A general model for attitude determination error analysis

NASA Technical Reports Server (NTRS)

Markley, F. Landis; Seidewitz, ED; Nicholson, Mark

1988-01-01

An overview is given of a comprehensive approach to filter and dynamics modeling for attitude determination error analysis. The models presented include both batch least-squares and sequential attitude estimation processes for both spin-stabilized and three-axis stabilized spacecraft. The discussion includes a brief description of a dynamics model of strapdown gyros, but it does not cover other sensor models. Model parameters can be chosen to be solve-for parameters, which are assumed to be estimated as part of the determination process, or consider parameters, which are assumed to have errors but not to be estimated. The only restriction on this choice is that the time evolution of the consider parameters must not depend on any of the solve-for parameters. The result of an error analysis is an indication of the contributions of the various error sources to the uncertainties in the determination of the spacecraft solve-for parameters. The model presented gives the uncertainty due to errors in the a priori estimates of the solve-for parameters, the uncertainty due to measurement noise, the uncertainty due to dynamic noise (also known as process noise or measurement noise), the uncertainty due to the consider parameters, and the overall uncertainty due to all these sources of error.

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

Bao, C.; Hanany, S.; Baccigalupi, C.

We extend a general maximum likelihood foreground estimation for cosmic microwave background (CMB) polarization data to include estimation of instrumental systematic effects. We focus on two particular effects: frequency band measurement uncertainty and instrumentally induced frequency dependent polarization rotation. We assess the bias induced on the estimation of the B-mode polarization signal by these two systematic effects in the presence of instrumental noise and uncertainties in the polarization and spectral index of Galactic dust. Degeneracies between uncertainties in the band and polarization angle calibration measurements and in the dust spectral index and polarization increase the uncertainty in the extracted CMBmore » B-mode power, and may give rise to a biased estimate. We provide a quantitative assessment of the potential bias and increased uncertainty in an example experimental configuration. For example, we find that with 10% polarized dust, a tensor to scalar ratio of r = 0.05, and the instrumental configuration of the E and B experiment balloon payload, the estimated CMB B-mode power spectrum is recovered without bias when the frequency band measurement has 5% uncertainty or less, and the polarization angle calibration has an uncertainty of up to 4°.« less

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.

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...

Climate Projections and Uncertainty Communication.

PubMed

Joslyn, Susan L; LeClerc, Jared E

2016-01-01

Lingering skepticism about climate change might be due in part to the way climate projections are perceived by members of the public. Variability between scientists' estimates might give the impression that scientists disagree about the fact of climate change rather than about details concerning the extent or timing. Providing uncertainty estimates might clarify that the variability is due in part to quantifiable uncertainty inherent in the prediction process, thereby increasing people's trust in climate projections. This hypothesis was tested in two experiments. Results suggest that including uncertainty estimates along with climate projections leads to an increase in participants' trust in the information. Analyses explored the roles of time, place, demographic differences (e.g., age, gender, education level, political party affiliation), and initial belief in climate change. Implications are discussed in terms of the potential benefit of adding uncertainty estimates to public climate projections. Copyright © 2015 Cognitive Science Society, Inc.

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.

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

Estimation of environment-related properties of chemicals for design of sustainable processes: development of group-contribution+ (GC+) property models and uncertainty analysis.

PubMed

Hukkerikar, Amol Shivajirao; Kalakul, Sawitree; Sarup, Bent; Young, Douglas M; Sin, Gürkan; Gani, Rafiqul

2012-11-26

The aim of this work is to develop group-contribution(+) (GC(+)) method (combined group-contribution (GC) method and atom connectivity index (CI) method) based property models to provide reliable estimations of environment-related properties of organic chemicals together with uncertainties of estimated property values. For this purpose, a systematic methodology for property modeling and uncertainty analysis is used. The methodology includes a parameter estimation step to determine parameters of property models and an uncertainty analysis step to establish statistical information about the quality of parameter estimation, such as the parameter covariance, the standard errors in predicted properties, and the confidence intervals. For parameter estimation, large data sets of experimentally measured property values of a wide range of chemicals (hydrocarbons, oxygenated chemicals, nitrogenated chemicals, poly functional chemicals, etc.) taken from the database of the US Environmental Protection Agency (EPA) and from the database of USEtox is used. For property modeling and uncertainty analysis, the Marrero and Gani GC method and atom connectivity index method have been considered. In total, 22 environment-related properties, which include the fathead minnow 96-h LC(50), Daphnia magna 48-h LC(50), oral rat LD(50), aqueous solubility, bioconcentration factor, permissible exposure limit (OSHA-TWA), photochemical oxidation potential, global warming potential, ozone depletion potential, acidification potential, emission to urban air (carcinogenic and noncarcinogenic), emission to continental rural air (carcinogenic and noncarcinogenic), emission to continental fresh water (carcinogenic and noncarcinogenic), emission to continental seawater (carcinogenic and noncarcinogenic), emission to continental natural soil (carcinogenic and noncarcinogenic), and emission to continental agricultural soil (carcinogenic and noncarcinogenic) have been modeled and analyzed. The application of the developed property models for the estimation of environment-related properties and uncertainties of the estimated property values is highlighted through an illustrative example. The developed property models provide reliable estimates of environment-related properties needed to perform process synthesis, design, and analysis of sustainable chemical processes and allow one to evaluate the effect of uncertainties of estimated property values on the calculated performance of processes giving useful insights into quality and reliability of the design of sustainable processes.

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.

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

Impact of Exposure Uncertainty on the Association between Perfluorooctanoate and Preeclampsia in the C8 Health Project Population.

PubMed

Avanasi, Raghavendhran; Shin, Hyeong-Moo; Vieira, Verónica M; Savitz, David A; Bartell, Scott M

2016-01-01

Uncertainty in exposure estimates from models can result in exposure measurement error and can potentially affect the validity of epidemiological studies. We recently used a suite of environmental models and an integrated exposure and pharmacokinetic model to estimate individual perfluorooctanoate (PFOA) serum concentrations and assess the association with preeclampsia from 1990 through 2006 for the C8 Health Project participants. The aims of the current study are to evaluate impact of uncertainty in estimated PFOA drinking-water concentrations on estimated serum concentrations and their reported epidemiological association with preeclampsia. For each individual public water district, we used Monte Carlo simulations to vary the year-by-year PFOA drinking-water concentration by randomly sampling from lognormal distributions for random error in the yearly public water district PFOA concentrations, systematic error specific to each water district, and global systematic error in the release assessment (using the estimated concentrations from the original fate and transport model as medians and a range of 2-, 5-, and 10-fold uncertainty). Uncertainty in PFOA water concentrations could cause major changes in estimated serum PFOA concentrations among participants. However, there is relatively little impact on the resulting epidemiological association in our simulations. The contribution of exposure uncertainty to the total uncertainty (including regression parameter variance) ranged from 5% to 31%, and bias was negligible. We found that correlated exposure uncertainty can substantially change estimated PFOA serum concentrations, but results in only minor impacts on the epidemiological association between PFOA and preeclampsia. Avanasi R, Shin HM, Vieira VM, Savitz DA, Bartell SM. 2016. Impact of exposure uncertainty on the association between perfluorooctanoate and preeclampsia in the C8 Health Project population. Environ Health Perspect 124:126-132; http://dx.doi.org/10.1289/ehp.1409044.

Estimating Coastal Digital Elevation Model (DEM) Uncertainty

NASA Astrophysics Data System (ADS)

Amante, C.; Mesick, S.

2017-12-01

Integrated bathymetric-topographic digital elevation models (DEMs) are representations of the Earth's solid surface and are fundamental to the modeling of coastal processes, including tsunami, storm surge, and sea-level rise inundation. Deviations in elevation values from the actual seabed or land surface constitute errors in DEMs, which originate from numerous sources, including: (i) the source elevation measurements (e.g., multibeam sonar, lidar), (ii) the interpolative gridding technique (e.g., spline, kriging) used to estimate elevations in areas unconstrained by source measurements, and (iii) the datum transformation used to convert bathymetric and topographic data to common vertical reference systems. The magnitude and spatial distribution of the errors from these sources are typically unknown, and the lack of knowledge regarding these errors represents the vertical uncertainty in the DEM. The National Oceanic and Atmospheric Administration (NOAA) National Centers for Environmental Information (NCEI) has developed DEMs for more than 200 coastal communities. This study presents a methodology developed at NOAA NCEI to derive accompanying uncertainty surfaces that estimate DEM errors at the individual cell-level. The development of high-resolution (1/9th arc-second), integrated bathymetric-topographic DEMs along the southwest coast of Florida serves as the case study for deriving uncertainty surfaces. The estimated uncertainty can then be propagated into the modeling of coastal processes that utilize DEMs. Incorporating the uncertainty produces more reliable modeling results, and in turn, better-informed coastal management decisions.

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.

An Applied Framework for Incorporating Multiple Sources of Uncertainty in Fisheries Stock Assessments.

PubMed

Scott, Finlay; Jardim, Ernesto; Millar, Colin P; Cerviño, Santiago

2016-01-01

Estimating fish stock status is very challenging given the many sources and high levels of uncertainty surrounding the biological processes (e.g. natural variability in the demographic rates), model selection (e.g. choosing growth or stock assessment models) and parameter estimation. Incorporating multiple sources of uncertainty in a stock assessment allows advice to better account for the risks associated with proposed management options, promoting decisions that are more robust to such uncertainty. However, a typical assessment only reports the model fit and variance of estimated parameters, thereby underreporting the overall uncertainty. Additionally, although multiple candidate models may be considered, only one is selected as the 'best' result, effectively rejecting the plausible assumptions behind the other models. We present an applied framework to integrate multiple sources of uncertainty in the stock assessment process. The first step is the generation and conditioning of a suite of stock assessment models that contain different assumptions about the stock and the fishery. The second step is the estimation of parameters, including fitting of the stock assessment models. The final step integrates across all of the results to reconcile the multi-model outcome. The framework is flexible enough to be tailored to particular stocks and fisheries and can draw on information from multiple sources to implement a broad variety of assumptions, making it applicable to stocks with varying levels of data availability The Iberian hake stock in International Council for the Exploration of the Sea (ICES) Divisions VIIIc and IXa is used to demonstrate the framework, starting from length-based stock and indices data. Process and model uncertainty are considered through the growth, natural mortality, fishing mortality, survey catchability and stock-recruitment relationship. Estimation uncertainty is included as part of the fitting process. Simple model averaging is used to integrate across the results and produce a single assessment that considers the multiple sources of uncertainty.

What’s Driving Uncertainty? The Model or the Model Parameters (What’s Driving Uncertainty? The influences of model and model parameters in data analysis)

DOE PAGES

Anderson-Cook, Christine Michaela

2017-03-01

Here, one of the substantial improvements to the practice of data analysis in recent decades is the change from reporting just a point estimate for a parameter or characteristic, to now including a summary of uncertainty for that estimate. Understanding the precision of the estimate for the quantity of interest provides better understanding of what to expect and how well we are able to predict future behavior from the process. For example, when we report a sample average as an estimate of the population mean, it is good practice to also provide a confidence interval (or credible interval, if youmore » are doing a Bayesian analysis) to accompany that summary. This helps to calibrate what ranges of values are reasonable given the variability observed in the sample and the amount of data that were included in producing the summary.« less

Synthesizing Global and Local Datasets to Estimate Jurisdictional Forest Carbon Fluxes in Berau, Indonesia

PubMed Central

Griscom, Bronson W.; Ellis, Peter W.; Baccini, Alessandro; Marthinus, Delon; Evans, Jeffrey S.; Ruslandi

2016-01-01

Background Forest conservation efforts are increasingly being implemented at the scale of sub-national jurisdictions in order to mitigate global climate change and provide other ecosystem services. We see an urgent need for robust estimates of historic forest carbon emissions at this scale, as the basis for credible measures of climate and other benefits achieved. Despite the arrival of a new generation of global datasets on forest area change and biomass, confusion remains about how to produce credible jurisdictional estimates of forest emissions. We demonstrate a method for estimating the relevant historic forest carbon fluxes within the Regency of Berau in eastern Borneo, Indonesia. Our method integrates best available global and local datasets, and includes a comprehensive analysis of uncertainty at the regency scale. Principal Findings and Significance We find that Berau generated 8.91 ± 1.99 million tonnes of net CO2 emissions per year during 2000–2010. Berau is an early frontier landscape where gross emissions are 12 times higher than gross sequestration. Yet most (85%) of Berau’s original forests are still standing. The majority of net emissions were due to conversion of native forests to unspecified agriculture (43% of total), oil palm (28%), and fiber plantations (9%). Most of the remainder was due to legal commercial selective logging (17%). Our overall uncertainty estimate offers an independent basis for assessing three other estimates for Berau. Two other estimates were above the upper end of our uncertainty range. We emphasize the importance of including an uncertainty range for all parameters of the emissions equation to generate a comprehensive uncertainty estimate–which has not been done before. We believe comprehensive estimates of carbon flux uncertainty are increasingly important as national and international institutions are challenged with comparing alternative estimates and identifying a credible range of historic emissions values. PMID:26752298

Impact of uncertainty in soil, climatic, and chemical information in a pesticide leaching assessment

NASA Astrophysics Data System (ADS)

Loague, Keith; Green, Richard E.; Giambelluca, Thomas W.; Liang, Tony C.; Yost, Russell S.

1990-01-01

A simple mobility index, when combined with a geographic information system, can be used to generate rating maps which indicate qualitatively the potential for various organic chemicals to leach to groundwater. In this paper we investigate the magnitude of uncertainty associated with pesticide mobility estimates as a result of data uncertainties. Our example is for the Pearl Harbor Basin, Oahu, Hawaii. The two pesticides included in our analysis are atrazine (2-chloro-4-ethylamino-6-isopropylamino-s-triazine) and diuron [3-(3,4-dichlorophenyul)-1,1-dimethylarea]. The mobility index used here is known as the Attenuation Factor ( AF); it requires soil, hydrogeologic, climatic and chemical information as input data. We employ first-order uncertainty analysis to characterize the uncertainty in estimates of AF resulting from uncertainties in the various input data. Soils in the Pearl Harbor Basin are delineated at the order taxonomic category for this study. Our results show that there can be a significant amount of uncertainty in estimates of pesticide mobility for the Pearl Harbor Basin. This information needs to be considered if future decisions concerning chemical regulation are to be based on estimates of pesticide mobility determined from simple indices.

Erratum to "Impact of uncertainty in soil, climatic, and chemical information in a pesticide leaching assessment"

NASA Astrophysics Data System (ADS)

Loague, Keith; Green, Richard E.; Giambelluca, Thomas W.; Liang, Tony C.; Yost, Russell S.

2016-11-01

A simple mobility index, when combined with a geographic information system, can be used to generate rating maps which indicate qualitatively the potential for various organic chemicals to leach to groundwater. In this paper we investigate the magnitude of uncertainty associated with pesticide mobility estimates as a result of data uncertainties. Our example is for the Pearl Harbor Basin, Oahu, Hawaii. The two pesticides included in our analysis are atrazine (2-chloro-4-ethylamino-6-isopropylamino-s-triazine) and diuron [3-(3,4-dichlorophenyl)-1,1-dimethylarea]. The mobility index used here is known as the Attenuation Factor (AF); it requires soil, hydrogeologic, climatic, and chemical information as input data. We employ first-order uncertainty analysis to characterize the uncertainty in estimates of AF resulting from uncertainties in the various input data. Soils in the Pearl Harbor Basin are delineated at the order taxonomic category for this study. Our results show that there can be a significant amount of uncertainty in estimates of pesticide mobility for the Pearl Harbor Basin. This information needs to be considered if future decisions concerning chemical regulation are to be based on estimates of pesticide mobility determined from simple indices.

Constraining uncertainties in water supply reliability in a tropical data scarce basin

NASA Astrophysics Data System (ADS)

Kaune, Alexander; Werner, Micha; Rodriguez, Erasmo; de Fraiture, Charlotte

2015-04-01

Assessing the water supply reliability in river basins is essential for adequate planning and development of irrigated agriculture and urban water systems. In many cases hydrological models are applied to determine the surface water availability in river basins. However, surface water availability and variability is often not appropriately quantified due to epistemic uncertainties, leading to water supply insecurity. The objective of this research is to determine the water supply reliability in order to support planning and development of irrigated agriculture in a tropical, data scarce environment. The approach proposed uses a simple hydrological model, but explicitly includes model parameter uncertainty. A transboundary river basin in the tropical region of Colombia and Venezuela with an approximately area of 2100 km² was selected as a case study. The Budyko hydrological framework was extended to consider climatological input variability and model parameter uncertainty, and through this the surface water reliability to satisfy the irrigation and urban demand was estimated. This provides a spatial estimate of the water supply reliability across the basin. For the middle basin the reliability was found to be less than 30% for most of the months when the water is extracted from an upstream source. Conversely, the monthly water supply reliability was high (r>98%) in the lower basin irrigation areas when water was withdrawn from a source located further downstream. Including model parameter uncertainty provides a complete estimate of the water supply reliability, but that estimate is influenced by the uncertainty in the model. Reducing the uncertainty in the model through improved data and perhaps improved model structure will improve the estimate of the water supply reliability allowing better planning of irrigated agriculture and dependable water allocation decisions.

Quantifying the uncertainty introduced by discretization and time-averaging in two-fluid model predictions

DOE PAGES

Syamlal, Madhava; Celik, Ismail B.; Benyahia, Sofiane

2017-07-12

The two-fluid model (TFM) has become a tool for the design and troubleshooting of industrial fluidized bed reactors. To use TFM for scale up with confidence, the uncertainty in its predictions must be quantified. Here, we study two sources of uncertainty: discretization and time-averaging. First, we show that successive grid refinement may not yield grid-independent transient quantities, including cross-section–averaged quantities. Successive grid refinement would yield grid-independent time-averaged quantities on sufficiently fine grids. A Richardson extrapolation can then be used to estimate the discretization error, and the grid convergence index gives an estimate of the uncertainty. Richardson extrapolation may not workmore » for industrial-scale simulations that use coarse grids. We present an alternative method for coarse grids and assess its ability to estimate the discretization error. Second, we assess two methods (autocorrelation and binning) and find that the autocorrelation method is more reliable for estimating the uncertainty introduced by time-averaging TFM data.« less

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

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

A new retrieval algorithm for tropospheric temperature, humidity and pressure profiling based on GNSS radio occultation data

NASA Astrophysics Data System (ADS)

Kirchengast, Gottfried; Li, Ying; Scherllin-Pirscher, Barbara; Schwärz, Marc; Schwarz, Jakob; Nielsen, Johannes K.

2017-04-01

The GNSS radio occultation (RO) technique is an important remote sensing technique for obtaining thermodynamic profiles of temperature, humidity, and pressure in the Earth's troposphere. However, due to refraction effects of both dry ambient air and water vapor in the troposphere, retrieval of accurate thermodynamic profiles at these lower altitudes is challenging and requires suitable background information in addition to the RO refractivity information. Here we introduce a new moist air retrieval algorithm aiming to improve the quality and robustness of retrieving temperature, humidity and pressure profiles in moist air tropospheric conditions. The new algorithm consists of four steps: (1) use of prescribed specific humidity and its uncertainty to retrieve temperature and its associated uncertainty; (2) use of prescribed temperature and its uncertainty to retrieve specific humidity and its associated uncertainty; (3) use of the previous results to estimate final temperature and specific humidity profiles through optimal estimation; (4) determination of air pressure and density profiles from the results obtained before. The new algorithm does not require elaborated matrix inversions which are otherwise widely used in 1D-Var retrieval algorithms, and it allows a transparent uncertainty propagation, whereby the uncertainties of prescribed variables are dynamically estimated accounting for their spatial and temporal variations. Estimated random uncertainties are calculated by constructing error covariance matrices from co-located ECMWF short-range forecast and corresponding analysis profiles. Systematic uncertainties are estimated by empirical modeling. The influence of regarding or disregarding vertical error correlations is quantified. The new scheme is implemented with static input uncertainty profiles in WEGC's current OPSv5.6 processing system and with full scope in WEGC's next-generation system, the Reference Occultation Processing System (rOPS). Results from both WEGC systems, current OPSv5.6 and next-generation rOPS, are shown and discussed, based on both insights from individual profiles and statistical ensembles, and compared to moist air retrieval results from the UCAR Boulder and ROM-SAF Copenhagen centers. The results show that the new algorithmic scheme improves the temperature, humidity and pressure retrieval performance, in particular also the robustness including for integrated uncertainty estimation for large-scale applications, over the previous algorithms. The new rOPS-implemented algorithm will therefore be used in the first large-scale reprocessing towards a tropospheric climate data record 2001-2016 by the rOPS, including its integrated uncertainty propagation.

New Methods for Assessing and Reducing Uncertainty in Microgravity Studies

NASA Astrophysics Data System (ADS)

Giniaux, J. M.; Hooper, A. J.; Bagnardi, M.

2017-12-01

Microgravity surveying, also known as dynamic or 4D gravimetry is a time-dependent geophysical method used to detect mass fluctuations within the shallow crust, by analysing temporal changes in relative gravity measurements. We present here a detailed uncertainty analysis of temporal gravity measurements, considering for the first time all possible error sources, including tilt, error in drift estimations and timing errors. We find that some error sources that are actually ignored, can have a significant impact on the total error budget and it is therefore likely that some gravity signals may have been misinterpreted in previous studies. Our analysis leads to new methods for reducing some of the uncertainties associated with residual gravity estimation. In particular, we propose different approaches for drift estimation and free air correction depending on the survey set up. We also provide formulae to recalculate uncertainties for past studies and lay out a framework for best practice in future studies. We demonstrate our new approach on volcanic case studies, which include Kilauea in Hawaii and Askja in Iceland.

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.

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.

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.

Uncertainties in Estimates of the Risks of Late Effects from Space Radiation

NASA Technical Reports Server (NTRS)

Cucinotta, F. A.; Schimmerling, W.; Wilson, J. W.; Peterson, L. E.; Saganti, P.; Dicelli, J. F.

2002-01-01

The health risks faced by astronauts from space radiation include cancer, cataracts, hereditary effects, and non-cancer morbidity and mortality risks related to the diseases of the old age. Methods used to project risks in low-Earth orbit are of questionable merit for exploration missions because of the limited radiobiology data and knowledge of galactic cosmic ray (GCR) heavy ions, which causes estimates of the risk of late effects to be highly uncertain. Risk projections involve a product of many biological and physical factors, each of which has a differential range of uncertainty due to lack of data and knowledge. Within the linear-additivity model, we use Monte-Carlo sampling from subjective uncertainty distributions in each factor to obtain a Maximum Likelihood estimate of the overall uncertainty in risk projections. The resulting methodology is applied to several human space exploration mission scenarios including ISS, lunar station, deep space outpost, and Mar's missions of duration of 360, 660, and 1000 days. The major results are the quantification of the uncertainties in current risk estimates, the identification of factors that dominate risk projection uncertainties, and the development of a method to quantify candidate approaches to reduce uncertainties or mitigate risks. The large uncertainties in GCR risk projections lead to probability distributions of risk that mask any potential risk reduction using the "optimization" of shielding materials or configurations. In contrast, the design of shielding optimization approaches for solar particle events and trapped protons can be made at this time, and promising technologies can be shown to have merit using our approach. The methods used also make it possible to express risk management objectives in terms of quantitative objective's, i.e., the number of days in space without exceeding a given risk level within well defined confidence limits.

Study of the uncertainty in estimation of the exposure of non-human biota to ionising radiation.

PubMed

Avila, R; Beresford, N A; Agüero, A; Broed, R; Brown, J; Iospje, M; Robles, B; Suañez, A

2004-12-01

Uncertainty in estimations of the exposure of non-human biota to ionising radiation may arise from a number of sources including values of the model parameters, empirical data, measurement errors and biases in the sampling. The significance of the overall uncertainty of an exposure assessment will depend on how the estimated dose compares with reference doses used for risk characterisation. In this paper, we present the results of a study of the uncertainty in estimation of the exposure of non-human biota using some of the models and parameters recommended in the FASSET methodology. The study was carried out for semi-natural terrestrial, agricultural and marine ecosystems, and for four radionuclides (137Cs, 239Pu, 129I and 237Np). The parameters of the radionuclide transfer models showed the highest sensitivity and contributed the most to the uncertainty in the predictions of doses to biota. The most important ones were related to the bioavailability and mobility of radionuclides in the environment, for example soil-to-plant transfer factors, the bioaccumulation factors for marine biota and the gut uptake fraction for terrestrial mammals. In contrast, the dose conversion coefficients showed low sensitivity and contributed little to the overall uncertainty. Radiobiological effectiveness contributed to the overall uncertainty of the dose estimations for alpha emitters although to a lesser degree than a number of transfer model parameters.

Sampling in freshwater environments: suspended particle traps and variability in the final data.

PubMed

Barbizzi, Sabrina; Pati, Alessandra

2008-11-01

This paper reports one practical method to estimate the measurement uncertainty including sampling, derived by the approach implemented by Ramsey for soil investigations. The methodology has been applied to estimate the measurements uncertainty (sampling and analyses) of (137)Cs activity concentration (Bq kg(-1)) and total carbon content (%) in suspended particle sampling in a freshwater ecosystem. Uncertainty estimates for between locations, sampling and analysis components have been evaluated. For the considered measurands, the relative expanded measurement uncertainties are 12.3% for (137)Cs and 4.5% for total carbon. For (137)Cs, the measurement (sampling+analysis) variance gives the major contribution to the total variance, while for total carbon the spatial variance is the dominant contributor to the total variance. The limitations and advantages of this basic method are discussed.

Accounting for Age Uncertainty in Growth Modeling, the Case Study of Yellowfin Tuna (Thunnus albacares) of the Indian Ocean

PubMed Central

Dortel, Emmanuelle; Massiot-Granier, Félix; Rivot, Etienne; Million, Julien; Hallier, Jean-Pierre; Morize, Eric; Munaron, Jean-Marie; Bousquet, Nicolas; Chassot, Emmanuel

2013-01-01

Age estimates, typically determined by counting periodic growth increments in calcified structures of vertebrates, are the basis of population dynamics models used for managing exploited or threatened species. In fisheries research, the use of otolith growth rings as an indicator of fish age has increased considerably in recent decades. However, otolith readings include various sources of uncertainty. Current ageing methods, which converts an average count of rings into age, only provide periodic age estimates in which the range of uncertainty is fully ignored. In this study, we describe a hierarchical model for estimating individual ages from repeated otolith readings. The model was developed within a Bayesian framework to explicitly represent the sources of uncertainty associated with age estimation, to allow for individual variations and to include knowledge on parameters from expertise. The performance of the proposed model was examined through simulations, and then it was coupled to a two-stanza somatic growth model to evaluate the impact of the age estimation method on the age composition of commercial fisheries catches. We illustrate our approach using the saggital otoliths of yellowfin tuna of the Indian Ocean collected through large-scale mark-recapture experiments. The simulation performance suggested that the ageing error model was able to estimate the ageing biases and provide accurate age estimates, regardless of the age of the fish. Coupled with the growth model, this approach appeared suitable for modeling the growth of Indian Ocean yellowfin and is consistent with findings of previous studies. The simulations showed that the choice of the ageing method can strongly affect growth estimates with subsequent implications for age-structured data used as inputs for population models. Finally, our modeling approach revealed particularly useful to reflect uncertainty around age estimates into the process of growth estimation and it can be applied to any study relying on age estimation. PMID:23637773

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.

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.

A new software for deformation source optimization, the Bayesian Earthquake Analysis Tool (BEAT)

NASA Astrophysics Data System (ADS)

Vasyura-Bathke, H.; Dutta, R.; Jonsson, S.; Mai, P. M.

2017-12-01

Modern studies of crustal deformation and the related source estimation, including magmatic and tectonic sources, increasingly use non-linear optimization strategies to estimate geometric and/or kinematic source parameters and often consider both jointly, geodetic and seismic data. Bayesian inference is increasingly being used for estimating posterior distributions of deformation source model parameters, given measured/estimated/assumed data and model uncertainties. For instance, some studies consider uncertainties of a layered medium and propagate these into source parameter uncertainties, while others use informative priors to reduce the model parameter space. In addition, innovative sampling algorithms have been developed to efficiently explore the high-dimensional parameter spaces. Compared to earlier studies, these improvements have resulted in overall more robust source model parameter estimates that include uncertainties. However, the computational burden of these methods is high and estimation codes are rarely made available along with the published results. Even if the codes are accessible, it is usually challenging to assemble them into a single optimization framework as they are typically coded in different programing languages. Therefore, further progress and future applications of these methods/codes are hampered, while reproducibility and validation of results has become essentially impossible. In the spirit of providing open-access and modular codes to facilitate progress and reproducible research in deformation source estimations, we undertook the effort of developing BEAT, a python package that comprises all the above-mentioned features in one single programing environment. The package builds on the pyrocko seismological toolbox (www.pyrocko.org), and uses the pymc3 module for Bayesian statistical model fitting. BEAT is an open-source package (https://github.com/hvasbath/beat), and we encourage and solicit contributions to the project. Here, we present our strategy for developing BEAT and show application examples; especially the effect of including the model prediction uncertainty of the velocity model in following source optimizations: full moment tensor, Mogi source, moderate strike-slip earth-quake.

Towards SI-traceable radio occultation excess phase processing with integrated uncertainty estimation for climate applications

NASA Astrophysics Data System (ADS)

Innerkofler, Josef; Pock, Christian; Kirchengast, Gottfried; Schwaerz, Marc; Jaeggi, Adrian; Schwarz, Jakob

2016-04-01

The GNSS Radio Occultation (RO) measurement technique is highly valuable for climate monitoring of the atmosphere as it provides accurate and precise measurements in the troposphere and stratosphere regions with global coverage, long-term stability, and virtually all-weather capability. The novel Reference Occultation Processing System (rOPS), currently under development at the WEGC at University of Graz aims to process raw RO measurements into essential climate variables, such as temperature, pressure, and tropospheric water vapor, in a way which is SI-traceable to the universal time standard and which includes rigorous uncertainty propagation. As part of this rOPS climate-quality processing system, accurate atmospheric excess phase profiles with new approaches integrating uncertainty propagation are derived from the raw occultation tracking data and orbit data. Regarding the latter, highly accurate orbit positions and velocities of the GNSS transmitter satellites and the RO receiver satellites in low Earth orbit (LEO) need to be determined, in order to enable high accuracy of the excess phase profiles. Using several representative test days of GPS orbit data from the CODE and IGS archives, which are available at accuracies of about 3 cm (position) / 0.03 mm/s (velocity), and employing Bernese 5.2 and Napeos 3.3.1 software packages for the LEO orbit determination of the CHAMP, GRACE, and MetOp RO satellites, we achieved robust SI-traced LEO orbit uncertainty estimates of about 5 cm (position) / 0.05 mm/s (velocity) for the daily orbits, including estimates of systematic uncertainty bounds and of propagated random uncertainties. For COSMIC RO satellites, we found decreased accuracy estimates near 10-15 cm (position) / 0.1-0.15 mm/s (velocity), since the characteristics of the small COSMIC satellite platforms and antennas provide somewhat less favorable orbit determination conditions. We present the setup of how we (I) used the Bernese and Napeos package in mutual cross-check for this purpose, (II) integrated satellite laser-ranging validation of the estimated systematic uncertainty bounds, (III) expanded the Bernese 5.2 software for propagating random uncertainties from the GPS orbit data and LEO navigation tracking data input to the LEO data output. Preliminary excess phase results including propagated uncertainty estimates will also be shown. Except for disturbed space weather conditions, we expect a robust performance at millimeter level for the derived excess phases, which after large-scale processing of the RO data of many years can provide a new SI-traced fundamental climate data record.

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.

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

Erratum to "Impact of uncertainty in soil, climatic, and chemical information in a pesticide leaching assessment".

PubMed

Loague, Keith; Green, Richard E; Giambelluca, Thomas W; Liang, Tony C; Yost, Russell S

2016-11-01

A simple mobility index, when combined with a geographic information system, can be used to generate rating maps which indicate qualitatively the potential for various organic chemicals to leach to groundwater. In this paper we investigate the magnitude of uncertainty associated with pesticide mobility estimates as a result of data uncertainties. Our example is for the Pearl Harbor Basin, Oahu, Hawaii. The two pesticides included in our analysis are atrazine (2-chloro-4-ethylamino-6-isopropylamino-s-triazine) and diuron [3-(3,4-dichlorophenyl)-1,1-dimethylarea]. The mobility index used here is known as the Attenuation Factor (AF); it requires soil, hydrogeologic, climatic, and chemical information as input data. We employ first-order uncertainty analysis to characterize the uncertainty in estimates of AF resulting from uncertainties in the various input data. Soils in the Pearl Harbor Basin are delineated at the order taxonomic category for this study. Our results show that there can be a significant amount of uncertainty in estimates of pesticide mobility for the Pearl Harbor Basin. This information needs to be considered if future decisions concerning chemical regulation are to be based on estimates of pesticide mobility determined from simple indices. Copyright © 2016. Published by Elsevier B.V.

Implications of Uncertainty in Fossil Fuel Emissions for Terrestrial Ecosystem Modeling

NASA Astrophysics Data System (ADS)

King, A. W.; Ricciuto, D. M.; Mao, J.; Andres, R. J.

2017-12-01

Given observations of the increase in atmospheric CO2, estimates of anthropogenic emissions and models of oceanic CO2 uptake, one can estimate net global CO2 exchange between the atmosphere and terrestrial ecosystems as the residual of the balanced global carbon budget. Estimates from the Global Carbon Project 2016 show that terrestrial ecosystems are a growing sink for atmospheric CO2 (averaging 2.12 Gt C y-1 for the period 1959-2015 with a growth rate of 0.03 Gt C y-1 per year) but with considerable year-to-year variability (standard deviation of 1.07 Gt C y-1). Within the uncertainty of the observations, emissions estimates and ocean modeling, this residual calculation is a robust estimate of a global terrestrial sink for CO2. A task of terrestrial ecosystem science is to explain the trend and variability in this estimate. However, "within the uncertainty" is an important caveat. The uncertainty (2σ; 95% confidence interval) in fossil fuel emissions is 8.4% (±0.8 Gt C in 2015). Combined with uncertainty in other carbon budget components, the 2σ uncertainty surrounding the global net terrestrial ecosystem CO2 exchange is ±1.6 Gt C y-1. Ignoring the uncertainty, the estimate of a general terrestrial sink includes 2 years (1987 and 1998) in which terrestrial ecosystems are a small source of CO2 to the atmosphere. However, with 2σ uncertainty, terrestrial ecosystems may have been a source in as many as 18 years. We examine how well global terrestrial biosphere models simulate the trend and interannual variability of the global-budget estimate of the terrestrial sink within the context of this uncertainty (e.g., which models fall outside the 2σ uncertainty and in what years). Models are generally capable of reproducing the trend in net terrestrial exchange, but are less able to capture interannual variability and often fall outside the 2σ uncertainty. The trend in the residual carbon budget estimate is primarily associated with the increase in atmospheric CO2, while interannual variation is related to variations in global land-surface temperature with weaker sinks in warmer years. We examine whether these relationships are reproduced in models. Their absence might explain weaknesses in model simulations or in the reconstruction of historical climate used as drivers in model intercomparison projects (MIPs).

Mass discharge estimation from contaminated sites: Multi-model solutions for assessment of conceptual uncertainty

NASA Astrophysics Data System (ADS)

Thomsen, N. I.; Troldborg, M.; McKnight, U. S.; Binning, P. J.; Bjerg, P. L.

2012-04-01

Mass discharge estimates are increasingly being used in the management of contaminated sites. Such estimates have proven useful for supporting decisions related to the prioritization of contaminated sites in a groundwater catchment. Potential management options can be categorised as follows: (1) leave as is, (2) clean up, or (3) further investigation needed. However, mass discharge estimates are often very uncertain, which may hamper the management decisions. If option 1 is incorrectly chosen soil and water quality will decrease, threatening or destroying drinking water resources. The risk of choosing option 2 is to spend money on remediating a site that does not pose a problem. Choosing option 3 will often be safest, but may not be the optimal economic solution. Quantification of the uncertainty in mass discharge estimates can therefore greatly improve the foundation for selecting the appropriate management option. The uncertainty of mass discharge estimates depends greatly on the extent of the site characterization. A good approach for uncertainty estimation will be flexible with respect to the investigation level, and account for both parameter and conceptual model uncertainty. We propose a method for quantifying the uncertainty of dynamic mass discharge estimates from contaminant point sources on the local scale. The method considers both parameter and conceptual uncertainty through a multi-model approach. The multi-model approach evaluates multiple conceptual models for the same site. The different conceptual models consider different source characterizations and hydrogeological descriptions. The idea is to include a set of essentially different conceptual models where each model is believed to be realistic representation of the given site, based on the current level of information. Parameter uncertainty is quantified using Monte Carlo simulations. For each conceptual model we calculate a transient mass discharge estimate with uncertainty bounds resulting from the parametric uncertainty. To quantify the conceptual uncertainty from a given site, we combine the outputs from the different conceptual models using Bayesian model averaging. The weight for each model is obtained by integrating available data and expert knowledge using Bayesian belief networks. The multi-model approach is applied to a contaminated site. At the site a DNAPL (dense non aqueous phase liquid) spill consisting of PCE (perchloroethylene) has contaminated a fractured clay till aquitard overlaying a limestone aquifer. The exact shape and nature of the source is unknown and so is the importance of transport in the fractures. The result of the multi-model approach is a visual representation of the uncertainty of the mass discharge estimates for the site which can be used to support the management options.

Uncertainty Forecasts Improve Weather-Related Decisions and Attenuate the Effects of Forecast Error

ERIC Educational Resources Information Center

Joslyn, Susan L.; LeClerc, Jared E.

2012-01-01

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…

Quantifying the uncertainty in site amplification modeling and its effects on site-specific seismic-hazard estimation in the upper Mississippi embayment and adjacent areas

USGS Publications Warehouse

Cramer, C.H.

2006-01-01

The Mississippi embayment, located in the central United States, and its thick deposits of sediments (over 1 km in places) have a large effect on earthquake ground motions. Several previous studies have addressed how these thick sediments might modify probabilistic seismic-hazard maps. The high seismic hazard associated with the New Madrid seismic zone makes it particularly important to quantify the uncertainty in modeling site amplification to better represent earthquake hazard in seismic-hazard maps. The methodology of the Memphis urban seismic-hazard-mapping project (Cramer et al., 2004) is combined with the reference profile approach of Toro and Silva (2001) to better estimate seismic hazard in the Mississippi embayment. Improvements over previous approaches include using the 2002 national seismic-hazard model, fully probabilistic hazard calculations, calibration of site amplification with improved nonlinear soil-response estimates, and estimates of uncertainty. Comparisons are made with the results of several previous studies, and estimates of uncertainty inherent in site-amplification modeling for the upper Mississippi embayment are developed. I present new seismic-hazard maps for the upper Mississippi embayment with the effects of site geology incorporating these uncertainties.

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.

Time-Resolved Particle Image Velocimetry Measurements with Wall Shear Stress and Uncertainty Quantification for the FDA Nozzle Model.

PubMed

Raben, Jaime S; Hariharan, Prasanna; Robinson, Ronald; Malinauskas, Richard; Vlachos, Pavlos P

2016-03-01

We present advanced particle image velocimetry (PIV) processing, post-processing, and uncertainty estimation techniques to support the validation of computational fluid dynamics analyses of medical devices. This work is an extension of a previous FDA-sponsored multi-laboratory study, which used a medical device mimicking geometry referred to as the FDA benchmark nozzle model. Experimental measurements were performed using time-resolved PIV at five overlapping regions of the model for Reynolds numbers in the nozzle throat of 500, 2000, 5000, and 8000. Images included a twofold increase in spatial resolution in comparison to the previous study. Data was processed using ensemble correlation, dynamic range enhancement, and phase correlations to increase signal-to-noise ratios and measurement accuracy, and to resolve flow regions with large velocity ranges and gradients, which is typical of many blood-contacting medical devices. Parameters relevant to device safety, including shear stress at the wall and in bulk flow, were computed using radial basis functions. In addition, in-field spatially resolved pressure distributions, Reynolds stresses, and energy dissipation rates were computed from PIV measurements. Velocity measurement uncertainty was estimated directly from the PIV correlation plane, and uncertainty analysis for wall shear stress at each measurement location was performed using a Monte Carlo model. Local velocity uncertainty varied greatly and depended largely on local conditions such as particle seeding, velocity gradients, and particle displacements. Uncertainty in low velocity regions in the sudden expansion section of the nozzle was greatly reduced by over an order of magnitude when dynamic range enhancement was applied. Wall shear stress uncertainty was dominated by uncertainty contributions from velocity estimations, which were shown to account for 90-99% of the total uncertainty. This study provides advancements in the PIV processing methodologies over the previous work through increased PIV image resolution, use of robust image processing algorithms for near-wall velocity measurements and wall shear stress calculations, and uncertainty analyses for both velocity and wall shear stress measurements. The velocity and shear stress analysis, with spatially distributed uncertainty estimates, highlights the challenges of flow quantification in medical devices and provides potential methods to overcome such challenges.

Uncertainty Estimation using Bootstrapped Kriging Predictions for Precipitation Isoscapes

NASA Astrophysics Data System (ADS)

Ma, C.; Bowen, G. J.; Vander Zanden, H.; Wunder, M.

2017-12-01

Isoscapes are spatial models representing the distribution of stable isotope values across landscapes. Isoscapes of hydrogen and oxygen in precipitation are now widely used in a diversity of fields, including geology, biology, hydrology, and atmospheric science. To generate isoscapes, geostatistical methods are typically applied to extend predictions from limited data measurements. Kriging is a popular method in isoscape modeling, but quantifying the uncertainty associated with the resulting isoscapes is challenging. Applications that use precipitation isoscapes to determine sample origin require estimation of uncertainty. Here we present a simple bootstrap method (SBM) to estimate the mean and uncertainty of the krigged isoscape and compare these results with a generalized bootstrap method (GBM) applied in previous studies. We used hydrogen isotopic data from IsoMAP to explore these two approaches for estimating uncertainty. We conducted 10 simulations for each bootstrap method and found that SBM results in more kriging predictions (9/10) compared to GBM (4/10). Prediction from SBM was closer to the original prediction generated without bootstrapping and had less variance than GBM. SBM was tested on different datasets from IsoMAP with different numbers of observation sites. We determined that predictions from the datasets with fewer than 40 observation sites using SBM were more variable than the original prediction. The approaches we used for estimating uncertainty will be compiled in an R package that is under development. We expect that these robust estimates of precipitation isoscape uncertainty can be applied in diagnosing the origin of samples ranging from various type of waters to migratory animals, food products, and humans.

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

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.

Uncertainty in temperature response of current consumption-based emissions estimates

NASA Astrophysics Data System (ADS)

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

2015-05-01

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 along the entire causal chain. 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. Based on our assumptions, which exclude correlations in the economic data, the uncertainty in the economic data appears to have a relatively small impact on uncertainty at the national level in comparison to emissions and metric uncertainty. 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 ±10 to ±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 to ±25 %, with metric and emission uncertainties contributing similarly. The absolute global temperature potential (AGTP) 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.

Uncertainty analysis of gross primary production partitioned from net ecosystem exchange measurements

NASA Astrophysics Data System (ADS)

Raj, Rahul; Hamm, Nicholas Alexander Samuel; van der Tol, Christiaan; Stein, Alfred

2016-03-01

Gross primary production (GPP) can be separated from flux tower measurements of net ecosystem exchange (NEE) of CO2. This is used increasingly to validate process-based simulators and remote-sensing-derived estimates of simulated GPP at various time steps. Proper validation includes the uncertainty associated with this separation. In this study, uncertainty assessment was done in a Bayesian framework. It was applied to data from the Speulderbos forest site, The Netherlands. We estimated the uncertainty in GPP at half-hourly time steps, using a non-rectangular hyperbola (NRH) model for its separation from the flux tower measurements. The NRH model provides a robust empirical relationship between radiation and GPP. It includes the degree of curvature of the light response curve, radiation and temperature. Parameters of the NRH model were fitted to the measured NEE data for every 10-day period during the growing season (April to October) in 2009. We defined the prior distribution of each NRH parameter and used Markov chain Monte Carlo (MCMC) simulation to estimate the uncertainty in the separated GPP from the posterior distribution at half-hourly time steps. This time series also allowed us to estimate the uncertainty at daily time steps. We compared the informative with the non-informative prior distributions of the NRH parameters and found that both choices produced similar posterior distributions of GPP. This will provide relevant and important information for the validation of process-based simulators in the future. Furthermore, the obtained posterior distributions of NEE and the NRH parameters are of interest for a range of applications.

Multivariate Copula Analysis Toolbox (MvCAT): Describing dependence and underlying uncertainty using a Bayesian framework

NASA Astrophysics Data System (ADS)

Sadegh, Mojtaba; Ragno, Elisa; AghaKouchak, Amir

2017-06-01

We present a newly developed Multivariate Copula Analysis Toolbox (MvCAT) which includes a wide range of copula families with different levels of complexity. MvCAT employs a Bayesian framework with a residual-based Gaussian likelihood function for inferring copula parameters and estimating the underlying uncertainties. The contribution of this paper is threefold: (a) providing a Bayesian framework to approximate the predictive uncertainties of fitted copulas, (b) introducing a hybrid-evolution Markov Chain Monte Carlo (MCMC) approach designed for numerical estimation of the posterior distribution of copula parameters, and (c) enabling the community to explore a wide range of copulas and evaluate them relative to the fitting uncertainties. We show that the commonly used local optimization methods for copula parameter estimation often get trapped in local minima. The proposed method, however, addresses this limitation and improves describing the dependence structure. MvCAT also enables evaluation of uncertainties relative to the length of record, which is fundamental to a wide range of applications such as multivariate frequency analysis.

An integrated uncertainty analysis and data assimilation approach for improved streamflow predictions

NASA Astrophysics Data System (ADS)

Hogue, T. S.; He, M.; Franz, K. J.; Margulis, S. A.; Vrugt, J. A.

2010-12-01

The current study presents an integrated uncertainty analysis and data assimilation approach to improve streamflow predictions while simultaneously providing meaningful estimates of the associated uncertainty. Study models include the National Weather Service (NWS) operational snow model (SNOW17) and rainfall-runoff model (SAC-SMA). The proposed approach uses the recently developed DiffeRential Evolution Adaptive Metropolis (DREAM) to simultaneously estimate uncertainties in model parameters, forcing, and observations. An ensemble Kalman filter (EnKF) is configured with the DREAM-identified uncertainty structure and applied to assimilating snow water equivalent data into the SNOW17 model for improved snowmelt simulations. Snowmelt estimates then serves as an input to the SAC-SMA model to provide streamflow predictions at the basin outlet. The robustness and usefulness of the approach is evaluated for a snow-dominated watershed in the northern Sierra Mountains. This presentation describes the implementation of DREAM and EnKF into the coupled SNOW17 and SAC-SMA models and summarizes study results and findings.

Bayesian characterization of uncertainty in species interaction strengths.

PubMed

Wolf, Christopher; Novak, Mark; Gitelman, Alix I

2017-06-01

Considerable effort has been devoted to the estimation of species interaction strengths. This effort has focused primarily on statistical significance testing and obtaining point estimates of parameters that contribute to interaction strength magnitudes, leaving the characterization of uncertainty associated with those estimates unconsidered. We consider a means of characterizing the uncertainty of a generalist predator's interaction strengths by formulating an observational method for estimating a predator's prey-specific per capita attack rates as a Bayesian statistical model. This formulation permits the explicit incorporation of multiple sources of uncertainty. A key insight is the informative nature of several so-called non-informative priors that have been used in modeling the sparse data typical of predator feeding surveys. We introduce to ecology a new neutral prior and provide evidence for its superior performance. We use a case study to consider the attack rates in a New Zealand intertidal whelk predator, and we illustrate not only that Bayesian point estimates can be made to correspond with those obtained by frequentist approaches, but also that estimation uncertainty as described by 95% intervals is more useful and biologically realistic using the Bayesian method. In particular, unlike in bootstrap confidence intervals, the lower bounds of the Bayesian posterior intervals for attack rates do not include zero when a predator-prey interaction is in fact observed. We conclude that the Bayesian framework provides a straightforward, probabilistic characterization of interaction strength uncertainty, enabling future considerations of both the deterministic and stochastic drivers of interaction strength and their impact on food webs.

Quantifying Uncertainties in N2O Emission Due to N Fertilizer Application in Cultivated Areas

PubMed Central

Philibert, Aurore; Loyce, Chantal; Makowski, David

2012-01-01

Nitrous oxide (N2O) is a greenhouse gas with a global warming potential approximately 298 times greater than that of CO2. In 2006, the Intergovernmental Panel on Climate Change (IPCC) estimated N2O emission due to synthetic and organic nitrogen (N) fertilization at 1% of applied N. We investigated the uncertainty on this estimated value, by fitting 13 different models to a published dataset including 985 N2O measurements. These models were characterized by (i) the presence or absence of the explanatory variable “applied N”, (ii) the function relating N2O emission to applied N (exponential or linear function), (iii) fixed or random background (i.e. in the absence of N application) N2O emission and (iv) fixed or random applied N effect. We calculated ranges of uncertainty on N2O emissions from a subset of these models, and compared them with the uncertainty ranges currently used in the IPCC-Tier 1 method. The exponential models outperformed the linear models, and models including one or two random effects outperformed those including fixed effects only. The use of an exponential function rather than a linear function has an important practical consequence: the emission factor is not constant and increases as a function of applied N. Emission factors estimated using the exponential function were lower than 1% when the amount of N applied was below 160 kg N ha−1. Our uncertainty analysis shows that the uncertainty range currently used by the IPCC-Tier 1 method could be reduced. PMID:23226430

Agriculture-driven deforestation in the tropics from 1990-2015: emissions, trends and uncertainties

NASA Astrophysics Data System (ADS)

Carter, Sarah; Herold, Martin; Avitabile, Valerio; de Bruin, Sytze; De Sy, Veronique; Kooistra, Lammert; Rufino, Mariana C.

2018-01-01

Limited data exists on emissions from agriculture-driven deforestation, and available data are typically uncertain. In this paper, we provide comparable estimates of emissions from both all deforestation and agriculture-driven deforestation, with uncertainties for 91 countries across the tropics between 1990 and 2015. Uncertainties associated with input datasets (activity data and emissions factors) were used to combine the datasets, where most certain datasets contribute the most. This method utilizes all the input data, while minimizing the uncertainty of the emissions estimate. The uncertainty of input datasets was influenced by the quality of the data, the sample size (for sample-based datasets), and the extent to which the timeframe of the data matches the period of interest. Area of deforestation, and the agriculture-driver factor (extent to which agriculture drives deforestation), were the most uncertain components of the emissions estimates, thus improvement in the uncertainties related to these estimates will provide the greatest reductions in uncertainties of emissions estimates. Over the period of the study, Latin America had the highest proportion of deforestation driven by agriculture (78%), and Africa had the lowest (62%). Latin America had the highest emissions from agriculture-driven deforestation, and these peaked at 974 ± 148 Mt CO2 yr-1 in 2000-2005. Africa saw a continuous increase in emissions between 1990 and 2015 (from 154 ± 21-412 ± 75 Mt CO2 yr-1), so mitigation initiatives could be prioritized there. Uncertainties for emissions from agriculture-driven deforestation are ± 62.4% (average over 1990-2015), and uncertainties were highest in Asia and lowest in Latin America. Uncertainty information is crucial for transparency when reporting, and gives credibility to related mitigation initiatives. We demonstrate that uncertainty data can also be useful when combining multiple open datasets, so we recommend new data providers to include this information.

Methodology for Uncertainty Analysis of Dynamic Computational Toxicology Models

EPA Science Inventory

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

Variances and uncertainties of the sample laboratory-to-laboratory variance (S(L)2) and standard deviation (S(L)) associated with an interlaboratory study.

PubMed

McClure, Foster D; Lee, Jung K

2012-01-01

The validation process for an analytical method usually employs an interlaboratory study conducted as a balanced completely randomized model involving a specified number of randomly chosen laboratories, each analyzing a specified number of randomly allocated replicates. For such studies, formulas to obtain approximate unbiased estimates of the variance and uncertainty of the sample laboratory-to-laboratory (lab-to-lab) STD (S(L)) have been developed primarily to account for the uncertainty of S(L) when there is a need to develop an uncertainty budget that includes the uncertainty of S(L). For the sake of completeness on this topic, formulas to estimate the variance and uncertainty of the sample lab-to-lab variance (S(L)2) were also developed. In some cases, it was necessary to derive the formulas based on an approximate distribution for S(L)2.

Space shuttle propulsion estimation development verification

NASA Technical Reports Server (NTRS)

Rogers, Robert M.

1989-01-01

The application of extended Kalman filtering to estimating the Space Shuttle Propulsion performance, i.e., specific impulse, from flight data in a post-flight processing computer program is detailed. The flight data used include inertial platform acceleration, SRB head pressure, SSME chamber pressure and flow rates, and ground based radar tracking data. The key feature in this application is the model used for the SRB's, which is a nominal or reference quasi-static internal ballistics model normalized to the propellant burn depth. Dynamic states of mass overboard and propellant burn depth are included in the filter model to account for real-time deviations from the reference model used. Aerodynamic, plume, wind and main engine uncertainties are also included for an integrated system model. Assuming uncertainty within the propulsion system model and attempts to estimate its deviations represent a new application of parameter estimation for rocket powered vehicles. Illustrations from the results of applying this estimation approach to several missions show good quality propulsion estimates.

Uncertainty analysis in vulnerability estimations for elements at risk- a review of concepts and some examples on landslides

NASA Astrophysics Data System (ADS)

Ciurean, R. L.; Glade, T.

2012-04-01

Decision under uncertainty is a constant of everyday life and an important component of risk management and governance. Recently, experts have emphasized the importance of quantifying uncertainty in all phases of landslide risk analysis. Due to its multi-dimensional and dynamic nature, (physical) vulnerability is inherently complex and the "degree of loss" estimates imprecise and to some extent even subjective. Uncertainty analysis introduces quantitative modeling approaches that allow for a more explicitly objective output, improving the risk management process as well as enhancing communication between various stakeholders for better risk governance. This study presents a review of concepts for uncertainty analysis in vulnerability of elements at risk to landslides. Different semi-quantitative and quantitative methods are compared based on their feasibility in real-world situations, hazard dependency, process stage in vulnerability assessment (i.e. input data, model, output), and applicability within an integrated landslide hazard and risk framework. The resulted observations will help to identify current gaps and future needs in vulnerability assessment, including estimation of uncertainty propagation, transferability of the methods, development of visualization tools, but also address basic questions like what is uncertainty and how uncertainty can be quantified or treated in a reliable and reproducible way.

A New Formulation of the Filter-Error Method for Aerodynamic Parameter Estimation in Turbulence

NASA Technical Reports Server (NTRS)

Grauer, Jared A.; Morelli, Eugene A.

2015-01-01

A new formulation of the filter-error method for estimating aerodynamic parameters in nonlinear aircraft dynamic models during turbulence was developed and demonstrated. The approach uses an estimate of the measurement noise covariance to identify the model parameters, their uncertainties, and the process noise covariance, in a relaxation method analogous to the output-error method. Prior information on the model parameters and uncertainties can be supplied, and a post-estimation correction to the uncertainty was included to account for colored residuals not considered in the theory. No tuning parameters, needing adjustment by the analyst, are used in the estimation. The method was demonstrated in simulation using the NASA Generic Transport Model, then applied to the subscale T-2 jet-engine transport aircraft flight. Modeling results in different levels of turbulence were compared with results from time-domain output error and frequency- domain equation error methods to demonstrate the effectiveness of the approach.

An approach for estimating measurement uncertainty in medical laboratories using data from long-term quality control and external quality assessment schemes.

PubMed

Padoan, Andrea; Antonelli, Giorgia; Aita, Ada; Sciacovelli, Laura; Plebani, Mario

2017-10-26

The present study was prompted by the ISO 15189 requirements that medical laboratories should estimate measurement uncertainty (MU). The method used to estimate MU included the: a) identification of quantitative tests, b) classification of tests in relation to their clinical purpose, and c) identification of criteria to estimate the different MU components. Imprecision was estimated using long-term internal quality control (IQC) results of the year 2016, while external quality assessment schemes (EQAs) results obtained in the period 2015-2016 were used to estimate bias and bias uncertainty. A total of 263 measurement procedures (MPs) were analyzed. On the basis of test purpose, in 51 MPs imprecision only was used to estimate MU; in the remaining MPs, the bias component was not estimable for 22 MPs because EQAs results did not provide reliable statistics. For a total of 28 MPs, two or more MU values were calculated on the basis of analyte concentration levels. Overall, results showed that uncertainty of bias is a minor factor contributing to MU, the bias component being the most relevant contributor to all the studied sample matrices. The model chosen for MU estimation allowed us to derive a standardized approach for bias calculation, with respect to the fitness-for-purpose of test results. Measurement uncertainty estimation could readily be implemented in medical laboratories as a useful tool in monitoring the analytical quality of test results since they are calculated using a combination of both the long-term imprecision IQC results and bias, on the basis of EQAs results.

Prioritizing Chemicals and Data Requirements for Screening-Level Exposure and Risk Assessment

PubMed Central

Brown, Trevor N.; Wania, Frank; Breivik, Knut; McLachlan, Michael S.

2012-01-01

Background: Scientists and regulatory agencies strive to identify chemicals that may cause harmful effects to humans and the environment; however, prioritization is challenging because of the large number of chemicals requiring evaluation and limited data and resources. Objectives: We aimed to prioritize chemicals for exposure and exposure potential and obtain a quantitative perspective on research needs to better address uncertainty in screening assessments. Methods: We used a multimedia mass balance model to prioritize > 12,000 organic chemicals using four far-field human exposure metrics. The propagation of variance (uncertainty) in key chemical information used as model input for calculating exposure metrics was quantified. Results: Modeled human concentrations and intake rates span approximately 17 and 15 orders of magnitude, respectively. Estimates of exposure potential using human concentrations and a unit emission rate span approximately 13 orders of magnitude, and intake fractions span 7 orders of magnitude. The actual chemical emission rate contributes the greatest variance (uncertainty) in exposure estimates. The human biotransformation half-life is the second greatest source of uncertainty in estimated concentrations. In general, biotransformation and biodegradation half-lives are greater sources of uncertainty in modeled exposure and exposure potential than chemical partition coefficients. Conclusions: Mechanistic exposure modeling is suitable for screening and prioritizing large numbers of chemicals. By including uncertainty analysis and uncertainty in chemical information in the exposure estimates, these methods can help identify and address the important sources of uncertainty in human exposure and risk assessment in a systematic manner. PMID:23008278

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.

A Global Emission Inventory of Black Carbon and Primary Organic Carbon from Fossil-Fuel and Biofuel Combustion

NASA Astrophysics Data System (ADS)

Bond, T. C.; Streets, D. G.; Nelson, S. M.

2001-12-01

Regional and global climate models rely on emission inventories of black carbon and organic carbon to determine the climatic effects of primary particulate matter (PM) from combustion. The emission of primary carbonaceous particles is highly dependent on fuel type and combustion practice. Therefore, simple categories such as "domestic" or "industrial" combustion are not sufficient to quantify emissions, and the black-carbon and organic-carbon fractions of PM vary with combustion type. We present a global inventory of primary carbonaceous particles that improves on previous "bottom-up" tabulations (e.g. \\textit{Cooke et al.,} 1999) by considering approximately 100 technologies, each representing one combination of fuel, combustion type, and emission controls. For fossil-fuel combustion, we include several categories not found in previous inventories, including "superemitting" and two-stroke vehicles, steel-making. We also include emissions from waste burning and biofuels used for heating and cooking. Open biomass burning is not included. Fuel use, drawn from International Energy Agency (IEA) and United Nations (UN) data, is divided into technologies on a regional basis. We suggest that emissions in developing countries are better characterized by including high-emitting technologies than by invoking emission multipliers. Due to lack of information on emission factors and technologies in use, uncertainties are high. We estimate central values and uncertainties by combining the range of emission factors found in the literature with reasonable estimates of technology divisions. We provide regional totals of central, low and high estimates, identify the sources of greatest uncertainty to be targeted for future work, and compare our results with previous emission inventories. Both central estimates and uncertainties are given on a 1\\deg x1\\deg grid. As we have reported previously for the case of China (\\textit{Streets et al.,} 2001), low-technology combustion contributes greatly to the emissions and to the uncertainties.

Robust gaze-steering of an active vision system against errors in the estimated parameters

NASA Astrophysics Data System (ADS)

Han, Youngmo

2015-01-01

Gaze-steering is often used to broaden the viewing range of an active vision system. Gaze-steering procedures are usually based on estimated parameters such as image position, image velocity, depth and camera calibration parameters. However, there may be uncertainties in these estimated parameters because of measurement noise and estimation errors. In this case, robust gaze-steering cannot be guaranteed. To compensate for such problems, this paper proposes a gaze-steering method based on a linear matrix inequality (LMI). In this method, we first propose a proportional derivative (PD) control scheme on the unit sphere that does not use depth parameters. This proposed PD control scheme can avoid uncertainties in the estimated depth and camera calibration parameters, as well as inconveniences in their estimation process, including the use of auxiliary feature points and highly non-linear computation. Furthermore, the control gain of the proposed PD control scheme on the unit sphere is designed using LMI such that the designed control is robust in the presence of uncertainties in the other estimated parameters, such as image position and velocity. Simulation results demonstrate that the proposed method provides a better compensation for uncertainties in the estimated parameters than the contemporary linear method and steers the gaze of the camera more steadily over time than the contemporary non-linear method.

Historic emissions from deforestation and forest degradation in Mato Grosso, Brazil: 1) source data uncertainties

PubMed Central

2011-01-01

Background Historic carbon emissions are an important foundation for proposed efforts to Reduce Emissions from Deforestation and forest Degradation and enhance forest carbon stocks through conservation and sustainable forest management (REDD+). The level of uncertainty in historic carbon emissions estimates is also critical for REDD+, since high uncertainties could limit climate benefits from credited mitigation actions. Here, we analyzed source data uncertainties based on the range of available deforestation, forest degradation, and forest carbon stock estimates for the Brazilian state of Mato Grosso during 1990-2008. Results Deforestation estimates showed good agreement for multi-year periods of increasing and decreasing deforestation during the study period. However, annual deforestation rates differed by > 20% in more than half of the years between 1997-2008, even for products based on similar input data. Tier 2 estimates of average forest carbon stocks varied between 99-192 Mg C ha-1, with greatest differences in northwest Mato Grosso. Carbon stocks in deforested areas increased over the study period, yet this increasing trend in deforested biomass was smaller than the difference among carbon stock datasets for these areas. Conclusions Estimates of source data uncertainties are essential for REDD+. Patterns of spatial and temporal disagreement among available data products provide a roadmap for future efforts to reduce source data uncertainties for estimates of historic forest carbon emissions. Specifically, regions with large discrepancies in available estimates of both deforestation and forest carbon stocks are priority areas for evaluating and improving existing estimates. Full carbon accounting for REDD+ will also require filling data gaps, including forest degradation and secondary forest, with annual data on all forest transitions. PMID:22208947

VizieR Online Data Catalog: SDSS bulge, disk and total stellar mass estimates (Mendel+, 2014)

NASA Astrophysics Data System (ADS)

Mendel, J. T.; Simard, L.; Palmer, M.; Ellison, S. L.; Patton, D. R.

2014-01-01

We present a catalog of bulge, disk, and total stellar mass estimates for ~660000 galaxies in the Legacy area of the Sloan Digital Sky Survey Data (SDSS) Release 7. These masses are based on a homogeneous catalog of g- and r-band photometry described by Simard et al. (2011, Cat. J/ApJS/196/11), which we extend here with bulge+disk and Sersic profile photometric decompositions in the SDSS u, i, and z bands. We discuss the methodology used to derive stellar masses from these data via fitting to broadband spectral energy distributions (SEDs), and show that the typical statistical uncertainty on total, bulge, and disk stellar mass is ~0.15 dex. Despite relatively small formal uncertainties, we argue that SED modeling assumptions, including the choice of synthesis model, extinction law, initial mass function, and details of stellar evolution likely contribute an additional 60% systematic uncertainty in any mass estimate based on broadband SED fitting. We discuss several approaches for identifying genuine bulge+disk systems based on both their statistical likelihood and an analysis of their one-dimensional surface-brightness profiles, and include these metrics in the catalogs. Estimates of the total, bulge and disk stellar masses for both normal and dust-free models and their uncertainties are made publicly available here. (4 data files).

Estimating uncertainty in subsurface glider position using transmissions from fixed acoustic tomography sources.

PubMed

Van Uffelen, Lora J; Nosal, Eva-Marie; Howe, Bruce M; Carter, Glenn S; Worcester, Peter F; Dzieciuch, Matthew A; Heaney, Kevin D; Campbell, Richard L; Cross, Patrick S

2013-10-01

Four acoustic Seagliders were deployed in the Philippine Sea November 2010 to April 2011 in the vicinity of an acoustic tomography array. The gliders recorded over 2000 broadband transmissions at ranges up to 700 km from moored acoustic sources as they transited between mooring sites. The precision of glider positioning at the time of acoustic reception is important to resolve the fundamental ambiguity between position and sound speed. The Seagliders utilized GPS at the surface and a kinematic model below for positioning. The gliders were typically underwater for about 6.4 h, diving to depths of 1000 m and traveling on average 3.6 km during a dive. Measured acoustic arrival peaks were unambiguously associated with predicted ray arrivals. Statistics of travel-time offsets between received arrivals and acoustic predictions were used to estimate range uncertainty. Range (travel time) uncertainty between the source and the glider position from the kinematic model is estimated to be 639 m (426 ms) rms. Least-squares solutions for glider position estimated from acoustically derived ranges from 5 sources differed by 914 m rms from modeled positions, with estimated uncertainty of 106 m rms in horizontal position. Error analysis included 70 ms rms of uncertainty due to oceanic sound-speed variability.

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.

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.

Age-Related Differences in Susceptibility to Carcinogenesis. II. Approaches for Application and Uncertainty Analyses for Individual Genetically Acting Carcinogens

PubMed Central

Hattis, Dale; Goble, Robert; Chu, Margaret

2005-01-01

In an earlier report we developed a quantitative likelihood-based analysis of the differences in sensitivity of rodents to mutagenic carcinogens across three life stages (fetal, birth to weaning, and weaning to 60 days) relative to exposures in adult life. Here we draw implications for assessing human risks for full lifetime exposures, taking into account three types of uncertainties in making projections from the rodent data: uncertainty in the central estimates of the life-stage–specific sensitivity factors estimated earlier, uncertainty from chemical-to-chemical differences in life-stage–specific sensitivities for carcinogenesis, and uncertainty in the mapping of rodent life stages to human ages/exposure periods. Among the uncertainties analyzed, the mapping of rodent life stages to human ages/exposure periods is most important quantitatively (a range of several-fold in estimates of the duration of the human equivalent of the highest sensitivity “birth to weaning” period in rodents). The combined effects of these uncertainties are estimated with Monte Carlo analyses. Overall, the estimated population arithmetic mean risk from lifetime exposures at a constant milligrams per kilogram body weight level to a generic mutagenic carcinogen is about 2.8-fold larger than expected from adult-only exposure with 5–95% confidence limits of 1.5-to 6-fold. The mean estimates for the 0- to 2-year and 2- to 15-year periods are about 35–55% larger than the 10- and 3-fold sensitivity factor adjustments recently proposed by the U.S. Environmental Protection Agency. The present results are based on data for only nine chemicals, including five mutagens. Risk inferences will be altered as data become available for other chemicals. PMID:15811844

Estimating rate uncertainty with maximum likelihood: differences between power-law and flicker–random-walk models

USGS Publications Warehouse

Langbein, John O.

2012-01-01

Recent studies have documented that global positioning system (GPS) time series of position estimates have temporal correlations which have been modeled as a combination of power-law and white noise processes. When estimating quantities such as a constant rate from GPS time series data, the estimated uncertainties on these quantities are more realistic when using a noise model that includes temporal correlations than simply assuming temporally uncorrelated noise. However, the choice of the specific representation of correlated noise can affect the estimate of uncertainty. For many GPS time series, the background noise can be represented by either: (1) a sum of flicker and random-walk noise or, (2) as a power-law noise model that represents an average of the flicker and random-walk noise. For instance, if the underlying noise model is a combination of flicker and random-walk noise, then incorrectly choosing the power-law model could underestimate the rate uncertainty by a factor of two. Distinguishing between the two alternate noise models is difficult since the flicker component can dominate the assessment of the noise properties because it is spread over a significant portion of the measurable frequency band. But, although not necessarily detectable, the random-walk component can be a major constituent of the estimated rate uncertainty. None the less, it is possible to determine the upper bound on the random-walk noise.

Incorporating structure from motion uncertainty into image-based pose estimation

NASA Astrophysics Data System (ADS)

Ludington, Ben T.; Brown, Andrew P.; Sheffler, Michael J.; Taylor, Clark N.; Berardi, Stephen

2015-05-01

A method for generating and utilizing structure from motion (SfM) uncertainty estimates within image-based pose estimation is presented. The method is applied to a class of problems in which SfM algorithms are utilized to form a geo-registered reference model of a particular ground area using imagery gathered during flight by a small unmanned aircraft. The model is then used to form camera pose estimates in near real-time from imagery gathered later. The resulting pose estimates can be utilized by any of the other onboard systems (e.g. as a replacement for GPS data) or downstream exploitation systems, e.g., image-based object trackers. However, many of the consumers of pose estimates require an assessment of the pose accuracy. The method for generating the accuracy assessment is presented. First, the uncertainty in the reference model is estimated. Bundle Adjustment (BA) is utilized for model generation. While the high-level approach for generating a covariance matrix of the BA parameters is straightforward, typical computing hardware is not able to support the required operations due to the scale of the optimization problem within BA. Therefore, a series of sparse matrix operations is utilized to form an exact covariance matrix for only the parameters that are needed at a particular moment. Once the uncertainty in the model has been determined, it is used to augment Perspective-n-Point pose estimation algorithms to improve the pose accuracy and to estimate the resulting pose uncertainty. The implementation of the described method is presented along with results including results gathered from flight test data.

Uncertainties in Projecting Risks of Late Effects from Space Radiation

NASA Astrophysics Data System (ADS)

Cucinotta, F.; Schimmerling, W.; Peterson, L.; Wilson, J.; Saganti, P.; Dicello, J.

The health risks faced by astronauts from space radiation include cancer, cataracts, hereditary effects, CNS risks, and non - cancer morbidity and mortality risks related to the diseases of the old age. Methods used to project risks in low -Earth orbit are of questionable merit for exploration missions because of the limited radiobiology data and knowledge of galactic cosmic ray (GCR) heavy ions, which causes estimates of the risk of late effects to be highly uncertain. Risk projections involve a product of many biological and physical factors, each of which has a differential range of uncertainty due to lack of data and knowledge. Within the linear-additivity model, we use Monte-Carlo sampling from subjective uncertainty distributions in each factor to obtain a maximum likelihood estimate of the overall uncertainty in risk projections. The resulting methodology is applied to several human space exploration mission scenarios including ISS, lunar station, deep space outpost, and Mar's missions of duration of 360, 660, and 1000 days. The major results are the quantification of the uncertainties in current risk estimates, the identification of the primary factors that dominate risk projection uncertainties, and the development of a method to quantify candidate approaches to reduce uncertainties or mitigate risks. The large uncertainties in GCR risk projections lead to probability distributions of risk that mask any potential risk reduction using the "optimization" of shielding materials or configurations. In contrast, the design of shielding optimization approaches for solar particle events and trapped protons can be made at this time, and promising technologies can be shown to have merit using our approach. The methods used also make it possible to express risk management objectives in terms of quantitative objectives, i.e., number of days in space without exceeding a given risk level within well defined confidence limits

Uncertainty of streamwater solute fluxes in five contrasting headwater catchments including model uncertainty and natural variability (Invited)

NASA Astrophysics Data System (ADS)

Aulenbach, B. T.; Burns, D. A.; Shanley, J. B.; Yanai, R. D.; Bae, K.; Wild, A.; Yang, Y.; Dong, Y.

2013-12-01

There are many sources of uncertainty in estimates of streamwater solute flux. Flux is the product of discharge and concentration (summed over time), each of which has measurement uncertainty of its own. Discharge can be measured almost continuously, but concentrations are usually determined from discrete samples, which increases uncertainty dependent on sampling frequency and how concentrations are assigned for the periods between samples. Gaps between samples can be estimated by linear interpolation or by models that that use the relations between concentration and continuously measured or known variables such as discharge, season, temperature, and time. For this project, developed in cooperation with QUEST (Quantifying Uncertainty in Ecosystem Studies), we evaluated uncertainty for three flux estimation methods and three different sampling frequencies (monthly, weekly, and weekly plus event). The constituents investigated were dissolved NO3, Si, SO4, and dissolved organic carbon (DOC), solutes whose concentration dynamics exhibit strongly contrasting behavior. The evaluation was completed for a 10-year period at five small, forested watersheds in Georgia, New Hampshire, New York, Puerto Rico, and Vermont. Concentration regression models were developed for each solute at each of the three sampling frequencies for all five watersheds. Fluxes were then calculated using (1) a linear interpolation approach, (2) a regression-model method, and (3) the composite method - which combines the regression-model method for estimating concentrations and the linear interpolation method for correcting model residuals to the observed sample concentrations. We considered the best estimates of flux to be derived using the composite method at the highest sampling frequencies. We also evaluated the importance of sampling frequency and estimation method on flux estimate uncertainty; flux uncertainty was dependent on the variability characteristics of each solute and varied for different reporting periods (e.g. 10-year, study period vs. annually vs. monthly). The usefulness of the two regression model based flux estimation approaches was dependent upon the amount of variance in concentrations the regression models could explain. Our results can guide the development of optimal sampling strategies by weighing sampling frequency with improvements in uncertainty in stream flux estimates for solutes with particular characteristics of variability. The appropriate flux estimation method is dependent on a combination of sampling frequency and the strength of concentration regression models. Sites: Biscuit Brook (Frost Valley, NY), Hubbard Brook Experimental Forest and LTER (West Thornton, NH), Luquillo Experimental Forest and LTER (Luquillo, Puerto Rico), Panola Mountain (Stockbridge, GA), Sleepers River Research Watershed (Danville, VT)

Gaussian Process Interpolation for Uncertainty Estimation in Image Registration

PubMed Central

Wachinger, Christian; Golland, Polina; Reuter, Martin; Wells, William

2014-01-01

Intensity-based image registration requires resampling images on a common grid to evaluate the similarity function. The uncertainty of interpolation varies across the image, depending on the location of resampled points relative to the base grid. We propose to perform Bayesian inference with Gaussian processes, where the covariance matrix of the Gaussian process posterior distribution estimates the uncertainty in interpolation. The Gaussian process replaces a single image with a distribution over images that we integrate into a generative model for registration. Marginalization over resampled images leads to a new similarity measure that includes the uncertainty of the interpolation. We demonstrate that our approach increases the registration accuracy and propose an efficient approximation scheme that enables seamless integration with existing registration methods. PMID:25333127

Robust Flutter Margin Analysis that Incorporates Flight Data

NASA Technical Reports Server (NTRS)

Lind, Rick; Brenner, Martin J.

1998-01-01

An approach for computing worst-case flutter margins has been formulated in a robust stability framework. Uncertainty operators are included with a linear model to describe modeling errors and flight variations. The structured singular value, mu, computes a stability margin that directly accounts for these uncertainties. This approach introduces a new method of computing flutter margins and an associated new parameter for describing these margins. The mu margins are robust margins that indicate worst-case stability estimates with respect to the defined uncertainty. Worst-case flutter margins are computed for the F/A-18 Systems Research Aircraft using uncertainty sets generated by flight data analysis. The robust margins demonstrate flight conditions for flutter may lie closer to the flight envelope than previously estimated by p-k analysis.

Uncertainty analysis of gross primary production partitioned from net ecosystem exchange measurements

NASA Astrophysics Data System (ADS)

Raj, R.; Hamm, N. A. S.; van der Tol, C.; Stein, A.

2015-08-01

Gross primary production (GPP), separated from flux tower measurements of net ecosystem exchange (NEE) of CO2, is used increasingly to validate process-based simulators and remote sensing-derived estimates of simulated GPP at various time steps. Proper validation should include the uncertainty associated with this separation at different time steps. This can be achieved by using a Bayesian framework. In this study, we estimated the uncertainty in GPP at half hourly time steps. We used a non-rectangular hyperbola (NRH) model to separate GPP from flux tower measurements of NEE at the Speulderbos forest site, The Netherlands. The NRH model included the variables that influence GPP, in particular radiation, and temperature. In addition, the NRH model provided a robust empirical relationship between radiation and GPP by including the degree of curvature of the light response curve. Parameters of the NRH model were fitted to the measured NEE data for every 10-day period during the growing season (April to October) in 2009. Adopting a Bayesian approach, we defined the prior distribution of each NRH parameter. Markov chain Monte Carlo (MCMC) simulation was used to update the prior distribution of each NRH parameter. This allowed us to estimate the uncertainty in the separated GPP at half-hourly time steps. This yielded the posterior distribution of GPP at each half hour and allowed the quantification of uncertainty. The time series of posterior distributions thus obtained allowed us to estimate the uncertainty at daily time steps. We compared the informative with non-informative prior distributions of the NRH parameters. The results showed that both choices of prior produced similar posterior distributions GPP. This will provide relevant and important information for the validation of process-based simulators in the future. Furthermore, the obtained posterior distributions of NEE and the NRH parameters are of interest for a range of applications.

Prediction and assimilation of surf-zone processes using a Bayesian network: Part II: Inverse models

USGS Publications Warehouse

Plant, Nathaniel G.; Holland, K. Todd

2011-01-01

A Bayesian network model has been developed to simulate a relatively simple problem of wave propagation in the surf zone (detailed in Part I). Here, we demonstrate that this Bayesian model can provide both inverse modeling and data-assimilation solutions for predicting offshore wave heights and depth estimates given limited wave-height and depth information from an onshore location. The inverse method is extended to allow data assimilation using observational inputs that are not compatible with deterministic solutions of the problem. These inputs include sand bar positions (instead of bathymetry) and estimates of the intensity of wave breaking (instead of wave-height observations). Our results indicate that wave breaking information is essential to reduce prediction errors. In many practical situations, this information could be provided from a shore-based observer or from remote-sensing systems. We show that various combinations of the assimilated inputs significantly reduce the uncertainty in the estimates of water depths and wave heights in the model domain. Application of the Bayesian network model to new field data demonstrated significant predictive skill (R2 = 0.7) for the inverse estimate of a month-long time series of offshore wave heights. The Bayesian inverse results include uncertainty estimates that were shown to be most accurate when given uncertainty in the inputs (e.g., depth and tuning parameters). Furthermore, the inverse modeling was extended to directly estimate tuning parameters associated with the underlying wave-process model. The inverse estimates of the model parameters not only showed an offshore wave height dependence consistent with results of previous studies but the uncertainty estimates of the tuning parameters also explain previously reported variations in the model parameters.

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.

A New Formulation of Equivalent Effective Stratospheric Chlorine (EESC)

NASA Technical Reports Server (NTRS)

Newman, P. A.; Daniel, J. S.; Waugh, D. W.; Nash, E. R.

2007-01-01

Equivalent effective stratospheric chlorine (EESC) is a convenient parameter to quantify the effects of halogens (chlorine and bromine) on ozone depletion in the stratosphere. We show and discuss a new formulation of EESC that now includes the effects of age-of-air dependent fractional release values and an age-of-air spectrum. This new formulation provides quantitative estimates of EESC that can be directly related to inorganic chlorine and bromine throughout the stratosphere. Using this EESC formulation, we estimate that human-produced ozone depleting substances will recover to 1980 levels in 2041 in the midlatitudes, and 2067 over Antarctica. These recovery dates are based upon the assumption that the international agreements for regulating ozone-depleting substances are adhered to. In addition to recovery dates, we also estimate the uncertainties in the estimated time of recovery. The midlatitude recovery of 2041 has a 95% confidence uncertainty from 2028 to 2049, while the 2067 Antarctic recovery has a 95% confidence uncertainty from 2056 to 2078. The principal uncertainties are from the estimated mean age-of-air, and the assumption that the mean age-of-air and fractional release values are time independent. Using other model estimates of age decrease due to climate change, we estimate that midlatitude recovery may be accelerated from 2041 to 2031.

Performance of Trajectory Models with Wind Uncertainty

NASA Technical Reports Server (NTRS)

Lee, Alan G.; Weygandt, Stephen S.; Schwartz, Barry; Murphy, James R.

2009-01-01

Typical aircraft trajectory predictors use wind forecasts but do not account for the forecast uncertainty. A method for generating estimates of wind prediction uncertainty is described and its effect on aircraft trajectory prediction uncertainty is investigated. The procedure for estimating the wind prediction uncertainty relies uses a time-lagged ensemble of weather model forecasts from the hourly updated Rapid Update Cycle (RUC) weather prediction system. Forecast uncertainty is estimated using measures of the spread amongst various RUC time-lagged ensemble forecasts. This proof of concept study illustrates the estimated uncertainty and the actual wind errors, and documents the validity of the assumed ensemble-forecast accuracy relationship. Aircraft trajectory predictions are made using RUC winds with provision for the estimated uncertainty. Results for a set of simulated flights indicate this simple approach effectively translates the wind uncertainty estimate into an aircraft trajectory uncertainty. A key strength of the method is the ability to relate uncertainty to specific weather phenomena (contained in the various ensemble members) allowing identification of regional variations in uncertainty.

The use of multiwavelets for uncertainty estimation in seismic surface wave dispersion.

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

Poppeliers, Christian

This report describes a new single-station analysis method to estimate the dispersion and uncer- tainty of seismic surface waves using the multiwavelet transform. Typically, when estimating the dispersion of a surface wave using only a single seismic station, the seismogram is decomposed into a series of narrow-band realizations using a bank of narrow-band filters. By then enveloping and normalizing the filtered seismograms and identifying the maximum power as a function of frequency, the group velocity can be estimated if the source-receiver distance is known. However, using the filter bank method, there is no robust way to estimate uncertainty. In thismore » report, I in- troduce a new method of estimating the group velocity that includes an estimate of uncertainty. The method is similar to the conventional filter bank method, but uses a class of functions, called Slepian wavelets, to compute a series of wavelet transforms of the data. Each wavelet transform is mathematically similar to a filter bank, however, the time-frequency tradeoff is optimized. By taking multiple wavelet transforms, I form a population of dispersion estimates from which stan- dard statistical methods can be used to estimate uncertainty. I demonstrate the utility of this new method by applying it to synthetic data as well as ambient-noise surface-wave cross-correlelograms recorded by the University of Nevada Seismic Network.« less

Combined Uncertainty and A-Posteriori Error Bound Estimates for General CFD Calculations: Theory and Software Implementation

NASA Technical Reports Server (NTRS)

Barth, Timothy J.

2014-01-01

This workshop presentation discusses the design and implementation of numerical methods for the quantification of statistical uncertainty, including a-posteriori error bounds, for output quantities computed using CFD methods. Hydrodynamic realizations often contain numerical error arising from finite-dimensional approximation (e.g. numerical methods using grids, basis functions, particles) and statistical uncertainty arising from incomplete information and/or statistical characterization of model parameters and random fields. The first task at hand is to derive formal error bounds for statistics given realizations containing finite-dimensional numerical error [1]. The error in computed output statistics contains contributions from both realization error and the error resulting from the calculation of statistics integrals using a numerical method. A second task is to devise computable a-posteriori error bounds by numerically approximating all terms arising in the error bound estimates. For the same reason that CFD calculations including error bounds but omitting uncertainty modeling are only of limited value, CFD calculations including uncertainty modeling but omitting error bounds are only of limited value. To gain maximum value from CFD calculations, a general software package for uncertainty quantification with quantified error bounds has been developed at NASA. The package provides implementations for a suite of numerical methods used in uncertainty quantification: Dense tensorization basis methods [3] and a subscale recovery variant [1] for non-smooth data, Sparse tensorization methods[2] utilizing node-nested hierarchies, Sampling methods[4] for high-dimensional random variable spaces.

A Bayesian geostatistical approach for evaluating the uncertainty of contaminant mass discharges from point sources

NASA Astrophysics Data System (ADS)

Troldborg, M.; Nowak, W.; Binning, P. J.; Bjerg, P. L.

2012-12-01

Estimates of mass discharge (mass/time) are increasingly being used when assessing risks of groundwater contamination and designing remedial systems at contaminated sites. Mass discharge estimates are, however, prone to rather large uncertainties as they integrate uncertain spatial distributions of both concentration and groundwater flow velocities. For risk assessments or any other decisions that are being based on mass discharge estimates, it is essential to address these uncertainties. We present a novel Bayesian geostatistical approach for quantifying the uncertainty of the mass discharge across a multilevel control plane. The method decouples the flow and transport simulation and has the advantage of avoiding the heavy computational burden of three-dimensional numerical flow and transport simulation coupled with geostatistical inversion. It may therefore be of practical relevance to practitioners compared to existing methods that are either too simple or computationally demanding. The method is based on conditional geostatistical simulation and accounts for i) heterogeneity of both the flow field and the concentration distribution through Bayesian geostatistics (including the uncertainty in covariance functions), ii) measurement uncertainty, and iii) uncertain source zone geometry and transport parameters. The method generates multiple equally likely realizations of the spatial flow and concentration distribution, which all honour the measured data at the control plane. The flow realizations are generated by analytical co-simulation of the hydraulic conductivity and the hydraulic gradient across the control plane. These realizations are made consistent with measurements of both hydraulic conductivity and head at the site. An analytical macro-dispersive transport solution is employed to simulate the mean concentration distribution across the control plane, and a geostatistical model of the Box-Cox transformed concentration data is used to simulate observed deviations from this mean solution. By combining the flow and concentration realizations, a mass discharge probability distribution is obtained. Tests show that the decoupled approach is both efficient and able to provide accurate uncertainty estimates. The method is demonstrated on a Danish field site contaminated with chlorinated ethenes. For this site, we show that including a physically meaningful concentration trend and the co-simulation of hydraulic conductivity and hydraulic gradient across the transect helps constrain the mass discharge uncertainty. The number of sampling points required for accurate mass discharge estimation and the relative influence of different data types on mass discharge uncertainty is discussed.

Wave-optics uncertainty propagation and regression-based bias model in GNSS radio occultation bending angle retrievals

NASA Astrophysics Data System (ADS)

Gorbunov, Michael E.; Kirchengast, Gottfried

2018-01-01

A new reference occultation processing system (rOPS) will include a Global Navigation Satellite System (GNSS) radio occultation (RO) retrieval chain with integrated uncertainty propagation. In this paper, we focus on wave-optics bending angle (BA) retrieval in the lower troposphere and introduce (1) an empirically estimated boundary layer bias (BLB) model then employed to reduce the systematic uncertainty of excess phases and bending angles in about the lowest 2 km of the troposphere and (2) the estimation of (residual) systematic uncertainties and their propagation together with random uncertainties from excess phase to bending angle profiles. Our BLB model describes the estimated bias of the excess phase transferred from the estimated bias of the bending angle, for which the model is built, informed by analyzing refractivity fluctuation statistics shown to induce such biases. The model is derived from regression analysis using a large ensemble of Constellation Observing System for Meteorology, Ionosphere, and Climate (COSMIC) RO observations and concurrent European Centre for Medium-Range Weather Forecasts (ECMWF) analysis fields. It is formulated in terms of predictors and adaptive functions (powers and cross products of predictors), where we use six main predictors derived from observations: impact altitude, latitude, bending angle and its standard deviation, canonical transform (CT) amplitude, and its fluctuation index. Based on an ensemble of test days, independent of the days of data used for the regression analysis to establish the BLB model, we find the model very effective for bias reduction and capable of reducing bending angle and corresponding refractivity biases by about a factor of 5. The estimated residual systematic uncertainty, after the BLB profile subtraction, is lower bounded by the uncertainty from the (indirect) use of ECMWF analysis fields but is significantly lower than the systematic uncertainty without BLB correction. The systematic and random uncertainties are propagated from excess phase to bending angle profiles, using a perturbation approach and the wave-optical method recently introduced by Gorbunov and Kirchengast (2015), starting with estimated excess phase uncertainties. The results are encouraging and this uncertainty propagation approach combined with BLB correction enables a robust reduction and quantification of the uncertainties of excess phases and bending angles in the lower troposphere.

Space Radiation Cancer Risk Projections and Uncertainties - 2010

NASA Technical Reports Server (NTRS)

Cucinotta, Francis A.; Kim, Myung-Hee Y.; Chappell, Lori J.

2011-01-01

Uncertainties in estimating health risks from galactic cosmic rays greatly limit space mission lengths and potential risk mitigation evaluations. NASA limits astronaut exposures to a 3% risk of exposure-induced death and protects against uncertainties using an assessment of 95% confidence intervals in the projection model. Revisions to this model for lifetime cancer risks from space radiation and new estimates of model uncertainties are described here. We review models of space environments and transport code predictions of organ exposures, and characterize uncertainties in these descriptions. We summarize recent analysis of low linear energy transfer radio-epidemiology data, including revision to Japanese A-bomb survivor dosimetry, longer follow-up of exposed cohorts, and reassessments of dose and dose-rate reduction effectiveness factors. We compare these projections and uncertainties with earlier estimates. Current understanding of radiation quality effects and recent data on factors of relative biological effectiveness and particle track structure are reviewed. Recent radiobiology experiment results provide new information on solid cancer and leukemia risks from heavy ions. We also consider deviations from the paradigm of linearity at low doses of heavy ions motivated by non-targeted effects models. New findings and knowledge are used to revise the NASA risk projection model for space radiation cancer risks.

Uncertainties in estimates of the risks of late effects from space radiation

NASA Astrophysics Data System (ADS)

Cucinotta, F. A.; Schimmerling, W.; Wilson, J. W.; Peterson, L. E.; Saganti, P. B.; Dicello, J. F.

2004-01-01

Methods used to project risks in low-Earth orbit are of questionable merit for exploration missions because of the limited radiobiology data and knowledge of galactic cosmic ray (GCR) heavy ions, which causes estimates of the risk of late effects to be highly uncertain. Risk projections involve a product of many biological and physical factors, each of which has a differential range of uncertainty due to lack of data and knowledge. Using the linear-additivity model for radiation risks, we use Monte-Carlo sampling from subjective uncertainty distributions in each factor to obtain an estimate of the overall uncertainty in risk projections. The resulting methodology is applied to several human space exploration mission scenarios including a deep space outpost and Mars missions of duration of 360, 660, and 1000 days. The major results are the quantification of the uncertainties in current risk estimates, the identification of factors that dominate risk projection uncertainties, and the development of a method to quantify candidate approaches to reduce uncertainties or mitigate risks. The large uncertainties in GCR risk projections lead to probability distributions of risk that mask any potential risk reduction using the "optimization" of shielding materials or configurations. In contrast, the design of shielding optimization approaches for solar particle events and trapped protons can be made at this time and promising technologies can be shown to have merit using our approach. The methods used also make it possible to express risk management objectives in terms of quantitative metrics, e.g., the number of days in space without exceeding a given risk level within well-defined confidence limits.

Application of at-site peak-streamflow frequency analyses for very low annual exceedance probabilities

USGS Publications Warehouse

Asquith, William H.; Kiang, Julie E.; Cohn, Timothy A.

2017-07-17

The U.S. Geological Survey (USGS), in cooperation with the U.S. Nuclear Regulatory Commission, has investigated statistical methods for probabilistic flood hazard assessment to provide guidance on very low annual exceedance probability (AEP) estimation of peak-streamflow frequency and the quantification of corresponding uncertainties using streamgage-specific data. The term “very low AEP” implies exceptionally rare events defined as those having AEPs less than about 0.001 (or 1 × 10–3 in scientific notation or for brevity 10–3). Such low AEPs are of great interest to those involved with peak-streamflow frequency analyses for critical infrastructure, such as nuclear power plants. Flood frequency analyses at streamgages are most commonly based on annual instantaneous peak streamflow data and a probability distribution fit to these data. The fitted distribution provides a means to extrapolate to very low AEPs. Within the United States, the Pearson type III probability distribution, when fit to the base-10 logarithms of streamflow, is widely used, but other distribution choices exist. The USGS-PeakFQ software, implementing the Pearson type III within the Federal agency guidelines of Bulletin 17B (method of moments) and updates to the expected moments algorithm (EMA), was specially adapted for an “Extended Output” user option to provide estimates at selected AEPs from 10–3 to 10–6. Parameter estimation methods, in addition to product moments and EMA, include L-moments, maximum likelihood, and maximum product of spacings (maximum spacing estimation). This study comprehensively investigates multiple distributions and parameter estimation methods for two USGS streamgages (01400500 Raritan River at Manville, New Jersey, and 01638500 Potomac River at Point of Rocks, Maryland). The results of this study specifically involve the four methods for parameter estimation and up to nine probability distributions, including the generalized extreme value, generalized log-normal, generalized Pareto, and Weibull. Uncertainties in streamflow estimates for corresponding AEP are depicted and quantified as two primary forms: quantile (aleatoric [random sampling] uncertainty) and distribution-choice (epistemic [model] uncertainty). Sampling uncertainties of a given distribution are relatively straightforward to compute from analytical or Monte Carlo-based approaches. Distribution-choice uncertainty stems from choices of potentially applicable probability distributions for which divergence among the choices increases as AEP decreases. Conventional goodness-of-fit statistics, such as Cramér-von Mises, and L-moment ratio diagrams are demonstrated in order to hone distribution choice. The results generally show that distribution choice uncertainty is larger than sampling uncertainty for very low AEP values.

Assessment of uncertainties of the models used in thermal-hydraulic computer codes

NASA Astrophysics Data System (ADS)

Gricay, A. S.; Migrov, Yu. A.

2015-09-01

The article deals with matters concerned with the problem of determining the statistical characteristics of variable parameters (the variation range and distribution law) in analyzing the uncertainty and sensitivity of calculation results to uncertainty in input data. A comparative analysis of modern approaches to uncertainty in input data is presented. The need to develop an alternative method for estimating the uncertainty of model parameters used in thermal-hydraulic computer codes, in particular, in the closing correlations of the loop thermal hydraulics block, is shown. Such a method shall feature the minimal degree of subjectivism and must be based on objective quantitative assessment criteria. The method includes three sequential stages: selecting experimental data satisfying the specified criteria, identifying the key closing correlation using a sensitivity analysis, and carrying out case calculations followed by statistical processing of the results. By using the method, one can estimate the uncertainty range of a variable parameter and establish its distribution law in the above-mentioned range provided that the experimental information is sufficiently representative. Practical application of the method is demonstrated taking as an example the problem of estimating the uncertainty of a parameter appearing in the model describing transition to post-burnout heat transfer that is used in the thermal-hydraulic computer code KORSAR. The performed study revealed the need to narrow the previously established uncertainty range of this parameter and to replace the uniform distribution law in the above-mentioned range by the Gaussian distribution law. The proposed method can be applied to different thermal-hydraulic computer codes. In some cases, application of the method can make it possible to achieve a smaller degree of conservatism in the expert estimates of uncertainties pertinent to the model parameters used in computer codes.

Quantifying the Uncertainty in Discharge Data Using Hydraulic Knowledge and Uncertain Gaugings

NASA Astrophysics Data System (ADS)

Renard, B.; Le Coz, J.; Bonnifait, L.; Branger, F.; Le Boursicaud, R.; Horner, I.; Mansanarez, V.; Lang, M.

2014-12-01

River discharge is a crucial variable for Hydrology: as the output variable of most hydrologic models, it is used for sensitivity analyses, model structure identification, parameter estimation, data assimilation, prediction, etc. A major difficulty stems from the fact that river discharge is not measured continuously. Instead, discharge time series used by hydrologists are usually based on simple stage-discharge relations (rating curves) calibrated using a set of direct stage-discharge measurements (gaugings). In this presentation, we present a Bayesian approach to build such hydrometric rating curves, to estimate the associated uncertainty and to propagate this uncertainty to discharge time series. The three main steps of this approach are described: (1) Hydraulic analysis: identification of the hydraulic controls that govern the stage-discharge relation, identification of the rating curve equation and specification of prior distributions for the rating curve parameters; (2) Rating curve estimation: Bayesian inference of the rating curve parameters, accounting for the individual uncertainties of available gaugings, which often differ according to the discharge measurement procedure and the flow conditions; (3) Uncertainty propagation: quantification of the uncertainty in discharge time series, accounting for both the rating curve uncertainties and the uncertainty of recorded stage values. In addition, we also discuss current research activities, including the treatment of non-univocal stage-discharge relationships (e.g. due to hydraulic hysteresis, vegetation growth, sudden change of the geometry of the section, etc.).

Estimating winter wheat phenological parameters: Implications for crop modeling

USDA-ARS?s Scientific Manuscript database

Crop parameters, such as the timing of developmental events, are critical for accurate simulation results in crop simulation models, yet uncertainty often exists in determining the parameters. Factors contributing to the uncertainty include: a) sources of variation within a plant (i.e., within diffe...

Hybrid Gibbs Sampling and MCMC for CMB Analysis at Small Angular Scales

NASA Technical Reports Server (NTRS)

Jewell, Jeffrey B.; Eriksen, H. K.; Wandelt, B. D.; Gorski, K. M.; Huey, G.; O'Dwyer, I. J.; Dickinson, C.; Banday, A. J.; Lawrence, C. R.

2008-01-01

A) Gibbs Sampling has now been validated as an efficient, statistically exact, and practically useful method for "low-L" (as demonstrated on WMAP temperature polarization data). B) We are extending Gibbs sampling to directly propagate uncertainties in both foreground and instrument models to total uncertainty in cosmological parameters for the entire range of angular scales relevant for Planck. C) Made possible by inclusion of foreground model parameters in Gibbs sampling and hybrid MCMC and Gibbs sampling for the low signal to noise (high-L) regime. D) Future items to be included in the Bayesian framework include: 1) Integration with Hybrid Likelihood (or posterior) code for cosmological parameters; 2) Include other uncertainties in instrumental systematics? (I.e. beam uncertainties, noise estimation, calibration errors, other).

Impact of nuclear data uncertainty on safety calculations for spent nuclear fuel geological disposal

NASA Astrophysics Data System (ADS)

Herrero, J. J.; Rochman, D.; Leray, O.; Vasiliev, A.; Pecchia, M.; Ferroukhi, H.; Caruso, S.

2017-09-01

In the design of a spent nuclear fuel disposal system, one necessary condition is to show that the configuration remains subcritical at time of emplacement but also during long periods covering up to 1,000,000 years. In the context of criticality safety applying burn-up credit, k-eff eigenvalue calculations are affected by nuclear data uncertainty mainly in the burnup calculations simulating reactor operation and in the criticality calculation for the disposal canister loaded with the spent fuel assemblies. The impact of nuclear data uncertainty should be included in the k-eff value estimation to enforce safety. Estimations of the uncertainty in the discharge compositions from the CASMO5 burn-up calculation phase are employed in the final MCNP6 criticality computations for the intact canister configuration; in between, SERPENT2 is employed to get the spent fuel composition along the decay periods. In this paper, nuclear data uncertainty was propagated by Monte Carlo sampling in the burn-up, decay and criticality calculation phases and representative values for fuel operated in a Swiss PWR plant will be presented as an estimation of its impact.

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

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

NASA Astrophysics Data System (ADS)

Vesselinov, V. V.; Harp, D.

2010-12-01

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

Dating Tips for Divergence-Time Estimation.

PubMed

O'Reilly, Joseph E; Dos Reis, Mario; Donoghue, Philip C J

2015-11-01

The molecular clock is the only viable means of establishing an accurate timescale for Life on Earth, but it remains reliant on a capricious fossil record for calibration. 'Tip-dating' promises a conceptual advance, integrating fossil species among their living relatives using molecular/morphological datasets and evolutionary models. Fossil species of known age establish calibration directly, and their phylogenetic uncertainty is accommodated through the co-estimation of time and topology. However, challenges remain, including a dearth of effective models of morphological evolution, rate correlation, the non-random nature of missing characters in fossil data, and, most importantly, accommodating uncertainty in fossil age. We show uncertainty in fossil-dating propagates to divergence-time estimates, yielding estimates that are older and less precise than those based on traditional node calibration. Ultimately, node and tip calibrations are not mutually incompatible and may be integrated to achieve more accurate and precise evolutionary timescales. Copyright © 2015 Elsevier Ltd. All rights reserved.

Uncertainty in countrywide forest biomass estimates.

Treesearch

C.E. Peterson; D. Turner

1994-01-01

Country-wide estimates of forest biomass are the major driver for estimating and understanding carbon pools and flux, a critical component of global change research. Important determinants in making these estimates include the areal extent of forested lands and their associated biomass. Estimates for these parameters may be derived from surface-based data, photo...

Quantifying Uncertainty of Wind Power Production Through an Analog Ensemble

NASA Astrophysics Data System (ADS)

Shahriari, M.; Cervone, G.

2016-12-01

The Analog Ensemble (AnEn) method is used to generate probabilistic weather forecasts that quantify the uncertainty in power estimates at hypothetical wind farm locations. The data are from the NREL Eastern Wind Dataset that includes more than 1,300 modeled wind farms. The AnEn model uses a two-dimensional grid to estimate the probability distribution of wind speed (the predictand) given the values of predictor variables such as temperature, pressure, geopotential height, U-component and V-component of wind. The meteorological data is taken from the NCEP GFS which is available on a 0.25 degree grid resolution. The methodology first divides the data into two classes: training period and verification period. The AnEn selects a point in the verification period and searches for the best matching estimates (analogs) in the training period. The predictand value at those analogs are the ensemble prediction for the point in the verification period. The model provides a grid of wind speed values and the uncertainty (probability index) associated with each estimate. Each wind farm is associated with a probability index which quantifies the degree of difficulty to estimate wind power. Further, the uncertainty in estimation is related to other factors such as topography, land cover and wind resources. This is achieved by using a GIS system to compute the correlation between the probability index and geographical characteristics. This study has significant applications for investors in renewable energy sector especially wind farm developers. Lower level of uncertainty facilitates the process of submitting bids into day ahead and real time electricity markets. Thus, building wind farms in regions with lower levels of uncertainty will reduce the real-time operational risks and create a hedge against volatile real-time prices. Further, the links between wind estimate uncertainty and factors such as topography and wind resources, provide wind farm developers with valuable information regarding wind farm siting.

Assessment of the magnitude of ammonia emissions in the United Kingdom

NASA Astrophysics Data System (ADS)

Sutton, M. A.; Place, C. J.; Eager, M.; Fowler, D.; Smith, R. I.

Estimates of ammonia emission in the U.K. have been critically reviewed with the aim of establishing the magnitude and uncertainty of each of the sources. European studies are also reviewed, with the U.K. providing a useful case study to highlight the uncertainties common to all ammonia emission inventories. This analysis of the emission factors and their application to U.K. sources supports an emission of 450 (231-715) Gg NH 3 yr -1. Agricultural activities are confirmed as the major source, providing 406 (215-630) Gg NH 3yr -1 (90% of the total), and therefore dominate uncertainties. Non-agricultural sources include sewage, pets, horses, humans, combustion and wild animals, though these contribute only 44 (16-85) Gg yr -1. Cattle represent the largest single uncertainty, accounting for 245 (119-389) Gg yr -1. The major uncertainties for cattle derive from estimation of the amount of nitrogen (N) excreted, the % N volatilized from land spreading of wastes, and the % N volatilized from stored farm-yard manure. Similar relative uncertainties apply to each of sheep, pigs and poultry, as well as fertilized crops, though these are quantitatively less important. Accounting; for regional differences in livestock demography, emission of 347, 63 and 40 Gg yr -1 are estimated for England & Wales, Scotland, and Northern Ireland, respectively. Though very uncertain, the total is in good agreement with estimates required to balance the U.K. atmospheric NH. budget.

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 uncertain fault geometry in earthquake source inversions - I: theory and simplified application

NASA Astrophysics Data System (ADS)

Ragon, Théa; Sladen, Anthony; Simons, Mark

2018-05-01

The ill-posed nature of earthquake source estimation derives from several factors including the quality and quantity of available observations and the fidelity of our forward theory. Observational errors are usually accounted for in the inversion process. Epistemic errors, which stem from our simplified description of the forward problem, are rarely dealt with despite their potential to bias the estimate of a source model. In this study, we explore the impact of uncertainties related to the choice of a fault geometry in source inversion problems. The geometry of a fault structure is generally reduced to a set of parameters, such as position, strike and dip, for one or a few planar fault segments. While some of these parameters can be solved for, more often they are fixed to an uncertain value. We propose a practical framework to address this limitation by following a previously implemented method exploring the impact of uncertainties on the elastic properties of our models. We develop a sensitivity analysis to small perturbations of fault dip and position. The uncertainties in fault geometry are included in the inverse problem under the formulation of the misfit covariance matrix that combines both prediction and observation uncertainties. We validate this approach with the simplified case of a fault that extends infinitely along strike, using both Bayesian and optimization formulations of a static inversion. If epistemic errors are ignored, predictions are overconfident in the data and source parameters are not reliably estimated. In contrast, inclusion of uncertainties in fault geometry allows us to infer a robust posterior source model. Epistemic uncertainties can be many orders of magnitude larger than observational errors for great earthquakes (Mw > 8). Not accounting for uncertainties in fault geometry may partly explain observed shallow slip deficits for continental earthquakes. Similarly, ignoring the impact of epistemic errors can also bias estimates of near surface slip and predictions of tsunamis induced by megathrust earthquakes. (Mw > 8)

Bayesian Methods for Effective Field Theories

NASA Astrophysics Data System (ADS)

Wesolowski, Sarah

Microscopic predictions of the properties of atomic nuclei have reached a high level of precision in the past decade. This progress mandates improved uncertainty quantification (UQ) for a robust comparison of experiment with theory. With the uncertainty from many-body methods under control, calculations are now sensitive to the input inter-nucleon interactions. These interactions include parameters that must be fit to experiment, inducing both uncertainty from the fit and from missing physics in the operator structure of the Hamiltonian. Furthermore, the implementation of the inter-nucleon interactions is not unique, which presents the additional problem of assessing results using different interactions. Effective field theories (EFTs) take advantage of a separation of high- and low-energy scales in the problem to form a power-counting scheme that allows the organization of terms in the Hamiltonian based on their expected contribution to observable predictions. This scheme gives a natural framework for quantification of uncertainty due to missing physics. The free parameters of the EFT, called the low-energy constants (LECs), must be fit to data, but in a properly constructed EFT these constants will be natural-sized, i.e., of order unity. The constraints provided by the EFT, namely the size of the systematic uncertainty from truncation of the theory and the natural size of the LECs, are assumed information even before a calculation is performed or a fit is done. Bayesian statistical methods provide a framework for treating uncertainties that naturally incorporates prior information as well as putting stochastic and systematic uncertainties on an equal footing. For EFT UQ Bayesian methods allow the relevant EFT properties to be incorporated quantitatively as prior probability distribution functions (pdfs). Following the logic of probability theory, observable quantities and underlying physical parameters such as the EFT breakdown scale may be expressed as pdfs that incorporate the prior pdfs. Problems of model selection, such as distinguishing between competing EFT implementations, are also natural in a Bayesian framework. In this thesis we focus on two complementary topics for EFT UQ using Bayesian methods--quantifying EFT truncation uncertainty and parameter estimation for LECs. Using the order-by-order calculations and underlying EFT constraints as prior information, we show how to estimate EFT truncation uncertainties. We then apply the result to calculating truncation uncertainties on predictions of nucleon-nucleon scattering in chiral effective field theory. We apply model-checking diagnostics to our calculations to ensure that the statistical model of truncation uncertainty produces consistent results. A framework for EFT parameter estimation based on EFT convergence properties and naturalness is developed which includes a series of diagnostics to ensure the extraction of the maximum amount of available information from data to estimate LECs with minimal bias. We develop this framework using model EFTs and apply it to the problem of extrapolating lattice quantum chromodynamics results for the nucleon mass. We then apply aspects of the parameter estimation framework to perform case studies in chiral EFT parameter estimation, investigating a possible operator redundancy at fourth order in the chiral expansion and the appropriate inclusion of truncation uncertainty in estimating LECs.

Satellite Power Systems (SPS) space transportation cost analysis and evaluation

NASA Technical Reports Server (NTRS)

1980-01-01

A picture of Space Power Systems space transportation costs at the present time is given with respect to accuracy as stated, reasonableness of the methods used, assumptions made, and uncertainty associated with the estimates. The approach used consists of examining space transportation costs from several perspectives to perform a variety of sensitivity analyses or reviews and examine the findings in terms of internal consistency and external comparison with analogous systems. These approaches are summarized as a theoretical and historical review including a review of stated and unstated assumptions used to derive the costs, and a performance or technical review. These reviews cover the overall transportation program as well as the individual vehicles proposed. The review of overall cost assumptions is the principal means used for estimating the cost uncertainty derived. The cost estimates used as the best current estimate are included.

Quantifying the uncertainty in discharge data using hydraulic knowledge and uncertain gaugings: a Bayesian method named BaRatin

NASA Astrophysics Data System (ADS)

Le Coz, Jérôme; Renard, Benjamin; Bonnifait, Laurent; Branger, Flora; Le Boursicaud, Raphaël; Horner, Ivan; Mansanarez, Valentin; Lang, Michel; Vigneau, Sylvain

2015-04-01

River discharge is a crucial variable for Hydrology: as the output variable of most hydrologic models, it is used for sensitivity analyses, model structure identification, parameter estimation, data assimilation, prediction, etc. A major difficulty stems from the fact that river discharge is not measured continuously. Instead, discharge time series used by hydrologists are usually based on simple stage-discharge relations (rating curves) calibrated using a set of direct stage-discharge measurements (gaugings). In this presentation, we present a Bayesian approach (cf. Le Coz et al., 2014) to build such hydrometric rating curves, to estimate the associated uncertainty and to propagate this uncertainty to discharge time series. The three main steps of this approach are described: (1) Hydraulic analysis: identification of the hydraulic controls that govern the stage-discharge relation, identification of the rating curve equation and specification of prior distributions for the rating curve parameters; (2) Rating curve estimation: Bayesian inference of the rating curve parameters, accounting for the individual uncertainties of available gaugings, which often differ according to the discharge measurement procedure and the flow conditions; (3) Uncertainty propagation: quantification of the uncertainty in discharge time series, accounting for both the rating curve uncertainties and the uncertainty of recorded stage values. The rating curve uncertainties combine the parametric uncertainties and the remnant uncertainties that reflect the limited accuracy of the mathematical model used to simulate the physical stage-discharge relation. In addition, we also discuss current research activities, including the treatment of non-univocal stage-discharge relationships (e.g. due to hydraulic hysteresis, vegetation growth, sudden change of the geometry of the section, etc.). An operational version of the BaRatin software and its graphical interface are made available free of charge on request to the authors. J. Le Coz, B. Renard, L. Bonnifait, F. Branger, R. Le Boursicaud (2014). Combining hydraulic knowledge and uncertain gaugings in the estimation of hydrometric rating curves: a Bayesian approach, Journal of Hydrology, 509, 573-587.

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.

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.

Worst-Case Flutter Margins from F/A-18 Aircraft Aeroelastic Data

NASA Technical Reports Server (NTRS)

Lind, Rick; Brenner, Marty

1997-01-01

An approach for computing worst-case flutter margins has been formulated in a robust stability framework. Uncertainty operators are included with a linear model to describe modeling errors and flight variations. The structured singular value, micron, computes a stability margin which directly accounts for these uncertainties. This approach introduces a new method of computing flutter margins and an associated new parameter for describing these margins. The micron margins are robust margins which indicate worst-case stability estimates with respect to the defined uncertainty. Worst-case flutter margins are computed for the F/A-18 SRA using uncertainty sets generated by flight data analysis. The robust margins demonstrate flight conditions for flutter may lie closer to the flight envelope than previously estimated by p-k analysis.

Improved Event Location Uncertainty Estimates

DTIC Science & Technology

2006-09-21

validation purposes, we use GT0-2 event clusters. These include the Nevada Lop Nor, Semipalatinsk , and Novaya Zemlys test sites , as well as the Azgir...uncertainties. Furthermore, the tails of real seismic data distributions are heavier than Gaussian. The main objectives of this project are to develop, test

Quantifying uncertainty in geoacoustic inversion. II. Application to broadband, shallow-water data.

PubMed

Dosso, Stan E; Nielsen, Peter L

2002-01-01

This paper applies the new method of fast Gibbs sampling (FGS) to estimate the uncertainties of seabed geoacoustic parameters in a broadband, shallow-water acoustic survey, with the goal of interpreting the survey results and validating the method for experimental data. FGS applies a Bayesian approach to geoacoustic inversion based on sampling the posterior probability density to estimate marginal probability distributions and parameter covariances. This requires knowledge of the statistical distribution of the data errors, including both measurement and theory errors, which is generally not available. Invoking the simplifying assumption of independent, identically distributed Gaussian errors allows a maximum-likelihood estimate of the data variance and leads to a practical inversion algorithm. However, it is necessary to validate these assumptions, i.e., to verify that the parameter uncertainties obtained represent meaningful estimates. To this end, FGS is applied to a geoacoustic experiment carried out at a site off the west coast of Italy where previous acoustic and geophysical studies have been performed. The parameter uncertainties estimated via FGS are validated by comparison with: (i) the variability in the results of inverting multiple independent data sets collected during the experiment; (ii) the results of FGS inversion of synthetic test cases designed to simulate the experiment and data errors; and (iii) the available geophysical ground truth. Comparisons are carried out for a number of different source bandwidths, ranges, and levels of prior information, and indicate that FGS provides reliable and stable uncertainty estimates for the geoacoustic inverse problem.

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.

The two-dimensional Monte Carlo: a new methodologic paradigm for dose reconstruction for epidemiological studies.

PubMed

Simon, Steven L; Hoffman, F Owen; Hofer, Eduard

2015-01-01

Retrospective dose estimation, particularly dose reconstruction that supports epidemiological investigations of health risk, relies on various strategies that include models of physical processes and exposure conditions with detail ranging from simple to complex. Quantification of dose uncertainty is an essential component of assessments for health risk studies since, as is well understood, it is impossible to retrospectively determine the true dose for each person. To address uncertainty in dose estimation, numerical simulation tools have become commonplace and there is now an increased understanding about the needs and what is required for models used to estimate cohort doses (in the absence of direct measurement) to evaluate dose response. It now appears that for dose-response algorithms to derive the best, unbiased estimate of health risk, we need to understand the type, magnitude and interrelationships of the uncertainties of model assumptions, parameters and input data used in the associated dose estimation models. Heretofore, uncertainty analysis of dose estimates did not always properly distinguish between categories of errors, e.g., uncertainty that is specific to each subject (i.e., unshared error), and uncertainty of doses from a lack of understanding and knowledge about parameter values that are shared to varying degrees by numbers of subsets of the cohort. While mathematical propagation of errors by Monte Carlo simulation methods has been used for years to estimate the uncertainty of an individual subject's dose, it was almost always conducted without consideration of dependencies between subjects. In retrospect, these types of simple analyses are not suitable for studies with complex dose models, particularly when important input data are missing or otherwise not available. The dose estimation strategy presented here is a simulation method that corrects the previous deficiencies of analytical or simple Monte Carlo error propagation methods and is termed, due to its capability to maintain separation between shared and unshared errors, the two-dimensional Monte Carlo (2DMC) procedure. Simply put, the 2DMC method simulates alternative, possibly true, sets (or vectors) of doses for an entire cohort rather than a single set that emerges when each individual's dose is estimated independently from other subjects. Moreover, estimated doses within each simulated vector maintain proper inter-relationships such that the estimated doses for members of a cohort subgroup that share common lifestyle attributes and sources of uncertainty are properly correlated. The 2DMC procedure simulates inter-individual variability of possibly true doses within each dose vector and captures the influence of uncertainty in the values of dosimetric parameters across multiple realizations of possibly true vectors of cohort doses. The primary characteristic of the 2DMC approach, as well as its strength, are defined by the proper separation between uncertainties shared by members of the entire cohort or members of defined cohort subsets, and uncertainties that are individual-specific and therefore unshared.

An uncertainty analysis of the hydrogen source term for a station blackout accident in Sequoyah using MELCOR 1.8.5

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

Gauntt, Randall O.; Bixler, Nathan E.; Wagner, Kenneth Charles

2014-03-01

A methodology for using the MELCOR code with the Latin Hypercube Sampling method was developed to estimate uncertainty in various predicted quantities such as hydrogen generation or release of fission products under severe accident conditions. In this case, the emphasis was on estimating the range of hydrogen sources in station blackout conditions in the Sequoyah Ice Condenser plant, taking into account uncertainties in the modeled physics known to affect hydrogen generation. The method uses user-specified likelihood distributions for uncertain model parameters, which may include uncertainties of a stochastic nature, to produce a collection of code calculations, or realizations, characterizing themore » range of possible outcomes. Forty MELCOR code realizations of Sequoyah were conducted that included 10 uncertain parameters, producing a range of in-vessel hydrogen quantities. The range of total hydrogen produced was approximately 583kg 131kg. Sensitivity analyses revealed expected trends with respected to the parameters of greatest importance, however, considerable scatter in results when plotted against any of the uncertain parameters was observed, with no parameter manifesting dominant effects on hydrogen generation. It is concluded that, with respect to the physics parameters investigated, in order to further reduce predicted hydrogen uncertainty, it would be necessary to reduce all physics parameter uncertainties similarly, bearing in mind that some parameters are inherently uncertain within a range. It is suspected that some residual uncertainty associated with modeling complex, coupled and synergistic phenomena, is an inherent aspect of complex systems and cannot be reduced to point value estimates. The probabilistic analyses such as the one demonstrated in this work are important to properly characterize response of complex systems such as severe accident progression in nuclear power plants.« 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.

The Importance of Behavioral Thresholds and Objective Functions in Contaminant Transport Uncertainty Analysis

NASA Astrophysics Data System (ADS)

Sykes, J. F.; Kang, M.; Thomson, N. R.

2007-12-01

The TCE release from The Lockformer Company in Lisle Illinois resulted in a plume in a confined aquifer that is more than 4 km long and impacted more than 300 residential wells. Many of the wells are on the fringe of the plume and have concentrations that did not exceed 5 ppb. The settlement for the Chapter 11 bankruptcy protection of Lockformer involved the establishment of a trust fund that compensates individuals with cancers with payments being based on cancer type, estimated TCE concentration in the well and the duration of exposure to TCE. The estimation of early arrival times and hence low likelihood events is critical in the determination of the eligibility of an individual for compensation. Thus, an emphasis must be placed on the accuracy of the leading tail region in the likelihood distribution of possible arrival times at a well. The estimation of TCE arrival time, using a three-dimensional analytical solution, involved parameter estimation and uncertainty analysis. Parameters in the model included TCE source parameters, groundwater velocities, dispersivities and the TCE decay coefficient for both the confining layer and the bedrock aquifer. Numerous objective functions, which include the well-known L2-estimator, robust estimators (L1-estimators and M-estimators), penalty functions, and dead zones, were incorporated in the parameter estimation process to treat insufficiencies in both the model and observational data due to errors, biases, and limitations. The concept of equifinality was adopted and multiple maximum likelihood parameter sets were accepted if pre-defined physical criteria were met. The criteria ensured that a valid solution predicted TCE concentrations for all TCE impacted areas. Monte Carlo samples are found to be inadequate for uncertainty analysis of this case study due to its inability to find parameter sets that meet the predefined physical criteria. Successful results are achieved using a Dynamically-Dimensioned Search sampling methodology that inherently accounts for parameter correlations and does not require assumptions regarding parameter distributions. For uncertainty analysis, multiple parameter sets were obtained using a modified Cauchy's M-estimator. Penalty functions had to be incorporated into the objective function definitions to generate a sufficient number of acceptable parameter sets. The combined effect of optimization and the application of the physical criteria perform the function of behavioral thresholds by reducing anomalies and by removing parameter sets with high objective function values. The factors that are important to the creation of an uncertainty envelope for TCE arrival at wells are outlined in the work. In general, greater uncertainty appears to be present at the tails of the distribution. For a refinement of the uncertainty envelopes, the application of additional physical criteria or behavioral thresholds is recommended.

Variability and epistemic uncertainty in water ingestion rates and pharmacokinetic parameters, and impact on the association between perfluorooctanoate and preeclampsia in the C8 Health Project population.

PubMed

Avanasi, Raghavendhran; Shin, Hyeong-Moo; Vieira, Veronica M; Bartell, Scott M

2016-04-01

We recently utilized a suite of environmental fate and transport models and an integrated exposure and pharmacokinetic model to estimate individual perfluorooctanoate (PFOA) serum concentrations, and also assessed the association of those concentrations with preeclampsia for participants in the C8 Health Project (a cross-sectional study of over 69,000 people who were environmentally exposed to PFOA near a major U.S. fluoropolymer production facility located in West Virginia). However, the exposure estimates from this integrated model relied on default values for key independent exposure parameters including water ingestion rates, the serum PFOA half-life, and the volume of distribution for PFOA. The aim of the present study is to assess the impact of inter-individual variability and epistemic uncertainty in these parameters on the exposure estimates and subsequently, the epidemiological association between PFOA exposure and preeclampsia. We used Monte Carlo simulation to propagate inter-individual variability/epistemic uncertainty in the exposure assessment and reanalyzed the epidemiological association. Inter-individual variability in these parameters mildly impacted the serum PFOA concentration predictions (the lowest mean rank correlation between the estimated serum concentrations in our study and the original predicted serum concentrations was 0.95) and there was a negligible impact on the epidemiological association with preeclampsia (no change in the mean adjusted odds ratio (AOR) and the contribution of exposure uncertainty to the total uncertainty including sampling variability was 7%). However, when epistemic uncertainty was added along with the inter-individual variability, serum PFOA concentration predictions and their association with preeclampsia were moderately impacted (the mean AOR of preeclampsia occurrence was reduced from 1.12 to 1.09, and the contribution of exposure uncertainty to the total uncertainty was increased up to 33%). In conclusion, our study shows that the change of the rank exposure among the study participants due to variability and epistemic uncertainty in the independent exposure parameters was large enough to cause a 25% bias towards the null. This suggests that the true AOR of the association between PFOA and preeclampsia in this population might be higher than the originally reported AOR and has more uncertainty than indicated by the originally reported confidence interval. Copyright © 2016 Elsevier Inc. All rights reserved.

Historic Emissions from Deforestation and Forest Degradation in Mato Grosso, Brazil: 1. Source Data Uncertainties

NASA Technical Reports Server (NTRS)

Morton, Douglas C.; Sales, Marcio H.; Souza, Carlos M., Jr.; Griscom, Bronson

2011-01-01

Historic carbon emissions are an important foundation for proposed efforts to Reduce Emissions from Deforestation and forest Degradation and enhance forest carbon stocks through conservation and sustainable forest management (REDD+). The level of uncertainty in historic carbon emissions estimates is also critical for REDD+, since high uncertainties could limit climate benefits from mitigation actions. Here, we analyzed source data uncertainties based on the range of available deforestation, forest degradation, and forest carbon stock estimates for the Brazilian state of Mato Grosso during 1990-2008. Results: Deforestation estimates showed good agreement for multi-year trends of increasing and decreasing deforestation during the study period. However, annual deforestation rates differed by >20% in more than half of the years between 1997-2008, even for products based on similar input data. Tier 2 estimates of average forest carbon stocks varied between 99-192 Mg C/ha, with greatest differences in northwest Mato Grosso. Carbon stocks in deforested areas increased over the study period, yet this increasing trend in deforested biomass was smaller than the difference among carbon stock datasets for these areas. Conclusions: Patterns of spatial and temporal disagreement among available data products provide a roadmap for future efforts to reduce source data uncertainties for estimates of historic forest carbon emissions. Specifically, regions with large discrepancies in available estimates of both deforestation and forest carbon stocks are priority areas for evaluating and improving existing estimates. Full carbon accounting for REDD+ will also require filling data gaps, including forest degradation and secondary forest, with annual data on all forest transitions.

NASA space cancer risk model-2014: Uncertainties due to qualitative differences in biological effects of HZE particles

NASA Astrophysics Data System (ADS)

Cucinotta, Francis

Uncertainties in estimating health risks from exposures to galactic cosmic rays (GCR) — comprised of protons and high-energy and charge (HZE) nuclei are an important limitation to long duration space travel. HZE nuclei produce both qualitative and quantitative differences in biological effects compared to terrestrial radiation leading to large uncertainties in predicting risks to humans. Our NASA Space Cancer Risk Model-2012 (NSCR-2012) for estimating lifetime cancer risks from space radiation included several new features compared to earlier models from the National Council on Radiation Protection and Measurements (NCRP) used at NASA. New features of NSCR-2012 included the introduction of NASA defined radiation quality factors based on track structure concepts, a Bayesian analysis of the dose and dose-rate reduction effectiveness factor (DDREF) and its uncertainty, and the use of a never-smoker population to represent astronauts. However, NSCR-2012 did not include estimates of the role of qualitative differences between HZE particles and low LET radiation. In this report we discuss evidence for non-targeted effects increasing cancer risks at space relevant HZE particle absorbed doses in tissue (<0.2 Gy), and for increased tumor lethality due to the propensity for higher rates of metastatic tumors from high LET radiation suggested by animal experiments. The NSCR-2014 model considers how these qualitative differences modify the overall probability distribution functions (PDF) for cancer mortality risk estimates from space radiation. Predictions of NSCR-2014 for International Space Station missions and Mars exploration will be described, and compared to those of our earlier NSCR-2012 model.

Quantifying the uncertainties of a bottom-up emission inventory of anthropogenic atmospheric pollutants in China

NASA Astrophysics Data System (ADS)

Zhao, Y.; Nielsen, C. P.; Lei, Y.; McElroy, M. B.; Hao, J.

2010-11-01

The uncertainties of a national, bottom-up inventory of Chinese emissions of anthropogenic SO2, NOx, and particulate matter (PM) of different size classes and carbonaceous species are comprehensively quantified, for the first time, using Monte Carlo simulation. The inventory is structured by seven dominant sectors: coal-fired electric power, cement, iron and steel, other industry (boiler combustion), other industry (non-combustion processes), transportation, and residential. For each parameter related to emission factors or activity-level calculations, the uncertainties, represented as probability distributions, are either statistically fitted using results of domestic field tests or, when these are lacking, estimated based on foreign or other domestic data. The uncertainties (i.e., 95% confidence intervals around the central estimates) of Chinese emissions of SO2, NOx, total PM, PM10, PM2.5, black carbon (BC), and organic carbon (OC) in 2005 are estimated to be -14%~12%, -10%~36%, -10%~36%, -12%~42% -16%~52%, -23%~130%, and -37%~117%, respectively. Variations at activity levels (e.g., energy consumption or industrial production) are not the main source of emission uncertainties. Due to narrow classification of source types, large sample sizes, and relatively high data quality, the coal-fired power sector is estimated to have the smallest emission uncertainties for all species except BC and OC. Due to poorer source classifications and a wider range of estimated emission factors, considerable uncertainties of NOx and PM emissions from cement production and boiler combustion in other industries are found. The probability distributions of emission factors for biomass burning, the largest source of BC and OC, are fitted based on very limited domestic field measurements, and special caution should thus be taken interpreting these emission uncertainties. Although Monte Carlo simulation yields narrowed estimates of uncertainties compared to previous bottom-up emission studies, the results are not always consistent with those derived from satellite observations. The results thus represent an incremental research advance; while the analysis provides current estimates of uncertainty to researchers investigating Chinese and global atmospheric transport and chemistry, it also identifies specific needs in data collection and analysis to improve on them. Strengthened quantification of emissions of the included species and other, closely associated ones - notably CO2, generated largely by the same processes and thus subject to many of the same parameter uncertainties - is essential not only for science but for the design of policies to redress critical atmospheric environmental hazards at local, regional, and global scales.

Quantifying the uncertainties of a bottom-up emission inventory of anthropogenic atmospheric pollutants in China

NASA Astrophysics Data System (ADS)

Zhao, Y.; Nielsen, C. P.; Lei, Y.; McElroy, M. B.; Hao, J.

2011-03-01

The uncertainties of a national, bottom-up inventory of Chinese emissions of anthropogenic SO2, NOx, and particulate matter (PM) of different size classes and carbonaceous species are comprehensively quantified, for the first time, using Monte Carlo simulation. The inventory is structured by seven dominant sectors: coal-fired electric power, cement, iron and steel, other industry (boiler combustion), other industry (non-combustion processes), transportation, and residential. For each parameter related to emission factors or activity-level calculations, the uncertainties, represented as probability distributions, are either statistically fitted using results of domestic field tests or, when these are lacking, estimated based on foreign or other domestic data. The uncertainties (i.e., 95% confidence intervals around the central estimates) of Chinese emissions of SO2, NOx, total PM, PM10, PM2.5, black carbon (BC), and organic carbon (OC) in 2005 are estimated to be -14%~13%, -13%~37%, -11%~38%, -14%~45%, -17%~54%, -25%~136%, and -40%~121%, respectively. Variations at activity levels (e.g., energy consumption or industrial production) are not the main source of emission uncertainties. Due to narrow classification of source types, large sample sizes, and relatively high data quality, the coal-fired power sector is estimated to have the smallest emission uncertainties for all species except BC and OC. Due to poorer source classifications and a wider range of estimated emission factors, considerable uncertainties of NOx and PM emissions from cement production and boiler combustion in other industries are found. The probability distributions of emission factors for biomass burning, the largest source of BC and OC, are fitted based on very limited domestic field measurements, and special caution should thus be taken interpreting these emission uncertainties. Although Monte Carlo simulation yields narrowed estimates of uncertainties compared to previous bottom-up emission studies, the results are not always consistent with those derived from satellite observations. The results thus represent an incremental research advance; while the analysis provides current estimates of uncertainty to researchers investigating Chinese and global atmospheric transport and chemistry, it also identifies specific needs in data collection and analysis to improve on them. Strengthened quantification of emissions of the included species and other, closely associated ones - notably CO2, generated largely by the same processes and thus subject to many of the same parameter uncertainties - is essential not only for science but for the design of policies to redress critical atmospheric environmental hazards at local, regional, and global scales.

Hydraulic Conductivity Estimation using Bayesian Model Averaging and Generalized Parameterization

NASA Astrophysics Data System (ADS)

Tsai, F. T.; Li, X.

2006-12-01

Non-uniqueness in parameterization scheme is an inherent problem in groundwater inverse modeling due to limited data. To cope with the non-uniqueness problem of parameterization, we introduce a Bayesian Model Averaging (BMA) method to integrate a set of selected parameterization methods. The estimation uncertainty in BMA includes the uncertainty in individual parameterization methods as the within-parameterization variance and the uncertainty from using different parameterization methods as the between-parameterization variance. Moreover, the generalized parameterization (GP) method is considered in the geostatistical framework in this study. The GP method aims at increasing the flexibility of parameterization through the combination of a zonation structure and an interpolation method. The use of BMP with GP avoids over-confidence in a single parameterization method. A normalized least-squares estimation (NLSE) is adopted to calculate the posterior probability for each GP. We employee the adjoint state method for the sensitivity analysis on the weighting coefficients in the GP method. The adjoint state method is also applied to the NLSE problem. The proposed methodology is implemented to the Alamitos Barrier Project (ABP) in California, where the spatially distributed hydraulic conductivity is estimated. The optimal weighting coefficients embedded in GP are identified through the maximum likelihood estimation (MLE) where the misfits between the observed and calculated groundwater heads are minimized. The conditional mean and conditional variance of the estimated hydraulic conductivity distribution using BMA are obtained to assess the estimation uncertainty.

Improving the precision of lake ecosystem metabolism estimates by identifying predictors of model uncertainty

USGS Publications Warehouse

Rose, Kevin C.; Winslow, Luke A.; Read, Jordan S.; Read, Emily K.; Solomon, Christopher T.; Adrian, Rita; Hanson, Paul C.

2014-01-01

Diel changes in dissolved oxygen are often used to estimate gross primary production (GPP) and ecosystem respiration (ER) in aquatic ecosystems. Despite the widespread use of this approach to understand ecosystem metabolism, we are only beginning to understand the degree and underlying causes of uncertainty for metabolism model parameter estimates. Here, we present a novel approach to improve the precision and accuracy of ecosystem metabolism estimates by identifying physical metrics that indicate when metabolism estimates are highly uncertain. Using datasets from seventeen instrumented GLEON (Global Lake Ecological Observatory Network) lakes, we discovered that many physical characteristics correlated with uncertainty, including PAR (photosynthetically active radiation, 400-700 nm), daily variance in Schmidt stability, and wind speed. Low PAR was a consistent predictor of high variance in GPP model parameters, but also corresponded with low ER model parameter variance. We identified a threshold (30% of clear sky PAR) below which GPP parameter variance increased rapidly and was significantly greater in nearly all lakes compared with variance on days with PAR levels above this threshold. The relationship between daily variance in Schmidt stability and GPP model parameter variance depended on trophic status, whereas daily variance in Schmidt stability was consistently positively related to ER model parameter variance. Wind speeds in the range of ~0.8-3 m s–1 were consistent predictors of high variance for both GPP and ER model parameters, with greater uncertainty in eutrophic lakes. Our findings can be used to reduce ecosystem metabolism model parameter uncertainty and identify potential sources of that uncertainty.

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

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

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

2014-10-20

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

Quantification of uncertainty associated with United States high resolution fossil fuel CO2 emissions: updates, challenges and future plans

NASA Astrophysics Data System (ADS)

Gurney, K. R.; Chandrasekaran, V.; Mendoza, D. L.; Geethakumar, S.

2010-12-01

The Vulcan Project has estimated United States fossil fuel CO2 emissions at the hourly time scale and at spatial scales below the county level for the year 2002. Vulcan is built from a wide variety of observational data streams including regulated air pollutant emissions reporting, traffic monitoring, energy statistics, and US census data. In addition to these data sets, Vulcan relies on a series of modeling assumptions and constructs to interpolate in space, time and transform non-CO2 reporting into an estimate of CO2 combustion emissions. The recent version 2.0 of the Vulcan inventory has produced advances in a number of categories with particular emphasis on improved temporal structure. Onroad transportation emissions now avail of roughly 5000 automated traffic count monitors allowing for much improved diurnal and weekly time structure in our onroad transportation emissions. Though the inventory shows excellent agreement with independent national-level CO2 emissions estimates, uncertainty quantification has been a challenging task given the large number of data sources and numerous modeling assumptions. However, we have now accomplished a complete uncertainty estimate across all the Vulcan economic sectors and will present uncertainty estimates as a function of space, time, sector and fuel. We find that, like the underlying distribution of CO2 emissions themselves, the uncertainty is also strongly lognormal with high uncertainty associated with a relatively small number of locations. These locations typically are locations reliant upon coal combustion as the dominant CO2 source. We will also compare and contrast Vulcan fossil fuel CO2 emissions estimates against estimates built from DOE fuel-based surveys at the state level. We conclude that much of the difference between the Vulcan inventory and DOE statistics are not due to biased estimation but mechanistic differences in supply versus demand and combustion in space/time.

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.

Uncertainties in estimates of the risks of late effects from space radiation

NASA Technical Reports Server (NTRS)

Cucinotta, F. A.; Schimmerling, W.; Wilson, J. W.; Peterson, L. E.; Saganti, P. B.; Dicello, J. F.

2004-01-01

Methods used to project risks in low-Earth orbit are of questionable merit for exploration missions because of the limited radiobiology data and knowledge of galactic cosmic ray (GCR) heavy ions, which causes estimates of the risk of late effects to be highly uncertain. Risk projections involve a product of many biological and physical factors, each of which has a differential range of uncertainty due to lack of data and knowledge. Using the linear-additivity model for radiation risks, we use Monte-Carlo sampling from subjective uncertainty distributions in each factor to obtain an estimate of the overall uncertainty in risk projections. The resulting methodology is applied to several human space exploration mission scenarios including a deep space outpost and Mars missions of duration of 360, 660, and 1000 days. The major results are the quantification of the uncertainties in current risk estimates, the identification of factors that dominate risk projection uncertainties, and the development of a method to quantify candidate approaches to reduce uncertainties or mitigate risks. The large uncertainties in GCR risk projections lead to probability distributions of risk that mask any potential risk reduction using the "optimization" of shielding materials or configurations. In contrast, the design of shielding optimization approaches for solar particle events and trapped protons can be made at this time and promising technologies can be shown to have merit using our approach. The methods used also make it possible to express risk management objectives in terms of quantitative metrics, e.g., the number of days in space without exceeding a given risk level within well-defined confidence limits. Published by Elsevier Ltd on behalf of COSPAR.

Hydrogeologic setting and preliminary estimates of hydrologic components for Bull Run Lake and the Bull Run Lake drainage basin, Multnomah and Clackamas counties, Oregon

USGS Publications Warehouse

Snyder, Daniel T.; Brownell, Dorie L.

1996-01-01

Suggestions for further study include (1) evaluation of the surface-runoff component of inflow to the lake; (2) use of a cross-sectional ground-water flow model to estimate ground-water inflow, outflow, and storage; (3) additional data collection to reduce the uncertainties of the hydrologic components that have large relative uncertainties; and (4) determination of long-term trends for a wide range of climatic and hydrologic conditions.

Entry Debris Field Estimation Methods and Application to Compton Gamma Ray Observatory Disposal

NASA Technical Reports Server (NTRS)

Mrozinski, Richard B.

2001-01-01

For public safety reasons, the Compton Gamma Ray Observatory (CGRO) was intentionally deorbited on June 4, 2000. This deorbit was NASA's first intentional controlled deorbit of a satellite, and more will come including the eventual deorbit of the International Space Station. To maximize public safety, satellite deorbit planning requires conservative estimates of the debris footprint size and location. These estimates are needed to properly design a deorbit sequence that places the debris footprint over unpopulated areas, including protection for deorbit contingencies. This paper details a method for estimating the length (range), width (crossrange), and location of entry and breakup debris footprints. This method utilizes a three degree-of-freedom Monte Carlo simulation incorporating uncertainties in all aspects of the problem, including vehicle and environment uncertainties. The method incorporates a range of debris characteristics based on historical data in addition to any vehicle-specific debris catalog information. This paper describes the method in detail, and presents results of its application as used in planning the deorbit of the CGRO.

Integrating uncertainty propagation in GNSS radio occultation retrieval: from excess phase to atmospheric bending angle profiles

NASA Astrophysics Data System (ADS)

Schwarz, Jakob; Kirchengast, Gottfried; Schwaerz, Marc

2018-05-01

Global Navigation Satellite System (GNSS) radio occultation (RO) observations are highly accurate, long-term stable data sets and are globally available as a continuous record from 2001. Essential climate variables for the thermodynamic state of the free atmosphere - such as pressure, temperature, and tropospheric water vapor profiles (involving background information) - can be derived from these records, which therefore have the potential to serve as climate benchmark data. However, to exploit this potential, atmospheric profile retrievals need to be very accurate and the remaining uncertainties quantified and traced throughout the retrieval chain from raw observations to essential climate variables. The new Reference Occultation Processing System (rOPS) at the Wegener Center aims to deliver such an accurate RO retrieval chain with integrated uncertainty propagation. Here we introduce and demonstrate the algorithms implemented in the rOPS for uncertainty propagation from excess phase to atmospheric bending angle profiles, for estimated systematic and random uncertainties, including vertical error correlations and resolution estimates. We estimated systematic uncertainty profiles with the same operators as used for the basic state profiles retrieval. The random uncertainty is traced through covariance propagation and validated using Monte Carlo ensemble methods. The algorithm performance is demonstrated using test day ensembles of simulated data as well as real RO event data from the satellite missions CHAllenging Minisatellite Payload (CHAMP); Constellation Observing System for Meteorology, Ionosphere, and Climate (COSMIC); and Meteorological Operational Satellite A (MetOp). The results of the Monte Carlo validation show that our covariance propagation delivers correct uncertainty quantification from excess phase to bending angle profiles. The results from the real RO event ensembles demonstrate that the new uncertainty estimation chain performs robustly. Together with the other parts of the rOPS processing chain this part is thus ready to provide integrated uncertainty propagation through the whole RO retrieval chain for the benefit of climate monitoring and other applications.

Characterizing Uncertainty and Variability in PBPK Models ...

EPA Pesticide Factsheets

Mode-of-action based risk and safety assessments can rely upon tissue dosimetry estimates in animals and humans obtained from physiologically-based pharmacokinetic (PBPK) modeling. However, risk assessment also increasingly requires characterization of uncertainty and variability; such characterization for PBPK model predictions represents a continuing challenge to both modelers and users. Current practices show significant progress in specifying deterministic biological models and the non-deterministic (often statistical) models, estimating their parameters using diverse data sets from multiple sources, and using them to make predictions and characterize uncertainty and variability. The International Workshop on Uncertainty and Variability in PBPK Models, held Oct 31-Nov 2, 2006, sought to identify the state-of-the-science in this area and recommend priorities for research and changes in practice and implementation. For the short term, these include: (1) multidisciplinary teams to integrate deterministic and non-deterministic/statistical models; (2) broader use of sensitivity analyses, including for structural and global (rather than local) parameter changes; and (3) enhanced transparency and reproducibility through more complete documentation of the model structure(s) and parameter values, the results of sensitivity and other analyses, and supporting, discrepant, or excluded data. Longer-term needs include: (1) theoretic and practical methodological impro

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.

Alternative configurations of Quantile Regression for estimating predictive uncertainty in water level forecasts for the Upper Severn River: a comparison

NASA Astrophysics Data System (ADS)

Lopez, Patricia; Verkade, Jan; Weerts, Albrecht; Solomatine, Dimitri

2014-05-01

Hydrological forecasting is subject to many sources of uncertainty, including those originating in initial state, boundary conditions, model structure and model parameters. Although uncertainty can be reduced, it can never be fully eliminated. Statistical post-processing techniques constitute an often used approach to estimate the hydrological predictive uncertainty, where a model of forecast error is built using a historical record of past forecasts and observations. The present study focuses on the use of the Quantile Regression (QR) technique as a hydrological post-processor. It estimates the predictive distribution of water levels using deterministic water level forecasts as predictors. This work aims to thoroughly verify uncertainty estimates using the implementation of QR that was applied in an operational setting in the UK National Flood Forecasting System, and to inter-compare forecast quality and skill in various, differing configurations of QR. These configurations are (i) 'classical' QR, (ii) QR constrained by a requirement that quantiles do not cross, (iii) QR derived on time series that have been transformed into the Normal domain (Normal Quantile Transformation - NQT), and (iv) a piecewise linear derivation of QR models. The QR configurations are applied to fourteen hydrological stations on the Upper Severn River with different catchments characteristics. Results of each QR configuration are conditionally verified for progressively higher flood levels, in terms of commonly used verification metrics and skill scores. These include Brier's probability score (BS), the continuous ranked probability score (CRPS) and corresponding skill scores as well as the Relative Operating Characteristic score (ROCS). Reliability diagrams are also presented and analysed. The results indicate that none of the four Quantile Regression configurations clearly outperforms the others.

Bayesian dose-response analysis for epidemiological studies with complex uncertainty in dose estimation.

PubMed

Kwon, Deukwoo; Hoffman, F Owen; Moroz, Brian E; Simon, Steven L

2016-02-10

Most conventional risk analysis methods rely on a single best estimate of exposure per person, which does not allow for adjustment for exposure-related uncertainty. Here, we propose a Bayesian model averaging method to properly quantify the relationship between radiation dose and disease outcomes by accounting for shared and unshared uncertainty in estimated dose. Our Bayesian risk analysis method utilizes multiple realizations of sets (vectors) of doses generated by a two-dimensional Monte Carlo simulation method that properly separates shared and unshared errors in dose estimation. The exposure model used in this work is taken from a study of the risk of thyroid nodules among a cohort of 2376 subjects who were exposed to fallout from nuclear testing in Kazakhstan. We assessed the performance of our method through an extensive series of simulations and comparisons against conventional regression risk analysis methods. When the estimated doses contain relatively small amounts of uncertainty, the Bayesian method using multiple a priori plausible draws of dose vectors gave similar results to the conventional regression-based methods of dose-response analysis. However, when large and complex mixtures of shared and unshared uncertainties are present, the Bayesian method using multiple dose vectors had significantly lower relative bias than conventional regression-based risk analysis methods and better coverage, that is, a markedly increased capability to include the true risk coefficient within the 95% credible interval of the Bayesian-based risk estimate. An evaluation of the dose-response using our method is presented for an epidemiological study of thyroid disease following radiation exposure. Copyright © 2015 John Wiley & Sons, Ltd.

Estimation of parameter uncertainty for an activated sludge model using Bayesian inference: a comparison with the frequentist method.

PubMed

Zonta, Zivko J; Flotats, Xavier; Magrí, Albert

2014-08-01

The procedure commonly used for the assessment of the parameters included in activated sludge models (ASMs) relies on the estimation of their optimal value within a confidence region (i.e. frequentist inference). Once optimal values are estimated, parameter uncertainty is computed through the covariance matrix. However, alternative approaches based on the consideration of the model parameters as probability distributions (i.e. Bayesian inference), may be of interest. The aim of this work is to apply (and compare) both Bayesian and frequentist inference methods when assessing uncertainty for an ASM-type model, which considers intracellular storage and biomass growth, simultaneously. Practical identifiability was addressed exclusively considering respirometric profiles based on the oxygen uptake rate and with the aid of probabilistic global sensitivity analysis. Parameter uncertainty was thus estimated according to both the Bayesian and frequentist inferential procedures. Results were compared in order to evidence the strengths and weaknesses of both approaches. Since it was demonstrated that Bayesian inference could be reduced to a frequentist approach under particular hypotheses, the former can be considered as a more generalist methodology. Hence, the use of Bayesian inference is encouraged for tackling inferential issues in ASM environments.

GNSS Radio Occultation Excess Phase Processing with Integrated Uncertainty Estimation for Thermodynamic Cal/Val of Passive Atmospheric Sounders and Climate Science

NASA Astrophysics Data System (ADS)

Innerkofler, J.; Pock, C.; Kirchengast, G.; Schwaerz, M.; Jaeggi, A.; Andres, Y.; Marquardt, C.; Hunt, D.; Schreiner, W. S.; Schwarz, J.

2017-12-01

Global Navigation Satellite System (GNSS) radio occultation (RO) is a highly valuable satellite remote sensing technique for atmospheric and climate sciences, including calibration and validation (cal/val) of passive sounding instruments such as radiometers. It is providing accurate and precise measurements in the troposphere and stratosphere regions with global coverage, long-term stability, and virtually all-weather capability since 2001. For fully exploiting the potential of RO data as a cal/val reference and climate data record, uncertainties attributed to the data need to be assessed. Here we focus on the atmospheric excess phase data, based on the raw occultation tracking and orbit data, and its integrated uncertainty estimation within the new Reference Occultation Processing System (rOPS) developed at the WEGC. These excess phases correspond to integrated refractivity, proportional to pressure/temperature and water vapor, and are therefore highly valuable reference data for thermodynamic cal/val of passive (radiometric) sounder data. In order to enable high accuracy of the excess phase profiles, accurate orbit positions and velocities as well as clock estimates of the GNSS transmitter satellites and RO receiver satellites are determined using the Bernese and Napeos orbit determination software packages. We find orbit uncertainty estimates of about 5 cm (position) / 0.05 mm/s (velocity) for daily orbits for the MetOp, GRACE, and CHAMP RO missions, and decreased uncertainty estimates near 20 cm (position) / 0.2 mm/s (velocity) for the COSMIC RO mission. The strict evaluation and quality control of the position, velocity, and clock accuracies of the daily LEO and GNSS orbits assure smallest achievable uncertainties in the excess phase data. We compared the excess phase profiles from WEGC against profiles from EUMETSAT and UCAR. Results show good agreement in line with the estimated uncertainties, with millimetric differences in the upper stratosphere and mesosphere and centimetric differences in the troposphere, where the excess phases amount to beyond 100 m. This underlines the potential for a new fundamental cal/val reference and climate data record based on atmospheric excess phases from RO, given their narrow uncertainty and independence from background data.

GRAVSAT/GEOPAUSE covariance analysis including geopotential aliasing

NASA Technical Reports Server (NTRS)

Koch, D. W.

1975-01-01

A conventional covariance analysis for the GRAVSAT/GEOPAUSE mission is described in which the uncertainties of approximately 200 parameters, including the geopotential coefficients to degree and order 12, are estimated over three different tracking intervals. The estimated orbital uncertainties for both GRAVSAT and GEOPAUSE reach levels more accurate than presently available. The adjusted measurement bias errors approach the mission goal. Survey errors in the low centimeter range are achieved after ten days of tracking. The ability of the mission to obtain accuracies of geopotential terms to (12, 12) one to two orders of magnitude superior to present accuracy levels is clearly shown. A unique feature of this report is that the aliasing structure of this (12, 12) field is examined. It is shown that uncertainties for unadjusted terms to (12, 12) still exert a degrading effect upon the adjusted error of an arbitrarily selected term of lower degree and order. Finally, the distribution of the aliasing from the unestimated uncertainty of a particular high degree and order geopotential term upon the errors of all remaining adjusted terms is listed in detail.

A revised ground-motion and intensity interpolation scheme for shakemap

USGS Publications Warehouse

Worden, C.B.; Wald, D.J.; Allen, T.I.; Lin, K.; Garcia, D.; Cua, G.

2010-01-01

We describe a weighted-average approach for incorporating various types of data (observed peak ground motions and intensities and estimates from groundmotion prediction equations) into the ShakeMap ground motion and intensity mapping framework. This approach represents a fundamental revision of our existing ShakeMap methodology. In addition, the increased availability of near-real-time macroseismic intensity data, the development of newrelationships between intensity and peak ground motions, and new relationships to directly predict intensity from earthquake source information have facilitated the inclusion of intensity measurements directly into ShakeMap computations. Our approach allows for the combination of (1) direct observations (ground-motion measurements or reported intensities), (2) observations converted from intensity to ground motion (or vice versa), and (3) estimated ground motions and intensities from prediction equations or numerical models. Critically, each of the aforementioned data types must include an estimate of its uncertainties, including those caused by scaling the influence of observations to surrounding grid points and those associated with estimates given an unknown fault geometry. The ShakeMap ground-motion and intensity estimates are an uncertainty-weighted combination of these various data and estimates. A natural by-product of this interpolation process is an estimate of total uncertainty at each point on the map, which can be vital for comprehensive inventory loss calculations. We perform a number of tests to validate this new methodology and find that it produces a substantial improvement in the accuracy of ground-motion predictions over empirical prediction equations alone.

A COMPREHENSIVE ANALYSIS OF UNCERTAINTIES AFFECTING THE STELLAR MASS-HALO MASS RELATION FOR 0 < z < 4

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

Behroozi, Peter S.; Wechsler, Risa H.; Conroy, Charlie

2010-07-01

We conduct a comprehensive analysis of the relationship between central galaxies and their host dark matter halos, as characterized by the stellar mass-halo mass (SM-HM) relation, with rigorous consideration of uncertainties. Our analysis focuses on results from the abundance matching technique, which assumes that every dark matter halo or subhalo above a specific mass threshold hosts one galaxy. We provide a robust estimate of the SM-HM relation for 0 < z < 1 and discuss the quantitative effects of uncertainties in observed galaxy stellar mass functions (including stellar mass estimates and counting uncertainties), halo mass functions (including cosmology and uncertaintiesmore » from substructure), and the abundance matching technique used to link galaxies to halos (including scatter in this connection). Our analysis results in a robust estimate of the SM-HM relation and its evolution from z = 0 to z = 4. The shape and the evolution are well constrained for z < 1. The largest uncertainties at these redshifts are due to stellar mass estimates (0.25 dex uncertainty in normalization); however, failure to account for scatter in stellar masses at fixed halo mass can lead to errors of similar magnitude in the SM-HM relation for central galaxies in massive halos. We also investigate the SM-HM relation to z = 4, although the shape of the relation at higher redshifts remains fairly unconstrained when uncertainties are taken into account. We find that the integrated star formation at a given halo mass peaks at 10%-20% of available baryons for all redshifts from 0 to 4. This peak occurs at a halo mass of 7 x 10{sup 11} M{sub sun} at z = 0 and this mass increases by a factor of 5 to z = 4. At lower and higher masses, star formation is substantially less efficient, with stellar mass scaling as M{sub *} {approx} M {sup 2.3}{sub h} at low masses and M{sub *} {approx} M {sup 0.29}{sub h} at high masses. The typical stellar mass for halos with mass less than 10{sup 12} M{sub sun} has increased by 0.3-0.45 dex for halos since z {approx} 1. These results will provide a powerful tool to inform galaxy evolution models.« less

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.

Effects of measurement resolution on the analysis of temperature time series for stream-aquifer flux estimation

NASA Astrophysics Data System (ADS)

Soto-López, Carlos D.; Meixner, Thomas; Ferré, Ty P. A.

2011-12-01

From its inception in the mid-1960s, the use of temperature time series (thermographs) to estimate vertical fluxes has found increasing use in the hydrologic community. Beginning in 2000, researchers have examined the impacts of measurement and parameter uncertainty on the estimates of vertical fluxes. To date, the effects of temperature measurement discretization (resolution), a characteristic of all digital temperature loggers, on the determination of vertical fluxes has not been considered. In this technical note we expand the analysis of recently published work to include the effects of temperature measurement resolution on estimates of vertical fluxes using temperature amplitude and phase shift information. We show that errors in thermal front velocity estimation introduced by discretizing thermographs differ when amplitude or phase shift data are used to estimate vertical fluxes. We also show that under similar circumstances sensor resolution limits the range over which vertical velocities are accurately reproduced more than uncertainty in temperature measurements, uncertainty in sensor separation distance, and uncertainty in the thermal diffusivity combined. These effects represent the baseline error present and thus the best-case scenario when discrete temperature measurements are used to infer vertical fluxes. The errors associated with measurement resolution can be minimized by using the highest-resolution sensors available. But thoughtful experimental design could allow users to select the most cost-effective temperature sensors to fit their measurement needs.

Uncertainty Analysis of Inertial Model Attitude Sensor Calibration and Application with a Recommended New Calibration Method

NASA Technical Reports Server (NTRS)

Tripp, John S.; Tcheng, Ping

1999-01-01

Statistical tools, previously developed for nonlinear least-squares estimation of multivariate sensor calibration parameters and the associated calibration uncertainty analysis, have been applied to single- and multiple-axis inertial model attitude sensors used in wind tunnel testing to measure angle of attack and roll angle. The analysis provides confidence and prediction intervals of calibrated sensor measurement uncertainty as functions of applied input pitch and roll angles. A comparative performance study of various experimental designs for inertial sensor calibration is presented along with corroborating experimental data. The importance of replicated calibrations over extended time periods has been emphasized; replication provides independent estimates of calibration precision and bias uncertainties, statistical tests for calibration or modeling bias uncertainty, and statistical tests for sensor parameter drift over time. A set of recommendations for a new standardized model attitude sensor calibration method and usage procedures is included. The statistical information provided by these procedures is necessary for the uncertainty analysis of aerospace test results now required by users of industrial wind tunnel test facilities.

Effects of Ensemble Configuration on Estimates of Regional Climate Uncertainties

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

Goldenson, N.; Mauger, G.; Leung, L. R.

Internal variability in the climate system can contribute substantial uncertainty in climate projections, particularly at regional scales. Internal variability can be quantified using large ensembles of simulations that are identical but for perturbed initial conditions. Here we compare methods for quantifying internal variability. Our study region spans the west coast of North America, which is strongly influenced by El Niño and other large-scale dynamics through their contribution to large-scale internal variability. Using a statistical framework to simultaneously account for multiple sources of uncertainty, we find that internal variability can be quantified consistently using a large ensemble or an ensemble ofmore » opportunity that includes small ensembles from multiple models and climate scenarios. The latter also produce estimates of uncertainty due to model differences. We conclude that projection uncertainties are best assessed using small single-model ensembles from as many model-scenario pairings as computationally feasible, which has implications for ensemble design in large modeling efforts.« less

NASA Team 2 Sea Ice Concentration Algorithm Retrieval Uncertainty

NASA Technical Reports Server (NTRS)

Brucker, Ludovic; Cavalieri, Donald J.; Markus, Thorsten; Ivanoff, Alvaro

2014-01-01

Satellite microwave radiometers are widely used to estimate sea ice cover properties (concentration, extent, and area) through the use of sea ice concentration (IC) algorithms. Rare are the algorithms providing associated IC uncertainty estimates. Algorithm uncertainty estimates are needed to assess accurately global and regional trends in IC (and thus extent and area), and to improve sea ice predictions on seasonal to interannual timescales using data assimilation approaches. This paper presents a method to provide relative IC uncertainty estimates using the enhanced NASA Team (NT2) IC algorithm. The proposed approach takes advantage of the NT2 calculations and solely relies on the brightness temperatures (TBs) used as input. NT2 IC and its associated relative uncertainty are obtained for both the Northern and Southern Hemispheres using the Advanced Microwave Scanning Radiometer for the Earth Observing System (AMSR-E) TB. NT2 IC relative uncertainties estimated on a footprint-by-footprint swath-by-swath basis were averaged daily over each 12.5-km grid cell of the polar stereographic grid. For both hemispheres and throughout the year, the NT2 relative uncertainty is less than 5%. In the Southern Hemisphere, it is low in the interior ice pack, and it increases in the marginal ice zone up to 5%. In the Northern Hemisphere, areas with high uncertainties are also found in the high IC area of the Central Arctic. Retrieval uncertainties are greater in areas corresponding to NT2 ice types associated with deep snow and new ice. Seasonal variations in uncertainty show larger values in summer as a result of melt conditions and greater atmospheric contributions. Our analysis also includes an evaluation of the NT2 algorithm sensitivity to AMSR-E sensor noise. There is a 60% probability that the IC does not change (to within the computed retrieval precision of 1%) due to sensor noise, and the cumulated probability shows that there is a 90% chance that the IC varies by less than +/-3%. We also examined the daily IC variability, which is dominated by sea ice drift and ice formation/melt. Daily IC variability is the highest, year round, in the MIZ (often up to 20%, locally 30%). The temporal and spatial distributions of the retrieval uncertainties and the daily IC variability is expected to be useful for algorithm intercomparisons, climate trend assessments, and possibly IC assimilation in models.

A Monte-Carlo Bayesian framework for urban rainfall error modelling

NASA Astrophysics Data System (ADS)

Ochoa Rodriguez, Susana; Wang, Li-Pen; Willems, Patrick; Onof, Christian

2016-04-01

Rainfall estimates of the highest possible accuracy and resolution are required for urban hydrological applications, given the small size and fast response which characterise urban catchments. While significant progress has been made in recent years towards meeting rainfall input requirements for urban hydrology -including increasing use of high spatial resolution radar rainfall estimates in combination with point rain gauge records- rainfall estimates will never be perfect and the true rainfall field is, by definition, unknown [1]. Quantifying the residual errors in rainfall estimates is crucial in order to understand their reliability, as well as the impact that their uncertainty may have in subsequent runoff estimates. The quantification of errors in rainfall estimates has been an active topic of research for decades. However, existing rainfall error models have several shortcomings, including the fact that they are limited to describing errors associated to a single data source (i.e. errors associated to rain gauge measurements or radar QPEs alone) and to a single representative error source (e.g. radar-rain gauge differences, spatial temporal resolution). Moreover, rainfall error models have been mostly developed for and tested at large scales. Studies at urban scales are mostly limited to analyses of propagation of errors in rain gauge records-only through urban drainage models and to tests of model sensitivity to uncertainty arising from unmeasured rainfall variability. Only few radar rainfall error models -originally developed for large scales- have been tested at urban scales [2] and have been shown to fail to well capture small-scale storm dynamics, including storm peaks, which are of utmost important for urban runoff simulations. In this work a Monte-Carlo Bayesian framework for rainfall error modelling at urban scales is introduced, which explicitly accounts for relevant errors (arising from insufficient accuracy and/or resolution) in multiple data sources (in this case radar and rain gauge estimates typically available at present), while at the same time enabling dynamic combination of these data sources (thus not only quantifying uncertainty, but also reducing it). This model generates an ensemble of merged rainfall estimates, which can then be used as input to urban drainage models in order to examine how uncertainties in rainfall estimates propagate to urban runoff estimates. The proposed model is tested using as case study a detailed rainfall and flow dataset, and a carefully verified urban drainage model of a small (~9 km2) pilot catchment in North-East London. The model has shown to well characterise residual errors in rainfall data at urban scales (which remain after the merging), leading to improved runoff estimates. In fact, the majority of measured flow peaks are bounded within the uncertainty area produced by the runoff ensembles generated with the ensemble rainfall inputs. REFERENCES: [1] Ciach, G. J. & Krajewski, W. F. (1999). On the estimation of radar rainfall error variance. Advances in Water Resources, 22 (6), 585-595. [2] Rico-Ramirez, M. A., Liguori, S. & Schellart, A. N. A. (2015). Quantifying radar-rainfall uncertainties in urban drainage flow modelling. Journal of Hydrology, 528, 17-28.

Estimates of CO2 fluxes over the city of Cape Town, South Africa, through Bayesian inverse modelling

NASA Astrophysics Data System (ADS)

Nickless, Alecia; Rayner, Peter J.; Engelbrecht, Francois; Brunke, Ernst-Günther; Erni, Birgit; Scholes, Robert J.

2018-04-01

We present a city-scale inversion over Cape Town, South Africa. Measurement sites for atmospheric CO2 concentrations were installed at Robben Island and Hangklip lighthouses, located downwind and upwind of the metropolis. Prior estimates of the fossil fuel fluxes were obtained from a bespoke inventory analysis where emissions were spatially and temporally disaggregated and uncertainty estimates determined by means of error propagation techniques. Net ecosystem exchange (NEE) fluxes from biogenic processes were obtained from the land atmosphere exchange model CABLE (Community Atmosphere Biosphere Land Exchange). Uncertainty estimates were based on the estimates of net primary productivity. CABLE was dynamically coupled to the regional climate model CCAM (Conformal Cubic Atmospheric Model), which provided the climate inputs required to drive the Lagrangian particle dispersion model. The Bayesian inversion framework included a control vector where fossil fuel and NEE fluxes were solved for separately.Due to the large prior uncertainty prescribed to the NEE fluxes, the current inversion framework was unable to adequately distinguish between the fossil fuel and NEE fluxes, but the inversion was able to obtain improved estimates of the total fluxes within pixels and across the domain. The median of the uncertainty reductions of the total weekly flux estimates for the inversion domain of Cape Town was 28 %, but reach as high as 50 %. At the pixel level, uncertainty reductions of the total weekly flux reached up to 98 %, but these large uncertainty reductions were for NEE-dominated pixels. Improved corrections to the fossil fuel fluxes would be possible if the uncertainty around the prior NEE fluxes could be reduced. In order for this inversion framework to be operationalised for monitoring, reporting, and verification (MRV) of emissions from Cape Town, the NEE component of the CO2 budget needs to be better understood. Additional measurements of Δ14C and δ13C isotope measurements would be a beneficial component of an atmospheric monitoring programme aimed at MRV of CO2 for any city which has significant biogenic influence, allowing improved separation of contributions from NEE and fossil fuel fluxes to the observed CO2 concentration.

Large Uncertainty in Estimating pCO2 From Carbonate Equilibria in Lakes

NASA Astrophysics Data System (ADS)

Golub, Malgorzata; Desai, Ankur R.; McKinley, Galen A.; Remucal, Christina K.; Stanley, Emily H.

2017-11-01

Most estimates of carbon dioxide (CO2) evasion from freshwaters rely on calculating partial pressure of aquatic CO2 (pCO2) from two out of three CO2-related parameters using carbonate equilibria. However, the pCO2 uncertainty has not been systematically evaluated across multiple lake types and equilibria. We quantified random errors in pH, dissolved inorganic carbon, alkalinity, and temperature from the North Temperate Lakes Long-Term Ecological Research site in four lake groups across a broad gradient of chemical composition. These errors were propagated onto pCO2 calculated from three carbonate equilibria, and for overlapping observations, compared against uncertainties in directly measured pCO2. The empirical random errors in CO2-related parameters were mostly below 2% of their median values. Resulting random pCO2 errors ranged from ±3.7% to ±31.5% of the median depending on alkalinity group and choice of input parameter pairs. Temperature uncertainty had a negligible effect on pCO2. When compared with direct pCO2 measurements, all parameter combinations produced biased pCO2 estimates with less than one third of total uncertainty explained by random pCO2 errors, indicating that systematic uncertainty dominates over random error. Multidecadal trend of pCO2 was difficult to reconstruct from uncertain historical observations of CO2-related parameters. Given poor precision and accuracy of pCO2 estimates derived from virtually any combination of two CO2-related parameters, we recommend direct pCO2 measurements where possible. To achieve consistently robust estimates of CO2 emissions from freshwater components of terrestrial carbon balances, future efforts should focus on improving accuracy and precision of CO2-related parameters (including direct pCO2) measurements and associated pCO2 calculations.

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.

Laser network survey and orbit recovery. [altimeter evaluation in GEOS-C project

NASA Technical Reports Server (NTRS)

Berbert, J. H.

1974-01-01

Simulations were performed for the anticipated GEOS-C laser network stations at Goddard, Bermuda, and Florida to predict how well survey and orbit will be recovered. Lasers were added one at a time at Grand Turk, Antigua, and Panama to estimate the contribution from these additional sites. Time tag biases of 50 microseconds, survey uncertainties of 10 meters in each coordinate, laser range biases and noise estimates of 20 cm each, and conventional gravity uncertainties were included in the simulations. The results indicate that survey can be recovered to about 1 meter and Grand Turk can be recovered better than Antigua or Panama. Reducing the probably pessimistic assumed time tag biases and gravity field uncertainties improves the results. Using these survey recovery estimates, the short arc GEOS-C satellite heights for altimeter intercomparison orbits can be recovered within the calibration area to better than the required two meters.

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.

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

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.

Communications circuit including a linear quadratic estimator

DOEpatents

Ferguson, Dennis D.

2015-07-07

A circuit includes a linear quadratic estimator (LQE) configured to receive a plurality of measurements a signal. The LQE is configured to weight the measurements based on their respective uncertainties to produce weighted averages. The circuit further includes a controller coupled to the LQE and configured to selectively adjust at least one data link parameter associated with a communication channel in response to receiving the weighted averages.

Cost Implications of Uncertainty in CO{sub 2} Storage Resource Estimates: A Review

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

Anderson, Steven T., E-mail: sanderson@usgs.gov

Carbon capture from stationary sources and geologic storage of carbon dioxide (CO{sub 2}) is an important option to include in strategies to mitigate greenhouse gas emissions. However, the potential costs of commercial-scale CO{sub 2} storage are not well constrained, stemming from the inherent uncertainty in storage resource estimates coupled with a lack of detailed estimates of the infrastructure needed to access those resources. Storage resource estimates are highly dependent on storage efficiency values or storage coefficients, which are calculated based on ranges of uncertain geological and physical reservoir parameters. If dynamic factors (such as variability in storage efficiencies, pressure interference,more » and acceptable injection rates over time), reservoir pressure limitations, boundaries on migration of CO{sub 2}, consideration of closed or semi-closed saline reservoir systems, and other possible constraints on the technically accessible CO{sub 2} storage resource (TASR) are accounted for, it is likely that only a fraction of the TASR could be available without incurring significant additional costs. Although storage resource estimates typically assume that any issues with pressure buildup due to CO{sub 2} injection will be mitigated by reservoir pressure management, estimates of the costs of CO{sub 2} storage generally do not include the costs of active pressure management. Production of saline waters (brines) could be essential to increasing the dynamic storage capacity of most reservoirs, but including the costs of this critical method of reservoir pressure management could increase current estimates of the costs of CO{sub 2} storage by two times, or more. Even without considering the implications for reservoir pressure management, geologic uncertainty can significantly impact CO{sub 2} storage capacities and costs, and contribute to uncertainty in carbon capture and storage (CCS) systems. Given the current state of available information and the scarcity of (data from) long-term commercial-scale CO{sub 2} storage projects, decision makers may experience considerable difficulty in ascertaining the realistic potential, the likely costs, and the most beneficial pattern of deployment of CCS as an option to reduce CO{sub 2} concentrations in the atmosphere.« less

Cost implications of uncertainty in CO2 storage resource estimates: A review

USGS Publications Warehouse

Anderson, Steven T.

2017-01-01

Carbon capture from stationary sources and geologic storage of carbon dioxide (CO2) is an important option to include in strategies to mitigate greenhouse gas emissions. However, the potential costs of commercial-scale CO2 storage are not well constrained, stemming from the inherent uncertainty in storage resource estimates coupled with a lack of detailed estimates of the infrastructure needed to access those resources. Storage resource estimates are highly dependent on storage efficiency values or storage coefficients, which are calculated based on ranges of uncertain geological and physical reservoir parameters. If dynamic factors (such as variability in storage efficiencies, pressure interference, and acceptable injection rates over time), reservoir pressure limitations, boundaries on migration of CO2, consideration of closed or semi-closed saline reservoir systems, and other possible constraints on the technically accessible CO2 storage resource (TASR) are accounted for, it is likely that only a fraction of the TASR could be available without incurring significant additional costs. Although storage resource estimates typically assume that any issues with pressure buildup due to CO2 injection will be mitigated by reservoir pressure management, estimates of the costs of CO2 storage generally do not include the costs of active pressure management. Production of saline waters (brines) could be essential to increasing the dynamic storage capacity of most reservoirs, but including the costs of this critical method of reservoir pressure management could increase current estimates of the costs of CO2 storage by two times, or more. Even without considering the implications for reservoir pressure management, geologic uncertainty can significantly impact CO2 storage capacities and costs, and contribute to uncertainty in carbon capture and storage (CCS) systems. Given the current state of available information and the scarcity of (data from) long-term commercial-scale CO2 storage projects, decision makers may experience considerable difficulty in ascertaining the realistic potential, the likely costs, and the most beneficial pattern of deployment of CCS as an option to reduce CO2 concentrations in the atmosphere.

A bayesian approach for determining velocity and uncertainty estimates from seismic cone penetrometer testing or vertical seismic profiling data

USGS Publications Warehouse

Pidlisecky, Adam; Haines, S.S.

2011-01-01

Conventional processing methods for seismic cone penetrometer data present several shortcomings, most notably the absence of a robust velocity model uncertainty estimate. We propose a new seismic cone penetrometer testing (SCPT) data-processing approach that employs Bayesian methods to map measured data errors into quantitative estimates of model uncertainty. We first calculate travel-time differences for all permutations of seismic trace pairs. That is, we cross-correlate each trace at each measurement location with every trace at every other measurement location to determine travel-time differences that are not biased by the choice of any particular reference trace and to thoroughly characterize data error. We calculate a forward operator that accounts for the different ray paths for each measurement location, including refraction at layer boundaries. We then use a Bayesian inversion scheme to obtain the most likely slowness (the reciprocal of velocity) and a distribution of probable slowness values for each model layer. The result is a velocity model that is based on correct ray paths, with uncertainty bounds that are based on the data error. ?? NRC Research Press 2011.

Development and comparison in uncertainty assessment based Bayesian modularization method in hydrological modeling

NASA Astrophysics Data System (ADS)

Li, Lu; Xu, Chong-Yu; Engeland, Kolbjørn

2013-04-01

SummaryWith respect to model calibration, parameter estimation and analysis of uncertainty sources, various regression and probabilistic approaches are used in hydrological modeling. A family of Bayesian methods, which incorporates different sources of information into a single analysis through Bayes' theorem, is widely used for uncertainty assessment. However, none of these approaches can well treat the impact of high flows in hydrological modeling. This study proposes a Bayesian modularization uncertainty assessment approach in which the highest streamflow observations are treated as suspect information that should not influence the inference of the main bulk of the model parameters. This study includes a comprehensive comparison and evaluation of uncertainty assessments by our new Bayesian modularization method and standard Bayesian methods using the Metropolis-Hastings (MH) algorithm with the daily hydrological model WASMOD. Three likelihood functions were used in combination with standard Bayesian method: the AR(1) plus Normal model independent of time (Model 1), the AR(1) plus Normal model dependent on time (Model 2) and the AR(1) plus Multi-normal model (Model 3). The results reveal that the Bayesian modularization method provides the most accurate streamflow estimates measured by the Nash-Sutcliffe efficiency and provide the best in uncertainty estimates for low, medium and entire flows compared to standard Bayesian methods. The study thus provides a new approach for reducing the impact of high flows on the discharge uncertainty assessment of hydrological models via Bayesian method.

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

Ramuhalli, Pradeep; Hirt, Evelyn H.; Veeramany, Arun

This research report summaries the development and evaluation of a prototypic enhanced risk monitor (ERM) methodology (framework) that includes alternative risk metrics and uncertainty analysis. This updated ERM methodology accounts for uncertainty in the equipment condition assessment (ECA), the prognostic result, and the probabilistic risk assessment (PRA) model. It is anticipated that the ability to characterize uncertainty in the estimated risk and update the risk estimates in real time based on equipment condition assessment (ECA) will provide a mechanism for optimizing plant performance while staying within specified safety margins. These results (based on impacting active component O&M using real-time equipmentmore » condition information) are a step towards ERMs that, if integrated with AR supervisory plant control systems, can help control O&M costs and improve affordability of advanced reactors.« less

NWS Operational Requirements for Ensemble-Based Hydrologic Forecasts

NASA Astrophysics Data System (ADS)

Hartman, R. K.

2008-12-01

Ensemble-based hydrologic forecasts have been developed and issued by National Weather Service (NWS) staff at River Forecast Centers (RFCs) for many years. Used principally for long-range water supply forecasts, only the uncertainty associated with weather and climate have been traditionally considered. As technology and societal expectations of resource managers increase, the use and desire for risk-based decision support tools has also increased. These tools require forecast information that includes reliable uncertainty estimates across all time and space domains. The development of reliable uncertainty estimates associated with hydrologic forecasts is being actively pursued within the United States and internationally. This presentation will describe the challenges, components, and requirements for operational hydrologic ensemble-based forecasts from the perspective of a NOAA/NWS River Forecast Center.

Uncertainty estimation and multi sensor fusion for kinematic laser tracker measurements

NASA Astrophysics Data System (ADS)

Ulrich, Thomas

2013-08-01

Laser trackers are widely used to measure kinematic tasks such as tracking robot movements. Common methods to evaluate the uncertainty in the kinematic measurement include approximations specified by the manufacturers, various analytical adjustment methods and the Kalman filter. In this paper a new, real-time technique is proposed, which estimates the 4D-path (3D-position + time) uncertainty of an arbitrary path in space. Here a hybrid system estimator is applied in conjunction with the kinematic measurement model. This method can be applied to processes, which include various types of kinematic behaviour, constant velocity, variable acceleration or variable turn rates. The new approach is compared with the Kalman filter and a manufacturer's approximations. The comparison was made using data obtained by tracking an industrial robot's tool centre point with a Leica laser tracker AT901 and a Leica laser tracker LTD500. It shows that the new approach is more appropriate to analysing kinematic processes than the Kalman filter, as it reduces overshoots and decreases the estimated variance. In comparison with the manufacturer's approximations, the new approach takes account of kinematic behaviour with an improved description of the real measurement process and a reduction in estimated variance. This approach is therefore well suited to the analysis of kinematic processes with unknown changes in kinematic behaviour as well as the fusion among laser trackers.

Quantification of water resources uncertainties in the Luvuvhu sub-basin of the Limpopo river basin

NASA Astrophysics Data System (ADS)

Oosthuizen, N.; Hughes, D.; Kapangaziwiri, E.; Mwenge Kahinda, J.; Mvandaba, V.

2018-06-01

In the absence of historical observed data, models are generally used to describe the different hydrological processes and generate data and information that will inform management and policy decision making. Ideally, any hydrological model should be based on a sound conceptual understanding of the processes in the basin and be backed by quantitative information for the parameterization of the model. However, these data are often inadequate in many sub-basins, necessitating the incorporation of the uncertainty related to the estimation process. This paper reports on the impact of the uncertainty related to the parameterization of the Pitman monthly model and water use data on the estimates of the water resources of the Luvuvhu, a sub-basin of the Limpopo river basin. The study reviews existing information sources associated with the quantification of water balance components and gives an update of water resources of the sub-basin. The flows generated by the model at the outlet of the basin were between 44.03 Mm3 and 45.48 Mm3 per month when incorporating +20% uncertainty to the main physical runoff generating parameters. The total predictive uncertainty of the model increased when water use data such as small farm and large reservoirs and irrigation were included. The dam capacity data was considered at an average of 62% uncertainty mainly as a result of the large differences between the available information in the national water resources database and that digitised from satellite imagery. Water used by irrigated crops was estimated with an average of about 50% uncertainty. The mean simulated monthly flows were between 38.57 Mm3 and 54.83 Mm3 after the water use uncertainty was added. However, it is expected that the uncertainty could be reduced by using higher resolution remote sensing imagery.

How much swamp are we talking here?: Propagating uncertainty about the area of coastal wetlands into the U.S. greenhouse gas inventory

NASA Astrophysics Data System (ADS)

Holmquist, J. R.; Crooks, S.; Windham-Myers, L.; Megonigal, P.; Weller, D.; Lu, M.; Bernal, B.; Byrd, K. B.; Morris, J. T.; Troxler, T.; McCombs, J.; Herold, N.

2017-12-01

Stable coastal wetlands can store substantial amounts of carbon (C) that can be released when they are degraded or eroded. The EPA recently incorporated coastal wetland net-storage and emissions within the Agricultural Forested and Other Land Uses category of the U.S. National Greenhouse Gas Inventory (NGGI). This was a seminal analysis, but its quantification of uncertainty needs improvement. We provide a value-added analysis by estimating that uncertainty, focusing initially on the most basic assumption, the area of coastal wetlands. We considered three sources: uncertainty in the areas of vegetation and salinity subclasses, uncertainty in the areas of changing or stable wetlands, and uncertainty in the inland extent of coastal wetlands. The areas of vegetation and salinity subtypes, as well as stable or changing, were estimated from 2006 and 2010 maps derived from Landsat imagery by the Coastal Change Analysis Program (C-CAP). We generated unbiased area estimates and confidence intervals for C-CAP, taking into account mapped area, proportional areas of commission and omission errors, as well as the number of observations. We defined the inland extent of wetlands as all land below the current elevation of twice monthly highest tides. We generated probabilistic inundation maps integrating wetland-specific bias and random error in light-detection and ranging elevation maps, with the spatially explicit random error in tidal surfaces generated from tide gauges. This initial uncertainty analysis will be extended to calculate total propagated uncertainty in the NGGI by including the uncertainties in the amount of C lost from eroded and degraded wetlands, stored annually in stable wetlands, and emitted in the form of methane by tidal freshwater wetlands.

Information theoretic quantification of diagnostic uncertainty.

PubMed

Westover, M Brandon; Eiseman, Nathaniel A; Cash, Sydney S; Bianchi, Matt T

2012-01-01

Diagnostic test interpretation remains a challenge in clinical practice. Most physicians receive training in the use of Bayes' rule, which specifies how the sensitivity and specificity of a test for a given disease combine with the pre-test probability to quantify the change in disease probability incurred by a new test result. However, multiple studies demonstrate physicians' deficiencies in probabilistic reasoning, especially with unexpected test results. Information theory, a branch of probability theory dealing explicitly with the quantification of uncertainty, has been proposed as an alternative framework for diagnostic test interpretation, but is even less familiar to physicians. We have previously addressed one key challenge in the practical application of Bayes theorem: the handling of uncertainty in the critical first step of estimating the pre-test probability of disease. This essay aims to present the essential concepts of information theory to physicians in an accessible manner, and to extend previous work regarding uncertainty in pre-test probability estimation by placing this type of uncertainty within a principled information theoretic framework. We address several obstacles hindering physicians' application of information theoretic concepts to diagnostic test interpretation. These include issues of terminology (mathematical meanings of certain information theoretic terms differ from clinical or common parlance) as well as the underlying mathematical assumptions. Finally, we illustrate how, in information theoretic terms, one can understand the effect on diagnostic uncertainty of considering ranges instead of simple point estimates of pre-test probability.

Independent Qualification of the CIAU Tool Based on the Uncertainty Estimate in the Prediction of Angra 1 NPP Inadvertent Load Rejection Transient

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

Borges, Ronaldo C.; D'Auria, Francesco; Alvim, Antonio Carlos M.

2002-07-01

The Code with - the capability of - Internal Assessment of Uncertainty (CIAU) is a tool proposed by the 'Dipartimento di Ingegneria Meccanica, Nucleare e della Produzione (DIMNP)' of the University of Pisa. Other Institutions including the nuclear regulatory body from Brazil, 'Comissao Nacional de Energia Nuclear', contributed to the development of the tool. The CIAU aims at providing the currently available Relap5/Mod3.2 system code with the integrated capability of performing not only relevant transient calculations but also the related estimates of uncertainty bands. The Uncertainty Methodology based on Accuracy Extrapolation (UMAE) is used to characterize the uncertainty in themore » prediction of system code calculations for light water reactors and is internally coupled with the above system code. Following an overview of the CIAU development, the present paper deals with the independent qualification of the tool. The qualification test is performed by estimating the uncertainty bands that should envelope the prediction of the Angra 1 NPP transient RES-11. 99 originated by an inadvertent complete load rejection that caused the reactor scram when the unit was operating at 99% of nominal power. The current limitation of the 'error' database, implemented into the CIAU prevented a final demonstration of the qualification. However, all the steps for the qualification process are demonstrated. (authors)« less

Multifidelity Analysis and Optimization for Supersonic Design

NASA Technical Reports Server (NTRS)

Kroo, Ilan; Willcox, Karen; March, Andrew; Haas, Alex; Rajnarayan, Dev; Kays, Cory

2010-01-01

Supersonic aircraft design is a computationally expensive optimization problem and multifidelity approaches over a significant opportunity to reduce design time and computational cost. This report presents tools developed to improve supersonic aircraft design capabilities including: aerodynamic tools for supersonic aircraft configurations; a systematic way to manage model uncertainty; and multifidelity model management concepts that incorporate uncertainty. The aerodynamic analysis tools developed are appropriate for use in a multifidelity optimization framework, and include four analysis routines to estimate the lift and drag of a supersonic airfoil, a multifidelity supersonic drag code that estimates the drag of aircraft configurations with three different methods: an area rule method, a panel method, and an Euler solver. In addition, five multifidelity optimization methods are developed, which include local and global methods as well as gradient-based and gradient-free techniques.

Very High Resolution Tree Cover Mapping for Continental United States using Deep Convolutional Neural Networks

NASA Technical Reports Server (NTRS)

Ganguly, Sangram; Kalia, Subodh; Li, Shuang; Michaelis, Andrew; Nemani, Ramakrishna R.; Saatchi, Sassan A

2017-01-01

Uncertainties in input land cover estimates contribute to a significant bias in modeled above ground biomass (AGB) and carbon estimates from satellite-derived data. The resolution of most currently used passive remote sensing products is not sufficient to capture tree canopy cover of less than ca. 10-20 percent, limiting their utility to estimate canopy cover and AGB for trees outside of forest land. In our study, we created a first of its kind Continental United States (CONUS) tree cover map at a spatial resolution of 1-m for the 2010-2012 epoch using the USDA NAIP imagery to address the present uncertainties in AGB estimates. The process involves different tasks including data acquisition ingestion to pre-processing and running a state-of-art encoder-decoder based deep convolutional neural network (CNN) algorithm for automatically generating a tree non-tree map for almost a quarter million scenes. The entire processing chain including generation of the largest open source existing aerial satellite image training database was performed at the NEX supercomputing and storage facility. We believe the resulting forest cover product will substantially contribute to filling the gaps in ongoing carbon and ecological monitoring research and help quantifying the errors and uncertainties in derived products.

TSS concentration in sewers estimated from turbidity measurements by means of linear regression accounting for uncertainties in both variables.

PubMed

Bertrand-Krajewski, J L

2004-01-01

In order to replace traditional sampling and analysis techniques, turbidimeters can be used to estimate TSS concentration in sewers, by means of sensor and site specific empirical equations established by linear regression of on-site turbidity Tvalues with TSS concentrations C measured in corresponding samples. As the ordinary least-squares method is not able to account for measurement uncertainties in both T and C variables, an appropriate regression method is used to solve this difficulty and to evaluate correctly the uncertainty in TSS concentrations estimated from measured turbidity. The regression method is described, including detailed calculations of variances and covariance in the regression parameters. An example of application is given for a calibrated turbidimeter used in a combined sewer system, with data collected during three dry weather days. In order to show how the established regression could be used, an independent 24 hours long dry weather turbidity data series recorded at 2 min time interval is used, transformed into estimated TSS concentrations, and compared to TSS concentrations measured in samples. The comparison appears as satisfactory and suggests that turbidity measurements could replace traditional samples. Further developments, including wet weather periods and other types of sensors, are suggested.

Very High Resolution Tree Cover Mapping for Continental United States using Deep Convolutional Neural Networks

NASA Astrophysics Data System (ADS)

Ganguly, S.; Kalia, S.; Li, S.; Michaelis, A.; Nemani, R. R.; Saatchi, S.

2017-12-01

Uncertainties in input land cover estimates contribute to a significant bias in modeled above gound biomass (AGB) and carbon estimates from satellite-derived data. The resolution of most currently used passive remote sensing products is not sufficient to capture tree canopy cover of less than ca. 10-20 percent, limiting their utility to estimate canopy cover and AGB for trees outside of forest land. In our study, we created a first of its kind Continental United States (CONUS) tree cover map at a spatial resolution of 1-m for the 2010-2012 epoch using the USDA NAIP imagery to address the present uncertainties in AGB estimates. The process involves different tasks including data acquisition/ingestion to pre-processing and running a state-of-art encoder-decoder based deep convolutional neural network (CNN) algorithm for automatically generating a tree/non-tree map for almost a quarter million scenes. The entire processing chain including generation of the largest open source existing aerial/satellite image training database was performed at the NEX supercomputing and storage facility. We believe the resulting forest cover product will substantially contribute to filling the gaps in ongoing carbon and ecological monitoring research and help quantifying the errors and uncertainties in derived products.

On the predictivity of pore-scale simulations: Estimating uncertainties with multilevel Monte Carlo

NASA Astrophysics Data System (ADS)

Icardi, Matteo; Boccardo, Gianluca; Tempone, Raúl

2016-09-01

A fast method with tunable accuracy is proposed to estimate errors and uncertainties in pore-scale and Digital Rock Physics (DRP) problems. The overall predictivity of these studies can be, in fact, hindered by many factors including sample heterogeneity, computational and imaging limitations, model inadequacy and not perfectly known physical parameters. The typical objective of pore-scale studies is the estimation of macroscopic effective parameters such as permeability, effective diffusivity and hydrodynamic dispersion. However, these are often non-deterministic quantities (i.e., results obtained for specific pore-scale sample and setup are not totally reproducible by another ;equivalent; sample and setup). The stochastic nature can arise due to the multi-scale heterogeneity, the computational and experimental limitations in considering large samples, and the complexity of the physical models. These approximations, in fact, introduce an error that, being dependent on a large number of complex factors, can be modeled as random. We propose a general simulation tool, based on multilevel Monte Carlo, that can reduce drastically the computational cost needed for computing accurate statistics of effective parameters and other quantities of interest, under any of these random errors. This is, to our knowledge, the first attempt to include Uncertainty Quantification (UQ) in pore-scale physics and simulation. The method can also provide estimates of the discretization error and it is tested on three-dimensional transport problems in heterogeneous materials, where the sampling procedure is done by generation algorithms able to reproduce realistic consolidated and unconsolidated random sphere and ellipsoid packings and arrangements. A totally automatic workflow is developed in an open-source code [1], that include rigid body physics and random packing algorithms, unstructured mesh discretization, finite volume solvers, extrapolation and post-processing techniques. The proposed method can be efficiently used in many porous media applications for problems such as stochastic homogenization/upscaling, propagation of uncertainty from microscopic fluid and rock properties to macro-scale parameters, robust estimation of Representative Elementary Volume size for arbitrary physics.

Covariance expressions for eigenvalue and eigenvector problems

NASA Astrophysics Data System (ADS)

Liounis, Andrew J.

There are a number of important scientific and engineering problems whose solutions take the form of an eigenvalue--eigenvector problem. Some notable examples include solutions to linear systems of ordinary differential equations, controllability of linear systems, finite element analysis, chemical kinetics, fitting ellipses to noisy data, and optimal estimation of attitude from unit vectors. In many of these problems, having knowledge of the eigenvalue and eigenvector Jacobians is either necessary or is nearly as important as having the solution itself. For instance, Jacobians are necessary to find the uncertainty in a computed eigenvalue or eigenvector estimate. This uncertainty, which is usually represented as a covariance matrix, has been well studied for problems similar to the eigenvalue and eigenvector problem, such as singular value decomposition. There has been substantially less research on the covariance of an optimal estimate originating from an eigenvalue-eigenvector problem. In this thesis we develop two general expressions for the Jacobians of eigenvalues and eigenvectors with respect to the elements of their parent matrix. The expressions developed make use of only the parent matrix and the eigenvalue and eigenvector pair under consideration. In addition, they are applicable to any general matrix (including complex valued matrices, eigenvalues, and eigenvectors) as long as the eigenvalues are simple. Alongside this, we develop expressions that determine the uncertainty in a vector estimate obtained from an eigenvalue-eigenvector problem given the uncertainty of the terms of the matrix. The Jacobian expressions developed are numerically validated with forward finite, differencing and the covariance expressions are validated using Monte Carlo analysis. Finally, the results from this work are used to determine covariance expressions for a variety of estimation problem examples and are also applied to the design of a dynamical system.

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.

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....

ESTIMATING ACUTE AND CRONIC TOXICITY OF CHEMICALS FOR ENDANGERED FISHES

EPA Science Inventory

Predictive toxicological models, including estimates of uncertainty, are necessary to perform probability-based ecological risk assessments. This is particularly true for the protection of endangered species that are not prudent to test, other species that have not been tested o...

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.

Estimating the burden of disease attributable to indoor air pollution from household use of solid fuels in South Africa in 2000.

PubMed

Norman, Rosana; Barnes, Brendon; Mathee, Angela; Bradshaw, Debbie

2007-08-01

To estimate the burden of respiratory ill health in South African children and adults in 2000 from exposure to indoor air pollution associated with household use of solid fuels. World Health Organization comparative risk assessment (CRA) methodology was followed. The South African Census 2001 was used to derive the proportion of households using solid fuels for cooking and heating by population group. Exposure estimates were adjusted by a ventilation factor taking into account the general level of ventilation in the households. Population-attributable fractions were calculated and applied to revised burden of disease estimates for each population group. Monte Carlo simulation-modelling techniques were used for uncertainty analysis. South Africa. Black African, coloured, white and Indian children under 5 years of age and adults aged 30 years and older. Mortality and disability-adjusted life years (DALYs) from acute lower respiratory infections in children under 5 years, and chronic obstructive pulmonary disease and lung cancer in adults 30 years and older. An estimated 20% of South African households were exposed to indoor smoke from solid fuels, with marked variation by population group. This exposure was estimated to have caused 2,489 deaths (95% uncertainty interval 1,672 - 3,324) or 0.5% (95% uncertainty interval 0.3 - 0.6%) of all deaths in South Africa in 2000. The loss of healthy life years comprised a slightly smaller proportion of the total: 60,934 DALYs (95% uncertainty interval 41,170 - 81,246) or 0.4% of all DALYs (95% uncertainty interval 0.3 - 0.5%) in South Africa in 2000. Almost 99% of this burden occurred in the black African population. The most important interventions to reduce this impact include access to cleaner household fuels, improved stoves, and better ventilation.

Global, Regional, and National Burden of Rheumatic Heart Disease, 1990-2015.

PubMed

Watkins, David A; Johnson, Catherine O; Colquhoun, Samantha M; Karthikeyan, Ganesan; Beaton, Andrea; Bukhman, Gene; Forouzanfar, Mohammed H; Longenecker, Christopher T; Mayosi, Bongani M; Mensah, George A; Nascimento, Bruno R; Ribeiro, Antonio L P; Sable, Craig A; Steer, Andrew C; Naghavi, Mohsen; Mokdad, Ali H; Murray, Christopher J L; Vos, Theo; Carapetis, Jonathan R; Roth, Gregory A

2017-08-24

Rheumatic heart disease remains an important preventable cause of cardiovascular death and disability, particularly in low-income and middle-income countries. We estimated global, regional, and national trends in the prevalence of and mortality due to rheumatic heart disease as part of the 2015 Global Burden of Disease study. We systematically reviewed data on fatal and nonfatal rheumatic heart disease for the period from 1990 through 2015. Two Global Burden of Disease analytic tools, the Cause of Death Ensemble model and DisMod-MR 2.1, were used to produce estimates of mortality and prevalence, including estimates of uncertainty. We estimated that there were 319,400 (95% uncertainty interval, 297,300 to 337,300) deaths due to rheumatic heart disease in 2015. Global age-standardized mortality due to rheumatic heart disease decreased by 47.8% (95% uncertainty interval, 44.7 to 50.9) from 1990 to 2015, but large differences were observed across regions. In 2015, the highest age-standardized mortality due to and prevalence of rheumatic heart disease were observed in Oceania, South Asia, and central sub-Saharan Africa. We estimated that in 2015 there were 33.4 million (95% uncertainty interval, 29.7 million to 43.1 million) cases of rheumatic heart disease and 10.5 million (95% uncertainty interval, 9.6 million to 11.5 million) disability-adjusted life-years due to rheumatic heart disease globally. We estimated the global disease prevalence of and mortality due to rheumatic heart disease over a 25-year period. The health-related burden of rheumatic heart disease has declined worldwide, but high rates of disease persist in some of the poorest regions in the world. (Funded by the Bill and Melinda Gates Foundation and the Medtronic Foundation.).

A Comprehensive Analysis of Uncertainties Affecting the Stellar Mass-Halo Mass Relation for 0

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

Behroozi, Peter S.; Conroy, Charlie; Wechsler, Risa H.

2010-06-07

We conduct a comprehensive analysis of the relationship between central galaxies and their host dark matter halos, as characterized by the stellar mass - halo mass (SM-HM) relation, with rigorous consideration of uncertainties. Our analysis focuses on results from the abundance matching technique, which assumes that every dark matter halo or subhalo above a specific mass threshold hosts one galaxy. We provide a robust estimate of the SM-HM relation for 0 < z < 1 and discuss the quantitative effects of uncertainties in observed galaxy stellar mass functions (GSMFs) (including stellar mass estimates and counting uncertainties), halo mass functions (includingmore » cosmology and uncertainties from substructure), and the abundance matching technique used to link galaxies to halos (including scatter in this connection). Our analysis results in a robust estimate of the SM-HM relation and its evolution from z=0 to z=4. The shape and evolution are well constrained for z < 1. The largest uncertainties at these redshifts are due to stellar mass estimates (0.25 dex uncertainty in normalization); however, failure to account for scatter in stellar masses at fixed halo mass can lead to errors of similar magnitude in the SM-HM relation for central galaxies in massive halos. We also investigate the SM-HM relation to z = 4, although the shape of the relation at higher redshifts remains fairly unconstrained when uncertainties are taken into account. We find that the integrated star formation at a given halo mass peaks at 10-20% of available baryons for all redshifts from 0 to 4. This peak occurs at a halo mass of 7 x 10{sup 11} M{sub {circle_dot}} at z = 0 and this mass increases by a factor of 5 to z = 4. At lower and higher masses, star formation is substantially less efficient, with stellar mass scaling as M{sub *} {approx} M{sub h}{sup 2.3} at low masses and M{sub *} {approx} M{sub h}{sup 0.29} at high masses. The typical stellar mass for halos with mass less than 10{sup 12} M{sub {circle_dot}} has increased by 0.3-0.45 dex for halos since z {approx} 1. These results will provide a powerful tool to inform galaxy evolution models.« less

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.

Bayesian Mass Estimates of the Milky Way: Including Measurement Uncertainties with Hierarchical Bayes

NASA Astrophysics Data System (ADS)

Eadie, Gwendolyn M.; Springford, Aaron; Harris, William E.

2017-02-01

We present a hierarchical Bayesian method for estimating the total mass and mass profile of the Milky Way Galaxy. The new hierarchical Bayesian approach further improves the framework presented by Eadie et al. and Eadie and Harris and builds upon the preliminary reports by Eadie et al. The method uses a distribution function f({ E },L) to model the Galaxy and kinematic data from satellite objects, such as globular clusters (GCs), to trace the Galaxy’s gravitational potential. A major advantage of the method is that it not only includes complete and incomplete data simultaneously in the analysis, but also incorporates measurement uncertainties in a coherent and meaningful way. We first test the hierarchical Bayesian framework, which includes measurement uncertainties, using the same data and power-law model assumed in Eadie and Harris and find the results are similar but more strongly constrained. Next, we take advantage of the new statistical framework and incorporate all possible GC data, finding a cumulative mass profile with Bayesian credible regions. This profile implies a mass within 125 kpc of 4.8× {10}11{M}⊙ with a 95% Bayesian credible region of (4.0{--}5.8)× {10}11{M}⊙ . Our results also provide estimates of the true specific energies of all the GCs. By comparing these estimated energies to the measured energies of GCs with complete velocity measurements, we observe that (the few) remote tracers with complete measurements may play a large role in determining a total mass estimate of the Galaxy. Thus, our study stresses the need for more remote tracers with complete velocity measurements.

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.

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.

Interval Estimation of Seismic Hazard Parameters

NASA Astrophysics Data System (ADS)

Orlecka-Sikora, Beata; Lasocki, Stanislaw

2017-03-01

The paper considers Poisson temporal occurrence of earthquakes and presents a way to integrate uncertainties of the estimates of mean activity rate and magnitude cumulative distribution function in the interval estimation of the most widely used seismic hazard functions, such as the exceedance probability and the mean return period. The proposed algorithm can be used either when the Gutenberg-Richter model of magnitude distribution is accepted or when the nonparametric estimation is in use. When the Gutenberg-Richter model of magnitude distribution is used the interval estimation of its parameters is based on the asymptotic normality of the maximum likelihood estimator. When the nonparametric kernel estimation of magnitude distribution is used, we propose the iterated bias corrected and accelerated method for interval estimation based on the smoothed bootstrap and second-order bootstrap samples. The changes resulted from the integrated approach in the interval estimation of the seismic hazard functions with respect to the approach, which neglects the uncertainty of the mean activity rate estimates have been studied using Monte Carlo simulations and two real dataset examples. The results indicate that the uncertainty of mean activity rate affects significantly the interval estimates of hazard functions only when the product of activity rate and the time period, for which the hazard is estimated, is no more than 5.0. When this product becomes greater than 5.0, the impact of the uncertainty of cumulative distribution function of magnitude dominates the impact of the uncertainty of mean activity rate in the aggregated uncertainty of the hazard functions. Following, the interval estimates with and without inclusion of the uncertainty of mean activity rate converge. The presented algorithm is generic and can be applied also to capture the propagation of uncertainty of estimates, which are parameters of a multiparameter function, onto this function.

Expanded uncertainty estimation methodology in determining the sandy soils filtration coefficient

NASA Astrophysics Data System (ADS)

Rusanova, A. D.; Malaja, L. D.; Ivanov, R. N.; Gruzin, A. V.; Shalaj, V. V.

2018-04-01

The combined standard uncertainty estimation methodology in determining the sandy soils filtration coefficient has been developed. The laboratory researches were carried out which resulted in filtration coefficient determination and combined uncertainty estimation obtaining.

Parameter Uncertainty for Aircraft Aerodynamic Modeling using Recursive Least Squares

NASA Technical Reports Server (NTRS)

Grauer, Jared A.; Morelli, Eugene A.

2016-01-01

A real-time method was demonstrated for determining accurate uncertainty levels of stability and control derivatives estimated using recursive least squares and time-domain data. The method uses a recursive formulation of the residual autocorrelation to account for colored residuals, which are routinely encountered in aircraft parameter estimation and change the predicted uncertainties. Simulation data and flight test data for a subscale jet transport aircraft were used to demonstrate the approach. Results showed that the corrected uncertainties matched the observed scatter in the parameter estimates, and did so more accurately than conventional uncertainty estimates that assume white residuals. Only small differences were observed between batch estimates and recursive estimates at the end of the maneuver. It was also demonstrated that the autocorrelation could be reduced to a small number of lags to minimize computation and memory storage requirements without significantly degrading the accuracy of predicted uncertainty levels.

Uncertainty and the Social Cost of Methane Using Bayesian Constrained Climate Models

NASA Astrophysics Data System (ADS)

Errickson, F. C.; Anthoff, D.; Keller, K.

2016-12-01

Social cost estimates of greenhouse gases are important for the design of sound climate policies and are also plagued by uncertainty. One major source of uncertainty stems from the simplified representation of the climate system used in the integrated assessment models that provide these social cost estimates. We explore how uncertainty over the social cost of methane varies with the way physical processes and feedbacks in the methane cycle are modeled by (i) coupling three different methane models to a simple climate model, (ii) using MCMC to perform a Bayesian calibration of the three coupled climate models that simulates direct sampling from the joint posterior probability density function (pdf) of model parameters, and (iii) producing probabilistic climate projections that are then used to calculate the Social Cost of Methane (SCM) with the DICE and FUND integrated assessment models. We find that including a temperature feedback in the methane cycle acts as an additional constraint during the calibration process and results in a correlation between the tropospheric lifetime of methane and several climate model parameters. This correlation is not seen in the models lacking this feedback. Several of the estimated marginal pdfs of the model parameters also exhibit different distributional shapes and expected values depending on the methane model used. As a result, probabilistic projections of the climate system out to the year 2300 exhibit different levels of uncertainty and magnitudes of warming for each of the three models under an RCP8.5 scenario. We find these differences in climate projections result in differences in the distributions and expected values for our estimates of the SCM. We also examine uncertainty about the SCM by performing a Monte Carlo analysis using a distribution for the climate sensitivity while holding all other climate model parameters constant. Our SCM estimates using the Bayesian calibration are lower and exhibit less uncertainty about extremely high values in the right tail of the distribution compared to the Monte Carlo approach. This finding has important climate policy implications and suggests previous work that accounts for climate model uncertainty by only varying the climate sensitivity parameter may overestimate the SCM.

Assessing model uncertainty using hexavalent chromium and ...

EPA Pesticide Factsheets

Introduction: The National Research Council recommended quantitative evaluation of uncertainty in effect estimates for risk assessment. This analysis considers uncertainty across model forms and model parameterizations with hexavalent chromium [Cr(VI)] and lung cancer mortality as an example. The objective of this analysis is to characterize model uncertainty by evaluating the variance in estimates across several epidemiologic analyses.Methods: This analysis compared 7 publications analyzing two different chromate production sites in Ohio and Maryland. The Ohio cohort consisted of 482 workers employed from 1940-72, while the Maryland site employed 2,357 workers from 1950-74. Cox and Poisson models were the only model forms considered by study authors to assess the effect of Cr(VI) on lung cancer mortality. All models adjusted for smoking and included a 5-year exposure lag, however other latency periods and model covariates such as age and race were considered. Published effect estimates were standardized to the same units and normalized by their variances to produce a standardized metric to compare variability in estimates across and within model forms. A total of 7 similarly parameterized analyses were considered across model forms, and 23 analyses with alternative parameterizations were considered within model form (14 Cox; 9 Poisson). Results: Across Cox and Poisson model forms, adjusted cumulative exposure coefficients for 7 similar analyses ranged from 2.47

A new Bayesian Earthquake Analysis Tool (BEAT)

NASA Astrophysics Data System (ADS)

Vasyura-Bathke, Hannes; Dutta, Rishabh; Jónsson, Sigurjón; Mai, Martin

2017-04-01

Modern earthquake source estimation studies increasingly use non-linear optimization strategies to estimate kinematic rupture parameters, often considering geodetic and seismic data jointly. However, the optimization process is complex and consists of several steps that need to be followed in the earthquake parameter estimation procedure. These include pre-describing or modeling the fault geometry, calculating the Green's Functions (often assuming a layered elastic half-space), and estimating the distributed final slip and possibly other kinematic source parameters. Recently, Bayesian inference has become popular for estimating posterior distributions of earthquake source model parameters given measured/estimated/assumed data and model uncertainties. For instance, some research groups consider uncertainties of the layered medium and propagate these to the source parameter uncertainties. Other groups make use of informative priors to reduce the model parameter space. In addition, innovative sampling algorithms have been developed that efficiently explore the often high-dimensional parameter spaces. Compared to earlier studies, these improvements have resulted in overall more robust source model parameter estimates that include uncertainties. However, the computational demands of these methods are high and estimation codes are rarely distributed along with the published results. Even if codes are made available, it is often difficult to assemble them into a single optimization framework as they are typically coded in different programing languages. Therefore, further progress and future applications of these methods/codes are hampered, while reproducibility and validation of results has become essentially impossible. In the spirit of providing open-access and modular codes to facilitate progress and reproducible research in earthquake source estimations, we undertook the effort of producing BEAT, a python package that comprises all the above-mentioned features in one single programing environment. The package is build on top of the pyrocko seismological toolbox (www.pyrocko.org) and makes use of the pymc3 module for Bayesian statistical model fitting. BEAT is an open-source package (https://github.com/hvasbath/beat) and we encourage and solicit contributions to the project. In this contribution, we present our strategy for developing BEAT, show application examples, and discuss future developments.

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.

Evaluation of the information content of long-term wastewater characteristics data in relation to activated sludge model parameters.

PubMed

Alikhani, Jamal; Takacs, Imre; Al-Omari, Ahmed; Murthy, Sudhir; Massoudieh, Arash

2017-03-01

A parameter estimation framework was used to evaluate the ability of observed data from a full-scale nitrification-denitrification bioreactor to reduce the uncertainty associated with the bio-kinetic and stoichiometric parameters of an activated sludge model (ASM). Samples collected over a period of 150 days from the effluent as well as from the reactor tanks were used. A hybrid genetic algorithm and Bayesian inference were used to perform deterministic and parameter estimations, respectively. The main goal was to assess the ability of the data to obtain reliable parameter estimates for a modified version of the ASM. The modified ASM model includes methylotrophic processes which play the main role in methanol-fed denitrification. Sensitivity analysis was also used to explain the ability of the data to provide information about each of the parameters. The results showed that the uncertainty in the estimates of the most sensitive parameters (including growth rate, decay rate, and yield coefficients) decreased with respect to the prior information.

Prediction and assimilation of surf-zone processes using a Bayesian network: Part I: Forward models

USGS Publications Warehouse

Plant, Nathaniel G.; Holland, K. Todd

2011-01-01

Prediction of coastal processes, including waves, currents, and sediment transport, can be obtained from a variety of detailed geophysical-process models with many simulations showing significant skill. This capability supports a wide range of research and applied efforts that can benefit from accurate numerical predictions. However, the predictions are only as accurate as the data used to drive the models and, given the large temporal and spatial variability of the surf zone, inaccuracies in data are unavoidable such that useful predictions require corresponding estimates of uncertainty. We demonstrate how a Bayesian-network model can be used to provide accurate predictions of wave-height evolution in the surf zone given very sparse and/or inaccurate boundary-condition data. The approach is based on a formal treatment of a data-assimilation problem that takes advantage of significant reduction of the dimensionality of the model system. We demonstrate that predictions of a detailed geophysical model of the wave evolution are reproduced accurately using a Bayesian approach. In this surf-zone application, forward prediction skill was 83%, and uncertainties in the model inputs were accurately transferred to uncertainty in output variables. We also demonstrate that if modeling uncertainties were not conveyed to the Bayesian network (i.e., perfect data or model were assumed), then overly optimistic prediction uncertainties were computed. More consistent predictions and uncertainties were obtained by including model-parameter errors as a source of input uncertainty. Improved predictions (skill of 90%) were achieved because the Bayesian network simultaneously estimated optimal parameters while predicting wave heights.

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 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.

2016-01-01

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 functions and maximum damages can have large effects on flood damage estimates. This explanation is then used to quantify the uncertainty in the damage estimates with a Monte Carlo analysis. The Monte Carlo analysis uses a damage function library with 272 functions from seven different flood damage models. The paper shows that the resulting uncertainties in estimated damages are in the order of magnitude of a factor of 2 to 5. The uncertainty is typically larger for flood events with 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.

Proficiency testing as a basis for estimating uncertainty of measurement: application to forensic alcohol and toxicology quantitations.

PubMed

Wallace, Jack

2010-05-01

While forensic laboratories will soon be required to estimate uncertainties of measurement for those quantitations reported to the end users of the information, the procedures for estimating this have been little discussed in the forensic literature. This article illustrates how proficiency test results provide the basis for estimating uncertainties in three instances: (i) For breath alcohol analyzers the interlaboratory precision is taken as a direct measure of uncertainty. This approach applies when the number of proficiency tests is small. (ii) For blood alcohol, the uncertainty is calculated from the differences between the laboratory's proficiency testing results and the mean quantitations determined by the participants; this approach applies when the laboratory has participated in a large number of tests. (iii) For toxicology, either of these approaches is useful for estimating comparability between laboratories, but not for estimating absolute accuracy. It is seen that data from proficiency tests enable estimates of uncertainty that are empirical, simple, thorough, and applicable to a wide range of concentrations.

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.

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.

Uncertainty in Population Estimates for Endangered Animals and Improving the Recovery Process

PubMed Central

Haines, Aaron M.; Zak, Matthew; Hammond, Katie; Scott, J. Michael; Goble, Dale D.; Rachlow, Janet L.

2013-01-01

Simple Summary The objective of our study was to evaluate the mention of uncertainty (i.e., variance) associated with population size estimates within U.S. recovery plans for endangered animals. To do this we reviewed all finalized recovery plans for listed terrestrial vertebrate species. We found that more recent recovery plans reported more estimates of population size and uncertainty. Also, bird and mammal recovery plans reported more estimates of population size and uncertainty. We recommend that updated recovery plans combine uncertainty of population size estimates with a minimum detectable difference to aid in successful recovery. Abstract United States recovery plans contain biological information for a species listed under the Endangered Species Act and specify recovery criteria to provide basis for species recovery. The objective of our study was to evaluate whether recovery plans provide uncertainty (e.g., variance) with estimates of population size. We reviewed all finalized recovery plans for listed terrestrial vertebrate species to record the following data: (1) if a current population size was given, (2) if a measure of uncertainty or variance was associated with current estimates of population size and (3) if population size was stipulated for recovery. We found that 59% of completed recovery plans specified a current population size, 14.5% specified a variance for the current population size estimate and 43% specified population size as a recovery criterion. More recent recovery plans reported more estimates of current population size, uncertainty and population size as a recovery criterion. Also, bird and mammal recovery plans reported more estimates of population size and uncertainty compared to reptiles and amphibians. We suggest the use of calculating minimum detectable differences to improve confidence when delisting endangered animals and we identified incentives for individuals to get involved in recovery planning to improve access to quantitative data. PMID:26479531

On the ultimate uncertainty of the top quark pole mass

NASA Astrophysics Data System (ADS)

Beneke, M.; Marquard, P.; Nason, P.; Steinhauser, M.

2017-12-01

We combine the known asymptotic behaviour of the QCD perturbation series expansion, which relates the pole mass of a heavy quark to the MS ‾ mass, with the exact series coefficients up to the four-loop order to determine the ultimate uncertainty of the top-quark pole mass due to the renormalon divergence. We perform extensive tests of our procedure by varying the number of colours and flavours, as well as the scale of the strong coupling and the MS ‾ mass. Including an estimate of the internal bottom and charm quark mass effect, we conclude that this uncertainty is around 110 MeV. We further estimate the additional contribution to the mass relation from the five-loop correction and beyond to be around 300 MeV.

Reducing Contingency through Sampling at the Luckey FUSRAP Site - 13186

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

Frothingham, David; Barker, Michelle; Buechi, Steve

2013-07-01

Typically, the greatest risk in developing accurate cost estimates for the remediation of hazardous, toxic, and radioactive waste sites is the uncertainty in the estimated volume of contaminated media requiring remediation. Efforts to address this risk in the remediation cost estimate can result in large cost contingencies that are often considered unacceptable when budgeting for site cleanups. Such was the case for the Luckey Formerly Utilized Sites Remedial Action Program (FUSRAP) site near Luckey, Ohio, which had significant uncertainty surrounding the estimated volume of site soils contaminated with radium, uranium, thorium, beryllium, and lead. Funding provided by the American Recoverymore » and Reinvestment Act (ARRA) allowed the U.S. Army Corps of Engineers (USACE) to conduct additional environmental sampling and analysis at the Luckey Site between November 2009 and April 2010, with the objective to further delineate the horizontal and vertical extent of contaminated soils in order to reduce the uncertainty in the soil volume estimate. Investigative work included radiological, geophysical, and topographic field surveys, subsurface borings, and soil sampling. Results from the investigative sampling were used in conjunction with Argonne National Laboratory's Bayesian Approaches for Adaptive Spatial Sampling (BAASS) software to update the contaminated soil volume estimate for the site. This updated volume estimate was then used to update the project cost-to-complete estimate using the USACE Cost and Schedule Risk Analysis process, which develops cost contingencies based on project risks. An investment of $1.1 M of ARRA funds for additional investigative work resulted in a reduction of 135,000 in-situ cubic meters (177,000 in-situ cubic yards) in the estimated base volume estimate. This refinement of the estimated soil volume resulted in a $64.3 M reduction in the estimated project cost-to-complete, through a reduction in the uncertainty in the contaminated soil volume estimate and the associated contingency costs. (authors)« less

Estimating the uncertainty from sampling in pollution crime investigation: The importance of metrology in the forensic interpretation of environmental data.

PubMed

Barazzetti Barbieri, Cristina; de Souza Sarkis, Jorge Eduardo

2018-07-01

The forensic interpretation of environmental analytical data is usually challenging due to the high geospatial variability of these data. The measurements' uncertainty includes contributions from the sampling and from the sample handling and preparation processes. These contributions are often disregarded in analytical techniques results' quality assurance. A pollution crime investigation case was used to carry out a methodology able to address these uncertainties in two different environmental compartments, freshwater sediments and landfill leachate. The methodology used to estimate the uncertainty was the duplicate method (that replicates predefined steps of the measurement procedure in order to assess its precision) and the parameters used to investigate the pollution were metals (Cr, Cu, Ni, and Zn) in the leachate, the suspect source, and in the sediment, the possible sink. The metal analysis results were compared to statutory limits and it was demonstrated that Cr and Ni concentrations in sediment samples exceeded the threshold levels at all sites downstream the pollution sources, considering the expanded uncertainty U of the measurements and a probability of contamination >0.975, at most sites. Cu and Zn concentrations were above the statutory limits at two sites, but the classification was inconclusive considering the uncertainties of the measurements. Metal analyses in leachate revealed that Cr concentrations were above the statutory limits with a probability of contamination >0.975 in all leachate ponds while the Cu, Ni and Zn probability of contamination was below 0.025. The results demonstrated that the estimation of the sampling uncertainty, which was the dominant component of the combined uncertainty, is required for a comprehensive interpretation of the environmental analyses results, particularly in forensic cases. Copyright © 2018 Elsevier B.V. All rights reserved.

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.

Radiation protection issues in galactic cosmic ray risk assessment

NASA Technical Reports Server (NTRS)

Sinclair, W. K.

1994-01-01

Radiation protection involves the limitation of exposure to below threshold doses for direct (or deterministic) effects and a knowledge of the risk of stochastic effects after low doses. The principal stochastic risk associated with low dose rate galactic cosmic rays is the increased risk of cancer. Estimates of this risk depend on two factors (a) estimates of cancer risk for low-LET radiation and (b) values of the appropriate radiation weighting factors, WR, for the high-LET radiations of galactic cosmic rays. Both factors are subject to considerable uncertainty. The low-LET cancer risk derived from the late effects of the atomic bombs is vulnerable to a number of uncertainties including especially that from projection in time, and from extrapolation from high to low dose rate. Nevertheless, recent low dose studies of workers and others tend to confirm these estimates. WR, relies on biological effects studied mainly in non-human systems. Additional laboratory studies could reduce the uncertainties in WR and thus produce a more confident estimate of the overall risk of galactic cosmic rays.

Probability judgments under ambiguity and conflict

PubMed Central

Smithson, Michael

2015-01-01

Whether conflict and ambiguity are distinct kinds of uncertainty remains an open question, as does their joint impact on judgments of overall uncertainty. This paper reviews recent advances in our understanding of human judgment and decision making when both ambiguity and conflict are present, and presents two types of testable models of judgments under conflict and ambiguity. The first type concerns estimate-pooling to arrive at “best” probability estimates. The second type is models of subjective assessments of conflict and ambiguity. These models are developed for dealing with both described and experienced information. A framework for testing these models in the described-information setting is presented, including a reanalysis of a multi-nation data-set to test best-estimate models, and a study of participants' assessments of conflict, ambiguity, and overall uncertainty reported by Smithson (2013). A framework for research in the experienced-information setting is then developed, that differs substantially from extant paradigms in the literature. This framework yields new models of “best” estimates and perceived conflict. The paper concludes with specific suggestions for future research on judgment and decision making under conflict and ambiguity. PMID:26042081

Probability judgments under ambiguity and conflict.

PubMed

Smithson, Michael

2015-01-01

Whether conflict and ambiguity are distinct kinds of uncertainty remains an open question, as does their joint impact on judgments of overall uncertainty. This paper reviews recent advances in our understanding of human judgment and decision making when both ambiguity and conflict are present, and presents two types of testable models of judgments under conflict and ambiguity. The first type concerns estimate-pooling to arrive at "best" probability estimates. The second type is models of subjective assessments of conflict and ambiguity. These models are developed for dealing with both described and experienced information. A framework for testing these models in the described-information setting is presented, including a reanalysis of a multi-nation data-set to test best-estimate models, and a study of participants' assessments of conflict, ambiguity, and overall uncertainty reported by Smithson (2013). A framework for research in the experienced-information setting is then developed, that differs substantially from extant paradigms in the literature. This framework yields new models of "best" estimates and perceived conflict. The paper concludes with specific suggestions for future research on judgment and decision making under conflict and ambiguity.

Radiation protection issues in galactic cosmic ray risk assessment.

PubMed

Sinclair, W K

1994-01-01

Radiation protection involves the limitation of exposure to below threshold doses for direct (or deterministic) effects and a knowledge of the risk of stochastic effects after low doses. The principal stochastic risk associated with low dose rate galactic cosmic rays is the increased risk of cancer. Estimates of this risk depend on two factors (a) estimates of cancer risk for low-LET radiation and (b) values of the appropriate radiation weighting factors, WR, for the high-LET radiations of galactic cosmic rays. Both factors are subject to considerable uncertainty. The low-LET cancer risk derived from the late effects of the atomic bombs is vulnerable to a number of uncertainties including especially that from projection in time, and from extrapolation from high to low dose rate. Nevertheless, recent low dose studies of workers and others tend to confirm these estimates. WR, relies on biological effects studied mainly in non-human systems. Additional laboratory studies could reduce the uncertainties in WR and thus produce a more confident estimate of the overall risk of galactic cosmic rays.

New Multi-objective Uncertainty-based Algorithm for Water Resource Models' Calibration

NASA Astrophysics Data System (ADS)

Keshavarz, Kasra; Alizadeh, Hossein

2017-04-01

Water resource models are powerful tools to support water management decision making process and are developed to deal with a broad range of issues including land use and climate change impacts analysis, water allocation, systems design and operation, waste load control and allocation, etc. These models are divided into two categories of simulation and optimization models whose calibration has been addressed in the literature where great relevant efforts in recent decades have led to two main categories of auto-calibration methods of uncertainty-based algorithms such as GLUE, MCMC and PEST and optimization-based algorithms including single-objective optimization such as SCE-UA and multi-objective optimization such as MOCOM-UA and MOSCEM-UA. Although algorithms which benefit from capabilities of both types, such as SUFI-2, were rather developed, this paper proposes a new auto-calibration algorithm which is capable of both finding optimal parameters values regarding multiple objectives like optimization-based algorithms and providing interval estimations of parameters like uncertainty-based algorithms. The algorithm is actually developed to improve quality of SUFI-2 results. Based on a single-objective, e.g. NSE and RMSE, SUFI-2 proposes a routine to find the best point and interval estimation of parameters and corresponding prediction intervals (95 PPU) of time series of interest. To assess the goodness of calibration, final results are presented using two uncertainty measures of p-factor quantifying percentage of observations covered by 95PPU and r-factor quantifying degree of uncertainty, and the analyst has to select the point and interval estimation of parameters which are actually non-dominated regarding both of the uncertainty measures. Based on the described properties of SUFI-2, two important questions are raised, answering of which are our research motivation: Given that in SUFI-2, final selection is based on the two measures or objectives and on the other hand, knowing that there is no multi-objective optimization mechanism in SUFI-2, are the final estimations Pareto-optimal? Can systematic methods be applied to select the final estimations? Dealing with these questions, a new auto-calibration algorithm was proposed where the uncertainty measures were considered as two objectives to find non-dominated interval estimations of parameters by means of coupling Monte Carlo simulation and Multi-Objective Particle Swarm Optimization. Both the proposed algorithm and SUFI-2 were applied to calibrate parameters of water resources planning model of Helleh river basin, Iran. The model is a comprehensive water quantity-quality model developed in the previous researches using WEAP software in order to analyze the impacts of different water resources management strategies including dam construction, increasing cultivation area, utilization of more efficient irrigation technologies, changing crop pattern, etc. Comparing the Pareto frontier resulted from the proposed auto-calibration algorithm with SUFI-2 results, it was revealed that the new algorithm leads to a better and also continuous Pareto frontier, even though it is more computationally expensive. Finally, Nash and Kalai-Smorodinsky bargaining methods were used to choose compromised interval estimation regarding Pareto frontier.

Oscillator strengths of the Si II 181 nanometer resonance multiplet

NASA Technical Reports Server (NTRS)

Bergeson, S. D.; Lawler, J. E.

1993-01-01

We report Si II experimental log (gf)-values of -2.38(4) for the 180.801 nm line, of -2.18(4) for the 181.693 nm line, and of -3.29(5) for the 181.745 nm line, where the number in parentheses is the uncertainty in the last digit. The overall uncertainties (about 10 percent) include the 1 sigma random uncertainty (about 6 percent) and an estimate of the systematic uncertainty. The oscillator strengths are determined by combining branching fractions and radiative lifetimes. The branching fractions are measured using standard spectroradiometry on an optically thin source; the radiative lifetimes are measured using time-resolved laser-induced fluorescence.

NASA MEaSUREs Combined ASTER and MODIS Emissivity over Land (CAMEL) Uncertainty Estimation

NASA Astrophysics Data System (ADS)

Feltz, M.; Borbas, E. E.; Knuteson, R. O.; Hulley, G. C.; Hook, S. J.

2016-12-01

Under the NASA MEASUREs project a new global, land surface emissivity database is being made available as part of the Unified and Coherent Land Surface Temperature and Emissivity Earth System Data Record. This new CAMEL emissivity database is created by the merging of the MODIS baseline-fit emissivity database (UWIREMIS) developed at the University of Wisconsin-Madison and the ASTER Global Emissivity Dataset v4 produced at the Jet Propulsion Labratory. The combined CAMEL product leverages the ability of ASTER's 5 bands to more accurately resolve the TIR (8-12 micron) region and the ability of UWIREMIS to provide information throughout the 3.6-12 micron IR region. It will be made available for 2000 through 2017 at monthly mean, 5 km resolution for 13 bands within the 3.6-14.3 micron region, and will also be extended to 417 infrared spectral channels using a principal component regression approach. Uncertainty estimates of the CAMEL will be provided that combine temporal, spatial, and algorithm variability as part of a total uncertainty estimate for the emissivity product. The spatial and temporal uncertainties are calculated as the standard deviation of the surrounding 5x5 pixels and 3 neighboring months respectively while the algorithm uncertainty is calculated using a measure of the difference between the two CAMEL emissivity inputs—the ASTER GED and MODIS baseline-fit products. This work describes these uncertainty estimation methods in detail and shows first results. Global, monthly results for different seasons are shown as well as case study examples at locations with different land surface types. Comparisons of the case studies to both lab values and an independent emissivity climatology derived from IASI measurements (Dan Zhou et al., IEEE Trans., 2011) are included.

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

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.

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

SUB-PIXEL RAINFALL VARIABILITY AND THE IMPLICATIONS FOR UNCERTAINTIES IN RADAR RAINFALL ESTIMATES

EPA Science Inventory

Radar estimates of rainfall are subject to significant measurement uncertainty. Typically, uncertainties are measured by the discrepancies between real rainfall estimates based on radar reflectivity and point rainfall records of rain gauges. This study investigates how the disc...

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.

Using FOSM-Based Data Worth Analyses to Design Geophysical Surveys to Reduce Uncertainty in a Regional Groundwater Model Update

NASA Astrophysics Data System (ADS)

Smith, B. D.; White, J.; Kress, W. H.; Clark, B. R.; Barlow, J.

2016-12-01

Hydrogeophysical surveys have become an integral part of understanding hydrogeological frameworks used in groundwater models. Regional models cover a large area where water well data is, at best, scattered and irregular. Since budgets are finite, priorities must be assigned to select optimal areas for geophysical surveys. For airborne electromagnetic (AEM) geophysical surveys, optimization of mapping depth and line spacing needs to take in account the objectives of the groundwater models. The approach discussed here uses a first-order, second-moment (FOSM) uncertainty analyses which assumes an approximate linear relation between model parameters and observations. This assumption allows FOSM analyses to be applied to estimate the value of increased parameter knowledge to reduce forecast uncertainty. FOSM is used to facilitate optimization of yet-to-be-completed geophysical surveying to reduce model forecast uncertainty. The main objective of geophysical surveying is assumed to estimate values and spatial variation in hydrologic parameters (i.e. hydraulic conductivity) as well as map lower permeability layers that influence the spatial distribution of recharge flux. The proposed data worth analysis was applied to Mississippi Embayment Regional Aquifer Study (MERAS) which is being updated. The objective of MERAS is to assess the ground-water availability (status and trends) of the Mississippi embayment aquifer system. The study area covers portions of eight states including Alabama, Arkansas, Illinois, Kentucky, Louisiana, Mississippi, Missouri, and Tennessee. The active model grid covers approximately 70,000 square miles, and incorporates some 6,000 miles of major rivers and over 100,000 water wells. In the FOSM analysis, a dense network of pilot points was used to capture uncertainty in hydraulic conductivity and recharge. To simulate the effect of AEM flight lines, the prior uncertainty for hydraulic conductivity and recharge pilots along potential flight lines was reduced. The FOSM forecast uncertainty estimates were then recalculated and compared to the base forecast uncertainty estimates. The resulting reduction in forecast uncertainty is a measure of the effect on the model from the AEM survey. Iterations through this process, results in optimization of flight line location.

Ab Initio Values of the Thermophysical Properties of Helium as Standards

PubMed Central

Hurly, John J.; Moldover, Michael R.

2000-01-01

Recent quantum mechanical calculations of the interaction energy of pairs of helium atoms are accurate and some include reliable estimates of their uncertainty. We combined these ab initio results with earlier published results to obtain a helium-helium interatomic potential that includes relativistic retardation effects over all ranges of interaction. From this potential, we calculated the thermophysical properties of helium, i.e., the second virial coefficients, the dilute-gas viscosities, and the dilute-gas thermal conductivities of 3He, 4He, and their equimolar mixture from 1 K to 104 K. We also calculated the diffusion and thermal diffusion coefficients of mixtures of 3He and 4He. For the pure fluids, the uncertainties of the calculated values are dominated by the uncertainties of the potential; for the mixtures, the uncertainties of the transport properties also include contributions from approximations in the transport theory. In all cases, the uncertainties are smaller than the corresponding experimental uncertainties; therefore, we recommend the ab initio results be used as standards for calibrating instruments relying on these thermophysical properties. We present the calculated thermophysical properties in easy-to-use tabular form. PMID:27551630

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.

Uncertainty of inhalation dose coefficients for representative physical and chemical forms of iodine-131

NASA Astrophysics Data System (ADS)

Harvey, Richard Paul, III

Releases of radioactive material have occurred at various Department of Energy (DOE) weapons facilities and facilities associated with the nuclear fuel cycle in the generation of electricity. Many different radionuclides have been released to the environment with resulting exposure of the population to these various sources of radioactivity. Radioiodine has been released from a number of these facilities and is a potential public health concern due to its physical and biological characteristics. Iodine exists as various isotopes, but our focus is on 131I due to its relatively long half-life, its prevalence in atmospheric releases and its contribution to offsite dose. The assumption of physical and chemical form is speculated to have a profound impact on the deposition of radioactive material within the respiratory tract. In the case of iodine, it has been shown that more than one type of physical and chemical form may be released to, or exist in, the environment; iodine can exist as a particle or as a gas. The gaseous species can be further segregated based on chemical form: elemental, inorganic, and organic iodides. Chemical compounds in each class are assumed to behave similarly with respect to biochemistry. Studies at Oak Ridge National Laboratories have demonstrated that 131I is released as a particulate, as well as in elemental, inorganic and organic chemical form. The internal dose estimate from 131I may be very different depending on the effect that chemical form has on fractional deposition, gas uptake, and clearance in the respiratory tract. There are many sources of uncertainty in the estimation of environmental dose including source term, airborne transport of radionuclides, and internal dosimetry. Knowledge of uncertainty in internal dosimetry is essential for estimating dose to members of the public and for determining total uncertainty in dose estimation. Important calculational steps in any lung model is regional estimation of deposition fractions and gas uptake of radionuclides in various regions of the lung. Variability in regional radionuclide deposition within lung compartments may significantly contribute to the overall uncertainty of the lung model. The uncertainty of lung deposition and biological clearance is dependent upon physiological and anatomical parameters of individuals as well as characteristic parameters of the particulate material. These parameters introduce uncertainty into internal dose estimates due to their inherent variability. Anatomical and physiological input parameters are age and gender dependent. This work has determined the uncertainty in internal dose estimates and the sensitive parameters involved in modeling particulate deposition and gas uptake of different physical and chemical forms of 131I with age and gender dependencies.

Fractional order uncertainty estimator based hierarchical sliding mode design for a class of fractional order non-holonomic chained system.

PubMed

Deepika; Kaur, Sandeep; Narayan, Shiv

2018-06-01

This paper proposes a novel fractional order sliding mode control approach to address the issues of stabilization as well as tracking of an N-dimensional extended chained form of fractional order non-holonomic system. Firstly, the hierarchical fractional order terminal sliding manifolds are selected to procure the desired objectives in finite time. Then, a sliding mode control law is formulated which provides robustness against various system uncertainties or external disturbances. In addition, a novel fractional order uncertainty estimator is deduced mathematically to estimate and mitigate the effects of uncertainties, which also excludes the requirement of their upper bounds. Due to the omission of discontinuous control action, the proposed algorithm ensures a chatter-free control input. Moreover, the finite time stability of the closed loop system has been proved analytically through well known Mittag-Leffler and Fractional Lyapunov theorems. Finally, the proposed methodology is validated with MATLAB simulations on two examples including an application of fractional order non-holonomic wheeled mobile robot and its performances are also compared with the existing control approach. Copyright © 2018 ISA. Published by Elsevier Ltd. All rights reserved.

What drives uncertainty in model diagnoses of carbon dynamics in southern US forests: climate, vegetation, disturbance, or model parameters?

NASA Astrophysics Data System (ADS)

Zhou, Y.; Gu, H.; Williams, C. A.

2017-12-01

Results from terrestrial carbon cycle models have multiple sources of uncertainty, each with its behavior and range. Their relative importance and how they combine has received little attention. This study investigates how various sources of uncertainty propagate, temporally and spatially, in CASA-Disturbance (CASA-D). CASA-D simulates the impact of climatic forcing and disturbance legacies on forest carbon dynamics with the following steps. Firstly, we infer annual growth and mortality rates from measured biomass stocks (FIA) over time and disturbance (e.g., fire, harvest, bark beetle) to represent annual post-disturbance carbon fluxes trajectories across forest types and site productivity settings. Then, annual carbon fluxes are estimated from these trajectories by using time since disturbance which is inferred from biomass (NBCD 2000) and disturbance maps (NAFD, MTBS and ADS). Finally, we apply monthly climatic scalars derived from default CASA to temporally distribute annual carbon fluxes to each month. This study assesses carbon flux uncertainty from two sources: driving data including climatic and forest biomass inputs, and three most sensitive parameters in CASA-D including maximum light use efficiency, temperature sensitivity of soil respiration (Q10) and optimum temperature identified by using EFAST (Extended Fourier Amplitude Sensitivity Testing). We quantify model uncertainties from each, and report their relative importance in estimating forest carbon sink/source in southeast United States from 2003 to 2010.

Measuring and modeling carbon stock change estimates for US forests and uncertainties from apparent inter-annual variability

Treesearch

James E. Smith; Linda S. Heath

2015-01-01

Our approach is based on a collection of models that convert or augment the USDA Forest Inventory and Analysis program survey data to estimate all forest carbon component stocks, including live and standing dead tree aboveground and belowground biomass, forest floor (litter), down deadwood, and soil organic carbon, for each inventory plot. The data, which include...

The Seventh SeaWiFS Intercalibration Round-Robin Experiment (SIRREX-7), March 1999

NASA Technical Reports Server (NTRS)

Hooker, Stanford B. (Editor); Firestone, Elaine R. (Editor); McLean, Scott; Sherman, Jennifer; Small, Mark; Lazin, Gordana; Zibordi, Giuseppe; Brown, James W.; McClain, Charles R. (Technical Monitor)

2002-01-01

This report documents the scientific activities during the seventh SeaWiFS Intercalibration Round-Robin Experiment (SIRREX-7) held at Satlantic, Inc. (Halifax, Canada). The overall objective of SIRREX-7 was to determine the uncertainties of radiometric calibrations and measurements at a single calibration facility. Specifically, this involved the estimation of the uncertainties in a) lamp standards, b) plaque standards (including the uncertainties associated with plaque illumination non-uniformity), c) radiance calibrations, and d) irradiance calibrations. The investigation of the uncertainties in lamp standards included a comparison between a calibration of a new FEL by the National Institute of Standards and Technology (NIST) and Optronic Laboratories, Inc. In addition, the rotation and polarization sensitivity of radiometers were determined, and a procedure for transferring an absolute calibration to portable light sources was defined and executed.

Development of a banding database for North Pacific albatross: Implications for future data collection

USGS Publications Warehouse

Doherty, P.F.; Kendall, W.L.; Sillett, S.; Gustafson, M.; Flint, B.; Naughton, M.; Robbins, C.S.; Pyle, P.; Macintyre, Ian G.

2006-01-01

The effects of fishery practices on black-footed (Phoebastria nigripes) and Laysan albatross (Phoebastria immutabilis) continue to be a source of contention and uncertainty. Some of this uncertainty is a result of a lack of estimates of albatross demographic parameters such as survival. To begin to address these informational needs, a database of albatross banding and encounter records was constructed. Due to uncertainty concerning data collection and validity of assumptions required for mark-recapture analyses, these data should be used with caution. Although demographic parameter estimates are of interest to many, band loss rates, temporary emigration rates, and discontinuous banding effort can confound these estimates. We suggest a number of improvements in data collection that can help ameliorate problems, including the use of double banding and collecting data using a `robust? design. Additionally, sustained banding and encounter efforts are needed to maximize the value of these data. With these modifications, the usefulness of the banding data could be improved markedly.

Part 2. Development of Enhanced Statistical Methods for Assessing Health Effects Associated with an Unknown Number of Major Sources of Multiple Air Pollutants.

PubMed

Park, Eun Sug; Symanski, Elaine; Han, Daikwon; Spiegelman, Clifford

2015-06-01

A major difficulty with assessing source-specific health effects is that source-specific exposures cannot be measured directly; rather, they need to be estimated by a source-apportionment method such as multivariate receptor modeling. The uncertainty in source apportionment (uncertainty in source-specific exposure estimates and model uncertainty due to the unknown number of sources and identifiability conditions) has been largely ignored in previous studies. Also, spatial dependence of multipollutant data collected from multiple monitoring sites has not yet been incorporated into multivariate receptor modeling. The objectives of this project are (1) to develop a multipollutant approach that incorporates both sources of uncertainty in source-apportionment into the assessment of source-specific health effects and (2) to develop enhanced multivariate receptor models that can account for spatial correlations in the multipollutant data collected from multiple sites. We employed a Bayesian hierarchical modeling framework consisting of multivariate receptor models, health-effects models, and a hierarchical model on latent source contributions. For the health model, we focused on the time-series design in this project. Each combination of number of sources and identifiability conditions (additional constraints on model parameters) defines a different model. We built a set of plausible models with extensive exploratory data analyses and with information from previous studies, and then computed posterior model probability to estimate model uncertainty. Parameter estimation and model uncertainty estimation were implemented simultaneously by Markov chain Monte Carlo (MCMC*) methods. We validated the methods using simulated data. We illustrated the methods using PM2.5 (particulate matter ≤ 2.5 μm in aerodynamic diameter) speciation data and mortality data from Phoenix, Arizona, and Houston, Texas. The Phoenix data included counts of cardiovascular deaths and daily PM2.5 speciation data from 1995-1997. The Houston data included respiratory mortality data and 24-hour PM2.5 speciation data sampled every six days from a region near the Houston Ship Channel in years 2002-2005. We also developed a Bayesian spatial multivariate receptor modeling approach that, while simultaneously dealing with the unknown number of sources and identifiability conditions, incorporated spatial correlations in the multipollutant data collected from multiple sites into the estimation of source profiles and contributions based on the discrete process convolution model for multivariate spatial processes. This new modeling approach was applied to 24-hour ambient air concentrations of 17 volatile organic compounds (VOCs) measured at nine monitoring sites in Harris County, Texas, during years 2000 to 2005. Simulation results indicated that our methods were accurate in identifying the true model and estimated parameters were close to the true values. The results from our methods agreed in general with previous studies on the source apportionment of the Phoenix data in terms of estimated source profiles and contributions. However, we had a greater number of statistically insignificant findings, which was likely a natural consequence of incorporating uncertainty in the estimated source contributions into the health-effects parameter estimation. For the Houston data, a model with five sources (that seemed to be Sulfate-Rich Secondary Aerosol, Motor Vehicles, Industrial Combustion, Soil/Crustal Matter, and Sea Salt) showed the highest posterior model probability among the candidate models considered when fitted simultaneously to the PM2.5 and mortality data. There was a statistically significant positive association between respiratory mortality and same-day PM2.5 concentrations attributed to one of the sources (probably industrial combustion). The Bayesian spatial multivariate receptor modeling approach applied to the VOC data led to a highest posterior model probability for a model with five sources (that seemed to be refinery, petrochemical production, gasoline evaporation, natural gas, and vehicular exhaust) among several candidate models, with the number of sources varying between three and seven and with different identifiability conditions. Our multipollutant approach assessing source-specific health effects is more advantageous than a single-pollutant approach in that it can estimate total health effects from multiple pollutants and can also identify emission sources that are responsible for adverse health effects. Our Bayesian approach can incorporate not only uncertainty in the estimated source contributions, but also model uncertainty that has not been addressed in previous studies on assessing source-specific health effects. The new Bayesian spatial multivariate receptor modeling approach enables predictions of source contributions at unmonitored sites, minimizing exposure misclassification and providing improved exposure estimates along with their uncertainty estimates, as well as accounting for uncertainty in the number of sources and identifiability conditions.

Multiscale Structure of UXO Site Characterization: Spatial Estimation and Uncertainty Quantification

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

Ostrouchov, George; Doll, William E.; Beard, Les P.

2009-01-01

Unexploded ordnance (UXO) site characterization must consider both how the contamination is generated and how we observe that contamination. Within the generation and observation processes, dependence structures can be exploited at multiple scales. We describe a conceptual site characterization process, the dependence structures available at several scales, and consider their statistical estimation aspects. It is evident that most of the statistical methods that are needed to address the estimation problems are known but their application-specific implementation may not be available. We demonstrate estimation at one scale and propose a representation for site contamination intensity that takes full account of uncertainty,more » is flexible enough to answer regulatory requirements, and is a practical tool for managing detailed spatial site characterization and remediation. The representation is based on point process spatial estimation methods that require modern computational resources for practical application. These methods have provisions for including prior and covariate information.« less

Development and comparison of Bayesian modularization method in uncertainty assessment of hydrological models

NASA Astrophysics Data System (ADS)

Li, L.; Xu, C.-Y.; Engeland, K.

2012-04-01

With respect to model calibration, parameter estimation and analysis of uncertainty sources, different approaches have been used in hydrological models. Bayesian method is one of the most widely used methods for uncertainty assessment of hydrological models, which incorporates different sources of information into a single analysis through Bayesian theorem. However, none of these applications can well treat the uncertainty in extreme flows of hydrological models' simulations. This study proposes a Bayesian modularization method approach in uncertainty assessment of conceptual hydrological models by considering the extreme flows. It includes a comprehensive comparison and evaluation of uncertainty assessments by a new Bayesian modularization method approach and traditional Bayesian models using the Metropolis Hasting (MH) algorithm with the daily hydrological model WASMOD. Three likelihood functions are used in combination with traditional Bayesian: the AR (1) plus Normal and time period independent model (Model 1), the AR (1) plus Normal and time period dependent model (Model 2) and the AR (1) plus multi-normal model (Model 3). The results reveal that (1) the simulations derived from Bayesian modularization method are more accurate with the highest Nash-Sutcliffe efficiency value, and (2) the Bayesian modularization method performs best in uncertainty estimates of entire flows and in terms of the application and computational efficiency. The study thus introduces a new approach for reducing the extreme flow's effect on the discharge uncertainty assessment of hydrological models via Bayesian. Keywords: extreme flow, uncertainty assessment, Bayesian modularization, hydrological model, WASMOD

Development, evaluation and application of a modified micrometeorological gradient method for long-term estimation of gaseous dry deposition over forest canopies.

EPA Science Inventory

Small pollutant concentration gradients between levels above a plant canopy result in large uncertainties in estimated air–surface exchange fluxes when using existing micrometeorological gradient methods, including the aerodynamic gradient method (AGM) and the modified Bowen rati...

Estimation of Vickers hardness uncertainty for a heterogeneous welded joint (S235JR+AR and X2CrNiMo17-12-2)

NASA Astrophysics Data System (ADS)

Dijmărescu, M. C.; Dijmărescu, M. R.

2017-08-01

When talking about tests that include measurements, the uncertainty of measurement is an essential element because it is important to know the limits within the obtained results may be assumed to lie and the influence the measurement technological system elements have on these results. The research presented in this paper focuses on the estimation of the Vickers hardness uncertainty of measurement for the heterogeneous welded joint between S235JR+AR and X2CrNiMo17-12-2 materials in order to establish the results relevance and the quality assessment of this joint. The paper contents are structured in three main parts. In the first part, the initial data necessary for the experiment is presented in terms of the welded joint and technological means characterisation. The second part presents the physical experiment development and its results and in the third part the uncertainty of the measurements is calculated and a results discussion is undertaken.

Automated parton-shower variations in PYTHIA 8

DOE PAGES

Mrenna, S.; Skands, P.

2016-10-03

In the era of precision physics measurements at the LHC, efficient and exhaustive estimations of theoretical uncertainties play an increasingly crucial role. In the context of Monte Carlo (MC) event generators, the estimation of such uncertainties traditionally requires independent MC runs for each variation, for a linear increase in total run time. In this work, we report on an automated evaluation of the dominant (renormalization-scale and nonsingular) perturbative uncertainties in the pythia 8 event generator, with only a modest computational overhead. Each generated event is accompanied by a vector of alternative weights (one for each uncertainty variation), with each set separatelymore » preserving the total cross section. Explicit scale-compensating terms can be included, reflecting known coefficients of higher-order splitting terms and reducing the effect of the variations. In conclusion, the formalism also allows for the enhancement of rare partonic splittings, such as g→bb¯ and q→qγ, to obtain weighted samples enriched in these splittings while preserving the correct physical Sudakov factors.« less

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.

Quantifying uncertainty in carbon and nutrient pools of coarse woody debris

NASA Astrophysics Data System (ADS)

See, C. R.; Campbell, J. L.; Fraver, S.; Domke, G. M.; Harmon, M. E.; Knoepp, J. D.; Woodall, C. W.

2016-12-01

Woody detritus constitutes a major pool of both carbon and nutrients in forested ecosystems. Estimating coarse wood stocks relies on many assumptions, even when full surveys are conducted. Researchers rarely report error in coarse wood pool estimates, despite the importance to ecosystem budgets and modelling efforts. To date, no study has attempted a comprehensive assessment of error rates and uncertainty inherent in the estimation of this pool. Here, we use Monte Carlo analysis to propagate the error associated with the major sources of uncertainty present in the calculation of coarse wood carbon and nutrient (i.e., N, P, K, Ca, Mg, Na) pools. We also evaluate individual sources of error to identify the importance of each source of uncertainty in our estimates. We quantify sampling error by comparing the three most common field methods used to survey coarse wood (two transect methods and a whole-plot survey). We quantify the measurement error associated with length and diameter measurement, and technician error in species identification and decay class using plots surveyed by multiple technicians. We use previously published values of model error for the four most common methods of volume estimation: Smalian's, conical frustum, conic paraboloid, and average-of-ends. We also use previously published values for error in the collapse ratio (cross-sectional height/width) of decayed logs that serves as a surrogate for the volume remaining. We consider sampling error in chemical concentration and density for all decay classes, using distributions from both published and unpublished studies. Analytical uncertainty is calculated using standard reference plant material from the National Institute of Standards. Our results suggest that technician error in decay classification can have a large effect on uncertainty, since many of the error distributions included in the calculation (e.g. density, chemical concentration, volume-model selection, collapse ratio) are decay-class specific.

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.

Effect of correlated observation error on parameters, predictions, and uncertainty

USGS Publications Warehouse

Tiedeman, Claire; Green, Christopher T.

2013-01-01

Correlations among observation errors are typically omitted when calculating observation weights for model calibration by inverse methods. We explore the effects of omitting these correlations on estimates of parameters, predictions, and uncertainties. First, we develop a new analytical expression for the difference in parameter variance estimated with and without error correlations for a simple one-parameter two-observation inverse model. Results indicate that omitting error correlations from both the weight matrix and the variance calculation can either increase or decrease the parameter variance, depending on the values of error correlation (ρ) and the ratio of dimensionless scaled sensitivities (rdss). For small ρ, the difference in variance is always small, but for large ρ, the difference varies widely depending on the sign and magnitude of rdss. Next, we consider a groundwater reactive transport model of denitrification with four parameters and correlated geochemical observation errors that are computed by an error-propagation approach that is new for hydrogeologic studies. We compare parameter estimates, predictions, and uncertainties obtained with and without the error correlations. Omitting the correlations modestly to substantially changes parameter estimates, and causes both increases and decreases of parameter variances, consistent with the analytical expression. Differences in predictions for the models calibrated with and without error correlations can be greater than parameter differences when both are considered relative to their respective confidence intervals. These results indicate that including observation error correlations in weighting for nonlinear regression can have important effects on parameter estimates, predictions, and their respective uncertainties.

Uncertainties estimation in surveying measurands: application to lengths, perimeters and areas

NASA Astrophysics Data System (ADS)

Covián, E.; Puente, V.; Casero, M.

2017-10-01

The present paper develops a series of methods for the estimation of uncertainty when measuring certain measurands of interest in surveying practice, such as points elevation given a planimetric position within a triangle mesh, 2D and 3D lengths (including perimeters enclosures), 2D areas (horizontal surfaces) and 3D areas (natural surfaces). The basis for the proposed methodology is the law of propagation of variance-covariance, which, applied to the corresponding model for each measurand, allows calculating the resulting uncertainty from known measurement errors. The methods are tested first in a small example, with a limited number of measurement points, and then in two real-life measurements. In addition, the proposed methods have been incorporated to commercial software used in the field of surveying engineering and focused on the creation of digital terrain models. The aim of this evolution is, firstly, to comply with the guidelines of the BIPM (Bureau International des Poids et Mesures), as the international reference agency in the field of metrology, in relation to the determination and expression of uncertainty; and secondly, to improve the quality of the measurement by indicating the uncertainty associated with a given level of confidence. The conceptual and mathematical developments for the uncertainty estimation in the aforementioned cases were conducted by researchers from the AssIST group at the University of Oviedo, eventually resulting in several different mathematical algorithms implemented in the form of MATLAB code. Based on these prototypes, technicians incorporated the referred functionality to commercial software, developed in C++. As a result of this collaboration, in early 2016 a new version of this commercial software was made available, which will be the first, as far as the authors are aware, that incorporates the possibility of estimating the uncertainty for a given level of confidence when computing the aforementioned surveying measurands.

The Value of Learning about Natural History in Biodiversity Markets

PubMed Central

Bruggeman, Douglas J.

2015-01-01

Markets for biodiversity have generated much controversy because of the often unstated and untested assumptions included in transactions rules. Simple trading rules are favored to reduce transaction costs, but others have argued that this leads to markets that favor development and erode biodiversity. Here, I describe how embracing complexity and uncertainty within a tradable credit system for the Red-cockaded Woodpecker (Picoides borealis) creates opportunities to achieve financial and conservation goals simultaneously. Reversing the effects of habitat fragmentation is one of the main reasons for developing markets. I include uncertainty in habitat fragmentation effects by evaluating market transactions using five alternative dispersal models that were able to approximate observed patterns of occupancy and movement. Further, because dispersal habitat is often not included in market transactions, I contrast how changes in breeding versus dispersal habitat affect credit values. I use an individually-based, spatially-explicit population model for the Red-cockaded Woodpecker (Picoides borealis) to predict spatial- and temporal- influences of landscape change on species occurrence and genetic diversity. Results indicated that the probability of no net loss of abundance and genetic diversity responded differently to the transient dynamics in breeding and dispersal habitat. Trades that do not violate the abundance cap may simultaneously violate the cap for the erosion of genetic diversity. To highlight how economic incentives may help reduce uncertainty, I demonstrate tradeoffs between the value of tradable credits and the value of information needed to predict the influence of habitat trades on population viability. For the trade with the greatest uncertainty regarding the change in habitat fragmentation, I estimate that the value of using 13-years of data to reduce uncertainty in dispersal behaviors is $6.2 million. Future guidance for biodiversity markets should at least encourage the use of spatially- and temporally-explicit techniques that include population genetic estimates and the influence of uncertainty. PMID:26675488

The Value of Learning about Natural History in Biodiversity Markets.

PubMed

Bruggeman, Douglas J

2015-01-01

Markets for biodiversity have generated much controversy because of the often unstated and untested assumptions included in transactions rules. Simple trading rules are favored to reduce transaction costs, but others have argued that this leads to markets that favor development and erode biodiversity. Here, I describe how embracing complexity and uncertainty within a tradable credit system for the Red-cockaded Woodpecker (Picoides borealis) creates opportunities to achieve financial and conservation goals simultaneously. Reversing the effects of habitat fragmentation is one of the main reasons for developing markets. I include uncertainty in habitat fragmentation effects by evaluating market transactions using five alternative dispersal models that were able to approximate observed patterns of occupancy and movement. Further, because dispersal habitat is often not included in market transactions, I contrast how changes in breeding versus dispersal habitat affect credit values. I use an individually-based, spatially-explicit population model for the Red-cockaded Woodpecker (Picoides borealis) to predict spatial- and temporal- influences of landscape change on species occurrence and genetic diversity. Results indicated that the probability of no net loss of abundance and genetic diversity responded differently to the transient dynamics in breeding and dispersal habitat. Trades that do not violate the abundance cap may simultaneously violate the cap for the erosion of genetic diversity. To highlight how economic incentives may help reduce uncertainty, I demonstrate tradeoffs between the value of tradable credits and the value of information needed to predict the influence of habitat trades on population viability. For the trade with the greatest uncertainty regarding the change in habitat fragmentation, I estimate that the value of using 13-years of data to reduce uncertainty in dispersal behaviors is $6.2 million. Future guidance for biodiversity markets should at least encourage the use of spatially- and temporally-explicit techniques that include population genetic estimates and the influence of uncertainty.

A probabilistic approach to emissions from transportation sector in the coming decades

NASA Astrophysics Data System (ADS)

Yan, F.; Winijkul, E.; Bond, T. C.; Streets, D. G.

2010-12-01

Future emission estimates are necessary for understanding climate change, designing national and international strategies for air quality control and evaluating mitigation policies. Emission inventories are uncertain and future projections even more so. Most current emission projection models are deterministic; in other words, there is only single answer for each scenario. As a result, uncertainties have not been included in the estimation of climate forcing or other environmental effects, but it is important to quantify the uncertainty inherent in emission projections. We explore uncertainties of emission projections from transportation sector in the coming decades by sensitivity analysis and Monte Carlo simulations. These projections are based on a technology driven model: the Speciated Pollutants Emission Wizard (SPEW)-Trend, which responds to socioeconomic conditions in different economic and mitigation scenarios. The model contains detail about technology stock, including consumption growth rates, retirement rates, timing of emission standards, deterioration rates and transition rates from normal vehicles to vehicles with extremely high emission factors (termed “superemitters”). However, understanding of these parameters, as well as relationships with socioeconomic conditions, is uncertain. We project emissions from transportation sectors under four different IPCC scenarios (A1B, A2, B1, and B2). Due to the later implementation of advanced emission standards, Africa has the highest annual growth rate (1.2-3.1%) from 2010 to 2050. Superemitters begin producing more than 50% of global emissions around year 2020. We estimate uncertainties from the relationships between technological change and socioeconomic conditions and examine their impact on future emissions. Sensitivities to parameters governing retirement rates are highest, causing changes in global emissions from-26% to +55% on average from 2010 to 2050. We perform Monte Carlo simulations to examine how these uncertainties will affect total emissions if any input parameter that has inherent the uncertainties is substituted by a range of values-probability distribution and varies at the same time; the 95% confidence interval of global emission annual growth rate is -1.9% to +0.2% per year.

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

Communicating Geographical Risks in Crisis Management: The Need for Research.

PubMed

French, Simon; Argyris, Nikolaos; Haywood, Stephanie M; Hort, Matthew C; Smith, Jim Q

2017-10-23

In any crisis, there is a great deal of uncertainty, often geographical uncertainty or, more precisely, spatiotemporal uncertainty. Examples include the spread of contamination from an industrial accident, drifting volcanic ash, and the path of a hurricane. Estimating spatiotemporal probabilities is usually a difficult task, but that is not our primary concern. Rather, we ask how analysts can communicate spatiotemporal uncertainty to those handling the crisis. We comment on the somewhat limited literature on the representation of spatial uncertainty on maps. We note that many cognitive issues arise and that the potential for confusion is high. We note that in the early stages of handling a crisis, the uncertainties involved may be deep, i.e., difficult or impossible to quantify in the time available. In such circumstance, we suggest the idea of presenting multiple scenarios. © 2017 Society for Risk Analysis.

Estimating uncertainty of Full Waveform Inversion with Ensemble-based methods

NASA Astrophysics Data System (ADS)

Thurin, J.; Brossier, R.; Métivier, L.

2017-12-01

Uncertainty estimation is one key feature of tomographic applications for robust interpretation. However, this information is often missing in the frame of large scale linearized inversions, and only the results at convergence are shown, despite the ill-posed nature of the problem. This issue is common in the Full Waveform Inversion community.While few methodologies have already been proposed in the literature, standard FWI workflows do not include any systematic uncertainty quantifications methods yet, but often try to assess the result's quality through cross-comparison with other results from seismic or comparison with other geophysical data. With the development of large seismic networks/surveys, the increase in computational power and the more and more systematic application of FWI, it is crucial to tackle this problem and to propose robust and affordable workflows, in order to address the uncertainty quantification problem faced for near surface targets, crustal exploration, as well as regional and global scales.In this work (Thurin et al., 2017a,b), we propose an approach which takes advantage of the Ensemble Transform Kalman Filter (ETKF) proposed by Bishop et al., (2001), in order to estimate a low-rank approximation of the posterior covariance matrix of the FWI problem, allowing us to evaluate some uncertainty information of the solution. Instead of solving the FWI problem through a Bayesian inversion with the ETKF, we chose to combine a conventional FWI, based on local optimization, and the ETKF strategies. This scheme allows combining the efficiency of local optimization for solving large scale inverse problems and make the sampling of the local solution space possible thanks to its embarrassingly parallel property. References:Bishop, C. H., Etherton, B. J. and Majumdar, S. J., 2001. Adaptive sampling with the ensemble transform Kalman filter. Part I: Theoretical aspects. Monthly weather review, 129(3), 420-436.Thurin, J., Brossier, R. and Métivier, L. 2017,a.: Ensemble-Based Uncertainty Estimation in Full Waveform Inversion. 79th EAGE Conference and Exhibition 2017, (12 - 15 June, 2017)Thurin, J., Brossier, R. and Métivier, L. 2017,b.: An Ensemble-Transform Kalman Filter - Full Waveform Inversion scheme for Uncertainty estimation; SEG Technical Program Expanded Abstracts 2012

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

PubMed

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

2013-09-01

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

Estimation of the probability of success in petroleum exploration

USGS Publications Warehouse

Davis, J.C.

1977-01-01

A probabilistic model for oil exploration can be developed by assessing the conditional relationship between perceived geologic variables and the subsequent discovery of petroleum. Such a model includes two probabilistic components, the first reflecting the association between a geologic condition (structural closure, for example) and the occurrence of oil, and the second reflecting the uncertainty associated with the estimation of geologic variables in areas of limited control. Estimates of the conditional relationship between geologic variables and subsequent production can be found by analyzing the exploration history of a "training area" judged to be geologically similar to the exploration area. The geologic variables are assessed over the training area using an historical subset of the available data, whose density corresponds to the present control density in the exploration area. The success or failure of wells drilled in the training area subsequent to the time corresponding to the historical subset provides empirical estimates of the probability of success conditional upon geology. Uncertainty in perception of geological conditions may be estimated from the distribution of errors made in geologic assessment using the historical subset of control wells. These errors may be expressed as a linear function of distance from available control. Alternatively, the uncertainty may be found by calculating the semivariogram of the geologic variables used in the analysis: the two procedures will yield approximately equivalent results. The empirical probability functions may then be transferred to the exploration area and used to estimate the likelihood of success of specific exploration plays. These estimates will reflect both the conditional relationship between the geological variables used to guide exploration and the uncertainty resulting from lack of control. The technique is illustrated with case histories from the mid-Continent area of the U.S.A. ?? 1977 Plenum Publishing Corp.

Lidar-derived estimate and uncertainty of carbon sink in successional phases of woody encroachment

NASA Astrophysics Data System (ADS)

Sankey, Temuulen; Shrestha, Rupesh; Sankey, Joel B.; Hardegree, Stuart; Strand, Eva

2013-07-01

encroachment is a globally occurring phenomenon that contributes to the global carbon sink. The magnitude of this contribution needs to be estimated at regional and local scales to address uncertainties present in the global- and continental-scale estimates, and guide regional policy and management in balancing restoration activities, including removal of woody plants, with greenhouse gas mitigation goals. The objective of this study was to estimate carbon stored in various successional phases of woody encroachment. Using lidar measurements of individual trees, we present high-resolution estimates of aboveground carbon storage in juniper woodlands. Segmentation analysis of lidar point cloud data identified a total of 60,628 juniper tree crowns across four watersheds. Tree heights, canopy cover, and density derived from lidar were strongly correlated with field measurements of 2613 juniper stems measured in 85 plots (30 × 30 m). Aboveground total biomass of individual trees was estimated using a regression model with lidar-derived height and crown area as predictors (Adj. R2 = 0.76, p < 0.001, RMSE = 0.58 kg). The predicted mean aboveground woody carbon storage for the study area was 677 g/m2. Uncertainty in carbon storage estimates was examined with a Monte Carlo approach that addressed major error sources. Ranges predicted with uncertainty analysis in the mean, individual tree, aboveground woody C, and associated standard deviation were 0.35 - 143.6 kg and 0.5 - 1.25 kg, respectively. Later successional phases of woody encroachment had, on average, twice the aboveground carbon relative to earlier phases. Woody encroachment might be more successfully managed and balanced with carbon storage goals by identifying priority areas in earlier phases of encroachment where intensive treatments are most effective.

Lidar-derived estimate and uncertainty of carbon sink in successional phases of woody encroachment

USGS Publications Warehouse

Sankey, Temuulen; Shrestha, Rupesh; Sankey, Joel B.; Hardgree, Stuart; Strand, Eva

2013-01-01

Woody encroachment is a globally occurring phenomenon that contributes to the global carbon sink. The magnitude of this contribution needs to be estimated at regional and local scales to address uncertainties present in the global- and continental-scale estimates, and guide regional policy and management in balancing restoration activities, including removal of woody plants, with greenhouse gas mitigation goals. The objective of this study was to estimate carbon stored in various successional phases of woody encroachment. Using lidar measurements of individual trees, we present high-resolution estimates of aboveground carbon storage in juniper woodlands. Segmentation analysis of lidar point cloud data identified a total of 60,628 juniper tree crowns across four watersheds. Tree heights, canopy cover, and density derived from lidar were strongly correlated with field measurements of 2613 juniper stems measured in 85 plots (30 × 30 m). Aboveground total biomass of individual trees was estimated using a regression model with lidar-derived height and crown area as predictors (Adj. R2 = 0.76, p 2. Uncertainty in carbon storage estimates was examined with a Monte Carlo approach that addressed major error sources. Ranges predicted with uncertainty analysis in the mean, individual tree, aboveground woody C, and associated standard deviation were 0.35 – 143.6 kg and 0.5 – 1.25 kg, respectively. Later successional phases of woody encroachment had, on average, twice the aboveground carbon relative to earlier phases. Woody encroachment might be more successfully managed and balanced with carbon storage goals by identifying priority areas in earlier phases of encroachment where intensive treatments are most effective.

Assessing uncertainty in published risk estimates using ...

EPA Pesticide Factsheets

Introduction: The National Research Council recommended quantitative evaluation of uncertainty in effect estimates for risk assessment. This analysis considers uncertainty across model forms and model parameterizations with hexavalent chromium [Cr(VI)] and lung cancer mortality as an example. The objective is to characterize model uncertainty by evaluating estimates across published epidemiologic studies of the same cohort.Methods: This analysis was based on 5 studies analyzing a cohort of 2,357 workers employed from 1950-74 in a chromate production plant in Maryland. Cox and Poisson models were the only model forms considered by study authors to assess the effect of Cr(VI) on lung cancer mortality. All models adjusted for smoking and included a 5-year exposure lag, however other latency periods and model covariates such as age and race were considered. Published effect estimates were standardized to the same units and normalized by their variances to produce a standardized metric to compare variability within and between model forms. A total of 5 similarly parameterized analyses were considered across model form, and 16 analyses with alternative parameterizations were considered within model form (10 Cox; 6 Poisson). Results: Across Cox and Poisson model forms, adjusted cumulative exposure coefficients (betas) for 5 similar analyses ranged from 2.47 to 4.33 (mean=2.97, σ2=0.63). Within the 10 Cox models, coefficients ranged from 2.53 to 4.42 (mean=3.29, σ2=0.

Estimating the empirical probability of submarine landslide occurrence

USGS Publications Warehouse

Geist, Eric L.; Parsons, Thomas E.; Mosher, David C.; Shipp, Craig; Moscardelli, Lorena; Chaytor, Jason D.; Baxter, Christopher D. P.; Lee, Homa J.; Urgeles, Roger

2010-01-01

The empirical probability for the occurrence of submarine landslides at a given location can be estimated from age dates of past landslides. In this study, tools developed to estimate earthquake probability from paleoseismic horizons are adapted to estimate submarine landslide probability. In both types of estimates, one has to account for the uncertainty associated with age-dating individual events as well as the open time intervals before and after the observed sequence of landslides. For observed sequences of submarine landslides, we typically only have the age date of the youngest event and possibly of a seismic horizon that lies below the oldest event in a landslide sequence. We use an empirical Bayes analysis based on the Poisson-Gamma conjugate prior model specifically applied to the landslide probability problem. This model assumes that landslide events as imaged in geophysical data are independent and occur in time according to a Poisson distribution characterized by a rate parameter λ. With this method, we are able to estimate the most likely value of λ and, importantly, the range of uncertainty in this estimate. Examples considered include landslide sequences observed in the Santa Barbara Channel, California, and in Port Valdez, Alaska. We confirm that given the uncertainties of age dating that landslide complexes can be treated as single events by performing statistical test of age dates representing the main failure episode of the Holocene Storegga landslide complex.

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

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

Connolly, J.R.

Petrologic, bulk chemical, and mineralogic data are presented for 49 samples of tuffaceous rocks from core holes USW G-1 and UE-25a{number_sign}1 at Yucca Mountain, Nevada. Included, in descending stratigraphic order, are 11 samples from the Topopah Spring Member of the Paintbrush Tuff, 12 samples from the Tuffaceous Beds of Calico Hills, 3 samples from the Prow Pass Member of the Crater Flat Tuff, 20 samples from the Bullfrog Member of the Crater Flat Tuff and 3 samples from the Tram Member of the Crater Flat Tuff. The suite of samples contains a wide variety of petrologic types, including zeolitized, glassy,more » and devitrified tuffs. Data vary considerably between groups of samples, and include thin section descriptions (some with modal analyses for which uncertainties are estimated), electron microprobe analyses of mineral phases and matrix, mineral identifications by X-ray diffraction, and major element analyses with uncertainty estimates.« less

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

NASA Astrophysics Data System (ADS)

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

2018-04-01

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

Mixed oxidizer hybrid propulsion system optimization under uncertainty using applied response surface methodology and Monte Carlo simulation

NASA Astrophysics Data System (ADS)

Whitehead, James Joshua

The analysis documented herein provides an integrated approach for the conduct of optimization under uncertainty (OUU) using Monte Carlo Simulation (MCS) techniques coupled with response surface-based methods for characterization of mixture-dependent variables. This novel methodology provides an innovative means of conducting optimization studies under uncertainty in propulsion system design. Analytic inputs are based upon empirical regression rate information obtained from design of experiments (DOE) mixture studies utilizing a mixed oxidizer hybrid rocket concept. Hybrid fuel regression rate was selected as the target response variable for optimization under uncertainty, with maximization of regression rate chosen as the driving objective. Characteristic operational conditions and propellant mixture compositions from experimental efforts conducted during previous foundational work were combined with elemental uncertainty estimates as input variables. Response surfaces for mixture-dependent variables and their associated uncertainty levels were developed using quadratic response equations incorporating single and two-factor interactions. These analysis inputs, response surface equations and associated uncertainty contributions were applied to a probabilistic MCS to develop dispersed regression rates as a function of operational and mixture input conditions within design space. Illustrative case scenarios were developed and assessed using this analytic approach including fully and partially constrained operational condition sets over all of design mixture space. In addition, optimization sets were performed across an operationally representative region in operational space and across all investigated mixture combinations. These scenarios were selected as representative examples relevant to propulsion system optimization, particularly for hybrid and solid rocket platforms. Ternary diagrams, including contour and surface plots, were developed and utilized to aid in visualization. The concept of Expanded-Durov diagrams was also adopted and adapted to this study to aid in visualization of uncertainty bounds. Regions of maximum regression rate and associated uncertainties were determined for each set of case scenarios. Application of response surface methodology coupled with probabilistic-based MCS allowed for flexible and comprehensive interrogation of mixture and operating design space during optimization cases. Analyses were also conducted to assess sensitivity of uncertainty to variations in key elemental uncertainty estimates. The methodology developed during this research provides an innovative optimization tool for future propulsion design efforts.

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.

Plurality of Type A evaluations of uncertainty

NASA Astrophysics Data System (ADS)

Possolo, Antonio; Pintar, Adam L.

2017-10-01

The evaluations of measurement uncertainty involving the application of statistical methods to measurement data (Type A evaluations as specified in the Guide to the Expression of Uncertainty in Measurement, GUM) comprise the following three main steps: (i) developing a statistical model that captures the pattern of dispersion or variability in the experimental data, and that relates the data either to the measurand directly or to some intermediate quantity (input quantity) that the measurand depends on; (ii) selecting a procedure for data reduction that is consistent with this model and that is fit for the purpose that the results are intended to serve; (iii) producing estimates of the model parameters, or predictions based on the fitted model, and evaluations of uncertainty that qualify either those estimates or these predictions, and that are suitable for use in subsequent uncertainty propagation exercises. We illustrate these steps in uncertainty evaluations related to the measurement of the mass fraction of vanadium in a bituminous coal reference material, including the assessment of the homogeneity of the material, and to the calibration and measurement of the amount-of-substance fraction of a hydrochlorofluorocarbon in air, and of the age of a meteorite. Our goal is to expose the plurality of choices that can reasonably be made when taking each of the three steps outlined above, and to show that different choices typically lead to different estimates of the quantities of interest, and to different evaluations of the associated uncertainty. In all the examples, the several alternatives considered represent choices that comparably competent statisticians might make, but who differ in the assumptions that they are prepared to rely on, and in their selection of approach to statistical inference. They represent also alternative treatments that the same statistician might give to the same data when the results are intended for different purposes.

Algorithms and uncertainties for the determination of multispectral irradiance components and aerosol optical depth from a shipborne rotating shadowband radiometer

NASA Astrophysics Data System (ADS)

Witthuhn, Jonas; Deneke, Hartwig; Macke, Andreas; Bernhard, Germar

2017-03-01

The 19-channel rotating shadowband radiometer GUVis-3511 built by Biospherical Instruments provides automated shipborne measurements of the direct, diffuse and global spectral irradiance components without a requirement for platform stabilization. Several direct sun products, including spectral direct beam transmittance, aerosol optical depth, Ångström exponent and precipitable water, can be derived from these observations. The individual steps of the data analysis are described, and the different sources of uncertainty are discussed. The total uncertainty of the observed direct beam transmittances is estimated to be about 4 % for most channels within a 95 % confidence interval for shipborne operation. The calibration is identified as the dominating contribution to the total uncertainty. A comparison of direct beam transmittance with those obtained from a Cimel sunphotometer at a land site and a manually operated Microtops II sunphotometer on a ship is presented. Measurements deviate by less than 3 and 4 % on land and on ship, respectively, for most channels and in agreement with our previous uncertainty estimate. These numbers demonstrate that the instrument is well suited for shipborne operation, and the applied methods for motion correction work accurately. Based on spectral direct beam transmittance, aerosol optical depth can be retrieved with an uncertainty of 0.02 for all channels within a 95 % confidence interval. The different methods to account for Rayleigh scattering and gas absorption in our scheme and in the Aerosol Robotic Network processing for Cimel sunphotometers lead to minor deviations. Relying on the cross calibration of the 940 nm water vapor channel with the Cimel sunphotometer, the column amount of precipitable water can be estimated with an uncertainty of ±0.034 cm.

Uncertainty quantification in volumetric Particle Image Velocimetry

NASA Astrophysics Data System (ADS)

Bhattacharya, Sayantan; Charonko, John; Vlachos, Pavlos

2016-11-01

Particle Image Velocimetry (PIV) uncertainty quantification is challenging due to coupled sources of elemental uncertainty and complex data reduction procedures in the measurement chain. Recent developments in this field have led to uncertainty estimation methods for planar PIV. However, no framework exists for three-dimensional volumetric PIV. In volumetric PIV the measurement uncertainty is a function of reconstructed three-dimensional particle location that in turn is very sensitive to the accuracy of the calibration mapping function. Furthermore, the iterative correction to the camera mapping function using triangulated particle locations in space (volumetric self-calibration) has its own associated uncertainty due to image noise and ghost particle reconstructions. Here we first quantify the uncertainty in the triangulated particle position which is a function of particle detection and mapping function uncertainty. The location uncertainty is then combined with the three-dimensional cross-correlation uncertainty that is estimated as an extension of the 2D PIV uncertainty framework. Finally the overall measurement uncertainty is quantified using an uncertainty propagation equation. The framework is tested with both simulated and experimental cases. For the simulated cases the variation of estimated uncertainty with the elemental volumetric PIV error sources are also evaluated. The results show reasonable prediction of standard uncertainty with good coverage.

Aerosol-type retrieval and uncertainty quantification from OMI data

NASA Astrophysics Data System (ADS)

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

2017-11-01

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

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.

Effect of precipitation spatial distribution uncertainty on the uncertainty bounds of a snowmelt runoff model output

NASA Astrophysics Data System (ADS)

Jacquin, A. P.

2012-04-01

This study analyses the effect of precipitation spatial distribution uncertainty on the uncertainty bounds of a snowmelt runoff model's discharge estimates. Prediction uncertainty bounds are derived using the Generalized Likelihood Uncertainty Estimation (GLUE) methodology. The model analysed is a conceptual watershed model operating at a monthly time step. The model divides the catchment into five elevation zones, where the fifth zone corresponds to the catchment glaciers. Precipitation amounts at each elevation zone i are estimated as the product between observed precipitation (at a single station within the catchment) and a precipitation factor FPi. Thus, these factors provide a simplified representation of the spatial variation of precipitation, specifically the shape of the functional relationship between precipitation and height. In the absence of information about appropriate values of the precipitation factors FPi, these are estimated through standard calibration procedures. The catchment case study is Aconcagua River at Chacabuquito, located in the Andean region of Central Chile. Monte Carlo samples of the model output are obtained by randomly varying the model parameters within their feasible ranges. In the first experiment, the precipitation factors FPi are considered unknown and thus included in the sampling process. The total number of unknown parameters in this case is 16. In the second experiment, precipitation factors FPi are estimated a priori, by means of a long term water balance between observed discharge at the catchment outlet, evapotranspiration estimates and observed precipitation. In this case, the number of unknown parameters reduces to 11. The feasible ranges assigned to the precipitation factors in the first experiment are slightly wider than the range of fixed precipitation factors used in the second experiment. The mean squared error of the Box-Cox transformed discharge during the calibration period is used for the evaluation of the goodness of fit of the model realizations. GLUE-type uncertainty bounds during the verification period are derived at the probability levels p=85%, 90% and 95%. Results indicate that, as expected, prediction uncertainty bounds indeed change if precipitation factors FPi are estimated a priori rather than being allowed to vary, but that this change is not dramatic. Firstly, the width of the uncertainty bounds at the same probability level only slightly reduces compared to the case where precipitation factors are allowed to vary. Secondly, the ability to enclose the observations improves, but the decrease in the fraction of outliers is not significant. These results are probably due to the narrow range of variability allowed to the precipitation factors FPi in the first experiment, which implies that although they indicate the shape of the functional relationship between precipitation and height, the magnitude of precipitation estimates were mainly determined by the magnitude of the observations at the available raingauge. It is probable that the situation where no prior information is available on the realistic ranges of variation of the precipitation factors, and the inclusion of precipitation data uncertainty, would have led to a different conclusion. Acknowledgements: This research was funded by FONDECYT, Research Project 1110279.

Testing Map Features Designed to Convey the Uncertainty of Cancer Risk: Insights Gained From Assessing Judgments of Information Adequacy and Communication Goals

PubMed Central

Severtson, Dolores J.

2015-01-01

Barriers to communicating the uncertainty of environmental health risks include preferences for certain information and low numeracy. Map features designed to communicate the magnitude and uncertainty of estimated cancer risk from air pollution were tested among 826 participants to assess how map features influenced judgments of adequacy and the intended communication goals. An uncertain versus certain visual feature was judged as less adequate but met both communication goals and addressed numeracy barriers. Expressing relative risk using words communicated uncertainty and addressed numeracy barriers but was judged as highly inadequate. Risk communication and visual cognition concepts were applied to explain findings. PMID:26412960

Testing Map Features Designed to Convey the Uncertainty of Cancer Risk: Insights Gained From Assessing Judgments of Information Adequacy and Communication Goals.

PubMed

Severtson, Dolores J

2015-02-01

Barriers to communicating the uncertainty of environmental health risks include preferences for certain information and low numeracy. Map features designed to communicate the magnitude and uncertainty of estimated cancer risk from air pollution were tested among 826 participants to assess how map features influenced judgments of adequacy and the intended communication goals. An uncertain versus certain visual feature was judged as less adequate but met both communication goals and addressed numeracy barriers. Expressing relative risk using words communicated uncertainty and addressed numeracy barriers but was judged as highly inadequate. Risk communication and visual cognition concepts were applied to explain findings.

The deuteron-radius puzzle is alive: A new analysis of nuclear structure uncertainties

NASA Astrophysics Data System (ADS)

Hernandez, O. J.; Ekström, A.; Nevo Dinur, N.; Ji, C.; Bacca, S.; Barnea, N.

2018-03-01

To shed light on the deuteron radius puzzle we analyze the theoretical uncertainties of the nuclear structure corrections to the Lamb shift in muonic deuterium. We find that the discrepancy between the calculated two-photon exchange correction and the corresponding experimentally inferred value by Pohl et al. [1] remain. The present result is consistent with our previous estimate, although the discrepancy is reduced from 2.6 σ to about 2 σ. The error analysis includes statistic as well as systematic uncertainties stemming from the use of nucleon-nucleon interactions derived from chiral effective field theory at various orders. We therefore conclude that nuclear theory uncertainty is more likely not the source of the discrepancy.

Incorporating uncertainty regarding applicability of evidence from meta-analyses into clinical decision making.

PubMed

Kriston, Levente; Meister, Ramona

2014-03-01

Judging applicability (relevance) of meta-analytical findings to particular clinical decision-making situations remains challenging. We aimed to describe an evidence synthesis method that accounts for possible uncertainty regarding applicability of the evidence. We conceptualized uncertainty regarding applicability of the meta-analytical estimates to a decision-making situation as the result of uncertainty regarding applicability of the findings of the trials that were included in the meta-analysis. This trial-level applicability uncertainty can be directly assessed by the decision maker and allows for the definition of trial inclusion probabilities, which can be used to perform a probabilistic meta-analysis with unequal probability resampling of trials (adaptive meta-analysis). A case study with several fictitious decision-making scenarios was performed to demonstrate the method in practice. We present options to elicit trial inclusion probabilities and perform the calculations. The result of an adaptive meta-analysis is a frequency distribution of the estimated parameters from traditional meta-analysis that provides individually tailored information according to the specific needs and uncertainty of the decision maker. The proposed method offers a direct and formalized combination of research evidence with individual clinical expertise and may aid clinicians in specific decision-making situations. Copyright © 2014 Elsevier Inc. All rights reserved.

Estimate of uncertainties in polarized parton distributions

NASA Astrophysics Data System (ADS)

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

2001-10-01

From \\chi^2 analysis of polarized deep inelastic scattering data, we determined polarized parton distribution functions (Y. Goto et al. (AAC), Phys. Rev. D 62, 34017 (2000).). In order to clarify the reliability of the obtained distributions, we should estimate uncertainties of the distributions. In this talk, we discuss the pol-PDF uncertainties by using a Hessian method. A Hessian matrix H_ij is given by second derivatives of the \\chi^2, and the error matrix \\varepsilon_ij is defined as the inverse matrix of H_ij. Using the error matrix, we calculate the error of a function F by (δ F)^2 = sum_i,j fracpartial Fpartial ai \\varepsilon_ij fracpartial Fpartial aj , where a_i,j are the parameters in the \\chi^2 analysis. Using this method, we show the uncertainties of the pol-PDF, structure functions g_1, and spin asymmetries A_1. Furthermore, we show a role of future experiments such as the RHIC-Spin. An important purpose of planned experiments in the near future is to determine the polarized gluon distribution function Δ g (x) in detail. We reanalyze the pol-PDF uncertainties including the gluon fake data which are expected to be given by the upcoming experiments. From this analysis, we discuss how much the uncertainties of Δ g (x) can be improved by such measurements.

Deriving Continuous Fields of Tree Cover at 1-m over the Continental United States From the National Agriculture Imagery Program (NAIP) Imagery to Reduce Uncertainties in Forest Carbon Stock Estimation

NASA Astrophysics Data System (ADS)

Ganguly, S.; Basu, S.; Mukhopadhyay, S.; Michaelis, A.; Milesi, C.; Votava, P.; Nemani, R. R.

2013-12-01

An unresolved issue with coarse-to-medium resolution satellite-based forest carbon mapping over regional to continental scales is the high level of uncertainty in above ground biomass (AGB) estimates caused by the absence of forest cover information at a high enough spatial resolution (current spatial resolution is limited to 30-m). To put confidence in existing satellite-derived AGB density estimates, it is imperative to create continuous fields of tree cover at a sufficiently high resolution (e.g. 1-m) such that large uncertainties in forested area are reduced. The proposed work will provide means to reduce uncertainty in present satellite-derived AGB maps and Forest Inventory and Analysis (FIA) based regional estimates. Our primary objective will be to create Very High Resolution (VHR) estimates of tree cover at a spatial resolution of 1-m for the Continental United States using all available National Agriculture Imaging Program (NAIP) color-infrared imagery from 2010 till 2012. We will leverage the existing capabilities of the NASA Earth Exchange (NEX) high performance computing and storage facilities. The proposed 1-m tree cover map can be further aggregated to provide percent tree cover at any medium-to-coarse resolution spatial grid, which will aid in reducing uncertainties in AGB density estimation at the respective grid and overcome current limitations imposed by medium-to-coarse resolution land cover maps. We have implemented a scalable and computationally-efficient parallelized framework for tree-cover delineation - the core components of the algorithm [that] include a feature extraction process, a Statistical Region Merging image segmentation algorithm and a classification algorithm based on Deep Belief Network and a Feedforward Backpropagation Neural Network algorithm. An initial pilot exercise has been performed over the state of California (~11,000 scenes) to create a wall-to-wall 1-m tree cover map and the classification accuracy has been assessed. Results show an improvement in accuracy of tree-cover delineation as compared to existing forest cover maps from NLCD, especially over fragmented, heterogeneous and urban landscapes. Estimates of VHR tree cover will complement and enhance the accuracy of present remote-sensing based AGB modeling approaches and forest inventory based estimates at both national and local scales. A requisite step will be to characterize the inherent uncertainties in tree cover estimates and propagate them to estimate AGB.

On the representation and estimation of spatial uncertainty. [for mobile robot

NASA Technical Reports Server (NTRS)

Smith, Randall C.; Cheeseman, Peter

1987-01-01

This paper describes a general method for estimating the nominal relationship and expected error (covariance) between coordinate frames representing the relative locations of objects. The frames may be known only indirectly through a series of spatial relationships, each with its associated error, arising from diverse causes, including positioning errors, measurement errors, or tolerances in part dimensions. This estimation method can be used to answer such questions as whether a camera attached to a robot is likely to have a particular reference object in its field of view. The calculated estimates agree well with those from an independent Monte Carlo simulation. The method makes it possible to decide in advance whether an uncertain relationship is known accurately enough for some task and, if not, how much of an improvement in locational knowledge a proposed sensor will provide. The method presented can be generalized to six degrees of freedom and provides a practical means of estimating the relationships (position and orientation) among objects, as well as estimating the uncertainty associated with the relationships.

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.

Methods for Estimating Uncertainty in PMF Solutions: Examples with Ambient Air and Water Quality Data and Guidance on Reporting PMF Results

EPA Science Inventory

The new version of EPA’s positive matrix factorization (EPA PMF) software, 5.0, includes three error estimation (EE) methods for analyzing factor analytic solutions: classical bootstrap (BS), displacement of factor elements (DISP), and bootstrap enhanced by displacement (BS-DISP)...

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.

Evaluation of incremental reactivity and its uncertainty in Southern California.

PubMed

Martien, Philip T; Harley, Robert A; Milford, Jana B; Russell, Armistead G

2003-04-15

The incremental reactivity (IR) and relative incremental reactivity (RIR) of carbon monoxide and 30 individual volatile organic compounds (VOC) were estimated for the South Coast Air Basin using two photochemical air quality models: a 3-D, grid-based model and a vertically resolved trajectory model. Both models include an extended version of the SAPRC99 chemical mechanism. For the 3-D modeling, the decoupled direct method (DDM-3D) was used to assess reactivities. The trajectory model was applied to estimate uncertainties in reactivities due to uncertainties in chemical rate parameters, deposition parameters, and emission rates using Monte Carlo analysis with Latin hypercube sampling. For most VOC, RIRs were found to be consistent in rankings with those produced by Carter using a box model. However, 3-D simulations show that coastal regions, upwind of most of the emissions, have comparatively low IR but higher RIR than predicted by box models for C4-C5 alkenes and carbonyls that initiate the production of HOx radicals. Biogenic VOC emissions were found to have a lower RIR than predicted by box model estimates, because emissions of these VOC were mostly downwind of the areas of primary ozone production. Uncertainties in RIR of individual VOC were found to be dominated by uncertainties in the rate parameters of their primary oxidation reactions. The coefficient of variation (COV) of most RIR values ranged from 20% to 30%, whereas the COV of absolute incremental reactivity ranged from about 30% to 40%. In general, uncertainty and variability both decreased when relative rather than absolute reactivity metrics were used.

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.

Using cost-benefit concepts in design floods improves communication of uncertainty

NASA Astrophysics Data System (ADS)

Ganora, Daniele; Botto, Anna; Laio, Francesco; Claps, Pierluigi

2017-04-01

Flood frequency analysis, i.e. the study of the relationships between the magnitude and the rarity of high flows in a river, is the usual procedure adopted to assess flood hazard, preliminary to the plan/design of flood protection measures. It grounds on the fit of a probability distribution to the peak discharge values recorded in gauging stations and the final estimates over a region are thus affected by uncertainty, due to the limited sample availability and of the possible alternatives in terms of the probabilistic model and the parameter estimation methods used. In the last decade, the scientific community dealt with this issue by developing a number of methods to quantify such uncertainty components. Usually, uncertainty is visually represented through confidence bands, which are easy to understand, but are not yet demonstrated to be useful for design purposes: they usually disorient decision makers, as the design flood is no longer univocally defined, making the decision process undetermined. These considerations motivated the development of the uncertainty-compliant design flood estimator (UNCODE) procedure (Botto et al., 2014) that allows one to select meaningful flood design values accounting for the associated uncertainty by considering additional constraints based on cost-benefit criteria. This method suggests an explicit multiplication factor that corrects the traditional (without uncertainty) design flood estimates to incorporate the effects of uncertainty in the estimate at the same safety level. Even though the UNCODE method was developed for design purposes, it can represent a powerful and robust tool to help clarifying the effects of the uncertainty in statistical estimation. As the process produces increased design flood estimates, this outcome demonstrates how uncertainty leads to more expensive flood protection measures, or insufficiency of current defenses. Moreover, the UNCODE approach can be used to assess the "value" of data, as the costs of flood prevention can get down by reducing uncertainty with longer observed flood records. As the multiplication factor is dimensionless, some examples of application provided show how this approach allows simple comparisons of the effects of uncertainty in different catchments, helping to build ranking procedures for planning purposes. REFERENCES Botto, A., Ganora, D., Laio, F., and Claps, P.: Uncertainty compliant design flood estimation, Water Resources Research, 50, doi:10.1002/2013WR014981, 2014.

A Quantitative Method to Identify Lithology Beneath Cover

NASA Astrophysics Data System (ADS)

Gettings, M. E.

2008-12-01

Geophysical terranes (map areas of similar potential field data response) can be used in the estimation of geological map units beneath cover (bedrock, alluvium, or tectonic block). Potential field data over nearby bedrock terranes defines "candidate terranes". Geophysical anomaly dimensions, shapes, amplitudes, trends/structural grain, and fractal measures yield a vector of measures characterizing the terrane. To compare candidate terranes fields with those for covered areas, the effect of depth of cover must be taken into account. Gravity anomaly data yields depth estimates by which the aeromagnetic data of candidate terranes are then upward continued. Comparison of characteristics of the upward continued fields from the candidate terranes to those of covered areas rank the candidates. Because of signal loss in upward continuation and overlap of physical properties, the vectors of measures for the candidate terranes are usually not unique. Possibility theory offers a relatively objective and robust method that can be used to rank terrane types that includes uncertainty. The strategy is to prepare membership functions for each measure of each candidate terrane and the covered area, based on observed values and degree of knowledge, and then form the fuzzy-logical combination of these to estimate the possibility and its uncertainty for each candidate terrane. Membership functions include uncertainty by the degree of membership for various possibility values. With no other information, uncertainty is based on information content from survey specifications and geologic features dimensions. Geologic data can also be included, such as structural trends, proximity, and tectonic history. Little knowledge implies wide membership functions; perfect knowledge, a delta function. This and the combination rules in fuzzy logic yield a robust estimation method. An uncertain membership function of a characteristic contributes much less to the possibility than a precise one. The final result for each covered area is a ranked possibility function for each candidate terrane as the underlying bedrock of the covered area that honors the aeromagnetic field and the geologic constraints that have been included. An example of the application of this method is presented for an area in south central Arizona.

Trans-dimensional matched-field geoacoustic inversion with hierarchical error models and interacting Markov chains.

PubMed

Dettmer, Jan; Dosso, Stan E

2012-10-01

This paper develops a trans-dimensional approach to matched-field geoacoustic inversion, including interacting Markov chains to improve efficiency and an autoregressive model to account for correlated errors. The trans-dimensional approach and hierarchical seabed model allows inversion without assuming any particular parametrization by relaxing model specification to a range of plausible seabed models (e.g., in this case, the number of sediment layers is an unknown parameter). Data errors are addressed by sampling statistical error-distribution parameters, including correlated errors (covariance), by applying a hierarchical autoregressive error model. The well-known difficulty of low acceptance rates for trans-dimensional jumps is addressed with interacting Markov chains, resulting in a substantial increase in efficiency. The trans-dimensional seabed model and the hierarchical error model relax the degree of prior assumptions required in the inversion, resulting in substantially improved (more realistic) uncertainty estimates and a more automated algorithm. In particular, the approach gives seabed parameter uncertainty estimates that account for uncertainty due to prior model choice (layering and data error statistics). The approach is applied to data measured on a vertical array in the Mediterranean Sea.

Probability of Causation for Space Radiation Carcinogenesis Following International Space Station, Near Earth Asteroid, and Mars Missions

NASA Technical Reports Server (NTRS)

Cucinotta, Francis A.; Kim, Myung-Hee Y.; Chappell, Lori J.

2012-01-01

Cancer risk is an important concern for International Space Station (ISS) missions and future exploration missions. An important question concerns the likelihood of a causal association between a crew members radiation exposure and the occurrence of cancer. The probability of causation (PC), also denoted as attributable risk, is used to make such an estimate. This report summarizes the NASA model of space radiation cancer risks and uncertainties, including improvements to represent uncertainties in tissue-specific cancer incidence models for never-smokers and the U.S. average population. We report on tissue-specific cancer incidence estimates and PC for different post-mission times for ISS and exploration missions. An important conclusion from our analysis is that the NASA policy to limit the risk of exposure-induced death to 3% at the 95% confidence level largely ensures that estimates of the PC for most cancer types would not reach a level of significance. Reducing uncertainties through radiobiological research remains the most efficient method to extend mission length and establish effective mitigators for cancer risks. Efforts to establish biomarkers of space radiation-induced tumors and to estimate PC for rarer tumor types are briefly discussed.

Review of NASA approach to space radiation risk assessments for Mars exploration.

PubMed

Cucinotta, Francis A

2015-02-01

Long duration space missions present unique radiation protection challenges due to the complexity of the space radiation environment, which includes high charge and energy particles and other highly ionizing radiation such as neutrons. Based on a recommendation by the National Council on Radiation Protection and Measurements, a 3% lifetime risk of exposure-induced death for cancer has been used as a basis for risk limitation by the National Aeronautics and Space Administration (NASA) for low-Earth orbit missions. NASA has developed a risk-based approach to radiation exposure limits that accounts for individual factors (age, gender, and smoking history) and assesses the uncertainties in risk estimates. New radiation quality factors with associated probability distribution functions to represent the quality factor's uncertainty have been developed based on track structure models and recent radiobiology data for high charge and energy particles. The current radiation dose limits are reviewed for spaceflight and the various qualitative and quantitative uncertainties that impact the risk of exposure-induced death estimates using the NASA Space Cancer Risk (NSCR) model. NSCR estimates of the number of "safe days" in deep space to be within exposure limits and risk estimates for a Mars exploration mission are described.

A model-based approach to wildland fire reconstruction using sediment charcoal records

USGS Publications Warehouse

Itter, Malcolm S.; Finley, Andrew O.; Hooten, Mevin B.; Higuera, Philip E.; Marlon, Jennifer R.; Kelly, Ryan; McLachlan, Jason S.

2017-01-01

Lake sediment charcoal records are used in paleoecological analyses to reconstruct fire history, including the identification of past wildland fires. One challenge of applying sediment charcoal records to infer fire history is the separation of charcoal associated with local fire occurrence and charcoal originating from regional fire activity. Despite a variety of methods to identify local fires from sediment charcoal records, an integrated statistical framework for fire reconstruction is lacking. We develop a Bayesian point process model to estimate the probability of fire associated with charcoal counts from individual-lake sediments and estimate mean fire return intervals. A multivariate extension of the model combines records from multiple lakes to reduce uncertainty in local fire identification and estimate a regional mean fire return interval. The univariate and multivariate models are applied to 13 lakes in the Yukon Flats region of Alaska. Both models resulted in similar mean fire return intervals (100–350 years) with reduced uncertainty under the multivariate model due to improved estimation of regional charcoal deposition. The point process model offers an integrated statistical framework for paleofire reconstruction and extends existing methods to infer regional fire history from multiple lake records with uncertainty following directly from posterior distributions.

A Review On Accuracy and Uncertainty of Spatial Data and Analyses with special reference to Urban and Hydrological Modelling

NASA Astrophysics Data System (ADS)

Devendran, A. A.; Lakshmanan, G.

2014-11-01

Data quality for GIS processing and analysis is becoming an increased concern due to the accelerated application of GIS technology for problem solving and decision making roles. Uncertainty in the geographic representation of the real world arises as these representations are incomplete. Identification of the sources of these uncertainties and the ways in which they operate in GIS based representations become crucial in any spatial data representation and geospatial analysis applied to any field of application. This paper reviews the articles on the various components of spatial data quality and various uncertainties inherent in them and special focus is paid to two fields of application such as Urban Simulation and Hydrological Modelling. Urban growth is a complicated process involving the spatio-temporal changes of all socio-economic and physical components at different scales. Cellular Automata (CA) model is one of the simulation models, which randomly selects potential cells for urbanisation and the transition rules evaluate the properties of the cell and its neighbour. Uncertainty arising from CA modelling is assessed mainly using sensitivity analysis including Monte Carlo simulation method. Likewise, the importance of hydrological uncertainty analysis has been emphasized in recent years and there is an urgent need to incorporate uncertainty estimation into water resources assessment procedures. The Soil and Water Assessment Tool (SWAT) is a continuous time watershed model to evaluate various impacts of land use management and climate on hydrology and water quality. Hydrological model uncertainties using SWAT model are dealt primarily by Generalized Likelihood Uncertainty Estimation (GLUE) method.

Bayesian effect estimation accounting for adjustment uncertainty.

PubMed

Wang, Chi; Parmigiani, Giovanni; Dominici, Francesca

2012-09-01

Model-based estimation of the effect of an exposure on an outcome is generally sensitive to the choice of which confounding factors are included in the model. We propose a new approach, which we call Bayesian adjustment for confounding (BAC), to estimate the effect of an exposure of interest on the outcome, while accounting for the uncertainty in the choice of confounders. Our approach is based on specifying two models: (1) the outcome as a function of the exposure and the potential confounders (the outcome model); and (2) the exposure as a function of the potential confounders (the exposure model). We consider Bayesian variable selection on both models and link the two by introducing a dependence parameter, ω, denoting the prior odds of including a predictor in the outcome model, given that the same predictor is in the exposure model. In the absence of dependence (ω= 1), BAC reduces to traditional Bayesian model averaging (BMA). In simulation studies, we show that BAC, with ω > 1, estimates the exposure effect with smaller bias than traditional BMA, and improved coverage. We, then, compare BAC, a recent approach of Crainiceanu, Dominici, and Parmigiani (2008, Biometrika 95, 635-651), and traditional BMA in a time series data set of hospital admissions, air pollution levels, and weather variables in Nassau, NY for the period 1999-2005. Using each approach, we estimate the short-term effects of on emergency admissions for cardiovascular diseases, accounting for confounding. This application illustrates the potentially significant pitfalls of misusing variable selection methods in the context of adjustment uncertainty. © 2012, The International Biometric Society.

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.

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.

The first Australian gravimetric quasigeoid model with location-specific uncertainty estimates

NASA Astrophysics Data System (ADS)

Featherstone, W. E.; McCubbine, J. C.; Brown, N. J.; Claessens, S. J.; Filmer, M. S.; Kirby, J. F.

2018-02-01

We describe the computation of the first Australian quasigeoid model to include error estimates as a function of location that have been propagated from uncertainties in the EGM2008 global model, land and altimeter-derived gravity anomalies and terrain corrections. The model has been extended to include Australia's offshore territories and maritime boundaries using newer datasets comprising an additional {˜ }280,000 land gravity observations, a newer altimeter-derived marine gravity anomaly grid, and terrain corrections at 1^' ' }× 1^' ' } resolution. The error propagation uses a remove-restore approach, where the EGM2008 quasigeoid and gravity anomaly error grids are augmented by errors propagated through a modified Stokes integral from the errors in the altimeter gravity anomalies, land gravity observations and terrain corrections. The gravimetric quasigeoid errors (one sigma) are 50-60 mm across most of the Australian landmass, increasing to {˜ }100 mm in regions of steep horizontal gravity gradients or the mountains, and are commensurate with external estimates.

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.

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.

Carbon dioxide and methane measurements from the Los Angeles Megacity Carbon Project - Part 1: calibration, urban enhancements, and uncertainty estimates

NASA Astrophysics Data System (ADS)

Verhulst, Kristal R.; Karion, Anna; Kim, Jooil; Salameh, Peter K.; Keeling, Ralph F.; Newman, Sally; Miller, John; Sloop, Christopher; Pongetti, Thomas; Rao, Preeti; Wong, Clare; Hopkins, Francesca M.; Yadav, Vineet; Weiss, Ray F.; Duren, Riley M.; Miller, Charles E.

2017-07-01

We report continuous surface observations of carbon dioxide (CO2) and methane (CH4) from the Los Angeles (LA) Megacity Carbon Project during 2015. We devised a calibration strategy, methods for selection of background air masses, calculation of urban enhancements, and a detailed algorithm for estimating uncertainties in urban-scale CO2 and CH4 measurements. These methods are essential for understanding carbon fluxes from the LA megacity and other complex urban environments globally. We estimate background mole fractions entering LA using observations from four extra-urban sites including two marine sites located south of LA in La Jolla (LJO) and offshore on San Clemente Island (SCI), one continental site located in Victorville (VIC), in the high desert northeast of LA, and one continental/mid-troposphere site located on Mount Wilson (MWO) in the San Gabriel Mountains. We find that a local marine background can be established to within ˜ 1 ppm CO2 and ˜ 10 ppb CH4 using these local measurement sites. Overall, atmospheric carbon dioxide and methane levels are highly variable across Los Angeles. Urban and suburban sites show moderate to large CO2 and CH4 enhancements relative to a marine background estimate. The USC (University of Southern California) site near downtown LA exhibits median hourly enhancements of ˜ 20 ppm CO2 and ˜ 150 ppb CH4 during 2015 as well as ˜ 15 ppm CO2 and ˜ 80 ppb CH4 during mid-afternoon hours (12:00-16:00 LT, local time), which is the typical period of focus for flux inversions. The estimated measurement uncertainty is typically better than 0.1 ppm CO2 and 1 ppb CH4 based on the repeated standard gas measurements from the LA sites during the last 2 years, similar to Andrews et al. (2014). The largest component of the measurement uncertainty is due to the single-point calibration method; however, the uncertainty in the background mole fraction is much larger than the measurement uncertainty. The background uncertainty for the marine background estimate is ˜ 10 and ˜ 15 % of the median mid-afternoon enhancement near downtown LA for CO2 and CH4, respectively. Overall, analytical and background uncertainties are small relative to the local CO2 and CH4 enhancements; however, our results suggest that reducing the uncertainty to less than 5 % of the median mid-afternoon enhancement will require detailed assessment of the impact of meteorology on background conditions.

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.

Approximate Bayesian evaluations of measurement uncertainty

NASA Astrophysics Data System (ADS)

Possolo, Antonio; Bodnar, Olha

2018-04-01

The Guide to the Expression of Uncertainty in Measurement (GUM) includes formulas that produce an estimate of a scalar output quantity that is a function of several input quantities, and an approximate evaluation of the associated standard uncertainty. This contribution presents approximate, Bayesian counterparts of those formulas for the case where the output quantity is a parameter of the joint probability distribution of the input quantities, also taking into account any information about the value of the output quantity available prior to measurement expressed in the form of a probability distribution on the set of possible values for the measurand. The approximate Bayesian estimates and uncertainty evaluations that we present have a long history and illustrious pedigree, and provide sufficiently accurate approximations in many applications, yet are very easy to implement in practice. Differently from exact Bayesian estimates, which involve either (analytical or numerical) integrations, or Markov Chain Monte Carlo sampling, the approximations that we describe involve only numerical optimization and simple algebra. Therefore, they make Bayesian methods widely accessible to metrologists. We illustrate the application of the proposed techniques in several instances of measurement: isotopic ratio of silver in a commercial silver nitrate; odds of cryptosporidiosis in AIDS patients; height of a manometer column; mass fraction of chromium in a reference material; and potential-difference in a Zener voltage standard.

Empirically Estimating the Potential for Farm-Level Adaptation to Climate Change in Western European Agriculture

NASA Astrophysics Data System (ADS)

Moore, F. C.; Lobell, D. B.

2013-12-01

Agriculture is one of the economic sectors most exposed to climate change and estimating the sensitivity of food production to these changes is critical for determining the severity of climate change impacts and for informing both adaptation and mitigation policy. While climate change might have adverse effects in many areas, it has long been recognized that farmers have a suite of adaptation options at their disposal including, inter alia, changing planting date, varieties, crops, or the mix and quantity of inputs applied. These adaptations may significantly reduce the adverse impacts of climate change but the potential effectiveness of these options and the speed with which farmers will adopt them remain uncertain. We estimate the sensitivity of crop yields and farm profits in western Europe to climate change with and without the adoption of on-farm adaptations. We use cross-sectional variation across farms to define the long-run response function that includes adaptation and inter-annual variation within farms to define the short-run response function without adaptation. The difference between these can be interpreted as the potential for adaptation. We find that future warming will have a large adverse impact on wheat and barley yields and that adaptation will only be able to mitigate a small fraction of this. Maize, oilseed and sugarbeet yields are more modestly affected and adaptation is more effective for these crops. Farm profits could increase slightly under moderate amounts of warming if adaptations are adopted but will decline in the absence of adaptation. A decomposition of variance gives the relative importance of different sources of uncertainty in projections of climate change impacts. We find that in most cases uncertainty over future adaptation pathways (whether farmers will or will not adopt beneficial adaptations) is the most important source of uncertainty in projecting the effect of temperature changes on crop yields and farm profits. This source of uncertainty dominates both uncertainty over temperature projections (climate uncertainty) and uncertainty over how sensitive crops or profits are to changes in temperature (response uncertainty). Therefore, constraining how quickly farmers are likely to adapt will be essential for improving our understanding of how climate change will affect food production over the next few decades.

Obsessions and worry beliefs in an inpatient OCD population.

PubMed

Calleo, Jessica S; Hart, John; Björgvinsson, Thröstur; Stanley, Melinda A

2010-12-01

Dysfunctional beliefs in obsessive-compulsive disorder (OCD) and worry are thought to contribute to vulnerability and maintenance of pathological anxiety. In this study, five belief domains concerning responsibility/threat estimation, perfectionism, intolerance of uncertainty, importance/control of thoughts and thought-action fusion were examined to see whether they differentially predicted worry and obsession severity in patients with severe OCD. Correlational analysis revealed that perfectionism and intolerance of uncertainty were associated with worry, whereas beliefs in the importance and control of thoughts and thought-action fusion were associated with obsession severity when obsession severity and worry, respectively, were controlled. In regression analyses, thought-action fusion and intolerance of uncertainty predicted OCD severity. The relation between dysfunctional beliefs and specific subtypes of OCD symptoms was also examined. Specific relationships were identified, including perfectionism with ordering, obsessions with control/importance of thoughts and checking and washing with threat estimation. Copyright © 2010 Elsevier Ltd. All rights reserved.

CXTFIT/Excel A modular adaptable code for parameter estimation, sensitivity analysis and uncertainty analysis for laboratory or field tracer experiments

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

Tang, Guoping; Mayes, Melanie; Parker, Jack C

2010-01-01

We implemented the widely used CXTFIT code in Excel to provide flexibility and added sensitivity and uncertainty analysis functions to improve transport parameter estimation and to facilitate model discrimination for multi-tracer experiments on structured soils. Analytical solutions for one-dimensional equilibrium and nonequilibrium convection dispersion equations were coded as VBA functions so that they could be used as ordinary math functions in Excel for forward predictions. Macros with user-friendly interfaces were developed for optimization, sensitivity analysis, uncertainty analysis, error propagation, response surface calculation, and Monte Carlo analysis. As a result, any parameter with transformations (e.g., dimensionless, log-transformed, species-dependent reactions, etc.) couldmore » be estimated with uncertainty and sensitivity quantification for multiple tracer data at multiple locations and times. Prior information and observation errors could be incorporated into the weighted nonlinear least squares method with a penalty function. Users are able to change selected parameter values and view the results via embedded graphics, resulting in a flexible tool applicable to modeling transport processes and to teaching students about parameter estimation. The code was verified by comparing to a number of benchmarks with CXTFIT 2.0. It was applied to improve parameter estimation for four typical tracer experiment data sets in the literature using multi-model evaluation and comparison. Additional examples were included to illustrate the flexibilities and advantages of CXTFIT/Excel. The VBA macros were designed for general purpose and could be used for any parameter estimation/model calibration when the forward solution is implemented in Excel. A step-by-step tutorial, example Excel files and the code are provided as supplemental material.« less

An uncertainty and sensitivity analysis applied to the prioritisation of pharmaceuticals as surface water contaminants from wastewater treatment plant direct emissions.

PubMed

Morais, Sérgio Alberto; Delerue-Matos, Cristina; Gabarrell, Xavier

2014-08-15

In this study, the concentration probability distributions of 82 pharmaceutical compounds detected in the effluents of 179 European wastewater treatment plants were computed and inserted into a multimedia fate model. The comparative ecotoxicological impact of the direct emission of these compounds from wastewater treatment plants on freshwater ecosystems, based on a potentially affected fraction (PAF) of species approach, was assessed to rank compounds based on priority. As many pharmaceuticals are acids or bases, the multimedia fate model accounts for regressions to estimate pH-dependent fate parameters. An uncertainty analysis was performed by means of Monte Carlo analysis, which included the uncertainty of fate and ecotoxicity model input variables, as well as the spatial variability of landscape characteristics on the European continental scale. Several pharmaceutical compounds were identified as being of greatest concern, including 7 analgesics/anti-inflammatories, 3 β-blockers, 3 psychiatric drugs, and 1 each of 6 other therapeutic classes. The fate and impact modelling relied extensively on estimated data, given that most of these compounds have little or no experimental fate or ecotoxicity data available, as well as a limited reported occurrence in effluents. The contribution of estimated model input variables to the variance of freshwater ecotoxicity impact, as well as the lack of experimental abiotic degradation data for most compounds, helped in establishing priorities for further testing. Generally, the effluent concentration and the ecotoxicity effect factor were the model input variables with the most significant effect on the uncertainty of output results. Copyright © 2014. Published by Elsevier B.V.

Parameter and input data uncertainty estimation for the assessment of water resources in two sub-basins of the Limpopo River Basin

NASA Astrophysics Data System (ADS)

Oosthuizen, Nadia; Hughes, Denis A.; Kapangaziwiri, Evison; Mwenge Kahinda, Jean-Marc; Mvandaba, Vuyelwa

2018-05-01

The demand for water resources is rapidly growing, placing more strain on access to water and its management. In order to appropriately manage water resources, there is a need to accurately quantify available water resources. Unfortunately, the data required for such assessment are frequently far from sufficient in terms of availability and quality, especially in southern Africa. In this study, the uncertainty related to the estimation of water resources of two sub-basins of the Limpopo River Basin - the Mogalakwena in South Africa and the Shashe shared between Botswana and Zimbabwe - is assessed. Input data (and model parameters) are significant sources of uncertainty that should be quantified. In southern Africa water use data are among the most unreliable sources of model input data because available databases generally consist of only licensed information and actual use is generally unknown. The study assesses how these uncertainties impact the estimation of surface water resources of the sub-basins. Data on farm reservoirs and irrigated areas from various sources were collected and used to run the model. Many farm dams and large irrigation areas are located in the upper parts of the Mogalakwena sub-basin. Results indicate that water use uncertainty is small. Nevertheless, the medium to low flows are clearly impacted. The simulated mean monthly flows at the outlet of the Mogalakwena sub-basin were between 22.62 and 24.68 Mm3 per month when incorporating only the uncertainty related to the main physical runoff generating parameters. The range of total predictive uncertainty of the model increased to between 22.15 and 24.99 Mm3 when water use data such as small farm and large reservoirs and irrigation were included. For the Shashe sub-basin incorporating only uncertainty related to the main runoff parameters resulted in mean monthly flows between 11.66 and 14.54 Mm3. The range of predictive uncertainty changed to between 11.66 and 17.72 Mm3 after the uncertainty in water use information was added.

Comparison of methods for measuring atmospheric deposition of arsenic, cadmium, nickel and lead.

PubMed

Aas, Wenche; Alleman, Laurent Y; Bieber, Elke; Gladtke, Dieter; Houdret, Jean-Luc; Karlsson, Vuokko; Monies, Christian

2009-06-01

A comprehensive field intercomparison at four different types of European sites (two rural, one urban and one industrial) comparing three different collectors (wet only, bulk and Bergerhoff samplers) was conducted in the framework of the European Committee for Standardization (CEN) to create an European standard for the deposition of the four elements As, Cd, Ni and Pb. The purpose was to determine whether the proposed methods lead to results within the uncertainty required by the EU's daughter directive (70%). The main conclusion is that a different sampling strategy is needed for rural and industrial sites. Thus, the conclusions on uncertainties and sample approach are presented separately for the different approaches. The wet only and bulk collector ("bulk bottle method") are comparable at wet rural sites where the total deposition arises mainly from precipitation, the expanded uncertainty when comparing these two types of sampler are below 45% for As, Cd and Pb, 67% for Ni. At industrial sites and possibly very dry rural and urban sites it is necessary to use Bergerhoff samplers or a "bulk bottle+funnel method". It is not possible to address the total deposition estimation with these methods, but they will give the lowest estimate of the total deposition. The expanded uncertainties when comparing the Bergerhoff and the bulk bottle+funnel methods are below 50% for As and Cd, and 63% for Pb. The uncertainty for Ni was not addressed since the bulk bottle+funnel method did not include a full digestion procedure which is necessary for sites with high loads of undissolved metals. The lowest estimate can however be calculated by comparing parallel Bergerhoff samplers where the expanded uncertainty for Ni was 24%. The reproducibility is comparable to the between sampler/method uncertainties. Sampling and sample preparation were proved to be the main factors in the uncertainty budget of deposition measurements.

FERRET data analysis code

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

Schmittroth, F.

1979-09-01

A documentation of the FERRET data analysis code is given. The code provides a way to combine related measurements and calculations in a consistent evaluation. Basically a very general least-squares code, it is oriented towards problems frequently encountered in nuclear data and reactor physics. A strong emphasis is on the proper treatment of uncertainties and correlations and in providing quantitative uncertainty estimates. Documentation includes a review of the method, structure of the code, input formats, and examples.

Model uncertainty and multimodel inference in reliability estimation within a longitudinal framework.

PubMed

Alonso, Ariel; Laenen, Annouschka

2013-05-01

Laenen, Alonso, and Molenberghs (2007) and Laenen, Alonso, Molenberghs, and Vangeneugden (2009) proposed a method to assess the reliability of rating scales in a longitudinal context. The methodology is based on hierarchical linear models, and reliability coefficients are derived from the corresponding covariance matrices. However, finding a good parsimonious model to describe complex longitudinal data is a challenging task. Frequently, several models fit the data equally well, raising the problem of model selection uncertainty. When model uncertainty is high one may resort to model averaging, where inferences are based not on one but on an entire set of models. We explored the use of different model building strategies, including model averaging, in reliability estimation. We found that the approach introduced by Laenen et al. (2007, 2009) combined with some of these strategies may yield meaningful results in the presence of high model selection uncertainty and when all models are misspecified, in so far as some of them manage to capture the most salient features of the data. Nonetheless, when all models omit prominent regularities in the data, misleading results may be obtained. The main ideas are further illustrated on a case study in which the reliability of the Hamilton Anxiety Rating Scale is estimated. Importantly, the ambit of model selection uncertainty and model averaging transcends the specific setting studied in the paper and may be of interest in other areas of psychometrics. © 2012 The British Psychological Society.

Uncertainty and inference in the world of paleoecological data

NASA Astrophysics Data System (ADS)

McLachlan, J. S.; Dawson, A.; Dietze, M.; Finley, M.; Hooten, M.; Itter, M.; Jackson, S. T.; Marlon, J. R.; Raiho, A.; Tipton, J.; Williams, J.

2017-12-01

Proxy data in paleoecology and paleoclimatology share a common set of biases and uncertainties: spatiotemporal error associated with the taphonomic processes of deposition, preservation, and dating; calibration error between proxy data and the ecosystem states of interest; and error in the interpolation of calibrated estimates across space and time. Researchers often account for this daunting suite of challenges by applying qualitave expert judgment: inferring the past states of ecosystems and assessing the level of uncertainty in those states subjectively. The effectiveness of this approach can be seen by the extent to which future observations confirm previous assertions. Hierarchical Bayesian (HB) statistical approaches allow an alternative approach to accounting for multiple uncertainties in paleo data. HB estimates of ecosystem state formally account for each of the common uncertainties listed above. HB approaches can readily incorporate additional data, and data of different types into estimates of ecosystem state. And HB estimates of ecosystem state, with associated uncertainty, can be used to constrain forecasts of ecosystem dynamics based on mechanistic ecosystem models using data assimilation. Decisions about how to structure an HB model are also subjective, which creates a parallel framework for deciding how to interpret data from the deep past.Our group, the Paleoecological Observatory Network (PalEON), has applied hierarchical Bayesian statistics to formally account for uncertainties in proxy based estimates of past climate, fire, primary productivity, biomass, and vegetation composition. Our estimates often reveal new patterns of past ecosystem change, which is an unambiguously good thing, but we also often estimate a level of uncertainty that is uncomfortably high for many researchers. High levels of uncertainty are due to several features of the HB approach: spatiotemporal smoothing, the formal aggregation of multiple types of uncertainty, and a coarseness in statistical models of taphonomic process. Each of these features provides useful opportunities for statisticians and data-generating researchers to assess what we know about the signal and the noise in paleo data and to improve inference about past changes in ecosystem state.

Procedure for calculating estimated ultimate recoveries of Bakken and Three Forks Formations horizontal wells in the Williston Basin

USGS Publications Warehouse

Cook, Troy A.

2013-01-01

Estimated ultimate recoveries (EURs) are a key component in determining productivity of wells in continuous-type oil and gas reservoirs. EURs form the foundation of a well-performance-based assessment methodology initially developed by the U.S. Geological Survey (USGS; Schmoker, 1999). This methodology was formally reviewed by the American Association of Petroleum Geologists Committee on Resource Evaluation (Curtis and others, 2001). The EUR estimation methodology described in this paper was used in the 2013 USGS assessment of continuous oil resources in the Bakken and Three Forks Formations and incorporates uncertainties that would not normally be included in a basic decline-curve calculation. These uncertainties relate to (1) the mean time before failure of the entire well-production system (excluding economics), (2) the uncertainty of when (and if) a stable hyperbolic-decline profile is revealed in the production data, (3) the particular formation involved, (4) relations between initial production rates and a stable hyperbolic-decline profile, and (5) the final behavior of the decline extrapolation as production becomes more dependent on matrix storage.

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.

The uncertainty of nitrous oxide emissions from grazed grasslands: A New Zealand case study

NASA Astrophysics Data System (ADS)

Kelliher, Francis M.; Henderson, Harold V.; Cox, Neil R.

2017-01-01

Agricultural soils emit nitrous oxide (N2O), a greenhouse gas and the primary source of nitrogen oxides which deplete stratospheric ozone. Agriculture has been estimated to be the largest anthropogenic N2O source. In New Zealand (NZ), pastoral agriculture uses half the land area. To estimate the annual N2O emissions from NZ's agricultural soils, the nitrogen (N) inputs have been determined and multiplied by an emission factor (EF), the mass fraction of N inputs emitted as N2Osbnd N. To estimate the associated uncertainty, we developed an analytical method. For comparison, another estimate was determined by Monte Carlo numerical simulation. For both methods, expert judgement was used to estimate the N input uncertainty. The EF uncertainty was estimated by meta-analysis of the results from 185 NZ field trials. For the analytical method, assuming a normal distribution and independence of the terms used to calculate the emissions (correlation = 0), the estimated 95% confidence limit was ±57%. When there was a normal distribution and an estimated correlation of 0.4 between N input and EF, the latter inferred from experimental data involving six NZ soils, the analytical method estimated a 95% confidence limit of ±61%. The EF data from 185 NZ field trials had a logarithmic normal distribution. For the Monte Carlo method, assuming a logarithmic normal distribution for EF, a normal distribution for the other terms and independence of all terms, the estimated 95% confidence limits were -32% and +88% or ±60% on average. When there were the same distribution assumptions and a correlation of 0.4 between N input and EF, the Monte Carlo method estimated 95% confidence limits were -34% and +94% or ±64% on average. For the analytical and Monte Carlo methods, EF uncertainty accounted for 95% and 83% of the emissions uncertainty when the correlation between N input and EF was 0 and 0.4, respectively. As the first uncertainty analysis of an agricultural soils N2O emissions inventory using "country-specific" field trials to estimate EF uncertainty, this can be a potentially informative case study for the international scientific community.

The role of the uncertainty in assessing future scenarios of water shortage in alluvial aquifers

NASA Astrophysics Data System (ADS)

Romano, Emanuele; Camici, Stefania; Brocca, Luca; Moramarco, Tommaso; Guyennon, Nicolas; Preziosi, Elisabetta

2015-04-01

There are many evidences that the combined effects of variations in precipitation and temperature due to climate change can result in a significant change of the recharge to groundwater at different time scales. A possible reduction of effective infiltration can result in a significant decrease, temporary or permanent, of the availability of the resource and, consequently, the sustainable pumping rate should be reassessed. In addition to this, one should also consider the so called indirect impacts of climate change, resulting from human intervention (e.g. augmentation of abstractions) which are feared to be even more important than the direct ones in the medium term: thus, a possible increase of episodes of shortage (i.e. the inability of the groundwater system to completely supply the water demand) can result both from change in the climate forcing and change in the demand. In order to assess future scenarios of water shortage a modelling chain is often used. It includes: 1) the use of General Circulation Models to estimate changes in temperature and precipitation; 2) downscaling procedures to match modeling scenarios to the observed meteorological time series; 3) soil-atmosphere modelling to estimate the time variation of the recharge to the aquifer; 4) groundwater flow models to simulate the water budget and piezometric head evolution; 5) future scenarios of groundwater quantitative status that include scenarios of demand variation. It is well known that each of these processing steps is affected by an intrinsic uncertainty that propagates through the whole chain leading to a final uncertainty on the piezometric head scenarios. The estimate of such an uncertainty is a key point for a correct management of groundwater resources, in case of water shortage due to prolonged droughts as well as for planning purposes. This study analyzes the uncertainty of the processing chain from GCM scenarios to its impact on an alluvial aquifer in terms of exploitation sustainability. To this goal, three GCMs (ECHAM5, PCM and CCSM3) and two downscaling methods (Linear Rescaling and Quantile Mapping) are used to generate future scenarios of precipitation and temperature; the Thornthwaite-Mather soil water balance model is used to estimate the recharge to the aquifer; the evolution in time of the piezometric heads is estimated through a numerical model developed using the MODFLOW2005 code. Finally, different scenarios of water demand are applied. Final results show that the uncertainty due to the groundwater flow model calibration/validation in steady-state conditions is comparable to that arising from the whole processing chain from the GCM choice to the effective infiltration estimates. Simulations in transient conditions show the high impact of the uncertainty related to the calibration of the storage coefficient, that significantly drives the resilience of the system, thus the ability of the aquifer to sustain the demand during the periods of prolonged drought.

Estimate of methane emissions from oil and gas operations in the Uintah Basin using airborne measurements and Lidar wind data

NASA Astrophysics Data System (ADS)

Karion, A.; Sweeney, C.; Petron, G.; Frost, G. J.; Trainer, M.; Brewer, A.; Hardesty, R.; Conley, S. A.; Wolter, S.; Newberger, T.; Kofler, J.; Tans, P. P.

2012-12-01

During a February 2012 campaign in the Uintah oil and gas basin in northeastern Utah, thirteen research flights were conducted in conjunction with a variety of ground-based measurements. Using aircraft-based high-resolution (0.5 Hz) observations of methane (CH4) and carbon dioxide (CO2), along with High-Resolution Doppler Lidar wind observations from a ground site in the basin, we have calculated the basin-wide CH4 flux on several days. Uncertainty estimates are calculated for each day and are generally large for all but one flight day. On one day, February 3, uncertainty on the estimate from a mass balance approach is better than 30% due to ideal meteorological conditions, including a well-mixed boundary layer and low wind variability both in time and altitude, as determined from the Lidar wind observations. This aircraft-based mass balance approach to flux estimates is a critical and valuable tool for estimating CH4 emissions from oil and gas basins.

Quantifying and reducing statistical uncertainty in sample-based health program costing studies in low- and middle-income countries.

PubMed

Rivera-Rodriguez, Claudia L; Resch, Stephen; Haneuse, Sebastien

2018-01-01

In many low- and middle-income countries, the costs of delivering public health programs such as for HIV/AIDS, nutrition, and immunization are not routinely tracked. A number of recent studies have sought to estimate program costs on the basis of detailed information collected on a subsample of facilities. While unbiased estimates can be obtained via accurate measurement and appropriate analyses, they are subject to statistical uncertainty. Quantification of this uncertainty, for example, via standard errors and/or 95% confidence intervals, provides important contextual information for decision-makers and for the design of future costing studies. While other forms of uncertainty, such as that due to model misspecification, are considered and can be investigated through sensitivity analyses, statistical uncertainty is often not reported in studies estimating the total program costs. This may be due to a lack of awareness/understanding of (1) the technical details regarding uncertainty estimation and (2) the availability of software with which to calculate uncertainty for estimators resulting from complex surveys. We provide an overview of statistical uncertainty in the context of complex costing surveys, emphasizing the various potential specific sources that contribute to overall uncertainty. We describe how analysts can compute measures of uncertainty, either via appropriately derived formulae or through resampling techniques such as the bootstrap. We also provide an overview of calibration as a means of using additional auxiliary information that is readily available for the entire program, such as the total number of doses administered, to decrease uncertainty and thereby improve decision-making and the planning of future studies. A recent study of the national program for routine immunization in Honduras shows that uncertainty can be reduced by using information available prior to the study. This method can not only be used when estimating the total cost of delivering established health programs but also to decrease uncertainty when the interest lies in assessing the incremental effect of an intervention. Measures of statistical uncertainty associated with survey-based estimates of program costs, such as standard errors and 95% confidence intervals, provide important contextual information for health policy decision-making and key inputs for the design of future costing studies. Such measures are often not reported, possibly because of technical challenges associated with their calculation and a lack of awareness of appropriate software. Modern statistical analysis methods for survey data, such as calibration, provide a means to exploit additional information that is readily available but was not used in the design of the study to significantly improve the estimation of total cost through the reduction of statistical uncertainty.

Quantifying and reducing statistical uncertainty in sample-based health program costing studies in low- and middle-income countries

PubMed Central

Resch, Stephen

2018-01-01

Objectives: In many low- and middle-income countries, the costs of delivering public health programs such as for HIV/AIDS, nutrition, and immunization are not routinely tracked. A number of recent studies have sought to estimate program costs on the basis of detailed information collected on a subsample of facilities. While unbiased estimates can be obtained via accurate measurement and appropriate analyses, they are subject to statistical uncertainty. Quantification of this uncertainty, for example, via standard errors and/or 95% confidence intervals, provides important contextual information for decision-makers and for the design of future costing studies. While other forms of uncertainty, such as that due to model misspecification, are considered and can be investigated through sensitivity analyses, statistical uncertainty is often not reported in studies estimating the total program costs. This may be due to a lack of awareness/understanding of (1) the technical details regarding uncertainty estimation and (2) the availability of software with which to calculate uncertainty for estimators resulting from complex surveys. We provide an overview of statistical uncertainty in the context of complex costing surveys, emphasizing the various potential specific sources that contribute to overall uncertainty. Methods: We describe how analysts can compute measures of uncertainty, either via appropriately derived formulae or through resampling techniques such as the bootstrap. We also provide an overview of calibration as a means of using additional auxiliary information that is readily available for the entire program, such as the total number of doses administered, to decrease uncertainty and thereby improve decision-making and the planning of future studies. Results: A recent study of the national program for routine immunization in Honduras shows that uncertainty can be reduced by using information available prior to the study. This method can not only be used when estimating the total cost of delivering established health programs but also to decrease uncertainty when the interest lies in assessing the incremental effect of an intervention. Conclusion: Measures of statistical uncertainty associated with survey-based estimates of program costs, such as standard errors and 95% confidence intervals, provide important contextual information for health policy decision-making and key inputs for the design of future costing studies. Such measures are often not reported, possibly because of technical challenges associated with their calculation and a lack of awareness of appropriate software. Modern statistical analysis methods for survey data, such as calibration, provide a means to exploit additional information that is readily available but was not used in the design of the study to significantly improve the estimation of total cost through the reduction of statistical uncertainty. PMID:29636964

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.

Characterizing uncertainty and variability in physiologically based pharmacokinetic models: state of the science and needs for research and implementation.

PubMed

Barton, Hugh A; Chiu, Weihsueh A; Setzer, R Woodrow; Andersen, Melvin E; Bailer, A John; Bois, Frédéric Y; Dewoskin, Robert S; Hays, Sean; Johanson, Gunnar; Jones, Nancy; Loizou, George; Macphail, Robert C; Portier, Christopher J; Spendiff, Martin; Tan, Yu-Mei

2007-10-01

Physiologically based pharmacokinetic (PBPK) models are used in mode-of-action based risk and safety assessments to estimate internal dosimetry in animals and humans. When used in risk assessment, these models can provide a basis for extrapolating between species, doses, and exposure routes or for justifying nondefault values for uncertainty factors. Characterization of uncertainty and variability is increasingly recognized as important for risk assessment; this represents a continuing challenge for both PBPK modelers and users. Current practices show significant progress in specifying deterministic biological models and nondeterministic (often statistical) models, estimating parameters using diverse data sets from multiple sources, using them to make predictions, and characterizing uncertainty and variability of model parameters and predictions. The International Workshop on Uncertainty and Variability in PBPK Models, held 31 Oct-2 Nov 2006, identified the state-of-the-science, needed changes in practice and implementation, and research priorities. For the short term, these include (1) multidisciplinary teams to integrate deterministic and nondeterministic/statistical models; (2) broader use of sensitivity analyses, including for structural and global (rather than local) parameter changes; and (3) enhanced transparency and reproducibility through improved documentation of model structure(s), parameter values, sensitivity and other analyses, and supporting, discrepant, or excluded data. Longer-term needs include (1) theoretical and practical methodological improvements for nondeterministic/statistical modeling; (2) better methods for evaluating alternative model structures; (3) peer-reviewed databases of parameters and covariates, and their distributions; (4) expanded coverage of PBPK models across chemicals with different properties; and (5) training and reference materials, such as cases studies, bibliographies/glossaries, model repositories, and enhanced software. The multidisciplinary dialogue initiated by this Workshop will foster the collaboration, research, data collection, and training necessary to make characterizing uncertainty and variability a standard practice in PBPK modeling and risk assessment.

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

PubMed Central

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

2014-01-01

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

Investigating the Impact of Uncertainty about Item Parameters on Ability Estimation

ERIC Educational Resources Information Center

Zhang, Jinming; Xie, Minge; Song, Xiaolan; Lu, Ting

2011-01-01

Asymptotic expansions of the maximum likelihood estimator (MLE) and weighted likelihood estimator (WLE) of an examinee's ability are derived while item parameter estimators are treated as covariates measured with error. The asymptotic formulae present the amount of bias of the ability estimators due to the uncertainty of item parameter estimators.…

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.

Challenges and regulatory considerations in the acoustic measurement of high-frequency (>20 MHz) ultrasound.

PubMed

Nagle, Samuel M; Sundar, Guru; Schafer, Mark E; Harris, Gerald R; Vaezy, Shahram; Gessert, James M; Howard, Samuel M; Moore, Mary K; Eaton, Richard M

2013-11-01

This article examines the challenges associated with making acoustic output measurements at high ultrasound frequencies (>20 MHz) in the context of regulatory considerations contained in the US Food and Drug Administration industry guidance document for diagnostic ultrasound devices. Error sources in the acoustic measurement, including hydrophone calibration and spatial averaging, nonlinear distortion, and mechanical alignment, are evaluated, and the limitations of currently available acoustic measurement instruments are discussed. An uncertainty analysis of acoustic intensity and power measurements is presented, and an example uncertainty calculation is done on a hypothetical 30-MHz high-frequency ultrasound system. This analysis concludes that the estimated measurement uncertainty of the acoustic intensity is +73%/-86%, and the uncertainty in the mechanical index is +37%/-43%. These values exceed the respective levels in the Food and Drug Administration guidance document of 30% and 15%, respectively, which are more representative of the measurement uncertainty associated with characterizing lower-frequency ultrasound systems. Recommendations made for minimizing the measurement uncertainty include implementing a mechanical positioning system that has sufficient repeatability and precision, reconstructing the time-pressure waveform via deconvolution using the hydrophone frequency response, and correcting for hydrophone spatial averaging.

Technical Evaluation Report for Symposium AVT-147: Computational Uncertainty in Military Vehicle Design

NASA Technical Reports Server (NTRS)

Radespiel, Rolf; Hemsch, Michael J.

2007-01-01

The complexity of modern military systems, as well as the cost and difficulty associated with experimentally verifying system and subsystem design makes the use of high-fidelity based simulation a future alternative for design and development. The predictive ability of such simulations such as computational fluid dynamics (CFD) and computational structural mechanics (CSM) have matured significantly. However, for numerical simulations to be used with confidence in design and development, quantitative measures of uncertainty must be available. The AVT 147 Symposium has been established to compile state-of-the art methods of assessing computational uncertainty, to identify future research and development needs associated with these methods, and to present examples of how these needs are being addressed and how the methods are being applied. Papers were solicited that address uncertainty estimation associated with high fidelity, physics-based simulations. The solicitation included papers that identify sources of error and uncertainty in numerical simulation from either the industry perspective or from the disciplinary or cross-disciplinary research perspective. Examples of the industry perspective were to include how computational uncertainty methods are used to reduce system risk in various stages of design or development.

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...

Particle Dark Matter constraints: the effect of Galactic uncertainties

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

Benito, Maria; Bernal, Nicolás; Iocco, Fabio

2017-02-01

Collider, space, and Earth based experiments are now able to probe several extensions of the Standard Model of particle physics which provide viable dark matter candidates. Direct and indirect dark matter searches rely on inputs of astrophysical nature, such as the local dark matter density or the shape of the dark matter density profile in the target in object. The determination of these quantities is highly affected by astrophysical uncertainties. The latter, especially those for our own Galaxy, are ill-known, and often not fully accounted for when analyzing the phenomenology of particle physics models. In this paper we present amore » systematic, quantitative estimate of how astrophysical uncertainties on Galactic quantities (such as the local galactocentric distance, circular velocity, or the morphology of the stellar disk and bulge) propagate to the determination of the phenomenology of particle physics models, thus eventually affecting the determination of new physics parameters. We present results in the context of two specific extensions of the Standard Model (the Singlet Scalar and the Inert Doublet) that we adopt as case studies for their simplicity in illustrating the magnitude and impact of such uncertainties on the parameter space of the particle physics model itself. Our findings point toward very relevant effects of current Galactic uncertainties on the determination of particle physics parameters, and urge a systematic estimate of such uncertainties in more complex scenarios, in order to achieve constraints on the determination of new physics that realistically include all known uncertainties.« less

Uncertainty estimation in the determination of metals in superficial water by ICP-OES

NASA Astrophysics Data System (ADS)

Faustino, Mainara G.; Marques, Joyce R.; Monteiro, Lucilena R.; Stellato, Thamiris B.; Soares, Sabrina M. V.; Silva, Tatiane B. S. C.; da Silva, Douglas B.; Pires, Maria Aparecida F.; Cotrim, Marycel E. B.

2016-07-01

From validation studies, it was possible to estimate a measurement uncertainty of several elements such as Al, Ba, Ca, Cu, Cr, Cd, Fe, Mg, Mn, Ni and K in water samples from Guarapiranga Dam. These elements were analyzed by optical emission spectrometry with inductively coupled plasma (ICP-OES). The value of relative estimated uncertainties were between 3% and 15%. The greatest uncertainty contributions were analytical curve, and the recovery method, which were related with elements concentrations and the equipment response. Water samples analyzed were compared with CONAMA Resolution #357/2005.

Adaptation to Climate Change: A Comparative Analysis of Modeling Methods for Heat-Related Mortality.

PubMed

Gosling, Simon N; Hondula, David M; Bunker, Aditi; Ibarreta, Dolores; Liu, Junguo; Zhang, Xinxin; Sauerborn, Rainer

2017-08-16

Multiple methods are employed for modeling adaptation when projecting the impact of climate change on heat-related mortality. The sensitivity of impacts to each is unknown because they have never been systematically compared. In addition, little is known about the relative sensitivity of impacts to "adaptation uncertainty" (i.e., the inclusion/exclusion of adaptation modeling) relative to using multiple climate models and emissions scenarios. This study had three aims: a ) Compare the range in projected impacts that arises from using different adaptation modeling methods; b ) compare the range in impacts that arises from adaptation uncertainty with ranges from using multiple climate models and emissions scenarios; c ) recommend modeling method(s) to use in future impact assessments. We estimated impacts for 2070-2099 for 14 European cities, applying six different methods for modeling adaptation; we also estimated impacts with five climate models run under two emissions scenarios to explore the relative effects of climate modeling and emissions uncertainty. The range of the difference (percent) in impacts between including and excluding adaptation, irrespective of climate modeling and emissions uncertainty, can be as low as 28% with one method and up to 103% with another (mean across 14 cities). In 13 of 14 cities, the ranges in projected impacts due to adaptation uncertainty are larger than those associated with climate modeling and emissions uncertainty. Researchers should carefully consider how to model adaptation because it is a source of uncertainty that can be greater than the uncertainty in emissions and climate modeling. We recommend absolute threshold shifts and reductions in slope. https://doi.org/10.1289/EHP634.

Predictive Uncertainty And Parameter Sensitivity Of A Sediment-Flux Model: Nitrogen Flux and Sediment Oxygen Demand

EPA Science Inventory

Estimating model predictive uncertainty is imperative to informed environmental decision making and management of water resources. This paper applies the Generalized Sensitivity Analysis (GSA) to examine parameter sensitivity and the Generalized Likelihood Uncertainty Estimation...

Rapid Non-Gaussian Uncertainty Quantification of Seismic Velocity Models and Images

NASA Astrophysics Data System (ADS)

Ely, G.; Malcolm, A. E.; Poliannikov, O. V.

2017-12-01

Conventional seismic imaging typically provides a single estimate of the subsurface without any error bounds. Noise in the observed raw traces as well as the uncertainty of the velocity model directly impact the uncertainty of the final seismic image and its resulting interpretation. We present a Bayesian inference framework to quantify uncertainty in both the velocity model and seismic images, given noise statistics of the observed data.To estimate velocity model uncertainty, we combine the field expansion method, a fast frequency domain wave equation solver, with the adaptive Metropolis-Hastings algorithm. The speed of the field expansion method and its reduced parameterization allows us to perform the tens or hundreds of thousands of forward solves needed for non-parametric posterior estimations. We then migrate the observed data with the distribution of velocity models to generate uncertainty estimates of the resulting subsurface image. This procedure allows us to create both qualitative descriptions of seismic image uncertainty and put error bounds on quantities of interest such as the dip angle of a subduction slab or thickness of a stratigraphic layer.

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.

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

NASA Technical Reports Server (NTRS)

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

2016-01-01

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

Monte Carlo uncertainty analysis of dose estimates in radiochromic film dosimetry with single-channel and multichannel algorithms.

PubMed

Vera-Sánchez, Juan Antonio; Ruiz-Morales, Carmen; González-López, Antonio

2018-03-01

To provide a multi-stage model to calculate uncertainty in radiochromic film dosimetry with Monte-Carlo techniques. This new approach is applied to single-channel and multichannel algorithms. Two lots of Gafchromic EBT3 are exposed in two different Varian linacs. They are read with an EPSON V800 flatbed scanner. The Monte-Carlo techniques in uncertainty analysis provide a numerical representation of the probability density functions of the output magnitudes. From this numerical representation, traditional parameters of uncertainty analysis as the standard deviations and bias are calculated. Moreover, these numerical representations are used to investigate the shape of the probability density functions of the output magnitudes. Also, another calibration film is read in four EPSON scanners (two V800 and two 10000XL) and the uncertainty analysis is carried out with the four images. The dose estimates of single-channel and multichannel algorithms show a Gaussian behavior and low bias. The multichannel algorithms lead to less uncertainty in the final dose estimates when the EPSON V800 is employed as reading device. In the case of the EPSON 10000XL, the single-channel algorithms provide less uncertainty in the dose estimates for doses higher than four Gy. A multi-stage model has been presented. With the aid of this model and the use of the Monte-Carlo techniques, the uncertainty of dose estimates for single-channel and multichannel algorithms are estimated. The application of the model together with Monte-Carlo techniques leads to a complete characterization of the uncertainties in radiochromic film dosimetry. Copyright © 2018 Associazione Italiana di Fisica Medica. Published by Elsevier Ltd. All rights reserved.

Estimate of the uncertainty in measurement for the determination of mercury in seafood by TDA AAS.

PubMed

Torres, Daiane Placido; Olivares, Igor R B; Queiroz, Helena Müller

2015-01-01

An approach for the estimate of the uncertainty in measurement considering the individual sources related to the different steps of the method under evaluation as well as the uncertainties estimated from the validation data for the determination of mercury in seafood by using thermal decomposition/amalgamation atomic absorption spectrometry (TDA AAS) is proposed. The considered method has been fully optimized and validated in an official laboratory of the Ministry of Agriculture, Livestock and Food Supply of Brazil, in order to comply with national and international food regulations and quality assurance. The referred method has been accredited under the ISO/IEC 17025 norm since 2010. The approach of the present work in order to reach the aim of estimating of the uncertainty in measurement was based on six sources of uncertainty for mercury determination in seafood by TDA AAS, following the validation process, which were: Linear least square regression, Repeatability, Intermediate precision, Correction factor of the analytical curve, Sample mass, and Standard reference solution. Those that most influenced the uncertainty in measurement were sample weight, repeatability, intermediate precision and calibration curve. The obtained result for the estimate of uncertainty in measurement in the present work reached a value of 13.39%, which complies with the European Regulation EC 836/2011. This figure represents a very realistic estimate of the routine conditions, since it fairly encompasses the dispersion obtained from the value attributed to the sample and the value measured by the laboratory analysts. From this outcome, it is possible to infer that the validation data (based on calibration curve, recovery and precision), together with the variation on sample mass, can offer a proper estimate of uncertainty in measurement.

Estimation of Uncertainties in Stage-Discharge Curve for an Experimental Himalayan Watershed

NASA Astrophysics Data System (ADS)

Kumar, V.; Sen, S.

2016-12-01

Various water resource projects developed on rivers originating from the Himalayan region, the "Water Tower of Asia", plays an important role on downstream development. Flow measurements at the desired river site are very critical for river engineers and hydrologists for water resources planning and management, flood forecasting, reservoir operation and flood inundation studies. However, an accurate discharge assessment of these mountainous rivers is costly, tedious and frequently dangerous to operators during flood events. Currently, in India, discharge estimation is linked to stage-discharge relationship known as rating curve. This relationship would be affected by a high degree of uncertainty. Estimating the uncertainty of rating curve remains a relevant challenge because it is not easy to parameterize. Main source of rating curve uncertainty are errors because of incorrect discharge measurement, variation in hydraulic conditions and depth measurement. In this study our objective is to obtain best parameters of rating curve that fit the limited record of observations and to estimate uncertainties at different depth obtained from rating curve. The rating curve parameters of standard power law are estimated for three different streams of Aglar watershed located in lesser Himalayas by maximum-likelihood estimator. Quantification of uncertainties in the developed rating curves is obtained from the estimate of variances and covariances of the rating curve parameters. Results showed that the uncertainties varied with catchment behavior with error varies between 0.006-1.831 m3/s. Discharge uncertainty in the Aglar watershed streams significantly depend on the extent of extrapolation outside the range of observed water levels. Extrapolation analysis confirmed that more than 15% for maximum discharges and 5% for minimum discharges are not strongly recommended for these mountainous gauging sites.

Hierarchical Bayesian Model Averaging for Non-Uniqueness and Uncertainty Analysis of Artificial Neural Networks

NASA Astrophysics Data System (ADS)

Fijani, E.; Chitsazan, N.; Nadiri, A.; Tsai, F. T.; Asghari Moghaddam, A.

2012-12-01

Artificial Neural Networks (ANNs) have been widely used to estimate concentration of chemicals in groundwater systems. However, estimation uncertainty is rarely discussed in the literature. Uncertainty in ANN output stems from three sources: ANN inputs, ANN parameters (weights and biases), and ANN structures. Uncertainty in ANN inputs may come from input data selection and/or input data error. ANN parameters are naturally uncertain because they are maximum-likelihood estimated. ANN structure is also uncertain because there is no unique ANN model given a specific case. Therefore, multiple plausible AI models are generally resulted for a study. One might ask why good models have to be ignored in favor of the best model in traditional estimation. What is the ANN estimation variance? How do the variances from different ANN models accumulate to the total estimation variance? To answer these questions we propose a Hierarchical Bayesian Model Averaging (HBMA) framework. Instead of choosing one ANN model (the best ANN model) for estimation, HBMA averages outputs of all plausible ANN models. The model weights are based on the evidence of data. Therefore, the HBMA avoids overconfidence on the single best ANN model. In addition, HBMA is able to analyze uncertainty propagation through aggregation of ANN models in a hierarchy framework. This method is applied for estimation of fluoride concentration in the Poldasht plain and the Bazargan plain in Iran. Unusually high fluoride concentration in the Poldasht and Bazargan plains has caused negative effects on the public health. Management of this anomaly requires estimation of fluoride concentration distribution in the area. The results show that the HBMA provides a knowledge-decision-based framework that facilitates analyzing and quantifying ANN estimation uncertainties from different sources. In addition HBMA allows comparative evaluation of the realizations for each source of uncertainty by segregating the uncertainty sources in a hierarchical framework. Fluoride concentration estimation using the HBMA method shows better agreement to the observation data in the test step because they are not based on a single model with a non-dominate weights.

Bayesian forecasting and uncertainty quantifying of stream flows using Metropolis-Hastings Markov Chain Monte Carlo algorithm

NASA Astrophysics Data System (ADS)

Wang, Hongrui; Wang, Cheng; Wang, Ying; Gao, Xiong; Yu, Chen

2017-06-01

This paper presents a Bayesian approach using Metropolis-Hastings Markov Chain Monte Carlo algorithm and applies this method for daily river flow rate forecast and uncertainty quantification for Zhujiachuan River using data collected from Qiaotoubao Gage Station and other 13 gage stations in Zhujiachuan watershed in China. The proposed method is also compared with the conventional maximum likelihood estimation (MLE) for parameter estimation and quantification of associated uncertainties. While the Bayesian method performs similarly in estimating the mean value of daily flow rate, it performs over the conventional MLE method on uncertainty quantification, providing relatively narrower reliable interval than the MLE confidence interval and thus more precise estimation by using the related information from regional gage stations. The Bayesian MCMC method might be more favorable in the uncertainty analysis and risk management.

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.

Numerical Uncertainty Analysis for Computational Fluid Dynamics using Student T Distribution -- Application of CFD Uncertainty Analysis Compared to Exact Analytical Solution

NASA Technical Reports Server (NTRS)

Groves, Curtis E.; Ilie, marcel; Shallhorn, Paul A.

2014-01-01

Computational Fluid Dynamics (CFD) is the standard numerical tool used by Fluid Dynamists to estimate solutions to many problems in academia, government, and industry. CFD is known to have errors and uncertainties and there is no universally adopted method to estimate such quantities. This paper describes an approach to estimate CFD uncertainties strictly numerically using inputs and the Student-T distribution. The approach is compared to an exact analytical solution of fully developed, laminar flow between infinite, stationary plates. It is shown that treating all CFD input parameters as oscillatory uncertainty terms coupled with the Student-T distribution can encompass the exact solution.

Reassessing biases and other uncertainties in sea surface temperature observations measured in situ since 1850: 2. Biases and homogenization

NASA Astrophysics Data System (ADS)

Kennedy, J. J.; Rayner, N. A.; Smith, R. O.; Parker, D. E.; Saunby, M.

2011-07-01

Changes in instrumentation and data availability have caused time-varying biases in estimates of global and regional average sea surface temperature. The size of the biases arising from these changes are estimated and their uncertainties evaluated. The estimated biases and their associated uncertainties are largest during the period immediately following the Second World War, reflecting the rapid and incompletely documented changes in shipping and data availability at the time. Adjustments have been applied to reduce these effects in gridded data sets of sea surface temperature and the results are presented as a set of interchangeable realizations. Uncertainties of estimated trends in global and regional average sea surface temperature due to bias adjustments since the Second World War are found to be larger than uncertainties arising from the choice of analysis technique, indicating that this is an important source of uncertainty in analyses of historical sea surface temperatures. Despite this, trends over the twentieth century remain qualitatively consistent.

Uncertainty in estimates of the number of extraterrestrial civilizations

NASA Technical Reports Server (NTRS)

Sturrock, P. A.

1980-01-01

An estimation of the number N of communicative civilizations is made by means of Drake's formula which involves the combination of several quantities, each of which is to some extent uncertain. It is shown that the uncertainty in any quantity may be represented by a probability distribution function, even if that quantity is itself a probability. The uncertainty of current estimates of N is derived principally from uncertainty in estimates of the lifetime of advanced civilizations. It is argued that this is due primarily to uncertainty concerning the existence of a Galactic Federation which is in turn contingent upon uncertainty about whether the limitations of present-day physics are absolute or (in the event that there exists a yet undiscovered hyperphysics) transient. It is further argued that it is advantageous to consider explicitly these underlying assumptions in order to compare the probable numbers of civilizations operating radio beacons, permitting radio leakage, dispatching probes for radio surveillance for dispatching vehicles for manned surveillance.

Combined Uncertainty and A-Posteriori Error Bound Estimates for CFD Calculations: Theory and Implementation

NASA Technical Reports Server (NTRS)

Barth, Timothy J.

2014-01-01

Simulation codes often utilize finite-dimensional approximation resulting in numerical error. Some examples include, numerical methods utilizing grids and finite-dimensional basis functions, particle methods using a finite number of particles. These same simulation codes also often contain sources of uncertainty, for example, uncertain parameters and fields associated with the imposition of initial and boundary data,uncertain physical model parameters such as chemical reaction rates, mixture model parameters, material property parameters, etc.

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

DOE PAGES

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

2015-09-05

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

'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.

Uncertainty of exploitation estimates made from tag returns

USGS Publications Warehouse

Miranda, L.E.; Brock, R.E.; Dorr, B.S.

2002-01-01

Over 6,000 crappies Pomoxis spp. were tagged in five water bodies to estimate exploitation rates by anglers. Exploitation rates were computed as the percentage of tags returned after adjustment for three sources of uncertainty: postrelease mortality due to the tagging process, tag loss, and the reporting rate of tagged fish. Confidence intervals around exploitation rates were estimated by resampling from the probability distributions of tagging mortality, tag loss, and reporting rate. Estimates of exploitation rates ranged from 17% to 54% among the five study systems. Uncertainty around estimates of tagging mortality, tag loss, and reporting resulted in 90% confidence intervals around the median exploitation rate as narrow as 15 percentage points and as broad as 46 percentage points. The greatest source of estimation error was uncertainty about tag reporting. Because the large investments required by tagging and reward operations produce imprecise estimates of the exploitation rate, it may be worth considering other approaches to estimating it or simply circumventing the exploitation question altogether.

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.

Estimation of the uncertainty of analyte concentration from the measurement uncertainty.

PubMed

Brown, Simon; Cooke, Delwyn G; Blackwell, Leonard F

2015-09-01

Ligand-binding assays, such as immunoassays, are usually analysed using standard curves based on the four-parameter and five-parameter logistic models. An estimate of the uncertainty of an analyte concentration obtained from such curves is needed for confidence intervals or precision profiles. Using a numerical simulation approach, it is shown that the uncertainty of the analyte concentration estimate becomes significant at the extremes of the concentration range and that this is affected significantly by the steepness of the standard curve. We also provide expressions for the coefficient of variation of the analyte concentration estimate from which confidence intervals and the precision profile can be obtained. Using three examples, we show that the expressions perform well.

A revised probabilistic estimate of the maternal methyl mercury intake dose corresponding to a measured cord blood mercury concentration.

PubMed

Stern, Alan H

2005-02-01

In 2001, the U.S. Environmental Protection Agency (EPA) adopted a revised reference dose (RfD) for methyl mercury (MeHg) of 0.1 microg/kg/day. The RfD is based on neurologic developmental effects measured in children associated with exposure in utero to MeHg from the maternal diet. The RfD derivation proceeded from a point of departure based on measured concentration of mercury in fetal cord blood (micrograms per liter). The RfD, however, is a maternal dose (micrograms per kilogram per day). Reconstruction of the maternal dose corresponding to this cord blood concentration, including the variability around this estimate, is a critical step in the RfD derivation. The dose reconstruction employed by the U.S. EPA using the one-compartment pharmacokinetic model contains two areas of significant uncertainty: It does not directly account for the influence of the ratio of cord blood: maternal blood Hg concentration, and it does not resolve uncertainty regarding the most appropriate central tendency estimates for pregnancy and third-trimester-specific model parameters. A probabilistic reassessment of this dose reconstruction was undertaken to address these areas of uncertainty and generally to reconsider the specification of model input parameters. On the basis of a thorough review of the literature and recalculation of the one-compartment model including sensitivity analyses, I estimated that the 95th and 99th percentiles (i.e., the lower 5th and 1st percentiles) of the maternal intake dose corresponding to a fetal cord blood Hg concentration of 58 microg/L are 0.3 and 0.2 microg/kg/day, respectively. For the 99th percentile, this is half the value previously estimated by the U.S. EPA.

Estimating groundwater recharge uncertainty from joint application of an aquifer test and the water-table fluctuation method

NASA Astrophysics Data System (ADS)

Delottier, H.; Pryet, A.; Lemieux, J. M.; Dupuy, A.

2018-05-01

Specific yield and groundwater recharge of unconfined aquifers are both essential parameters for groundwater modeling and sustainable groundwater development, yet the collection of reliable estimates of these parameters remains challenging. Here, a joint approach combining an aquifer test with application of the water-table fluctuation (WTF) method is presented to estimate these parameters and quantify their uncertainty. The approach requires two wells: an observation well instrumented with a pressure probe for long-term monitoring and a pumping well, located in the vicinity, for the aquifer test. The derivative of observed drawdown levels highlights the necessity to represent delayed drainage from the unsaturated zone when interpreting the aquifer test results. Groundwater recharge is estimated with an event-based WTF method in order to minimize the transient effects of flow dynamics in the unsaturated zone. The uncertainty on groundwater recharge is obtained by the propagation of the uncertainties on specific yield (Bayesian inference) and groundwater recession dynamics (regression analysis) through the WTF equation. A major portion of the uncertainty on groundwater recharge originates from the uncertainty on the specific yield. The approach was applied to a site in Bordeaux (France). Groundwater recharge was estimated to be 335 mm with an associated uncertainty of 86.6 mm at 2σ. By the use of cost-effective instrumentation and parsimonious methods of interpretation, the replication of such a joint approach should be encouraged to provide reliable estimates of specific yield and groundwater recharge over a region of interest. This is necessary to reduce the predictive uncertainty of groundwater management models.

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.

Application of a Monte Carlo framework with bootstrapping for quantification of uncertainty in baseline map of carbon emissions from deforestation in Tropical Regions

Treesearch

William Salas; Steve Hagen

2013-01-01

This presentation will provide an overview of an approach for quantifying uncertainty in spatial estimates of carbon emission from land use change. We generate uncertainty bounds around our final emissions estimate using a randomized, Monte Carlo (MC)-style sampling technique. This approach allows us to combine uncertainty from different sources without making...

Probabilistic simulation of uncertainties in composite uniaxial strengths

NASA Technical Reports Server (NTRS)

Chamis, C. C.; Stock, T. A.

1990-01-01

Probabilistic composite micromechanics methods are developed that simulate uncertainties in unidirectional fiber composite strengths. These methods are in the form of computational procedures using composite mechanics with Monte Carlo simulation. The variables for which uncertainties are accounted include constituent strengths and their respective scatter. A graphite/epoxy unidirectional composite (ply) is studied to illustrate the procedure and its effectiveness to formally estimate the probable scatter in the composite uniaxial strengths. The results show that ply longitudinal tensile and compressive, transverse compressive and intralaminar shear strengths are not sensitive to single fiber anomalies (breaks, intergacial disbonds, matrix microcracks); however, the ply transverse tensile strength is.

A new Method for the Estimation of Initial Condition Uncertainty Structures in Mesoscale Models

NASA Astrophysics Data System (ADS)

Keller, J. D.; Bach, L.; Hense, A.

2012-12-01

The estimation of fast growing error modes of a system is a key interest of ensemble data assimilation when assessing uncertainty in initial conditions. Over the last two decades three methods (and variations of these methods) have evolved for global numerical weather prediction models: ensemble Kalman filter, singular vectors and breeding of growing modes (or now ensemble transform). While the former incorporates a priori model error information and observation error estimates to determine ensemble initial conditions, the latter two techniques directly address the error structures associated with Lyapunov vectors. However, in global models these structures are mainly associated with transient global wave patterns. When assessing initial condition uncertainty in mesoscale limited area models, several problems regarding the aforementioned techniques arise: (a) additional sources of uncertainty on the smaller scales contribute to the error and (b) error structures from the global scale may quickly move through the model domain (depending on the size of the domain). To address the latter problem, perturbation structures from global models are often included in the mesoscale predictions as perturbed boundary conditions. However, the initial perturbations (when used) are often generated with a variant of an ensemble Kalman filter which does not necessarily focus on the large scale error patterns. In the framework of the European regional reanalysis project of the Hans-Ertel-Center for Weather Research we use a mesoscale model with an implemented nudging data assimilation scheme which does not support ensemble data assimilation at all. In preparation of an ensemble-based regional reanalysis and for the estimation of three-dimensional atmospheric covariance structures, we implemented a new method for the assessment of fast growing error modes for mesoscale limited area models. The so-called self-breeding is development based on the breeding of growing modes technique. Initial perturbations are integrated forward for a short time period and then rescaled and added to the initial state again. Iterating this rapid breeding cycle provides estimates for the initial uncertainty structure (or local Lyapunov vectors) given a specific norm. To avoid that all ensemble perturbations converge towards the leading local Lyapunov vector we apply an ensemble transform variant to orthogonalize the perturbations in the sub-space spanned by the ensemble. By choosing different kind of norms to measure perturbation growth, this technique allows for estimating uncertainty patterns targeted at specific sources of errors (e.g. convection, turbulence). With case study experiments we show applications of the self-breeding method for different sources of uncertainty and different horizontal scales.

REDD+ emissions estimation and reporting: dealing with uncertainty

NASA Astrophysics Data System (ADS)

Pelletier, Johanne; Martin, Davy; Potvin, Catherine

2013-09-01

The United Nations Framework Convention on Climate Change (UNFCCC) defined the technical and financial modalities of policy approaches and incentives to reduce emissions from deforestation and forest degradation in developing countries (REDD+). Substantial technical challenges hinder precise and accurate estimation of forest-related emissions and removals, as well as the setting and assessment of reference levels. These challenges could limit country participation in REDD+, especially if REDD+ emission reductions were to meet quality standards required to serve as compliance grade offsets for developed countries’ emissions. Using Panama as a case study, we tested the matrix approach proposed by Bucki et al (2012 Environ. Res. Lett. 7 024005) to perform sensitivity and uncertainty analysis distinguishing between ‘modelling sources’ of uncertainty, which refers to model-specific parameters and assumptions, and ‘recurring sources’ of uncertainty, which refers to random and systematic errors in emission factors and activity data. The sensitivity analysis estimated differences in the resulting fluxes ranging from 4.2% to 262.2% of the reference emission level. The classification of fallows and the carbon stock increment or carbon accumulation of intact forest lands were the two key parameters showing the largest sensitivity. The highest error propagated using Monte Carlo simulations was caused by modelling sources of uncertainty, which calls for special attention to ensure consistency in REDD+ reporting which is essential for securing environmental integrity. Due to the role of these modelling sources of uncertainty, the adoption of strict rules for estimation and reporting would favour comparability of emission reductions between countries. We believe that a reduction of the bias in emission factors will arise, among other things, from a globally concerted effort to improve allometric equations for tropical forests. Public access to datasets and methodology used to evaluate reference level and emission reductions would strengthen the credibility of the system by promoting accountability and transparency. To secure conservativeness and deal with uncertainty, we consider the need for further research using real data available to developing countries to test the applicability of conservative discounts including the trend uncertainty and other possible options that would allow real incentives and stimulate improvements over time. Finally, we argue that REDD+ result-based actions assessed on the basis of a dashboard of performance indicators, not only in ‘tonnes CO2 equ. per year’ might provide a more holistic approach, at least until better accuracy and certainty of forest carbon stocks emission and removal estimates to support a REDD+ policy can be reached.

Uncertainty Estimation for the Determination of Ni, Pb and Al in Natural Water Samples by SPE-ICP-OES

NASA Astrophysics Data System (ADS)

Ghorbani, A.; Farahani, M. Mahmoodi; Rabbani, M.; Aflaki, F.; Waqifhosain, Syed

2008-01-01

In this paper we propose uncertainty estimation for the analytical results we obtained from determination of Ni, Pb and Al by solidphase extraction and inductively coupled plasma optical emission spectrometry (SPE-ICP-OES). The procedure is based on the retention of analytes in the form of 8-hydroxyquinoline (8-HQ) complexes on a mini column of XAD-4 resin and subsequent elution with nitric acid. The influence of various analytical parameters including the amount of solid phase, pH, elution factors (concentration and volume of eluting solution), volume of sample solution, and amount of ligand on the extraction efficiency of analytes was investigated. To estimate the uncertainty of analytical result obtained, we propose assessing trueness by employing spiked sample. Two types of bias are calculated in the assessment of trueness: a proportional bias and a constant bias. We applied Nested design for calculating proportional bias and Youden method to calculate the constant bias. The results we obtained for proportional bias are calculated from spiked samples. In this case, the concentration found is plotted against the concentration added and the slop of standard addition curve is an estimate of the method recovery. Estimated method of average recovery in Karaj river water is: (1.004±0.0085) for Ni, (0.999±0.010) for Pb and (0.987±0.008) for Al.

Estimating parameters of hidden Markov models based on marked individuals: use of robust design data

USGS Publications Warehouse

Kendall, William L.; White, Gary C.; Hines, James E.; Langtimm, Catherine A.; Yoshizaki, Jun

2012-01-01

Development and use of multistate mark-recapture models, which provide estimates of parameters of Markov processes in the face of imperfect detection, have become common over the last twenty years. Recently, estimating parameters of hidden Markov models, where the state of an individual can be uncertain even when it is detected, has received attention. Previous work has shown that ignoring state uncertainty biases estimates of survival and state transition probabilities, thereby reducing the power to detect effects. Efforts to adjust for state uncertainty have included special cases and a general framework for a single sample per period of interest. We provide a flexible framework for adjusting for state uncertainty in multistate models, while utilizing multiple sampling occasions per period of interest to increase precision and remove parameter redundancy. These models also produce direct estimates of state structure for each primary period, even for the case where there is just one sampling occasion. We apply our model to expected value data, and to data from a study of Florida manatees, to provide examples of the improvement in precision due to secondary capture occasions. We also provide user-friendly software to implement these models. This general framework could also be used by practitioners to consider constrained models of particular interest, or model the relationship between within-primary period parameters (e.g., state structure) and between-primary period parameters (e.g., state transition probabilities).

Uncertainty Analysis in Large Area Aboveground Biomass Mapping

NASA Astrophysics Data System (ADS)

Baccini, A.; Carvalho, L.; Dubayah, R.; Goetz, S. J.; Friedl, M. A.

2011-12-01

Satellite and aircraft-based remote sensing observations are being more frequently used to generate spatially explicit estimates of aboveground carbon stock of forest ecosystems. Because deforestation and forest degradation account for circa 10% of anthropogenic carbon emissions to the atmosphere, policy mechanisms are increasingly recognized as a low-cost mitigation option to reduce carbon emission. They are, however, contingent upon the capacity to accurately measures carbon stored in the forests. Here we examine the sources of uncertainty and error propagation in generating maps of aboveground biomass. We focus on characterizing uncertainties associated with maps at the pixel and spatially aggregated national scales. We pursue three strategies to describe the error and uncertainty properties of aboveground biomass maps, including: (1) model-based assessment using confidence intervals derived from linear regression methods; (2) data-mining algorithms such as regression trees and ensembles of these; (3) empirical assessments using independently collected data sets.. The latter effort explores error propagation using field data acquired within satellite-based lidar (GLAS) acquisitions versus alternative in situ methods that rely upon field measurements that have not been systematically collected for this purpose (e.g. from forest inventory data sets). A key goal of our effort is to provide multi-level characterizations that provide both pixel and biome-level estimates of uncertainties at different scales.

Estimation of the quantification uncertainty from flow injection and liquid chromatography transient signals in inductively coupled plasma mass spectrometry

NASA Astrophysics Data System (ADS)

Laborda, Francisco; Medrano, Jesús; Castillo, Juan R.

2004-06-01

The quality of the quantitative results obtained from transient signals in high-performance liquid chromatography-inductively coupled plasma mass spectrometry (HPLC-ICPMS) and flow injection-inductively coupled plasma mass spectrometry (FI-ICPMS) was investigated under multielement conditions. Quantification methods were based on multiple-point calibration by simple and weighted linear regression, and double-point calibration (measurement of the baseline and one standard). An uncertainty model, which includes the main sources of uncertainty from FI-ICPMS and HPLC-ICPMS (signal measurement, sample flow rate and injection volume), was developed to estimate peak area uncertainties and statistical weights used in weighted linear regression. The behaviour of the ICPMS instrument was characterized in order to be considered in the model, concluding that the instrument works as a concentration detector when it is used to monitorize transient signals from flow injection or chromatographic separations. Proper quantification by the three calibration methods was achieved when compared to reference materials, although the double-point calibration allowed to obtain results of the same quality as the multiple-point calibration, shortening the calibration time. Relative expanded uncertainties ranged from 10-20% for concentrations around the LOQ to 5% for concentrations higher than 100 times the LOQ.

Funnel plot control limits to identify poorly performing healthcare providers when there is uncertainty in the value of the benchmark.

PubMed

Manktelow, Bradley N; Seaton, Sarah E; Evans, T Alun

2016-12-01

There is an increasing use of statistical methods, such as funnel plots, to identify poorly performing healthcare providers. Funnel plots comprise the construction of control limits around a benchmark and providers with outcomes falling outside the limits are investigated as potential outliers. The benchmark is usually estimated from observed data but uncertainty in this estimate is usually ignored when constructing control limits. In this paper, the use of funnel plots in the presence of uncertainty in the value of the benchmark is reviewed for outcomes from a Binomial distribution. Two methods to derive the control limits are shown: (i) prediction intervals; (ii) tolerance intervals Tolerance intervals formally include the uncertainty in the value of the benchmark while prediction intervals do not. The probability properties of 95% control limits derived using each method were investigated through hypothesised scenarios. Neither prediction intervals nor tolerance intervals produce funnel plot control limits that satisfy the nominal probability characteristics when there is uncertainty in the value of the benchmark. This is not necessarily to say that funnel plots have no role to play in healthcare, but that without the development of intervals satisfying the nominal probability characteristics they must be interpreted with care. © The Author(s) 2014.

Covariance generation and uncertainty propagation for thermal and fast neutron induced fission yields

NASA Astrophysics Data System (ADS)

Terranova, Nicholas; Serot, Olivier; Archier, Pascal; De Saint Jean, Cyrille; Sumini, Marco

2017-09-01

Fission product yields (FY) are fundamental nuclear data for several applications, including decay heat, shielding, dosimetry, burn-up calculations. To be safe and sustainable, modern and future nuclear systems require accurate knowledge on reactor parameters, with reduced margins of uncertainty. Present nuclear data libraries for FY do not provide consistent and complete uncertainty information which are limited, in many cases, to only variances. In the present work we propose a methodology to evaluate covariance matrices for thermal and fast neutron induced fission yields. The semi-empirical models adopted to evaluate the JEFF-3.1.1 FY library have been used in the Generalized Least Square Method available in CONRAD (COde for Nuclear Reaction Analysis and Data assimilation) to generate covariance matrices for several fissioning systems such as the thermal fission of U235, Pu239 and Pu241 and the fast fission of U238, Pu239 and Pu240. The impact of such covariances on nuclear applications has been estimated using deterministic and Monte Carlo uncertainty propagation techniques. We studied the effects on decay heat and reactivity loss uncertainty estimation for simplified test case geometries, such as PWR and SFR pin-cells. The impact on existing nuclear reactors, such as the Jules Horowitz Reactor under construction at CEA-Cadarache, has also been considered.

A Unified Geodetic Vertical Velocity Field (UGVVF), Version 1.0

NASA Astrophysics Data System (ADS)

Schmalzle, G.; Wdowinski, S.

2014-12-01

Tectonic motion, volcanic inflation or deflation, as well as oil, gas and water pumping can induce vertical motion. In southern California these signals are inter-mingled. In tectonics, properly identifying regions that are contaminated by other signals can be important when estimating fault slip rates. Until recently vertical deformation rates determined by high precision Global Positioning Systems (GPS) had large uncertainties compared to horizontal components and were rarely used to constrain tectonic models of fault motion. However, many continuously occupied GPS stations have been operating for ten or more years, often delivering uncertainties of ~1 mm/yr or less, providing better constraints for tectonic modeling. Various processing centers produced GPS time series and estimated vertical velocity fields, each with their own set of processing techniques and assumptions. We compare vertical velocity solutions estimated by seven data processing groups as well as two combined solutions (Figure 1). These groups include: Central Washington University (CWU) and New Mexico Institute of Technology (NMT), and their combined solution provided by the Plate Boundary Observatory (PBO) through the UNAVCO website. Also compared are the Jet Propulsion Laboratory (JPL) and Scripps Orbit and Permanent Array Center (SOPAC) and their combined solution provided as part of the NASA MEaSUREs project. Smaller velocity fields included are from Amos et al., 2014, processed at the Nevada Geodetic Laboratory, Shen et al., 2011, processed by UCLA and called the Crustal Motion Map 4.0 (CMM4) dataset, and a new velocity field provided by the University of Miami (UM). Our analysis includes estimating and correcting for systematic vertical velocity and uncertainty differences between groups. Our final product is a unified velocity field that contains the median values of the adjusted velocity fields and their uncertainties. This product will be periodically updated when new velocity fields become available. A database and scripts to access the database will be available through the University of Miami (http://www.geodesy.miami.edu) website. Figure 1. Vertical velocity comparisons between processing groups (blue dots). Red line indicates equal velocities. Weighted Root Mean Square (WRMS) is shown.

Uncertainties for two-dimensional models of solar rotation from helioseismic eigenfrequency splitting

NASA Technical Reports Server (NTRS)

Genovese, Christopher R.; Stark, Philip B.; Thompson, Michael J.

1995-01-01

Observed solar p-mode frequency splittings can be used to estimate angular velocity as a function of position in the solar interior. Formal uncertainties of such estimates depend on the method of estimation (e.g., least-squares), the distribution of errors in the observations, and the parameterization imposed on the angular velocity. We obtain lower bounds on the uncertainties that do not depend on the method of estimation; the bounds depend on an assumed parameterization, but the fact that they are lower bounds for the 'true' uncertainty does not. Ninety-five percent confidence intervals for estimates of the angular velocity from 1986 Big Bear Solar Observatory (BBSO) data, based on a 3659 element tensor-product cubic-spline parameterization, are everywhere wider than 120 nHz, and exceed 60,000 nHz near the core. When compared with estimates of the solar rotation, these bounds reveal that useful inferences based on pointwise estimates of the angular velocity using 1986 BBSO splitting data are not feasible over most of the Sun's volume. The discouraging size of the uncertainties is due principally to the fact that helioseismic measurements are insensitive to changes in the angular velocity at individual points, so estimates of point values based on splittings are extremely uncertain. Functionals that measure distributed 'smooth' properties are, in general, better constrained than estimates of the rotation at a point. For example, the uncertainties in estimated differences of average rotation between adjacent blocks of about 0.001 solar volumes across the base of the convective zone are much smaller, and one of several estimated differences we compute appears significant at the 95% level.

Activity Recognition for Agent Teams

DTIC Science & Technology

2007-07-01

Uncertainty in Artifcial Intelligence (UAI), 1994. [47] S. Intille and A. Bobick. Visual tracking using closed-worlds. Technical Report 294, MIT Media Lab...information is estimated to average 1 hour per response, including the time for reviewing instructions, searching existing data sources, gathering and...maintaining the data needed, and completing and reviewing the collection of information. Send comments regarding this burden estimate or any other

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.

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.

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

Computation of European carbon balance components through synergistic use of CARBOEUROPE eddy covariance, MODIS remote sensing data and advanced ecosystem and statistical modeling

NASA Astrophysics Data System (ADS)

Reichstein, M.; Dinh, N.; Running, S.; Seufert, G.; Tenhunen, J.; Valentini, R.

2003-04-01

Here we present spatially distributed bottom-up estimates of European carbon balance components for the year 2001, that stem from a newly built modeling system that integrates CARBOEUROPE eddy covariance CO_2 exchange data, remotely sensed vegetation properties via the MODIS-Terra sensor, European-wide soils data, and a suite of carbon balance models of different complexity. These estimates are able to better constrain top-down atmospheric-inversion carbon balance estimates within the dual-constraint approach for estimating continental carbon balances. The models that are used to calculate gross primary production (GPP) include a detailed layered canopy model with Farquhar-type photosynthesis (PROXELNEE), sun-shade big-leaf formulations operating at a daily time-step and a simple radiation-use efficiency model. These models are parameterized from eddy covariance data through inverse estimation techniques. Also for the estimation of soil and ecosystem respiration (Rsoil, Reco) we profit from a large data set of eddy covariance and soil chamber measurements, that enables us to the parameterize and validate a recently developed semi-empirical model, that includes a variable temperature sensitivity of respiration. As the outcome of the modeling system we present the most likely daily to annual numbers of carbon balance components (GPP, Reco, Rsoil), but we also issue a thorough analysis of biases and uncertainties in carbon balance estimates that are introduced through errors in the meteorological and remote sensing input data and through uncertainties in the model parameterization. In particular, we analyze 1) the effect of cloud contamination of the MODIS data, 2) the sensitivity to the land-use classification (Corine versus MODIS), 3) the effect of different soil parameterizations as derived from new continental-scale soil maps, and 4) the necessity to include soil drought effects into models of GPP and respiration. While the models describe the eddy covariance data quite well with r^2 values always greater than 0.7, there are still uncertainties in the European carbon balance estimate that exceed 0.3 PgC/yr. In northern (boreal) regions the carbon balance estimate is very much contingent on a high-quality filling of cloud contaminated remote sensing data, while in the southern (Mediterranean) regions a correct description of the soil water holding capacity is crucial. A major source of uncertainty also still is the estimation of heterotrophic respiration at continental scales. Consequently more spatial surveys on soil carbon stocks, turnover and history are needed. The study demonstrates that both, the inclusion of considerable geo-biological variability into a carbon balance modeling system, a high-quality cloud screening and gap-filling of the MODIS remote sensing data, and a correct description of soil drought effects are mandatory for realistic bottom-up estimates of European carbon balance components.

The use of the multiwavelet transform for the estimation of surface wave group and phase velocities and their associated uncertainties

NASA Astrophysics Data System (ADS)

Poppeliers, C.; Preston, L. A.

2017-12-01

Measurements of seismic surface wave dispersion can be used to infer the structure of the Earth's subsurface. Typically, to identify group- and phase-velocity, a series of narrow-band filters are applied to surface wave seismograms. Frequency dependent arrival times of surface waves can then be identified from the resulting suite of narrow band seismograms. The frequency-dependent velocity estimates are then inverted for subsurface velocity structure. However, this technique has no method to estimate the uncertainty of the measured surface wave velocities, and subsequently there is no estimate of uncertainty on, for example, tomographic results. For the work here, we explore using the multiwavelet transform (MWT) as an alternate method to estimate surface wave speeds. The MWT decomposes a signal similarly to the conventional filter bank technique, but with two primary advantages: 1) the time-frequency localization is optimized in regard to the time-frequency tradeoff, and 2) we can use the MWT to estimate the uncertainty of the resulting surface wave group- and phase-velocities. The uncertainties of the surface wave speed measurements can then be propagated into tomographic inversions to provide uncertainties of resolved Earth structure. As proof-of-concept, we apply our technique to four seismic ambient noise correlograms that were collected from the University of Nevada Reno seismic network near the Nevada National Security Site. We invert the estimated group- and phase-velocities, as well the uncertainties, for 1-D Earth structure for each station pair. These preliminary results generally agree with 1-D velocities that are obtained from inverting dispersion curves estimated from a conventional Gaussian filter bank.

A modified micrometeorological gradient method for estimating O3 dry depositions over a forest canopy

NASA Astrophysics Data System (ADS)

Wu, Z. Y.; Zhang, L.; Wang, X. M.; Munger, J. W.

2015-07-01

Small pollutant concentration gradients between levels above a plant canopy result in large uncertainties in estimated air-surface exchange fluxes when using existing micrometeorological gradient methods, including the aerodynamic gradient method (AGM) and the modified Bowen ratio method (MBR). A modified micrometeorological gradient method (MGM) is proposed in this study for estimating O3 dry deposition fluxes over a forest canopy using concentration gradients between a level above and a level below the canopy top, taking advantage of relatively large gradients between these levels due to significant pollutant uptake in the top layers of the canopy. The new method is compared with the AGM and MBR methods and is also evaluated using eddy-covariance (EC) flux measurements collected at the Harvard Forest Environmental Measurement Site, Massachusetts, during 1993-2000. All three gradient methods (AGM, MBR, and MGM) produced similar diurnal cycles of O3 dry deposition velocity (Vd(O3)) to the EC measurements, with the MGM method being the closest in magnitude to the EC measurements. The multi-year average Vd(O3) differed significantly between these methods, with the AGM, MBR, and MGM method being 2.28, 1.45, and 1.18 times that of the EC, respectively. Sensitivity experiments identified several input parameters for the MGM method as first-order parameters that affect the estimated Vd(O3). A 10% uncertainty in the wind speed attenuation coefficient or canopy displacement height can cause about 10% uncertainty in the estimated Vd(O3). An unrealistic leaf area density vertical profile can cause an uncertainty of a factor of 2.0 in the estimated Vd(O3). Other input parameters or formulas for stability functions only caused an uncertainly of a few percent. The new method provides an alternative approach to monitoring/estimating long-term deposition fluxes of similar pollutants over tall canopies.

Tissue resistivity estimation in the presence of positional and geometrical uncertainties.

PubMed

Baysal, U; Eyüboğlu, B M

2000-08-01

Geometrical uncertainties (organ boundary variation and electrode position uncertainties) are the biggest sources of error in estimating electrical resistivity of tissues from body surface measurements. In this study, in order to decrease estimation errors, the statistically constrained minimum mean squared error estimation algorithm (MiMSEE) is constrained with a priori knowledge of the geometrical uncertainties in addition to the constraints based on geometry, resistivity range, linearization and instrumentation errors. The MiMSEE calculates an optimum inverse matrix, which maps the surface measurements to the unknown resistivity distribution. The required data are obtained from four-electrode impedance measurements, similar to injected-current electrical impedance tomography (EIT). In this study, the surface measurements are simulated by using a numerical thorax model. The data are perturbed with additive instrumentation noise. Simulated surface measurements are then used to estimate the tissue resistivities by using the proposed algorithm. The results are compared with the results of conventional least squares error estimator (LSEE). Depending on the region, the MiMSEE yields an estimation error between 0.42% and 31.3% compared with 7.12% to 2010% for the LSEE. It is shown that the MiMSEE is quite robust even in the case of geometrical uncertainties.

Parameter and model uncertainty in a life-table model for fine particles (PM2.5): a statistical modeling study

PubMed Central

Tainio, Marko; Tuomisto, Jouni T; Hänninen, Otto; Ruuskanen, Juhani; Jantunen, Matti J; Pekkanen, Juha

2007-01-01

Background The estimation of health impacts involves often uncertain input variables and assumptions which have to be incorporated into the model structure. These uncertainties may have significant effects on the results obtained with model, and, thus, on decision making. Fine particles (PM2.5) are believed to cause major health impacts, and, consequently, uncertainties in their health impact assessment have clear relevance to policy-making. We studied the effects of various uncertain input variables by building a life-table model for fine particles. Methods Life-expectancy of the Helsinki metropolitan area population and the change in life-expectancy due to fine particle exposures were predicted using a life-table model. A number of parameter and model uncertainties were estimated. Sensitivity analysis for input variables was performed by calculating rank-order correlations between input and output variables. The studied model uncertainties were (i) plausibility of mortality outcomes and (ii) lag, and parameter uncertainties (iii) exposure-response coefficients for different mortality outcomes, and (iv) exposure estimates for different age groups. The monetary value of the years-of-life-lost and the relative importance of the uncertainties related to monetary valuation were predicted to compare the relative importance of the monetary valuation on the health effect uncertainties. Results The magnitude of the health effects costs depended mostly on discount rate, exposure-response coefficient, and plausibility of the cardiopulmonary mortality. Other mortality outcomes (lung cancer, other non-accidental and infant mortality) and lag had only minor impact on the output. The results highlight the importance of the uncertainties associated with cardiopulmonary mortality in the fine particle impact assessment when compared with other uncertainties. Conclusion When estimating life-expectancy, the estimates used for cardiopulmonary exposure-response coefficient, discount rate, and plausibility require careful assessment, while complicated lag estimates can be omitted without this having any major effect on the results. PMID:17714598

Parameter and model uncertainty in a life-table model for fine particles (PM2.5): a statistical modeling study.

PubMed

Tainio, Marko; Tuomisto, Jouni T; Hänninen, Otto; Ruuskanen, Juhani; Jantunen, Matti J; Pekkanen, Juha

2007-08-23

The estimation of health impacts involves often uncertain input variables and assumptions which have to be incorporated into the model structure. These uncertainties may have significant effects on the results obtained with model, and, thus, on decision making. Fine particles (PM2.5) are believed to cause major health impacts, and, consequently, uncertainties in their health impact assessment have clear relevance to policy-making. We studied the effects of various uncertain input variables by building a life-table model for fine particles. Life-expectancy of the Helsinki metropolitan area population and the change in life-expectancy due to fine particle exposures were predicted using a life-table model. A number of parameter and model uncertainties were estimated. Sensitivity analysis for input variables was performed by calculating rank-order correlations between input and output variables. The studied model uncertainties were (i) plausibility of mortality outcomes and (ii) lag, and parameter uncertainties (iii) exposure-response coefficients for different mortality outcomes, and (iv) exposure estimates for different age groups. The monetary value of the years-of-life-lost and the relative importance of the uncertainties related to monetary valuation were predicted to compare the relative importance of the monetary valuation on the health effect uncertainties. The magnitude of the health effects costs depended mostly on discount rate, exposure-response coefficient, and plausibility of the cardiopulmonary mortality. Other mortality outcomes (lung cancer, other non-accidental and infant mortality) and lag had only minor impact on the output. The results highlight the importance of the uncertainties associated with cardiopulmonary mortality in the fine particle impact assessment when compared with other uncertainties. When estimating life-expectancy, the estimates used for cardiopulmonary exposure-response coefficient, discount rate, and plausibility require careful assessment, while complicated lag estimates can be omitted without this having any major effect on the results.

Trajectory Dispersed Vehicle Process for Space Launch System

NASA Technical Reports Server (NTRS)

Statham, Tamara; Thompson, Seth

2017-01-01

The Space Launch System (SLS) vehicle is part of NASA's deep space exploration plans that includes manned missions to Mars. Manufacturing uncertainties in design parameters are key considerations throughout SLS development as they have significant effects on focus parameters such as lift-off-thrust-to-weight, vehicle payload, maximum dynamic pressure, and compression loads. This presentation discusses how the SLS program captures these uncertainties by utilizing a 3 degree of freedom (DOF) process called Trajectory Dispersed (TD) analysis. This analysis biases nominal trajectories to identify extremes in the design parameters for various potential SLS configurations and missions. This process utilizes a Design of Experiments (DOE) and response surface methodologies (RSM) to statistically sample uncertainties, and develop resulting vehicles using a Maximum Likelihood Estimate (MLE) process for targeting uncertainties bias. These vehicles represent various missions and configurations which are used as key inputs into a variety of analyses in the SLS design process, including 6 DOF dispersions, separation clearances, and engine out failure studies.

Impact of in-Sewer Degradation of Pharmaceutical and Personal Care Products (PPCPs) Population Markers on a Population Model.

PubMed

O'Brien, Jake William; Banks, Andrew Phillip William; Novic, Andrew Joseph; Mueller, Jochen F; Jiang, Guangming; Ort, Christoph; Eaglesham, Geoff; Yuan, Zhiguo; Thai, Phong K

2017-04-04

A key uncertainty of wastewater-based epidemiology is the size of the population which contributed to a given wastewater sample. We previously developed and validated a Bayesian inference model to estimate population size based on 14 population markers which: (1) are easily measured and (2) have mass loads which correlate with population size. However, the potential uncertainty of the model prediction due to in-sewer degradation of these markers was not evaluated. In this study, we addressed this gap by testing their stability under sewer conditions and assessed whether degradation impacts the model estimates. Five markers, which formed the core of our model, were stable in the sewers while the others were not. Our evaluation showed that the presence of unstable population markers in the model did not decrease the precision of the population estimates providing that stable markers such as acesulfame remained in the model. However, to achieve the minimum uncertainty in population estimates, we propose that the core markers to be included in population models for other sites should meet two additional criteria: (3) negligible degradation in wastewater to ensure the stability of chemicals during collection; and (4) < 10% in-sewer degradation could occur during the mean residence time of the sewer network.

Merging gauge and satellite rainfall with specification of associated uncertainty across Australia

NASA Astrophysics Data System (ADS)

Woldemeskel, Fitsum M.; Sivakumar, Bellie; Sharma, Ashish

2013-08-01

Accurate estimation of spatial rainfall is crucial for modelling hydrological systems and planning and management of water resources. While spatial rainfall can be estimated either using rain gauge-based measurements or using satellite-based measurements, such estimates are subject to uncertainties due to various sources of errors in either case, including interpolation and retrieval errors. The purpose of the present study is twofold: (1) to investigate the benefit of merging rain gauge measurements and satellite rainfall data for Australian conditions and (2) to produce a database of retrospective rainfall along with a new uncertainty metric for each grid location at any timestep. The analysis involves four steps: First, a comparison of rain gauge measurements and the Tropical Rainfall Measuring Mission (TRMM) 3B42 data at such rain gauge locations is carried out. Second, gridded monthly rain gauge rainfall is determined using thin plate smoothing splines (TPSS) and modified inverse distance weight (MIDW) method. Third, the gridded rain gauge rainfall is merged with the monthly accumulated TRMM 3B42 using a linearised weighting procedure, the weights at each grid being calculated based on the error variances of each dataset. Finally, cross validation (CV) errors at rain gauge locations and standard errors at gridded locations for each timestep are estimated. The CV error statistics indicate that merging of the two datasets improves the estimation of spatial rainfall, and more so where the rain gauge network is sparse. The provision of spatio-temporal standard errors with the retrospective dataset is particularly useful for subsequent modelling applications where input error knowledge can help reduce the uncertainty associated with modelling outcomes.

Uncertainty in action-value estimation affects both action choice and learning rate of the choice behaviors of rats

PubMed Central

Funamizu, Akihiro; Ito, Makoto; Doya, Kenji; Kanzaki, Ryohei; Takahashi, Hirokazu

2012-01-01

The estimation of reward outcomes for action candidates is essential for decision making. In this study, we examined whether and how the uncertainty in reward outcome estimation affects the action choice and learning rate. We designed a choice task in which rats selected either the left-poking or right-poking hole and received a reward of a food pellet stochastically. The reward probabilities of the left and right holes were chosen from six settings (high, 100% vs. 66%; mid, 66% vs. 33%; low, 33% vs. 0% for the left vs. right holes, and the opposites) in every 20–549 trials. We used Bayesian Q-learning models to estimate the time course of the probability distribution of action values and tested if they better explain the behaviors of rats than standard Q-learning models that estimate only the mean of action values. Model comparison by cross-validation revealed that a Bayesian Q-learning model with an asymmetric update for reward and non-reward outcomes fit the choice time course of the rats best. In the action-choice equation of the Bayesian Q-learning model, the estimated coefficient for the variance of action value was positive, meaning that rats were uncertainty seeking. Further analysis of the Bayesian Q-learning model suggested that the uncertainty facilitated the effective learning rate. These results suggest that the rats consider uncertainty in action-value estimation and that they have an uncertainty-seeking action policy and uncertainty-dependent modulation of the effective learning rate. PMID:22487046

Estimating the Triple-Point Isotope Effect and the Corresponding Uncertainties for Cryogenic Fixed Points

NASA Astrophysics Data System (ADS)

Tew, W. L.

2008-02-01

The sensitivities of melting temperatures to isotopic variations in monatomic and diatomic atmospheric gases using both theoretical and semi-empirical methods are estimated. The current state of knowledge of the vapor-pressure isotope effects (VPIE) and triple-point isotope effects (TPIE) is briefly summarized for the noble gases (except He), and for selected diatomic molecules including oxygen. An approximate expression is derived to estimate the relative shift in the melting temperature with isotopic substitution. In general, the magnitude of the effects diminishes with increasing molecular mass and increasing temperature. Knowledge of the VPIE, molar volumes, and heat of fusion are sufficient to estimate the temperature shift or isotopic sensitivity coefficient via the derived expression. The usefulness of this approach is demonstrated in the estimation of isotopic sensitivities and uncertainties for triple points of xenon and molecular oxygen for which few documented estimates were previously available. The calculated sensitivities from this study are considerably higher than previous estimates for Xe, and lower than other estimates in the case of oxygen. In both these cases, the predicted sensitivities are small and the resulting variations in triple point temperatures due to mass fractionation effects are less than 20 μK.

Uncertainties for seismic moment tensors and applications to nuclear explosions, volcanic events, and earthquakes

NASA Astrophysics Data System (ADS)

Tape, C.; Alvizuri, C. R.; Silwal, V.; Tape, W.

2017-12-01

When considered as a point source, a seismic source can be characterized in terms of its origin time, hypocenter, moment tensor, and source time function. The seismologist's task is to estimate these parameters--and their uncertainties--from three-component ground motion recorded at irregularly spaced stations. We will focus on one portion of this problem: the estimation of the moment tensor and its uncertainties. With magnitude estimated separately, we are left with five parameters describing the normalized moment tensor. A lune of normalized eigenvalue triples can be used to visualize the two parameters (lune longitude and lune latitude) describing the source type, while the conventional strike, dip, and rake angles can be used to characterize the orientation. Slight modifications of these five parameters lead to a uniform parameterization of moment tensors--uniform in the sense that equal volumes in the coordinate domain of the parameterization correspond to equal volumes of moment tensors. For a moment tensor m that we have inferred from seismic data for an earthquake, we define P(V) to be the probability that the true moment tensor for the earthquake lies in the neighborhood of m that has fractional volume V. The average value of P(V) is then a measure of our confidence in our inference of m. The calculation of P(V) requires knowing both the probability P(w) and the fractional volume V(w) of the set of moment tensors within a given angular radius w of m. We apply this approach to several different data sets, including nuclear explosions from the Nevada Test Site, volcanic events from Uturuncu (Bolivia), and earthquakes. Several challenges remain: choosing an appropriate misfit function, handling time shifts between data and synthetic waveforms, and extending the uncertainty estimation to include more source parameters (e.g., hypocenter and source time function).

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

NASA Astrophysics Data System (ADS)

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

2016-12-01

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

Uncertainty 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.

Evaluation of dose uncertainty in radiation processing using EPR spectroscopy and butylated hydroxytoluene rods as dosimetry system

NASA Astrophysics Data System (ADS)

Alkhorayef, M.; Mansour, A.; Sulieman, A.; Alnaaimi, M.; Alduaij, M.; Babikir, E.; Bradley, D. A.

2017-12-01

Butylatedhydroxytoluene (BHT) rods represent a potential dosimeter in radiation processing, with readout via electron paramagnetic resonance (EPR) spectroscopy. Among the possible sources of uncertainty are those associated with the performance of the dosimetric medium and the conditions under which measurements are made, including sampling and environmental conditions. Present study makes estimate of the uncertainties, investigating physical response in different resonance regions. BHT, a white crystalline solid with a melting point of between 70-73 °C, was investigated using 60Co gamma irradiation over the dose range 0.1-100 kGy. The intensity of the EPR signal increases linearly in the range 0.1-35 kGy, the uncertainty budget for high doses being 3.3% at the 2σ confidence level. The rod form represents an excellent alternative dosimeter for high level dosimetry, of small uncertainty compared to powder form.

Measurement uncertainty and feasibility study of a flush airdata system for a hypersonic flight experiment

NASA Technical Reports Server (NTRS)

Whitmore, Stephen A.; Moes, Timothy R.

1994-01-01

Presented is a feasibility and error analysis for a hypersonic flush airdata system on a hypersonic flight experiment (HYFLITE). HYFLITE heating loads make intrusive airdata measurement impractical. Although this analysis is specifically for the HYFLITE vehicle and trajectory, the problems analyzed are generally applicable to hypersonic vehicles. A layout of the flush-port matrix is shown. Surface pressures are related airdata parameters using a simple aerodynamic model. The model is linearized using small perturbations and inverted using nonlinear least-squares. Effects of various error sources on the overall uncertainty are evaluated using an error simulation. Error sources modeled include boundarylayer/viscous interactions, pneumatic lag, thermal transpiration in the sensor pressure tubing, misalignment in the matrix layout, thermal warping of the vehicle nose, sampling resolution, and transducer error. Using simulated pressure data for input to the estimation algorithm, effects caused by various error sources are analyzed by comparing estimator outputs with the original trajectory. To obtain ensemble averages the simulation is run repeatedly and output statistics are compiled. Output errors resulting from the various error sources are presented as a function of Mach number. Final uncertainties with all modeled error sources included are presented as a function of Mach number.

Phylogenetic analysis of molecular and morphological data highlights uncertainty in the relationships of fossil and living species of Elopomorpha (Actinopterygii: Teleostei).

PubMed

Dornburg, Alex; Friedman, Matt; Near, Thomas J

2015-08-01

Elopomorpha is one of the three main clades of living teleost fishes and includes a range of disparate lineages including eels, tarpons, bonefishes, and halosaurs. Elopomorphs were among the first groups of fishes investigated using Hennigian phylogenetic methods and continue to be the object of intense phylogenetic scrutiny due to their economic significance, diversity, and crucial evolutionary status as the sister group of all other teleosts. While portions of the phylogenetic backbone for Elopomorpha are consistent between studies, the relationships among Albula, Pterothrissus, Notacanthiformes, and Anguilliformes remain contentious and difficult to evaluate. This lack of phylogenetic resolution is problematic as fossil lineages are often described and placed taxonomically based on an assumed sister group relationship between Albula and Pterothrissus. In addition, phylogenetic studies using morphological data that sample elopomorph fossil lineages often do not include notacanthiform or anguilliform lineages, potentially introducing a bias toward interpreting fossils as members of the common stem of Pterothrissus and Albula. Here we provide a phylogenetic analysis of DNA sequences sampled from multiple nuclear genes that include representative taxa from Albula, Pterothrissus, Notacanthiformes and Anguilliformes. We integrate our molecular dataset with a morphological character matrix that spans both living and fossil elopomorph lineages. Our results reveal substantial uncertainty in the placement of Pterothrissus as well as all sampled fossil lineages, questioning the stability of the taxonomy of fossil Elopomorpha. However, despite topological uncertainty, our integration of fossil lineages into a Bayesian time calibrated framework provides divergence time estimates for the clade that are consistent with previously published age estimates based on the elopomorph fossil record and molecular estimates resulting from traditional node-dating methods. Copyright © 2015 Elsevier Inc. All rights reserved.

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

NASA Astrophysics Data System (ADS)

Mahdyiar, M.; Guin, J.

2005-12-01

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

Data Anonymization that Leads to the Most Accurate Estimates of Statistical Characteristics: Fuzzy-Motivated Approach

PubMed Central

Xiang, G.; Ferson, S.; Ginzburg, L.; Longpré, L.; Mayorga, E.; Kosheleva, O.

2013-01-01

To preserve privacy, the original data points (with exact values) are replaced by boxes containing each (inaccessible) data point. This privacy-motivated uncertainty leads to uncertainty in the statistical characteristics computed based on this data. In a previous paper, we described how to minimize this uncertainty under the assumption that we use the same standard statistical estimates for the desired characteristics. In this paper, we show that we can further decrease the resulting uncertainty if we allow fuzzy-motivated weighted estimates, and we explain how to optimally select the corresponding weights. PMID:25187183

Bayesian forecasting and uncertainty quantifying of stream flows using Metropolis–Hastings Markov Chain Monte Carlo algorithm

DOE PAGES

Wang, Hongrui; Wang, Cheng; Wang, Ying; ...

2017-04-05

This paper presents a Bayesian approach using Metropolis-Hastings Markov Chain Monte Carlo algorithm and applies this method for daily river flow rate forecast and uncertainty quantification for Zhujiachuan River using data collected from Qiaotoubao Gage Station and other 13 gage stations in Zhujiachuan watershed in China. The proposed method is also compared with the conventional maximum likelihood estimation (MLE) for parameter estimation and quantification of associated uncertainties. While the Bayesian method performs similarly in estimating the mean value of daily flow rate, it performs over the conventional MLE method on uncertainty quantification, providing relatively narrower reliable interval than the MLEmore » confidence interval and thus more precise estimation by using the related information from regional gage stations. As a result, the Bayesian MCMC method might be more favorable in the uncertainty analysis and risk management.« less

Global Nitrous Oxide Emissions from Agricultural Soils: Magnitude and Uncertainties Associated with Input Data and Model Parameters

NASA Astrophysics Data System (ADS)

Xu, R.; Tian, H.; Pan, S.; Yang, J.; Lu, C.; Zhang, B.

2016-12-01

Human activities have caused significant perturbations of the nitrogen (N) cycle, resulting in about 21% increase of atmospheric N2O concentration since the pre-industrial era. This large increase is mainly caused by intensive agricultural activities including the application of nitrogen fertilizer and the expansion of leguminous crops. Substantial efforts have been made to quantify the global and regional N2O emission from agricultural soils in the last several decades using a wide variety of approaches, such as ground-based observation, atmospheric inversion, and process-based model. However, large uncertainties exist in those estimates as well as methods themselves. In this study, we used a coupled biogeochemical model (DLEM) to estimate magnitude, spatial, and temporal patterns of N2O emissions from global croplands in the past five decades (1961-2012). To estimate uncertainties associated with input data and model parameters, we have implemented a number of simulation experiments with DLEM, accounting for key parameter values that affect calculation of N2O fluxes (i.e., maximum nitrification and denitrification rates, N fixation rate, and the adsorption coefficient for soil ammonium and nitrate), different sets of input data including climate, land management practices (i.e., nitrogen fertilizer types, application rates and timings, with/without irrigation), N deposition, and land use and land cover change. This work provides a robust estimate of global N2O emissions from agricultural soils as well as identifies key gaps and limitations in the existing model and data that need to be investigated in the future.

Uncertainties in Parameters Estimated with Neural Networks: Application to Strong Gravitational Lensing

DOE PAGES

Perreault Levasseur, Laurence; Hezaveh, Yashar D.; Wechsler, Risa H.

2017-11-15

In Hezaveh et al. (2017) we showed that deep learning can be used for model parameter estimation and trained convolutional neural networks to determine the parameters of strong gravitational lensing systems. Here we demonstrate a method for obtaining the uncertainties of these parameters. We review the framework of variational inference to obtain approximate posteriors of Bayesian neural networks and apply it to a network trained to estimate the parameters of the Singular Isothermal Ellipsoid plus external shear and total flux magnification. We show that the method can capture the uncertainties due to different levels of noise in the input data,more » as well as training and architecture-related errors made by the network. To evaluate the accuracy of the resulting uncertainties, we calculate the coverage probabilities of marginalized distributions for each lensing parameter. By tuning a single hyperparameter, the dropout rate, we obtain coverage probabilities approximately equal to the confidence levels for which they were calculated, resulting in accurate and precise uncertainty estimates. Our results suggest that neural networks can be a fast alternative to Monte Carlo Markov Chains for parameter uncertainty estimation in many practical applications, allowing more than seven orders of magnitude improvement in speed.« less

Uncertainties in Parameters Estimated with Neural Networks: Application to Strong Gravitational Lensing

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

Perreault Levasseur, Laurence; Hezaveh, Yashar D.; Wechsler, Risa H.

In Hezaveh et al. (2017) we showed that deep learning can be used for model parameter estimation and trained convolutional neural networks to determine the parameters of strong gravitational lensing systems. Here we demonstrate a method for obtaining the uncertainties of these parameters. We review the framework of variational inference to obtain approximate posteriors of Bayesian neural networks and apply it to a network trained to estimate the parameters of the Singular Isothermal Ellipsoid plus external shear and total flux magnification. We show that the method can capture the uncertainties due to different levels of noise in the input data,more » as well as training and architecture-related errors made by the network. To evaluate the accuracy of the resulting uncertainties, we calculate the coverage probabilities of marginalized distributions for each lensing parameter. By tuning a single hyperparameter, the dropout rate, we obtain coverage probabilities approximately equal to the confidence levels for which they were calculated, resulting in accurate and precise uncertainty estimates. Our results suggest that neural networks can be a fast alternative to Monte Carlo Markov Chains for parameter uncertainty estimation in many practical applications, allowing more than seven orders of magnitude improvement in speed.« less

Comparison of Two Methods for Estimating the Sampling-Related Uncertainty of Satellite Rainfall Averages Based on a Large Radar Data Set

NASA Technical Reports Server (NTRS)

Lau, William K. M. (Technical Monitor); Bell, Thomas L.; Steiner, Matthias; Zhang, Yu; Wood, Eric F.

2002-01-01

The uncertainty of rainfall estimated from averages of discrete samples collected by a satellite is assessed using a multi-year radar data set covering a large portion of the United States. The sampling-related uncertainty of rainfall estimates is evaluated for all combinations of 100 km, 200 km, and 500 km space domains, 1 day, 5 day, and 30 day rainfall accumulations, and regular sampling time intervals of 1 h, 3 h, 6 h, 8 h, and 12 h. These extensive analyses are combined to characterize the sampling uncertainty as a function of space and time domain, sampling frequency, and rainfall characteristics by means of a simple scaling law. Moreover, it is shown that both parametric and non-parametric statistical techniques of estimating the sampling uncertainty produce comparable results. Sampling uncertainty estimates, however, do depend on the choice of technique for obtaining them. They can also vary considerably from case to case, reflecting the great variability of natural rainfall, and should therefore be expressed in probabilistic terms. Rainfall calibration errors are shown to affect comparison of results obtained by studies based on data from different climate regions and/or observation platforms.

Section summary: Uncertainty and design considerations

Treesearch

Stephen Hagen

2013-01-01

Well planned sampling designs and robust approaches to estimating uncertainty are critical components of forest monitoring. The importance of uncertainty estimation increases as deforestation and degradation issues become more closely tied to financing incentives for reducing greenhouse gas emissions in the forest sector. Investors like to know risk and risk is tightly...

AN OVERVIEW OF THE UNCERTAINTY ANALYSIS, SENSITIVITY ANALYSIS, AND PARAMETER ESTIMATION (UA/SA/PE) API AND HOW TO IMPLEMENT IT

EPA Science Inventory

The Application Programming Interface (API) for Uncertainty Analysis, Sensitivity Analysis, and
Parameter Estimation (UA/SA/PE API) (also known as Calibration, Optimization and Sensitivity and Uncertainty (CUSO)) was developed in a joint effort between several members of both ...

Uncertainties in estimating heart doses from 2D-tangential breast cancer radiotherapy.

PubMed

Lorenzen, Ebbe L; Brink, Carsten; Taylor, Carolyn W; Darby, Sarah C; Ewertz, Marianne

2016-04-01

We evaluated the accuracy of three methods of estimating radiation dose to the heart from two-dimensional tangential radiotherapy for breast cancer, as used in Denmark during 1982-2002. Three tangential radiotherapy regimens were reconstructed using CT-based planning scans for 40 patients with left-sided and 10 with right-sided breast cancer. Setup errors and organ motion were simulated using estimated uncertainties. For left-sided patients, mean heart dose was related to maximum heart distance in the medial field. For left-sided breast cancer, mean heart dose estimated from individual CT-scans varied from <1Gy to >8Gy, and maximum dose from 5 to 50Gy for all three regimens, so that estimates based only on regimen had substantial uncertainty. When maximum heart distance was taken into account, the uncertainty was reduced and was comparable to the uncertainty of estimates based on individual CT-scans. For right-sided breast cancer patients, mean heart dose based on individual CT-scans was always <1Gy and maximum dose always <5Gy for all three regimens. The use of stored individual simulator films provides a method for estimating heart doses in left-tangential radiotherapy for breast cancer that is almost as accurate as estimates based on individual CT-scans. Copyright © 2016. Published by Elsevier Ireland Ltd.

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

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

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

2017-08-01

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

Quantifying the Terrestrial Surface Energy Fluxes Using Remotely-Sensed Satellite Data

NASA Astrophysics Data System (ADS)

Siemann, Amanda Lynn

The dynamics of the energy fluxes between the land surface and the atmosphere drive local and regional climate and are paramount to understand the past, present, and future changes in climate. Although global reanalysis datasets, land surface models (LSMs), and climate models estimate these fluxes by simulating the physical processes involved, they merely simulate our current understanding of these processes. Global estimates of the terrestrial, surface energy fluxes based on observations allow us to capture the dynamics of the full climate system. Remotely-sensed satellite data is the source of observations of the land surface which provide the widest spatial coverage. Although net radiation and latent heat flux global, terrestrial, surface estimates based on remotely-sensed satellite data have progressed, comparable sensible heat data products and ground heat flux products have not progressed at this scale. Our primary objective is quantifying and understanding the terrestrial energy fluxes at the Earth's surface using remotely-sensed satellite data with consistent development among all energy budget components [through the land surface temperature (LST) and input meteorology], including validation of these products against in-situ data, uncertainty assessments, and long-term trend analysis. The turbulent fluxes are constrained by the available energy using the Bowen ratio of the un-constrained products to ensure energy budget closure. All final products are within uncertainty ranges of literature values, globally. When validated against the in-situ estimates, the sensible heat flux estimates using the CFSR air temperature and constrained with the products using the MODIS albedo produce estimates closest to the FLUXNET in-situ observations. Poor performance over South America is consistent with the largest uncertainties in the energy budget. From 1984-2007, the longwave upward flux increase due to the LST increase drives the net radiation decrease, and the decrease in the available energy balances the decrease in the sensible heat flux. These datasets are useful for benchmarking climate models and LSM output at the global annual scale and the regional scale subject to the regional uncertainties and performance. Future work should improve the input data, particularly the temperature gradient and Zilitinkevich empirical constant, to reduce uncertainties.

The Harm that Underestimation of Uncertainty Does to Our Community: A Case Study Using Sunspot Area Measurements

NASA Astrophysics Data System (ADS)

Munoz-Jaramillo, Andres

2017-08-01

Data products in heliospheric physics are very often provided without clear estimates of uncertainty. From helioseismology in the solar interior, all the way to in situ solar wind measurements beyond 1AU, uncertainty estimates are typically hard for users to find (buried inside long documents that are separate from the data products), or simply non-existent.There are two main reasons why uncertainty measurements are hard to find:1. Understanding instrumental systematic errors is given a much higher priority inside instrumental teams.2. The desire to perfectly understand all sources of uncertainty postpones indefinitely the actual quantification of uncertainty in our measurements.Using the cross calibration of 200 years of sunspot area measurements as a case study, in this presentation we will discuss the negative impact that inadequate measurements of uncertainty have on users, through the appearance of toxic and unnecessary controversies, and data providers, through the creation of unrealistic expectations regarding the information that can be extracted from their data. We will discuss how empirical estimates of uncertainty represent a very good alternative to not providing any estimates at all, and finalize by discussing the bare essentials that should become our standard practice for future instruments and surveys.

Simulating estimation of California fossil fuel and biosphere carbon dioxide exchanges combining in situ tower and satellite column observations

DOE PAGES

Fischer, Marc L.; Parazoo, Nicholas; Brophy, Kieran; ...

2017-03-09

Here, we report simulation experiments estimating the uncertainties in California regional fossil fuel and biosphere CO 2 exchanges that might be obtained by using an atmospheric inverse modeling system driven by the combination of ground-based observations of radiocarbon and total CO 2, together with column-mean CO 2 observations from NASA's Orbiting Carbon Observatory (OCO-2). The work includes an initial examination of statistical uncertainties in prior models for CO 2 exchange, in radiocarbon-based fossil fuel CO 2 measurements, in OCO-2 measurements, and in a regional atmospheric transport modeling system. Using these nominal assumptions for measurement and model uncertainties, we find thatmore » flask measurements of radiocarbon and total CO 2 at 10 towers can be used to distinguish between different fossil fuel emission data products for major urban regions of California. We then show that the combination of flask and OCO-2 observations yields posterior uncertainties in monthly-mean fossil fuel emissions of ~5–10%, levels likely useful for policy relevant evaluation of bottom-up fossil fuel emission estimates. Similarly, we find that inversions yield uncertainties in monthly biosphere CO 2 exchange of ~6%–12%, depending on season, providing useful information on net carbon uptake in California's forests and agricultural lands. Finally, initial sensitivity analysis suggests that obtaining the above results requires control of systematic biases below approximately 0.5 ppm, placing requirements on accuracy of the atmospheric measurements, background subtraction, and atmospheric transport modeling.« less

Estimating Prediction Uncertainty from Geographical Information System Raster Processing: A User's Manual for the Raster Error Propagation Tool (REPTool)

USGS Publications Warehouse

Gurdak, Jason J.; Qi, Sharon L.; Geisler, Michael L.

2009-01-01

The U.S. Geological Survey Raster Error Propagation Tool (REPTool) is a custom tool for use with the Environmental System Research Institute (ESRI) ArcGIS Desktop application to estimate error propagation and prediction uncertainty in raster processing operations and geospatial modeling. REPTool is designed to introduce concepts of error and uncertainty in geospatial data and modeling and provide users of ArcGIS Desktop a geoprocessing tool and methodology to consider how error affects geospatial model output. Similar to other geoprocessing tools available in ArcGIS Desktop, REPTool can be run from a dialog window, from the ArcMap command line, or from a Python script. REPTool consists of public-domain, Python-based packages that implement Latin Hypercube Sampling within a probabilistic framework to track error propagation in geospatial models and quantitatively estimate the uncertainty of the model output. Users may specify error for each input raster or model coefficient represented in the geospatial model. The error for the input rasters may be specified as either spatially invariant or spatially variable across the spatial domain. Users may specify model output as a distribution of uncertainty for each raster cell. REPTool uses the Relative Variance Contribution method to quantify the relative error contribution from the two primary components in the geospatial model - errors in the model input data and coefficients of the model variables. REPTool is appropriate for many types of geospatial processing operations, modeling applications, and related research questions, including applications that consider spatially invariant or spatially variable error in geospatial data.

Application of Bayesian geostatistics for evaluation of mass discharge uncertainty at contaminated sites

NASA Astrophysics Data System (ADS)

Troldborg, Mads; Nowak, Wolfgang; Lange, Ida V.; Santos, Marta C.; Binning, Philip J.; Bjerg, Poul L.

2012-09-01

Mass discharge estimates are increasingly being used when assessing risks of groundwater contamination and designing remedial systems at contaminated sites. Such estimates are, however, rather uncertain as they integrate uncertain spatial distributions of both concentration and groundwater flow. Here a geostatistical simulation method for quantifying the uncertainty of the mass discharge across a multilevel control plane is presented. The method accounts for (1) heterogeneity of both the flow field and the concentration distribution through Bayesian geostatistics, (2) measurement uncertainty, and (3) uncertain source zone and transport parameters. The method generates conditional realizations of the spatial flow and concentration distribution. An analytical macrodispersive transport solution is employed to simulate the mean concentration distribution, and a geostatistical model of the Box-Cox transformed concentration data is used to simulate observed deviations from this mean solution. By combining the flow and concentration realizations, a mass discharge probability distribution is obtained. The method has the advantage of avoiding the heavy computational burden of three-dimensional numerical flow and transport simulation coupled with geostatistical inversion. It may therefore be of practical relevance to practitioners compared to existing methods that are either too simple or computationally demanding. The method is demonstrated on a field site contaminated with chlorinated ethenes. For this site, we show that including a physically meaningful concentration trend and the cosimulation of hydraulic conductivity and hydraulic gradient across the transect helps constrain the mass discharge uncertainty. The number of sampling points required for accurate mass discharge estimation and the relative influence of different data types on mass discharge uncertainty is discussed.

Reducing uncertainty in estimating virus reduction by advanced water treatment processes.

PubMed

Gerba, Charles P; Betancourt, Walter Q; Kitajima, Masaaki; Rock, Channah M

2018-04-15

Treatment of wastewater for potable reuse requires the reduction of enteric viruses to levels that pose no significant risk to human health. Advanced water treatment trains (e.g., chemical clarification, reverse osmosis, ultrafiltration, advanced oxidation) have been developed to provide reductions of viruses to differing levels of regulatory control depending upon the levels of human exposure and associated health risks. Importance in any assessment is information on the concentration and types of viruses in the untreated wastewater, as well as the degree of removal by each treatment process. However, it is critical that the uncertainty associated with virus concentration and removal or inactivation by wastewater treatment be understood to improve these estimates and identifying research needs. We reviewed the critically literature to assess to identify uncertainty in these estimates. Biological diversity within families and genera of viruses (e.g. enteroviruses, rotaviruses, adenoviruses, reoviruses, noroviruses) and specific virus types (e.g. serotypes or genotypes) creates the greatest uncertainty. These aspects affect the methods for detection and quantification of viruses and anticipated removal efficiency by treatment processes. Approaches to reduce uncertainty may include; 1) inclusion of a virus indicator for assessing efficiency of virus concentration and detection by molecular methods for each sample, 2) use of viruses most resistant to individual treatment processes (e.g. adenoviruses for UV light disinfection and reoviruses for chlorination), 3) data on ratio of virion or genome copies to infectivity in untreated wastewater, and 4) assessment of virus removal at field scale treatment systems to verify laboratory and pilot plant data for virus removal. Copyright © 2018 Elsevier Ltd. All rights reserved.

Simulating estimation of California fossil fuel and biosphere carbon dioxide exchanges combining in situ tower and satellite column observations

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

Fischer, Marc L.; Parazoo, Nicholas; Brophy, Kieran

Here, we report simulation experiments estimating the uncertainties in California regional fossil fuel and biosphere CO 2 exchanges that might be obtained by using an atmospheric inverse modeling system driven by the combination of ground-based observations of radiocarbon and total CO 2, together with column-mean CO 2 observations from NASA's Orbiting Carbon Observatory (OCO-2). The work includes an initial examination of statistical uncertainties in prior models for CO 2 exchange, in radiocarbon-based fossil fuel CO 2 measurements, in OCO-2 measurements, and in a regional atmospheric transport modeling system. Using these nominal assumptions for measurement and model uncertainties, we find thatmore » flask measurements of radiocarbon and total CO 2 at 10 towers can be used to distinguish between different fossil fuel emission data products for major urban regions of California. We then show that the combination of flask and OCO-2 observations yields posterior uncertainties in monthly-mean fossil fuel emissions of ~5–10%, levels likely useful for policy relevant evaluation of bottom-up fossil fuel emission estimates. Similarly, we find that inversions yield uncertainties in monthly biosphere CO 2 exchange of ~6%–12%, depending on season, providing useful information on net carbon uptake in California's forests and agricultural lands. Finally, initial sensitivity analysis suggests that obtaining the above results requires control of systematic biases below approximately 0.5 ppm, placing requirements on accuracy of the atmospheric measurements, background subtraction, and atmospheric transport modeling.« less

Defining Tsunami Magnitude as Measure of Potential Impact

NASA Astrophysics Data System (ADS)

Titov, V. V.; Tang, L.

2016-12-01

The goal of tsunami forecast, as a system for predicting potential impact of a tsunami at coastlines, requires quick estimate of a tsunami magnitude. This goal has been recognized since the beginning of tsunami research. The work of Kajiura, Soloviev, Abe, Murty, and many others discussed several scales for tsunami magnitude based on estimates of tsunami energy. However, difficulties of estimating tsunami energy based on available tsunami measurements at coastal sea-level stations has carried significant uncertainties and has been virtually impossible in real time, before tsunami impacts coastlines. The slow process of tsunami magnitude estimates, including collection of vast amount of available coastal sea-level data from affected coastlines, made it impractical to use any tsunami magnitude scales in tsunami warning operations. Uncertainties of estimates made tsunami magnitudes difficult to use as universal scale for tsunami analysis. Historically, the earthquake magnitude has been used as a proxy of tsunami impact estimates, since real-time seismic data is available of real-time processing and ample amount of seismic data is available for an elaborate post event analysis. This measure of tsunami impact carries significant uncertainties in quantitative tsunami impact estimates, since the relation between the earthquake and generated tsunami energy varies from case to case. In this work, we argue that current tsunami measurement capabilities and real-time modeling tools allow for establishing robust tsunami magnitude that will be useful for tsunami warning as a quick estimate for tsunami impact and for post-event analysis as a universal scale for tsunamis inter-comparison. We present a method for estimating the tsunami magnitude based on tsunami energy and present application of the magnitude analysis for several historical events for inter-comparison with existing methods.

Uncertainty analysis of absorbed dose calculations from thermoluminescence dosimeters.

PubMed

Kirby, T H; Hanson, W F; Johnston, D A

1992-01-01

Thermoluminescence dosimeters (TLD) are widely used to verify absorbed doses delivered from radiation therapy beams. Specifically, they are used by the Radiological Physics Center for mailed dosimetry for verification of therapy machine output. The effects of the random experimental uncertainties of various factors on dose calculations from TLD signals are examined, including: fading, dose response nonlinearity, and energy response corrections; reproducibility of TL signal measurements and TLD reader calibration. Individual uncertainties are combined to estimate the total uncertainty due to random fluctuations. The Radiological Physics Center's (RPC) mail out TLD system, utilizing throwaway LiF powder to monitor high-energy photon and electron beam outputs, is analyzed in detail. The technique may also be applicable to other TLD systems. It is shown that statements of +/- 2% dose uncertainty and +/- 5% action criterion for TLD dosimetry are reasonable when related to uncertainties in the dose calculations, provided the standard deviation (s.d.) of TL readings is 1.5% or better.

Multivariate Probabilistic Analysis of an Hydrological Model

NASA Astrophysics Data System (ADS)

Franceschini, Samuela; Marani, Marco

2010-05-01

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

Autonomous frequency domain identification: Theory and experiment

NASA Technical Reports Server (NTRS)

Yam, Yeung; Bayard, D. S.; Hadaegh, F. Y.; Mettler, E.; Milman, M. H.; Scheid, R. E.

1989-01-01

The analysis, design, and on-orbit tuning of robust controllers require more information about the plant than simply a nominal estimate of the plant transfer function. Information is also required concerning the uncertainty in the nominal estimate, or more generally, the identification of a model set within which the true plant is known to lie. The identification methodology that was developed and experimentally demonstrated makes use of a simple but useful characterization of the model uncertainty based on the output error. This is a characterization of the additive uncertainty in the plant model, which has found considerable use in many robust control analysis and synthesis techniques. The identification process is initiated by a stochastic input u which is applied to the plant p giving rise to the output. Spectral estimation (h = P sub uy/P sub uu) is used as an estimate of p and the model order is estimated using the produce moment matrix (PMM) method. A parametric model unit direction vector p is then determined by curve fitting the spectral estimate to a rational transfer function. The additive uncertainty delta sub m = p - unit direction vector p is then estimated by the cross spectral estimate delta = P sub ue/P sub uu where e = y - unit direction vectory y is the output error, and unit direction vector y = unit direction vector pu is the computed output of the parametric model subjected to the actual input u. The experimental results demonstrate the curve fitting algorithm produces the reduced-order plant model which minimizes the additive uncertainty. The nominal transfer function estimate unit direction vector p and the estimate delta of the additive uncertainty delta sub m are subsequently available to be used for optimization of robust controller performance and stability.

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.

System Reliability-Based Design Optimization Under Input and Model Uncertainties

DTIC Science & Technology

2014-02-02

Standard Form 298 (Rev 8/98) Prescribed by ANSI Std. Z39.18 W911NF-09- 1 -0250 319-335-5684 Final Report 56025-NS.40 a. REPORT 14. ABSTRACT 16...resource. In such cases the inaccuracy and uncertainty of the surrogate model needs to 1 . REPORT DATE (DD-MM-YYYY) 4. TITLE AND SUBTITLE 13...estimated to average 1 hour per response, including the time for reviewing instructions, searching existing data sources, gathering and maintaining the

Analysing uncertainties of supply and demand in the future use of hydrogen as an energy vector

NASA Astrophysics Data System (ADS)

Lenel, U. R.; Davies, D. G. S.; Moore, M. A.

An analytical technique (Analysis with Uncertain Qualities), developed at Fulmer, is being used to examine the sensitivity of the outcome to uncertainties in input quantities in order to highlight which input quantities critically affect the potential role of hydrogen. The work presented here includes an outline of the model and the analysis technique, along with basic considerations of the input quantities to the model (demand, supply and constraints). Some examples are given of probabilistic estimates of input quantities.

Assessing the reliability of dose coefficients for exposure to radioiodine by members of the public, accounting for dosimetric and risk model uncertainties.

PubMed

Puncher, M; Zhang, W; Harrison, J D; Wakeford, R

2017-06-26

Assessments of risk to a specific population group resulting from internal exposure to a particular radionuclide can be used to assess the reliability of the appropriate International Commission on Radiological Protection (ICRP) dose coefficients used as a radiation protection device for the specified exposure pathway. An estimate of the uncertainty on the associated risk is important for informing judgments on reliability; a derived uncertainty factor, UF, is an estimate of the 95% probable geometric difference between the best risk estimate and the nominal risk and is a useful tool for making this assessment. This paper describes the application of parameter uncertainty analysis to quantify uncertainties resulting from internal exposures to radioiodine by members of the public, specifically 1, 10 and 20-year old females from the population of England and Wales. Best estimates of thyroid cancer incidence risk (lifetime attributable risk) are calculated for ingestion or inhalation of 129 I and 131 I, accounting for uncertainties in biokinetic model and cancer risk model parameter values. These estimates are compared with the equivalent ICRP derived nominal age-, sex- and population-averaged estimates of excess thyroid cancer incidence to obtain UFs. Derived UF values for ingestion or inhalation of 131 I for 1 year, 10-year and 20-year olds are around 28, 12 and 6, respectively, when compared with ICRP Publication 103 nominal values, and 9, 7 and 14, respectively, when compared with ICRP Publication 60 values. Broadly similar results were obtained for 129 I. The uncertainties on risk estimates are largely determined by uncertainties on risk model parameters rather than uncertainties on biokinetic model parameters. An examination of the sensitivity of the results to the risk models and populations used in the calculations show variations in the central estimates of risk of a factor of around 2-3. It is assumed that the direct proportionality of excess thyroid cancer risk and dose observed at low to moderate acute doses and incorporated in the risk models also applies to very small doses received at very low dose rates; the uncertainty in this assumption is considerable, but largely unquantifiable. The UF values illustrate the need for an informed approach to the use of ICRP dose and risk coefficients.

Modeling Sea-Level Change using Errors-in-Variables Integrated Gaussian Processes

NASA Astrophysics Data System (ADS)

Cahill, Niamh; Parnell, Andrew; Kemp, Andrew; Horton, Benjamin

2014-05-01

We perform Bayesian inference on historical and late Holocene (last 2000 years) rates of sea-level change. The data that form the input to our model are tide-gauge measurements and proxy reconstructions from cores of coastal sediment. To accurately estimate rates of sea-level change and reliably compare tide-gauge compilations with proxy reconstructions it is necessary to account for the uncertainties that characterize each dataset. Many previous studies used simple linear regression models (most commonly polynomial regression) resulting in overly precise rate estimates. The model we propose uses an integrated Gaussian process approach, where a Gaussian process prior is placed on the rate of sea-level change and the data itself is modeled as the integral of this rate process. The non-parametric Gaussian process model is known to be well suited to modeling time series data. The advantage of using an integrated Gaussian process is that it allows for the direct estimation of the derivative of a one dimensional curve. The derivative at a particular time point will be representative of the rate of sea level change at that time point. The tide gauge and proxy data are complicated by multiple sources of uncertainty, some of which arise as part of the data collection exercise. Most notably, the proxy reconstructions include temporal uncertainty from dating of the sediment core using techniques such as radiocarbon. As a result of this, the integrated Gaussian process model is set in an errors-in-variables (EIV) framework so as to take account of this temporal uncertainty. The data must be corrected for land-level change known as glacio-isostatic adjustment (GIA) as it is important to isolate the climate-related sea-level signal. The correction for GIA introduces covariance between individual age and sea level observations into the model. The proposed integrated Gaussian process model allows for the estimation of instantaneous rates of sea-level change and accounts for all available sources of uncertainty in tide-gauge and proxy-reconstruction data. Our response variable is sea level after correction for GIA. By embedding the integrated process in an errors-in-variables (EIV) framework, and removing the estimate of GIA, we can quantify rates with better estimates of uncertainty than previously possible. The model provides a flexible fit and enables us to estimate rates of change at any given time point, thus observing how rates have been evolving from the past to present day.

Parameter and uncertainty estimation for mechanistic, spatially explicit epidemiological models

NASA Astrophysics Data System (ADS)

Finger, Flavio; Schaefli, Bettina; Bertuzzo, Enrico; Mari, Lorenzo; Rinaldo, Andrea

2014-05-01

Epidemiological models can be a crucially important tool for decision-making during disease outbreaks. The range of possible applications spans from real-time forecasting and allocation of health-care resources to testing alternative intervention mechanisms such as vaccines, antibiotics or the improvement of sanitary conditions. Our spatially explicit, mechanistic models for cholera epidemics have been successfully applied to several epidemics including, the one that struck Haiti in late 2010 and is still ongoing. Calibration and parameter estimation of such models represents a major challenge because of properties unusual in traditional geoscientific domains such as hydrology. Firstly, the epidemiological data available might be subject to high uncertainties due to error-prone diagnosis as well as manual (and possibly incomplete) data collection. Secondly, long-term time-series of epidemiological data are often unavailable. Finally, the spatially explicit character of the models requires the comparison of several time-series of model outputs with their real-world counterparts, which calls for an appropriate weighting scheme. It follows that the usual assumption of a homoscedastic Gaussian error distribution, used in combination with classical calibration techniques based on Markov chain Monte Carlo algorithms, is likely to be violated, whereas the construction of an appropriate formal likelihood function seems close to impossible. Alternative calibration methods, which allow for accurate estimation of total model uncertainty, particularly regarding the envisaged use of the models for decision-making, are thus needed. Here we present the most recent developments regarding methods for parameter and uncertainty estimation to be used with our mechanistic, spatially explicit models for cholera epidemics, based on informal measures of goodness of fit.

Uncertainty analysis of practical structural health monitoring systems currently employed for tall buildings consisting of small number of sensors

NASA Astrophysics Data System (ADS)

Hirai, Kenta; Mita, Akira

2016-04-01

Because of social background, such as repeated large earthquakes and cheating in design and construction, structural health monitoring (SHM) systems are getting strong attention. The SHM systems are in a practical phase. An SHM system consisting of small number of sensors has been introduced to 6 tall buildings in Shinjuku area. Including them, there are 2 major issues in the SHM systems consisting of small number of sensors. First, optimal system number of sensors and the location are not well-defined. In the practice, system placement is determined based on rough prediction and experience. Second, there are some uncertainties in estimation results by the SHM systems. Thus, the purpose of this research is to provide useful information for increasing reliability of SHM system and to improve estimation results based on uncertainty analysis of the SHM systems. The important damage index used here is the inter-story drift angle. The uncertainty considered here are number of sensors, earthquake motion characteristics, noise in data, error between numerical model and real building, nonlinearity of parameter. Then I have analyzed influence of each factor to estimation accuracy. The analysis conducted here will help to decide sensor system design considering valance of cost and accuracy. Because of constraint on the number of sensors, estimation results by the SHM system has tendency to provide smaller values. To overcome this problem, a compensation algorithm was discussed and presented. The usefulness of this compensation method was demonstrated for 40 story S and RC building models with nonlinear response.

Using heat as a tracer to estimate spatially distributed mean residence times in the hyporheic zone of a riffle-pool sequence

USGS Publications Warehouse

Naranjo, Ramon C.

2013-01-01

Biochemical reactions that occur in the hyporheic zone are highly dependent on the time solutes that are in contact with sediments of the riverbed. In this investigation, we developed a 2-D longitudinal flow and solute-transport model to estimate the spatial distribution of mean residence time in the hyporheic zone. The flow model was calibrated using observations of temperature and pressure, and the mean residence times were simulated using the age-mass approach for steady-state flow conditions. The approach used in this investigation includes the mixing of different ages and flow paths of water through advection and dispersion. Uncertainty of flow and transport parameters was evaluated using standard Monte Carlo and the generalized likelihood uncertainty estimation method. Results of parameter estimation support the presence of a low-permeable zone in the riffle area that induced horizontal flow at a shallow depth within the riffle area. This establishes shallow and localized flow paths and limits deep vertical exchange. For the optimal model, mean residence times were found to be relatively long (9–40.0 days). The uncertainty of hydraulic conductivity resulted in a mean interquartile range (IQR) of 13 days across all piezometers and was reduced by 24% with the inclusion of temperature and pressure observations. To a lesser extent, uncertainty in streambed porosity and dispersivity resulted in a mean IQR of 2.2 and 4.7 days, respectively. Alternative conceptual models demonstrate the importance of accounting for the spatial distribution of hydraulic conductivity in simulating mean residence times in a riffle-pool sequence.

Uncertainty quantification metrics for whole product life cycle cost estimates in aerospace innovation

NASA Astrophysics Data System (ADS)

Schwabe, O.; Shehab, E.; Erkoyuncu, J.

2015-08-01

The lack of defensible methods for quantifying cost estimate uncertainty over the whole product life cycle of aerospace innovations such as propulsion systems or airframes poses a significant challenge to the creation of accurate and defensible cost estimates. Based on the axiomatic definition of uncertainty as the actual prediction error of the cost estimate, this paper provides a comprehensive overview of metrics used for the uncertainty quantification of cost estimates based on a literature review, an evaluation of publicly funded projects such as part of the CORDIS or Horizon 2020 programs, and an analysis of established approaches used by organizations such NASA, the U.S. Department of Defence, the ESA, and various commercial companies. The metrics are categorized based on their foundational character (foundations), their use in practice (state-of-practice), their availability for practice (state-of-art) and those suggested for future exploration (state-of-future). Insights gained were that a variety of uncertainty quantification metrics exist whose suitability depends on the volatility of available relevant information, as defined by technical and cost readiness level, and the number of whole product life cycle phases the estimate is intended to be valid for. Information volatility and number of whole product life cycle phases can hereby be considered as defining multi-dimensional probability fields admitting various uncertainty quantification metric families with identifiable thresholds for transitioning between them. The key research gaps identified were the lacking guidance grounded in theory for the selection of uncertainty quantification metrics and lacking practical alternatives to metrics based on the Central Limit Theorem. An innovative uncertainty quantification framework consisting of; a set-theory based typology, a data library, a classification system, and a corresponding input-output model are put forward to address this research gap as the basis for future work in this field.

Uncertainty in predicting soil hydraulic properties at the hillslope scale with indirect methods

NASA Astrophysics Data System (ADS)

Chirico, G. B.; Medina, H.; Romano, N.

2007-02-01

SummarySeveral hydrological applications require the characterisation of the soil hydraulic properties at large spatial scales. Pedotransfer functions (PTFs) are being developed as simplified methods to estimate soil hydraulic properties as an alternative to direct measurements, which are unfeasible for most practical circumstances. The objective of this study is to quantify the uncertainty in PTFs spatial predictions at the hillslope scale as related to the sampling density, due to: (i) the error in estimated soil physico-chemical properties and (ii) PTF model error. The analysis is carried out on a 2-km-long experimental hillslope in South Italy. The method adopted is based on a stochastic generation of patterns of soil variables using sequential Gaussian simulation, conditioned to the observed sample data. The following PTFs are applied: Vereecken's PTF [Vereecken, H., Diels, J., van Orshoven, J., Feyen, J., Bouma, J., 1992. Functional evaluation of pedotransfer functions for the estimation of soil hydraulic properties. Soil Sci. Soc. Am. J. 56, 1371-1378] and HYPRES PTF [Wösten, J.H.M., Lilly, A., Nemes, A., Le Bas, C., 1999. Development and use of a database of hydraulic properties of European soils. Geoderma 90, 169-185]. The two PTFs estimate reliably the soil water retention characteristic even for a relatively coarse sampling resolution, with prediction uncertainties comparable to the uncertainties in direct laboratory or field measurements. The uncertainty of soil water retention prediction due to the model error is as much as or more significant than the uncertainty associated with the estimated input, even for a relatively coarse sampling resolution. Prediction uncertainties are much more important when PTF are applied to estimate the saturated hydraulic conductivity. In this case model error dominates the overall prediction uncertainties, making negligible the effect of the input error.

Estimating statistical uncertainty of Monte Carlo efficiency-gain in the context of a correlated sampling Monte Carlo code for brachytherapy treatment planning with non-normal dose distribution.

PubMed

Mukhopadhyay, Nitai D; Sampson, Andrew J; Deniz, Daniel; Alm Carlsson, Gudrun; Williamson, Jeffrey; Malusek, Alexandr

2012-01-01

Correlated sampling Monte Carlo methods can shorten computing times in brachytherapy treatment planning. Monte Carlo efficiency is typically estimated via efficiency gain, defined as the reduction in computing time by correlated sampling relative to conventional Monte Carlo methods when equal statistical uncertainties have been achieved. The determination of the efficiency gain uncertainty arising from random effects, however, is not a straightforward task specially when the error distribution is non-normal. The purpose of this study is to evaluate the applicability of the F distribution and standardized uncertainty propagation methods (widely used in metrology to estimate uncertainty of physical measurements) for predicting confidence intervals about efficiency gain estimates derived from single Monte Carlo runs using fixed-collision correlated sampling in a simplified brachytherapy geometry. A bootstrap based algorithm was used to simulate the probability distribution of the efficiency gain estimates and the shortest 95% confidence interval was estimated from this distribution. It was found that the corresponding relative uncertainty was as large as 37% for this particular problem. The uncertainty propagation framework predicted confidence intervals reasonably well; however its main disadvantage was that uncertainties of input quantities had to be calculated in a separate run via a Monte Carlo method. The F distribution noticeably underestimated the confidence interval. These discrepancies were influenced by several photons with large statistical weights which made extremely large contributions to the scored absorbed dose difference. The mechanism of acquiring high statistical weights in the fixed-collision correlated sampling method was explained and a mitigation strategy was proposed. Copyright © 2011 Elsevier Ltd. All rights reserved.

Uncertainties of flood frequency estimation approaches based on continuous simulation using data resampling

NASA Astrophysics Data System (ADS)

Arnaud, Patrick; Cantet, Philippe; Odry, Jean

2017-11-01

Flood frequency analyses (FFAs) are needed for flood risk management. Many methods exist ranging from classical purely statistical approaches to more complex approaches based on process simulation. The results of these methods are associated with uncertainties that are sometimes difficult to estimate due to the complexity of the approaches or the number of parameters, especially for process simulation. This is the case of the simulation-based FFA approach called SHYREG presented in this paper, in which a rainfall generator is coupled with a simple rainfall-runoff model in an attempt to estimate the uncertainties due to the estimation of the seven parameters needed to estimate flood frequencies. The six parameters of the rainfall generator are mean values, so their theoretical distribution is known and can be used to estimate the generator uncertainties. In contrast, the theoretical distribution of the single hydrological model parameter is unknown; consequently, a bootstrap method is applied to estimate the calibration uncertainties. The propagation of uncertainty from the rainfall generator to the hydrological model is also taken into account. This method is applied to 1112 basins throughout France. Uncertainties coming from the SHYREG method and from purely statistical approaches are compared, and the results are discussed according to the length of the recorded observations, basin size and basin location. Uncertainties of the SHYREG method decrease as the basin size increases or as the length of the recorded flow increases. Moreover, the results show that the confidence intervals of the SHYREG method are relatively small despite the complexity of the method and the number of parameters (seven). This is due to the stability of the parameters and takes into account the dependence of uncertainties due to the rainfall model and the hydrological calibration. Indeed, the uncertainties on the flow quantiles are on the same order of magnitude as those associated with the use of a statistical law with two parameters (here generalised extreme value Type I distribution) and clearly lower than those associated with the use of a three-parameter law (here generalised extreme value Type II distribution). For extreme flood quantiles, the uncertainties are mostly due to the rainfall generator because of the progressive saturation of the hydrological model.

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.

Safe days in space with acceptable uncertainty from space radiation exposure.

PubMed

Cucinotta, Francis A; Alp, Murat; Rowedder, Blake; Kim, Myung-Hee Y

2015-04-01

The prediction of the risks of cancer and other late effects from space radiation exposure carries large uncertainties mostly due to the lack of information on the risks from high charge and energy (HZE) particles and other high linear energy transfer (LET) radiation. In our recent work new methods were used to consider NASA's requirement to protect against the acceptable risk of no more than 3% probability of cancer fatality estimated at the 95% confidence level. Because it is not possible that a zero-level of uncertainty could be achieved, we suggest that an acceptable uncertainty level should be defined in relationship to a probability distribution function (PDF) that only suffers from modest skewness with higher uncertainty allowed for a normal PDF. In this paper, we evaluate PDFs and the number or "safe days" in space, which are defined as the mission length where risk limits are not exceeded, for several mission scenarios at different acceptable levels of uncertainty. In addition, we briefly discuss several important issues in risk assessment including non-cancer effects, the distinct tumor spectra and lethality found in animal experiments for HZE particles compared to background or low LET radiation associated tumors, and the possibility of non-targeted effects (NTE) modifying low dose responses and increasing relative biological effectiveness (RBE) factors for tumor induction. Each of these issues skew uncertainty distributions to higher fatality probabilities with the potential to increase central values of risk estimates in the future. Therefore they will require significant research efforts to support space exploration within acceptable levels of risk and uncertainty. Copyright © 2015 The Committee on Space Research (COSPAR). Published by Elsevier Ltd. All rights reserved.

Reduced uncertainty of regional scale CLM predictions of net carbon fluxes and leaf area indices with estimated plant-specific parameters

NASA Astrophysics Data System (ADS)

Post, Hanna; Hendricks Franssen, Harrie-Jan; Han, Xujun; Baatz, Roland; Montzka, Carsten; Schmidt, Marius; Vereecken, Harry

2016-04-01

Reliable estimates of carbon fluxes and states at regional scales are required to reduce uncertainties in regional carbon balance estimates and to support decision making in environmental politics. In this work the Community Land Model version 4.5 (CLM4.5-BGC) was applied at a high spatial resolution (1 km2) for the Rur catchment in western Germany. In order to improve the model-data consistency of net ecosystem exchange (NEE) and leaf area index (LAI) for this study area, five plant functional type (PFT)-specific CLM4.5-BGC parameters were estimated with time series of half-hourly NEE data for one year in 2011/2012, using the DiffeRential Evolution Adaptive Metropolis (DREAM) algorithm, a Markov Chain Monte Carlo (MCMC) approach. The parameters were estimated separately for four different plant functional types (needleleaf evergreen temperate tree, broadleaf deciduous temperate tree, C3-grass and C3-crop) at four different sites. The four sites are located inside or close to the Rur catchment. We evaluated modeled NEE for one year in 2012/2013 with NEE measured at seven eddy covariance sites in the catchment, including the four parameter estimation sites. Modeled LAI was evaluated by means of LAI derived from remotely sensed RapidEye images of about 18 days in 2011/2012. Performance indices were based on a comparison between measurements and (i) a reference run with CLM default parameters, and (ii) a 60 instance CLM ensemble with parameters sampled from the DREAM posterior probability density functions (pdfs). The difference between the observed and simulated NEE sum reduced 23% if estimated parameters instead of default parameters were used as input. The mean absolute difference between modeled and measured LAI was reduced by 59% on average. Simulated LAI was not only improved in terms of the absolute value but in some cases also in terms of the timing (beginning of vegetation onset), which was directly related to a substantial improvement of the NEE estimates in spring. In order to obtain a more comprehensive estimate of the model uncertainty, a second CLM ensemble was set up, where initial conditions and atmospheric forcings were perturbed in addition to the parameter estimates. This resulted in very high standard deviations (STD) of the modeled annual NEE sums for C3-grass and C3-crop PFTs, ranging between 24.1 and 225.9 gC m-2 y-1, compared to STD = 0.1 - 3.4 gC m-2 y-1 (effect of parameter uncertainty only, without additional perturbation of initial states and atmospheric forcings). The higher spread of modeled NEE for the C3-crop and C3-grass indicated that the model uncertainty was notably higher for those PFTs compared to the forest-PFTs. Our findings highlight the potential of parameter and uncertainty estimation to support the understanding and further development of land surface models such as CLM.

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.

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

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

Determining an empirical estimate of the tracking inconsistency component for true astrometric uncertainties

NASA Astrophysics Data System (ADS)

Ramanjooloo, Yudish; Tholen, David J.; Fohring, Dora; Claytor, Zach; Hung, Denise

2017-10-01

The asteroid community is moving towards the implementation of a new astrometric reporting format. This new format will finally include of complementary astrometric uncertainties in the reported observations. The availability of uncertainties will allow ephemeris predictions and orbit solutions to be constrained with greater reliability, thereby improving the efficiency of the community's follow-up and recovery efforts.Our current uncertainty model involves our uncertainties in centroiding on the trailed stars and asteroid and the uncertainty due to the astrometric solution. The accuracy of our astrometric measurements are reliant on how well we can minimise the offset between the spatial and temporal centroids of the stars and the asteroid. This offset is currently unmodelled and can be caused by variations in the cloud transparency, the seeing and tracking inconsistencies. The magnitude zero point of the image, which is affected by fluctuating weather conditions and the catalog bias in the photometric magnitudes, can serve as an indicator of the presence and thickness of clouds. Through comparison of the astrometric uncertainties to the orbit solution residuals, it was apparent that a component of the error analysis remained unaccounted for, as a result of cloud coverage and thickness, telescope tracking inconsistencies and variable seeing. This work will attempt to quantify the tracking inconsistency component. We have acquired a rich dataset with the University of Hawaii 2.24 metre telescope (UH-88 inch) that is well positioned to construct an empirical estimate of the tracking inconsistency component. This work is funded by NASA grant NXX13AI64G.

Uncertainty Analysis in Space Radiation Protection

NASA Technical Reports Server (NTRS)

Cucinotta, Francis A.

2011-01-01

Space radiation is comprised of high energy and charge (HZE) nuclei, protons, and secondary radiation including neutrons. The uncertainties in estimating the health risks from galactic cosmic rays (GCR) are a major limitation to the length of space missions, the evaluation of potential risk mitigation approaches, and application of the As Low As Reasonably Achievable (ALARA) principle. For long duration space missio ns, risks may approach radiation exposure limits, therefore the uncertainties in risk projections become a major safety concern and methodologies used for ground-based works are not deemed to be sufficient. NASA limits astronaut exposures to a 3% risk of exposure induced death (REID) and protects against uncertainties in risks projections using an assessment of 95% confidence intervals in the projection model. We discuss NASA s approach to space radiation uncertainty assessments and applications for the International Space Station (ISS) program and design studies of future missions to Mars and other destinations. Several features of NASA s approach will be discussed. Radiation quality descriptions are based on the properties of radiation tracks rather than LET with probability distribution functions (PDF) for uncertainties derived from radiobiology experiments at particle accelerators. The application of age and gender specific models for individual astronauts is described. Because more than 90% of astronauts are never-smokers, an alternative risk calculation for never-smokers is used and will be compared to estimates for an average U.S. population. Because of the high energies of the GCR limits the benefits of shielding and the limited role expected for pharmaceutical countermeasures, uncertainty reduction continues to be the optimal approach to improve radiation safety for space missions.

Mars Entry Atmospheric Data System Modeling, Calibration, and Error Analysis

NASA Technical Reports Server (NTRS)

Karlgaard, Christopher D.; VanNorman, John; Siemers, Paul M.; Schoenenberger, Mark; Munk, Michelle M.

2014-01-01

The Mars Science Laboratory (MSL) Entry, Descent, and Landing Instrumentation (MEDLI)/Mars Entry Atmospheric Data System (MEADS) project installed seven pressure ports through the MSL Phenolic Impregnated Carbon Ablator (PICA) heatshield to measure heatshield surface pressures during entry. These measured surface pressures are used to generate estimates of atmospheric quantities based on modeled surface pressure distributions. In particular, the quantities to be estimated from the MEADS pressure measurements include the dynamic pressure, angle of attack, and angle of sideslip. This report describes the calibration of the pressure transducers utilized to reconstruct the atmospheric data and associated uncertainty models, pressure modeling and uncertainty analysis, and system performance results. The results indicate that the MEADS pressure measurement system hardware meets the project requirements.

Habitat suitability criteria via parametric distributions: estimation, model selection and uncertainty

USGS Publications Warehouse

Som, Nicholas A.; Goodman, Damon H.; Perry, Russell W.; Hardy, Thomas B.

2016-01-01

Previous methods for constructing univariate habitat suitability criteria (HSC) curves have ranged from professional judgement to kernel-smoothed density functions or combinations thereof. We present a new method of generating HSC curves that applies probability density functions as the mathematical representation of the curves. Compared with previous approaches, benefits of our method include (1) estimation of probability density function parameters directly from raw data, (2) quantitative methods for selecting among several candidate probability density functions, and (3) concise methods for expressing estimation uncertainty in the HSC curves. We demonstrate our method with a thorough example using data collected on the depth of water used by juvenile Chinook salmon (Oncorhynchus tschawytscha) in the Klamath River of northern California and southern Oregon. All R code needed to implement our example is provided in the appendix. Published 2015. This article is a U.S. Government work and is in the public domain in the USA.

Assessment of uncertainties in soil erosion and sediment yield estimates at ungauged basins: an application to the Garra River basin, India

NASA Astrophysics Data System (ADS)

Swarnkar, Somil; Malini, Anshu; Tripathi, Shivam; Sinha, Rajiv

2018-04-01

High soil erosion and excessive sediment load are serious problems in several Himalayan river basins. To apply mitigation procedures, precise estimation of soil erosion and sediment yield with associated uncertainties are needed. Here, the revised universal soil loss equation (RUSLE) and the sediment delivery ratio (SDR) equations are used to estimate the spatial pattern of soil erosion (SE) and sediment yield (SY) in the Garra River basin, a small Himalayan tributary of the River Ganga. A methodology is proposed for quantifying and propagating uncertainties in SE, SDR and SY estimates. Expressions for uncertainty propagation are derived by first-order uncertainty analysis, making the method viable even for large river basins. The methodology is applied to investigate the relative importance of different RUSLE factors in estimating the magnitude and uncertainties in SE over two distinct morphoclimatic regimes of the Garra River basin, namely the upper mountainous region and the lower alluvial plains. Our results suggest that average SE in the basin is very high (23 ± 4.7 t ha-1 yr-1) with higher values in the upper mountainous region (92 ± 15.2 t ha-1 yr-1) compared to the lower alluvial plains (19.3 ± 4 t ha-1 yr-1). Furthermore, the topographic steepness (LS) and crop practice (CP) factors exhibit higher uncertainties than other RUSLE factors. The annual average SY is estimated at two locations in the basin - Nanak Sagar Dam (NSD) for the period 1962-2008 and Husepur gauging station (HGS) for 1987-2002. The SY at NSD and HGS are estimated to be 6.9 ± 1.2 × 105 t yr-1 and 6.7 ± 1.4 × 106 t yr-1, respectively, and the estimated 90 % interval contains the observed values of 6.4 × 105 t yr-1 and 7.2 × 106 t yr-1, respectively. The study demonstrated the usefulness of the proposed methodology for quantifying uncertainty in SE and SY estimates at ungauged basins.

Assessment of source-specific health effects associated with an unknown number of major sources of multiple air pollutants: a unified Bayesian approach.

PubMed

Park, Eun Sug; Hopke, Philip K; Oh, Man-Suk; Symanski, Elaine; Han, Daikwon; Spiegelman, Clifford H

2014-07-01

There has been increasing interest in assessing health effects associated with multiple air pollutants emitted by specific sources. A major difficulty with achieving this goal is that the pollution source profiles are unknown and source-specific exposures cannot be measured directly; rather, they need to be estimated by decomposing ambient measurements of multiple air pollutants. This estimation process, called multivariate receptor modeling, is challenging because of the unknown number of sources and unknown identifiability conditions (model uncertainty). The uncertainty in source-specific exposures (source contributions) as well as uncertainty in the number of major pollution sources and identifiability conditions have been largely ignored in previous studies. A multipollutant approach that can deal with model uncertainty in multivariate receptor models while simultaneously accounting for parameter uncertainty in estimated source-specific exposures in assessment of source-specific health effects is presented in this paper. The methods are applied to daily ambient air measurements of the chemical composition of fine particulate matter ([Formula: see text]), weather data, and counts of cardiovascular deaths from 1995 to 1997 for Phoenix, AZ, USA. Our approach for evaluating source-specific health effects yields not only estimates of source contributions along with their uncertainties and associated health effects estimates but also estimates of model uncertainty (posterior model probabilities) that have been ignored in previous studies. The results from our methods agreed in general with those from the previously conducted workshop/studies on the source apportionment of PM health effects in terms of number of major contributing sources, estimated source profiles, and contributions. However, some of the adverse source-specific health effects identified in the previous studies were not statistically significant in our analysis, which probably resulted because we incorporated parameter uncertainty in estimated source contributions that has been ignored in the previous studies into the estimation of health effects parameters. © The Author 2014. Published by Oxford University Press. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com.

Modeling of structural uncertainties in Reynolds-averaged Navier-Stokes closures

NASA Astrophysics Data System (ADS)

Emory, Michael; Larsson, Johan; Iaccarino, Gianluca

2013-11-01

Estimation of the uncertainty in numerical predictions by Reynolds-averaged Navier-Stokes closures is a vital step in building confidence in such predictions. An approach to model-form uncertainty quantification that does not assume the eddy-viscosity hypothesis to be exact is proposed. The methodology for estimation of uncertainty is demonstrated for plane channel flow, for a duct with secondary flows, and for the shock/boundary-layer interaction over a transonic bump.

Propagation of Computational Uncertainty Using the Modern Design of Experiments

NASA Technical Reports Server (NTRS)

DeLoach, Richard

2007-01-01

This paper describes the use of formally designed experiments to aid in the error analysis of a computational experiment. A method is described by which the underlying code is approximated with relatively low-order polynomial graduating functions represented by truncated Taylor series approximations to the true underlying response function. A resource-minimal approach is outlined by which such graduating functions can be estimated from a minimum number of case runs of the underlying computational code. Certain practical considerations are discussed, including ways and means of coping with high-order response functions. The distributional properties of prediction residuals are presented and discussed. A practical method is presented for quantifying that component of the prediction uncertainty of a computational code that can be attributed to imperfect knowledge of independent variable levels. This method is illustrated with a recent assessment of uncertainty in computational estimates of Space Shuttle thermal and structural reentry loads attributable to ice and foam debris impact on ascent.

Systematic studies of Niv transitions of astrophysical importance

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

Fleming, J.; Brage, T.; Bell, K.L.

1995-12-01

E1, M2, M1, and E2 rates of transitions between {ital n}=2 levels of Niv have been calculated using the two independent codes CIV3 and MCHF. Convergence of each of the approaches has been studied and comparisons made as the complexity of the calculations increases to include valence, core-valence and core-core correlation. The agreement between the two methods is sufficiently good to allow us to set quite narrow uncertainty bars. For the {sup 1}{ital S}{endash}{sup 1}{ital P}{sup 0} resonance line, our recommended {ital f}-value is 0.609 with an estimated uncertainty of 0.002, while our recommended {ital A}-value for the {sup 1}{italmore » S}{sub 0}{endash}{sup 3}{ital P}{sup 0}{sub 1} intercombination line is 580 s{sup {minus}}{sup 1} with an estimated uncertainty of 10 s{sup {minus}}{sup 1}. {copyright} 1995 The American Astronomical Society.« less

Quantification for complex assessment: uncertainty estimation in final year project thesis assessment

NASA Astrophysics Data System (ADS)

Kim, Ho Sung

2013-12-01

A quantitative method for estimating an expected uncertainty (reliability and validity) in assessment results arising from the relativity between four variables, viz examiner's expertise, examinee's expertise achieved, assessment task difficulty and examinee's performance, was developed for the complex assessment applicable to final year project thesis assessment including peer assessment. A guide map can be generated by the method for finding expected uncertainties prior to the assessment implementation with a given set of variables. It employs a scale for visualisation of expertise levels, derivation of which is based on quantified clarities of mental images for levels of the examiner's expertise and the examinee's expertise achieved. To identify the relevant expertise areas that depend on the complexity in assessment format, a graphical continuum model was developed. The continuum model consists of assessment task, assessment standards and criterion for the transition towards the complex assessment owing to the relativity between implicitness and explicitness and is capable of identifying areas of expertise required for scale development.

Uncertainties in the cluster-cluster correlation function

NASA Astrophysics Data System (ADS)

Ling, E. N.; Frenk, C. S.; Barrow, J. D.

1986-12-01

The bootstrap resampling technique is applied to estimate sampling errors and significance levels of the two-point correlation functions determined for a subset of the CfA redshift survey of galaxies and a redshift sample of 104 Abell clusters. The angular correlation function for a sample of 1664 Abell clusters is also calculated. The standard errors in xi(r) for the Abell data are found to be considerably larger than quoted 'Poisson errors'. The best estimate for the ratio of the correlation length of Abell clusters (richness class R greater than or equal to 1, distance class D less than or equal to 4) to that of CfA galaxies is 4.2 + 1.4 or - 1.0 (68 percentile error). The enhancement of cluster clustering over galaxy clustering is statistically significant in the presence of resampling errors. The uncertainties found do not include the effects of possible systematic biases in the galaxy and cluster catalogs and could be regarded as lower bounds on the true uncertainty range.

A Calibration to Predict the Concentrations of Impurities in Plutonium Oxide by Prompt Gamma Analysis Revision 2

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

Narlesky, Joshua Edward; Kelly, Elizabeth J.

2015-09-10

This report documents the new PG calibration regression equation. These calibration equations incorporate new data that have become available since revision 1 of “A Calibration to Predict the Concentrations of Impurities in Plutonium Oxide by Prompt Gamma Analysis” was issued [3] The calibration equations are based on a weighted least squares (WLS) approach for the regression. The WLS method gives each data point its proper amount of influence over the parameter estimates. This gives two big advantages, more precise parameter estimates and better and more defensible estimates of uncertainties. The WLS approach makes sense both statistically and experimentally because themore » variances increase with concentration, and there are physical reasons that the higher measurements are less reliable and should be less influential. The new magnesium calibration includes a correction for sodium and separate calibration equation for items with and without chlorine. These additional calibration equations allow for better predictions and smaller uncertainties for sodium in materials with and without chlorine. Chlorine and sodium have separate equations for RICH materials. Again, these equations give better predictions and smaller uncertainties chlorine and sodium for RICH materials.« less

Location error uncertainties - an advanced using of probabilistic inverse theory

NASA Astrophysics Data System (ADS)

Debski, Wojciech

2016-04-01

The spatial location of sources of seismic waves is one of the first tasks when transient waves from natural (uncontrolled) sources are analyzed in many branches of physics, including seismology, oceanology, to name a few. Source activity and its spatial variability in time, the geometry of recording network, the complexity and heterogeneity of wave velocity distribution are all factors influencing the performance of location algorithms and accuracy of the achieved results. While estimating of the earthquake foci location is relatively simple a quantitative estimation of the location accuracy is really a challenging task even if the probabilistic inverse method is used because it requires knowledge of statistics of observational, modelling, and apriori uncertainties. In this presentation we addressed this task when statistics of observational and/or modeling errors are unknown. This common situation requires introduction of apriori constraints on the likelihood (misfit) function which significantly influence the estimated errors. Based on the results of an analysis of 120 seismic events from the Rudna copper mine operating in southwestern Poland we illustrate an approach based on an analysis of Shanon's entropy calculated for the aposteriori distribution. We show that this meta-characteristic of the aposteriori distribution carries some information on uncertainties of the solution found.

Uncertainties of wild-land fires emission in AQMEII phase 2 case study

NASA Astrophysics Data System (ADS)

Soares, J.; Sofiev, M.; Hakkarainen, J.

2015-08-01

The paper discusses the main uncertainties of wild-land fire emission estimates used in the AQMEII-II case study. The wild-land fire emission of particulate matter for the summer fire season of 2010 in Eurasia was generated by the Integrated System for wild-land Fires (IS4FIRES). The emission calculation procedure included two steps: bottom-up emission compilation from radiative energy of individual fires observed by MODIS instrument on-board of Terra and Aqua satellites; and top-down calibration of emission factors based on the comparison between observations and modelled results. The approach inherits various uncertainties originating from imperfect information on fires, inaccuracies of the inverse problem solution, and simplifications in the fire description. These are analysed in regard to the Eurasian fires in 2010. It is concluded that the total emission is likely to be over-estimated by up to 50% with individual-fire emission accuracy likely to vary in a wide range. The first results of the new IS4FIRESv2 products and fire-resolving modelling are discussed in application to the 2010 events. It is shown that the new emission estimates have similar patterns but are lower than the IS4FIRESv1 values.

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

Savy, J.

New design and evaluation guidelines for department of energy facilities subjected to natural phenomena hazard, are being finalized. Although still in draft form at this time, the document describing those guidelines should be considered to be an update of previously available guidelines. The recommendations in the guidelines document mentioned above, and simply referred to as the guidelines'' thereafter, are based on the best information at the time of its development. In particular, the seismic hazard model for the Princeton site was based on a study performed in 1981 for Lawrence Livermore National Laboratory (LLNL), which relied heavily on the resultsmore » of the NRC's Systematic Evaluation Program and was based on a methodology and data sets developed in 1977 and 1978. Considerable advances have been made in the last ten years in the domain of seismic hazard modeling. Thus, it is recommended to update the estimate of the seismic hazard at the DOE sites whenever possible. The major differences between previous estimates and the ones proposed in this study for the PPPL are in the modeling of the strong ground motion at the site, and the treatment of the total uncertainty in the estimates to include knowledge uncertainty, random uncertainty, and expert opinion diversity as well. 28 refs.« less

Explicitly integrating parameter, input, and structure uncertainties into Bayesian Neural Networks for probabilistic hydrologic forecasting

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

Zhang, Xuesong; Liang, Faming; Yu, Beibei

2011-11-09

Estimating uncertainty of hydrologic forecasting is valuable to water resources and other relevant decision making processes. Recently, Bayesian Neural Networks (BNNs) have been proved powerful tools for quantifying uncertainty of streamflow forecasting. In this study, we propose a Markov Chain Monte Carlo (MCMC) framework to incorporate the uncertainties associated with input, model structure, and parameter into BNNs. This framework allows the structure of the neural networks to change by removing or adding connections between neurons and enables scaling of input data by using rainfall multipliers. The results show that the new BNNs outperform the BNNs that only consider uncertainties associatedmore » with parameter and model structure. Critical evaluation of posterior distribution of neural network weights, number of effective connections, rainfall multipliers, and hyper-parameters show that the assumptions held in our BNNs are not well supported. Further understanding of characteristics of different uncertainty sources and including output error into the MCMC framework are expected to enhance the application of neural networks for uncertainty analysis of hydrologic forecasting.« less

Chapter 8: Uncertainty assessment for quantifying greenhouse gas sources and sinks

Treesearch

Jay Breidt; Stephen M. Ogle; Wendy Powers; Coeli Hoover

2014-01-01

Quantifying the uncertainty of greenhouse gas (GHG) emissions and reductions from agriculture and forestry practices is an important aspect of decision�]making for farmers, ranchers and forest landowners as the uncertainty range for each GHG estimate communicates our level of confidence that the estimate reflects the actual balance of GHG exchange between...

Estimating Uncertainty in N2O Emissions from US Cropland Soils

USDA-ARS?s Scientific Manuscript database

A Monte Carlo analysis was combined with an empirically-based approach to quantify uncertainties in soil N2O emissions from US croplands estimated with the DAYCENT simulation model. Only a subset of croplands was simulated in the Monte Carlo analysis which was used to infer uncertainties across the ...

Potential of European 14CO2 observation network to estimate the fossil fuel CO2 emissions via atmospheric inversions

NASA Astrophysics Data System (ADS)

Wang, Yilong; Broquet, Grégoire; Ciais, Philippe; Chevallier, Frédéric; Vogel, Felix; Wu, Lin; Yin, Yi; Wang, Rong; Tao, Shu

2018-03-01

Combining measurements of atmospheric CO2 and its radiocarbon (14CO2) fraction and transport modeling in atmospheric inversions offers a way to derive improved estimates of CO2 emitted from fossil fuel (FFCO2). In this study, we solve for the monthly FFCO2 emission budgets at regional scale (i.e., the size of a medium-sized country in Europe) and investigate the performance of different observation networks and sampling strategies across Europe. The inversion system is built on the LMDZv4 global transport model at 3.75° × 2.5° resolution. We conduct Observing System Simulation Experiments (OSSEs) and use two types of diagnostics to assess the potential of the observation and inverse modeling frameworks. The first one relies on the theoretical computation of the uncertainty in the estimate of emissions from the inversion, known as posterior uncertainty, and on the uncertainty reduction compared to the uncertainty in the inventories of these emissions, which are used as a prior knowledge by the inversion (called prior uncertainty). The second one is based on comparisons of prior and posterior estimates of the emission to synthetic true emissions when these true emissions are used beforehand to generate the synthetic fossil fuel CO2 mixing ratio measurements that are assimilated in the inversion. With 17 stations currently measuring 14CO2 across Europe using 2-week integrated sampling, the uncertainty reduction for monthly FFCO2 emissions in a country where the network is rather dense like Germany, is larger than 30 %. With the 43 14CO2 measurement stations planned in Europe, the uncertainty reduction for monthly FFCO2 emissions is increased for the UK, France, Italy, eastern Europe and the Balkans, depending on the configuration of prior uncertainty. Further increasing the number of stations or the sampling frequency improves the uncertainty reduction (up to 40 to 70 %) in high emitting regions, but the performance of the inversion remains limited over low-emitting regions, even assuming a dense observation network covering the whole of Europe. This study also shows that both the theoretical uncertainty reduction (and resulting posterior uncertainty) from the inversion and the posterior estimate of emissions itself, for a given prior and true estimate of the emissions, are highly sensitive to the choice between two configurations of the prior uncertainty derived from the general estimate by inventory compilers or computations on existing inventories. In particular, when the configuration of the prior uncertainty statistics in the inversion system does not match the difference between these prior and true estimates, the posterior estimate of emissions deviates significantly from the truth. This highlights the difficulty of filtering the targeted signal in the model-data misfit for this specific inversion framework, the need to strongly rely on the prior uncertainty characterization for this and, consequently, the need for improved estimates of the uncertainties in current emission inventories for real applications with actual data. We apply the posterior uncertainty in annual emissions to the problem of detecting a trend of FFCO2, showing that increasing the monitoring period (e.g., more than 20 years) is more efficient than reducing uncertainty in annual emissions by adding stations. The coarse spatial resolution of the atmospheric transport model used in this OSSE (typical of models used for global inversions of natural CO2 fluxes) leads to large representation errors (related to the inability of the transport model to capture the spatial variability of the actual fluxes and mixing ratios at subgrid scales), which is a key limitation of our OSSE setup to improve the accuracy of the monitoring of FFCO2 emissions in European regions. Using a high-resolution transport model should improve the potential to retrieve FFCO2 emissions, and this needs to be investigated.

`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.

Uncertainty in action-value estimation affects both action choice and learning rate of the choice behaviors of rats.

PubMed

Funamizu, Akihiro; Ito, Makoto; Doya, Kenji; Kanzaki, Ryohei; Takahashi, Hirokazu

2012-04-01

The estimation of reward outcomes for action candidates is essential for decision making. In this study, we examined whether and how the uncertainty in reward outcome estimation affects the action choice and learning rate. We designed a choice task in which rats selected either the left-poking or right-poking hole and received a reward of a food pellet stochastically. The reward probabilities of the left and right holes were chosen from six settings (high, 100% vs. 66%; mid, 66% vs. 33%; low, 33% vs. 0% for the left vs. right holes, and the opposites) in every 20-549 trials. We used Bayesian Q-learning models to estimate the time course of the probability distribution of action values and tested if they better explain the behaviors of rats than standard Q-learning models that estimate only the mean of action values. Model comparison by cross-validation revealed that a Bayesian Q-learning model with an asymmetric update for reward and non-reward outcomes fit the choice time course of the rats best. In the action-choice equation of the Bayesian Q-learning model, the estimated coefficient for the variance of action value was positive, meaning that rats were uncertainty seeking. Further analysis of the Bayesian Q-learning model suggested that the uncertainty facilitated the effective learning rate. These results suggest that the rats consider uncertainty in action-value estimation and that they have an uncertainty-seeking action policy and uncertainty-dependent modulation of the effective learning rate. © 2012 The Authors. European Journal of Neuroscience © 2012 Federation of European Neuroscience Societies and Blackwell Publishing Ltd.

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.

Validation and Uncertainty Estimates for MODIS Collection 6 "Deep Blue" Aerosol Data

NASA Technical Reports Server (NTRS)

Sayer, A. M.; Hsu, N. C.; Bettenhausen, C.; Jeong, M.-J.

2013-01-01

The "Deep Blue" aerosol optical depth (AOD) retrieval algorithm was introduced in Collection 5 of the Moderate Resolution Imaging Spectroradiometer (MODIS) product suite, and complemented the existing "Dark Target" land and ocean algorithms by retrieving AOD over bright arid land surfaces, such as deserts. The forthcoming Collection 6 of MODIS products will include a "second generation" Deep Blue algorithm, expanding coverage to all cloud-free and snow-free land surfaces. The Deep Blue dataset will also provide an estimate of the absolute uncertainty on AOD at 550 nm for each retrieval. This study describes the validation of Deep Blue Collection 6 AOD at 550 nm (Tau(sub M)) from MODIS Aqua against Aerosol Robotic Network (AERONET) data from 60 sites to quantify these uncertainties. The highest quality (denoted quality assurance flag value 3) data are shown to have an absolute uncertainty of approximately (0.086+0.56Tau(sub M))/AMF, where AMF is the geometric air mass factor. For a typical AMF of 2.8, this is approximately 0.03+0.20Tau(sub M), comparable in quality to other satellite AOD datasets. Regional variability of retrieval performance and comparisons against Collection 5 results are also discussed.

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

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

DTIC Science & Technology

2011-06-28

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

Particle image velocimetry correlation signal-to-noise ratio metrics and measurement uncertainty quantification

NASA Astrophysics Data System (ADS)

Xue, Zhenyu; Charonko, John J.; Vlachos, Pavlos P.

2014-11-01

In particle image velocimetry (PIV) the measurement signal is contained in the recorded intensity of the particle image pattern superimposed on a variety of noise sources. The signal-to-noise-ratio (SNR) strength governs the resulting PIV cross correlation and ultimately the accuracy and uncertainty of the resulting PIV measurement. Hence we posit that correlation SNR metrics calculated from the correlation plane can be used to quantify the quality of the correlation and the resulting uncertainty of an individual measurement. In this paper we extend the original work by Charonko and Vlachos and present a framework for evaluating the correlation SNR using a set of different metrics, which in turn are used to develop models for uncertainty estimation. Several corrections have been applied in this work. The SNR metrics and corresponding models presented herein are expanded to be applicable to both standard and filtered correlations by applying a subtraction of the minimum correlation value to remove the effect of the background image noise. In addition, the notion of a ‘valid’ measurement is redefined with respect to the correlation peak width in order to be consistent with uncertainty quantification principles and distinct from an ‘outlier’ measurement. Finally the type and significance of the error distribution function is investigated. These advancements lead to more robust and reliable uncertainty estimation models compared with the original work by Charonko and Vlachos. The models are tested against both synthetic benchmark data as well as experimental measurements. In this work, {{U}68.5} uncertainties are estimated at the 68.5% confidence level while {{U}95} uncertainties are estimated at 95% confidence level. For all cases the resulting calculated coverage factors approximate the expected theoretical confidence intervals, thus demonstrating the applicability of these new models for estimation of uncertainty for individual PIV measurements.

Uncertainty in Population Estimates for Endangered Animals and Improving the Recovery Process.

PubMed

Haines, Aaron M; Zak, Matthew; Hammond, Katie; Scott, J Michael; Goble, Dale D; Rachlow, Janet L

2013-08-13

United States recovery plans contain biological information for a species listed under the Endangered Species Act and specify recovery criteria to provide basis for species recovery. The objective of our study was to evaluate whether recovery plans provide uncertainty (e.g., variance) with estimates of population size. We reviewed all finalized recovery plans for listed terrestrial vertebrate species to record the following data: (1) if a current population size was given, (2) if a measure of uncertainty or variance was associated with current estimates of population size and (3) if population size was stipulated for recovery. We found that 59% of completed recovery plans specified a current population size, 14.5% specified a variance for the current population size estimate and 43% specified population size as a recovery criterion. More recent recovery plans reported more estimates of current population size, uncertainty and population size as a recovery criterion. Also, bird and mammal recovery plans reported more estimates of population size and uncertainty compared to reptiles and amphibians. We suggest the use of calculating minimum detectable differences to improve confidence when delisting endangered animals and we identified incentives for individuals to get involved in recovery planning to improve access to quantitative data.

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

USGS Publications Warehouse

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

2011-01-01

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

Characterization and Uncertainty Analysis of a Reference Pressure Measurement System for Wind Tunnels

NASA Technical Reports Server (NTRS)

Amer, Tahani; Tripp, John; Tcheng, Ping; Burkett, Cecil; Sealey, Bradley

2004-01-01

This paper presents the calibration results and uncertainty analysis of a high-precision reference pressure measurement system currently used in wind tunnels at the NASA Langley Research Center (LaRC). Sensors, calibration standards, and measurement instruments are subject to errors due to aging, drift with time, environment effects, transportation, the mathematical model, the calibration experimental design, and other factors. Errors occur at every link in the chain of measurements and data reduction from the sensor to the final computed results. At each link of the chain, bias and precision uncertainties must be separately estimated for facility use, and are combined to produce overall calibration and prediction confidence intervals for the instrument, typically at a 95% confidence level. The uncertainty analysis and calibration experimental designs used herein, based on techniques developed at LaRC, employ replicated experimental designs for efficiency, separate estimation of bias and precision uncertainties, and detection of significant parameter drift with time. Final results, including calibration confidence intervals and prediction intervals given as functions of the applied inputs, not as a fixed percentage of the full-scale value are presented. System uncertainties are propagated beginning with the initial reference pressure standard, to the calibrated instrument as a working standard in the facility. Among the several parameters that can affect the overall results are operating temperature, atmospheric pressure, humidity, and facility vibration. Effects of factors such as initial zeroing and temperature are investigated. The effects of the identified parameters on system performance and accuracy are discussed.

Comparison of SOC estimates and uncertainties from aerosol chemical composition and gas phase data in Atlanta

NASA Astrophysics Data System (ADS)

Pachon, Jorge E.; Balachandran, Sivaraman; Hu, Yongtao; Weber, Rodney J.; Mulholland, James A.; Russell, Armistead G.

2010-10-01

In the Southeastern US, organic carbon (OC) comprises about 30% of the PM 2.5 mass. A large fraction of OC is estimated to be of secondary origin. Long-term estimates of SOC and uncertainties are necessary in the evaluation of air quality policy effectiveness and epidemiologic studies. Four methods to estimate secondary organic carbon (SOC) and respective uncertainties are compared utilizing PM 2.5 chemical composition and gas phase data available in Atlanta from 1999 to 2007. The elemental carbon (EC) tracer and the regression methods, which rely on the use of tracer species of primary and secondary OC formation, provided intermediate estimates of SOC as 30% of OC. The other two methods, chemical mass balance (CMB) and positive matrix factorization (PMF) solve mass balance equations to estimate primary and secondary fractions based on source profiles and statistically-derived common factors, respectively. CMB had the highest estimate of SOC (46% of OC) while PMF led to the lowest (26% of OC). The comparison of SOC uncertainties, estimated based on propagation of errors, led to the regression method having the lowest uncertainty among the four methods. We compared the estimates with the water soluble fraction of the OC, which has been suggested as a surrogate of SOC when biomass burning is negligible, and found a similar trend with SOC estimates from the regression method. The regression method also showed the strongest correlation with daily SOC estimates from CMB using molecular markers. The regression method shows advantages over the other methods in the calculation of a long-term series of SOC estimates.

The role of correlations in uncertainty quantification of transportation relevant fuel models

DOE PAGES

Fridlyand, Aleksandr; Johnson, Matthew S.; Goldsborough, S. Scott; ...

2017-02-03

Large reaction mechanisms are often used to describe the combustion behavior of transportation-relevant fuels like gasoline, where these are typically represented by surrogate blends, e.g., n-heptane/iso-octane/toluene. We describe efforts to quantify the uncertainty in the predictions of such mechanisms at realistic engine conditions, seeking to better understand the robustness of the model as well as the important reaction pathways and their impacts on combustion behavior. In this work, we examine the importance of taking into account correlations among reactions that utilize the same rate rules and those with multiple product channels on forward propagation of uncertainty by Monte Carlo simulations.more » Automated means are developed to generate the uncertainty factor assignment for a detailed chemical kinetic mechanism, by first uniquely identifying each reacting species, then sorting each of the reactions based on the rate rule utilized. Simulation results reveal that in the low temperature combustion regime for iso-octane, the majority of the uncertainty in the model predictions can be attributed to low temperature reactions of the fuel sub-mechanism. The foundational, or small-molecule chemistry (C 0-C 4) only contributes significantly to uncertainties in the predictions at the highest temperatures (Tc=900 K). Accounting for correlations between important reactions is shown to produce non-negligible differences in the estimates of uncertainty. Including correlations among reactions that use the same rate rules increases uncertainty in the model predictions, while accounting for correlations among reactions with multiple branches decreases uncertainty in some cases. Significant non-linear response is observed in the model predictions depending on how the probability distributions of the uncertain rate constants are defined.Finally, we concluded that care must be exercised in defining these probability distributions in order to reduce bias, and physically unrealistic estimates in the forward propagation of uncertainty for a range of UQ activities.« less

Bounding the role of black carbon in the climate system: A scientific assessment

NASA Astrophysics Data System (ADS)

Bond, T. C.; Doherty, S. J.; Fahey, D. W.; Forster, P. M.; Berntsen, T.; DeAngelo, B. J.; Flanner, M. G.; Ghan, S.; Kärcher, B.; Koch, D.; Kinne, S.; Kondo, Y.; Quinn, P. K.; Sarofim, M. C.; Schultz, M. G.; Schulz, M.; Venkataraman, C.; Zhang, H.; Zhang, S.; Bellouin, N.; Guttikunda, S. K.; Hopke, P. K.; Jacobson, M. Z.; Kaiser, J. W.; Klimont, Z.; Lohmann, U.; Schwarz, J. P.; Shindell, D.; Storelvmo, T.; Warren, S. G.; Zender, C. S.

2013-06-01

carbon aerosol plays a unique and important role in Earth's climate system. Black carbon is a type of carbonaceous material with a unique combination of physical properties. This assessment provides an evaluation of black-carbon climate forcing that is comprehensive in its inclusion of all known and relevant processes and that is quantitative in providing best estimates and uncertainties of the main forcing terms: direct solar absorption; influence on liquid, mixed phase, and ice clouds; and deposition on snow and ice. These effects are calculated with climate models, but when possible, they are evaluated with both microphysical measurements and field observations. Predominant sources are combustion related, namely, fossil fuels for transportation, solid fuels for industrial and residential uses, and open burning of biomass. Total global emissions of black carbon using bottom-up inventory methods are 7500 Gg yr-1 in the year 2000 with an uncertainty range of 2000 to 29000. However, global atmospheric absorption attributable to black carbon is too low in many models and should be increased by a factor of almost 3. After this scaling, the best estimate for the industrial-era (1750 to 2005) direct radiative forcing of atmospheric black carbon is +0.71 W m-2 with 90% uncertainty bounds of (+0.08, +1.27) W m-2. Total direct forcing by all black carbon sources, without subtracting the preindustrial background, is estimated as +0.88 (+0.17, +1.48) W m-2. Direct radiative forcing alone does not capture important rapid adjustment mechanisms. A framework is described and used for quantifying climate forcings, including rapid adjustments. The best estimate of industrial-era climate forcing of black carbon through all forcing mechanisms, including clouds and cryosphere forcing, is +1.1 W m-2 with 90% uncertainty bounds of +0.17 to +2.1 W m-2. Thus, there is a very high probability that black carbon emissions, independent of co-emitted species, have a positive forcing and warm the climate. We estimate that black carbon, with a total climate forcing of +1.1 W m-2, is the second most important human emission in terms of its climate forcing in the present-day atmosphere; only carbon dioxide is estimated to have a greater forcing. Sources that emit black carbon also emit other short-lived species that may either cool or warm climate. Climate forcings from co-emitted species are estimated and used in the framework described herein. When the principal effects of short-lived co-emissions, including cooling agents such as sulfur dioxide, are included in net forcing, energy-related sources (fossil fuel and biofuel) have an industrial-era climate forcing of +0.22 (-0.50 to +1.08) W m-2 during the first year after emission. For a few of these sources, such as diesel engines and possibly residential biofuels, warming is strong enough that eliminating all short-lived emissions from these sources would reduce net climate forcing (i.e., produce cooling). When open burning emissions, which emit high levels of organic matter, are included in the total, the best estimate of net industrial-era climate forcing by all short-lived species from black-carbon-rich sources becomes slightly negative (-0.06 W m-2 with 90% uncertainty bounds of -1.45 to +1.29 W m-2). The uncertainties in net climate forcing from black-carbon-rich sources are substantial, largely due to lack of knowledge about cloud interactions with both black carbon and co-emitted organic carbon. In prioritizing potential black-carbon mitigation actions, non-science factors, such as technical feasibility, costs, policy design, and implementation feasibility play important roles. The major sources of black carbon are presently in different stages with regard to the feasibility for near-term mitigation. This assessment, by evaluating the large number and complexity of the associated physical and radiative processes in black-carbon climate forcing, sets a baseline from which to improve future climate forcing estimates.

Bounding the Role of Black Carbon in the Climate System: a Scientific Assessment

NASA Technical Reports Server (NTRS)

Bond, T. C.; Doherty, S. J.; Fahey, D. W.; Forster, P. M.; Bernsten, T.; DeAngelo, B. J.; Flanner, M. G.; Ghan, S.; Karcher, B.; Koch, D.;

Uncertainty of InSAR velocity fields for measuring long-wavelength displacement

NASA Astrophysics Data System (ADS)

Fattahi, H.; Amelung, F.

2014-12-01

Long-wavelength artifacts in InSAR data are the main limitation to measure long-wavelength displacement; they are traditionally attributed mainly to the inaccuracy of the satellite orbits (orbital errors). However, most satellites are precisely tracked resulting in uncertainties of orbits of 2-10 cm. Orbits of these satellites are thus precise enough to obtain precise velocity fields with uncertainties better than 1 mm/yr/100 km for older satellites (e.g. Envisat) and better than 0.2 mm/yr/100 km for modern satellites (e.g. TerraSAR-X and Sentinel-1) [Fattahi & Amelung, 2014]. Such accurate velocity fields are achievable if long-wavelength artifacts from sources other than orbital errors are identified and corrected for. We present a modified Small Baseline approach to measure long-wavelength deformation and evaluate the uncertainty of these measurements. We use a redundant network of interferograms for detection and correction of unwrapping errors to ensure the unbiased estimation of phase history. We distinguish between different sources of long-wavelength artifacts and correct those introduced by atmospheric delay, topographic residuals, timing errors, processing approximations and hardware issues. We evaluate the uncertainty of the velocity fields using a covariance matrix with the contributions from orbital errors and residual atmospheric delay. For contributions from the orbital errors we consider the standard deviation of velocity gradients in range and azimuth directions as a function of orbital uncertainty. For contributions from the residual atmospheric delay we use several approaches including the structure functions of InSAR time-series epochs, the predicted delay from numerical weather models and estimated wet delay from optical imagery. We validate this InSAR approach for measuring long-wavelength deformation by comparing InSAR velocity fields over ~500 km long swath across the southern San Andreas fault system with independent GPS velocities and examine the estimated uncertainties in several non-deforming areas. We show the efficiency of the approach to study the continental deformation across the Chaman fault system at the western Indian plate boundary. Ref: Fattahi, H., & Amelung, F., (2014), InSAR uncertainty due to orbital errors, Geophys, J. Int (in press).

Using Kriging with a heterogeneous measurement error to improve the accuracy of extreme precipitation return level estimation

NASA Astrophysics Data System (ADS)

Yin, Shui-qing; Wang, Zhonglei; Zhu, Zhengyuan; Zou, Xu-kai; Wang, Wen-ting

2018-07-01

Extreme precipitation can cause flooding and may result in great economic losses and deaths. The return level is a commonly used measure of extreme precipitation events and is required for hydrological engineer designs, including those of sewerage systems, dams, reservoirs and bridges. In this paper, we propose a two-step method to estimate the return level and its uncertainty for a study region. In the first step, we use the generalized extreme value distribution, the L-moment method and the stationary bootstrap to estimate the return level and its uncertainty at the site with observations. In the second step, a spatial model incorporating the heterogeneous measurement errors and covariates is trained to estimate return levels at sites with no observations and to improve the estimates at sites with limited information. The proposed method is applied to the daily rainfall data from 273 weather stations in the Haihe river basin of North China. We compare the proposed method with two alternatives: the first one is based on the ordinary Kriging method without measurement error, and the second one smooths the estimated location and scale parameters of the generalized extreme value distribution by the universal Kriging method. Results show that the proposed method outperforms its counterparts. We also propose a novel approach to assess the two-step method by comparing it with the at-site estimation method with a series of reduced length of observations. Estimates of the 2-, 5-, 10-, 20-, 50- and 100-year return level maps and the corresponding uncertainties are provided for the Haihe river basin, and a comparison with those released by the Hydrology Bureau of Ministry of Water Resources of China is made.

Estimate of the potential impact of folic acid fortification of corn masa flour on the prevention of neural tube defects.

PubMed

Tinker, Sarah C; Devine, Owen; Mai, Cara; Hamner, Heather C; Reefhuis, Jennita; Gilboa, Suzanne M; Dowling, Nicole F; Honein, Margaret A

2013-10-01

Hispanics in the US have a higher prevalence of neural tube defect (NTD) -affected pregnancies than non-Hispanic whites, and lower median total folic acid (FA) intake. FA fortification of corn masa flour (CMF) is a policy-level intervention for NTD prevention; however, the impact on NTD prevalence has not been estimated. We developed a model to estimate the percentage reduction in prevalence of spina bifida and anencephaly (NTDs) that could occur with FA fortification of CMF. Model inputs included estimates of the percentage reduction in United States NTD prevalence attributed to FA fortification of enriched cereal grain products (1995-1996 vs. 1998-2002), the increase in median FA intake after enriched cereal grain product fortification, and the estimated increase in median FA intake that could occur with CMF fortification at the same level as enriched cereal grain products (140 μg/100 g). We used Monte Carlo simulation to quantify uncertainty. We stratified analyses by racial/ethnic group and rounded results to the nearest 10. We estimated CMF fortification could prevent 30 Hispanic infants from having spina bifida (95% uncertainty interval: 0, 80) and 10 infants from having anencephaly (95% uncertainty interval: 0, 40) annually. The estimated impact among non-Hispanic whites and blacks was smaller. CMF fortification with FA could prevent from 0 to 120 infants, with the most likely value of approximately 40, from having spina bifida or anencephaly among Hispanics, the population most likely to benefit from the proposed intervention. While this estimated reduction is unlikely to be discernible using current birth defect surveillance methods, it still suggests an important benefit to the target population. Copyright © 2013 Wiley Periodicals, Inc.

Estimation of Pre-industrial Nitrous Oxide Emission from the Terrestrial Biosphere

NASA Astrophysics Data System (ADS)

Xu, R.; Tian, H.; Lu, C.; Zhang, B.; Pan, S.; Yang, J.

2015-12-01

Nitrous oxide (N2O) is currently the third most important greenhouse gases (GHG) after methane (CH4) and carbon dioxide (CO2). Global N2O emission increased substantially primarily due to reactive nitrogen (N) enrichment through fossil fuel combustion, fertilizer production, and legume crop cultivation etc. In order to understand how climate system is perturbed by anthropogenic N2O emissions from the terrestrial biosphere, it is necessary to better estimate the pre-industrial N2O emissions. Previous estimations of natural N2O emissions from the terrestrial biosphere range from 3.3-9.0 Tg N2O-N yr-1. This large uncertainty in the estimation of pre-industrial N2O emissions from the terrestrial biosphere may be caused by uncertainty associated with key parameters such as maximum nitrification and denitrification rates, half-saturation coefficients of soil ammonium and nitrate, N fixation rate, and maximum N uptake rate. In addition to the large estimation range, previous studies did not provide an estimate on preindustrial N2O emissions at regional and biome levels. In this study, we applied a process-based coupled biogeochemical model to estimate the magnitude and spatial patterns of pre-industrial N2O fluxes at biome and continental scales as driven by multiple input data, including pre-industrial climate data, atmospheric CO2 concentration, N deposition, N fixation, and land cover types and distributions. Uncertainty associated with key parameters is also evaluated. Finally, we generate sector-based estimates of pre-industrial N2O emission, which provides a reference for assessing the climate forcing of anthropogenic N2O emission from the land biosphere.

Scaling uncertainties in estimating canopy foliar maintenance respiration for black spruce ecosystems in Alaska

USGS Publications Warehouse

Zhang, X.; McGuire, A.D.; Ruess, Roger W.

2006-01-01

A major challenge confronting the scientific community is to understand both patterns of and controls over spatial and temporal variability of carbon exchange between boreal forest ecosystems and the atmosphere. An understanding of the sources of variability of carbon processes at fine scales and how these contribute to uncertainties in estimating carbon fluxes is relevant to representing these processes at coarse scales. To explore some of the challenges and uncertainties in estimating carbon fluxes at fine to coarse scales, we conducted a modeling analysis of canopy foliar maintenance respiration for black spruce ecosystems of Alaska by scaling empirical hourly models of foliar maintenance respiration (Rm) to estimate canopy foliar Rm for individual stands. We used variation in foliar N concentration among stands to develop hourly stand-specific models and then developed an hourly pooled model. An uncertainty analysis identified that the most important parameter affecting estimates of canopy foliar Rm was one that describes R m at 0??C per g N, which explained more than 55% of variance in annual estimates of canopy foliar Rm. The comparison of simulated annual canopy foliar Rm identified significant differences between stand-specific and pooled models for each stand. This result indicates that control over foliar N concentration should be considered in models that estimate canopy foliar Rm of black spruce stands across the landscape. In this study, we also temporally scaled the hourly stand-level models to estimate canopy foliar Rm of black spruce stands using mean monthly temperature data. Comparisons of monthly Rm between the hourly and monthly versions of the models indicated that there was very little difference between the estimates of hourly and monthly models, suggesting that hourly models can be aggregated to use monthly input data with little loss of precision. We conclude that uncertainties in the use of a coarse-scale model for estimating canopy foliar Rm at regional scales depend on uncertainties in representing needle-level respiration and on uncertainties in representing the spatial variability of canopy foliar N across a region. The development of spatial data sets of canopy foliar N represents a major challenge in estimating canopy foliar maintenance respiration at regional scales. ?? Springer 2006.

The Spatial Distribution of Forest Biomass in the Brazilian Amazon: A Comparison of Estimates

NASA Technical Reports Server (NTRS)

Houghton, R. A.; Lawrence, J. L.; Hackler, J. L.; Brown, S.

2001-01-01

The amount of carbon released to the atmosphere as a result of deforestation is determined, in part, by the amount of carbon held in the biomass of the forests converted to other uses. Uncertainty in forest biomass is responsible for much of the uncertainty in current estimates of the flux of carbon from land-use change. We compared several estimates of forest biomass for the Brazilian Amazon, based on spatial interpolations of direct measurements, relationships to climatic variables, and remote sensing data. We asked three questions. First, do the methods yield similar estimates? Second, do they yield similar spatial patterns of distribution of biomass? And, third, what factors need most attention if we are to predict more accurately the distribution of forest biomass over large areas? Amazonian forests (including dead and below-ground biomass) vary by more than a factor of two, from a low of 39 PgC to a high of 93 PgC. Furthermore, the estimates disagree as to the regions of high and low biomass. The lack of agreement among estimates confirms the need for reliable determination of aboveground biomass over large areas. Potential methods include direct measurement of biomass through forest inventories with improved allometric regression equations, dynamic modeling of forest recovery following observed stand-replacing disturbances (the approach used in this research), and estimation of aboveground biomass from airborne or satellite-based instruments sensitive to the vertical structure plant canopies.

A subagging regression method for estimating the qualitative and quantitative state of groundwater

NASA Astrophysics Data System (ADS)

Jeong, J.; Park, E.; Choi, J.; Han, W. S.; Yun, S. T.

2016-12-01

A subagging regression (SBR) method for the analysis of groundwater data pertaining to the estimation of trend and the associated uncertainty is proposed. The SBR method is validated against synthetic data competitively with other conventional robust and non-robust methods. From the results, it is verified that the estimation accuracies of the SBR method are consistent and superior to those of the other methods and the uncertainties are reasonably estimated where the others have no uncertainty analysis option. To validate further, real quantitative and qualitative data are employed and analyzed comparatively with Gaussian process regression (GPR). For all cases, the trend and the associated uncertainties are reasonably estimated by SBR, whereas the GPR has limitations in representing the variability of non-Gaussian skewed data. From the implementations, it is determined that the SBR method has potential to be further developed as an effective tool of anomaly detection or outlier identification in groundwater state data.

Characterizing Variability and Uncertainty in Exposure Assessments Improves links to Environmental Decision-Making

EPA Science Inventory

Environmental Decisions often rely upon observational data or model estimates. For instance, the evaluation of human health or ecological risks often includes information on pollutant emission rates, environmental concentrations, exposures, and exposure/dose-response data. Whet...

Improving uncertainty estimates: Inter-annual variability in Ireland

NASA Astrophysics Data System (ADS)

Pullinger, D.; Zhang, M.; Hill, N.; Crutchley, T.

2017-11-01

This paper addresses the uncertainty associated with inter-annual variability used within wind resource assessments for Ireland in order to more accurately represent the uncertainties within wind resource and energy yield assessments. The study was undertaken using a total of 16 ground stations (Met Eireann) and corresponding reanalysis datasets to provide an update to previous work on this topic undertaken nearly 20 years ago. The results of the work demonstrate that the previously reported 5.4% of wind speed inter-annual variability is considered to be appropriate, guidance is given on how to provide a robust assessment of IAV using available sources of data including ground stations, MERRA-2 and ERA-Interim.

The effect of model uncertainty on some optimal routing problems

NASA Technical Reports Server (NTRS)

Mohanty, Bibhu; Cassandras, Christos G.

1991-01-01

The effect of model uncertainties on optimal routing in a system of parallel queues is examined. The uncertainty arises in modeling the service time distribution for the customers (jobs, packets) to be served. For a Poisson arrival process and Bernoulli routing, the optimal mean system delay generally depends on the variance of this distribution. However, as the input traffic load approaches the system capacity the optimal routing assignment and corresponding mean system delay are shown to converge to a variance-invariant point. The implications of these results are examined in the context of gradient-based routing algorithms. An example of a model-independent algorithm using online gradient estimation is also included.

A new way to ask the experts: Rating radioactive waste risks

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

Kerr, R.A.

1996-11-08

The possible risks of a proposed nuclear waste repository at Yucca Mountain include the dozen or more young volcanos near by. Now some earth scientists have a new approach to evaluating hazards accounting for uncertainty at every step - `expert elicitation.` This pulls together a panel of experts, carefully assesses the uncertainties of each of their views then mathematically combines their risk estimates along with the accompanying uncertainties. The article goes on to describe just such a panel which considered seismic hazards to Yucca Mountain, how they came to their conclusions, the arguments about the conclusions, and the future ofmore » expert elicitation in evaluating the risks of nuclear waste disposal.« less

Uncertainty analysis of signal deconvolution using a measured instrument response function

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

Hartouni, E. P.; Beeman, B.; Caggiano, J. A.

2016-10-05

A common analysis procedure minimizes the ln-likelihood that a set of experimental observables matches a parameterized model of the observation. The model includes a description of the underlying physical process as well as the instrument response function (IRF). Here, we investigate the National Ignition Facility (NIF) neutron time-of-flight (nTOF) spectrometers, the IRF is constructed from measurements and models. IRF measurements have a finite precision that can make significant contributions to the uncertainty estimate of the physical model’s parameters. Finally, we apply a Bayesian analysis to properly account for IRF uncertainties in calculating the ln-likelihood function used to find the optimummore » physical parameters.« less

Application of data fusion modeling (DFM) to site characterization

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

Porter, D.W.; Gibbs, B.P.; Jones, W.F.

1996-01-01

Subsurface characterization is faced with substantial uncertainties because the earth is very heterogeneous, and typical data sets are fragmented and disparate. DFM removes many of the data limitations of current methods to quantify and reduce uncertainty for a variety of data types and models. DFM is a methodology to compute hydrogeological state estimates and their uncertainties from three sources of information: measured data, physical laws, and statistical models for spatial heterogeneities. The benefits of DFM are savings in time and cost through the following: the ability to update models in real time to help guide site assessment, improved quantification ofmore » uncertainty for risk assessment, and improved remedial design by quantifying the uncertainty in safety margins. A Bayesian inverse modeling approach is implemented with a Gauss Newton method where spatial heterogeneities are viewed as Markov random fields. Information from data, physical laws, and Markov models is combined in a Square Root Information Smoother (SRIS). Estimates and uncertainties can be computed for heterogeneous hydraulic conductivity fields in multiple geological layers from the usually sparse hydraulic conductivity data and the often more plentiful head data. An application of DFM to the Old Burial Ground at the DOE Savannah River Site will be presented. DFM estimates and quantifies uncertainty in hydrogeological parameters using variably saturated flow numerical modeling to constrain the estimation. Then uncertainties are propagated through the transport modeling to quantify the uncertainty in tritium breakthrough curves at compliance points.« less

Application of data fusion modeling (DFM) to site characterization

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

Porter, D.W.; Gibbs, B.P.; Jones, W.F.

1996-12-31

Subsurface characterization is faced with substantial uncertainties because the earth is very heterogeneous, and typical data sets are fragmented and disparate. DFM removes many of the data limitations of current methods to quantify and reduce uncertainty for a variety of data types and models. DFM is a methodology to compute hydrogeological state estimates and their uncertainties from three sources of information: measured data, physical laws, and statistical models for spatial heterogeneities. The benefits of DFM are savings in time and cost through the following: the ability to update models in real time to help guide site assessment, improved quantification ofmore » uncertainty for risk assessment, and improved remedial design by quantifying the uncertainty in safety margins. A Bayesian inverse modeling approach is implemented with a Gauss Newton method where spatial heterogeneities are viewed as Markov random fields. Information from data, physical laws, and Markov models is combined in a Square Root Information Smoother (SRIS). Estimates and uncertainties can be computed for heterogeneous hydraulic conductivity fields in multiple geological layers from the usually sparse hydraulic conductivity data and the often more plentiful head data. An application of DFM to the Old Burial Ground at the DOE Savannah River Site will be presented. DFM estimates and quantifies uncertainty in hydrogeological parameters using variably saturated flow numerical modeling to constrain the estimation. Then uncertainties are propagated through the transport modeling to quantify the uncertainty in tritium breakthrough curves at compliance points.« less

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

NASA Astrophysics Data System (ADS)

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

2007-03-01

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

Impact of meteorological inflow uncertainty on tracer transport and source estimation in urban atmospheres

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

Lucas, Donald D.; Gowardhan, Akshay; Cameron-Smith, Philip

2015-08-08

Here, a computational Bayesian inverse technique is used to quantify the effects of meteorological inflow uncertainty on tracer transport and source estimation in a complex urban environment. We estimate a probability distribution of meteorological inflow by comparing wind observations to Monte Carlo simulations from the Aeolus model. Aeolus is a computational fluid dynamics model that simulates atmospheric and tracer flow around buildings and structures at meter-scale resolution. Uncertainty in the inflow is propagated through forward and backward Lagrangian dispersion calculations to determine the impact on tracer transport and the ability to estimate the release location of an unknown source. Ourmore » uncertainty methods are compared against measurements from an intensive observation period during the Joint Urban 2003 tracer release experiment conducted in Oklahoma City.« less

Evaluating Variability and Uncertainty of Geological Strength Index at a Specific Site

NASA Astrophysics Data System (ADS)

Wang, Yu; Aladejare, Adeyemi Emman

2016-09-01

Geological Strength Index (GSI) is an important parameter for estimating rock mass properties. GSI can be estimated from quantitative GSI chart, as an alternative to the direct observational method which requires vast geological experience of rock. GSI chart was developed from past observations and engineering experience, with either empiricism or some theoretical simplifications. The GSI chart thereby contains model uncertainty which arises from its development. The presence of such model uncertainty affects the GSI estimated from GSI chart at a specific site; it is, therefore, imperative to quantify and incorporate the model uncertainty during GSI estimation from the GSI chart. A major challenge for quantifying the GSI chart model uncertainty is a lack of the original datasets that have been used to develop the GSI chart, since the GSI chart was developed from past experience without referring to specific datasets. This paper intends to tackle this problem by developing a Bayesian approach for quantifying the model uncertainty in GSI chart when using it to estimate GSI at a specific site. The model uncertainty in the GSI chart and the inherent spatial variability in GSI are modeled explicitly in the Bayesian approach. The Bayesian approach generates equivalent samples of GSI from the integrated knowledge of GSI chart, prior knowledge and observation data available from site investigation. Equations are derived for the Bayesian approach, and the proposed approach is illustrated using data from a drill and blast tunnel project. The proposed approach effectively tackles the problem of how to quantify the model uncertainty that arises from using GSI chart for characterization of site-specific GSI in a transparent manner.

Uncertainty of chromatic dispersion estimation from transmitted waveforms in direct detection systems

NASA Astrophysics Data System (ADS)

Lach, Zbigniew T.

2017-08-01

A possibility is shown of a non-disruptive estimation of chromatic dispersion in a fiber of an intensity modulation communication line under work conditions. Uncertainty of the chromatic dispersion estimates is analyzed and quantified with the use of confidence intervals.

Kalman filter approach for uncertainty quantification in time-resolved laser-induced incandescence.

PubMed

Hadwin, Paul J; Sipkens, Timothy A; Thomson, Kevin A; Liu, Fengshan; Daun, Kyle J

2018-03-01

Time-resolved laser-induced incandescence (TiRe-LII) data can be used to infer spatially and temporally resolved volume fractions and primary particle size distributions of soot-laden aerosols, but these estimates are corrupted by measurement noise as well as uncertainties in the spectroscopic and heat transfer submodels used to interpret the data. Estimates of the temperature, concentration, and size distribution of soot primary particles within a sample aerosol are typically made by nonlinear regression of modeled spectral incandescence decay, or effective temperature decay, to experimental data. In this work, we employ nonstationary Bayesian estimation techniques to infer aerosol properties from simulated and experimental LII signals, specifically the extended Kalman filter and Schmidt-Kalman filter. These techniques exploit the time-varying nature of both the measurements and the models, and they reveal how uncertainty in the estimates computed from TiRe-LII data evolves over time. Both techniques perform better when compared with standard deterministic estimates; however, we demonstrate that the Schmidt-Kalman filter produces more realistic uncertainty estimates.

Improving the quantification of flash flood hydrographs and reducing their uncertainty using noncontact streamgauging methods

NASA Astrophysics Data System (ADS)

Branger, Flora; Dramais, Guillaume; Horner, Ivan; Le Boursicaud, Raphaël; Le Coz, Jérôme; Renard, Benjamin

2015-04-01

Continuous river discharge data are crucial for the study and management of floods. In most river discharge monitoring networks, these data are obtained at gauging stations, where the stage-discharge relation is modelled with a rating curve to derive discharge from the measurement of water level in the river. Rating curves are usually established using individual ratings (or gaugings). However, using traditional gauging methods during flash floods is challenging for many reasons including hazardous flow conditions (for both equipment and people), short duration of the flood events, transient flows during the time needed to perform the gauging, etc. The lack of gaugings implies that the rating curve is often extrapolated well beyond the gauged range for the highest floods, inducing large uncertainties in the computed discharges. We deployed two remote techniques for gauging floods and improving stage-discharge relations for high flow conditions at several hydrometric stations throughout the Ardèche river catchment in France : (1) permanent video-recording stations enabling the implementation of the image analysis LS-PIV technique (Large Scale Particle Image Velocimetry) ; (2) and mobile gaugings using handheld Surface Velocity Radars (SVR). These gaugings were used to estimate the rating curve and its uncertainty using the Bayesian method BaRatin (Le Coz et al., 2014). Importantly, this method explicitly accounts for the uncertainty of individual gaugings, which is especially relevant for remote gaugings since their uncertainty is generally much higher than that of standard intrusive gauging methods. Then, the uncertainty of streamflow records was derived by combining the uncertainty of the rating curve and the uncertainty of stage records. We assessed the impact of these methodological developments for peak flow estimation and for flood descriptors at various time steps. The combination of field measurement innovation and statistical developments allows efficiently quantifying and reducing the uncertainties of flood peak estimates and flood descriptors at gauging stations. The noncontact streamgauging techniques used in our field campaign strategy have complementary interests. Permanent LSPIV stations, once installed and calibrated, can monitor floods automatically and perform many gaugings during a single event, thus documenting the rise, peak and recession of floods. SVR gaugings are more "one shot" gaugings but can be deployed quickly and at minimal cost over a large territory. Both of these noncontact techniques contribute to a significant reduction of uncertainty on peak hydrographs and flood descriptors at different time steps for a given catchment. 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 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

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.

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.

A Decision Support System for effective use of probability forecasts

NASA Astrophysics Data System (ADS)

De Kleermaeker, Simone; Verkade, Jan

2013-04-01

Often, water management decisions are based on hydrological forecasts. These forecasts, however, are affected by inherent uncertainties. It is increasingly common for forecasting agencies to make explicit estimates of these uncertainties and thus produce probabilistic forecasts. Associated benefits include the decision makers' increased awareness of forecasting uncertainties and the potential for risk-based decision-making. Also, a stricter separation of responsibilities between forecasters and decision maker can be made. However, simply having probabilistic forecasts available is not sufficient to realise the associated benefits. Additional effort is required in areas such as forecast visualisation and communication, decision making in uncertainty and forecast verification. Also, revised separation of responsibilities requires a shift in institutional arrangements and responsibilities. A recent study identified a number of additional issues related to the effective use of probability forecasts. When moving from deterministic to probability forecasting, a dimension is added to an already multi-dimensional problem; this makes it increasingly difficult for forecast users to extract relevant information from a forecast. A second issue is that while probability forecasts provide a necessary ingredient for risk-based decision making, other ingredients may not be present. For example, in many cases no estimates of flood damage, of costs of management measures and of damage reduction are available. This paper presents the results of the study, including some suggestions for resolving these issues and the integration of those solutions in a prototype decision support system (DSS). A pathway for further development of the DSS is outlined.

Estimating the Uncertainty In Diameter Growth Model Predictions and Its Effects On The Uncertainty of Annual Inventory Estimates

Treesearch

Ronald E. McRoberts; Veronica C. Lessard

2001-01-01

Uncertainty in diameter growth predictions is attributed to three general sources: measurement error or sampling variability in predictor variables, parameter covariances, and residual or unexplained variation around model expectations. Using measurement error and sampling variability distributions obtained from the literature and Monte Carlo simulation methods, the...

An assessment of uncertainty in forest carbon budget projections

Treesearch

Linda S. Heath; James E. Smith

2000-01-01

Estimates of uncertainty are presented for projections of forest carbon inventory and average annual net carbon flux on private timberland in the US using the model FORCARB. Uncertainty in carbon inventory was approximately ±9% (2000 million metric tons) of the estimated median in the year 2000, rising to 11% (2800 million metric tons) in projection year 2040...

Leaf area index uncertainty estimates for model-data fusion applications

Treesearch

Andrew D. Richardson; D. Bryan Dail; D.Y. Hollinger

2011-01-01

Estimates of data uncertainties are required to integrate different observational data streams as model constraints using model-data fusion. We describe an approach with which random and systematic uncertainties in optical measurements of leaf area index [LAI] can be quantified. We use data from a measurement campaign at the spruce-dominated Howland Forest AmeriFlux...

The AgMIP Wheat Pilot: A multi-model approach for climate change impact assessments.

NASA Astrophysics Data System (ADS)

Asseng, S.

2012-12-01

Asseng S., F. Ewert, C. Rosenzweig, J.W. Jones, J.L. Hatfield, A. Ruane, K.J. Boote, P. Thorburn, R.P. Rötter, D. Cammarano, N. Brisson, B. Basso, P. Martre, D. Ripoche, P. Bertuzzi, P. Steduto, L. Heng, M.A. Semenov, P. Stratonovitch, C. Stockle, G. O'Leary, P.K. Aggarwal, S. Naresh Kumar, C. Izaurralde, J.W. White, L.A. Hunt, R. Grant, K.C. Kersebaum, T. Palosuo, J. Hooker, T. Osborne, J. Wolf, I. Supit, J.E. Olesen, J. Doltra, C. Nendel, S. Gayler, J. Ingwersen, E. Priesack, T. Streck, F. Tao, C. Müller, K. Waha, R. Goldberg, C. Angulo, I. Shcherbak, C. Biernath, D. Wallach, M. Travasso, A. Challinor. Abstract: Crop simulation models have been used to assess the impact of climate change on agriculture. These assessments are often carried out with a single model in a limited number of environments and without determining the uncertainty of simulated impacts. There is a need for a coordinated effort bringing together multiple modeling teams which has been recognized by the Agricultural Model Intercomparison and Improvement Project (AgMIP; www.agmip.org). AgMIP aims to provide more robust estimates of climate impacts on crop yields and agricultural trade, including estimates of associated uncertainties. Here, we present the AgMIP Wheat Pilot Study, the most comprehensive model intercomparison of the response of wheat crops to climate change to date, including 27 wheat models. Crop model uncertainties in assessing climate change impacts are explored and compared with field experimental and Global Circulation Model uncertainties. Causes of impact uncertainties and ways to reduce these are discussed.

Calibration of TOPEX/POSEIDON at Platform Harvest

NASA Technical Reports Server (NTRS)

Christensen, E. J.; Haines, B. J.; Keihm, S. J.; Morris, C. S.; Norman, R. A.; Purcell, G. H.; Williams, B. G.; Wilson, B. D.; Born, G. H.; Parke, M. E.

1994-01-01

We present estimates for the mean bias of the TOPEX/POSEIDON NASA altimeter (ALT) and the Centre National d'Etudes Spatiales altimeter (SSALT) using in-situ data gathered at Platform Harvest during the first 36 cycles of the mission. Data for 21 overflights of the ALT and six overflights of the SSALT have been analyzed. The analysis includes an independent assessment of in-situ measurements of sea level, the radial component of the orbit, wet tropospheric path delay, and ionospheric path delay. (The sign convention used is such that, to correct the geophysical data record values for sea level, add the bias algebraically. Unless otherwise stated, the uncertainty in a given parameter is depicted by +/- sigma(sub x), where sigma(sub x) is the sample standard deviation of x about the mean.) Tide gauges at Harvest provide estimates of sea level with an uncertainty of +/- 1.5 cm. The uncertainty in the radial component of the orbit is estimated to be +/- 1.3 cm. In-situ measurements of tropopsheric path delay at Harvest compare to within +/- 1.3 cm of the TOPEX/POSEIDON microwave radiometer, and in-situ measurements of the ionospheric path delay compare to within -0.4 +/- 0.7 cm of the dual-frequency ALT and 1.1 +/- 0.6 cm of Doppler orbitography and radiopositioning integrated by satellite. We obtain mean bias estimates of -14.5 +/- 2.9 cm for the ALT and +0.9 +/- 3.1 cm for the SSALT (where the uncertainties are based on the standard deviation of the estimated mean (sigma(sub bar x/y), which is derived from sample statistics and estimates for errors that cannot be observed). These results are consistent with independent estimates for the relative bias between the two altimeters. A linear regression applied to the complete set of data shows that there is a discernable secular trend in the time series for the ALT bias estimates. A preliminary analysis of data obtained through cycle 48 suggests that the apparent secular drift may be the result of a poorly sampled annual signal.

Estimation of Land Surface Fluxes and Their Uncertainty via Variational Data Assimilation Approach

NASA Astrophysics Data System (ADS)

Abdolghafoorian, A.; Farhadi, L.

2016-12-01

Accurate estimation of land surface heat and moisture fluxes as well as root zone soil moisture is crucial in various hydrological, meteorological, and agricultural applications. "In situ" measurements of these fluxes are costly and cannot be readily scaled to large areas relevant to weather and climate studies. Therefore, there is a need for techniques to make quantitative estimates of heat and moisture fluxes using land surface state variables. In this work, we applied a novel approach based on the variational data assimilation (VDA) methodology to estimate land surface fluxes and soil moisture profile from the land surface states. This study accounts for the strong linkage between terrestrial water and energy cycles by coupling the dual source energy balance equation with the water balance equation through the mass flux of evapotranspiration (ET). Heat diffusion and moisture diffusion into the column of soil are adjoined to the cost function as constraints. This coupling results in more accurate prediction of land surface heat and moisture fluxes and consequently soil moisture at multiple depths with high temporal frequency as required in many hydrological, environmental and agricultural applications. One of the key limitations of VDA technique is its tendency to be ill-posed, meaning that a continuum of possibilities exists for different parameters that produce essentially identical measurement-model misfit errors. On the other hand, the value of heat and moisture flux estimation to decision-making processes is limited if reasonable estimates of the corresponding uncertainty are not provided. In order to address these issues, in this research uncertainty analysis will be performed to estimate the uncertainty of retrieved fluxes and root zone soil moisture. The assimilation algorithm is tested with a series of experiments using a synthetic data set generated by the simultaneous heat and water (SHAW) model. We demonstrate the VDA performance by comparing the (synthetic) true measurements (including profile of soil moisture and temperature, land surface water and heat fluxes, and root water uptake) with VDA estimates. In addition, the feasibility of extending the proposed approach to use remote sensing observations is tested by limiting the number of LST observations and soil moisture observations.

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.

Estimating decades-long trends in petroleum field energy return on investment (EROI) with an engineering-based model.

PubMed

Tripathi, Vinay S; Brandt, Adam R

2017-01-01

This paper estimates changes in the energy return on investment (EROI) for five large petroleum fields over time using the Oil Production Greenhouse Gas Emissions Estimator (OPGEE). The modeled fields include Cantarell (Mexico), Forties (U.K.), Midway-Sunset (U.S.), Prudhoe Bay (U.S.), and Wilmington (U.S.). Data on field properties and production/processing parameters were obtained from a combination of government and technical literature sources. Key areas of uncertainty include details of the oil and gas surface processing schemes. We aim to explore how long-term trends in depletion at major petroleum fields change the effective energetic productivity of petroleum extraction. Four EROI ratios are estimated for each field as follows: The net energy ratio (NER) and external energy ratio (EER) are calculated, each using two measures of energy outputs, (1) oil-only and (2) all energy outputs. In all cases, engineering estimates of inputs are used rather than expenditure-based estimates (including off-site indirect energy use and embodied energy). All fields display significant declines in NER over the modeling period driven by a combination of (1) reduced petroleum production and (2) increased energy expenditures on recovery methods such as the injection of water, steam, or gas. The fields studied had NER reductions ranging from 46% to 88% over the modeling periods (accounting for all energy outputs). The reasons for declines in EROI differ by field. Midway-Sunset experienced a 5-fold increase in steam injected per barrel of oil produced. In contrast, Prudhoe Bay has experienced nearly a 30-fold increase in amount of gas processed and reinjected per unit of oil produced. In contrast, EER estimates are subject to greater variability and uncertainty due to the relatively small magnitude of external energy investments in most cases.

Estimating decades-long trends in petroleum field energy return on investment (EROI) with an engineering-based model

PubMed Central

Tripathi, Vinay S.

2017-01-01

This paper estimates changes in the energy return on investment (EROI) for five large petroleum fields over time using the Oil Production Greenhouse Gas Emissions Estimator (OPGEE). The modeled fields include Cantarell (Mexico), Forties (U.K.), Midway-Sunset (U.S.), Prudhoe Bay (U.S.), and Wilmington (U.S.). Data on field properties and production/processing parameters were obtained from a combination of government and technical literature sources. Key areas of uncertainty include details of the oil and gas surface processing schemes. We aim to explore how long-term trends in depletion at major petroleum fields change the effective energetic productivity of petroleum extraction. Four EROI ratios are estimated for each field as follows: The net energy ratio (NER) and external energy ratio (EER) are calculated, each using two measures of energy outputs, (1) oil-only and (2) all energy outputs. In all cases, engineering estimates of inputs are used rather than expenditure-based estimates (including off-site indirect energy use and embodied energy). All fields display significant declines in NER over the modeling period driven by a combination of (1) reduced petroleum production and (2) increased energy expenditures on recovery methods such as the injection of water, steam, or gas. The fields studied had NER reductions ranging from 46% to 88% over the modeling periods (accounting for all energy outputs). The reasons for declines in EROI differ by field. Midway-Sunset experienced a 5-fold increase in steam injected per barrel of oil produced. In contrast, Prudhoe Bay has experienced nearly a 30-fold increase in amount of gas processed and reinjected per unit of oil produced. In contrast, EER estimates are subject to greater variability and uncertainty due to the relatively small magnitude of external energy investments in most cases. PMID:28178318

Uncertainty in cloud optical depth estimates made from satellite radiance measurements

NASA Technical Reports Server (NTRS)

Pincus, Robert; Szczodrak, Malgorzata; Gu, Jiujing; Austin, Philip

1995-01-01

The uncertainty in optical depths retrieved from satellite measurements of visible wavelength radiance at the top of the atmosphere is quantified. Techniques are briefly reviewed for the estimation of optical depth from measurements of radiance, and it is noted that these estimates are always more uncertain at greater optical depths and larger solar zenith angles. The lack of radiometric calibration for visible wavelength imagers on operational satellites dominates the uncertainty retrievals of optical depth. This is true for both single-pixel retrievals and for statistics calculated from a population of individual retrievals. For individual estimates or small samples, sensor discretization can also be significant, but the sensitivity of the retrieval to the specification of the model atmosphere is less important. The relative uncertainty in calibration affects the accuracy with which optical depth distributions measured by different sensors may be quantitatively compared, while the absolute calibration uncertainty, acting through the nonlinear mapping of radiance to optical depth, limits the degree to which distributions measured by the same sensor may be distinguished.

Mapping Plasmodium falciparum Mortality in Africa between 1990 and 2015

PubMed Central

Gething, Peter W.; Casey, Daniel C.; Weiss, Daniel J.; Bisanzio, Donal; Bhatt, Samir; Cameron, Ewan; Battle, Katherine E.; Dalrymple, Ursula; Rozier, Jennifer; Rao, Puja C.; Kutz, Michael J.; Barber, Ryan M.; Huynh, Chantal; Shackelford, Katya A.; Coates, Matthew M.; Nguyen, Grant; Fraser, Maya S.; Kulikoff, Rachel; Wang, Haidong; Naghavi, Mohsen; Smith, David L.; Murray, Christopher J.L.; Hay, Simon I.; Lim, Stephen S.

2017-01-01

Background Malaria control has not been routinely informed by the assessment of subnational variation in malaria deaths. We combined data from the Malaria Atlas Project and the Global Burden of Disease Study to estimate malaria mortality across sub-Saharan Africa on a grid of 5 km2 from 1990 through 2015. Methods We estimated malaria mortality using a spatiotemporal modeling framework of geolocated data (i.e., with known latitude and longitude) on the clinical incidence of malaria, coverage of antimalarial drug treatment, case fatality rate, and population distribution according to age. Results Across sub-Saharan Africa during the past 15 years, we estimated that there was an overall decrease of 57% (95% uncertainty interval, 46 to 65) in the rate of malaria deaths, from 12.5 (95% uncertainty interval, 8.3 to 17.0) per 10,000 population in 2000 to 5.4 (95% uncertainty interval, 3.4 to 7.9) in 2015. This led to an overall decrease of 37% (95% uncertainty interval, 36 to 39) in the number of malaria deaths annually, from 1,007,000 (95% uncertainty interval, 666,000 to 1,376,000) to 631,000 (95% uncertainty interval, 394,000 to 914,000). The share of malaria deaths among children younger than 5 years of age ranged from more than 80% at a rate of death of more than 25 per 10,000 to less than 40% at rates below 1 per 10,000. Areas with high malaria mortality (>10 per 10,000) and low coverage (<50%) of insecticide-treated bed nets and antimalarial drugs included much of Nigeria, Angola, and Cameroon and parts of the Central African Republic, Congo, Guinea, and Equatorial Guinea. Conclusions We estimated that there was an overall decrease of 57% in the rate of death from malaria across sub-Saharan Africa over the past 15 years and identified several countries in which high rates of death were associated with low coverage of antimalarial treatment and prevention programs. (Funded by the Bill and Melinda Gates Foundation and others.) PMID:27723434

Assessing the controls and uncertainties on mean transit times in contrasting headwater catchments

NASA Astrophysics Data System (ADS)

Cartwright, Ian; Irvine, Dylan; Burton, Chad; Morgenstern, Uwe

2018-02-01

Estimating the time required for water to travel through headwater catchments from where it recharges to where it discharges into streams (the transit time) is important for understanding catchment behaviour. This study uses tritium (3H) activities of stream water to estimate the mean transit times of water in the upper Latrobe and Yarra catchments, southeast Australia, at different flow conditions. The 3H activities of the stream water were between 1.26 and 1.99 TU, which are lower than those of local rainfall (2.6 to 3.0 TU). 3H activities in individual subcatchments are almost invariably lowest at low streamflows. Mean transit times calculated from the 3H activities using a range of lumped parameter models are between 7 and 62 years and are longest during low streamflows. Uncertainties in the estimated mean transit times result from uncertainties in the geometry of the flow systems, uncertainties in the 3H input, and macroscopic mixing. In addition, simulation of 3H activities in FEFLOW indicates that heterogeneous hydraulic conductivities increase the range of mean transit times corresponding to a specific 3H activity. The absolute uncertainties in the mean transit times may be up to ±30 years. However, differences between mean transit times at different streamflows in the same catchment or between different subcatchments in the same area are more reliably estimated. Despite the uncertainties, the conclusions that the mean transit times are years to decades and decrease with increasing streamflow are robust. The seasonal variation in major ion geochemistry and 3H activities indicate that the higher general streamflows in winter are sustained by water displaced from shallower younger stores (e.g., soils or regolith). Poor correlations between 3H activities and catchment area, drainage density, mean slope, distance to stream, and landuse, imply that mean transit times are controlled by a variety of factors including the hydraulic properties of the soils and aquifers that are difficult to characterise spatially. The long mean transit times imply that there are long-lived stores of water in these catchments that may sustain streamflow over drought periods. Additionally, there may be considerable delay in contaminants reaching the stream.