Uncertainty in temperature response of current consumption-based emissions estimates

NASA Astrophysics Data System (ADS)

Karstensen, J.; Peters, G. P.; Andrew, R. M.

2014-09-01

Several studies have connected emissions of greenhouse gases to economic and trade data to quantify the causal chain from consumption to emissions and climate change. These studies usually combine data and models originating from different sources, making it difficult to estimate uncertainties in the end results. We estimate uncertainties in economic data, multi-pollutant emission statistics and metric parameters, and use Monte Carlo analysis to quantify contributions to uncertainty and to determine how uncertainty propagates to estimates of global temperature change from regional and sectoral territorial- and consumption-based emissions for the year 2007. We find that the uncertainties are sensitive to the emission allocations, mix of pollutants included, the metric and its time horizon, and the level of aggregation of the results. Uncertainties in the final results are largely dominated by the climate sensitivity and the parameters associated with the warming effects of CO2. The economic data have a relatively small impact on uncertainty at the global and national level, while much higher uncertainties are found at the sectoral level. Our results suggest that consumption-based national emissions are not significantly more uncertain than the corresponding production based emissions, since the largest uncertainties are due to metric and emissions which affect both perspectives equally. The two perspectives exhibit different sectoral uncertainties, due to changes of pollutant compositions. We find global sectoral consumption uncertainties in the range of ±9-±27% using the global temperature potential with a 50 year time horizon, with metric uncertainties dominating. National level uncertainties are similar in both perspectives due to the dominance of CO2 over other pollutants. The consumption emissions of the top 10 emitting regions have a broad uncertainty range of ±9-±25%, with metric and emissions uncertainties contributing similarly. The Absolute global temperature potential with a 50 year time horizon has much higher uncertainties, with considerable uncertainty overlap for regions and sectors, indicating that the ranking of countries is uncertain.

Is my bottom-up uncertainty estimation on metal measurement adequate?

NASA Astrophysics Data System (ADS)

Marques, J. R.; Faustino, M. G.; Monteiro, L. R.; Ulrich, J. C.; Pires, M. A. F.; Cotrim, M. E. B.

2018-03-01

Is the estimated uncertainty under GUM recommendation associated with metal measurement adequately estimated? How to evaluate if the measurement uncertainty really covers all uncertainty that is associated with the analytical procedure? Considering that, many laboratories frequently underestimate or less frequently overestimate uncertainties on its results; this paper presents the evaluation of estimated uncertainties on two ICP-OES procedures of seven metal measurements according to GUM approach. Horwitz function and proficiency tests scaled standard uncertainties were used in this evaluation. Our data shows that most elements expanded uncertainties were from two to four times underestimated. Possible causes and corrections are discussed herein.

Uncertainty in hydrological signatures

NASA Astrophysics Data System (ADS)

McMillan, Hilary; Westerberg, Ida

2015-04-01

Information that summarises the hydrological behaviour or flow regime of a catchment is essential for comparing responses of different catchments to understand catchment organisation and similarity, and for many other modelling and water-management applications. Such information types derived as an index value from observed data are known as hydrological signatures, and can include descriptors of high flows (e.g. mean annual flood), low flows (e.g. mean annual low flow, recession shape), the flow variability, flow duration curve, and runoff ratio. Because the hydrological signatures are calculated from observed data such as rainfall and flow records, they are affected by uncertainty in those data. Subjective choices in the method used to calculate the signatures create a further source of uncertainty. Uncertainties in the signatures may affect our ability to compare different locations, to detect changes, or to compare future water resource management scenarios. The aim of this study was to contribute to the hydrological community's awareness and knowledge of data uncertainty in hydrological signatures, including typical sources, magnitude and methods for its assessment. We proposed a generally applicable method to calculate these uncertainties based on Monte Carlo sampling and demonstrated it for a variety of commonly used signatures. The study was made for two data rich catchments, the 50 km2 Mahurangi catchment in New Zealand and the 135 km2 Brue catchment in the UK. For rainfall data the uncertainty sources included point measurement uncertainty, the number of gauges used in calculation of the catchment spatial average, and uncertainties relating to lack of quality control. For flow data the uncertainty sources included uncertainties in stage/discharge measurement and in the approximation of the true stage-discharge relation by a rating curve. The resulting uncertainties were compared across the different signatures and catchments, to quantify uncertainty magnitude and bias, and to test how uncertainty depended on the density of the raingauge network and flow gauging station characteristics. The uncertainties were sometimes large (i.e. typical intervals of ±10-40% relative uncertainty) and highly variable between signatures. Uncertainty in the mean discharge was around ±10% for both catchments, while signatures describing the flow variability had much higher uncertainties in the Mahurangi where there was a fast rainfall-runoff response and greater high-flow rating uncertainty. Event and total runoff ratios had uncertainties from ±10% to ±15% depending on the number of rain gauges used; precipitation uncertainty was related to interpolation rather than point uncertainty. Uncertainty distributions in these signatures were skewed, and meant that differences in signature values between these catchments were often not significant. We hope that this study encourages others to use signatures in a way that is robust to data uncertainty.

Socializing Identity Through Practice: A Mixed Methods Approach to Family Medicine Resident Perspectives on Uncertainty.

PubMed

Ledford, Christy J W; Cafferty, Lauren A; Seehusen, Dean A

2015-01-01

Uncertainty is a central theme in the practice of medicine and particularly primary care. This study explored how family medicine resident physicians react to uncertainty in their practice. This study incorporated a two-phase mixed methods approach, including semi-structured personal interviews (n=21) and longitudinal self-report surveys (n=21) with family medicine residents. Qualitative analysis showed that though residents described uncertainty as an implicit part of their identity, they still developed tactics to minimize or manage uncertainty in their practice. Residents described increasing comfort with uncertainty the longer they practiced and anticipated that growth continuing throughout their careers. Quantitative surveys showed that reactions to uncertainty were more positive over time; however, the difference was not statistically significant. Qualitative and quantitative results show that as family medicine residents practice medicine their perception of uncertainty changes. To reduce uncertainty, residents use relational information-seeking strategies. From a broader view of practice, residents describe uncertainty neutrally, asserting that uncertainty is simply part of the practice of family medicine.

Physicians' reactions to uncertainty in the context of shared decision making.

PubMed

Politi, Mary C; Légaré, France

2010-08-01

Physicians' reactions towards uncertainty may influence their willingness to engage in shared decision making (SDM). This study aimed to identify variables associated with physician's anxiety from uncertainty and reluctance to disclose uncertainty to patients. We conducted a cross-sectional secondary analysis of longitudinal data of an implementation study of SDM among primary care professionals (n=122). Outcomes were anxiety from uncertainty and reluctance to disclose uncertainty to patients. Hypothesized factors that would be associated with outcomes included attitude, social norm, perceived behavioral control, intention to implement SDM in practice, and socio-demographics. Stepwise linear regression was used to identify predictors of anxiety from uncertainty and reluctance to disclose uncertainty to patients. In multivariate analyses, anxiety from uncertainty was influenced by female gender (beta=0.483; p=0.0039), residency status (1st year: beta=0.600; p=0.001; 2nd year: beta=0.972; p<0.001), and number of hours worked per week (beta=-0.012; p=0.048). Reluctance to disclose uncertainty to patients was influenced by having more years in formal education (beta=-1.996; p=0.012). Variables associated with anxiety from uncertainty differ from those associated with reluctance to disclose uncertainty to patients. Given the importance of communicating uncertainty during SDM, measuring physicians' reactions to uncertainty is essential in SDM implementation studies. Copyright 2009 Elsevier Ireland Ltd. All rights reserved.

Uncertainty in Climate Change Research: An Integrated Approach

NASA Astrophysics Data System (ADS)

Mearns, L.

2017-12-01

Uncertainty has been a major theme in research regarding climate change from virtually the very beginning. And appropriately characterizing and quantifying uncertainty has been an important aspect of this work. Initially, uncertainties were explored regarding the climate system and how it would react to future forcing. A concomitant area of concern was viewed in the future emissions and concentrations of important forcing agents such as greenhouse gases and aerosols. But, of course we know there are important uncertainties in all aspects of climate change research, not just that of the climate system and emissions. And as climate change research has become more important and of pragmatic concern as possible solutions to the climate change problem are addressed, exploring all the relevant uncertainties has become more relevant and urgent. More recently, over the past five years or so, uncertainties in impacts models, such as agricultural and hydrological models, have received much more attention, through programs such as AgMIP, and some research in this arena has indicated that the uncertainty in the impacts models can be as great or greater than that in the climate system. Still there remains other areas of uncertainty that remain underexplored and/or undervalued. This includes uncertainty in vulnerability and governance. Without more thoroughly exploring these last uncertainties, we likely will underestimate important uncertainties particularly regarding how different systems can successfully adapt to climate change . In this talk I will discuss these different uncertainties and how to combine them to give a complete picture of the total uncertainty individual systems are facing. And as part of this, I will discuss how the uncertainty can be successfully managed even if it is fairly large and deep. Part of my argument will be that large uncertainty is not the enemy, but rather false certainty is the true danger.

MO-E-BRE-01: Determination, Minimization and Communication of Uncertainties in Radiation Therapy

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

Van Dyk, J; Palta, J; Bortfeld, T

2014-06-15

Medical Physicists have a general understanding of uncertainties in the radiation treatment process, both with respect to dosimetry and geometry. However, there is a desire to be more quantitative about uncertainty estimation. A recent International Atomic Energy Agency (IAEA) report (about to be published) recommends that we should be as “accurate as reasonably achievable, technical and biological factors being taken into account”. Thus, a single recommendation as a goal for accuracy in radiation therapy is an oversimplification. That report also suggests that individual clinics should determine their own level of uncertainties for their specific treatment protocols. The question is “howmore » do we implement this in clinical practice”? AAPM Monograph 35 (2011 AAPM Summer School) addressed many specific aspects of uncertainties in each of the steps of a course of radiation treatment. The intent of this symposium is: (1) to review uncertainty considerations in the entire radiation treatment process including uncertainty determination for each step and uncertainty propagation for the total process, (2) to consider aspects of robust optimization which optimizes treatment plans while protecting them against uncertainties, and (3) to describe various methods of displaying uncertainties and communicating uncertainties to the relevant professionals. While the theoretical and research aspects will also be described, the emphasis will be on the practical considerations for the medical physicist in clinical practice. Learning Objectives: To review uncertainty determination in the overall radiation treatment process. To consider uncertainty modeling and uncertainty propagation. To highlight the basic ideas and clinical potential of robust optimization procedures to generate optimal treatment plans that are not severely affected by uncertainties. To describe methods of uncertainty communication and display.« less

Probabilistic accounting of uncertainty in forecasts of species distributions under climate change

USGS Publications Warehouse

Wenger, Seth J.; Som, Nicholas A.; Dauwalter, Daniel C.; Isaak, Daniel J.; Neville, Helen M.; Luce, Charles H.; Dunham, Jason B.; Young, Michael K.; Fausch, Kurt D.; Rieman, Bruce E.

2013-01-01

Forecasts of species distributions under future climates are inherently uncertain, but there have been few attempts to describe this uncertainty comprehensively in a probabilistic manner. We developed a Monte Carlo approach that accounts for uncertainty within generalized linear regression models (parameter uncertainty and residual error), uncertainty among competing models (model uncertainty), and uncertainty in future climate conditions (climate uncertainty) to produce site-specific frequency distributions of occurrence probabilities across a species’ range. We illustrated the method by forecasting suitable habitat for bull trout (Salvelinus confluentus) in the Interior Columbia River Basin, USA, under recent and projected 2040s and 2080s climate conditions. The 95% interval of total suitable habitat under recent conditions was estimated at 30.1–42.5 thousand km; this was predicted to decline to 0.5–7.9 thousand km by the 2080s. Projections for the 2080s showed that the great majority of stream segments would be unsuitable with high certainty, regardless of the climate data set or bull trout model employed. The largest contributor to uncertainty in total suitable habitat was climate uncertainty, followed by parameter uncertainty and model uncertainty. Our approach makes it possible to calculate a full distribution of possible outcomes for a species, and permits ready graphical display of uncertainty for individual locations and of total habitat.

[Influence of Uncertainty and Uncertainty Appraisal on Self-management in Hemodialysis Patients].

PubMed

Jang, Hyung Suk; Lee, Chang Suk; Yang, Young Hee

2015-04-01

This study was done to examine the relation of uncertainty, uncertainty appraisal, and self-management in patients undergoing hemodialysis, and to identify factors influencing self-management. A convenience sample of 92 patients receiving hemodialysis was selected. Data were collected using a structured questionnaire and medical records. The collected data were analyzed using descriptive statistics, t-test, ANOVA, Pearson correlations and multiple regression analysis with the SPSS/WIN 20.0 program. The participants showed a moderate level of uncertainty with the highest score being for ambiguity among the four uncertainty subdomains. Scores for uncertainty danger or opportunity appraisals were under the mid points. The participants were found to perform a high level of self-management such as diet control, management of arteriovenous fistula, exercise, medication, physical management, measurements of body weight and blood pressure, and social activity. The self-management of participants undergoing hemodialysis showed a significant relationship with uncertainty and uncertainty appraisal. The significant factors influencing self-management were uncertainty, uncertainty opportunity appraisal, hemodialysis duration, and having a spouse. These variables explained 32.8% of the variance in self-management. The results suggest that intervention programs to reduce the level of uncertainty and to increase the level of uncertainty opportunity appraisal among patients would improve the self-management of hemodialysis patients.

Uncertainty as knowledge

PubMed Central

Lewandowsky, Stephan; Ballard, Timothy; Pancost, Richard D.

2015-01-01

This issue of Philosophical Transactions examines the relationship between scientific uncertainty about climate change and knowledge. Uncertainty is an inherent feature of the climate system. Considerable effort has therefore been devoted to understanding how to effectively respond to a changing, yet uncertain climate. Politicians and the public often appeal to uncertainty as an argument to delay mitigative action. We argue that the appropriate response to uncertainty is exactly the opposite: uncertainty provides an impetus to be concerned about climate change, because greater uncertainty increases the risks associated with climate change. We therefore suggest that uncertainty can be a source of actionable knowledge. We survey the papers in this issue, which address the relationship between uncertainty and knowledge from physical, economic and social perspectives. We also summarize the pervasive psychological effects of uncertainty, some of which may militate against a meaningful response to climate change, and we provide pointers to how those difficulties may be ameliorated. PMID:26460108

Information Seeking in Uncertainty Management Theory: Exposure to Information About Medical Uncertainty and Information-Processing Orientation as Predictors of Uncertainty Management Success.

PubMed

Rains, Stephen A; Tukachinsky, Riva

2015-01-01

Uncertainty management theory outlines the processes through which individuals cope with health-related uncertainty. Information seeking has been frequently documented as an important uncertainty management strategy. The reported study investigates exposure to specific types of medical information during a search, and one's information-processing orientation as predictors of successful uncertainty management (i.e., a reduction in the discrepancy between the level of uncertainty one feels and the level one desires). A lab study was conducted in which participants were primed to feel more or less certain about skin cancer and then were allowed to search the World Wide Web for skin cancer information. Participants' search behavior was recorded and content analyzed. The results indicate that exposure to two health communication constructs that pervade medical forms of uncertainty (i.e., severity and susceptibility) and information-processing orientation predicted uncertainty management success.

Probabilistic Methods for Uncertainty Propagation Applied to Aircraft Design

NASA Technical Reports Server (NTRS)

Green, Lawrence L.; Lin, Hong-Zong; Khalessi, Mohammad R.

2002-01-01

Three methods of probabilistic uncertainty propagation and quantification (the method of moments, Monte Carlo simulation, and a nongradient simulation search method) are applied to an aircraft analysis and conceptual design program to demonstrate design under uncertainty. The chosen example problems appear to have discontinuous design spaces and thus these examples pose difficulties for many popular methods of uncertainty propagation and quantification. However, specific implementation features of the first and third methods chosen for use in this study enable successful propagation of small uncertainties through the program. Input uncertainties in two configuration design variables are considered. Uncertainties in aircraft weight are computed. The effects of specifying required levels of constraint satisfaction with specified levels of input uncertainty are also demonstrated. The results show, as expected, that the designs under uncertainty are typically heavier and more conservative than those in which no input uncertainties exist.

A Unified Approach for Reporting ARM Measurement Uncertainties Technical Report: Updated in 2016

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

Sisterson, Douglas

The U.S. Department of Energy (DOE) Atmospheric Radiation Measurement (ARM) Climate Research Facility is observationally based, and quantifying the uncertainty of its measurements is critically important. With over 300 widely differing instruments providing over 2,500 datastreams, concise expression of measurement uncertainty is quite challenging. ARM currently provides data and supporting metadata (information about the data or data quality) to its users through several sources. Because the continued success of the ARM Facility depends on the known quality of its measurements, ARM relies on Instrument Mentors and the ARM Data Quality Office to ensure, assess, and report measurement quality. Therefore, anmore » easily accessible, well-articulated estimate of ARM measurement uncertainty is needed. This report is a continuation of the work presented by Campos and Sisterson (2015) and provides additional uncertainty information from instruments not available in their report. As before, a total measurement uncertainty has been calculated as a function of the instrument uncertainty (calibration factors), the field uncertainty (environmental factors), and the retrieval uncertainty (algorithm factors). This study will not expand on methods for computing these uncertainties. As before, it will focus on the practical identification, characterization, and inventory of the measurement uncertainties already available to the ARM community through the ARM Instrument Mentors and their ARM instrument handbooks. This study continues the first steps towards reporting ARM measurement uncertainty as: (1) identifying how the uncertainty of individual ARM measurements is currently expressed, (2) identifying a consistent approach to measurement uncertainty, and then (3) reclassifying ARM instrument measurement uncertainties in a common framework.« less

Decision-Making under Criteria Uncertainty

NASA Astrophysics Data System (ADS)

Kureychik, V. M.; Safronenkova, I. B.

2018-05-01

Uncertainty is an essential part of a decision-making procedure. The paper deals with the problem of decision-making under criteria uncertainty. In this context, decision-making under uncertainty, types and conditions of uncertainty were examined. The decision-making problem under uncertainty was formalized. A modification of the mathematical decision support method under uncertainty via ontologies was proposed. A critical distinction of the developed method is ontology usage as its base elements. The goal of this work is a development of a decision-making method under criteria uncertainty with the use of ontologies in the area of multilayer board designing. This method is oriented to improvement of technical-economic values of the examined domain.

Embracing uncertainty in applied ecology.

PubMed

Milner-Gulland, E J; Shea, K

2017-12-01

Applied ecologists often face uncertainty that hinders effective decision-making.Common traps that may catch the unwary are: ignoring uncertainty, acknowledging uncertainty but ploughing on, focussing on trivial uncertainties, believing your models, and unclear objectives.We integrate research insights and examples from a wide range of applied ecological fields to illustrate advances that are generally underused, but could facilitate ecologists' ability to plan and execute research to support management.Recommended approaches to avoid uncertainty traps are: embracing models, using decision theory, using models more effectively, thinking experimentally, and being realistic about uncertainty. Synthesis and applications . Applied ecologists can become more effective at informing management by using approaches that explicitly take account of uncertainty.

Confronting uncertainty in wildlife management: performance of grizzly bear management.

PubMed

Artelle, Kyle A; Anderson, Sean C; Cooper, Andrew B; Paquet, Paul C; Reynolds, John D; Darimont, Chris T

2013-01-01

Scientific management of wildlife requires confronting the complexities of natural and social systems. Uncertainty poses a central problem. Whereas the importance of considering uncertainty has been widely discussed, studies of the effects of unaddressed uncertainty on real management systems have been rare. We examined the effects of outcome uncertainty and components of biological uncertainty on hunt management performance, illustrated with grizzly bears (Ursus arctos horribilis) in British Columbia, Canada. We found that both forms of uncertainty can have serious impacts on management performance. Outcome uncertainty alone--discrepancy between expected and realized mortality levels--led to excess mortality in 19% of cases (population-years) examined. Accounting for uncertainty around estimated biological parameters (i.e., biological uncertainty) revealed that excess mortality might have occurred in up to 70% of cases. We offer a general method for identifying targets for exploited species that incorporates uncertainty and maintains the probability of exceeding mortality limits below specified thresholds. Setting targets in our focal system using this method at thresholds of 25% and 5% probability of overmortality would require average target mortality reductions of 47% and 81%, respectively. Application of our transparent and generalizable framework to this or other systems could improve management performance in the presence of uncertainty.

Sonic Boom Pressure Signature Uncertainty Calculation and Propagation to Ground Noise

NASA Technical Reports Server (NTRS)

West, Thomas K., IV; Bretl, Katherine N.; Walker, Eric L.; Pinier, Jeremy T.

2015-01-01

The objective of this study was to outline an approach for the quantification of uncertainty in sonic boom measurements and to investigate the effect of various near-field uncertainty representation approaches on ground noise predictions. These approaches included a symmetric versus asymmetric uncertainty band representation and a dispersion technique based on a partial sum Fourier series that allows for the inclusion of random error sources in the uncertainty. The near-field uncertainty was propagated to the ground level, along with additional uncertainty in the propagation modeling. Estimates of perceived loudness were obtained for the various types of uncertainty representation in the near-field. Analyses were performed on three configurations of interest to the sonic boom community: the SEEB-ALR, the 69o DeltaWing, and the LM 1021-01. Results showed that representation of the near-field uncertainty plays a key role in ground noise predictions. Using a Fourier series based dispersion approach can double the amount of uncertainty in the ground noise compared to a pure bias representation. Compared to previous computational fluid dynamics results, uncertainty in ground noise predictions were greater when considering the near-field experimental uncertainty.

Conflict or Caveats? Effects of Media Portrayals of Scientific Uncertainty on Audience Perceptions of New Technologies.

PubMed

Binder, Andrew R; Hillback, Elliott D; Brossard, Dominique

2016-04-01

Research indicates that uncertainty in science news stories affects public assessment of risk and uncertainty. However, the form in which uncertainty is presented may also affect people's risk and uncertainty assessments. For example, a news story that features an expert discussing both what is known and what is unknown about a topic may convey a different form of scientific uncertainty than a story that features two experts who hold conflicting opinions about the status of scientific knowledge of the topic, even when both stories contain the same information about knowledge and its boundaries. This study focuses on audience uncertainty and risk perceptions regarding the emerging science of nanotechnology by manipulating whether uncertainty in a news story about potential risks is attributed to expert sources in the form of caveats (individual uncertainty) or conflicting viewpoints (collective uncertainty). Results suggest that the type of uncertainty portrayed does not impact audience feelings of uncertainty or risk perceptions directly. Rather, the presentation of the story influences risk perceptions only among those who are highly deferent to scientific authority. Implications for risk communication theory and practice are discussed. © 2015 Society for Risk Analysis.

Responses to clinical uncertainty in Australian general practice trainees: a cross-sectional analysis.

PubMed

Cooke, Georga; Tapley, Amanda; Holliday, Elizabeth; Morgan, Simon; Henderson, Kim; Ball, Jean; van Driel, Mieke; Spike, Neil; Kerr, Rohan; Magin, Parker

2017-12-01

Tolerance for ambiguity is essential for optimal learning and professional competence. General practice trainees must be, or must learn to be, adept at managing clinical uncertainty. However, few studies have examined associations of intolerance of uncertainty in this group. The aim of this study was to establish levels of tolerance of uncertainty in Australian general practice trainees and associations of uncertainty with demographic, educational and training practice factors. A cross-sectional analysis was performed on the Registrar Clinical Encounters in Training (ReCEnT) project, an ongoing multi-site cohort study. Scores on three of the four independent subscales of the Physicians' Reaction to Uncertainty (PRU) instrument were analysed as outcome variables in linear regression models with trainee and practice factors as independent variables. A total of 594 trainees contributed data on a total of 1209 occasions. Trainees in earlier training terms had higher scores for 'Anxiety due to uncertainty', 'Concern about bad outcomes' and 'Reluctance to disclose diagnosis/treatment uncertainty to patients'. Beyond this, findings suggest two distinct sets of associations regarding reaction to uncertainty. Firstly, affective aspects of uncertainty (the 'Anxiety' and 'Concern' subscales) were associated with female gender, less experience in hospital prior to commencing general practice training, and graduation overseas. Secondly, a maladaptive response to uncertainty (the 'Reluctance to disclose' subscale) was associated with urban practice, health qualifications prior to studying medicine, practice in an area of higher socio-economic status, and being Australian-trained. This study has established levels of three measures of trainees' responses to uncertainty and associations with these responses. The current findings suggest differing 'phenotypes' of trainees with high 'affective' responses to uncertainty and those reluctant to disclose uncertainty to patients. More research is needed to examine the relationship between clinical uncertainty and clinical outcomes, temporal changes in tolerance for uncertainty, and strategies that might assist physicians in developing adaptive responses to clinical uncertainty. © 2017 John Wiley & Sons Ltd and The Association for the Study of Medical Education.

Forward and backward uncertainty propagation: an oxidation ditch modelling example.

PubMed

Abusam, A; Keesman, K J; van Straten, G

2003-01-01

In the field of water technology, forward uncertainty propagation is frequently used, whereas backward uncertainty propagation is rarely used. In forward uncertainty analysis, one moves from a given (or assumed) parameter subspace towards the corresponding distribution of the output or objective function. However, in the backward uncertainty propagation, one moves in the reverse direction, from the distribution function towards the parameter subspace. Backward uncertainty propagation, which is a generalisation of parameter estimation error analysis, gives information essential for designing experimental or monitoring programmes, and for tighter bounding of parameter uncertainty intervals. The procedure of carrying out backward uncertainty propagation is illustrated in this technical note by working example for an oxidation ditch wastewater treatment plant. Results obtained have demonstrated that essential information can be achieved by carrying out backward uncertainty propagation analysis.

Evaluation of assigned-value uncertainty for complex calibrator value assignment processes: a prealbumin example.

PubMed

Middleton, John; Vaks, Jeffrey E

2007-04-01

Errors of calibrator-assigned values lead to errors in the testing of patient samples. The ability to estimate the uncertainties of calibrator-assigned values and other variables minimizes errors in testing processes. International Organization of Standardization guidelines provide simple equations for the estimation of calibrator uncertainty with simple value-assignment processes, but other methods are needed to estimate uncertainty in complex processes. We estimated the assigned-value uncertainty with a Monte Carlo computer simulation of a complex value-assignment process, based on a formalized description of the process, with measurement parameters estimated experimentally. This method was applied to study uncertainty of a multilevel calibrator value assignment for a prealbumin immunoassay. The simulation results showed that the component of the uncertainty added by the process of value transfer from the reference material CRM470 to the calibrator is smaller than that of the reference material itself (<0.8% vs 3.7%). Varying the process parameters in the simulation model allowed for optimizing the process, while keeping the added uncertainty small. The patient result uncertainty caused by the calibrator uncertainty was also found to be small. This method of estimating uncertainty is a powerful tool that allows for estimation of calibrator uncertainty for optimization of various value assignment processes, with a reduced number of measurements and reagent costs, while satisfying the requirements to uncertainty. The new method expands and augments existing methods to allow estimation of uncertainty in complex processes.

Characterizing Epistemic Uncertainty for Launch Vehicle Designs

NASA Technical Reports Server (NTRS)

Novack, Steven D.; Rogers, Jim; Al Hassan, Mohammad; Hark, Frank

2016-01-01

NASA Probabilistic Risk Assessment (PRA) has the task of estimating the aleatory (randomness) and epistemic (lack of knowledge) uncertainty of launch vehicle loss of mission and crew risk, and communicating the results. Launch vehicles are complex engineered systems designed with sophisticated subsystems that are built to work together to accomplish mission success. Some of these systems or subsystems are in the form of heritage equipment, while some have never been previously launched. For these cases, characterizing the epistemic uncertainty is of foremost importance, and it is anticipated that the epistemic uncertainty of a modified launch vehicle design versus a design of well understood heritage equipment would be greater. For reasons that will be discussed, standard uncertainty propagation methods using Monte Carlo simulation produce counter intuitive results, and significantly underestimate epistemic uncertainty for launch vehicle models. Furthermore, standard PRA methods, such as Uncertainty-Importance analyses used to identify components that are significant contributors to uncertainty, are rendered obsolete, since sensitivity to uncertainty changes are not reflected in propagation of uncertainty using Monte Carlo methods. This paper provides a basis of the uncertainty underestimation for complex systems and especially, due to nuances of launch vehicle logic, for launch vehicles. It then suggests several alternative methods for estimating uncertainty and provides examples of estimation results. Lastly, the paper describes how to implement an Uncertainty-Importance analysis using one alternative approach, describes the results, and suggests ways to reduce epistemic uncertainty by focusing on additional data or testing of selected components.

Characterizing Epistemic Uncertainty for Launch Vehicle Designs

NASA Technical Reports Server (NTRS)

Novack, Steven D.; Rogers, Jim; Hark, Frank; Al Hassan, Mohammad

2016-01-01

NASA Probabilistic Risk Assessment (PRA) has the task of estimating the aleatory (randomness) and epistemic (lack of knowledge) uncertainty of launch vehicle loss of mission and crew risk and communicating the results. Launch vehicles are complex engineered systems designed with sophisticated subsystems that are built to work together to accomplish mission success. Some of these systems or subsystems are in the form of heritage equipment, while some have never been previously launched. For these cases, characterizing the epistemic uncertainty is of foremost importance, and it is anticipated that the epistemic uncertainty of a modified launch vehicle design versus a design of well understood heritage equipment would be greater. For reasons that will be discussed, standard uncertainty propagation methods using Monte Carlo simulation produce counter intuitive results and significantly underestimate epistemic uncertainty for launch vehicle models. Furthermore, standard PRA methods such as Uncertainty-Importance analyses used to identify components that are significant contributors to uncertainty are rendered obsolete since sensitivity to uncertainty changes are not reflected in propagation of uncertainty using Monte Carlo methods.This paper provides a basis of the uncertainty underestimation for complex systems and especially, due to nuances of launch vehicle logic, for launch vehicles. It then suggests several alternative methods for estimating uncertainty and provides examples of estimation results. Lastly, the paper shows how to implement an Uncertainty-Importance analysis using one alternative approach, describes the results, and suggests ways to reduce epistemic uncertainty by focusing on additional data or testing of selected components.

Uncertainty analysis and robust trajectory linearization control of a flexible air-breathing hypersonic vehicle

NASA Astrophysics Data System (ADS)

Pu, Zhiqiang; Tan, Xiangmin; Fan, Guoliang; Yi, Jianqiang

2014-08-01

Flexible air-breathing hypersonic vehicles feature significant uncertainties which pose huge challenges to robust controller designs. In this paper, four major categories of uncertainties are analyzed, that is, uncertainties associated with flexible effects, aerodynamic parameter variations, external environmental disturbances, and control-oriented modeling errors. A uniform nonlinear uncertainty model is explored for the first three uncertainties which lumps all uncertainties together and consequently is beneficial for controller synthesis. The fourth uncertainty is additionally considered in stability analysis. Based on these analyses, the starting point of the control design is to decompose the vehicle dynamics into five functional subsystems. Then a robust trajectory linearization control (TLC) scheme consisting of five robust subsystem controllers is proposed. In each subsystem controller, TLC is combined with the extended state observer (ESO) technique for uncertainty compensation. The stability of the overall closed-loop system with the four aforementioned uncertainties and additional singular perturbations is analyzed. Particularly, the stability of nonlinear ESO is also discussed from a Liénard system perspective. At last, simulations demonstrate the great control performance and the uncertainty rejection ability of the robust scheme.

A Method to Estimate Uncertainty in Radiometric Measurement Using the Guide to the Expression of Uncertainty in Measurement (GUM) Method; NREL (National Renewable Energy Laboratory)

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

Habte, A.; Sengupta, M.; Reda, I.

Radiometric data with known and traceable uncertainty is essential for climate change studies to better understand cloud radiation interactions and the earth radiation budget. Further, adopting a known and traceable method of estimating uncertainty with respect to SI ensures that the uncertainty quoted for radiometric measurements can be compared based on documented methods of derivation.Therefore, statements about the overall measurement uncertainty can only be made on an individual basis, taking all relevant factors into account. This poster provides guidelines and recommended procedures for estimating the uncertainty in calibrations and measurements from radiometers. The approach follows the Guide to the Expressionmore » of Uncertainty in Measurement (GUM). derivation.Therefore, statements about the overall measurement uncertainty can only be made on an individual basis, taking all relevant factors into account. This poster provides guidelines and recommended procedures for estimating the uncertainty in calibrations and measurements from radiometers. The approach follows the Guide to the Expression of Uncertainty in Measurement (GUM).« less

Uncertainty Analysis of Instrument Calibration and Application

NASA Technical Reports Server (NTRS)

Tripp, John S.; Tcheng, Ping

1999-01-01

Experimental aerodynamic researchers require estimated precision and bias uncertainties of measured physical quantities, typically at 95 percent confidence levels. Uncertainties of final computed aerodynamic parameters are obtained by propagation of individual measurement uncertainties through the defining functional expressions. In this paper, rigorous mathematical techniques are extended to determine precision and bias uncertainties of any instrument-sensor system. Through this analysis, instrument uncertainties determined through calibration are now expressed as functions of the corresponding measurement for linear and nonlinear univariate and multivariate processes. Treatment of correlated measurement precision error is developed. During laboratory calibration, calibration standard uncertainties are assumed to be an order of magnitude less than those of the instrument being calibrated. Often calibration standards do not satisfy this assumption. This paper applies rigorous statistical methods for inclusion of calibration standard uncertainty and covariance due to the order of their application. The effects of mathematical modeling error on calibration bias uncertainty are quantified. The effects of experimental design on uncertainty are analyzed. The importance of replication is emphasized, techniques for estimation of both bias and precision uncertainties using replication are developed. Statistical tests for stationarity of calibration parameters over time are obtained.

Uncertainty information in climate data records from Earth observation

NASA Astrophysics Data System (ADS)

Merchant, C. J.

2017-12-01

How to derive and present uncertainty in climate data records (CDRs) has been debated within the European Space Agency Climate Change Initiative, in search of common principles applicable across a range of essential climate variables. Various points of consensus have been reached, including the importance of improving provision of uncertainty information and the benefit of adopting international norms of metrology for language around the distinct concepts of uncertainty and error. Providing an estimate of standard uncertainty per datum (or the means to readily calculate it) emerged as baseline good practice, and should be highly relevant to users of CDRs when the uncertainty in data is variable (the usual case). Given this baseline, the role of quality flags is clarified as being complementary to and not repetitive of uncertainty information. Data with high uncertainty are not poor quality if a valid estimate of the uncertainty is available. For CDRs and their applications, the error correlation properties across spatio-temporal scales present important challenges that are not fully solved. Error effects that are negligible in the uncertainty of a single pixel may dominate uncertainty in the large-scale and long-term. A further principle is that uncertainty estimates should themselves be validated. The concepts of estimating and propagating uncertainty are generally acknowledged in geophysical sciences, but less widely practised in Earth observation and development of CDRs. Uncertainty in a CDR depends in part (and usually significantly) on the error covariance of the radiances and auxiliary data used in the retrieval. Typically, error covariance information is not available in the fundamental CDR (FCDR) (i.e., with the level-1 radiances), since provision of adequate level-1 uncertainty information is not yet standard practice. Those deriving CDRs thus cannot propagate the radiance uncertainty to their geophysical products. The FIDUCEO project (www.fiduceo.eu) is demonstrating metrologically sound methodologies addressing this problem for four key historical CDRs. FIDUCEO methods of uncertainty analysis (which also tend to lead to improved FCDRs and CDRs) could support coherent treatment of uncertainty across FCDRs to CDRs and higher level products for a wide range of essential climate variables.

Coupled semivariogram uncertainty of hydrogeological and geophysical data on capture zone uncertainty analysis

USGS Publications Warehouse

Rahman, A.; Tsai, F.T.-C.; White, C.D.; Willson, C.S.

2008-01-01

This study investigates capture zone uncertainty that relates to the coupled semivariogram uncertainty of hydrogeological and geophysical data. Semivariogram uncertainty is represented by the uncertainty in structural parameters (range, sill, and nugget). We used the beta distribution function to derive the prior distributions of structural parameters. The probability distributions of structural parameters were further updated through the Bayesian approach with the Gaussian likelihood functions. Cokriging of noncollocated pumping test data and electrical resistivity data was conducted to better estimate hydraulic conductivity through autosemivariograms and pseudo-cross-semivariogram. Sensitivities of capture zone variability with respect to the spatial variability of hydraulic conductivity, porosity and aquifer thickness were analyzed using ANOVA. The proposed methodology was applied to the analysis of capture zone uncertainty at the Chicot aquifer in Southwestern Louisiana, where a regional groundwater flow model was developed. MODFLOW-MODPATH was adopted to delineate the capture zone. The ANOVA results showed that both capture zone area and compactness were sensitive to hydraulic conductivity variation. We concluded that the capture zone uncertainty due to the semivariogram uncertainty is much higher than that due to the kriging uncertainty for given semivariograms. In other words, the sole use of conditional variances of kriging may greatly underestimate the flow response uncertainty. Semivariogram uncertainty should also be taken into account in the uncertainty analysis. ?? 2008 ASCE.

Uncertainty in BRCA1 cancer susceptibility testing.

PubMed

Baty, Bonnie J; Dudley, William N; Musters, Adrian; Kinney, Anita Y

2006-11-15

This study investigated uncertainty in individuals undergoing genetic counseling/testing for breast/ovarian cancer susceptibility. Sixty-three individuals from a single kindred with a known BRCA1 mutation rated uncertainty about 12 items on a five-point Likert scale before and 1 month after genetic counseling/testing. Factor analysis identified a five-item total uncertainty scale that was sensitive to changes before and after testing. The items in the scale were related to uncertainty about obtaining health care, positive changes after testing, and coping well with results. The majority of participants (76%) rated reducing uncertainty as an important reason for genetic testing. The importance of reducing uncertainty was stable across time and unrelated to anxiety or demographics. Yet, at baseline, total uncertainty was low and decreased after genetic counseling/testing (P = 0.004). Analysis of individual items showed that after genetic counseling/testing, there was less uncertainty about the participant detecting cancer early (P = 0.005) and coping well with their result (P < 0.001). Our findings support the importance to clients of genetic counseling/testing as a means of reducing uncertainty. Testing may help clients to reduce the uncertainty about items they can control, and it may be important to differentiate the sources of uncertainty that are more or less controllable. Genetic counselors can help clients by providing anticipatory guidance about the role of uncertainty in genetic testing. (c) 2006 Wiley-Liss, Inc.

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

Uncertainty As a Trigger for a Paradigm Change in Science Communication

NASA Astrophysics Data System (ADS)

Schneider, S.

2014-12-01

Over the last decade, the need to communicate uncertainty increased. Climate sciences and environmental sciences have faced massive propaganda campaigns by global industry and astroturf organizations. These organizations use the deep societal mistrust in uncertainty to point out alleged unethical and intentional delusion of decision makers and the public by scientists and their consultatory function. Scientists, who openly communicate uncertainty of climate model calculations, earthquake occurrence frequencies, or possible side effects of genetic manipulated semen have to face massive campaigns against their research, and sometimes against their person and live as well. Hence, new strategies to communicate uncertainty have to face the societal roots of the misunderstanding of the concept of uncertainty itself. Evolutionary biology has shown, that human mind is well suited for practical decision making by its sensory structures. Therefore, many of the irrational concepts about uncertainty are mitigated if data is presented in formats the brain is adapted to understand. At the end, the impact of uncertainty to the decision-making process is finally dominantly driven by preconceptions about terms such as uncertainty, vagueness or probabilities. Parallel to the increasing role of scientific uncertainty in strategic communication, science communicators for example at the Research and Development Program GEOTECHNOLOGIEN developed a number of techniques to master the challenge of putting uncertainty in the focus. By raising the awareness of scientific uncertainty as a driving force for scientific development and evolution, the public perspective on uncertainty is changing. While first steps to implement this process are under way, the value of uncertainty still is underestimated in the public and in politics. Therefore, science communicators are in need for new and innovative ways to talk about scientific uncertainty.

Neural Mechanisms of Updating under Reducible and Irreducible Uncertainty.

PubMed

Kobayashi, Kenji; Hsu, Ming

2017-07-19

Adaptive decision making depends on an agent's ability to use environmental signals to reduce uncertainty. However, because of multiple types of uncertainty, agents must take into account not only the extent to which signals violate prior expectations but also whether uncertainty can be reduced in the first place. Here we studied how human brains of both sexes respond to signals under conditions of reducible and irreducible uncertainty. We show behaviorally that subjects' value updating was sensitive to the reducibility of uncertainty, and could be quantitatively characterized by a Bayesian model where agents ignore expectancy violations that do not update beliefs or values. Using fMRI, we found that neural processes underlying belief and value updating were separable from responses to expectancy violation, and that reducibility of uncertainty in value modulated connections from belief-updating regions to value-updating regions. Together, these results provide insights into how agents use knowledge about uncertainty to make better decisions while ignoring mere expectancy violation. SIGNIFICANCE STATEMENT To make good decisions, a person must observe the environment carefully, and use these observations to reduce uncertainty about consequences of actions. Importantly, uncertainty should not be reduced purely based on how surprising the observations are, particularly because in some cases uncertainty is not reducible. Here we show that the human brain indeed reduces uncertainty adaptively by taking into account the nature of uncertainty and ignoring mere surprise. Behaviorally, we show that human subjects reduce uncertainty in a quasioptimal Bayesian manner. Using fMRI, we characterize brain regions that may be involved in uncertainty reduction, as well as the network they constitute, and dissociate them from brain regions that respond to mere surprise. Copyright © 2017 the authors 0270-6474/17/376972-11$15.00/0.

Neural Mechanisms of Updating under Reducible and Irreducible Uncertainty

PubMed Central

2017-01-01

Adaptive decision making depends on an agent's ability to use environmental signals to reduce uncertainty. However, because of multiple types of uncertainty, agents must take into account not only the extent to which signals violate prior expectations but also whether uncertainty can be reduced in the first place. Here we studied how human brains of both sexes respond to signals under conditions of reducible and irreducible uncertainty. We show behaviorally that subjects' value updating was sensitive to the reducibility of uncertainty, and could be quantitatively characterized by a Bayesian model where agents ignore expectancy violations that do not update beliefs or values. Using fMRI, we found that neural processes underlying belief and value updating were separable from responses to expectancy violation, and that reducibility of uncertainty in value modulated connections from belief-updating regions to value-updating regions. Together, these results provide insights into how agents use knowledge about uncertainty to make better decisions while ignoring mere expectancy violation. SIGNIFICANCE STATEMENT To make good decisions, a person must observe the environment carefully, and use these observations to reduce uncertainty about consequences of actions. Importantly, uncertainty should not be reduced purely based on how surprising the observations are, particularly because in some cases uncertainty is not reducible. Here we show that the human brain indeed reduces uncertainty adaptively by taking into account the nature of uncertainty and ignoring mere surprise. Behaviorally, we show that human subjects reduce uncertainty in a quasioptimal Bayesian manner. Using fMRI, we characterize brain regions that may be involved in uncertainty reduction, as well as the network they constitute, and dissociate them from brain regions that respond to mere surprise. PMID:28626019

Maximizing the probability of satisfying the clinical goals in radiation therapy treatment planning under setup uncertainty

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

Fredriksson, Albin, E-mail: albin.fredriksson@raysearchlabs.com; Hårdemark, Björn; Forsgren, Anders

2015-07-15

Purpose: This paper introduces a method that maximizes the probability of satisfying the clinical goals in intensity-modulated radiation therapy treatments subject to setup uncertainty. Methods: The authors perform robust optimization in which the clinical goals are constrained to be satisfied whenever the setup error falls within an uncertainty set. The shape of the uncertainty set is included as a variable in the optimization. The goal of the optimization is to modify the shape of the uncertainty set in order to maximize the probability that the setup error will fall within the modified set. Because the constraints enforce the clinical goalsmore » to be satisfied under all setup errors within the uncertainty set, this is equivalent to maximizing the probability of satisfying the clinical goals. This type of robust optimization is studied with respect to photon and proton therapy applied to a prostate case and compared to robust optimization using an a priori defined uncertainty set. Results: Slight reductions of the uncertainty sets resulted in plans that satisfied a larger number of clinical goals than optimization with respect to a priori defined uncertainty sets, both within the reduced uncertainty sets and within the a priori, nonreduced, uncertainty sets. For the prostate case, the plans taking reduced uncertainty sets into account satisfied 1.4 (photons) and 1.5 (protons) times as many clinical goals over the scenarios as the method taking a priori uncertainty sets into account. Conclusions: Reducing the uncertainty sets enabled the optimization to find better solutions with respect to the errors within the reduced as well as the nonreduced uncertainty sets and thereby achieve higher probability of satisfying the clinical goals. This shows that asking for a little less in the optimization sometimes leads to better overall plan quality.« less

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.

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.

Some applications of uncertainty relations in quantum information

NASA Astrophysics Data System (ADS)

Majumdar, A. S.; Pramanik, T.

2016-08-01

We discuss some applications of various versions of uncertainty relations for both discrete and continuous variables in the context of quantum information theory. The Heisenberg uncertainty relation enables demonstration of the Einstein, Podolsky and Rosen (EPR) paradox. Entropic uncertainty relations (EURs) are used to reveal quantum steering for non-Gaussian continuous variable states. EURs for discrete variables are studied in the context of quantum memory where fine-graining yields the optimum lower bound of uncertainty. The fine-grained uncertainty relation is used to obtain connections between uncertainty and the nonlocality of retrieval games for bipartite and tripartite systems. The Robertson-Schrödinger (RS) uncertainty relation is applied for distinguishing pure and mixed states of discrete variables.

Dynamics of entropic uncertainty for atoms immersed in thermal fluctuating massless scalar field

NASA Astrophysics Data System (ADS)

Huang, Zhiming

2018-04-01

In this article, the dynamics of quantum memory-assisted entropic uncertainty relation for two atoms immersed in a thermal bath of fluctuating massless scalar field is investigated. The master equation that governs the system evolution process is derived. It is found that the mixedness is closely associated with entropic uncertainty. For equilibrium state, the tightness of uncertainty vanishes. For the initial maximum entangled state, the tightness of uncertainty undergoes a slight increase and then declines to zero with evolution time. It is found that temperature can increase the uncertainty, but two-atom separation does not always increase the uncertainty. The uncertainty evolves to different relatively stable values for different temperatures and converges to a fixed value for different two-atom distances with evolution time. Furthermore, weak measurement reversal is employed to control the entropic uncertainty.

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.

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.

Uncertainties in s -process nucleosynthesis in low mass stars determined from Monte Carlo variations

NASA Astrophysics Data System (ADS)

Cescutti, G.; Hirschi, R.; Nishimura, N.; den Hartogh, J. W.; Rauscher, T.; Murphy, A. St J.; Cristallo, S.

2018-05-01

The main s-process taking place in low mass stars produces about half of the elements heavier than iron. It is therefore very important to determine the importance and impact of nuclear physics uncertainties on this process. We have performed extensive nuclear reaction network calculations using individual and temperature-dependent uncertainties for reactions involving elements heavier than iron, within a Monte Carlo framework. Using this technique, we determined the uncertainty in the main s-process abundance predictions due to nuclear uncertainties link to weak interactions and neutron captures on elements heavier than iron. We also identified the key nuclear reactions dominating these uncertainties. We found that β-decay rate uncertainties affect only a few nuclides near s-process branchings, whereas most of the uncertainty in the final abundances is caused by uncertainties in neutron capture rates, either directly producing or destroying the nuclide of interest. Combined total nuclear uncertainties due to reactions on heavy elements are in general small (less than 50%). Three key reactions, nevertheless, stand out because they significantly affect the uncertainties of a large number of nuclides. These are 56Fe(n,γ), 64Ni(n,γ), and 138Ba(n,γ). We discuss the prospect of reducing uncertainties in the key reactions identified in this study with future experiments.

Uncertainty in the delayed neutron fraction in fuel assembly depletion calculations

NASA Astrophysics Data System (ADS)

Aures, Alexander; Bostelmann, Friederike; Kodeli, Ivan A.; Velkov, Kiril; Zwermann, Winfried

2017-09-01

This study presents uncertainty and sensitivity analyses of the delayed neutron fraction of light water reactor and sodium-cooled fast reactor fuel assemblies. For these analyses, the sampling-based XSUSA methodology is used to propagate cross section uncertainties in neutron transport and depletion calculations. Cross section data is varied according to the SCALE 6.1 covariance library. Since this library includes nu-bar uncertainties only for the total values, it has been supplemented by delayed nu-bar uncertainties from the covariance data of the JENDL-4.0 nuclear data library. The neutron transport and depletion calculations are performed with the TRITON/NEWT sequence of the SCALE 6.1 package. The evolution of the delayed neutron fraction uncertainty over burn-up is analysed without and with the consideration of delayed nu-bar uncertainties. Moreover, the main contributors to the result uncertainty are determined. In all cases, the delayed nu-bar uncertainties increase the delayed neutron fraction uncertainty. Depending on the fuel composition, the delayed nu-bar values of uranium and plutonium in fact give the main contributions to the delayed neutron fraction uncertainty for the LWR fuel assemblies. For the SFR case, the uncertainty of the scattering cross section of U-238 is the main contributor.

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.

Method for estimating effects of unknown correlations in spectral irradiance data on uncertainties of spectrally integrated colorimetric quantities

NASA Astrophysics Data System (ADS)

Kärhä, Petri; Vaskuri, Anna; Mäntynen, Henrik; Mikkonen, Nikke; Ikonen, Erkki

2017-08-01

Spectral irradiance data are often used to calculate colorimetric properties, such as color coordinates and color temperatures of light sources by integration. The spectral data may contain unknown correlations that should be accounted for in the uncertainty estimation. We propose a new method for estimating uncertainties in such cases. The method goes through all possible scenarios of deviations using Monte Carlo analysis. Varying spectral error functions are produced by combining spectral base functions, and the distorted spectra are used to calculate the colorimetric quantities. Standard deviations of the colorimetric quantities at different scenarios give uncertainties assuming no correlations, uncertainties assuming full correlation, and uncertainties for an unfavorable case of unknown correlations, which turn out to be a significant source of uncertainty. With 1% standard uncertainty in spectral irradiance, the expanded uncertainty of the correlated color temperature of a source corresponding to the CIE Standard Illuminant A may reach as high as 37.2 K in unfavorable conditions, when calculations assuming full correlation give zero uncertainty, and calculations assuming no correlations yield the expanded uncertainties of 5.6 K and 12.1 K, with wavelength steps of 1 nm and 5 nm used in spectral integrations, respectively. We also show that there is an absolute limit of 60.2 K in the error of the correlated color temperature for Standard Illuminant A when assuming 1% standard uncertainty in the spectral irradiance. A comparison of our uncorrelated uncertainties with those obtained using analytical methods by other research groups shows good agreement. We re-estimated the uncertainties for the colorimetric properties of our 1 kW photometric standard lamps using the new method. The revised uncertainty of color temperature is a factor of 2.5 higher than the uncertainty assuming no correlations.

A Unified Approach for Reporting ARM Measurement Uncertainties Technical Report

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

Campos, E; Sisterson, Douglas

The U.S. Department of Energy (DOE) Atmospheric Radiation Measurement (ARM) Climate Research Facility is observationally based, and quantifying the uncertainty of its measurements is critically important. With over 300 widely differing instruments providing over 2,500 datastreams, concise expression of measurement uncertainty is quite challenging. The ARM Facility currently provides data and supporting metadata (information about the data or data quality) to its users through a number of sources. Because the continued success of the ARM Facility depends on the known quality of its measurements, the Facility relies on instrument mentors and the ARM Data Quality Office (DQO) to ensure, assess,more » and report measurement quality. Therefore, an easily accessible, well-articulated estimate of ARM measurement uncertainty is needed. Note that some of the instrument observations require mathematical algorithms (retrievals) to convert a measured engineering variable into a useful geophysical measurement. While those types of retrieval measurements are identified, this study does not address particular methods for retrieval uncertainty. As well, the ARM Facility also provides engineered data products, or value-added products (VAPs), based on multiple instrument measurements. This study does not include uncertainty estimates for those data products. We propose here that a total measurement uncertainty should be calculated as a function of the instrument uncertainty (calibration factors), the field uncertainty (environmental factors), and the retrieval uncertainty (algorithm factors). The study will not expand on methods for computing these uncertainties. Instead, it will focus on the practical identification, characterization, and inventory of the measurement uncertainties already available in the ARM community through the ARM instrument mentors and their ARM instrument handbooks. As a result, this study will address the first steps towards reporting ARM measurement uncertainty: 1) identifying how the uncertainty of individual ARM measurements is currently expressed, 2) identifying a consistent approach to measurement uncertainty, and then 3) reclassifying ARM instrument measurement uncertainties in a common framework.« less

How to Avoid Errors in Error Propagation: Prediction Intervals and Confidence Intervals in Forest Biomass

NASA Astrophysics Data System (ADS)

Lilly, P.; Yanai, R. D.; Buckley, H. L.; Case, B. S.; Woollons, R. C.; Holdaway, R. J.; Johnson, J.

2016-12-01

Calculations of forest biomass and elemental content require many measurements and models, each contributing uncertainty to the final estimates. While sampling error is commonly reported, based on replicate plots, error due to uncertainty in the regression used to estimate biomass from tree diameter is usually not quantified. Some published estimates of uncertainty due to the regression models have used the uncertainty in the prediction of individuals, ignoring uncertainty in the mean, while others have propagated uncertainty in the mean while ignoring individual variation. Using the simple case of the calcium concentration of sugar maple leaves, we compare the variation among individuals (the standard deviation) to the uncertainty in the mean (the standard error) and illustrate the declining importance in the prediction of individual concentrations as the number of individuals increases. For allometric models, the analogous statistics are the prediction interval (or the residual variation in the model fit) and the confidence interval (describing the uncertainty in the best fit model). The effect of propagating these two sources of error is illustrated using the mass of sugar maple foliage. The uncertainty in individual tree predictions was large for plots with few trees; for plots with 30 trees or more, the uncertainty in individuals was less important than the uncertainty in the mean. Authors of previously published analyses have reanalyzed their data to show the magnitude of these two sources of uncertainty in scales ranging from experimental plots to entire countries. The most correct analysis will take both sources of uncertainty into account, but for practical purposes, country-level reports of uncertainty in carbon stocks, as required by the IPCC, can ignore the uncertainty in individuals. Ignoring the uncertainty in the mean will lead to exaggerated estimates of confidence in estimates of forest biomass and carbon and nutrient contents.

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.

Linear Programming Problems for Generalized Uncertainty

ERIC Educational Resources Information Center

Thipwiwatpotjana, Phantipa

2010-01-01

Uncertainty occurs when there is more than one realization that can represent an information. This dissertation concerns merely discrete realizations of an uncertainty. Different interpretations of an uncertainty and their relationships are addressed when the uncertainty is not a probability of each realization. A well known model that can handle…

The state of the art of the impact of sampling uncertainty on measurement uncertainty

NASA Astrophysics Data System (ADS)

Leite, V. J.; Oliveira, E. C.

2018-03-01

The measurement uncertainty is a parameter that marks the reliability and can be divided into two large groups: sampling and analytical variations. Analytical uncertainty is a controlled process, performed in the laboratory. The same does not occur with the sampling uncertainty, which, because it faces several obstacles and there is no clarity on how to perform the procedures, has been neglected, although it is admittedly indispensable to the measurement process. This paper aims at describing the state of the art of sampling uncertainty and at assessing its relevance to measurement uncertainty.

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

Computation and visualization of uncertainty in surgical navigation.

PubMed

Simpson, Amber L; Ma, Burton; Vasarhelyi, Edward M; Borschneck, Dan P; Ellis, Randy E; James Stewart, A

2014-09-01

Surgical displays do not show uncertainty information with respect to the position and orientation of instruments. Data is presented as though it were perfect; surgeons unaware of this uncertainty could make critical navigational mistakes. The propagation of uncertainty to the tip of a surgical instrument is described and a novel uncertainty visualization method is proposed. An extensive study with surgeons has examined the effect of uncertainty visualization on surgical performance with pedicle screw insertion, a procedure highly sensitive to uncertain data. It is shown that surgical performance (time to insert screw, degree of breach of pedicle, and rotation error) is not impeded by the additional cognitive burden imposed by uncertainty visualization. Uncertainty can be computed in real time and visualized without adversely affecting surgical performance, and the best method of uncertainty visualization may depend upon the type of navigation display. Copyright © 2013 John Wiley & Sons, Ltd.

Reducing uncertainty about objective functions in adaptive management

USGS Publications Warehouse

Williams, B.K.

2012-01-01

This paper extends the uncertainty framework of adaptive management to include uncertainty about the objectives to be used in guiding decisions. Adaptive decision making typically assumes explicit and agreed-upon objectives for management, but allows for uncertainty as to the structure of the decision process that generates change through time. Yet it is not unusual for there to be uncertainty (or disagreement) about objectives, with different stakeholders expressing different views not only about resource responses to management but also about the appropriate management objectives. In this paper I extend the treatment of uncertainty in adaptive management, and describe a stochastic structure for the joint occurrence of uncertainty about objectives as well as models, and show how adaptive decision making and the assessment of post-decision monitoring data can be used to reduce uncertainties of both kinds. Different degrees of association between model and objective uncertainty lead to different patterns of learning about objectives. ?? 2011.

Uncertainty Analysis of the NASA Glenn 8x6 Supersonic Wind Tunnel

NASA Technical Reports Server (NTRS)

Stephens, Julia; Hubbard, Erin; Walter, Joel; McElroy, Tyler

2016-01-01

This paper presents methods and results of a detailed measurement uncertainty analysis that was performed for the 8- by 6-foot Supersonic Wind Tunnel located at the NASA Glenn Research Center. The statistical methods and engineering judgments used to estimate elemental uncertainties are described. The Monte Carlo method of propagating uncertainty was selected to determine the uncertainty of calculated variables of interest. A detailed description of the Monte Carlo method as applied for this analysis is provided. Detailed uncertainty results for the uncertainty in average free stream Mach number as well as other variables of interest are provided. All results are presented as random (variation in observed values about a true value), systematic (potential offset between observed and true value), and total (random and systematic combined) uncertainty. The largest sources contributing to uncertainty are determined and potential improvement opportunities for the facility are investigated.

Role of information theoretic uncertainty relations in quantum theory

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

Jizba, Petr, E-mail: p.jizba@fjfi.cvut.cz; ITP, Freie Universität Berlin, Arnimallee 14, D-14195 Berlin; Dunningham, Jacob A., E-mail: J.Dunningham@sussex.ac.uk

2015-04-15

Uncertainty relations based on information theory for both discrete and continuous distribution functions are briefly reviewed. We extend these results to account for (differential) Rényi entropy and its related entropy power. This allows us to find a new class of information-theoretic uncertainty relations (ITURs). The potency of such uncertainty relations in quantum mechanics is illustrated with a simple two-energy-level model where they outperform both the usual Robertson–Schrödinger uncertainty relation and Shannon entropy based uncertainty relation. In the continuous case the ensuing entropy power uncertainty relations are discussed in the context of heavy tailed wave functions and Schrödinger cat states. Again,more » improvement over both the Robertson–Schrödinger uncertainty principle and Shannon ITUR is demonstrated in these cases. Further salient issues such as the proof of a generalized entropy power inequality and a geometric picture of information-theoretic uncertainty relations are also discussed.« less

The uncertainty of reference standards--a guide to understanding factors impacting uncertainty, uncertainty calculations, and vendor certifications.

PubMed

Gates, Kevin; Chang, Ning; Dilek, Isil; Jian, Huahua; Pogue, Sherri; Sreenivasan, Uma

2009-10-01

Certified solution standards are widely used in forensic toxicological, clinical/diagnostic, and environmental testing. Typically, these standards are purchased as ampouled solutions with a certified concentration. Vendors present concentration and uncertainty differently on their Certificates of Analysis. Understanding the factors that impact uncertainty and which factors have been considered in the vendor's assignment of uncertainty are critical to understanding the accuracy of the standard and the impact on testing results. Understanding these variables is also important for laboratories seeking to comply with ISO/IEC 17025 requirements and for those preparing reference solutions from neat materials at the bench. The impact of uncertainty associated with the neat material purity (including residual water, residual solvent, and inorganic content), mass measurement (weighing techniques), and solvent addition (solution density) on the overall uncertainty of the certified concentration is described along with uncertainty calculations.

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.

Reusable launch vehicle model uncertainties impact analysis

NASA Astrophysics Data System (ADS)

Chen, Jiaye; Mu, Rongjun; Zhang, Xin; Deng, Yanpeng

2018-03-01

Reusable launch vehicle(RLV) has the typical characteristics of complex aerodynamic shape and propulsion system coupling, and the flight environment is highly complicated and intensely changeable. So its model has large uncertainty, which makes the nominal system quite different from the real system. Therefore, studying the influences caused by the uncertainties on the stability of the control system is of great significance for the controller design. In order to improve the performance of RLV, this paper proposes the approach of analyzing the influence of the model uncertainties. According to the typical RLV, the coupling dynamic and kinematics models are built. Then different factors that cause uncertainties during building the model are analyzed and summed up. After that, the model uncertainties are expressed according to the additive uncertainty model. Choosing the uncertainties matrix's maximum singular values as the boundary model, and selecting the uncertainties matrix's norm to show t how much the uncertainty factors influence is on the stability of the control system . The simulation results illustrate that the inertial factors have the largest influence on the stability of the system, and it is necessary and important to take the model uncertainties into consideration before the designing the controller of this kind of aircraft( like RLV, etc).

The Interplay between Uncertainty Monitoring and Working Memory: Can Metacognition Become Automatic?

PubMed Central

Coutinho, Mariana V. C.; Redford, Joshua S.; Church, Barbara A.; Zakrzewski, Alexandria C.; Couchman, Justin J.; Smith, J. David

2016-01-01

The uncertainty response has grounded the study of metacognition in nonhuman animals. Recent research has explored the processes supporting uncertainty monitoring in monkeys. It revealed that uncertainty responding in contrast to perceptual responding depends on significant working memory resources. The aim of the present study was to expand this research by examining whether uncertainty monitoring is also working memory demanding in humans. To explore this issue, human participants were tested with or without a cognitive load on a psychophysical discrimination task including either an uncertainty response (allowing the decline of difficult trials) or a middle-perceptual response (labeling the same intermediate trial levels). The results demonstrated that cognitive load reduced uncertainty responding, but increased middle responding. However, this dissociation between uncertainty and middle responding was only observed when participants either lacked training or had very little training with the uncertainty response. If more training was provided, the effect of load was small. These results suggest that uncertainty responding is resource demanding, but with sufficient training, human participants can respond to uncertainty either by using minimal working memory resources or effectively sharing resources. These results are discussed in relation to the literature on animal and human metacognition. PMID:25971878

Direct Aerosol Forcing Uncertainty

DOE Data Explorer

Mccomiskey, Allison

2008-01-15

Understanding sources of uncertainty in aerosol direct radiative forcing (DRF), the difference in a given radiative flux component with and without aerosol, is essential to quantifying changes in Earth's radiation budget. We examine the uncertainty in DRF due to measurement uncertainty in the quantities on which it depends: aerosol optical depth, single scattering albedo, asymmetry parameter, solar geometry, and surface albedo. Direct radiative forcing at the top of the atmosphere and at the surface as well as sensitivities, the changes in DRF in response to unit changes in individual aerosol or surface properties, are calculated at three locations representing distinct aerosol types and radiative environments. The uncertainty in DRF associated with a given property is computed as the product of the sensitivity and typical measurement uncertainty in the respective aerosol or surface property. Sensitivity and uncertainty values permit estimation of total uncertainty in calculated DRF and identification of properties that most limit accuracy in estimating forcing. Total uncertainties in modeled local diurnally averaged forcing range from 0.2 to 1.3 W m-2 (42 to 20%) depending on location (from tropical to polar sites), solar zenith angle, surface reflectance, aerosol type, and aerosol optical depth. The largest contributor to total uncertainty in DRF is usually single scattering albedo; however decreasing measurement uncertainties for any property would increase accuracy in DRF. Comparison of two radiative transfer models suggests the contribution of modeling error is small compared to the total uncertainty although comparable to uncertainty arising from some individual properties.

Incorporating the effects of socioeconomic uncertainty into priority setting for conservation investment.

PubMed

McBride, Marissa F; Wilson, Kerrie A; Bode, Michael; Possingham, Hugh P

2007-12-01

Uncertainty in the implementation and outcomes of conservation actions that is not accounted for leaves conservation plans vulnerable to potential changes in future conditions. We used a decision-theoretic approach to investigate the effects of two types of investment uncertainty on the optimal allocation of global conservation resources for land acquisition in the Mediterranean Basin. We considered uncertainty about (1) whether investment will continue and (2) whether the acquired biodiversity assets are secure, which we termed transaction uncertainty and performance uncertainty, respectively. We also developed and tested the robustness of different rules of thumb for guiding the allocation of conservation resources when these sources of uncertainty exist. In the presence of uncertainty in future investment ability (transaction uncertainty), the optimal strategy was opportunistic, meaning the investment priority should be to act where uncertainty is highest while investment remains possible. When there was a probability that investments would fail (performance uncertainty), the optimal solution became a complex trade-off between the immediate biodiversity benefits of acting in a region and the perceived longevity of the investment. In general, regions were prioritized for investment when they had the greatest performance certainty, even if an alternative region was highly threatened or had higher biodiversity value. The improved performance of rules of thumb when accounting for uncertainty highlights the importance of explicitly incorporating sources of investment uncertainty and evaluating potential conservation investments in the context of their likely long-term success.

Comprehensive analysis of proton range uncertainties related to stopping-power-ratio estimation using dual-energy CT imaging

NASA Astrophysics Data System (ADS)

Li, B.; Lee, H. C.; Duan, X.; Shen, C.; Zhou, L.; Jia, X.; Yang, M.

2017-09-01

The dual-energy CT-based (DECT) approach holds promise in reducing the overall uncertainty in proton stopping-power-ratio (SPR) estimation as compared to the conventional stoichiometric calibration approach. The objective of this study was to analyze the factors contributing to uncertainty in SPR estimation using the DECT-based approach and to derive a comprehensive estimate of the range uncertainty associated with SPR estimation in treatment planning. Two state-of-the-art DECT-based methods were selected and implemented on a Siemens SOMATOM Force DECT scanner. The uncertainties were first divided into five independent categories. The uncertainty associated with each category was estimated for lung, soft and bone tissues separately. A single composite uncertainty estimate was eventually determined for three tumor sites (lung, prostate and head-and-neck) by weighting the relative proportion of each tissue group for that specific site. The uncertainties associated with the two selected DECT methods were found to be similar, therefore the following results applied to both methods. The overall uncertainty (1σ) in SPR estimation with the DECT-based approach was estimated to be 3.8%, 1.2% and 2.0% for lung, soft and bone tissues, respectively. The dominant factor contributing to uncertainty in the DECT approach was the imaging uncertainties, followed by the DECT modeling uncertainties. Our study showed that the DECT approach can reduce the overall range uncertainty to approximately 2.2% (2σ) in clinical scenarios, in contrast to the previously reported 1%.

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.

Approximating uncertainty of annual runoff and reservoir yield using stochastic replicates of global climate model data

NASA Astrophysics Data System (ADS)

Peel, M. C.; Srikanthan, R.; McMahon, T. A.; Karoly, D. J.

2015-04-01

Two key sources of uncertainty in projections of future runoff for climate change impact assessments are uncertainty between global climate models (GCMs) and within a GCM. Within-GCM uncertainty is the variability in GCM output that occurs when running a scenario multiple times but each run has slightly different, but equally plausible, initial conditions. The limited number of runs available for each GCM and scenario combination within the Coupled Model Intercomparison Project phase 3 (CMIP3) and phase 5 (CMIP5) data sets, limits the assessment of within-GCM uncertainty. In this second of two companion papers, the primary aim is to present a proof-of-concept approximation of within-GCM uncertainty for monthly precipitation and temperature projections and to assess the impact of within-GCM uncertainty on modelled runoff for climate change impact assessments. A secondary aim is to assess the impact of between-GCM uncertainty on modelled runoff. Here we approximate within-GCM uncertainty by developing non-stationary stochastic replicates of GCM monthly precipitation and temperature data. These replicates are input to an off-line hydrologic model to assess the impact of within-GCM uncertainty on projected annual runoff and reservoir yield. We adopt stochastic replicates of available GCM runs to approximate within-GCM uncertainty because large ensembles, hundreds of runs, for a given GCM and scenario are unavailable, other than the Climateprediction.net data set for the Hadley Centre GCM. To date within-GCM uncertainty has received little attention in the hydrologic climate change impact literature and this analysis provides an approximation of the uncertainty in projected runoff, and reservoir yield, due to within- and between-GCM uncertainty of precipitation and temperature projections. In the companion paper, McMahon et al. (2015) sought to reduce between-GCM uncertainty by removing poorly performing GCMs, resulting in a selection of five better performing GCMs from CMIP3 for use in this paper. Here we present within- and between-GCM uncertainty results in mean annual precipitation (MAP), mean annual temperature (MAT), mean annual runoff (MAR), the standard deviation of annual precipitation (SDP), standard deviation of runoff (SDR) and reservoir yield for five CMIP3 GCMs at 17 worldwide catchments. Based on 100 stochastic replicates of each GCM run at each catchment, within-GCM uncertainty was assessed in relative form as the standard deviation expressed as a percentage of the mean of the 100 replicate values of each variable. The average relative within-GCM uncertainties from the 17 catchments and 5 GCMs for 2015-2044 (A1B) were MAP 4.2%, SDP 14.2%, MAT 0.7%, MAR 10.1% and SDR 17.6%. The Gould-Dincer Gamma (G-DG) procedure was applied to each annual runoff time series for hypothetical reservoir capacities of 1 × MAR and 3 × MAR and the average uncertainties in reservoir yield due to within-GCM uncertainty from the 17 catchments and 5 GCMs were 25.1% (1 × MAR) and 11.9% (3 × MAR). Our approximation of within-GCM uncertainty is expected to be an underestimate due to not replicating the GCM trend. However, our results indicate that within-GCM uncertainty is important when interpreting climate change impact assessments. Approximately 95% of values of MAP, SDP, MAT, MAR, SDR and reservoir yield from 1 × MAR or 3 × MAR capacity reservoirs are expected to fall within twice their respective relative uncertainty (standard deviation/mean). Within-GCM uncertainty has significant implications for interpreting climate change impact assessments that report future changes within our range of uncertainty for a given variable - these projected changes may be due solely to within-GCM uncertainty. Since within-GCM variability is amplified from precipitation to runoff and then to reservoir yield, climate change impact assessments that do not take into account within-GCM uncertainty risk providing water resources management decision makers with a sense of certainty that is unjustified.

Lived Experiences of "Illness Uncertainty" of Iranian Cancer Patients: A Phenomenological Hermeneutic Study.

PubMed

Sajjadi, Moosa; Rassouli, Maryam; Abbaszadeh, Abbas; Brant, Jeannine; Majd, Hamid Alavi

2016-01-01

For cancer patients, uncertainty is a pervasive experience and a major psychological stressor that affects many aspects of their lives. Uncertainty is a multifaceted concept, and its understanding for patients depends on many factors, including factors associated with various sociocultural contexts. Unfortunately, little is known about the concept of uncertainty in Iranian society and culture. This study aimed to clarify the concept and explain lived experiences of illness uncertainty in Iranian cancer patients. In this hermeneutic phenomenological study, 8 cancer patients participated in semistructured in-depth interviews about their experiences of uncertainty in illness. Interviews continued until data saturation was reached. All interviews were recorded, transcribed, analyzed, and interpreted using 6 stages of the van Manen phenomenological approach. Seven main themes emerged from patients' experiences of illness uncertainty of cancer. Four themes contributed to uncertainty including "Complexity of Cancer," "Confusion About Cancer," "Contradictory Information," and "Unknown Future." Two themes facilitated coping with uncertainty including "Seeking Knowledge" and "Need for Spiritual Peace." One theme, "Knowledge Ambivalence," revealed the struggle between wanting to know and not wanting to know, especially if bad news was delivered. Uncertainty experience for cancer patients in different societies is largely similar. However, some experiences (eg, ambiguity in access to medical resources) seemed unique to Iranian patients. This study provided an outlook of cancer patients' experiences of illness uncertainty in Iran. Cancer patients' coping ability to deal with uncertainty can be improved.

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.

Residual uncertainty estimation using instance-based learning with applications to hydrologic forecasting

NASA Astrophysics Data System (ADS)

Wani, Omar; Beckers, Joost V. L.; Weerts, Albrecht H.; Solomatine, Dimitri P.

2017-08-01

A non-parametric method is applied to quantify residual uncertainty in hydrologic streamflow forecasting. This method acts as a post-processor on deterministic model forecasts and generates a residual uncertainty distribution. Based on instance-based learning, it uses a k nearest-neighbour search for similar historical hydrometeorological conditions to determine uncertainty intervals from a set of historical errors, i.e. discrepancies between past forecast and observation. The performance of this method is assessed using test cases of hydrologic forecasting in two UK rivers: the Severn and Brue. Forecasts in retrospect were made and their uncertainties were estimated using kNN resampling and two alternative uncertainty estimators: quantile regression (QR) and uncertainty estimation based on local errors and clustering (UNEEC). Results show that kNN uncertainty estimation produces accurate and narrow uncertainty intervals with good probability coverage. Analysis also shows that the performance of this technique depends on the choice of search space. Nevertheless, the accuracy and reliability of uncertainty intervals generated using kNN resampling are at least comparable to those produced by QR and UNEEC. It is concluded that kNN uncertainty estimation is an interesting alternative to other post-processors, like QR and UNEEC, for estimating forecast uncertainty. Apart from its concept being simple and well understood, an advantage of this method is that it is relatively easy to implement.

Probabilistic accounting of uncertainty in forecasts of species distributions under climate change

Treesearch

Seth J. Wenger; Nicholas A. Som; Daniel C. Dauwalter; Daniel J. Isaak; Helen M. Neville; Charles H. Luce; Jason B. Dunham; Michael K. Young; Kurt D. Fausch; Bruce E. Rieman

2013-01-01

Forecasts of species distributions under future climates are inherently uncertain, but there have been few attempts to describe this uncertainty comprehensively in a probabilistic manner. We developed a Monte Carlo approach that accounts for uncertainty within generalized linear regression models (parameter uncertainty and residual error), uncertainty among competing...

Uncertainty as Knowledge: Constraints on Policy Choices Provided by Analysis of Uncertainty

NASA Astrophysics Data System (ADS)

Lewandowsky, S.; Risbey, J.; Smithson, M.; Newell, B. R.

2012-12-01

Uncertainty forms an integral part of climate science, and it is often cited in connection with arguments against mitigative action. We argue that an analysis of uncertainty must consider existing knowledge as well as uncertainty, and the two must be evaluated with respect to the outcomes and risks associated with possible policy options. Although risk judgments are inherently subjective, an analysis of the role of uncertainty within the climate system yields two constraints that are robust to a broad range of assumptions. Those constraints are that (a) greater uncertainty about the climate system is necessarily associated with greater expected damages from warming, and (b) greater uncertainty translates into a greater risk of the failure of mitigation efforts. These ordinal constraints are unaffected by subjective or cultural risk-perception factors, they are independent of the discount rate, and they are independent of the magnitude of the estimate for climate sensitivity. The constraints mean that any appeal to uncertainty must imply a stronger, rather than weaker, need to cut greenhouse gas emissions than in the absence of uncertainty.

Uncertainty in Agricultural Impact Assessment

NASA Technical Reports Server (NTRS)

Wallach, Daniel; Mearns, Linda O.; Rivington, Michael; Antle, John M.; Ruane, Alexander C.

2014-01-01

This chapter considers issues concerning uncertainty associated with modeling and its use within agricultural impact assessments. Information about uncertainty is important for those who develop assessment methods, since that information indicates the need for, and the possibility of, improvement of the methods and databases. Such information also allows one to compare alternative methods. Information about the sources of uncertainties is an aid in prioritizing further work on the impact assessment method. Uncertainty information is also necessary for those who apply assessment methods, e.g., for projecting climate change impacts on agricultural production and for stakeholders who want to use the results as part of a decision-making process (e.g., for adaptation planning). For them, uncertainty information indicates the degree of confidence they can place in the simulated results. Quantification of uncertainty also provides stakeholders with an important guideline for making decisions that are robust across the known uncertainties. Thus, uncertainty information is important for any decision based on impact assessment. Ultimately, we are interested in knowledge about uncertainty so that information can be used to achieve positive outcomes from agricultural modeling and impact assessment.

Communicating mega-projects in the face of uncertainties: Israeli mass media treatment of the Dead Sea Water Canal.

PubMed

Fischhendler, Itay; Cohen-Blankshtain, Galit; Shuali, Yoav; Boykoff, Max

2015-10-01

Given the potential for uncertainties to influence mega-projects, this study examines how mega-projects are deliberated in the public arena. The paper traces the strategies used to promote the Dead Sea Water Canal. Findings show that the Dead Sea mega-project was encumbered by ample uncertainties. Treatment of uncertainties in early coverage was dominated by economics and raised primarily by politicians, while more contemporary media discourses have been dominated by ecological uncertainties voiced by environmental non-governmental organizations. This change in uncertainty type is explained by the changing nature of the project and by shifts in societal values over time. The study also reveals that 'uncertainty reduction' and to a lesser degree, 'project cancellation', are still the strategies most often used to address uncertainties. Statistical analysis indicates that although uncertainties and strategies are significantly correlated, there may be other intervening variables that affect this correlation. This research also therefore contributes to wider and ongoing considerations of uncertainty in the public arena through various media representational practices. © The Author(s) 2013.

[Evaluation of measurement uncertainty of welding fume in welding workplace of a shipyard].

PubMed

Ren, Jie; Wang, Yanrang

2015-12-01

To evaluate the measurement uncertainty of welding fume in the air of the welding workplace of a shipyard, and to provide quality assurance for measurement. According to GBZ/T 192.1-2007 "Determination of dust in the air of workplace-Part 1: Total dust concentration" and JJF 1059-1999 "Evaluation and expression of measurement uncertainty", the uncertainty for determination of welding fume was evaluated and the measurement results were completely described. The concentration of welding fume was 3.3 mg/m(3), and the expanded uncertainty was 0.24 mg/m(3). The repeatability for determination of dust concentration introduced an uncertainty of 1.9%, the measurement using electronic balance introduced a standard uncertainty of 0.3%, and the measurement of sample quality introduced a standard uncertainty of 3.2%. During the determination of welding fume, the standard uncertainty introduced by the measurement of sample quality is the dominant uncertainty. In the process of sampling and measurement, quality control should be focused on the collection efficiency of dust, air humidity, sample volume, and measuring instruments.

Uncertainties in scaling factors for ab initio vibrational zero-point energies

NASA Astrophysics Data System (ADS)

Irikura, Karl K.; Johnson, Russell D.; Kacker, Raghu N.; Kessel, Rüdiger

2009-03-01

Vibrational zero-point energies (ZPEs) determined from ab initio calculations are often scaled by empirical factors. An empirical scaling factor partially compensates for the effects arising from vibrational anharmonicity and incomplete treatment of electron correlation. These effects are not random but are systematic. We report scaling factors for 32 combinations of theory and basis set, intended for predicting ZPEs from computed harmonic frequencies. An empirical scaling factor carries uncertainty. We quantify and report, for the first time, the uncertainties associated with scaling factors for ZPE. The uncertainties are larger than generally acknowledged; the scaling factors have only two significant digits. For example, the scaling factor for B3LYP/6-31G(d) is 0.9757±0.0224 (standard uncertainty). The uncertainties in the scaling factors lead to corresponding uncertainties in predicted ZPEs. The proposed method for quantifying the uncertainties associated with scaling factors is based upon the Guide to the Expression of Uncertainty in Measurement, published by the International Organization for Standardization. We also present a new reference set of 60 diatomic and 15 polyatomic "experimental" ZPEs that includes estimated uncertainties.

Uncertainty in structural interpretation: Lessons to be learnt

NASA Astrophysics Data System (ADS)

Bond, Clare E.

2015-05-01

Uncertainty in the interpretation of geological data is an inherent element of geology. Datasets from different sources: remotely sensed seismic imagery, field data and borehole data, are often combined and interpreted to create a geological model of the sub-surface. The data have limited resolution and spatial distribution that results in uncertainty in the interpretation of the data and in the subsequent geological model(s) created. Methods to determine the extent of interpretational uncertainty of a dataset, how to capture and express that uncertainty, and consideration of uncertainties in terms of risk have been investigated. Here I review the work that has taken place and discuss best practice in accounting for uncertainties in structural interpretation workflows. Barriers to best practice are reflected on, including the use of software packages for interpretation. Experimental evidence suggests that minimising interpretation error through the use of geological reasoning and rules can help decrease interpretation uncertainty; through identification of inadmissible interpretations and in highlighting areas of uncertainty. Understanding expert thought processes and reasoning, including the use of visuospatial skills, during interpretation may aid in the identification of uncertainties, and in the education of new geoscientists.

Uncertainty Analysis in 3D Equilibrium Reconstruction

DOE PAGES

Cianciosa, Mark R.; Hanson, James D.; Maurer, David A.

2018-02-21

Reconstruction is an inverse process where a parameter space is searched to locate a set of parameters with the highest probability of describing experimental observations. Due to systematic errors and uncertainty in experimental measurements, this optimal set of parameters will contain some associated uncertainty. This uncertainty in the optimal parameters leads to uncertainty in models derived using those parameters. V3FIT is a three-dimensional (3D) equilibrium reconstruction code that propagates uncertainty from the input signals, to the reconstructed parameters, and to the final model. Here in this paper, we describe the methods used to propagate uncertainty in V3FIT. Using the resultsmore » of whole shot 3D equilibrium reconstruction of the Compact Toroidal Hybrid, this propagated uncertainty is validated against the random variation in the resulting parameters. Two different model parameterizations demonstrate how the uncertainty propagation can indicate the quality of a reconstruction. As a proxy for random sampling, the whole shot reconstruction results in a time interval that will be used to validate the propagated uncertainty from a single time slice.« less

Methods for handling uncertainty within pharmaceutical funding decisions

NASA Astrophysics Data System (ADS)

Stevenson, Matt; Tappenden, Paul; Squires, Hazel

2014-01-01

This article provides a position statement regarding decision making under uncertainty within the economic evaluation of pharmaceuticals, with a particular focus upon the National Institute for Health and Clinical Excellence context within England and Wales. This area is of importance as funding agencies have a finite budget from which to purchase a selection of competing health care interventions. The objective function generally used is that of maximising societal health with an explicit acknowledgement that there will be opportunity costs associated with purchasing a particular intervention. Three components of uncertainty are discussed within a pharmaceutical funding perspective: methodological uncertainty, parameter uncertainty and structural uncertainty, alongside a discussion of challenges that are particularly pertinent to health economic evaluation. The discipline has focused primarily on handling methodological and parameter uncertainty and a clear reference case has been developed for consistency across evaluations. However, uncertainties still remain. Less attention has been given to methods for handling structural uncertainty. The lack of adequate methods to explicitly incorporate this aspect of model development may result in the true uncertainty surrounding health care investment decisions being underestimated. Research in this area is ongoing as we review.

Uncertainty Analysis in 3D Equilibrium Reconstruction

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

Cianciosa, Mark R.; Hanson, James D.; Maurer, David A.

Reconstruction is an inverse process where a parameter space is searched to locate a set of parameters with the highest probability of describing experimental observations. Due to systematic errors and uncertainty in experimental measurements, this optimal set of parameters will contain some associated uncertainty. This uncertainty in the optimal parameters leads to uncertainty in models derived using those parameters. V3FIT is a three-dimensional (3D) equilibrium reconstruction code that propagates uncertainty from the input signals, to the reconstructed parameters, and to the final model. Here in this paper, we describe the methods used to propagate uncertainty in V3FIT. Using the resultsmore » of whole shot 3D equilibrium reconstruction of the Compact Toroidal Hybrid, this propagated uncertainty is validated against the random variation in the resulting parameters. Two different model parameterizations demonstrate how the uncertainty propagation can indicate the quality of a reconstruction. As a proxy for random sampling, the whole shot reconstruction results in a time interval that will be used to validate the propagated uncertainty from a single time slice.« less

Defining the measurand in radius of curvature measurements

NASA Astrophysics Data System (ADS)

Davies, Angela; Schmitz, Tony L.

2003-11-01

Traceable radius of curvature measurements are critical for precision optics manufacture. An optical bench measurement of radius is very repeatable and is the preferred method for low-uncertainty applications. On an optical bench, the displacement of the optic is measured as it is moved between the cat's eye and confocal positions, each identified using a figure measuring interferometer. Traceability requires connection to a basic unit (the meter, here) in addition to a defensible uncertainty analysis, and the identification and proper propagation of all uncertainty sources in this measurement is challenging. Recent work has focused on identifying all uncertainty contributions; measurement biases have been approximately taken into account and uncertainties combined in an RSS sense for a final measurement estimate and uncertainty. In this paper we report on a new mathematical definition of the radius measurand, which is a single function that depends on all uncertainty sources, such as error motions, alignment uncertainty, displacement gauge uncertainty, etc. The method is based on a homogeneous transformation matrix (HTM) formalism, and intrinsically defines an unbiased estimate for radius, providing a single mathematical expression for uncertainty propagation through a Taylor-series expansion.

Fear of self-annihilation and existential uncertainty as predictors of worldview defense: Comparing terror management and uncertainty theories.

PubMed

Rubin, Mark

2018-01-01

Terror management theory (TMT) proposes that thoughts of death trigger a concern about self-annihilation that motivates the defense of cultural worldviews. In contrast, uncertainty theorists propose that thoughts of death trigger feelings of uncertainty that motivate worldview defense. University students (N = 414) completed measures of the chronic fear of self-annihilation and existential uncertainty as well as the need for closure. They then evaluated either a meaning threat stimulus or a control stimulus. Consistent with TMT, participants with a high fear of self-annihilation and a high need for closure showed the greatest dislike of the meaning threat stimulus, even after controlling for their existential uncertainty. Contrary to the uncertainty perspective, fear of existential uncertainty showed no significant effects.

Uncertainty Categorization, Modeling, and Management for Regional Water Supply Planning

NASA Astrophysics Data System (ADS)

Fletcher, S.; Strzepek, K. M.; AlSaati, A.; Alhassan, A.

2016-12-01

Many water planners face increased pressure on water supply systems from growing demands, variability in supply and a changing climate. Short-term variation in water availability and demand; long-term uncertainty in climate, groundwater storage, and sectoral competition for water; and varying stakeholder perspectives on the impacts of water shortages make it difficult to assess the necessity of expensive infrastructure investments. We categorize these uncertainties on two dimensions: whether they are the result of stochastic variation or epistemic uncertainty, and whether the uncertainties can be described probabilistically or are deep uncertainties whose likelihood is unknown. We develop a decision framework that combines simulation for probabilistic uncertainty, sensitivity analysis for deep uncertainty and Bayesian decision analysis for uncertainties that are reduced over time with additional information. We apply this framework to two contrasting case studies - drought preparedness in Melbourne, Australia and fossil groundwater depletion in Riyadh, Saudi Arabia - to assess the impacts of different types of uncertainty on infrastructure decisions. Melbourne's water supply system relies on surface water, which is impacted by natural variation in rainfall, and a market-based system for managing water rights. Our results show that small, flexible investment increases can mitigate shortage risk considerably at reduced cost. Riyadh, by contrast, relies primarily on desalination for municipal use and fossil groundwater for agriculture, and a centralized planner makes allocation decisions. Poor regional groundwater measurement makes it difficult to know when groundwater pumping will become uneconomical, resulting in epistemic uncertainty. However, collecting more data can reduce the uncertainty, suggesting the need for different uncertainty modeling and management strategies in Riyadh than in Melbourne. We will categorize the two systems and propose appropriate decision making under uncertainty methods from the state of the art. We will compare the efficiency of alternative approaches to the two case studies. Finally, we will present a hybrid decision analytic tool to address the synthesis of uncertainties.

Assessment of the uncertainty in future projection for summer climate extremes over the East Asia

NASA Astrophysics Data System (ADS)

Park, Changyong; Min, Seung-Ki; Cha, Dong-Hyun

2017-04-01

Future projections of climate extremes in regional and local scales are essential information needed for better adapting to climate changes. However, future projections hold larger uncertainty factors arising from internal and external processes which reduce the projection confidence. Using CMIP5 (Coupled Model Intercomparison Project Phase 5) multi-model simulations, we assess uncertainties in future projections of the East Asian temperature and precipitation extremes focusing on summer. In examining future projection, summer mean and extreme projections of the East Asian temperature and precipitation would be larger as time. Moreover, uncertainty cascades represent wider scenario difference and inter-model ranges with increasing time. A positive mean-extreme relation is found in projections for both temperature and precipitation. For the assessment of uncertainty factors for these projections, dominant uncertainty factors from temperature and precipitation change as time. For uncertainty of mean and extreme temperature, contributions of internal variability and model uncertainty declines after mid-21st century while role of scenario uncertainty grows rapidly. For uncertainty of mean precipitation projections, internal variability is more important than the scenario uncertainty. Unlike mean precipitation, extreme precipitation shows that the scenario uncertainty is expected to be a dominant factor in 2090s. The model uncertainty holds as an important factor for both mean and extreme precipitation until late 21st century. The spatial changes for the uncertainty factors of mean and extreme projections generally are expressed according to temporal changes of the fraction of total variance from uncertainty factors in many grids of the East Asia. ACKNOWLEDGEMENTS The research was supported by the Korea Meteorological Administration Research and Development program under grant KMIPA 2015-2083 and the National Research Foundation of Korea Grant funded by the Ministry of Science, ICT and Future Planning of Korea (NRF-2016M3C4A7952637) for its support and assistant in completion of the study.

Effect of uncertainties on probabilistic-based design capacity of hydrosystems

NASA Astrophysics Data System (ADS)

Tung, Yeou-Koung

2018-02-01

Hydrosystems engineering designs involve analysis of hydrometric data (e.g., rainfall, floods) and use of hydrologic/hydraulic models, all of which contribute various degrees of uncertainty to the design process. Uncertainties in hydrosystem designs can be generally categorized into aleatory and epistemic types. The former arises from the natural randomness of hydrologic processes whereas the latter are due to knowledge deficiency in model formulation and model parameter specification. This study shows that the presence of epistemic uncertainties induces uncertainty in determining the design capacity. Hence, the designer needs to quantify the uncertainty features of design capacity to determine the capacity with a stipulated performance reliability under the design condition. Using detention basin design as an example, the study illustrates a methodological framework by considering aleatory uncertainty from rainfall and epistemic uncertainties from the runoff coefficient, curve number, and sampling error in design rainfall magnitude. The effects of including different items of uncertainty and performance reliability on the design detention capacity are examined. A numerical example shows that the mean value of the design capacity of the detention basin increases with the design return period and this relation is found to be practically the same regardless of the uncertainty types considered. The standard deviation associated with the design capacity, when subject to epistemic uncertainty, increases with both design frequency and items of epistemic uncertainty involved. It is found that the epistemic uncertainty due to sampling error in rainfall quantiles should not be ignored. Even with a sample size of 80 (relatively large for a hydrologic application) the inclusion of sampling error in rainfall quantiles resulted in a standard deviation about 2.5 times higher than that considering only the uncertainty of the runoff coefficient and curve number. Furthermore, the presence of epistemic uncertainties in the design would result in under-estimation of the annual failure probability of the hydrosystem and has a discounting effect on the anticipated design return period.

Adjoint-Based Uncertainty Quantification with MCNP

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

Seifried, Jeffrey E.

2011-09-01

This work serves to quantify the instantaneous uncertainties in neutron transport simulations born from nuclear data and statistical counting uncertainties. Perturbation and adjoint theories are used to derive implicit sensitivity expressions. These expressions are transformed into forms that are convenient for construction with MCNP6, creating the ability to perform adjoint-based uncertainty quantification with MCNP6. These new tools are exercised on the depleted-uranium hybrid LIFE blanket, quantifying its sensitivities and uncertainties to important figures of merit. Overall, these uncertainty estimates are small (< 2%). Having quantified the sensitivities and uncertainties, physical understanding of the system is gained and some confidence inmore » the simulation is acquired.« less

Uncertainty Estimation Cheat Sheet for Probabilistic Risk Assessment

NASA Technical Reports Server (NTRS)

Britton, Paul T.; Al Hassan, Mohammad; Ring, Robert W.

2017-01-01

"Uncertainty analysis itself is uncertain, therefore, you cannot evaluate it exactly," Source Uncertain Quantitative results for aerospace engineering problems are influenced by many sources of uncertainty. Uncertainty analysis aims to make a technical contribution to decision-making through the quantification of uncertainties in the relevant variables as well as through the propagation of these uncertainties up to the result. Uncertainty can be thought of as a measure of the 'goodness' of a result and is typically represented as statistical dispersion. This paper will explain common measures of centrality and dispersion; and-with examples-will provide guidelines for how they may be estimated to ensure effective technical contributions to decision-making.

Lognormal Uncertainty Estimation for Failure Rates

NASA Technical Reports Server (NTRS)

Britton, Paul T.; Al Hassan, Mohammad; Ring, Robert W.

2017-01-01

"Uncertainty analysis itself is uncertain, therefore, you cannot evaluate it exactly," Source Uncertain. Quantitative results for aerospace engineering problems are influenced by many sources of uncertainty. Uncertainty analysis aims to make a technical contribution to decision-making through the quantification of uncertainties in the relevant variables as well as through the propagation of these uncertainties up to the result. Uncertainty can be thought of as a measure of the 'goodness' of a result and is typically represented as statistical dispersion. This presentation will explain common measures of centrality and dispersion; and-with examples-will provide guidelines for how they may be estimated to ensure effective technical contributions to decision-making.

Strong Unitary and Overlap Uncertainty Relations: Theory and Experiment

NASA Astrophysics Data System (ADS)

Bong, Kok-Wei; Tischler, Nora; Patel, Raj B.; Wollmann, Sabine; Pryde, Geoff J.; Hall, Michael J. W.

2018-06-01

We derive and experimentally investigate a strong uncertainty relation valid for any n unitary operators, which implies the standard uncertainty relation and others as special cases, and which can be written in terms of geometric phases. It is saturated by every pure state of any n -dimensional quantum system, generates a tight overlap uncertainty relation for the transition probabilities of any n +1 pure states, and gives an upper bound for the out-of-time-order correlation function. We test these uncertainty relations experimentally for photonic polarization qubits, including the minimum uncertainty states of the overlap uncertainty relation, via interferometric measurements of generalized geometric phases.

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.

Impacts of Process and Prediction Uncertainties on Projected Hanford Waste Glass Amount

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

Gervasio, Vivianaluxa; Vienna, John D.; Kim, Dong-Sang

Analyses were performed to evaluate the impacts of using the advanced glass models, constraints (Vienna et al. 2016), and uncertainty descriptions on projected Hanford glass mass. The maximum allowable WOL was estimated for waste compositions while simultaneously satisfying all applicable glass property and composition constraints with sufficient confidence. Different components of prediction and composition/process uncertainties were systematically included in the calculations to evaluate their impacts on glass mass. The analyses estimated the production of 23,360 MT of IHLW glass when no uncertainties were taken into accound. Accounting for prediction and composition/process uncertainties resulted in 5.01 relative percent increase in estimatedmore » glass mass 24,531 MT. Roughly equal impacts were found for prediction uncertainties (2.58 RPD) and composition/process uncertainties (2.43 RPD). ILAW mass was predicted to be 282,350 MT without uncertainty and with weaste loading “line” rules in place. Accounting for prediction and composition/process uncertainties resulted in only 0.08 relative percent increase in estimated glass mass of 282,562 MTG. Without application of line rules the glass mass decreases by 10.6 relative percent (252,490 MT) for the case with no uncertainties. Addition of prediction uncertainties increases glass mass by 1.32 relative percent and the addition of composition/process uncertainties increase glass mass by an additional 7.73 relative percent (9.06 relative percent increase combined). The glass mass estimate without line rules (275,359 MT) was 2.55 relative percent lower than that with the line rules (282,562 MT), after accounting for all applicable uncertainties.« less

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.

Method to Calculate Uncertainty Estimate of Measuring Shortwave Solar Irradiance using Thermopile and Semiconductor Solar Radiometers

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

Reda, I.

2011-07-01

The uncertainty of measuring solar irradiance is fundamentally important for solar energy and atmospheric science applications. Without an uncertainty statement, the quality of a result, model, or testing method cannot be quantified, the chain of traceability is broken, and confidence cannot be maintained in the measurement. Measurement results are incomplete and meaningless without a statement of the estimated uncertainty with traceability to the International System of Units (SI) or to another internationally recognized standard. This report explains how to use International Guidelines of Uncertainty in Measurement (GUM) to calculate such uncertainty. The report also shows that without appropriate corrections tomore » solar measuring instruments (solar radiometers), the uncertainty of measuring shortwave solar irradiance can exceed 4% using present state-of-the-art pyranometers and 2.7% using present state-of-the-art pyrheliometers. Finally, the report demonstrates that by applying the appropriate corrections, uncertainties may be reduced by at least 50%. The uncertainties, with or without the appropriate corrections might not be compatible with the needs of solar energy and atmospheric science applications; yet, this report may shed some light on the sources of uncertainties and the means to reduce overall uncertainty in measuring solar irradiance.« less

Where do uncertainties reside within environmental risk assessments? Testing UnISERA, a guide for uncertainty assessment.

PubMed

Skinner, Daniel J C; Rocks, Sophie A; Pollard, Simon J T

2017-06-01

A means for identifying and prioritising the treatment of uncertainty (UnISERA) in environmental risk assessments (ERAs) is tested, using three risk domains where ERA is an established requirement and one in which ERA practice is emerging. UnISERA's development draws on 19 expert elicitations across genetically modified higher plants, particulate matter, and agricultural pesticide release and is stress tested here for engineered nanomaterials (ENM). We are concerned with the severity of uncertainty; its nature; and its location across four accepted stages of ERAs. Using an established uncertainty scale, the risk characterisation stage of ERA harbours the highest severity level of uncertainty, associated with estimating, aggregating and evaluating expressions of risk. Combined epistemic and aleatory uncertainty is the dominant nature of uncertainty. The dominant location of uncertainty is associated with data in problem formulation, exposure assessment and effects assessment. Testing UnISERA produced agreements of 55%, 90%, and 80% for the severity level, nature and location dimensions of uncertainty between the combined case studies and the ENM stress test. UnISERA enables environmental risk analysts to prioritise risk assessment phases, groups of tasks, or individual ERA tasks and it can direct them towards established methods for uncertainty treatment. Copyright © 2017 The Authors. Published by Elsevier Ltd.. All rights reserved.

ACCOUNTING FOR CALIBRATION UNCERTAINTIES IN X-RAY ANALYSIS: EFFECTIVE AREAS IN SPECTRAL FITTING

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

Lee, Hyunsook; Kashyap, Vinay L.; Drake, Jeremy J.

2011-04-20

While considerable advance has been made to account for statistical uncertainties in astronomical analyses, systematic instrumental uncertainties have been generally ignored. This can be crucial to a proper interpretation of analysis results because instrumental calibration uncertainty is a form of systematic uncertainty. Ignoring it can underestimate error bars and introduce bias into the fitted values of model parameters. Accounting for such uncertainties currently requires extensive case-specific simulations if using existing analysis packages. Here, we present general statistical methods that incorporate calibration uncertainties into spectral analysis of high-energy data. We first present a method based on multiple imputation that can bemore » applied with any fitting method, but is necessarily approximate. We then describe a more exact Bayesian approach that works in conjunction with a Markov chain Monte Carlo based fitting. We explore methods for improving computational efficiency, and in particular detail a method of summarizing calibration uncertainties with a principal component analysis of samples of plausible calibration files. This method is implemented using recently codified Chandra effective area uncertainties for low-resolution spectral analysis and is verified using both simulated and actual Chandra data. Our procedure for incorporating effective area uncertainty is easily generalized to other types of calibration uncertainties.« less

CALiPER Exploratory Study: Accounting for Uncertainty in Lumen Measurements

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

Bergman, Rolf; Paget, Maria L.; Richman, Eric E.

2011-03-31

With a well-defined and shared understanding of uncertainty in lumen measurements, testing laboratories can better evaluate their processes, contributing to greater consistency and credibility of lighting testing a key component of the U.S. Department of Energy (DOE) Commercially Available LED Product Evaluation and Reporting (CALiPER) program. Reliable lighting testing is a crucial underlying factor contributing toward the success of many energy-efficient lighting efforts, such as the DOE GATEWAY demonstrations, Lighting Facts Label, ENERGY STAR® energy efficient lighting programs, and many others. Uncertainty in measurements is inherent to all testing methodologies, including photometric and other lighting-related testing. Uncertainty exists for allmore » equipment, processes, and systems of measurement in individual as well as combined ways. A major issue with testing and the resulting accuracy of the tests is the uncertainty of the complete process. Individual equipment uncertainties are typically identified, but their relative value in practice and their combined value with other equipment and processes in the same test are elusive concepts, particularly for complex types of testing such as photometry. The total combined uncertainty of a measurement result is important for repeatable and comparative measurements for light emitting diode (LED) products in comparison with other technologies as well as competing products. This study provides a detailed and step-by-step method for determining uncertainty in lumen measurements, working closely with related standards efforts and key industry experts. This report uses the structure proposed in the Guide to Uncertainty Measurements (GUM) for evaluating and expressing uncertainty in measurements. The steps of the procedure are described and a spreadsheet format adapted for integrating sphere and goniophotometric uncertainty measurements is provided for entering parameters, ordering the information, calculating intermediate values and, finally, obtaining expanded uncertainties. Using this basis and examining each step of the photometric measurement and calibration methods, mathematical uncertainty models are developed. Determination of estimated values of input variables is discussed. Guidance is provided for the evaluation of the standard uncertainties of each input estimate, covariances associated with input estimates and the calculation of the result measurements. With this basis, the combined uncertainty of the measurement results and finally, the expanded uncertainty can be determined.« less

Variability And Uncertainty Analysis Of Contaminant Transport Model Using Fuzzy Latin Hypercube Sampling Technique

NASA Astrophysics Data System (ADS)

Kumar, V.; Nayagum, D.; Thornton, S.; Banwart, S.; Schuhmacher2, M.; Lerner, D.

2006-12-01

Characterization of uncertainty associated with groundwater quality models is often of critical importance, as for example in cases where environmental models are employed in risk assessment. Insufficient data, inherent variability and estimation errors of environmental model parameters introduce uncertainty into model predictions. However, uncertainty analysis using conventional methods such as standard Monte Carlo sampling (MCS) may not be efficient, or even suitable, for complex, computationally demanding models and involving different nature of parametric variability and uncertainty. General MCS or variant of MCS such as Latin Hypercube Sampling (LHS) assumes variability and uncertainty as a single random entity and the generated samples are treated as crisp assuming vagueness as randomness. Also when the models are used as purely predictive tools, uncertainty and variability lead to the need for assessment of the plausible range of model outputs. An improved systematic variability and uncertainty analysis can provide insight into the level of confidence in model estimates, and can aid in assessing how various possible model estimates should be weighed. The present study aims to introduce, Fuzzy Latin Hypercube Sampling (FLHS), a hybrid approach of incorporating cognitive and noncognitive uncertainties. The noncognitive uncertainty such as physical randomness, statistical uncertainty due to limited information, etc can be described by its own probability density function (PDF); whereas the cognitive uncertainty such estimation error etc can be described by the membership function for its fuzziness and confidence interval by ?-cuts. An important property of this theory is its ability to merge inexact generated data of LHS approach to increase the quality of information. The FLHS technique ensures that the entire range of each variable is sampled with proper incorporation of uncertainty and variability. A fuzzified statistical summary of the model results will produce indices of sensitivity and uncertainty that relate the effects of heterogeneity and uncertainty of input variables to model predictions. The feasibility of the method is validated to assess uncertainty propagation of parameter values for estimation of the contamination level of a drinking water supply well due to transport of dissolved phenolics from a contaminated site in the UK.

Uncertainty in projected climate change arising from uncertain fossil-fuel emission factors

NASA Astrophysics Data System (ADS)

Quilcaille, Y.; Gasser, T.; Ciais, P.; Lecocq, F.; Janssens-Maenhout, G.; Mohr, S.

2018-04-01

Emission inventories are widely used by the climate community, but their uncertainties are rarely accounted for. In this study, we evaluate the uncertainty in projected climate change induced by uncertainties in fossil-fuel emissions, accounting for non-CO2 species co-emitted with the combustion of fossil-fuels and their use in industrial processes. Using consistent historical reconstructions and three contrasted future projections of fossil-fuel extraction from Mohr et al we calculate CO2 emissions and their uncertainties stemming from estimates of fuel carbon content, net calorific value and oxidation fraction. Our historical reconstructions of fossil-fuel CO2 emissions are consistent with other inventories in terms of average and range. The uncertainties sum up to a ±15% relative uncertainty in cumulative CO2 emissions by 2300. Uncertainties in the emissions of non-CO2 species associated with the use of fossil fuels are estimated using co-emission ratios varying with time. Using these inputs, we use the compact Earth system model OSCAR v2.2 and a Monte Carlo setup, in order to attribute the uncertainty in projected global surface temperature change (ΔT) to three sources of uncertainty, namely on the Earth system’s response, on fossil-fuel CO2 emission and on non-CO2 co-emissions. Under the three future fuel extraction scenarios, we simulate the median ΔT to be 1.9, 2.7 or 4.0 °C in 2300, with an associated 90% confidence interval of about 65%, 52% and 42%. We show that virtually all of the total uncertainty is attributable to the uncertainty in the future Earth system’s response to the anthropogenic perturbation. We conclude that the uncertainty in emission estimates can be neglected for global temperature projections in the face of the large uncertainty in the Earth system response to the forcing of emissions. We show that this result does not hold for all variables of the climate system, such as the atmospheric partial pressure of CO2 and the radiative forcing of tropospheric ozone, that have an emissions-induced uncertainty representing more than 40% of the uncertainty in the Earth system’s response.

[The uncertainty evaluation of analytical results of 27 elements in geological samples by X-ray fluorescence spectrometry].

PubMed

Wang, Yi-Ya; Zhan, Xiu-Chun

2014-04-01

Evaluating uncertainty of analytical results with 165 geological samples by polarized dispersive X-ray fluorescence spectrometry (P-EDXRF) has been reported according to the internationally accepted guidelines. One hundred sixty five pressed pellets of similar matrix geological samples with reliable values were analyzed by P-EDXRF. These samples were divided into several different concentration sections in the concentration ranges of every component. The relative uncertainties caused by precision and accuracy of 27 components were evaluated respectively. For one element in one concentration, the relative uncertainty caused by precision can be calculated according to the average value of relative standard deviation with different concentration level in one concentration section, n = 6 stands for the 6 results of one concentration level. The relative uncertainty caused by accuracy in one concentration section can be evaluated by the relative standard deviation of relative deviation with different concentration level in one concentration section. According to the error propagation theory, combining the precision uncertainty and the accuracy uncertainty into a global uncertainty, this global uncertainty acted as method uncertainty. This model of evaluating uncertainty can solve a series of difficult questions in the process of evaluating uncertainty, such as uncertainties caused by complex matrix of geological samples, calibration procedure, standard samples, unknown samples, matrix correction, overlap correction, sample preparation, instrument condition and mathematics model. The uncertainty of analytical results in this method can act as the uncertainty of the results of the similar matrix unknown sample in one concentration section. This evaluation model is a basic statistical method owning the practical application value, which can provide a strong base for the building of model of the following uncertainty evaluation function. However, this model used a lot of samples which cannot simply be applied to other types of samples with different matrix samples. The number of samples is too large to adapt to other type's samples. We will strive for using this study as a basis to establish a reasonable basis of mathematical statistics function mode to be applied to different types of samples.

Numerical Uncertainty Quantification for Radiation Analysis Tools

NASA Technical Reports Server (NTRS)

Anderson, Brooke; Blattnig, Steve; Clowdsley, Martha

2007-01-01

Recently a new emphasis has been placed on engineering applications of space radiation analyses and thus a systematic effort of Verification, Validation and Uncertainty Quantification (VV&UQ) of the tools commonly used for radiation analysis for vehicle design and mission planning has begun. There are two sources of uncertainty in geometric discretization addressed in this paper that need to be quantified in order to understand the total uncertainty in estimating space radiation exposures. One source of uncertainty is in ray tracing, as the number of rays increase the associated uncertainty decreases, but the computational expense increases. Thus, a cost benefit analysis optimizing computational time versus uncertainty is needed and is addressed in this paper. The second source of uncertainty results from the interpolation over the dose vs. depth curves that is needed to determine the radiation exposure. The question, then, is what is the number of thicknesses that is needed to get an accurate result. So convergence testing is performed to quantify the uncertainty associated with interpolating over different shield thickness spatial grids.

Detailed Uncertainty Analysis of the ZEM-3 Measurement System

NASA Technical Reports Server (NTRS)

Mackey, Jon; Sehirlioglu, Alp; Dynys, Fred

2014-01-01

The measurement of Seebeck coefficient and electrical resistivity are critical to the investigation of all thermoelectric systems. Therefore, it stands that the measurement uncertainty must be well understood to report ZT values which are accurate and trustworthy. A detailed uncertainty analysis of the ZEM-3 measurement system has been performed. The uncertainty analysis calculates error in the electrical resistivity measurement as a result of sample geometry tolerance, probe geometry tolerance, statistical error, and multi-meter uncertainty. The uncertainty on Seebeck coefficient includes probe wire correction factors, statistical error, multi-meter uncertainty, and most importantly the cold-finger effect. The cold-finger effect plagues all potentiometric (four-probe) Seebeck measurement systems, as heat parasitically transfers through thermocouple probes. The effect leads to an asymmetric over-estimation of the Seebeck coefficient. A thermal finite element analysis allows for quantification of the phenomenon, and provides an estimate on the uncertainty of the Seebeck coefficient. The thermoelectric power factor has been found to have an uncertainty of +9-14 at high temperature and 9 near room temperature.

Exploring entropic uncertainty relation in the Heisenberg XX model with inhomogeneous magnetic field

NASA Astrophysics Data System (ADS)

Huang, Ai-Jun; Wang, Dong; Wang, Jia-Ming; Shi, Jia-Dong; Sun, Wen-Yang; Ye, Liu

2017-08-01

In this work, we investigate the quantum-memory-assisted entropic uncertainty relation in a two-qubit Heisenberg XX model with inhomogeneous magnetic field. It has been found that larger coupling strength J between the two spin-chain qubits can effectively reduce the entropic uncertainty. Besides, we observe the mechanics of how the inhomogeneous field influences the uncertainty, and find out that when the inhomogeneous field parameter b<1, the uncertainty will decrease with the decrease of the inhomogeneous field parameter b, conversely, the uncertainty will increase with decreasing b under the condition that b>1. Intriguingly, the entropic uncertainty can shrink to zero when the coupling coefficients are relatively large, while the entropic uncertainty only reduces to 1 with the increase of the homogeneous magnetic field. Additionally, we observe the purity of the state and Bell non-locality and obtain that the entropic uncertainty is anticorrelated with both the purity and Bell non-locality of the evolution state.

Analogy as a strategy for supporting complex problem solving under uncertainty.

PubMed

Chan, Joel; Paletz, Susannah B F; Schunn, Christian D

2012-11-01

Complex problem solving in naturalistic environments is fraught with uncertainty, which has significant impacts on problem-solving behavior. Thus, theories of human problem solving should include accounts of the cognitive strategies people bring to bear to deal with uncertainty during problem solving. In this article, we present evidence that analogy is one such strategy. Using statistical analyses of the temporal dynamics between analogy and expressed uncertainty in the naturalistic problem-solving conversations among scientists on the Mars Rover Mission, we show that spikes in expressed uncertainty reliably predict analogy use (Study 1) and that expressed uncertainty reduces to baseline levels following analogy use (Study 2). In addition, in Study 3, we show with qualitative analyses that this relationship between uncertainty and analogy is not due to miscommunication-related uncertainty but, rather, is primarily concentrated on substantive problem-solving issues. Finally, we discuss a hypothesis about how analogy might serve as an uncertainty reduction strategy in naturalistic complex problem solving.

Dealing with uncertainties in environmental burden of disease assessment

PubMed Central

2009-01-01

Disability Adjusted Life Years (DALYs) combine the number of people affected by disease or mortality in a population and the duration and severity of their condition into one number. The environmental burden of disease is the number of DALYs that can be attributed to environmental factors. Environmental burden of disease estimates enable policy makers to evaluate, compare and prioritize dissimilar environmental health problems or interventions. These estimates often have various uncertainties and assumptions which are not always made explicit. Besides statistical uncertainty in input data and parameters – which is commonly addressed – a variety of other types of uncertainties may substantially influence the results of the assessment. We have reviewed how different types of uncertainties affect environmental burden of disease assessments, and we give suggestions as to how researchers could address these uncertainties. We propose the use of an uncertainty typology to identify and characterize uncertainties. Finally, we argue that uncertainties need to be identified, assessed, reported and interpreted in order for assessment results to adequately support decision making. PMID:19400963

Uncertainty associated with the gravimetric measurement of particulate matter concentration in ambient air.

PubMed

Lacey, Ronald E; Faulkner, William Brock

2015-07-01

This work applied a propagation of uncertainty method to typical total suspended particulate (TSP) sampling apparatus in order to estimate the overall measurement uncertainty. The objectives of this study were to estimate the uncertainty for three TSP samplers, develop an uncertainty budget, and determine the sensitivity of the total uncertainty to environmental parameters. The samplers evaluated were the TAMU High Volume TSP Sampler at a nominal volumetric flow rate of 1.42 m3 min(-1) (50 CFM), the TAMU Low Volume TSP Sampler at a nominal volumetric flow rate of 17 L min(-1) (0.6 CFM) and the EPA TSP Sampler at the nominal volumetric flow rates of 1.1 and 1.7 m3 min(-1) (39 and 60 CFM). Under nominal operating conditions the overall measurement uncertainty was found to vary from 6.1x10(-6) g m(-3) to 18.0x10(-6) g m(-3), which represented an uncertainty of 1.7% to 5.2% of the measurement. Analysis of the uncertainty budget determined that three of the instrument parameters contributed significantly to the overall uncertainty: the uncertainty in the pressure drop measurement across the orifice meter during both calibration and testing and the uncertainty of the airflow standard used during calibration of the orifice meter. Five environmental parameters occurring during field measurements were considered for their effect on overall uncertainty: ambient TSP concentration, volumetric airflow rate, ambient temperature, ambient pressure, and ambient relative humidity. Of these, only ambient TSP concentration and volumetric airflow rate were found to have a strong effect on the overall uncertainty. The technique described in this paper can be applied to other measurement systems and is especially useful where there are no methods available to generate these values empirically. This work addresses measurement uncertainty of TSP samplers used in ambient conditions. Estimation of uncertainty in gravimetric measurements is of particular interest, since as ambient particulate matter (PM) concentrations approach regulatory limits, the uncertainty of the measurement is essential in determining the sample size and the probability of type II errors in hypothesis testing. This is an important factor in determining if ambient PM concentrations exceed regulatory limits. The technique described in this paper can be applied to other measurement systems and is especially useful where there are no methods available to generate these values empirically.

Insights into water managers' perception and handling of uncertainties - a study of the role of uncertainty in practitioners' planning and decision-making

NASA Astrophysics Data System (ADS)

Höllermann, Britta; Evers, Mariele

2017-04-01

Planning and decision-making under uncertainty is common in water management due to climate variability, simplified models, societal developments, planning restrictions just to name a few. Dealing with uncertainty can be approached from two sites, hereby affecting the process and form of communication: Either improve the knowledge base by reducing uncertainties or apply risk-based approaches to acknowledge uncertainties throughout the management process. Current understanding is that science more strongly focusses on the former approach, while policy and practice are more actively applying a risk-based approach to handle incomplete and/or ambiguous information. The focus of this study is on how water managers perceive and handle uncertainties at the knowledge/decision interface in their daily planning and decision-making routines. How they evaluate the role of uncertainties for their decisions and how they integrate this information into the decision-making process. Expert interviews and questionnaires among practitioners and scientists provided an insight into their perspectives on uncertainty handling allowing a comparison of diverse strategies between science and practice as well as between different types of practitioners. Our results confirmed the practitioners' bottom up approach from potential measures upwards instead of impact assessment downwards common in science-based approaches. This science-practice gap may hinder effective uncertainty integration and acknowledgement in final decisions. Additionally, the implementation of an adaptive and flexible management approach acknowledging uncertainties is often stalled by rigid regulations favouring a predict-and-control attitude. However, the study showed that practitioners' level of uncertainty recognition varies with respect to his or her affiliation to type of employer and business unit, hence, affecting the degree of the science-practice-gap with respect to uncertainty recognition. The level of working experience was examined as a cross-cutting property of science and practice with increasing levels of uncertainty awareness and integration among more experienced researchers and practitioners. In conclusion, our study of water managers' perception and handling of uncertainties provides valuable insights for finding routines for uncertainty communication and integration into planning and decision-making processes by acknowledging the divers perceptions among producers, users and receivers of uncertainty information. These results can contribute to more effective integration of hydrological forecast and improved decisions.

Assessing Uncertainty in Risk Assessment Models (BOSC CSS meeting)

EPA Science Inventory

In vitro assays are increasingly being used in risk assessments Uncertainty in assays leads to uncertainty in models used for risk assessments. This poster assesses uncertainty in the ER and AR models.

The NASA Langley Multidisciplinary Uncertainty Quantification Challenge

NASA Technical Reports Server (NTRS)

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

2014-01-01

This paper presents the formulation of an uncertainty quantification challenge problem consisting of five subproblems. These problems focus on key aspects of uncertainty characterization, sensitivity analysis, uncertainty propagation, extreme-case analysis, and robust design.

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.

Uncertainty Budget Analysis for Dimensional Inspection Processes (U)

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

Valdez, Lucas M.

2012-07-26

This paper is intended to provide guidance and describe how to prepare an uncertainty analysis of a dimensional inspection process through the utilization of an uncertainty budget analysis. The uncertainty analysis is stated in the same methodology as that of the ISO GUM standard for calibration and testing. There is a specific distinction between how Type A and Type B uncertainty analysis is used in a general and specific process. All theory and applications are utilized to represent both a generalized approach to estimating measurement uncertainty and how to report and present these estimations for dimensional measurements in a dimensionalmore » inspection process. The analysis of this uncertainty budget shows that a well-controlled dimensional inspection process produces a conservative process uncertainty, which can be attributed to the necessary assumptions in place for best possible results.« less

Uncertainty representation of grey numbers and grey sets.

PubMed

Yang, Yingjie; Liu, Sifeng; John, Robert

2014-09-01

In the literature, there is a presumption that a grey set and an interval-valued fuzzy set are equivalent. This presumption ignores the existence of discrete components in a grey number. In this paper, new measurements of uncertainties of grey numbers and grey sets, consisting of both absolute and relative uncertainties, are defined to give a comprehensive representation of uncertainties in a grey number and a grey set. Some simple examples are provided to illustrate that the proposed uncertainty measurement can give an effective representation of both absolute and relative uncertainties in a grey number and a grey set. The relationships between grey sets and interval-valued fuzzy sets are also analyzed from the point of view of the proposed uncertainty representation. The analysis demonstrates that grey sets and interval-valued fuzzy sets provide different but overlapping models for uncertainty representation in sets.

Uncertainty after treatment for prostate cancer: definition, assessment, and management.

PubMed

Yu Ko, Wellam F; Degner, Lesley F

2008-10-01

Prostate cancer is the second most common type of cancer in men living in the United States and the most common type of malignancy in Canadian men, accounting for 186,320 new cases in the United States and 24,700 in Canada in 2008. Uncertainty, a component of all illness experiences, influences how men perceive the processes of treatment and adaptation. The Reconceptualized Uncertainty in Illness Theory explains the chronic nature of uncertainty in cancer survivorship by describing a shift from an emergent acute phase of uncertainty in survivors to a new level of uncertainty that is no longer acute and becomes a part of daily life. Proper assessment of certainty and uncertainty may allow nurses to maximize the effectiveness of patient-provider communication, cognitive reframing, and problem-solving interventions to reduce uncertainty after cancer treatment.

Monthly Fossil-Fuel CO2 Emissions: Uncertainty of Emissions Gridded by On Degree Latitude by One Degree Longitude (Uncertainties, V.2016)

DOE Data Explorer

Andres, J.A. [Oak Ridge National Lab. (ORNL), Oak Ridge, TN (United States); Boden, T.A. [Oak Ridge National Lab. (ORNL), Oak Ridge, TN (United States)

2016-01-01

The monthly, gridded fossil-fuel CO2 emissions uncertainty estimates from 1950-2013 provided in this database are derived from time series of global, regional, and national fossil-fuel CO2 emissions (Boden et al. 2016). Andres et al. (2016) describes the basic methodology in estimating the uncertainty in the (gridded fossil fuel data product ). This uncertainty is gridded at the same spatial and temporal scales as the mass magnitude maps. This gridded uncertainty includes uncertainty contributions from the spatial, temporal, proxy, and magnitude components used to create the magnitude map of FFCO2 emissions. Throughout this process, when assumptions had to be made or expert judgment employed, the general tendency in most cases was toward overestimating or increasing the magnitude of uncertainty.

Measurement uncertainty of liquid chromatographic analyses visualized by Ishikawa diagrams.

PubMed

Meyer, Veronika R

2003-09-01

Ishikawa, or cause-and-effect diagrams, help to visualize the parameters that influence a chromatographic analysis. Therefore, they facilitate the set up of the uncertainty budget of the analysis, which can then be expressed in mathematical form. If the uncertainty is calculated as the Gaussian sum of all uncertainty parameters, it is necessary to quantitate them all, a task that is usually not practical. The other possible approach is to use the intermediate precision as a base for the uncertainty calculation. In this case, it is at least necessary to consider the uncertainty of the purity of the reference material in addition to the precision data. The Ishikawa diagram is then very simple, and so is the uncertainty calculation. This advantage is given by the loss of information about the parameters that influence the measurement uncertainty.

Uncertainties in historical pollution data from sedimentary records from an Australian urban floodplain lake

NASA Astrophysics Data System (ADS)

Lintern, A.; Leahy, P.; Deletic, A.; Heijnis, H.; Zawadzki, A.; Gadd, P.; McCarthy, D.

2018-05-01

Sediment cores from aquatic environments can provide valuable information about historical pollution levels and sources. However, there is little understanding of the uncertainties associated with these findings. The aim of this study is to fill this knowledge gap by proposing a framework for quantifying the uncertainties in historical heavy metal pollution records reconstructed from sediment cores. This uncertainty framework consists of six sources of uncertainty: uncertainties in (1) metals analysis methods, (2) spatial variability of sediment core heavy metal profiles, (3) sub-sampling intervals, (4) the sediment chronology, (5) the assumption that metal levels in bed sediments reflect the magnitude of metal inputs into the aquatic system, and (6) post-depositional transformation of metals. We apply this uncertainty framework to an urban floodplain lake in South-East Australia (Willsmere Billabong). We find that for this site, uncertainties in historical dated heavy metal profiles can be up to 176%, largely due to uncertainties in the sediment chronology, and in the assumption that the settled heavy metal mass is equivalent to the heavy metal mass entering the aquatic system. As such, we recommend that future studies reconstructing historical pollution records using sediment cores from aquatic systems undertake an investigation of the uncertainties in the reconstructed pollution record, using the uncertainty framework provided in this study. We envisage that quantifying and understanding the uncertainties associated with the reconstructed pollution records will facilitate the practical application of sediment core heavy metal profiles in environmental management projects.

Overall uncertainty study of the hydrological impacts of climate change for a Canadian watershed

NASA Astrophysics Data System (ADS)

Chen, Jie; Brissette, FrançOis P.; Poulin, Annie; Leconte, Robert

2011-12-01

General circulation models (GCMs) and greenhouse gas emissions scenarios (GGES) are generally considered to be the two major sources of uncertainty in quantifying the climate change impacts on hydrology. Other sources of uncertainty have been given less attention. This study considers overall uncertainty by combining results from an ensemble of two GGES, six GCMs, five GCM initial conditions, four downscaling techniques, three hydrological model structures, and 10 sets of hydrological model parameters. Each climate projection is equally weighted to predict the hydrology on a Canadian watershed for the 2081-2100 horizon. The results show that the choice of GCM is consistently a major contributor to uncertainty. However, other sources of uncertainty, such as the choice of a downscaling method and the GCM initial conditions, also have a comparable or even larger uncertainty for some hydrological variables. Uncertainties linked to GGES and the hydrological model structure are somewhat less than those related to GCMs and downscaling techniques. Uncertainty due to the hydrological model parameter selection has the least important contribution among all the variables considered. Overall, this research underlines the importance of adequately covering all sources of uncertainty. A failure to do so may result in moderately to severely biased climate change impact studies. Results further indicate that the major contributors to uncertainty vary depending on the hydrological variables selected, and that the methodology presented in this paper is successful at identifying the key sources of uncertainty to consider for a climate change impact study.

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.

Analysis of uncertainties in the estimates of nitrous oxide and methane emissions in the UK's greenhouse gas inventory for agriculture

NASA Astrophysics Data System (ADS)

Milne, Alice E.; Glendining, Margaret J.; Bellamy, Pat; Misselbrook, Tom; Gilhespy, Sarah; Rivas Casado, Monica; Hulin, Adele; van Oijen, Marcel; Whitmore, Andrew P.

2014-01-01

The UK's greenhouse gas inventory for agriculture uses a model based on the IPCC Tier 1 and Tier 2 methods to estimate the emissions of methane and nitrous oxide from agriculture. The inventory calculations are disaggregated at country level (England, Wales, Scotland and Northern Ireland). Before now, no detailed assessment of the uncertainties in the estimates of emissions had been done. We used Monte Carlo simulation to do such an analysis. We collated information on the uncertainties of each of the model inputs. The uncertainties propagate through the model and result in uncertainties in the estimated emissions. Using a sensitivity analysis, we found that in England and Scotland the uncertainty in the emission factor for emissions from N inputs (EF1) affected uncertainty the most, but that in Wales and Northern Ireland, the emission factor for N leaching and runoff (EF5) had greater influence. We showed that if the uncertainty in any one of these emission factors is reduced by 50%, the uncertainty in emissions of nitrous oxide reduces by 10%. The uncertainty in the estimate for the emissions of methane emission factors for enteric fermentation in cows and sheep most affected the uncertainty in methane emissions. When inventories are disaggregated (as that for the UK is) correlation between separate instances of each emission factor will affect the uncertainty in emissions. As more countries move towards inventory models with disaggregation, it is important that the IPCC give firm guidance on this topic.

Identifying and Analyzing Uncertainty Structures in the TRMM Microwave Imager Precipitation Product over Tropical Ocean Basins

NASA Technical Reports Server (NTRS)

Liu, Jianbo; Kummerow, Christian D.; Elsaesser, Gregory S.

2016-01-01

Despite continuous improvements in microwave sensors and retrieval algorithms, our understanding of precipitation uncertainty is quite limited, due primarily to inconsistent findings in studies that compare satellite estimates to in situ observations over different parts of the world. This study seeks to characterize the temporal and spatial properties of uncertainty in the Tropical Rainfall Measuring Mission Microwave Imager surface rainfall product over tropical ocean basins. Two uncertainty analysis frameworks are introduced to qualitatively evaluate the properties of uncertainty under a hierarchy of spatiotemporal data resolutions. The first framework (i.e. 'climate method') demonstrates that, apart from random errors and regionally dependent biases, a large component of the overall precipitation uncertainty is manifested in cyclical patterns that are closely related to large-scale atmospheric modes of variability. By estimating the magnitudes of major uncertainty sources independently, the climate method is able to explain 45-88% of the monthly uncertainty variability. The percentage is largely resolution dependent (with the lowest percentage explained associated with a 1 deg x 1 deg spatial/1 month temporal resolution, and highest associated with a 3 deg x 3 deg spatial/3 month temporal resolution). The second framework (i.e. 'weather method') explains regional mean precipitation uncertainty as a summation of uncertainties associated with individual precipitation systems. By further assuming that self-similar recurring precipitation systems yield qualitatively comparable precipitation uncertainties, the weather method can consistently resolve about 50 % of the daily uncertainty variability, with only limited dependence on the regions of interest.

Bias and robustness of uncertainty components estimates in transient climate projections

NASA Astrophysics Data System (ADS)

Hingray, Benoit; Blanchet, Juliette; Jean-Philippe, Vidal

2016-04-01

A critical issue in climate change studies is the estimation of uncertainties in projections along with the contribution of the different uncertainty sources, including scenario uncertainty, the different components of model uncertainty and internal variability. Quantifying the different uncertainty sources faces actually different problems. For instance and for the sake of simplicity, an estimate of model uncertainty is classically obtained from the empirical variance of the climate responses obtained for the different modeling chains. These estimates are however biased. Another difficulty arises from the limited number of members that are classically available for most modeling chains. In this case, the climate response of one given chain and the effect of its internal variability may be actually difficult if not impossible to separate. The estimate of scenario uncertainty, model uncertainty and internal variability components are thus likely to be not really robust. We explore the importance of the bias and the robustness of the estimates for two classical Analysis of Variance (ANOVA) approaches: a Single Time approach (STANOVA), based on the only data available for the considered projection lead time and a time series based approach (QEANOVA), which assumes quasi-ergodicity of climate outputs over the whole available climate simulation period (Hingray and Saïd, 2014). We explore both issues for a simple but classical configuration where uncertainties in projections are composed of two single sources: model uncertainty and internal climate variability. The bias in model uncertainty estimates is explored from theoretical expressions of unbiased estimators developed for both ANOVA approaches. The robustness of uncertainty estimates is explored for multiple synthetic ensembles of time series projections generated with MonteCarlo simulations. For both ANOVA approaches, when the empirical variance of climate responses is used to estimate model uncertainty, the bias is always positive. It can be especially high with STANOVA. In the most critical configurations, when the number of members available for each modeling chain is small (< 3) and when internal variability explains most of total uncertainty variance (75% or more), the overestimation is higher than 100% of the true model uncertainty variance. The bias can be considerably reduced with a time series ANOVA approach, owing to the multiple time steps accounted for. The longer the transient time period used for the analysis, the larger the reduction. When a quasi-ergodic ANOVA approach is applied to decadal data for the whole 1980-2100 period, the bias is reduced by a factor 2.5 to 20 depending on the projection lead time. In all cases, the bias is likely to be not negligible for a large number of climate impact studies resulting in a likely large overestimation of the contribution of model uncertainty to total variance. For both approaches, the robustness of all uncertainty estimates is higher when more members are available, when internal variability is smaller and/or the response-to-uncertainty ratio is higher. QEANOVA estimates are much more robust than STANOVA ones: QEANOVA simulated confidence intervals are roughly 3 to 5 times smaller than STANOVA ones. Excepted for STANOVA when less than 3 members is available, the robustness is rather high for total uncertainty and moderate for internal variability estimates. For model uncertainty or response-to-uncertainty ratio estimates, the robustness is conversely low for QEANOVA to very low for STANOVA. In the most critical configurations (small number of member, large internal variability), large over- or underestimation of uncertainty components is very thus likely. To propose relevant uncertainty analyses and avoid misleading interpretations, estimates of uncertainty components should be therefore bias corrected and ideally come with estimates of their robustness. This work is part of the COMPLEX Project (European Collaborative Project FP7-ENV-2012 number: 308601; http://www.complex.ac.uk/). Hingray, B., Saïd, M., 2014. Partitioning internal variability and model uncertainty components in a multimodel multireplicate ensemble of climate projections. J.Climate. doi:10.1175/JCLI-D-13-00629.1 Hingray, B., Blanchet, J. (revision) Unbiased estimators for uncertainty components in transient climate projections. J. Climate Hingray, B., Blanchet, J., Vidal, J.P. (revision) Robustness of uncertainty components estimates in climate projections. J.Climate

Framework for Uncertainty Assessment - Hanford Site-Wide Groundwater Flow and Transport Modeling

NASA Astrophysics Data System (ADS)

Bergeron, M. P.; Cole, C. R.; Murray, C. J.; Thorne, P. D.; Wurstner, S. K.

2002-05-01

Pacific Northwest National Laboratory is in the process of development and implementation of an uncertainty estimation methodology for use in future site assessments that addresses parameter uncertainty as well as uncertainties related to the groundwater conceptual model. The long-term goals of the effort are development and implementation of an uncertainty estimation methodology for use in future assessments and analyses being made with the Hanford site-wide groundwater model. The basic approach in the framework developed for uncertainty assessment consists of: 1) Alternate conceptual model (ACM) identification to identify and document the major features and assumptions of each conceptual model. The process must also include a periodic review of the existing and proposed new conceptual models as data or understanding become available. 2) ACM development of each identified conceptual model through inverse modeling with historical site data. 3) ACM evaluation to identify which of conceptual models are plausible and should be included in any subsequent uncertainty assessments. 4) ACM uncertainty assessments will only be carried out for those ACMs determined to be plausible through comparison with historical observations and model structure identification measures. The parameter uncertainty assessment process generally involves: a) Model Complexity Optimization - to identify the important or relevant parameters for the uncertainty analysis; b) Characterization of Parameter Uncertainty - to develop the pdfs for the important uncertain parameters including identification of any correlations among parameters; c) Propagation of Uncertainty - to propagate parameter uncertainties (e.g., by first order second moment methods if applicable or by a Monte Carlo approach) through the model to determine the uncertainty in the model predictions of interest. 5)Estimation of combined ACM and scenario uncertainty by a double sum with each component of the inner sum (an individual CCDF) representing parameter uncertainty associated with a particular scenario and ACM and the outer sum enumerating the various plausible ACM and scenario combinations in order to represent the combined estimate of uncertainty (a family of CCDFs). A final important part of the framework includes identification, enumeration, and documentation of all the assumptions, which include those made during conceptual model development, required by the mathematical model, required by the numerical model, made during the spatial and temporal descretization process, needed to assign the statistical model and associated parameters that describe the uncertainty in the relevant input parameters, and finally those assumptions required by the propagation method. Pacific Northwest National Laboratory is operated for the U.S. Department of Energy under Contract DE-AC06-76RL01830.

Determination of Uncertainties for the New SSME Model

NASA Technical Reports Server (NTRS)

Coleman, Hugh W.; Hawk, Clark W.

1996-01-01

This report discusses the uncertainty analysis performed in support of a new test analysis and performance prediction model for the Space Shuttle Main Engine. The new model utilizes uncertainty estimates for experimental data and for the analytical model to obtain the most plausible operating condition for the engine system. This report discusses the development of the data sets and uncertainty estimates to be used in the development of the new model. It also presents the application of uncertainty analysis to analytical models and the uncertainty analysis for the conservation of mass and energy balance relations is presented. A new methodology for the assessment of the uncertainty associated with linear regressions is presented.

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.

Uncertainty relations with the generalized Wigner-Yanase-Dyson skew information

NASA Astrophysics Data System (ADS)

Fan, Yajing; Cao, Huaixin; Wang, Wenhua; Meng, Huixian; Chen, Liang

2018-07-01

The uncertainty principle in quantum mechanics is a fundamental relation with different forms, including Heisenberg's uncertainty relation and Schrödinger's uncertainty relation. We introduce the generalized Wigner-Yanase-Dyson correlation and the related quantities. Various properties of them are discussed. Finally, we establish several generalizations of uncertainty relation expressed in terms of the generalized Wigner-Yanase-Dyson skew information.

Estimation of uncertainty in pKa values determined by potentiometric titration.

PubMed

Koort, Eve; Herodes, Koit; Pihl, Viljar; Leito, Ivo

2004-06-01

A procedure is presented for estimation of uncertainty in measurement of the pK(a) of a weak acid by potentiometric titration. The procedure is based on the ISO GUM. The core of the procedure is a mathematical model that involves 40 input parameters. A novel approach is used for taking into account the purity of the acid, the impurities are not treated as inert compounds only, their possible acidic dissociation is also taken into account. Application to an example of practical pK(a) determination is presented. Altogether 67 different sources of uncertainty are identified and quantified within the example. The relative importance of different uncertainty sources is discussed. The most important source of uncertainty (with the experimental set-up of the example) is the uncertainty of pH measurement followed by the accuracy of the burette and the uncertainty of weighing. The procedure gives uncertainty separately for each point of the titration curve. The uncertainty depends on the amount of titrant added, being lowest in the central part of the titration curve. The possibilities of reducing the uncertainty and interpreting the drift of the pK(a) values obtained from the same curve are discussed.

Estimation of uncertainty for contour method residual stress measurements

DOE PAGES

Olson, Mitchell D.; DeWald, Adrian T.; Prime, Michael B.; ...

2014-12-03

This paper describes a methodology for the estimation of measurement uncertainty for the contour method, where the contour method is an experimental technique for measuring a two-dimensional map of residual stress over a plane. Random error sources including the error arising from noise in displacement measurements and the smoothing of the displacement surfaces are accounted for in the uncertainty analysis. The output is a two-dimensional, spatially varying uncertainty estimate such that every point on the cross-section where residual stress is determined has a corresponding uncertainty value. Both numerical and physical experiments are reported, which are used to support the usefulnessmore » of the proposed uncertainty estimator. The uncertainty estimator shows the contour method to have larger uncertainty near the perimeter of the measurement plane. For the experiments, which were performed on a quenched aluminum bar with a cross section of 51 × 76 mm, the estimated uncertainty was approximately 5 MPa (σ/E = 7 · 10⁻⁵) over the majority of the cross-section, with localized areas of higher uncertainty, up to 10 MPa (σ/E = 14 · 10⁻⁵).« less

Uncertainty propagation in the calibration equations for NTC thermistors

NASA Astrophysics Data System (ADS)

Liu, Guang; Guo, Liang; Liu, Chunlong; Wu, Qingwen

2018-06-01

The uncertainty propagation problem is quite important for temperature measurements, since we rely so much on the sensors and calibration equations. Although uncertainty propagation for platinum resistance or radiation thermometers is well known, there have been few publications concerning negative temperature coefficient (NTC) thermistors. Insight into the propagation characteristics of uncertainty that develop when equations are determined using the Lagrange interpolation or least-squares fitting method is presented here with respect to several of the most common equations used in NTC thermistor calibration. Within this work, analytical expressions of the propagated uncertainties for both fitting methods are derived for the uncertainties in the measured temperature and resistance at each calibration point. High-precision calibration of an NTC thermistor in a precision water bath was performed by means of the comparison method. Results show that, for both fitting methods, the propagated uncertainty is flat in the interpolation region but rises rapidly beyond the calibration range. Also, for temperatures interpolated between calibration points, the propagated uncertainty is generally no greater than that associated with the calibration points. For least-squares fitting, the propagated uncertainty is significantly reduced by increasing the number of calibration points and can be well kept below the uncertainty of the calibration points.

Unveiling the decoherence effect of noise on the entropic uncertainty relation and its control by partially collapsed operations

NASA Astrophysics Data System (ADS)

Chen, Min-Nan; Sun, Wen-Yang; Huang, Ai-Jun; Ming, Fei; Wang, Dong; Ye, Liu

2018-01-01

In this work, we investigate the dynamics of quantum-memory-assisted entropic uncertainty relations under open systems, and how to steer the uncertainty under different types of decoherence. Specifically, we develop the dynamical behaviors of the uncertainty of interest under two typical categories of noise; bit flipping and depolarizing channels. It has been shown that the measurement uncertainty firstly increases and then decreases with the growth of the decoherence strength in bit flipping channels. In contrast, the uncertainty monotonically increases with the increase of the decoherence strength in depolarizing channels. Notably, and to a large degree, it is shown that the uncertainty depends on both the systematic quantum correlation and the minimal conditional entropy of the observed subsystem. Moreover, we present a possible physical interpretation for these distinctive behaviors of the uncertainty within such scenarios. Furthermore, we propose a simple and effective strategy to reduce the entropic uncertainty by means of a partially collapsed operation—quantum weak measurement. Therefore, our investigations might offer an insight into the dynamics of the measurment uncertainty under decoherence, and be of importance to quantum precision measurement in open systems.

Detailed Uncertainty Analysis of the Ares I A106 Liftoff/Transition Database

NASA Technical Reports Server (NTRS)

Hanke, Jeremy L.

2011-01-01

The Ares I A106 Liftoff/Transition Force and Moment Aerodynamics Database describes the aerodynamics of the Ares I Crew Launch Vehicle (CLV) from the moment of liftoff through the transition from high to low total angles of attack at low subsonic Mach numbers. The database includes uncertainty estimates that were developed using a detailed uncertainty quantification procedure. The Ares I Aerodynamics Panel developed both the database and the uncertainties from wind tunnel test data acquired in the NASA Langley Research Center s 14- by 22-Foot Subsonic Wind Tunnel Test 591 using a 1.75 percent scale model of the Ares I and the tower assembly. The uncertainty modeling contains three primary uncertainty sources: experimental uncertainty, database modeling uncertainty, and database query interpolation uncertainty. The final database and uncertainty model represent a significant improvement in the quality of the aerodynamic predictions for this regime of flight over the estimates previously used by the Ares Project. The maximum possible aerodynamic force pushing the vehicle towards the launch tower assembly in a dispersed case using this database saw a 40 percent reduction from the worst-case scenario in previously released data for Ares I.

Evaluating measurement uncertainty in fluid phase equilibrium calculations

NASA Astrophysics Data System (ADS)

van der Veen, Adriaan M. H.

2018-04-01

The evaluation of measurement uncertainty in accordance with the ‘Guide to the expression of uncertainty in measurement’ (GUM) has not yet become widespread in physical chemistry. With only the law of the propagation of uncertainty from the GUM, many of these uncertainty evaluations would be cumbersome, as models are often non-linear and require iterative calculations. The methods from GUM supplements 1 and 2 enable the propagation of uncertainties under most circumstances. Experimental data in physical chemistry are used, for example, to derive reference property data and support trade—all applications where measurement uncertainty plays an important role. This paper aims to outline how the methods for evaluating and propagating uncertainty can be applied to some specific cases with a wide impact: deriving reference data from vapour pressure data, a flash calculation, and the use of an equation-of-state to predict the properties of both phases in a vapour-liquid equilibrium. The three uncertainty evaluations demonstrate that the methods of GUM and its supplements are a versatile toolbox that enable us to evaluate the measurement uncertainty of physical chemical measurements, including the derivation of reference data, such as the equilibrium thermodynamical properties of fluids.

Intolerance of uncertainty, causal uncertainty, causal importance, self-concept clarity and their relations to generalized anxiety disorder.

PubMed

Kusec, Andrea; Tallon, Kathleen; Koerner, Naomi

2016-06-01

Although numerous studies have provided support for the notion that intolerance of uncertainty plays a key role in pathological worry (the hallmark feature of generalized anxiety disorder (GAD)), other uncertainty-related constructs may also have relevance for the understanding of individuals who engage in pathological worry. Three constructs from the social cognition literature, causal uncertainty, causal importance, and self-concept clarity, were examined in the present study to assess the degree to which these explain unique variance in GAD, over and above intolerance of uncertainty. N = 235 participants completed self-report measures of trait worry, GAD symptoms, and uncertainty-relevant constructs. A subgroup was subsequently classified as low in GAD symptoms (n = 69) or high in GAD symptoms (n = 54) based on validated cut scores on measures of trait worry and GAD symptoms. In logistic regressions, only elevated intolerance of uncertainty and lower self-concept clarity emerged as unique correlates of high (vs. low) GAD symptoms. The possible role of self-concept uncertainty in GAD and the utility of integrating social cognition theories and constructs into clinical research on intolerance of uncertainty are discussed.

Experimental Research Examining How People Can Cope with Uncertainty Through Soft Haptic Sensations.

PubMed

van Horen, Femke; Mussweiler, Thomas

2015-09-16

Human beings are constantly surrounded by uncertainty and change. The question arises how people cope with such uncertainty. To date, most research has focused on the cognitive strategies people adopt to deal with uncertainty. However, especially when uncertainty is due to unpredictable societal events (e.g., economical crises, political revolutions, terrorism threats) of which one is unable to judge the impact on one's future live, cognitive strategies (like seeking additional information) is likely to fail to combat uncertainty. Instead, the current paper discusses a method demonstrating that people might deal with uncertainty experientially through soft haptic sensations. More specifically, because touching something soft creates a feeling of comfort and security, people prefer objects with softer as compared to harder properties when feeling uncertain. Seeking for softness is a highly efficient and effective tool to deal with uncertainty as our hands are available at all times. This protocol describes a set of methods demonstrating 1) how environmental (un)certainty can be situationally activated with an experiential priming procedure, 2) that the quality of the softness experience (what type of softness and how it is experienced) matters and 3) how uncertainty can be reduced using different methods.

Calculation of surface and top of atmosphere radiative fluxes from physical quantities based on ISCCP data sets. 1: Method and sensitivity to input data uncertainties

NASA Technical Reports Server (NTRS)

Zhang, Y.-C.; Rossow, W. B.; Lacis, A. A.

1995-01-01

The largest uncertainty in upwelling shortwave (SW) fluxes (approximately equal 10-15 W/m(exp 2), regional daily mean) is caused by uncertainties in land surface albedo, whereas the largest uncertainty in downwelling SW at the surface (approximately equal 5-10 W/m(exp 2), regional daily mean) is related to cloud detection errors. The uncertainty of upwelling longwave (LW) fluxes (approximately 10-20 W/m(exp 2), regional daily mean) depends on the accuracy of the surface temperature for the surface LW fluxes and the atmospheric temperature for the top of atmosphere LW fluxes. The dominant source of uncertainty is downwelling LW fluxes at the surface (approximately equal 10-15 W/m(exp 2)) is uncertainty in atmospheric temperature and, secondarily, atmospheric humidity; clouds play little role except in the polar regions. The uncertainties of the individual flux components and the total net fluxes are largest over land (15-20 W/m(exp 2)) because of uncertainties in surface albedo (especially its spectral dependence) and surface temperature and emissivity (including its spectral dependence). Clouds are the most important modulator of the SW fluxes, but over land areas, uncertainties in net SW at the surface depend almost as much on uncertainties in surface albedo. Although atmospheric and surface temperature variations cause larger LW flux variations, the most notable feature of the net LW fluxes is the changing relative importance of clouds and water vapor with latitude. Uncertainty in individual flux values is dominated by sampling effects because of large natrual variations, but uncertainty in monthly mean fluxes is dominated by bias errors in the input quantities.

Uncertainty of Acute Stroke Patients: A Cross-sectional Descriptive and Correlational Study.

PubMed

Ni, Chunping; Peng, Jing; Wei, Yuanyuan; Hua, Yan; Ren, Xiaoran; Su, Xiangni; Shi, Ruijie

2018-06-12

Uncertainty is a chronic and pervasive source of psychological distress for patients and plays an important role in the rehabilitation of stroke survivors. Little is known about the level and correlates of uncertainty among patients in the acute phase of stroke. The purposes of this study were to describe the uncertainty of patients in the acute phase of stroke and to explore characteristics of patients associated with that uncertainty. A cross-sectional descriptive and correlational study was conducted with a convenience sample of 451 consecutive hospitalized acute stroke patients recruited from the neurology department of 2 general hospitals of China. Uncertainty was measured using Chinese versions of Mishel Uncertainty in Illness Scale for Adults on the fourth day of patients' admission. The patients had moderately high Mishel Uncertainty in Illness Scale for Adults scores (mean [SD], 74.37 [9.22]) in the acute phase of stroke. A total of 95.2% and 2.9% of patients were in moderate and high levels of uncertainty, respectively. The mean (SD) score of ambiguity (3.05 [0.39]) was higher than that of complexity (2.88 [0.52]). Each of the following characteristics was independently associated with greater uncertainty: functional status (P = .000), suffering from other chronic diseases (P = .000), time since the first-ever stroke (P = .000), self-evaluated economic pressure (P = .000), family monthly income (P = .001), educational level (P = .006), and self-evaluated severity of disease (P = .000). Patients experienced persistently, moderately high uncertainty in the acute phase of stroke. Ameliorating uncertainty should be an integral part of the rehabilitation program. Better understanding of uncertainty and its associated characteristics may help nurses identify patients at the highest risk who may benefit from targeted interventions.

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.

Impacts of Process and Prediction Uncertainties on Projected Hanford Waste Glass Amount

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

Gervasio, V.; Kim, D. S.; Vienna, J. D.

Analyses were performed to evaluate the impacts of using the advanced glass models, constraints, and uncertainty descriptions on projected Hanford glass mass. The maximum allowable waste oxide loading (WOL) was estimated for waste compositions while simultaneously satisfying all applicable glass property and composition constraints with sufficient confidence. Different components of prediction and composition/process uncertainties were systematically included in the calculations to evaluate their impacts on glass mass. The analyses estimated the production of 23,360 MT of immobilized high-level waste (IHLW) glass when no uncertainties were taken into account. Accounting for prediction and composition/process uncertainties resulted in 5.01 relative percent increasemore » in estimated glass mass of 24,531 MT. Roughly equal impacts were found for prediction uncertainties (2.58 RPD) and composition/process uncertainties (2.43 RPD). The immobilized low-activity waste (ILAW) mass was predicted to be 282,350 MT without uncertainty and with waste loading “line” rules in place. Accounting for prediction and composition/process uncertainties resulted in only 0.08 relative percent increase in estimated glass mass of 282,562 MT. Without application of line rules the glass mass decreases by 10.6 relative percent (252,490 MT) for the case with no uncertainties. Addition of prediction uncertainties increases glass mass by 1.32 relative percent and the addition of composition/process uncertainties increase glass mass by an additional 7.73 relative percent (9.06 relative percent increase combined). The glass mass estimate without line rules (275,359 MT) was 2.55 relative percent lower than that with the line rules (282,562 MT), after accounting for all applicable uncertainties.« less

What is the uncertainty principle of non-relativistic quantum mechanics?

NASA Astrophysics Data System (ADS)

Riggs, Peter J.

2018-05-01

After more than ninety years of discussions over the uncertainty principle, there is still no universal agreement on what the principle states. The Robertson uncertainty relation (incorporating standard deviations) is given as the mathematical expression of the principle in most quantum mechanics textbooks. However, the uncertainty principle is not merely a statement of what any of the several uncertainty relations affirm. It is suggested that a better approach would be to present the uncertainty principle as a statement about the probability distributions of incompatible variables and the resulting restrictions on quantum states.

Moving Beyond 2% Uncertainty: A New Framework for Quantifying Lidar Uncertainty

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

Newman, Jennifer F.; Clifton, Andrew

2017-03-08

Remote sensing of wind using lidar is revolutionizing wind energy. However, current generations of wind lidar are ascribed a climatic value of uncertainty, which is based on a poor description of lidar sensitivity to external conditions. In this presentation, we show how it is important to consider the complete lidar measurement process to define the measurement uncertainty, which in turn offers the ability to define a much more granular and dynamic measurement uncertainty. This approach is a progression from the 'white box' lidar uncertainty method.

Comprehensive Approach to Verification and Validation of CFD Simulations Applied to Backward Facing Step-Application of CFD Uncertainty Analysis

NASA Technical Reports Server (NTRS)

Groves, Curtis E.; LLie, Marcel; Shallhorn, Paul A.

2012-01-01

There are inherent uncertainties and errors associated with using Computational Fluid Dynamics (CFD) to predict the flow field and there is no standard method for evaluating uncertainty in the CFD community. This paper describes an approach to -validate the . uncertainty in using CFD. The method will use the state of the art uncertainty analysis applying different turbulence niodels and draw conclusions on which models provide the least uncertainty and which models most accurately predict the flow of a backward facing step.

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.

Estimating uncertainties in complex joint inverse problems

NASA Astrophysics Data System (ADS)

Afonso, Juan Carlos

2016-04-01

Sources of uncertainty affecting geophysical inversions can be classified either as reflective (i.e. the practitioner is aware of her/his ignorance) or non-reflective (i.e. the practitioner does not know that she/he does not know!). Although we should be always conscious of the latter, the former are the ones that, in principle, can be estimated either empirically (by making measurements or collecting data) or subjectively (based on the experience of the researchers). For complex parameter estimation problems in geophysics, subjective estimation of uncertainty is the most common type. In this context, probabilistic (aka Bayesian) methods are commonly claimed to offer a natural and realistic platform from which to estimate model uncertainties. This is because in the Bayesian approach, errors (whatever their nature) can be naturally included as part of the global statistical model, the solution of which represents the actual solution to the inverse problem. However, although we agree that probabilistic inversion methods are the most powerful tool for uncertainty estimation, the common claim that they produce "realistic" or "representative" uncertainties is not always justified. Typically, ALL UNCERTAINTY ESTIMATES ARE MODEL DEPENDENT, and therefore, besides a thorough characterization of experimental uncertainties, particular care must be paid to the uncertainty arising from model errors and input uncertainties. We recall here two quotes by G. Box and M. Gunzburger, respectively, of special significance for inversion practitioners and for this session: "…all models are wrong, but some are useful" and "computational results are believed by no one, except the person who wrote the code". In this presentation I will discuss and present examples of some problems associated with the estimation and quantification of uncertainties in complex multi-observable probabilistic inversions, and how to address them. Although the emphasis will be on sources of uncertainty related to the forward and statistical models, I will also address other uncertainties associated with data and uncertainty propagation.

Defining and Measuring Diagnostic Uncertainty in Medicine: A Systematic Review.

PubMed

Bhise, Viraj; Rajan, Suja S; Sittig, Dean F; Morgan, Robert O; Chaudhary, Pooja; Singh, Hardeep

2018-01-01

Physicians routinely encounter diagnostic uncertainty in practice. Despite its impact on health care utilization, costs and error, measurement of diagnostic uncertainty is poorly understood. We conducted a systematic review to describe how diagnostic uncertainty is defined and measured in medical practice. We searched OVID Medline and PsycINFO databases from inception until May 2017 using a combination of keywords and Medical Subject Headings (MeSH). Additional search strategies included manual review of references identified in the primary search, use of a topic-specific database (AHRQ-PSNet) and expert input. We specifically focused on articles that (1) defined diagnostic uncertainty; (2) conceptualized diagnostic uncertainty in terms of its sources, complexity of its attributes or strategies for managing it; or (3) attempted to measure diagnostic uncertainty. We identified 123 articles for full review, none of which defined diagnostic uncertainty. Three attributes of diagnostic uncertainty were relevant for measurement: (1) it is a subjective perception experienced by the clinician; (2) it has the potential to impact diagnostic evaluation-for example, when inappropriately managed, it can lead to diagnostic delays; and (3) it is dynamic in nature, changing with time. Current methods for measuring diagnostic uncertainty in medical practice include: (1) asking clinicians about their perception of uncertainty (surveys and qualitative interviews), (2) evaluating the patient-clinician encounter (such as by reviews of medical records, transcripts of patient-clinician communication and observation), and (3) experimental techniques (patient vignette studies). The term "diagnostic uncertainty" lacks a clear definition, and there is no comprehensive framework for its measurement in medical practice. Based on review findings, we propose that diagnostic uncertainty be defined as a "subjective perception of an inability to provide an accurate explanation of the patient's health problem." Methodological advancements in measuring diagnostic uncertainty can improve our understanding of diagnostic decision-making and inform interventions to reduce diagnostic errors and overuse of health care resources.

SU-F-T-316: A Model to Deal with Dosimetric and Delivery Uncertainties in Radiotherapy Treatment Planning

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

Haering, P; Lang, C; Splinter, M

2016-06-15

Purpose The conventional way of dealing with uncertainties resulting from dose calculation or beam delivery in IMRT, is to do verification measurements for the plan in question. Here we present an alternative based on recommendations given in the AAPM 142 report and treatment specific parameters that model the uncertainties for the plan delivery. Methods Basis of the model is the assignment of uncertainty parameters to all segment fields or control point sequences of a plan. The given field shape is analyzed for complexity, dose rate, number of MU, field size related output as well as factors for in/out field positionmore » and penumbra regions. Together with depth related uncertainties, a 3D matrix is generated by a projection algorithm. Patient anatomy is included as uncertainty CT data set as well. Therefore, object density is classified in 4 categories close to water, lung, bone and gradient regions with additional uncertainties. The result is then exported as a DICOM dose file by the software tool (written in IDL, Exelis), having the given resolution and target point. Results Uncertainty matrixes for several patient cases have been calculated and compared side by side in the planning system. The result is not quite always intuitive but it clearly indicates high and low uncertainties related to OARs and target volumes as well as to measured gamma distributions.ConclusionThe imported uncertainty datasets may help the treatment planner to understand the complexity of the treatment plan. He then might decide to change the plan to produce a more suited uncertainty distribution, e.g. by changing the beam angles the high uncertainty spots can be influenced or try to use another treatment setup, resulting in a plan with lower uncertainties. A next step could be to include such a model into the optimization algorithm to add a new dose uncertainty constraint.« less

Known unknowns: building an ethics of uncertainty into genomic medicine.

PubMed

Newson, Ainsley J; Leonard, Samantha J; Hall, Alison; Gaff, Clara L

2016-09-01

Genomic testing has reached the point where, technically at least, it can be cheaper to undertake panel-, exome- or whole genome testing than it is to sequence a single gene. An attribute of these approaches is that information gleaned will often have uncertain significance. In addition to the challenges this presents for pre-test counseling and informed consent, a further consideration emerges over how - ethically - we should conceive of and respond to this uncertainty. To date, the ethical aspects of uncertainty in genomics have remained under-explored. In this paper, we draft a conceptual and ethical response to the question of how to conceive of and respond to uncertainty in genomic medicine. After introducing the problem, we articulate a concept of 'genomic uncertainty'. Drawing on this, together with exemplar clinical cases and related empirical literature, we then critique the presumption that uncertainty is always problematic and something to be avoided, or eradicated. We conclude by outlining an 'ethics of genomic uncertainty'; describing how we might handle uncertainty in genomic medicine. This involves fostering resilience, welfare, autonomy and solidarity. Uncertainty will be an inherent aspect of clinical practice in genomics for some time to come. Genomic testing should not be offered with the explicit aim to reduce uncertainty. Rather, uncertainty should be appraised, adapted to and communicated about as part of the process of offering and providing genomic information.

Evaluation of measurement uncertainty of glucose in clinical chemistry.

PubMed

Berçik Inal, B; Koldas, M; Inal, H; Coskun, C; Gümüs, A; Döventas, Y

2007-04-01

The definition of the uncertainty of measurement used in the International Vocabulary of Basic and General Terms in Metrology (VIM) is a parameter associated with the result of a measurement, which characterizes the dispersion of the values that could reasonably be attributed to the measurand. Uncertainty of measurement comprises many components. In addition to every parameter, the measurement uncertainty is that a value should be given by all institutions that have been accredited. This value shows reliability of the measurement. GUM, published by NIST, contains uncertainty directions. Eurachem/CITAC Guide CG4 was also published by Eurachem/CITAC Working Group in the year 2000. Both of them offer a mathematical model, for uncertainty can be calculated. There are two types of uncertainty in measurement. Type A is the evaluation of uncertainty through the statistical analysis and type B is the evaluation of uncertainty through other means, for example, certificate reference material. Eurachem Guide uses four types of distribution functions: (1) rectangular distribution that gives limits without specifying a level of confidence (u(x)=a/ radical3) to a certificate; (2) triangular distribution that values near to the same point (u(x)=a/ radical6); (3) normal distribution in which an uncertainty is given in the form of a standard deviation s, a relative standard deviation s/ radicaln, or a coefficient of variance CV% without specifying the distribution (a = certificate value, u = standard uncertainty); and (4) confidence interval.

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.

Irreducible Uncertainty in Terrestrial Carbon Projections

NASA Astrophysics Data System (ADS)

Lovenduski, N. S.; Bonan, G. B.

2016-12-01

We quantify and isolate the sources of uncertainty in projections of carbon accumulation by the ocean and terrestrial biosphere over 2006-2100 using output from Earth System Models participating in the 5th Coupled Model Intercomparison Project. We consider three independent sources of uncertainty in our analysis of variance: (1) internal variability, driven by random, internal variations in the climate system, (2) emission scenario, driven by uncertainty in future radiative forcing, and (3) model structure, wherein different models produce different projections given the same emission scenario. Whereas uncertainty in projections of ocean carbon accumulation by 2100 is 100 Pg C and driven primarily by emission scenario, uncertainty in projections of terrestrial carbon accumulation by 2100 is 50% larger than that of the ocean, and driven primarily by model structure. This structural uncertainty is correlated with emission scenario: the variance associated with model structure is an order of magnitude larger under a business-as-usual scenario (RCP8.5) than a mitigation scenario (RCP2.6). In an effort to reduce this structural uncertainty, we apply various model weighting schemes to our analysis of variance in terrestrial carbon accumulation projections. The largest reductions in uncertainty are achieved when giving all the weight to a single model; here the uncertainty is of a similar magnitude to the ocean projections. Such an analysis suggests that this structural uncertainty is irreducible given current terrestrial model development efforts.

Uncertainty of climate change impact on groundwater reserves - Application to a chalk aquifer

NASA Astrophysics Data System (ADS)

Goderniaux, Pascal; Brouyère, Serge; Wildemeersch, Samuel; Therrien, René; Dassargues, Alain

2015-09-01

Recent studies have evaluated the impact of climate change on groundwater resources for different geographical and climatic contexts. However, most studies have either not estimated the uncertainty around projected impacts or have limited the analysis to the uncertainty related to climate models. In this study, the uncertainties around impact projections from several sources (climate models, natural variability of the weather, hydrological model calibration) are calculated and compared for the Geer catchment (465 km2) in Belgium. We use a surface-subsurface integrated model implemented using the finite element code HydroGeoSphere, coupled with climate change scenarios (2010-2085) and the UCODE_2005 inverse model, to assess the uncertainty related to the calibration of the hydrological model. This integrated model provides a more realistic representation of the water exchanges between surface and subsurface domains and constrains more the calibration with the use of both surface and subsurface observed data. Sensitivity and uncertainty analyses were performed on predictions. The linear uncertainty analysis is approximate for this nonlinear system, but it provides some measure of uncertainty for computationally demanding models. Results show that, for the Geer catchment, the most important uncertainty is related to calibration of the hydrological model. The total uncertainty associated with the prediction of groundwater levels remains large. By the end of the century, however, the uncertainty becomes smaller than the predicted decline in groundwater levels.

Uncertainties in stormwater runoff data collection from a small urban catchment, Southeast China.

PubMed

Huang, Jinliang; Tu, Zhenshun; Du, Pengfei; Lin, Jie; Li, Qingsheng

2010-01-01

Monitoring data are often used to identify stormwater runoff characteristics and in stormwater runoff modelling without consideration of their inherent uncertainties. Integrated with discrete sample analysis and error propagation analysis, this study attempted to quantify the uncertainties of discrete chemical oxygen demand (COD), total suspended solids (TSS) concentration, stormwater flowrate, stormwater event volumes, COD event mean concentration (EMC), and COD event loads in terms of flow measurement, sample collection, storage and laboratory analysis. The results showed that the uncertainties due to sample collection, storage and laboratory analysis of COD from stormwater runoff are 13.99%, 19.48% and 12.28%. Meanwhile, flow measurement uncertainty was 12.82%, and the sample collection uncertainty of TSS from stormwater runoff was 31.63%. Based on the law of propagation of uncertainties, the uncertainties regarding event flow volume, COD EMC and COD event loads were quantified as 7.03%, 10.26% and 18.47%.

A structured analysis of uncertainty surrounding modeled impacts of groundwater-extraction rules

NASA Astrophysics Data System (ADS)

Guillaume, Joseph H. A.; Qureshi, M. Ejaz; Jakeman, Anthony J.

2012-08-01

Integrating economic and groundwater models for groundwater-management can help improve understanding of trade-offs involved between conflicting socioeconomic and biophysical objectives. However, there is significant uncertainty in most strategic decision-making situations, including in the models constructed to represent them. If not addressed, this uncertainty may be used to challenge the legitimacy of the models and decisions made using them. In this context, a preliminary uncertainty analysis was conducted of a dynamic coupled economic-groundwater model aimed at assessing groundwater extraction rules. The analysis demonstrates how a variety of uncertainties in such a model can be addressed. A number of methods are used including propagation of scenarios and bounds on parameters, multiple models, block bootstrap time-series sampling and robust linear regression for model calibration. These methods are described within the context of a theoretical uncertainty management framework, using a set of fundamental uncertainty management tasks and an uncertainty typology.

Model structures amplify uncertainty in predicted soil carbon responses to climate change.

PubMed

Shi, Zheng; Crowell, Sean; Luo, Yiqi; Moore, Berrien

2018-06-04

Large model uncertainty in projected future soil carbon (C) dynamics has been well documented. However, our understanding of the sources of this uncertainty is limited. Here we quantify the uncertainties arising from model parameters, structures and their interactions, and how those uncertainties propagate through different models to projections of future soil carbon stocks. Both the vertically resolved model and the microbial explicit model project much greater uncertainties to climate change than the conventional soil C model, with both positive and negative C-climate feedbacks, whereas the conventional model consistently predicts positive soil C-climate feedback. Our findings suggest that diverse model structures are necessary to increase confidence in soil C projection. However, the larger uncertainty in the complex models also suggests that we need to strike a balance between model complexity and the need to include diverse model structures in order to forecast soil C dynamics with high confidence and low uncertainty.

Quantum-memory-assisted entropic uncertainty in spin models with Dzyaloshinskii-Moriya interaction

NASA Astrophysics Data System (ADS)

Huang, Zhiming

2018-02-01

In this article, we investigate the dynamics and correlations of quantum-memory-assisted entropic uncertainty, the tightness of the uncertainty, entanglement, quantum correlation and mixedness for various spin chain models with Dzyaloshinskii-Moriya (DM) interaction, including the XXZ model with DM interaction, the XY model with DM interaction and the Ising model with DM interaction. We find that the uncertainty grows to a stable value with growing temperature but reduces as the coupling coefficient, anisotropy parameter and DM values increase. It is found that the entropic uncertainty is closely correlated with the mixedness of the system. The increasing quantum correlation can result in a decrease in the uncertainty, and the robustness of quantum correlation is better than entanglement since entanglement means sudden birth and death. The tightness of the uncertainty drops to zero, apart from slight volatility as various parameters increase. Furthermore, we propose an effective approach to steering the uncertainty by weak measurement reversal.

Assessment of the uncertainties in the Radiological Protection Institute of Ireland (RPII) radon measurements service.

PubMed

Hanley, O; Gutiérrez-Villanueva, J L; Currivan, L; Pollard, D

2008-10-01

The RPII radon (Rn) laboratory holds accreditation for the International Standard ISO/IEC 17025. A requirement of this standard is an estimate of the uncertainty of measurement. This work shows two approaches to estimate the uncertainty. The bottom-up approach involved identifying the components that were found to contribute to the uncertainty. Estimates were made for each of these components, which were combined to give a combined uncertainty of 13.5% at a Rn concentration of approximately 2500 Bq m(-3) at the 68% confidence level. By applying a coverage factor of k=2, the expanded uncertainty is +/-27% at the 95% confidence level. The top-down approach used information previously gathered from intercomparison exercises to estimate the uncertainty. This investigation found an expanded uncertainty of +/-22% at approximately 95% confidence level. This is good agreement for such independent estimates.

Study of Uncertainties of Predicting Space Shuttle Thermal Environment. [impact of heating rate prediction errors on weight of thermal protection system

NASA Technical Reports Server (NTRS)

Fehrman, A. L.; Masek, R. V.

1972-01-01

Quantitative estimates of the uncertainty in predicting aerodynamic heating rates for a fully reusable space shuttle system are developed and the impact of these uncertainties on Thermal Protection System (TPS) weight are discussed. The study approach consisted of statistical evaluations of the scatter of heating data on shuttle configurations about state-of-the-art heating prediction methods to define the uncertainty in these heating predictions. The uncertainties were then applied as heating rate increments to the nominal predicted heating rate to define the uncertainty in TPS weight. Separate evaluations were made for the booster and orbiter, for trajectories which included boost through reentry and touchdown. For purposes of analysis, the vehicle configuration is divided into areas in which a given prediction method is expected to apply, and separate uncertainty factors and corresponding uncertainty in TPS weight derived for each area.

Estimating discharge measurement uncertainty using the interpolated variance estimator

USGS Publications Warehouse

Cohn, T.; Kiang, J.; Mason, R.

2012-01-01

Methods for quantifying the uncertainty in discharge measurements typically identify various sources of uncertainty and then estimate the uncertainty from each of these sources by applying the results of empirical or laboratory studies. If actual measurement conditions are not consistent with those encountered in the empirical or laboratory studies, these methods may give poor estimates of discharge uncertainty. This paper presents an alternative method for estimating discharge measurement uncertainty that uses statistical techniques and at-site observations. This Interpolated Variance Estimator (IVE) estimates uncertainty based on the data collected during the streamflow measurement and therefore reflects the conditions encountered at the site. The IVE has the additional advantage of capturing all sources of random uncertainty in the velocity and depth measurements. It can be applied to velocity-area discharge measurements that use a velocity meter to measure point velocities at multiple vertical sections in a channel cross section.

Conceptual, Methodological, and Ethical Problems in Communicating Uncertainty in Clinical Evidence

PubMed Central

Han, Paul K. J.

2014-01-01

The communication of uncertainty in clinical evidence is an important endeavor that poses difficult conceptual, methodological, and ethical problems. Conceptual problems include logical paradoxes in the meaning of probability and “ambiguity”— second-order uncertainty arising from the lack of reliability, credibility, or adequacy of probability information. Methodological problems include questions about optimal methods for representing fundamental uncertainties and for communicating these uncertainties in clinical practice. Ethical problems include questions about whether communicating uncertainty enhances or diminishes patient autonomy and produces net benefits or harms. This article reviews the limited but growing literature on these problems and efforts to address them and identifies key areas of focus for future research. It is argued that the critical need moving forward is for greater conceptual clarity and consistent representational methods that make the meaning of various uncertainties understandable, and for clinical interventions to support patients in coping with uncertainty in decision making. PMID:23132891

Uncertainty and Cognitive Control

PubMed Central

Mushtaq, Faisal; Bland, Amy R.; Schaefer, Alexandre

2011-01-01

A growing trend of neuroimaging, behavioral, and computational research has investigated the topic of outcome uncertainty in decision-making. Although evidence to date indicates that humans are very effective in learning to adapt to uncertain situations, the nature of the specific cognitive processes involved in the adaptation to uncertainty are still a matter of debate. In this article, we reviewed evidence suggesting that cognitive control processes are at the heart of uncertainty in decision-making contexts. Available evidence suggests that: (1) There is a strong conceptual overlap between the constructs of uncertainty and cognitive control; (2) There is a remarkable overlap between the neural networks associated with uncertainty and the brain networks subserving cognitive control; (3) The perception and estimation of uncertainty might play a key role in monitoring processes and the evaluation of the “need for control”; (4) Potential interactions between uncertainty and cognitive control might play a significant role in several affective disorders. PMID:22007181

Combining uncertainty factors in deriving human exposure levels of noncarcinogenic toxicants.

PubMed

Kodell, R L; Gaylor, D W

1999-01-01

Acceptable levels of human exposure to noncarcinogenic toxicants in environmental and occupational settings generally are derived by reducing experimental no-observed-adverse-effect levels (NOAELs) or benchmark doses (BDs) by a product of uncertainty factors (Barnes and Dourson, Ref. 1). These factors are presumed to ensure safety by accounting for uncertainty in dose extrapolation, uncertainty in duration extrapolation, differential sensitivity between humans and animals, and differential sensitivity among humans. The common default value for each uncertainty factor is 10. This paper shows how estimates of means and standard deviations of the approximately log-normal distributions of individual uncertainty factors can be used to estimate percentiles of the distribution of the product of uncertainty factors. An appropriately selected upper percentile, for example, 95th or 99th, of the distribution of the product can be used as a combined uncertainty factor to replace the conventional product of default factors.

Information-integration category learning and the human uncertainty response.

PubMed

Paul, Erick J; Boomer, Joseph; Smith, J David; Ashby, F Gregory

2011-04-01

The human response to uncertainty has been well studied in tasks requiring attention and declarative memory systems. However, uncertainty monitoring and control have not been studied in multi-dimensional, information-integration categorization tasks that rely on non-declarative procedural memory. Three experiments are described that investigated the human uncertainty response in such tasks. Experiment 1 showed that following standard categorization training, uncertainty responding was similar in information-integration tasks and rule-based tasks requiring declarative memory. In Experiment 2, however, uncertainty responding in untrained information-integration tasks impaired the ability of many participants to master those tasks. Finally, Experiment 3 showed that the deficit observed in Experiment 2 was not because of the uncertainty response option per se, but rather because the uncertainty response provided participants a mechanism via which to eliminate stimuli that were inconsistent with a simple declarative response strategy. These results are considered in the light of recent models of category learning and metacognition.

Selective and graded coding of reward-uncertainty by neurons in the primate anterodorsal septal region

PubMed Central

Monosov, Ilya E.; Hikosaka, Okihide

2014-01-01

Natural environments are uncertain. Uncertainty of emotional outcomes can induce anxiety and raise vigilance, promote and signal the opportunity for learning, modulate economic choice, and regulate risk seeking. Here we demonstrate that a subset of neurons in the anterodorsal region of the primate septum (ADS) are primarily devoted to processing uncertainty in a highly specific manner. Those neurons were selectively activated by visual cues indicating probabilistic delivery of reward (e.g. 25%, 50%, 75% reward) and did not respond to cues indicating certain outcomes (0% and 100% reward). The average ADS uncertainty response was graded with the magnitude of reward uncertainty, and selectively signaled uncertainty about rewards rather than punishments. The selective and graded information about reward uncertainty encoded by many neurons in the ADS may underlie uncertainty-modulation of value- and sensorimotor- related areas to regulate goal-directed behavior. PMID:23666181

Transfer Standard Uncertainty Can Cause Inconclusive Inter-Laboratory Comparisons

PubMed Central

Wright, John; Toman, Blaza; Mickan, Bodo; Wübbeler, Gerd; Bodnar, Olha; Elster, Clemens

2016-01-01

Inter-laboratory comparisons use the best available transfer standards to check the participants’ uncertainty analyses, identify underestimated uncertainty claims or unknown measurement biases, and improve the global measurement system. For some measurands, instability of the transfer standard can lead to an inconclusive comparison result. If the transfer standard uncertainty is large relative to a participating laboratory’s uncertainty, the commonly used standardized degree of equivalence ≤ 1 criterion does not always correctly assess whether a participant is working within their uncertainty claims. We show comparison results that demonstrate this issue and propose several criteria for assessing a comparison result as passing, failing, or inconclusive. We investigate the behavior of the standardized degree of equivalence and alternative comparison measures for a range of values of the transfer standard uncertainty relative to the individual laboratory uncertainty values. The proposed alternative criteria successfully discerned between passing, failing, and inconclusive comparison results for the cases we examined. PMID:28090123

Uncertainty analysis for low-level radioactive waste disposal performance assessment at Oak Ridge National Laboratory

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

Lee, D.W.; Yambert, M.W.; Kocher, D.C.

1994-12-31

A performance assessment of the operating Solid Waste Storage Area 6 (SWSA 6) facility for the disposal of low-level radioactive waste at the Oak Ridge National Laboratory has been prepared to provide the technical basis for demonstrating compliance with the performance objectives of DOE Order 5820.2A, Chapter 111.2 An analysis of the uncertainty incorporated into the assessment was performed which addressed the quantitative uncertainty in the data used by the models, the subjective uncertainty associated with the models used for assessing performance of the disposal facility and site, and the uncertainty in the models used for estimating dose and humanmore » exposure. The results of the uncertainty analysis were used to interpret results and to formulate conclusions about the performance assessment. This paper discusses the approach taken in analyzing the uncertainty in the performance assessment and the role of uncertainty in performance assessment.« less

Radiologist Uncertainty and the Interpretation of Screening

PubMed Central

Carney, Patricia A.; Elmore, Joann G.; Abraham, Linn A.; Gerrity, Martha S.; Hendrick, R. Edward; Taplin, Stephen H.; Barlow, William E.; Cutter, Gary R.; Poplack, Steven P.; D’Orsi, Carl J.

2011-01-01

Objective To determine radiologists’ reactions to uncertainty when interpreting mammography and the extent to which radiologist uncertainty explains variability in interpretive performance. Methods The authors used a mailed survey to assess demographic and clinical characteristics of radiologists and reactions to uncertainty associated with practice. Responses were linked to radiologists’ actual interpretive performance data obtained from 3 regionally located mammography registries. Results More than 180 radiologists were eligible to participate, and 139 consented for a response rate of 76.8%. Radiologist gender, more years interpreting, and higher volume were associated with lower uncertainty scores. Positive predictive value, recall rates, and specificity were more affected by reactions to uncertainty than sensitivity or negative predictive value; however, none of these relationships was statistically significant. Conclusion Certain practice factors, such as gender and years of interpretive experience, affect uncertainty scores. Radiologists’ reactions to uncertainty do not appear to affect interpretive performance. PMID:15155014

Quantifying the impact of the longitudinal dispersion coefficient parameter uncertainty on the physical transport processes in rivers

NASA Astrophysics Data System (ADS)

Camacho Suarez, V. V.; Shucksmith, J.; Schellart, A.

2016-12-01

Analytical and numerical models can be used to represent the advection-dispersion processes governing the transport of pollutants in rivers (Fan et al., 2015; Van Genuchten et al., 2013). Simplifications, assumptions and parameter estimations in these models result in various uncertainties within the modelling process and estimations of pollutant concentrations. In this study, we explore both: 1) the structural uncertainty due to the one dimensional simplification of the Advection Dispersion Equation (ADE) and 2) the parameter uncertainty due to the semi empirical estimation of the longitudinal dispersion coefficient. The relative significance of these uncertainties has not previously been examined. By analysing both the relative structural uncertainty of analytical solutions of the ADE, and the parameter uncertainty due to the longitudinal dispersion coefficient via a Monte Carlo analysis, an evaluation of the dominant uncertainties for a case study in the river Chillan, Chile is presented over a range of spatial scales.

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...

Estimating the uncertainty in thermochemical calculations for oxygen-hydrogen combustors

NASA Astrophysics Data System (ADS)

Sims, Joseph David

The thermochemistry program CEA2 was combined with the statistical thermodynamics program PAC99 in a Monte Carlo simulation to determine the uncertainty in several CEA2 output variables due to uncertainty in thermodynamic reference values for the reactant and combustion species. In all, six typical performance parameters were examined, along with the required intermediate calculations (five gas properties and eight stoichiometric coefficients), for three hydrogen-oxygen combustors: a main combustor, an oxidizer preburner and a fuel preburner. The three combustors were analyzed in two different modes: design mode, where, for the first time, the uncertainty in thermodynamic reference values---taken from the literature---was considered (inputs to CEA2 were specified and so had no uncertainty); and data reduction mode, where inputs to CEA2 did have uncertainty. The inputs to CEA2 were contrived experimental measurements that were intended to represent the typical combustor testing facility. In design mode, uncertainties in the performance parameters were on the order of 0.1% for the main combustor, on the order of 0.05% for the oxidizer preburner and on the order of 0.01% for the fuel preburner. Thermodynamic reference values for H2O were the dominant sources of uncertainty, as was the assigned enthalpy for liquid oxygen. In data reduction mode, uncertainties in performance parameters increased significantly as a result of the uncertainties in experimental measurements compared to uncertainties in thermodynamic reference values. Main combustor and fuel preburner theoretical performance values had uncertainties of about 0.5%, while the oxidizer preburner had nearly 2%. Associated experimentally-determined performance values for all three combustors were 3% to 4%. The dominant sources of uncertainty in this mode were the propellant flowrates. These results only apply to hydrogen-oxygen combustors and should not be generalized to every propellant combination. Species for a hydrogen-oxygen system are relatively simple, thereby resulting in low thermodynamic reference value uncertainties. Hydrocarbon combustors, solid rocket motors and hybrid rocket motors have combustion gases containing complex molecules that will likely have thermodynamic reference values with large uncertainties. Thus, every chemical system should be analyzed in a similar manner as that shown in this work.

Communication about scientific uncertainty in environmental nanoparticle research - a comparison of scientific literature and mass media

NASA Astrophysics Data System (ADS)

Heidmann, Ilona; Milde, Jutta

2014-05-01

The research about the fate and behavior of engineered nanoparticles in the environment is despite its wide applications still in the early stages. 'There is a high level of scientific uncertainty in nanoparticle research' is often stated in the scientific community. Knowledge about these uncertainties might be of interest to other scientists, experts and laymen. But how could these uncertainties be characterized and are they communicated within the scientific literature and the mass media? To answer these questions, the current state of scientific knowledge about scientific uncertainty through the example of environmental nanoparticle research was characterized and the communication of these uncertainties within the scientific literature is compared with its media coverage in the field of nanotechnologies. The scientific uncertainty within the field of environmental fate of nanoparticles is by method uncertainties and a general lack of data concerning the fate and effects of nanoparticles and their mechanisms in the environment, and by the uncertain transferability of results to the environmental system. In the scientific literature, scientific uncertainties, their sources, and consequences are mentioned with different foci and to a different extent. As expected, the authors in research papers focus on the certainty of specific results within their specific research question, whereas in review papers, the uncertainties due to a general lack of data are emphasized and the sources and consequences are discussed in a broader environmental context. In the mass media, nanotechnology is often framed as rather certain and positive aspects and benefits are emphasized. Although reporting about a new technology, only in one-third of the reports scientific uncertainties are mentioned. Scientific uncertainties are most often mentioned together with risk and they arise primarily from unknown harmful effects to human health. Environmental issues itself are seldom mentioned. Scientific uncertainties, sources, and consequences have been most widely discussed in the review papers. Research papers and mass media tend to emphasize more the certainty of their scientific results or the benefits of the nanotechnology applications. Neither the broad spectrum nor any specifications of uncertainties have been communicated. This indicates that there has been no effective dialogue over scientific uncertainty with the public so far.

When, not if: The inescapability of an uncertain future

NASA Astrophysics Data System (ADS)

Lewandowsky, S.; Ballard, T.

2014-12-01

Uncertainty is an inherent feature of most scientific endeavours, and many political decisions must be made in the presence of scientific uncertainty. In the case of climate change, there is evidence that greater scientific uncertainty increases the risk associated with the impact of climate change. Scientific uncertainty thus provides an impetus for cutting emissions rather than delaying action. In contrast to those normative considerations, uncertainty is frequently cited in political and public discourse as a reason to delay mitigation. We examine ways in which this gap between public and scientific understanding of uncertainty can be bridged. In particular, we sought ways to communicate uncertainty in a way that better calibrates people's risk perceptions with the projected impact of climate change. We report two behavioural experiments in which uncertainty about the future was expressed either as outcome uncertainty or temporal uncertainty. The conventional presentation of uncertainty involves uncertainty about an outcome at a given time—for example, the range of possible sea level rise (say 50cm +/- 20cm) by a certain date. An alternative presentation of the same situation presents a certain outcome ("sea levels will rise by 50cm") but places the uncertainty into the time of arrival ("this may occur as early as 2040 or as late as 2080"). We presented participants with a series of statements and graphs indicating projected increases in temperature, sea levels, ocean acidification, and a decrease in artic sea ice. In the uncertain magnitude condition, the statements and graphs reported the upper and lower confidence bounds of the projected magnitude and the mean projected time of arrival. In the uncertain time of arrival condition, they reported the upper and lower confidence bounds of the projected time of arrival and the mean projected magnitude. The results show that when uncertainty was presented as uncertain time of arrival rather than an uncertain outcome, people expressed greater concern about the projected outcomes. In a further experiment involving repeated "games" with a simulated economy, we similarly showed that people allocate more resources to mitigation if there is uncertainty about the timing of an adverse event rather than about the magnitude of its impact.

Uncertainty in monitoring E. coli concentrations in streams and stormwater runoff

NASA Astrophysics Data System (ADS)

Harmel, R. D.; Hathaway, J. M.; Wagner, K. L.; Wolfe, J. E.; Karthikeyan, R.; Francesconi, W.; McCarthy, D. T.

2016-03-01

Microbial contamination of surface waters, a substantial public health concern throughout the world, is typically identified by fecal indicator bacteria such as Escherichia coli. Thus, monitoring E. coli concentrations is critical to evaluate current conditions, determine restoration effectiveness, and inform model development and calibration. An often overlooked component of these monitoring and modeling activities is understanding the inherent random and systematic uncertainty present in measured data. In this research, a review and subsequent analysis was performed to identify, document, and analyze measurement uncertainty of E. coli data collected in stream flow and stormwater runoff as individual discrete samples or throughout a single runoff event. Data on the uncertainty contributed by sample collection, sample preservation/storage, and laboratory analysis in measured E. coli concentrations were compiled and analyzed, and differences in sampling method and data quality scenarios were compared. The analysis showed that: (1) manual integrated sampling produced the lowest random and systematic uncertainty in individual samples, but automated sampling typically produced the lowest uncertainty when sampling throughout runoff events; (2) sample collection procedures often contributed the highest amount of uncertainty, although laboratory analysis introduced substantial random uncertainty and preservation/storage introduced substantial systematic uncertainty under some scenarios; and (3) the uncertainty in measured E. coli concentrations was greater than that of sediment and nutrients, but the difference was not as great as may be assumed. This comprehensive analysis of uncertainty in E. coli concentrations measured in streamflow and runoff should provide valuable insight for designing E. coli monitoring projects, reducing uncertainty in quality assurance efforts, regulatory and policy decision making, and fate and transport modeling.

Comparison between bottom-up and top-down approaches in the estimation of measurement uncertainty.

PubMed

Lee, Jun Hyung; Choi, Jee-Hye; Youn, Jae Saeng; Cha, Young Joo; Song, Woonheung; Park, Ae Ja

2015-06-01

Measurement uncertainty is a metrological concept to quantify the variability of measurement results. There are two approaches to estimate measurement uncertainty. In this study, we sought to provide practical and detailed examples of the two approaches and compare the bottom-up and top-down approaches to estimating measurement uncertainty. We estimated measurement uncertainty of the concentration of glucose according to CLSI EP29-A guideline. Two different approaches were used. First, we performed a bottom-up approach. We identified the sources of uncertainty and made an uncertainty budget and assessed the measurement functions. We determined the uncertainties of each element and combined them. Second, we performed a top-down approach using internal quality control (IQC) data for 6 months. Then, we estimated and corrected systematic bias using certified reference material of glucose (NIST SRM 965b). The expanded uncertainties at the low glucose concentration (5.57 mmol/L) by the bottom-up approach and top-down approaches were ±0.18 mmol/L and ±0.17 mmol/L, respectively (all k=2). Those at the high glucose concentration (12.77 mmol/L) by the bottom-up and top-down approaches were ±0.34 mmol/L and ±0.36 mmol/L, respectively (all k=2). We presented practical and detailed examples for estimating measurement uncertainty by the two approaches. The uncertainties by the bottom-up approach were quite similar to those by the top-down approach. Thus, we demonstrated that the two approaches were approximately equivalent and interchangeable and concluded that clinical laboratories could determine measurement uncertainty by the simpler top-down approach.

The Key Role of Experiential Uncertainty when Dealing with Risks: Its Relationships with Demand for Regulation and Institutional Trust.

PubMed

Poortvliet, P Marijn; Lokhorst, Anne Marike

2016-08-01

The results of a survey and an experiment show that experiential uncertainty-people's experience of uncertainty in risk contexts-plays a moderating role in individuals' risk-related demand for government regulation and trust in risk-managing government institutions. First, descriptions of risks were presented to respondents in a survey (N = 1,017) and their reactions to questions about experiential uncertainty, risk perception, and demand for government regulation were measured, as well as levels of risk-specific knowledge. When experiential uncertainty was high, risk perceptions had a positive relationship with demand for government regulation of risk; no such relationship showed under low experiential uncertainty. Conversely, when people experience little experiential uncertainty, having more knowledge about the risk topic involved was associated with a weaker demand for government regulation of risk. For people experiencing uncertainty, this relationship between knowledge and demand for regulation did not emerge. Second, in an experiment (N = 120), experiential uncertainty and openness in risk communication were manipulated to investigate effects on trust. In the uncertainty condition, the results showed that open versus nonopen government communication about Q-fever-a zoonosis-led to higher levels of trust in the government agency, but not in in the control condition. Altogether, this research suggests that only when people experience relatively little uncertainty about the risk, knowledge provision may preclude them from demanding government action. Also, only when persons experience uncertainty are stronger risk perceptions associated with a demand for government regulation, and they are affected by openness of risk communication in forming institutional trust. © 2016 Society for Risk Analysis.

A model-averaging method for assessing groundwater conceptual model uncertainty.

PubMed

Ye, Ming; Pohlmann, Karl F; Chapman, Jenny B; Pohll, Greg M; Reeves, Donald M

2010-01-01

This study evaluates alternative groundwater models with different recharge and geologic components at the northern Yucca Flat area of the Death Valley Regional Flow System (DVRFS), USA. Recharge over the DVRFS has been estimated using five methods, and five geological interpretations are available at the northern Yucca Flat area. Combining the recharge and geological components together with additional modeling components that represent other hydrogeological conditions yields a total of 25 groundwater flow models. As all the models are plausible given available data and information, evaluating model uncertainty becomes inevitable. On the other hand, hydraulic parameters (e.g., hydraulic conductivity) are uncertain in each model, giving rise to parametric uncertainty. Propagation of the uncertainty in the models and model parameters through groundwater modeling causes predictive uncertainty in model predictions (e.g., hydraulic head and flow). Parametric uncertainty within each model is assessed using Monte Carlo simulation, and model uncertainty is evaluated using the model averaging method. Two model-averaging techniques (on the basis of information criteria and GLUE) are discussed. This study shows that contribution of model uncertainty to predictive uncertainty is significantly larger than that of parametric uncertainty. For the recharge and geological components, uncertainty in the geological interpretations has more significant effect on model predictions than uncertainty in the recharge estimates. In addition, weighted residuals vary more for the different geological models than for different recharge models. Most of the calibrated observations are not important for discriminating between the alternative models, because their weighted residuals vary only slightly from one model to another.

Accounting for Epistemic Uncertainty in Mission Supportability Assessment: A Necessary Step in Understanding Risk and Logistics Requirements

NASA Technical Reports Server (NTRS)

Owens, Andrew; De Weck, Olivier L.; Stromgren, Chel; Goodliff, Kandyce; Cirillo, William

2017-01-01

Future crewed missions to Mars present a maintenance logistics challenge that is unprecedented in human spaceflight. Mission endurance – defined as the time between resupply opportunities – will be significantly longer than previous missions, and therefore logistics planning horizons are longer and the impact of uncertainty is magnified. Maintenance logistics forecasting typically assumes that component failure rates are deterministically known and uses them to represent aleatory uncertainty, or uncertainty that is inherent to the process being examined. However, failure rates cannot be directly measured; rather, they are estimated based on similarity to other components or statistical analysis of observed failures. As a result, epistemic uncertainty – that is, uncertainty in knowledge of the process – exists in failure rate estimates that must be accounted for. Analyses that neglect epistemic uncertainty tend to significantly underestimate risk. Epistemic uncertainty can be reduced via operational experience; for example, the International Space Station (ISS) failure rate estimates are refined using a Bayesian update process. However, design changes may re-introduce epistemic uncertainty. Thus, there is a tradeoff between changing a design to reduce failure rates and operating a fixed design to reduce uncertainty. This paper examines the impact of epistemic uncertainty on maintenance logistics requirements for future Mars missions, using data from the ISS Environmental Control and Life Support System (ECLS) as a baseline for a case study. Sensitivity analyses are performed to investigate the impact of variations in failure rate estimates and epistemic uncertainty on spares mass. The results of these analyses and their implications for future system design and mission planning are discussed.

Visual Semiotics & Uncertainty Visualization: An Empirical Study.

PubMed

MacEachren, A M; Roth, R E; O'Brien, J; Li, B; Swingley, D; Gahegan, M

2012-12-01

This paper presents two linked empirical studies focused on uncertainty visualization. The experiments are framed from two conceptual perspectives. First, a typology of uncertainty is used to delineate kinds of uncertainty matched with space, time, and attribute components of data. Second, concepts from visual semiotics are applied to characterize the kind of visual signification that is appropriate for representing those different categories of uncertainty. This framework guided the two experiments reported here. The first addresses representation intuitiveness, considering both visual variables and iconicity of representation. The second addresses relative performance of the most intuitive abstract and iconic representations of uncertainty on a map reading task. Combined results suggest initial guidelines for representing uncertainty and discussion focuses on practical applicability of results.

An uncertainty budget for VHF and UHF reflectometers

NASA Astrophysics Data System (ADS)

Ridler, N. M.; Medley, C. J.

1992-05-01

Details of the derivation of an uncertainty budget for one port immittance or complex voltage reflection coefficient measuring instruments, operating at VHF and UHF in the 14 mm 50 ohm coaxial line size, are reported. The principles of the uncertainty budget are given along with experimental results obtained using six ports and a network analyzer as the measuring instruments. Details of the types of calibration for which the uncertainty budget is suitable are reported. Various aspects of the uncertainty budget are considered and general principles and treatment of the type A and type B contributions are discussed. Experimental results obtained using the uncertainty budget are given. A summary of uncertainties for the six ports and HP8753B automatic network analyzer are also given.

Uncertainty of fast biological radiation dose assessment for emergency response scenarios.

PubMed

Ainsbury, Elizabeth A; Higueras, Manuel; Puig, Pedro; Einbeck, Jochen; Samaga, Daniel; Barquinero, Joan Francesc; Barrios, Lleonard; Brzozowska, Beata; Fattibene, Paola; Gregoire, Eric; Jaworska, Alicja; Lloyd, David; Oestreicher, Ursula; Romm, Horst; Rothkamm, Kai; Roy, Laurence; Sommer, Sylwester; Terzoudi, Georgia; Thierens, Hubert; Trompier, Francois; Vral, Anne; Woda, Clemens

2017-01-01

Reliable dose estimation is an important factor in appropriate dosimetric triage categorization of exposed individuals to support radiation emergency response. Following work done under the EU FP7 MULTIBIODOSE and RENEB projects, formal methods for defining uncertainties on biological dose estimates are compared using simulated and real data from recent exercises. The results demonstrate that a Bayesian method of uncertainty assessment is the most appropriate, even in the absence of detailed prior information. The relative accuracy and relevance of techniques for calculating uncertainty and combining assay results to produce single dose and uncertainty estimates is further discussed. Finally, it is demonstrated that whatever uncertainty estimation method is employed, ignoring the uncertainty on fast dose assessments can have an important impact on rapid biodosimetric categorization.

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.

Impact of uncertainty on modeling and testing

NASA Technical Reports Server (NTRS)

Coleman, Hugh W.; Brown, Kendall K.

1995-01-01

A thorough understanding of the uncertainties associated with the modeling and testing of the Space Shuttle Main Engine (SSME) Engine will greatly aid decisions concerning hardware performance and future development efforts. This report will describe the determination of the uncertainties in the modeling and testing of the Space Shuttle Main Engine test program at the Technology Test Bed facility at Marshall Space Flight Center. Section 2 will present a summary of the uncertainty analysis methodology used and discuss the specific applications to the TTB SSME test program. Section 3 will discuss the application of the uncertainty analysis to the test program and the results obtained. Section 4 presents the results of the analysis of the SSME modeling effort from an uncertainty analysis point of view. The appendices at the end of the report contain a significant amount of information relative to the analysis, including discussions of venturi flowmeter data reduction and uncertainty propagation, bias uncertainty documentations, technical papers published, the computer code generated to determine the venturi uncertainties, and the venturi data and results used in the analysis.

Position uncertainty distribution for articulated arm coordinate measuring machine based on simplified definite integration

NASA Astrophysics Data System (ADS)

You, Xu; Zhi-jian, Zong; Qun, Gao

2018-07-01

This paper describes a methodology for the position uncertainty distribution of an articulated arm coordinate measuring machine (AACMM). First, a model of the structural parameter uncertainties was established by statistical method. Second, the position uncertainty space volume of the AACMM in a certain configuration was expressed using a simplified definite integration method based on the structural parameter uncertainties; it was then used to evaluate the position accuracy of the AACMM in a certain configuration. Third, the configurations of a certain working point were calculated by an inverse solution, and the position uncertainty distribution of a certain working point was determined; working point uncertainty can be evaluated by the weighting method. Lastly, the position uncertainty distribution in the workspace of the ACCMM was described by a map. A single-point contrast test of a 6-joint AACMM was carried out to verify the effectiveness of the proposed method, and it was shown that the method can describe the position uncertainty of the AACMM and it was used to guide the calibration of the AACMM and the choice of AACMM’s accuracy area.

Quantifying and Reducing Curve-Fitting Uncertainty in Isc

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

Campanelli, Mark; Duck, Benjamin; Emery, Keith

2015-06-14

Current-voltage (I-V) curve measurements of photovoltaic (PV) devices are used to determine performance parameters and to establish traceable calibration chains. Measurement standards specify localized curve fitting methods, e.g., straight-line interpolation/extrapolation of the I-V curve points near short-circuit current, Isc. By considering such fits as statistical linear regressions, uncertainties in the performance parameters are readily quantified. However, the legitimacy of such a computed uncertainty requires that the model be a valid (local) representation of the I-V curve and that the noise be sufficiently well characterized. Using more data points often has the advantage of lowering the uncertainty. However, more data pointsmore » can make the uncertainty in the fit arbitrarily small, and this fit uncertainty misses the dominant residual uncertainty due to so-called model discrepancy. Using objective Bayesian linear regression for straight-line fits for Isc, we investigate an evidence-based method to automatically choose data windows of I-V points with reduced model discrepancy. We also investigate noise effects. Uncertainties, aligned with the Guide to the Expression of Uncertainty in Measurement (GUM), are quantified throughout.« less

Matching experimental and three dimensional numerical models for structural vibration problems with uncertainties

NASA Astrophysics Data System (ADS)

Langer, P.; Sepahvand, K.; Guist, C.; Bär, J.; Peplow, A.; Marburg, S.

2018-03-01

The simulation model which examines the dynamic behavior of real structures needs to address the impact of uncertainty in both geometry and material parameters. This article investigates three-dimensional finite element models for structural dynamics problems with respect to both model and parameter uncertainties. The parameter uncertainties are determined via laboratory measurements on several beam-like samples. The parameters are then considered as random variables to the finite element model for exploring the uncertainty effects on the quality of the model outputs, i.e. natural frequencies. The accuracy of the output predictions from the model is compared with the experimental results. To this end, the non-contact experimental modal analysis is conducted to identify the natural frequency of the samples. The results show a good agreement compared with experimental data. Furthermore, it is demonstrated that geometrical uncertainties have more influence on the natural frequencies compared to material parameters and material uncertainties are about two times higher than geometrical uncertainties. This gives valuable insights for improving the finite element model due to various parameter ranges required in a modeling process involving uncertainty.

Uncertainty, God, and scrupulosity: Uncertainty salience and priming God concepts interact to cause greater fears of sin.

PubMed

Fergus, Thomas A; Rowatt, Wade C

2015-03-01

Difficulties tolerating uncertainty are considered central to scrupulosity, a moral/religious presentation of obsessive-compulsive disorder (OCD). We examined whether uncertainty salience (i.e., exposure to a state of uncertainty) caused fears of sin and fears of God, as well as whether priming God concepts affected the impact of uncertainty salience on those fears. An internet sample of community adults (N = 120) who endorsed holding a belief in God or a higher power were randomly assigned to an experimental manipulation of (1) salience (uncertainty or insecurity) and (2) prime (God concepts or neutral). As predicted, participants who received the uncertainty salience and God concept priming reported the greatest fears of sin. There were no mean-level differences in the other conditions. The effect was not attributable to religiosity and the manipulations did not cause negative affect. We used a nonclinical sample recruited from the internet. These results support cognitive-behavioral models suggesting that religious uncertainty is important to scrupulosity. Implications of these results for future research are discussed. Copyright © 2014 Elsevier Ltd. All rights reserved.

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.

Quantifying and Reducing Curve-Fitting Uncertainty in Isc: Preprint

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

Campanelli, Mark; Duck, Benjamin; Emery, Keith

Current-voltage (I-V) curve measurements of photovoltaic (PV) devices are used to determine performance parameters and to establish traceable calibration chains. Measurement standards specify localized curve fitting methods, e.g., straight-line interpolation/extrapolation of the I-V curve points near short-circuit current, Isc. By considering such fits as statistical linear regressions, uncertainties in the performance parameters are readily quantified. However, the legitimacy of such a computed uncertainty requires that the model be a valid (local) representation of the I-V curve and that the noise be sufficiently well characterized. Using more data points often has the advantage of lowering the uncertainty. However, more data pointsmore » can make the uncertainty in the fit arbitrarily small, and this fit uncertainty misses the dominant residual uncertainty due to so-called model discrepancy. Using objective Bayesian linear regression for straight-line fits for Isc, we investigate an evidence-based method to automatically choose data windows of I-V points with reduced model discrepancy. We also investigate noise effects. Uncertainties, aligned with the Guide to the Expression of Uncertainty in Measurement (GUM), are quantified throughout.« less

Accounting for uncertainty in marine reserve design.

PubMed

Halpern, Benjamin S; Regan, Helen M; Possingham, Hugh P; McCarthy, Michael A

2006-01-01

Ecosystems and the species and communities within them are highly complex systems that defy predictions with any degree of certainty. Managing and conserving these systems in the face of uncertainty remains a daunting challenge, particularly with respect to developing networks of marine reserves. Here we review several modelling frameworks that explicitly acknowledge and incorporate uncertainty, and then use these methods to evaluate reserve spacing rules given increasing levels of uncertainty about larval dispersal distances. Our approach finds similar spacing rules as have been proposed elsewhere - roughly 20-200 km - but highlights several advantages provided by uncertainty modelling over more traditional approaches to developing these estimates. In particular, we argue that uncertainty modelling can allow for (1) an evaluation of the risk associated with any decision based on the assumed uncertainty; (2) a method for quantifying the costs and benefits of reducing uncertainty; and (3) a useful tool for communicating to stakeholders the challenges in managing highly uncertain systems. We also argue that incorporating rather than avoiding uncertainty will increase the chances of successfully achieving conservation and management goals.

Accounting for methodological, structural, and parameter uncertainty in decision-analytic models: a practical guide.

PubMed

Bilcke, Joke; Beutels, Philippe; Brisson, Marc; Jit, Mark

2011-01-01

Accounting for uncertainty is now a standard part of decision-analytic modeling and is recommended by many health technology agencies and published guidelines. However, the scope of such analyses is often limited, even though techniques have been developed for presenting the effects of methodological, structural, and parameter uncertainty on model results. To help bring these techniques into mainstream use, the authors present a step-by-step guide that offers an integrated approach to account for different kinds of uncertainty in the same model, along with a checklist for assessing the way in which uncertainty has been incorporated. The guide also addresses special situations such as when a source of uncertainty is difficult to parameterize, resources are limited for an ideal exploration of uncertainty, or evidence to inform the model is not available or not reliable. for identifying the sources of uncertainty that influence results most are also described. Besides guiding analysts, the guide and checklist may be useful to decision makers who need to assess how well uncertainty has been accounted for in a decision-analytic model before using the results to make a decision.

Autistic Heterogeneity: Linking Uncertainties and Indeterminacies

PubMed Central

Hollin, Gregory

2017-01-01

Abstract Autism is a highly uncertain entity and little is said about it with any degree of certainty. Scientists must, and do, work through these uncertainties in the course of their work. Scientists explain uncertainty in autism research through discussion of epistemological uncertainties which suggest that diverse methods and techniques make results hard to reconcile, ontological uncertainties which suggest doubt over taxonomic coherence, but also through reference to autism’s indeterminacy which suggests that the condition is inherently heterogeneous. Indeed, indeterminacy takes two forms—an inter-personal form which suggests that there are fundamental differences between individuals with autism and an intra-personal form which suggests that no one factor is able to explain all features of autism within a given individual. What is apparent in the case of autism is that scientists put uncertainty and indeterminacy into discussion with one another and, rather than a well-policed epistemic-ontic boundary, there is a movement between, and an entwinement of, the two. Understanding scientists’ dialogue concerning uncertainty and indeterminacy is of importance for understanding autism and autistic heterogeneity but also for understanding uncertainty and ‘uncertainty work’ within science more generally. PMID:28515574

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

Associating uncertainty with datasets using Linked Data and allowing propagation via provenance chains

NASA Astrophysics Data System (ADS)

Car, Nicholas; Cox, Simon; Fitch, Peter

2015-04-01

With earth-science datasets increasingly being published to enable re-use in projects disassociated from the original data acquisition or generation, there is an urgent need for associated metadata to be connected, in order to guide their application. In particular, provenance traces should support the evaluation of data quality and reliability. However, while standards for describing provenance are emerging (e.g. PROV-O), these do not include the necessary statistical descriptors and confidence assessments. UncertML has a mature conceptual model that may be used to record uncertainty metadata. However, by itself UncertML does not support the representation of uncertainty of multi-part datasets, and provides no direct way of associating the uncertainty information - metadata in relation to a dataset - with dataset objects.We present a method to address both these issues by combining UncertML with PROV-O, and delivering resulting uncertainty-enriched provenance traces through the Linked Data API. UncertProv extends the PROV-O provenance ontology with an RDF formulation of the UncertML conceptual model elements, adds further elements to support uncertainty representation without a conceptual model and the integration of UncertML through links to documents. The Linked ID API provides a systematic way of navigating from dataset objects to their UncertProv metadata and back again. The Linked Data API's 'views' capability enables access to UncertML and non-UncertML uncertainty metadata representations for a dataset. With this approach, it is possible to access and navigate the uncertainty metadata associated with a published dataset using standard semantic web tools, such as SPARQL queries. Where the uncertainty data follows the UncertML model it can be automatically interpreted and may also support automatic uncertainty propagation . Repositories wishing to enable uncertainty propagation for all datasets must ensure that all elements that are associated with uncertainty (PROV-O Entity and Activity classes) have UncertML elements recorded. This methodology is intentionally flexible to allow uncertainty metadata in many forms, not limited to UncertML. While the more formal representation of uncertainty metadata is desirable (using UncertProv elements to implement the UncertML conceptual model ), this will not always be possible, and any uncertainty data stored will be better than none. Since the UncertProv ontology contains a superset of UncertML elements to facilitate the representation of non-UncertML uncertainty data, it could easily be extended to include other formal uncertainty conceptual models thus allowing non-UncertML propagation calculations.

A TIERED APPROACH TO PERFORMING UNCERTAINTY ANALYSIS IN CONDUCTING EXPOSURE ANALYSIS FOR CHEMICALS

EPA Science Inventory

The WHO/IPCS draft Guidance Document on Characterizing and Communicating Uncertainty in Exposure Assessment provides guidance on recommended strategies for conducting uncertainty analysis as part of human exposure analysis. Specifically, a tiered approach to uncertainty analysis ...

Sensitivity-Uncertainty Techniques for Nuclear Criticality Safety

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

Brown, Forrest B.; Rising, Michael Evan; Alwin, Jennifer Louise

2017-08-07

The sensitivity and uncertainty analysis course will introduce students to k eff sensitivity data, cross-section uncertainty data, how k eff sensitivity data and k eff uncertainty data are generated and how they can be used. Discussion will include how sensitivity/uncertainty data can be used to select applicable critical experiments, to quantify a defensible margin to cover validation gaps and weaknesses, and in development of upper subcritical limits.

Uncertainty Estimation Cheat Sheet for Probabilistic Risk Assessment

NASA Technical Reports Server (NTRS)

Britton, Paul; Al Hassan, Mohammad; Ring, Robert

2017-01-01

Quantitative results for aerospace engineering problems are influenced by many sources of uncertainty. Uncertainty analysis aims to make a technical contribution to decision-making through the quantification of uncertainties in the relevant variables as well as through the propagation of these uncertainties up to the result. Uncertainty can be thought of as a measure of the 'goodness' of a result and is typically represented as statistical dispersion. This paper will explain common measures of centrality and dispersion; and-with examples-will provide guidelines for how they may be estimated to ensure effective technical contributions to decision-making.

CASMO5/TSUNAMI-3D spent nuclear fuel reactivity uncertainty analysis

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

Ferrer, R.; Rhodes, J.; Smith, K.

2012-07-01

The CASMO5 lattice physics code is used in conjunction with the TSUNAMI-3D sequence in ORNL's SCALE 6 code system to estimate the uncertainties in hot-to-cold reactivity changes due to cross-section uncertainty for PWR assemblies at various burnup points. The goal of the analysis is to establish the multiplication factor uncertainty similarity between various fuel assemblies at different conditions in a quantifiable manner and to obtain a bound on the hot-to-cold reactivity uncertainty over the various assembly types and burnup attributed to fundamental cross-section data uncertainty. (authors)

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.

Assessing and reporting uncertainties in dietary exposure analysis - Part II: Application of the uncertainty template to a practical example of exposure assessment.

PubMed

Tennant, David; Bánáti, Diána; Kennedy, Marc; König, Jürgen; O'Mahony, Cian; Kettler, Susanne

2017-11-01

A previous publication described methods for assessing and reporting uncertainty in dietary exposure assessments. This follow-up publication uses a case study to develop proposals for representing and communicating uncertainty to risk managers. The food ingredient aspartame is used as the case study in a simple deterministic model (the EFSA FAIM template) and with more sophisticated probabilistic exposure assessment software (FACET). Parameter and model uncertainties are identified for each modelling approach and tabulated. The relative importance of each source of uncertainty is then evaluated using a semi-quantitative scale and the results expressed using two different forms of graphical summary. The value of this approach in expressing uncertainties in a manner that is relevant to the exposure assessment and useful to risk managers is then discussed. It was observed that the majority of uncertainties are often associated with data sources rather than the model itself. However, differences in modelling methods can have the greatest impact on uncertainties overall, particularly when the underlying data are the same. It was concluded that improved methods for communicating uncertainties for risk management is the research area where the greatest amount of effort is suggested to be placed in future. Copyright © 2017 The Authors. Published by Elsevier Ltd.. All rights reserved.

Uncertainty evaluation in the chloroquine phosphate potentiometric titration: application of three different approaches.

PubMed

Rodomonte, Andrea Luca; Montinaro, Annalisa; Bartolomei, Monica

2006-09-11

A measurement result cannot be properly interpreted if not accompanied by its uncertainty. Several methods to estimate uncertainty have been developed. From those methods three in particular were chosen in this work to estimate the uncertainty of the Eu. Ph. chloroquine phosphate assay, a potentiometric titration commonly used in medicinal control laboratories. The famous error-budget approach (also called bottom-up or step-by-step) described by the ISO Guide to the expression of Uncertainty in Measurement (GUM) was the first method chosen. It is based on the combination of uncertainty contributions that have to be directly derived from the measurement process. The second method employed was the Analytical Method Committee top-down which estimates uncertainty through reproducibility obtained during inter-laboratory studies. Data for its application were collected in a proficiency testing study carried out by over 50 laboratories throughout Europe. The last method chosen was the one proposed by Barwick and Ellison. It uses a combination of precision, trueness and ruggedness data to estimate uncertainty. These data were collected from a validation process specifically designed for uncertainty estimation. All the three approaches presented a distinctive set of advantages and drawbacks in their implementation. An expanded uncertainty of about 1% was assessed for the assay investigated.

Facing uncertainty in ecosystem services-based resource management.

PubMed

Grêt-Regamey, Adrienne; Brunner, Sibyl H; Altwegg, Jürg; Bebi, Peter

2013-09-01

The concept of ecosystem services is increasingly used as a support for natural resource management decisions. While the science for assessing ecosystem services is improving, appropriate methods to address uncertainties in a quantitative manner are missing. Ignoring parameter uncertainties, modeling uncertainties and uncertainties related to human-environment interactions can modify decisions and lead to overlooking important management possibilities. In this contribution, we present a new approach for mapping the uncertainties in the assessment of multiple ecosystem services. The spatially explicit risk approach links Bayesian networks to a Geographic Information System for forecasting the value of a bundle of ecosystem services and quantifies the uncertainties related to the outcomes in a spatially explicit manner. We demonstrate that mapping uncertainties in ecosystem services assessments provides key information for decision-makers seeking critical areas in the delivery of ecosystem services in a case study in the Swiss Alps. The results suggest that not only the total value of the bundle of ecosystem services is highly dependent on uncertainties, but the spatial pattern of the ecosystem services values changes substantially when considering uncertainties. This is particularly important for the long-term management of mountain forest ecosystems, which have long rotation stands and are highly sensitive to pressing climate and socio-economic changes. Copyright © 2012 Elsevier Ltd. All rights reserved.

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

Quantifying uncertainty in health impact assessment: a case-study example on indoor housing ventilation.

PubMed

Mesa-Frias, Marco; Chalabi, Zaid; Foss, Anna M

2014-01-01

Quantitative health impact assessment (HIA) is increasingly being used to assess the health impacts attributable to an environmental policy or intervention. As a consequence, there is a need to assess uncertainties in the assessments because of the uncertainty in the HIA models. In this paper, a framework is developed to quantify the uncertainty in the health impacts of environmental interventions and is applied to evaluate the impacts of poor housing ventilation. The paper describes the development of the framework through three steps: (i) selecting the relevant exposure metric and quantifying the evidence of potential health effects of the exposure; (ii) estimating the size of the population affected by the exposure and selecting the associated outcome measure; (iii) quantifying the health impact and its uncertainty. The framework introduces a novel application for the propagation of uncertainty in HIA, based on fuzzy set theory. Fuzzy sets are used to propagate parametric uncertainty in a non-probabilistic space and are applied to calculate the uncertainty in the morbidity burdens associated with three indoor ventilation exposure scenarios: poor, fair and adequate. The case-study example demonstrates how the framework can be used in practice, to quantify the uncertainty in health impact assessment where there is insufficient information to carry out a probabilistic uncertainty analysis. © 2013.

Visualizing Uncertainty of Point Phenomena by Redesigned Error Ellipses

NASA Astrophysics Data System (ADS)

Murphy, Christian E.

2018-05-01

Visualizing uncertainty remains one of the great challenges in modern cartography. There is no overarching strategy to display the nature of uncertainty, as an effective and efficient visualization depends, besides on the spatial data feature type, heavily on the type of uncertainty. This work presents a design strategy to visualize uncertainty con-nected to point features. The error ellipse, well-known from mathematical statistics, is adapted to display the uncer-tainty of point information originating from spatial generalization. Modified designs of the error ellipse show the po-tential of quantitative and qualitative symbolization and simultaneous point based uncertainty symbolization. The user can intuitively depict the centers of gravity, the major orientation of the point arrays as well as estimate the ex-tents and possible spatial distributions of multiple point phenomena. The error ellipse represents uncertainty in an intuitive way, particularly suitable for laymen. Furthermore it is shown how applicable an adapted design of the er-ror ellipse is to display the uncertainty of point features originating from incomplete data. The suitability of the error ellipse to display the uncertainty of point information is demonstrated within two showcases: (1) the analysis of formations of association football players, and (2) uncertain positioning of events on maps for the media.

Resolving structural uncertainty in natural resources management using POMDP approaches

USGS Publications Warehouse

Williams, B.K.

2011-01-01

In recent years there has been a growing focus on the uncertainties of natural resources management, and the importance of accounting for uncertainty in assessing management effectiveness. This paper focuses on uncertainty in resource management in terms of discrete-state Markov decision processes (MDP) under structural uncertainty and partial observability. It describes the treatment of structural uncertainty with approaches developed for partially observable resource systems. In particular, I show how value iteration for partially observable MDPs (POMDP) can be extended to structurally uncertain MDPs. A key difference between these process classes is that structurally uncertain MDPs require the tracking of system state as well as a probability structure for the structure uncertainty, whereas with POMDPs require only a probability structure for the observation uncertainty. The added complexity of the optimization problem under structural uncertainty is compensated by reduced dimensionality in the search for optimal strategy. A solution algorithm for structurally uncertain processes is outlined for a simple example in conservation biology. By building on the conceptual framework developed for POMDPs, natural resource analysts and decision makers who confront structural uncertainties in natural resources can take advantage of the rapid growth in POMDP methods and approaches, and thereby produce better conservation strategies over a larger class of resource problems. ?? 2011.

Living with uncertainty and hope: A qualitative study exploring parents' experiences of living with childhood multiple sclerosis.

PubMed

Hinton, Denise; Kirk, Susan

2017-06-01

Background There is growing recognition that multiple sclerosis is a possible, albeit uncommon, diagnosis in childhood. However, very little is known about the experiences of families living with childhood multiple sclerosis and this is the first study to explore this in depth. Objective Our objective was to explore the experiences of parents of children with multiple sclerosis. Methods Qualitative in-depth interviews with 31 parents using a grounded theory approach were conducted. Parents were sampled and recruited via health service and voluntary sector organisations in the United Kingdom. Results Parents' accounts of life with childhood multiple sclerosis were dominated by feelings of uncertainty associated with four sources; diagnostic uncertainty, daily uncertainty, interaction uncertainty and future uncertainty. Parents attempted to manage these uncertainties using specific strategies, which could in turn create further uncertainties about their child's illness. However, over time, ongoing uncertainty appeared to give parents hope for their child's future with multiple sclerosis. Conclusion Illness-related uncertainties appear to play a role in generating hope among parents of a child with multiple sclerosis. However, this may lead parents to avoid sources of information and support that threatens their fragile optimism. Professionals need to be sensitive to the role hope plays in supporting parental coping with childhood multiple sclerosis.

Observation of quantum-memory-assisted entropic uncertainty relation under open systems, and its steering

NASA Astrophysics Data System (ADS)

Chen, Peng-Fei; Sun, Wen-Yang; Ming, Fei; Huang, Ai-Jun; Wang, Dong; Ye, Liu

2018-01-01

Quantum objects are susceptible to noise from their surrounding environments, interaction with which inevitably gives rise to quantum decoherence or dissipation effects. In this work, we examine how different types of local noise under an open system affect entropic uncertainty relations for two incompatible measurements. Explicitly, we observe the dynamics of the entropic uncertainty in the presence of quantum memory under two canonical categories of noisy environments: unital (phase flip) and nonunital (amplitude damping). Our study shows that the measurement uncertainty exhibits a non-monotonic dynamical behavior—that is, the amount of the uncertainty will first inflate, and subsequently decrease, with the growth of decoherence strengths in the two channels. In contrast, the uncertainty decreases monotonically with the growth of the purity of the initial state shared in prior. In order to reduce the measurement uncertainty in noisy environments, we put forward a remarkably effective strategy to steer the magnitude of uncertainty by means of a local non-unitary operation (i.e. weak measurement) on the qubit of interest. It turns out that this non-unitary operation can greatly reduce the entropic uncertainty, upon tuning the operation strength. Our investigations might thereby offer an insight into the dynamics and steering of entropic uncertainty in open systems.

Exploring Best Practice Skills to Predict Uncertainties in Venture Capital Investment Decision-Making

NASA Astrophysics Data System (ADS)

Blum, David Arthur

Algae biodiesel is the sole sustainable and abundant transportation fuel source that can replace petrol diesel use; however, high competition and economic uncertainties exist, influencing independent venture capital decision making. Technology, market, management, and government action uncertainties influence competition and economic uncertainties in the venture capital industry. The purpose of this qualitative case study was to identify the best practice skills at IVC firms to predict uncertainty between early and late funding stages. The basis of the study was real options theory, a framework used to evaluate and understand the economic and competition uncertainties inherent in natural resource investment and energy derived from plant-based oils. Data were collected from interviews of 24 venture capital partners based in the United States who invest in algae and other renewable energy solutions. Data were analyzed by coding and theme development interwoven with the conceptual framework. Eight themes emerged: (a) expected returns model, (b) due diligence, (c) invest in specific sectors, (d) reduced uncertainty-late stage, (e) coopetition, (f) portfolio firm relationships, (g) differentiation strategy, and (h) modeling uncertainty and best practice. The most noteworthy finding was that predicting uncertainty at the early stage was impractical; at the expansion and late funding stages, however, predicting uncertainty was possible. The implications of these findings will affect social change by providing independent venture capitalists with best practice skills to increase successful exits, lessen uncertainty, and encourage increased funding of renewable energy firms, contributing to cleaner and healthier communities throughout the United States..

Dealing with uncertainty in landscape genetic resistance models: a case of three co-occurring marsupials.

PubMed

Dudaniec, Rachael Y; Worthington Wilmer, Jessica; Hanson, Jeffrey O; Warren, Matthew; Bell, Sarah; Rhodes, Jonathan R

2016-01-01

Landscape genetics lacks explicit methods for dealing with the uncertainty in landscape resistance estimation, which is particularly problematic when sample sizes of individuals are small. Unless uncertainty can be quantified, valuable but small data sets may be rendered unusable for conservation purposes. We offer a method to quantify uncertainty in landscape resistance estimates using multimodel inference as an improvement over single model-based inference. We illustrate the approach empirically using co-occurring, woodland-preferring Australian marsupials within a common study area: two arboreal gliders (Petaurus breviceps, and Petaurus norfolcensis) and one ground-dwelling antechinus (Antechinus flavipes). First, we use maximum-likelihood and a bootstrap procedure to identify the best-supported isolation-by-resistance model out of 56 models defined by linear and non-linear resistance functions. We then quantify uncertainty in resistance estimates by examining parameter selection probabilities from the bootstrapped data. The selection probabilities provide estimates of uncertainty in the parameters that drive the relationships between landscape features and resistance. We then validate our method for quantifying uncertainty using simulated genetic and landscape data showing that for most parameter combinations it provides sensible estimates of uncertainty. We conclude that small data sets can be informative in landscape genetic analyses provided uncertainty can be explicitly quantified. Being explicit about uncertainty in landscape genetic models will make results more interpretable and useful for conservation decision-making, where dealing with uncertainty is critical. © 2015 John Wiley & Sons Ltd.

Assessing uncertainties in surface water security: An empirical multimodel approach

NASA Astrophysics Data System (ADS)

Rodrigues, Dulce B. B.; Gupta, Hoshin V.; Mendiondo, Eduardo M.; Oliveira, Paulo Tarso S.

2015-11-01

Various uncertainties are involved in the representation of processes that characterize interactions among 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 multimodel 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 method for defining Environmental Flow Requirement; (v) the definition of critical conditions for water provision; and (vi) the critical demand imposed by human activities. We estimate the overall hydrological model uncertainty 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 multimodel framework and the uncertainty estimates provided by each model uncertainty estimation approach. The range of values obtained for the water security indicators suggests that the models/methods are robust and performs well in a range of plausible situations. The method is general and can be easily extended, thereby 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.

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.

An algorithm for U-Pb isotope dilution data reduction and uncertainty propagation

NASA Astrophysics Data System (ADS)

McLean, N. M.; Bowring, J. F.; Bowring, S. A.

2011-06-01

High-precision U-Pb geochronology by isotope dilution-thermal ionization mass spectrometry is integral to a variety of Earth science disciplines, but its ultimate resolving power is quantified by the uncertainties of calculated U-Pb dates. As analytical techniques have advanced, formerly small sources of uncertainty are increasingly important, and thus previous simplifications for data reduction and uncertainty propagation are no longer valid. Although notable previous efforts have treated propagation of correlated uncertainties for the U-Pb system, the equations, uncertainties, and correlations have been limited in number and subject to simplification during propagation through intermediary calculations. We derive and present a transparent U-Pb data reduction algorithm that transforms raw isotopic data and measured or assumed laboratory parameters into the isotopic ratios and dates geochronologists interpret without making assumptions about the relative size of sample components. To propagate uncertainties and their correlations, we describe, in detail, a linear algebraic algorithm that incorporates all input uncertainties and correlations without limiting or simplifying covariance terms to propagate them though intermediate calculations. Finally, a weighted mean algorithm is presented that utilizes matrix elements from the uncertainty propagation algorithm to propagate random and systematic uncertainties for data comparison between other U-Pb labs and other geochronometers. The linear uncertainty propagation algorithms are verified with Monte Carlo simulations of several typical analyses. We propose that our algorithms be considered by the community for implementation to improve the collaborative science envisioned by the EARTHTIME initiative.

Assessing the relative importance of parameter and forcing uncertainty and their interactions in conceptual hydrological model simulations

NASA Astrophysics Data System (ADS)

Mockler, E. M.; Chun, K. P.; Sapriza-Azuri, G.; Bruen, M.; Wheater, H. S.

2016-11-01

Predictions of river flow dynamics provide vital information for many aspects of water management including water resource planning, climate adaptation, and flood and drought assessments. Many of the subjective choices that modellers make including model and criteria selection can have a significant impact on the magnitude and distribution of the output uncertainty. Hydrological modellers are tasked with understanding and minimising the uncertainty surrounding streamflow predictions before communicating the overall uncertainty to decision makers. Parameter uncertainty in conceptual rainfall-runoff models has been widely investigated, and model structural uncertainty and forcing data have been receiving increasing attention. This study aimed to assess uncertainties in streamflow predictions due to forcing data and the identification of behavioural parameter sets in 31 Irish catchments. By combining stochastic rainfall ensembles and multiple parameter sets for three conceptual rainfall-runoff models, an analysis of variance model was used to decompose the total uncertainty in streamflow simulations into contributions from (i) forcing data, (ii) identification of model parameters and (iii) interactions between the two. The analysis illustrates that, for our subjective choices, hydrological model selection had a greater contribution to overall uncertainty, while performance criteria selection influenced the relative intra-annual uncertainties in streamflow predictions. Uncertainties in streamflow predictions due to the method of determining parameters were relatively lower for wetter catchments, and more evenly distributed throughout the year when the Nash-Sutcliffe Efficiency of logarithmic values of flow (lnNSE) was the evaluation criterion.

Uncertainty relation in Schwarzschild spacetime

NASA Astrophysics Data System (ADS)

Feng, Jun; Zhang, Yao-Zhong; Gould, Mark D.; Fan, Heng

2015-04-01

We explore the entropic uncertainty relation in the curved background outside a Schwarzschild black hole, and find that Hawking radiation introduces a nontrivial modification on the uncertainty bound for particular observer, therefore it could be witnessed by proper uncertainty game experimentally. We first investigate an uncertainty game between a free falling observer and his static partner holding a quantum memory initially entangled with the quantum system to be measured. Due to the information loss from Hawking decoherence, we find an inevitable increase of the uncertainty on the outcome of measurements in the view of static observer, which is dependent on the mass of the black hole, the distance of observer from event horizon, and the mode frequency of quantum memory. To illustrate the generality of this paradigm, we relate the entropic uncertainty bound with other uncertainty probe, e.g., time-energy uncertainty. In an alternative game between two static players, we show that quantum information of qubit can be transferred to quantum memory through a bath of fluctuating quantum fields outside the black hole. For a particular choice of initial state, we show that the Hawking decoherence cannot counteract entanglement generation after the dynamical evolution of system, which triggers an effectively reduced uncertainty bound that violates the intrinsic limit -log2 ⁡ c. Numerically estimation for a proper choice of initial state shows that our result is comparable with possible real experiments. Finally, a discussion on the black hole firewall paradox in the context of entropic uncertainty relation is given.

Public Perception of Uncertainties Within Climate Change Science.

PubMed

Visschers, Vivianne H M

2018-01-01

Climate change is a complex, multifaceted problem involving various interacting systems and actors. Therefore, the intensities, locations, and timeframes of the consequences of climate change are hard to predict and cause uncertainties. Relatively little is known about how the public perceives this scientific uncertainty and how this relates to their concern about climate change. In this article, an online survey among 306 Swiss people is reported that investigated whether people differentiate between different types of uncertainty in climate change research. Also examined was the way in which the perception of uncertainty is related to people's concern about climate change, their trust in science, their knowledge about climate change, and their political attitude. The results of a principal component analysis showed that respondents differentiated between perceived ambiguity in climate research, measurement uncertainty, and uncertainty about the future impact of climate change. Using structural equation modeling, it was found that only perceived ambiguity was directly related to concern about climate change, whereas measurement uncertainty and future uncertainty were not. Trust in climate science was strongly associated with each type of uncertainty perception and was indirectly associated with concern about climate change. Also, more knowledge about climate change was related to less strong perceptions of each type of climate science uncertainty. Hence, it is suggested that to increase public concern about climate change, it may be especially important to consider the perceived ambiguity about climate research. Efforts that foster trust in climate science also appear highly worthwhile. © 2017 Society for Risk Analysis.

Uncertainty Analysis of Air Radiation for Lunar Return Shock Layers

NASA Technical Reports Server (NTRS)

Kleb, Bil; Johnston, Christopher O.

2008-01-01

By leveraging a new uncertainty markup technique, two risk analysis methods are used to compute the uncertainty of lunar-return shock layer radiation predicted by the High temperature Aerothermodynamic Radiation Algorithm (HARA). The effects of epistemic uncertainty, or uncertainty due to a lack of knowledge, is considered for the following modeling parameters: atomic line oscillator strengths, atomic line Stark broadening widths, atomic photoionization cross sections, negative ion photodetachment cross sections, molecular bands oscillator strengths, and electron impact excitation rates. First, a simplified shock layer problem consisting of two constant-property equilibrium layers is considered. The results of this simplified problem show that the atomic nitrogen oscillator strengths and Stark broadening widths in both the vacuum ultraviolet and infrared spectral regions, along with the negative ion continuum, are the dominant uncertainty contributors. Next, three variable property stagnation-line shock layer cases are analyzed: a typical lunar return case and two Fire II cases. For the near-equilibrium lunar return and Fire 1643-second cases, the resulting uncertainties are very similar to the simplified case. Conversely, the relatively nonequilibrium 1636-second case shows significantly larger influence from electron impact excitation rates of both atoms and molecules. For all cases, the total uncertainty in radiative heat flux to the wall due to epistemic uncertainty in modeling parameters is 30% as opposed to the erroneously-small uncertainty levels (plus or minus 6%) found when treating model parameter uncertainties as aleatory (due to chance) instead of epistemic (due to lack of knowledge).

"I Don't Want to Be an Ostrich": Managing Mothers' Uncertainty during BRCA1/2 Genetic Counseling.

PubMed

Fisher, Carla L; Roccotagliata, Thomas; Rising, Camella J; Kissane, David W; Glogowski, Emily A; Bylund, Carma L

2017-06-01

Families who face genetic disease risk must learn how to grapple with complicated uncertainties about their health and future on a long-term basis. Women who undergo BRCA 1/2 genetic testing describe uncertainty related to personal risk as well as their loved ones', particularly daughters', risk. The genetic counseling setting is a prime opportunity for practitioners to help mothers manage uncertainty in the moment but also once they leave a session. Uncertainty Management Theory (UMT) helps to illuminate the various types of uncertainty women encounter and the important role of communication in uncertainty management. Informed by UMT, we conducted a thematic analysis of 16 genetic counseling sessions between practitioners and mothers at risk for, or carriers of, a BRCA1/2 mutation. Five themes emerged that represent communication strategies used to manage uncertainty: 1) addresses myths, misunderstandings, or misconceptions; 2) introduces uncertainty related to science; 3) encourages information seeking or sharing about family medical history; 4) reaffirms or validates previous behavior or decisions; and 5) minimizes the probability of personal risk or family members' risk. Findings illustrate the critical role of genetic counseling for families in managing emotionally challenging risk-related uncertainty. The analysis may prove beneficial to not only genetic counseling practice but generations of families at high risk for cancer who must learn strategic approaches to managing a complex web of uncertainty that can challenge them for a lifetime.

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.

A framework for modeling uncertainty in regional climate change

EPA Science Inventory

In this study, we present a new modeling framework and a large ensemble of climate projections to investigate the uncertainty in regional climate change over the United States associated with four dimensions of uncertainty. The sources of uncertainty considered in this framework ...

Accounting For Uncertainty in The Application Of High Throughput Datasets

EPA Science Inventory

The use of high throughput screening (HTS) datasets will need to adequately account for uncertainties in the data generation process and propagate these uncertainties through to ultimate use. Uncertainty arises at multiple levels in the construction of predictors using in vitro ...

Nuclear Data Uncertainty Propagation in Depletion Calculations Using Cross Section Uncertainties in One-group or Multi-group

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

Díez, C.J., E-mail: cj.diez@upm.es; Cabellos, O.; Instituto de Fusión Nuclear, Universidad Politécnica de Madrid, 28006 Madrid

Several approaches have been developed in last decades to tackle nuclear data uncertainty propagation problems of burn-up calculations. One approach proposed was the Hybrid Method, where uncertainties in nuclear data are propagated only on the depletion part of a burn-up problem. Because only depletion is addressed, only one-group cross sections are necessary, and hence, their collapsed one-group uncertainties. This approach has been applied successfully in several advanced reactor systems like EFIT (ADS-like reactor) or ESFR (Sodium fast reactor) to assess uncertainties on the isotopic composition. However, a comparison with using multi-group energy structures was not carried out, and has tomore » be performed in order to analyse the limitations of using one-group uncertainties.« less

Incorporating uncertainty in RADTRAN 6.0 input files.

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

Dennis, Matthew L.; Weiner, Ruth F.; Heames, Terence John

Uncertainty may be introduced into RADTRAN analyses by distributing input parameters. The MELCOR Uncertainty Engine (Gauntt and Erickson, 2004) has been adapted for use in RADTRAN to determine the parameter shape and minimum and maximum of the distribution, to sample on the distribution, and to create an appropriate RADTRAN batch file. Coupling input parameters is not possible in this initial application. It is recommended that the analyst be very familiar with RADTRAN and able to edit or create a RADTRAN input file using a text editor before implementing the RADTRAN Uncertainty Analysis Module. Installation of the MELCOR Uncertainty Engine ismore » required for incorporation of uncertainty into RADTRAN. Gauntt and Erickson (2004) provides installation instructions as well as a description and user guide for the uncertainty engine.« less

Nuclear Data Uncertainty Propagation in Depletion Calculations Using Cross Section Uncertainties in One-group or Multi-group

NASA Astrophysics Data System (ADS)

Díez, C. J.; Cabellos, O.; Martínez, J. S.

2015-01-01

Several approaches have been developed in last decades to tackle nuclear data uncertainty propagation problems of burn-up calculations. One approach proposed was the Hybrid Method, where uncertainties in nuclear data are propagated only on the depletion part of a burn-up problem. Because only depletion is addressed, only one-group cross sections are necessary, and hence, their collapsed one-group uncertainties. This approach has been applied successfully in several advanced reactor systems like EFIT (ADS-like reactor) or ESFR (Sodium fast reactor) to assess uncertainties on the isotopic composition. However, a comparison with using multi-group energy structures was not carried out, and has to be performed in order to analyse the limitations of using one-group uncertainties.

A method to estimate the effect of deformable image registration uncertainties on daily dose mapping

PubMed Central

Murphy, Martin J.; Salguero, Francisco J.; Siebers, Jeffrey V.; Staub, David; Vaman, Constantin

2012-01-01

Purpose: To develop a statistical sampling procedure for spatially-correlated uncertainties in deformable image registration and then use it to demonstrate their effect on daily dose mapping. Methods: Sequential daily CT studies are acquired to map anatomical variations prior to fractionated external beam radiotherapy. The CTs are deformably registered to the planning CT to obtain displacement vector fields (DVFs). The DVFs are used to accumulate the dose delivered each day onto the planning CT. Each DVF has spatially-correlated uncertainties associated with it. Principal components analysis (PCA) is applied to measured DVF error maps to produce decorrelated principal component modes of the errors. The modes are sampled independently and reconstructed to produce synthetic registration error maps. The synthetic error maps are convolved with dose mapped via deformable registration to model the resulting uncertainty in the dose mapping. The results are compared to the dose mapping uncertainty that would result from uncorrelated DVF errors that vary randomly from voxel to voxel. Results: The error sampling method is shown to produce synthetic DVF error maps that are statistically indistinguishable from the observed error maps. Spatially-correlated DVF uncertainties modeled by our procedure produce patterns of dose mapping error that are different from that due to randomly distributed uncertainties. Conclusions: Deformable image registration uncertainties have complex spatial distributions. The authors have developed and tested a method to decorrelate the spatial uncertainties and make statistical samples of highly correlated error maps. The sample error maps can be used to investigate the effect of DVF uncertainties on daily dose mapping via deformable image registration. An initial demonstration of this methodology shows that dose mapping uncertainties can be sensitive to spatial patterns in the DVF uncertainties. PMID:22320766

Framing of Uncertainty in Scientific Publications: Towards Recommendations for Decision Support

NASA Astrophysics Data System (ADS)

Guillaume, J. H. A.; Helgeson, C.; Elsawah, S.; Jakeman, A. J.; Kummu, M.

2016-12-01

Uncertainty is recognised as an essential issue in environmental decision making and decision support. As modellers, we notably use a variety of tools and techniques within an analysis, for example related to uncertainty quantification and model validation. We also address uncertainty by how we present results. For example, experienced modellers are careful to distinguish robust conclusions from those that need further work, and the precision of quantitative results is tailored to their accuracy. In doing so, the modeller frames how uncertainty should be interpreted by their audience. This is an area which extends beyond modelling to fields such as philosophy of science, semantics, discourse analysis, intercultural communication and rhetoric. We propose that framing of uncertainty deserves greater attention in the context of decision support, and that there are opportunities in this area for fundamental research, synthesis and knowledge transfer, development of teaching curricula, and significant advances in managing uncertainty in decision making. This presentation reports preliminary results of a study of framing practices. Specifically, we analyse the framing of uncertainty that is visible in the abstracts from a corpus of scientific articles. We do this through textual analysis of the content and structure of those abstracts. Each finding that appears in an abstract is classified according to the uncertainty framing approach used, using a classification scheme that was iteratively revised based on reflection and comparison amongst three coders. This analysis indicates how frequently the different framing approaches are used, and provides initial insights into relationships between frames, how the frames relate to interpretation of uncertainty, and how rhetorical devices are used by modellers to communicate uncertainty in their work. We propose initial hypotheses for how the resulting insights might influence decision support, and help advance decision making to better address uncertainty.

Dynamic rating curve assessment for hydrometric stations and computation of the associated uncertainties: Quality and station management indicators

NASA Astrophysics Data System (ADS)

Morlot, Thomas; Perret, Christian; Favre, Anne-Catherine; Jalbert, Jonathan

2014-09-01

A rating curve is used to indirectly estimate the discharge in rivers based on water level measurements. The discharge values obtained from a rating curve include uncertainties related to the direct stage-discharge measurements (gaugings) used to build the curves, the quality of fit of the curve to these measurements and the constant changes in the river bed morphology. Moreover, the uncertainty of discharges estimated from a rating curve increases with the “age” of the rating curve. The level of uncertainty at a given point in time is therefore particularly difficult to assess. A “dynamic” method has been developed to compute rating curves while calculating associated uncertainties, thus making it possible to regenerate streamflow data with uncertainty estimates. The method is based on historical gaugings at hydrometric stations. A rating curve is computed for each gauging and a model of the uncertainty is fitted for each of them. The model of uncertainty takes into account the uncertainties in the measurement of the water level, the quality of fit of the curve, the uncertainty of gaugings and the increase of the uncertainty of discharge estimates with the age of the rating curve computed with a variographic analysis (Jalbert et al., 2011). The presented dynamic method can answer important questions in the field of hydrometry such as “How many gaugings a year are required to produce streamflow data with an average uncertainty of X%?” and “When and in what range of water flow rates should these gaugings be carried out?”. The Rocherousse hydrometric station (France, Haute-Durance watershed, 946 [km2]) is used as an example throughout the paper. Others stations are used to illustrate certain points.

Entropic uncertainty relations in the Heisenberg XXZ model and its controlling via filtering operations

NASA Astrophysics Data System (ADS)

Ming, Fei; Wang, Dong; Shi, Wei-Nan; Huang, Ai-Jun; Sun, Wen-Yang; Ye, Liu

2018-04-01

The uncertainty principle is recognized as an elementary ingredient of quantum theory and sets up a significant bound to predict outcome of measurement for a couple of incompatible observables. In this work, we develop dynamical features of quantum memory-assisted entropic uncertainty relations (QMA-EUR) in a two-qubit Heisenberg XXZ spin chain with an inhomogeneous magnetic field. We specifically derive the dynamical evolutions of the entropic uncertainty with respect to the measurement in the Heisenberg XXZ model when spin A is initially correlated with quantum memory B. It has been found that the larger coupling strength J of the ferromagnetism ( J < 0 ) and the anti-ferromagnetism ( J > 0 ) chains can effectively degrade the measuring uncertainty. Besides, it turns out that the higher temperature can induce the inflation of the uncertainty because the thermal entanglement becomes relatively weak in this scenario, and there exists a distinct dynamical behavior of the uncertainty when an inhomogeneous magnetic field emerges. With the growing magnetic field | B | , the variation of the entropic uncertainty will be non-monotonic. Meanwhile, we compare several different optimized bounds existing with the initial bound proposed by Berta et al. and consequently conclude Adabi et al.'s result is optimal. Moreover, we also investigate the mixedness of the system of interest, dramatically associated with the uncertainty. Remarkably, we put forward a possible physical interpretation to explain the evolutionary phenomenon of the uncertainty. Finally, we take advantage of a local filtering operation to steer the magnitude of the uncertainty. Therefore, our explorations may shed light on the entropic uncertainty under the Heisenberg XXZ model and hence be of importance to quantum precision measurement over solid state-based quantum information processing.

Different top-down approaches to estimate measurement uncertainty of whole blood tacrolimus mass concentration values.

PubMed

Rigo-Bonnin, Raül; Blanco-Font, Aurora; Canalias, Francesca

2018-05-08

Values of mass concentration of tacrolimus in whole blood are commonly used by the clinicians for monitoring the status of a transplant patient and for checking whether the administered dose of tacrolimus is effective. So, clinical laboratories must provide results as accurately as possible. Measurement uncertainty can allow ensuring reliability of these results. The aim of this study was to estimate measurement uncertainty of whole blood mass concentration tacrolimus values obtained by UHPLC-MS/MS using two top-down approaches: the single laboratory validation approach and the proficiency testing approach. For the single laboratory validation approach, we estimated the uncertainties associated to the intermediate imprecision (using long-term internal quality control data) and the bias (utilizing a certified reference material). Next, we combined them together with the uncertainties related to the calibrators-assigned values to obtain a combined uncertainty for, finally, to calculate the expanded uncertainty. For the proficiency testing approach, the uncertainty was estimated in a similar way that the single laboratory validation approach but considering data from internal and external quality control schemes to estimate the uncertainty related to the bias. The estimated expanded uncertainty for single laboratory validation, proficiency testing using internal and external quality control schemes were 11.8%, 13.2%, and 13.0%, respectively. After performing the two top-down approaches, we observed that their uncertainty results were quite similar. This fact would confirm that either two approaches could be used to estimate the measurement uncertainty of whole blood mass concentration tacrolimus values in clinical laboratories. Copyright © 2018 The Canadian Society of Clinical Chemists. Published by Elsevier Inc. All rights reserved.

Uncertainty of rotating shadowband irradiometers and Si-pyranometers including the spectral irradiance error

NASA Astrophysics Data System (ADS)

Wilbert, Stefan; Kleindiek, Stefan; Nouri, Bijan; Geuder, Norbert; Habte, Aron; Schwandt, Marko; Vignola, Frank

2016-05-01

Concentrating solar power projects require accurate direct normal irradiance (DNI) data including uncertainty specifications for plant layout and cost calculations. Ground measured data are necessary to obtain the required level of accuracy and are often obtained with Rotating Shadowband Irradiometers (RSI) that use photodiode pyranometers and correction functions to account for systematic effects. The uncertainty of Si-pyranometers has been investigated, but so far basically empirical studies were published or decisive uncertainty influences had to be estimated based on experience in analytical studies. One of the most crucial estimated influences is the spectral irradiance error because Si-photodiode-pyranometers only detect visible and color infrared radiation and have a spectral response that varies strongly within this wavelength interval. Furthermore, analytic studies did not discuss the role of correction functions and the uncertainty introduced by imperfect shading. In order to further improve the bankability of RSI and Si-pyranometer data, a detailed uncertainty analysis following the Guide to the Expression of Uncertainty in Measurement (GUM) has been carried out. The study defines a method for the derivation of the spectral error and spectral uncertainties and presents quantitative values of the spectral and overall uncertainties. Data from the PSA station in southern Spain was selected for the analysis. Average standard uncertainties for corrected 10 min data of 2 % for global horizontal irradiance (GHI), and 2.9 % for DNI (for GHI and DNI over 300 W/m²) were found for the 2012 yearly dataset when separate GHI and DHI calibration constants were used. Also the uncertainty in 1 min resolution was analyzed. The effect of correction functions is significant. The uncertainties found in this study are consistent with results of previous empirical studies.

Uncertainty of Rotating Shadowband Irradiometers and Si-Pyranometers Including the Spectral Irradiance Error

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

Wilbert, Stefan; Kleindiek, Stefan; Nouri, Bijan

2016-05-31

Concentrating solar power projects require accurate direct normal irradiance (DNI) data including uncertainty specifications for plant layout and cost calculations. Ground measured data are necessary to obtain the required level of accuracy and are often obtained with Rotating Shadowband Irradiometers (RSI) that use photodiode pyranometers and correction functions to account for systematic effects. The uncertainty of Si-pyranometers has been investigated, but so far basically empirical studies were published or decisive uncertainty influences had to be estimated based on experience in analytical studies. One of the most crucial estimated influences is the spectral irradiance error because Si-photodiode-pyranometers only detect visible andmore » color infrared radiation and have a spectral response that varies strongly within this wavelength interval. Furthermore, analytic studies did not discuss the role of correction functions and the uncertainty introduced by imperfect shading. In order to further improve the bankability of RSI and Si-pyranometer data, a detailed uncertainty analysis following the Guide to the Expression of Uncertainty in Measurement (GUM) has been carried out. The study defines a method for the derivation of the spectral error and spectral uncertainties and presents quantitative values of the spectral and overall uncertainties. Data from the PSA station in southern Spain was selected for the analysis. Average standard uncertainties for corrected 10 min data of 2% for global horizontal irradiance (GHI), and 2.9% for DNI (for GHI and DNI over 300 W/m2) were found for the 2012 yearly dataset when separate GHI and DHI calibration constants were used. Also the uncertainty in 1 min resolution was analyzed. The effect of correction functions is significant. The uncertainties found in this study are consistent with results of previous empirical studies.« less

On the Monte Carlo simulation of electron transport in the sub-1 keV energy range.

PubMed

Thomson, Rowan M; Kawrakow, Iwan

2011-08-01

The validity of "classic" Monte Carlo (MC) simulations of electron and positron transport at sub-1 keV energies is investigated in the context of quantum theory. Quantum theory dictates that uncertainties on the position and energy-momentum four-vectors of radiation quanta obey Heisenberg's uncertainty relation; however, these uncertainties are neglected in "classical" MC simulations of radiation transport in which position and momentum are known precisely. Using the quantum uncertainty relation and electron mean free path, the magnitudes of uncertainties on electron position and momentum are calculated for different kinetic energies; a validity bound on the classical simulation of electron transport is derived. In order to satisfy the Heisenberg uncertainty principle, uncertainties of 5% must be assigned to position and momentum for 1 keV electrons in water; at 100 eV, these uncertainties are 17 to 20% and are even larger at lower energies. In gaseous media such as air, these uncertainties are much smaller (less than 1% for electrons with energy 20 eV or greater). The classical Monte Carlo transport treatment is questionable for sub-1 keV electrons in condensed water as uncertainties on position and momentum must be large (relative to electron momentum and mean free path) to satisfy the quantum uncertainty principle. Simulations which do not account for these uncertainties are not faithful representations of the physical processes, calling into question the results of MC track structure codes simulating sub-1 keV electron transport. Further, the large difference in the scale at which quantum effects are important in gaseous and condensed media suggests that track structure measurements in gases are not necessarily representative of track structure in condensed materials on a micrometer or a nanometer scale.

An optimization based sampling approach for multiple metrics uncertainty analysis using generalized likelihood uncertainty estimation

NASA Astrophysics Data System (ADS)

Zhou, Rurui; Li, Yu; Lu, Di; Liu, Haixing; Zhou, Huicheng

2016-09-01

This paper investigates the use of an epsilon-dominance non-dominated sorted genetic algorithm II (ɛ-NSGAII) as a sampling approach with an aim to improving sampling efficiency for multiple metrics uncertainty analysis using Generalized Likelihood Uncertainty Estimation (GLUE). The effectiveness of ɛ-NSGAII based sampling is demonstrated compared with Latin hypercube sampling (LHS) through analyzing sampling efficiency, multiple metrics performance, parameter uncertainty and flood forecasting uncertainty with a case study of flood forecasting uncertainty evaluation based on Xinanjiang model (XAJ) for Qing River reservoir, China. Results obtained demonstrate the following advantages of the ɛ-NSGAII based sampling approach in comparison to LHS: (1) The former performs more effective and efficient than LHS, for example the simulation time required to generate 1000 behavioral parameter sets is shorter by 9 times; (2) The Pareto tradeoffs between metrics are demonstrated clearly with the solutions from ɛ-NSGAII based sampling, also their Pareto optimal values are better than those of LHS, which means better forecasting accuracy of ɛ-NSGAII parameter sets; (3) The parameter posterior distributions from ɛ-NSGAII based sampling are concentrated in the appropriate ranges rather than uniform, which accords with their physical significance, also parameter uncertainties are reduced significantly; (4) The forecasted floods are close to the observations as evaluated by three measures: the normalized total flow outside the uncertainty intervals (FOUI), average relative band-width (RB) and average deviation amplitude (D). The flood forecasting uncertainty is also reduced a lot with ɛ-NSGAII based sampling. This study provides a new sampling approach to improve multiple metrics uncertainty analysis under the framework of GLUE, and could be used to reveal the underlying mechanisms of parameter sets under multiple conflicting metrics in the uncertainty analysis process.

Uncertainty of Polarized Parton Distributions

NASA Astrophysics Data System (ADS)

Hirai, M.; Goto, Y.; Horaguchi, T.; Kobayashi, H.; Kumano, S.; Miyama, M.; Saito, N.; Shibata, T.-A.

Polarized parton distribution functions are determined by a χ2 analysis of polarized deep inelastic experimental data. In this paper, uncertainty of obtained distribution functions is investigated by a Hessian method. We find that the uncertainty of the polarized gluon distribution is fairly large. Then, we estimate the gluon uncertainty by including the fake data which are generated from prompt photon process at RHIC. We observed that the uncertainty could be reduced with these data.

Assessment of Radiative Heating Uncertainty for Hyperbolic Earth Entry

NASA Technical Reports Server (NTRS)

Johnston, Christopher O.; Mazaheri, Alireza; Gnoffo, Peter A.; Kleb, W. L.; Sutton, Kenneth; Prabhu, Dinesh K.; Brandis, Aaron M.; Bose, Deepak

2011-01-01

This paper investigates the shock-layer radiative heating uncertainty for hyperbolic Earth entry, with the main focus being a Mars return. In Part I of this work, a baseline simulation approach involving the LAURA Navier-Stokes code with coupled ablation and radiation is presented, with the HARA radiation code being used for the radiation predictions. Flight cases representative of peak-heating Mars or asteroid return are de ned and the strong influence of coupled ablation and radiation on their aerothermodynamic environments are shown. Structural uncertainties inherent in the baseline simulations are identified, with turbulence modeling, precursor absorption, grid convergence, and radiation transport uncertainties combining for a +34% and ..24% structural uncertainty on the radiative heating. A parametric uncertainty analysis, which assumes interval uncertainties, is presented. This analysis accounts for uncertainties in the radiation models as well as heat of formation uncertainties in the flow field model. Discussions and references are provided to support the uncertainty range chosen for each parameter. A parametric uncertainty of +47.3% and -28.3% is computed for the stagnation-point radiative heating for the 15 km/s Mars-return case. A breakdown of the largest individual uncertainty contributors is presented, which includes C3 Swings cross-section, photoionization edge shift, and Opacity Project atomic lines. Combining the structural and parametric uncertainty components results in a total uncertainty of +81.3% and ..52.3% for the Mars-return case. In Part II, the computational technique and uncertainty analysis presented in Part I are applied to 1960s era shock-tube and constricted-arc experimental cases. It is shown that experiments contain shock layer temperatures and radiative ux values relevant to the Mars-return cases of present interest. Comparisons between the predictions and measurements, accounting for the uncertainty in both, are made for a range of experiments. A measure of comparison quality is de ned, which consists of the percent overlap of the predicted uncertainty bar with the corresponding measurement uncertainty bar. For nearly all cases, this percent overlap is greater than zero, and for most of the higher temperature cases (T >13,000 K) it is greater than 50%. These favorable comparisons provide evidence that the baseline computational technique and uncertainty analysis presented in Part I are adequate for Mars-return simulations. In Part III, the computational technique and uncertainty analysis presented in Part I are applied to EAST shock-tube cases. These experimental cases contain wavelength dependent intensity measurements in a wavelength range that covers 60% of the radiative intensity for the 11 km/s, 5 m radius flight case studied in Part I. Comparisons between the predictions and EAST measurements are made for a range of experiments. The uncertainty analysis presented in Part I is applied to each prediction, and comparisons are made using the metrics defined in Part II. The agreement between predictions and measurements is excellent for velocities greater than 10.5 km/s. Both the wavelength dependent and wavelength integrated intensities agree within 30% for nearly all cases considered. This agreement provides confidence in the computational technique and uncertainty analysis presented in Part I, and provides further evidence that this approach is adequate for Mars-return simulations. Part IV of this paper reviews existing experimental data that include the influence of massive ablation on radiative heating. It is concluded that this existing data is not sufficient for the present uncertainty analysis. Experiments to capture the influence of massive ablation on radiation are suggested as future work, along with further studies of the radiative precursor and improvements in the radiation properties of ablation products.

Evaluating a multispecies adaptive management framework: Must uncertainty impede effective decision-making?

USGS Publications Warehouse

Smith, David R.; McGowan, Conor P.; Daily, Jonathan P.; Nichols, James D.; Sweka, John A.; Lyons, James E.

2013-01-01

Application of adaptive management to complex natural resource systems requires careful evaluation to ensure that the process leads to improved decision-making. As part of that evaluation, adaptive policies can be compared with alternative nonadaptive management scenarios. Also, the value of reducing structural (ecological) uncertainty to achieving management objectives can be quantified.A multispecies adaptive management framework was recently adopted by the Atlantic States Marine Fisheries Commission for sustainable harvest of Delaware Bay horseshoe crabs Limulus polyphemus, while maintaining adequate stopover habitat for migrating red knots Calidris canutus rufa, the focal shorebird species. The predictive model set encompassed the structural uncertainty in the relationships between horseshoe crab spawning, red knot weight gain and red knot vital rates. Stochastic dynamic programming was used to generate a state-dependent strategy for harvest decisions given that uncertainty. In this paper, we employed a management strategy evaluation approach to evaluate the performance of this adaptive management framework. Active adaptive management was used by including model weights as state variables in the optimization and reducing structural uncertainty by model weight updating.We found that the value of information for reducing structural uncertainty is expected to be low, because the uncertainty does not appear to impede effective management. Harvest policy responded to abundance levels of both species regardless of uncertainty in the specific relationship that generated those abundances. Thus, the expected horseshoe crab harvest and red knot abundance were similar when the population generating model was uncertain or known, and harvest policy was robust to structural uncertainty as specified.Synthesis and applications. The combination of management strategy evaluation with state-dependent strategies from stochastic dynamic programming was an informative approach to evaluate adaptive management performance and value of learning. Although natural resource decisions are characterized by uncertainty, not all uncertainty will cause decisions to be altered substantially, as we found in this case. It is important to incorporate uncertainty into the decision framing and evaluate the effect of reducing that uncertainty on achieving the desired outcomes

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.

Quantifying acoustic doppler current profiler discharge uncertainty: A Monte Carlo based tool for moving-boat measurements

USGS Publications Warehouse

Mueller, David S.

2017-01-01

This paper presents a method using Monte Carlo simulations for assessing uncertainty of moving-boat acoustic Doppler current profiler (ADCP) discharge measurements using a software tool known as QUant, which was developed for this purpose. Analysis was performed on 10 data sets from four Water Survey of Canada gauging stations in order to evaluate the relative contribution of a range of error sources to the total estimated uncertainty. The factors that differed among data sets included the fraction of unmeasured discharge relative to the total discharge, flow nonuniformity, and operator decisions about instrument programming and measurement cross section. As anticipated, it was found that the estimated uncertainty is dominated by uncertainty of the discharge in the unmeasured areas, highlighting the importance of appropriate selection of the site, the instrument, and the user inputs required to estimate the unmeasured discharge. The main contributor to uncertainty was invalid data, but spatial inhomogeneity in water velocity and bottom-track velocity also contributed, as did variation in the edge velocity, uncertainty in the edge distances, edge coefficients, and the top and bottom extrapolation methods. To a lesser extent, spatial inhomogeneity in the bottom depth also contributed to the total uncertainty, as did uncertainty in the ADCP draft at shallow sites. The estimated uncertainties from QUant can be used to assess the adequacy of standard operating procedures. They also provide quantitative feedback to the ADCP operators about the quality of their measurements, indicating which parameters are contributing most to uncertainty, and perhaps even highlighting ways in which uncertainty can be reduced. Additionally, QUant can be used to account for self-dependent error sources such as heading errors, which are a function of heading. The results demonstrate the importance of a Monte Carlo method tool such as QUant for quantifying random and bias errors when evaluating the uncertainty of moving-boat ADCP measurements.

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

The Scientific Basis of Uncertainty Factors Used in Setting Occupational Exposure Limits.

PubMed

Dankovic, D A; Naumann, B D; Maier, A; Dourson, M L; Levy, L S

2015-01-01

The uncertainty factor concept is integrated into health risk assessments for all aspects of public health practice, including by most organizations that derive occupational exposure limits. The use of uncertainty factors is predicated on the assumption that a sufficient reduction in exposure from those at the boundary for the onset of adverse effects will yield a safe exposure level for at least the great majority of the exposed population, including vulnerable subgroups. There are differences in the application of the uncertainty factor approach among groups that conduct occupational assessments; however, there are common areas of uncertainty which are considered by all or nearly all occupational exposure limit-setting organizations. Five key uncertainties that are often examined include interspecies variability in response when extrapolating from animal studies to humans, response variability in humans, uncertainty in estimating a no-effect level from a dose where effects were observed, extrapolation from shorter duration studies to a full life-time exposure, and other insufficiencies in the overall health effects database indicating that the most sensitive adverse effect may not have been evaluated. In addition, a modifying factor is used by some organizations to account for other remaining uncertainties-typically related to exposure scenarios or accounting for the interplay among the five areas noted above. Consideration of uncertainties in occupational exposure limit derivation is a systematic process whereby the factors applied are not arbitrary, although they are mathematically imprecise. As the scientific basis for uncertainty factor application has improved, default uncertainty factors are now used only in the absence of chemical-specific data, and the trend is to replace them with chemical-specific adjustment factors whenever possible. The increased application of scientific data in the development of uncertainty factors for individual chemicals also has the benefit of increasing the transparency of occupational exposure limit derivation. Improved characterization of the scientific basis for uncertainty factors has led to increasing rigor and transparency in their application as part of the overall occupational exposure limit derivation process.

Uncertainty in recharge estimation: impact on groundwater vulnerability assessments for the Pearl Harbor Basin, O'ahu, Hawai'i, U.S.A.

NASA Astrophysics Data System (ADS)

Giambelluca, Thomas W.; Loague, Keith; Green, Richard E.; Nullet, Michael A.

1996-06-01

In this paper, uncertainty in recharge estimates is investigated relative to its impact on assessments of groundwater contamination vulnerability using a relatively simple pesticide mobility index, attenuation factor (AF). We employ a combination of first-order uncertainty analysis (FOUA) and sensitivity analysis to investigate recharge uncertainties for agricultural land on the island of O'ahu, Hawai'i, that is currently, or has been in the past, under sugarcane or pineapple cultivation. Uncertainty in recharge due to recharge component uncertainties is 49% of the mean for sugarcane and 58% of the mean for pineapple. The components contributing the largest amounts of uncertainty to the recharge estimate are irrigation in the case of sugarcane and precipitation in the case of pineapple. For a suite of pesticides formerly or currently used in the region, the contribution to AF uncertainty of recharge uncertainty was compared with the contributions of other AF components: retardation factor (RF), a measure of the effects of sorption; soil-water content at field capacity (ΘFC); and pesticide half-life (t1/2). Depending upon the pesticide, the contribution of recharge to uncertainty ranks second or third among the four AF components tested. The natural temporal variability of recharge is another source of uncertainty in AF, because the index is calculated using the time-averaged recharge rate. Relative to the mean, recharge variability is 10%, 44%, and 176% for the annual, monthly, and daily time scales, respectively, under sugarcane, and 31%, 112%, and 344%, respectively, under pineapple. In general, uncertainty in AF associated with temporal variability in recharge at all time scales exceeds AF. For chemicals such as atrazine or diuron under sugarcane, and atrazine or bromacil under pineapple, the range of AF uncertainty due to temporal variability in recharge encompasses significantly higher levels of leaching potential at some locations than that indicated by the AF estimate.

Gap Size Uncertainty Quantification in Advanced Gas Reactor TRISO Fuel Irradiation Experiments

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

Pham, Binh T.; Einerson, Jeffrey J.; Hawkes, Grant L.

The Advanced Gas Reactor (AGR)-3/4 experiment is the combination of the third and fourth tests conducted within the tristructural isotropic fuel development and qualification research program. The AGR-3/4 test consists of twelve independent capsules containing a fuel stack in the center surrounded by three graphite cylinders and shrouded by a stainless steel shell. This capsule design enables temperature control of both the fuel and the graphite rings by varying the neon/helium gas mixture flowing through the four resulting gaps. Knowledge of fuel and graphite temperatures is crucial for establishing the functional relationship between fission product release and irradiation thermal conditions.more » These temperatures are predicted for each capsule using the commercial finite-element heat transfer code ABAQUS. Uncertainty quantification reveals that the gap size uncertainties are among the dominant factors contributing to predicted temperature uncertainty due to high input sensitivity and uncertainty. Gap size uncertainty originates from the fact that all gap sizes vary with time due to dimensional changes of the fuel compacts and three graphite rings caused by extended exposure to high temperatures and fast neutron irradiation. Gap sizes are estimated using as-fabricated dimensional measurements at the start of irradiation and post irradiation examination dimensional measurements at the end of irradiation. Uncertainties in these measurements provide a basis for quantifying gap size uncertainty. However, lack of gap size measurements during irradiation and lack of knowledge about the dimension change rates lead to gap size modeling assumptions, which could increase gap size uncertainty. In addition, the dimensional measurements are performed at room temperature, and must be corrected to account for thermal expansion of the materials at high irradiation temperatures. Uncertainty in the thermal expansion coefficients for the graphite materials used in the AGR-3/4 capsules also increases gap size uncertainty. This study focuses on analysis of modeling assumptions and uncertainty sources to evaluate their impacts on the gap size uncertainty.« less

The importance of hydrological uncertainty assessment methods in climate change impact studies

NASA Astrophysics Data System (ADS)

Honti, M.; Scheidegger, A.; Stamm, C.

2014-08-01

Climate change impact assessments have become more and more popular in hydrology since the middle 1980s with a recent boost after the publication of the IPCC AR4 report. From hundreds of impact studies a quasi-standard methodology has emerged, to a large extent shaped by the growing public demand for predicting how water resources management or flood protection should change in the coming decades. The "standard" workflow relies on a model cascade from global circulation model (GCM) predictions for selected IPCC scenarios to future catchment hydrology. Uncertainty is present at each level and propagates through the model cascade. There is an emerging consensus between many studies on the relative importance of the different uncertainty sources. The prevailing perception is that GCM uncertainty dominates hydrological impact studies. Our hypothesis was that the relative importance of climatic and hydrologic uncertainty is (among other factors) heavily influenced by the uncertainty assessment method. To test this we carried out a climate change impact assessment and estimated the relative importance of the uncertainty sources. The study was performed on two small catchments in the Swiss Plateau with a lumped conceptual rainfall runoff model. In the climatic part we applied the standard ensemble approach to quantify uncertainty but in hydrology we used formal Bayesian uncertainty assessment with two different likelihood functions. One was a time series error model that was able to deal with the complicated statistical properties of hydrological model residuals. The second was an approximate likelihood function for the flow quantiles. The results showed that the expected climatic impact on flow quantiles was small compared to prediction uncertainty. The choice of uncertainty assessment method actually determined what sources of uncertainty could be identified at all. This demonstrated that one could arrive at rather different conclusions about the causes behind predictive uncertainty for the same hydrological model and calibration data when considering different objective functions for calibration.

Relating Data and Models to Characterize Parameter and Prediction Uncertainty

EPA Science Inventory

Applying PBPK models in risk analysis requires that we realistically assess the uncertainty of relevant model predictions in as quantitative a way as possible. The reality of human variability may add a confusing feature to the overall uncertainty assessment, as uncertainty and v...

Students' Uncertainty Management in the College Classroom

ERIC Educational Resources Information Center

Sollitto, Michael; Brott, Jan; Cole, Catherine; Gil, Elia; Selim, Heather

2018-01-01

The uncertainty experienced by college students can have serious repercussions for their success and subsequent retention. Drawing parallels between instructional context and organizational context will enrich theory and research about students' experiences of uncertainty in their college courses. Therefore, this study used Uncertainty Management…

Preparing Teachers for Uncertainty.

ERIC Educational Resources Information Center

Floden, Robert E.; Clark, Christopher M.

An examination of the various ways in which teaching is uncertain and how uncertainty pervades teachers' lives points out that teachers face uncertainties in their instructional content, ranging from difficult concepts, to unclarity about how teaching might be improved. These forms of uncertainty undermine teachers' authority, creating situations…

Sense of control under uncertainty depends on people's childhood environment: a life history theory approach.

PubMed

Mittal, Chiraag; Griskevicius, Vladas

2014-10-01

Past research found that environmental uncertainty leads people to behave differently depending on their childhood environment. For example, economic uncertainty leads people from poor childhoods to become more impulsive while leading people from wealthy childhoods to become less impulsive. Drawing on life history theory, we examine the psychological mechanism driving such diverging responses to uncertainty. Five experiments show that uncertainty alters people's sense of control over the environment. Exposure to uncertainty led people from poorer childhoods to have a significantly lower sense of control than those from wealthier childhoods. In addition, perceptions of control statistically mediated the effect of uncertainty on impulsive behavior. These studies contribute by demonstrating that sense of control is a psychological driver of behaviors associated with fast and slow life history strategies. We discuss the implications of this for theory and future research, including that environmental uncertainty might lead people who grew up poor to quit challenging tasks sooner than people who grew up wealthy. 2014 APA, all rights reserved

Uncertainties in atmospheric muon-neutrino fluxes arising from cosmic-ray primaries

NASA Astrophysics Data System (ADS)

Evans, Justin; Garcia Gamez, Diego; Porzio, Salvatore Davide; Söldner-Rembold, Stefan; Wren, Steven

2017-01-01

We present an updated calculation of the uncertainties on the atmospheric muon-neutrino flux arising from cosmic-ray primaries. For the first time, we include recent measurements of the cosmic-ray primaries collected since 2005. We apply a statistical technique that allows the determination of correlations between the parameters of the Gaisser, Stanev, Honda, and Lipari primary-flux parametrization and the incorporation of these correlations into the uncertainty on the muon-neutrino flux. We obtain an uncertainty related to the primary cosmic rays of around (5-15)%, depending on energy, which is about a factor of 2 smaller than the previously determined uncertainty. The hadron production uncertainty is added in quadrature to obtain the total uncertainty on the neutrino flux, which is reduced by ≈5 % . To take into account an unexpected hardening of the spectrum of primaries above energies of 100 GeV observed in recent measurements, we propose an alternative parametrization and discuss its impact on the neutrino flux uncertainties.

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.

Health information seeking and the World Wide Web: an uncertainty management perspective.

PubMed

Rains, Stephen A

2014-01-01

Uncertainty management theory was applied in the present study to offer one theoretical explanation for how individuals use the World Wide Web to acquire health information and to help better understand the implications of the Web for information seeking. The diversity of information sources available on the Web and potential to exert some control over the depth and breadth of one's information-acquisition effort is argued to facilitate uncertainty management. A total of 538 respondents completed a questionnaire about their uncertainty related to cancer prevention and information-seeking behavior. Consistent with study predictions, use of the Web for information seeking interacted with respondents' desired level of uncertainty to predict their actual level of uncertainty about cancer prevention. The results offer evidence that respondents who used the Web to search for cancer information were better able than were respondents who did not seek information to achieve a level of uncertainty commensurate with the level of uncertainty they desired.

A (Creative) Portrait of the Uncertain Individual: Self-Uncertainty and Individualism Enhance Creative Generation.

PubMed

Rios, Kimberly; Markman, Keith D; Schroeder, Juliana; Dyczewski, Elizabeth A

2014-08-01

Building on findings that self-uncertainty motivates attempts to restore certainty about the self, particularly in ways that highlight one's distinctiveness from others, we show that self-uncertainty, relative to uncertainty in general, increases creative generation among individualists. In Studies 1 to 3, high (but not low) individualists performed better on a creative generation task after being primed with self-uncertainty as opposed to general uncertainty. In Study 4, this effect emerged only among those who were told that the task measured creative as opposed to analytical thinking, suggesting that the positive effects of self-uncertainty on performance are specific to tasks that bolster perceptions of uniqueness. In Study 5, self-uncertain individualists experienced a restoration of self-clarity after being induced to think about themselves as more (vs. less) creative. Implications for compensatory responses to self-uncertainty and factors that influence creativity are discussed. © 2014 by the Society for Personality and Social Psychology, Inc.

The relationship of parental overprotection, perceived child vulnerability, and parenting stress to uncertainty in youth with chronic illness.

PubMed

Mullins, Larry L; Wolfe-Christensen, Cortney; Pai, Ahna L Hoff; Carpentier, Melissa Y; Gillaspy, Stephen; Cheek, Jeff; Page, Melanie

2007-09-01

To examine the relationship of parent-reported overprotection (OP), perceived child vulnerability (PCV), and parenting stress (PS) to youth-reported illness uncertainty, and to explore potential developmental differences. Eighty-two children and 82 adolescents (n = 164) diagnosed with Type 1 diabetes mellitus (DM1) or asthma, completed a measure of illness uncertainty, while their parents completed measures of OP, PCV, and PS. After controlling for demographic and illness parameters, both PCV and PS significantly predicted youth illness uncertainty in the combined sample. Within the child group, only PS significantly predicted illness uncertainty, whereas only PCV significantly predicted uncertainty for adolescents. Specific parenting variables are associated with youth-reported illness uncertainty; however, their relationship varies according to developmental level. Although OP has been identified as a predictor of child psychological outcomes in other studies, it does not appear to be associated with illness uncertainty in youth with DM1 or asthma.

Remaining Useful Life Estimation in Prognosis: An Uncertainty Propagation Problem

NASA Technical Reports Server (NTRS)

Sankararaman, Shankar; Goebel, Kai

2013-01-01

The estimation of remaining useful life is significant in the context of prognostics and health monitoring, and the prediction of remaining useful life is essential for online operations and decision-making. However, it is challenging to accurately predict the remaining useful life in practical aerospace applications due to the presence of various uncertainties that affect prognostic calculations, and in turn, render the remaining useful life prediction uncertain. It is challenging to identify and characterize the various sources of uncertainty in prognosis, understand how each of these sources of uncertainty affect the uncertainty in the remaining useful life prediction, and thereby compute the overall uncertainty in the remaining useful life prediction. In order to achieve these goals, this paper proposes that the task of estimating the remaining useful life must be approached as an uncertainty propagation problem. In this context, uncertainty propagation methods which are available in the literature are reviewed, and their applicability to prognostics and health monitoring are discussed.

Instrumentation-related uncertainty of reflectance and transmittance measurements with a two-channel spectrophotometer.

PubMed

Peest, Christian; Schinke, Carsten; Brendel, Rolf; Schmidt, Jan; Bothe, Karsten

2017-01-01

Spectrophotometers are operated in numerous fields of science and industry for a variety of applications. In order to provide confidence for the measured data, analyzing the associated uncertainty is valuable. However, the uncertainty of the measurement results is often unknown or reduced to sample-related contributions. In this paper, we describe our approach for the systematic determination of the measurement uncertainty of the commercially available two-channel spectrophotometer Agilent Cary 5000 in accordance with the Guide to the expression of uncertainty in measurements. We focus on the instrumentation-related uncertainty contributions rather than the specific application and thus outline a general procedure which can be adapted for other instruments. Moreover, we discover a systematic signal deviation due to the inertia of the measurement amplifier and develop and apply a correction procedure. Thereby we increase the usable dynamic range of the instrument by more than one order of magnitude. We present methods for the quantification of the uncertainty contributions and combine them into an uncertainty budget for the device.

Uncertainty as Impetus for Climate Mitigation

NASA Astrophysics Data System (ADS)

Lewandowsky, S.; Oreskes, N.; Risbey, J.

2015-12-01

For decades, the scientific community has called for actions to be taken to mitigate the adverse consequences of climate change. To date, those calls have found little substantial traction, and politicians and the general public are instead engaged in a debate about the causes and effects of climate change that bears little resemblance to the state of scientific knowledge. Uncertainty plays a pivotal role in that public debate, and arguments against mitigation are frequently couched in terms of uncertainty. We show that the rhetorical uses of scientific uncertainty in public debate by some actors (often with vested interests or political agendas) contrast with the mathematical result that greater uncertainty about the extent of warming is virtually always associated with an increased risk: The expected damage costs increase as a function of uncertainty about future warming. We suggest ways in which the actual implications of scientific uncertainty can be better communicated and how scientific uncertainty should be understood as an impetus, rather than a barrier, for climate mitigation.

Confronting the Uncertainty in Aerosol Forcing Using Comprehensive Observational Data

NASA Astrophysics Data System (ADS)

Johnson, J. S.; Regayre, L. A.; Yoshioka, M.; Pringle, K.; Sexton, D.; Lee, L.; Carslaw, K. S.

2017-12-01

The effect of aerosols on cloud droplet concentrations and radiative properties is the largest uncertainty in the overall radiative forcing of climate over the industrial period. In this study, we take advantage of a large perturbed parameter ensemble of simulations from the UK Met Office HadGEM-UKCA model (the aerosol component of the UK Earth System Model) to comprehensively sample uncertainty in aerosol forcing. Uncertain aerosol and atmospheric parameters cause substantial aerosol forcing uncertainty in climatically important regions. As the aerosol radiative forcing itself is unobservable, we investigate the potential for observations of aerosol and radiative properties to act as constraints on the large forcing uncertainty. We test how eight different theoretically perfect aerosol and radiation observations can constrain the forcing uncertainty over Europe. We find that the achievable constraint is weak unless many diverse observations are used simultaneously. This is due to the complex relationships between model output responses and the multiple interacting parameter uncertainties: compensating model errors mean there are many ways to produce the same model output (known as model equifinality) which impacts on the achievable constraint. However, using all eight observable quantities together we show that the aerosol forcing uncertainty can potentially be reduced by around 50%. This reduction occurs as we reduce a large sample of model variants (over 1 million) that cover the full parametric uncertainty to around 1% that are observationally plausible.Constraining the forcing uncertainty using real observations is a more complex undertaking, in which we must account for multiple further uncertainties including measurement uncertainties, structural model uncertainties and the model discrepancy from reality. Here, we make a first attempt to determine the true potential constraint on the forcing uncertainty from our model that is achievable using a comprehensive set of real aerosol and radiation observations taken from ground stations, flight campaigns and satellite. This research has been supported by the UK-China Research & Innovation Partnership Fund through the Met Office Climate Science for Service Partnership (CSSP) China as part of the Newton Fund, and by the NERC funded GASSP project.

A new framework for quantifying uncertainties in modelling studies for future climates - how more certain are CMIP5 precipitation and temperature simulations compared to CMIP3?

NASA Astrophysics Data System (ADS)

Sharma, A.; Woldemeskel, F. M.; Sivakumar, B.; Mehrotra, R.

2014-12-01

We outline a new framework for assessing uncertainties in model simulations, be they hydro-ecological simulations for known scenarios, or climate simulations for assumed scenarios representing the future. This framework is illustrated here using GCM projections for future climates for hydrologically relevant variables (precipitation and temperature), with the uncertainty segregated into three dominant components - model uncertainty, scenario uncertainty (representing greenhouse gas emission scenarios), and ensemble uncertainty (representing uncertain initial conditions and states). A novel uncertainty metric, the Square Root Error Variance (SREV), is used to quantify the uncertainties involved. The SREV requires: (1) Interpolating raw and corrected GCM outputs to a common grid; (2) Converting these to percentiles; (3) Estimating SREV for model, scenario, initial condition and total uncertainty at each percentile; and (4) Transforming SREV to a time series. The outcome is a spatially varying series of SREVs associated with each model that can be used to assess how uncertain the system is at each simulated point or time. This framework, while illustrated in a climate change context, is completely applicable for assessment of uncertainties any modelling framework may be subject to. The proposed method is applied to monthly precipitation and temperature from 6 CMIP3 and 13 CMIP5 GCMs across the world. For CMIP3, B1, A1B and A2 scenarios whereas for CMIP5, RCP2.6, RCP4.5 and RCP8.5 representing low, medium and high emissions are considered. For both CMIP3 and CMIP5, model structure is the largest source of uncertainty, which reduces significantly after correcting for biases. Scenario uncertainly increases, especially for temperature, in future due to divergence of the three emission scenarios analysed. While CMIP5 precipitation simulations exhibit a small reduction in total uncertainty over CMIP3, there is almost no reduction observed for temperature projections. Estimation of uncertainty in both space and time sheds lights on the spatial and temporal patterns of uncertainties in GCM outputs, providing an effective platform for risk-based assessments of any alternate plans or decisions that may be formulated using GCM simulations.

Percentiles of the product of uncertainty factors for establishing probabilistic reference doses.

PubMed

Gaylor, D W; Kodell, R L

2000-04-01

Exposure guidelines for potentially toxic substances are often based on a reference dose (RfD) that is determined by dividing a no-observed-adverse-effect-level (NOAEL), lowest-observed-adverse-effect-level (LOAEL), or benchmark dose (BD) corresponding to a low level of risk, by a product of uncertainty factors. The uncertainty factors for animal to human extrapolation, variable sensitivities among humans, extrapolation from measured subchronic effects to unknown results for chronic exposures, and extrapolation from a LOAEL to a NOAEL can be thought of as random variables that vary from chemical to chemical. Selected databases are examined that provide distributions across chemicals of inter- and intraspecies effects, ratios of LOAELs to NOAELs, and differences in acute and chronic effects, to illustrate the determination of percentiles for uncertainty factors. The distributions of uncertainty factors tend to be approximately lognormally distributed. The logarithm of the product of independent uncertainty factors is approximately distributed as the sum of normally distributed variables, making it possible to estimate percentiles for the product. Hence, the size of the products of uncertainty factors can be selected to provide adequate safety for a large percentage (e.g., approximately 95%) of RfDs. For the databases used to describe the distributions of uncertainty factors, using values of 10 appear to be reasonable and conservative. For the databases examined the following simple "Rule of 3s" is suggested that exceeds the estimated 95th percentile of the product of uncertainty factors: If only a single uncertainty factor is required use 33, for any two uncertainty factors use 3 x 33 approximately 100, for any three uncertainty factors use a combined factor of 3 x 100 = 300, and if all four uncertainty factors are needed use a total factor of 3 x 300 = 900. If near the 99th percentile is desired use another factor of 3. An additional factor may be needed for inadequate data or a modifying factor for other uncertainties (e.g., different routes of exposure) not covered above.

Capturing the complexity of uncertainty language to maximise its use.

NASA Astrophysics Data System (ADS)

Juanchich, Marie; Sirota, Miroslav

2016-04-01

Uncertainty is often communicated verbally, using uncertainty phrases such as 'there is a small risk of earthquake', 'flooding is possible' or 'it is very likely the sea level will rise'. Prior research has only examined a limited number of properties of uncertainty phrases: mainly the probability conveyed (e.g., 'a small chance' convey a small probability whereas 'it is likely' convey a high probability). We propose a new analytical framework that captures more of the complexity of uncertainty phrases by studying their semantic, pragmatic and syntactic properties. Further, we argue that the complexity of uncertainty phrases is functional and can be leveraged to best describe uncertain outcomes and achieve the goals of speakers. We will present findings from a corpus study and an experiment where we assessed the following properties of uncertainty phrases: probability conveyed, subjectivity, valence, nature of the subject, grammatical category of the uncertainty quantifier and whether the quantifier elicits a positive or a negative framing. Natural language processing techniques applied to corpus data showed that people use a very large variety of uncertainty phrases representing different configurations of the properties of uncertainty phrases (e.g., phrases that convey different levels of subjectivity, phrases with different grammatical construction). In addition, the corpus analysis uncovered that uncertainty phrases commonly studied in psychology are not the most commonly used in real life. In the experiment we manipulated the amount of evidence indicating that a fact was true and whether the participant was required to prove the fact was true or that it was false. Participants produced a phrase to communicate the likelihood that the fact was true (e.g., 'it is not sure…', 'I am convinced that…'). The analyses of the uncertainty phrases produced showed that participants leveraged the properties of uncertainty phrases to reflect the strength of evidence but also to achieve their personal goals. For example, participants aiming to prove that the fact was true chose words that conveyed a more positive polarity and a higher probability than participants aiming to prove that the fact was false. We discuss the utility of the framework for harnessing the properties of uncertainty phrases in geosciences.

Uncertainty quantification of Antarctic contribution to sea-level rise using the fast Elementary Thermomechanical Ice Sheet (f.ETISh) model

NASA Astrophysics Data System (ADS)

Bulthuis, Kevin; Arnst, Maarten; Pattyn, Frank; Favier, Lionel

2017-04-01

Uncertainties in sea-level rise projections are mostly due to uncertainties in Antarctic ice-sheet predictions (IPCC AR5 report, 2013), because key parameters related to the current state of the Antarctic ice sheet (e.g. sub-ice-shelf melting) and future climate forcing are poorly constrained. Here, we propose to improve the predictions of Antarctic ice-sheet behaviour using new uncertainty quantification methods. As opposed to ensemble modelling (Bindschadler et al., 2013) which provides a rather limited view on input and output dispersion, new stochastic methods (Le Maître and Knio, 2010) can provide deeper insight into the impact of uncertainties on complex system behaviour. Such stochastic methods usually begin with deducing a probabilistic description of input parameter uncertainties from the available data. Then, the impact of these input parameter uncertainties on output quantities is assessed by estimating the probability distribution of the outputs by means of uncertainty propagation methods such as Monte Carlo methods or stochastic expansion methods. The use of such uncertainty propagation methods in glaciology may be computationally costly because of the high computational complexity of ice-sheet models. This challenge emphasises the importance of developing reliable and computationally efficient ice-sheet models such as the f.ETISh ice-sheet model (Pattyn, 2015), a new fast thermomechanical coupled ice sheet/ice shelf model capable of handling complex and critical processes such as the marine ice-sheet instability mechanism. Here, we apply these methods to investigate the role of uncertainties in sub-ice-shelf melting, calving rates and climate projections in assessing Antarctic contribution to sea-level rise for the next centuries using the f.ETISh model. We detail the methods and show results that provide nominal values and uncertainty bounds for future sea-level rise as a reflection of the impact of the input parameter uncertainties under consideration, as well as a ranking of the input parameter uncertainties in the order of the significance of their contribution to uncertainty in future sea-level rise. In addition, we discuss how limitations posed by the available information (poorly constrained data) pose challenges that motivate our current research.

Strain Gauge Balance Uncertainty Analysis at NASA Langley: A Technical Review

NASA Technical Reports Server (NTRS)

Tripp, John S.

1999-01-01

This paper describes a method to determine the uncertainties of measured forces and moments from multi-component force balances used in wind tunnel tests. A multivariate regression technique is first employed to estimate the uncertainties of the six balance sensitivities and 156 interaction coefficients derived from established balance calibration procedures. These uncertainties are then employed to calculate the uncertainties of force-moment values computed from observed balance output readings obtained during tests. Confidence and prediction intervals are obtained for each computed force and moment as functions of the actual measurands. Techniques are discussed for separate estimation of balance bias and precision uncertainties.

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.

Representativeness Uncertainty in Chemical Data Assimilation Highlight Mixing Barriers

NASA Technical Reports Server (NTRS)

Lary, David John

2003-01-01

When performing chemical data assimilation the observational, representativeness, and theoretical uncertainties have very different characteristics. In this study we have accurately characterized the representativeness uncertainty by studying the probability distribution function (PDF) of the observations. The average deviation has been used as a measure of the width of the PDF and of the variability (representativeness uncertainty) for the grid cell. It turns out that for long-lived tracers such as N2O and CH4 the representativeness uncertainty is markedly different from the observational uncertainty and clearly delineates mixing barriers such as the polar vortex edge, the tropical pipe and the tropopause.

Space shuttle launch vehicle aerodynamic uncertainties: Lessons learned

NASA Technical Reports Server (NTRS)

Hamilton, J. T.

1983-01-01

The chronological development and evolution of an uncertainties model which defines the complex interdependency and interaction of the individual Space Shuttle element and component uncertainties for the launch vehicle are presented. Emphasis is placed on user requirements which dictated certain concessions, simplifications, and assumptions in the analytical model. The use of the uncertainty model in the vehicle design process and flight planning support is discussed. The terminology and justification associated with tolerances as opposed to variations are also presented. Comparisons of and conclusions drawn from flight minus predicted data and uncertainties are given. Lessons learned from the Space Shuttle program concerning aerodynamic uncertainties are examined.

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.

Uncertainty during breast diagnostic evaluation: state of the science.

PubMed

Montgomery, Mariann

2010-01-01

To present the state of the science on uncertainty in relationship to the experiences of women undergoing diagnostic evaluation for suspected breast cancer. Published articles from Medline, CINAHL, PubMED, and PsycINFO from 1983-2008 using the following key words: breast biopsy, mammography, uncertainty, reframing, inner strength, and disruption. Fifty research studies were examined with all reporting the presence of anxiety persisting throughout the diagnostic evaluation until certitude is achieved through the establishment of a definitive diagnosis. Indirect determinants of uncertainty for women undergoing breast diagnostic evaluation include measures of anxiety, depression, social support, emotional responses, defense mechanisms, and the psychological impact of events. Understanding and influencing the uncertainty experience have been suggested to be key in relieving psychosocial distress and positively influencing future screening behaviors. Several studies examine correlational relationships among anxiety, selection of coping methods, and demographic factors that influence uncertainty. A gap exists in the literature with regard to the relationship of inner strength and uncertainty. Nurses can be invaluable in assisting women in coping with the uncertainty experience by providing positive communication and support. Nursing interventions should be designed and tested for their effects on uncertainty experienced by women undergoing a breast diagnostic evaluation.

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.

Performance Assessment Uncertainty Analysis for Japan's HLW Program Feasibility Study (H12)

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

BABA,T.; ISHIGURO,K.; ISHIHARA,Y.

1999-08-30

Most HLW programs in the world recognize that any estimate of long-term radiological performance must be couched in terms of the uncertainties derived from natural variation, changes through time and lack of knowledge about the essential processes. The Japan Nuclear Cycle Development Institute followed a relatively standard procedure to address two major categories of uncertainty. First, a FEatures, Events and Processes (FEPs) listing, screening and grouping activity was pursued in order to define the range of uncertainty in system processes as well as possible variations in engineering design. A reference and many alternative cases representing various groups of FEPs weremore » defined and individual numerical simulations performed for each to quantify the range of conceptual uncertainty. Second, parameter distributions were developed for the reference case to represent the uncertainty in the strength of these processes, the sequencing of activities and geometric variations. Both point estimates using high and low values for individual parameters as well as a probabilistic analysis were performed to estimate parameter uncertainty. A brief description of the conceptual model uncertainty analysis is presented. This paper focuses on presenting the details of the probabilistic parameter uncertainty assessment.« less

A taxonomy of medical uncertainties in clinical genome sequencing.

PubMed

Han, Paul K J; Umstead, Kendall L; Bernhardt, Barbara A; Green, Robert C; Joffe, Steven; Koenig, Barbara; Krantz, Ian; Waterston, Leo B; Biesecker, Leslie G; Biesecker, Barbara B

2017-08-01

Clinical next-generation sequencing (CNGS) is introducing new opportunities and challenges into the practice of medicine. Simultaneously, these technologies are generating uncertainties of an unprecedented scale that laboratories, clinicians, and patients are required to address and manage. We describe in this report the conceptual design of a new taxonomy of uncertainties around the use of CNGS in health care. Interviews to delineate the dimensions of uncertainty in CNGS were conducted with genomics experts and themes were extracted in order to expand on a previously published three-dimensional taxonomy of medical uncertainty. In parallel, we developed an interactive website to disseminate the CNGS taxonomy to researchers and engage them in its continued refinement. The proposed taxonomy divides uncertainty along three axes-source, issue, and locus-and further discriminates the uncertainties into five layers with multiple domains. Using a hypothetical clinical example, we illustrate how the taxonomy can be applied to findings from CNGS and used to guide stakeholders through interpretation and implementation of variant results. The utility of the proposed taxonomy lies in promoting consistency in describing dimensions of uncertainty in publications and presentations, to facilitate research design and management of the uncertainties inherent in the implementation of CNGS.Genet Med advance online publication 19 January 2017.

A Taxonomy of Medical Uncertainties in Clinical Genome Sequencing

PubMed Central

Han, Paul K. J.; Umstead, Kendall L.; Bernhardt, Barbara A.; Green, Robert C.; Joffe, Steven; Koenig, Barbara; Krantz, Ian; Waterston, Leo B.; Biesecker, Leslie G.; Biesecker, Barbara B.

2017-01-01

Purpose Clinical next generation sequencing (CNGS) is introducing new opportunities and challenges into the practice of medicine. Simultaneously, these technologies are generating uncertainties of unprecedented scale that laboratories, clinicians, and patients are required to address and manage. We describe in this report the conceptual design of a new taxonomy of uncertainties around the use of CNGS in health care. Methods Interviews to delineate the dimensions of uncertainty in CNGS were conducted with genomics experts, and themes were extracted in order to expand upon a previously published three-dimensional taxonomy of medical uncertainty. In parallel we developed an interactive website to disseminate the CNGS taxonomy to researchers and engage them in its continued refinement. Results The proposed taxonomy divides uncertainty along three axes: source, issue, and locus, and further discriminates the uncertainties into five layers with multiple domains. Using a hypothetical clinical example, we illustrate how the taxonomy can be applied to findings from CNGS and used to guide stakeholders through interpretation and implementation of variant results. Conclusion The utility of the proposed taxonomy lies in promoting consistency in describing dimensions of uncertainty in publications and presentations, to facilitate research design and management of the uncertainties inherent in the implementation of CNGS. PMID:28102863

The known unknowns: neural representation of second-order uncertainty, and ambiguity

PubMed Central

Bach, Dominik R.; Hulme, Oliver; Penny, William D.; Dolan, Raymond J.

2011-01-01

Predictions provided by action-outcome probabilities entail a degree of (first-order) uncertainty. However, these probabilities themselves can be imprecise and embody second-order uncertainty. Tracking second-order uncertainty is important for optimal decision making and reinforcement learning. Previous functional magnetic resonance imaging investigations of second-order uncertainty in humans have drawn on an economic concept of ambiguity, where action-outcome associations in a gamble are either known (unambiguous) or completely unknown (ambiguous). Here, we relaxed the constraints associated with a purely categorical concept of ambiguity and varied the second-order uncertainty of gambles continuously, quantified as entropy over second-order probabilities. We show that second-order uncertainty influences decisions in a pessimistic way by biasing second-order probabilities, and that second-order uncertainty is negatively correlated with posterior cingulate cortex activity. The category of ambiguous (compared to non-ambiguous) gambles also biased choice in a similar direction, but was associated with distinct activation of a posterior parietal cortical area; an activation that we show reflects a different computational mechanism. Our findings indicate that behavioural and neural responses to second-order uncertainty are distinct from those associated with ambiguity and may call for a reappraisal of previous data. PMID:21451019

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

Reward uncertainty enhances incentive salience attribution as sign-tracking

PubMed Central

Anselme, Patrick; Robinson, Mike J. F.; Berridge, Kent C.

2014-01-01

Conditioned stimuli (CSs) come to act as motivational magnets following repeated association with unconditioned stimuli (UCSs) such as sucrose rewards. By traditional views, the more reliably predictive a Pavlovian CS-UCS association, the more the CS becomes attractive. However, in some cases, less predictability might equal more motivation. Here we examined the effect of introducing uncertainty in CS-UCS association on CS strength as an attractive motivation magnet. In the present study, Experiment 1 assessed the effects of Pavlovian predictability versus uncertainty about reward probability and/or reward magnitude on the acquisition and expression of sign-tracking (ST) and goal-tracking (GT) responses in an autoshaping procedure. Results suggested that uncertainty produced strongest incentive salience expressed as sign-tracking. Experiment 2 examined whether a within-individual temporal shift from certainty to uncertainty conditions could produce a stronger CS motivational magnet when uncertainty began, and found that sign-tracking still increased after the shift. Overall, our results support earlier reports that ST responses become more pronounced in the presence of uncertainty regarding CS-UCS associations, especially when uncertainty combines both probability and magnitude. These results suggest that Pavlovian uncertainty, although diluting predictability, is still able to enhance the incentive motivational power of particular CSs. PMID:23078951

Quantifying and Qualifying USGS ShakeMap Uncertainty

USGS Publications Warehouse

Wald, David J.; Lin, Kuo-Wan; Quitoriano, Vincent

2008-01-01

We describe algorithms for quantifying and qualifying uncertainties associated with USGS ShakeMap ground motions. The uncertainty values computed consist of latitude/longitude grid-based multiplicative factors that scale the standard deviation associated with the ground motion prediction equation (GMPE) used within the ShakeMap algorithm for estimating ground motions. The resulting grid-based 'uncertainty map' is essential for evaluation of losses derived using ShakeMaps as the hazard input. For ShakeMap, ground motion uncertainty at any point is dominated by two main factors: (i) the influence of any proximal ground motion observations, and (ii) the uncertainty of estimating ground motions from the GMPE, most notably, elevated uncertainty due to initial, unconstrained source rupture geometry. The uncertainty is highest for larger magnitude earthquakes when source finiteness is not yet constrained and, hence, the distance to rupture is also uncertain. In addition to a spatially-dependant, quantitative assessment, many users may prefer a simple, qualitative grading for the entire ShakeMap. We developed a grading scale that allows one to quickly gauge the appropriate level of confidence when using rapidly produced ShakeMaps as part of the post-earthquake decision-making process or for qualitative assessments of archived or historical earthquake ShakeMaps. We describe an uncertainty letter grading ('A' through 'F', for high to poor quality, respectively) based on the uncertainty map. A middle-range ('C') grade corresponds to a ShakeMap for a moderate-magnitude earthquake suitably represented with a point-source location. Lower grades 'D' and 'F' are assigned for larger events (M>6) where finite-source dimensions are not yet constrained. The addition of ground motion observations (or observed macroseismic intensities) reduces uncertainties over data-constrained portions of the map. Higher grades ('A' and 'B') correspond to ShakeMaps with constrained fault dimensions and numerous stations, depending on the density of station/data coverage. Due to these dependencies, the letter grade can change with subsequent ShakeMap revisions if more data are added or when finite-faulting dimensions are added. We emphasize that the greatest uncertainties are associated with unconstrained source dimensions for large earthquakes where the distance term in the GMPE is most uncertain; this uncertainty thus scales with magnitude (and consequently rupture dimension). Since this distance uncertainty produces potentially large uncertainties in ShakeMap ground-motion estimates, this factor dominates over compensating constraints for all but the most dense station distributions.

Uncertainty assessment of urban pluvial flood risk in a context of climate change adaptation decision making

NASA Astrophysics Data System (ADS)

Arnbjerg-Nielsen, Karsten; Zhou, Qianqian

2014-05-01

There has been a significant increase in climatic extremes in many regions. In Central and Northern Europe, this has led to more frequent and more severe floods. Along with improved flood modelling technologies this has enabled development of economic assessment of climate change adaptation to increasing urban flood risk. Assessment of adaptation strategies often requires a comprehensive risk-based economic analysis of current risk, drivers of change of risk over time, and measures to reduce the risk. However, such studies are often associated with large uncertainties. The uncertainties arise from basic assumptions in the economic analysis and the hydrological model, but also from the projection of future societies to local climate change impacts and suitable adaptation options. This presents a challenge to decision makers when trying to identify robust measures. We present an integrated uncertainty analysis, which can assess and quantify the overall uncertainty in relation to climate change adaptation to urban flash floods. The analysis is based on an uncertainty cascade that by means of Monte Carlo simulations of flood risk assessments incorporates climate change impacts as a key driver of risk changes over time. The overall uncertainty is then attributed to six bulk processes: climate change impact, urban rainfall-runoff processes, stage-depth functions, unit cost of repair, cost of adaptation measures, and discount rate. We apply the approach on an urban hydrological catchment in Odense, Denmark, and find that the uncertainty on the climate change impact appears to have the least influence on the net present value of the studied adaptation measures-. This does not imply that the climate change impact is not important, but that the uncertainties are not dominating when deciding on action or in-action. We then consider the uncertainty related to choosing between adaptation options given that a decision of action has been taken. In this case the major part of the uncertainty on the estimated net present values is identical for all adaptation options and will therefore not affect a comparison between adaptation measures. This makes the chose among the options easier. Furthermore, the explicit attribution of uncertainty also enables a reduction of the overall uncertainty by identifying the processes which contributes the most. This knowledge can then be used to further reduce the uncertainty related to decision making, as a substantial part of the remaining uncertainty is epistemic.

A Framework for Quantifying Measurement Uncertainties and Uncertainty Propagation in HCCI/LTGC Engine Experiments

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

Petitpas, Guillaume; McNenly, Matthew J.; Whitesides, Russell A.

In this study, a framework for estimating experimental measurement uncertainties for a Homogenous Charge Compression Ignition (HCCI)/Low-Temperature Gasoline Combustion (LTGC) engine testing facility is presented. Detailed uncertainty quantification is first carried out for the measurement of the in-cylinder pressure, whose variations during the cycle provide most of the information for performance evaluation. Standard uncertainties of other measured quantities, such as the engine geometry and speed, the air and fuel flow rate and the intake/exhaust dry molar fractions are also estimated. Propagating those uncertainties using a Monte Carlo simulation and Bayesian inference methods then allows for estimation of uncertainties of themore » mass-average temperature and composition at IVC and throughout the cycle; and also of the engine performances such as gross Integrated Mean Effective Pressure, Heat Release and Ringing Intensity. Throughout the analysis, nominal values for uncertainty inputs were taken from a well-characterized engine test facility. However, the analysis did not take into account the calibration practice of experiments run in that facility and the resulting uncertainty values are therefore not indicative of the expected accuracy of those experimental results. A future study will employ the methodology developed here to explore the effects of different calibration methods on the different uncertainty values in order to evaluate best practices for accurate engine measurements.« less

Uncertainty Measurement for Trace Element Analysis of Uranium and Plutonium Samples by Inductively Coupled Plasma-Atomic Emission Spectrometry (ICP-AES) and Inductively Coupled Plasma-Mass Spectrometry (ICP-MS)

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

Gallimore, David L.

2012-06-13

The measurement uncertainty estimatino associated with trace element analysis of impurities in U and Pu was evaluated using the Guide to the Expression of Uncertainty Measurement (GUM). I this evalution the uncertainty sources were identified and standard uncertainties for the components were categorized as either Type A or B. The combined standard uncertainty was calculated and a coverage factor k = 2 was applied to obtain the expanded uncertainty, U. The ICP-AES and ICP-MS methods used were deveoped for the multi-element analysis of U and Pu samples. A typical analytical run consists of standards, process blanks, samples, matrix spiked samples,more » post digestion spiked samples and independent calibration verification standards. The uncertainty estimation was performed on U and Pu samples that have been analyzed previously as part of the U and Pu Sample Exchange Programs. Control chart results and data from the U and Pu metal exchange programs were combined with the GUM into a concentration dependent estimate of the expanded uncertainty. Comparison of trace element uncertainties obtained using this model was compared to those obtained for trace element results as part of the Exchange programs. This process was completed for all trace elements that were determined to be above the detection limit for the U and Pu samples.« less

Presentation of uncertainties on web platforms for climate change information

NASA Astrophysics Data System (ADS)

Nocke, Thomas; Wrobel, Markus; Reusser, Dominik

2014-05-01

Climate research has a long tradition, however there is still uncertainty about the specific effects of climate change. One of the key tasks is - beyond discussing climate change and its impacts in specialist groups - to present these to a wider audience. In that respect, decision-makers in the public sector as well as directly affected professional groups require to obtain easy-to-understand information. These groups are not made up of specialist scientists. This gives rise to the challenge that the scientific information must be presented such that it is commonly understood, however, the complexity of the science behind needs to be incorporated. In particular, this requires the explicit representation of spatial and temporal uncertainty information to lay people. Within this talk/poster we survey how climate change and climate impact uncertainty information is presented on various climate service web-based platforms. We outline how the specifics of this medium make it challenging to find adequate and readable representations of uncertainties. First, we introduce a multi-step approach in communicating the uncertainty basing on a typology of uncertainty distinguishing between epistemic, natural stochastic, and human reflexive uncertainty. Then, we compare existing concepts and representations for uncertainty communication with current practices on web-based platforms, including own solutions within our web platforms ClimateImpactsOnline and ci:grasp. Finally, we review surveys on how spatial uncertainty visualization techniques are conceived by untrainded users.

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 Framework for Quantifying Measurement Uncertainties and Uncertainty Propagation in HCCI/LTGC Engine Experiments

DOE PAGES

Petitpas, Guillaume; McNenly, Matthew J.; Whitesides, Russell A.

2017-03-28

In this study, a framework for estimating experimental measurement uncertainties for a Homogenous Charge Compression Ignition (HCCI)/Low-Temperature Gasoline Combustion (LTGC) engine testing facility is presented. Detailed uncertainty quantification is first carried out for the measurement of the in-cylinder pressure, whose variations during the cycle provide most of the information for performance evaluation. Standard uncertainties of other measured quantities, such as the engine geometry and speed, the air and fuel flow rate and the intake/exhaust dry molar fractions are also estimated. Propagating those uncertainties using a Monte Carlo simulation and Bayesian inference methods then allows for estimation of uncertainties of themore » mass-average temperature and composition at IVC and throughout the cycle; and also of the engine performances such as gross Integrated Mean Effective Pressure, Heat Release and Ringing Intensity. Throughout the analysis, nominal values for uncertainty inputs were taken from a well-characterized engine test facility. However, the analysis did not take into account the calibration practice of experiments run in that facility and the resulting uncertainty values are therefore not indicative of the expected accuracy of those experimental results. A future study will employ the methodology developed here to explore the effects of different calibration methods on the different uncertainty values in order to evaluate best practices for accurate engine measurements.« less

Fundamental uncertainty limit of optical flow velocimetry according to Heisenberg's uncertainty principle.

PubMed

Fischer, Andreas

2016-11-01

Optical flow velocity measurements are important for understanding the complex behavior of flows. Although a huge variety of methods exist, they are either based on a Doppler or a time-of-flight measurement principle. Doppler velocimetry evaluates the velocity-dependent frequency shift of light scattered at a moving particle, whereas time-of-flight velocimetry evaluates the traveled distance of a scattering particle per time interval. Regarding the aim of achieving a minimal measurement uncertainty, it is unclear if one principle allows to achieve lower uncertainties or if both principles can achieve equal uncertainties. For this reason, the natural, fundamental uncertainty limit according to Heisenberg's uncertainty principle is derived for Doppler and time-of-flight measurement principles, respectively. The obtained limits of the velocity uncertainty are qualitatively identical showing, e.g., a direct proportionality for the absolute value of the velocity to the power of 32 and an indirect proportionality to the square root of the scattered light power. Hence, both measurement principles have identical potentials regarding the fundamental uncertainty limit due to the quantum mechanical behavior of photons. This fundamental limit can be attained (at least asymptotically) in reality either with Doppler or time-of-flight methods, because the respective Cramér-Rao bounds for dominating photon shot noise, which is modeled as white Poissonian noise, are identical with the conclusions from Heisenberg's uncertainty principle.

IDENTIFYING WOMEN AT RISK OF UNCERTAINTY AND POOR QUALITY OF LIFE WHEN UNDERGOING BREAST CANCER SURGERY: A SURVEY-BASED DESCRIPTIVE STUDY.

PubMed

Van Straten, S K; Xu, M; Rayne, S R

2017-06-01

Breast cancer is a leading cause of morbidity and mortality in South African women. In resource-limited settings emphasis for disease management is often concentrated on biological control and survival. However, understanding the full biopsychosocial experience of breast cancer is essential in improving access and patient uptake of care. A quantitative cross-sectional study was carried out in patients prior to breast surgery. Each participant completed the survey including validated questionnaires of uncertainty, QoL index, social support scale and demographics. Of the 59 women approached, 53 (89.9%) participated. Uncertainty was found in 86.8% (28.3% severe uncertainty) with all newly-diagnosed patients experiencing uncertainty. Patients above 45 years made up 80% of all those who were severely uncertain. Good social support did not affect levels of uncertainty. Conversely QoL was improved in women with at least primary education, and in women above 45 years. Pre-surgical chemotherapy was not associated with either uncertainty or QoL. Greatest uncertainty was reported about the roles of the treating staff and the presence of unanswered questions. Older women and those with education more commonly experienced uncertainty, but reported better QoL. The areas of uncertainty can help direct clinicians in limited resources settings to better direct services to help support patients, instituting simple measures of education and orientation.

Insight from uncertainty: bootstrap-derived diffusion metrics differentially predict memory function among older adults.

PubMed

Vorburger, Robert S; Habeck, Christian G; Narkhede, Atul; Guzman, Vanessa A; Manly, Jennifer J; Brickman, Adam M

2016-01-01

Diffusion tensor imaging suffers from an intrinsic low signal-to-noise ratio. Bootstrap algorithms have been introduced to provide a non-parametric method to estimate the uncertainty of the measured diffusion parameters. To quantify the variability of the principal diffusion direction, bootstrap-derived metrics such as the cone of uncertainty have been proposed. However, bootstrap-derived metrics are not independent of the underlying diffusion profile. A higher mean diffusivity causes a smaller signal-to-noise ratio and, thus, increases the measurement uncertainty. Moreover, the goodness of the tensor model, which relies strongly on the complexity of the underlying diffusion profile, influences bootstrap-derived metrics as well. The presented simulations clearly depict the cone of uncertainty as a function of the underlying diffusion profile. Since the relationship of the cone of uncertainty and common diffusion parameters, such as the mean diffusivity and the fractional anisotropy, is not linear, the cone of uncertainty has a different sensitivity. In vivo analysis of the fornix reveals the cone of uncertainty to be a predictor of memory function among older adults. No significant correlation occurs with the common diffusion parameters. The present work not only demonstrates the cone of uncertainty as a function of the actual diffusion profile, but also discloses the cone of uncertainty as a sensitive predictor of memory function. Future studies should incorporate bootstrap-derived metrics to provide more comprehensive analysis.

Proposed standardized definitions for vertical resolution and uncertainty in the NDACC lidar ozone and temperature algorithms - Part 3: Temperature uncertainty budget

NASA Astrophysics Data System (ADS)

Leblanc, Thierry; Sica, Robert J.; van Gijsel, Joanna A. E.; Haefele, Alexander; Payen, Guillaume; Liberti, Gianluigi

2016-08-01

A standardized approach for the definition, propagation, and reporting of uncertainty in the temperature lidar data products contributing to the Network for the Detection for Atmospheric Composition Change (NDACC) database is proposed. One important aspect of the proposed approach is the ability to propagate all independent uncertainty components in parallel through the data processing chain. The individual uncertainty components are then combined together at the very last stage of processing to form the temperature combined standard uncertainty. The identified uncertainty sources comprise major components such as signal detection, saturation correction, background noise extraction, temperature tie-on at the top of the profile, and absorption by ozone if working in the visible spectrum, as well as other components such as molecular extinction, the acceleration of gravity, and the molecular mass of air, whose magnitudes depend on the instrument, data processing algorithm, and altitude range of interest. The expression of the individual uncertainty components and their step-by-step propagation through the temperature data processing chain are thoroughly estimated, taking into account the effect of vertical filtering and the merging of multiple channels. All sources of uncertainty except detection noise imply correlated terms in the vertical dimension, which means that covariance terms must be taken into account when vertical filtering is applied and when temperature is integrated from the top of the profile. Quantitatively, the uncertainty budget is presented in a generic form (i.e., as a function of instrument performance and wavelength), so that any NDACC temperature lidar investigator can easily estimate the expected impact of individual uncertainty components in the case of their own instrument. Using this standardized approach, an example of uncertainty budget is provided for the Jet Propulsion Laboratory (JPL) lidar at Mauna Loa Observatory, Hawai'i, which is typical of the NDACC temperature lidars transmitting at 355 nm. The combined temperature uncertainty ranges between 0.1 and 1 K below 60 km, with detection noise, saturation correction, and molecular extinction correction being the three dominant sources of uncertainty. Above 60 km and up to 10 km below the top of the profile, the total uncertainty increases exponentially from 1 to 10 K due to the combined effect of random noise and temperature tie-on. In the top 10 km of the profile, the accuracy of the profile mainly depends on that of the tie-on temperature. All other uncertainty components remain below 0.1 K throughout the entire profile (15-90 km), except the background noise correction uncertainty, which peaks around 0.3-0.5 K. It should be kept in mind that these quantitative estimates may be very different for other lidar instruments, depending on their altitude range and the wavelengths used.

Quantifying Mixed Uncertainties in Cyber Attacker Payoffs

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

Chatterjee, Samrat; Halappanavar, Mahantesh; Tipireddy, Ramakrishna

Representation and propagation of uncertainty in cyber attacker payoffs is a key aspect of security games. Past research has primarily focused on representing the defender’s beliefs about attacker payoffs as point utility estimates. More recently, within the physical security domain, attacker payoff uncertainties have been represented as Uniform and Gaussian probability distributions, and intervals. Within cyber-settings, continuous probability distributions may still be appropriate for addressing statistical (aleatory) uncertainties where the defender may assume that the attacker’s payoffs differ over time. However, systematic (epistemic) uncertainties may exist, where the defender may not have sufficient knowledge or there is insufficient information aboutmore » the attacker’s payoff generation mechanism. Such epistemic uncertainties are more suitably represented as probability boxes with intervals. In this study, we explore the mathematical treatment of such mixed payoff uncertainties.« less

A detailed description of the uncertainty analysis for high area ratio rocket nozzle tests at the NASA Lewis Research Center

NASA Technical Reports Server (NTRS)

Davidian, Kenneth J.; Dieck, Ronald H.; Chuang, Isaac

1987-01-01

A preliminary uncertainty analysis was performed for the High Area Ratio Rocket Nozzle test program which took place at the altitude test capsule of the Rocket Engine Test Facility at the NASA Lewis Research Center. Results from the study establish the uncertainty of measured and calculated parameters required for the calculation of rocket engine specific impulse. A generalized description of the uncertainty methodology used is provided. Specific equations and a detailed description of the analysis is presented. Verification of the uncertainty analysis model was performed by comparison with results from the experimental program's data reduction code. Final results include an uncertainty for specific impulse of 1.30 percent. The largest contributors to this uncertainty were calibration errors from the test capsule pressure and thrust measurement devices.

Quantifying uncertainty in discharge measurements: A new approach

USGS Publications Warehouse

Kiang, J.E.; Cohn, T.A.; Mason, R.R.

2009-01-01

The accuracy of discharge measurements using velocity meters and the velocity-area method is typically assessed based on empirical studies that may not correspond to conditions encountered in practice. In this paper, a statistical approach for assessing uncertainty based on interpolated variance estimation (IVE) is introduced. The IVE method quantifies all sources of random uncertainty in the measured data. This paper presents results employing data from sites where substantial over-sampling allowed for the comparison of IVE-estimated uncertainty and observed variability among repeated measurements. These results suggest that the IVE approach can provide approximate estimates of measurement uncertainty. The use of IVE to estimate the uncertainty of a discharge measurement would provide the hydrographer an immediate determination of uncertainty and help determine whether there is a need for additional sampling in problematic river cross sections. ?? 2009 ASCE.

A detailed description of the uncertainty analysis for High Area Ratio Rocket Nozzle tests at the NASA Lewis Research Center

NASA Technical Reports Server (NTRS)

Davidian, Kenneth J.; Dieck, Ronald H.; Chuang, Isaac

1987-01-01

A preliminary uncertainty analysis has been performed for the High Area Ratio Rocket Nozzle test program which took place at the altitude test capsule of the Rocket Engine Test Facility at the NASA Lewis Research Center. Results from the study establish the uncertainty of measured and calculated parameters required for the calculation of rocket engine specific impulse. A generalized description of the uncertainty methodology used is provided. Specific equations and a detailed description of the analysis are presented. Verification of the uncertainty analysis model was performed by comparison with results from the experimental program's data reduction code. Final results include an uncertainty for specific impulse of 1.30 percent. The largest contributors to this uncertainty were calibration errors from the test capsule pressure and thrust measurement devices.

Annual Fossil-Fuel CO2 Emissions: Uncertainty of Emissions Gridded by On Degree Latitude by One Degree Longitude (1950-2013) (V. 2016)

DOE Data Explorer

Andres, R. J. [CDIAC; Boden, T. A. [CDIAC

2016-01-01

The annual, gridded fossil-fuel CO2 emissions uncertainty estimates from 1950-2013 provided in this database are derived from time series of global, regional, and national fossil-fuel CO2 emissions (Boden et al. 2016). Andres et al. (2016) describes the basic methodology in estimating the uncertainty in the (gridded fossil fuel data product ). This uncertainty is gridded at the same spatial and temporal scales as the mass magnitude maps. This gridded uncertainty includes uncertainty contributions from the spatial, temporal, proxy, and magnitude components used to create the magnitude map of FFCO2 emissions. Throughout this process, when assumptions had to be made or expert judgment employed, the general tendency in most cases was toward overestimating or increasing the magnitude of uncertainty.

Uncertainty in prostate cancer. Ethnic and family patterns.

PubMed

Germino, B B; Mishel, M H; Belyea, M; Harris, L; Ware, A; Mohler, J

1998-01-01

Prostate cancer occurs 37% more often in African-American men than in white men. Patients and their family care providers (FCPs) may have different experiences of cancer and its treatment. This report addresses two questions: 1) What is the relationship of uncertainty to family coping, psychological adjustment to illness, and spiritual factors? and 2) Are these patterns of relationship similar for patients and their family care givers and for whites and African-Americans? A sample of white and African-American men and their family care givers (N = 403) was drawn from an ongoing study, testing the efficacy of an uncertainty management intervention with men with stage B prostate cancer. Data were collected at study entry, either 1 week after post-surgical catheter removal or at the beginning of primary radiation treatment. Measures of uncertainty, adult role behavior, problem solving, social support, importance of God in one's life, family coping, psychological adjustment to illness, and perceptions of health and illness met standard criteria for internal consistency. Analyses of baseline data using Pearson's product moment correlations were conducted to examine the relationships of person, disease, and contextual factors to uncertainty. For family coping, uncertainty was significantly and positively related to two domains in white family care providers only. In African-American and white family care providers, the more uncertainty experienced, the less positive they felt about treatment. Uncertainty for all care givers was related inversely to positive feelings about the patient recovering from the illness. For all patients and for white family members, uncertainty was related inversely to the quality of the domestic environment. For everyone, uncertainty was related inversely to psychological distress. Higher levels of uncertainty were related to a poorer social environment for African-American patients and for white family members. For white patients and their family members, higher levels of uncertainty were related to lower scores on adult role behavior (shopping, running errands). For white family members, higher levels of uncertainty were related to less active problem solving and less perceived social support. Finally, higher levels of uncertainty were related to the importance of God for white patients and family care providers. The clearest finding of the present study is that there are ethnic differences in the relationship of uncertainty to a number of quality-of-life and coping variables. This has immediate implications for the assessment of psychosocial responses to cancer and cancer treatment. Much of what is in curricula is based on clinical and research experience primarily with white individuals. The experience of uncertainty related to cancer and its treatment is influenced by the cultural perspectives of patients and their families. To assist patients and families with the inevitable uncertainties of the cancer experience, healthcare providers need to reconsider their ethnocentric assumptions and develop more skill in assessing patient and family beliefs, values, cultural perspectives, and the influence of these on patient and family uncertainties.

Accounting for downscaling and model uncertainty in fine-resolution seasonal climate projections over the Columbia River Basin

NASA Astrophysics Data System (ADS)

Ahmadalipour, Ali; Moradkhani, Hamid; Rana, Arun

2018-01-01

Climate change is expected to have severe impacts on natural systems as well as various socio-economic aspects of human life. This has urged scientific communities to improve the understanding of future climate and reduce the uncertainties associated with projections. In the present study, ten statistically downscaled CMIP5 GCMs at 1/16th deg. spatial resolution from two different downscaling procedures are utilized over the Columbia River Basin (CRB) to assess the changes in climate variables and characterize the associated uncertainties. Three climate variables, i.e. precipitation, maximum temperature, and minimum temperature, are studied for the historical period of 1970-2000 as well as future period of 2010-2099, simulated with representative concentration pathways of RCP4.5 and RCP8.5. Bayesian Model Averaging (BMA) is employed to reduce the model uncertainty and develop a probabilistic projection for each variable in each scenario. Historical comparison of long-term attributes of GCMs and observation suggests a more accurate representation for BMA than individual models. Furthermore, BMA projections are used to investigate future seasonal to annual changes of climate variables. Projections indicate significant increase in annual precipitation and temperature, with varied degree of change across different sub-basins of CRB. We then characterized uncertainty of future projections for each season over CRB. Results reveal that model uncertainty is the main source of uncertainty, among others. However, downscaling uncertainty considerably contributes to the total uncertainty of future projections, especially in summer. On the contrary, downscaling uncertainty appears to be higher than scenario uncertainty for precipitation.

Joint analysis of input and parametric uncertainties in watershed water quality modeling: A formal Bayesian approach

NASA Astrophysics Data System (ADS)

Han, Feng; Zheng, Yi

2018-06-01

Significant Input uncertainty is a major source of error in watershed water quality (WWQ) modeling. It remains challenging to address the input uncertainty in a rigorous Bayesian framework. This study develops the Bayesian Analysis of Input and Parametric Uncertainties (BAIPU), an approach for the joint analysis of input and parametric uncertainties through a tight coupling of Markov Chain Monte Carlo (MCMC) analysis and Bayesian Model Averaging (BMA). The formal likelihood function for this approach is derived considering a lag-1 autocorrelated, heteroscedastic, and Skew Exponential Power (SEP) distributed error model. A series of numerical experiments were performed based on a synthetic nitrate pollution case and on a real study case in the Newport Bay Watershed, California. The Soil and Water Assessment Tool (SWAT) and Differential Evolution Adaptive Metropolis (DREAM(ZS)) were used as the representative WWQ model and MCMC algorithm, respectively. The major findings include the following: (1) the BAIPU can be implemented and used to appropriately identify the uncertain parameters and characterize the predictive uncertainty; (2) the compensation effect between the input and parametric uncertainties can seriously mislead the modeling based management decisions, if the input uncertainty is not explicitly accounted for; (3) the BAIPU accounts for the interaction between the input and parametric uncertainties and therefore provides more accurate calibration and uncertainty results than a sequential analysis of the uncertainties; and (4) the BAIPU quantifies the credibility of different input assumptions on a statistical basis and can be implemented as an effective inverse modeling approach to the joint inference of parameters and inputs.

Public perception and communication of scientific uncertainty.

PubMed

Broomell, Stephen B; Kane, Patrick Bodilly

2017-02-01

Understanding how the public perceives uncertainty in scientific research is fundamental for effective communication about research and its inevitable uncertainty. Previous work found that scientific evidence differentially influenced beliefs from individuals with different political ideologies. Evidence that threatens an individual's political ideology is perceived as more uncertain than nonthreatening evidence. The authors present 3 studies examining perceptions of scientific uncertainty more broadly by including sciences that are not politically polarizing. Study 1 develops scales measuring perceptions of scientific uncertainty. It finds (a) 3 perceptual dimensions of scientific uncertainty, with the primary dimension representing a perception of precision; (b) the precision dimension of uncertainty is strongly associated with the perceived value of a research field; and (c) differences in perceived uncertainty across political affiliations. Study 2 manipulated these dimensions, finding that Republicans were more sensitive than Democrats to descriptions of uncertainty associated with a research field (e.g., psychology). Study 3 found that these views of a research field did not extend to the evaluation of individual results produced by the field. Together, these studies show that perceptions of scientific uncertainty associated with entire research fields are valid predictors of abstract perceptions of scientific quality, benefit, and allocation of funding. Yet, they do not inform judgments about individual results. Therefore, polarization in the acceptance of specific results is not likely due to individual differences in perceived scientific uncertainty. Further, the direction of influence potentially could be reversed, such that perceived quality of scientific results could be used to influence perceptions about scientific research fields. (PsycINFO Database Record (c) 2017 APA, all rights reserved).

Analysis of the uncertainty in the monetary valuation of ecosystem services--A case study at the river basin scale.

PubMed

Boithias, Laurie; Terrado, Marta; Corominas, Lluís; Ziv, Guy; Kumar, Vikas; Marqués, Montse; Schuhmacher, Marta; Acuña, Vicenç

2016-02-01

Ecosystem services provide multiple benefits to human wellbeing and are increasingly considered by policy-makers in environmental management. However, the uncertainty related with the monetary valuation of these benefits is not yet adequately defined or integrated by policy-makers. Given this background, our aim was to quantify different sources of uncertainty when performing monetary valuation of ecosystem services, in order to provide a series of guidelines to reduce them. With an example of 4 ecosystem services (i.e., water provisioning, waste treatment, erosion protection, and habitat for species) provided at the river basin scale, we quantified the uncertainty associated with the following sources: (1) the number of services considered, (2) the number of benefits considered for each service, (3) the valuation metrics (i.e. valuation methods) used to value benefits, and (4) the uncertainty of the parameters included in the valuation metrics. Results indicate that the highest uncertainty was caused by the number of services considered, as well as by the number of benefits considered for each service, whereas the parametric uncertainty was similar to the one related to the selection of valuation metric, thus suggesting that the parametric uncertainty, which is the only uncertainty type commonly considered, was less critical than the structural uncertainty, which is in turn mainly dependent on the decision-making context. Given the uncertainty associated to the valuation structure, special attention should be given to the selection of services, benefits and metrics according to a given context. Copyright © 2015 Elsevier B.V. All rights reserved.

Classifying the Sizes of Explosive Eruptions using Tephra Deposits: The Advantages of a Numerical Inversion Approach

NASA Astrophysics Data System (ADS)

Connor, C.; Connor, L.; White, J.

2015-12-01

Explosive volcanic eruptions are often classified by deposit mass and eruption column height. How well are these eruption parameters determined in older deposits, and how well can we reduce uncertainty using robust numerical and statistical methods? We describe an efficient and effective inversion and uncertainty quantification approach for estimating eruption parameters given a dataset of tephra deposit thickness and granulometry. The inversion and uncertainty quantification is implemented using the open-source PEST++ code. Inversion with PEST++ can be used with a variety of forward models and here is applied using Tephra2, a code that simulates advective and dispersive tephra transport and deposition. The Levenburg-Marquardt algorithm is combined with formal Tikhonov and subspace regularization to invert eruption parameters; a linear equation for conditional uncertainty propagation is used to estimate posterior parameter uncertainty. Both the inversion and uncertainty analysis support simultaneous analysis of the full eruption and wind-field parameterization. The combined inversion/uncertainty-quantification approach is applied to the 1992 eruption of Cerro Negro (Nicaragua), the 2011 Kirishima-Shinmoedake (Japan), and the 1913 Colima (Mexico) eruptions. These examples show that although eruption mass uncertainty is reduced by inversion against tephra isomass data, considerable uncertainty remains for many eruption and wind-field parameters, such as eruption column height. Supplementing the inversion dataset with tephra granulometry data is shown to further reduce the uncertainty of most eruption and wind-field parameters. We think the use of such robust models provides a better understanding of uncertainty in eruption parameters, and hence eruption classification, than is possible with more qualitative methods that are widely used.

Uncertainty in gridded CO 2 emissions estimates

DOE PAGES

Hogue, Susannah; Marland, Eric; Andres, Robert J.; ...

2016-05-19

We are interested in the spatial distribution of fossil-fuel-related emissions of CO 2 for both geochemical and geopolitical reasons, but it is important to understand the uncertainty that exists in spatially explicit emissions estimates. Working from one of the widely used gridded data sets of CO 2 emissions, we examine the elements of uncertainty, focusing on gridded data for the United States at the scale of 1° latitude by 1° longitude. Uncertainty is introduced in the magnitude of total United States emissions, the magnitude and location of large point sources, the magnitude and distribution of non-point sources, and from themore » use of proxy data to characterize emissions. For the United States, we develop estimates of the contribution of each component of uncertainty. At 1° resolution, in most grid cells, the largest contribution to uncertainty comes from how well the distribution of the proxy (in this case population density) represents the distribution of emissions. In other grid cells, the magnitude and location of large point sources make the major contribution to uncertainty. Uncertainty in population density can be important where a large gradient in population density occurs near a grid cell boundary. Uncertainty is strongly scale-dependent with uncertainty increasing as grid size decreases. In conclusion, uncertainty for our data set with 1° grid cells for the United States is typically on the order of ±150%, but this is perhaps not excessive in a data set where emissions per grid cell vary over 8 orders of magnitude.« less

Uncertainty for calculating transport on Titan: A probabilistic description of bimolecular diffusion parameters

NASA Astrophysics Data System (ADS)

Plessis, S.; McDougall, D.; Mandt, K.; Greathouse, T.; Luspay-Kuti, A.

2015-11-01

Bimolecular diffusion coefficients are important parameters used by atmospheric models to calculate altitude profiles of minor constituents in an atmosphere. Unfortunately, laboratory measurements of these coefficients were never conducted at temperature conditions relevant to the atmosphere of Titan. Here we conduct a detailed uncertainty analysis of the bimolecular diffusion coefficient parameters as applied to Titan's upper atmosphere to provide a better understanding of the impact of uncertainty for this parameter on models. Because temperature and pressure conditions are much lower than the laboratory conditions in which bimolecular diffusion parameters were measured, we apply a Bayesian framework, a problem-agnostic framework, to determine parameter estimates and associated uncertainties. We solve the Bayesian calibration problem using the open-source QUESO library which also performs a propagation of uncertainties in the calibrated parameters to temperature and pressure conditions observed in Titan's upper atmosphere. Our results show that, after propagating uncertainty through the Massman model, the uncertainty in molecular diffusion is highly correlated to temperature and we observe no noticeable correlation with pressure. We propagate the calibrated molecular diffusion estimate and associated uncertainty to obtain an estimate with uncertainty due to bimolecular diffusion for the methane molar fraction as a function of altitude. Results show that the uncertainty in methane abundance due to molecular diffusion is in general small compared to eddy diffusion and the chemical kinetics description. However, methane abundance is most sensitive to uncertainty in molecular diffusion above 1200 km where the errors are nontrivial and could have important implications for scientific research based on diffusion models in this altitude range.

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.

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

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.

On the uncertainty of phenological responses to climate change, and implications for a terrestrial biosphere model

NASA Astrophysics Data System (ADS)

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

2012-06-01

Phenology, the timing of recurring life cycle events, controls numerous land surface feedbacks to the climate system through the regulation of exchanges of carbon, water and energy between the biosphere and atmosphere. Terrestrial biosphere 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 used the Harvard Forest phenology record to investigate and characterize sources of uncertainty in predicting phenology, and the subsequent impacts on model forecasts of carbon and water cycling. Using a model-data fusion approach, we combined information from 20 yr of phenological observations of 11 North American woody species, with 12 leaf bud-burst models that varied in complexity. Akaike's Information Criterion indicated support for spring warming models with photoperiod limitations and, to a lesser extent, models that included chilling requirements. 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% Confidence Interval - CI: 2.4 days century-1 for scenario B1 and 4.5 days century-1 for A1fi), whereas driver uncertainty was the largest (up to 8.4 days 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 among models (±7.7 days century-1 for A1fi, ±3.6 days 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 days °C-1 and 5.2 days °C-1 depending on model structure. We quantified the impact of uncertainties in bud-burst forecasts on simulated photosynthetic CO2 uptake and evapotranspiration (ET) using a process-based terrestrial biosphere model. Uncertainty in phenology model structure led to uncertainty in the description of forest seasonality, which accumulated to uncertainty in annual model estimates of gross primary productivity (GPP) and 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 (i.e. driver) represent the largest source of uncertainty, followed by uncertainties related to model structure, and finally, related to model parameterization. The uncertainties we have quantified will affect the description of the seasonality of ecosystem 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.

Proton and neutron electromagnetic form factors and uncertainties

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

Ye, Zhihong; Arrington, John; Hill, Richard J.

We determine the nucleon electromagnetic form factors and their uncertainties from world electron scattering data. The analysis incorporates two-photon exchange corrections, constraints on the low-Q 2 and high-Q 2 behavior, and additional uncertainties to account for tensions between different data sets and uncertainties in radiative corrections.

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

Luis, Alfredo

We show within a very simple framework that different measures of fluctuations lead to uncertainty relations resulting in contradictory conclusions. More specifically we focus on Tsallis and Renyi entropic uncertainty relations and we get that the minimum joint uncertainty states for some fluctuation measures are the maximum joint uncertainty states of other fluctuation measures, and vice versa.

Proton and neutron electromagnetic form factors and uncertainties

DOE PAGES

Ye, Zhihong; Arrington, John; Hill, Richard J.; ...

2017-12-06

We determine the nucleon electromagnetic form factors and their uncertainties from world electron scattering data. The analysis incorporates two-photon exchange corrections, constraints on the low-Q 2 and high-Q 2 behavior, and additional uncertainties to account for tensions between different data sets and uncertainties in radiative corrections.

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...

Quantifying uncertainty in forest nutrient budgets

Treesearch

Ruth D. Yanai; Carrie R. Levine; Mark B. Green; John L. Campbell

2012-01-01

Nutrient budgets for forested ecosystems have rarely included error analysis, in spite of the importance of uncertainty to interpretation and extrapolation of the results. Uncertainty derives from natural spatial and temporal variation and also from knowledge uncertainty in measurement and models. For example, when estimating forest biomass, researchers commonly report...

Uncertainty of a hybrid surface temperature sensor for silicon wafers and comparison with an embedded thermocouple.

PubMed

Iuchi, Tohru; Gogami, Atsushi

2009-12-01

We have developed a user-friendly hybrid surface temperature sensor. The uncertainties of temperature readings associated with this sensor and a thermocouple embedded in a silicon wafer are compared. The expanded uncertainties (k=2) of the hybrid temperature sensor and the embedded thermocouple are 2.11 and 2.37 K, respectively, in the temperature range between 600 and 1000 K. In the present paper, the uncertainty evaluation and the sources of uncertainty are described.

Uncertainty and Anticipation in Anxiety

PubMed Central

Grupe, Dan W.; Nitschke, Jack B.

2014-01-01

Uncertainty about a possible future threat disrupts our ability to avoid it or to mitigate its negative impact, and thus results in anxiety. Here, we focus the broad literature on the neurobiology of anxiety through the lens of uncertainty. We identify five processes essential for adaptive anticipatory responses to future threat uncertainty, and propose that alterations to the neural instantiation of these processes results in maladaptive responses to uncertainty in pathological anxiety. This framework has the potential to advance the classification, diagnosis, and treatment of clinical anxiety. PMID:23783199

Measuring, Estimating, and Deciding under Uncertainty.

PubMed

Michel, Rolf

2016-03-01

The problem of uncertainty as a general consequence of incomplete information and the approach to quantify uncertainty in metrology is addressed. Then, this paper discusses some of the controversial aspects of the statistical foundation of the concepts of uncertainty in measurements. The basics of the ISO Guide to the Expression of Uncertainty in Measurement as well as of characteristic limits according to ISO 11929 are described and the needs for a revision of the latter standard are explained. Copyright © 2015 Elsevier Ltd. All rights reserved.

Erratum: Evidence of b -jet quenching in PbPb collisions at s N N = 2.76 TeV [Phys. Rev. Lett. 113 , 132301 (2014)

DOE PAGES

Chatrchyan, S.

2015-07-10

In our Letter, there was a component of the statistical uncertainty from the simulated PbPb Monte Carlo samples. This uncertainty was not propagated to all of the results. Figures 3 and 4 have been updated to reflect this source of uncertainty. In this case, the statistical uncertainties remain smaller than the systematic uncertainties in all cases such that the conclusions of the Letter are unaltered.

The effect of short-range spatial variability on soil sampling uncertainty.

PubMed

Van der Perk, Marcel; de Zorzi, Paolo; Barbizzi, Sabrina; Belli, Maria; Fajgelj, Ales; Sansone, Umberto; Jeran, Zvonka; Jaćimović, Radojko

2008-11-01

This paper aims to quantify the soil sampling uncertainty arising from the short-range spatial variability of elemental concentrations in the topsoils of agricultural, semi-natural, and contaminated environments. For the agricultural site, the relative standard sampling uncertainty ranges between 1% and 5.5%. For the semi-natural area, the sampling uncertainties are 2-4 times larger than in the agricultural area. The contaminated site exhibited significant short-range spatial variability in elemental composition, which resulted in sampling uncertainties of 20-30%.

Representativeness uncertainty in chemical data assimilation highlight mixing barriers

NASA Astrophysics Data System (ADS)

Lary, David John

2004-02-01

When performing chemical data assimilation the observational, representativeness, and theoretical uncertainties have very different characteristics. In this study, we have accurately characterized the representativeness uncertainty by studying the probability distribution function (PDF) of the observations. The average deviation has been used as a measure of the width of the PDF and of the variability (representativeness uncertainty) for the grid cell. It turns out that for long-lived tracers such as N2O and CH4 the representativeness uncertainty is markedly different from the observational uncertainty and clearly delineates mixing barriers such as the polar vortex edge, the tropical pipe and the tropopause. Published by Elsevier Ltd on behalf of Royal Meteorological Society.

Uncertainty in flood damage estimates and its potential effect on investment decisions

NASA Astrophysics Data System (ADS)

Wagenaar, Dennis; de Bruijn, Karin; Bouwer, Laurens; de Moel, Hans

2015-04-01

This paper addresses the large differences that are found between damage estimates of different flood damage models. It explains how implicit assumptions in flood damage models can lead to large uncertainties in flood damage estimates. This explanation is used to quantify this uncertainty with a Monte Carlo Analysis. This Monte Carlo analysis uses a damage function library with 272 functions from 7 different flood damage models. This results in uncertainties in the order of magnitude of a factor 2 to 5. This uncertainty is typically larger for small water depths and for smaller flood events. The implications of the uncertainty in damage estimates for flood risk management are illustrated by a case study in which the economic optimal investment strategy for a dike segment in the Netherlands is determined. The case study shows that the uncertainty in flood damage estimates can lead to significant over- or under-investments.

Uncertainty in flood damage estimates and its potential effect on investment decisions

NASA Astrophysics Data System (ADS)

Wagenaar, D. J.; de Bruijn, K. M.; Bouwer, L. M.; De Moel, H.

2015-01-01

This paper addresses the large differences that are found between damage estimates of different flood damage models. It explains how implicit assumptions in flood damage models can lead to large uncertainties in flood damage estimates. This explanation is used to quantify this uncertainty with a Monte Carlo Analysis. As input the Monte Carlo analysis uses a damage function library with 272 functions from 7 different flood damage models. This results in uncertainties in the order of magnitude of a factor 2 to 5. The resulting uncertainty is typically larger for small water depths and for smaller flood events. The implications of the uncertainty in damage estimates for flood risk management are illustrated by a case study in which the economic optimal investment strategy for a dike segment in the Netherlands is determined. The case study shows that the uncertainty in flood damage estimates can lead to significant over- or under-investments.

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.

Propagation of nuclear data uncertainties for fusion power measurements

NASA Astrophysics Data System (ADS)

Sjöstrand, Henrik; Conroy, Sean; Helgesson, Petter; Hernandez, Solis Augusto; Koning, Arjan; Pomp, Stephan; Rochman, Dimitri

2017-09-01

Neutron measurements using neutron activation systems are an essential part of the diagnostic system at large fusion machines such as JET and ITER. Nuclear data is used to infer the neutron yield. Consequently, high-quality nuclear data is essential for the proper determination of the neutron yield and fusion power. However, uncertainties due to nuclear data are not fully taken into account in uncertainty analysis for neutron yield calibrations using activation foils. This paper investigates the neutron yield uncertainty due to nuclear data using the so-called Total Monte Carlo Method. The work is performed using a detailed MCNP model of the JET fusion machine; the uncertainties due to the cross-sections and angular distributions in JET structural materials, as well as the activation cross-sections in the activation foils, are analysed. It is found that a significant contribution to the neutron yield uncertainty can come from uncertainties in the nuclear data.

An empirical application of transaction-costs theory to organizational design characteristics.

PubMed

Williams, S

2000-01-01

The environmental uncertainty component of transaction-costs theory was used to predict the organizational structural characteristics of size (number of employees) and horizontal differentiation (number of vice presidents) using financial and management information from the COMPACT DISCLOSURE data base (which contains the most recent annual and periodic reports for more than 12,000 public companies). Organizations were categorized as low- or high-uncertainty industries according to Dess and Beard's (1984) Dynamism Scale, and net sales volume was controlled. As predicted, high-uncertainty companies had significantly higher horizontal differentiation than low-uncertainty firms, a finding that supports the transaction-costs expectation that organizations may require more departments or personnel to cope with increasing uncertainty. Surprisingly, low-uncertainty firms were found to have significantly more employees than high-uncertainty organizations, which is the opposite of what transaction-costs theory predicts. Possible explanations for this unexpected finding and further potential limitations are discussed.

The uncertainty room: strategies for managing uncertainty in a surgical waiting room.

PubMed

Stone, Anne M; Lammers, John C

2012-01-01

To describe experiences of uncertainty and management strategies for staff working with families in a hospital waiting room. A 288-bed, nonprofit community hospital in a Midwestern city. Data were collected during individual, semistructured interviews with 3 volunteers, 3 technical staff members, and 1 circulating nurse (n = 7), and during 40 hours of observation in a surgical waiting room. Interview transcripts were analyzed using constant comparative techniques. The surgical waiting room represents the intersection of several sources of uncertainty that families experience. Findings also illustrate the ways in which staff manage the uncertainty of families in the waiting room by communicating support. Staff in surgical waiting rooms are responsible for managing family members' uncertainty related to insufficient information. Practically, this study provided some evidence that staff are expected to help manage the uncertainty that is typical in a surgical waiting room, further highlighting the important role of communication in improving family members' experiences.

Decision making with epistemic uncertainty under safety constraints: An application to seismic design

USGS Publications Warehouse

Veneziano, D.; Agarwal, A.; Karaca, E.

2009-01-01

The problem of accounting for epistemic uncertainty in risk management decisions is conceptually straightforward, but is riddled with practical difficulties. Simple approximations are often used whereby future variations in epistemic uncertainty are ignored or worst-case scenarios are postulated. These strategies tend to produce sub-optimal decisions. We develop a general framework based on Bayesian decision theory and exemplify it for the case of seismic design of buildings. When temporal fluctuations of the epistemic uncertainties and regulatory safety constraints are included, the optimal level of seismic protection exceeds the normative level at the time of construction. Optimal Bayesian decisions do not depend on the aleatory or epistemic nature of the uncertainties, but only on the total (epistemic plus aleatory) uncertainty and how that total uncertainty varies randomly during the lifetime of the project. ?? 2009 Elsevier Ltd. All rights reserved.

Examples of measurement uncertainty evaluations in accordance with the revised GUM

NASA Astrophysics Data System (ADS)

Runje, B.; Horvatic, A.; Alar, V.; Medic, S.; Bosnjakovic, A.

2016-11-01

The paper presents examples of the evaluation of uncertainty components in accordance with the current and revised Guide to the expression of uncertainty in measurement (GUM). In accordance with the proposed revision of the GUM a Bayesian approach was conducted for both type A and type B evaluations.The law of propagation of uncertainty (LPU) and the law of propagation of distribution applied through the Monte Carlo method, (MCM) were used to evaluate associated standard uncertainties, expanded uncertainties and coverage intervals. Furthermore, the influence of the non-Gaussian dominant input quantity and asymmetric distribution of the output quantity y on the evaluation of measurement uncertainty was analyzed. In the case when the probabilistically coverage interval is not symmetric, the coverage interval for the probability P is estimated from the experimental probability density function using the Monte Carlo method. Key highlights of the proposed revision of the GUM were analyzed through a set of examples.

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.

Sources of uncertainty in annual forest inventory estimates

Treesearch

Ronald E. McRoberts

2000-01-01

Although design and estimation aspects of annual forest inventories have begun to receive considerable attention within the forestry and natural resources communities, little attention has been devoted to identifying the sources of uncertainty inherent in these systems or to assessing the impact of those uncertainties on the total uncertainties of inventory estimates....

Uncertainty Representation in Visualizations of Learning Analytics for Learners: Current Approaches and Opportunities

ERIC Educational Resources Information Center

Demmans Epp, Carrie; Bull, Susan

2015-01-01

Adding uncertainty information to visualizations is becoming increasingly common across domains since its addition helps ensure that informed decisions are made. This work has shown the difficulty that is inherent to representing uncertainty. Moreover, the representation of uncertainty has yet to be thoroughly explored in educational domains even…

Uncertainty in the Work-Place: Hierarchical Differences of Uncertainty Levels and Reduction Strategies.

ERIC Educational Resources Information Center

Petelle, John L.; And Others

A study examined the uncertainty levels and types reported by supervisors and employees at three hierarchical levels of an organization: first-line supervisors, full-time employees, and part-time employees. It investigated differences in uncertainty-reduction strategies employed by these three hierarchical groups. The 61 subjects who completed…

Pretest uncertainty analysis for chemical rocket engine tests

NASA Technical Reports Server (NTRS)

Davidian, Kenneth J.

1987-01-01

A parametric pretest uncertainty analysis has been performed for a chemical rocket engine test at a unique 1000:1 area ratio altitude test facility. Results from the parametric study provide the error limits required in order to maintain a maximum uncertainty of 1 percent on specific impulse. Equations used in the uncertainty analysis are presented.

Assessing Uncertainty in Expert Judgments About Natural Resources

Treesearch

David A. Cleaves

1994-01-01

Judgments are necessary in natural resources management, but uncertainty about these judgments should be assessed. When all judgments are rejected in the absence of hard data, valuable professional experience and knowledge are not utilized fully. The objective of assessing uncertainty is to get the best representation of knowledge and its bounds. Uncertainty...

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...

The Impact of Uncertainty and Irreversibility on Investments in Online Learning

ERIC Educational Resources Information Center

Oslington, Paul

2004-01-01

Uncertainty and irreversibility are central to online learning projects, but have been neglected in the existing educational cost-benefit analysis literature. This paper builds some simple illustrative models of the impact of irreversibility and uncertainty, and shows how different types of cost and demand uncertainty can have substantial impacts…

Are You Sure? The Role of Uncertainty in Career

ERIC Educational Resources Information Center

Trevor-Roberts, Edwin

2006-01-01

Although uncertainty is a fundamental human experience, professionals in the career field have largely overlooked the role that it plays in people's careers. The changed nature of careers has resulted in people experiencing increased uncertainty in their career that is beyond the uncertainty experienced in their job. The author explores the role…

Improving Forecasts Through Realistic Uncertainty Estimates: A Novel Data Driven Method for Model Uncertainty Quantification in Data Assimilation

NASA Astrophysics Data System (ADS)

Pathiraja, S. D.; Moradkhani, H.; Marshall, L. A.; Sharma, A.; Geenens, G.

2016-12-01

Effective combination of model simulations and observations through Data Assimilation (DA) depends heavily on uncertainty characterisation. Many traditional methods for quantifying model uncertainty in DA require some level of subjectivity (by way of tuning parameters or by assuming Gaussian statistics). Furthermore, the focus is typically on only estimating the first and second moments. We propose a data-driven methodology to estimate the full distributional form of model uncertainty, i.e. the transition density p(xt|xt-1). All sources of uncertainty associated with the model simulations are considered collectively, without needing to devise stochastic perturbations for individual components (such as model input, parameter and structural uncertainty). A training period is used to derive the distribution of errors in observed variables conditioned on hidden states. Errors in hidden states are estimated from the conditional distribution of observed variables using non-linear optimization. The theory behind the framework and case study applications are discussed in detail. Results demonstrate improved predictions and more realistic uncertainty bounds compared to a standard perturbation approach.

Uncertainty in Population Growth Rates: Determining Confidence Intervals from Point Estimates of Parameters

PubMed Central

Devenish Nelson, Eleanor S.; Harris, Stephen; Soulsbury, Carl D.; Richards, Shane A.; Stephens, Philip A.

2010-01-01

Background Demographic models are widely used in conservation and management, and their parameterisation often relies on data collected for other purposes. When underlying data lack clear indications of associated uncertainty, modellers often fail to account for that uncertainty in model outputs, such as estimates of population growth. Methodology/Principal Findings We applied a likelihood approach to infer uncertainty retrospectively from point estimates of vital rates. Combining this with resampling techniques and projection modelling, we show that confidence intervals for population growth estimates are easy to derive. We used similar techniques to examine the effects of sample size on uncertainty. Our approach is illustrated using data on the red fox, Vulpes vulpes, a predator of ecological and cultural importance, and the most widespread extant terrestrial mammal. We show that uncertainty surrounding estimated population growth rates can be high, even for relatively well-studied populations. Halving that uncertainty typically requires a quadrupling of sampling effort. Conclusions/Significance Our results compel caution when comparing demographic trends between populations without accounting for uncertainty. Our methods will be widely applicable to demographic studies of many species. PMID:21049049

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.

Dealing with Uncertainty in Water Management: Finding the Right Balance Between Risk and Opportunity to Build Trust and Create Value

NASA Astrophysics Data System (ADS)

Islam, S.; Susskind, L.

2012-12-01

Most difficulties in water management are the product of rigid assumptions about how water ought to be allocated in the face of ever-increasing demand and growing uncertainty. When stakeholders face contending water claims, one of the biggest obstacles to reaching agreement is uncertainty. Specifically, there are three types of uncertainty that need to be addressed: uncertainty of information, uncertainty of action and uncertainty of perception. All three shape water management decisions. Contrary to traditional approaches, we argue that management of uncertainty needs to include both risks and opportunities. When parties treat water as a flexible rather than a fixed resource, opportunities to create value can be invented. When they use the right processes and mechanisms to enhance trust, even parties in conflict can reach agreements that satisfy their competing water needs and interests simultaneously. Using examples from several boundary crossing water cases we will show how this balance between risks and opportunities can be found to manage water resources for an uncertain future.

Perseveration induces dissociative uncertainty in obsessive-compulsive disorder.

PubMed

Giele, Catharina L; van den Hout, Marcel A; Engelhard, Iris M; Dek, Eliane C P; Toffolo, Marieke B J; Cath, Danielle C

2016-09-01

Obsessive compulsive (OC)-like perseveration paradoxically increases feelings of uncertainty. We studied whether the underlying mechanism between perseveration and uncertainty is a reduced accessibility of meaning ('semantic satiation'). OCD patients (n = 24) and matched non-clinical controls (n = 24) repeated words 2 (non-perseveration) or 20 times (perseveration). They decided whether this word was related to another target word. Speed of relatedness judgments and feelings of dissociative uncertainty were measured. The effects of real-life perseveration on dissociative uncertainty were tested in a smaller subsample of the OCD group (n = 9). Speed of relatedness judgments was not affected by perseveration. However, both groups reported more dissociative uncertainty after perseveration compared to non-perseveration, which was higher in OCD patients. Patients reported more dissociative uncertainty after 'clinical' perseveration compared to non-perseveration.. Both parts of this study are limited by some methodological issues and a small sample size. Although the mechanism behind 'perseveration → uncertainty' is still unclear, results suggest that the effects of perseveration are counterproductive. Copyright © 2016 Elsevier Ltd. All rights reserved.

Determining the nuclear data uncertainty on MONK10 and WIMS10 criticality calculations

NASA Astrophysics Data System (ADS)

Ware, Tim; Dobson, Geoff; Hanlon, David; Hiles, Richard; Mason, Robert; Perry, Ray

2017-09-01

The ANSWERS Software Service is developing a number of techniques to better understand and quantify uncertainty on calculations of the neutron multiplication factor, k-effective, in nuclear fuel and other systems containing fissile material. The uncertainty on the calculated k-effective arises from a number of sources, including nuclear data uncertainties, manufacturing tolerances, modelling approximations and, for Monte Carlo simulation, stochastic uncertainty. For determining the uncertainties due to nuclear data, a set of application libraries have been generated for use with the MONK10 Monte Carlo and the WIMS10 deterministic criticality and reactor physics codes. This paper overviews the generation of these nuclear data libraries by Latin hypercube sampling of JEFF-3.1.2 evaluated data based upon a library of covariance data taken from JEFF, ENDF/B, JENDL and TENDL evaluations. Criticality calculations have been performed with MONK10 and WIMS10 using these sampled libraries for a number of benchmark models of fissile systems. Results are presented which show the uncertainty on k-effective for these systems arising from the uncertainty on the input nuclear data.

Effect of pesticide fate parameters and their uncertainty on the selection of 'worst-case' scenarios of pesticide leaching to groundwater.

PubMed

Vanderborght, Jan; Tiktak, Aaldrik; Boesten, Jos J T I; Vereecken, Harry

2011-03-01

For the registration of pesticides in the European Union, model simulations for worst-case scenarios are used to demonstrate that leaching concentrations to groundwater do not exceed a critical threshold. A worst-case scenario is a combination of soil and climate properties for which predicted leaching concentrations are higher than a certain percentile of the spatial concentration distribution within a region. The derivation of scenarios is complicated by uncertainty about soil and pesticide fate parameters. As the ranking of climate and soil property combinations according to predicted leaching concentrations is different for different pesticides, the worst-case scenario for one pesticide may misrepresent the worst case for another pesticide, which leads to 'scenario uncertainty'. Pesticide fate parameter uncertainty led to higher concentrations in the higher percentiles of spatial concentration distributions, especially for distributions in smaller and more homogeneous regions. The effect of pesticide fate parameter uncertainty on the spatial concentration distribution was small when compared with the uncertainty of local concentration predictions and with the scenario uncertainty. Uncertainty in pesticide fate parameters and scenario uncertainty can be accounted for using higher percentiles of spatial concentration distributions and considering a range of pesticides for the scenario selection. Copyright © 2010 Society of Chemical Industry.

Propagation of stage measurement uncertainties to streamflow time series

NASA Astrophysics Data System (ADS)

Horner, Ivan; Le Coz, Jérôme; Renard, Benjamin; Branger, Flora; McMillan, Hilary

2016-04-01

Streamflow uncertainties due to stage measurements errors are generally overlooked in the promising probabilistic approaches that have emerged in the last decade. We introduce an original error model for propagating stage uncertainties through a stage-discharge rating curve within a Bayesian probabilistic framework. The method takes into account both rating curve (parametric errors and structural errors) and stage uncertainty (systematic and non-systematic errors). Practical ways to estimate the different types of stage errors are also presented: (1) non-systematic errors due to instrument resolution and precision and non-stationary waves and (2) systematic errors due to gauge calibration against the staff gauge. The method is illustrated at a site where the rating-curve-derived streamflow can be compared with an accurate streamflow reference. The agreement between the two time series is overall satisfying. Moreover, the quantification of uncertainty is also satisfying since the streamflow reference is compatible with the streamflow uncertainty intervals derived from the rating curve and the stage uncertainties. Illustrations from other sites are also presented. Results are much contrasted depending on the site features. In some cases, streamflow uncertainty is mainly due to stage measurement errors. The results also show the importance of discriminating systematic and non-systematic stage errors, especially for long term flow averages. Perspectives for improving and validating the streamflow uncertainty estimates are eventually discussed.

Robust control of seismically excited cable stayed bridges with MR dampers

NASA Astrophysics Data System (ADS)

YeganehFallah, Arash; Khajeh Ahamd Attari, Nader

2017-03-01

In recent decades active and semi-active structural control are becoming attractive alternatives for enhancing performance of civil infrastructures subjected to seismic and winds loads. However, in order to have reliable active and semi-active control, there is a need to include information of uncertainties in design of the controller. In real world for civil structures, parameters such as loading places, stiffness, mass and damping are time variant and uncertain. These uncertainties in many cases model as parametric uncertainties. The motivation of this research is to design a robust controller for attenuating the vibrational responses of civil infrastructures, regarding their dynamical uncertainties. Uncertainties in structural dynamic’s parameters are modeled as affine uncertainties in state space modeling. These uncertainties are decoupled from the system through Linear Fractional Transformation (LFT) and are assumed to be unknown input to the system but norm bounded. The robust H ∞ controller is designed for the decoupled system to regulate the evaluation outputs and it is robust to effects of uncertainties, disturbance and sensors noise. The cable stayed bridge benchmark which is equipped with MR damper is considered for the numerical simulation. The simulated results show that the proposed robust controller can effectively mitigate undesired uncertainties effects on systems’ responds under seismic loading.

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.

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.

Development of an Uncertainty Model for the National Transonic Facility

NASA Technical Reports Server (NTRS)

Walter, Joel A.; Lawrence, William R.; Elder, David W.; Treece, Michael D.

2010-01-01

This paper introduces an uncertainty model being developed for the National Transonic Facility (NTF). The model uses a Monte Carlo technique to propagate standard uncertainties of measured values through the NTF data reduction equations to calculate the combined uncertainties of the key aerodynamic force and moment coefficients and freestream properties. The uncertainty propagation approach to assessing data variability is compared with ongoing data quality assessment activities at the NTF, notably check standard testing using statistical process control (SPC) techniques. It is shown that the two approaches are complementary and both are necessary tools for data quality assessment and improvement activities. The SPC approach is the final arbiter of variability in a facility. Its result encompasses variation due to people, processes, test equipment, and test article. The uncertainty propagation approach is limited mainly to the data reduction process. However, it is useful because it helps to assess the causes of variability seen in the data and consequently provides a basis for improvement. For example, it is shown that Mach number random uncertainty is dominated by static pressure variation over most of the dynamic pressure range tested. However, the random uncertainty in the drag coefficient is generally dominated by axial and normal force uncertainty with much less contribution from freestream conditions.

Uncertainty Propagation for Terrestrial Mobile Laser Scanner

NASA Astrophysics Data System (ADS)

Mezian, c.; Vallet, Bruno; Soheilian, Bahman; Paparoditis, Nicolas

2016-06-01

Laser scanners are used more and more in mobile mapping systems. They provide 3D point clouds that are used for object reconstruction and registration of the system. For both of those applications, uncertainty analysis of 3D points is of great interest but rarely investigated in the literature. In this paper we present a complete pipeline that takes into account all the sources of uncertainties and allows to compute a covariance matrix per 3D point. The sources of uncertainties are laser scanner, calibration of the scanner in relation to the vehicle and direct georeferencing system. We suppose that all the uncertainties follow the Gaussian law. The variances of the laser scanner measurements (two angles and one distance) are usually evaluated by the constructors. This is also the case for integrated direct georeferencing devices. Residuals of the calibration process were used to estimate the covariance matrix of the 6D transformation between scanner laser and the vehicle system. Knowing the variances of all sources of uncertainties, we applied uncertainty propagation technique to compute the variance-covariance matrix of every obtained 3D point. Such an uncertainty analysis enables to estimate the impact of different laser scanners and georeferencing devices on the quality of obtained 3D points. The obtained uncertainty values were illustrated using error ellipsoids on different datasets.

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.

Doppler Global Velocimeter Development for the Large Wind Tunnels at Ames Research Center

NASA Technical Reports Server (NTRS)

Reinath, Michael S.

1997-01-01

Development of an optical, laser-based flow-field measurement technique for large wind tunnels is described. The technique uses laser sheet illumination and charged coupled device detectors to rapidly measure flow-field velocity distributions over large planar regions of the flow. Sample measurements are presented that illustrate the capability of the technique. An analysis of measurement uncertainty, which focuses on the random component of uncertainty, shows that precision uncertainty is not dependent on the measured velocity magnitude. For a single-image measurement, the analysis predicts a precision uncertainty of +/-5 m/s. When multiple images are averaged, this uncertainty is shown to decrease. For an average of 100 images, for example, the analysis shows that a precision uncertainty of +/-0.5 m/s can be expected. Sample applications show that vectors aligned with an orthogonal coordinate system are difficult to measure directly. An algebraic transformation is presented which converts measured vectors to the desired orthogonal components. Uncertainty propagation is then used to show how the uncertainty propagates from the direct measurements to the orthogonal components. For a typical forward-scatter viewing geometry, the propagation analysis predicts precision uncertainties of +/-4, +/-7, and +/-6 m/s, respectively, for the U, V, and W components at 68% confidence.

Performance and Reliability Optimization for Aerospace Systems subject to Uncertainty and Degradation

NASA Technical Reports Server (NTRS)

Miller, David W.; Uebelhart, Scott A.; Blaurock, Carl

2004-01-01

This report summarizes work performed by the Space Systems Laboratory (SSL) for NASA Langley Research Center in the field of performance optimization for systems subject to uncertainty. The objective of the research is to develop design methods and tools to the aerospace vehicle design process which take into account lifecycle uncertainties. It recognizes that uncertainty between the predictions of integrated models and data collected from the system in its operational environment is unavoidable. Given the presence of uncertainty, the goal of this work is to develop means of identifying critical sources of uncertainty, and to combine these with the analytical tools used with integrated modeling. In this manner, system uncertainty analysis becomes part of the design process, and can motivate redesign. The specific program objectives were: 1. To incorporate uncertainty modeling, propagation and analysis into the integrated (controls, structures, payloads, disturbances, etc.) design process to derive the error bars associated with performance predictions. 2. To apply modern optimization tools to guide in the expenditure of funds in a way that most cost-effectively improves the lifecycle productivity of the system by enhancing the subsystem reliability and redundancy. The results from the second program objective are described. This report describes the work and results for the first objective: uncertainty modeling, propagation, and synthesis with integrated modeling.

Methodology for quantifying uncertainty in coal assessments with an application to a Texas lignite deposit

USGS Publications Warehouse

Olea, R.A.; Luppens, J.A.; Tewalt, S.J.

2011-01-01

A common practice for characterizing uncertainty in coal resource assessments has been the itemization of tonnage at the mining unit level and the classification of such units according to distance to drilling holes. Distance criteria, such as those used in U.S. Geological Survey Circular 891, are still widely used for public disclosure. A major deficiency of distance methods is that they do not provide a quantitative measure of uncertainty. Additionally, relying on distance between data points alone does not take into consideration other factors known to have an influence on uncertainty, such as spatial correlation, type of probability distribution followed by the data, geological discontinuities, and boundary of the deposit. Several geostatistical methods have been combined to formulate a quantitative characterization for appraising uncertainty. Drill hole datasets ranging from widespread exploration drilling to detailed development drilling from a lignite deposit in Texas were used to illustrate the modeling. The results show that distance to the nearest drill hole is almost completely unrelated to uncertainty, which confirms the inadequacy of characterizing uncertainty based solely on a simple classification of resources by distance classes. The more complex statistical methods used in this study quantify uncertainty and show good agreement between confidence intervals in the uncertainty predictions and data from additional drilling. ?? 2010.

Latin hypercube approach to estimate uncertainty in ground water vulnerability

USGS Publications Warehouse

Gurdak, J.J.; McCray, J.E.; Thyne, G.; Qi, S.L.

2007-01-01

A methodology is proposed to quantify prediction uncertainty associated with ground water vulnerability models that were developed through an approach that coupled multivariate logistic regression with a geographic information system (GIS). This method uses Latin hypercube sampling (LHS) to illustrate the propagation of input error and estimate uncertainty associated with the logistic regression predictions of ground water vulnerability. Central to the proposed method is the assumption that prediction uncertainty in ground water vulnerability models is a function of input error propagation from uncertainty in the estimated logistic regression model coefficients (model error) and the values of explanatory variables represented in the GIS (data error). Input probability distributions that represent both model and data error sources of uncertainty were simultaneously sampled using a Latin hypercube approach with logistic regression calculations of probability of elevated nonpoint source contaminants in ground water. The resulting probability distribution represents the prediction intervals and associated uncertainty of the ground water vulnerability predictions. The method is illustrated through a ground water vulnerability assessment of the High Plains regional aquifer. Results of the LHS simulations reveal significant prediction uncertainties that vary spatially across the regional aquifer. Additionally, the proposed method enables a spatial deconstruction of the prediction uncertainty that can lead to improved prediction of ground water vulnerability. ?? 2007 National Ground Water Association.

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/

Estimation and impact assessment of input and parameter uncertainty in predicting groundwater flow with a fully distributed model

NASA Astrophysics Data System (ADS)

Touhidul Mustafa, Syed Md.; Nossent, Jiri; Ghysels, Gert; Huysmans, Marijke

2017-04-01

Transient numerical groundwater flow models have been used to understand and forecast groundwater flow systems under anthropogenic and climatic effects, but the reliability of the predictions is strongly influenced by different sources of uncertainty. Hence, researchers in hydrological sciences are developing and applying methods for uncertainty quantification. Nevertheless, spatially distributed flow models pose significant challenges for parameter and spatially distributed input estimation and uncertainty quantification. In this study, we present a general and flexible approach for input and parameter estimation and uncertainty analysis of groundwater models. The proposed approach combines a fully distributed groundwater flow model (MODFLOW) with the DiffeRential Evolution Adaptive Metropolis (DREAM) algorithm. To avoid over-parameterization, the uncertainty of the spatially distributed model input has been represented by multipliers. The posterior distributions of these multipliers and the regular model parameters were estimated using DREAM. The proposed methodology has been applied in an overexploited aquifer in Bangladesh where groundwater pumping and recharge data are highly uncertain. The results confirm that input uncertainty does have a considerable effect on the model predictions and parameter distributions. Additionally, our approach also provides a new way to optimize the spatially distributed recharge and pumping data along with the parameter values under uncertain input conditions. It can be concluded from our approach that considering model input uncertainty along with parameter uncertainty is important for obtaining realistic model predictions and a correct estimation of the uncertainty bounds.

Making framing of uncertainty in water management practice explicit by using a participant-structured approach.

PubMed

Isendahl, Nicola; Dewulf, Art; Pahl-Wostl, Claudia

2010-01-01

By now, the need for addressing uncertainty in the management of water resources is widely recognized, yet there is little expertise and experience how to effectively deal with uncertainty in practice. Uncertainties in water management practice so far are mostly dealt with intuitively or based on experience. That way decisions can be quickly taken but analytic processes of deliberate reasoning are bypassed. To meet the desire of practitioners for better guidance and tools how to deal with uncertainty more practice-oriented systematic approaches are needed. For that purpose we consider it important to understand how practitioners frame uncertainties. In this paper we present an approach where water managers developed criteria of relevance to understand and address uncertainties. The empirical research took place in the Doñana region of the Guadalquivir estuary in southern Spain making use of the method of card sorting. Through the card sorting exercise a broad range of criteria to make sense of and describe uncertainties was produced by different subgroups, which were then merged into a shared list of criteria. That way framing differences were made explicit and communication on uncertainty and on framing differences was enhanced. In that, the present approach constitutes a first step to enabling reframing and overcoming framing differences, which are important features on the way to robust decision-making. Moreover, the elaborated criteria build a basis for the development of more structured approaches to deal with uncertainties in water management practice. Copyright 2009 Elsevier Ltd. All rights reserved.

Illness uncertainty and quality of life in children with cancer.

PubMed

Fortier, Michelle A; Batista, Melissa L; Wahi, Aditi; Kain, Alexandra; Strom, Suzanne; Sender, Leonard S

2013-07-01

Illness uncertainty is prevalent in children with cancer and has been associated with increased psychological distress. The relationship between illness uncertainty and quality of life in pediatric cancer patients remains unclear. The aim of the present study was to examine illness uncertainty as a predictor of health-related quality of life in children diagnosed with cancer. It was hypothesized that child-reported illness uncertainty would be negatively associated with child health-related quality of life. Children aged 8 to 18 years old and receiving treatment for cancer were recruited to participate in this study. One hundred twenty children and their parent(s) completed measures of illness uncertainty, pain, anxiety, and quality of life during a routine visit to the Cancer Center at Children's Hospital of Orange County. Illness uncertainty was significantly associated with child age (P=0.02), overall health-related (P<0.001) and cancer-related (P<0.001) quality of life, but not with treatment status (on/off chemotherapy) or demographic variables including sex and household income. Regression analyses statistically controlling for age, anxiety, and pain revealed that illness uncertainty significantly predicted child-reported cancer-related and health-related quality of life (P<0.01) as well as parent-reported cancer-specific quality of life (P<0.01). Illness uncertainty is prevalent and associated with lower quality of life in children diagnosed with cancer. Improved communication with children regarding disease state, treatment expectations, and prognosis may alleviate uncertainty and improve functioning in this vulnerable patient population.

Characterizing spatial uncertainty when integrating social data in conservation planning.

PubMed

Lechner, A M; Raymond, C M; Adams, V M; Polyakov, M; Gordon, A; Rhodes, J R; Mills, M; Stein, A; Ives, C D; Lefroy, E C

2014-12-01

Recent conservation planning studies have presented approaches for integrating spatially referenced social (SRS) data with a view to improving the feasibility of conservation action. We reviewed the growing conservation literature on SRS data, focusing on elicited or stated preferences derived through social survey methods such as choice experiments and public participation geographic information systems. Elicited SRS data includes the spatial distribution of willingness to sell, willingness to pay, willingness to act, and assessments of social and cultural values. We developed a typology for assessing elicited SRS data uncertainty which describes how social survey uncertainty propagates when projected spatially and the importance of accounting for spatial uncertainty such as scale effects and data quality. These uncertainties will propagate when elicited SRS data is integrated with biophysical data for conservation planning and may have important consequences for assessing the feasibility of conservation actions. To explore this issue further, we conducted a systematic review of the elicited SRS data literature. We found that social survey uncertainty was commonly tested for, but that these uncertainties were ignored when projected spatially. Based on these results we developed a framework which will help researchers and practitioners estimate social survey uncertainty and use these quantitative estimates to systematically address uncertainty within an analysis. This is important when using SRS data in conservation applications because decisions need to be made irrespective of data quality and well characterized uncertainty can be incorporated into decision theoretic approaches. © 2014 Society for Conservation Biology.

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.

Uncertainty Estimate for the Outdoor Calibration of Solar Pyranometers: A Metrologist Perspective

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

Reda, I.; Myers, D.; Stoffel, T.

2008-12-01

Pyranometers are used outdoors to measure solar irradiance. By design, this type of radiometer can measure the; total hemispheric (global) or diffuse (sky) irradiance when the detector is unshaded or shaded from the sun disk, respectively. These measurements are used in a variety of applications including solar energy conversion, atmospheric studies, agriculture, and materials science. Proper calibration of pyranometers is essential to ensure measurement quality. This paper describes a step-by-step method for calculating and reporting the uncertainty of the calibration, using the guidelines of the ISO 'Guide to the Expression of Uncertainty in Measurement' or GUM, that is applied tomore » the pyranometer; calibration procedures used at the National Renewable Energy Laboratory (NREL). The NREL technique; characterizes a responsivity function of a pyranometer as a function of the zenith angle, as well as reporting a single; calibration responsivity value for a zenith angle of 45 ..deg... The uncertainty analysis shows that a lower uncertainty can be achieved by using the response function of a pyranometer determined as a function of zenith angle, in lieu of just using; the average value at 45..deg... By presenting the contribution of each uncertainty source to the total uncertainty; users will be able to troubleshoot and improve their calibration process. The uncertainty analysis method can also be used to determine the uncertainty of different calibration techniques and applications, such as deriving the uncertainty of field measurements.« less

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.

Entropic uncertainty for spin-1/2 XXX chains in the presence of inhomogeneous magnetic fields and its steering via weak measurement reversals

NASA Astrophysics Data System (ADS)

Wang, Dong; Ming, Fei; Huang, Ai-Jun; Sun, Wen-Yang; Ye, Liu

2017-09-01

The uncertainty principle configures a low bound to the measuring precision for a pair of non-commuting observables, and hence is considerably nontrivial to quantum precision measurement in the field of quantum information theory. In this letter, we consider the entropic uncertainty relation (EUR) in the context of quantum memory in a two-qubit isotropic Heisenberg spin chain. Specifically, we explore the dynamics of EUR in a practical scenario, where two associated nodes of a one-dimensional XXX-spin chain, under an inhomogeneous magnetic field, are connected to a thermal entanglement. We show that the temperature and magnetic field effect can lead to the inflation of the measuring uncertainty, stemming from the reduction of systematic quantum correlation. Notably, we reveal that, firstly, the uncertainty is not fully dependent on the observed quantum correlation of the system; secondly, the dynamical behaviors of the measuring uncertainty are relatively distinct with respect to ferromagnetism and antiferromagnetism chains. Meanwhile, we deduce that the measuring uncertainty is dramatically correlated with the mixedness of the system, implying that smaller mixedness tends to reduce the uncertainty. Furthermore, we propose an effective strategy to control the uncertainty of interest by means of quantum weak measurement reversal. Therefore, our work may shed light on the dynamics of the measuring uncertainty in the Heisenberg spin chain, and thus be important to quantum precision measurement in various solid-state systems.

Comprehensive analysis of proton range uncertainties related to patient stopping-power-ratio estimation using the stoichiometric calibration

PubMed Central

Yang, M; Zhu, X R; Park, PC; Titt, Uwe; Mohan, R; Virshup, G; Clayton, J; Dong, L

2012-01-01

The purpose of this study was to analyze factors affecting proton stopping-power-ratio (SPR) estimations and range uncertainties in proton therapy planning using the standard stoichiometric calibration. The SPR uncertainties were grouped into five categories according to their origins and then estimated based on previously published reports or measurements. For the first time, the impact of tissue composition variations on SPR estimation was assessed and the uncertainty estimates of each category were determined for low-density (lung), soft, and high-density (bone) tissues. A composite, 95th percentile water-equivalent-thickness uncertainty was calculated from multiple beam directions in 15 patients with various types of cancer undergoing proton therapy. The SPR uncertainties (1σ) were quite different (ranging from 1.6% to 5.0%) in different tissue groups, although the final combined uncertainty (95th percentile) for different treatment sites was fairly consistent at 3.0–3.4%, primarily because soft tissue is the dominant tissue type in human body. The dominant contributing factor for uncertainties in soft tissues was the degeneracy of Hounsfield Numbers in the presence of tissue composition variations. To reduce the overall uncertainties in SPR estimation, the use of dual-energy computed tomography is suggested. The values recommended in this study based on typical treatment sites and a small group of patients roughly agree with the commonly referenced value (3.5%) used for margin design. By using tissue-specific range uncertainties, one could estimate the beam-specific range margin by accounting for different types and amounts of tissues along a beam, which may allow for customization of range uncertainty for each beam direction. PMID:22678123

`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.

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.

To be or not to be: How do we speak about uncertainty in public?

NASA Astrophysics Data System (ADS)

Todesco, Micol; Lolli, Barbara; Sheldrake, Tom; Odbert, Henry

2016-04-01

One of the challenges related to hazard communication concerns the public perception and understanding of scientific uncertainties, and of its implications in terms of hazard assessment and mitigation. Often science is perceived as an effective dispenser of resolving answers to the main issues posed by the complexities of life and nature. In this perspective, uncertainty is seen as a pernicious lack of knowledge that hinders our ability to face complex problems. From a scientific perspective, however, the definition of uncertainty is the only valuable tool we have to handle errors affecting our data and propagating through the increasingly complex models we develop to describe reality. Through uncertainty, scientists acknowledge the great variability that characterises natural systems and account for it in their assessment of possible scenarios. From this point of view, uncertainty is not ignorance, but it rather provides a great deal of information that is needed to inform decision making. To find effective ways to bridge the gap between these different meaning of uncertainty, we asked high-school students for assistance. With their help, we gathered definitions of the term 'uncertainty' interviewing different categories of peoples, including schoolmates and professors, neighbours, families and friends. These definitions will be compared with those provided by scientists, to find differences and similarity. To understand the role of uncertainty on judgment, a hands-on experiment is performed where students will have to estimate the exact time of explosion of party poppers subjected to a variable degree of pull. At the end of the project, the students will express their own understanding of uncertainty in a video, which will be made available for sharing. Materials collected during all the activities will contribute to our understanding of how uncertainty is portrayed and can be better expressed to improve our hazard communication.

A unified approach for squeal instability analysis of disc brakes with two types of random-fuzzy uncertainties

NASA Astrophysics Data System (ADS)

Lü, Hui; Shangguan, Wen-Bin; Yu, Dejie

2017-09-01

Automotive brake systems are always subjected to various types of uncertainties and two types of random-fuzzy uncertainties may exist in the brakes. In this paper, a unified approach is proposed for squeal instability analysis of disc brakes with two types of random-fuzzy uncertainties. In the proposed approach, two uncertainty analysis models with mixed variables are introduced to model the random-fuzzy uncertainties. The first one is the random and fuzzy model, in which random variables and fuzzy variables exist simultaneously and independently. The second one is the fuzzy random model, in which uncertain parameters are all treated as random variables while their distribution parameters are expressed as fuzzy numbers. Firstly, the fuzziness is discretized by using α-cut technique and the two uncertainty analysis models are simplified into random-interval models. Afterwards, by temporarily neglecting interval uncertainties, the random-interval models are degraded into random models, in which the expectations, variances, reliability indexes and reliability probabilities of system stability functions are calculated. And then, by reconsidering the interval uncertainties, the bounds of the expectations, variances, reliability indexes and reliability probabilities are computed based on Taylor series expansion. Finally, by recomposing the analysis results at each α-cut level, the fuzzy reliability indexes and probabilities can be obtained, by which the brake squeal instability can be evaluated. The proposed approach gives a general framework to deal with both types of random-fuzzy uncertainties that may exist in the brakes and its effectiveness is demonstrated by numerical examples. It will be a valuable supplement to the systematic study of brake squeal considering uncertainty.

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 approach to forecasting health expenditures, with application to the U.S. Medicare system.

PubMed

Lee, Ronald; Miller, Timoth

2002-10-01

To quantify uncertainty in forecasts of health expenditures. Stochastic time series models are estimated for historical variations in fertility, mortality, and health spending per capita in the United States, and used to generate stochastic simulations of the growth of Medicare expenditures. Individual health spending is modeled to depend on the number of years until death. A simple accounting model is developed for forecasting health expenditures, using the U.S. Medicare system as an example. Medicare expenditures are projected to rise from 2.2 percent of GDP (gross domestic product) to about 8 percent of GDP by 2075. This increase is due in equal measure to increasing health spending per beneficiary and to population aging. The traditional projection method constructs high, medium, and low scenarios to assess uncertainty, an approach that has many problems. Using stochastic forecasting, we find a 95 percent probability that Medicare spending in 2075 will fall between 4 percent and 18 percent of GDP, indicating a wide band of uncertainty. Although there is substantial uncertainty about future mortality decline, it contributed little to uncertainty about future Medicare spending, since lower mortality both raises the number of elderly, tending to raise spending, and is associated with improved health of the elderly, tending to reduce spending. Uncertainty about fertility, by contrast, leads to great uncertainty about the future size of the labor force, and therefore adds importantly to uncertainty about the health-share of GDP. In the shorter term, the major source of uncertainty is health spending per capita. History is a valuable guide for quantifying our uncertainty about future health expenditures. The probabilistic model we present has several advantages over the high-low scenario approach to forecasting. It indicates great uncertainty about future Medicare expenditures relative to GDP.

Relative Gains, Losses, and Reference Points in Probabilistic Choice in Rats

PubMed Central

Marshall, Andrew T.; Kirkpatrick, Kimberly

2015-01-01

Theoretical reference points have been proposed to differentiate probabilistic gains from probabilistic losses in humans, but such a phenomenon in non-human animals has yet to be thoroughly elucidated. Three experiments evaluated the effect of reward magnitude on probabilistic choice in rats, seeking to determine reference point use by examining the effect of previous outcome magnitude(s) on subsequent choice behavior. Rats were trained to choose between an outcome that always delivered reward (low-uncertainty choice) and one that probabilistically delivered reward (high-uncertainty). The probability of high-uncertainty outcome receipt and the magnitudes of low-uncertainty and high-uncertainty outcomes were manipulated within and between experiments. Both the low- and high-uncertainty outcomes involved variable reward magnitudes, so that either a smaller or larger magnitude was probabilistically delivered, as well as reward omission following high-uncertainty choices. In Experiments 1 and 2, the between groups factor was the magnitude of the high-uncertainty-smaller (H-S) and high-uncertainty-larger (H-L) outcome, respectively. The H-S magnitude manipulation differentiated the groups, while the H-L magnitude manipulation did not. Experiment 3 showed that manipulating the probability of differential losses as well as the expected value of the low-uncertainty choice produced systematic effects on choice behavior. The results suggest that the reference point for probabilistic gains and losses was the expected value of the low-uncertainty choice. Current theories of probabilistic choice behavior have difficulty accounting for the present results, so an integrated theoretical framework is proposed. Overall, the present results have implications for understanding individual differences and corresponding underlying mechanisms of probabilistic choice behavior. PMID:25658448

A framework to quantify uncertainties of seafloor backscatter from swath mapping echosounders

NASA Astrophysics Data System (ADS)

Malik, Mashkoor; Lurton, Xavier; Mayer, Larry

2018-06-01

Multibeam echosounders (MBES) have become a widely used acoustic remote sensing tool to map and study the seafloor, providing co-located bathymetry and seafloor backscatter. Although the uncertainty associated with MBES-derived bathymetric data has been studied extensively, the question of backscatter uncertainty has been addressed only minimally and hinders the quantitative use of MBES seafloor backscatter. This paper explores approaches to identifying uncertainty sources associated with MBES-derived backscatter measurements. The major sources of uncertainty are catalogued and the magnitudes of their relative contributions to the backscatter uncertainty budget are evaluated. These major uncertainty sources include seafloor insonified area (1-3 dB), absorption coefficient (up to > 6 dB), random fluctuations in echo level (5.5 dB for a Rayleigh distribution), and sonar calibration (device dependent). The magnitudes of these uncertainty sources vary based on how these effects are compensated for during data acquisition and processing. Various cases (no compensation, partial compensation and full compensation) for seafloor insonified area, transmission losses and random fluctuations were modeled to estimate their uncertainties in different scenarios. Uncertainty related to the seafloor insonified area can be reduced significantly by accounting for seafloor slope during backscatter processing while transmission losses can be constrained by collecting full water column absorption coefficient profiles (temperature and salinity profiles). To reduce random fluctuations to below 1 dB, at least 20 samples are recommended to be used while computing mean values. The estimation of uncertainty in backscatter measurements is constrained by the fact that not all instrumental components are characterized and documented sufficiently for commercially available MBES. Further involvement from manufacturers in providing this essential information is critically required.

Parameter and prediction uncertainty in an optimized terrestrial carbon cycle model: Effects of constraining variables and data record length

NASA Astrophysics Data System (ADS)

Ricciuto, Daniel M.; King, Anthony W.; Dragoni, D.; Post, Wilfred M.

2011-03-01

Many parameters in terrestrial biogeochemical models are inherently uncertain, leading to uncertainty in predictions of key carbon cycle variables. At observation sites, this uncertainty can be quantified by applying model-data fusion techniques to estimate model parameters using eddy covariance observations and associated biometric data sets as constraints. Uncertainty is reduced as data records become longer and different types of observations are added. We estimate parametric and associated predictive uncertainty at the Morgan Monroe State Forest in Indiana, USA. Parameters in the Local Terrestrial Ecosystem Carbon (LoTEC) are estimated using both synthetic and actual constraints. These model parameters and uncertainties are then used to make predictions of carbon flux for up to 20 years. We find a strong dependence of both parametric and prediction uncertainty on the length of the data record used in the model-data fusion. In this model framework, this dependence is strongly reduced as the data record length increases beyond 5 years. If synthetic initial biomass pool constraints with realistic uncertainties are included in the model-data fusion, prediction uncertainty is reduced by more than 25% when constraining flux records are less than 3 years. If synthetic annual aboveground woody biomass increment constraints are also included, uncertainty is similarly reduced by an additional 25%. When actual observed eddy covariance data are used as constraints, there is still a strong dependence of parameter and prediction uncertainty on data record length, but the results are harder to interpret because of the inability of LoTEC to reproduce observed interannual variations and the confounding effects of model structural error.

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.

Techniques for analyses of trends in GRUAN data

NASA Astrophysics Data System (ADS)

Bodeker, G. E.; Kremser, S.

2015-04-01

The Global Climate Observing System (GCOS) Reference Upper Air Network (GRUAN) provides reference quality RS92 radiosonde measurements of temperature, pressure and humidity. A key attribute of reference quality measurements, and hence GRUAN data, is that each datum has a well characterized and traceable estimate of the measurement uncertainty. The long-term homogeneity of the measurement records, and their well characterized uncertainties, make these data suitable for reliably detecting changes in global and regional climate on decadal time scales. Considerable effort is invested in GRUAN operations to (i) describe and analyse all sources of measurement uncertainty to the extent possible, (ii) quantify and synthesize the contribution of each source of uncertainty to the total measurement uncertainty, and (iii) verify that the evaluated net uncertainty is within the required target uncertainty. However, if the climate science community is not sufficiently well informed on how to capitalize on this added value, the significant investment in estimating meaningful measurement uncertainties is largely wasted. This paper presents and discusses the techniques that will need to be employed to reliably quantify long-term trends in GRUAN data records. A pedagogical approach is taken whereby numerical recipes for key parts of the trend analysis process are explored. The paper discusses the construction of linear least squares regression models for trend analysis, boot-strapping approaches to determine uncertainties in trends, dealing with the combined effects of autocorrelation in the data and measurement uncertainties in calculating the uncertainty on trends, best practice for determining seasonality in trends, how to deal with co-linear basis functions, and interpreting derived trends. Synthetic data sets are used to demonstrate these concepts which are then applied to a first analysis of temperature trends in RS92 radiosonde upper air soundings at the GRUAN site at Lindenberg, Germany (52.21° N, 14.12° E).

Techniques for analyses of trends in GRUAN data

NASA Astrophysics Data System (ADS)

Bodeker, G. E.; Kremser, S.

2014-12-01

The Global Climate Observing System (GCOS) Reference Upper Air Network (GRUAN) provides reference quality RS92 radiosonde measurements of temperature, pressure and humidity. A key attribute of reference quality measurements, and hence GRUAN data, is that each datum has a well characterised and traceable estimate of the measurement uncertainty. The long-term homogeneity of the measurement records, and their well characterised uncertainties, make these data suitable for reliably detecting changes in global and regional climate on decadal time scales. Considerable effort is invested in GRUAN operations to (i) describe and analyse all sources of measurement uncertainty to the extent possible, (ii) quantify and synthesize the contribution of each source of uncertainty to the total measurement uncertainty, and (iii) verify that the evaluated net uncertainty is within the required target uncertainty. However, if the climate science community is not sufficiently well informed on how to capitalize on this added value, the significant investment in estimating meaningful measurement uncertainties is largely wasted. This paper presents and discusses the techniques that will need to be employed to reliably quantify long-term trends in GRUAN data records. A pedagogical approach is taken whereby numerical recipes for key parts of the trend analysis process are explored. The paper discusses the construction of linear least squares regression models for trend analysis, boot-strapping approaches to determine uncertainties in trends, dealing with the combined effects of autocorrelation in the data and measurement uncertainties in calculating the uncertainty on trends, best practice for determining seasonality in trends, how to deal with co-linear basis functions, and interpreting derived trends. Synthetic data sets are used to demonstrate these concepts which are then applied to a first analysis of temperature trends in RS92 radiosonde upper air soundings at the GRUAN site at Lindenberg, Germany (52.21° N, 14.12° E).

Quantification and propagation of disciplinary uncertainty via Bayesian statistics

NASA Astrophysics Data System (ADS)

Mantis, George Constantine

2002-08-01

Several needs exist in the military, commercial, and civil sectors for new hypersonic systems. These needs remain unfulfilled, due in part to the uncertainty encountered in designing these systems. This uncertainty takes a number of forms, including disciplinary uncertainty, that which is inherent in the analytical tools utilized during the design process. Yet, few efforts to date empower the designer with the means to account for this uncertainty within the disciplinary analyses. In the current state-of-the-art in design, the effects of this unquantifiable uncertainty significantly increase the risks associated with new design efforts. Typically, the risk proves too great to allow a given design to proceed beyond the conceptual stage. To that end, the research encompasses the formulation and validation of a new design method, a systematic process for probabilistically assessing the impact of disciplinary uncertainty. The method implements Bayesian Statistics theory to quantify this source of uncertainty, and propagate its effects to the vehicle system level. Comparison of analytical and physical data for existing systems, modeled a priori in the given analysis tools, leads to quantification of uncertainty in those tools' calculation of discipline-level metrics. Then, after exploration of the new vehicle's design space, the quantified uncertainty is propagated probabilistically through the design space. This ultimately results in the assessment of the impact of disciplinary uncertainty on the confidence in the design solution: the final shape and variability of the probability functions defining the vehicle's system-level metrics. Although motivated by the hypersonic regime, the proposed treatment of uncertainty applies to any class of aerospace vehicle, just as the problem itself affects the design process of any vehicle. A number of computer programs comprise the environment constructed for the implementation of this work. Application to a single-stage-to-orbit (SSTO) reusable launch vehicle concept, developed by the NASA Langley Research Center under the Space Launch Initiative, provides the validation case for this work, with the focus placed on economics, aerothermodynamics, propulsion, and structures metrics. (Abstract shortened by UMI.)

'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.

Effect of soil property uncertainties on permafrost thaw projections: a calibration-constrained analysis

NASA Astrophysics Data System (ADS)

Harp, D. R.; Atchley, A. L.; Painter, S. L.; Coon, E. T.; Wilson, C. J.; Romanovsky, V. E.; Rowland, J. C.

2016-02-01

The effects of soil property uncertainties on permafrost thaw projections are studied using a three-phase subsurface thermal hydrology model and calibration-constrained uncertainty analysis. The null-space Monte Carlo method is used to identify soil hydrothermal parameter combinations that are consistent with borehole temperature measurements at the study site, the Barrow Environmental Observatory. Each parameter combination is then used in a forward projection of permafrost conditions for the 21st century (from calendar year 2006 to 2100) using atmospheric forcings from the Community Earth System Model (CESM) in the Representative Concentration Pathway (RCP) 8.5 greenhouse gas concentration trajectory. A 100-year projection allows for the evaluation of predictive uncertainty (due to soil property (parametric) uncertainty) and the inter-annual climate variability due to year to year differences in CESM climate forcings. After calibrating to measured borehole temperature data at this well-characterized site, soil property uncertainties are still significant and result in significant predictive uncertainties in projected active layer thickness and annual thaw depth-duration even with a specified future climate. Inter-annual climate variability in projected soil moisture content and Stefan number are small. A volume- and time-integrated Stefan number decreases significantly, indicating a shift in subsurface energy utilization in the future climate (latent heat of phase change becomes more important than heat conduction). Out of 10 soil parameters, ALT, annual thaw depth-duration, and Stefan number are highly dependent on mineral soil porosity, while annual mean liquid saturation of the active layer is highly dependent on the mineral soil residual saturation and moderately dependent on peat residual saturation. By comparing the ensemble statistics to the spread of projected permafrost metrics using different climate models, we quantify the relative magnitude of soil property uncertainty to another source of permafrost uncertainty, structural climate model uncertainty. We show that the effect of calibration-constrained uncertainty in soil properties, although significant, is less than that produced by structural climate model uncertainty for this location.

Uncertainties in selected river water quality data

NASA Astrophysics Data System (ADS)

Rode, M.; Suhr, U.

2007-02-01

Monitoring of surface waters is primarily done to detect the status and trends in water quality and to identify whether observed trends arise from natural or anthropogenic causes. Empirical quality of river water quality data is rarely certain and knowledge of their uncertainties is essential to assess the reliability of water quality models and their predictions. The objective of this paper is to assess the uncertainties in selected river water quality data, i.e. suspended sediment, nitrogen fraction, phosphorus fraction, heavy metals and biological compounds. The methodology used to structure the uncertainty is based on the empirical quality of data and the sources of uncertainty in data (van Loon et al., 2005). A literature review was carried out including additional experimental data of the Elbe river. All data of compounds associated with suspended particulate matter have considerable higher sampling uncertainties than soluble concentrations. This is due to high variability within the cross section of a given river. This variability is positively correlated with total suspended particulate matter concentrations. Sampling location has also considerable effect on the representativeness of a water sample. These sampling uncertainties are highly site specific. The estimation of uncertainty in sampling can only be achieved by taking at least a proportion of samples in duplicates. Compared to sampling uncertainties, measurement and analytical uncertainties are much lower. Instrument quality can be stated well suited for field and laboratory situations for all considered constituents. Analytical errors can contribute considerably to the overall uncertainty of river water quality data. Temporal autocorrelation of river water quality data is present but literature on general behaviour of water quality compounds is rare. For meso scale river catchments (500-3000 km2) reasonable yearly dissolved load calculations can be achieved using biweekly sample frequencies. For suspended sediments none of the methods investigated produced very reliable load estimates when weekly concentrations data were used. Uncertainties associated with loads estimates based on infrequent samples will decrease with increasing size of rivers.

Uncertainties in selected surface water quality data

NASA Astrophysics Data System (ADS)

Rode, M.; Suhr, U.

2006-09-01

Monitoring of surface waters is primarily done to detect the status and trends in water quality and to identify whether observed trends arise form natural or anthropogenic causes. Empirical quality of surface water quality data is rarely certain and knowledge of their uncertainties is essential to assess the reliability of water quality models and their predictions. The objective of this paper is to assess the uncertainties in selected surface water quality data, i.e. suspended sediment, nitrogen fraction, phosphorus fraction, heavy metals and biological compounds. The methodology used to structure the uncertainty is based on the empirical quality of data and the sources of uncertainty in data (van Loon et al., 2006). A literature review was carried out including additional experimental data of the Elbe river. All data of compounds associated with suspended particulate matter have considerable higher sampling uncertainties than soluble concentrations. This is due to high variability's within the cross section of a given river. This variability is positively correlated with total suspended particulate matter concentrations. Sampling location has also considerable effect on the representativeness of a water sample. These sampling uncertainties are highly site specific. The estimation of uncertainty in sampling can only be achieved by taking at least a proportion of samples in duplicates. Compared to sampling uncertainties measurement and analytical uncertainties are much lower. Instrument quality can be stated well suited for field and laboratory situations for all considered constituents. Analytical errors can contribute considerable to the overall uncertainty of surface water quality data. Temporal autocorrelation of surface water quality data is present but literature on general behaviour of water quality compounds is rare. For meso scale river catchments reasonable yearly dissolved load calculations can be achieved using biweekly sample frequencies. For suspended sediments none of the methods investigated produced very reliable load estimates when weekly concentrations data were used. Uncertainties associated with loads estimates based on infrequent samples will decrease with increasing size of rivers.

Uncertainty in Measurement: A Review of Monte Carlo Simulation Using Microsoft Excel for the Calculation of Uncertainties Through Functional Relationships, Including Uncertainties in Empirically Derived Constants

PubMed Central

Farrance, Ian; Frenkel, Robert

2014-01-01

The Guide to the Expression of Uncertainty in Measurement (usually referred to as the GUM) provides the basic framework for evaluating uncertainty in measurement. The GUM however does not always provide clearly identifiable procedures suitable for medical laboratory applications, particularly when internal quality control (IQC) is used to derive most of the uncertainty estimates. The GUM modelling approach requires advanced mathematical skills for many of its procedures, but Monte Carlo simulation (MCS) can be used as an alternative for many medical laboratory applications. In particular, calculations for determining how uncertainties in the input quantities to a functional relationship propagate through to the output can be accomplished using a readily available spreadsheet such as Microsoft Excel. The MCS procedure uses algorithmically generated pseudo-random numbers which are then forced to follow a prescribed probability distribution. When IQC data provide the uncertainty estimates the normal (Gaussian) distribution is generally considered appropriate, but MCS is by no means restricted to this particular case. With input variations simulated by random numbers, the functional relationship then provides the corresponding variations in the output in a manner which also provides its probability distribution. The MCS procedure thus provides output uncertainty estimates without the need for the differential equations associated with GUM modelling. The aim of this article is to demonstrate the ease with which Microsoft Excel (or a similar spreadsheet) can be used to provide an uncertainty estimate for measurands derived through a functional relationship. In addition, we also consider the relatively common situation where an empirically derived formula includes one or more ‘constants’, each of which has an empirically derived numerical value. Such empirically derived ‘constants’ must also have associated uncertainties which propagate through the functional relationship and contribute to the combined standard uncertainty of the measurand. PMID:24659835

Uncertainty in measurement: a review of monte carlo simulation using microsoft excel for the calculation of uncertainties through functional relationships, including uncertainties in empirically derived constants.

PubMed

Farrance, Ian; Frenkel, Robert

2014-02-01

The Guide to the Expression of Uncertainty in Measurement (usually referred to as the GUM) provides the basic framework for evaluating uncertainty in measurement. The GUM however does not always provide clearly identifiable procedures suitable for medical laboratory applications, particularly when internal quality control (IQC) is used to derive most of the uncertainty estimates. The GUM modelling approach requires advanced mathematical skills for many of its procedures, but Monte Carlo simulation (MCS) can be used as an alternative for many medical laboratory applications. In particular, calculations for determining how uncertainties in the input quantities to a functional relationship propagate through to the output can be accomplished using a readily available spreadsheet such as Microsoft Excel. The MCS procedure uses algorithmically generated pseudo-random numbers which are then forced to follow a prescribed probability distribution. When IQC data provide the uncertainty estimates the normal (Gaussian) distribution is generally considered appropriate, but MCS is by no means restricted to this particular case. With input variations simulated by random numbers, the functional relationship then provides the corresponding variations in the output in a manner which also provides its probability distribution. The MCS procedure thus provides output uncertainty estimates without the need for the differential equations associated with GUM modelling. The aim of this article is to demonstrate the ease with which Microsoft Excel (or a similar spreadsheet) can be used to provide an uncertainty estimate for measurands derived through a functional relationship. In addition, we also consider the relatively common situation where an empirically derived formula includes one or more 'constants', each of which has an empirically derived numerical value. Such empirically derived 'constants' must also have associated uncertainties which propagate through the functional relationship and contribute to the combined standard uncertainty of the measurand.

Managing uncertainty in flood protection planning with climate projections

NASA Astrophysics Data System (ADS)

Dittes, Beatrice; Špačková, Olga; Schoppa, Lukas; Straub, Daniel

2018-04-01

Technical flood protection is a necessary part of integrated strategies to protect riverine settlements from extreme floods. Many technical flood protection measures, such as dikes and protection walls, are costly to adapt after their initial construction. This poses a challenge to decision makers as there is large uncertainty in how the required protection level will change during the measure lifetime, which is typically many decades long. Flood protection requirements should account for multiple future uncertain factors: socioeconomic, e.g., whether the population and with it the damage potential grows or falls; technological, e.g., possible advancements in flood protection; and climatic, e.g., whether extreme discharge will become more frequent or not. This paper focuses on climatic uncertainty. Specifically, we devise methodology to account for uncertainty associated with the use of discharge projections, ultimately leading to planning implications. For planning purposes, we categorize uncertainties as either visible, if they can be quantified from available catchment data, or hidden, if they cannot be quantified from catchment data and must be estimated, e.g., from the literature. It is vital to consider the hidden uncertainty, since in practical applications only a limited amount of information (e.g., a finite projection ensemble) is available. We use a Bayesian approach to quantify the visible uncertainties and combine them with an estimate of the hidden uncertainties to learn a joint probability distribution of the parameters of extreme discharge. The methodology is integrated into an optimization framework and applied to a pre-alpine case study to give a quantitative, cost-optimal recommendation on the required amount of flood protection. The results show that hidden uncertainty ought to be considered in planning, but the larger the uncertainty already present, the smaller the impact of adding more. The recommended planning is robust to moderate changes in uncertainty as well as in trend. In contrast, planning without consideration of bias and dependencies in and between uncertainty components leads to strongly suboptimal planning recommendations.

A dosimetric uncertainty analysis for photon-emitting brachytherapy sources: Report of AAPM Task Group No. 138 and GEC-ESTRO

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

DeWerd, Larry A.; Ibbott, Geoffrey S.; Meigooni, Ali S.

2011-02-15

This report addresses uncertainties pertaining to brachytherapy single-source dosimetry preceding clinical use. The International Organization for Standardization (ISO) Guide to the Expression of Uncertainty in Measurement (GUM) and the National Institute of Standards and Technology (NIST) Technical Note 1297 are taken as reference standards for uncertainty formalism. Uncertainties in using detectors to measure or utilizing Monte Carlo methods to estimate brachytherapy dose distributions are provided with discussion of the components intrinsic to the overall dosimetric assessment. Uncertainties provided are based on published observations and cited when available. The uncertainty propagation from the primary calibration standard through transfer to the clinicmore » for air-kerma strength is covered first. Uncertainties in each of the brachytherapy dosimetry parameters of the TG-43 formalism are then explored, ending with transfer to the clinic and recommended approaches. Dosimetric uncertainties during treatment delivery are considered briefly but are not included in the detailed analysis. For low- and high-energy brachytherapy sources of low dose rate and high dose rate, a combined dosimetric uncertainty <5% (k=1) is estimated, which is consistent with prior literature estimates. Recommendations are provided for clinical medical physicists, dosimetry investigators, and source and treatment planning system manufacturers. These recommendations include the use of the GUM and NIST reports, a requirement of constancy of manufacturer source design, dosimetry investigator guidelines, provision of the lowest uncertainty for patient treatment dosimetry, and the establishment of an action level based on dosimetric uncertainty. These recommendations reflect the guidance of the American Association of Physicists in Medicine (AAPM) and the Groupe Europeen de Curietherapie-European Society for Therapeutic Radiology and Oncology (GEC-ESTRO) for their members and may also be used as guidance to manufacturers and regulatory agencies in developing good manufacturing practices for sources used in routine clinical treatments.« less

Uncertainties in Past and Future Global Water Availability

NASA Astrophysics Data System (ADS)

Sheffield, J.; Kam, J.

2014-12-01

Understanding how water availability changes on inter-annual to decadal time scales and how it may change in the future under climate change are a key part of understanding future stresses on water and food security. Historic evaluations of water availability on regional to global scales are generally based on large-scale model simulations with their associated uncertainties, in particular for long-term changes. Uncertainties are due to model errors and missing processes, parameter uncertainty, and errors in meteorological forcing data. Recent multi-model inter-comparisons and impact studies have highlighted large differences for past reconstructions, due to different simplifying assumptions in the models or the inclusion of physical processes such as CO2 fertilization. Modeling of direct anthropogenic factors such as water and land management also carry large uncertainties in their physical representation and from lack of socio-economic data. Furthermore, there is little understanding of the impact of uncertainties in the meteorological forcings that underpin these historic simulations. Similarly, future changes in water availability are highly uncertain due to climate model diversity, natural variability and scenario uncertainty, each of which dominates at different time scales. In particular, natural climate variability is expected to dominate any externally forced signal over the next several decades. We present results from multi-land surface model simulations of the historic global availability of water in the context of natural variability (droughts) and long-term changes (drying). The simulations take into account the impact of uncertainties in the meteorological forcings and the incorporation of water management in the form of reservoirs and irrigation. The results indicate that model uncertainty is important for short-term drought events, and forcing uncertainty is particularly important for long-term changes, especially uncertainty in precipitation due to reduced gauge density in recent years. We also discuss uncertainties in future projections from these models as driven by bias-corrected and downscaled CMIP5 climate projections, in the context of the balance between climate model robustness and climate model diversity.

Uncertainty analysis of thermocouple measurements used in normal and abnormal thermal environment experiments at Sandia's Radiant Heat Facility and Lurance Canyon Burn Site.

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

Nakos, James Thomas

2004-04-01

It would not be possible to confidently qualify weapon systems performance or validate computer codes without knowing the uncertainty of the experimental data used. This report provides uncertainty estimates associated with thermocouple data for temperature measurements from two of Sandia's large-scale thermal facilities. These two facilities (the Radiant Heat Facility (RHF) and the Lurance Canyon Burn Site (LCBS)) routinely gather data from normal and abnormal thermal environment experiments. They are managed by Fire Science & Technology Department 09132. Uncertainty analyses were performed for several thermocouple (TC) data acquisition systems (DASs) used at the RHF and LCBS. These analyses apply tomore » Type K, chromel-alumel thermocouples of various types: fiberglass sheathed TC wire, mineral-insulated, metal-sheathed (MIMS) TC assemblies, and are easily extended to other TC materials (e.g., copper-constantan). Several DASs were analyzed: (1) A Hewlett-Packard (HP) 3852A system, and (2) several National Instrument (NI) systems. The uncertainty analyses were performed on the entire system from the TC to the DAS output file. Uncertainty sources include TC mounting errors, ANSI standard calibration uncertainty for Type K TC wire, potential errors due to temperature gradients inside connectors, extension wire uncertainty, DAS hardware uncertainties including noise, common mode rejection ratio, digital voltmeter accuracy, mV to temperature conversion, analog to digital conversion, and other possible sources. Typical results for 'normal' environments (e.g., maximum of 300-400 K) showed the total uncertainty to be about {+-}1% of the reading in absolute temperature. In high temperature or high heat flux ('abnormal') thermal environments, total uncertainties range up to {+-}2-3% of the reading (maximum of 1300 K). The higher uncertainties in abnormal thermal environments are caused by increased errors due to the effects of imperfect TC attachment to the test item. 'Best practices' are provided in Section 9 to help the user to obtain the best measurements possible.« less

Evaluating land cover influences on model uncertainties—A case study of cropland carbon dynamics in the Mid-Continent Intensive Campaign region

USGS Publications Warehouse

Li, Zhengpeng; Liu, Shuguang; Zhang, Xuesong; West, Tristram O.; Ogle, Stephen M.; Zhou, Naijun

2016-01-01

Quantifying spatial and temporal patterns of carbon sources and sinks and their uncertainties across agriculture-dominated areas remains challenging for understanding regional carbon cycles. Characteristics of local land cover inputs could impact the regional carbon estimates but the effect has not been fully evaluated in the past. Within the North American Carbon Program Mid-Continent Intensive (MCI) Campaign, three models were developed to estimate carbon fluxes on croplands: an inventory-based model, the Environmental Policy Integrated Climate (EPIC) model, and the General Ensemble biogeochemical Modeling System (GEMS) model. They all provided estimates of three major carbon fluxes on cropland: net primary production (NPP), net ecosystem production (NEP), and soil organic carbon (SOC) change. Using data mining and spatial statistics, we studied the spatial distribution of the carbon fluxes uncertainties and the relationships between the uncertainties and the land cover characteristics. Results indicated that uncertainties for all three carbon fluxes were not randomly distributed, but instead formed multiple clusters within the MCI region. We investigated the impacts of three land cover characteristics on the fluxes uncertainties: cropland percentage, cropland richness and cropland diversity. The results indicated that cropland percentage significantly influenced the uncertainties of NPP and NEP, but not on the uncertainties of SOC change. Greater uncertainties of NPP and NEP were found in counties with small cropland percentage than the counties with large cropland percentage. Cropland species richness and diversity also showed negative correlations with the model uncertainties. Our study demonstrated that the land cover characteristics contributed to the uncertainties of regional carbon fluxes estimates. The approaches we used in this study can be applied to other ecosystem models to identify the areas with high uncertainties and where models can be improved to reduce overall uncertainties for regional carbon flux estimates.

Proposed standardized definitions for vertical resolution and uncertainty in the NDACC lidar ozone and temperature algorithms - Part 2: Ozone DIAL uncertainty budget

NASA Astrophysics Data System (ADS)

Leblanc, Thierry; Sica, Robert J.; van Gijsel, Joanna A. E.; Godin-Beekmann, Sophie; Haefele, Alexander; Trickl, Thomas; Payen, Guillaume; Liberti, Gianluigi

2016-08-01

A standardized approach for the definition, propagation, and reporting of uncertainty in the ozone differential absorption lidar data products contributing to the Network for the Detection for Atmospheric Composition Change (NDACC) database is proposed. One essential aspect of the proposed approach is the propagation in parallel of all independent uncertainty components through the data processing chain before they are combined together to form the ozone combined standard uncertainty. The independent uncertainty components contributing to the overall budget include random noise associated with signal detection, uncertainty due to saturation correction, background noise extraction, the absorption cross sections of O3, NO2, SO2, and O2, the molecular extinction cross sections, and the number densities of the air, NO2, and SO2. The expression of the individual uncertainty components and their step-by-step propagation through the ozone differential absorption lidar (DIAL) processing chain are thoroughly estimated. All sources of uncertainty except detection noise imply correlated terms in the vertical dimension, which requires knowledge of the covariance matrix when the lidar signal is vertically filtered. In addition, the covariance terms must be taken into account if the same detection hardware is shared by the lidar receiver channels at the absorbed and non-absorbed wavelengths. The ozone uncertainty budget is presented as much as possible in a generic form (i.e., as a function of instrument performance and wavelength) so that all NDACC ozone DIAL investigators across the network can estimate, for their own instrument and in a straightforward manner, the expected impact of each reviewed uncertainty component. In addition, two actual examples of full uncertainty budget are provided, using nighttime measurements from the tropospheric ozone DIAL located at the Jet Propulsion Laboratory (JPL) Table Mountain Facility, California, and nighttime measurements from the JPL stratospheric ozone DIAL located at Mauna Loa Observatory, Hawai'i.

Assessment of uncertainties in radiation-induced cancer risk predictions at clinically relevant doses

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

Nguyen, J.; Moteabbed, M.; Paganetti, H., E-mail: hpaganetti@mgh.harvard.edu

2015-01-15

Purpose: Theoretical dose–response models offer the possibility to assess second cancer induction risks after external beam therapy. The parameters used in these models are determined with limited data from epidemiological studies. Risk estimations are thus associated with considerable uncertainties. This study aims at illustrating uncertainties when predicting the risk for organ-specific second cancers in the primary radiation field illustrated by choosing selected treatment plans for brain cancer patients. Methods: A widely used risk model was considered in this study. The uncertainties of the model parameters were estimated with reported data of second cancer incidences for various organs. Standard error propagationmore » was then subsequently applied to assess the uncertainty in the risk model. Next, second cancer risks of five pediatric patients treated for cancer in the head and neck regions were calculated. For each case, treatment plans for proton and photon therapy were designed to estimate the uncertainties (a) in the lifetime attributable risk (LAR) for a given treatment modality and (b) when comparing risks of two different treatment modalities. Results: Uncertainties in excess of 100% of the risk were found for almost all organs considered. When applied to treatment plans, the calculated LAR values have uncertainties of the same magnitude. A comparison between cancer risks of different treatment modalities, however, does allow statistically significant conclusions. In the studied cases, the patient averaged LAR ratio of proton and photon treatments was 0.35, 0.56, and 0.59 for brain carcinoma, brain sarcoma, and bone sarcoma, respectively. Their corresponding uncertainties were estimated to be potentially below 5%, depending on uncertainties in dosimetry. Conclusions: The uncertainty in the dose–response curve in cancer risk models makes it currently impractical to predict the risk for an individual external beam treatment. On the other hand, the ratio of absolute risks between two modalities is less sensitive to the uncertainties in the risk model and can provide statistically significant estimates.« less

A dosimetric uncertainty analysis for photon-emitting brachytherapy sources: Report of AAPM Task Group No. 138 and GEC-ESTRO

PubMed Central

DeWerd, Larry A.; Ibbott, Geoffrey S.; Meigooni, Ali S.; Mitch, Michael G.; Rivard, Mark J.; Stump, Kurt E.; Thomadsen, Bruce R.; Venselaar, Jack L. M.

2011-01-01

This report addresses uncertainties pertaining to brachytherapy single-source dosimetry preceding clinical use. The International Organization for Standardization (ISO) Guide to the Expression of Uncertainty in Measurement (GUM) and the National Institute of Standards and Technology (NIST) Technical Note 1297 are taken as reference standards for uncertainty formalism. Uncertainties in using detectors to measure or utilizing Monte Carlo methods to estimate brachytherapy dose distributions are provided with discussion of the components intrinsic to the overall dosimetric assessment. Uncertainties provided are based on published observations and cited when available. The uncertainty propagation from the primary calibration standard through transfer to the clinic for air-kerma strength is covered first. Uncertainties in each of the brachytherapy dosimetry parameters of the TG-43 formalism are then explored, ending with transfer to the clinic and recommended approaches. Dosimetric uncertainties during treatment delivery are considered briefly but are not included in the detailed analysis. For low- and high-energy brachytherapy sources of low dose rate and high dose rate, a combined dosimetric uncertainty <5% (k=1) is estimated, which is consistent with prior literature estimates. Recommendations are provided for clinical medical physicists, dosimetry investigators, and source and treatment planning system manufacturers. These recommendations include the use of the GUM and NIST reports, a requirement of constancy of manufacturer source design, dosimetry investigator guidelines, provision of the lowest uncertainty for patient treatment dosimetry, and the establishment of an action level based on dosimetric uncertainty. These recommendations reflect the guidance of the American Association of Physicists in Medicine (AAPM) and the Groupe Européen de Curiethérapie–European Society for Therapeutic Radiology and Oncology (GEC-ESTRO) for their members and may also be used as guidance to manufacturers and regulatory agencies in developing good manufacturing practices for sources used in routine clinical treatments. PMID:21452716

A dosimetric uncertainty analysis for photon-emitting brachytherapy sources: report of AAPM Task Group No. 138 and GEC-ESTRO.

PubMed

DeWerd, Larry A; Ibbott, Geoffrey S; Meigooni, Ali S; Mitch, Michael G; Rivard, Mark J; Stump, Kurt E; Thomadsen, Bruce R; Venselaar, Jack L M

2011-02-01

This report addresses uncertainties pertaining to brachytherapy single-source dosimetry preceding clinical use. The International Organization for Standardization (ISO) Guide to the Expression of Uncertainty in Measurement (GUM) and the National Institute of Standards and Technology (NIST) Technical Note 1297 are taken as reference standards for uncertainty formalism. Uncertainties in using detectors to measure or utilizing Monte Carlo methods to estimate brachytherapy dose distributions are provided with discussion of the components intrinsic to the overall dosimetric assessment. Uncertainties provided are based on published observations and cited when available. The uncertainty propagation from the primary calibration standard through transfer to the clinic for air-kerma strength is covered first. Uncertainties in each of the brachytherapy dosimetry parameters of the TG-43 formalism are then explored, ending with transfer to the clinic and recommended approaches. Dosimetric uncertainties during treatment delivery are considered briefly but are not included in the detailed analysis. For low- and high-energy brachytherapy sources of low dose rate and high dose rate, a combined dosimetric uncertainty <5% (k=1) is estimated, which is consistent with prior literature estimates. Recommendations are provided for clinical medical physicists, dosimetry investigators, and source and treatment planning system manufacturers. These recommendations include the use of the GUM and NIST reports, a requirement of constancy of manufacturer source design, dosimetry investigator guidelines, provision of the lowest uncertainty for patient treatment dosimetry, and the establishment of an action level based on dosimetric uncertainty. These recommendations reflect the guidance of the American Association of Physicists in Medicine (AAPM) and the Groupe Européen de Curiethérapie-European Society for Therapeutic Radiology and Oncology (GEC-ESTRO) for their members and may also be used as guidance to manufacturers and regulatory agencies in developing good manufacturing practices for sources used in routine clinical treatments.

Water supply infrastructure planning under multiple uncertainties: A differentiated approach

NASA Astrophysics Data System (ADS)

Fletcher, S.; Strzepek, K.

2017-12-01

Many water planners face increased pressure on water supply systems from increasing demands from population and economic growth in combination with uncertain water supply. Supply uncertainty arises from short-term climate variability and long-term climate change as well as uncertainty in groundwater availability. Social and economic uncertainties - such as sectoral competition for water, food and energy security, urbanization, and environmental protection - compound physical uncertainty. Further, the varying risk aversion of stakeholders and water managers makes it difficult to assess the necessity of expensive infrastructure investments to reduce risk. We categorize these uncertainties on two dimensions: whether they can be updated over time by collecting additional information, and whether the uncertainties can be described probabilistically or are "deep" uncertainties whose likelihood is unknown. Based on this, we apply a decision framework that combines simulation for probabilistic uncertainty, scenario analysis for deep uncertainty, and multi-stage decision analysis for uncertainties that are reduced over time with additional information. In light of these uncertainties and the investment costs of large infrastructure, we propose the assessment of staged, modular infrastructure and information updating as a hedge against risk. We apply this framework to cases in Melbourne, Australia and Riyadh, Saudi Arabia. Melbourne is a surface water system facing uncertain population growth and variable rainfall and runoff. A severe drought from 1997 to 2009 prompted investment in a 150 MCM/y reverse osmosis desalination plan with a capital cost of 3.5 billion. Our analysis shows that flexible design in which a smaller portion of capacity is developed initially with the option to add modular capacity in the future can mitigate uncertainty and reduce the expected lifetime costs by up to 1 billion. In Riyadh, urban water use relies on fossil groundwater aquifers and desalination. Intense withdrawals for urban and agricultural use will lead to lowering of the water table in the aquifer at rapid but uncertain rates due to poor groundwater characterization. We assess the potential for additional groundwater data collection and a flexible infrastructure approach similar to that in Melbourne to mitigate risk.

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.

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.

Review of clinical brachytherapy uncertainties: Analysis guidelines of GEC-ESTRO and the AAPM☆

PubMed Central

Kirisits, Christian; Rivard, Mark J.; Baltas, Dimos; Ballester, Facundo; De Brabandere, Marisol; van der Laarse, Rob; Niatsetski, Yury; Papagiannis, Panagiotis; Hellebust, Taran Paulsen; Perez-Calatayud, Jose; Tanderup, Kari; Venselaar, Jack L.M.; Siebert, Frank-André

2014-01-01

Background and purpose A substantial reduction of uncertainties in clinical brachytherapy should result in improved outcome in terms of increased local control and reduced side effects. Types of uncertainties have to be identified, grouped, and quantified. Methods A detailed literature review was performed to identify uncertainty components and their relative importance to the combined overall uncertainty. Results Very few components (e.g., source strength and afterloader timer) are independent of clinical disease site and location of administered dose. While the influence of medium on dose calculation can be substantial for low energy sources or non-deeply seated implants, the influence of medium is of minor importance for high-energy sources in the pelvic region. The level of uncertainties due to target, organ, applicator, and/or source movement in relation to the geometry assumed for treatment planning is highly dependent on fractionation and the level of image guided adaptive treatment. Most studies to date report the results in a manner that allows no direct reproduction and further comparison with other studies. Often, no distinction is made between variations, uncertainties, and errors or mistakes. The literature review facilitated the drafting of recommendations for uniform uncertainty reporting in clinical BT, which are also provided. The recommended comprehensive uncertainty investigations are key to obtain a general impression of uncertainties, and may help to identify elements of the brachytherapy treatment process that need improvement in terms of diminishing their dosimetric uncertainties. It is recommended to present data on the analyzed parameters (distance shifts, volume changes, source or applicator position, etc.), and also their influence on absorbed dose for clinically-relevant dose parameters (e.g., target parameters such as D90 or OAR doses). Publications on brachytherapy should include a statement of total dose uncertainty for the entire treatment course, taking into account the fractionation schedule and level of image guidance for adaptation. Conclusions This report on brachytherapy clinical uncertainties represents a working project developed by the Brachytherapy Physics Quality Assurances System (BRAPHYQS) subcommittee to the Physics Committee within GEC-ESTRO. Further, this report has been reviewed and approved by the American Association of Physicists in Medicine. PMID:24299968

Uncertainty quantification of surface-water/groundwater exchange estimates in large wetland systems using Python

NASA Astrophysics Data System (ADS)

Hughes, J. D.; Metz, P. A.

2014-12-01

Most watershed studies include observation-based water budget analyses to develop first-order estimates of significant flow terms. Surface-water/groundwater (SWGW) exchange is typically assumed to be equal to the residual of the sum of inflows and outflows in a watershed. These estimates of SWGW exchange, however, are highly uncertain as a result of the propagation of uncertainty inherent in the calculation or processing of the other terms of the water budget, such as stage-area-volume relations, and uncertainties associated with land-cover based evapotranspiration (ET) rate estimates. Furthermore, the uncertainty of estimated SWGW exchanges can be magnified in large wetland systems that transition from dry to wet during wet periods. Although it is well understood that observation-based estimates of SWGW exchange are uncertain it is uncommon for the uncertainty of these estimates to be directly quantified. High-level programming languages like Python can greatly reduce the effort required to (1) quantify the uncertainty of estimated SWGW exchange in large wetland systems and (2) evaluate how different approaches for partitioning land-cover data in a watershed may affect the water-budget uncertainty. We have used Python with the Numpy, Scipy.stats, and pyDOE packages to implement an unconstrained Monte Carlo approach with Latin Hypercube sampling to quantify the uncertainty of monthly estimates of SWGW exchange in the Floral City watershed of the Tsala Apopka wetland system in west-central Florida, USA. Possible sources of uncertainty in the water budget analysis include rainfall, ET, canal discharge, and land/bathymetric surface elevations. Each of these input variables was assigned a probability distribution based on observation error or spanning the range of probable values. The Monte Carlo integration process exposes the uncertainties in land-cover based ET rate estimates as the dominant contributor to the uncertainty in SWGW exchange estimates. We will discuss the uncertainty of SWGW exchange estimates using an ET model that partitions the watershed into open water and wetland land-cover types. We will also discuss the uncertainty of SWGW exchange estimates calculated using ET models partitioned into additional land-cover types.

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.

Evaluation on uncertainty sources in projecting hydrological changes over the Xijiang River basin in South China

NASA Astrophysics Data System (ADS)

Yuan, Fei; Zhao, Chongxu; Jiang, Yong; Ren, Liliang; Shan, Hongcui; Zhang, Limin; Zhu, Yonghua; Chen, Tao; Jiang, Shanhu; Yang, Xiaoli; Shen, Hongren

2017-11-01

Projections of hydrological changes are associated with large uncertainties from different sources, which should be quantified for an effective implementation of water management policies adaptive to future climate change. In this study, a modeling chain framework to project future hydrological changes and the associated uncertainties in the Xijiang River basin, South China, was established. The framework consists of three emission scenarios (ESs), four climate models (CMs), four statistical downscaling (SD) methods, four hydrological modeling (HM) schemes, and four probability distributions (PDs) for extreme flow frequency analyses. Direct variance method was adopted to analyze the manner by which uncertainty sources such as ES, CM, SD, and HM affect the estimates of future evapotranspiration (ET) and streamflow, and to quantify the uncertainties of PDs in future flood and drought risk assessment. Results show that ES is one of the least important uncertainty sources in most situations. CM, in general, is the dominant uncertainty source for the projections of monthly ET and monthly streamflow during most of the annual cycle, daily streamflow below the 99.6% quantile level, and extreme low flow. SD is the most predominant uncertainty source in the projections of extreme high flow, and has a considerable percentage of uncertainty contribution in monthly streamflow projections in July-September. The effects of SD in other cases are negligible. HM is a non-ignorable uncertainty source that has the potential to produce much larger uncertainties for the projections of low flow and ET in warm and wet seasons than for the projections of high flow. PD contributes a larger percentage of uncertainty in extreme flood projections than it does in extreme low flow estimates. Despite the large uncertainties in hydrological projections, this work found that future extreme low flow would undergo a considerable reduction, and a noticeable increase in drought risk in the Xijiang River basin would be expected. Thus, the necessity of employing effective water-saving techniques and adaptive water resources management strategies for drought disaster mitigation should be addressed.

Gridded Uncertainty Maps of Fossil Fuel Carbon Dioxide Emissions: A New Data Product

NASA Astrophysics Data System (ADS)

Andres, R. J.; Boden, T.

2014-12-01

With the publication of a new assessment of the uncertainty associated with the mass of fossil fuel carbon dioxide (FFCO2) emissions (2014, Tellus B, 66, 23616, doi:10.3402/tellusb.v66.23616), it is now possible to extend that work with a gridded map of fossil fuel emission uncertainties. The new data product was created to be paired with the long-used, Carbon Dioxide Information Analysis Center (CDIAC), emission year 1751-present, one degree latitude by one degree longitude (1x1) mass of emissions data product (http://cdiac.ornl.gov/epubs/ndp/ndp058/ndp058_v2013.html). Now, for the first time, data users will have FFCO2 emission information that represents both mass and uncertainty, each of which varies in both time and space. The new data product was constructed by examining the individual uncertainties in each of the input data sets to the gridded mass maps and then combining these individual uncertainties into an overall uncertainty for the mass maps. The input data sets include a table of the mass of FFCO2 emissions by country and year, the one degree geographic map of emissions which includes changing borders on an annual time scale and ties the mass of emissions to location, and the one degree population proxy used to distribute the mass of emissions within each country. As the three input data sets are independent of each other, their combination for the overall uncertainty is accomplished by a simple square root of the sum of the squares procedure. The resulting uncertainty data product is gridded at 1x1 and exactly overlays the 1x1 mass emission maps. The default temporal resolution is annual, but a companion product is also available at monthly time scales. The monthly uncertainty product uses the same input data sets, but the mass uncertainty is scaled as described in the monthly mass product description paper (2011, Tellus B, 63:309-327, doi: 10.1111/j.1600-0889.2011.00530.x). The gridded uncertainty maps cover emission year 1950 to 2010. The start year is determined by the mass uncertainty study which began its analysis in 1950. The end year reflects the latest emission year in the current CDIAC data set; as new years are added to the CDIAC data set, the uncertainty map time series can also be extended.

Communicating spatial uncertainty to non-experts using R

NASA Astrophysics Data System (ADS)

Luzzi, Damiano; Sawicka, Kasia; Heuvelink, Gerard; de Bruin, Sytze

2016-04-01

Effective visualisation methods are important for the efficient use of uncertainty information for various groups of users. Uncertainty propagation analysis is often used with spatial environmental models to quantify the uncertainty within the information. A challenge arises when trying to effectively communicate the uncertainty information to non-experts (not statisticians) in a wide range of cases. Due to the growing popularity and applicability of the open source programming language R, we undertook a project to develop an R package that facilitates uncertainty propagation analysis in spatial environmental modelling. The package has implemented Monte Carlo algorithms for uncertainty propagation, the output of which is represented by an ensemble of model outputs (i.e. a sample from a probability distribution). Numerous visualisation methods exist that aim to present such spatial uncertainty information both statically, dynamically and interactively. To provide the most universal visualisation tools for non-experts, we conducted a survey on a group of 20 university students and assessed the effectiveness of selected static and interactive methods for visualising uncertainty in spatial variables such as DEM and land cover. The static methods included adjacent maps and glyphs for continuous variables. Both allow for displaying maps with information about the ensemble mean, variance/standard deviation and prediction intervals. Adjacent maps were also used for categorical data, displaying maps of the most probable class, as well as its associated probability. The interactive methods included a graphical user interface, which in addition to displaying the previously mentioned variables also allowed for comparison of joint uncertainties at multiple locations. The survey indicated that users could understand the basics of the uncertainty information displayed in the static maps, with the interactive interface allowing for more in-depth information. Subsequently, the R package included a collation of the plotting functions that were evaluated in the survey. The implementation of static visualisations was done via calls to the 'ggplot2' package. This allowed the user to provide control over the content, legend, colours, axes and titles. The interactive methods were implemented using the 'shiny' package allowing users to activate the visualisation of statistical descriptions of uncertainty through interaction with a plotted map of means. This research brings uncertainty visualisation to a broader audience through the development of tools for visualising uncertainty using open source software.

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.

Multi-model approach to assess the impact of climate change on runoff

NASA Astrophysics Data System (ADS)

Dams, J.; Nossent, J.; Senbeta, T. B.; Willems, P.; Batelaan, O.

2015-10-01

The assessment of climate change impacts on hydrology is subject to uncertainties related to the climate change scenarios, stochastic uncertainties of the hydrological model and structural uncertainties of the hydrological model. This paper focuses on the contribution of structural uncertainty of hydrological models to the overall uncertainty of the climate change impact assessment. To quantify the structural uncertainty of hydrological models, four physically based hydrological models (SWAT, PRMS and a semi- and fully distributed version of the WetSpa model) are set up for a catchment in Belgium. Each model is calibrated using four different objective functions. Three climate change scenarios with a high, mean and low hydrological impact are statistically perturbed from a large ensemble of climate change scenarios and are used to force the hydrological models. This methodology allows assessing and comparing the uncertainty introduced by the climate change scenarios with the uncertainty introduced by the hydrological model structure. Results show that the hydrological model structure introduces a large uncertainty on both the average monthly discharge and the extreme peak and low flow predictions under the climate change scenarios. For the low impact climate change scenario, the uncertainty range of the mean monthly runoff is comparable to the range of these runoff values in the reference period. However, for the mean and high impact scenarios, this range is significantly larger. The uncertainty introduced by the climate change scenarios is larger than the uncertainty due to the hydrological model structure for the low and mean hydrological impact scenarios, but the reverse is true for the high impact climate change scenario. The mean and high impact scenarios project increasing peak discharges, while the low impact scenario projects increasing peak discharges only for peak events with return periods larger than 1.6 years. All models suggest for all scenarios a decrease of the lowest flows, except for the SWAT model with the mean hydrological impact climate change scenario. The results of this study indicate that besides the uncertainty introduced by the climate change scenarios also the hydrological model structure uncertainty should be taken into account in the assessment of climate change impacts on hydrology. To make it more straightforward and transparent to include model structural uncertainty in hydrological impact studies, there is a need for hydrological modelling tools that allow flexible structures and methods to validate model structures in their ability to assess impacts under unobserved future climatic conditions.

Assessment and visualization of uncertainty for countrywide soil organic matter map of Hungary using local entropy

NASA Astrophysics Data System (ADS)

Szatmári, Gábor; Pásztor, László

2016-04-01

Uncertainty is a general term expressing our imperfect knowledge in describing an environmental process and we are aware of it (Bárdossy and Fodor, 2004). Sampling, laboratory measurements, models and so on are subject to uncertainty. Effective quantification and visualization of uncertainty would be indispensable to stakeholders (e.g. policy makers, society). Soil related features and their spatial models should be stressfully targeted to uncertainty assessment because their inferences are further used in modelling and decision making process. The aim of our present study was to assess and effectively visualize the local uncertainty of the countrywide soil organic matter (SOM) spatial distribution model of Hungary using geostatistical tools and concepts. The Hungarian Soil Information and Monitoring System's SOM data (approximately 1,200 observations) and environmental related, spatially exhaustive secondary information (i.e. digital elevation model, climatic maps, MODIS satellite images and geological map) were used to model the countrywide SOM spatial distribution by regression kriging. It would be common to use the calculated estimation (or kriging) variance as a measure of uncertainty, however the normality and homoscedasticity hypotheses have to be refused according to our preliminary analysis on the data. Therefore, a normal score transformation and a sequential stochastic simulation approach was introduced to be able to model and assess the local uncertainty. Five hundred equally probable realizations (i.e. stochastic images) were generated. The number of the stochastic images is fairly enough to provide a model of uncertainty at each location, which is a complete description of uncertainty in geostatistics (Deutsch and Journel, 1998). Furthermore, these models can be applied e.g. to contour the probability of any events, which can be regarded as goal oriented digital soil maps and are of interest for agricultural management and decision making as well. A standardized measure of the local entropy was used to visualize uncertainty, where entropy values close to 1 correspond to high uncertainty, whilst values close to 0 correspond low uncertainty. The advantage of the usage of local entropy in this context is that it combines probabilities from multiple members into a single number for each location of the model. In conclusion, it is straightforward to use a sequential stochastic simulation approach to the assessment of uncertainty, when normality and homoscedasticity are violated. The visualization of uncertainty using the local entropy is effective and communicative to stakeholders because it represents the uncertainty through a single number within a [0, 1] scale. References: Bárdossy, Gy. & Fodor, J., 2004. Evaluation of Uncertainties and Risks in Geology. Springer-Verlag, Berlin Heidelberg. Deutsch, C.V. & Journel, A.G., 1998. GSLIB: geostatistical software library and user's guide. Oxford University Press, New York. Acknowledgement: Our work was supported by the Hungarian National Scientific Research Foundation (OTKA, Grant No. K105167).

Uncertainty of a hydrological climate change impact assessment - Is it really all about climate uncertainty?

NASA Astrophysics Data System (ADS)

Honti, Mark; Reichert, Peter; Scheidegger, Andreas; Stamm, Christian

2013-04-01

Climate change impact assessments have become more and more popular in hydrology since the middle 1980's with another boost after the publication of the IPCC AR4 report. During hundreds of impact studies a quasi-standard methodology emerged, which is mainly shaped by the growing public demand for predicting how water resources management or flood protection should change in the close future. The ``standard'' workflow considers future climate under a specific IPCC emission scenario simulated by global circulation models (GCMs), possibly downscaled by a regional climate model (RCM) and/or a stochastic weather generator. The output from the climate models is typically corrected for bias before feeding it into a calibrated hydrological model, which is run on the past and future meteorological data to analyse the impacts of climate change on the hydrological indicators of interest. The impact predictions are as uncertain as any forecast that tries to describe the behaviour of an extremely complex system decades into the future. Future climate predictions are uncertain due to the scenario uncertainty and the GCM model uncertainty that is obvious on finer resolution than continental scale. Like in any hierarchical model system, uncertainty propagates through the descendant components. Downscaling increases uncertainty with the deficiencies of RCMs and/or weather generators. Bias correction adds a strong deterministic shift to the input data. Finally the predictive uncertainty of the hydrological model ends the cascade that leads to the total uncertainty of the hydrological impact assessment. There is an emerging consensus between many studies on the relative importance of the different uncertainty sources. The prevailing perception is that GCM uncertainty dominates hydrological impact studies. There are only few studies, which found that the predictive uncertainty of hydrological models can be in the same range or even larger than climatic uncertainty. We carried out a climate change impact assessment and estimated the relative importance of the uncertainty sources. The study was performed on 2 small catchments in the Swiss Plateau with a lumped conceptual rainfall runoff model. In the climatic part we applied the standard ensemble approach to quantify uncertainty but in hydrology we used formal Bayesian uncertainty assessment method with 2 different likelihood functions. One was a time-series error model that was able to deal with the complicated statistical properties of hydrological model residuals. The second was a likelihood function for the flow quantiles directly. Due to the better data coverage and smaller hydrological complexity in one of our test catchments we had better performance from the hydrological model and thus could observe that the relative importance of different uncertainty sources varied between sites, boundary conditions and flow indicators. The uncertainty of future climate was important, but not dominant. The deficiencies of the hydrological model were on the same scale, especially for the sites and flow components where model performance for the past observations was further from optimal (Nash-Sutcliffe index = 0.5 - 0.7). The overall uncertainty of predictions was well beyond the expected change signal even for the best performing site and flow indicator.

Management of California Oak Woodlands: Uncertainties and Modeling

Treesearch

Jay E. Noel; Richard P. Thompson

1995-01-01

A mathematical policy model of oak woodlands is presented. The model illustrates the policy uncertainties that exist in the management of oak woodlands. These uncertainties include: (1) selection of a policy criterion function, (2) woodland dynamics, (3) initial and final state of the woodland stock. The paper provides a review of each of the uncertainty issues. The...

Modeling Radioactive Decay Chains with Branching Fraction Uncertainties

DTIC Science & Technology

2013-03-01

moments methods with transmutation matrices. Uncertainty from both half-lives and branching fractions is carried through these calculations by Monte...moment methods, method for sampling from normal distributions for half- life uncertainty, and use of transmutation matrices were leveraged. This...distributions for half-life and branching fraction uncertainties, building decay chains and generating the transmutation matrix (T-matrix

Evaluation of Sources of Uncertainties in Solar Resource Measurement

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

Habte, Aron M; Sengupta, Manajit

This poster presents a high-level overview of sources of uncertainties in solar resource measurement, demonstrating the impact of various sources of uncertainties -- such as cosine response, thermal offset, spectral response, and others -- on the accuracy of data from several radiometers. The study provides insight on how to reduce the impact of some of the sources of uncertainties.

Encouraging Uncertainty in the "Scientific Method": Promoting Understanding in the Processes of Science with Preservice Teachers

ERIC Educational Resources Information Center

Melville, Wayne; Bartley, Anthony; Fazio, Xavier

2012-01-01

Teachers' feelings of uncertainty are an overlooked, though crucial, condition necessary for the promotion of educational change. This article investigates the feelings of uncertainty that preservice teachers have toward the conduct of science as inquiry and the extent to which methods courses can confront and embrace those uncertainties. Our work…

Verification of the Uncertainty Principle by Using Diffraction of Light Waves

ERIC Educational Resources Information Center

Nikolic, D.; Nesic, Lj

2011-01-01

We described a simple idea for experimental verification of the uncertainty principle for light waves. We used a single-slit diffraction of a laser beam for measuring the angular width of zero-order diffraction maximum and obtained the corresponding wave number uncertainty. We will assume that the uncertainty in position is the slit width. For the…

Robust stability of fractional order polynomials with complicated uncertainty structure

PubMed Central

Şenol, Bilal; Pekař, Libor

2017-01-01

The main aim of this article is to present a graphical approach to robust stability analysis for families of fractional order (quasi-)polynomials with complicated uncertainty structure. More specifically, the work emphasizes the multilinear, polynomial and general structures of uncertainty and, moreover, the retarded quasi-polynomials with parametric uncertainty are studied. Since the families with these complex uncertainty structures suffer from the lack of analytical tools, their robust stability is investigated by numerical calculation and depiction of the value sets and subsequent application of the zero exclusion condition. PMID:28662173

Uncertainty relation for non-Hamiltonian quantum systems

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

Tarasov, Vasily E.

2013-01-15

General forms of uncertainty relations for quantum observables of non-Hamiltonian quantum systems are considered. Special cases of uncertainty relations are discussed. The uncertainty relations for non-Hamiltonian quantum systems are considered in the Schroedinger-Robertson form since it allows us to take into account Lie-Jordan algebra of quantum observables. In uncertainty relations, the time dependence of quantum observables and the properties of this dependence are discussed. We take into account that a time evolution of observables of a non-Hamiltonian quantum system is not an endomorphism with respect to Lie, Jordan, and associative multiplications.

Process for estimating likelihood and confidence in post detonation nuclear forensics.

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

Darby, John L.; Craft, Charles M.

2014-07-01

Technical nuclear forensics (TNF) must provide answers to questions of concern to the broader community, including an estimate of uncertainty. There is significant uncertainty associated with post-detonation TNF. The uncertainty consists of a great deal of epistemic (state of knowledge) as well as aleatory (random) uncertainty, and many of the variables of interest are linguistic (words) and not numeric. We provide a process by which TNF experts can structure their process for answering questions and provide an estimate of uncertainty. The process uses belief and plausibility, fuzzy sets, and approximate reasoning.

Uncertainty Analysis of Consequence Management (CM) Data Products.

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

Hunt, Brian D.; Eckert-Gallup, Aubrey Celia; Cochran, Lainy Dromgoole

The goal of this project is to develop and execute methods for characterizing uncertainty in data products that are deve loped and distributed by the DOE Consequence Management (CM) Program. A global approach to this problem is necessary because multiple sources of error and uncertainty from across the CM skill sets contribute to the ultimate p roduction of CM data products. This report presents the methods used to develop a probabilistic framework to characterize this uncertainty and provides results for an uncertainty analysis for a study scenario analyzed using this framework.

Parametric robust control and system identification: Unified approach

NASA Technical Reports Server (NTRS)

Keel, Leehyun

1994-01-01

Despite significant advancement in the area of robust parametric control, the problem of synthesizing such a controller is still a wide open problem. Thus, we attempt to give a solution to this important problem. Our approach captures the parametric uncertainty as an H(sub infinity) unstructured uncertainty so that H(sub infinity) synthesis techniques are applicable. Although the techniques cannot cope with the exact parametric uncertainty, they give a reasonable guideline to model the unstructured uncertainty that contains the parametric uncertainty. An additional loop shaping technique is also introduced to relax its conservatism.

Physical Uncertainty Bounds (PUB)

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

Vaughan, Diane Elizabeth; Preston, Dean L.

2015-03-19

This paper introduces and motivates the need for a new methodology for determining upper bounds on the uncertainties in simulations of engineered systems due to limited fidelity in the composite continuum-level physics models needed to simulate the systems. We show that traditional uncertainty quantification methods provide, at best, a lower bound on this uncertainty. We propose to obtain bounds on the simulation uncertainties by first determining bounds on the physical quantities or processes relevant to system performance. By bounding these physics processes, as opposed to carrying out statistical analyses of the parameter sets of specific physics models or simply switchingmore » out the available physics models, one can obtain upper bounds on the uncertainties in simulated quantities of interest.« less

Impact of Nuclear Data Uncertainties on Advanced Fuel Cycles and their Irradiated Fuel - a Comparison between Libraries

NASA Astrophysics Data System (ADS)

Díez, C. J.; Cabellos, O.; Martínez, J. S.

2014-04-01

The uncertainties on the isotopic composition throughout the burnup due to the nuclear data uncertainties are analysed. The different sources of uncertainties: decay data, fission yield and cross sections; are propagated individually, and their effect assessed. Two applications are studied: EFIT (an ADS-like reactor) and ESFR (Sodium Fast Reactor). The impact of the uncertainties on cross sections provided by the EAF-2010, SCALE6.1 and COMMARA-2.0 libraries are compared. These Uncertainty Quantification (UQ) studies have been carried out with a Monte Carlo sampling approach implemented in the depletion/activation code ACAB. Such implementation has been improved to overcome depletion/activation problems with variations of the neutron spectrum.

Uncertainty evaluation in normalization of isotope delta measurement results against international reference materials.

PubMed

Meija, Juris; Chartrand, Michelle M G

2018-01-01

Isotope delta measurements are normalized against international reference standards. Although multi-point normalization is becoming a standard practice, the existing uncertainty evaluation practices are either undocumented or are incomplete. For multi-point normalization, we present errors-in-variables regression models for explicit accounting of the measurement uncertainty of the international standards along with the uncertainty that is attributed to their assigned values. This manuscript presents framework to account for the uncertainty that arises due to a small number of replicate measurements and discusses multi-laboratory data reduction while accounting for inevitable correlations between the laboratories due to the use of identical reference materials for calibration. Both frequentist and Bayesian methods of uncertainty analysis are discussed.

Seniors' uncertainty management of direct-to-consumer prescription drug advertising usefulness.

PubMed

DeLorme, Denise E; Huh, Jisu

2009-09-01

This study provides insight into seniors' perceptions of and responses to direct-to-consumer prescription drug advertising (DTCA) usefulness, examines support for DTCA regulation as a type of uncertainty management, and extends and gives empirical voice to previous survey results through methodological triangulation. In-depth interview findings revealed that, for most informants, DTCA usefulness was uncertain and this uncertainty stemmed from 4 sources. The majority had negative responses to DTCA uncertainty and relied on 2 uncertainty-management strategies: information seeking from physicians, and inferences of and support for some government regulation of DTCA. Overall, the findings demonstrate the viability of uncertainty management theory (Brashers, 2001, 2007) for mass-mediated health communication, specifically DTCA. The article concludes with practical implications and research recommendations.

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

Uncertainty forecasts improve weather-related decisions and attenuate the effects of forecast error.

PubMed

Joslyn, Susan L; LeClerc, Jared E

2012-03-01

Although uncertainty is inherent in weather forecasts, explicit numeric uncertainty estimates are rarely included in public forecasts for fear that they will be misunderstood. Of particular concern are situations in which precautionary action is required at low probabilities, often the case with severe events. At present, a categorical weather warning system is used. The work reported here tested the relative benefits of several forecast formats, comparing decisions made with and without uncertainty forecasts. In three experiments, participants assumed the role of a manager of a road maintenance company in charge of deciding whether to pay to salt the roads and avoid a potential penalty associated with icy conditions. Participants used overnight low temperature forecasts accompanied in some conditions by uncertainty estimates and in others by decision advice comparable to categorical warnings. Results suggested that uncertainty information improved decision quality overall and increased trust in the forecast. Participants with uncertainty forecasts took appropriate precautionary action and withheld unnecessary action more often than did participants using deterministic forecasts. When error in the forecast increased, participants with conventional forecasts were reluctant to act. However, this effect was attenuated by uncertainty forecasts. Providing categorical decision advice alone did not improve decisions. However, combining decision advice with uncertainty estimates resulted in the best performance overall. The results reported here have important implications for the development of forecast formats to increase compliance with severe weather warnings as well as other domains in which one must act in the face of uncertainty. PsycINFO Database Record (c) 2012 APA, all rights reserved.

A Probabilistic Framework for Quantifying Mixed Uncertainties in Cyber Attacker Payoffs

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

Chatterjee, Samrat; Tipireddy, Ramakrishna; Oster, Matthew R.

Quantification and propagation of uncertainties in cyber attacker payoffs is a key aspect within multiplayer, stochastic security games. These payoffs may represent penalties or rewards associated with player actions and are subject to various sources of uncertainty, including: (1) cyber-system state, (2) attacker type, (3) choice of player actions, and (4) cyber-system state transitions over time. Past research has primarily focused on representing defender beliefs about attacker payoffs as point utility estimates. More recently, within the physical security domain, attacker payoff uncertainties have been represented as Uniform and Gaussian probability distributions, and mathematical intervals. For cyber-systems, probability distributions may helpmore » address statistical (aleatory) uncertainties where the defender may assume inherent variability or randomness in the factors contributing to the attacker payoffs. However, systematic (epistemic) uncertainties may exist, where the defender may not have sufficient knowledge or there is insufficient information about the attacker’s payoff generation mechanism. Such epistemic uncertainties are more suitably represented as generalizations of probability boxes. This paper explores the mathematical treatment of such mixed payoff uncertainties. A conditional probabilistic reasoning approach is adopted to organize the dependencies between a cyber-system’s state, attacker type, player actions, and state transitions. This also enables the application of probabilistic theories to propagate various uncertainties in the attacker payoffs. An example implementation of this probabilistic framework and resulting attacker payoff distributions are discussed. A goal of this paper is also to highlight this uncertainty quantification problem space to the cyber security research community and encourage further advancements in this area.« less

Uncertainty and stress: Why it causes diseases and how it is mastered by the brain.

PubMed

Peters, Achim; McEwen, Bruce S; Friston, Karl

2017-09-01

The term 'stress' - coined in 1936 - has many definitions, but until now has lacked a theoretical foundation. Here we present an information-theoretic approach - based on the 'free energy principle' - defining the essence of stress; namely, uncertainty. We address three questions: What is uncertainty? What does it do to us? What are our resources to master it? Mathematically speaking, uncertainty is entropy or 'expected surprise'. The 'free energy principle' rests upon the fact that self-organizing biological agents resist a tendency to disorder and must therefore minimize the entropy of their sensory states. Applied to our everyday life, this means that we feel uncertain, when we anticipate that outcomes will turn out to be something other than expected - and that we are unable to avoid surprise. As all cognitive systems strive to reduce their uncertainty about future outcomes, they face a critical constraint: Reducing uncertainty requires cerebral energy. The characteristic of the vertebrate brain to prioritize its own high energy is captured by the notion of the 'selfish brain'. Accordingly, in times of uncertainty, the selfish brain demands extra energy from the body. If, despite all this, the brain cannot reduce uncertainty, a persistent cerebral energy crisis may develop, burdening the individual by 'allostatic load' that contributes to systemic and brain malfunction (impaired memory, atherogenesis, diabetes and subsequent cardio- and cerebrovascular events). Based on the basic tenet that stress originates from uncertainty, we discuss the strategies our brain uses to avoid surprise and thereby resolve uncertainty. Copyright © 2017 The Authors. Published by Elsevier Ltd.. All rights reserved.

Understanding differences in electronic health record (EHR) use: linking individual physicians' perceptions of uncertainty and EHR use patterns in ambulatory care.

PubMed

Lanham, Holly Jordan; Sittig, Dean F; Leykum, Luci K; Parchman, Michael L; Pugh, Jacqueline A; McDaniel, Reuben R

2014-01-01

Electronic health records (EHR) hold great promise for managing patient information in ways that improve healthcare delivery. Physicians differ, however, in their use of this health information technology (IT), and these differences are not well understood. The authors study the differences in individual physicians' EHR use patterns and identify perceptions of uncertainty as an important new variable in understanding EHR use. Qualitative study using semi-structured interviews and direct observation of physicians (n=28) working in a multispecialty outpatient care organization. We identified physicians' perceptions of uncertainty as an important variable in understanding differences in EHR use patterns. Drawing on theories from the medical and organizational literatures, we identified three categories of perceptions of uncertainty: reduction, absorption, and hybrid. We used an existing model of EHR use to categorize physician EHR use patterns as high, medium, and low based on degree of feature use, level of EHR-enabled communication, and frequency that EHR use patterns change. Physicians' perceptions of uncertainty were distinctly associated with their EHR use patterns. Uncertainty reductionists tended to exhibit high levels of EHR use, uncertainty absorbers tended to exhibit low levels of EHR use, and physicians demonstrating both perspectives of uncertainty (hybrids) tended to exhibit medium levels of EHR use. We find evidence linking physicians' perceptions of uncertainty with EHR use patterns. Study findings have implications for health IT research, practice, and policy, particularly in terms of impacting health IT design and implementation efforts in ways that consider differences in physicians' perceptions of uncertainty.

Assessment of spectral, misregistration, and spatial uncertainties inherent in the cross-calibration study

USGS Publications Warehouse

Chander, G.; Helder, D.L.; Aaron, David; Mishra, N.; Shrestha, A.K.

2013-01-01

Cross-calibration of satellite sensors permits the quantitative comparison of measurements obtained from different Earth Observing (EO) systems. Cross-calibration studies usually use simultaneous or near-simultaneous observations from several spaceborne sensors to develop band-by-band relationships through regression analysis. The investigation described in this paper focuses on evaluation of the uncertainties inherent in the cross-calibration process, including contributions due to different spectral responses, spectral resolution, spectral filter shift, geometric misregistrations, and spatial resolutions. The hyperspectral data from the Environmental Satellite SCanning Imaging Absorption SpectroMeter for Atmospheric CartograpHY and the EO-1 Hyperion, along with the relative spectral responses (RSRs) from the Landsat 7 Enhanced Thematic Mapper (TM) Plus and the Terra Moderate Resolution Imaging Spectroradiometer sensors, were used for the spectral uncertainty study. The data from Landsat 5 TM over five representative land cover types (desert, rangeland, grassland, deciduous forest, and coniferous forest) were used for the geometric misregistrations and spatial-resolution study. The spectral resolution uncertainty was found to be within 0.25%, spectral filter shift within 2.5%, geometric misregistrations within 0.35%, and spatial-resolution effects within 0.1% for the Libya 4 site. The one-sigma uncertainties presented in this paper are uncorrelated, and therefore, the uncertainties can be summed orthogonally. Furthermore, an overall total uncertainty was developed. In general, the results suggested that the spectral uncertainty is more dominant compared to other uncertainties presented in this paper. Therefore, the effect of the sensor RSR differences needs to be quantified and compensated to avoid large uncertainties in cross-calibration results.

Risk analysis under uncertainty, the precautionary principle, and the new EU chemicals strategy.

PubMed

Rogers, Michael D

2003-06-01

Three categories of uncertainty in relation to risk assessment are defined; uncertainty in effect, uncertainty in cause, and uncertainty in the relationship between a hypothesised cause and effect. The Precautionary Principle (PP) relates to the third type of uncertainty. Three broad descriptions of the PP are set out, uncertainty justifies action, uncertainty requires action, and uncertainty requires a reversal of the burden of proof for risk assessments. The application of the PP is controversial but what matters in practise is the precautionary action (PA) that follows. The criteria by which the PAs should be judged are detailed. This framework for risk assessment and management under uncertainty is then applied to the envisaged European system for the regulation of chemicals. A new EU regulatory system has been proposed which shifts the burden of proof concerning risk assessments from the regulator to the producer, and embodies the PP in all three of its main regulatory stages. The proposals are critically discussed in relation to three chemicals, namely, atrazine (an endocrine disrupter), cadmium (toxic and possibly carcinogenic), and hydrogen fluoride (a toxic, high-production-volume chemical). Reversing the burden of proof will speed up the regulatory process but the examples demonstrate that applying the PP appropriately, and balancing the countervailing risks and the socio-economic benefits, will continue to be a difficult task for the regulator. The paper concludes with a discussion of the role of precaution in the management of change and of the importance of trust in the effective regulation of uncertain risks.

Uncertainty Quantification of CFD Data Generated for a Model Scramjet Isolator Flowfield

NASA Technical Reports Server (NTRS)

Baurle, R. A.; Axdahl, E. L.

2017-01-01

Computational fluid dynamics is now considered to be an indispensable tool for the design and development of scramjet engine components. Unfortunately, the quantification of uncertainties is rarely addressed with anything other than sensitivity studies, so the degree of confidence associated with the numerical results remains exclusively with the subject matter expert that generated them. This practice must be replaced with a formal uncertainty quantification process for computational fluid dynamics to play an expanded role in the system design, development, and flight certification process. Given the limitations of current hypersonic ground test facilities, this expanded role is believed to be a requirement by some in the hypersonics community if scramjet engines are to be given serious consideration as a viable propulsion system. The present effort describes a simple, relatively low cost, nonintrusive approach to uncertainty quantification that includes the basic ingredients required to handle both aleatoric (random) and epistemic (lack of knowledge) sources of uncertainty. The nonintrusive nature of the approach allows the computational fluid dynamicist to perform the uncertainty quantification with the flow solver treated as a "black box". Moreover, a large fraction of the process can be automated, allowing the uncertainty assessment to be readily adapted into the engineering design and development workflow. In the present work, the approach is applied to a model scramjet isolator problem where the desire is to validate turbulence closure models in the presence of uncertainty. In this context, the relevant uncertainty sources are determined and accounted for to allow the analyst to delineate turbulence model-form errors from other sources of uncertainty associated with the simulation of the facility flow.

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

Post, Wilfred M; King, Anthony Wayne; Dragoni, Danilo

Many parameters in terrestrial biogeochemical models are inherently uncertain, leading to uncertainty in predictions of key carbon cycle variables. At observation sites, this uncertainty can be quantified by applying model-data fusion techniques to estimate model parameters using eddy covariance observations and associated biometric data sets as constraints. Uncertainty is reduced as data records become longer and different types of observations are added. We estimate parametric and associated predictive uncertainty at the Morgan Monroe State Forest in Indiana, USA. Parameters in the Local Terrestrial Ecosystem Carbon (LoTEC) are estimated using both synthetic and actual constraints. These model parameters and uncertainties aremore » then used to make predictions of carbon flux for up to 20 years. We find a strong dependence of both parametric and prediction uncertainty on the length of the data record used in the model-data fusion. In this model framework, this dependence is strongly reduced as the data record length increases beyond 5 years. If synthetic initial biomass pool constraints with realistic uncertainties are included in the model-data fusion, prediction uncertainty is reduced by more than 25% when constraining flux records are less than 3 years. If synthetic annual aboveground woody biomass increment constraints are also included, uncertainty is similarly reduced by an additional 25%. When actual observed eddy covariance data are used as constraints, there is still a strong dependence of parameter and prediction uncertainty on data record length, but the results are harder to interpret because of the inability of LoTEC to reproduce observed interannual variations and the confounding effects of model structural error.« less

Reducing Spatial Uncertainty Through Attentional Cueing Improves Contrast Sensitivity in Regions of the Visual Field With Glaucomatous Defects

PubMed Central

Phu, Jack; Kalloniatis, Michael; Khuu, Sieu K.

2018-01-01

Purpose Current clinical perimetric test paradigms present stimuli randomly to various locations across the visual field (VF), inherently introducing spatial uncertainty, which reduces contrast sensitivity. In the present study, we determined the extent to which spatial uncertainty affects contrast sensitivity in glaucoma patients by minimizing spatial uncertainty through attentional cueing. Methods Six patients with open-angle glaucoma and six healthy subjects underwent laboratory-based psychophysical testing to measure contrast sensitivity at preselected locations at two eccentricities (9.5° and 17.5°) with two stimulus sizes (Goldmann sizes III and V) under different cueing conditions: 1, 2, 4, or 8 points verbally cued. Method of Constant Stimuli and a single-interval forced-choice procedure were used to generate frequency of seeing (FOS) curves at locations with and without VF defects. Results At locations with VF defects, cueing minimizes spatial uncertainty and improves sensitivity under all conditions. The effect of cueing was maximal when one point was cued, and rapidly diminished when more points were cued (no change to baseline with 8 points cued). The slope of the FOS curve steepened with reduced spatial uncertainty. Locations with normal sensitivity in glaucomatous eyes had similar performance to that of healthy subjects. There was a systematic increase in uncertainty with the depth of VF loss. Conclusions Sensitivity measurements across the VF are negatively affected by spatial uncertainty, which increases with greater VF loss. Minimizing uncertainty can improve sensitivity at locations of deficit. Translational Relevance Current perimetric techniques introduce spatial uncertainty and may therefore underestimate sensitivity in regions of VF loss. PMID:29600116

Improving the driver-automation interaction: an approach using automation uncertainty.

PubMed

Beller, Johannes; Heesen, Matthias; Vollrath, Mark

2013-12-01

The aim of this study was to evaluate whether communicating automation uncertainty improves the driver-automation interaction. A false system understanding of infallibility may provoke automation misuse and can lead to severe consequences in case of automation failure. The presentation of automation uncertainty may prevent this false system understanding and, as was shown by previous studies, may have numerous benefits. Few studies, however, have clearly shown the potential of communicating uncertainty information in driving. The current study fills this gap. We conducted a driving simulator experiment, varying the presented uncertainty information between participants (no uncertainty information vs. uncertainty information) and the automation reliability (high vs.low) within participants. Participants interacted with a highly automated driving system while engaging in secondary tasks and were required to cooperate with the automation to drive safely. Quantile regressions and multilevel modeling showed that the presentation of uncertainty information increases the time to collision in the case of automation failure. Furthermore, the data indicated improved situation awareness and better knowledge of fallibility for the experimental group. Consequently, the automation with the uncertainty symbol received higher trust ratings and increased acceptance. The presentation of automation uncertaintythrough a symbol improves overall driver-automation cooperation. Most automated systems in driving could benefit from displaying reliability information. This display might improve the acceptance of fallible systems and further enhances driver-automation cooperation.

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.

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.

Simplified methods for real-time prediction of storm surge uncertainty: The city of Venice case study

NASA Astrophysics Data System (ADS)

Mel, Riccardo; Viero, Daniele Pietro; Carniello, Luca; Defina, Andrea; D'Alpaos, Luigi

2014-09-01

Providing reliable and accurate storm surge forecasts is important for a wide range of problems related to coastal environments. In order to adequately support decision-making processes, it also become increasingly important to be able to estimate the uncertainty associated with the storm surge forecast. The procedure commonly adopted to do this uses the results of a hydrodynamic model forced by a set of different meteorological forecasts; however, this approach requires a considerable, if not prohibitive, computational cost for real-time application. In the present paper we present two simplified methods for estimating the uncertainty affecting storm surge prediction with moderate computational effort. In the first approach we use a computationally fast, statistical tidal model instead of a hydrodynamic numerical model to estimate storm surge uncertainty. The second approach is based on the observation that the uncertainty in the sea level forecast mainly stems from the uncertainty affecting the meteorological fields; this has led to the idea to estimate forecast uncertainty via a linear combination of suitable meteorological variances, directly extracted from the meteorological fields. The proposed methods were applied to estimate the uncertainty in the storm surge forecast in the Venice Lagoon. The results clearly show that the uncertainty estimated through a linear combination of suitable meteorological variances nicely matches the one obtained using the deterministic approach and overcomes some intrinsic limitations in the use of a statistical tidal model.

Micro-Pulse Lidar Signals: Uncertainty Analysis

NASA Technical Reports Server (NTRS)

Welton, Ellsworth J.; Campbell, James R.; Starr, David OC. (Technical Monitor)

2002-01-01

Micro-pulse lidar (MPL) systems are small, autonomous, eye-safe lidars used for continuous observations of the vertical distribution of cloud and aerosol layers. Since the construction of the first MPL in 1993, procedures have been developed to correct for various instrument effects present in MPL signals. The primary instrument effects include afterpulse, laser-detector cross-talk, and overlap, poor near-range (less than 6 km) focusing. The accurate correction of both afterpulse and overlap effects are required to study both clouds and aerosols. Furthermore, the outgoing energy of the laser pulses and the statistical uncertainty of the MPL detector must also be correctly determined in order to assess the accuracy of MPL observations. The uncertainties associated with the afterpulse, overlap, pulse energy, detector noise, and all remaining quantities affecting measured MPL signals, are determined in this study. The uncertainties are propagated through the entire MPL correction process to give a net uncertainty on the final corrected MPL signal. The results show that in the near range, the overlap uncertainty dominates. At altitudes above the overlap region, the dominant source of uncertainty is caused by uncertainty in the pulse energy. However, if the laser energy is low, then during mid-day, high solar background levels can significantly reduce the signal-to-noise of the detector. In such a case, the statistical uncertainty of the detector count rate becomes dominant at altitudes above the overlap region.

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.

Task uncertainty and communication during nursing shift handovers.

PubMed

Mayor, Eric; Bangerter, Adrian; Aribot, Myriam

2012-09-01

We explore variations in handover duration and communication in nursing units. We hypothesize that duration per patient is higher in units facing high task uncertainty. We expect both topics and functions of communication to vary depending on task uncertainty. Handovers are changing in modern healthcare organizations, where standardized procedures are increasingly advocated for efficiency and reliability reasons. However, redesign of handover should take environmental contingencies of different clinical unit types into account. An important contingency in institutions is task uncertainty, which may affect how communicative routines like handover are accomplished. Nurse unit managers of 80 care units in 18 hospitals were interviewed in 2008 about topics and functions of handover communication and duration in their unit. Interviews were content-analysed. Clinical units were classified into a theory-based typology (unit type) that gradually increases on task uncertainty. Quantitative analyses were performed. Unit type affected resource allocation. Unit types facing higher uncertainty had higher handover duration per patient. As expected, unit type also affected communication content. Clinical units facing higher uncertainty discussed fewer topics, discussing treatment and care and organization of work less frequently. Finally, unit type affected functions of handover: sharing emotions was less often mentioned in unit types facing higher uncertainty. Task uncertainty and its relationship with functions and topics of handover should be taken into account during the design of handover procedures. © 2011 Blackwell Publishing Ltd.

A methodology to estimate uncertainty for emission projections through sensitivity analysis.

PubMed

Lumbreras, Julio; de Andrés, Juan Manuel; Pérez, Javier; Borge, Rafael; de la Paz, David; Rodríguez, María Encarnación

2015-04-01

Air pollution abatement policies must be based on quantitative information on current and future emissions of pollutants. As emission projections uncertainties are inevitable and traditional statistical treatments of uncertainty are highly time/resources consuming, a simplified methodology for nonstatistical uncertainty estimation based on sensitivity analysis is presented in this work. The methodology was applied to the "with measures" scenario for Spain, concretely over the 12 highest emitting sectors regarding greenhouse gas and air pollutants emissions. Examples of methodology application for two important sectors (power plants, and agriculture and livestock) are shown and explained in depth. Uncertainty bands were obtained up to 2020 by modifying the driving factors of the 12 selected sectors and the methodology was tested against a recomputed emission trend in a low economic-growth perspective and official figures for 2010, showing a very good performance. A solid understanding and quantification of uncertainties related to atmospheric emission inventories and projections provide useful information for policy negotiations. However, as many of those uncertainties are irreducible, there is an interest on how they could be managed in order to derive robust policy conclusions. Taking this into account, a method developed to use sensitivity analysis as a source of information to derive nonstatistical uncertainty bands for emission projections is presented and applied to Spain. This method simplifies uncertainty assessment and allows other countries to take advantage of their sensitivity analyses.

Assessing concentration uncertainty estimates from passive microwave sea ice products

NASA Astrophysics Data System (ADS)

Meier, W.; Brucker, L.; Miller, J. A.

2017-12-01

Sea ice concentration is an essential climate variable and passive microwave derived estimates of concentration are one of the longest satellite-derived climate records. However, until recently uncertainty estimates were not provided. Numerous validation studies provided insight into general error characteristics, but the studies have found that concentration error varied greatly depending on sea ice conditions. Thus, an uncertainty estimate from each observation is desired, particularly for initialization, assimilation, and validation of models. Here we investigate three sea ice products that include an uncertainty for each concentration estimate: the NASA Team 2 algorithm product, the EUMETSAT Ocean and Sea Ice Satellite Application Facility (OSI-SAF) product, and the NOAA/NSIDC Climate Data Record (CDR) product. Each product estimates uncertainty with a completely different approach. The NASA Team 2 product derives uncertainty internally from the algorithm method itself. The OSI-SAF uses atmospheric reanalysis fields and a radiative transfer model. The CDR uses spatial variability from two algorithms. Each approach has merits and limitations. Here we evaluate the uncertainty estimates by comparing the passive microwave concentration products with fields derived from the NOAA VIIRS sensor. The results show that the relationship between the product uncertainty estimates and the concentration error (relative to VIIRS) is complex. This may be due to the sea ice conditions, the uncertainty methods, as well as the spatial and temporal variability of the passive microwave and VIIRS products.

Striatal dopamine release codes uncertainty in pathological gambling.

PubMed

Linnet, Jakob; Mouridsen, Kim; Peterson, Ericka; Møller, Arne; Doudet, Doris Jeanne; Gjedde, Albert

2012-10-30

Two mechanisms of midbrain and striatal dopaminergic projections may be involved in pathological gambling: hypersensitivity to reward and sustained activation toward uncertainty. The midbrain-striatal dopamine system distinctly codes reward and uncertainty, where dopaminergic activation is a linear function of expected reward and an inverse U-shaped function of uncertainty. In this study, we investigated the dopaminergic coding of reward and uncertainty in 18 pathological gambling sufferers and 16 healthy controls. We used positron emission tomography (PET) with the tracer [(11)C]raclopride to measure dopamine release, and we used performance on the Iowa Gambling Task (IGT) to determine overall reward and uncertainty. We hypothesized that we would find a linear function between dopamine release and IGT performance, if dopamine release coded reward in pathological gambling. If, on the other hand, dopamine release coded uncertainty, we would find an inversely U-shaped function. The data supported an inverse U-shaped relation between striatal dopamine release and IGT performance if the pathological gambling group, but not in the healthy control group. These results are consistent with the hypothesis of dopaminergic sensitivity toward uncertainty, and suggest that dopaminergic sensitivity to uncertainty is pronounced in pathological gambling, but not among non-gambling healthy controls. The findings have implications for understanding dopamine dysfunctions in pathological gambling and addictive behaviors. Copyright © 2012 Elsevier Ireland Ltd. All rights reserved.

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.

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...

HPC Analytics Support. Requirements for Uncertainty Quantification Benchmarks

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

Paulson, Patrick R.; Purohit, Sumit; Rodriguez, Luke R.

2015-05-01

This report outlines techniques for extending benchmark generation products so they support uncertainty quantification by benchmarked systems. We describe how uncertainty quantification requirements can be presented to candidate analytical tools supporting SPARQL. We describe benchmark data sets for evaluating uncertainty quantification, as well as an approach for using our benchmark generator to produce data sets for generating benchmark data sets.

Uncertainties of Mayak urine data

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

Miller, Guthrie; Vostrotin, Vadim; Vvdensky, Vladimir

2008-01-01

For internal dose calculations for the Mayak worker epidemiological study, quantitative estimates of uncertainty of the urine measurements are necessary. Some of the data consist of measurements of 24h urine excretion on successive days (e.g. 3 or 4 days). In a recent publication, dose calculations were done where the uncertainty of the urine measurements was estimated starting from the statistical standard deviation of these replicate mesurements. This approach is straightforward and accurate when the number of replicate measurements is large, however, a Monte Carlo study showed it to be problematic for the actual number of replicate measurements (median from 3more » to 4). Also, it is sometimes important to characterize the uncertainty of a single urine measurement. Therefore this alternate method has been developed. A method of parameterizing the uncertainty of Mayak urine bioassay measmements is described. The Poisson lognormal model is assumed and data from 63 cases (1099 urine measurements in all) are used to empirically determine the lognormal normalization uncertainty, given the measurement uncertainties obtained from count quantities. The natural logarithm of the geometric standard deviation of the normalization uncertainty is found to be in the range 0.31 to 0.35 including a measurement component estimated to be 0.2.« less

Helium Mass Spectrometer Leak Detection: A Method to Quantify Total Measurement Uncertainty

NASA Technical Reports Server (NTRS)

Mather, Janice L.; Taylor, Shawn C.

2015-01-01

In applications where leak rates of components or systems are evaluated against a leak rate requirement, the uncertainty of the measured leak rate must be included in the reported result. However, in the helium mass spectrometer leak detection method, the sensitivity, or resolution, of the instrument is often the only component of the total measurement uncertainty noted when reporting results. To address this shortfall, a measurement uncertainty analysis method was developed that includes the leak detector unit's resolution, repeatability, hysteresis, and drift, along with the uncertainty associated with the calibration standard. In a step-wise process, the method identifies the bias and precision components of the calibration standard, the measurement correction factor (K-factor), and the leak detector unit. Together these individual contributions to error are combined and the total measurement uncertainty is determined using the root-sum-square method. It was found that the precision component contributes more to the total uncertainty than the bias component, but the bias component is not insignificant. For helium mass spectrometer leak rate tests where unit sensitivity alone is not enough, a thorough evaluation of the measurement uncertainty such as the one presented herein should be performed and reported along with the leak rate value.

Measurement uncertainty: Friend or foe?

PubMed

Infusino, Ilenia; Panteghini, Mauro

2018-02-02

The definition and enforcement of a reference measurement system, based on the implementation of metrological traceability of patients' results to higher order reference methods and materials, together with a clinically acceptable level of measurement uncertainty, are fundamental requirements to produce accurate and equivalent laboratory results. The uncertainty associated with each step of the traceability chain should be governed to obtain a final combined uncertainty on clinical samples fulfilling the requested performance specifications. It is important that end-users (i.e., clinical laboratory) may know and verify how in vitro diagnostics (IVD) manufacturers have implemented the traceability of their calibrators and estimated the corresponding uncertainty. However, full information about traceability and combined uncertainty of calibrators is currently very difficult to obtain. Laboratory professionals should investigate the need to reduce the uncertainty of the higher order metrological references and/or to increase the precision of commercial measuring systems. Accordingly, the measurement uncertainty should not be considered a parameter to be calculated by clinical laboratories just to fulfil the accreditation standards, but it must become a key quality indicator to describe both the performance of an IVD measuring system and the laboratory itself. Copyright © 2018 The Canadian Society of Clinical Chemists. Published by Elsevier Inc. All rights reserved.

Gridded uncertainty in fossil fuel carbon dioxide emission maps, a CDIAC example

DOE PAGES

Andres, Robert J.; Boden, Thomas A.; Higdon, David M.

2016-12-05

Due to a current lack of physical measurements at appropriate spatial and temporal scales, all current global maps and distributions of fossil fuel carbon dioxide (FFCO2) emissions use one or more proxies to distribute those emissions. These proxies and distribution schemes introduce additional uncertainty into these maps. This paper examines the uncertainty associated with the magnitude of gridded FFCO2 emissions. This uncertainty is gridded at the same spatial and temporal scales as the mass magnitude maps. This gridded uncertainty includes uncertainty contributions from the spatial, temporal, proxy, and magnitude components used to create the magnitude map of FFCO2 emissions. Throughoutmore » this process, when assumptions had to be made or expert judgment employed, the general tendency in most cases was toward overestimating or increasing the magnitude of uncertainty. The results of the uncertainty analysis reveal a range of 4–190 %, with an average of 120 % (2 σ) for populated and FFCO2-emitting grid spaces over annual timescales. This paper also describes a methodological change specific to the creation of the Carbon Dioxide Information Analysis Center (CDIAC) FFCO2 emission maps: the change from a temporally fixed population proxy to a temporally varying population proxy.« less

Structural and parameteric uncertainty quantification in cloud microphysics parameterization schemes

NASA Astrophysics Data System (ADS)

van Lier-Walqui, M.; Morrison, H.; Kumjian, M. R.; Prat, O. P.; Martinkus, C.

2017-12-01

Atmospheric model parameterization schemes employ approximations to represent the effects of unresolved processes. These approximations are a source of error in forecasts, caused in part by considerable uncertainty about the optimal value of parameters within each scheme -- parameteric uncertainty. Furthermore, there is uncertainty regarding the best choice of the overarching structure of the parameterization scheme -- structrual uncertainty. Parameter estimation can constrain the first, but may struggle with the second because structural choices are typically discrete. We address this problem in the context of cloud microphysics parameterization schemes by creating a flexible framework wherein structural and parametric uncertainties can be simultaneously constrained. Our scheme makes no assuptions about drop size distribution shape or the functional form of parametrized process rate terms. Instead, these uncertainties are constrained by observations using a Markov Chain Monte Carlo sampler within a Bayesian inference framework. Our scheme, the Bayesian Observationally-constrained Statistical-physical Scheme (BOSS), has flexibility to predict various sets of prognostic drop size distribution moments as well as varying complexity of process rate formulations. We compare idealized probabilistic forecasts from versions of BOSS with varying levels of structural complexity. This work has applications in ensemble forecasts with model physics uncertainty, data assimilation, and cloud microphysics process studies.

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

Gridded uncertainty in fossil fuel carbon dioxide emission maps, a CDIAC example

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

Andres, Robert J.; Boden, Thomas A.; Higdon, David M.

Due to a current lack of physical measurements at appropriate spatial and temporal scales, all current global maps and distributions of fossil fuel carbon dioxide (FFCO2) emissions use one or more proxies to distribute those emissions. These proxies and distribution schemes introduce additional uncertainty into these maps. This paper examines the uncertainty associated with the magnitude of gridded FFCO2 emissions. This uncertainty is gridded at the same spatial and temporal scales as the mass magnitude maps. This gridded uncertainty includes uncertainty contributions from the spatial, temporal, proxy, and magnitude components used to create the magnitude map of FFCO2 emissions. Throughoutmore » this process, when assumptions had to be made or expert judgment employed, the general tendency in most cases was toward overestimating or increasing the magnitude of uncertainty. The results of the uncertainty analysis reveal a range of 4–190 %, with an average of 120 % (2 σ) for populated and FFCO2-emitting grid spaces over annual timescales. This paper also describes a methodological change specific to the creation of the Carbon Dioxide Information Analysis Center (CDIAC) FFCO2 emission maps: the change from a temporally fixed population proxy to a temporally varying population proxy.« less

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.

Host Model Uncertainty in Aerosol Radiative Effects: the AeroCom Prescribed Experiment and Beyond

NASA Astrophysics Data System (ADS)

Stier, Philip; Schutgens, Nick; Bian, Huisheng; Boucher, Olivier; Chin, Mian; Ghan, Steven; Huneeus, Nicolas; Kinne, Stefan; Lin, Guangxing; Myhre, Gunnar; Penner, Joyce; Randles, Cynthia; Samset, Bjorn; Schulz, Michael; Yu, Hongbin; Zhou, Cheng; Bellouin, Nicolas; Ma, Xiaoyan; Yu, Fangqun; Takemura, Toshihiko

2013-04-01

Anthropogenic and natural aerosol radiative effects are recognized to affect global and regional climate. Multi-model "diversity" in estimates of the aerosol radiative effect is often perceived as a measure of the uncertainty in modelling aerosol itself. However, current aerosol models vary considerably in model components relevant for the calculation of aerosol radiative forcings and feedbacks and the associated "host-model uncertainties" are generally convoluted with the actual uncertainty in aerosol modelling. In the AeroCom Prescribed intercomparison study we systematically isolate and quantify host model uncertainties on aerosol forcing experiments through prescription of identical aerosol radiative properties in eleven participating models. Host model errors in aerosol radiative forcing are largest in regions of uncertain host model components, such as stratocumulus cloud decks or areas with poorly constrained surface albedos, such as sea ice. Our results demonstrate that host model uncertainties are an important component of aerosol forcing uncertainty that require further attention. However, uncertainties in aerosol radiative effects also include short-term and long-term feedback processes that will be systematically explored in future intercomparison studies. Here we will present an overview of the proposals for discussion and results from early scoping studies.

MOMENTS OF UNCERTAINTY: ETHICAL CONSIDERATIONS AND EMERGING CONTAMINANTS

PubMed Central

Cordner, Alissa; Brown, Phil

2013-01-01

Science on emerging environmental health threats involves numerous ethical concerns related to scientific uncertainty about conducting, interpreting, communicating, and acting upon research findings, but the connections between ethical decision making and scientific uncertainty are under-studied in sociology. Under conditions of scientific uncertainty, researcher conduct is not fully prescribed by formal ethical codes of conduct, increasing the importance of ethical reflection by researchers, conflicts over research conduct, and reliance on informal ethical standards. This paper draws on in-depth interviews with scientists, regulators, activists, industry representatives, and fire safety experts to explore ethical considerations of moments of uncertainty using a case study of flame retardants, chemicals widely used in consumer products with potential negative health and environmental impacts. We focus on the uncertainty that arises in measuring people’s exposure to these chemicals through testing of their personal environments or bodies. We identify four sources of ethical concerns relevant to scientific uncertainty: 1) choosing research questions or methods, 2) interpreting scientific results, 3) communicating results to multiple publics, and 4) applying results for policy-making. This research offers lessons about professional conduct under conditions of uncertainty, ethical research practice, democratization of scientific knowledge, and science’s impact on policy. PMID:24249964

Gridded uncertainty in fossil fuel carbon dioxide emission maps, a CDIAC example

NASA Astrophysics Data System (ADS)

Andres, Robert J.; Boden, Thomas A.; Higdon, David M.

2016-12-01

Due to a current lack of physical measurements at appropriate spatial and temporal scales, all current global maps and distributions of fossil fuel carbon dioxide (FFCO2) emissions use one or more proxies to distribute those emissions. These proxies and distribution schemes introduce additional uncertainty into these maps. This paper examines the uncertainty associated with the magnitude of gridded FFCO2 emissions. This uncertainty is gridded at the same spatial and temporal scales as the mass magnitude maps. This gridded uncertainty includes uncertainty contributions from the spatial, temporal, proxy, and magnitude components used to create the magnitude map of FFCO2 emissions. Throughout this process, when assumptions had to be made or expert judgment employed, the general tendency in most cases was toward overestimating or increasing the magnitude of uncertainty. The results of the uncertainty analysis reveal a range of 4-190 %, with an average of 120 % (2σ) for populated and FFCO2-emitting grid spaces over annual timescales. This paper also describes a methodological change specific to the creation of the Carbon Dioxide Information Analysis Center (CDIAC) FFCO2 emission maps: the change from a temporally fixed population proxy to a temporally varying population proxy.

The short-term impact of economic uncertainty on motor vehicle collisions.

PubMed

Vandoros, Sotiris; Avendano, Mauricio; Kawachi, Ichiro

2018-06-01

Stress and anxiety lead to attention loss and sleep deprivation and may reduce driving performance, increasing the risk of motor vehicle collision. We used evidence from a natural experiment to examine whether daily changes in economic uncertainty, potentially leading to attention or sleep loss, are associated with collisions in Great Britain. Daily data from the economic policy uncertainty index, derived from analysis of daily UK newspapers, were linked to the daily number of motor vehicle collisions in Great Britain over the period 2005-2015, obtained from the Department for Transport. Exploiting daily variations in economic uncertainty, we used a GARCH approach to model daily rates of motor vehicle collisions as a function of economic uncertainty, controlling for month and day of the week, monthly unemployment rates and weekly unleaded petrol prices. A spike in the daily economic uncertainty index was associated with an immediate increase in the number of motor vehicle collisions. Results were robust to various sensitivity analyses. Overall, daily increases in economic uncertainty are associated with short-term spikes in motor vehicle collisions. Preventive and traffic control measures may need to increase during periods of economic uncertainty. Copyright © 2018 Elsevier Inc. All rights reserved.

Uncertainties propagation and global sensitivity analysis of the frequency response function of piezoelectric energy harvesters

NASA Astrophysics Data System (ADS)

Ruiz, Rafael O.; Meruane, Viviana

2017-06-01

The goal of this work is to describe a framework to propagate uncertainties in piezoelectric energy harvesters (PEHs). These uncertainties are related to the incomplete knowledge of the model parameters. The framework presented could be employed to conduct prior robust stochastic predictions. The prior analysis assumes a known probability density function for the uncertain variables and propagates the uncertainties to the output voltage. The framework is particularized to evaluate the behavior of the frequency response functions (FRFs) in PEHs, while its implementation is illustrated by the use of different unimorph and bimorph PEHs subjected to different scenarios: free of uncertainties, common uncertainties, and uncertainties as a product of imperfect clamping. The common variability associated with the PEH parameters are tabulated and reported. A global sensitivity analysis is conducted to identify the Sobol indices. Results indicate that the elastic modulus, density, and thickness of the piezoelectric layer are the most relevant parameters of the output variability. The importance of including the model parameter uncertainties in the estimation of the FRFs is revealed. In this sense, the present framework constitutes a powerful tool in the robust design and prediction of PEH performance.

Comparison of the uncertainties of several European low-dose calibration facilities

NASA Astrophysics Data System (ADS)

Dombrowski, H.; Cornejo Díaz, N. A.; Toni, M. P.; Mihelic, M.; Röttger, A.

2018-04-01

The typical uncertainty of a low-dose rate calibration of a detector, which is calibrated in a dedicated secondary national calibration laboratory, is investigated, including measurements in the photon field of metrology institutes. Calibrations at low ambient dose equivalent rates (at the level of the natural ambient radiation) are needed when environmental radiation monitors are to be characterised. The uncertainties of calibration measurements in conventional irradiation facilities above ground are compared with those obtained in a low-dose rate irradiation facility located deep underground. Four laboratories quantitatively evaluated the uncertainties of their calibration facilities, in particular for calibrations at low dose rates (250 nSv/h and 1 μSv/h). For the first time, typical uncertainties of European calibration facilities are documented in a comparison and the main sources of uncertainty are revealed. All sources of uncertainties are analysed, including the irradiation geometry, scattering, deviations of real spectra from standardised spectra, etc. As a fundamental metrological consequence, no instrument calibrated in such a facility can have a lower total uncertainty in subsequent measurements. For the first time, the need to perform calibrations at very low dose rates (< 100 nSv/h) deep underground is underpinned on the basis of quantitative data.

A Two-Step Approach to Uncertainty Quantification of Core Simulators

DOE PAGES

Yankov, Artem; Collins, Benjamin; Klein, Markus; ...

2012-01-01

For the multiple sources of error introduced into the standard computational regime for simulating reactor cores, rigorous uncertainty analysis methods are available primarily to quantify the effects of cross section uncertainties. Two methods for propagating cross section uncertainties through core simulators are the XSUSA statistical approach and the “two-step” method. The XSUSA approach, which is based on the SUSA code package, is fundamentally a stochastic sampling method. Alternatively, the two-step method utilizes generalized perturbation theory in the first step and stochastic sampling in the second step. The consistency of these two methods in quantifying uncertainties in the multiplication factor andmore » in the core power distribution was examined in the framework of phase I-3 of the OECD Uncertainty Analysis in Modeling benchmark. With the Three Mile Island Unit 1 core as a base model for analysis, the XSUSA and two-step methods were applied with certain limitations, and the results were compared to those produced by other stochastic sampling-based codes. Based on the uncertainty analysis results, conclusions were drawn as to the method that is currently more viable for computing uncertainties in burnup and transient calculations.« less

INSPECTION SHOP: PLAN TO PROVIDE UNCERTAINTY ANALYSIS WITH MEASUREMENTS

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

Nederbragt, W W

The LLNL inspection shop is chartered to make dimensional measurements of components for critical programmatic experiments. These measurements ensure that components are within tolerance and provide geometric details that can be used to further refine simulations. For these measurements to be useful, they must be significantly more accurate than the tolerances that are being checked. For example, if a part has a specified dimension of 100 millimeters and a tolerance of 1 millimeter, then the precision and/or accuracy of the measurement should be less than 1 millimeter. Using the ''10-to-1 gaugemaker's rule of thumb'', the desired precision of the measurementmore » should be less than 100 micrometers. Currently, the process for associating measurement uncertainty with data is not standardized, nor is the uncertainty based on a thorough uncertainty analysis. The goal of this project is to begin providing measurement uncertainty statements with critical measurements performed in the inspection shop. To accomplish this task, comprehensive knowledge about the underlying sources of uncertainty for measurement instruments need to be understood and quantified. Moreover, measurements of elemental uncertainties for each physical source need to be combined in a meaningful way to obtain an overall measurement uncertainty.« less

Quantifying the Effects of Historical Land Cover Conversion Uncertainty on Global Carbon and Climate Estimates

NASA Astrophysics Data System (ADS)

Di Vittorio, A. V.; Mao, J.; Shi, X.; Chini, L.; Hurtt, G.; Collins, W. D.

2018-01-01

Previous studies have examined land use change as a driver of global change, but the translation of land use change into land cover conversion has been largely unconstrained. Here we quantify the effects of land cover conversion uncertainty on the global carbon and climate system using the integrated Earth System Model. Our experiments use identical land use change data and vary land cover conversions to quantify associated uncertainty in carbon and climate estimates. Land cover conversion uncertainty is large, constitutes a 5 ppmv range in estimated atmospheric CO2 in 2004, and generates carbon uncertainty that is equivalent to 80% of the net effects of CO2 and climate and 124% of the effects of nitrogen deposition during 1850-2004. Additionally, land cover uncertainty generates differences in local surface temperature of over 1°C. We conclude that future studies addressing land use, carbon, and climate need to constrain and reduce land cover conversion uncertainties.

Large contribution of natural aerosols to uncertainty in indirect forcing

NASA Astrophysics Data System (ADS)

Carslaw, K. S.; Lee, L. A.; Reddington, C. L.; Pringle, K. J.; Rap, A.; Forster, P. M.; Mann, G. W.; Spracklen, D. V.; Woodhouse, M. T.; Regayre, L. A.; Pierce, J. R.

2013-11-01

The effect of anthropogenic aerosols on cloud droplet concentrations and radiative properties is the source of one of the largest uncertainties in the radiative forcing of climate over the industrial period. This uncertainty affects our ability to estimate how sensitive the climate is to greenhouse gas emissions. Here we perform a sensitivity analysis on a global model to quantify the uncertainty in cloud radiative forcing over the industrial period caused by uncertainties in aerosol emissions and processes. Our results show that 45 per cent of the variance of aerosol forcing since about 1750 arises from uncertainties in natural emissions of volcanic sulphur dioxide, marine dimethylsulphide, biogenic volatile organic carbon, biomass burning and sea spray. Only 34 per cent of the variance is associated with anthropogenic emissions. The results point to the importance of understanding pristine pre-industrial-like environments, with natural aerosols only, and suggest that improved measurements and evaluation of simulated aerosols in polluted present-day conditions will not necessarily result in commensurate reductions in the uncertainty of forcing estimates.

Uncertainty Quantification in Remaining Useful Life of Aerospace Components using State Space Models and Inverse FORM

NASA Technical Reports Server (NTRS)

Sankararaman, Shankar; Goebel, Kai

2013-01-01

This paper investigates the use of the inverse first-order reliability method (inverse- FORM) to quantify the uncertainty in the remaining useful life (RUL) of aerospace components. The prediction of remaining useful life is an integral part of system health prognosis, and directly helps in online health monitoring and decision-making. However, the prediction of remaining useful life is affected by several sources of uncertainty, and therefore it is necessary to quantify the uncertainty in the remaining useful life prediction. While system parameter uncertainty and physical variability can be easily included in inverse-FORM, this paper extends the methodology to include: (1) future loading uncertainty, (2) process noise; and (3) uncertainty in the state estimate. The inverse-FORM method has been used in this paper to (1) quickly obtain probability bounds on the remaining useful life prediction; and (2) calculate the entire probability distribution of remaining useful life prediction, and the results are verified against Monte Carlo sampling. The proposed methodology is illustrated using a numerical example.

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.

Mitigating Provider Uncertainty in Service Provision Contracts

NASA Astrophysics Data System (ADS)

Smith, Chris; van Moorsel, Aad

Uncertainty is an inherent property of open, distributed and multiparty systems. The viability of the mutually beneficial relationships which motivate these systems relies on rational decision-making by each constituent party under uncertainty. Service provision in distributed systems is one such relationship. Uncertainty is experienced by the service provider in his ability to deliver a service with selected quality level guarantees due to inherent non-determinism, such as load fluctuations and hardware failures. Statistical estimators utilized to model this non-determinism introduce additional uncertainty through sampling error. Inability of the provider to accurately model and analyze uncertainty in the quality level guarantees can result in the formation of sub-optimal service provision contracts. Emblematic consequences include loss of revenue, inefficient resource utilization and erosion of reputation and consumer trust. We propose a utility model for contract-based service provision to provide a systematic approach to optimal service provision contract formation under uncertainty. Performance prediction methods to enable the derivation of statistical estimators for quality level are introduced, with analysis of their resultant accuracy and cost.

Effects of model structural uncertainty on carbon cycle projections: biological nitrogen fixation as a case study

NASA Astrophysics Data System (ADS)

Wieder, William R.; Cleveland, Cory C.; Lawrence, David M.; Bonan, Gordon B.

2015-04-01

Uncertainties in terrestrial carbon (C) cycle projections increase uncertainty of potential climate feedbacks. Efforts to improve model performance often include increased representation of biogeochemical processes, such as coupled carbon-nitrogen (N) cycles. In doing so, models are becoming more complex, generating structural uncertainties in model form that reflect incomplete knowledge of how to represent underlying processes. Here, we explore structural uncertainties associated with biological nitrogen fixation (BNF) and quantify their effects on C cycle projections. We find that alternative plausible structures to represent BNF result in nearly equivalent terrestrial C fluxes and pools through the twentieth century, but the strength of the terrestrial C sink varies by nearly a third (50 Pg C) by the end of the twenty-first century under a business-as-usual climate change scenario representative concentration pathway 8.5. These results indicate that actual uncertainty in future C cycle projections may be larger than previously estimated, and this uncertainty will limit C cycle projections until model structures can be evaluated and refined.

Effects of Parameter Uncertainty on Long-Term Simulations of Lake Alkalinity

NASA Astrophysics Data System (ADS)

Lee, Sijin; Georgakakos, Konstantine P.; Schnoor, Jerald L.

1990-03-01

A first-order second-moment uncertainty analysis has been applied to two lakes in the Adirondack Park, New York, to assess the long-term response of lakes to acid deposition. Uncertainty due to parameter error and initial condition error was considered. Because the enhanced trickle-down (ETD) model is calibrated with only 3 years of field data and is used to simulate a 50-year period, the uncertainty in the lake alkalinity prediction is relatively large. When a best estimate of parameter uncertainty is used, the annual average alkalinity is predicted to be -11 ±28 μeq/L for Lake Woods and 142 ± 139 μeq/L for Lake Panther after 50 years. Hydrologic parameters and chemical weathering rate constants contributed most to the uncertainty of the simulations. Results indicate that the uncertainty in long-range predictions of lake alkalinity increased significantly over a 5- to 10-year period and then reached a steady state.

Sources of uncertainty in flood inundation maps

USGS Publications Warehouse

Bales, J.D.; Wagner, C.R.

2009-01-01

Flood inundation maps typically have been used to depict inundated areas for floods having specific exceedance levels. The uncertainty associated with the inundation boundaries is seldom quantified, in part, because all of the sources of uncertainty are not recognized and because data available to quantify uncertainty seldom are available. Sources of uncertainty discussed in this paper include hydrologic data used for hydraulic model development and validation, topographic data, and the hydraulic model. The assumption of steady flow, which typically is made to produce inundation maps, has less of an effect on predicted inundation at lower flows than for higher flows because more time typically is required to inundate areas at high flows than at low flows. Difficulties with establishing reasonable cross sections that do not intersect and that represent water-surface slopes in tributaries contribute additional uncertainties in the hydraulic modelling. As a result, uncertainty in the flood inundation polygons simulated with a one-dimensional model increases with distance from the main channel.

Effects of radiobiological uncertainty on vehicle and habitat shield design for missions to the moon and Mars

NASA Technical Reports Server (NTRS)

Wilson, John W.; Nealy, John E.; Schimmerling, Walter; Cucinotta, Francis A.; Wood, James S.

1993-01-01

Some consequences of uncertainties in radiobiological risk due to galactic cosmic ray (GCR) exposure are analyzed for their effect on engineering designs for the first lunar outpost and a mission to explore Mars. This report presents the plausible effect of biological uncertainties, the design changes necessary to reduce the uncertainties to acceptable levels for a safe mission, and an evaluation of the mission redesign cost. Estimates of the amount of shield mass required to compensate for radiobiological uncertainty are given for a simplified vehicle and habitat. The additional amount of shield mass required to provide a safety factor for uncertainty compensation is calculated from the expected response to GCR exposure. The amount of shield mass greatly increases in the estimated range of biological uncertainty, thus, escalating the estimated cost of the mission. The estimates are used as a quantitative example for the cost-effectiveness of research in radiation biophysics and radiation physics.

Position-momentum uncertainty relations in the presence of quantum memory

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

Furrer, Fabian, E-mail: furrer@eve.phys.s.u-tokyo.ac.jp; Berta, Mario; Institute for Theoretical Physics, ETH Zurich, Wolfgang-Pauli-Str. 27, 8093 Zürich

2014-12-15

A prominent formulation of the uncertainty principle identifies the fundamental quantum feature that no particle may be prepared with certain outcomes for both position and momentum measurements. Often the statistical uncertainties are thereby measured in terms of entropies providing a clear operational interpretation in information theory and cryptography. Recently, entropic uncertainty relations have been used to show that the uncertainty can be reduced in the presence of entanglement and to prove security of quantum cryptographic tasks. However, much of this recent progress has been focused on observables with only a finite number of outcomes not including Heisenberg’s original setting ofmore » position and momentum observables. Here, we show entropic uncertainty relations for general observables with discrete but infinite or continuous spectrum that take into account the power of an entangled observer. As an illustration, we evaluate the uncertainty relations for position and momentum measurements, which is operationally significant in that it implies security of a quantum key distribution scheme based on homodyne detection of squeezed Gaussian states.« less

ISO/GUM UNCERTAINTIES AND CIAAW (UNCERTAINTY TREATMENT FOR RECOMMENDED ATOMIC WEIGHTS AND ISOTOPIC ABUNDANCES)

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

HOLDEN,N.E.

2007-07-23

The International Organization for Standardization (ISO) has published a Guide to the expression of Uncertainty in Measurement (GUM). The IUPAC Commission on Isotopic Abundance and Atomic Weight (CIAAW) began attaching uncertainty limits to their recommended values about forty years ago. CIAAW's method for determining and assigning uncertainties has evolved over time. We trace this evolution to their present method and their effort to incorporate the basic ISO/GUM procedures into evaluations of these uncertainties. We discuss some dilemma the CIAAW faces in their present method and whether it is consistent with the application of the ISO/GUM rules. We discuss the attemptmore » to incorporate variations in measured isotope ratios, due to natural fractionation, into the ISO/GUM system. We make some observations about the inconsistent treatment in the incorporation of natural variations into recommended data and uncertainties. A recommendation for expressing atomic weight values using a tabulated range of values for various chemical elements is discussed.« less

Impact of Pitot tube calibration on the uncertainty of water flow rate measurement

NASA Astrophysics Data System (ADS)

de Oliveira Buscarini, Icaro; Costa Barsaglini, Andre; Saiz Jabardo, Paulo Jose; Massami Taira, Nilson; Nader, Gilder

2015-10-01

Water utility companies often use Cole type Pitot tubes to map velocity profiles and thus measure flow rate. Frequent monitoring and measurement of flow rate is an important step in identifying leaks and other types of losses. In Brazil losses as high as 42% are common and in some places even higher values are found. When using Cole type Pitot tubes to measure the flow rate, the uncertainty of the calibration coefficient (Cd) is a major component of the overall flow rate measurement uncertainty. A common practice is to employ the usual value Cd = 0.869, in use since Cole proposed his Pitot tube in 1896. Analysis of 414 calibrations of Cole type Pitot tubes show that Cd varies considerably and values as high 0.020 for the expanded uncertainty are common. Combined with other uncertainty sources, the overall velocity measurement uncertainty is 0.02, increasing flowrate measurement uncertainty by 1.5% which, for the Sao Paulo metropolitan area (Brazil) corresponds to 3.5 × 107 m3/year.

Quantifying the Effects of Historical Land Cover Conversion Uncertainty on Global Carbon and Climate Estimates

DOE PAGES

Di Vittorio, A. V.; Mao, J.; Shi, X.; ...

2018-01-03

Previous studies have examined land use change as a driver of global change, but the translation of land use change into land cover conversion has been largely unconstrained. In this paper, we quantify the effects of land cover conversion uncertainty on the global carbon and climate system using the integrated Earth System Model. Our experiments use identical land use change data and vary land cover conversions to quantify associated uncertainty in carbon and climate estimates. Land cover conversion uncertainty is large, constitutes a 5 ppmv range in estimated atmospheric CO 2 in 2004, and generates carbon uncertainty that is equivalentmore » to 80% of the net effects of CO 2 and climate and 124% of the effects of nitrogen deposition during 1850–2004. Additionally, land cover uncertainty generates differences in local surface temperature of over 1°C. Finally, we conclude that future studies addressing land use, carbon, and climate need to constrain and reduce land cover conversion uncertainties.« less

Steering the measured uncertainty under decoherence through local PT -symmetric operations

NASA Astrophysics Data System (ADS)

Shi, Wei-Nan; Wang, Dong; Sun, Wen-Yang; Ming, Fei; Huang, Ai-Jun; Ye, Liu

2018-07-01

The uncertainty principle is viewed as one of the appealing properties in the context of quantum mechanics, which intrinsically offers a lower bound with regard to the measurement outcomes of a pair of incompatible observables within a given system. In this letter, we attempt to observe entropic uncertainty in the presence of quantum memory under different local noisy channels. To be specific, we develop the dynamics of the measured uncertainty under local bit-phase-flipping (unital) and depolarization (nonunital) noise, respectively, and attractively put forward an effective strategy to manipulate its magnitude of the uncertainty of interest by means of parity-time symmetric (-symmetric) operations on the subsystem to be measured. It is interesting to find that there exist different evolution characteristics of the uncertainty in the channels considered here, i.e. the monotonic behavior in the nonunital channels, and the non-monotonic behavior in the unital channels. Moreover, the amount of the measured uncertainty can be reduced to some degree by properly modulating the -symmetric operations.

Quantifying the Effects of Historical Land Cover Conversion Uncertainty on Global Carbon and Climate Estimates

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

Di Vittorio, A. V.; Mao, J.; Shi, X.

Previous studies have examined land use change as a driver of global change, but the translation of land use change into land cover conversion has been largely unconstrained. In this paper, we quantify the effects of land cover conversion uncertainty on the global carbon and climate system using the integrated Earth System Model. Our experiments use identical land use change data and vary land cover conversions to quantify associated uncertainty in carbon and climate estimates. Land cover conversion uncertainty is large, constitutes a 5 ppmv range in estimated atmospheric CO 2 in 2004, and generates carbon uncertainty that is equivalentmore » to 80% of the net effects of CO 2 and climate and 124% of the effects of nitrogen deposition during 1850–2004. Additionally, land cover uncertainty generates differences in local surface temperature of over 1°C. Finally, we conclude that future studies addressing land use, carbon, and climate need to constrain and reduce land cover conversion uncertainties.« less

Ensembles vs. information theory: supporting science under uncertainty

NASA Astrophysics Data System (ADS)

Nearing, Grey S.; Gupta, Hoshin V.

2018-05-01

Multi-model ensembles are one of the most common ways to deal with epistemic uncertainty in hydrology. This is a problem because there is no known way to sample models such that the resulting ensemble admits a measure that has any systematic (i.e., asymptotic, bounded, or consistent) relationship with uncertainty. Multi-model ensembles are effectively sensitivity analyses and cannot - even partially - quantify uncertainty. One consequence of this is that multi-model approaches cannot support a consistent scientific method - in particular, multi-model approaches yield unbounded errors in inference. In contrast, information theory supports a coherent hypothesis test that is robust to (i.e., bounded under) arbitrary epistemic uncertainty. This paper may be understood as advocating a procedure for hypothesis testing that does not require quantifying uncertainty, but is coherent and reliable (i.e., bounded) in the presence of arbitrary (unknown and unknowable) uncertainty. We conclude by offering some suggestions about how this proposed philosophy of science suggests new ways to conceptualize and construct simulation models of complex, dynamical systems.

Religion in the face of uncertainty: an uncertainty-identity theory account of religiousness.

PubMed

Hogg, Michael A; Adelman, Janice R; Blagg, Robert D

2010-02-01

The authors characterize religions as social groups and religiosity as the extent to which a person identifies with a religion, subscribes to its ideology or worldview, and conforms to its normative practices. They argue that religions have attributes that make them well suited to reduce feelings of self-uncertainty. According to uncertainty-identity theory, people are motivated to reduce feelings of uncertainty about or reflecting on self; and identification with groups, particularly highly entitative groups, is a very effective way to reduce uncertainty. All groups provide belief systems and normative prescriptions related to everyday life. However, religions also address the nature of existence, invoking sacred entities and associated rituals and ceremonies. They are entitative groups that provide a moral compass and rules for living that pervade a person's life, making them particularly attractive in times of uncertainty. The authors document data supporting their analysis and discuss conditions that transform religiosity into religious zealotry and extremism.

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

Knudsen, J.K.; Smith, C.L.

The steps involved to incorporate parameter uncertainty into the Nuclear Regulatory Commission (NRC) accident sequence precursor (ASP) models is covered in this paper. Three different uncertainty distributions (i.e., lognormal, beta, gamma) were evaluated to Determine the most appropriate distribution. From the evaluation, it was Determined that the lognormal distribution will be used for the ASP models uncertainty parameters. Selection of the uncertainty parameters for the basic events is also discussed. This paper covers the process of determining uncertainty parameters for the supercomponent basic events (i.e., basic events that are comprised of more than one component which can have more thanmore » one failure mode) that are utilized in the ASP models. Once this is completed, the ASP model is ready to be utilized to propagate parameter uncertainty for event assessments.« less

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.

State-independent uncertainty relations and entanglement detection

NASA Astrophysics Data System (ADS)

Qian, Chen; Li, Jun-Li; Qiao, Cong-Feng

2018-04-01

The uncertainty relation is one of the key ingredients of quantum theory. Despite the great efforts devoted to this subject, most of the variance-based uncertainty relations are state-dependent and suffering from the triviality problem of zero lower bounds. Here we develop a method to get uncertainty relations with state-independent lower bounds. The method works by exploring the eigenvalues of a Hermitian matrix composed by Bloch vectors of incompatible observables and is applicable for both pure and mixed states and for arbitrary number of N-dimensional observables. The uncertainty relation for the incompatible observables can be explained by geometric relations related to the parallel postulate and the inequalities in Horn's conjecture on Hermitian matrix sum. Practical entanglement criteria are also presented based on the derived uncertainty relations.

Calculating Measurement Uncertainty of the “Conventional Value of the Result of Weighing in Air”

DOE PAGES

Flicker, Celia J.; Tran, Hy D.

2016-04-02

The conventional value of the result of weighing in air is frequently used in commercial calibrations of balances. The guidance in OIML D-028 for reporting uncertainty of the conventional value is too terse. When calibrating mass standards at low measurement uncertainties, it is necessary to perform a buoyancy correction before reporting the result. When calculating the conventional result after calibrating true mass, the uncertainty due to calculating the conventional result is correlated with the buoyancy correction. We show through Monte Carlo simulations that the measurement uncertainty of the conventional result is less than the measurement uncertainty when reporting true mass.more » The Monte Carlo simulation tool is available in the online version of this article.« less

Uncertainties in derived temperature-height profiles

NASA Technical Reports Server (NTRS)

Minzner, R. A.

1974-01-01

Nomographs were developed for relating uncertainty in temperature T to uncertainty in the observed height profiles of both pressure p and density rho. The relative uncertainty delta T/T is seen to depend not only upon the relative uncertainties delta P/P or delta rho/rho, and to a small extent upon the value of T or H, but primarily upon the sampling-height increment Delta h, the height increment between successive observations of p or delta. For a fixed value of delta p/p, the value of delta T/T varies inversely with Delta h. No limit exists in the fineness of usable height resolution of T which may be derived from densities, while a fine height resolution in pressure-height data leads to temperatures with unacceptably large uncertainties.

Estimation of the measurement uncertainty in magnetic resonance velocimetry based on statistical models

NASA Astrophysics Data System (ADS)

Bruschewski, Martin; Freudenhammer, Daniel; Buchenberg, Waltraud B.; Schiffer, Heinz-Peter; Grundmann, Sven

2016-05-01

Velocity measurements with magnetic resonance velocimetry offer outstanding possibilities for experimental fluid mechanics. The purpose of this study was to provide practical guidelines for the estimation of the measurement uncertainty in such experiments. Based on various test cases, it is shown that the uncertainty estimate can vary substantially depending on how the uncertainty is obtained. The conventional approach to estimate the uncertainty from the noise in the artifact-free background can lead to wrong results. A deviation of up to -75 % is observed with the presented experiments. In addition, a similarly high deviation is demonstrated with the data from other studies. As a more accurate approach, the uncertainty is estimated directly from the image region with the flow sample. Two possible estimation methods are presented.

Uncertainty-accounting environmental policy and management of water systems.

PubMed

Baresel, Christian; Destouni, Georgia

2007-05-15

Environmental policies for water quality and ecosystem management do not commonly require explicit stochastic accounts of uncertainty and risk associated with the quantification and prediction of waterborne pollutant loads and abatement effects. In this study, we formulate and investigate a possible environmental policy that does require an explicit stochastic uncertainty account. We compare both the environmental and economic resource allocation performance of such an uncertainty-accounting environmental policy with that of deterministic, risk-prone and risk-averse environmental policies under a range of different hypothetical, yet still possible, scenarios. The comparison indicates that a stochastic uncertainty-accounting policy may perform better than deterministic policies over a range of different scenarios. Even in the absence of reliable site-specific data, reported literature values appear to be useful for such a stochastic account of uncertainty.

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

Advanced probabilistic methods for quantifying the effects of various uncertainties in structural response

NASA Technical Reports Server (NTRS)

Nagpal, Vinod K.

1988-01-01

The effects of actual variations, also called uncertainties, in geometry and material properties on the structural response of a space shuttle main engine turbopump blade are evaluated. A normal distribution was assumed to represent the uncertainties statistically. Uncertainties were assumed to be totally random, partially correlated, and fully correlated. The magnitude of these uncertainties were represented in terms of mean and variance. Blade responses, recorded in terms of displacements, natural frequencies, and maximum stress, was evaluated and plotted in the form of probabilistic distributions under combined uncertainties. These distributions provide an estimate of the range of magnitudes of the response and probability of occurrence of a given response. Most importantly, these distributions provide the information needed to estimate quantitatively the risk in a structural design.

Probability and Confidence Trade-space (PACT) Evaluation: Accounting for Uncertainty in Sparing Assessments

NASA Technical Reports Server (NTRS)

Anderson, Leif; Box, Neil; Carter, Katrina; DiFilippo, Denise; Harrington, Sean; Jackson, David; Lutomski, Michael

2012-01-01

There are two general shortcomings to the current annual sparing assessment: 1. The vehicle functions are currently assessed according to confidence targets, which can be misleading- overly conservative or optimistic. 2. The current confidence levels are arbitrarily determined and do not account for epistemic uncertainty (lack of knowledge) in the ORU failure rate. There are two major categories of uncertainty that impact Sparing Assessment: (a) Aleatory Uncertainty: Natural variability in distribution of actual failures around an Mean Time Between Failure (MTBF) (b) Epistemic Uncertainty : Lack of knowledge about the true value of an Orbital Replacement Unit's (ORU) MTBF We propose an approach to revise confidence targets and account for both categories of uncertainty, an approach we call Probability and Confidence Trade-space (PACT) evaluation.

The Social Construction of Uncertainty in Healthcare Delivery

NASA Astrophysics Data System (ADS)

Begun, James W.; Kaissi, Amer A.

We explore the following question: How would healthcare delivery be different if uncertainty were widely recognized, accurately diagnosed, and appropriately managed? Unlike most studies of uncertainty, we examine uncertainty at more than one level of analysis, considering uncertainty that arises at the patient-clinician interaction level and at the organizational level of healthcare delivery. We consider the effects of history, as the forces and systems that currently shape and manage uncertainty have emerged over a long time period. The purpose of this broad and speculative "thought exercise" is to generate greater sensemaking of the current state of healthcare delivery, particularly in the realm of organizational and public policy, and to generate new research questions about healthcare delivery. The discussion is largely based on experience in the United States, which may limit its generalizability.

Sensitivity of a radiative transfer model to the uncertainty in the aerosol optical depth used as input

NASA Astrophysics Data System (ADS)

Román, Roberto; Bilbao, Julia; de Miguel, Argimiro; Pérez-Burgos, Ana

2014-05-01

The radiative transfer models can be used to obtain solar radiative quantities in the Earth surface as the erythemal ultraviolet (UVER) irradiance, which is the spectral irradiance weighted with the erythemal (sunburn) action spectrum, and the total shortwave irradiance (SW; 305-2,8000 nm). Aerosol and atmospheric properties are necessary as inputs in the model in order to calculate the UVER and SW irradiances under cloudless conditions, however the uncertainty in these inputs causes another uncertainty in the simulations. The objective of this work is to quantify the uncertainty in UVER and SW simulations generated by the aerosol optical depth (AOD) uncertainty. The data from different satellite retrievals were downloaded at nine Spanish places located in the Iberian Peninsula: Total ozone column from different databases, spectral surface albedo and water vapour column from MODIS instrument, AOD at 443 nm and Angström Exponent (between 443 nm and 670 nm) from MISR instrument onboard Terra satellite, single scattering albedo from OMI instrument onboard Aura satellite. The obtained AOD at 443 nm data from MISR were compared with AERONET measurements in six Spanish sites finding an uncertainty in the AOD from MISR of 0.074. In this work the radiative transfer model UVSPEC/Libradtran (1.7 version) was used to obtain the SW and UVER irradiance under cloudless conditions for each month and for different solar zenith angles (SZA) in the nine mentioned locations. The inputs used for these simulations were monthly climatology tables obtained with the available data in each location. Once obtained the UVER and SW simulations, they were repeated twice but changing the AOD monthly values by the same AOD plus/minus its uncertainty. The maximum difference between the irradiance run with AOD and the irradiance run with AOD plus/minus its uncertainty was calculated for each month, SZA, and location. This difference was considered as the uncertainty on the model caused by the AOD uncertainty. The uncertainty in the simulated global SW and UVER varies with the location, but the behaviour is similar: high uncertainty in specific months. The averages of the uncertainty at the nine locations were calculated. Uncertainty in the global SW is lower than 5% for SZA values lower than 70º, and the uncertainty in global UVER is between 2 and 6%. The uncertainty in the direct and diffuse components is higher than in the global case for both SW and UVER irradiances, but a balance between the changes with AOD in direct and diffuse components provide a lower uncertainty in global SW and UVER irradiance. References Bilbao, J., Román, R., de Miguel, A., Mateos, D.: Long-term solar erythemal UV irradiance data reconstruction in Spain using a semiempirical method, J. Geophys. Res., 116, D22211, 2011. Kylling, A., Stamnes, K., Tsay, S. C.: A reliable and efficient two-stream algorithm for spherical radiative transfer: Documentation of acciracy in realistic layered media, J. Atmos. Chem, 21, 115-150, 1995. Ricchiazzi, P., Yang, S., Gautier, C., Sowle, D.: SBDART: A research and Teaching software tool for plane-parallel radiative transfer in the Earth's atmosphere, Bulletin of the American Meteorological

Managing the uncertainties of the streamflow data produced by the French national hydrological services

NASA Astrophysics Data System (ADS)

Puechberty, Rachel; Bechon, Pierre-Marie; Le Coz, Jérôme; Renard, Benjamin

2015-04-01

The French national hydrological services (NHS) manage the production of streamflow time series throughout the national territory. The hydrological data are made available to end-users through different web applications and the national hydrological archive (Banque Hydro). Providing end-users with qualitative and quantitative information on the uncertainty of the hydrological data is key to allow them drawing relevant conclusions and making appropriate decisions. Due to technical and organisational issues that are specific to the field of hydrometry, quantifying the uncertainty of hydrological measurements is still challenging and not yet standardized. The French NHS have made progress on building a consistent strategy to assess the uncertainty of their streamflow data. The strategy consists of addressing the uncertainties produced and propagated at each step of the data production with uncertainty analysis tools that are compatible with each other and compliant with international uncertainty guidance and standards. Beyond the necessary research and methodological developments, operational software tools and procedures are absolutely necessary to the data management and uncertainty analysis by field hydrologists. A first challenge is to assess, and if possible reduce, the uncertainty of streamgauging data, i.e. direct stage-discharge measurements. Interlaboratory experiments proved to be a very efficient way to empirically measure the uncertainty of a given streamgauging technique in given measurement conditions. The Q+ method (Le Coz et al., 2012) was developed to improve the uncertainty propagation method proposed in the ISO748 standard for velocity-area gaugings. Both empirical or computed (with Q+) uncertainty values can now be assigned in BAREME, which is the software used by the French NHS for managing streamgauging measurements. A second pivotal step is to quantify the uncertainty related to stage-discharge rating curves and their application to water level records to produce continuous discharge time series. The management of rating curves is also done using BAREME. The BaRatin method (Le Coz et al., 2014) was developed as a Bayesian approach of rating curve development and uncertainty analysis. Since BaRatin accounts for the individual uncertainties of gauging data used to build the rating curve, it was coupled with BAREME. The BaRatin method is still undergoing development and research, in particular to address non univocal or time-varying stage-discharge relations, due to hysteresis, variable backwater, rating shifts, etc. A new interface including new options is under development. The next steps are now to propagate the uncertainties of water level records, through uncertain rating curves, up to discharge time series and derived variables (e.g. annual mean flow) and statistics (e.g. flood quantiles). Bayesian tools are already available for both tasks but further validation and development is necessary for their integration in the operational data workflow of the French NHS. References Le Coz, J., Camenen, B., Peyrard, X., Dramais, G., 2012. Uncertainty in open-channel discharges measured with the velocity-area method. Flow Measurement and Instrumentation 26, 18-29. Le Coz, J., Renard, B., Bonnifait, L., Branger, F., Le Boursicaud, R., 2014. Combining hydraulic knowledge and uncertain gaugings in the estimation of hydrometric rating curves: a Bayesian approach, Journal of Hydrology, 509, 573-587.

Uncertainty quantification in fission cross section measurements at LANSCE

DOE PAGES

Tovesson, F.

2015-01-09

Neutron-induced fission cross sections have been measured for several isotopes of uranium and plutonium at the Los Alamos Neutron Science Center (LANSCE) over a wide range of incident neutron energies. The total uncertainties in these measurements are in the range 3–5% above 100 keV of incident neutron energy, which results from uncertainties in the target, neutron source, and detector system. The individual sources of uncertainties are assumed to be uncorrelated, however correlation in the cross section across neutron energy bins are considered. The quantification of the uncertainty contributions will be described here.

Estimating Uncertainties in the Multi-Instrument SBUV Profile Ozone Merged Data Set

NASA Technical Reports Server (NTRS)

Frith, Stacey; Stolarski, Richard

2015-01-01

The MOD data set is uniquely qualified for use in long-term ozone analysis because of its long record, high spatial coverage, and consistent instrument design and algorithm. The estimated MOD uncertainty term significantly increases the uncertainty over the statistical error alone. Trends in the post-2000 period are generally positive in the upper stratosphere, but only significant at 1-1.6 hPa. Remaining uncertainties not yet included in the Monte Carlo model are Smoothing Error ( 1 from 10 to 1 hPa) Relative calibration uncertainty between N11 and N17Seasonal cycle differences between SBUV records.

Modeling uncertainty: quicksand for water temperature modeling

USGS Publications Warehouse

Bartholow, John M.

2003-01-01

Uncertainty has been a hot topic relative to science generally, and modeling specifically. Modeling uncertainty comes in various forms: measured data, limited model domain, model parameter estimation, model structure, sensitivity to inputs, modelers themselves, and users of the results. This paper will address important components of uncertainty in modeling water temperatures, and discuss several areas that need attention as the modeling community grapples with how to incorporate uncertainty into modeling without getting stuck in the quicksand that prevents constructive contributions to policy making. The material, and in particular the reference, are meant to supplement the presentation given at this conference.

Measurement Uncertainty

NASA Astrophysics Data System (ADS)

Koch, Michael

Measurement uncertainty is one of the key issues in quality assurance. It became increasingly important for analytical chemistry laboratories with the accreditation to ISO/IEC 17025. The uncertainty of a measurement is the most important criterion for the decision whether a measurement result is fit for purpose. It also delivers help for the decision whether a specification limit is exceeded or not. Estimation of measurement uncertainty often is not trivial. Several strategies have been developed for this purpose that will shortly be described in this chapter. In addition the different possibilities to take into account the uncertainty in compliance assessment are explained.

Uncertainty Analysis of NASA Glenn's 8- by 6-Foot Supersonic Wind Tunnel

NASA Technical Reports Server (NTRS)

Stephens, Julia E.; Hubbard, Erin P.; Walter, Joel A.; McElroy, Tyler

2016-01-01

An analysis was performed to determine the measurement uncertainty of the Mach Number of the 8- by 6-foot Supersonic Wind Tunnel at the NASA Glenn Research Center. This paper details the analysis process used, including methods for handling limited data and complicated data correlations. Due to the complexity of the equations used, a Monte Carlo Method was utilized for this uncertainty analysis. A summary of the findings are presented as pertains to understanding what the uncertainties are, how they impact various research tests in the facility, and methods of reducing the uncertainties in the future.