Bayesian methods and Information Gap theory to address the data/model uncertainties discussed above. We also use these tools to evaluate different models, which vary in complexity, in order to weigh and rank models based on model accuracy (in representation of existing observations), model parsimony (everything else being equal, models with smaller number of model parameters are preferred), and model robustness (related to model predictions of unknown future states). These analyses are carried out on synthetic problems, but are directly related to real-world problems; for example, the modeled processes and data inputs are consistent with the conditions at the Los Alamos National Laboratory contamination sites (RDX and Chromium).

Advanced Atmospheric Ensemble Modeling Techniques

DOE Office of Scientific and Technical Information (OSTI.GOV)

Buckley, R.; Chiswell, S.; Kurzeja, R.

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

Ice particle morphology and microphysical properties of cirrus clouds inferred from combined CALIOP-IIR measurements

NASA Astrophysics Data System (ADS)

Saito, Masanori; Iwabuchi, Hironobu; Yang, Ping; Tang, Guanglin; King, Michael D.; Sekiguchi, Miho

2017-04-01

Ice particle morphology and microphysical properties of cirrus clouds are essential for assessing radiative forcing associated with these clouds. We develop an optimal estimation-based algorithm to infer cirrus cloud optical thickness (COT), cloud effective radius (CER), plate fraction including quasi-horizontally oriented plates (HOPs), and the degree of surface roughness from the Cloud Aerosol Lidar with Orthogonal Polarization (CALIOP) and the Infrared Imaging Radiometer (IIR) on the Cloud Aerosol Lidar and Infrared Pathfinder Satellite Observation (CALIPSO) platform. A simple but realistic ice particle model is used, and the relevant bulk optical properties are computed using state-of-the-art light-scattering computational capabilities. Rigorous estimation of uncertainties related to surface properties, atmospheric gases, and cloud heterogeneity is performed. The results based on the present method show that COTs are quite consistent with other satellite products and CERs essentially agree with the other counterparts. A 1 month global analysis for April 2007, in which CALIPSO off-nadir angle is 0.3°, shows that the HOP has significant temperature-dependence and is critical to the lidar ratio when cloud temperature is warmer than -40°C. The lidar ratio is calculated from the bulk optical properties based on the inferred parameters, showing robust temperature dependence. The median lidar ratio of cirrus clouds is 27-31 sr over the globe.

A data-driven SVR model for long-term runoff prediction and uncertainty analysis based on the Bayesian framework

NASA Astrophysics Data System (ADS)

Liang, Zhongmin; Li, Yujie; Hu, Yiming; Li, Binquan; Wang, Jun

2017-06-01

Accurate and reliable long-term forecasting plays an important role in water resources management and utilization. In this paper, a hybrid model called SVR-HUP is presented to predict long-term runoff and quantify the prediction uncertainty. The model is created based on three steps. First, appropriate predictors are selected according to the correlations between meteorological factors and runoff. Second, a support vector regression (SVR) model is structured and optimized based on the LibSVM toolbox and a genetic algorithm. Finally, using forecasted and observed runoff, a hydrologic uncertainty processor (HUP) based on a Bayesian framework is used to estimate the posterior probability distribution of the simulated values, and the associated uncertainty of prediction was quantitatively analyzed. Six precision evaluation indexes, including the correlation coefficient (CC), relative root mean square error (RRMSE), relative error (RE), mean absolute percentage error (MAPE), Nash-Sutcliffe efficiency (NSE), and qualification rate (QR), are used to measure the prediction accuracy. As a case study, the proposed approach is applied in the Han River basin, South Central China. Three types of SVR models are established to forecast the monthly, flood season and annual runoff volumes. The results indicate that SVR yields satisfactory accuracy and reliability at all three scales. In addition, the results suggest that the HUP cannot only quantify the uncertainty of prediction based on a confidence interval but also provide a more accurate single value prediction than the initial SVR forecasting result. Thus, the SVR-HUP model provides an alternative method for long-term runoff forecasting.

Uncertainty Analysis via Failure Domain Characterization: Unrestricted Requirement Functions

NASA Technical Reports Server (NTRS)

Crespo, Luis G.; Kenny, Sean P.; Giesy, Daniel P.

2011-01-01

This paper proposes an uncertainty analysis framework based on the characterization of the uncertain parameter space. This characterization enables the identification of worst-case uncertainty combinations and the approximation of the failure and safe domains with a high level of accuracy. Because these approximations are comprised of subsets of readily computable probability, they enable the calculation of arbitrarily tight upper and lower bounds to the failure probability. The methods developed herein, which are based on nonlinear constrained optimization, are applicable to requirement functions whose functional dependency on the uncertainty is arbitrary and whose explicit form may even be unknown. Some of the most prominent features of the methodology are the substantial desensitization of the calculations from the assumed uncertainty model (i.e., the probability distribution describing the uncertainty) as well as the accommodation for changes in such a model with a practically insignificant amount of computational effort.

Uncertainty analysis on simple mass balance model to calculate critical loads for soil acidity.

PubMed

Li, Harbin; McNulty, Steven G

2007-10-01

Simple mass balance equations (SMBE) of critical acid loads (CAL) in forest soil were developed to assess potential risks of air pollutants to ecosystems. However, to apply SMBE reliably at large scales, SMBE must be tested for adequacy and uncertainty. Our goal was to provide a detailed analysis of uncertainty in SMBE so that sound strategies for scaling up CAL estimates to the national scale could be developed. Specifically, we wanted to quantify CAL uncertainty under natural variability in 17 model parameters, and determine their relative contributions in predicting CAL. Results indicated that uncertainty in CAL came primarily from components of base cation weathering (BC(w); 49%) and acid neutralizing capacity (46%), whereas the most critical parameters were BC(w) base rate (62%), soil depth (20%), and soil temperature (11%). Thus, improvements in estimates of these factors are crucial to reducing uncertainty and successfully scaling up SMBE for national assessments of CAL.

Nutrient non-equivalence: Does restricting high-potassium plant foods help to prevent hyperkalemia in hemodialysis patients?

PubMed Central

St-Jules, DE; Goldfarb, DS; Sevick, MA

2016-01-01

Hemodialysis patients are often advised to limit their intake of high-potassium foods to help manage hyperkalemia. However, the benefits of this practice are entirely theoretical and not supported by rigorous randomized controlled trials. The hypothesis that potassium restriction is useful is based on the assumption that different sources of dietary potassium are therapeutically equivalent. In fact, animal and plant sources of potassium may differ in their potential to contribute to hyperkalemia. In this commentary, we summarize the historical research basis for limiting high-potassium foods. Ultimately, we conclude that this approach is not evidence-based and may actually present harm to patients. However, given the uncertainty arising from the paucity of conclusive data, we agree that until the appropriate intervention studies are conducted, practitioners should continue to advise restriction of high-potassium foods. PMID:26975777

Nutrient Non-equivalence: Does Restricting High-Potassium Plant Foods Help to Prevent Hyperkalemia in Hemodialysis Patients?

PubMed

St-Jules, David E; Goldfarb, David S; Sevick, Mary Ann

2016-09-01

Hemodialysis patients are often advised to limit their intake of high-potassium foods to help manage hyperkalemia. However, the benefits of this practice are entirely theoretical and not supported by rigorous randomized controlled trials. The hypothesis that potassium restriction is useful is based on the assumption that different sources of dietary potassium are therapeutically equivalent. In fact, animal and plant sources of potassium may differ in their potential to contribute to hyperkalemia. In this commentary, we summarize the historical research basis for limiting high-potassium foods. Ultimately, we conclude that this approach is not evidence-based and may actually present harm to patients. However, given the uncertainty arising from the paucity of conclusive data, we agree that until the appropriate intervention studies are conducted, practitioners should continue to advise restriction of high-potassium foods. Copyright © 2016 National Kidney Foundation, Inc. Published by Elsevier Inc. All rights reserved.

Can reduction of uncertainties in cervix cancer brachytherapy potentially improve clinical outcome?

PubMed

Nesvacil, Nicole; Tanderup, Kari; Lindegaard, Jacob C; Pötter, Richard; Kirisits, Christian

2016-09-01

The aim of this study was to quantify the impact of different types and magnitudes of dosimetric uncertainties in cervix cancer brachytherapy (BT) on tumour control probability (TCP) and normal tissue complication probability (NTCP) curves. A dose-response simulation study was based on systematic and random dose uncertainties and TCP/NTCP models for CTV and rectum. Large patient cohorts were simulated assuming different levels of dosimetric uncertainties. TCP and NTCP were computed, based on the planned doses, the simulated dose uncertainty, and an underlying TCP/NTCP model. Systematic uncertainties of 3-20% and random uncertainties with a 5-30% standard deviation per BT fraction were analysed. Systematic dose uncertainties of 5% lead to a 1% decrease/increase of TCP/NTCP, while random uncertainties of 10% had negligible impact on the dose-response curve at clinically relevant dose levels for target and OAR. Random OAR dose uncertainties of 30% resulted in an NTCP increase of 3-4% for planned doses of 70-80Gy EQD2. TCP is robust to dosimetric uncertainties when dose prescription is in the more flat region of the dose-response curve at doses >75Gy. For OARs, improved clinical outcome is expected by reduction of uncertainties via sophisticated dose delivery and treatment verification. Copyright © 2016 Elsevier Ireland Ltd. All rights reserved.

Characterizing uncertainty and variability in physiologically based pharmacokinetic models: state of the science and needs for research and implementation.

PubMed

Barton, Hugh A; Chiu, Weihsueh A; Setzer, R Woodrow; Andersen, Melvin E; Bailer, A John; Bois, Frédéric Y; Dewoskin, Robert S; Hays, Sean; Johanson, Gunnar; Jones, Nancy; Loizou, George; Macphail, Robert C; Portier, Christopher J; Spendiff, Martin; Tan, Yu-Mei

2007-10-01

Physiologically based pharmacokinetic (PBPK) models are used in mode-of-action based risk and safety assessments to estimate internal dosimetry in animals and humans. When used in risk assessment, these models can provide a basis for extrapolating between species, doses, and exposure routes or for justifying nondefault values for uncertainty factors. Characterization of uncertainty and variability is increasingly recognized as important for risk assessment; this represents a continuing challenge for both PBPK modelers and users. Current practices show significant progress in specifying deterministic biological models and nondeterministic (often statistical) models, estimating parameters using diverse data sets from multiple sources, using them to make predictions, and characterizing uncertainty and variability of model parameters and predictions. The International Workshop on Uncertainty and Variability in PBPK Models, held 31 Oct-2 Nov 2006, identified the state-of-the-science, needed changes in practice and implementation, and research priorities. For the short term, these include (1) multidisciplinary teams to integrate deterministic and nondeterministic/statistical models; (2) broader use of sensitivity analyses, including for structural and global (rather than local) parameter changes; and (3) enhanced transparency and reproducibility through improved documentation of model structure(s), parameter values, sensitivity and other analyses, and supporting, discrepant, or excluded data. Longer-term needs include (1) theoretical and practical methodological improvements for nondeterministic/statistical modeling; (2) better methods for evaluating alternative model structures; (3) peer-reviewed databases of parameters and covariates, and their distributions; (4) expanded coverage of PBPK models across chemicals with different properties; and (5) training and reference materials, such as cases studies, bibliographies/glossaries, model repositories, and enhanced software. The multidisciplinary dialogue initiated by this Workshop will foster the collaboration, research, data collection, and training necessary to make characterizing uncertainty and variability a standard practice in PBPK modeling and risk assessment.

Testing Atmospheric Retrieval Modeling Assumptions for Transiting Planet Atmospheres: Preparatory science for the James Webb Space Telescope and beyond.

NASA Astrophysics Data System (ADS)

Line, Michael

The field of transiting exoplanet atmosphere characterization has grown considerably over the past decade given the wealth of photometric and spectroscopic data from the Hubble and Spitzer space telescopes. In order to interpret these data, atmospheric models combined with Bayesian approaches are required. From spectra, these approaches permit us to infer fundamental atmospheric properties and how their compositions can relate back to planet formation. However, such approaches must make a wide range of assumptions regarding the physics/parameterizations included in the model atmospheres. There has yet to be a comprehensive investigation exploring how these model assumptions influence our interpretations of exoplanetary spectra. Understanding the impact of these assumptions is especially important since the James Webb Space Telescope (JWST) is expected to invest a substantial portion of its time observing transiting planet atmospheres. It is therefore prudent to optimize and enhance our tools to maximize the scientific return from the revolutionary data to come. The primary goal of the proposed work is to determine the pieces of information we can robustly learn from transiting planet spectra as obtained by JWST and other future, space-based platforms, by investigating commonly overlooked model assumptions. We propose to explore the following effects and how they impact our ability to infer exoplanet atmospheric properties: 1. Stellar/Planetary Uncertainties: Transit/occultation eclipse depths and subsequent planetary spectra are measured relative to their host stars. How do stellar uncertainties, on radius, effective temperature, metallicity, and gravity, as well as uncertainties in the planetary radius and gravity, propagate into the uncertainties on atmospheric composition and thermal structure? Will these uncertainties significantly bias our atmospheric interpretations? Is it possible to use the relative measurements of the planetary spectra to provide additional constraints on the stellar properties? 2. The "1D" Assumption: Atmospheres are inherently three-dimensional. Many exoplanet atmosphere models, especially within retrieval frameworks, assume 1D physics and chemistry when interpreting spectra. How does this "1D" atmosphere assumption bias our interpretation of exoplanet spectra? Do we have to consider global temperature variations such as day-night contrasts or hot spots? What about spatially inhomogeneous molecular abundances and clouds? How will this change our interpretations of phase resolved spectra? 3. Clouds/Hazes: Understanding how clouds/hazes impact transit spectra is absolutely critical if we are to obtain proper estimates of basic atmospheric quantities. How do the assumptions in cloud physics bias our inferences of molecular abundances in transmission? What kind of data (wavelengths, signal-to-noise, resolution) do we need to infer cloud composition, vertical extent, spatial distribution (patchy or global), and size distributions? The proposed work is relevant and timely to the scope of the NASA Exoplanet Research program. The proposed work aims to further develop the critical theoretical modeling tools required to rigorously interpret transiting exoplanet atmosphere data in order to maximize the science return from JWST and beyond. This work will serve as a benchmark study for defining the data (wavelength ranges, signal-to-noises, and resolutions) required from a modeling perspective to "characterize exoplanets and their atmospheres in order to inform target and operational choices for current NASA missions, and/or targeting, operational, and formulation data for future NASA observatories". Doing so will allow us to better "understand the chemical and physical processes of exoplanets (their atmospheres)" which will ultimately " improve understanding of the origins of exoplanetary systems" through robust planetary elemental abundance determinations.

A reduced-order adaptive neuro-fuzzy inference system model as a software sensor for rapid estimation of five-day biochemical oxygen demand

NASA Astrophysics Data System (ADS)

Noori, Roohollah; Safavi, Salman; Nateghi Shahrokni, Seyyed Afshin

2013-07-01

The five-day biochemical oxygen demand (BOD5) is one of the key parameters in water quality management. In this study, a novel approach, i.e., reduced-order adaptive neuro-fuzzy inference system (ROANFIS) model was developed for rapid estimation of BOD5. In addition, an uncertainty analysis of adaptive neuro-fuzzy inference system (ANFIS) and ROANFIS models was carried out based on Monte-Carlo simulation. Accuracy analysis of ANFIS and ROANFIS models based on both developed discrepancy ratio and threshold statistics revealed that the selected ROANFIS model was superior. Pearson correlation coefficient (R) and root mean square error for the best fitted ROANFIS model were 0.96 and 7.12, respectively. Furthermore, uncertainty analysis of the developed models indicated that the selected ROANFIS had less uncertainty than the ANFIS model and accurately forecasted BOD5 in the Sefidrood River Basin. Besides, the uncertainty analysis also showed that bracketed predictions by 95% confidence bound and d-factor in the testing steps for the selected ROANFIS model were 94% and 0.83, respectively.

Towards the next generation of climate change assessment: learning from past experiences to inform a sustainable future

NASA Astrophysics Data System (ADS)

Mach, K. J.; Field, C. B.

2017-12-01

Over decades, assessment by the Intergovernmental Panel on Climate Change and many others has bolstered understanding of the climate problem: unequivocal warming, pervasive impacts, and serious risks from continued high emissions of heat-trapping gases. Societies are increasingly responding with early actions to decarbonize energy systems and prepare for impacts. This emerging era of climate solutions creates a need for new approaches to assessment that emphasize learning from ongoing real-world experiences and that help close the gap between aspirations and the pace of progress. Against this backdrop, the presentation will take stock of recent advances and challenges in assessment, especially drawing from analysis of climate change assessment. Four assessment priorities will be considered: (1) integrating diverse evidence including quantitative and qualitative results, (2) applying rigorous expert judgment in evaluating knowledge and uncertainties, (3) exploring widely ranging futures and their connections to ongoing choices and actions, and (4) incorporating interactions among experts and decision-makers in assessment processes. Across these assessment priorities, the presentation will critique both opportunities and pitfalls, outlining possibilities for future experimentation, innovation, and learning. It will evaluate, in particular, lessons from risk-based approaches; strategies for transparently acknowledging persistent uncertainties and contested priorities; ways to minimize biases and foster creativity in expert judgments; scenario-based assessment of surprises, deep uncertainties, and decision-making implications; and opportunities for broadening the conception of expertise and engaging different decision-makers and stakeholders. Overall, these approaches can advance assessment products and processes as a basis for sustained dialogue supporting decision-making.

Development of the X-33 Aerodynamic Uncertainty Model

NASA Technical Reports Server (NTRS)

Cobleigh, Brent R.

1998-01-01

An aerodynamic uncertainty model for the X-33 single-stage-to-orbit demonstrator aircraft has been developed at NASA Dryden Flight Research Center. The model is based on comparisons of historical flight test estimates to preflight wind-tunnel and analysis code predictions of vehicle aerodynamics documented during six lifting-body aircraft and the Space Shuttle Orbiter flight programs. The lifting-body and Orbiter data were used to define an appropriate uncertainty magnitude in the subsonic and supersonic flight regions, and the Orbiter data were used to extend the database to hypersonic Mach numbers. The uncertainty data consist of increments or percentage variations in the important aerodynamic coefficients and derivatives as a function of Mach number along a nominal trajectory. The uncertainty models will be used to perform linear analysis of the X-33 flight control system and Monte Carlo mission simulation studies. Because the X-33 aerodynamic uncertainty model was developed exclusively using historical data rather than X-33 specific characteristics, the model may be useful for other lifting-body studies.

Constraining ozone-precursor responsiveness using ambient measurements

EPA Science Inventory

This study develops probabilistic estimates of ozone (O3) sensitivities to precursoremissions by incorporating uncertainties in photochemical modeling and evaluating modelperformance based on ground-level observations of O3 and oxides of nitrogen (NOx).Uncertainties in model form...

Uncertainty Quantification of Turbulence Model Closure Coefficients for Transonic Wall-Bounded Flows

NASA Technical Reports Server (NTRS)

Schaefer, John; West, Thomas; Hosder, Serhat; Rumsey, Christopher; Carlson, Jan-Renee; Kleb, William

2015-01-01

The goal of this work was to quantify the uncertainty and sensitivity of commonly used turbulence models in Reynolds-Averaged Navier-Stokes codes due to uncertainty in the values of closure coefficients for transonic, wall-bounded flows and to rank the contribution of each coefficient to uncertainty in various output flow quantities of interest. Specifically, uncertainty quantification of turbulence model closure coefficients was performed for transonic flow over an axisymmetric bump at zero degrees angle of attack and the RAE 2822 transonic airfoil at a lift coefficient of 0.744. Three turbulence models were considered: the Spalart-Allmaras Model, Wilcox (2006) k-w Model, and the Menter Shear-Stress Trans- port Model. The FUN3D code developed by NASA Langley Research Center was used as the flow solver. The uncertainty quantification analysis employed stochastic expansions based on non-intrusive polynomial chaos as an efficient means of uncertainty propagation. Several integrated and point-quantities are considered as uncertain outputs for both CFD problems. All closure coefficients were treated as epistemic uncertain variables represented with intervals. Sobol indices were used to rank the relative contributions of each closure coefficient to the total uncertainty in the output quantities of interest. This study identified a number of closure coefficients for each turbulence model for which more information will reduce the amount of uncertainty in the output significantly for transonic, wall-bounded flows.

Assessments of species' vulnerability to climate change: From pseudo to science

USGS Publications Warehouse

Wade, Alisa A.; Hand, Brian K.; Kovach, Ryan; Muhlfeld, Clint C.; Waples, Robin S.; Luikart, Gordon

2017-01-01

Climate change vulnerability assessments (CCVAs) are important tools to plan for and mitigate potential impacts of climate change. However, CCVAs often lack scientific rigor, which can ultimately lead to poor conservation prioritization and associated ecological and economic costs. We discuss the need to improve comparability and consistency of CCVAs and either validate their findings or improve assessment of CCVA uncertainty and sensitivity to methodological assumptions.

An Efficient Deterministic Approach to Model-based Prediction Uncertainty Estimation

DTIC Science & Technology

2012-09-01

94035, USA abhinav.saxena@nasa.gov ABSTRACT Prognostics deals with the prediction of the end of life ( EOL ) of a system. EOL is a random variable, due...future evolution of the system, accumulating additional uncertainty into the predicted EOL . Prediction algorithms that do not account for these sources of...uncertainty are misrepresenting the EOL and can lead to poor decisions based on their results. In this paper, we explore the impact of uncertainty in

A Hierarchical Multi-Model Approach for Uncertainty Segregation, Prioritization and Comparative Evaluation of Competing Modeling Propositions

NASA Astrophysics Data System (ADS)

Tsai, F. T.; Elshall, A. S.; Hanor, J. S.

2012-12-01

Subsurface modeling is challenging because of many possible competing propositions for each uncertain model component. How can we judge that we are selecting the correct proposition for an uncertain model component out of numerous competing propositions? How can we bridge the gap between synthetic mental principles such as mathematical expressions on one hand, and empirical observation such as observation data on the other hand when uncertainty exists on both sides? In this study, we introduce hierarchical Bayesian model averaging (HBMA) as a multi-model (multi-proposition) framework to represent our current state of knowledge and decision for hydrogeological structure modeling. The HBMA framework allows for segregating and prioritizing different sources of uncertainty, and for comparative evaluation of competing propositions for each source of uncertainty. We applied the HBMA to a study of hydrostratigraphy and uncertainty propagation of the Southern Hills aquifer system in the Baton Rouge area, Louisiana. We used geophysical data for hydrogeological structure construction through indictor hydrostratigraphy method and used lithologic data from drillers' logs for model structure calibration. However, due to uncertainty in model data, structure and parameters, multiple possible hydrostratigraphic models were produced and calibrated. The study considered four sources of uncertainties. To evaluate mathematical structure uncertainty, the study considered three different variogram models and two geological stationarity assumptions. With respect to geological structure uncertainty, the study considered two geological structures with respect to the Denham Springs-Scotlandville fault. With respect to data uncertainty, the study considered two calibration data sets. These four sources of uncertainty with their corresponding competing modeling propositions resulted in 24 calibrated models. The results showed that by segregating different sources of uncertainty, HBMA analysis provided insights on uncertainty priorities and propagation. In addition, it assisted in evaluating the relative importance of competing modeling propositions for each uncertain model component. By being able to dissect the uncertain model components and provide weighted representation of the competing propositions for each uncertain model component based on the background knowledge, the HBMA functions as an epistemic framework for advancing knowledge about the system under study.

Parametric uncertainties in global model simulations of black carbon column mass concentration

NASA Astrophysics Data System (ADS)

Pearce, Hana; Lee, Lindsay; Reddington, Carly; Carslaw, Ken; Mann, Graham

2016-04-01

Previous studies have deduced that the annual mean direct radiative forcing from black carbon (BC) aerosol may regionally be up to 5 W m-2 larger than expected due to underestimation of global atmospheric BC absorption in models. We have identified the magnitude and important sources of parametric uncertainty in simulations of BC column mass concentration from a global aerosol microphysics model (GLOMAP-Mode). A variance-based uncertainty analysis of 28 parameters has been performed, based on statistical emulators trained on model output from GLOMAP-Mode. This is the largest number of uncertain model parameters to be considered in a BC uncertainty analysis to date and covers primary aerosol emissions, microphysical processes and structural parameters related to the aerosol size distribution. We will present several recommendations for further research to improve the fidelity of simulated BC. In brief, we find that the standard deviation around the simulated mean annual BC column mass concentration varies globally between 2.5 x 10-9 g cm-2 in remote marine regions and 1.25 x 10-6 g cm-2 near emission sources due to parameter uncertainty Between 60 and 90% of the variance over source regions is due to uncertainty associated with primary BC emission fluxes, including biomass burning, fossil fuel and biofuel emissions. While the contributions to BC column uncertainty from microphysical processes, for example those related to dry and wet deposition, are increased over remote regions, we find that emissions still make an important contribution in these areas. It is likely, however, that the importance of structural model error, i.e. differences between models, is greater than parametric uncertainty. We have extended our analysis to emulate vertical BC profiles at several locations in the mid-Pacific Ocean and identify the parameters contributing to uncertainty in the vertical distribution of black carbon at these locations. We will present preliminary comparisons of emulated BC vertical profiles from the AeroCom multi-model ensemble and Hiaper Pole-to-Pole (HIPPO) observations.

Ensemble-based uncertainty quantification for coordination and control of thermostatically controlled loads

DOE Office of Scientific and Technical Information (OSTI.GOV)

Li, Weixuan; Lian, Jianming; Engel, Dave

2017-07-27

This paper presents a general uncertainty quantification (UQ) framework that provides a systematic analysis of the uncertainty involved in the modeling of a control system, and helps to improve the performance of a control strategy.

Addressing uncertainty in modelling cumulative impacts within maritime spatial planning in the Adriatic and Ionian region.

PubMed

Gissi, Elena; Menegon, Stefano; Sarretta, Alessandro; Appiotti, Federica; Maragno, Denis; Vianello, Andrea; Depellegrin, Daniel; Venier, Chiara; Barbanti, Andrea

2017-01-01

Maritime spatial planning (MSP) is envisaged as a tool to apply an ecosystem-based approach to the marine and coastal realms, aiming at ensuring that the collective pressure of human activities is kept within acceptable limits. Cumulative impacts (CI) assessment can support science-based MSP, in order to understand the existing and potential impacts of human uses on the marine environment. A CI assessment includes several sources of uncertainty that can hinder the correct interpretation of its results if not explicitly incorporated in the decision-making process. This study proposes a three-level methodology to perform a general uncertainty analysis integrated with the CI assessment for MSP, applied to the Adriatic and Ionian Region (AIR). We describe the nature and level of uncertainty with the help of expert judgement and elicitation to include all of the possible sources of uncertainty related to the CI model with assumptions and gaps related to the case-based MSP process in the AIR. Next, we use the results to tailor the global uncertainty analysis to spatially describe the uncertainty distribution and variations of the CI scores dependent on the CI model factors. The results show the variability of the uncertainty in the AIR, with only limited portions robustly identified as the most or the least impacted areas under multiple model factors hypothesis. The results are discussed for the level and type of reliable information and insights they provide to decision-making. The most significant uncertainty factors are identified to facilitate the adaptive MSP process and to establish research priorities to fill knowledge gaps for subsequent planning cycles. The method aims to depict the potential CI effects, as well as the extent and spatial variation of the data and scientific uncertainty; therefore, this method constitutes a suitable tool to inform the potential establishment of the precautionary principle in MSP.

Comparing WSA coronal and solar wind model predictions driven by line-of-sight and vector HMI ADAPT maps

NASA Astrophysics Data System (ADS)

Arge, C. N.; Henney, C. J.; Shurkin, K.; Wallace, S.

2017-12-01

As the primary input to nearly all coronal models, reliable estimates of the global solar photospheric magnetic field distribution are critical for accurate modeling and understanding of solar and heliospheric magnetic fields. The Air Force Data Assimilative Photospheric flux Transport (ADAPT) model generates synchronic (i.e., globally instantaneous) maps by evolving observed solar magnetic flux using relatively well understood transport processes when measurements are not available and then updating modeled flux with new observations (available from both the Earth and the far-side of the Sun) using data assimilation methods that rigorously take into account model and observational uncertainties. ADAPT is capable of assimilating line-of-sight and vector magnetic field data from all observatory sources including the expected photospheric vector magnetograms from the Polarimetric and Helioseismic Imager (PHI) on the Solar Orbiter, as well as those generated using helioseismic methods. This paper compares Wang-Sheeley-Arge (WSA) coronal and solar wind modeling results at Earth and STEREO A & B using ADAPT input model maps derived from both line-of-site and vector SDO/HMI magnetograms that include methods for incorporating observations of a large, newly emerged (July 2010) far-side active region (AR11087).

Modeling of profilometry with laser focus sensors

NASA Astrophysics Data System (ADS)

Bischoff, Jörg; Manske, Eberhard; Baitinger, Henner

2011-05-01

Metrology is of paramount importance in submicron patterning. Particularly, line width and overlay have to be measured very accurately. Appropriated metrology techniques are scanning electron microscopy and optical scatterometry. The latter is non-invasive, highly accurate and enables optical cross sections of layer stacks but it requires periodic patterns. Scanning laser focus sensors are a viable alternative enabling the measurement of non-periodic features. Severe limitations are imposed by the diffraction limit determining the edge location accuracy. It will be shown that the accuracy can be greatly improved by means of rigorous modeling. To this end, a fully vectorial 2.5-dimensional model has been developed based on rigorous Maxwell solvers and combined with models for the scanning and various autofocus principles. The simulations are compared with experimental results. Moreover, the simulations are directly utilized to improve the edge location accuracy.

Atomic Radius and Charge Parameter Uncertainty in Biomolecular Solvation Energy Calculations

DOE Office of Scientific and Technical Information (OSTI.GOV)

Yang, Xiu; Lei, Huan; Gao, Peiyuan

Atomic radii and charges are two major parameters used in implicit solvent electrostatics and energy calculations. The optimization problem for charges and radii is under-determined, leading to uncertainty in the values of these parameters and in the results of solvation energy calculations using these parameters. This paper presents a method for quantifying this uncertainty in solvation energies using surrogate models based on generalized polynomial chaos (gPC) expansions. There are relatively few atom types used to specify radii parameters in implicit solvation calculations; therefore, surrogate models for these low-dimensional spaces could be constructed using least-squares fitting. However, there are many moremore » types of atomic charges; therefore, construction of surrogate models for the charge parameter space required compressed sensing combined with an iterative rotation method to enhance problem sparsity. We present results for the uncertainty in small molecule solvation energies based on these approaches. Additionally, we explore the correlation between uncertainties due to radii and charges which motivates the need for future work in uncertainty quantification methods for high-dimensional parameter spaces.« less

'spup' - an R package for uncertainty propagation in spatial environmental modelling

NASA Astrophysics Data System (ADS)

Sawicka, Kasia; Heuvelink, Gerard

2016-04-01

Computer models have become a crucial tool in engineering and environmental sciences for simulating the behaviour of complex static and dynamic systems. However, while many models are deterministic, the uncertainty in their predictions needs to be estimated before they are used for decision support. Currently, advances in uncertainty propagation and assessment have been paralleled by a growing number of software tools for uncertainty analysis, but none has gained recognition for a universal applicability, including case studies with spatial models and spatial model inputs. Due to the growing popularity and applicability of the open source R programming language we undertook a project to develop an R package that facilitates uncertainty propagation analysis in spatial environmental modelling. In particular, the 'spup' package provides functions for examining the uncertainty propagation starting from input data and model parameters, via the environmental model onto model predictions. The functions include uncertainty model specification, stochastic simulation and propagation of uncertainty using Monte Carlo (MC) techniques, as well as several uncertainty visualization functions. Uncertain environmental variables are represented in the package as objects whose attribute values may be uncertain and described by probability distributions. Both numerical and categorical data types are handled. Spatial auto-correlation within an attribute and cross-correlation between attributes is also accommodated for. For uncertainty propagation the package has implemented the MC approach with efficient sampling algorithms, i.e. stratified random sampling and Latin hypercube sampling. The design includes facilitation of parallel computing to speed up MC computation. The MC realizations may be used as an input to the environmental models called from R, or externally. Selected static and interactive visualization methods that are understandable by non-experts with limited background in statistics can be used to summarize and visualize uncertainty about the measured input, model parameters and output of the uncertainty propagation. We demonstrate that the 'spup' package is an effective and easy tool to apply and can be used in multi-disciplinary research and model-based decision support.

'spup' - an R package for uncertainty propagation analysis in spatial environmental modelling

NASA Astrophysics Data System (ADS)

Sawicka, Kasia; Heuvelink, Gerard

2017-04-01

Computer models have become a crucial tool in engineering and environmental sciences for simulating the behaviour of complex static and dynamic systems. However, while many models are deterministic, the uncertainty in their predictions needs to be estimated before they are used for decision support. Currently, advances in uncertainty propagation and assessment have been paralleled by a growing number of software tools for uncertainty analysis, but none has gained recognition for a universal applicability and being able to deal with case studies with spatial models and spatial model inputs. Due to the growing popularity and applicability of the open source R programming language we undertook a project to develop an R package that facilitates uncertainty propagation analysis in spatial environmental modelling. In particular, the 'spup' package provides functions for examining the uncertainty propagation starting from input data and model parameters, via the environmental model onto model predictions. The functions include uncertainty model specification, stochastic simulation and propagation of uncertainty using Monte Carlo (MC) techniques, as well as several uncertainty visualization functions. Uncertain environmental variables are represented in the package as objects whose attribute values may be uncertain and described by probability distributions. Both numerical and categorical data types are handled. Spatial auto-correlation within an attribute and cross-correlation between attributes is also accommodated for. For uncertainty propagation the package has implemented the MC approach with efficient sampling algorithms, i.e. stratified random sampling and Latin hypercube sampling. The design includes facilitation of parallel computing to speed up MC computation. The MC realizations may be used as an input to the environmental models called from R, or externally. Selected visualization methods that are understandable by non-experts with limited background in statistics can be used to summarize and visualize uncertainty about the measured input, model parameters and output of the uncertainty propagation. We demonstrate that the 'spup' package is an effective and easy tool to apply and can be used in multi-disciplinary research and model-based decision support.

Using CV-GLUE procedure in analysis of wetland model predictive uncertainty.

PubMed

Huang, Chun-Wei; Lin, Yu-Pin; Chiang, Li-Chi; Wang, Yung-Chieh

2014-07-01

This study develops a procedure that is related to Generalized Likelihood Uncertainty Estimation (GLUE), called the CV-GLUE procedure, for assessing the predictive uncertainty that is associated with different model structures with varying degrees of complexity. The proposed procedure comprises model calibration, validation, and predictive uncertainty estimation in terms of a characteristic coefficient of variation (characteristic CV). The procedure first performed two-stage Monte-Carlo simulations to ensure predictive accuracy by obtaining behavior parameter sets, and then the estimation of CV-values of the model outcomes, which represent the predictive uncertainties for a model structure of interest with its associated behavior parameter sets. Three commonly used wetland models (the first-order K-C model, the plug flow with dispersion model, and the Wetland Water Quality Model; WWQM) were compared based on data that were collected from a free water surface constructed wetland with paddy cultivation in Taipei, Taiwan. The results show that the first-order K-C model, which is simpler than the other two models, has greater predictive uncertainty. This finding shows that predictive uncertainty does not necessarily increase with the complexity of the model structure because in this case, the more simplistic representation (first-order K-C model) of reality results in a higher uncertainty in the prediction made by the model. The CV-GLUE procedure is suggested to be a useful tool not only for designing constructed wetlands but also for other aspects of environmental management. Copyright © 2014 Elsevier Ltd. All rights reserved.

Mathematical Rigor vs. Conceptual Change: Some Early Results

NASA Astrophysics Data System (ADS)

Alexander, W. R.

2003-05-01

Results from two different pedagogical approaches to teaching introductory astronomy at the college level will be presented. The first of these approaches is a descriptive, conceptually based approach that emphasizes conceptual change. This descriptive class is typically an elective for non-science majors. The other approach is a mathematically rigorous treatment that emphasizes problem solving and is designed to prepare students for further study in astronomy. The mathematically rigorous class is typically taken by science majors. It also fulfills an elective science requirement for these science majors. The Astronomy Diagnostic Test version 2 (ADT 2.0) was used as an assessment instrument since the validity and reliability have been investigated by previous researchers. The ADT 2.0 was administered as both a pre-test and post-test to both groups. Initial results show no significant difference between the two groups in the post-test. However, there is a slightly greater improvement for the descriptive class between the pre and post testing compared to the mathematically rigorous course. There was great care to account for variables. These variables included: selection of text, class format as well as instructor differences. Results indicate that the mathematically rigorous model, doesn't improve conceptual understanding any better than the conceptual change model. Additional results indicate that there is a similar gender bias in favor of males that has been measured by previous investigators. This research has been funded by the College of Science and Mathematics at James Madison University.

Adjoint-Based Implicit Uncertainty Analysis for Figures of Merit in a Laser Inertial Fusion Engine

DOE Office of Scientific and Technical Information (OSTI.GOV)

Seifried, J E; Fratoni, M; Kramer, K J

A primary purpose of computational models is to inform design decisions and, in order to make those decisions reliably, the confidence in the results of such models must be estimated. Monte Carlo neutron transport models are common tools for reactor designers. These types of models contain several sources of uncertainty that propagate onto the model predictions. Two uncertainties worthy of note are (1) experimental and evaluation uncertainties of nuclear data that inform all neutron transport models and (2) statistical counting precision, which all results of a Monte Carlo codes contain. Adjoint-based implicit uncertainty analyses allow for the consideration of anymore » number of uncertain input quantities and their effects upon the confidence of figures of merit with only a handful of forward and adjoint transport calculations. When considering a rich set of uncertain inputs, adjoint-based methods remain hundreds of times more computationally efficient than Direct Monte-Carlo methods. The LIFE (Laser Inertial Fusion Energy) engine is a concept being developed at Lawrence Livermore National Laboratory. Various options exist for the LIFE blanket, depending on the mission of the design. The depleted uranium hybrid LIFE blanket design strives to close the fission fuel cycle without enrichment or reprocessing, while simultaneously achieving high discharge burnups with reduced proliferation concerns. Neutron transport results that are central to the operation of the design are tritium production for fusion fuel, fission of fissile isotopes for energy multiplication, and production of fissile isotopes for sustained power. In previous work, explicit cross-sectional uncertainty analyses were performed for reaction rates related to the figures of merit for the depleted uranium hybrid LIFE blanket. Counting precision was also quantified for both the figures of merit themselves and the cross-sectional uncertainty estimates to gauge the validity of the analysis. All cross-sectional uncertainties were small (0.1-0.8%), bounded counting uncertainties, and were precise with regard to counting precision. Adjoint/importance distributions were generated for the same reaction rates. The current work leverages those adjoint distributions to transition from explicit sensitivities, in which the neutron flux is constrained, to implicit sensitivities, in which the neutron flux responds to input perturbations. This treatment vastly expands the set of data that contribute to uncertainties to produce larger, more physically accurate uncertainty estimates.« less

A comprehensive evaluation of input data-induced uncertainty in nonpoint source pollution modeling

NASA Astrophysics Data System (ADS)

Chen, L.; Gong, Y.; Shen, Z.

2015-11-01

Watershed models have been used extensively for quantifying nonpoint source (NPS) pollution, but few studies have been conducted on the error-transitivity from different input data sets to NPS modeling. In this paper, the effects of four input data, including rainfall, digital elevation models (DEMs), land use maps, and the amount of fertilizer, on NPS simulation were quantified and compared. A systematic input-induced uncertainty was investigated using watershed model for phosphorus load prediction. Based on the results, the rain gauge density resulted in the largest model uncertainty, followed by DEMs, whereas land use and fertilizer amount exhibited limited impacts. The mean coefficient of variation for errors in single rain gauges-, multiple gauges-, ASTER GDEM-, NFGIS DEM-, land use-, and fertilizer amount information was 0.390, 0.274, 0.186, 0.073, 0.033 and 0.005, respectively. The use of specific input information, such as key gauges, is also highlighted to achieve the required model accuracy. In this sense, these results provide valuable information to other model-based studies for the control of prediction uncertainty.

Modifying climate change habitat models using tree species-specific assessments of model uncertainty and life history-factors

Treesearch

Stephen N. Matthews; Louis R. Iverson; Anantha M. Prasad; Matthew P. Peters; Paul G. Rodewald

2011-01-01

Species distribution models (SDMs) to evaluate trees' potential responses to climate change are essential for developing appropriate forest management strategies. However, there is a great need to better understand these models' limitations and evaluate their uncertainties. We have previously developed statistical models of suitable habitat, based on both...

Assessing Uncertainties in Surface Water Security: A Probabilistic Multi-model Resampling approach

NASA Astrophysics Data System (ADS)

Rodrigues, D. B. B.

2015-12-01

Various uncertainties are involved in the representation of processes that characterize interactions between societal needs, ecosystem functioning, and hydrological conditions. Here, we develop an empirical uncertainty assessment of water security indicators that characterize scarcity and vulnerability, based on a multi-model and resampling framework. We consider several uncertainty sources including those related to: i) observed streamflow data; ii) hydrological model structure; iii) residual analysis; iv) the definition of Environmental Flow Requirement method; v) the definition of critical conditions for water provision; and vi) the critical demand imposed by human activities. We estimate the overall uncertainty coming from the hydrological model by means of a residual bootstrap resampling approach, and by uncertainty propagation through different methodological arrangements applied to a 291 km² agricultural basin within the Cantareira water supply system in Brazil. Together, the two-component hydrograph residual analysis and the block bootstrap resampling approach result in a more accurate and precise estimate of the uncertainty (95% confidence intervals) in the simulated time series. We then compare the uncertainty estimates associated with water security indicators using a multi-model framework and provided by each model uncertainty estimation approach. The method is general and can be easily extended forming the basis for meaningful support to end-users facing water resource challenges by enabling them to incorporate a viable uncertainty analysis into a robust decision making process.

A dominance-based approach to map risks of ecological invasions in the presence of severe uncertainty

Treesearch

Denys Yemshanov; Frank H. Koch; D. Barry Lyons; Mark Ducey; Klaus Koehler

2012-01-01

Aim Uncertainty has been widely recognized as one of the most critical issues in predicting the expansion of ecological invasions. The uncertainty associated with the introduction and spread of invasive organisms influences how pest management decision makers respond to expanding incursions. We present a model-based approach to map risk of ecological invasions that...

Effects of temporal and spatial resolution of calibration data on integrated hydrologic water quality model identification

NASA Astrophysics Data System (ADS)

Jiang, Sanyuan; Jomaa, Seifeddine; Büttner, Olaf; Rode, Michael

2014-05-01

Hydrological water quality modeling is increasingly used for investigating runoff and nutrient transport processes as well as watershed management but it is mostly unclear how data availablity determins model identification. In this study, the HYPE (HYdrological Predictions for the Environment) model, which is a process-based, semi-distributed hydrological water quality model, was applied in two different mesoscale catchments (Selke (463 km2) and Weida (99 km2)) located in central Germany to simulate discharge and inorganic nitrogen (IN) transport. PEST and DREAM(ZS) were combined with the HYPE model to conduct parameter calibration and uncertainty analysis. Split-sample test was used for model calibration (1994-1999) and validation (1999-2004). IN concentration and daily IN load were found to be highly correlated with discharge, indicating that IN leaching is mainly controlled by runoff. Both dynamics and balances of water and IN load were well captured with NSE greater than 0.83 during validation period. Multi-objective calibration (calibrating hydrological and water quality parameters simultaneously) was found to outperform step-wise calibration in terms of model robustness. Multi-site calibration was able to improve model performance at internal sites, decrease parameter posterior uncertainty and prediction uncertainty. Nitrogen-process parameters calibrated using continuous daily averages of nitrate-N concentration observations produced better and more robust simulations of IN concentration and load, lower posterior parameter uncertainty and IN concentration prediction uncertainty compared to the calibration against uncontinuous biweekly nitrate-N concentration measurements. Both PEST and DREAM(ZS) are efficient in parameter calibration. However, DREAM(ZS) is more sound in terms of parameter identification and uncertainty analysis than PEST because of its capability to evolve parameter posterior distributions and estimate prediction uncertainty based on global search and Bayesian inference schemes.

Flood risk assessment and robust management under deep uncertainty: Application to Dhaka City

NASA Astrophysics Data System (ADS)

Mojtahed, Vahid; Gain, Animesh Kumar; Giupponi, Carlo

2014-05-01

The socio-economic changes as well as climatic changes have been the main drivers of uncertainty in environmental risk assessment and in particular flood. The level of future uncertainty that researchers face when dealing with problems in a future perspective with focus on climate change is known as Deep Uncertainty (also known as Knightian uncertainty), since nobody has already experienced and undergone those changes before and our knowledge is limited to the extent that we have no notion of probabilities, and therefore consolidated risk management approaches have limited potential.. Deep uncertainty is referred to circumstances that analysts and experts do not know or parties to decision making cannot agree on: i) the appropriate models describing the interaction among system variables, ii) probability distributions to represent uncertainty about key parameters in the model 3) how to value the desirability of alternative outcomes. The need thus emerges to assist policy-makers by providing them with not a single and optimal solution to the problem at hand, such as crisp estimates for the costs of damages of natural hazards considered, but instead ranges of possible future costs, based on the outcomes of ensembles of assessment models and sets of plausible scenarios. Accordingly, we need to substitute optimality as a decision criterion with robustness. Under conditions of deep uncertainty, the decision-makers do not have statistical and mathematical bases to identify optimal solutions, while instead they should prefer to implement "robust" decisions that perform relatively well over all conceivable outcomes out of all future unknown scenarios. Under deep uncertainty, analysts cannot employ probability theory or other statistics that usually can be derived from observed historical data and therefore, we turn to non-statistical measures such as scenario analysis. We construct several plausible scenarios with each scenario being a full description of what may happen in future and based on a meaningful synthesis of parameters' values with control of their correlations for maintaining internal consistencies. This paper aims at incorporating a set of data mining and sampling tools to assess uncertainty of model outputs under future climatic and socio-economic changes for Dhaka city and providing a decision support system for robust flood management and mitigation policies. After constructing an uncertainty matrix to identify the main sources of uncertainty for Dhaka City, we identify several hazard and vulnerability maps based on future climatic and socio-economic scenarios. The vulnerability of each flood management alternative under different set of scenarios is determined and finally the robustness of each plausible solution considered is defined based on the above assessment.

Uncertainty quantification and global sensitivity analysis of the Los Alamos sea ice model

NASA Astrophysics Data System (ADS)

Urrego-Blanco, Jorge R.; Urban, Nathan M.; Hunke, Elizabeth C.; Turner, Adrian K.; Jeffery, Nicole

2016-04-01

Changes in the high-latitude climate system have the potential to affect global climate through feedbacks with the atmosphere and connections with midlatitudes. Sea ice and climate models used to understand these changes have uncertainties that need to be characterized and quantified. We present a quantitative way to assess uncertainty in complex computer models, which is a new approach in the analysis of sea ice models. We characterize parametric uncertainty in the Los Alamos sea ice model (CICE) in a standalone configuration and quantify the sensitivity of sea ice area, extent, and volume with respect to uncertainty in 39 individual model parameters. Unlike common sensitivity analyses conducted in previous studies where parameters are varied one at a time, this study uses a global variance-based approach in which Sobol' sequences are used to efficiently sample the full 39-dimensional parameter space. We implement a fast emulator of the sea ice model whose predictions of sea ice extent, area, and volume are used to compute the Sobol' sensitivity indices of the 39 parameters. Main effects and interactions among the most influential parameters are also estimated by a nonparametric regression technique based on generalized additive models. A ranking based on the sensitivity indices indicates that model predictions are most sensitive to snow parameters such as snow conductivity and grain size, and the drainage of melt ponds. It is recommended that research be prioritized toward more accurately determining these most influential parameter values by observational studies or by improving parameterizations in the sea ice model.

Uncertainties in Predicting Rice Yield by Current Crop Models Under a Wide Range of Climatic Conditions

NASA Technical Reports Server (NTRS)

Li, Tao; Hasegawa, Toshihiro; Yin, Xinyou; Zhu, Yan; Boote, Kenneth; Adam, Myriam; Bregaglio, Simone; Buis, Samuel; Confalonieri, Roberto; Fumoto, Tamon;

Predictions on the Development Dimensions of Provincial Tourism Discipline Based on the Artificial Neural Network BP Model

ERIC Educational Resources Information Center

Yang, Yang; Hu, Jun; Lv, Yingchun; Zhang, Mu

2013-01-01

As the tourism industry has gradually become the strategic mainstay industry of the national economy, the scope of the tourism discipline has developed rigorously. This paper makes a predictive study on the development of the scope of Guangdong provincial tourism discipline based on the artificial neural network BP model in order to find out how…

Can climate variability information constrain a hydrological model for an ungauged Costa Rican catchment?

NASA Astrophysics Data System (ADS)

Quesada-Montano, Beatriz; Westerberg, Ida K.; Fuentes-Andino, Diana; Hidalgo-Leon, Hugo; Halldin, Sven

2017-04-01

Long-term hydrological data are key to understanding catchment behaviour and for decision making within water management and planning. Given the lack of observed data in many regions worldwide, hydrological models are an alternative for reproducing historical streamflow series. Additional types of information - to locally observed discharge - can be used to constrain model parameter uncertainty for ungauged catchments. Climate variability exerts a strong influence on streamflow variability on long and short time scales, in particular in the Central-American region. We therefore explored the use of climate variability knowledge to constrain the simulated discharge uncertainty of a conceptual hydrological model applied to a Costa Rican catchment, assumed to be ungauged. To reduce model uncertainty we first rejected parameter relationships that disagreed with our understanding of the system. We then assessed how well climate-based constraints applied at long-term, inter-annual and intra-annual time scales could constrain model uncertainty. Finally, we compared the climate-based constraints to a constraint on low-flow statistics based on information obtained from global maps. We evaluated our method in terms of the ability of the model to reproduce the observed hydrograph and the active catchment processes in terms of two efficiency measures, a statistical consistency measure, a spread measure and 17 hydrological signatures. We found that climate variability knowledge was useful for reducing model uncertainty, in particular, unrealistic representation of deep groundwater processes. The constraints based on global maps of low-flow statistics provided more constraining information than those based on climate variability, but the latter rejected slow rainfall-runoff representations that the low flow statistics did not reject. The use of such knowledge, together with information on low-flow statistics and constraints on parameter relationships showed to be useful to constrain model uncertainty for an - assumed to be - ungauged basin. This shows that our method is promising for reconstructing long-term flow data for ungauged catchments on the Pacific side of Central America, and that similar methods can be developed for ungauged basins in other regions where climate variability exerts a strong control on streamflow variability.

Breaking Computational Barriers: Real-time Analysis and Optimization with Large-scale Nonlinear Models via Model Reduction

DOE Office of Scientific and Technical Information (OSTI.GOV)

Carlberg, Kevin Thomas; Drohmann, Martin; Tuminaro, Raymond S.

2014-10-01

Model reduction for dynamical systems is a promising approach for reducing the computational cost of large-scale physics-based simulations to enable high-fidelity models to be used in many- query (e.g., Bayesian inference) and near-real-time (e.g., fast-turnaround simulation) contexts. While model reduction works well for specialized problems such as linear time-invariant systems, it is much more difficult to obtain accurate, stable, and efficient reduced-order models (ROMs) for systems with general nonlinearities. This report describes several advances that enable nonlinear reduced-order models (ROMs) to be deployed in a variety of time-critical settings. First, we present an error bound for the Gauss-Newton with Approximatedmore » Tensors (GNAT) nonlinear model reduction technique. This bound allows the state-space error for the GNAT method to be quantified when applied with the backward Euler time-integration scheme. Second, we present a methodology for preserving classical Lagrangian structure in nonlinear model reduction. This technique guarantees that important properties--such as energy conservation and symplectic time-evolution maps--are preserved when performing model reduction for models described by a Lagrangian formalism (e.g., molecular dynamics, structural dynamics). Third, we present a novel technique for decreasing the temporal complexity --defined as the number of Newton-like iterations performed over the course of the simulation--by exploiting time-domain data. Fourth, we describe a novel method for refining projection-based reduced-order models a posteriori using a goal-oriented framework similar to mesh-adaptive h -refinement in finite elements. The technique allows the ROM to generate arbitrarily accurate solutions, thereby providing the ROM with a 'failsafe' mechanism in the event of insufficient training data. Finally, we present the reduced-order model error surrogate (ROMES) method for statistically quantifying reduced- order-model errors. This enables ROMs to be rigorously incorporated in uncertainty-quantification settings, as the error model can be treated as a source of epistemic uncertainty. This work was completed as part of a Truman Fellowship appointment. We note that much additional work was performed as part of the Fellowship. One salient project is the development of the Trilinos-based model-reduction software module Razor , which is currently bundled with the Albany PDE code and currently allows nonlinear reduced-order models to be constructed for any application supported in Albany. Other important projects include the following: 1. ROMES-equipped ROMs for Bayesian inference: K. Carlberg, M. Drohmann, F. Lu (Lawrence Berkeley National Laboratory), M. Morzfeld (Lawrence Berkeley National Laboratory). 2. ROM-enabled Krylov-subspace recycling: K. Carlberg, V. Forstall (University of Maryland), P. Tsuji, R. Tuminaro. 3. A pseudo balanced POD method using only dual snapshots: K. Carlberg, M. Sarovar. 4. An analysis of discrete v. continuous optimality in nonlinear model reduction: K. Carlberg, M. Barone, H. Antil (George Mason University). Journal articles for these projects are in progress at the time of this writing.« less

The integrated effects of future climate and hydrologic uncertainty on sustainable flood risk management

NASA Astrophysics Data System (ADS)

Steinschneider, S.; Wi, S.; Brown, C. M.

2013-12-01

Flood risk management performance is investigated within the context of integrated climate and hydrologic modeling uncertainty to explore system robustness. The research question investigated is whether structural and hydrologic parameterization uncertainties are significant relative to other uncertainties such as climate change when considering water resources system performance. Two hydrologic models are considered, a conceptual, lumped parameter model that preserves the water balance and a physically-based model that preserves both water and energy balances. In the conceptual model, parameter and structural uncertainties are quantified and propagated through the analysis using a Bayesian modeling framework with an innovative error model. Mean climate changes and internal climate variability are explored using an ensemble of simulations from a stochastic weather generator. The approach presented can be used to quantify the sensitivity of flood protection adequacy to different sources of uncertainty in the climate and hydrologic system, enabling the identification of robust projects that maintain adequate performance despite the uncertainties. The method is demonstrated in a case study for the Coralville Reservoir on the Iowa River, where increased flooding over the past several decades has raised questions about potential impacts of climate change on flood protection adequacy.

Post-processing of multi-hydrologic model simulations for improved streamflow projections

NASA Astrophysics Data System (ADS)

khajehei, sepideh; Ahmadalipour, Ali; Moradkhani, Hamid

2016-04-01

Hydrologic model outputs are prone to bias and uncertainty due to knowledge deficiency in model and data. Uncertainty in hydroclimatic projections arises due to uncertainty in hydrologic model as well as the epistemic or aleatory uncertainties in GCM parameterization and development. This study is conducted to: 1) evaluate the recently developed multi-variate post-processing method for historical simulations and 2) assess the effect of post-processing on uncertainty and reliability of future streamflow projections in both high-flow and low-flow conditions. The first objective is performed for historical period of 1970-1999. Future streamflow projections are generated for 10 statistically downscaled GCMs from two widely used downscaling methods: Bias Corrected Statistically Downscaled (BCSD) and Multivariate Adaptive Constructed Analogs (MACA), over the period of 2010-2099 for two representative concentration pathways of RCP4.5 and RCP8.5. Three semi-distributed hydrologic models were employed and calibrated at 1/16 degree latitude-longitude resolution for over 100 points across the Columbia River Basin (CRB) in the pacific northwest USA. Streamflow outputs are post-processed through a Bayesian framework based on copula functions. The post-processing approach is relying on a transfer function developed based on bivariate joint distribution between the observation and simulation in historical period. Results show that application of post-processing technique leads to considerably higher accuracy in historical simulations and also reducing model uncertainty in future streamflow projections.

“Wrong, but Useful”: Negotiating Uncertainty in Infectious Disease Modelling

PubMed Central

Christley, Robert M.; Mort, Maggie; Wynne, Brian; Wastling, Jonathan M.; Heathwaite, A. Louise; Pickup, Roger; Austin, Zoë; Latham, Sophia M.

2013-01-01

For infectious disease dynamical models to inform policy for containment of infectious diseases the models must be able to predict; however, it is well recognised that such prediction will never be perfect. Nevertheless, the consensus is that although models are uncertain, some may yet inform effective action. This assumes that the quality of a model can be ascertained in order to evaluate sufficiently model uncertainties, and to decide whether or not, or in what ways or under what conditions, the model should be ‘used’. We examined uncertainty in modelling, utilising a range of data: interviews with scientists, policy-makers and advisors, and analysis of policy documents, scientific publications and reports of major inquiries into key livestock epidemics. We show that the discourse of uncertainty in infectious disease models is multi-layered, flexible, contingent, embedded in context and plays a critical role in negotiating model credibility. We argue that usability and stability of a model is an outcome of the negotiation that occurs within the networks and discourses surrounding it. This negotiation employs a range of discursive devices that renders uncertainty in infectious disease modelling a plastic quality that is amenable to ‘interpretive flexibility’. The utility of models in the face of uncertainty is a function of this flexibility, the negotiation this allows, and the contexts in which model outputs are framed and interpreted in the decision making process. We contend that rather than being based predominantly on beliefs about quality, the usefulness and authority of a model may at times be primarily based on its functional status within the broad social and political environment in which it acts. PMID:24146851

Near infrared spectroscopy as an on-line method to quantitatively determine glycogen and predict ultimate pH in pre rigor bovine M. longissimus dorsi.

PubMed

Lomiwes, D; Reis, M M; Wiklund, E; Young, O A; North, M

2010-12-01

The potential of near infrared (NIR) spectroscopy as an on-line method to quantify glycogen and predict ultimate pH (pH(u)) of pre rigor beef M. longissimus dorsi (LD) was assessed. NIR spectra (538 to 1677 nm) of pre rigor LD from steers, cows and bulls were collected early post mortem and measurements were made for pre rigor glycogen concentration and pH(u). Spectral and measured data were combined to develop models to quantify glycogen and predict the pH(u) of pre rigor LD. NIR spectra and pre rigor predicted values obtained from quantitative models were shown to be poorly correlated against glycogen and pH(u) (r(2)=0.23 and 0.20, respectively). Qualitative models developed to categorize each muscle according to their pH(u) were able to correctly categorize 42% of high pH(u) samples. Optimum qualitative and quantitative models derived from NIR spectra found low correlation between predicted values and reference measurements. Copyright © 2010 The American Meat Science Association. Published by Elsevier Ltd.. All rights reserved.

Delaunay-based derivative-free optimization for efficient minimization of time-averaged statistics of turbulent flows

NASA Astrophysics Data System (ADS)

Beyhaghi, Pooriya

2016-11-01

This work considers the problem of the efficient minimization of the infinite time average of a stationary ergodic process in the space of a handful of independent parameters which affect it. Problems of this class, derived from physical or numerical experiments which are sometimes expensive to perform, are ubiquitous in turbulence research. In such problems, any given function evaluation, determined with finite sampling, is associated with a quantifiable amount of uncertainty, which may be reduced via additional sampling. This work proposes the first algorithm of this type. Our algorithm remarkably reduces the overall cost of the optimization process for problems of this class. Further, under certain well-defined conditions, rigorous proof of convergence is established to the global minimum of the problem considered.

Bulk Enthalpy Calculations in the Arc Jet Facility at NASA ARC

NASA Technical Reports Server (NTRS)

Thompson, Corinna S.; Prabhu, Dinesh; Terrazas-Salinas, Imelda; Mach, Jeffrey J.

2011-01-01

The Arc Jet Facilities at NASA Ames Research Center generate test streams with enthalpies ranging from 5 MJ/kg to 25 MJ/kg. The present work describes a rigorous method, based on equilibrium thermodynamics, for calculating the bulk enthalpy of the flow produced in two of these facilities. The motivation for this work is to determine a dimensionally-correct formula for calculating the bulk enthalpy that is at least as accurate as the conventional formulas that are currently used. Unlike previous methods, the new method accounts for the amount of argon that is present in the flow. Comparisons are made with bulk enthalpies computed from an energy balance method. An analysis of primary facility operating parameters and their associated uncertainties is presented in order to further validate the enthalpy calculations reported herein.

A GIS based spatially-explicit sensitivity and uncertainty analysis approach for multi-criteria decision analysis.

PubMed

Feizizadeh, Bakhtiar; Jankowski, Piotr; Blaschke, Thomas

2014-03-01

GIS multicriteria decision analysis (MCDA) techniques are increasingly used in landslide susceptibility mapping for the prediction of future hazards, land use planning, as well as for hazard preparedness. However, the uncertainties associated with MCDA techniques are inevitable and model outcomes are open to multiple types of uncertainty. In this paper, we present a systematic approach to uncertainty and sensitivity analysis. We access the uncertainty of landslide susceptibility maps produced with GIS-MCDA techniques. A new spatially-explicit approach and Dempster-Shafer Theory (DST) are employed to assess the uncertainties associated with two MCDA techniques, namely Analytical Hierarchical Process (AHP) and Ordered Weighted Averaging (OWA) implemented in GIS. The methodology is composed of three different phases. First, weights are computed to express the relative importance of factors (criteria) for landslide susceptibility. Next, the uncertainty and sensitivity of landslide susceptibility is analyzed as a function of weights using Monte Carlo Simulation and Global Sensitivity Analysis. Finally, the results are validated using a landslide inventory database and by applying DST. The comparisons of the obtained landslide susceptibility maps of both MCDA techniques with known landslides show that the AHP outperforms OWA. However, the OWA-generated landslide susceptibility map shows lower uncertainty than the AHP-generated map. The results demonstrate that further improvement in the accuracy of GIS-based MCDA can be achieved by employing an integrated uncertainty-sensitivity analysis approach, in which the uncertainty of landslide susceptibility model is decomposed and attributed to model's criteria weights.

A GIS based spatially-explicit sensitivity and uncertainty analysis approach for multi-criteria decision analysis

NASA Astrophysics Data System (ADS)

Feizizadeh, Bakhtiar; Jankowski, Piotr; Blaschke, Thomas

2014-03-01

GIS multicriteria decision analysis (MCDA) techniques are increasingly used in landslide susceptibility mapping for the prediction of future hazards, land use planning, as well as for hazard preparedness. However, the uncertainties associated with MCDA techniques are inevitable and model outcomes are open to multiple types of uncertainty. In this paper, we present a systematic approach to uncertainty and sensitivity analysis. We access the uncertainty of landslide susceptibility maps produced with GIS-MCDA techniques. A new spatially-explicit approach and Dempster-Shafer Theory (DST) are employed to assess the uncertainties associated with two MCDA techniques, namely Analytical Hierarchical Process (AHP) and Ordered Weighted Averaging (OWA) implemented in GIS. The methodology is composed of three different phases. First, weights are computed to express the relative importance of factors (criteria) for landslide susceptibility. Next, the uncertainty and sensitivity of landslide susceptibility is analyzed as a function of weights using Monte Carlo Simulation and Global Sensitivity Analysis. Finally, the results are validated using a landslide inventory database and by applying DST. The comparisons of the obtained landslide susceptibility maps of both MCDA techniques with known landslides show that the AHP outperforms OWA. However, the OWA-generated landslide susceptibility map shows lower uncertainty than the AHP-generated map. The results demonstrate that further improvement in the accuracy of GIS-based MCDA can be achieved by employing an integrated uncertainty-sensitivity analysis approach, in which the uncertainty of landslide susceptibility model is decomposed and attributed to model's criteria weights.

Alarms about structural alerts.

PubMed

Alves, Vinicius; Muratov, Eugene; Capuzzi, Stephen; Politi, Regina; Low, Yen; Braga, Rodolpho; Zakharov, Alexey V; Sedykh, Alexander; Mokshyna, Elena; Farag, Sherif; Andrade, Carolina; Kuz'min, Victor; Fourches, Denis; Tropsha, Alexander

2016-08-21

Structural alerts are widely accepted in chemical toxicology and regulatory decision support as a simple and transparent means to flag potential chemical hazards or group compounds into categories for read-across. However, there has been a growing concern that alerts disproportionally flag too many chemicals as toxic, which questions their reliability as toxicity markers. Conversely, the rigorously developed and properly validated statistical QSAR models can accurately and reliably predict the toxicity of a chemical; however, their use in regulatory toxicology has been hampered by the lack of transparency and interpretability. We demonstrate that contrary to the common perception of QSAR models as "black boxes" they can be used to identify statistically significant chemical substructures (QSAR-based alerts) that influence toxicity. We show through several case studies, however, that the mere presence of structural alerts in a chemical, irrespective of the derivation method (expert-based or QSAR-based), should be perceived only as hypotheses of possible toxicological effect. We propose a new approach that synergistically integrates structural alerts and rigorously validated QSAR models for a more transparent and accurate safety assessment of new chemicals.

Simulation's Ensemble is Better Than Ensemble Simulation

NASA Astrophysics Data System (ADS)

Yan, X.

2017-12-01

Simulation's ensemble is better than ensemble simulation Yan Xiaodong State Key Laboratory of Earth Surface Processes and Resource Ecology (ESPRE) Beijing Normal University,19 Xinjiekouwai Street, Haidian District, Beijing 100875, China Email: yxd@bnu.edu.cnDynamical system is simulated from initial state. However initial state data is of great uncertainty, which leads to uncertainty of simulation. Therefore, multiple possible initial states based simulation has been used widely in atmospheric science, which has indeed been proved to be able to lower the uncertainty, that was named simulation's ensemble because multiple simulation results would be fused . In ecological field, individual based model simulation (forest gap models for example) can be regarded as simulation's ensemble compared with community based simulation (most ecosystem models). In this talk, we will address the advantage of individual based simulation and even their ensembles.

Ensemble urban flood simulation in comparison with laboratory-scale experiments: Impact of interaction models for manhole, sewer pipe, and surface flow

NASA Astrophysics Data System (ADS)

Noh, Seong Jin; Lee, Seungsoo; An, Hyunuk; Kawaike, Kenji; Nakagawa, Hajime

2016-11-01

An urban flood is an integrated phenomenon that is affected by various uncertainty sources such as input forcing, model parameters, complex geometry, and exchanges of flow among different domains in surfaces and subsurfaces. Despite considerable advances in urban flood modeling techniques, limited knowledge is currently available with regard to the impact of dynamic interaction among different flow domains on urban floods. In this paper, an ensemble method for urban flood modeling is presented to consider the parameter uncertainty of interaction models among a manhole, a sewer pipe, and surface flow. Laboratory-scale experiments on urban flood and inundation are performed under various flow conditions to investigate the parameter uncertainty of interaction models. The results show that ensemble simulation using interaction models based on weir and orifice formulas reproduces experimental data with high accuracy and detects the identifiability of model parameters. Among interaction-related parameters, the parameters of the sewer-manhole interaction show lower uncertainty than those of the sewer-surface interaction. Experimental data obtained under unsteady-state conditions are more informative than those obtained under steady-state conditions to assess the parameter uncertainty of interaction models. Although the optimal parameters vary according to the flow conditions, the difference is marginal. Simulation results also confirm the capability of the interaction models and the potential of the ensemble-based approaches to facilitate urban flood simulation.

Quantification of uncertainty in flood risk assessment for flood protection planning: a Bayesian approach

NASA Astrophysics Data System (ADS)

Dittes, Beatrice; Špačková, Olga; Ebrahimian, Negin; Kaiser, Maria; Rieger, Wolfgang; Disse, Markus; Straub, Daniel

2017-04-01

Flood risk estimates are subject to significant uncertainties, e.g. due to limited records of historic flood events, uncertainty in flood modeling, uncertain impact of climate change or uncertainty in the exposure and loss estimates. In traditional design of flood protection systems, these uncertainties are typically just accounted for implicitly, based on engineering judgment. In the AdaptRisk project, we develop a fully quantitative framework for planning of flood protection systems under current and future uncertainties using quantitative pre-posterior Bayesian decision analysis. In this contribution, we focus on the quantification of the uncertainties and study their relative influence on the flood risk estimate and on the planning of flood protection systems. The following uncertainty components are included using a Bayesian approach: 1) inherent and statistical (i.e. limited record length) uncertainty; 2) climate uncertainty that can be learned from an ensemble of GCM-RCM models; 3) estimates of climate uncertainty components not covered in 2), such as bias correction, incomplete ensemble, local specifics not captured by the GCM-RCM models; 4) uncertainty in the inundation modelling; 5) uncertainty in damage estimation. We also investigate how these uncertainties are possibly reduced in the future when new evidence - such as new climate models, observed extreme events, and socio-economic data - becomes available. Finally, we look into how this new evidence influences the risk assessment and effectivity of flood protection systems. We demonstrate our methodology for a pre-alpine catchment in southern Germany: the Mangfall catchment in Bavaria that includes the city of Rosenheim, which suffered significant losses during the 2013 flood event.

Model uncertainty and multimodel inference in reliability estimation within a longitudinal framework.

PubMed

Alonso, Ariel; Laenen, Annouschka

2013-05-01

Laenen, Alonso, and Molenberghs (2007) and Laenen, Alonso, Molenberghs, and Vangeneugden (2009) proposed a method to assess the reliability of rating scales in a longitudinal context. The methodology is based on hierarchical linear models, and reliability coefficients are derived from the corresponding covariance matrices. However, finding a good parsimonious model to describe complex longitudinal data is a challenging task. Frequently, several models fit the data equally well, raising the problem of model selection uncertainty. When model uncertainty is high one may resort to model averaging, where inferences are based not on one but on an entire set of models. We explored the use of different model building strategies, including model averaging, in reliability estimation. We found that the approach introduced by Laenen et al. (2007, 2009) combined with some of these strategies may yield meaningful results in the presence of high model selection uncertainty and when all models are misspecified, in so far as some of them manage to capture the most salient features of the data. Nonetheless, when all models omit prominent regularities in the data, misleading results may be obtained. The main ideas are further illustrated on a case study in which the reliability of the Hamilton Anxiety Rating Scale is estimated. Importantly, the ambit of model selection uncertainty and model averaging transcends the specific setting studied in the paper and may be of interest in other areas of psychometrics. © 2012 The British Psychological Society.

Impacts of climate change and internal climate variability on french rivers streamflows

NASA Astrophysics Data System (ADS)

Dayon, Gildas; Boé, Julien; Martin, Eric

2016-04-01

The assessment of the impacts of climate change often requires to set up long chains of modeling, from the model to estimate the future concentration of greenhouse gases to the impact model. Throughout the modeling chain, sources of uncertainty accumulate making the exploitation of results for the development of adaptation strategies difficult. It is proposed here to assess the impacts of climate change on the hydrological cycle over France and the associated uncertainties. The contribution of the uncertainties from greenhouse gases emission scenario, climate models and internal variability are addressed in this work. To have a large ensemble of climate simulations, the study is based on Global Climate Models (GCM) simulations from the Coupled Model Intercomparison Phase 5 (CMIP5), including several simulations from the same GCM to properly assess uncertainties from internal climate variability. Simulations from the four Radiative Concentration Pathway (RCP) are downscaled with a statistical method developed in a previous study (Dayon et al. 2015). The hydrological system Isba-Modcou is then driven by the downscaling results on a 8 km grid over France. Isba is a land surface model that calculates the energy and water balance and Modcou a hydrogeological model that routes the surface runoff given by Isba. Based on that framework, uncertainties uncertainties from greenhouse gases emission scenario, climate models and climate internal variability are evaluated. Their relative importance is described for the next decades and the end of this century. In a last part, uncertainties due to internal climate variability on streamflows simulated with downscaled GCM and Isba-Modcou are evaluated against observations and hydrological reconstructions on the whole 20th century. Hydrological reconstructions are based on the downscaling of recent atmospheric reanalyses of the 20th century and observations of temperature and precipitation. We show that the multi-decadal variability of streamflows observed in the 20th century is generally weaker in the hydrological simulations done with the historical simulations from climate models. References: Dayon et al. (2015), Transferability in the future climate of a statistical downscaling mehtod for precipitation in France, J. Geophys. Res. Atmos., 120, 1023-1043, doi:10.1002/2014JD022236

A new algorithm for construction of coarse-grained sites of large biomolecules.

PubMed

Li, Min; Zhang, John Z H; Xia, Fei

2016-04-05

The development of coarse-grained (CG) models for large biomolecules remains a challenge in multiscale simulations, including a rigorous definition of CG representations for them. In this work, we proposed a new stepwise optimization imposed with the boundary-constraint (SOBC) algorithm to construct the CG sites of large biomolecules, based on the s cheme of essential dynamics CG. By means of SOBC, we can rigorously derive the CG representations of biomolecules with less computational cost. The SOBC is particularly efficient for the CG definition of large systems with thousands of residues. The resulted CG sites can be parameterized as a CG model using the normal mode analysis based fluctuation matching method. Through normal mode analysis, the obtained modes of CG model can accurately reflect the functionally related slow motions of biomolecules. The SOBC algorithm can be used for the construction of CG sites of large biomolecules such as F-actin and for the study of mechanical properties of biomaterials. © 2015 Wiley Periodicals, Inc.