A parallel calibration utility for WRF-Hydro on high performance computers

NASA Astrophysics Data System (ADS)

Wang, J.; Wang, C.; Kotamarthi, V. R.

2017-12-01

A successful modeling of complex hydrological processes comprises establishing an integrated hydrological model which simulates the hydrological processes in each water regime, calibrates and validates the model performance based on observation data, and estimates the uncertainties from different sources especially those associated with parameters. Such a model system requires large computing resources and often have to be run on High Performance Computers (HPC). The recently developed WRF-Hydro modeling system provides a significant advancement in the capability to simulate regional water cycles more completely. The WRF-Hydro model has a large range of parameters such as those in the input table files — GENPARM.TBL, SOILPARM.TBL and CHANPARM.TBL — and several distributed scaling factors such as OVROUGHRTFAC. These parameters affect the behavior and outputs of the model and thus may need to be calibrated against the observations in order to obtain a good modeling performance. Having a parameter calibration tool specifically for automate calibration and uncertainty estimates of WRF-Hydro model can provide significant convenience for the modeling community. In this study, we developed a customized tool using the parallel version of the model-independent parameter estimation and uncertainty analysis tool, PEST, to enabled it to run on HPC with PBS and SLURM workload manager and job scheduler. We also developed a series of PEST input file templates that are specifically for WRF-Hydro model calibration and uncertainty analysis. Here we will present a flood case study occurred in April 2013 over Midwest. The sensitivity and uncertainties are analyzed using the customized PEST tool we developed.

Design and performance evaluation of a simplified dynamic model for combined sewer overflows in pumped sewer systems

NASA Astrophysics Data System (ADS)

van Daal-Rombouts, Petra; Sun, Siao; Langeveld, Jeroen; Bertrand-Krajewski, Jean-Luc; Clemens, François

2016-07-01

Optimisation or real time control (RTC) studies in wastewater systems increasingly require rapid simulations of sewer systems in extensive catchments. To reduce the simulation time calibrated simplified models are applied, with the performance generally based on the goodness of fit of the calibration. In this research the performance of three simplified and a full hydrodynamic (FH) model for two catchments are compared based on the correct determination of CSO event occurrences and of the total discharged volumes to the surface water. Simplified model M1 consists of a rainfall runoff outflow (RRO) model only. M2 combines the RRO model with a static reservoir model for the sewer behaviour. M3 comprises the RRO model and a dynamic reservoir model. The dynamic reservoir characteristics were derived from FH model simulations. It was found that M2 and M3 are able to describe the sewer behaviour of the catchments, contrary to M1. The preferred model structure depends on the quality of the information (geometrical database and monitoring data) available for the design and calibration of the model. Finally, calibrated simplified models are shown to be preferable to uncalibrated FH models when performing optimisation or RTC studies.

Performance measures and criteria for hydrologic and water quality models

USDA-ARS?s Scientific Manuscript database

Performance measures and criteria are essential for model calibration and validation. This presentation will include a summary of one of the papers that will be included in the 2014 Hydrologic and Water Quality Model Calibration & Validation Guidelines Special Collection of the ASABE Transactions. T...

Meta-analysis of prediction model performance across multiple studies: Which scale helps ensure between-study normality for the C-statistic and calibration measures?

PubMed

Snell, Kym Ie; Ensor, Joie; Debray, Thomas Pa; Moons, Karel Gm; Riley, Richard D

2017-01-01

If individual participant data are available from multiple studies or clusters, then a prediction model can be externally validated multiple times. This allows the model's discrimination and calibration performance to be examined across different settings. Random-effects meta-analysis can then be used to quantify overall (average) performance and heterogeneity in performance. This typically assumes a normal distribution of 'true' performance across studies. We conducted a simulation study to examine this normality assumption for various performance measures relating to a logistic regression prediction model. We simulated data across multiple studies with varying degrees of variability in baseline risk or predictor effects and then evaluated the shape of the between-study distribution in the C-statistic, calibration slope, calibration-in-the-large, and E/O statistic, and possible transformations thereof. We found that a normal between-study distribution was usually reasonable for the calibration slope and calibration-in-the-large; however, the distributions of the C-statistic and E/O were often skewed across studies, particularly in settings with large variability in the predictor effects. Normality was vastly improved when using the logit transformation for the C-statistic and the log transformation for E/O, and therefore we recommend these scales to be used for meta-analysis. An illustrated example is given using a random-effects meta-analysis of the performance of QRISK2 across 25 general practices.

Decomposition of the Mean Squared Error and NSE Performance Criteria: Implications for Improving Hydrological Modelling

NASA Technical Reports Server (NTRS)

Gupta, Hoshin V.; Kling, Harald; Yilmaz, Koray K.; Martinez-Baquero, Guillermo F.

2009-01-01

The mean squared error (MSE) and the related normalization, the Nash-Sutcliffe efficiency (NSE), are the two criteria most widely used for calibration and evaluation of hydrological models with observed data. Here, we present a diagnostically interesting decomposition of NSE (and hence MSE), which facilitates analysis of the relative importance of its different components in the context of hydrological modelling, and show how model calibration problems can arise due to interactions among these components. The analysis is illustrated by calibrating a simple conceptual precipitation-runoff model to daily data for a number of Austrian basins having a broad range of hydro-meteorological characteristics. Evaluation of the results clearly demonstrates the problems that can be associated with any calibration based on the NSE (or MSE) criterion. While we propose and test an alternative criterion that can help to reduce model calibration problems, the primary purpose of this study is not to present an improved measure of model performance. Instead, we seek to show that there are systematic problems inherent with any optimization based on formulations related to the MSE. The analysis and results have implications to the manner in which we calibrate and evaluate environmental models; we discuss these and suggest possible ways forward that may move us towards an improved and diagnostically meaningful approach to model performance evaluation and identification.

Sky camera geometric calibration using solar observations

DOE PAGES

Urquhart, Bryan; Kurtz, Ben; Kleissl, Jan

2016-09-05

A camera model and associated automated calibration procedure for stationary daytime sky imaging cameras is presented. The specific modeling and calibration needs are motivated by remotely deployed cameras used to forecast solar power production where cameras point skyward and use 180° fisheye lenses. Sun position in the sky and on the image plane provides a simple and automated approach to calibration; special equipment or calibration patterns are not required. Sun position in the sky is modeled using a solar position algorithm (requiring latitude, longitude, altitude and time as inputs). Sun position on the image plane is detected using a simple image processing algorithm. Themore » performance evaluation focuses on the calibration of a camera employing a fisheye lens with an equisolid angle projection, but the camera model is general enough to treat most fixed focal length, central, dioptric camera systems with a photo objective lens. Calibration errors scale with the noise level of the sun position measurement in the image plane, but the calibration is robust across a large range of noise in the sun position. In conclusion, calibration performance on clear days ranged from 0.94 to 1.24 pixels root mean square error.« less

Impact of length of calibration period on the apex model output simulation performance

USDA-ARS?s Scientific Manuscript database

Datasets from long-term monitoring sites that can be used for calibration and validation of hydrologic and water quality models are rare due to resource constraints. As a result, hydrologic and water quality models are calibrated and, when possible, validated using short-term measured data. A previo...

Impact of length of dataset on streamflow calibration parameters and performance of APEX model

USDA-ARS?s Scientific Manuscript database

Due to resource constraints, long-term monitoring data for calibration and validation of hydrologic and water quality models are rare. As a result, most models are calibrated and, if possible, validated using limited measured data. However, little research has been done to determine the impact of le...

Impact of length of calibration period on the APEX model water quantity and quality simulation performance

USDA-ARS?s Scientific Manuscript database

Availability of continuous long-term measured data for model calibration and validation is limited due to time and resources constraints. As a result, hydrologic and water quality models are calibrated and, if possible, validated when measured data is available. Past work reported on the impact of t...

Evaluation and Enhancement of Calibration in the American College of Surgeons NSQIP Surgical Risk Calculator.

PubMed

Liu, Yaoming; Cohen, Mark E; Hall, Bruce L; Ko, Clifford Y; Bilimoria, Karl Y

2016-08-01

The American College of Surgeon (ACS) NSQIP Surgical Risk Calculator has been widely adopted as a decision aid and informed consent tool by surgeons and patients. Previous evaluations showed excellent discrimination and combined discrimination and calibration, but model calibration alone, and potential benefits of recalibration, were not explored. Because lack of calibration can lead to systematic errors in assessing surgical risk, our objective was to assess calibration and determine whether spline-based adjustments could improve it. We evaluated Surgical Risk Calculator model calibration, as well as discrimination, for each of 11 outcomes modeled from nearly 3 million patients (2010 to 2014). Using independent random subsets of data, we evaluated model performance for the Development (60% of records), Validation (20%), and Test (20%) datasets, where prediction equations from the Development dataset were recalibrated using restricted cubic splines estimated from the Validation dataset. We also evaluated performance on data subsets composed of higher-risk operations. The nonrecalibrated Surgical Risk Calculator performed well, but there was a slight tendency for predicted risk to be overestimated for lowest- and highest-risk patients and underestimated for moderate-risk patients. After recalibration, this distortion was eliminated, and p values for miscalibration were most often nonsignificant. Calibration was also excellent for subsets of higher-risk operations, though observed calibration was reduced due to instability associated with smaller sample sizes. Performance of NSQIP Surgical Risk Calculator models was shown to be excellent and improved with recalibration. Surgeons and patients can rely on the calculator to provide accurate estimates of surgical risk. Copyright © 2016 American College of Surgeons. Published by Elsevier Inc. All rights reserved.

Nitrous oxide emissions from cropland: a procedure for calibrating the DayCent biogeochemical model using inverse modelling

USGS Publications Warehouse

Rafique, Rashad; Fienen, Michael N.; Parkin, Timothy B.; Anex, Robert P.

2013-01-01

DayCent is a biogeochemical model of intermediate complexity widely used to simulate greenhouse gases (GHG), soil organic carbon and nutrients in crop, grassland, forest and savannah ecosystems. Although this model has been applied to a wide range of ecosystems, it is still typically parameterized through a traditional “trial and error” approach and has not been calibrated using statistical inverse modelling (i.e. algorithmic parameter estimation). The aim of this study is to establish and demonstrate a procedure for calibration of DayCent to improve estimation of GHG emissions. We coupled DayCent with the parameter estimation (PEST) software for inverse modelling. The PEST software can be used for calibration through regularized inversion as well as model sensitivity and uncertainty analysis. The DayCent model was analysed and calibrated using N2O flux data collected over 2 years at the Iowa State University Agronomy and Agricultural Engineering Research Farms, Boone, IA. Crop year 2003 data were used for model calibration and 2004 data were used for validation. The optimization of DayCent model parameters using PEST significantly reduced model residuals relative to the default DayCent parameter values. Parameter estimation improved the model performance by reducing the sum of weighted squared residual difference between measured and modelled outputs by up to 67 %. For the calibration period, simulation with the default model parameter values underestimated mean daily N2O flux by 98 %. After parameter estimation, the model underestimated the mean daily fluxes by 35 %. During the validation period, the calibrated model reduced sum of weighted squared residuals by 20 % relative to the default simulation. Sensitivity analysis performed provides important insights into the model structure providing guidance for model improvement.

Calibration of the computer model describing flows in the water supply system; example of the application of a genetic algorithm

NASA Astrophysics Data System (ADS)

Orłowska-Szostak, Maria; Orłowski, Ryszard

2017-11-01

The paper discusses some relevant aspects of the calibration of a computer model describing flows in the water supply system. The authors described an exemplary water supply system and used it as a practical illustration of calibration. A range of measures was discussed and applied, which improve the convergence and effective use of calculations in the calibration process and also the effect of such calibration which is the validity of the results obtained. Drawing up results of performed measurements, i.e. estimating pipe roughnesses, the authors performed using the genetic algorithm implementation of which is a software developed by Resan Labs company from Brazil.

Model Calibration with Censored Data

DOE PAGES

Cao, Fang; Ba, Shan; Brenneman, William A.; ...

2017-06-28

Here, the purpose of model calibration is to make the model predictions closer to reality. The classical Kennedy-O'Hagan approach is widely used for model calibration, which can account for the inadequacy of the computer model while simultaneously estimating the unknown calibration parameters. In many applications, the phenomenon of censoring occurs when the exact outcome of the physical experiment is not observed, but is only known to fall within a certain region. In such cases, the Kennedy-O'Hagan approach cannot be used directly, and we propose a method to incorporate the censoring information when performing model calibration. The method is applied tomore » study the compression phenomenon of liquid inside a bottle. The results show significant improvement over the traditional calibration methods, especially when the number of censored observations is large.« less

Method calibration of the model 13145 infrared target projectors

NASA Astrophysics Data System (ADS)

Huang, Jianxia; Gao, Yuan; Han, Ying

2014-11-01

The SBIR Model 13145 Infrared Target Projectors ( The following abbreviation Evaluation Unit ) used for characterizing the performances of infrared imaging system. Test items: SiTF, MTF, NETD, MRTD, MDTD, NPS. Infrared target projectors includes two area blackbodies, a 12 position target wheel, all reflective collimator. It provide high spatial frequency differential targets, Precision differential targets imaged by infrared imaging system. And by photoelectricity convert on simulate signal or digital signal. Applications software (IR Windows TM 2001) evaluate characterizing the performances of infrared imaging system. With regards to as a whole calibration, first differently calibration for distributed component , According to calibration specification for area blackbody to calibration area blackbody, by means of to amend error factor to calibration of all reflective collimator, radiance calibration of an infrared target projectors using the SR5000 spectral radiometer, and to analyze systematic error. With regards to as parameter of infrared imaging system, need to integrate evaluation method. According to regulation with -GJB2340-1995 General specification for military thermal imaging sets -testing parameters of infrared imaging system, the results compare with results from Optical Calibration Testing Laboratory . As a goal to real calibration performances of the Evaluation Unit.

Multi-metric calibration of hydrological model to capture overall flow regimes

NASA Astrophysics Data System (ADS)

Zhang, Yongyong; Shao, Quanxi; Zhang, Shifeng; Zhai, Xiaoyan; She, Dunxian

2016-08-01

Flow regimes (e.g., magnitude, frequency, variation, duration, timing and rating of change) play a critical role in water supply and flood control, environmental processes, as well as biodiversity and life history patterns in the aquatic ecosystem. The traditional flow magnitude-oriented calibration of hydrological model was usually inadequate to well capture all the characteristics of observed flow regimes. In this study, we simulated multiple flow regime metrics simultaneously by coupling a distributed hydrological model with an equally weighted multi-objective optimization algorithm. Two headwater watersheds in the arid Hexi Corridor were selected for the case study. Sixteen metrics were selected as optimization objectives, which could represent the major characteristics of flow regimes. Model performance was compared with that of the single objective calibration. Results showed that most metrics were better simulated by the multi-objective approach than those of the single objective calibration, especially the low and high flow magnitudes, frequency and variation, duration, maximum flow timing and rating. However, the model performance of middle flow magnitude was not significantly improved because this metric was usually well captured by single objective calibration. The timing of minimum flow was poorly predicted by both the multi-metric and single calibrations due to the uncertainties in model structure and input data. The sensitive parameter values of the hydrological model changed remarkably and the simulated hydrological processes by the multi-metric calibration became more reliable, because more flow characteristics were considered. The study is expected to provide more detailed flow information by hydrological simulation for the integrated water resources management, and to improve the simulation performances of overall flow regimes.

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

Xiong, Wei; Balkovic, Juraj; van der Velde, M.

Crop models are increasingly used to assess impacts of climate change/variability and management practices on productivity and environmental performance of alternative cropping systems. Calibration is an important procedure to improve reliability of model simulations, especially for large area applications. However, global-scale crop model calibration has rarely been exercised due to limited data availability and expensive computing cost. Here we present a simple approach to calibrate Environmental Policy Integrated Climate (EPIC) model for a global implementation of rice. We identify four parameters (potential heat unit – PHU, planting density – PD, harvest index – HI, and biomass energy ratio – BER)more » and calibrate them regionally to capture the spatial pattern of reported rice yield in 2000. Model performance is assessed by comparing simulated outputs with independent FAO national data. The comparison demonstrates that the global calibration scheme performs satisfactorily in reproducing the spatial pattern of rice yield, particularly in main rice production areas. Spatial agreement increases substantially when more parameters are selected and calibrated, but with varying efficiencies. Among the parameters, PHU and HI exhibit the highest efficiencies in increasing the spatial agreement. Simulations with different calibration strategies generate a pronounced discrepancy of 5–35% in mean yields across latitude bands, and a small to moderate difference in estimated yield variability and yield changing trend for the period of 1981–2000. Present calibration has little effects in improving simulated yield variability and trends at both regional and global levels, suggesting further works are needed to reproduce temporal variability of reported yields. This study highlights the importance of crop models’ calibration, and presents the possibility of a transparent and consistent up scaling approach for global crop simulations given current availability of global databases of weather, soil, crop calendar, fertilizer and irrigation management information, and reported yield.« less

Calibration and simulation of two large wastewater treatment plants operated for nutrient removal.

PubMed

Ferrer, J; Morenilla, J J; Bouzas, A; García-Usach, F

2004-01-01

Control and optimisation of plant processes has become a priority for WWTP managers. The calibration and verification of a mathematical model provides an important tool for the investigation of advanced control strategies that may assist in the design or optimization of WWTPs. This paper describes the calibration of the ASM2d model for two full scale biological nitrogen and phosphorus removal plants in order to characterize the biological process and to upgrade the plants' performance. Results from simulation showed a good correspondence with experimental data demonstrating that the model and the calibrated parameters were able to predict the behaviour of both WWTPs. Once the calibration and simulation process was finished, a study for each WWTP was done with the aim of improving its performance. Modifications focused on reactor configuration and operation strategies were proposed.

MoDOT pavement preservation research program volume VII, re-calibration of triggers and performance models.

DOT National Transportation Integrated Search

2015-10-01

The objective of this task is to develop the concept and framework for a procedure to routinely create, re-calibrate, and update the : Trigger Tables and Performance Models. The scope of work for Task 6 includes a limited review of the recent pavemen...

Digital filtering and model updating methods for improving the robustness of near-infrared multivariate calibrations.

PubMed

Kramer, Kirsten E; Small, Gary W

2009-02-01

Fourier transform near-infrared (NIR) transmission spectra are used for quantitative analysis of glucose for 17 sets of prediction data sampled as much as six months outside the timeframe of the corresponding calibration data. Aqueous samples containing physiological levels of glucose in a matrix of bovine serum albumin and triacetin are used to simulate clinical samples such as blood plasma. Background spectra of a single analyte-free matrix sample acquired during the instrumental warm-up period on the prediction day are used for calibration updating and for determining the optimal frequency response of a preprocessing infinite impulse response time-domain digital filter. By tuning the filter and the calibration model to the specific instrumental response associated with the prediction day, the calibration model is given enhanced ability to operate over time. This methodology is demonstrated in conjunction with partial least squares calibration models built with a spectral range of 4700-4300 cm(-1). By using a subset of the background spectra to evaluate the prediction performance of the updated model, projections can be made regarding the success of subsequent glucose predictions. If a threshold standard error of prediction (SEP) of 1.5 mM is used to establish successful model performance with the glucose samples, the corresponding threshold for the SEP of the background spectra is found to be 1.3 mM. For calibration updating in conjunction with digital filtering, SEP values of all 17 prediction sets collected over 3-178 days displaced from the calibration data are below 1.5 mM. In addition, the diagnostic based on the background spectra correctly assesses the prediction performance in 16 of the 17 cases.

When to Make Mountains out of Molehills: The Pros and Cons of Simple and Complex Model Calibration Procedures

NASA Astrophysics Data System (ADS)

Smith, K. A.; Barker, L. J.; Harrigan, S.; Prudhomme, C.; Hannaford, J.; Tanguy, M.; Parry, S.

2017-12-01

Earth and environmental models are relied upon to investigate system responses that cannot otherwise be examined. In simulating physical processes, models have adjustable parameters which may, or may not, have a physical meaning. Determining the values to assign to these model parameters is an enduring challenge for earth and environmental modellers. Selecting different error metrics by which the models results are compared to observations will lead to different sets of calibrated model parameters, and thus different model results. Furthermore, models may exhibit `equifinal' behaviour, where multiple combinations of model parameters lead to equally acceptable model performance against observations. These decisions in model calibration introduce uncertainty that must be considered when model results are used to inform environmental decision-making. This presentation focusses on the uncertainties that derive from the calibration of a four parameter lumped catchment hydrological model (GR4J). The GR models contain an inbuilt automatic calibration algorithm that can satisfactorily calibrate against four error metrics in only a few seconds. However, a single, deterministic model result does not provide information on parameter uncertainty. Furthermore, a modeller interested in extreme events, such as droughts, may wish to calibrate against more low flows specific error metrics. In a comprehensive assessment, the GR4J model has been run with 500,000 Latin Hypercube Sampled parameter sets across 303 catchments in the United Kingdom. These parameter sets have been assessed against six error metrics, including two drought specific metrics. This presentation compares the two approaches, and demonstrates that the inbuilt automatic calibration can outperform the Latin Hypercube experiment approach in single metric assessed performance. However, it is also shown that there are many merits of the more comprehensive assessment, which allows for probabilistic model results, multi-objective optimisation, and better tailoring to calibrate the model for specific applications such as drought event characterisation. Modellers and decision-makers may be constrained in their choice of calibration method, so it is important that they recognise the strengths and limitations of their chosen approach.

Calculations to support JET neutron yield calibration: Modelling of neutron emission from a compact DT neutron generator

NASA Astrophysics Data System (ADS)

Čufar, Aljaž; Batistoni, Paola; Conroy, Sean; Ghani, Zamir; Lengar, Igor; Milocco, Alberto; Packer, Lee; Pillon, Mario; Popovichev, Sergey; Snoj, Luka; JET Contributors

2017-03-01

At the Joint European Torus (JET) the ex-vessel fission chambers and in-vessel activation detectors are used as the neutron production rate and neutron yield monitors respectively. In order to ensure that these detectors produce accurate measurements they need to be experimentally calibrated. A new calibration of neutron detectors to 14 MeV neutrons, resulting from deuterium-tritium (DT) plasmas, is planned at JET using a compact accelerator based neutron generator (NG) in which a D/T beam impinges on a solid target containing T/D, producing neutrons by DT fusion reactions. This paper presents the analysis that was performed to model the neutron source characteristics in terms of energy spectrum, angle-energy distribution and the effect of the neutron generator geometry. Different codes capable of simulating the accelerator based DT neutron sources are compared and sensitivities to uncertainties in the generator's internal structure analysed. The analysis was performed to support preparation to the experimental measurements performed to characterize the NG as a calibration source. Further extensive neutronics analyses, performed with this model of the NG, will be needed to support the neutron calibration experiments and take into account various differences between the calibration experiment and experiments using the plasma as a source of neutrons.

Man vs. Machine: An interactive poll to evaluate hydrological model performance of a manual and an automatic calibration

NASA Astrophysics Data System (ADS)

Wesemann, Johannes; Burgholzer, Reinhard; Herrnegger, Mathew; Schulz, Karsten

2017-04-01

In recent years, a lot of research in hydrological modelling has been invested to improve the automatic calibration of rainfall-runoff models. This includes for example (1) the implementation of new optimisation methods, (2) the incorporation of new and different objective criteria and signatures in the optimisation and (3) the usage of auxiliary data sets apart from runoff. Nevertheless, in many applications manual calibration is still justifiable and frequently applied. The hydrologist performing the manual calibration, with his expert knowledge, is able to judge the hydrographs simultaneously concerning details but also in a holistic view. This integrated eye-ball verification procedure available to man can be difficult to formulate in objective criteria, even when using a multi-criteria approach. Comparing the results of automatic and manual calibration is not straightforward. Automatic calibration often solely involves objective criteria such as Nash-Sutcliffe Efficiency Coefficient or the Kling-Gupta-Efficiency as a benchmark during the calibration. Consequently, a comparison based on such measures is intrinsically biased towards automatic calibration. Additionally, objective criteria do not cover all aspects of a hydrograph leaving questions concerning the quality of a simulation open. This contribution therefore seeks to examine the quality of manually and automatically calibrated hydrographs by interactively involving expert knowledge in the evaluation. Simulations have been performed for the Mur catchment in Austria with the rainfall-runoff model COSERO using two parameter sets evolved from a manual and an automatic calibration. A subset of resulting hydrographs for observation and simulation, representing the typical flow conditions and events, will be evaluated in this study. In an interactive crowdsourcing approach experts attending the session can vote for their preferred simulated hydrograph without having information on the calibration method that produced the respective hydrograph. Therefore, the result of the poll can be seen as an additional quality criterion for the comparison of the two different approaches and help in the evaluation of the automatic calibration method.

Object-oriented simulation model of a parabolic trough solar collector: Static and dynamic validation

NASA Astrophysics Data System (ADS)

Ubieta, Eduardo; Hoyo, Itzal del; Valenzuela, Loreto; Lopez-Martín, Rafael; Peña, Víctor de la; López, Susana

2017-06-01

A simulation model of a parabolic-trough solar collector developed in Modelica® language is calibrated and validated. The calibration is performed in order to approximate the behavior of the solar collector model to a real one due to the uncertainty in some of the system parameters, i.e. measured data is used during the calibration process. Afterwards, the validation of this calibrated model is done. During the validation, the results obtained from the model are compared to the ones obtained during real operation in a collector from the Plataforma Solar de Almeria (PSA).

The worth of data to reduce predictive uncertainty of an integrated catchment model by multi-constraint calibration

NASA Astrophysics Data System (ADS)

Koch, J.; Jensen, K. H.; Stisen, S.

2017-12-01

Hydrological models that integrate numerical process descriptions across compartments of the water cycle are typically required to undergo thorough model calibration in order to estimate suitable effective model parameters. In this study, we apply a spatially distributed hydrological model code which couples the saturated zone with the unsaturated zone and the energy portioning at the land surface. We conduct a comprehensive multi-constraint model calibration against nine independent observational datasets which reflect both the temporal and the spatial behavior of hydrological response of a 1000km2 large catchment in Denmark. The datasets are obtained from satellite remote sensing and in-situ measurements and cover five keystone hydrological variables: discharge, evapotranspiration, groundwater head, soil moisture and land surface temperature. Results indicate that a balanced optimization can be achieved where errors on objective functions for all nine observational datasets can be reduced simultaneously. The applied calibration framework was tailored with focus on improving the spatial pattern performance; however results suggest that the optimization is still more prone to improve the temporal dimension of model performance. This study features a post-calibration linear uncertainty analysis. This allows quantifying parameter identifiability which is the worth of a specific observational dataset to infer values to model parameters through calibration. Furthermore the ability of an observation to reduce predictive uncertainty is assessed as well. Such findings determine concrete implications on the design of model calibration frameworks and, in more general terms, the acquisition of data in hydrological observatories.

Physical resist models and their calibration: their readiness for accurate EUV lithography simulation

NASA Astrophysics Data System (ADS)

Klostermann, U. K.; Mülders, T.; Schmöller, T.; Lorusso, G. F.; Hendrickx, E.

2010-04-01

In this paper, we discuss the performance of EUV resist models in terms of predictive accuracy, and we assess the readiness of the corresponding model calibration methodology. The study is done on an extensive OPC data set collected at IMEC for the ShinEtsu resist SEVR-59 on the ASML EUV Alpha Demo Tool (ADT), with the data set including more than thousand CD values. We address practical aspects such as the speed of calibration and selection of calibration patterns. The model is calibrated on 12 process window data series varying in pattern width (32, 36, 40 nm), orientation (H, V) and pitch (dense, isolated). The minimum measured feature size at nominal process condition is a 32 nm CD at a dense pitch of 64 nm. Mask metrology is applied to verify and eventually correct nominal width of the drawn CD. Cross-sectional SEM information is included in the calibration to tune the simulated resist loss and sidewall angle. The achieved calibration RMS is ~ 1.0 nm. We show what elements are important to obtain a well calibrated model. We discuss the impact of 3D mask effects on the Bossung tilt. We demonstrate that a correct representation of the flare level during the calibration is important to achieve a high predictability at various flare conditions. Although the model calibration is performed on a limited subset of the measurement data (one dimensional structures only), its accuracy is validated based on a large number of OPC patterns (at nominal dose and focus conditions) not included in the calibration; validation RMS results as small as 1 nm can be reached. Furthermore, we study the model's extendibility to two-dimensional end of line (EOL) structures. Finally, we correlate the experimentally observed fingerprint of the CD uniformity to a model, where EUV tool specific signatures are taken into account.

Impact of the calibration period on the conceptual rainfall-runoff model parameter estimates

NASA Astrophysics Data System (ADS)

Todorovic, Andrijana; Plavsic, Jasna

2015-04-01

A conceptual rainfall-runoff model is defined by its structure and parameters, which are commonly inferred through model calibration. Parameter estimates depend on objective function(s), optimisation method, and calibration period. Model calibration over different periods may result in dissimilar parameter estimates, while model efficiency decreases outside calibration period. Problem of model (parameter) transferability, which conditions reliability of hydrologic simulations, has been investigated for decades. In this paper, dependence of the parameter estimates and model performance on calibration period is analysed. The main question that is addressed is: are there any changes in optimised parameters and model efficiency that can be linked to the changes in hydrologic or meteorological variables (flow, precipitation and temperature)? Conceptual, semi-distributed HBV-light model is calibrated over five-year periods shifted by a year (sliding time windows). Length of the calibration periods is selected to enable identification of all parameters. One water year of model warm-up precedes every simulation, which starts with the beginning of a water year. The model is calibrated using the built-in GAP optimisation algorithm. The objective function used for calibration is composed of Nash-Sutcliffe coefficient for flows and logarithms of flows, and volumetric error, all of which participate in the composite objective function with approximately equal weights. Same prior parameter ranges are used in all simulations. The model is calibrated against flows observed at the Slovac stream gauge on the Kolubara River in Serbia (records from 1954 to 2013). There are no trends in precipitation nor in flows, however, there is a statistically significant increasing trend in temperatures at this catchment. Parameter variability across the calibration periods is quantified in terms of standard deviations of normalised parameters, enabling detection of the most variable parameters. Correlation coefficients among optimised model parameters and total precipitation P, mean temperature T and mean flow Q are calculated to give an insight into parameter dependence on the hydrometeorological drivers. The results reveal high sensitivity of almost all model parameters towards calibration period. The highest variability is displayed by the refreezing coefficient, water holding capacity, and temperature gradient. The only statistically significant (decreasing) trend is detected in the evapotranspiration reduction threshold. Statistically significant correlation is detected between the precipitation gradient and precipitation depth, and between the time-area histogram base and flows. All other correlations are not statistically significant, implying that changes in optimised parameters cannot generally be linked to the changes in P, T or Q. As for the model performance, the model reproduces the observed runoff satisfactorily, though the runoff is slightly overestimated in wet periods. The Nash-Sutcliffe efficiency coefficient (NSE) ranges from 0.44 to 0.79. Higher NSE values are obtained over wetter periods, what is supported by statistically significant correlation between NSE and flows. Overall, no systematic variations in parameters or in model performance are detected. Parameter variability may therefore rather be attributed to errors in data or inadequacies in the model structure. Further research is required to examine the impact of the calibration strategy or model structure on the variability in optimised parameters in time.

Calibration and validation of toxicokinetic-toxicodynamic models for three neonicotinoids and some aquatic macroinvertebrates.

PubMed

Focks, Andreas; Belgers, Dick; Boerwinkel, Marie-Claire; Buijse, Laura; Roessink, Ivo; Van den Brink, Paul J

2018-05-01

Exposure patterns in ecotoxicological experiments often do not match the exposure profiles for which a risk assessment needs to be performed. This limitation can be overcome by using toxicokinetic-toxicodynamic (TKTD) models for the prediction of effects under time-variable exposure. For the use of TKTD models in the environmental risk assessment of chemicals, it is required to calibrate and validate the model for specific compound-species combinations. In this study, the survival of macroinvertebrates after exposure to the neonicotinoid insecticide was modelled using TKTD models from the General Unified Threshold models of Survival (GUTS) framework. The models were calibrated on existing survival data from acute or chronic tests under static exposure regime. Validation experiments were performed for two sets of species-compound combinations: one set focussed on multiple species sensitivity to a single compound: imidacloprid, and the other set on the effects of multiple compounds for a single species, i.e., the three neonicotinoid compounds imidacloprid, thiacloprid and thiamethoxam, on the survival of the mayfly Cloeon dipterum. The calibrated models were used to predict survival over time, including uncertainty ranges, for the different time-variable exposure profiles used in the validation experiments. From the comparison between observed and predicted survival, it appeared that the accuracy of the model predictions was acceptable for four of five tested species in the multiple species data set. For compounds such as neonicotinoids, which are known to have the potential to show increased toxicity under prolonged exposure, the calibration and validation of TKTD models for survival needs to be performed ideally by considering calibration data from both acute and chronic tests.

Streamflow characteristics from modelled runoff time series: Importance of calibration criteria selection

USGS Publications Warehouse

Poole, Sandra; Vis, Marc; Knight, Rodney; Seibert, Jan

2017-01-01

Ecologically relevant streamflow characteristics (SFCs) of ungauged catchments are often estimated from simulated runoff of hydrologic models that were originally calibrated on gauged catchments. However, SFC estimates of the gauged donor catchments and subsequently the ungauged catchments can be substantially uncertain when models are calibrated using traditional approaches based on optimization of statistical performance metrics (e.g., Nash–Sutcliffe model efficiency). An improved calibration strategy for gauged catchments is therefore crucial to help reduce the uncertainties of estimated SFCs for ungauged catchments. The aim of this study was to improve SFC estimates from modeled runoff time series in gauged catchments by explicitly including one or several SFCs in the calibration process. Different types of objective functions were defined consisting of the Nash–Sutcliffe model efficiency, single SFCs, or combinations thereof. We calibrated a bucket-type runoff model (HBV – Hydrologiska Byråns Vattenavdelning – model) for 25 catchments in the Tennessee River basin and evaluated the proposed calibration approach on 13 ecologically relevant SFCs representing major flow regime components and different flow conditions. While the model generally tended to underestimate the tested SFCs related to mean and high-flow conditions, SFCs related to low flow were generally overestimated. The highest estimation accuracies were achieved by a SFC-specific model calibration. Estimates of SFCs not included in the calibration process were of similar quality when comparing a multi-SFC calibration approach to a traditional model efficiency calibration. For practical applications, this implies that SFCs should preferably be estimated from targeted runoff model calibration, and modeled estimates need to be carefully interpreted.

Calibration of the ARID robot

NASA Technical Reports Server (NTRS)

Doty, Keith L

1992-01-01

The author has formulated a new, general model for specifying the kinematic properties of serial manipulators. The new model kinematic parameters do not suffer discontinuities when nominally parallel adjacent axes deviate from exact parallelism. From this new theory the author develops a first-order, lumped-parameter, calibration-model for the ARID manipulator. Next, the author develops a calibration methodology for the ARID based on visual and acoustic sensing. A sensor platform, consisting of a camera and four sonars attached to the ARID end frame, performs calibration measurements. A calibration measurement consists of processing one visual frame of an accurately placed calibration image and recording four acoustic range measurements. A minimum of two measurement protocols determine the kinematics calibration-model of the ARID for a particular region: assuming the joint displacements are accurately measured, the calibration surface is planar, and the kinematic parameters do not vary rapidly in the region. No theoretical or practical limitations appear to contra-indicate the feasibility of the calibration method developed here.

A back-fitting algorithm to improve real-time flood forecasting

NASA Astrophysics Data System (ADS)

Zhang, Xiaojing; Liu, Pan; Cheng, Lei; Liu, Zhangjun; Zhao, Yan

2018-07-01

Real-time flood forecasting is important for decision-making with regards to flood control and disaster reduction. The conventional approach involves a postprocessor calibration strategy that first calibrates the hydrological model and then estimates errors. This procedure can simulate streamflow consistent with observations, but obtained parameters are not optimal. Joint calibration strategies address this issue by refining hydrological model parameters jointly with the autoregressive (AR) model. In this study, five alternative schemes are used to forecast floods. Scheme I uses only the hydrological model, while scheme II includes an AR model for error correction. In scheme III, differencing is used to remove non-stationarity in the error series. A joint inference strategy employed in scheme IV calibrates the hydrological and AR models simultaneously. The back-fitting algorithm, a basic approach for training an additive model, is adopted in scheme V to alternately recalibrate hydrological and AR model parameters. The performance of the five schemes is compared with a case study of 15 recorded flood events from China's Baiyunshan reservoir basin. Our results show that (1) schemes IV and V outperform scheme III during the calibration and validation periods and (2) scheme V is inferior to scheme IV in the calibration period, but provides better results in the validation period. Joint calibration strategies can therefore improve the accuracy of flood forecasting. Additionally, the back-fitting recalibration strategy produces weaker overcorrection and a more robust performance compared with the joint inference strategy.

A novel spatial performance metric for robust pattern optimization of distributed hydrological models

NASA Astrophysics Data System (ADS)

Stisen, S.; Demirel, C.; Koch, J.

2017-12-01

Evaluation of performance is an integral part of model development and calibration as well as it is of paramount importance when communicating modelling results to stakeholders and the scientific community. There exists a comprehensive and well tested toolbox of metrics to assess temporal model performance in the hydrological modelling community. On the contrary, the experience to evaluate spatial performance is not corresponding to the grand availability of spatial observations readily available and to the sophisticate model codes simulating the spatial variability of complex hydrological processes. This study aims at making a contribution towards advancing spatial pattern oriented model evaluation for distributed hydrological models. This is achieved by introducing a novel spatial performance metric which provides robust pattern performance during model calibration. The promoted SPAtial EFficiency (spaef) metric reflects three equally weighted components: correlation, coefficient of variation and histogram overlap. This multi-component approach is necessary in order to adequately compare spatial patterns. spaef, its three components individually and two alternative spatial performance metrics, i.e. connectivity analysis and fractions skill score, are tested in a spatial pattern oriented model calibration of a catchment model in Denmark. The calibration is constrained by a remote sensing based spatial pattern of evapotranspiration and discharge timeseries at two stations. Our results stress that stand-alone metrics tend to fail to provide holistic pattern information to the optimizer which underlines the importance of multi-component metrics. The three spaef components are independent which allows them to complement each other in a meaningful way. This study promotes the use of bias insensitive metrics which allow comparing variables which are related but may differ in unit in order to optimally exploit spatial observations made available by remote sensing platforms. We see great potential of spaef across environmental disciplines dealing with spatially distributed modelling.

Evaluating the role of evapotranspiration remote sensing data in improving hydrological modeling predictability

NASA Astrophysics Data System (ADS)

Herman, Matthew R.; Nejadhashemi, A. Pouyan; Abouali, Mohammad; Hernandez-Suarez, Juan Sebastian; Daneshvar, Fariborz; Zhang, Zhen; Anderson, Martha C.; Sadeghi, Ali M.; Hain, Christopher R.; Sharifi, Amirreza

2018-01-01

As the global demands for the use of freshwater resources continues to rise, it has become increasingly important to insure the sustainability of this resources. This is accomplished through the use of management strategies that often utilize monitoring and the use of hydrological models. However, monitoring at large scales is not feasible and therefore model applications are becoming challenging, especially when spatially distributed datasets, such as evapotranspiration, are needed to understand the model performances. Due to these limitations, most of the hydrological models are only calibrated for data obtained from site/point observations, such as streamflow. Therefore, the main focus of this paper is to examine whether the incorporation of remotely sensed and spatially distributed datasets can improve the overall performance of the model. In this study, actual evapotranspiration (ETa) data was obtained from the two different sets of satellite based remote sensing data. One dataset estimates ETa based on the Simplified Surface Energy Balance (SSEBop) model while the other one estimates ETa based on the Atmosphere-Land Exchange Inverse (ALEXI) model. The hydrological model used in this study is the Soil and Water Assessment Tool (SWAT), which was calibrated against spatially distributed ETa and single point streamflow records for the Honeyoey Creek-Pine Creek Watershed, located in Michigan, USA. Two different techniques, multi-variable and genetic algorithm, were used to calibrate the SWAT model. Using the aforementioned datasets, the performance of the hydrological model in estimating ETa was improved using both calibration techniques by achieving Nash-Sutcliffe efficiency (NSE) values >0.5 (0.73-0.85), percent bias (PBIAS) values within ±25% (±21.73%), and root mean squared error - observations standard deviation ratio (RSR) values <0.7 (0.39-0.52). However, the genetic algorithm technique was more effective with the ETa calibration while significantly reducing the model performance for estimating the streamflow (NSE: 0.32-0.52, PBIAS: ±32.73%, and RSR: 0.63-0.82). Meanwhile, using the multi-variable technique, the model performance for estimating the streamflow was maintained with a high level of accuracy (NSE: 0.59-0.61, PBIAS: ±13.70%, and RSR: 0.63-0.64) while the evapotranspiration estimations were improved. Results from this assessment shows that incorporation of remotely sensed and spatially distributed data can improve the hydrological model performance if it is coupled with a right calibration technique.

The impact of modeling the dependencies among patient findings on classification accuracy and calibration.

PubMed Central

Monti, S.; Cooper, G. F.

1998-01-01

We present a new Bayesian classifier for computer-aided diagnosis. The new classifier builds upon the naive-Bayes classifier, and models the dependencies among patient findings in an attempt to improve its performance, both in terms of classification accuracy and in terms of calibration of the estimated probabilities. This work finds motivation in the argument that highly calibrated probabilities are necessary for the clinician to be able to rely on the model's recommendations. Experimental results are presented, supporting the conclusion that modeling the dependencies among findings improves calibration. PMID:9929288

Effect of Using Extreme Years in Hydrologic Model Calibration Performance

NASA Astrophysics Data System (ADS)

Goktas, R. K.; Tezel, U.; Kargi, P. G.; Ayvaz, T.; Tezyapar, I.; Mesta, B.; Kentel, E.

2017-12-01

Hydrological models are useful in predicting and developing management strategies for controlling the system behaviour. Specifically they can be used for evaluating streamflow at ungaged catchments, effect of climate change, best management practices on water resources, or identification of pollution sources in a watershed. This study is a part of a TUBITAK project named "Development of a geographical information system based decision-making tool for water quality management of Ergene Watershed using pollutant fingerprints". Within the scope of this project, first water resources in Ergene Watershed is studied. Streamgages found in the basin are identified and daily streamflow measurements are obtained from State Hydraulic Works of Turkey. Streamflow data is analysed using box-whisker plots, hydrographs and flow-duration curves focusing on identification of extreme periods, dry or wet. Then a hydrological model is developed for Ergene Watershed using HEC-HMS in the Watershed Modeling System (WMS) environment. The model is calibrated for various time periods including dry and wet ones and the performance of calibration is evaluated using Nash-Sutcliffe Efficiency (NSE), correlation coefficient, percent bias (PBIAS) and root mean square error. It is observed that calibration period affects the model performance, and the main purpose of the development of the hydrological model should guide calibration period selection. Acknowledgement: This study is funded by The Scientific and Technological Research Council of Turkey (TUBITAK) under Project Number 115Y064.

Global-scale regionalization of hydrological model parameters using streamflow data from many small catchments

NASA Astrophysics Data System (ADS)

Beck, Hylke; de Roo, Ad; van Dijk, Albert; McVicar, Tim; Miralles, Diego; Schellekens, Jaap; Bruijnzeel, Sampurno; de Jeu, Richard

2015-04-01

Motivated by the lack of large-scale model parameter regionalization studies, a large set of 3328 small catchments (< 10000 km2) around the globe was used to set up and evaluate five model parameterization schemes at global scale. The HBV-light model was chosen because of its parsimony and flexibility to test the schemes. The catchments were calibrated against observed streamflow (Q) using an objective function incorporating both behavioral and goodness-of-fit measures, after which the catchment set was split into subsets of 1215 donor and 2113 evaluation catchments based on the calibration performance. The donor catchments were subsequently used to derive parameter sets that were transferred to similar grid cells based on a similarity measure incorporating climatic and physiographic characteristics, thereby producing parameter maps with global coverage. Overall, there was a lack of suitable donor catchments for mountainous and tropical environments. The schemes with spatially-uniform parameter sets (EXP2 and EXP3) achieved the worst Q estimation performance in the evaluation catchments, emphasizing the importance of parameter regionalization. The direct transfer of calibrated parameter sets from donor catchments to similar grid cells (scheme EXP1) performed best, although there was still a large performance gap between EXP1 and HBV-light calibrated against observed Q. The schemes with parameter sets obtained by simultaneously calibrating clusters of similar donor catchments (NC10 and NC58) performed worse than EXP1. The relatively poor Q estimation performance achieved by two (uncalibrated) macro-scale hydrological models suggests there is considerable merit in regionalizing the parameters of such models. The global HBV-light parameter maps and ancillary data are freely available via http://water.jrc.ec.europa.eu.

Modelling Hydrologic Processes in the Mekong River Basin Using a Distributed Model Driven by Satellite Precipitation and Rain Gauge Observations

PubMed Central

Wang, Wei; Lu, Hui; Yang, Dawen; Sothea, Khem; Jiao, Yang; Gao, Bin; Peng, Xueting; Pang, Zhiguo

2016-01-01

The Mekong River is the most important river in Southeast Asia. It has increasingly suffered from water-related problems due to economic development, population growth and climate change in the surrounding areas. In this study, we built a distributed Geomorphology-Based Hydrological Model (GBHM) of the Mekong River using remote sensing data and other publicly available data. Two numerical experiments were conducted using different rainfall data sets as model inputs. The data sets included rain gauge data from the Mekong River Commission (MRC) and remote sensing rainfall data from the Tropic Rainfall Measurement Mission (TRMM 3B42V7). Model calibration and validation were conducted for the two rainfall data sets. Compared to the observed discharge, both the gauge simulation and TRMM simulation performed well during the calibration period (1998–2001). However, the performance of the gauge simulation was worse than that of the TRMM simulation during the validation period (2002–2012). The TRMM simulation is more stable and reliable at different scales. Moreover, the calibration period was changed to 2, 4, and 8 years to test the impact of the calibration period length on the two simulations. The results suggest that longer calibration periods improved the GBHM performance during validation periods. In addition, the TRMM simulation is more stable and less sensitive to the calibration period length than is the gauge simulation. Further analysis reveals that the uneven distribution of rain gauges makes the input rainfall data less representative and more heterogeneous, worsening the simulation performance. Our results indicate that remotely sensed rainfall data may be more suitable for driving distributed hydrologic models, especially in basins with poor data quality or limited gauge availability. PMID:27010692

Modelling Hydrologic Processes in the Mekong River Basin Using a Distributed Model Driven by Satellite Precipitation and Rain Gauge Observations.

PubMed

Wang, Wei; Lu, Hui; Yang, Dawen; Sothea, Khem; Jiao, Yang; Gao, Bin; Peng, Xueting; Pang, Zhiguo

2016-01-01

The Mekong River is the most important river in Southeast Asia. It has increasingly suffered from water-related problems due to economic development, population growth and climate change in the surrounding areas. In this study, we built a distributed Geomorphology-Based Hydrological Model (GBHM) of the Mekong River using remote sensing data and other publicly available data. Two numerical experiments were conducted using different rainfall data sets as model inputs. The data sets included rain gauge data from the Mekong River Commission (MRC) and remote sensing rainfall data from the Tropic Rainfall Measurement Mission (TRMM 3B42V7). Model calibration and validation were conducted for the two rainfall data sets. Compared to the observed discharge, both the gauge simulation and TRMM simulation performed well during the calibration period (1998-2001). However, the performance of the gauge simulation was worse than that of the TRMM simulation during the validation period (2002-2012). The TRMM simulation is more stable and reliable at different scales. Moreover, the calibration period was changed to 2, 4, and 8 years to test the impact of the calibration period length on the two simulations. The results suggest that longer calibration periods improved the GBHM performance during validation periods. In addition, the TRMM simulation is more stable and less sensitive to the calibration period length than is the gauge simulation. Further analysis reveals that the uneven distribution of rain gauges makes the input rainfall data less representative and more heterogeneous, worsening the simulation performance. Our results indicate that remotely sensed rainfall data may be more suitable for driving distributed hydrologic models, especially in basins with poor data quality or limited gauge availability.

Texas flexible pavements and overlays : year 5 report - complete data documentation.

DOT National Transportation Integrated Search

2017-05-01

Proper calibration and validation of pavement design and performance models to Texas conditions is : essential for cost-effective flexible pavement design, performance predictions, and maintenance/rehab : strategies. The veracity of the calibration o...

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.

SWAT model uncertainty analysis, calibration and validation for runoff simulation in the Luvuvhu River catchment, South Africa

NASA Astrophysics Data System (ADS)

Thavhana, M. P.; Savage, M. J.; Moeletsi, M. E.

2018-06-01

The soil and water assessment tool (SWAT) was calibrated for the Luvuvhu River catchment, South Africa in order to simulate runoff. The model was executed through QSWAT which is an interface between SWAT and QGIS. Data from four weather stations and four weir stations evenly distributed over the catchment were used. The model was run for a 33-year period of 1983-2015. Sensitivity analysis, calibration and validation were conducted using the sequential uncertainty fitting (SUFI-2) algorithm through its interface with SWAT calibration and uncertainty procedure (SWAT-CUP). The calibration process was conducted for the period 1986 to 2005 while the validation process was from 2006 to 2015. Six model efficiency measures were used, namely: coefficient of determination (R2), Nash-Sutcliffe efficiency (NSE) index, root mean square error (RMSE)-observations standard deviation ratio (RSR), percent bias (PBIAS), probability (P)-factor and correlation coefficient (R)-factor were used. Initial results indicated an over-estimation of low flows with regression slope of less than 0.7. Twelve model parameters were applied for sensitivity analysis with four (ALPHA_BF, CN2, GW_DELAY and SOL_K) found to be more distinguishable and sensitive to streamflow (p < 0.05). The SUFI-2 algorithm through the interface with the SWAT-CUP was capable of capturing the model's behaviour, with calibration results showing an R2 of 0.63, NSE index of 0.66, RSR of 0.56 and a positive PBIAS of 16.3 while validation results revealed an R2 of 0.52, NSE of 0.48, RSR of 0.72 and PBIAS of 19.90. The model produced P-factor of 0.67 and R-factor of 0.68 during calibration and during validation, 0.69 and 0.53 respectively. Although performance indicators yielded fair and acceptable results, the P-factor was still below the recommended model performance of 70%. Apart from the unacceptable P-factor values, the results obtained in this study demonstrate acceptable model performance during calibration while validation results were still inconclusive. It can be concluded that calibration of the SWAT model yielded acceptable results with exception to validation results. Having said this, the model can be a useful tool for general water resources assessment and not for analysing hydrological extremes in the Luvuvhu River catchment.

Calibration transfer of a Raman spectroscopic quantification method for the assessment of liquid detergent compositions from at-line laboratory to in-line industrial scale.

PubMed

Brouckaert, D; Uyttersprot, J-S; Broeckx, W; De Beer, T

2018-03-01

Calibration transfer or standardisation aims at creating a uniform spectral response on different spectroscopic instruments or under varying conditions, without requiring a full recalibration for each situation. In the current study, this strategy is applied to construct at-line multivariate calibration models and consequently employ them in-line in a continuous industrial production line, using the same spectrometer. Firstly, quantitative multivariate models are constructed at-line at laboratory scale for predicting the concentration of two main ingredients in hard surface cleaners. By regressing the Raman spectra of a set of small-scale calibration samples against their reference concentration values, partial least squares (PLS) models are developed to quantify the surfactant levels in the liquid detergent compositions under investigation. After evaluating the models performance with a set of independent validation samples, a univariate slope/bias correction is applied in view of transporting these at-line calibration models to an in-line manufacturing set-up. This standardisation technique allows a fast and easy transfer of the PLS regression models, by simply correcting the model predictions on the in-line set-up, without adjusting anything to the original multivariate calibration models. An extensive statistical analysis is performed in order to assess the predictive quality of the transferred regression models. Before and after transfer, the R 2 and RMSEP of both models is compared for evaluating if their magnitude is similar. T-tests are then performed to investigate whether the slope and intercept of the transferred regression line are not statistically different from 1 and 0, respectively. Furthermore, it is inspected whether no significant bias can be noted. F-tests are executed as well, for assessing the linearity of the transfer regression line and for investigating the statistical coincidence of the transfer and validation regression line. Finally, a paired t-test is performed to compare the original at-line model to the slope/bias corrected in-line model, using interval hypotheses. It is shown that the calibration models of Surfactant 1 and Surfactant 2 yield satisfactory in-line predictions after slope/bias correction. While Surfactant 1 passes seven out of eight statistical tests, the recommended validation parameters are 100% successful for Surfactant 2. It is hence concluded that the proposed strategy for transferring at-line calibration models to an in-line industrial environment via a univariate slope/bias correction of the predicted values offers a successful standardisation approach. Copyright © 2017 Elsevier B.V. All rights reserved.

A new method to calibrate Lagrangian model with ASAR images for oil slick trajectory.

PubMed

Tian, Siyu; Huang, Xiaoxia; Li, Hongga

2017-03-15

Since Lagrangian model coefficients vary with different conditions, it is necessary to calibrate the model to obtain optimal coefficient combination for special oil spill accident. This paper focuses on proposing a new method to calibrate Lagrangian model with time series of Envisat ASAR images. Oil slicks extracted from time series images form a detected trajectory of special oil slick. Lagrangian model is calibrated by minimizing the difference between simulated trajectory and detected trajectory. mean center position distance difference (MCPD) and rotation difference (RD) of Oil slicks' or particles' standard deviational ellipses (SDEs) are calculated as two evaluations. The two parameters are taken to evaluate the performance of Lagrangian transport model with different coefficient combinations. This method is applied to Penglai 19-3 oil spill accident. The simulation result with calibrated model agrees well with related satellite observations. It is suggested the new method is effective to calibrate Lagrangian model. Copyright © 2016 Elsevier Ltd. All rights reserved.

Antenna Calibration and Measurement Equipment

NASA Technical Reports Server (NTRS)

Rochblatt, David J.; Cortes, Manuel Vazquez

2012-01-01

A document describes the Antenna Calibration & Measurement Equipment (ACME) system that will provide the Deep Space Network (DSN) with instrumentation enabling a trained RF engineer at each complex to perform antenna calibration measurements and to generate antenna calibration data. This data includes continuous-scan auto-bore-based data acquisition with all-sky data gathering in support of 4th order pointing model generation requirements. Other data includes antenna subreflector focus, system noise temperature and tipping curves, antenna efficiency, reports system linearity, and instrument calibration. The ACME system design is based on the on-the-fly (OTF) mapping technique and architecture. ACME has contributed to the improved RF performance of the DSN by approximately a factor of two. It improved the pointing performances of the DSN antennas and productivity of its personnel and calibration engineers.

The calibration and flight test performance of the space shuttle orbiter air data system

NASA Technical Reports Server (NTRS)

Dean, A. S.; Mena, A. L.

1983-01-01

The Space Shuttle air data system (ADS) is used by the guidance, navigation and control system (GN&C) to guide the vehicle to a safe landing. In addition, postflight aerodynamic analysis requires a precise knowledge of flight conditions. Since the orbiter is essentially an unpowered vehicle, the conventional methods of obtaining the ADS calibration were not available; therefore, the calibration was derived using a unique and extensive wind tunnel test program. This test program included subsonic tests with a 0.36-scale orbiter model, transonic and supersonic tests with a smaller 0.2-scale model, and numerous ADS probe-alone tests. The wind tunnel calibration was further refined with subsonic results from the approach and landing test (ALT) program, thus producing the ADS calibration for the orbital flight test (OFT) program. The calibration of the Space Shuttle ADS and its performance during flight are discussed in this paper. A brief description of the system is followed by a discussion of the calibration methodology, and then by a review of the wind tunnel and flight test programs. Finally, the flight results are presented, including an evaluation of the system performance for on-board systems use and a description of the calibration refinements developed to provide the best possible air data for postflight analysis work.

Identifying influential data points in hydrological model calibration and their impact on streamflow predictions

NASA Astrophysics Data System (ADS)

Wright, David; Thyer, Mark; Westra, Seth

2015-04-01

Highly influential data points are those that have a disproportionately large impact on model performance, parameters and predictions. However, in current hydrological modelling practice the relative influence of individual data points on hydrological model calibration is not commonly evaluated. This presentation illustrates and evaluates several influence diagnostics tools that hydrological modellers can use to assess the relative influence of data. The feasibility and importance of including influence detection diagnostics as a standard tool in hydrological model calibration is discussed. Two classes of influence diagnostics are evaluated: (1) computationally demanding numerical "case deletion" diagnostics; and (2) computationally efficient analytical diagnostics, based on Cook's distance. These diagnostics are compared against hydrologically orientated diagnostics that describe changes in the model parameters (measured through the Mahalanobis distance), performance (objective function displacement) and predictions (mean and maximum streamflow). These influence diagnostics are applied to two case studies: a stage/discharge rating curve model, and a conceptual rainfall-runoff model (GR4J). Removing a single data point from the calibration resulted in differences to mean flow predictions of up to 6% for the rating curve model, and differences to mean and maximum flow predictions of up to 10% and 17%, respectively, for the hydrological model. When using the Nash-Sutcliffe efficiency in calibration, the computationally cheaper Cook's distance metrics produce similar results to the case-deletion metrics at a fraction of the computational cost. However, Cooks distance is adapted from linear regression with inherit assumptions on the data and is therefore less flexible than case deletion. Influential point detection diagnostics show great potential to improve current hydrological modelling practices by identifying highly influential data points. The findings of this study establish the feasibility and importance of including influential point detection diagnostics as a standard tool in hydrological model calibration. They provide the hydrologist with important information on whether model calibration is susceptible to a small number of highly influent data points. This enables the hydrologist to make a more informed decision of whether to (1) remove/retain the calibration data; (2) adjust the calibration strategy and/or hydrological model to reduce the susceptibility of model predictions to a small number of influential observations.

Modeling and Calibration of a Novel One-Mirror Galvanometric Laser Scanner

PubMed Central

Yu, Chengyi; Chen, Xiaobo; Xi, Juntong

2017-01-01

A laser stripe sensor has limited application when a point cloud of geometric samples on the surface of the object needs to be collected, so a galvanometric laser scanner is designed by using a one-mirror galvanometer element as its mechanical device to drive the laser stripe to sweep along the object. A novel mathematical model is derived for the proposed galvanometer laser scanner without any position assumptions and then a model-driven calibration procedure is proposed. Compared with available model-driven approaches, the influence of machining and assembly errors is considered in the proposed model. Meanwhile, a plane-constraint-based approach is proposed to extract a large number of calibration points effectively and accurately to calibrate the galvanometric laser scanner. Repeatability and accuracy of the galvanometric laser scanner are evaluated on the automobile production line to verify the efficiency and accuracy of the proposed calibration method. Experimental results show that the proposed calibration approach yields similar measurement performance compared with a look-up table calibration method. PMID:28098844

Calibration and parameterization of a semi-distributed hydrological model to support sub-daily ensemble flood forecasting; a watershed in southeast Brazil

NASA Astrophysics Data System (ADS)

de Almeida Bressiani, D.; Srinivasan, R.; Mendiondo, E. M.

2013-12-01

The use of distributed or semi-distributed models to represent the processes and dynamics of a watershed in the last few years has increased. These models are important tools to predict and forecast the hydrological responses of the watersheds, and they can subside disaster risk management and planning. However they usually have a lot of parameters, of which, due to the spatial and temporal variability of the processes, are not known, specially in developing countries; therefore a robust and sensible calibration is very important. This study conduced a sub-daily calibration and parameterization of the Soil & Water Assessment Tool (SWAT) for a 12,600 km2 watershed in southeast Brazil, and uses ensemble forecasts to evaluate if the model can be used as a tool for flood forecasting. The Piracicaba Watershed, in São Paulo State, is mainly rural, but has about 4 million of population in highly relevant urban areas, and three cities in the list of critical cities of the National Center for Natural Disasters Monitoring and Alerts. For calibration: the watershed was divided in areas with similar hydrological characteristics, for each of these areas one gauge station was chosen for calibration; this procedure was performed to evaluate the effectiveness of calibrating in fewer places, since areas with the same group of groundwater, soil, land use and slope characteristics should have similar parameters; making calibration a less time-consuming task. The sensibility analysis and calibration were performed on the software SWAT-CUP with the optimization algorithm: Sequential Uncertainly Fitting Version 2 (SUFI-2), which uses Latin hypercube sampling scheme in an iterative process. The performance of the models to evaluate the calibration and validation was done with: Nash-Sutcliffe efficiency coefficient (NSE), determination coefficient (r2), root mean square error (RMSE), and percent bias (PBIAS), with monthly average values of NSE around 0.70, r2 of 0.9, normalized RMSE of 0.01, and PBIAS of 10. Past events were analysed to evaluate the possibility of using the SWAT developed model for Piracicaba watershed as a tool for ensemble flood forecasting. For the ensemble evaluation members from the numerical model Eta were used. Eta is an atmospheric model used for research and operational purposes, with 5km resolution, and is updated twice a day (00 e 12 UTC) for a ten day horizon, with precipitation and weather estimates for each hour. The parameterized SWAT model performed overall well for ensemble flood forecasting.

An investigation into force-moment calibration techniques applicable to a magnetic suspension and balance system. M.S. Thesis

NASA Technical Reports Server (NTRS)

Eskins, Jonathan

1988-01-01

The problem of determining the forces and moments acting on a wind tunnel model suspended in a Magnetic Suspension and Balance System is addressed. Two calibration methods were investigated for three types of model cores, i.e., Alnico, Samarium-Cobalt, and a superconducting solenoid. Both methods involve calibrating the currents in the electromagnetic array against known forces and moments. The first is a static calibration method using calibration weights and a system of pulleys. The other method, dynamic calibration, involves oscillating the model and using its inertia to provide calibration forces and moments. Static calibration data, found to produce the most reliable results, is presented for three degrees of freedom at 0, 15, and -10 deg angle of attack. Theoretical calculations are hampered by the inability to represent iron-cored electromagnets. Dynamic calibrations, despite being quicker and easier to perform, are not as accurate as static calibrations. Data for dynamic calibrations at 0 and 15 deg is compared with the relevant static data acquired. Distortion of oscillation traces is cited as a major source of error in dynamic calibrations.

Application of near-infrared spectroscopy for estimation of non-structural carbohydrates in foliar samples of Eucalyptus globulus Labilladière.

PubMed

Quentin, A G; Rodemann, T; Doutreleau, M-F; Moreau, M; Davies, N W; Millard, Peter

2017-01-31

Near-infrared reflectance spectroscopy (NIRS) is frequently used for the assessment of key nutrients of forage or crops but remains underused in ecological and physiological studies, especially to quantify non-structural carbohydrates. The aim of this study was to develop calibration models to assess the content in soluble sugars (fructose, glucose, sucrose) and starch in foliar material of Eucalyptus globulus. A partial least squares (PLS) regression was used on the sample spectral data and was compared to the contents measured using standard wet chemistry methods. The calibration models were validated using a completely independent set of samples. We used key indicators such as the ratio of prediction to deviation (RPD) and the range error ratio to give an assessment of the performance of the calibration models. Accurate calibration models were obtained for fructose and sucrose content (R2 > 0.85, root mean square error of prediction (RMSEP) of 0.95%–1.26% in the validation models), followed by sucrose and total soluble sugar content (R2 ~ 0.70 and RMSEP > 2.3%). In comparison to the others, calibration of the starch model performed very poorly with RPD = 1.70. This study establishes the ability of the NIRS calibration model to infer soluble sugar content in foliar samples of E. globulus in a rapid and cost-effective way. We suggest a complete redevelopment of the starch analysis using more specific quantification such as an HPLC-based technique to reach higher performance in the starch model. Overall, NIRS could serve as a high-throughput phenotyping tool to study plant response to stress factors.

Studies in Software Cost Model Behavior: Do We Really Understand Cost Model Performance?

NASA Technical Reports Server (NTRS)

Lum, Karen; Hihn, Jairus; Menzies, Tim

2006-01-01

While there exists extensive literature on software cost estimation techniques, industry practice continues to rely upon standard regression-based algorithms. These software effort models are typically calibrated or tuned to local conditions using local data. This paper cautions that current approaches to model calibration often produce sub-optimal models because of the large variance problem inherent in cost data and by including far more effort multipliers than the data supports. Building optimal models requires that a wider range of models be considered while correctly calibrating these models requires rejection rules that prune variables and records and use multiple criteria for evaluating model performance. The main contribution of this paper is to document a standard method that integrates formal model identification, estimation, and validation. It also documents what we call the large variance problem that is a leading cause of cost model brittleness or instability.

Simulating soil moisture change in a semiarid rangeland watershed with a process-based water-balance model

Treesearch

Howard Evan Canfield; Vicente L. Lopes

2000-01-01

A process-based, simulation model for evaporation, soil water and streamflow (BROOK903) was used to estimate soil moisture change on a semiarid rangeland watershed in southeastern Arizona. A sensitivity analysis was performed to select parameters affecting ET and soil moisture for calibration. Automatic parameter calibration was performed using a procedure based on a...

Texas flexible pavements overlays : review and analysis of existing databases.

DOT National Transportation Integrated Search

2011-12-01

Proper calibration of pavement design and rehabilitation performance models to : conditions in Texas is essential for cost-effective flexible pavement design. The degree of : excellence with which TxDOTs pavement design models is calibrated will d...

Development of a Tool for an Efficient Calibration of CORSIM Models

DOT National Transportation Integrated Search

2014-08-01

This project proposes a Memetic Algorithm (MA) for the calibration of microscopic traffic flow simulation models. The proposed MA includes a combination of genetic and simulated annealing algorithms. The genetic algorithm performs the exploration of ...

Collection of materials and performance data for Texas flexible pavements and overlays : project summary.

DOT National Transportation Integrated Search

2015-08-31

Proper calibration of mechanistic-empirical : (M-E) design and rehabilitation performance : models to meet Texas conditions is essential : for cost-effective flexible pavement designs. : Such a calibration effort would require a : reliable source of ...

Calibration by Hydrological Response Unit of a National Hydrologic Model to Improve Spatial Representation and Distribution of Parameters

NASA Astrophysics Data System (ADS)

Norton, P. A., II

2015-12-01

The U. S. Geological Survey is developing a National Hydrologic Model (NHM) to support consistent hydrologic modeling across the conterminous United States (CONUS). The Precipitation-Runoff Modeling System (PRMS) simulates daily hydrologic and energy processes in watersheds, and is used for the NHM application. For PRMS each watershed is divided into hydrologic response units (HRUs); by default each HRU is assumed to have a uniform hydrologic response. The Geospatial Fabric (GF) is a database containing initial parameter values for input to PRMS and was created for the NHM. The parameter values in the GF were derived from datasets that characterize the physical features of the entire CONUS. The NHM application is composed of more than 100,000 HRUs from the GF. Selected parameter values commonly are adjusted by basin in PRMS using an automated calibration process based on calibration targets, such as streamflow. Providing each HRU with distinct values that captures variability within the CONUS may improve simulation performance of the NHM. During calibration of the NHM by HRU, selected parameter values are adjusted for PRMS based on calibration targets, such as streamflow, snow water equivalent (SWE) and actual evapotranspiration (AET). Simulated SWE, AET, and runoff were compared to value ranges derived from multiple sources (e.g. the Snow Data Assimilation System, the Moderate Resolution Imaging Spectroradiometer (i.e. MODIS) Global Evapotranspiration Project, the Simplified Surface Energy Balance model, and the Monthly Water Balance Model). This provides each HRU with a distinct set of parameter values that captures the variability within the CONUS, leading to improved model performance. We present simulation results from the NHM after preliminary calibration, including the results of basin-level calibration for the NHM using: 1) default initial GF parameter values, and 2) parameter values calibrated by HRU.

Use of soft data for multi-criteria calibration and validation of APEX: Impact on model simulations

USDA-ARS?s Scientific Manuscript database

It is widely known that the use of soft data and multiple model performance criteria in model calibration and validation is critical to ensuring the model capture major hydrologic and water quality processes. The Agricultural Policy/Environmental eXtender (APEX) is a hydrologic and water quality mod...

Procedure for the Selection and Validation of a Calibration Model I-Description and Application.

PubMed

Desharnais, Brigitte; Camirand-Lemyre, Félix; Mireault, Pascal; Skinner, Cameron D

2017-05-01

Calibration model selection is required for all quantitative methods in toxicology and more broadly in bioanalysis. This typically involves selecting the equation order (quadratic or linear) and weighting factor correctly modelizing the data. A mis-selection of the calibration model will generate lower quality control (QC) accuracy, with an error up to 154%. Unfortunately, simple tools to perform this selection and tests to validate the resulting model are lacking. We present a stepwise, analyst-independent scheme for selection and validation of calibration models. The success rate of this scheme is on average 40% higher than a traditional "fit and check the QCs accuracy" method of selecting the calibration model. Moreover, the process was completely automated through a script (available in Supplemental Data 3) running in RStudio (free, open-source software). The need for weighting was assessed through an F-test using the variances of the upper limit of quantification and lower limit of quantification replicate measurements. When weighting was required, the choice between 1/x and 1/x2 was determined by calculating which option generated the smallest spread of weighted normalized variances. Finally, model order was selected through a partial F-test. The chosen calibration model was validated through Cramer-von Mises or Kolmogorov-Smirnov normality testing of the standardized residuals. Performance of the different tests was assessed using 50 simulated data sets per possible calibration model (e.g., linear-no weight, quadratic-no weight, linear-1/x, etc.). This first of two papers describes the tests, procedures and outcomes of the developed procedure using real LC-MS-MS results for the quantification of cocaine and naltrexone. © The Author 2017. Published by Oxford University Press. All rights reserved. For Permissions, please email: journals.permissions@oup.com.

Calibration of a complex activated sludge model for the full-scale wastewater treatment plant.

PubMed

Liwarska-Bizukojc, Ewa; Olejnik, Dorota; Biernacki, Rafal; Ledakowicz, Stanislaw

2011-08-01

In this study, the results of the calibration of the complex activated sludge model implemented in BioWin software for the full-scale wastewater treatment plant are presented. Within the calibration of the model, sensitivity analysis of its parameters and the fractions of carbonaceous substrate were performed. In the steady-state and dynamic calibrations, a successful agreement between the measured and simulated values of the output variables was achieved. Sensitivity analysis revealed that upon the calculations of normalized sensitivity coefficient (S(i,j)) 17 (steady-state) or 19 (dynamic conditions) kinetic and stoichiometric parameters are sensitive. Most of them are associated with growth and decay of ordinary heterotrophic organisms and phosphorus accumulating organisms. The rankings of ten most sensitive parameters established on the basis of the calculations of the mean square sensitivity measure (δ(msqr)j) indicate that irrespective of the fact, whether the steady-state or dynamic calibration was performed, there is an agreement in the sensitivity of parameters.

Multisite Evaluation of APEX for Water Quality: I. Best Professional Judgment Parameterization.

PubMed

Baffaut, Claire; Nelson, Nathan O; Lory, John A; Senaviratne, G M M M Anomaa; Bhandari, Ammar B; Udawatta, Ranjith P; Sweeney, Daniel W; Helmers, Matt J; Van Liew, Mike W; Mallarino, Antonio P; Wortmann, Charles S

2017-11-01

The Agricultural Policy Environmental eXtender (APEX) model is capable of estimating edge-of-field water, nutrient, and sediment transport and is used to assess the environmental impacts of management practices. The current practice is to fully calibrate the model for each site simulation, a task that requires resources and data not always available. The objective of this study was to compare model performance for flow, sediment, and phosphorus transport under two parameterization schemes: a best professional judgment (BPJ) parameterization based on readily available data and a fully calibrated parameterization based on site-specific soil, weather, event flow, and water quality data. The analysis was conducted using 12 datasets at four locations representing poorly drained soils and row-crop production under different tillage systems. Model performance was based on the Nash-Sutcliffe efficiency (NSE), the coefficient of determination () and the regression slope between simulated and measured annualized loads across all site years. Although the BPJ model performance for flow was acceptable (NSE = 0.7) at the annual time step, calibration improved it (NSE = 0.9). Acceptable simulation of sediment and total phosphorus transport (NSE = 0.5 and 0.9, respectively) was obtained only after full calibration at each site. Given the unacceptable performance of the BPJ approach, uncalibrated use of APEX for planning or management purposes may be misleading. Model calibration with water quality data prior to using APEX for simulating sediment and total phosphorus loss is essential. Copyright © by the American Society of Agronomy, Crop Science Society of America, and Soil Science Society of America, Inc.

Evaluation of the AnnAGNPS Model for Predicting Runoff and Nutrient Export in a Typical Small Watershed in the Hilly Region of Taihu Lake.

PubMed

Luo, Chuan; Li, Zhaofu; Li, Hengpeng; Chen, Xiaomin

2015-09-02

The application of hydrological and water quality models is an efficient approach to better understand the processes of environmental deterioration. This study evaluated the ability of the Annualized Agricultural Non-Point Source (AnnAGNPS) model to predict runoff, total nitrogen (TN) and total phosphorus (TP) loading in a typical small watershed of a hilly region near Taihu Lake, China. Runoff was calibrated and validated at both an annual and monthly scale, and parameter sensitivity analysis was performed for TN and TP before the two water quality components were calibrated. The results showed that the model satisfactorily simulated runoff at annual and monthly scales, both during calibration and validation processes. Additionally, results of parameter sensitivity analysis showed that the parameters Fertilizer rate, Fertilizer organic, Canopy cover and Fertilizer inorganic were more sensitive to TN output. In terms of TP, the parameters Residue mass ratio, Fertilizer rate, Fertilizer inorganic and Canopy cover were the most sensitive. Based on these sensitive parameters, calibration was performed. TN loading produced satisfactory results for both the calibration and validation processes, whereas the performance of TP loading was slightly poor. The simulation results showed that AnnAGNPS has the potential to be used as a valuable tool for the planning and management of watersheds.

Development and Calibration of Regional Dynamic Traffic Assignment Models for the Estimation of Traffic Performance Measures in Nevada

DOT National Transportation Integrated Search

2017-02-01

This project covered the development and calibration of a Dynamic Traffic Assignment (DTA) model and explained the procedures, constraints, and considerations for usage of this model for the Reno-Sparks area roadway network in Northern Nevada. A lite...

A machine learning calibration model using random forests to improve sensor performance for lower-cost air quality monitoring

NASA Astrophysics Data System (ADS)

Zimmerman, Naomi; Presto, Albert A.; Kumar, Sriniwasa P. N.; Gu, Jason; Hauryliuk, Aliaksei; Robinson, Ellis S.; Robinson, Allen L.; Subramanian, R.

2018-01-01

Low-cost sensing strategies hold the promise of denser air quality monitoring networks, which could significantly improve our understanding of personal air pollution exposure. Additionally, low-cost air quality sensors could be deployed to areas where limited monitoring exists. However, low-cost sensors are frequently sensitive to environmental conditions and pollutant cross-sensitivities, which have historically been poorly addressed by laboratory calibrations, limiting their utility for monitoring. In this study, we investigated different calibration models for the Real-time Affordable Multi-Pollutant (RAMP) sensor package, which measures CO, NO2, O3, and CO2. We explored three methods: (1) laboratory univariate linear regression, (2) empirical multiple linear regression, and (3) machine-learning-based calibration models using random forests (RF). Calibration models were developed for 16-19 RAMP monitors (varied by pollutant) using training and testing windows spanning August 2016 through February 2017 in Pittsburgh, PA, US. The random forest models matched (CO) or significantly outperformed (NO2, CO2, O3) the other calibration models, and their accuracy and precision were robust over time for testing windows of up to 16 weeks. Following calibration, average mean absolute error on the testing data set from the random forest models was 38 ppb for CO (14 % relative error), 10 ppm for CO2 (2 % relative error), 3.5 ppb for NO2 (29 % relative error), and 3.4 ppb for O3 (15 % relative error), and Pearson r versus the reference monitors exceeded 0.8 for most units. Model performance is explored in detail, including a quantification of model variable importance, accuracy across different concentration ranges, and performance in a range of monitoring contexts including the National Ambient Air Quality Standards (NAAQS) and the US EPA Air Sensors Guidebook recommendations of minimum data quality for personal exposure measurement. A key strength of the RF approach is that it accounts for pollutant cross-sensitivities. This highlights the importance of developing multipollutant sensor packages (as opposed to single-pollutant monitors); we determined this is especially critical for NO2 and CO2. The evaluation reveals that only the RF-calibrated sensors meet the US EPA Air Sensors Guidebook recommendations of minimum data quality for personal exposure measurement. We also demonstrate that the RF-model-calibrated sensors could detect differences in NO2 concentrations between a near-road site and a suburban site less than 1.5 km away. From this study, we conclude that combining RF models with carefully controlled state-of-the-art multipollutant sensor packages as in the RAMP monitors appears to be a very promising approach to address the poor performance that has plagued low-cost air quality sensors.

Calibration of limited-area ensemble precipitation forecasts for hydrological predictions

NASA Astrophysics Data System (ADS)

Diomede, Tommaso; Marsigli, Chiara; Montani, Andrea; Nerozzi, Fabrizio; Paccagnella, Tiziana

2015-04-01

The main objective of this study is to investigate the impact of calibration for limited-area ensemble precipitation forecasts, to be used for driving discharge predictions up to 5 days in advance. A reforecast dataset, which spans 30 years, based on the Consortium for Small Scale Modeling Limited-Area Ensemble Prediction System (COSMO-LEPS) was used for testing the calibration strategy. Three calibration techniques were applied: quantile-to-quantile mapping, linear regression, and analogs. The performance of these methodologies was evaluated in terms of statistical scores for the precipitation forecasts operationally provided by COSMO-LEPS in the years 2003-2007 over Germany, Switzerland, and the Emilia-Romagna region (northern Italy). The analog-based method seemed to be preferred because of its capability of correct position errors and spread deficiencies. A suitable spatial domain for the analog search can help to handle model spatial errors as systematic errors. However, the performance of the analog-based method may degrade in cases where a limited training dataset is available. A sensitivity test on the length of the training dataset over which to perform the analog search has been performed. The quantile-to-quantile mapping and linear regression methods were less effective, mainly because the forecast-analysis relation was not so strong for the available training dataset. A comparison between the calibration based on the deterministic reforecast and the calibration based on the full operational ensemble used as training dataset has been considered, with the aim to evaluate whether reforecasts are really worthy for calibration, given that their computational cost is remarkable. The verification of the calibration process was then performed by coupling ensemble precipitation forecasts with a distributed rainfall-runoff model. This test was carried out for a medium-sized catchment located in Emilia-Romagna, showing a beneficial impact of the analog-based method on the reduction of missed events for discharge predictions.

The preliminary checkout, evaluation and calibration of a 3-component force measurement system for calibrating propulsion simulators for wind tunnel models

NASA Technical Reports Server (NTRS)

Scott, W. A.

1984-01-01

The propulsion simulator calibration laboratory (PSCL) in which calibrations can be performed to determine the gross thrust and airflow of propulsion simulators installed in wind tunnel models is described. The preliminary checkout, evaluation and calibration of the PSCL's 3 component force measurement system is reported. Methods and equipment were developed for the alignment and calibration of the force measurement system. The initial alignment of the system demonstrated the need for more efficient means of aligning system's components. The use of precision alignment jigs increases both the speed and accuracy with which the system is aligned. The calibration of the force measurement system shows that the methods and equipment for this procedure can be successful.

Development of a Regional Glycerol Dialkyl Glycerol Tetraether (GDGT) - Temperature Calibration for Antarctic and sub-Antarctic Lakes

NASA Astrophysics Data System (ADS)

Roberts, S. J.; Foster, L. C.; Pearson, E. J.; Steve, J.; Hodgson, D.; Saunders, K. M.; Verleyen, E.

2016-12-01

Temperature calibration models based on the relative abundances of sedimentary glycerol dialkyl glycerol tetraethers (GDGTs) have been used to reconstruct past temperatures in both marine and terrestrial environments, but have not been widely applied in high latitude environments. This is mainly because the performance of GDGT-temperature calibrations at lower temperatures and GDGT provenance in many lacustrine settings remains uncertain. To address these issues, we examined surface sediments from 32 Antarctic, sub-Antarctic and Southern Chilean lakes. First, we quantified GDGT compositions present and then investigated modern-day environmental controls on GDGT composition. GDGTs were found in all 32 lakes studied. Branched GDGTs (brGDGTs) were dominant in 31 lakes and statistical analyses showed that their composition was strongly correlated with mean summer air temperature (MSAT) rather than pH, conductivity or water depth. Second, we developed the first regional brGDGT-temperature calibration for Antarctic and sub-Antarctic lakes based on four brGDGT compounds (GDGT-Ib, GDGT-II, GDGT-III and GDGT-IIIb). Of these, GDGT-IIIb proved particularly important in cold lacustrine environments. Our brGDGT-Antarctic temperature calibration dataset has an improved statistical performance at low temperatures compared to previous global calibrations (r2=0.83, RMSE=1.45°C, RMSEP-LOO=1.68°C, n=36 samples), highlighting the importance of basing palaeotemperature reconstructions on regional GDGT-temperature calibrations, especially if specific compounds lead to improved model performance. Finally, we applied the new Antarctic brGDGT-temperature calibration to two key lake records from the Antarctic Peninsula and South Georgia. In both, downcore temperature reconstructions show similarities to known Holocene warm periods, providing proof of concept for the new Antarctic calibration model.

An Open-Source Auto-Calibration Routine Supporting the Stormwater Management Model

NASA Astrophysics Data System (ADS)

Tiernan, E. D.; Hodges, B. R.

2017-12-01

The stormwater management model (SWMM) is a clustered model that relies on subcatchment-averaged parameter assignments to correctly capture catchment stormwater runoff behavior. Model calibration is considered a critical step for SWMM performance, an arduous task that most stormwater management designers undertake manually. This research presents an open-source, automated calibration routine that increases the efficiency and accuracy of the model calibration process. The routine makes use of a preliminary sensitivity analysis to reduce the dimensions of the parameter space, at which point a multi-objective function, genetic algorithm (modified Non-dominated Sorting Genetic Algorithm II) determines the Pareto front for the objective functions within the parameter space. The solutions on this Pareto front represent the optimized parameter value sets for the catchment behavior that could not have been reasonably obtained through manual calibration.

Fast hydrological model calibration based on the heterogeneous parallel computing accelerated shuffled complex evolution method

NASA Astrophysics Data System (ADS)

Kan, Guangyuan; He, Xiaoyan; Ding, Liuqian; Li, Jiren; Hong, Yang; Zuo, Depeng; Ren, Minglei; Lei, Tianjie; Liang, Ke

2018-01-01

Hydrological model calibration has been a hot issue for decades. The shuffled complex evolution method developed at the University of Arizona (SCE-UA) has been proved to be an effective and robust optimization approach. However, its computational efficiency deteriorates significantly when the amount of hydrometeorological data increases. In recent years, the rise of heterogeneous parallel computing has brought hope for the acceleration of hydrological model calibration. This study proposed a parallel SCE-UA method and applied it to the calibration of a watershed rainfall-runoff model, the Xinanjiang model. The parallel method was implemented on heterogeneous computing systems using OpenMP and CUDA. Performance testing and sensitivity analysis were carried out to verify its correctness and efficiency. Comparison results indicated that heterogeneous parallel computing-accelerated SCE-UA converged much more quickly than the original serial version and possessed satisfactory accuracy and stability for the task of fast hydrological model calibration.

A Case Study on a Combination NDVI Forecasting Model Based on the Entropy Weight Method

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

Huang, Shengzhi; Ming, Bo; Huang, Qiang

It is critically meaningful to accurately predict NDVI (Normalized Difference Vegetation Index), which helps guide regional ecological remediation and environmental managements. In this study, a combination forecasting model (CFM) was proposed to improve the performance of NDVI predictions in the Yellow River Basin (YRB) based on three individual forecasting models, i.e., the Multiple Linear Regression (MLR), Artificial Neural Network (ANN), and Support Vector Machine (SVM) models. The entropy weight method was employed to determine the weight coefficient for each individual model depending on its predictive performance. Results showed that: (1) ANN exhibits the highest fitting capability among the four orecastingmore » models in the calibration period, whilst its generalization ability becomes weak in the validation period; MLR has a poor performance in both calibration and validation periods; the predicted results of CFM in the calibration period have the highest stability; (2) CFM generally outperforms all individual models in the validation period, and can improve the reliability and stability of predicted results through combining the strengths while reducing the weaknesses of individual models; (3) the performances of all forecasting models are better in dense vegetation areas than in sparse vegetation areas.« less

Some advances in experimentation supporting development of viscoplastic constitutive models

NASA Technical Reports Server (NTRS)

Ellis, J. R.; Robinson, D. N.

1985-01-01

The development of a biaxial extensometer capable of measuring axial, torsion, and diametral strains to near-microstrain resolution at elevated temperatures is discussed. An instrument with this capability was needed to provide experimental support to the development of viscoplastic constitutive models. The advantages gained when torsional loading is used to investigate inelastic material response at elevated temperatures are highlighted. The development of the biaxial extensometer was conducted in two stages. The first involved a series of bench calibration experiments performed at room temperature. The second stage involved a series of in-place calibration experiments performed at room temperature. A review of the calibration data indicated that all performance requirements regarding resolution, range, stability, and crosstalk had been met by the subject instrument over the temperature range of interest, 21 C to 651 C. The scope of the in-placed calibration experiments was expanded to investigate the feasibility of generating stress relaxation data under torsional loading.

Combining satellite data and appropriate objective functions for improved spatial pattern performance of a distributed hydrologic model

NASA Astrophysics Data System (ADS)

Demirel, Mehmet C.; Mai, Juliane; Mendiguren, Gorka; Koch, Julian; Samaniego, Luis; Stisen, Simon

2018-02-01

Satellite-based earth observations offer great opportunities to improve spatial model predictions by means of spatial-pattern-oriented model evaluations. In this study, observed spatial patterns of actual evapotranspiration (AET) are utilised for spatial model calibration tailored to target the pattern performance of the model. The proposed calibration framework combines temporally aggregated observed spatial patterns with a new spatial performance metric and a flexible spatial parameterisation scheme. The mesoscale hydrologic model (mHM) is used to simulate streamflow and AET and has been selected due to its soil parameter distribution approach based on pedo-transfer functions and the build in multi-scale parameter regionalisation. In addition two new spatial parameter distribution options have been incorporated in the model in order to increase the flexibility of root fraction coefficient and potential evapotranspiration correction parameterisations, based on soil type and vegetation density. These parameterisations are utilised as they are most relevant for simulated AET patterns from the hydrologic model. Due to the fundamental challenges encountered when evaluating spatial pattern performance using standard metrics, we developed a simple but highly discriminative spatial metric, i.e. one comprised of three easily interpretable components measuring co-location, variation and distribution of the spatial data. The study shows that with flexible spatial model parameterisation used in combination with the appropriate objective functions, the simulated spatial patterns of actual evapotranspiration become substantially more similar to the satellite-based estimates. Overall 26 parameters are identified for calibration through a sequential screening approach based on a combination of streamflow and spatial pattern metrics. The robustness of the calibrations is tested using an ensemble of nine calibrations based on different seed numbers using the shuffled complex evolution optimiser. The calibration results reveal a limited trade-off between streamflow dynamics and spatial patterns illustrating the benefit of combining separate observation types and objective functions. At the same time, the simulated spatial patterns of AET significantly improved when an objective function based on observed AET patterns and a novel spatial performance metric compared to traditional streamflow-only calibration were included. Since the overall water balance is usually a crucial goal in hydrologic modelling, spatial-pattern-oriented optimisation should always be accompanied by traditional discharge measurements. In such a multi-objective framework, the current study promotes the use of a novel bias-insensitive spatial pattern metric, which exploits the key information contained in the observed patterns while allowing the water balance to be informed by discharge observations.

Parameter identification of the SWAT model on the BANI catchment (West Africa) under limited data condition

NASA Astrophysics Data System (ADS)

Chaibou Begou, Jamilatou; Jomaa, Seifeddine; Benabdallah, Sihem; Rode, Michael

2015-04-01

Due to the climate change, drier conditions have prevailed in West Africa, since the seventies, and the consequences are important on water resources. In order to identify and implement management strategies of adaptation to climate change in the sector of water, it is crucial to improve our physical understanding of water resources evolution in the region. To this end, hydrologic modelling is an appropriate tool for flow predictions under changing climate and land use conditions. In this study, the applicability and performance of the recent version of Soil and Water Assessment Tool (SWAT2012) model were tested on the Bani catchment in West Africa under limited data condition. Model parameters identification was also tested using one site and multisite calibration approaches. The Bani is located in the upper part of the Niger River and drains an area of about 101, 000 km2 at the outlet of Douna. The climate is tropical, humid to semi-arid from the South to the North with an average annual rainfall of 1050 mm (period 1981-2000). Global datasets were used for the model setup such as: USGS hydrosheds DEM, USGS LCI GlobCov2009 and the FAO Digital Soil Map of the World. Daily measured rainfall from nine rain gauges and maximum and minimum temperature from five weather stations covering the period 1981-1997 were used for model setup. Sensitivity analysis, calibration and validation are performed within SWATCUP using GLUE procedure at Douna station first (one site calibration), then at three additional internal stations, Bougouni, Pankourou and Kouoro1 (multi-site calibration). Model parameters were calibrated at daily time step for the period 1983-1992, then validated for the period 1993-1997. A period of two years (1981-1982) was used for model warming up. Results of one-site calibration showed that the model performance is evaluated by 0.76 and 0.79 for Nash-Sutcliffe (NS) and correlation coefficient (R2), respectively. While for the validation period the performance improved considerably with NS and R2 equal to 0.84 and 0.87, respectively. The degree of total uncertainties is quantified by a minimum P-factor of 0.61 and a maximum R-factor of 0.59. These statistics suggest that the model performance can be judged as very good, especially considering limited data condition and high climate, land use and soil variability in the studied basin. The most sensitive parameters (CN2, OVN and SLSUBBSN) are related to surface runoff reflecting the dominance of this process on the streamflow generation. In the next step, multisite calibration approach will be performed on the BANI basin to assess how much additional observations improve the model parameter identification.

Prediction of SOFC Performance with or without Experiments: A Study on Minimum Requirements for Experimental Data

DOE PAGES

Yang, Tao; Sezer, Hayri; Celik, Ismail B.; ...

2015-06-02

In the present paper, a physics-based procedure combining experiments and multi-physics numerical simulations is developed for overall analysis of SOFCs operational diagnostics and performance predictions. In this procedure, essential information for the fuel cell is extracted first by utilizing empirical polarization analysis in conjunction with experiments and refined by multi-physics numerical simulations via simultaneous analysis and calibration of polarization curve and impedance behavior. The performance at different utilization cases and operating currents is also predicted to confirm the accuracy of the proposed model. It is demonstrated that, with the present electrochemical model, three air/fuel flow conditions are needed to producemore » a set of complete data for better understanding of the processes occurring within SOFCs. After calibration against button cell experiments, the methodology can be used to assess performance of planar cell without further calibration. The proposed methodology would accelerate the calibration process and improve the efficiency of design and diagnostics.« less

Impact of influent data frequency and model structure on the quality of WWTP model calibration and uncertainty.

PubMed

Cierkens, Katrijn; Plano, Salvatore; Benedetti, Lorenzo; Weijers, Stefan; de Jonge, Jarno; Nopens, Ingmar

2012-01-01

Application of activated sludge models (ASMs) to full-scale wastewater treatment plants (WWTPs) is still hampered by the problem of model calibration of these over-parameterised models. This either requires expert knowledge or global methods that explore a large parameter space. However, a better balance in structure between the submodels (ASM, hydraulic, aeration, etc.) and improved quality of influent data result in much smaller calibration efforts. In this contribution, a methodology is proposed that links data frequency and model structure to calibration quality and output uncertainty. It is composed of defining the model structure, the input data, an automated calibration, confidence interval computation and uncertainty propagation to the model output. Apart from the last step, the methodology is applied to an existing WWTP using three models differing only in the aeration submodel. A sensitivity analysis was performed on all models, allowing the ranking of the most important parameters to select in the subsequent calibration step. The aeration submodel proved very important to get good NH(4) predictions. Finally, the impact of data frequency was explored. Lowering the frequency resulted in larger deviations of parameter estimates from their default values and larger confidence intervals. Autocorrelation due to high frequency calibration data has an opposite effect on the confidence intervals. The proposed methodology opens doors to facilitate and improve calibration efforts and to design measurement campaigns.

Calibration, event reconstruction, data analysis, and limit calculation for the LUX dark matter experiment

NASA Astrophysics Data System (ADS)

Akerib, D. S.; Alsum, S.; Araújo, H. M.; Bai, X.; Bailey, A. J.; Balajthy, J.; Beltrame, P.; Bernard, E. P.; Bernstein, A.; Biesiadzinski, T. P.; Boulton, E. M.; Brás, P.; Byram, D.; Cahn, S. B.; Carmona-Benitez, M. C.; Chan, C.; Currie, A.; Cutter, J. E.; Davison, T. J. R.; Dobi, A.; Dobson, J. E. Y.; Druszkiewicz, E.; Edwards, B. N.; Faham, C. H.; Fallon, S. R.; Fan, A.; Fiorucci, S.; Gaitskell, R. J.; Gehman, V. M.; Genovesi, J.; Ghag, C.; Gilchriese, M. G. D.; Hall, C. R.; Hanhardt, M.; Haselschwardt, S. J.; Hertel, S. A.; Hogan, D. P.; Horn, M.; Huang, D. Q.; Ignarra, C. M.; Jacobsen, R. G.; Ji, W.; Kamdin, K.; Kazkaz, K.; Khaitan, D.; Knoche, R.; Larsen, N. A.; Lee, C.; Lenardo, B. G.; Lesko, K. T.; Lindote, A.; Lopes, M. I.; Manalaysay, A.; Mannino, R. L.; Marzioni, M. F.; McKinsey, D. N.; Mei, D.-M.; Mock, J.; Moongweluwan, M.; Morad, J. A.; Murphy, A. St. J.; Nehrkorn, C.; Nelson, H. N.; Neves, F.; O'Sullivan, K.; Oliver-Mallory, K. C.; Palladino, K. J.; Pease, E. K.; Reichhart, L.; Rhyne, C.; Shaw, S.; Shutt, T. A.; Silva, C.; Solmaz, M.; Solovov, V. N.; Sorensen, P.; Sumner, T. J.; Szydagis, M.; Taylor, D. J.; Taylor, W. C.; Tennyson, B. P.; Terman, P. A.; Tiedt, D. R.; To, W. H.; Tripathi, M.; Tvrznikova, L.; Uvarov, S.; Velan, V.; Verbus, J. R.; Webb, R. C.; White, J. T.; Whitis, T. J.; Witherell, M. S.; Wolfs, F. L. H.; Xu, J.; Yazdani, K.; Young, S. K.; Zhang, C.; LUX Collaboration

2018-05-01

The LUX experiment has performed searches for dark-matter particles scattering elastically on xenon nuclei, leading to stringent upper limits on the nuclear scattering cross sections for dark matter. Here, for results derived from 1.4 ×104 kg days of target exposure in 2013, details of the calibration, event-reconstruction, modeling, and statistical tests that underlie the results are presented. Detector performance is characterized, including measured efficiencies, stability of response, position resolution, and discrimination between electron- and nuclear-recoil populations. Models are developed for the drift field, optical properties, background populations, the electron- and nuclear-recoil responses, and the absolute rate of low-energy background events. Innovations in the analysis include in situ measurement of the photomultipliers' response to xenon scintillation photons, verification of fiducial mass with a low-energy internal calibration source, and new empirical models for low-energy signal yield based on large-sample, in situ calibrations.

Recalibration of risk prediction models in a large multicenter cohort of admissions to adult, general critical care units in the United Kingdom.

PubMed

Harrison, David A; Brady, Anthony R; Parry, Gareth J; Carpenter, James R; Rowan, Kathy

2006-05-01

To assess the performance of published risk prediction models in common use in adult critical care in the United Kingdom and to recalibrate these models in a large representative database of critical care admissions. Prospective cohort study. A total of 163 adult general critical care units in England, Wales, and Northern Ireland, during the period of December 1995 to August 2003. A total of 231,930 admissions, of which 141,106 met inclusion criteria and had sufficient data recorded for all risk prediction models. None. The published versions of the Acute Physiology and Chronic Health Evaluation (APACHE) II, APACHE II UK, APACHE III, Simplified Acute Physiology Score (SAPS) II, and Mortality Probability Models (MPM) II were evaluated for discrimination and calibration by means of a combination of appropriate statistical measures recommended by an expert steering committee. All models showed good discrimination (the c index varied from 0.803 to 0.832) but imperfect calibration. Recalibration of the models, which was performed by both the Cox method and re-estimating coefficients, led to improved discrimination and calibration, although all models still showed significant departures from perfect calibration. Risk prediction models developed in another country require validation and recalibration before being used to provide risk-adjusted outcomes within a new country setting. Periodic reassessment is beneficial to ensure calibration is maintained.

A comparison of scope for growth (SFG) and dynamic energy budget (DEB) models applied to the blue mussel ( Mytilus edulis)

NASA Astrophysics Data System (ADS)

Filgueira, Ramón; Rosland, Rune; Grant, Jon

2011-11-01

Growth of Mytilus edulis was simulated using individual based models following both Scope For Growth (SFG) and Dynamic Energy Budget (DEB) approaches. These models were parameterized using independent studies and calibrated for each dataset by adjusting the half-saturation coefficient of the food ingestion function term, XK, a common parameter in both approaches related to feeding behavior. Auto-calibration was carried out using an optimization tool, which provides an objective way of tuning the model. Both approaches yielded similar performance, suggesting that although the basis for constructing the models is different, both can successfully reproduce M. edulis growth. The good performance of both models in different environments achieved by adjusting a single parameter, XK, highlights the potential of these models for (1) producing prospective analysis of mussel growth and (2) investigating mussel feeding response in different ecosystems. Finally, we emphasize that the convergence of two different modeling approaches via calibration of XK, indicates the importance of the feeding behavior and local trophic conditions for bivalve growth performance. Consequently, further investigations should be conducted to explore the relationship of XK to environmental variables and/or to the sophistication of the functional response to food availability with the final objective of creating a general model that can be applied to different ecosystems without the need for calibration.

Thematic mapper flight model preshipment review data package. Volume 3, part B: System data

NASA Technical Reports Server (NTRS)

1982-01-01

Procedures and results are presented for performance and systems integration tests of flight model-1 thematic mapper. Aspects considered cover electronic module integration, radiometric calibration, spectral matching, spatial coverage, radiometric calibration of the calibrator, coherent noise, dynamic square wave response, band to band registration, geometric accuracy, and self induced vibration. Thermal vacuum tests, EMI/EMS, and mass properties are included. Liens are summarized.

Multi-objective Calibration of DHSVM Based on Hydrologic Key Elements in Jinhua River Basin, East China

NASA Astrophysics Data System (ADS)

Pan, S.; Liu, L.; Xu, Y. P.

2017-12-01

Abstract: In physically based distributed hydrological model, large number of parameters, representing spatial heterogeneity of watershed and various processes in hydrologic cycle, are involved. For lack of calibration module in Distributed Hydrology Soil Vegetation Model, this study developed a multi-objective calibration module using Epsilon-Dominance Non-Dominated Sorted Genetic Algorithm II (ɛ-NSGAII) and based on parallel computing of Linux cluster for DHSVM (ɛP-DHSVM). In this study, two hydrologic key elements (i.e., runoff and evapotranspiration) are used as objectives in multi-objective calibration of model. MODIS evapotranspiration obtained by SEBAL is adopted to fill the gap of lack of observation for evapotranspiration. The results show that good performance of runoff simulation in single objective calibration cannot ensure good simulation performance of other hydrologic key elements. Self-developed ɛP-DHSVM model can make multi-objective calibration more efficiently and effectively. The running speed can be increased by more than 20-30 times via applying ɛP-DHSVM. In addition, runoff and evapotranspiration can be simulated very well simultaneously by ɛP-DHSVM, with superior values for two efficiency coefficients (0.74 for NS of runoff and 0.79 for NS of evapotranspiration, -10.5% and -8.6% for PBIAS of runoff and evapotranspiration respectively).

State updating and calibration period selection to improve dynamic monthly streamflow forecasts for an environmental flow management application

NASA Astrophysics Data System (ADS)

Gibbs, Matthew S.; McInerney, David; Humphrey, Greer; Thyer, Mark A.; Maier, Holger R.; Dandy, Graeme C.; Kavetski, Dmitri

2018-02-01

Monthly to seasonal streamflow forecasts provide useful information for a range of water resource management and planning applications. This work focuses on improving such forecasts by considering the following two aspects: (1) state updating to force the models to match observations from the start of the forecast period, and (2) selection of a shorter calibration period that is more representative of the forecast period, compared to a longer calibration period traditionally used. The analysis is undertaken in the context of using streamflow forecasts for environmental flow water management of an open channel drainage network in southern Australia. Forecasts of monthly streamflow are obtained using a conceptual rainfall-runoff model combined with a post-processor error model for uncertainty analysis. This model set-up is applied to two catchments, one with stronger evidence of non-stationarity than the other. A range of metrics are used to assess different aspects of predictive performance, including reliability, sharpness, bias and accuracy. The results indicate that, for most scenarios and metrics, state updating improves predictive performance for both observed rainfall and forecast rainfall sources. Using the shorter calibration period also improves predictive performance, particularly for the catchment with stronger evidence of non-stationarity. The results highlight that a traditional approach of using a long calibration period can degrade predictive performance when there is evidence of non-stationarity. The techniques presented can form the basis for operational monthly streamflow forecasting systems and provide support for environmental decision-making.

Calibration of hydrological models using flow-duration curves

NASA Astrophysics Data System (ADS)

Westerberg, I. K.; Guerrero, J.-L.; Younger, P. M.; Beven, K. J.; Seibert, J.; Halldin, S.; Freer, J. E.; Xu, C.-Y.

2011-07-01

The degree of belief we have in predictions from hydrologic models will normally depend on how well they can reproduce observations. Calibrations with traditional performance measures, such as the Nash-Sutcliffe model efficiency, are challenged by problems including: (1) uncertain discharge data, (2) variable sensitivity of different performance measures to different flow magnitudes, (3) influence of unknown input/output errors and (4) inability to evaluate model performance when observation time periods for discharge and model input data do not overlap. This paper explores a calibration method using flow-duration curves (FDCs) to address these problems. The method focuses on reproducing the observed discharge frequency distribution rather than the exact hydrograph. It consists of applying limits of acceptability for selected evaluation points (EPs) on the observed uncertain FDC in the extended GLUE approach. Two ways of selecting the EPs were tested - based on equal intervals of discharge and of volume of water. The method was tested and compared to a calibration using the traditional model efficiency for the daily four-parameter WASMOD model in the Paso La Ceiba catchment in Honduras and for Dynamic TOPMODEL evaluated at an hourly time scale for the Brue catchment in Great Britain. The volume method of selecting EPs gave the best results in both catchments with better calibrated slow flow, recession and evaporation than the other criteria. Observed and simulated time series of uncertain discharges agreed better for this method both in calibration and prediction in both catchments. An advantage with the method is that the rejection criterion is based on an estimation of the uncertainty in discharge data and that the EPs of the FDC can be chosen to reflect the aims of the modelling application, e.g. using more/less EPs at high/low flows. While the method appears less sensitive to epistemic input/output errors than previous use of limits of acceptability applied directly to the time series of discharge, it still requires a reasonable representation of the distribution of inputs. Additional constraints might therefore be required in catchments subject to snow and where peak-flow timing at sub-daily time scales is of high importance. The results suggest that the calibration method can be useful when observation time periods for discharge and model input data do not overlap. The method could also be suitable for calibration to regional FDCs while taking uncertainties in the hydrological model and data into account.

Calibration of hydrological models using flow-duration curves

NASA Astrophysics Data System (ADS)

Westerberg, I. K.; Guerrero, J.-L.; Younger, P. M.; Beven, K. J.; Seibert, J.; Halldin, S.; Freer, J. E.; Xu, C.-Y.

2010-12-01

The degree of belief we have in predictions from hydrologic models depends on how well they can reproduce observations. Calibrations with traditional performance measures such as the Nash-Sutcliffe model efficiency are challenged by problems including: (1) uncertain discharge data, (2) variable importance of the performance with flow magnitudes, (3) influence of unknown input/output errors and (4) inability to evaluate model performance when observation time periods for discharge and model input data do not overlap. A new calibration method using flow-duration curves (FDCs) was developed which addresses these problems. The method focuses on reproducing the observed discharge frequency distribution rather than the exact hydrograph. It consists of applying limits of acceptability for selected evaluation points (EPs) of the observed uncertain FDC in the extended GLUE approach. Two ways of selecting the EPs were tested - based on equal intervals of discharge and of volume of water. The method was tested and compared to a calibration using the traditional model efficiency for the daily four-parameter WASMOD model in the Paso La Ceiba catchment in Honduras and for Dynamic TOPMODEL evaluated at an hourly time scale for the Brue catchment in Great Britain. The volume method of selecting EPs gave the best results in both catchments with better calibrated slow flow, recession and evaporation than the other criteria. Observed and simulated time series of uncertain discharges agreed better for this method both in calibration and prediction in both catchments without resulting in overpredicted simulated uncertainty. An advantage with the method is that the rejection criterion is based on an estimation of the uncertainty in discharge data and that the EPs of the FDC can be chosen to reflect the aims of the modelling application e.g. using more/less EPs at high/low flows. While the new method is less sensitive to epistemic input/output errors than the normal use of limits of acceptability applied directly to the time series of discharge, it still requires a reasonable representation of the distribution of inputs. Additional constraints might therefore be required in catchments subject to snow. The results suggest that the new calibration method can be useful when observation time periods for discharge and model input data do not overlap. The new method could also be suitable for calibration to regional FDCs while taking uncertainties in the hydrological model and data into account.

Modeling Improvements and Users Manual for Axial-flow Turbine Off-design Computer Code AXOD

NASA Technical Reports Server (NTRS)

Glassman, Arthur J.

1994-01-01

An axial-flow turbine off-design performance computer code used for preliminary studies of gas turbine systems was modified and calibrated based on the experimental performance of large aircraft-type turbines. The flow- and loss-model modifications and calibrations are presented in this report. Comparisons are made between computed performances and experimental data for seven turbines over wide ranges of speed and pressure ratio. This report also serves as the users manual for the revised code, which is named AXOD.

A framework for streamflow prediction in the world's most severely data-limited regions: Test of applicability and performance in a poorly-gauged region of China

NASA Astrophysics Data System (ADS)

Alipour, M. H.; Kibler, Kelly M.

2018-02-01

A framework methodology is proposed for streamflow prediction in poorly-gauged rivers located within large-scale regions of sparse hydrometeorologic observation. A multi-criteria model evaluation is developed to select models that balance runoff efficiency with selection of accurate parameter values. Sparse observed data are supplemented by uncertain or low-resolution information, incorporated as 'soft' data, to estimate parameter values a priori. Model performance is tested in two catchments within a data-poor region of southwestern China, and results are compared to models selected using alternative calibration methods. While all models perform consistently with respect to runoff efficiency (NSE range of 0.67-0.78), models selected using the proposed multi-objective method may incorporate more representative parameter values than those selected by traditional calibration. Notably, parameter values estimated by the proposed method resonate with direct estimates of catchment subsurface storage capacity (parameter residuals of 20 and 61 mm for maximum soil moisture capacity (Cmax), and 0.91 and 0.48 for soil moisture distribution shape factor (B); where a parameter residual is equal to the centroid of a soft parameter value minus the calibrated parameter value). A model more traditionally calibrated to observed data only (single-objective model) estimates a much lower soil moisture capacity (residuals of Cmax = 475 and 518 mm and B = 1.24 and 0.7). A constrained single-objective model also underestimates maximum soil moisture capacity relative to a priori estimates (residuals of Cmax = 246 and 289 mm). The proposed method may allow managers to more confidently transfer calibrated models to ungauged catchments for streamflow predictions, even in the world's most data-limited regions.

An Accurate Projector Calibration Method Based on Polynomial Distortion Representation

PubMed Central

Liu, Miao; Sun, Changku; Huang, Shujun; Zhang, Zonghua

2015-01-01

In structure light measurement systems or 3D printing systems, the errors caused by optical distortion of a digital projector always affect the precision performance and cannot be ignored. Existing methods to calibrate the projection distortion rely on calibration plate and photogrammetry, so the calibration performance is largely affected by the quality of the plate and the imaging system. This paper proposes a new projector calibration approach that makes use of photodiodes to directly detect the light emitted from a digital projector. By analyzing the output sequence of the photoelectric module, the pixel coordinates can be accurately obtained by the curve fitting method. A polynomial distortion representation is employed to reduce the residuals of the traditional distortion representation model. Experimental results and performance evaluation show that the proposed calibration method is able to avoid most of the disadvantages in traditional methods and achieves a higher accuracy. This proposed method is also practically applicable to evaluate the geometric optical performance of other optical projection system. PMID:26492247

Use of model calibration to achieve high accuracy in analysis of computer networks

DOEpatents

Frogner, Bjorn; Guarro, Sergio; Scharf, Guy

2004-05-11

A system and method are provided for creating a network performance prediction model, and calibrating the prediction model, through application of network load statistical analyses. The method includes characterizing the measured load on the network, which may include background load data obtained over time, and may further include directed load data representative of a transaction-level event. Probabilistic representations of load data are derived to characterize the statistical persistence of the network performance variability and to determine delays throughout the network. The probabilistic representations are applied to the network performance prediction model to adapt the model for accurate prediction of network performance. Certain embodiments of the method and system may be used for analysis of the performance of a distributed application characterized as data packet streams.

Approaches in highly parameterized inversion: TSPROC, a general time-series processor to assist in model calibration and result summarization

USGS Publications Warehouse

Westenbroek, Stephen M.; Doherty, John; Walker, John F.; Kelson, Victor A.; Hunt, Randall J.; Cera, Timothy B.

2012-01-01

The TSPROC (Time Series PROCessor) computer software uses a simple scripting language to process and analyze time series. It was developed primarily to assist in the calibration of environmental models. The software is designed to perform calculations on time-series data commonly associated with surface-water models, including calculation of flow volumes, transformation by means of basic arithmetic operations, and generation of seasonal and annual statistics and hydrologic indices. TSPROC can also be used to generate some of the key input files required to perform parameter optimization by means of the PEST (Parameter ESTimation) computer software. Through the use of TSPROC, the objective function for use in the model-calibration process can be focused on specific components of a hydrograph.

Sentinel-2 diffuser on-ground calibration

NASA Astrophysics Data System (ADS)

Mazy, E.; Camus, F.; Chorvalli, V.; Domken, I.; Laborie, A.; Marcotte, S.; Stockman, Y.

2013-10-01

The Sentinel-2 multi-spectral instrument (MSI) will provide Earth imagery in the frame of the Global Monitoring for Environment and Security (GMES) initiative which is a joint undertaking of the European Commission and the Agency. MSI instrument, under Astrium SAS responsibility, is a push-broom spectro imager in 13 spectral channels in VNIR and SWIR. The instrument radiometric calibration is based on in-flight calibration with sunlight through a quasi Lambertian diffuser. The diffuser covers the full pupil and the full field of view of the instrument. The on-ground calibration of the diffuser BRDF is mandatory to fulfil the in-flight performances. The diffuser is a 779 x 278 mm2 rectangular flat area in Zenith-A material. It is mounted on a motorised door in front of the instrument optical system entrance. The diffuser manufacturing and calibration is under the Centre Spatial of Liege (CSL) responsibility. The CSL has designed and built a completely remote controlled BRDF test bench able to handle large diffusers in their mount. As the diffuser is calibrated directly in its mount with respect to a reference cube, the error budget is significantly improved. The BRDF calibration is performed directly in MSI instrument spectral bands by using dedicated band-pass filters (VNIR and SWIR up to 2200 nm). Absolute accuracy is better than 0.5% in VNIR spectral bands and 1% in SWIR spectral bands. Performances were cross checked with other laboratories. The first MSI diffuser for flight model was calibrated mid 2013 on CSL BRDF measurement bench. The calibration of the diffuser consists mainly in thermal vacuum cycles, BRDF uniformity characterisation and BRDF angular characterisation. The total amount of measurement for the first flight model diffuser corresponds to more than 17500 BRDF acquisitions. Performance results are discussed in comparison with requirements.

Calibration of Reduced Dynamic Models of Power Systems using Phasor Measurement Unit (PMU) Data

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

Zhou, Ning; Lu, Shuai; Singh, Ruchi

2011-09-23

Accuracy of a power system dynamic model is essential to the secure and efficient operation of the system. Lower confidence on model accuracy usually leads to conservative operation and lowers asset usage. To improve model accuracy, identification algorithms have been developed to calibrate parameters of individual components using measurement data from staged tests. To facilitate online dynamic studies for large power system interconnections, this paper proposes a model reduction and calibration approach using phasor measurement unit (PMU) data. First, a model reduction method is used to reduce the number of dynamic components. Then, a calibration algorithm is developed to estimatemore » parameters of the reduced model. This approach will help to maintain an accurate dynamic model suitable for online dynamic studies. The performance of the proposed method is verified through simulation studies.« less

Use of Two-Part Regression Calibration Model to Correct for Measurement Error in Episodically Consumed Foods in a Single-Replicate Study Design: EPIC Case Study

PubMed Central

Agogo, George O.; van der Voet, Hilko; Veer, Pieter van’t; Ferrari, Pietro; Leenders, Max; Muller, David C.; Sánchez-Cantalejo, Emilio; Bamia, Christina; Braaten, Tonje; Knüppel, Sven; Johansson, Ingegerd; van Eeuwijk, Fred A.; Boshuizen, Hendriek

2014-01-01

In epidemiologic studies, measurement error in dietary variables often attenuates association between dietary intake and disease occurrence. To adjust for the attenuation caused by error in dietary intake, regression calibration is commonly used. To apply regression calibration, unbiased reference measurements are required. Short-term reference measurements for foods that are not consumed daily contain excess zeroes that pose challenges in the calibration model. We adapted two-part regression calibration model, initially developed for multiple replicates of reference measurements per individual to a single-replicate setting. We showed how to handle excess zero reference measurements by two-step modeling approach, how to explore heteroscedasticity in the consumed amount with variance-mean graph, how to explore nonlinearity with the generalized additive modeling (GAM) and the empirical logit approaches, and how to select covariates in the calibration model. The performance of two-part calibration model was compared with the one-part counterpart. We used vegetable intake and mortality data from European Prospective Investigation on Cancer and Nutrition (EPIC) study. In the EPIC, reference measurements were taken with 24-hour recalls. For each of the three vegetable subgroups assessed separately, correcting for error with an appropriately specified two-part calibration model resulted in about three fold increase in the strength of association with all-cause mortality, as measured by the log hazard ratio. Further found is that the standard way of including covariates in the calibration model can lead to over fitting the two-part calibration model. Moreover, the extent of adjusting for error is influenced by the number and forms of covariates in the calibration model. For episodically consumed foods, we advise researchers to pay special attention to response distribution, nonlinearity, and covariate inclusion in specifying the calibration model. PMID:25402487

Accuracy, calibration and clinical performance of the EuroSCORE: can we reduce the number of variables?

PubMed

Ranucci, Marco; Castelvecchio, Serenella; Menicanti, Lorenzo; Frigiola, Alessandro; Pelissero, Gabriele

2010-03-01

The European system for cardiac operative risk evaluation (EuroSCORE) is currently used in many institutions and is considered a reference tool in many countries. We hypothesised that too many variables were included in the EuroSCORE using limited patient series. We tested different models using a limited number of variables. A total of 11150 adult patients undergoing cardiac operations at our institution (2001-2007) were retrospectively analysed. The 17 risk factors composing the EuroSCORE were separately analysed and ranked for accuracy of prediction of hospital mortality. Seventeen models were created by progressively including one factor at a time. The models were compared for accuracy with a receiver operating characteristics (ROC) analysis and area under the curve (AUC) evaluation. Calibration was tested with Hosmer-Lemeshow statistics. Clinical performance was assessed by comparing the predicted with the observed mortality rates. The best accuracy (AUC 0.76) was obtained using a model including only age, left ventricular ejection fraction, serum creatinine, emergency operation and non-isolated coronary operation. The EuroSCORE AUC (0.75) was not significantly different. Calibration and clinical performance were better in the five-factor model than in the EuroSCORE. Only in high-risk patients were 12 factors needed to achieve a good performance. Including many factors in multivariable logistic models increases the risk for overfitting, multicollinearity and human error. A five-factor model offers the same level of accuracy but demonstrated better calibration and clinical performance. Models with a limited number of factors may work better than complex models when applied to a limited number of patients. Copyright (c) 2009 European Association for Cardio-Thoracic Surgery. Published by Elsevier B.V. All rights reserved.

Evaluation of the AnnAGNPS Model for Predicting Runoff and Nutrient Export in a Typical Small Watershed in the Hilly Region of Taihu Lake

PubMed Central

Luo, Chuan; Li, Zhaofu; Li, Hengpeng; Chen, Xiaomin

2015-01-01

The application of hydrological and water quality models is an efficient approach to better understand the processes of environmental deterioration. This study evaluated the ability of the Annualized Agricultural Non-Point Source (AnnAGNPS) model to predict runoff, total nitrogen (TN) and total phosphorus (TP) loading in a typical small watershed of a hilly region near Taihu Lake, China. Runoff was calibrated and validated at both an annual and monthly scale, and parameter sensitivity analysis was performed for TN and TP before the two water quality components were calibrated. The results showed that the model satisfactorily simulated runoff at annual and monthly scales, both during calibration and validation processes. Additionally, results of parameter sensitivity analysis showed that the parameters Fertilizer rate, Fertilizer organic, Canopy cover and Fertilizer inorganic were more sensitive to TN output. In terms of TP, the parameters Residue mass ratio, Fertilizer rate, Fertilizer inorganic and Canopy cover were the most sensitive. Based on these sensitive parameters, calibration was performed. TN loading produced satisfactory results for both the calibration and validation processes, whereas the performance of TP loading was slightly poor. The simulation results showed that AnnAGNPS has the potential to be used as a valuable tool for the planning and management of watersheds. PMID:26364642

A simplified gross primary production and evapotranspiration model for boreal coniferous forests - is a generic calibration sufficient?

NASA Astrophysics Data System (ADS)

Minunno, F.; Peltoniemi, M.; Launiainen, S.; Aurela, M.; Lindroth, A.; Lohila, A.; Mammarella, I.; Minkkinen, K.; Mäkelä, A.

2015-07-01

The problem of model complexity has been lively debated in environmental sciences as well as in the forest modelling community. Simple models are less input demanding and their calibration involves a lower number of parameters, but they might be suitable only at local scale. In this work we calibrated a simplified ecosystem process model (PRELES) to data from multiple sites and we tested if PRELES can be used at regional scale to estimate the carbon and water fluxes of Boreal conifer forests. We compared a multi-site (M-S) with site-specific (S-S) calibrations. Model calibrations and evaluations were carried out by the means of the Bayesian method; Bayesian calibration (BC) and Bayesian model comparison (BMC) were used to quantify the uncertainty in model parameters and model structure. To evaluate model performances BMC results were combined with more classical analysis of model-data mismatch (M-DM). Evapotranspiration (ET) and gross primary production (GPP) measurements collected in 10 sites of Finland and Sweden were used in the study. Calibration results showed that similar estimates were obtained for the parameters at which model outputs are most sensitive. No significant differences were encountered in the predictions of the multi-site and site-specific versions of PRELES with exception of a site with agricultural history (Alkkia). Although PRELES predicted GPP better than evapotranspiration, we concluded that the model can be reliably used at regional scale to simulate carbon and water fluxes of Boreal forests. Our analyses underlined also the importance of using long and carefully collected flux datasets in model calibration. In fact, even a single site can provide model calibrations that can be applied at a wider spatial scale, since it covers a wide range of variability in climatic conditions.

Calibration of the 7—Equation Transition Model for High Reynolds Flows at Low Mach

NASA Astrophysics Data System (ADS)

Colonia, S.; Leble, V.; Steijl, R.; Barakos, G.

2016-09-01

The numerical simulation of flows over large-scale wind turbine blades without considering the transition from laminar to fully turbulent flow may result in incorrect estimates of the blade loads and performance. Thanks to its relative simplicity and promising results, the Local-Correlation based Transition Modelling concept represents a valid way to include transitional effects into practical CFD simulations. However, the model involves coefficients that need tuning. In this paper, the γ—equation transition model is assessed and calibrated, for a wide range of Reynolds numbers at low Mach, as needed for wind turbine applications. An aerofoil is used to evaluate the original model and calibrate it; while a large scale wind turbine blade is employed to show that the calibrated model can lead to reliable solutions for complex three-dimensional flows. The calibrated model shows promising results for both two-dimensional and three-dimensional flows, even if cross-flow instabilities are neglected.

A Method to Test Model Calibration Techniques

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

Judkoff, Ron; Polly, Ben; Neymark, Joel

This paper describes a method for testing model calibration techniques. Calibration is commonly used in conjunction with energy retrofit audit models. An audit is conducted to gather information about the building needed to assemble an input file for a building energy modeling tool. A calibration technique is used to reconcile model predictions with utility data, and then the 'calibrated model' is used to predict energy savings from a variety of retrofit measures and combinations thereof. Current standards and guidelines such as BPI-2400 and ASHRAE-14 set criteria for 'goodness of fit' and assume that if the criteria are met, then themore » calibration technique is acceptable. While it is logical to use the actual performance data of the building to tune the model, it is not certain that a good fit will result in a model that better predicts post-retrofit energy savings. Therefore, the basic idea here is that the simulation program (intended for use with the calibration technique) is used to generate surrogate utility bill data and retrofit energy savings data against which the calibration technique can be tested. This provides three figures of merit for testing a calibration technique, 1) accuracy of the post-retrofit energy savings prediction, 2) closure on the 'true' input parameter values, and 3) goodness of fit to the utility bill data. The paper will also discuss the pros and cons of using this synthetic surrogate data approach versus trying to use real data sets of actual buildings.« less

A Method to Test Model Calibration Techniques: Preprint

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

Judkoff, Ron; Polly, Ben; Neymark, Joel

This paper describes a method for testing model calibration techniques. Calibration is commonly used in conjunction with energy retrofit audit models. An audit is conducted to gather information about the building needed to assemble an input file for a building energy modeling tool. A calibration technique is used to reconcile model predictions with utility data, and then the 'calibrated model' is used to predict energy savings from a variety of retrofit measures and combinations thereof. Current standards and guidelines such as BPI-2400 and ASHRAE-14 set criteria for 'goodness of fit' and assume that if the criteria are met, then themore » calibration technique is acceptable. While it is logical to use the actual performance data of the building to tune the model, it is not certain that a good fit will result in a model that better predicts post-retrofit energy savings. Therefore, the basic idea here is that the simulation program (intended for use with the calibration technique) is used to generate surrogate utility bill data and retrofit energy savings data against which the calibration technique can be tested. This provides three figures of merit for testing a calibration technique, 1) accuracy of the post-retrofit energy savings prediction, 2) closure on the 'true' input parameter values, and 3) goodness of fit to the utility bill data. The paper will also discuss the pros and cons of using this synthetic surrogate data approach versus trying to use real data sets of actual buildings.« less

Transportation safety data and analysis : Volume 2, Calibration of the highway safety manual and development of new safety performance functions.

DOT National Transportation Integrated Search

2011-03-01

This report documents the calibration of the Highway Safety Manual (HSM) safety performance function (SPF) : for rural two-lane two-way roadway segments in Utah and the development of new models using negative : binomial and hierarchical Bayesian mod...

Automatic Calibration of a Semi-Distributed Hydrologic Model Using Particle Swarm Optimization

NASA Astrophysics Data System (ADS)

Bekele, E. G.; Nicklow, J. W.

2005-12-01

Hydrologic simulation models need to be calibrated and validated before using them for operational predictions. Spatially-distributed hydrologic models generally have a large number of parameters to capture the various physical characteristics of a hydrologic system. Manual calibration of such models is a very tedious and daunting task, and its success depends on the subjective assessment of a particular modeler, which includes knowledge of the basic approaches and interactions in the model. In order to alleviate these shortcomings, an automatic calibration model, which employs an evolutionary optimization technique known as Particle Swarm Optimizer (PSO) for parameter estimation, is developed. PSO is a heuristic search algorithm that is inspired by social behavior of bird flocking or fish schooling. The newly-developed calibration model is integrated to the U.S. Department of Agriculture's Soil and Water Assessment Tool (SWAT). SWAT is a physically-based, semi-distributed hydrologic model that was developed to predict the long term impacts of land management practices on water, sediment and agricultural chemical yields in large complex watersheds with varying soils, land use, and management conditions. SWAT was calibrated for streamflow and sediment concentration. The calibration process involves parameter specification, whereby sensitive model parameters are identified, and parameter estimation. In order to reduce the number of parameters to be calibrated, parameterization was performed. The methodology is applied to a demonstration watershed known as Big Creek, which is located in southern Illinois. Application results show the effectiveness of the approach and model predictions are significantly improved.

Low Frequency Error Analysis and Calibration for High-Resolution Optical Satellite's Uncontrolled Geometric Positioning

NASA Astrophysics Data System (ADS)

Wang, Mi; Fang, Chengcheng; Yang, Bo; Cheng, Yufeng

2016-06-01

The low frequency error is a key factor which has affected uncontrolled geometry processing accuracy of the high-resolution optical image. To guarantee the geometric quality of imagery, this paper presents an on-orbit calibration method for the low frequency error based on geometric calibration field. Firstly, we introduce the overall flow of low frequency error on-orbit analysis and calibration, which includes optical axis angle variation detection of star sensor, relative calibration among star sensors, multi-star sensor information fusion, low frequency error model construction and verification. Secondly, we use optical axis angle change detection method to analyze the law of low frequency error variation. Thirdly, we respectively use the method of relative calibration and information fusion among star sensors to realize the datum unity and high precision attitude output. Finally, we realize the low frequency error model construction and optimal estimation of model parameters based on DEM/DOM of geometric calibration field. To evaluate the performance of the proposed calibration method, a certain type satellite's real data is used. Test results demonstrate that the calibration model in this paper can well describe the law of the low frequency error variation. The uncontrolled geometric positioning accuracy of the high-resolution optical image in the WGS-84 Coordinate Systems is obviously improved after the step-wise calibration.

Objective calibration of regional climate models

NASA Astrophysics Data System (ADS)

Bellprat, O.; Kotlarski, S.; Lüthi, D.; SchäR, C.

2012-12-01

Climate models are subject to high parametric uncertainty induced by poorly confined model parameters of parameterized physical processes. Uncertain model parameters are typically calibrated in order to increase the agreement of the model with available observations. The common practice is to adjust uncertain model parameters manually, often referred to as expert tuning, which lacks objectivity and transparency in the use of observations. These shortcomings often haze model inter-comparisons and hinder the implementation of new model parameterizations. Methods which would allow to systematically calibrate model parameters are unfortunately often not applicable to state-of-the-art climate models, due to computational constraints facing the high dimensionality and non-linearity of the problem. Here we present an approach to objectively calibrate a regional climate model, using reanalysis driven simulations and building upon a quadratic metamodel presented by Neelin et al. (2010) that serves as a computationally cheap surrogate of the model. Five model parameters originating from different parameterizations are selected for the optimization according to their influence on the model performance. The metamodel accurately estimates spatial averages of 2 m temperature, precipitation and total cloud cover, with an uncertainty of similar magnitude as the internal variability of the regional climate model. The non-linearities of the parameter perturbations are well captured, such that only a limited number of 20-50 simulations are needed to estimate optimal parameter settings. Parameter interactions are small, which allows to further reduce the number of simulations. In comparison to an ensemble of the same model which has undergone expert tuning, the calibration yields similar optimal model configurations, but leading to an additional reduction of the model error. The performance range captured is much wider than sampled with the expert-tuned ensemble and the presented methodology is effective and objective. It is argued that objective calibration is an attractive tool and could become standard procedure after introducing new model implementations, or after a spatial transfer of a regional climate model. Objective calibration of parameterizations with regional models could also serve as a strategy toward improving parameterization packages of global climate models.

Definition and sensitivity of the conceptual MORDOR rainfall-runoff model parameters using different multi-criteria calibration strategies

NASA Astrophysics Data System (ADS)

Garavaglia, F.; Seyve, E.; Gottardi, F.; Le Lay, M.; Gailhard, J.; Garçon, R.

2014-12-01

MORDOR is a conceptual hydrological model extensively used in Électricité de France (EDF, French electric utility company) operational applications: (i) hydrological forecasting, (ii) flood risk assessment, (iii) water balance and (iv) climate change studies. MORDOR is a lumped, reservoir, elevation based model with hourly or daily areal rainfall and air temperature as the driving input data. The principal hydrological processes represented are evapotranspiration, direct and indirect runoff, ground water, snow accumulation and melt and routing. The model has been intensively used at EDF for more than 20 years, in particular for modeling French mountainous watersheds. In the matter of parameters calibration we propose and test alternative multi-criteria techniques based on two specific approaches: automatic calibration using single-objective functions and a priori parameter calibration founded on hydrological watershed features. The automatic calibration approach uses single-objective functions, based on Kling-Gupta efficiency, to quantify the good agreement between the simulated and observed runoff focusing on four different runoff samples: (i) time-series sample, (I) annual hydrological regime, (iii) monthly cumulative distribution functions and (iv) recession sequences.The primary purpose of this study is to analyze the definition and sensitivity of MORDOR parameters testing different calibration techniques in order to: (i) simplify the model structure, (ii) increase the calibration-validation performance of the model and (iii) reduce the equifinality problem of calibration process. We propose an alternative calibration strategy that reaches these goals. The analysis is illustrated by calibrating MORDOR model to daily data for 50 watersheds located in French mountainous regions.

Development of a calibration protocol and identification of the most sensitive parameters for the particulate biofilm models used in biological wastewater treatment.

PubMed

Eldyasti, Ahmed; Nakhla, George; Zhu, Jesse

2012-05-01

Biofilm models are valuable tools for process engineers to simulate biological wastewater treatment. In order to enhance the use of biofilm models implemented in contemporary simulation software, model calibration is both necessary and helpful. The aim of this work was to develop a calibration protocol of the particulate biofilm model with a help of the sensitivity analysis of the most important parameters in the biofilm model implemented in BioWin® and verify the predictability of the calibration protocol. A case study of a circulating fluidized bed bioreactor (CFBBR) system used for biological nutrient removal (BNR) with a fluidized bed respirometric study of the biofilm stoichiometry and kinetics was used to verify and validate the proposed calibration protocol. Applying the five stages of the biofilm calibration procedures enhanced the applicability of BioWin®, which was capable of predicting most of the performance parameters with an average percentage error (APE) of 0-20%. Copyright © 2012 Elsevier Ltd. All rights reserved.

Calibrating a numerical model's morphology using high-resolution spatial and temporal datasets from multithread channel flume experiments.

NASA Astrophysics Data System (ADS)

Javernick, L.; Bertoldi, W.; Redolfi, M.

2017-12-01

Accessing or acquiring high quality, low-cost topographic data has never been easier due to recent developments of the photogrammetric techniques of Structure-from-Motion (SfM). Researchers can acquire the necessary SfM imagery with various platforms, with the ability to capture millimetre resolution and accuracy, or large-scale areas with the help of unmanned platforms. Such datasets in combination with numerical modelling have opened up new opportunities to study river environments physical and ecological relationships. While numerical models overall predictive accuracy is most influenced by topography, proper model calibration requires hydraulic data and morphological data; however, rich hydraulic and morphological datasets remain scarce. This lack in field and laboratory data has limited model advancement through the inability to properly calibrate, assess sensitivity, and validate the models performance. However, new time-lapse imagery techniques have shown success in identifying instantaneous sediment transport in flume experiments and their ability to improve hydraulic model calibration. With new capabilities to capture high resolution spatial and temporal datasets of flume experiments, there is a need to further assess model performance. To address this demand, this research used braided river flume experiments and captured time-lapse observed sediment transport and repeat SfM elevation surveys to provide unprecedented spatial and temporal datasets. Through newly created metrics that quantified observed and modeled activation, deactivation, and bank erosion rates, the numerical model Delft3d was calibrated. This increased temporal data of both high-resolution time series and long-term temporal coverage provided significantly improved calibration routines that refined calibration parameterization. Model results show that there is a trade-off between achieving quantitative statistical and qualitative morphological representations. Specifically, statistical agreement simulations suffered to represent braiding planforms (evolving toward meandering), and parameterization that ensured braided produced exaggerated activation and bank erosion rates. Marie Sklodowska-Curie Individual Fellowship: River-HMV, 656917

Design of a tracked ultrasound calibration phantom made of LEGO bricks

NASA Astrophysics Data System (ADS)

Walsh, Ryan; Soehl, Marie; Rankin, Adam; Lasso, Andras; Fichtinger, Gabor

2014-03-01

PURPOSE: Spatial calibration of tracked ultrasound systems is commonly performed using precisely fabricated phantoms. Machining or 3D printing has relatively high cost and not easily available. Moreover, the possibilities for modifying the phantoms are very limited. Our goal was to find a method to construct a calibration phantom from affordable, widely available components, which can be built in short time, can be easily modified, and provides comparable accuracy to the existing solutions. METHODS: We designed an N-wire calibration phantom made of LEGO® bricks. To affirm the phantom's reproducibility and build time, ten builds were done by first-time users. The phantoms were used for a tracked ultrasound calibration by an experienced user. The success of each user's build was determined by the lowest root mean square (RMS) wire reprojection error of three calibrations. The accuracy and variance of calibrations were evaluated for the calibrations produced for various tracked ultrasound probes. The proposed model was compared to two of the currently available phantom models for both electromagnetic and optical tracking. RESULTS: The phantom was successfully built by all ten first-time users in an average time of 18.8 minutes. It cost approximately $10 CAD for the required LEGO® bricks and averaged a 0.69mm of error in the calibration reproducibility for ultrasound calibrations. It is one third the cost of similar 3D printed phantoms and takes much less time to build. The proposed phantom's image reprojections were 0.13mm more erroneous than those of the highest performing current phantom model The average standard deviation of multiple 3D image reprojections differed by 0.05mm between the phantoms CONCLUSION: It was found that the phantom could be built in less time, was one third the cost, compared to similar 3D printed models. The proposed phantom was found to be capable of producing equivalent calibrations to 3D printed phantoms.

Calibration of skill and judgment in driving: development of a conceptual framework and the implications for road safety.

PubMed

Horrey, William J; Lesch, Mary F; Mitsopoulos-Rubens, Eve; Lee, John D

2015-03-01

Humans often make inflated or erroneous estimates of their own ability or performance. Such errors in calibration can be due to incomplete processing, neglect of available information or due to improper weighing or integration of the information and can impact our decision-making, risk tolerance, and behaviors. In the driving context, these outcomes can have important implications for safety. The current paper discusses the notion of calibration in the context of self-appraisals and self-competence as well as in models of self-regulation in driving. We further develop a conceptual framework for calibration in the driving context borrowing from earlier models of momentary demand regulation, information processing, and lens models for information selection and utilization. Finally, using the model we describe the implications for calibration (or, more specifically, errors in calibration) for our understanding of driver distraction, in-vehicle automation and autonomous vehicles, and the training of novice and inexperienced drivers. Copyright © 2014 The Authors. Published by Elsevier Ltd.. All rights reserved.

Validation of Storm Water Management Model Storm Control Measures Modules

NASA Astrophysics Data System (ADS)

Simon, M. A.; Platz, M. C.

2017-12-01

EPA's Storm Water Management Model (SWMM) is a computational code heavily relied upon by industry for the simulation of wastewater and stormwater infrastructure performance. Many municipalities are relying on SWMM results to design multi-billion-dollar, multi-decade infrastructure upgrades. Since the 1970's, EPA and others have developed five major releases, the most recent ones containing storm control measures modules for green infrastructure. The main objective of this study was to quantify the accuracy with which SWMM v5.1.10 simulates the hydrologic activity of previously monitored low impact developments. Model performance was evaluated with a mathematical comparison of outflow hydrographs and total outflow volumes, using empirical data and a multi-event, multi-objective calibration method. The calibration methodology utilized PEST++ Version 3, a parameter estimation tool, which aided in the selection of unmeasured hydrologic parameters. From the validation study and sensitivity analysis, several model improvements were identified to advance SWMM LID Module performance for permeable pavements, infiltration units and green roofs, and these were performed and reported herein. Overall, it was determined that SWMM can successfully simulate low impact development controls given accurate model confirmation, parameter measurement, and model calibration.

Can we calibrate simultaneously groundwater recharge and aquifer hydrodynamic parameters ?

NASA Astrophysics Data System (ADS)

Hassane Maina, Fadji; Ackerer, Philippe; Bildstein, Olivier

2017-04-01

By groundwater model calibration, we consider here fitting the measured piezometric heads by estimating the hydrodynamic parameters (storage term and hydraulic conductivity) and the recharge. It is traditionally recommended to avoid simultaneous calibration of groundwater recharge and flow parameters because of correlation between recharge and the flow parameters. From a physical point of view, little recharge associated with low hydraulic conductivity can provide very similar piezometric changes than higher recharge and higher hydraulic conductivity. If this correlation is true under steady state conditions, we assume that this correlation is much weaker under transient conditions because recharge varies in time and the parameters do not. Moreover, the recharge is negligible during summer time for many climatic conditions due to reduced precipitation, increased evaporation and transpiration by vegetation cover. We analyze our hypothesis through global sensitivity analysis (GSA) in conjunction with the polynomial chaos expansion (PCE) methodology. We perform GSA by calculating the Sobol indices, which provide a variance-based 'measure' of the effects of uncertain parameters (storage and hydraulic conductivity) and recharge on the piezometric heads computed by the flow model. The choice of PCE has the following two benefits: (i) it provides the global sensitivity indices in a straightforward manner, and (ii) PCE can serve as a surrogate model for the calibration of parameters. The coefficients of the PCE are computed by probabilistic collocation. We perform the GSA on simplified real conditions coming from an already built groundwater model dedicated to a subdomain of the Upper-Rhine aquifer (geometry, boundary conditions, climatic data). GSA shows that the simultaneous calibration of recharge and flow parameters is possible if the calibration is performed over at least one year. It provides also the valuable information of the sensitivity versus time, depending on the aquifer inertia and climatic conditions. The groundwater levels variations during recharge (increase) are sensitive to the storage coefficient whereas the groundwater levels variations after recharge (decrease) are sensitive to the hydraulic conductivity. The performed model calibration on synthetic data sets shows that the parameters and recharge are estimated quite accurately.

Calibration Modeling Methodology to Optimize Performance for Low Range Applications

NASA Technical Reports Server (NTRS)

McCollum, Raymond A.; Commo, Sean A.; Parker, Peter A.

2010-01-01

Calibration is a vital process in characterizing the performance of an instrument in an application environment and seeks to obtain acceptable accuracy over the entire design range. Often, project requirements specify a maximum total measurement uncertainty, expressed as a percent of full-scale. However in some applications, we seek to obtain enhanced performance at the low range, therefore expressing the accuracy as a percent of reading should be considered as a modeling strategy. For example, it is common to desire to use a force balance in multiple facilities or regimes, often well below its designed full-scale capacity. This paper presents a general statistical methodology for optimizing calibration mathematical models based on a percent of reading accuracy requirement, which has broad application in all types of transducer applications where low range performance is required. A case study illustrates the proposed methodology for the Mars Entry Atmospheric Data System that employs seven strain-gage based pressure transducers mounted on the heatshield of the Mars Science Laboratory mission.

Effects of temporal and spatial resolution of calibration data on integrated hydrologic water quality model identification

NASA Astrophysics Data System (ADS)

Jiang, Sanyuan; Jomaa, Seifeddine; Büttner, Olaf; Rode, Michael

2014-05-01

Hydrological water quality modeling is increasingly used for investigating runoff and nutrient transport processes as well as watershed management but it is mostly unclear how data availablity determins model identification. In this study, the HYPE (HYdrological Predictions for the Environment) model, which is a process-based, semi-distributed hydrological water quality model, was applied in two different mesoscale catchments (Selke (463 km2) and Weida (99 km2)) located in central Germany to simulate discharge and inorganic nitrogen (IN) transport. PEST and DREAM(ZS) were combined with the HYPE model to conduct parameter calibration and uncertainty analysis. Split-sample test was used for model calibration (1994-1999) and validation (1999-2004). IN concentration and daily IN load were found to be highly correlated with discharge, indicating that IN leaching is mainly controlled by runoff. Both dynamics and balances of water and IN load were well captured with NSE greater than 0.83 during validation period. Multi-objective calibration (calibrating hydrological and water quality parameters simultaneously) was found to outperform step-wise calibration in terms of model robustness. Multi-site calibration was able to improve model performance at internal sites, decrease parameter posterior uncertainty and prediction uncertainty. Nitrogen-process parameters calibrated using continuous daily averages of nitrate-N concentration observations produced better and more robust simulations of IN concentration and load, lower posterior parameter uncertainty and IN concentration prediction uncertainty compared to the calibration against uncontinuous biweekly nitrate-N concentration measurements. Both PEST and DREAM(ZS) are efficient in parameter calibration. However, DREAM(ZS) is more sound in terms of parameter identification and uncertainty analysis than PEST because of its capability to evolve parameter posterior distributions and estimate prediction uncertainty based on global search and Bayesian inference schemes.

Comparative study on ATR-FTIR calibration models for monitoring solution concentration in cooling crystallization

NASA Astrophysics Data System (ADS)

Zhang, Fangkun; Liu, Tao; Wang, Xue Z.; Liu, Jingxiang; Jiang, Xiaobin

2017-02-01

In this paper calibration model building based on using an ATR-FTIR spectroscopy is investigated for in-situ measurement of the solution concentration during a cooling crystallization process. The cooling crystallization of L-glutamic Acid (LGA) as a case is studied here. It was found that using the metastable zone (MSZ) data for model calibration can guarantee the prediction accuracy for monitoring the operating window of cooling crystallization, compared to the usage of undersaturated zone (USZ) spectra for model building as traditionally practiced. Calibration experiments were made for LGA solution under different concentrations. Four candidate calibration models were established using different zone data for comparison, by using a multivariate partial least-squares (PLS) regression algorithm for the collected spectra together with the corresponding temperature values. Experiments under different process conditions including the changes of solution concentration and operating temperature were conducted. The results indicate that using the MSZ spectra for model calibration can give more accurate prediction of the solution concentration during the crystallization process, while maintaining accuracy in changing the operating temperature. The primary reason of prediction error was clarified as spectral nonlinearity for in-situ measurement between USZ and MSZ. In addition, an LGA cooling crystallization experiment was performed to verify the sensitivity of these calibration models for monitoring the crystal growth process.

[Outlier sample discriminating methods for building calibration model in melons quality detecting using NIR spectra].

PubMed

Tian, Hai-Qing; Wang, Chun-Guang; Zhang, Hai-Jun; Yu, Zhi-Hong; Li, Jian-Kang

2012-11-01

Outlier samples strongly influence the precision of the calibration model in soluble solids content measurement of melons using NIR Spectra. According to the possible sources of outlier samples, three methods (predicted concentration residual test; Chauvenet test; leverage and studentized residual test) were used to discriminate these outliers respectively. Nine suspicious outliers were detected from calibration set which including 85 fruit samples. Considering the 9 suspicious outlier samples maybe contain some no-outlier samples, they were reclaimed to the model one by one to see whether they influence the model and prediction precision or not. In this way, 5 samples which were helpful to the model joined in calibration set again, and a new model was developed with the correlation coefficient (r) 0. 889 and root mean square errors for calibration (RMSEC) 0.6010 Brix. For 35 unknown samples, the root mean square errors prediction (RMSEP) was 0.854 degrees Brix. The performance of this model was more better than that developed with non outlier was eliminated from calibration set (r = 0.797, RMSEC= 0.849 degrees Brix, RMSEP = 1.19 degrees Brix), and more representative and stable with all 9 samples were eliminated from calibration set (r = 0.892, RMSEC = 0.605 degrees Brix, RMSEP = 0.862 degrees).

Numerical simulation of groundwater flow in Dar es Salaam Coastal Plain (Tanzania)

NASA Astrophysics Data System (ADS)

Luciani, Giulia; Sappa, Giuseppe; Cella, Antonella

2016-04-01

They are presented the results of a groundwater modeling study on the Coastal Aquifer of Dar es Salaam (Tanzania). Dar es Salaam is one of the fastest-growing coastal cities in Sub-Saharan Africa, with with more than 4 million of inhabitants and a population growth rate of about 8 per cent per year. The city faces periodic water shortages, due to the lack of an adequate water supply network. These two factors have determined, in the last ten years, an increasing demand of groundwater exploitation, carried on by quite a number of private wells, which have been drilled to satisfy human demand. A steady-state three dimensional groundwater model has been set up by the MODFLOW code, and calibrated with the UCODE code for inverse modeling. The aim of the model was to carry out a characterization of groundwater flow system in the Dar es Salaam Coastal Plain. The inputs applied to the model included net recharge rate, calculated from time series of precipitation data (1961-2012), estimations of average groundwater extraction, and estimations of groundwater recharge, coming from zones, outside the area under study. Parametrization of the hydraulic conductivities was realized referring to the main geological features of the study area, based on available literature data and information. Boundary conditions were assigned based on hydrogeological boundaries. The conceptual model was defined in subsequent steps, which added some hydrogeological features and excluded other ones. Calibration was performed with UCODE 2014, using 76 measures of hydraulic head, taken in 2012 referred to the same season. Data were weighted on the basis of the expected errors. Sensitivity analysis of data was performed during calibration, and permitted to identify which parameters were possible to be estimated, and which data could support parameters estimation. Calibration was evaluated based on statistical index, maps of error distribution and test of independence of residuals. Further model analysis was performed after calibration, to test model performance under a range of variations of input variables.

On the importance of appropriate precipitation gauge catch correction for hydrological modelling at mid to high latitudes

NASA Astrophysics Data System (ADS)

Stisen, S.; Højberg, A. L.; Troldborg, L.; Refsgaard, J. C.; Christensen, B. S. B.; Olsen, M.; Henriksen, H. J.

2012-11-01

Precipitation gauge catch correction is often given very little attention in hydrological modelling compared to model parameter calibration. This is critical because significant precipitation biases often make the calibration exercise pointless, especially when supposedly physically-based models are in play. This study addresses the general importance of appropriate precipitation catch correction through a detailed modelling exercise. An existing precipitation gauge catch correction method addressing solid and liquid precipitation is applied, both as national mean monthly correction factors based on a historic 30 yr record and as gridded daily correction factors based on local daily observations of wind speed and temperature. The two methods, named the historic mean monthly (HMM) and the time-space variable (TSV) correction, resulted in different winter precipitation rates for the period 1990-2010. The resulting precipitation datasets were evaluated through the comprehensive Danish National Water Resources model (DK-Model), revealing major differences in both model performance and optimised model parameter sets. Simulated stream discharge is improved significantly when introducing the TSV correction, whereas the simulated hydraulic heads and multi-annual water balances performed similarly due to recalibration adjusting model parameters to compensate for input biases. The resulting optimised model parameters are much more physically plausible for the model based on the TSV correction of precipitation. A proxy-basin test where calibrated DK-Model parameters were transferred to another region without site specific calibration showed better performance for parameter values based on the TSV correction. Similarly, the performances of the TSV correction method were superior when considering two single years with a much dryer and a much wetter winter, respectively, as compared to the winters in the calibration period (differential split-sample tests). We conclude that TSV precipitation correction should be carried out for studies requiring a sound dynamic description of hydrological processes, and it is of particular importance when using hydrological models to make predictions for future climates when the snow/rain composition will differ from the past climate. This conclusion is expected to be applicable for mid to high latitudes, especially in coastal climates where winter precipitation types (solid/liquid) fluctuate significantly, causing climatological mean correction factors to be inadequate.

Calibrated Noise Measurements with Induced Receiver Gain Fluctuations

NASA Technical Reports Server (NTRS)

Racette, Paul; Walker, David; Gu, Dazhen; Rajola, Marco; Spevacek, Ashly

2011-01-01

The lack of well-developed techniques for modeling changing statistical moments in our observations has stymied the application of stochastic process theory in science and engineering. These limitations were encountered when modeling the performance of radiometer calibration architectures and algorithms in the presence of non stationary receiver fluctuations. Analyses of measured signals have traditionally been limited to a single measurement series. Whereas in a radiometer that samples a set of noise references, the data collection can be treated as an ensemble set of measurements of the receiver state. Noise Assisted Data Analysis is a growing field of study with significant potential for aiding the understanding and modeling of non stationary processes. Typically, NADA entails adding noise to a signal to produce an ensemble set on which statistical analysis is performed. Alternatively as in radiometric measurements, mixing a signal with calibrated noise provides, through the calibration process, the means to detect deviations from the stationary assumption and thereby a measurement tool to characterize the signal's non stationary properties. Data sets comprised of calibrated noise measurements have been limited to those collected with naturally occurring fluctuations in the radiometer receiver. To examine the application of NADA using calibrated noise, a Receiver Gain Modulation Circuit (RGMC) was designed and built to modulate the gain of a radiometer receiver using an external signal. In 2010, an RGMC was installed and operated at the National Institute of Standards and Techniques (NIST) using their Noise Figure Radiometer (NFRad) and national standard noise references. The data collected is the first known set of calibrated noise measurements from a receiver with an externally modulated gain. As an initial step, sinusoidal and step-function signals were used to modulate the receiver gain, to evaluate the circuit characteristics and to study the performance of a variety of calibration algorithms. The receiver noise temperature and time-bandwidth product of the NFRad are calculated from the data. Statistical analysis using temporal-dependent calibration algorithms reveals that the natural occurring fluctuations in the receiver are stationary over long intervals (100s of seconds); however the receiver exhibits local non stationarity over the interval over which one set of reference measurements are collected. A variety of calibration algorithms have been applied to the data to assess algorithms' performance with the gain fluctuation signals. This presentation will describe the RGMC, experiment design and a comparative analysis of calibration algorithms.

Calibration of a flexible measurement system based on industrial articulated robot and structured light sensor

NASA Astrophysics Data System (ADS)

Mu, Nan; Wang, Kun; Xie, Zexiao; Ren, Ping

2017-05-01

To realize online rapid measurement for complex workpieces, a flexible measurement system based on an articulated industrial robot with a structured light sensor mounted on the end-effector is developed. A method for calibrating the system parameters is proposed in which the hand-eye transformation parameters and the robot kinematic parameters are synthesized in the calibration process. An initial hand-eye calibration is first performed using a standard sphere as the calibration target. By applying the modified complete and parametrically continuous method, we establish a synthesized kinematic model that combines the initial hand-eye transformation and distal link parameters as a whole with the sensor coordinate system as the tool frame. According to the synthesized kinematic model, an error model is constructed based on spheres' center-to-center distance errors. Consequently, the error model parameters can be identified in a calibration experiment using a three-standard-sphere target. Furthermore, the redundancy of error model parameters is eliminated to ensure the accuracy and robustness of the parameter identification. Calibration and measurement experiments are carried out based on an ER3A-C60 robot. The experimental results show that the proposed calibration method enjoys high measurement accuracy, and this efficient and flexible system is suitable for online measurement in industrial scenes.

Performance Assessment and Geometric Calibration of RESOURCESAT-2

NASA Astrophysics Data System (ADS)

Radhadevi, P. V.; Solanki, S. S.; Akilan, A.; Jyothi, M. V.; Nagasubramanian, V.

2016-06-01

Resourcesat-2 (RS-2) has successfully completed five years of operations in its orbit. This satellite has multi-resolution and multi-spectral capabilities in a single platform. A continuous and autonomous co-registration, geo-location and radiometric calibration of image data from different sensors with widely varying view angles and resolution was one of the challenges of RS-2 data processing. On-orbit geometric performance of RS-2 sensors has been widely assessed and calibrated during the initial phase operations. Since then, as an ongoing activity, various geometric performance data are being generated periodically. This is performed with sites of dense ground control points (GCPs). These parameters are correlated to the direct geo-location accuracy of the RS-2 sensors and are monitored and validated to maintain the performance. This paper brings out the geometric accuracy assessment, calibration and validation done for about 500 datasets of RS-2. The objectives of this study are to ensure the best absolute and relative location accuracy of different cameras, location performance with payload steering and co-registration of multiple bands. This is done using a viewing geometry model, given ephemeris and attitude data, precise camera geometry and datum transformation. In the model, the forward and reverse transformations between the coordinate systems associated with the focal plane, payload, body, orbit and ground are rigorously and explicitly defined. System level tests using comparisons to ground check points have validated the operational geo-location accuracy performance and the stability of the calibration parameters.

External validation of a prediction model for surgical site infection after thoracolumbar spine surgery in a Western European cohort.

PubMed

Janssen, Daniël M C; van Kuijk, Sander M J; d'Aumerie, Boudewijn B; Willems, Paul C

2018-05-16

A prediction model for surgical site infection (SSI) after spine surgery was developed in 2014 by Lee et al. This model was developed to compute an individual estimate of the probability of SSI after spine surgery based on the patient's comorbidity profile and invasiveness of surgery. Before any prediction model can be validly implemented in daily medical practice, it should be externally validated to assess how the prediction model performs in patients sampled independently from the derivation cohort. We included 898 consecutive patients who underwent instrumented thoracolumbar spine surgery. To quantify overall performance using Nagelkerke's R 2 statistic, the discriminative ability was quantified as the area under the receiver operating characteristic curve (AUC). We computed the calibration slope of the calibration plot, to judge prediction accuracy. Sixty patients developed an SSI. The overall performance of the prediction model in our population was poor: Nagelkerke's R 2 was 0.01. The AUC was 0.61 (95% confidence interval (CI) 0.54-0.68). The estimated slope of the calibration plot was 0.52. The previously published prediction model showed poor performance in our academic external validation cohort. To predict SSI after instrumented thoracolumbar spine surgery for the present population, a better fitting prediction model should be developed.

Advancing computational methods for calibration of the Soil and Water Assessment Tool (SWAT): Application for modeling climate change impacts on water resources in the Upper Neuse Watershed of North Carolina

NASA Astrophysics Data System (ADS)

Ercan, Mehmet Bulent

Watershed-scale hydrologic models are used for a variety of applications from flood prediction, to drought analysis, to water quality assessments. A particular challenge in applying these models is calibration of the model parameters, many of which are difficult to measure at the watershed-scale. A primary goal of this dissertation is to contribute new computational methods and tools for calibration of watershed-scale hydrologic models and the Soil and Water Assessment Tool (SWAT) model, in particular. SWAT is a physically-based, watershed-scale hydrologic model developed to predict the impact of land management practices on water quality and quantity. The dissertation follows a manuscript format meaning it is comprised of three separate but interrelated research studies. The first two research studies focus on SWAT model calibration, and the third research study presents an application of the new calibration methods and tools to study climate change impacts on water resources in the Upper Neuse Watershed of North Carolina using SWAT. The objective of the first two studies is to overcome computational challenges associated with calibration of SWAT models. The first study evaluates a parallel SWAT calibration tool built using the Windows Azure cloud environment and a parallel version of the Dynamically Dimensioned Search (DDS) calibration method modified to run in Azure. The calibration tool was tested for six model scenarios constructed using three watersheds of increasing size (the Eno, Upper Neuse, and Neuse) for both a 2 year and 10 year simulation duration. Leveraging the cloud as an on demand computing resource allowed for a significantly reduced calibration time such that calibration of the Neuse watershed went from taking 207 hours on a personal computer to only 3.4 hours using 256 cores in the Azure cloud. The second study aims at increasing SWAT model calibration efficiency by creating an open source, multi-objective calibration tool using the Non-Dominated Sorting Genetic Algorithm II (NSGA-II). This tool was demonstrated through an application for the Upper Neuse Watershed in North Carolina, USA. The objective functions used for the calibration were Nash-Sutcliffe (E) and Percent Bias (PB), and the objective sites were the Flat, Little, and Eno watershed outlets. The results show that the use of multi-objective calibration algorithms for SWAT calibration improved model performance especially in terms of minimizing PB compared to the single objective model calibration. The third study builds upon the first two studies by leveraging the new calibration methods and tools to study future climate impacts on the Upper Neuse watershed. Statistically downscaled outputs from eight Global Circulation Models (GCMs) were used for both low and high emission scenarios to drive a well calibrated SWAT model of the Upper Neuse watershed. The objective of the study was to understand the potential hydrologic response of the watershed, which serves as a public water supply for the growing Research Triangle Park region of North Carolina, under projected climate change scenarios. The future climate change scenarios, in general, indicate an increase in precipitation and temperature for the watershed in coming decades. The SWAT simulations using the future climate scenarios, in general, suggest an increase in soil water and water yield, and a decrease in evapotranspiration within the Upper Neuse watershed. In summary, this dissertation advances the field of watershed-scale hydrologic modeling by (i) providing some of the first work to apply cloud computing for the computationally-demanding task of model calibration; (ii) providing a new, open source library that can be used by SWAT modelers to perform multi-objective calibration of their models; and (iii) advancing understanding of climate change impacts on water resources for an important watershed in the Research Triangle Park region of North Carolina. The third study leveraged the methodological advances presented in the first two studies. Therefore, the dissertation contains three independent by interrelated studies that collectively advance the field of watershed-scale hydrologic modeling and analysis.

Stepwise calibration procedure for regional coupled hydrological-hydrogeological models

NASA Astrophysics Data System (ADS)

Labarthe, Baptiste; Abasq, Lena; de Fouquet, Chantal; Flipo, Nicolas

2014-05-01

Stream-aquifer interaction is a complex process depending on regional and local processes. Indeed, the groundwater component of hydrosystem and large scale heterogeneities control the regional flows towards the alluvial plains and the rivers. In second instance, the local distribution of the stream bed permeabilities controls the dynamics of stream-aquifer water fluxes within the alluvial plain, and therefore the near-river piezometric head distribution. In order to better understand the water circulation and pollutant transport in watersheds, the integration of these multi-dimensional processes in modelling platform has to be performed. Thus, the nested interfaces concept in continental hydrosystem modelling (where regional fluxes, simulated by large scale models, are imposed at local stream-aquifer interfaces) has been presented in Flipo et al (2014). This concept has been implemented in EauDyssée modelling platform for a large alluvial plain model (900km2) part of a 11000km2 multi-layer aquifer system, located in the Seine basin (France). The hydrosystem modelling platform is composed of four spatially distributed modules (Surface, Sub-surface, River and Groundwater), corresponding to four components of the terrestrial water cycle. Considering the large number of parameters to be inferred simultaneously, the calibration process of coupled models is highly computationally demanding and therefore hardly applicable to a real case study of 10000km2. In order to improve the efficiency of the calibration process, a stepwise calibration procedure is proposed. The stepwise methodology involves determining optimal parameters of all components of the coupled model, to provide a near optimum prior information for the global calibration. It starts with the surface component parameters calibration. The surface parameters are optimised based on the comparison between simulated and observed discharges (or filtered discharges) at various locations. Once the surface parameters have been determined, the groundwater component is calibrated. The calibration procedure is performed under steady state hypothesis (to minimize the procedure time length) using recharge rates given by the surface component calibration and imposed fluxes boundary conditions given by the regional model. The calibration is performed using pilot point where the prior variogram is calculated from observed transmissivities values. This procedure uses PEST (http//:www.pesthomepage.org/Home.php) as the inverse modelling tool and EauDyssée as the direct model. During the stepwise calibration process, each modules, even if they are actually dependant from each other, are run and calibrated independently, therefore contributions between each module have to be determined. For the surface module, groundwater and runoff contributions have been determined by hydrograph separation. Among the automated base-flow separation methods, the one-parameter Chapman filter (Chapman et al 1999) has been chosen. This filter is a decomposition of the actual base-flow between the previous base-flow and the discharge gradient weighted by functions of the recession coefficient. For the groundwater module, the recharge has been determined from surface and sub-surface module. References : Flipo, N., A. Mourhi, B. Labarthe, and S. Biancamaria (2014). Continental hydrosystem modelling : the concept of nested stream-aquifer interfaces. Hydrol. Earth Syst. Sci. Discuss. 11, 451-500. Chapman,TG. (1999). A comparison of algorithms for stream flow recession and base-flow separation. hydrological Processes 13, 701-714.

Spectral characterization and calibration of AOTF spectrometers and hyper-spectral imaging system

NASA Astrophysics Data System (ADS)

Katrašnik, Jaka; Pernuš, Franjo; Likar, Boštjan

2010-02-01

The goal of this article is to present a novel method for spectral characterization and calibration of spectrometers and hyper-spectral imaging systems based on non-collinear acousto-optical tunable filters. The method characterizes the spectral tuning curve (frequency-wavelength characteristic) of the AOTF (Acousto-Optic Tunable Filter) filter by matching the acquired and modeled spectra of the HgAr calibration lamp, which emits line spectrum that can be well modeled via AOTF transfer function. In this way, not only tuning curve characterization and corresponding spectral calibration but also spectral resolution assessment is performed. The obtained results indicated that the proposed method is efficient, accurate and feasible for routine calibration of AOTF spectrometers and hyper-spectral imaging systems and thereby a highly competitive alternative to the existing calibration methods.

Enhancing the NOAA National Water Center WRF-Hydro model architecture to improve representation of the Midwest and Southwest CONUS climate regions

NASA Astrophysics Data System (ADS)

Lahmers, T. M.; Castro, C. L.; Gupta, H. V.; Gochis, D.; Dugger, A. L.; Smith, M.

2016-12-01

The NOAA National Water Model (NWM), which is based on the WRF-Hydro architecture, became operational in June of 2016 to produce streamflow forecasts nationwide. In order to improve the physical process representation of NWM/WRF-Hydro, a parameterized channel infiltration function is added to the Muskingum-Cunge channel routing scheme. Representation of transmission losses along streams was previously not supported by WRF-Hydro, even though most channels in the southwest CONUS have a high depth to groundwater, and are consequently a source for recharge throughout the region. The LSM, routing grid, baseflow bucket model, and channel parameters of the modified version of NWM/WRF-Hydro are calibrated using spatial regularization in selected basins in the Midwest and Southwest CONUS. WRF-Hydro is calibrated and tested in the Verde, San Pedro, Little Sioux, Nishnabotna, and Wapsipinicon basins. The model is forced with NCEP Stage-IV and NLDAS-2 precipitation for calibration, and the effects of the precipitation climatology, including extreme events, on model performance are considered. This work advances the regional performance of WRF-Hydro through process enhancement and calibration that is highly relevant for improving model fidelity in semi-arid climates.

Integrating ecosystems measurements from multiple eddy-covariance sites to a simple model of ecosystem process - Are there possibilities for a uniform model calibration?

NASA Astrophysics Data System (ADS)

Minunno, Francesco; Peltoniemi, Mikko; Launiainen, Samuli; Mäkelä, Annikki

2014-05-01

Biogeochemical models quantify the material and energy flux exchanges between biosphere, atmosphere and soil, however there is still considerable uncertainty underpinning model structure and parametrization. The increasing availability of data from of multiple sources provides useful information for model calibration and validation at different space and time scales. We calibrated a simplified ecosystem process model PRELES to data from multiple sites. In this work we had the following objective: to compare a multi-site calibration and site-specific calibrations, in order to test if PRELES is a model of general applicability, and to test how well one parameterization can predict ecosystem fluxes. Model calibration and evaluation were carried out by the means of the Bayesian method; Bayesian calibration (BC) and Bayesian model comparison (BMC) were used to quantify the uncertainty in model parameters and model structure. Evapotranspiration (ET) and gross primary production (GPP) measurements collected in 9 sites of Finland and Sweden were used in the study; half dataset was used for model calibrations and half for the comparative analyses. 10 BCs were performed; the model was independently calibrated for each of the nine sites (site-specific calibrations) and a multi-site calibration was achieved using the data from all the sites in one BC. Then 9 BMCs were carried out, one for each site, using output from the multi-site and the site-specific versions of PRELES. Similar estimates were obtained for the parameters at which model outputs are most sensitive. Not surprisingly, the joint posterior distribution achieved through the multi-site calibration was characterized by lower uncertainty, because more data were involved in the calibration process. No significant differences were encountered in the prediction of the multi-site and site-specific versions of PRELES, and after BMC, we concluded that the model can be reliably used at regional scale to simulate carbon and water fluxes of Boreal forests. Despite being a simple model, PRELES provided good estimates of GPP and ET; only for one site PRELES multi-site version underestimated water fluxes. Our study implies convergence of GPP and water processes in boreal zone to the extent that their plausible prediction is possible with a simple model using global parameterization.

Bayesian calibration for electrochemical thermal model of lithium-ion cells

NASA Astrophysics Data System (ADS)

Tagade, Piyush; Hariharan, Krishnan S.; Basu, Suman; Verma, Mohan Kumar Singh; Kolake, Subramanya Mayya; Song, Taewon; Oh, Dukjin; Yeo, Taejung; Doo, Seokgwang

2016-07-01

Pseudo-two dimensional electrochemical thermal (P2D-ECT) model contains many parameters that are difficult to evaluate experimentally. Estimation of these model parameters is challenging due to computational cost and the transient model. Due to lack of complete physical understanding, this issue gets aggravated at extreme conditions like low temperature (LT) operations. This paper presents a Bayesian calibration framework for estimation of the P2D-ECT model parameters. The framework uses a matrix variate Gaussian process representation to obtain a computationally tractable formulation for calibration of the transient model. Performance of the framework is investigated for calibration of the P2D-ECT model across a range of temperatures (333 Ksbnd 263 K) and operating protocols. In the absence of complete physical understanding, the framework also quantifies structural uncertainty in the calibrated model. This information is used by the framework to test validity of the new physical phenomena before incorporation in the model. This capability is demonstrated by introducing temperature dependence on Bruggeman's coefficient and lithium plating formation at LT. With the incorporation of new physics, the calibrated P2D-ECT model accurately predicts the cell voltage with high confidence. The accurate predictions are used to obtain new insights into the low temperature lithium ion cell behavior.

Soybean Physiology Calibration in the Community Land Model

NASA Astrophysics Data System (ADS)

Drewniak, B. A.; Bilionis, I.; Constantinescu, E. M.

2014-12-01

With the large influence of agricultural land use on biophysical and biogeochemical cycles, integrating cultivation into Earth System Models (ESMs) is increasingly important. The Community Land Model (CLM) was augmented with a CLM-Crop extension that simulates the development of three crop types: maize, soybean, and spring wheat. The CLM-Crop model is a complex system that relies on a suite of parametric inputs that govern plant growth under a given atmospheric forcing and available resources. However, the strong nonlinearity of ESMs makes parameter fitting a difficult task. In this study, our goal is to calibrate ten of the CLM-Crop parameters for one crop type, soybean, in order to improve model projection of plant development and carbon fluxes. We used measurements of gross primary productivity, net ecosystem exchange, and plant biomass from AmeriFlux sites to choose parameter values that optimize crop productivity in the model. Calibration is performed in a Bayesian framework by developing a scalable and adaptive scheme based on sequential Monte Carlo (SMC). Our scheme can perform model calibration using very few evaluations and, by exploiting parallelism, at a fraction of the time required by plain vanilla Markov Chain Monte Carlo (MCMC). We present the results from a twin experiment (self-validation) and calibration results and validation using real observations from an AmeriFlux tower site in the Midwestern United States, for the soybean crop type. The improved model will help researchers understand how climate affects crop production and resulting carbon fluxes, and additionally, how cultivation impacts climate.

Demonstration of emulator-based Bayesian calibration of safety analysis codes: Theory and formulation

DOE PAGES

Yurko, Joseph P.; Buongiorno, Jacopo; Youngblood, Robert

2015-05-28

System codes for simulation of safety performance of nuclear plants may contain parameters whose values are not known very accurately. New information from tests or operating experience is incorporated into safety codes by a process known as calibration, which reduces uncertainty in the output of the code and thereby improves its support for decision-making. The work reported here implements several improvements on classic calibration techniques afforded by modern analysis techniques. The key innovation has come from development of code surrogate model (or code emulator) construction and prediction algorithms. Use of a fast emulator makes the calibration processes used here withmore » Markov Chain Monte Carlo (MCMC) sampling feasible. This study uses Gaussian Process (GP) based emulators, which have been used previously to emulate computer codes in the nuclear field. The present work describes the formulation of an emulator that incorporates GPs into a factor analysis-type or pattern recognition-type model. This “function factorization” Gaussian Process (FFGP) model allows overcoming limitations present in standard GP emulators, thereby improving both accuracy and speed of the emulator-based calibration process. Calibration of a friction-factor example using a Method of Manufactured Solution is performed to illustrate key properties of the FFGP based process.« less

Calibration of a Distributed Hydrological Model using Remote Sensing Evapotranspiration data in the Semi-Arid Punjab Region of Pakista

NASA Astrophysics Data System (ADS)

Becker, R.; Usman, M.

2017-12-01

A SWAT (Soil Water Assessment Tool) model is applied in the semi-arid Punjab region in Pakistan. The physically based hydrological model is set up to simulate hydrological processes and water resources demands under future land use, climate change and irrigation management scenarios. In order to successfully run the model, detailed focus is laid on the calibration procedure of the model. The study deals with the following calibration issues:i. lack of reliable calibration/validation data, ii. difficulty to accurately model a highly managed system with a physically based hydrological model and iii. use of alternative and spatially distributed data sets for model calibration. In our study area field observations are rare and the entirely human controlled irrigation system renders central calibration parameters (e.g. runoff/curve number) unsuitable, as it can't be assumed that they represent the natural behavior of the hydrological system. From evapotranspiration (ET) however principal hydrological processes can still be inferred. Usman et al. (2015) derived satellite based monthly ET data for our study area based on SEBAL (Surface Energy Balance Algorithm) and created a reliable ET data set which we use in this study to calibrate our SWAT model. The initial SWAT model performance is evaluated with respect to the SEBAL results using correlation coefficients, RMSE, Nash-Sutcliffe efficiencies and mean differences. Particular focus is laid on the spatial patters, investigating the potential of a spatially differentiated parameterization instead of just using spatially uniform calibration data. A sensitivity analysis reveals the most sensitive parameters with respect to changes in ET, which are then selected for the calibration process.Using the SEBAL-ET product we calibrate the SWAT model for the time period 2005-2006 using a dynamically dimensioned global search algorithm to minimize RMSE. The model improvement after the calibration procedure is finally evaluated based on the previously chosen evaluation criteria for the time period 2007-2008. The study reveals the sensitivity of SWAT model parameters to changes in ET in a semi-arid and human controlled system and the potential of calibrating those parameters using satellite derived ET data.

Calibration, event reconstruction, data analysis and limits calculation for the LUX dark matter experiment

DOE PAGES

Akerib, DS; Alsum, S; Araújo, HM; ...

2018-01-05

The LUX experiment has performed searches for dark matter particles scattering elastically on xenon nuclei, leading to stringent upper limits on the nuclear scattering cross sections for dark matter. Here, for results derived frommore » $${1.4}\\times 10^{4}\\;\\mathrm{kg\\,days}$$ of target exposure in 2013, details of the calibration, event-reconstruction, modeling, and statistical tests that underlie the results are presented. Detector performance is characterized, including measured efficiencies, stability of response, position resolution, and discrimination between electron- and nuclear-recoil populations. Models are developed for the drift field, optical properties, background populations, the electron- and nuclear-recoil responses, and the absolute rate of low-energy background events. Innovations in the analysis include in situ measurement of the photomultipliers' response to xenon scintillation photons, verification of fiducial mass with a low-energy internal calibration source, and new empirical models for low-energy signal yield based on large-sample, in situ calibrations.« less

Calibration, event reconstruction, data analysis, and limit calculation for the LUX dark matter experiment

DOE PAGES

Akerib, D. S.; Alsum, S.; Araújo, H. M.; ...

2018-05-31

Here, the LUX experiment has performed searches for dark matter particles scattering elastically on xenon nuclei, leading to stringent upper limits on the nuclear scattering cross sections for dark matter. Here, for results derived frommore » $${1.4}\\times 10^{4}\\;\\mathrm{kg\\,days}$$ of target exposure in 2013, details of the calibration, event-reconstruction, modeling, and statistical tests that underlie the results are presented. Detector performance is characterized, including measured efficiencies, stability of response, position resolution, and discrimination between electron- and nuclear-recoil populations. Models are developed for the drift field, optical properties, background populations, the electron- and nuclear-recoil responses, and the absolute rate of low-energy background events. Innovations in the analysis include in situ measurement of the photomultipliers' response to xenon scintillation photons, verification of fiducial mass with a low-energy internal calibration source, and new empirical models for low-energy signal yield based on large-sample, in situ calibrations.« less

Calibration, event reconstruction, data analysis, and limit calculation for the LUX dark matter experiment

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

Akerib, D. S.; Alsum, S.; Araújo, H. M.

Here, the LUX experiment has performed searches for dark matter particles scattering elastically on xenon nuclei, leading to stringent upper limits on the nuclear scattering cross sections for dark matter. Here, for results derived frommore » $${1.4}\\times 10^{4}\\;\\mathrm{kg\\,days}$$ of target exposure in 2013, details of the calibration, event-reconstruction, modeling, and statistical tests that underlie the results are presented. Detector performance is characterized, including measured efficiencies, stability of response, position resolution, and discrimination between electron- and nuclear-recoil populations. Models are developed for the drift field, optical properties, background populations, the electron- and nuclear-recoil responses, and the absolute rate of low-energy background events. Innovations in the analysis include in situ measurement of the photomultipliers' response to xenon scintillation photons, verification of fiducial mass with a low-energy internal calibration source, and new empirical models for low-energy signal yield based on large-sample, in situ calibrations.« less

Prediction models for clustered data: comparison of a random intercept and standard regression model

PubMed Central

2013-01-01

Background When study data are clustered, standard regression analysis is considered inappropriate and analytical techniques for clustered data need to be used. For prediction research in which the interest of predictor effects is on the patient level, random effect regression models are probably preferred over standard regression analysis. It is well known that the random effect parameter estimates and the standard logistic regression parameter estimates are different. Here, we compared random effect and standard logistic regression models for their ability to provide accurate predictions. Methods Using an empirical study on 1642 surgical patients at risk of postoperative nausea and vomiting, who were treated by one of 19 anesthesiologists (clusters), we developed prognostic models either with standard or random intercept logistic regression. External validity of these models was assessed in new patients from other anesthesiologists. We supported our results with simulation studies using intra-class correlation coefficients (ICC) of 5%, 15%, or 30%. Standard performance measures and measures adapted for the clustered data structure were estimated. Results The model developed with random effect analysis showed better discrimination than the standard approach, if the cluster effects were used for risk prediction (standard c-index of 0.69 versus 0.66). In the external validation set, both models showed similar discrimination (standard c-index 0.68 versus 0.67). The simulation study confirmed these results. For datasets with a high ICC (≥15%), model calibration was only adequate in external subjects, if the used performance measure assumed the same data structure as the model development method: standard calibration measures showed good calibration for the standard developed model, calibration measures adapting the clustered data structure showed good calibration for the prediction model with random intercept. Conclusion The models with random intercept discriminate better than the standard model only if the cluster effect is used for predictions. The prediction model with random intercept had good calibration within clusters. PMID:23414436

Prediction models for clustered data: comparison of a random intercept and standard regression model.

PubMed

Bouwmeester, Walter; Twisk, Jos W R; Kappen, Teus H; van Klei, Wilton A; Moons, Karel G M; Vergouwe, Yvonne

2013-02-15

When study data are clustered, standard regression analysis is considered inappropriate and analytical techniques for clustered data need to be used. For prediction research in which the interest of predictor effects is on the patient level, random effect regression models are probably preferred over standard regression analysis. It is well known that the random effect parameter estimates and the standard logistic regression parameter estimates are different. Here, we compared random effect and standard logistic regression models for their ability to provide accurate predictions. Using an empirical study on 1642 surgical patients at risk of postoperative nausea and vomiting, who were treated by one of 19 anesthesiologists (clusters), we developed prognostic models either with standard or random intercept logistic regression. External validity of these models was assessed in new patients from other anesthesiologists. We supported our results with simulation studies using intra-class correlation coefficients (ICC) of 5%, 15%, or 30%. Standard performance measures and measures adapted for the clustered data structure were estimated. The model developed with random effect analysis showed better discrimination than the standard approach, if the cluster effects were used for risk prediction (standard c-index of 0.69 versus 0.66). In the external validation set, both models showed similar discrimination (standard c-index 0.68 versus 0.67). The simulation study confirmed these results. For datasets with a high ICC (≥15%), model calibration was only adequate in external subjects, if the used performance measure assumed the same data structure as the model development method: standard calibration measures showed good calibration for the standard developed model, calibration measures adapting the clustered data structure showed good calibration for the prediction model with random intercept. The models with random intercept discriminate better than the standard model only if the cluster effect is used for predictions. The prediction model with random intercept had good calibration within clusters.

Model calibration of a variable refrigerant flow system with a dedicated outdoor air system: A case study

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

Kim, Dongsu; Cox, Sam J.; Cho, Heejin

With increased use of variable refrigerant flow (VRF) systems in the U.S. building sector, interests in capability and rationality of various building energy modeling tools to simulate VRF systems are rising. This paper presents the detailed procedures for model calibration of a VRF system with a dedicated outdoor air system (DOAS) by comparing to detailed measured data from an occupancy emulated small office building. The building energy model is first developed based on as-built drawings, and building and system characteristics available. The whole building energy modeling tool used for the study is U.S. DOE’s EnergyPlus version 8.1. The initial modelmore » is, then, calibrated with the hourly measured data from the target building and VRF-DOAS system. In a detailed calibration procedures of the VRF-DOAS, the original EnergyPlus source code is modified to enable the modeling of the specific VRF-DOAS installed in the building. After a proper calibration during cooling and heating seasons, the VRF-DOAS model can reasonably predict the performance of the actual VRF-DOAS system based on the criteria from ASHRAE Guideline 14-2014. The calibration results show that hourly CV-RMSE and NMBE would be 15.7% and 3.8%, respectively, which is deemed to be calibrated. As a result, the whole-building energy usage after calibration of the VRF-DOAS model is 1.9% (78.8 kWh) lower than that of the measurements during comparison period.« less

External validation of prognostic models to predict risk of gestational diabetes mellitus in one Dutch cohort: prospective multicentre cohort study.

PubMed

Lamain-de Ruiter, Marije; Kwee, Anneke; Naaktgeboren, Christiana A; de Groot, Inge; Evers, Inge M; Groenendaal, Floris; Hering, Yolanda R; Huisjes, Anjoke J M; Kirpestein, Cornel; Monincx, Wilma M; Siljee, Jacqueline E; Van 't Zelfde, Annewil; van Oirschot, Charlotte M; Vankan-Buitelaar, Simone A; Vonk, Mariska A A W; Wiegers, Therese A; Zwart, Joost J; Franx, Arie; Moons, Karel G M; Koster, Maria P H

2016-08-30

 To perform an external validation and direct comparison of published prognostic models for early prediction of the risk of gestational diabetes mellitus, including predictors applicable in the first trimester of pregnancy.  External validation of all published prognostic models in large scale, prospective, multicentre cohort study.  31 independent midwifery practices and six hospitals in the Netherlands.  Women recruited in their first trimester (<14 weeks) of pregnancy between December 2012 and January 2014, at their initial prenatal visit. Women with pre-existing diabetes mellitus of any type were excluded.  Discrimination of the prognostic models was assessed by the C statistic, and calibration assessed by calibration plots.  3723 women were included for analysis, of whom 181 (4.9%) developed gestational diabetes mellitus in pregnancy. 12 prognostic models for the disorder could be validated in the cohort. C statistics ranged from 0.67 to 0.78. Calibration plots showed that eight of the 12 models were well calibrated. The four models with the highest C statistics included almost all of the following predictors: maternal age, maternal body mass index, history of gestational diabetes mellitus, ethnicity, and family history of diabetes. Prognostic models had a similar performance in a subgroup of nulliparous women only. Decision curve analysis showed that the use of these four models always had a positive net benefit.  In this external validation study, most of the published prognostic models for gestational diabetes mellitus show acceptable discrimination and calibration. The four models with the highest discriminative abilities in this study cohort, which also perform well in a subgroup of nulliparous women, are easy models to apply in clinical practice and therefore deserve further evaluation regarding their clinical impact. Published by the BMJ Publishing Group Limited. For permission to use (where not already granted under a licence) please go to http://group.bmj.com/group/rights-licensing/permissions.

Simulation of the Quantity, Variability, and Timing of Streamflow in the Dennys River Basin, Maine, by Use of a Precipitation-Runoff Watershed Model

USGS Publications Warehouse

Dudley, Robert W.

2008-01-01

The U.S. Geological Survey (USGS), in cooperation with the Maine Department of Marine Resources Bureau of Sea Run Fisheries and Habitat, began a study in 2004 to characterize the quantity, variability, and timing of streamflow in the Dennys River. The study included a synoptic summary of historical streamflow data at a long-term streamflow gage, collecting data from an additional four short-term streamflow gages, and the development and evaluation of a distributed-parameter watershed model for the Dennys River Basin. The watershed model used in this investigation was the USGS Precipitation-Runoff Modeling System (PRMS). The Geographic Information System (GIS) Weasel was used to delineate the Dennys River Basin and subbasins and derive parameters for their physical geographic features. Calibration of the models used in this investigation involved a four-step procedure in which model output was evaluated against four calibration data sets using computed objective functions for solar radiation, potential evapotranspiration, annual and seasonal water budgets, and daily streamflows. The calibration procedure involved thousands of model runs and was carried out using the USGS software application Luca (Let us calibrate). Luca uses the Shuffled Complex Evolution (SCE) global search algorithm to calibrate the model parameters. The SCE method reliably produces satisfactory solutions for large, complex optimization problems. The primary calibration effort went into the Dennys main stem watershed model. Calibrated parameter values obtained for the Dennys main stem model were transferred to the Cathance Stream model, and a similar four-step SCE calibration procedure was performed; this effort was undertaken to determine the potential to transfer modeling information to a nearby basin in the same region. The calibrated Dennys main stem watershed model performed with Nash-Sutcliffe efficiency (NSE) statistic values for the calibration period and evaluation period of 0.79 and 0.76, respectively. The Cathance Stream model had an NSE value of 0.68. The Dennys River Basin models make use of limited streamflow-gaging station data and provide information to characterize subbasin hydrology. The calibrated PRMS watershed models of the Dennys River Basin provide simulated daily streamflow time series from October 1, 1985, through September 30, 2006, for nearly any location within the basin. These models enable natural-resources managers to characterize the timing and quantity of water moving through the basin to support many endeavors including geochemical calculations, water-use assessment, Atlantic salmon population dynamics and migration modeling, habitat modeling and assessment, and other resource-management scenario evaluations. Characterizing streamflow contributions from subbasins in the basin and the relative amounts of surface- and ground-water contributions to streamflow throughout the basin will lead to a better understanding of water quantity and quality in the basin. Improved water-resources information will support Atlantic salmon protection efforts.

Calibration and LOD/LOQ estimation of a chemiluminescent hybridization assay for residual DNA in recombinant protein drugs expressed in E. coli using a four-parameter logistic model.

PubMed

Lee, K R; Dipaolo, B; Ji, X

2000-06-01

Calibration is the process of fitting a model based on reference data points (x, y), then using the model to estimate an unknown x based on a new measured response, y. In DNA assay, x is the concentration, and y is the measured signal volume. A four-parameter logistic model was used frequently for calibration of immunoassay when the response is optical density for enzyme-linked immunosorbent assay (ELISA) or adjusted radioactivity count for radioimmunoassay (RIA). Here, it is shown that the same model or a linearized version of the curve are equally useful for the calibration of a chemiluminescent hybridization assay for residual DNA in recombinant protein drugs and calculation of performance measures of the assay.

A Nonlinear Calibration Algorithm Based on Harmonic Decomposition for Two-Axis Fluxgate Sensors

PubMed Central

Liu, Shibin

2018-01-01

Nonlinearity is a prominent limitation to the calibration performance for two-axis fluxgate sensors. In this paper, a novel nonlinear calibration algorithm taking into account the nonlinearity of errors is proposed. In order to establish the nonlinear calibration model, the combined effort of all time-invariant errors is analyzed in detail, and then harmonic decomposition method is utilized to estimate the compensation coefficients. Meanwhile, the proposed nonlinear calibration algorithm is validated and compared with a classical calibration algorithm by experiments. The experimental results show that, after the nonlinear calibration, the maximum deviation of magnetic field magnitude is decreased from 1302 nT to 30 nT, which is smaller than 81 nT after the classical calibration. Furthermore, for the two-axis fluxgate sensor used as magnetic compass, the maximum error of heading is corrected from 1.86° to 0.07°, which is approximately 11% in contrast with 0.62° after the classical calibration. The results suggest an effective way to improve the calibration performance of two-axis fluxgate sensors. PMID:29789448

Model performance evaluation (validation and calibration) in model-based studies of therapeutic interventions for cardiovascular diseases : a review and suggested reporting framework.

PubMed

Haji Ali Afzali, Hossein; Gray, Jodi; Karnon, Jonathan

2013-04-01

Decision analytic models play an increasingly important role in the economic evaluation of health technologies. Given uncertainties around the assumptions used to develop such models, several guidelines have been published to identify and assess 'best practice' in the model development process, including general modelling approach (e.g., time horizon), model structure, input data and model performance evaluation. This paper focuses on model performance evaluation. In the absence of a sufficient level of detail around model performance evaluation, concerns regarding the accuracy of model outputs, and hence the credibility of such models, are frequently raised. Following presentation of its components, a review of the application and reporting of model performance evaluation is presented. Taking cardiovascular disease as an illustrative example, the review investigates the use of face validity, internal validity, external validity, and cross model validity. As a part of the performance evaluation process, model calibration is also discussed and its use in applied studies investigated. The review found that the application and reporting of model performance evaluation across 81 studies of treatment for cardiovascular disease was variable. Cross-model validation was reported in 55 % of the reviewed studies, though the level of detail provided varied considerably. We found that very few studies documented other types of validity, and only 6 % of the reviewed articles reported a calibration process. Considering the above findings, we propose a comprehensive model performance evaluation framework (checklist), informed by a review of best-practice guidelines. This framework provides a basis for more accurate and consistent documentation of model performance evaluation. This will improve the peer review process and the comparability of modelling studies. Recognising the fundamental role of decision analytic models in informing public funding decisions, the proposed framework should usefully inform guidelines for preparing submissions to reimbursement bodies.

Calibration and Propagation of Uncertainty for Independence

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

Holland, Troy Michael; Kress, Joel David; Bhat, Kabekode Ghanasham

This document reports on progress and methods for the calibration and uncertainty quantification of the Independence model developed at UT Austin. The Independence model is an advanced thermodynamic and process model framework for piperazine solutions as a high-performance CO 2 capture solvent. Progress is presented in the framework of the CCSI standard basic data model inference framework. Recent work has largely focused on the thermodynamic submodels of Independence.

Reconstructing the calibrated strain signal in the Advanced LIGO detectors

NASA Astrophysics Data System (ADS)

Viets, A. D.; Wade, M.; Urban, A. L.; Kandhasamy, S.; Betzwieser, J.; Brown, Duncan A.; Burguet-Castell, J.; Cahillane, C.; Goetz, E.; Izumi, K.; Karki, S.; Kissel, J. S.; Mendell, G.; Savage, R. L.; Siemens, X.; Tuyenbayev, D.; Weinstein, A. J.

2018-05-01

Advanced LIGO’s raw detector output needs to be calibrated to compute dimensionless strain h(t) . Calibrated strain data is produced in the time domain using both a low-latency, online procedure and a high-latency, offline procedure. The low-latency h(t) data stream is produced in two stages, the first of which is performed on the same computers that operate the detector’s feedback control system. This stage, referred to as the front-end calibration, uses infinite impulse response (IIR) filtering and performs all operations at a 16 384 Hz digital sampling rate. Due to several limitations, this procedure currently introduces certain systematic errors in the calibrated strain data, motivating the second stage of the low-latency procedure, known as the low-latency gstlal calibration pipeline. The gstlal calibration pipeline uses finite impulse response (FIR) filtering to apply corrections to the output of the front-end calibration. It applies time-dependent correction factors to the sensing and actuation components of the calibrated strain to reduce systematic errors. The gstlal calibration pipeline is also used in high latency to recalibrate the data, which is necessary due mainly to online dropouts in the calibrated data and identified improvements to the calibration models or filters.

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

Shen, Bo; Abdelaziz, Omar; Shrestha, Som S.

Based on the laboratory investigation in FY16, for R-22 and R-410A alternative low GWP refrigerants in two baseline rooftop air conditioners (RTU), we used the DOE/ORNL Heat Pump Design Model to model the two RTUs and calibrated the models against the experimental data. Using the calibrated equipment models, we compared the compressor efficiencies, heat exchanger performances. An efficiency-based compressor mapping method was developed, which is able to predict compressor performances of the alternative low GWP refrigerants accurately. Extensive model-based optimizations were conducted to provide a fair comparison between all the low GWP candidates by selecting their preferred configurations at themore » same cooling capacity and compressor efficiencies.« less

A proposed standard method for polarimetric calibration and calibration verification

NASA Astrophysics Data System (ADS)

Persons, Christopher M.; Jones, Michael W.; Farlow, Craig A.; Morell, L. Denise; Gulley, Michael G.; Spradley, Kevin D.

2007-09-01

Accurate calibration of polarimetric sensors is critical to reducing and analyzing phenomenology data, producing uniform polarimetric imagery for deployable sensors, and ensuring predictable performance of polarimetric algorithms. It is desirable to develop a standard calibration method, including verification reporting, in order to increase credibility with customers and foster communication and understanding within the polarimetric community. This paper seeks to facilitate discussions within the community on arriving at such standards. Both the calibration and verification methods presented here are performed easily with common polarimetric equipment, and are applicable to visible and infrared systems with either partial Stokes or full Stokes sensitivity. The calibration procedure has been used on infrared and visible polarimetric imagers over a six year period, and resulting imagery has been presented previously at conferences and workshops. The proposed calibration method involves the familiar calculation of the polarimetric data reduction matrix by measuring the polarimeter's response to a set of input Stokes vectors. With this method, however, linear combinations of Stokes vectors are used to generate highly accurate input states. This allows the direct measurement of all system effects, in contrast with fitting modeled calibration parameters to measured data. This direct measurement of the data reduction matrix allows higher order effects that are difficult to model to be discovered and corrected for in calibration. This paper begins with a detailed tutorial on the proposed calibration and verification reporting methods. Example results are then presented for a LWIR rotating half-wave retarder polarimeter.

Modelling carbon oxidation in pulp mill activated sludge systems: calibration of Activated Sludge Model No 3.

PubMed

Barañao, P A; Hall, E R

2004-01-01

Activated Sludge Model No 3 (ASM3) was chosen to model an activated sludge system treating effluents from a mechanical pulp and paper mill. The high COD concentration and the high content of readily biodegradable substrates of the wastewater make this model appropriate for this system. ASM3 was calibrated based on batch respirometric tests using fresh wastewater and sludge from the treatment plant, and on analytical measurements of COD, TSS and VSS. The model, developed for municipal wastewater, was found suitable for fitting a variety of respirometric batch tests, performed at different temperatures and food to microorganism ratios (F/M). Therefore, a set of calibrated parameters, as well as the wastewater COD fractions, was estimated for this industrial wastewater. The majority of the calibrated parameters were in the range of those found in the literature.

The feasibility of using explicit method for linear correction of the particle size variation using NIR Spectroscopy combined with PLS2regression method

NASA Astrophysics Data System (ADS)

Yulia, M.; Suhandy, D.

2018-03-01

NIR spectra obtained from spectral data acquisition system contains both chemical information of samples as well as physical information of the samples, such as particle size and bulk density. Several methods have been established for developing calibration models that can compensate for sample physical information variations. One common approach is to include physical information variation in the calibration model both explicitly and implicitly. The objective of this study was to evaluate the feasibility of using explicit method to compensate the influence of different particle size of coffee powder in NIR calibration model performance. A number of 220 coffee powder samples with two different types of coffee (civet and non-civet) and two different particle sizes (212 and 500 µm) were prepared. Spectral data was acquired using NIR spectrometer equipped with an integrating sphere for diffuse reflectance measurement. A discrimination method based on PLS-DA was conducted and the influence of different particle size on the performance of PLS-DA was investigated. In explicit method, we add directly the particle size as predicted variable results in an X block containing only the NIR spectra and a Y block containing the particle size and type of coffee. The explicit inclusion of the particle size into the calibration model is expected to improve the accuracy of type of coffee determination. The result shows that using explicit method the quality of the developed calibration model for type of coffee determination is a little bit superior with coefficient of determination (R2) = 0.99 and root mean square error of cross-validation (RMSECV) = 0.041. The performance of the PLS2 calibration model for type of coffee determination with particle size compensation was quite good and able to predict the type of coffee in two different particle sizes with relatively high R2 pred values. The prediction also resulted in low bias and RMSEP values.

A suggestion for computing objective function in model calibration

USGS Publications Warehouse

Wu, Yiping; Liu, Shuguang

2014-01-01

A parameter-optimization process (model calibration) is usually required for numerical model applications, which involves the use of an objective function to determine the model cost (model-data errors). The sum of square errors (SSR) has been widely adopted as the objective function in various optimization procedures. However, ‘square error’ calculation was found to be more sensitive to extreme or high values. Thus, we proposed that the sum of absolute errors (SAR) may be a better option than SSR for model calibration. To test this hypothesis, we used two case studies—a hydrological model calibration and a biogeochemical model calibration—to investigate the behavior of a group of potential objective functions: SSR, SAR, sum of squared relative deviation (SSRD), and sum of absolute relative deviation (SARD). Mathematical evaluation of model performance demonstrates that ‘absolute error’ (SAR and SARD) are superior to ‘square error’ (SSR and SSRD) in calculating objective function for model calibration, and SAR behaved the best (with the least error and highest efficiency). This study suggests that SSR might be overly used in real applications, and SAR may be a reasonable choice in common optimization implementations without emphasizing either high or low values (e.g., modeling for supporting resources management).

Normal tissue complication probability (NTCP) modelling using spatial dose metrics and machine learning methods for severe acute oral mucositis resulting from head and neck radiotherapy.

PubMed

Dean, Jamie A; Wong, Kee H; Welsh, Liam C; Jones, Ann-Britt; Schick, Ulrike; Newbold, Kate L; Bhide, Shreerang A; Harrington, Kevin J; Nutting, Christopher M; Gulliford, Sarah L

2016-07-01

Severe acute mucositis commonly results from head and neck (chemo)radiotherapy. A predictive model of mucositis could guide clinical decision-making and inform treatment planning. We aimed to generate such a model using spatial dose metrics and machine learning. Predictive models of severe acute mucositis were generated using radiotherapy dose (dose-volume and spatial dose metrics) and clinical data. Penalised logistic regression, support vector classification and random forest classification (RFC) models were generated and compared. Internal validation was performed (with 100-iteration cross-validation), using multiple metrics, including area under the receiver operating characteristic curve (AUC) and calibration slope, to assess performance. Associations between covariates and severe mucositis were explored using the models. The dose-volume-based models (standard) performed equally to those incorporating spatial information. Discrimination was similar between models, but the RFCstandard had the best calibration. The mean AUC and calibration slope for this model were 0.71 (s.d.=0.09) and 3.9 (s.d.=2.2), respectively. The volumes of oral cavity receiving intermediate and high doses were associated with severe mucositis. The RFCstandard model performance is modest-to-good, but should be improved, and requires external validation. Reducing the volumes of oral cavity receiving intermediate and high doses may reduce mucositis incidence. Copyright © 2016 The Author(s). Published by Elsevier Ireland Ltd.. All rights reserved.

Georgia Long-Term Pavement Performance (GALTPP) Program – Maintaining Georgia’s Calibration Sites and Identifying the Potential for Using MEPDG for Characterization of Non-Standard Materials and Methods (Phase 1)

DOT National Transportation Integrated Search

2016-10-01

The Georgia Department of Transportation (GDOT) has initiated a Georgia Long-Term Pavement Performance (GALTPP) monitoring program 1) to provide data for calibrating the prediction models in the AASHTO Mechanistic-Empirical Pavement Design Guide (MEP...

Improving Fifth Grade Students' Mathematics Self-Efficacy Calibration and Performance through Self-Regulation Training

ERIC Educational Resources Information Center

Ramdass, Darshanand H.

2009-01-01

This primary goal of this study was to investigate the effects of strategy training and self-reflection, two subprocesses of Zimmerman's cyclical model of self-regulation, on fifth grade students' mathematics performance, self-efficacy, self-evaluation, and calibration measures of self-efficacy bias, self-efficacy accuracy, self-evaluation bias,…

Exploring the performance of the SEDD model to predict sediment yield in eucalyptus plantations. Long-term results from an experimental catchment in Southern Italy

NASA Astrophysics Data System (ADS)

Porto, P.; Cogliandro, V.; Callegari, G.

2018-01-01

In this paper, long-term sediment yield data, collected in a small (1.38 ha) Calabrian catchment (W2), reafforested with eucalyptus trees (Eucalyptus occidentalis Engl.) are used to validate the performance of the SEdiment Delivery Distributed Model (SEDD) in areas with high erosion rates. At first step, the SEDD model was calibrated using field data collected in previous field campaigns undertaken during the period 1978-1994. This first phase allowed the model calibration parameter β to be calculated using direct measurements of rainfall, runoff, and sediment output. The model was then validated in its calibrated form for an independent period (2006-2016) for which new measurements of rainfall, runoff and sediment output are also available. The analysis, carried out at event and annual scale showed good agreement between measured and predicted values of sediment yield and suggested that the SEDD model can be seen as an appropriate means of evaluating erosion risk associated with manmade plantations in marginal areas. Further work is however required to test the performance of the SEDD model as a prediction tool in different geomorphic contexts.

In-flight calibration of the high-gain antenna pointing for the Mariner Venus-Mercury 1973 spacecraft

NASA Technical Reports Server (NTRS)

Hardman, J. M.; Havens, W. F.; Ohtakay, H.

1975-01-01

The methods used to in-flight calibrate the pointing direction of the Mariner Venus-Mercury 1973 spacecraft high gain antenna and the achieved antenna pointing accuracy are described. The overall pointing calibration was accomplished by performing calibration sequences at a number of points along the spacecraft trajectory. Each of these consisted of articulating the antenna about the expected spacecraft-earth vector to determine systematic pointing errors. The high gain antenna pointing system, the error model used in the calibration, and the calibration and pointing strategy and results are discussed.

The benefits of using remotely sensed soil moisture in parameter identification of large-scale hydrological models

NASA Astrophysics Data System (ADS)

Karssenberg, D.; Wanders, N.; de Roo, A.; de Jong, S.; Bierkens, M. F.

2013-12-01

Large-scale hydrological models are nowadays mostly calibrated using observed discharge. As a result, a large part of the hydrological system that is not directly linked to discharge, in particular the unsaturated zone, remains uncalibrated, or might be modified unrealistically. Soil moisture observations from satellites have the potential to fill this gap, as these provide the closest thing to a direct measurement of the state of the unsaturated zone, and thus are potentially useful in calibrating unsaturated zone model parameters. This is expected to result in a better identification of the complete hydrological system, potentially leading to improved forecasts of the hydrograph as well. Here we evaluate this added value of remotely sensed soil moisture in calibration of large-scale hydrological models by addressing two research questions: 1) Which parameters of hydrological models can be identified by calibration with remotely sensed soil moisture? 2) Does calibration with remotely sensed soil moisture lead to an improved calibration of hydrological models compared to approaches that calibrate only with discharge, such that this leads to improved forecasts of soil moisture content and discharge as well? To answer these questions we use a dual state and parameter ensemble Kalman filter to calibrate the hydrological model LISFLOOD for the Upper Danube area. Calibration is done with discharge and remotely sensed soil moisture acquired by AMSR-E, SMOS and ASCAT. Four scenarios are studied: no calibration (expert knowledge), calibration on discharge, calibration on remote sensing data (three satellites) and calibration on both discharge and remote sensing data. Using a split-sample approach, the model is calibrated for a period of 2 years and validated for the calibrated model parameters on a validation period of 10 years. Results show that calibration with discharge data improves the estimation of groundwater parameters (e.g., groundwater reservoir constant) and routing parameters. Calibration with only remotely sensed soil moisture results in an accurate calibration of parameters related to land surface process (e.g., the saturated conductivity of the soil), which is not possible when calibrating on discharge alone. For the upstream area up to 40000 km2, calibration on both discharge and soil moisture results in a reduction by 10-30 % in the RMSE for discharge simulations, compared to calibration on discharge alone. For discharge in the downstream area, the model performance due to assimilation of remotely sensed soil moisture is not increased or slightly decreased, most probably due to the longer relative importance of the routing and contribution of groundwater in downstream areas. When microwave soil moisture is used for calibration the RMSE of soil moisture simulations decreases from 0.072 m3m-3 to 0.062 m3m-3. The conclusion is that remotely sensed soil moisture holds potential for calibration of hydrological models leading to a better simulation of soil moisture content throughout and a better simulation of discharge in upstream areas, particularly if discharge observations are sparse.

Summary of Terra and Aqua MODIS Long-Term Performance

NASA Technical Reports Server (NTRS)

Xiong, Xiaoxiong (Jack); Wenny, Brian N.; Angal, Amit; Barnes, William; Salomonson, Vincent

2011-01-01

Since launch in December 1999, the MODIS ProtoFlight Model (PFM) onboard the Terra spacecraft has successfully operated for more than 11 years. Its Flight Model (FM) onboard the Aqua spacecraft, launched in May 2002, has also successfully operated for over 9 years. MODIS observations are made in 36 spectral bands at three nadir spatial resolutions and are calibrated and characterized regularly by a set of on-board calibrators (OBC). Nearly 40 science products, supporting a variety of land, ocean, and atmospheric applications, are continuously derived from the calibrated reflectances and radiances of each MODIS instrument and widely distributed to the world-wide user community. Following an overview of MODIS instrument operation and calibration activities, this paper provides a summary of both Terra and Aqua MODIS long-term performance. Special considerations that are critical to maintaining MODIS data quality and beneficial for future missions are also discussed.

Some advances in experimentation supporting development of viscoplastic constitutive models

NASA Technical Reports Server (NTRS)

Ellis, J. R.; Robinson, D. N.

1985-01-01

The development of a biaxial extensometer capable of measuring axial, torsion, and diametral strains to near-microstrain resolution at elevated temperatures is discussed. An instrument with this capability was needed to provide experimental support to the development of viscoplastic constitutive models. The advantages gained when torsional loading is used to investigate inelastic material response at elevated temperatures are highlighted. The development of the biaxial extensometer was conducted in two stages. The first involved a series of bench calibration experiments performed at room temperature. The second stage involved a series of in-place calibration experiments conducted at room and elevated temperature. A review of the calibration data indicated that all performance requirements regarding resolution, range, stability, and crosstalk had been met by the subject instrument over the temperature range of interest, 21 C to 651 C. The scope of the in-place calibration experiments was expanded to investigate the feasibility of generating stress relaxation data under torsional loading.

A Bayesian procedure for evaluating the frequency of calibration factor updates in highway safety manual (HSM) applications.

PubMed

Saha, Dibakar; Alluri, Priyanka; Gan, Albert

2017-01-01

The Highway Safety Manual (HSM) presents statistical models to quantitatively estimate an agency's safety performance. The models were developed using data from only a few U.S. states. To account for the effects of the local attributes and temporal factors on crash occurrence, agencies are required to calibrate the HSM-default models for crash predictions. The manual suggests updating calibration factors every two to three years, or preferably on an annual basis. Given that the calibration process involves substantial time, effort, and resources, a comprehensive analysis of the required calibration factor update frequency is valuable to the agencies. Accordingly, the objective of this study is to evaluate the HSM's recommendation and determine the required frequency of calibration factor updates. A robust Bayesian estimation procedure is used to assess the variation between calibration factors computed annually, biennially, and triennially using data collected from over 2400 miles of segments and over 700 intersections on urban and suburban facilities in Florida. Bayesian model yields a posterior distribution of the model parameters that give credible information to infer whether the difference between calibration factors computed at specified intervals is credibly different from the null value which represents unaltered calibration factors between the comparison years or in other words, zero difference. The concept of the null value is extended to include the range of values that are practically equivalent to zero. Bayesian inference shows that calibration factors based on total crash frequency are required to be updated every two years in cases where the variations between calibration factors are not greater than 0.01. When the variations are between 0.01 and 0.05, calibration factors based on total crash frequency could be updated every three years. Copyright © 2016 Elsevier Ltd. All rights reserved.

A simple topography-driven, calibration-free runoff generation model

NASA Astrophysics Data System (ADS)

Gao, H.; Birkel, C.; Hrachowitz, M.; Tetzlaff, D.; Soulsby, C.; Savenije, H. H. G.

2017-12-01

Determining the amount of runoff generation from rainfall occupies a central place in rainfall-runoff modelling. Moreover, reading landscapes and developing calibration-free runoff generation models that adequately reflect land surface heterogeneities remains the focus of much hydrological research. In this study, we created a new method to estimate runoff generation - HAND-based Storage Capacity curve (HSC) which uses a topographic index (HAND, Height Above the Nearest Drainage) to identify hydrological similarity and partially the saturated areas of catchments. We then coupled the HSC model with the Mass Curve Technique (MCT) method to estimate root zone storage capacity (SuMax), and obtained the calibration-free runoff generation model HSC-MCT. Both the two models (HSC and HSC-MCT) allow us to estimate runoff generation and simultaneously visualize the spatial dynamic of saturated area. We tested the two models in the data-rich Bruntland Burn (BB) experimental catchment in Scotland with an unusual time series of the field-mapped saturation area extent. The models were subsequently tested in 323 MOPEX (Model Parameter Estimation Experiment) catchments in the United States. HBV and TOPMODEL were used as benchmarks. We found that the HSC performed better in reproducing the spatio-temporal pattern of the observed saturated areas in the BB catchment compared with TOPMODEL which is based on the topographic wetness index (TWI). The HSC also outperformed HBV and TOPMODEL in the MOPEX catchments for both calibration and validation. Despite having no calibrated parameters, the HSC-MCT model also performed comparably well with the calibrated HBV and TOPMODEL, highlighting the robustness of the HSC model to both describe the spatial distribution of the root zone storage capacity and the efficiency of the MCT method to estimate the SuMax. Moreover, the HSC-MCT model facilitated effective visualization of the saturated area, which has the potential to be used for broader geoscience studies beyond hydrology.

Development of a generic auto-calibration package for regional ecological modeling and application in the Central Plains of the United States

USGS Publications Warehouse

Wu, Yiping; Liu, Shuguang; Li, Zhengpeng; Dahal, Devendra; Young, Claudia J.; Schmidt, Gail L.; Liu, Jinxun; Davis, Brian; Sohl, Terry L.; Werner, Jeremy M.; Oeding, Jennifer

2014-01-01

Process-oriented ecological models are frequently used for predicting potential impacts of global changes such as climate and land-cover changes, which can be useful for policy making. It is critical but challenging to automatically derive optimal parameter values at different scales, especially at regional scale, and validate the model performance. In this study, we developed an automatic calibration (auto-calibration) function for a well-established biogeochemical model—the General Ensemble Biogeochemical Modeling System (GEMS)-Erosion Deposition Carbon Model (EDCM)—using data assimilation technique: the Shuffled Complex Evolution algorithm and a model-inversion R package—Flexible Modeling Environment (FME). The new functionality can support multi-parameter and multi-objective auto-calibration of EDCM at the both pixel and regional levels. We also developed a post-processing procedure for GEMS to provide options to save the pixel-based or aggregated county-land cover specific parameter values for subsequent simulations. In our case study, we successfully applied the updated model (EDCM-Auto) for a single crop pixel with a corn–wheat rotation and a large ecological region (Level II)—Central USA Plains. The evaluation results indicate that EDCM-Auto is applicable at multiple scales and is capable to handle land cover changes (e.g., crop rotations). The model also performs well in capturing the spatial pattern of grain yield production for crops and net primary production (NPP) for other ecosystems across the region, which is a good example for implementing calibration and validation of ecological models with readily available survey data (grain yield) and remote sensing data (NPP) at regional and national levels. The developed platform for auto-calibration can be readily expanded to incorporate other model inversion algorithms and potential R packages, and also be applied to other ecological models.

Efficient Calibration of Distributed Catchment Models Using Perceptual Understanding and Hydrologic Signatures

NASA Astrophysics Data System (ADS)

Hutton, C.; Wagener, T.; Freer, J. E.; Duffy, C.; Han, D.

2015-12-01

Distributed models offer the potential to resolve catchment systems in more detail, and therefore simulate the hydrological impacts of spatial changes in catchment forcing (e.g. landscape change). Such models may contain a large number of model parameters which are computationally expensive to calibrate. Even when calibration is possible, insufficient data can result in model parameter and structural equifinality. In order to help reduce the space of feasible models and supplement traditional outlet discharge calibration data, semi-quantitative information (e.g. knowledge of relative groundwater levels), may also be used to identify behavioural models when applied to constrain spatially distributed predictions of states and fluxes. The challenge is to combine these different sources of information together to identify a behavioural region of state-space, and efficiently search a large, complex parameter space to identify behavioural parameter sets that produce predictions that fall within this behavioural region. Here we present a methodology to incorporate different sources of data to efficiently calibrate distributed catchment models. Metrics of model performance may be derived from multiple sources of data (e.g. perceptual understanding and measured or regionalised hydrologic signatures). For each metric, an interval or inequality is used to define the behaviour of the catchment system, accounting for data uncertainties. These intervals are then combined to produce a hyper-volume in state space. The state space is then recast as a multi-objective optimisation problem, and the Borg MOEA is applied to first find, and then populate the hyper-volume, thereby identifying acceptable model parameter sets. We apply the methodology to calibrate the PIHM model at Plynlimon, UK by incorporating perceptual and hydrologic data into the calibration problem. Furthermore, we explore how to improve calibration efficiency through search initialisation from shorter model runs.

Evaluation of chiller modeling approaches and their usability for fault detection

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

Sreedharan, Priya

Selecting the model is an important and essential step in model based fault detection and diagnosis (FDD). Several factors must be considered in model evaluation, including accuracy, training data requirements, calibration effort, generality, and computational requirements. All modeling approaches fall somewhere between pure first-principles models, and empirical models. The objective of this study was to evaluate different modeling approaches for their applicability to model based FDD of vapor compression air conditioning units, which are commonly known as chillers. Three different models were studied: two are based on first-principles and the third is empirical in nature. The first-principles models are themore » Gordon and Ng Universal Chiller model (2nd generation), and a modified version of the ASHRAE Primary Toolkit model, which are both based on first principles. The DOE-2 chiller model as implemented in CoolTools{trademark} was selected for the empirical category. The models were compared in terms of their ability to reproduce the observed performance of an older chiller operating in a commercial building, and a newer chiller in a laboratory. The DOE-2 and Gordon-Ng models were calibrated by linear regression, while a direct-search method was used to calibrate the Toolkit model. The ''CoolTools'' package contains a library of calibrated DOE-2 curves for a variety of different chillers, and was used to calibrate the building chiller to the DOE-2 model. All three models displayed similar levels of accuracy. Of the first principles models, the Gordon-Ng model has the advantage of being linear in the parameters, which allows more robust parameter estimation methods to be used and facilitates estimation of the uncertainty in the parameter values. The ASHRAE Toolkit Model may have advantages when refrigerant temperature measurements are also available. The DOE-2 model can be expected to have advantages when very limited data are available to calibrate the model, as long as one of the previously identified models in the CoolTools library matches the performance of the chiller in question.« less

Model-as-a-service (MaaS) using the cloud service innovation platform (CSIP)

USDA-ARS?s Scientific Manuscript database

Cloud infrastructures for modelling activities such as data processing, performing environmental simulations, or conducting model calibrations/optimizations provide a cost effective alternative to traditional high performance computing approaches. Cloud-based modelling examples emerged into the more...

How does higher frequency monitoring data affect the calibration of a process-based water quality model?

NASA Astrophysics Data System (ADS)

Jackson-Blake, Leah; Helliwell, Rachel

2015-04-01

Process-based catchment water quality models are increasingly used as tools to inform land management. However, for such models to be reliable they need to be well calibrated and shown to reproduce key catchment processes. Calibration can be challenging for process-based models, which tend to be complex and highly parameterised. Calibrating a large number of parameters generally requires a large amount of monitoring data, spanning all hydrochemical conditions. However, regulatory agencies and research organisations generally only sample at a fortnightly or monthly frequency, even in well-studied catchments, often missing peak flow events. The primary aim of this study was therefore to investigate how the quality and uncertainty of model simulations produced by a process-based, semi-distributed catchment model, INCA-P (the INtegrated CAtchment model of Phosphorus dynamics), were improved by calibration to higher frequency water chemistry data. Two model calibrations were carried out for a small rural Scottish catchment: one using 18 months of daily total dissolved phosphorus (TDP) concentration data, another using a fortnightly dataset derived from the daily data. To aid comparability, calibrations were carried out automatically using the Markov Chain Monte Carlo - DiffeRential Evolution Adaptive Metropolis (MCMC-DREAM) algorithm. Calibration to daily data resulted in improved simulation of peak TDP concentrations and improved model performance statistics. Parameter-related uncertainty in simulated TDP was large when fortnightly data was used for calibration, with a 95% credible interval of 26 μg/l. This uncertainty is comparable in size to the difference between Water Framework Directive (WFD) chemical status classes, and would therefore make it difficult to use this calibration to predict shifts in WFD status. The 95% credible interval reduced markedly with the higher frequency monitoring data, to 6 μg/l. The number of parameters that could be reliably auto-calibrated was lower for the fortnightly data, with a physically unrealistic TDP simulation being produced when too many parameters were allowed to vary during model calibration. Parameters should not therefore be varied spatially for models such as INCA-P unless there is solid evidence that this is appropriate, or there is a real need to do so for the model to fulfil its purpose. This study highlights the potential pitfalls of using low frequency timeseries of observed water quality to calibrate complex process-based models. For reliable model calibrations to be produced, monitoring programmes need to be designed which capture system variability, in particular nutrient dynamics during high flow events. In addition, there is a need for simpler models, so that all model parameters can be included in auto-calibration and uncertainty analysis, and to reduce the data needs during calibration.

Multisite Evaluation of APEX for Water Quality: II. Regional Parameterization.

PubMed

Nelson, Nathan O; Baffaut, Claire; Lory, John A; Anomaa Senaviratne, G M M M; Bhandari, Ammar B; Udawatta, Ranjith P; Sweeney, Daniel W; Helmers, Matt J; Van Liew, Mike W; Mallarino, Antonio P; Wortmann, Charles S

2017-11-01

Phosphorus (P) Index assessment requires independent estimates of long-term average annual P loss from fields, representing multiple climatic scenarios, management practices, and landscape positions. Because currently available measured data are insufficient to evaluate P Index performance, calibrated and validated process-based models have been proposed as tools to generate the required data. The objectives of this research were to develop a regional parameterization for the Agricultural Policy Environmental eXtender (APEX) model to estimate edge-of-field runoff, sediment, and P losses in restricted-layer soils of Missouri and Kansas and to assess the performance of this parameterization using monitoring data from multiple sites in this region. Five site-specific calibrated models (SSCM) from within the region were used to develop a regionally calibrated model (RCM), which was further calibrated and validated with measured data. Performance of the RCM was similar to that of the SSCMs for runoff simulation and had Nash-Sutcliffe efficiency (NSE) > 0.72 and absolute percent bias (|PBIAS|) < 18% for both calibration and validation. The RCM could not simulate sediment loss (NSE < 0, |PBIAS| > 90%) and was particularly ineffective at simulating sediment loss from locations with small sediment loads. The RCM had acceptable performance for simulation of total P loss (NSE > 0.74, |PBIAS| < 30%) but underperformed the SSCMs. Total P-loss estimates should be used with caution due to poor simulation of sediment loss. Although we did not attain our goal of a robust regional parameterization of APEX for estimating sediment and total P losses, runoff estimates with the RCM were acceptable for P Index evaluation. Copyright © by the American Society of Agronomy, Crop Science Society of America, and Soil Science Society of America, Inc.

Effect of experimental design on the prediction performance of calibration models based on near-infrared spectroscopy for pharmaceutical applications.

PubMed

Bondi, Robert W; Igne, Benoît; Drennen, James K; Anderson, Carl A

2012-12-01

Near-infrared spectroscopy (NIRS) is a valuable tool in the pharmaceutical industry, presenting opportunities for online analyses to achieve real-time assessment of intermediates and finished dosage forms. The purpose of this work was to investigate the effect of experimental designs on prediction performance of quantitative models based on NIRS using a five-component formulation as a model system. The following experimental designs were evaluated: five-level, full factorial (5-L FF); three-level, full factorial (3-L FF); central composite; I-optimal; and D-optimal. The factors for all designs were acetaminophen content and the ratio of microcrystalline cellulose to lactose monohydrate. Other constituents included croscarmellose sodium and magnesium stearate (content remained constant). Partial least squares-based models were generated using data from individual experimental designs that related acetaminophen content to spectral data. The effect of each experimental design was evaluated by determining the statistical significance of the difference in bias and standard error of the prediction for that model's prediction performance. The calibration model derived from the I-optimal design had similar prediction performance as did the model derived from the 5-L FF design, despite containing 16 fewer design points. It also outperformed all other models estimated from designs with similar or fewer numbers of samples. This suggested that experimental-design selection for calibration-model development is critical, and optimum performance can be achieved with efficient experimental designs (i.e., optimal designs).

A Novel Protocol for Model Calibration in Biological Wastewater Treatment

PubMed Central

Zhu, Ao; Guo, Jianhua; Ni, Bing-Jie; Wang, Shuying; Yang, Qing; Peng, Yongzhen

2015-01-01

Activated sludge models (ASMs) have been widely used for process design, operation and optimization in wastewater treatment plants. However, it is still a challenge to achieve an efficient calibration for reliable application by using the conventional approaches. Hereby, we propose a novel calibration protocol, i.e. Numerical Optimal Approaching Procedure (NOAP), for the systematic calibration of ASMs. The NOAP consists of three key steps in an iterative scheme flow: i) global factors sensitivity analysis for factors fixing; ii) pseudo-global parameter correlation analysis for non-identifiable factors detection; and iii) formation of a parameter subset through an estimation by using genetic algorithm. The validity and applicability are confirmed using experimental data obtained from two independent wastewater treatment systems, including a sequencing batch reactor and a continuous stirred-tank reactor. The results indicate that the NOAP can effectively determine the optimal parameter subset and successfully perform model calibration and validation for these two different systems. The proposed NOAP is expected to use for automatic calibration of ASMs and be applied potentially to other ordinary differential equations models. PMID:25682959

Utilization of Expert Knowledge in a Multi-Objective Hydrologic Model Automatic Calibration Process

NASA Astrophysics Data System (ADS)

Quebbeman, J.; Park, G. H.; Carney, S.; Day, G. N.; Micheletty, P. D.

2016-12-01

Spatially distributed continuous simulation hydrologic models have a large number of parameters for potential adjustment during the calibration process. Traditional manual calibration approaches of such a modeling system is extremely laborious, which has historically motivated the use of automatic calibration procedures. With a large selection of model parameters, achieving high degrees of objective space fitness - measured with typical metrics such as Nash-Sutcliffe, Kling-Gupta, RMSE, etc. - can easily be achieved using a range of evolutionary algorithms. A concern with this approach is the high degree of compensatory calibration, with many similarly performing solutions, and yet grossly varying parameter set solutions. To help alleviate this concern, and mimic manual calibration processes, expert knowledge is proposed for inclusion within the multi-objective functions, which evaluates the parameter decision space. As a result, Pareto solutions are identified with high degrees of fitness, but also create parameter sets that maintain and utilize available expert knowledge resulting in more realistic and consistent solutions. This process was tested using the joint SNOW-17 and Sacramento Soil Moisture Accounting method (SAC-SMA) within the Animas River basin in Colorado. Three different elevation zones, each with a range of parameters, resulted in over 35 model parameters simultaneously calibrated. As a result, high degrees of fitness were achieved, in addition to the development of more realistic and consistent parameter sets such as those typically achieved during manual calibration procedures.

Management of groundwater in-situ bioremediation system using reactive transport modelling under parametric uncertainty: field scale application

NASA Astrophysics Data System (ADS)

Verardo, E.; Atteia, O.; Rouvreau, L.

2015-12-01

In-situ bioremediation is a commonly used remediation technology to clean up the subsurface of petroleum-contaminated sites. Forecasting remedial performance (in terms of flux and mass reduction) is a challenge due to uncertainties associated with source properties and the uncertainties associated with contribution and efficiency of concentration reducing mechanisms. In this study, predictive uncertainty analysis of bio-remediation system efficiency is carried out with the null-space Monte Carlo (NSMC) method which combines the calibration solution-space parameters with the ensemble of null-space parameters, creating sets of calibration-constrained parameters for input to follow-on remedial efficiency. The first step in the NSMC methodology for uncertainty analysis is model calibration. The model calibration was conducted by matching simulated BTEX concentration to a total of 48 observations from historical data before implementation of treatment. Two different bio-remediation designs were then implemented in the calibrated model. The first consists in pumping/injection wells and the second in permeable barrier coupled with infiltration across slotted piping. The NSMC method was used to calculate 1000 calibration-constrained parameter sets for the two different models. Several variants of the method were implemented to investigate their effect on the efficiency of the NSMC method. The first variant implementation of the NSMC is based on a single calibrated model. In the second variant, models were calibrated from different initial parameter sets. NSMC calibration-constrained parameter sets were sampled from these different calibrated models. We demonstrate that in context of nonlinear model, second variant avoids to underestimate parameter uncertainty which may lead to a poor quantification of predictive uncertainty. Application of the proposed approach to manage bioremediation of groundwater in a real site shows that it is effective to provide support in management of the in-situ bioremediation systems. Moreover, this study demonstrates that the NSMC method provides a computationally efficient and practical methodology of utilizing model predictive uncertainty methods in environmental management.

Optical System Error Analysis and Calibration Method of High-Accuracy Star Trackers

PubMed Central

Sun, Ting; Xing, Fei; You, Zheng

2013-01-01

The star tracker is a high-accuracy attitude measurement device widely used in spacecraft. Its performance depends largely on the precision of the optical system parameters. Therefore, the analysis of the optical system parameter errors and a precise calibration model are crucial to the accuracy of the star tracker. Research in this field is relatively lacking a systematic and universal analysis up to now. This paper proposes in detail an approach for the synthetic error analysis of the star tracker, without the complicated theoretical derivation. This approach can determine the error propagation relationship of the star tracker, and can build intuitively and systematically an error model. The analysis results can be used as a foundation and a guide for the optical design, calibration, and compensation of the star tracker. A calibration experiment is designed and conducted. Excellent calibration results are achieved based on the calibration model. To summarize, the error analysis approach and the calibration method are proved to be adequate and precise, and could provide an important guarantee for the design, manufacture, and measurement of high-accuracy star trackers. PMID:23567527

Optimized star sensors laboratory calibration method using a regularization neural network.

PubMed

Zhang, Chengfen; Niu, Yanxiong; Zhang, Hao; Lu, Jiazhen

2018-02-10

High-precision ground calibration is essential to ensure the performance of star sensors. However, the complex distortion and multi-error coupling have brought great difficulties to traditional calibration methods, especially for large field of view (FOV) star sensors. Although increasing the complexity of models is an effective way to improve the calibration accuracy, it significantly increases the demand for calibration data. In order to achieve high-precision calibration of star sensors with large FOV, a novel laboratory calibration method based on a regularization neural network is proposed. A multi-layer structure neural network is designed to represent the mapping of the star vector and the corresponding star point coordinate directly. To ensure the generalization performance of the network, regularization strategies are incorporated into the net structure and the training algorithm. Simulation and experiment results demonstrate that the proposed method can achieve high precision with less calibration data and without any other priori information. Compared with traditional methods, the calibration error of the star sensor decreased by about 30%. The proposed method can satisfy the precision requirement for large FOV star sensors.

Pattern-Based Inverse Modeling for Characterization of Subsurface Flow Models with Complex Geologic Heterogeneity

NASA Astrophysics Data System (ADS)

Golmohammadi, A.; Jafarpour, B.; M Khaninezhad, M. R.

2017-12-01

Calibration of heterogeneous subsurface flow models leads to ill-posed nonlinear inverse problems, where too many unknown parameters are estimated from limited response measurements. When the underlying parameters form complex (non-Gaussian) structured spatial connectivity patterns, classical variogram-based geostatistical techniques cannot describe the underlying connectivity patterns. Modern pattern-based geostatistical methods that incorporate higher-order spatial statistics are more suitable for describing such complex spatial patterns. Moreover, when the underlying unknown parameters are discrete (geologic facies distribution), conventional model calibration techniques that are designed for continuous parameters cannot be applied directly. In this paper, we introduce a novel pattern-based model calibration method to reconstruct discrete and spatially complex facies distributions from dynamic flow response data. To reproduce complex connectivity patterns during model calibration, we impose a feasibility constraint to ensure that the solution follows the expected higher-order spatial statistics. For model calibration, we adopt a regularized least-squares formulation, involving data mismatch, pattern connectivity, and feasibility constraint terms. Using an alternating directions optimization algorithm, the regularized objective function is divided into a continuous model calibration problem, followed by mapping the solution onto the feasible set. The feasibility constraint to honor the expected spatial statistics is implemented using a supervised machine learning algorithm. The two steps of the model calibration formulation are repeated until the convergence criterion is met. Several numerical examples are used to evaluate the performance of the developed method.

Calibration transfer of a Raman spectroscopic quantification method for the assessment of liquid detergent compositions between two at-line instruments installed at two liquid detergent production plants.

PubMed

Brouckaert, D; Uyttersprot, J-S; Broeckx, W; De Beer, T

2017-09-01

Calibration transfer of partial least squares (PLS) quantification models is established between two Raman spectrometers located at two liquid detergent production plants. As full recalibration of existing calibration models is time-consuming, labour-intensive and costly, it is investigated whether the use of mathematical correction methods requiring only a handful of standardization samples can overcome the dissimilarities in spectral response observed between both measurement systems. Univariate and multivariate standardization approaches are investigated, ranging from simple slope/bias correction (SBC), local centring (LC) and single wavelength standardization (SWS) to more complex direct standardization (DS) and piecewise direct standardization (PDS). The results of these five calibration transfer methods are compared reciprocally, as well as with regard to a full recalibration. Four PLS quantification models, each predicting the concentration of one of the four main ingredients in the studied liquid detergent composition, are aimed at transferring. Accuracy profiles are established from the original and transferred quantification models for validation purposes. A reliable representation of the calibration models performance before and after transfer is thus established, based on β-expectation tolerance intervals. For each transferred model, it is investigated whether every future measurement that will be performed in routine will be close enough to the unknown true value of the sample. From this validation, it is concluded that instrument standardization is successful for three out of four investigated calibration models using multivariate (DS and PDS) transfer approaches. The fourth transferred PLS model could not be validated over the investigated concentration range, due to a lack of precision of the slave instrument. Comparing these transfer results to a full recalibration on the slave instrument allows comparison of the predictive power of both Raman systems and leads to the formulation of guidelines for further standardization projects. It is concluded that it is essential to evaluate the performance of the slave instrument prior to transfer, even when it is theoretically identical to the master apparatus. Copyright © 2017 Elsevier B.V. All rights reserved.

Global Sensitivity Analysis and Parameter Calibration for an Ecosystem Carbon Model

NASA Astrophysics Data System (ADS)

Safta, C.; Ricciuto, D. M.; Sargsyan, K.; Najm, H. N.; Debusschere, B.; Thornton, P. E.

2013-12-01

We present uncertainty quantification results for a process-based ecosystem carbon model. The model employs 18 parameters and is driven by meteorological data corresponding to years 1992-2006 at the Harvard Forest site. Daily Net Ecosystem Exchange (NEE) observations were available to calibrate the model parameters and test the performance of the model. Posterior distributions show good predictive capabilities for the calibrated model. A global sensitivity analysis was first performed to determine the important model parameters based on their contribution to the variance of NEE. We then proceed to calibrate the model parameters in a Bayesian framework. The daily discrepancies between measured and predicted NEE values were modeled as independent and identically distributed Gaussians with prescribed daily variance according to the recorded instrument error. All model parameters were assumed to have uninformative priors with bounds set according to expert opinion. The global sensitivity results show that the rate of leaf fall (LEAFALL) is responsible for approximately 25% of the total variance in the average NEE for 1992-2005. A set of 4 other parameters, Nitrogen use efficiency (NUE), base rate for maintenance respiration (BR_MR), growth respiration fraction (RG_FRAC), and allocation to plant stem pool (ASTEM) contribute between 5% and 12% to the variance in average NEE, while the rest of the parameters have smaller contributions. The posterior distributions, sampled with a Markov Chain Monte Carlo algorithm, exhibit significant correlations between model parameters. However LEAFALL, the most important parameter for the average NEE, is not informed by the observational data, while less important parameters show significant updates between their prior and posterior densities. The Fisher information matrix values, indicating which parameters are most informed by the experimental observations, are examined to augment the comparison between the calibration and global sensitivity analysis results.

A Monte-Carlo simulation analysis for evaluating the severity distribution functions (SDFs) calibration methodology and determining the minimum sample-size requirements.

PubMed

Shirazi, Mohammadali; Reddy Geedipally, Srinivas; Lord, Dominique

2017-01-01

Severity distribution functions (SDFs) are used in highway safety to estimate the severity of crashes and conduct different types of safety evaluations and analyses. Developing a new SDF is a difficult task and demands significant time and resources. To simplify the process, the Highway Safety Manual (HSM) has started to document SDF models for different types of facilities. As such, SDF models have recently been introduced for freeway and ramps in HSM addendum. However, since these functions or models are fitted and validated using data from a few selected number of states, they are required to be calibrated to the local conditions when applied to a new jurisdiction. The HSM provides a methodology to calibrate the models through a scalar calibration factor. However, the proposed methodology to calibrate SDFs was never validated through research. Furthermore, there are no concrete guidelines to select a reliable sample size. Using extensive simulation, this paper documents an analysis that examined the bias between the 'true' and 'estimated' calibration factors. It was indicated that as the value of the true calibration factor deviates further away from '1', more bias is observed between the 'true' and 'estimated' calibration factors. In addition, simulation studies were performed to determine the calibration sample size for various conditions. It was found that, as the average of the coefficient of variation (CV) of the 'KAB' and 'C' crashes increases, the analyst needs to collect a larger sample size to calibrate SDF models. Taking this observation into account, sample-size guidelines are proposed based on the average CV of crash severities that are used for the calibration process. Copyright © 2016 Elsevier Ltd. All rights reserved.

The fossilized birth–death process for coherent calibration of divergence-time estimates

PubMed Central

Heath, Tracy A.; Huelsenbeck, John P.; Stadler, Tanja

2014-01-01

Time-calibrated species phylogenies are critical for addressing a wide range of questions in evolutionary biology, such as those that elucidate historical biogeography or uncover patterns of coevolution and diversification. Because molecular sequence data are not informative on absolute time, external data—most commonly, fossil age estimates—are required to calibrate estimates of species divergence dates. For Bayesian divergence time methods, the common practice for calibration using fossil information involves placing arbitrarily chosen parametric distributions on internal nodes, often disregarding most of the information in the fossil record. We introduce the “fossilized birth–death” (FBD) process—a model for calibrating divergence time estimates in a Bayesian framework, explicitly acknowledging that extant species and fossils are part of the same macroevolutionary process. Under this model, absolute node age estimates are calibrated by a single diversification model and arbitrary calibration densities are not necessary. Moreover, the FBD model allows for inclusion of all available fossils. We performed analyses of simulated data and show that node age estimation under the FBD model results in robust and accurate estimates of species divergence times with realistic measures of statistical uncertainty, overcoming major limitations of standard divergence time estimation methods. We used this model to estimate the speciation times for a dataset composed of all living bears, indicating that the genus Ursus diversified in the Late Miocene to Middle Pliocene. PMID:25009181

Surrogate Based Uni/Multi-Objective Optimization and Distribution Estimation Methods

NASA Astrophysics Data System (ADS)

Gong, W.; Duan, Q.; Huo, X.

2017-12-01

Parameter calibration has been demonstrated as an effective way to improve the performance of dynamic models, such as hydrological models, land surface models, weather and climate models etc. Traditional optimization algorithms usually cost a huge number of model evaluations, making dynamic model calibration very difficult, or even computationally prohibitive. With the help of a serious of recently developed adaptive surrogate-modelling based optimization methods: uni-objective optimization method ASMO, multi-objective optimization method MO-ASMO, and probability distribution estimation method ASMO-PODE, the number of model evaluations can be significantly reduced to several hundreds, making it possible to calibrate very expensive dynamic models, such as regional high resolution land surface models, weather forecast models such as WRF, and intermediate complexity earth system models such as LOVECLIM. This presentation provides a brief introduction to the common framework of adaptive surrogate-based optimization algorithms of ASMO, MO-ASMO and ASMO-PODE, a case study of Common Land Model (CoLM) calibration in Heihe river basin in Northwest China, and an outlook of the potential applications of the surrogate-based optimization methods.

Calibration of a distributed hydrologic model using observed spatial patterns from MODIS data

NASA Astrophysics Data System (ADS)

Demirel, Mehmet C.; González, Gorka M.; Mai, Juliane; Stisen, Simon

2016-04-01

Distributed hydrologic models are typically calibrated against streamflow observations at the outlet of the basin. Along with these observations from gauging stations, satellite based estimates offer independent evaluation data such as remotely sensed actual evapotranspiration (aET) and land surface temperature. The primary objective of the study is to compare model calibrations against traditional downstream discharge measurements with calibrations against simulated spatial patterns and combinations of both types of observations. While the discharge based model calibration typically improves the temporal dynamics of the model, it seems to give rise to minimum improvement of the simulated spatial patterns. In contrast, objective functions specifically targeting the spatial pattern performance could potentially increase the spatial model performance. However, most modeling studies, including the model formulations and parameterization, are not designed to actually change the simulated spatial pattern during calibration. This study investigates the potential benefits of incorporating spatial patterns from MODIS data to calibrate the mesoscale hydrologic model (mHM). This model is selected as it allows for a change in the spatial distribution of key soil parameters through the optimization of pedo-transfer function parameters and includes options for using fully distributed daily Leaf Area Index (LAI) values directly as input. In addition the simulated aET can be estimated at a spatial resolution suitable for comparison to the spatial patterns observed with MODIS data. To increase our control on spatial calibration we introduced three additional parameters to the model. These new parameters are part of an empirical equation to the calculate crop coefficient (Kc) from daily LAI maps and used to update potential evapotranspiration (PET) as model inputs. This is done instead of correcting/updating PET with just a uniform (or aspect driven) factor used in the mHM model (version 5.3). We selected the 20 most important parameters out of 53 mHM parameters based on a comprehensive sensitivity analysis (Cuntz et al., 2015). We calibrated 1km-daily mHM for the Skjern basin in Denmark using the Shuffled Complex Evolution (SCE) algorithm and inputs at different spatial scales i.e. meteorological data at 10km and morphological data at 250 meters. We used correlation coefficients between observed monthly (summer months only) MODIS data calculated from cloud free days over the calibration period from 2001 to 2008 and simulated aET from mHM over the same period. Similarly other metrics, e.g mapcurves and fraction skill-score, are also included in our objective function to assess the co-location of the grid-cells. The preliminary results show that multi-objective calibration of mHM against observed streamflow and spatial patterns together does not significantly reduce the spatial errors in aET while it improves the streamflow simulations. This is a strong signal for further investigation of the multi parameter regionalization affecting spatial aET patterns and weighting the spatial metrics in the objective function relative to the streamflow metrics.

The Optical Field Angle Distortion Calibration of HST Fine Guidance Sensors 1R and 3

NASA Technical Reports Server (NTRS)

McArthur, B.; Benedict, G. F.; Jefferys, W. H.; Nelan, E.

2006-01-01

To date five OFAD (Optical Field Angle Distortion) calibrations have been performed with a star field in M35, four on FGS3 and one on FGS1, all analyzed by the Astrometry Science Team. We have recently completed an improved FGS1R OFAD calibration. The ongoing Long Term Stability Tests have also been analyzed and incorporated into these calibrations, which are time-dependent due to on-orbit changes in the FGS. Descriptions of these tests and the results of our OFAD modeling are given. Because all OFAD calibrations use the same star field, we calibrate FGS 1 and FGS 3 simultaneously. This increases the precision of our input catalog,resulting in an improvement in both the FGS 1 and FGS 3 calibrations. A redetermination of the proper motions,using 12 years of HST data has significantly improved our calibration. Residuals to our OFAD modeling indicate that FGS 1 will provide astrometry superior to FGS 3 by approx. 20%. Past and future FGS astrometric science supported by these calibrations is briefly reviewed.

How Does Higher Frequency Monitoring Data Affect the Calibration of a Process-Based Water Quality Model?

NASA Astrophysics Data System (ADS)

Jackson-Blake, L.

2014-12-01

Process-based catchment water quality models are increasingly used as tools to inform land management. However, for such models to be reliable they need to be well calibrated and shown to reproduce key catchment processes. Calibration can be challenging for process-based models, which tend to be complex and highly parameterised. Calibrating a large number of parameters generally requires a large amount of monitoring data, but even in well-studied catchments, streams are often only sampled at a fortnightly or monthly frequency. The primary aim of this study was therefore to investigate how the quality and uncertainty of model simulations produced by one process-based catchment model, INCA-P (the INtegrated CAtchment model of Phosphorus dynamics), were improved by calibration to higher frequency water chemistry data. Two model calibrations were carried out for a small rural Scottish catchment: one using 18 months of daily total dissolved phosphorus (TDP) concentration data, another using a fortnightly dataset derived from the daily data. To aid comparability, calibrations were carried out automatically using the MCMC-DREAM algorithm. Using daily rather than fortnightly data resulted in improved simulation of the magnitude of peak TDP concentrations, in turn resulting in improved model performance statistics. Marginal posteriors were better constrained by the higher frequency data, resulting in a large reduction in parameter-related uncertainty in simulated TDP (the 95% credible interval decreased from 26 to 6 μg/l). The number of parameters that could be reliably auto-calibrated was lower for the fortnightly data, leading to the recommendation that parameters should not be varied spatially for models such as INCA-P unless there is solid evidence that this is appropriate, or there is a real need to do so for the model to fulfil its purpose. Secondary study aims were to highlight the subjective elements involved in auto-calibration and suggest practical improvements that could make models such as INCA-P more suited to auto-calibration and uncertainty analyses. Two key improvements include model simplification, so that all model parameters can be included in an analysis of this kind, and better documenting of recommended ranges for each parameter, to help in choosing sensible priors.

Spatially distributed model calibration of flood inundation guided by consequences such as loss of property

NASA Astrophysics Data System (ADS)

Pappenberger, F.; Beven, K. J.; Frodsham, K.; Matgen, P.

2005-12-01

Flood inundation models play an increasingly important role in assessing flood risk. The growth of 2D inundation models that are intimately related to raster maps of floodplains is occurring at the same time as an increase in the availability of 2D remote data (e.g. SAR images and aerial photographs), against which model performancee can be evaluated. This requires new techniques to be explored in order to evaluate model performance in two dimensional space. In this paper we present a fuzzified pattern matching algorithm which compares favorably to a set of traditional measures. However, we further argue that model calibration has to go beyond the comparison of physical properties and should demonstrate how a weighting towards consequences, such as loss of property, can enhance model focus and prediction. Indeed, it will be necessary to abandon a fully spatial comparison in many scenarios to concentrate the model calibration exercise on specific points such as hospitals, police stations or emergency response centers. It can be shown that such point evaluations lead to significantly different flood hazard maps due to the averaging effect of a spatial performance measure. A strategy to balance the different needs (accuracy at certain spatial points and acceptable spatial performance) has to be based in a public and political decision making process.

The new camera calibration system at the US Geological Survey

USGS Publications Warehouse

Light, D.L.

1992-01-01

Modern computerized photogrammetric instruments are capable of utilizing both radial and decentering camera calibration parameters which can increase plotting accuracy over that of older analog instrumentation technology from previous decades. Also, recent design improvements in aerial cameras have minimized distortions and increased the resolving power of camera systems, which should improve the performance of the overall photogrammetric process. In concert with these improvements, the Geological Survey has adopted the rigorous mathematical model for camera calibration developed by Duane Brown. An explanation of the Geological Survey's calibration facility and the additional calibration parameters now being provided in the USGS calibration certificate are reviewed. -Author

Linear model correction: A method for transferring a near-infrared multivariate calibration model without standard samples.

PubMed

Liu, Yan; Cai, Wensheng; Shao, Xueguang

2016-12-05

Calibration transfer is essential for practical applications of near infrared (NIR) spectroscopy because the measurements of the spectra may be performed on different instruments and the difference between the instruments must be corrected. For most of calibration transfer methods, standard samples are necessary to construct the transfer model using the spectra of the samples measured on two instruments, named as master and slave instrument, respectively. In this work, a method named as linear model correction (LMC) is proposed for calibration transfer without standard samples. The method is based on the fact that, for the samples with similar physical and chemical properties, the spectra measured on different instruments are linearly correlated. The fact makes the coefficients of the linear models constructed by the spectra measured on different instruments are similar in profile. Therefore, by using the constrained optimization method, the coefficients of the master model can be transferred into that of the slave model with a few spectra measured on slave instrument. Two NIR datasets of corn and plant leaf samples measured with different instruments are used to test the performance of the method. The results show that, for both the datasets, the spectra can be correctly predicted using the transferred partial least squares (PLS) models. Because standard samples are not necessary in the method, it may be more useful in practical uses. Copyright © 2016 Elsevier B.V. All rights reserved.

High-Reynolds Number Active Blowing Semi-Span Force Measurement System Development

NASA Technical Reports Server (NTRS)

Lynn, Keith C.; Rhew, Ray D.; Acheson, Michael J.; Jones, Gregory S.; Milholen, William E.; Goodliff, Scott L.

2012-01-01

Recent wind-tunnel tests at the NASA Langley Research Center National Transonic Facility utilized high-pressure bellows to route air to the model for evaluating aircraft circulation control. The introduction of these bellows within the Sidewall Model Support System significantly impacted the performance of the external sidewall mounted semi-span balance. As a result of this impact on the semi-span balance measurement performance, it became apparent that a new capability needed to be built into the National Transonic Facility s infrastructure to allow for performing pressure tare calibrations on the balance in order to properly characterize its performance under the influence of static bellows pressure tare loads and bellows thermal effects. The objective of this study was to design both mechanical calibration hardware and an experimental calibration design that can be employed at the facility in order to efficiently and precisely perform the necessary loadings in order to characterize the semi-span balance under the influence of multiple calibration factors (balance forces/moments and bellows pressure/temperature). Using statistical design of experiments, an experimental design was developed allowing for strategically characterizing the behavior of the semi-span balance for use in circulation control and propulsion-type flow control testing at the National Transonic Facility.

On the importance of geological data for hydraulic tomography analysis: Laboratory sandbox study

NASA Astrophysics Data System (ADS)

Zhao, Zhanfeng; Illman, Walter A.; Berg, Steven J.

2016-11-01

This paper investigates the importance of geological data in Hydraulic Tomography (HT) through sandbox experiments. In particular, four groundwater models with homogeneous geological units constructed with borehole data of varying accuracy are jointly calibrated with multiple pumping test data of two different pumping and observation densities. The results are compared to those from a geostatistical inverse model. Model calibration and validation performances are quantitatively assessed using drawdown scatterplots. We find that accurate and inaccurate geological models can be well calibrated, despite the estimated K values for the poor geological models being quite different from the actual values. Model validation results reveal that inaccurate geological models yield poor drawdown predictions, but using more calibration data improves its predictive capability. Moreover, model comparisons among a highly parameterized geostatistical and layer-based geological models show that, (1) as the number of pumping tests and monitoring locations are reduced, the performance gap between the approaches decreases, and (2) a simplified geological model with a fewer number of layers is more reliable than the one based on the wrong description of stratigraphy. Finally, using a geological model as prior information in geostatistical inverse models results in the preservation of geological features, especially in areas where drawdown data are not available. Overall, our sandbox results emphasize the importance of incorporating geological data in HT surveys when data from pumping tests is sparse. These findings have important implications for field applications of HT where well distances are large.

The calibration of a model for simulating the thermal and electrical performance of a 2.8 kW AC solid-oxide fuel cell micro-cogeneration device

NASA Astrophysics Data System (ADS)

Beausoleil-Morrison, Ian; Lombardi, Kathleen

The concurrent production of heat and electricity within residential buildings using solid-oxide fuel cell (SOFC) micro-cogeneration devices has the potential to reduce primary energy consumption, greenhouse gas emissions, and air pollutants. A realistic assessment of this emerging technology requires the accurate simulation of the thermal and electrical production of SOFC micro-cogeneration devices concurrent with the simulation of the building, its occupants, and coupled plant components. The calibration of such a model using empirical data gathered from experiments conducted with a 2.8 kW AC SOFC micro-cogeneration device is demonstrated. The experimental configuration, types of instrumentation employed, and the operating scenarios examined are treated. The propagation of measurement uncertainty into the derived quantities that are necessary for model calibration are demonstrated by focusing upon the SOFC micro-cogeneration system's gas-to-water heat exchanger. The calibration coefficients necessary to accurately simulate the thermal and electrical performance of this prototype device are presented and the types of analyses enabled to study the potential of the technology are demonstrated.

Sample classification for improved performance of PLS models applied to the quality control of deep-frying oils of different botanic origins analyzed using ATR-FTIR spectroscopy.

PubMed

Kuligowski, Julia; Carrión, David; Quintás, Guillermo; Garrigues, Salvador; de la Guardia, Miguel

2011-01-01

The selection of an appropriate calibration set is a critical step in multivariate method development. In this work, the effect of using different calibration sets, based on a previous classification of unknown samples, on the partial least squares (PLS) regression model performance has been discussed. As an example, attenuated total reflection (ATR) mid-infrared spectra of deep-fried vegetable oil samples from three botanical origins (olive, sunflower, and corn oil), with increasing polymerized triacylglyceride (PTG) content induced by a deep-frying process were employed. The use of a one-class-classifier partial least squares-discriminant analysis (PLS-DA) and a rooted binary directed acyclic graph tree provided accurate oil classification. Oil samples fried without foodstuff could be classified correctly, independent of their PTG content. However, class separation of oil samples fried with foodstuff, was less evident. The combined use of double-cross model validation with permutation testing was used to validate the obtained PLS-DA classification models, confirming the results. To discuss the usefulness of the selection of an appropriate PLS calibration set, the PTG content was determined by calculating a PLS model based on the previously selected classes. In comparison to a PLS model calculated using a pooled calibration set containing samples from all classes, the root mean square error of prediction could be improved significantly using PLS models based on the selected calibration sets using PLS-DA, ranging between 1.06 and 2.91% (w/w).

Nondestructive evaluation of soluble solid content in strawberry by near infrared spectroscopy

NASA Astrophysics Data System (ADS)

Guo, Zhiming; Huang, Wenqian; Chen, Liping; Wang, Xiu; Peng, Yankun

This paper indicates the feasibility to use near infrared (NIR) spectroscopy combined with synergy interval partial least squares (siPLS) algorithms as a rapid nondestructive method to estimate the soluble solid content (SSC) in strawberry. Spectral preprocessing methods were optimized selected by cross-validation in the model calibration. Partial least squares (PLS) algorithm was conducted on the calibration of regression model. The performance of the final model was back-evaluated according to root mean square error of calibration (RMSEC) and correlation coefficient (R2 c) in calibration set, and tested by mean square error of prediction (RMSEP) and correlation coefficient (R2 p) in prediction set. The optimal siPLS model was obtained with after first derivation spectra preprocessing. The measurement results of best model were achieved as follow: RMSEC = 0.2259, R2 c = 0.9590 in the calibration set; and RMSEP = 0.2892, R2 p = 0.9390 in the prediction set. This work demonstrated that NIR spectroscopy and siPLS with efficient spectral preprocessing is a useful tool for nondestructively evaluation SSC in strawberry.

A Robust Bayesian Random Effects Model for Nonlinear Calibration Problems

PubMed Central

Fong, Y.; Wakefield, J.; De Rosa, S.; Frahm, N.

2013-01-01

Summary In the context of a bioassay or an immunoassay, calibration means fitting a curve, usually nonlinear, through the observations collected on a set of samples containing known concentrations of a target substance, and then using the fitted curve and observations collected on samples of interest to predict the concentrations of the target substance in these samples. Recent technological advances have greatly improved our ability to quantify minute amounts of substance from a tiny volume of biological sample. This has in turn led to a need to improve statistical methods for calibration. In this paper, we focus on developing calibration methods robust to dependent outliers. We introduce a novel normal mixture model with dependent error terms to model the experimental noise. In addition, we propose a re-parameterization of the five parameter logistic nonlinear regression model that allows us to better incorporate prior information. We examine the performance of our methods with simulation studies and show that they lead to a substantial increase in performance measured in terms of mean squared error of estimation and a measure of the average prediction accuracy. A real data example from the HIV Vaccine Trials Network Laboratory is used to illustrate the methods. PMID:22551415

On-ground re-calibration of the GOME-2 satellite spectrometer series

NASA Astrophysics Data System (ADS)

Otter, Gerard; Dijkhuizen, Niels; Vosteen, Amir; Brinkers, Sanneke; Gür, Bilgehan; Kenter, Pepijn; Sallusti, Marcello; Tomuta, Dana; Veratti, Rubes; Cappani, Annalisa

2017-11-01

The Global Ozone Monitoring Experiment-2[1] (GOME-2) represents one of the European instruments carried on board the MetOp satellite within the ESA's "Living Planet Program". Consisting of three flight models (FM's) it is intended to provide long-term monitoring of atmospheric ozone and other trace gases over a time frame of 15-20 years, thus contributing valuable input towards climate and atmospheric research and providing near real-time data for use in air quality forecasting. The ambition to achieve highly accurate scientific results requires a thorough calibration and characterization of the instrument prior to launch. These calibration campaigns were performed by TNO in Delft in the Netherlands, in the "Thermal Vacuum Calibration Facility" of the institute. Due to refurbishment and / or storage of the instruments over a period of a few years, several re-calibration campaigns were necessary. These re-calibrations provided the unique opportunity to study the effects of long term storage and build up statistics on the instrument as well as the calibration methods used. During the re-calibration of the second flight model a difference was found in the radiometric calibration output, which was not understood initially. In order to understand the anomalies on the radiometry, a deep investigation was performed using numerous variations of the setup and different sources. The major contributor was identified to be a systematic error in the alignment, for which a correction was applied. Apart from this, it was found that the geometry of the sources influenced the results. Based on the calibration results combined with a theoretical geometrical hypothesis inferred that the on-ground calibration should mimic as close as possible the in-orbit geometry.

Prediction models for successful external cephalic version: a systematic review.

PubMed

Velzel, Joost; de Hundt, Marcella; Mulder, Frederique M; Molkenboer, Jan F M; Van der Post, Joris A M; Mol, Ben W; Kok, Marjolein

2015-12-01

To provide an overview of existing prediction models for successful ECV, and to assess their quality, development and performance. We searched MEDLINE, EMBASE and the Cochrane Library to identify all articles reporting on prediction models for successful ECV published from inception to January 2015. We extracted information on study design, sample size, model-building strategies and validation. We evaluated the phases of model development and summarized their performance in terms of discrimination, calibration and clinical usefulness. We collected different predictor variables together with their defined significance, in order to identify important predictor variables for successful ECV. We identified eight articles reporting on seven prediction models. All models were subjected to internal validation. Only one model was also validated in an external cohort. Two prediction models had a low overall risk of bias, of which only one showed promising predictive performance at internal validation. This model also completed the phase of external validation. For none of the models their impact on clinical practice was evaluated. The most important predictor variables for successful ECV described in the selected articles were parity, placental location, breech engagement and the fetal head being palpable. One model was assessed using discrimination and calibration using internal (AUC 0.71) and external validation (AUC 0.64), while two other models were assessed with discrimination and calibration, respectively. We found one prediction model for breech presentation that was validated in an external cohort and had acceptable predictive performance. This model should be used to council women considering ECV. Copyright © 2015. Published by Elsevier Ireland Ltd.

Multispectral scanner flight model (F-1) radiometric calibration and alignment handbook

NASA Technical Reports Server (NTRS)

1981-01-01

This handbook on the calibration of the MSS-D flight model (F-1) provides both the relevant data and a summary description of how the data were obtained for the system radiometric calibration, system relative spectral response, and the filter response characteristics for all 24 channels of the four band MSS-D F-1 scanner. The calibration test procedure and resulting test data required to establish the reference light levels of the MSS-D internal calibration system are discussed. The final set of data ("nominal" calibration wedges for all 24 channels) for the internal calibration system is given. The system relative spectral response measurements for all 24 channels of MSS-D F-1 are included. These data are the spectral response of the complete scanner, which are the composite of the spectral responses of the scan mirror primary and secondary telescope mirrors, fiber optics, optical filters, and detectors. Unit level test data on the measurements of the individual channel optical transmission filters are provided. Measured performance is compared to specification values.

Daytime sky polarization calibration limitations

NASA Astrophysics Data System (ADS)

Harrington, David M.; Kuhn, Jeffrey R.; Ariste, Arturo López

2017-01-01

The daytime sky has recently been demonstrated as a useful calibration tool for deriving polarization cross-talk properties of large astronomical telescopes. The Daniel K. Inouye Solar Telescope and other large telescopes under construction can benefit from precise polarimetric calibration of large mirrors. Several atmospheric phenomena and instrumental errors potentially limit the technique's accuracy. At the 3.67-m AEOS telescope on Haleakala, we performed a large observing campaign with the HiVIS spectropolarimeter to identify limitations and develop algorithms for extracting consistent calibrations. Effective sampling of the telescope optical configurations and filtering of data for several derived parameters provide robustness to the derived Mueller matrix calibrations. Second-order scattering models of the sky show that this method is relatively insensitive to multiple-scattering in the sky, provided calibration observations are done in regions of high polarization degree. The technique is also insensitive to assumptions about telescope-induced polarization, provided the mirror coatings are highly reflective. Zemax-derived polarization models show agreement between the functional dependence of polarization predictions and the corresponding on-sky calibrations.

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

Akerib, DS; Alsum, S; Araújo, HM

The LUX experiment has performed searches for dark matter particles scattering elastically on xenon nuclei, leading to stringent upper limits on the nuclear scattering cross sections for dark matter. Here, for results derived frommore » $${1.4}\\times 10^{4}\\;\\mathrm{kg\\,days}$$ of target exposure in 2013, details of the calibration, event-reconstruction, modeling, and statistical tests that underlie the results are presented. Detector performance is characterized, including measured efficiencies, stability of response, position resolution, and discrimination between electron- and nuclear-recoil populations. Models are developed for the drift field, optical properties, background populations, the electron- and nuclear-recoil responses, and the absolute rate of low-energy background events. Innovations in the analysis include in situ measurement of the photomultipliers' response to xenon scintillation photons, verification of fiducial mass with a low-energy internal calibration source, and new empirical models for low-energy signal yield based on large-sample, in situ calibrations.« less

Wavelet Analysis Used for Spectral Background Removal in the Determination of Glucose from Near-Infrared Single-Beam Spectra

PubMed Central

Wan, Boyong; Small, Gary W.

2010-01-01

Wavelet analysis is developed as a preprocessing tool for use in removing background information from near-infrared (near-IR) single-beam spectra before the construction of multivariate calibration models. Three data sets collected with three different near-IR spectrometers are investigated that involve the determination of physiological levels of glucose (1-30 mM) in a simulated biological matrix containing alanine, ascorbate, lactate, triacetin, and urea in phosphate buffer. A factorial design is employed to optimize the specific wavelet function used and the level of decomposition applied, in addition to the spectral range and number of latent variables associated with a partial least-squares calibration model. The prediction performance of the computed models is studied with separate data acquired after the collection of the calibration spectra. This evaluation includes one data set collected over a period of more than six months. Preprocessing with wavelet analysis is also compared to the calculation of second-derivative spectra. Over the three data sets evaluated, wavelet analysis is observed to produce better-performing calibration models, with improvements in concentration predictions on the order of 30% being realized relative to models based on either second-derivative spectra or spectra preprocessed with simple additive and multiplicative scaling correction. This methodology allows the construction of stable calibrations directly with single-beam spectra, thereby eliminating the need for the collection of a separate background or reference spectrum. PMID:21035604

Wavelet analysis used for spectral background removal in the determination of glucose from near-infrared single-beam spectra.

PubMed

Wan, Boyong; Small, Gary W

2010-11-29

Wavelet analysis is developed as a preprocessing tool for use in removing background information from near-infrared (near-IR) single-beam spectra before the construction of multivariate calibration models. Three data sets collected with three different near-IR spectrometers are investigated that involve the determination of physiological levels of glucose (1-30 mM) in a simulated biological matrix containing alanine, ascorbate, lactate, triacetin, and urea in phosphate buffer. A factorial design is employed to optimize the specific wavelet function used and the level of decomposition applied, in addition to the spectral range and number of latent variables associated with a partial least-squares calibration model. The prediction performance of the computed models is studied with separate data acquired after the collection of the calibration spectra. This evaluation includes one data set collected over a period of more than 6 months. Preprocessing with wavelet analysis is also compared to the calculation of second-derivative spectra. Over the three data sets evaluated, wavelet analysis is observed to produce better-performing calibration models, with improvements in concentration predictions on the order of 30% being realized relative to models based on either second-derivative spectra or spectra preprocessed with simple additive and multiplicative scaling correction. This methodology allows the construction of stable calibrations directly with single-beam spectra, thereby eliminating the need for the collection of a separate background or reference spectrum. Copyright © 2010 Elsevier B.V. All rights reserved.

Process Consistency in Models: the Importance of System Signatures, Expert Knowledge and Process Complexity

NASA Astrophysics Data System (ADS)

Hrachowitz, Markus; Fovet, Ophelie; Ruiz, Laurent; Gascuel-Odoux, Chantal; Savenije, Hubert

2014-05-01

Hydrological models are frequently characterized by what is often considered to be adequate calibration performances. In many cases, however, these models experience a substantial uncertainty and performance decrease in validation periods, thus resulting in poor predictive power. Besides the likely presence of data errors, this observation can point towards wrong or insufficient representations of the underlying processes and their heterogeneity. In other words, right results are generated for the wrong reasons. Thus ways are sought to increase model consistency and to thereby satisfy the contrasting priorities of the need a) to increase model complexity and b) to limit model equifinality. In this study a stepwise model development approach is chosen to test the value of an exhaustive and systematic combined use of hydrological signatures, expert knowledge and readily available, yet anecdotal and rarely exploited, hydrological information for increasing model consistency towards generating the right answer for the right reasons. A simple 3-box, 7 parameter, conceptual HBV-type model, constrained by 4 calibration objective functions was able to adequately reproduce the hydrograph with comparatively high values for the 4 objective functions in the 5-year calibration period. However, closer inspection of the results showed a dramatic decrease of model performance in the 5-year validation period. In addition, assessing the model's skill to reproduce a range of 20 hydrological signatures including, amongst others, the flow duration curve, the autocorrelation function and the rising limb density, showed that it could not adequately reproduce the vast majority of these signatures, indicating a lack of model consistency. Subsequently model complexity was increased in a stepwise way to allow for more process heterogeneity. To limit model equifinality, increase in complexity was counter-balanced by a stepwise application of "realism constraints", inferred from expert knowledge (e.g. unsaturated storage capacity of hillslopes should exceed the one of wetlands) and anecdotal hydrological information (e.g. long-term estimates of actual evaporation obtained from the Budyko framework and long-term estimates of baseflow contribution) to ensure that the model is well behaved with respect to the modeller's perception of the system. A total of 11 model set-ups with increased complexity and an increased number of realism constraints was tested. It could be shown that in spite of largely unchanged calibration performance, compared to the simplest set-up, the most complex model set-up (12 parameters, 8 constraints) exhibited significantly increased performance in the validation period while uncertainty did not increase. In addition, the most complex model was characterized by a substantially increased skill to reproduce all 20 signatures, indicating a more suitable representation of the system. The results suggest that a model, "well" constrained by 4 calibration objective functions may still be an inadequate representation of the system and that increasing model complexity, if counter-balanced by realism constraints, can indeed increase predictive performance of a model and its skill to reproduce a range of hydrological signatures, but that it does not necessarily result in increased uncertainty. The results also strongly illustrate the need to move away from automated model calibration towards a more general expert-knowledge driven strategy of constraining models if a certain level of model consistency is to be achieved.

Parameter estimation for groundwater models under uncertain irrigation data

USGS Publications Warehouse

Demissie, Yonas; Valocchi, Albert J.; Cai, Ximing; Brozovic, Nicholas; Senay, Gabriel; Gebremichael, Mekonnen

2015-01-01

The success of modeling groundwater is strongly influenced by the accuracy of the model parameters that are used to characterize the subsurface system. However, the presence of uncertainty and possibly bias in groundwater model source/sink terms may lead to biased estimates of model parameters and model predictions when the standard regression-based inverse modeling techniques are used. This study first quantifies the levels of bias in groundwater model parameters and predictions due to the presence of errors in irrigation data. Then, a new inverse modeling technique called input uncertainty weighted least-squares (IUWLS) is presented for unbiased estimation of the parameters when pumping and other source/sink data are uncertain. The approach uses the concept of generalized least-squares method with the weight of the objective function depending on the level of pumping uncertainty and iteratively adjusted during the parameter optimization process. We have conducted both analytical and numerical experiments, using irrigation pumping data from the Republican River Basin in Nebraska, to evaluate the performance of ordinary least-squares (OLS) and IUWLS calibration methods under different levels of uncertainty of irrigation data and calibration conditions. The result from the OLS method shows the presence of statistically significant (p < 0.05) bias in estimated parameters and model predictions that persist despite calibrating the models to different calibration data and sample sizes. However, by directly accounting for the irrigation pumping uncertainties during the calibration procedures, the proposed IUWLS is able to minimize the bias effectively without adding significant computational burden to the calibration processes.

Simultaneous estimation of diet composition and calibration coefficients with fatty acid signature data

USGS Publications Warehouse

Bromaghin, Jeffrey F.; Budge, Suzanne M.; Thiemann, Gregory W.; Rode, Karyn D.

2017-01-01

Knowledge of animal diets provides essential insights into their life history and ecology, although diet estimation is challenging and remains an active area of research. Quantitative fatty acid signature analysis (QFASA) has become a popular method of estimating diet composition, especially for marine species. A primary assumption of QFASA is that constants called calibration coefficients, which account for the differential metabolism of individual fatty acids, are known. In practice, however, calibration coefficients are not known, but rather have been estimated in feeding trials with captive animals of a limited number of model species. The impossibility of verifying the accuracy of feeding trial derived calibration coefficients to estimate the diets of wild animals is a foundational problem with QFASA that has generated considerable criticism. We present a new model that allows simultaneous estimation of diet composition and calibration coefficients based only on fatty acid signature samples from wild predators and potential prey. Our model performed almost flawlessly in four tests with constructed examples, estimating both diet proportions and calibration coefficients with essentially no error. We also applied the model to data from Chukchi Sea polar bears, obtaining diet estimates that were more diverse than estimates conditioned on feeding trial calibration coefficients. Our model avoids bias in diet estimates caused by conditioning on inaccurate calibration coefficients, invalidates the primary criticism of QFASA, eliminates the need to conduct feeding trials solely for diet estimation, and consequently expands the utility of fatty acid data to investigate aspects of ecology linked to animal diets.

Multi-gauge Calibration for modeling the Semi-Arid Santa Cruz Watershed in Arizona-Mexico Border Area Using SWAT

USGS Publications Warehouse

Niraula, Rewati; Norman, Laura A.; Meixner, Thomas; Callegary, James B.

2012-01-01

In most watershed-modeling studies, flow is calibrated at one monitoring site, usually at the watershed outlet. Like many arid and semi-arid watersheds, the main reach of the Santa Cruz watershed, located on the Arizona-Mexico border, is discontinuous for most of the year except during large flood events, and therefore the flow characteristics at the outlet do not represent the entire watershed. Calibration is required at multiple locations along the Santa Cruz River to improve model reliability. The objective of this study was to best portray surface water flow in this semiarid watershed and evaluate the effect of multi-gage calibration on flow predictions. In this study, the Soil and Water Assessment Tool (SWAT) was calibrated at seven monitoring stations, which improved model performance and increased the reliability of flow, in the Santa Cruz watershed. The most sensitive parameters to affect flow were found to be curve number (CN2), soil evaporation and compensation coefficient (ESCO), threshold water depth in shallow aquifer for return flow to occur (GWQMN), base flow alpha factor (Alpha_Bf), and effective hydraulic conductivity of the soil layer (Ch_K2). In comparison, when the model was established with a single calibration at the watershed outlet, flow predictions at other monitoring gages were inaccurate. This study emphasizes the importance of multi-gage calibration to develop a reliable watershed model in arid and semiarid environments. The developed model, with further calibration of water quality parameters will be an integral part of the Santa Cruz Watershed Ecosystem Portfolio Model (SCWEPM), an online decision support tool, to assess the impacts of climate change and urban growth in the Santa Cruz watershed.

Simulation of natural flows in major river basins in Alabama

USGS Publications Warehouse

Hunt, Alexandria M.; García, Ana María

2014-01-01

The Office of Water Resources (OWR) in the Alabama Department of Economic and Community Affairs (ADECA) is charged with the assessment of the State’s water resources. This study developed a watershed model for the major river basins that are within Alabama or that cross Alabama’s borders, which serves as a planning tool for water-resource decisionmakers. The watershed model chosen to assess the natural amount of available water was the Precipitation-Runoff Modeling System (PRMS). Models were configured and calibrated for the following four river basins: Mobile, Gulf of Mexico, Middle Tennessee, and Chattahoochee. These models required calibrating unregulated U.S. Geological Survey (USGS) streamflow gaging stations to estimate natural flows, with emphases on low-flow calibration. The target calibration criteria required the errors be within the range of: (1) ±10 percent for total-streamflow volume, (2) ±10 percent for low-flow volume, (3) ±15 percent for high-flow volume, (4) ±30 percent for summer volume, and (5) above 0.5 for the correlation coefficient (R2). Seventy-one of the 90 calibration stations in the watershed models for the four major river basins within Alabama met the target calibration criteria. Variability in the model performance can be attributed to limitations in correctly representing certain hydrologic conditions that are characterized by some of the ecoregions in Alabama. Ecoregions consisting of predominantly clayey soils and (or) low topographic relief yield less successful calibration results, whereas ecoregions consisting of loamy and sandy soils and (or) high topographic relief yield more successful calibration results. Results indicate that the model does well in hilly regions with sandy soils because of rapid surface runoff and more direct interaction with subsurface flow.

LANDSAT-D conical scanner evaluation plan

NASA Technical Reports Server (NTRS)

Bilanow, S.; Chen, L. C. (Principal Investigator)

1982-01-01

The planned activities involved in the inflight sensor calibration and performance evaluation are discussed and the supporting software requirements are specified. The possible sensor error sources and their effects on sensor measurements are summarized. The methods by which the inflight sensor performance will be analyzed and the sensor modeling parameters will be calibrated are presented. In addition, a brief discussion on the data requirement for the study is provided.

Prediction of geomagnetic reversals using low-dimensional dynamical models and advanced data assimilation: a feasibility study

NASA Astrophysics Data System (ADS)

Fournier, A.; Morzfeld, M.; Hulot, G.

2013-12-01

For a suitable choice of parameters, the system of three ordinary differential equations (ODE) presented by Gissinger [1] was shown to exhibit chaotic reversals whose statistics compared well with those from the paleomagnetic record. In order to further assess the geophysical relevance of this low-dimensional model, we resort to data assimilation methods to calibrate it using reconstructions of the fluctuation of the virtual axial dipole moment spanning the past 2 millions years. Moreover, we test to which extent a properly calibrated model could possibly be used to predict a reversal of the geomagnetic field. We calibrate the ODE model to the geomagnetic field over the past 2 Ma using the SINT data set of Valet et al. [2]. To this end, we consider four data assimilation algorithms: the ensemble Kalman filter (EnKF), a variational method and two Monte Carlo (MC) schemes, prior importance sampling and implicit sampling. We observe that EnKF performs poorly and that prior importance sampling is inefficient. We obtain the most accurate reconstructions of the geomagnetic data using implicit sampling with five data points per assimilation sweep (of duration 5 kyr). The variational scheme performs equally well, but it does not provide us with quantitative information about the uncertainty of the estimates, which makes this method difficult to use for robust prediction under uncertainty. A calibration of the model using the PADM2M data set of Ziegler et al. [3] confirms these findings. We study the predictive capability of the ODE model using statistics computed from synthetic data experiments. For each experiment, we produce 2 Myr of synthetic data (with error levels similar to the ones found in real data), then calibrate the model to this record and then check if this calibrated model can correctly and reliably predict a reversal within the next 10 kyr (say). By performing 100 such experiments, we can assess how reliably our calibrated model can predict a (non-) reversal. It is found that the 5 kyr ahead predictions of reversals produced by the model appear to be accurate and reliable.These encouraging results prompted us to also test predictions of the five reversals of the SINT (and PADM2M) data set, using a similarly calibrated model. Results will be presented and discussed. [1] Gissinger, C., 2012, A new deterministic model for chaotic reversals, European Physical Journal B, 85:137 [2] Valet, J.-P., Meynadier, L. and Guyodo, Y., 2005, Geomagnetic field strength and reversal rate over the past 2 Million years, Nature, 435, 802-805. [3] Ziegler, L. B., Constable, C. G., Johnson, C. L. and Tauxe, L., 2011, PADM2M: a penalized maximum likelihood model of the 0-2 Ma paleomagnetic axial dipole moment, Geophysical Journal International, 184, 1069-1089.

Bayesian calibration of terrestrial ecosystem models: A study of advanced Markov chain Monte Carlo methods

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

Lu, Dan; Ricciuto, Daniel; Walker, Anthony

Calibration of terrestrial ecosystem models is important but challenging. Bayesian inference implemented by Markov chain Monte Carlo (MCMC) sampling provides a comprehensive framework to estimate model parameters and associated uncertainties using their posterior distributions. The effectiveness and efficiency of the method strongly depend on the MCMC algorithm used. In this study, a Differential Evolution Adaptive Metropolis (DREAM) algorithm was used to estimate posterior distributions of 21 parameters for the data assimilation linked ecosystem carbon (DALEC) model using 14 years of daily net ecosystem exchange data collected at the Harvard Forest Environmental Measurement Site eddy-flux tower. The DREAM is a multi-chainmore » method and uses differential evolution technique for chain movement, allowing it to be efficiently applied to high-dimensional problems, and can reliably estimate heavy-tailed and multimodal distributions that are difficult for single-chain schemes using a Gaussian proposal distribution. The results were evaluated against the popular Adaptive Metropolis (AM) scheme. DREAM indicated that two parameters controlling autumn phenology have multiple modes in their posterior distributions while AM only identified one mode. The calibration of DREAM resulted in a better model fit and predictive performance compared to the AM. DREAM provides means for a good exploration of the posterior distributions of model parameters. Lastly, it reduces the risk of false convergence to a local optimum and potentially improves the predictive performance of the calibrated model.« less

Bayesian calibration of terrestrial ecosystem models: A study of advanced Markov chain Monte Carlo methods

DOE PAGES

Lu, Dan; Ricciuto, Daniel; Walker, Anthony; ...

2017-02-22

Calibration of terrestrial ecosystem models is important but challenging. Bayesian inference implemented by Markov chain Monte Carlo (MCMC) sampling provides a comprehensive framework to estimate model parameters and associated uncertainties using their posterior distributions. The effectiveness and efficiency of the method strongly depend on the MCMC algorithm used. In this study, a Differential Evolution Adaptive Metropolis (DREAM) algorithm was used to estimate posterior distributions of 21 parameters for the data assimilation linked ecosystem carbon (DALEC) model using 14 years of daily net ecosystem exchange data collected at the Harvard Forest Environmental Measurement Site eddy-flux tower. The DREAM is a multi-chainmore » method and uses differential evolution technique for chain movement, allowing it to be efficiently applied to high-dimensional problems, and can reliably estimate heavy-tailed and multimodal distributions that are difficult for single-chain schemes using a Gaussian proposal distribution. The results were evaluated against the popular Adaptive Metropolis (AM) scheme. DREAM indicated that two parameters controlling autumn phenology have multiple modes in their posterior distributions while AM only identified one mode. The calibration of DREAM resulted in a better model fit and predictive performance compared to the AM. DREAM provides means for a good exploration of the posterior distributions of model parameters. Lastly, it reduces the risk of false convergence to a local optimum and potentially improves the predictive performance of the calibrated model.« less

Predicting the performance uncertainty of a 1-MW pilot-scale carbon capture system after hierarchical laboratory-scale calibration and validation

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

Xu, Zhijie; Lai, Canhai; Marcy, Peter William

2017-05-01

A challenging problem in designing pilot-scale carbon capture systems is to predict, with uncertainty, the adsorber performance and capture efficiency under various operating conditions where no direct experimental data exist. Motivated by this challenge, we previously proposed a hierarchical framework in which relevant parameters of physical models were sequentially calibrated from different laboratory-scale carbon capture unit (C2U) experiments. Specifically, three models of increasing complexity were identified based on the fundamental physical and chemical processes of the sorbent-based carbon capture technology. Results from the corresponding laboratory experiments were used to statistically calibrate the physical model parameters while quantifying some of theirmore » inherent uncertainty. The parameter distributions obtained from laboratory-scale C2U calibration runs are used in this study to facilitate prediction at a larger scale where no corresponding experimental results are available. In this paper, we first describe the multiphase reactive flow model for a sorbent-based 1-MW carbon capture system then analyze results from an ensemble of simulations with the upscaled model. The simulation results are used to quantify uncertainty regarding the design’s predicted efficiency in carbon capture. In particular, we determine the minimum gas flow rate necessary to achieve 90% capture efficiency with 95% confidence.« less

Using Active Learning for Speeding up Calibration in Simulation Models.

PubMed

Cevik, Mucahit; Ergun, Mehmet Ali; Stout, Natasha K; Trentham-Dietz, Amy; Craven, Mark; Alagoz, Oguzhan

2016-07-01

Most cancer simulation models include unobservable parameters that determine disease onset and tumor growth. These parameters play an important role in matching key outcomes such as cancer incidence and mortality, and their values are typically estimated via a lengthy calibration procedure, which involves evaluating a large number of combinations of parameter values via simulation. The objective of this study is to demonstrate how machine learning approaches can be used to accelerate the calibration process by reducing the number of parameter combinations that are actually evaluated. Active learning is a popular machine learning method that enables a learning algorithm such as artificial neural networks to interactively choose which parameter combinations to evaluate. We developed an active learning algorithm to expedite the calibration process. Our algorithm determines the parameter combinations that are more likely to produce desired outputs and therefore reduces the number of simulation runs performed during calibration. We demonstrate our method using the previously developed University of Wisconsin breast cancer simulation model (UWBCS). In a recent study, calibration of the UWBCS required the evaluation of 378 000 input parameter combinations to build a race-specific model, and only 69 of these combinations produced results that closely matched observed data. By using the active learning algorithm in conjunction with standard calibration methods, we identify all 69 parameter combinations by evaluating only 5620 of the 378 000 combinations. Machine learning methods hold potential in guiding model developers in the selection of more promising parameter combinations and hence speeding up the calibration process. Applying our machine learning algorithm to one model shows that evaluating only 1.49% of all parameter combinations would be sufficient for the calibration. © The Author(s) 2015.

Using Active Learning for Speeding up Calibration in Simulation Models

PubMed Central

Cevik, Mucahit; Ali Ergun, Mehmet; Stout, Natasha K.; Trentham-Dietz, Amy; Craven, Mark; Alagoz, Oguzhan

2015-01-01

Background Most cancer simulation models include unobservable parameters that determine the disease onset and tumor growth. These parameters play an important role in matching key outcomes such as cancer incidence and mortality and their values are typically estimated via lengthy calibration procedure, which involves evaluating large number of combinations of parameter values via simulation. The objective of this study is to demonstrate how machine learning approaches can be used to accelerate the calibration process by reducing the number of parameter combinations that are actually evaluated. Methods Active learning is a popular machine learning method that enables a learning algorithm such as artificial neural networks to interactively choose which parameter combinations to evaluate. We develop an active learning algorithm to expedite the calibration process. Our algorithm determines the parameter combinations that are more likely to produce desired outputs, therefore reduces the number of simulation runs performed during calibration. We demonstrate our method using previously developed University of Wisconsin Breast Cancer Simulation Model (UWBCS). Results In a recent study, calibration of the UWBCS required the evaluation of 378,000 input parameter combinations to build a race-specific model and only 69 of these combinations produced results that closely matched observed data. By using the active learning algorithm in conjunction with standard calibration methods, we identify all 69 parameter combinations by evaluating only 5620 of the 378,000 combinations. Conclusion Machine learning methods hold potential in guiding model developers in the selection of more promising parameter combinations and hence speeding up the calibration process. Applying our machine learning algorithm to one model shows that evaluating only 1.49% of all parameter combinations would be sufficient for the calibration. PMID:26471190

Spectral performance of Square Kilometre Array Antennas - II. Calibration performance

NASA Astrophysics Data System (ADS)

Trott, Cathryn M.; de Lera Acedo, Eloy; Wayth, Randall B.; Fagnoni, Nicolas; Sutinjo, Adrian T.; Wakley, Brett; Punzalan, Chris Ivan B.

2017-09-01

We test the bandpass smoothness performance of two prototype Square Kilometre Array (SKA) SKA1-Low log-periodic dipole antennas, SKALA2 and SKALA3 ('SKA Log-periodic Antenna'), and the current dipole from the Murchison Widefield Array (MWA) precursor telescope. Throughout this paper, we refer to the output complex-valued voltage response of an antenna when connected to a low-noise amplifier, as the dipole bandpass. In Paper I, the bandpass spectral response of the log-periodic antenna being developed for the SKA1-Low was estimated using numerical electromagnetic simulations and analysed using low-order polynomial fittings, and it was compared with the HERA antenna against the delay spectrum metric. In this work, realistic simulations of the SKA1-Low instrument, including frequency-dependent primary beam shapes and array configuration, are used with a weighted least-squares polynomial estimator to assess the ability of a given prototype antenna to perform the SKA Epoch of Reionisation (EoR) statistical experiments. This work complements the ideal estimator tolerances computed for the proposed EoR science experiments in Trott & Wayth, with the realized performance of an optimal and standard estimation (calibration) procedure. With a sufficient sky calibration model at higher frequencies, all antennas have bandpasses that are sufficiently smooth to meet the tolerances described in Trott & Wayth to perform the EoR statistical experiments, and these are primarily limited by an adequate sky calibration model and the thermal noise level in the calibration data. At frequencies of the Cosmic Dawn, which is of principal interest to SKA as one of the first next-generation telescopes capable of accessing higher redshifts, the MWA dipole and SKALA3 antenna have adequate performance, while the SKALA2 design will impede the ability to explore this era.

Uncertainty quantification in capacitive RF MEMS switches

NASA Astrophysics Data System (ADS)

Pax, Benjamin J.

Development of radio frequency micro electrical-mechanical systems (RF MEMS) has led to novel approaches to implement electrical circuitry. The introduction of capacitive MEMS switches, in particular, has shown promise in low-loss, low-power devices. However, the promise of MEMS switches has not yet been completely realized. RF-MEMS switches are known to fail after only a few months of operation, and nominally similar designs show wide variability in lifetime. Modeling switch operation using nominal or as-designed parameters cannot predict the statistical spread in the number of cycles to failure, and probabilistic methods are necessary. A Bayesian framework for calibration, validation and prediction offers an integrated approach to quantifying the uncertainty in predictions of MEMS switch performance. The objective of this thesis is to use the Bayesian framework to predict the creep-related deflection of the PRISM RF-MEMS switch over several thousand hours of operation. The PRISM switch used in this thesis is the focus of research at Purdue's PRISM center, and is a capacitive contacting RF-MEMS switch. It employs a fixed-fixed nickel membrane which is electrostatically actuated by applying voltage between the membrane and a pull-down electrode. Creep plays a central role in the reliability of this switch. The focus of this thesis is on the creep model, which is calibrated against experimental data measured for a frog-leg varactor fabricated and characterized at Purdue University. Creep plasticity is modeled using plate element theory with electrostatic forces being generated using either parallel plate approximations where appropriate, or solving for the full 3D potential field. For the latter, structure-electrostatics interaction is determined through immersed boundary method. A probabilistic framework using generalized polynomial chaos (gPC) is used to create surrogate models to mitigate the costly full physics simulations, and Bayesian calibration and forward propagation of uncertainty are performed using this surrogate model. The first step in the analysis is Bayesian calibration of the creep related parameters. A computational model of the frog-leg varactor is created, and the computed creep deflection of the device over 800 hours is used to generate a surrogate model using a polynomial chaos expansion in Hermite polynomials. Parameters related to the creep phenomenon are calibrated using Bayesian calibration with experimental deflection data from the frog-leg device. The calibrated input distributions are subsequently propagated through a surrogate gPC model for the PRISM MEMS switch to produce probability density functions of the maximum membrane deflection of the membrane over several thousand hours. The assumptions related to the Bayesian calibration and forward propagation are analyzed to determine the sensitivity to these assumptions of the calibrated input distributions and propagated output distributions of the PRISM device. The work is an early step in understanding the role of geometric variability, model uncertainty, numerical errors and experimental uncertainties in the long-term performance of RF-MEMS.

Calibration and validation of rockfall models

NASA Astrophysics Data System (ADS)

Frattini, Paolo; Valagussa, Andrea; Zenoni, Stefania; Crosta, Giovanni B.

2013-04-01

Calibrating and validating landslide models is extremely difficult due to the particular characteristic of landslides: limited recurrence in time, relatively low frequency of the events, short durability of post-event traces, poor availability of continuous monitoring data, especially for small landslide and rockfalls. For this reason, most of the rockfall models presented in literature completely lack calibration and validation of the results. In this contribution, we explore different strategies for rockfall model calibration and validation starting from both an historical event and a full-scale field test. The event occurred in 2012 in Courmayeur (Western Alps, Italy), and caused serious damages to quarrying facilities. This event has been studied soon after the occurrence through a field campaign aimed at mapping the blocks arrested along the slope, the shape and location of the detachment area, and the traces of scars associated to impacts of blocks on the slope. The full-scale field test was performed by Geovert Ltd in the Christchurch area (New Zealand) after the 2011 earthquake. During the test, a number of large blocks have been mobilized from the upper part of the slope and filmed with high velocity cameras from different viewpoints. The movies of each released block were analysed to identify the block shape, the propagation path, the location of impacts, the height of the trajectory and the velocity of the block along the path. Both calibration and validation of rockfall models should be based on the optimization of the agreement between the actual trajectories or location of arrested blocks and the simulated ones. A measure that describe this agreement is therefore needed. For calibration purpose, this measure should simple enough to allow trial and error repetitions of the model for parameter optimization. In this contribution we explore different calibration/validation measures: (1) the percentage of simulated blocks arresting within a buffer of the actual blocks, (2) the percentage of trajectories passing through the buffer of the actual rockfall path, (3) the mean distance between the location of arrest of each simulated blocks and the location of the nearest actual blocks; (4) the mean distance between the location of detachment of each simulated block and the location of detachment of the actual block located closer to the arrest position. By applying the four measures to the case studies, we observed that all measures are able to represent the model performance for validation purposes. However, the third measure is more simple and reliable than the others, and seems to be optimal for model calibration, especially when using a parameter estimation and optimization modelling software for automated calibration.

Evaluation of a laser scanner for large volume coordinate metrology: a comparison of results before and after factory calibration

NASA Astrophysics Data System (ADS)

Ferrucci, M.; Muralikrishnan, B.; Sawyer, D.; Phillips, S.; Petrov, P.; Yakovlev, Y.; Astrelin, A.; Milligan, S.; Palmateer, J.

2014-10-01

Large volume laser scanners are increasingly being used for a variety of dimensional metrology applications. Methods to evaluate the performance of these scanners are still under development and there are currently no documentary standards available. This paper describes the results of extensive ranging and volumetric performance tests conducted on a large volume laser scanner. The results demonstrated small but clear systematic errors that are explained in the context of a geometric error model for the instrument. The instrument was subsequently returned to the manufacturer for factory calibration. The ranging and volumetric tests were performed again and the results are compared against those obtained prior to the factory calibration.

Accuracy evaluation of optical distortion calibration by digital image correlation

NASA Astrophysics Data System (ADS)

Gao, Zeren; Zhang, Qingchuan; Su, Yong; Wu, Shangquan

2017-11-01

Due to its convenience of operation, the camera calibration algorithm, which is based on the plane template, is widely used in image measurement, computer vision and other fields. How to select a suitable distortion model is always a problem to be solved. Therefore, there is an urgent need for an experimental evaluation of the accuracy of camera distortion calibrations. This paper presents an experimental method for evaluating camera distortion calibration accuracy, which is easy to implement, has high precision, and is suitable for a variety of commonly used lens. First, we use the digital image correlation method to calculate the in-plane rigid body displacement field of an image displayed on a liquid crystal display before and after translation, as captured with a camera. Next, we use a calibration board to calibrate the camera to obtain calibration parameters which are used to correct calculation points of the image before and after deformation. The displacement field before and after correction is compared to analyze the distortion calibration results. Experiments were carried out to evaluate the performance of two commonly used industrial camera lenses for four commonly used distortion models.

Angle Measurement System (AMS) for Establishing Model Pitch and Roll Zero, and Performing Single Axis Angle Comparisons

NASA Technical Reports Server (NTRS)

Crawford, Bradley L.

2007-01-01

The angle measurement system (AMS) developed at NASA Langley Research Center (LaRC) is a system for many uses. It was originally developed to check taper fits in the wind tunnel model support system. The system was further developed to measure simultaneous pitch and roll angles using 3 orthogonally mounted accelerometers (3-axis). This 3-axis arrangement is used as a transfer standard from the calibration standard to the wind tunnel facility. It is generally used to establish model pitch and roll zero and performs the in-situ calibration on model attitude devices. The AMS originally used a laptop computer running DOS based software but has recently been upgraded to operate in a windows environment. Other improvements have also been made to the software to enhance its accuracy and add features. This paper will discuss the accuracy and calibration methodologies used in this system and some of the features that have contributed to its popularity.

TOD to TTP calibration

NASA Astrophysics Data System (ADS)

Bijl, Piet; Reynolds, Joseph P.; Vos, Wouter K.; Hogervorst, Maarten A.; Fanning, Jonathan D.

2011-05-01

The TTP (Targeting Task Performance) metric, developed at NVESD, is the current standard US Army model to predict EO/IR Target Acquisition performance. This model however does not have a corresponding lab or field test to empirically assess the performance of a camera system. The TOD (Triangle Orientation Discrimination) method, developed at TNO in The Netherlands, provides such a measurement. In this study, we make a direct comparison between TOD performance for a range of sensors and the extensive historical US observer performance database built to develop and calibrate the TTP metric. The US perception data were collected doing an identification task by military personnel on a standard 12 target, 12 aspect tactical vehicle image set that was processed through simulated sensors for which the most fundamental sensor parameters such as blur, sampling, spatial and temporal noise were varied. In the present study, we measured TOD sensor performance using exactly the same sensors processing a set of TOD triangle test patterns. The study shows that good overall agreement is obtained when the ratio between target characteristic size and TOD test pattern size at threshold equals 6.3. Note that this number is purely based on empirical data without any intermediate modeling. The calibration of the TOD to the TTP is highly beneficial to the sensor modeling and testing community for a variety of reasons. These include: i) a connection between requirement specification and acceptance testing, and ii) a very efficient method to quickly validate or extend the TTP range prediction model to new systems and tasks.

Linear and nonlinear trending and prediction for AVHRR time series data

NASA Technical Reports Server (NTRS)

Smid, J.; Volf, P.; Slama, M.; Palus, M.

1995-01-01

The variability of AVHRR calibration coefficient in time was analyzed using algorithms of linear and non-linear time series analysis. Specifically we have used the spline trend modeling, autoregressive process analysis, incremental neural network learning algorithm and redundancy functional testing. The analysis performed on available AVHRR data sets revealed that (1) the calibration data have nonlinear dependencies, (2) the calibration data depend strongly on the target temperature, (3) both calibration coefficients and the temperature time series can be modeled, in the first approximation, as autonomous dynamical systems, (4) the high frequency residuals of the analyzed data sets can be best modeled as an autoregressive process of the 10th degree. We have dealt with a nonlinear identification problem and the problem of noise filtering (data smoothing). The system identification and filtering are significant problems for AVHRR data sets. The algorithms outlined in this study can be used for the future EOS missions. Prediction and smoothing algorithms for time series of calibration data provide a functional characterization of the data. Those algorithms can be particularly useful when calibration data are incomplete or sparse.

Hydrochemical tracers in the middle Rio Grande Basin, USA: 2. Calibration of a groundwater-flow model

USGS Publications Warehouse

Sanford, W.E.; Plummer, Niel; McAda, D.P.; Bexfield, L.M.; Anderholm, S.K.

2004-01-01

The calibration of a groundwater model with the aid of hydrochemical data has demonstrated that low recharge rates in the Middle Rio Grande Basin may be responsible for a groundwater trough in the center of the basin and for a substantial amount of Rio Grande water in the regional flow system. Earlier models of the basin had difficulty reproducing these features without any hydrochemical data to constrain the rates and distribution of recharge. The objective of this study was to use the large quantity of available hydrochemical data to help calibrate the model parameters, including the recharge rates. The model was constructed using the US Geological Survey's software MODFLOW, MODPATH, and UCODE, and calibrated using 14C activities and the positions of certain flow zones defined by the hydrochemical data. Parameter estimation was performed using a combination of nonlinear regression techniques and a manual search for the minimum difference between field and simulated observations. The calibrated recharge values were substantially smaller than those used in previous models. Results from a 30,000-year transient simulation suggest that recharge was at a maximum about 20,000 years ago and at a minimum about 10,000 years ago. ?? Springer-Verlag 2004.

Texas flexible pavements and overlays : calibration plans for M-E models and related software.

DOT National Transportation Integrated Search

2013-06-01

This five-year project was initiated to collect materials and pavement performance data on a minimum of 100 highway test sections around the State of Texas, incorporating flexible pavements and overlays. Besides being used to calibrate and validate m...

Calibrating the orientation between a microlens array and a sensor based on projective geometry

NASA Astrophysics Data System (ADS)

Su, Lijuan; Yan, Qiangqiang; Cao, Jun; Yuan, Yan

2016-07-01

We demonstrate a method for calibrating a microlens array (MLA) with a sensor component by building a plenoptic camera with a conventional prime lens. This calibration method includes a geometric model, a setup to adjust the distance (L) between the prime lens and the MLA, a calibration procedure for determining the subimage centers, and an optimization algorithm. The geometric model introduces nine unknown parameters regarding the centers of the microlenses and their images, whereas the distance adjustment setup provides an initial guess for the distance L. The simulation results verify the effectiveness and accuracy of the proposed method. The experimental results demonstrate the calibration process can be performed with a commercial prime lens and the proposed method can be used to quantitatively evaluate whether a MLA and a sensor is assembled properly for plenoptic systems.

On Lack of Robustness in Hydrological Model Development Due to Absence of Guidelines for Selecting Calibration and Evaluation Data: Demonstration for Data-Driven Models

NASA Astrophysics Data System (ADS)

Zheng, Feifei; Maier, Holger R.; Wu, Wenyan; Dandy, Graeme C.; Gupta, Hoshin V.; Zhang, Tuqiao

2018-02-01

Hydrological models are used for a wide variety of engineering purposes, including streamflow forecasting and flood-risk estimation. To develop such models, it is common to allocate the available data to calibration and evaluation data subsets. Surprisingly, the issue of how this allocation can affect model evaluation performance has been largely ignored in the research literature. This paper discusses the evaluation performance bias that can arise from how available data are allocated to calibration and evaluation subsets. As a first step to assessing this issue in a statistically rigorous fashion, we present a comprehensive investigation of the influence of data allocation on the development of data-driven artificial neural network (ANN) models of streamflow. Four well-known formal data splitting methods are applied to 754 catchments from Australia and the U.S. to develop 902,483 ANN models. Results clearly show that the choice of the method used for data allocation has a significant impact on model performance, particularly for runoff data that are more highly skewed, highlighting the importance of considering the impact of data splitting when developing hydrological models. The statistical behavior of the data splitting methods investigated is discussed and guidance is offered on the selection of the most appropriate data splitting methods to achieve representative evaluation performance for streamflow data with different statistical properties. Although our results are obtained for data-driven models, they highlight the fact that this issue is likely to have a significant impact on all types of hydrological models, especially conceptual rainfall-runoff models.

The Geostationary Lightning Mapper: Its Performance and Calibration

NASA Astrophysics Data System (ADS)

Christian, H. J., Jr.

2015-12-01

The Geostationary Lightning Mapper (GLM) has been developed to be an operational instrument on the GOES-R series of spacecraft. The GLM is a unique instrument, unlike other meteorological instruments, both in how it operates and in the information content that it provides. Instrumentally, it is an event detector, rather than an imager. While processing almost a billion pixels per second with 14 bits of resolution, the event detection process reduces the required telemetry bandwidth by almost 105, thus keeping the telemetry requirements modest and enabling efficient ground processing that leads to rapid data distribution to operational users. The GLM was designed to detect about 90 percent of the total lightning flashes within its almost hemispherical field of view. Based on laboratory calibration, we expect the on-orbit detection efficiency to be closer to 85%, making it the highest performing, large area coverage total lightning detector. It has a number of unique design features that will enable it have near uniform special resolution over most of its field of view and to operate with minimal impact on performance during solar eclipses. The GLM has no dedicated on-orbit calibration system, thus the ground-based calibration provides the bases for the predicted radiometric performance. A number of problems were encountered during the calibration of Flight Model 1. The issues arouse from GLM design features including its wide field of view, fast lens, the narrow-band interference filters located in both object and collimated space and the fact that the GLM is inherently a event detector yet the calibration procedures required both calibration of images and events. The GLM calibration techniques were based on those developed for the Lightning Imaging Sensor calibration, but there are enough differences between the sensors that the initial GLM calibration suggested that it is significantly more sensitive than its design parameters. The calibration discrepancies have been resolved and will be discussed. Absolute calibration will be verified on-orbit using vicarious cloud reflections. In addition to details of the GLM calibration, the presentation will address the unique design of the GLM, its features, capabilities and performance.

Regional estimation of response routine parameters

NASA Astrophysics Data System (ADS)

Tøfte, Lena S.

2015-04-01

Reducing the number of calibration parameters is of a considerable advantage when area distributed hydrological models are to be calibrated, both due to equifinality and over-parameterization of the model in general, and for making the calibration process more efficient. A simple non-threshold response model for drainage in natural catchments based on among others Kirchner's article in WRR 2009 is implemented in the gridded hydrological model in the ENKI framework. This response model takes only the hydrogram into account; it has one state and two parameters, and is adapted to catchments that are dominated by terrain drainage. In former analyses of natural discharge series from a large number of catchments in different regions of Norway, we found that these response model parameters can be calculated from some known catchment characteristics, as catchment area and lake percentage, found in maps or data bases, meaning that the parameters can easily be found also for ungauged catchments. In the presented work from the EU project COMPLEX a large region in Mid-Norway containing 27 simulated catchments of different sizes and characteristics is calibrated. Results from two different calibration strategies are compared: 1) removing the response parameters from the calibration by calculating them in advance, based on the results from our former studies, and 2) including the response parameters in the calibration, both as maps with different values for each catchment, and as a constant number for the total region. The resulting simulation performances are compared and discussed.

Simulation of streamflow in the Pleasant, Narraguagus, Sheepscot, and Royal Rivers, Maine, using watershed models

USGS Publications Warehouse

Dudley, Robert W.; Nielsen, Martha G.

2011-01-01

The U.S. Geological Survey (USGS) began a study in 2008 to investigate anticipated changes in summer streamflows and stream temperatures in four coastal Maine river basins and the potential effects of those changes on populations of endangered Atlantic salmon. To achieve this purpose, it was necessary to characterize the quantity and timing of streamflow in these rivers by developing and evaluating a distributed-parameter watershed model for a part of each river basin by using the USGS Precipitation-Runoff Modeling System (PRMS). The GIS (geographic information system) Weasel, a USGS software application, was used to delineate the four study basins and their many subbasins, and to derive parameters for their geographic features. The models were calibrated using a four-step optimization procedure in which model output was evaluated against four datasets for calibrating solar radiation, potential evapotranspiration, annual and seasonal water balances, and daily streamflows. The calibration procedure involved thousands of model runs that used the USGS software application Luca (Let us calibrate). Luca uses the Shuffled Complex Evolution (SCE) global search algorithm to calibrate the model parameters. The calibrated watershed models performed satisfactorily, in that Nash-Sutcliffe efficiency (NSE) statistic values for the calibration periods ranged from 0.59 to 0.75 (on a scale of negative infinity to 1) and NSE statistic values for the evaluation periods ranged from 0.55 to 0.73. The calibrated watershed models simulate daily streamflow at many locations in each study basin. These models enable natural resources managers to characterize the timing and amount of streamflow in order to support a variety of water-resources efforts including water-quality calculations, assessments of water use, modeling of population dynamics and migration of Atlantic salmon, modeling and assessment of habitat, and simulation of anticipated changes to streamflow and water temperature resulting from changes forecast for air temperature and precipitation.

Diagnosing the impact of alternative calibration strategies on coupled hydrologic models

NASA Astrophysics Data System (ADS)

Smith, T. J.; Perera, C.; Corrigan, C.

2017-12-01

Hydrologic models represent a significant tool for understanding, predicting, and responding to the impacts of water on society and society on water resources and, as such, are used extensively in water resources planning and management. Given this important role, the validity and fidelity of hydrologic models is imperative. While extensive focus has been paid to improving hydrologic models through better process representation, better parameter estimation, and better uncertainty quantification, significant challenges remain. In this study, we explore a number of competing model calibration scenarios for simple, coupled snowmelt-runoff models to better understand the sensitivity / variability of parameterizations and its impact on model performance, robustness, fidelity, and transferability. Our analysis highlights the sensitivity of coupled snowmelt-runoff model parameterizations to alterations in calibration approach, underscores the concept of information content in hydrologic modeling, and provides insight into potential strategies for improving model robustness / fidelity.

Coupling HYDRUS-1D Code with PA-DDS Algorithms for Inverse Calibration

NASA Astrophysics Data System (ADS)

Wang, Xiang; Asadzadeh, Masoud; Holländer, Hartmut

2017-04-01

Numerical modelling requires calibration to predict future stages. A standard method for calibration is inverse calibration where generally multi-objective optimization algorithms are used to find a solution, e.g. to find an optimal solution of the van Genuchten Mualem (VGM) parameters to predict water fluxes in the vadose zone. We coupled HYDRUS-1D with PA-DDS to add a new, robust function for inverse calibration to the model. The PA-DDS method is a recently developed multi-objective optimization algorithm, which combines Dynamically Dimensioned Search (DDS) and Pareto Archived Evolution Strategy (PAES). The results were compared to a standard method (Marquardt-Levenberg method) implemented in HYDRUS-1D. Calibration performance is evaluated using observed and simulated soil moisture at two soil layers in the Southern Abbotsford, British Columbia, Canada in the terms of the root mean squared error (RMSE) and the Nash-Sutcliffe Efficiency (NSE). Results showed low RMSE values of 0.014 and 0.017 and strong NSE values of 0.961 and 0.939. Compared to the results by the Marquardt-Levenberg method, we received better calibration results for deeper located soil sensors. However, VGM parameters were similar comparing with previous studies. Both methods are equally computational efficient. We claim that a direct implementation of PA-DDS into HYDRUS-1D should reduce the computation effort further. This, the PA-DDS method is efficient for calibrating recharge for complex vadose zone modelling with multiple soil layer and can be a potential tool for calibration of heat and solute transport. Future work should focus on the effectiveness of PA-DDS for calibrating more complex versions of the model with complex vadose zone settings, with more soil layers, and against measured heat and solute transport. Keywords: Recharge, Calibration, HYDRUS-1D, Multi-objective Optimization

Max Tech Efficiency Electric HPWH with low-GWP Halogenated Refrigerant

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

Nawaz, Kashif; Shen, Bo; Elatar, Ahmed F.

A scoping-level analysis was conducted to determine the maximum performance of an electric heat pump water heater (HPWH) with low GWP refrigerants (hydroflouroolefins (HFO), hydrofluorocarbons (HFC), and blends). A baseline heat pump water heater (GE GeoSpring) deploying R-134a was analyzed first using the DOE/ORNL Heat Pump Design Model (HPDM) modeling tool. The model was calibrated using experimental data to match the water temperature stratification in tank, first hour rating, energy factor and coefficient of performance. A CFD modeling tool was used to further refine the HPDM tank model. After calibration, the model was used to simulate the performance of alternativemore » refrigerants. The parametric analysis concluded that by appropriate selection of equipment size and condenser tube wrap configuration the overall performance of emerging low GWP refrigerants for HPWH application not only exceed the Energy Star Energy Factor criteria i.e. 2.20, but is also comparable to some of the most efficient products in the market.« less

Prognostic models for complete recovery in ischemic stroke: a systematic review and meta-analysis.

PubMed

Jampathong, Nampet; Laopaiboon, Malinee; Rattanakanokchai, Siwanon; Pattanittum, Porjai

2018-03-09

Prognostic models have been increasingly developed to predict complete recovery in ischemic stroke. However, questions arise about the performance characteristics of these models. The aim of this study was to systematically review and synthesize performance of existing prognostic models for complete recovery in ischemic stroke. We searched journal publications indexed in PUBMED, SCOPUS, CENTRAL, ISI Web of Science and OVID MEDLINE from inception until 4 December, 2017, for studies designed to develop and/or validate prognostic models for predicting complete recovery in ischemic stroke patients. Two reviewers independently examined titles and abstracts, and assessed whether each study met the pre-defined inclusion criteria and also independently extracted information about model development and performance. We evaluated validation of the models by medians of the area under the receiver operating characteristic curve (AUC) or c-statistic and calibration performance. We used a random-effects meta-analysis to pool AUC values. We included 10 studies with 23 models developed from elderly patients with a moderately severe ischemic stroke, mainly in three high income countries. Sample sizes for each study ranged from 75 to 4441. Logistic regression was the only analytical strategy used to develop the models. The number of various predictors varied from one to 11. Internal validation was performed in 12 models with a median AUC of 0.80 (95% CI 0.73 to 0.84). One model reported good calibration. Nine models reported external validation with a median AUC of 0.80 (95% CI 0.76 to 0.82). Four models showed good discrimination and calibration on external validation. The pooled AUC of the two validation models of the same developed model was 0.78 (95% CI 0.71 to 0.85). The performance of the 23 models found in the systematic review varied from fair to good in terms of internal and external validation. Further models should be developed with internal and external validation in low and middle income countries.

Calibration of two complex ecosystem models with different likelihood functions

NASA Astrophysics Data System (ADS)

Hidy, Dóra; Haszpra, László; Pintér, Krisztina; Nagy, Zoltán; Barcza, Zoltán

2014-05-01

The biosphere is a sensitive carbon reservoir. Terrestrial ecosystems were approximately carbon neutral during the past centuries, but they became net carbon sinks due to climate change induced environmental change and associated CO2 fertilization effect of the atmosphere. Model studies and measurements indicate that the biospheric carbon sink can saturate in the future due to ongoing climate change which can act as a positive feedback. Robustness of carbon cycle models is a key issue when trying to choose the appropriate model for decision support. The input parameters of the process-based models are decisive regarding the model output. At the same time there are several input parameters for which accurate values are hard to obtain directly from experiments or no local measurements are available. Due to the uncertainty associated with the unknown model parameters significant bias can be experienced if the model is used to simulate the carbon and nitrogen cycle components of different ecosystems. In order to improve model performance the unknown model parameters has to be estimated. We developed a multi-objective, two-step calibration method based on Bayesian approach in order to estimate the unknown parameters of PaSim and Biome-BGC models. Biome-BGC and PaSim are a widely used biogeochemical models that simulate the storage and flux of water, carbon, and nitrogen between the ecosystem and the atmosphere, and within the components of the terrestrial ecosystems (in this research the developed version of Biome-BGC is used which is referred as BBGC MuSo). Both models were calibrated regardless the simulated processes and type of model parameters. The calibration procedure is based on the comparison of measured data with simulated results via calculating a likelihood function (degree of goodness-of-fit between simulated and measured data). In our research different likelihood function formulations were used in order to examine the effect of the different model goodness metric on calibration. The different likelihoods are different functions of RMSE (root mean squared error) weighted by measurement uncertainty: exponential / linear / quadratic / linear normalized by correlation. As a first calibration step sensitivity analysis was performed in order to select the influential parameters which have strong effect on the output data. In the second calibration step only the sensitive parameters were calibrated (optimal values and confidence intervals were calculated). In case of PaSim more parameters were found responsible for the 95% of the output data variance than is case of BBGC MuSo. Analysis of the results of the optimized models revealed that the exponential likelihood estimation proved to be the most robust (best model simulation with optimized parameter, highest confidence interval increase). The cross-validation of the model simulations can help in constraining the highly uncertain greenhouse gas budget of grasslands.

Firefly algorithm versus genetic algorithm as powerful variable selection tools and their effect on different multivariate calibration models in spectroscopy: A comparative study

NASA Astrophysics Data System (ADS)

Attia, Khalid A. M.; Nassar, Mohammed W. I.; El-Zeiny, Mohamed B.; Serag, Ahmed

2017-01-01

For the first time, a new variable selection method based on swarm intelligence namely firefly algorithm is coupled with three different multivariate calibration models namely, concentration residual augmented classical least squares, artificial neural network and support vector regression in UV spectral data. A comparative study between the firefly algorithm and the well-known genetic algorithm was developed. The discussion revealed the superiority of using this new powerful algorithm over the well-known genetic algorithm. Moreover, different statistical tests were performed and no significant differences were found between all the models regarding their predictabilities. This ensures that simpler and faster models were obtained without any deterioration of the quality of the calibration.

Metric Calibration of a Focused Plenoptic Camera Based on a 3d Calibration Target

NASA Astrophysics Data System (ADS)

Zeller, N.; Noury, C. A.; Quint, F.; Teulière, C.; Stilla, U.; Dhome, M.

2016-06-01

In this paper we present a new calibration approach for focused plenoptic cameras. We derive a new mathematical projection model of a focused plenoptic camera which considers lateral as well as depth distortion. Therefore, we derive a new depth distortion model directly from the theory of depth estimation in a focused plenoptic camera. In total the model consists of five intrinsic parameters, the parameters for radial and tangential distortion in the image plane and two new depth distortion parameters. In the proposed calibration we perform a complete bundle adjustment based on a 3D calibration target. The residual of our optimization approach is three dimensional, where the depth residual is defined by a scaled version of the inverse virtual depth difference and thus conforms well to the measured data. Our method is evaluated based on different camera setups and shows good accuracy. For a better characterization of our approach we evaluate the accuracy of virtual image points projected back to 3D space.

Bayesian model calibration of computational models in velocimetry diagnosed dynamic compression experiments.

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

Brown, Justin; Hund, Lauren

2017-02-01

Dynamic compression experiments are being performed on complicated materials using increasingly complex drivers. The data produced in these experiments are beginning to reach a regime where traditional analysis techniques break down; requiring the solution of an inverse problem. A common measurement in dynamic experiments is an interface velocity as a function of time, and often this functional output can be simulated using a hydrodynamics code. Bayesian model calibration is a statistical framework to estimate inputs into a computational model in the presence of multiple uncertainties, making it well suited to measurements of this type. In this article, we apply Bayesianmore » model calibration to high pressure (250 GPa) ramp compression measurements in tantalum. We address several issues speci c to this calibration including the functional nature of the output as well as parameter and model discrepancy identi ability. Speci cally, we propose scaling the likelihood function by an e ective sample size rather than modeling the autocorrelation function to accommodate the functional output and propose sensitivity analyses using the notion of `modularization' to assess the impact of experiment-speci c nuisance input parameters on estimates of material properties. We conclude that the proposed Bayesian model calibration procedure results in simple, fast, and valid inferences on the equation of state parameters for tantalum.« less

Desert soil clay content estimation using reflectance spectroscopy preprocessed by fractional derivative

PubMed Central

Tiyip, Tashpolat; Ding, Jianli; Zhang, Dong; Liu, Wei; Wang, Fei; Tashpolat, Nigara

2017-01-01

Effective pretreatment of spectral reflectance is vital to model accuracy in soil parameter estimation. However, the classic integer derivative has some disadvantages, including spectral information loss and the introduction of high-frequency noise. In this paper, the fractional order derivative algorithm was applied to the pretreatment and partial least squares regression (PLSR) was used to assess the clay content of desert soils. Overall, 103 soil samples were collected from the Ebinur Lake basin in the Xinjiang Uighur Autonomous Region of China, and used as data sets for calibration and validation. Following laboratory measurements of spectral reflectance and clay content, the raw spectral reflectance and absorbance data were treated using the fractional derivative order from the 0.0 to the 2.0 order (order interval: 0.2). The ratio of performance to deviation (RPD), determinant coefficients of calibration (Rc2), root mean square errors of calibration (RMSEC), determinant coefficients of prediction (Rp2), and root mean square errors of prediction (RMSEP) were applied to assess the performance of predicting models. The results showed that models built on the fractional derivative order performed better than when using the classic integer derivative. Comparison of the predictive effects of 22 models for estimating clay content, calibrated by PLSR, showed that those models based on the fractional derivative 1.8 order of spectral reflectance (Rc2 = 0.907, RMSEC = 0.425%, Rp2 = 0.916, RMSEP = 0.364%, and RPD = 2.484 ≥ 2.000) and absorbance (Rc2 = 0.888, RMSEC = 0.446%, Rp2 = 0.918, RMSEP = 0.383% and RPD = 2.511 ≥ 2.000) were most effective. Furthermore, they performed well in quantitative estimations of the clay content of soils in the study area. PMID:28934274

Evaluation of global climate model on performances of precipitation simulation and prediction in the Huaihe River basin

NASA Astrophysics Data System (ADS)

Wu, Yenan; Zhong, Ping-an; Xu, Bin; Zhu, Feilin; Fu, Jisi

2017-06-01

Using climate models with high performance to predict the future climate changes can increase the reliability of results. In this paper, six kinds of global climate models that selected from the Coupled Model Intercomparison Project Phase 5 (CMIP5) under Representative Concentration Path (RCP) 4.5 scenarios were compared to the measured data during baseline period (1960-2000) and evaluate the simulation performance on precipitation. Since the results of single climate models are often biased and highly uncertain, we examine the back propagation (BP) neural network and arithmetic mean method in assembling the precipitation of multi models. The delta method was used to calibrate the result of single model and multimodel ensembles by arithmetic mean method (MME-AM) during the validation period (2001-2010) and the predicting period (2011-2100). We then use the single models and multimodel ensembles to predict the future precipitation process and spatial distribution. The result shows that BNU-ESM model has the highest simulation effect among all the single models. The multimodel assembled by BP neural network (MME-BP) has a good simulation performance on the annual average precipitation process and the deterministic coefficient during the validation period is 0.814. The simulation capability on spatial distribution of precipitation is: calibrated MME-AM > MME-BP > calibrated BNU-ESM. The future precipitation predicted by all models tends to increase as the time period increases. The order of average increase amplitude of each season is: winter > spring > summer > autumn. These findings can provide useful information for decision makers to make climate-related disaster mitigation plans.

Calibrating a hydraulic model using water levels derived from time series high-resolution Radarsat-2 synthetic aperture radar images and elevation data

NASA Astrophysics Data System (ADS)

Trudel, M.; Desrochers, N.; Leconte, R.

2017-12-01

Knowledge of water extent (WE) and level (WL) of rivers is necessary to calibrate and validate hydraulic models and thus to better simulate and forecast floods. Synthetic aperture radar (SAR) has demonstrated its potential for delineating water bodies, as backscattering of water is much lower than that of other natural surfaces. The ability of SAR to obtain information despite cloud cover makes it an interesting tool for temporal monitoring of water bodies. The delineation of WE combined with a high-resolution digital terrain model (DTM) allows extracting WL. However, most research using SAR data to calibrate hydraulic models has been carried out using one or two images. The objectives of this study is to use WL derived from time series high resolution Radarsat-2 SAR images for the calibration of a 1-D hydraulic model (HEC-RAS). Twenty high-resolution (5 m) Radarsat-2 images were acquired over a 40 km reach of the Athabasca River, in northern Alberta, Canada, between 2012 and 2016, covering both low and high flow regimes. A high-resolution (2m) DTM was generated combining information from LIDAR data and bathymetry acquired between 2008 and 2016 by boat surveying. The HEC-RAS model was implemented on the Athabasca River to simulate WL using cross-sections spaced by 100 m. An image histogram thresholding method was applied on each Radarsat-2 image to derive WE. WE were then compared against each cross-section to identify those were the slope of the banks is not too abrupt and therefore amenable to extract WL. 139 observations of WL at different locations along the river reach and with streamflow measurements were used to calibrate the HEC-RAS model. The RMSE between SAR-derived and simulated WL is under 0.35 m. Validation was performed using in situ observations of WL measured in 2008, 2012 and 2016. The RMSE between the simulated water levels calibrated with SAR images and in situ observations is less than 0.20 m. In addition, a critical success index (CSI) was performed to compare the WE simulated by HEC-RAS and that derived from SARs images. The CSI is higher than 0.85 for each date, which means that simulated WE is highly similar to the WE derived from SARs images. Thereby, the results of our analysis indicate that calibration of a hydraulic model can be performed from WL derived from time series of high-resolution SAR images.

Reducing calibration parameters to increase insight in catchment organization and similarity

NASA Astrophysics Data System (ADS)

Skaugen, Thomas; Onof, Christian

2013-04-01

Ideally, hydrological models should be built from equations parameterised from observed catchment characteristics and data. This state of affairs may never be reached, but a governing principle in hydrological modelling should be to keep the number of calibration parameters to a minimum. A reduced number of parameters to be calibrated, while maintaining the accuracy and detail required by modern hydrological models, will reduce parameter and model structure uncertainty and improve model diagnostics. The dynamics of runoff for small catchments are derived from the distribution of distances from points in the catchments to the nearest stream in a catchment. This distribution is unique for each catchment and can be determined from a geographical information system (GIS). The distribution of distances, will, when a celerity of (subsurface) flow is introduced, provide a distribution of travel times, or a unit hydrograph (UH). For spatially varying levels of saturation deficit we have different celerities and, hence, different UHs. Runoff is derived from the super-positioning of the different UHs. This study shows how celerities can be estimated if we assume that recession events represent the superpositioned UH for different levels of saturation deficit. The performance of the DDD (Distance Distribution Dynamics) model is compared to that of the Swedish HBV model and is found to perform equally well for eight Norwegian catchments although the number of parameters to be calibrated in the module concerning soil moisture and runoff dynamics is reduced from 7 in the HBV model to 1 in the DDD model. It is also shown that the DDD model has a more realistic representation of the subsurface hydrology. The transparency of the DDD model makes model diagnostics more easy and experience with DDD shows that differences in model performance may be related to differences in catchment characteristics. More specifically, it appears that the hydrological dynamics of bogs have to be taken especially into account when modelling Norwegian catchments.

Absolute sensitivity calibration of an extreme ultraviolet spectrometer for tokamak measurements

NASA Astrophysics Data System (ADS)

Guirlet, R.; Schwob, J. L.; Meyer, O.; Vartanian, S.

2017-01-01

An extreme ultraviolet spectrometer installed on the Tore Supra tokamak has been calibrated in absolute units of brightness in the range 10-340 Å. This has been performed by means of a combination of techniques. The range 10-113 Å was absolutely calibrated by using an ultrasoft-X ray source emitting six spectral lines in this range. The calibration transfer to the range 113-182 Å was performed using the spectral line intensity branching ratio method. The range 182-340 Å was calibrated thanks to radiative-collisional modelling of spectral line intensity ratios. The maximum sensitivity of the spectrometer was found to lie around 100 Å. Around this wavelength, the sensitivity is fairly flat in a 80 Å wide interval. The spatial variations of sensitivity along the detector assembly were also measured. The observed trend is related to the quantum efficiency decrease as the angle of the incoming photon trajectories becomes more grazing.

Basic Geometric Support of Systems for Earth Observation from Geostationary and Highly Elliptical Orbits

NASA Astrophysics Data System (ADS)

Gektin, Yu. M.; Egoshkin, N. A.; Eremeev, V. V.; Kuznecov, A. E.; Moskatinyev, I. V.; Smelyanskiy, M. B.

2017-12-01

A set of standardized models and algorithms for geometric normalization and georeferencing images from geostationary and highly elliptical Earth observation systems is considered. The algorithms can process information from modern scanning multispectral sensors with two-coordinate scanning and represent normalized images in optimal projection. Problems of the high-precision ground calibration of the imaging equipment using reference objects, as well as issues of the flight calibration and refinement of geometric models using the absolute and relative reference points, are considered. Practical testing of the models, algorithms, and technologies is performed in the calibration of sensors for spacecrafts of the Electro-L series and during the simulation of the Arktika prospective system.

Microbial models with data-driven parameters predict stronger soil carbon responses to climate change.

PubMed

Hararuk, Oleksandra; Smith, Matthew J; Luo, Yiqi

2015-06-01

Long-term carbon (C) cycle feedbacks to climate depend on the future dynamics of soil organic carbon (SOC). Current models show low predictive accuracy at simulating contemporary SOC pools, which can be improved through parameter estimation. However, major uncertainty remains in global soil responses to climate change, particularly uncertainty in how the activity of soil microbial communities will respond. To date, the role of microbes in SOC dynamics has been implicitly described by decay rate constants in most conventional global carbon cycle models. Explicitly including microbial biomass dynamics into C cycle model formulations has shown potential to improve model predictive performance when assessed against global SOC databases. This study aimed to data-constrained parameters of two soil microbial models, evaluate the improvements in performance of those calibrated models in predicting contemporary carbon stocks, and compare the SOC responses to climate change and their uncertainties between microbial and conventional models. Microbial models with calibrated parameters explained 51% of variability in the observed total SOC, whereas a calibrated conventional model explained 41%. The microbial models, when forced with climate and soil carbon input predictions from the 5th Coupled Model Intercomparison Project (CMIP5), produced stronger soil C responses to 95 years of climate change than any of the 11 CMIP5 models. The calibrated microbial models predicted between 8% (2-pool model) and 11% (4-pool model) soil C losses compared with CMIP5 model projections which ranged from a 7% loss to a 22.6% gain. Lastly, we observed unrealistic oscillatory SOC dynamics in the 2-pool microbial model. The 4-pool model also produced oscillations, but they were less prominent and could be avoided, depending on the parameter values. © 2014 John Wiley & Sons Ltd.

Polarized-pixel performance model for DoFP polarimeter

NASA Astrophysics Data System (ADS)

Feng, Bin; Shi, Zelin; Liu, Haizheng; Liu, Li; Zhao, Yaohong; Zhang, Junchao

2018-06-01

A division of a focal plane (DoFP) polarimeter is manufactured by placing a micropolarizer array directly onto the focal plane array (FPA) of a detector. Each element of the DoFP polarimeter is a polarized pixel. This paper proposes a performance model for a polarized pixel. The proposed model characterizes the optical and electronic performance of a polarized pixel by three parameters. They are respectively major polarization responsivity, minor polarization responsivity and polarization orientation. Each parameter corresponds to an intuitive physical feature of a polarized pixel. This paper further extends this model to calibrate polarization images from a DoFP (division of focal plane) polarimeter. This calibration work is evaluated quantitatively by a developed DoFP polarimeter under varying illumination intensity and angle of linear polarization. The experiment proves that our model reduces nonuniformity to 6.79% of uncalibrated DoLP (degree of linear polarization) images, and significantly improves the visual effect of DoLP images.

Conceptual modelling to predict unobserved system states - the case of groundwater flooding in the UK Chalk

NASA Astrophysics Data System (ADS)

Hartmann, A. J.; Ireson, A. M.

2017-12-01

Chalk aquifers represent an important source of drinking water in the UK. Due to its fractured-porous structure, Chalk aquifers are characterized by highly dynamic groundwater fluctuations that enhance the risk of groundwater flooding. The risk of groundwater flooding can be assessed by physically-based groundwater models. But for reliable results, a-priori information about the distribution of hydraulic conductivities and porosities is necessary, which is often not available. For that reason, conceptual simulation models are often used to predict groundwater behaviour. They commonly require calibration by historic groundwater observations. Consequently, their prediction performance may reduce significantly, when it comes to system states that did not occur within the calibration time series. In this study, we calibrate a conceptual model to the observed groundwater level observations at several locations within a Chalk system in Southern England. During the calibration period, no groundwater flooding occurred. We then apply our model to predict the groundwater dynamics of the system at a time that includes a groundwater flooding event. We show that the calibrated model provides reasonable predictions before and after the flooding event but it over-estimates groundwater levels during the event. After modifying the model structure to include topographic information, the model is capable of prediction the groundwater flooding event even though groundwater flooding never occurred in the calibration period. Although straight forward, our approach shows how conceptual process-based models can be applied to predict system states and dynamics that did not occur in the calibration period. We believe such an approach can be transferred to similar cases, especially to regions where rainfall intensities are expected to trigger processes and system states that may have not yet been observed.

A Taxonomy-Based Approach to Shed Light on the Babel of Mathematical Models for Rice Simulation

NASA Technical Reports Server (NTRS)

Confalonieri, Roberto; Bregaglio, Simone; Adam, Myriam; Ruget, Francoise; Li, Tao; Hasegawa, Toshihiro; Yin, Xinyou; Zhu, Yan; Boote, Kenneth; Buis, Samuel;

Longitudinal train dynamics model for a rail transit simulation system

DOE PAGES

Wang, Jinghui; Rakha, Hesham A.

2018-01-01

The paper develops a longitudinal train dynamics model in support of microscopic railway transportation simulation. The model can be calibrated without any mechanical data making it ideal for implementation in transportation simulators. The calibration and validation work is based on data collected from the Portland light rail train fleet. The calibration procedure is mathematically formulated as a constrained non-linear optimization problem. The validity of the model is assessed by comparing instantaneous model predictions against field observations, and also evaluated in the domains of acceleration/deceleration versus speed and acceleration/deceleration versus distance. A test is conducted to investigate the adequacy of themore » model in simulation implementation. The results demonstrate that the proposed model can adequately capture instantaneous train dynamics, and provides good performance in the simulation test. Thus, the model provides a simple theoretical foundation for microscopic simulators and will significantly support the planning, management and control of railway transportation systems.« less

Global sensitivity analysis of a filtration model for submerged anaerobic membrane bioreactors (AnMBR).

PubMed

Robles, A; Ruano, M V; Ribes, J; Seco, A; Ferrer, J

2014-04-01

The results of a global sensitivity analysis of a filtration model for submerged anaerobic MBRs (AnMBRs) are assessed in this paper. This study aimed to (1) identify the less- (or non-) influential factors of the model in order to facilitate model calibration and (2) validate the modelling approach (i.e. to determine the need for each of the proposed factors to be included in the model). The sensitivity analysis was conducted using a revised version of the Morris screening method. The dynamic simulations were conducted using long-term data obtained from an AnMBR plant fitted with industrial-scale hollow-fibre membranes. Of the 14 factors in the model, six were identified as influential, i.e. those calibrated using off-line protocols. A dynamic calibration (based on optimisation algorithms) of these influential factors was conducted. The resulting estimated model factors accurately predicted membrane performance. Copyright © 2014 Elsevier Ltd. All rights reserved.

Longitudinal train dynamics model for a rail transit simulation system

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

Wang, Jinghui; Rakha, Hesham A.

The paper develops a longitudinal train dynamics model in support of microscopic railway transportation simulation. The model can be calibrated without any mechanical data making it ideal for implementation in transportation simulators. The calibration and validation work is based on data collected from the Portland light rail train fleet. The calibration procedure is mathematically formulated as a constrained non-linear optimization problem. The validity of the model is assessed by comparing instantaneous model predictions against field observations, and also evaluated in the domains of acceleration/deceleration versus speed and acceleration/deceleration versus distance. A test is conducted to investigate the adequacy of themore » model in simulation implementation. The results demonstrate that the proposed model can adequately capture instantaneous train dynamics, and provides good performance in the simulation test. Thus, the model provides a simple theoretical foundation for microscopic simulators and will significantly support the planning, management and control of railway transportation systems.« less

Multivariate calibration standardization across instruments for the determination of glucose by Fourier transform near-infrared spectrometry.

PubMed

Zhang, Lin; Small, Gary W; Arnold, Mark A

2003-11-01

The transfer of multivariate calibration models is investigated between a primary (A) and two secondary Fourier transform near-infrared (near-IR) spectrometers (B, C). The application studied in this work is the use of bands in the near-IR combination region of 5000-4000 cm(-)(1) to determine physiological levels of glucose in a buffered aqueous matrix containing varying levels of alanine, ascorbate, lactate, triacetin, and urea. The three spectrometers are used to measure 80 samples produced through a randomized experimental design that minimizes correlations between the component concentrations and between the concentrations of glucose and water. Direct standardization (DS), piecewise direct standardization (PDS), and guided model reoptimization (GMR) are evaluated for use in transferring partial least-squares calibration models developed with the spectra of 64 samples from the primary instrument to the prediction of glucose concentrations in 16 prediction samples measured with each secondary spectrometer. The three algorithms are evaluated as a function of the number of standardization samples used in transferring the calibration models. Performance criteria for judging the success of the calibration transfer are established as the standard error of prediction (SEP) for internal calibration models built with the spectra of the 64 calibration samples collected with each secondary spectrometer. These SEP values are 1.51 and 1.14 mM for spectrometers B and C, respectively. When calibration standardization is applied, the GMR algorithm is observed to outperform DS and PDS. With spectrometer C, the calibration transfer is highly successful, producing an SEP value of 1.07 mM. However, an SEP of 2.96 mM indicates unsuccessful calibration standardization with spectrometer B. This failure is attributed to differences in the variance structure of the spectra collected with spectrometers A and B. Diagnostic procedures are presented for use with the GMR algorithm that forecasts the successful calibration transfer with spectrometer C and the unsatisfactory results with spectrometer B.

Texas flexible pavements and overlays : interim report for phases 2 and 3 - data collection and model calibration.

DOT National Transportation Integrated Search

2017-05-01

This five-year project was initiated to collect materials and pavement performance data on a minimum of 100 : highway test sections around the state of Texas, incorporating both flexible pavements and overlays. Besides : being used to calibrate and v...

NREL Improves Building Energy Simulation Programs Through Diagnostic Testing (Fact Sheet)

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

Not Available

2012-01-01

This technical highlight describes NREL research to develop Building Energy Simulation Test for Existing Homes (BESTEST-EX) to increase the quality and accuracy of energy analysis tools for the building retrofit market. Researchers at the National Renewable Energy Laboratory (NREL) have developed a new test procedure to increase the quality and accuracy of energy analysis tools for the building retrofit market. The Building Energy Simulation Test for Existing Homes (BESTEST-EX) is a test procedure that enables software developers to evaluate the performance of their audit tools in modeling energy use and savings in existing homes when utility bills are available formore » model calibration. Similar to NREL's previous energy analysis tests, such as HERS BESTEST and other BESTEST suites included in ANSI/ASHRAE Standard 140, BESTEST-EX compares software simulation findings to reference results generated with state-of-the-art simulation tools such as EnergyPlus, SUNREL, and DOE-2.1E. The BESTEST-EX methodology: (1) Tests software predictions of retrofit energy savings in existing homes; (2) Ensures building physics calculations and utility bill calibration procedures perform to a minimum standard; and (3) Quantifies impacts of uncertainties in input audit data and occupant behavior. BESTEST-EX includes building physics and utility bill calibration test cases. The diagram illustrates the utility bill calibration test cases. Participants are given input ranges and synthetic utility bills. Software tools use the utility bills to calibrate key model inputs and predict energy savings for the retrofit cases. Participant energy savings predictions using calibrated models are compared to NREL predictions using state-of-the-art building energy simulation programs.« less

Estimation of adsorption isotherm and mass transfer parameters in protein chromatography using artificial neural networks.

PubMed

Wang, Gang; Briskot, Till; Hahn, Tobias; Baumann, Pascal; Hubbuch, Jürgen

2017-03-03

Mechanistic modeling has been repeatedly successfully applied in process development and control of protein chromatography. For each combination of adsorbate and adsorbent, the mechanistic models have to be calibrated. Some of the model parameters, such as system characteristics, can be determined reliably by applying well-established experimental methods, whereas others cannot be measured directly. In common practice of protein chromatography modeling, these parameters are identified by applying time-consuming methods such as frontal analysis combined with gradient experiments, curve-fitting, or combined Yamamoto approach. For new components in the chromatographic system, these traditional calibration approaches require to be conducted repeatedly. In the presented work, a novel method for the calibration of mechanistic models based on artificial neural network (ANN) modeling was applied. An in silico screening of possible model parameter combinations was performed to generate learning material for the ANN model. Once the ANN model was trained to recognize chromatograms and to respond with the corresponding model parameter set, it was used to calibrate the mechanistic model from measured chromatograms. The ANN model's capability of parameter estimation was tested by predicting gradient elution chromatograms. The time-consuming model parameter estimation process itself could be reduced down to milliseconds. The functionality of the method was successfully demonstrated in a study with the calibration of the transport-dispersive model (TDM) and the stoichiometric displacement model (SDM) for a protein mixture. Copyright © 2017 The Author(s). Published by Elsevier B.V. All rights reserved.

A Preliminary Assessment of the SURF Reactive Burn Model Implementation in FLAG

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

Johnson, Carl Edward; McCombe, Ryan Patrick; Carver, Kyle

Properly validated and calibrated reactive burn models (RBM) can be useful engineering tools for assessing high explosive performance and safety. Experiments with high explosives are expensive. Inexpensive RBM calculations are increasingly relied on for predictive analysis for performance and safety. This report discusses the validation of Menikoff and Shaw’s SURF reactive burn model, which has recently been implemented in the FLAG code. The LANL Gapstick experiment is discussed as is its’ utility in reactive burn model validation. Data obtained from pRad for the LT-63 series is also presented along with FLAG simulations using SURF for both PBX 9501 and PBXmore » 9502. Calibration parameters for both explosives are presented.« less

Accurate evaluation of sensitivity for calibration between a LiDAR and a panoramic camera used for remote sensing

NASA Astrophysics Data System (ADS)

García-Moreno, Angel-Iván; González-Barbosa, José-Joel; Ramírez-Pedraza, Alfonso; Hurtado-Ramos, Juan B.; Ornelas-Rodriguez, Francisco-Javier

2016-04-01

Computer-based reconstruction models can be used to approximate urban environments. These models are usually based on several mathematical approximations and the usage of different sensors, which implies dependency on many variables. The sensitivity analysis presented in this paper is used to weigh the relative importance of each uncertainty contributor into the calibration of a panoramic camera-LiDAR system. Both sensors are used for three-dimensional urban reconstruction. Simulated and experimental tests were conducted. For the simulated tests we analyze and compare the calibration parameters using the Monte Carlo and Latin hypercube sampling techniques. Sensitivity analysis for each variable involved into the calibration was computed by the Sobol method, which is based on the analysis of the variance breakdown, and the Fourier amplitude sensitivity test method, which is based on Fourier's analysis. Sensitivity analysis is an essential tool in simulation modeling and for performing error propagation assessments.

Hearing Tests on Mobile Devices: Evaluation of the Reference Sound Level by Means of Biological Calibration.

PubMed

Masalski, Marcin; Kipiński, Lech; Grysiński, Tomasz; Kręcicki, Tomasz

2016-05-30

Hearing tests carried out in home setting by means of mobile devices require previous calibration of the reference sound level. Mobile devices with bundled headphones create a possibility of applying the predefined level for a particular model as an alternative to calibrating each device separately. The objective of this study was to determine the reference sound level for sets composed of a mobile device and bundled headphones. Reference sound levels for Android-based mobile devices were determined using an open access mobile phone app by means of biological calibration, that is, in relation to the normal-hearing threshold. The examinations were conducted in 2 groups: an uncontrolled and a controlled one. In the uncontrolled group, the fully automated self-measurements were carried out in home conditions by 18- to 35-year-old subjects, without prior hearing problems, recruited online. Calibration was conducted as a preliminary step in preparation for further examination. In the controlled group, audiologist-assisted examinations were performed in a sound booth, on normal-hearing subjects verified through pure-tone audiometry, recruited offline from among the workers and patients of the clinic. In both the groups, the reference sound levels were determined on a subject's mobile device using the Bekesy audiometry. The reference sound levels were compared between the groups. Intramodel and intermodel analyses were carried out as well. In the uncontrolled group, 8988 calibrations were conducted on 8620 different devices representing 2040 models. In the controlled group, 158 calibrations (test and retest) were conducted on 79 devices representing 50 models. Result analysis was performed for 10 most frequently used models in both the groups. The difference in reference sound levels between uncontrolled and controlled groups was 1.50 dB (SD 4.42). The mean SD of the reference sound level determined for devices within the same model was 4.03 dB (95% CI 3.93-4.11). Statistically significant differences were found across models. Reference sound levels determined in the uncontrolled group are comparable to the values obtained in the controlled group. This validates the use of biological calibration in the uncontrolled group for determining the predefined reference sound level for new devices. Moreover, due to a relatively small deviation of the reference sound level for devices of the same model, it is feasible to conduct hearing screening on devices calibrated with the predefined reference sound level.

Predicting ecological flow regime at ungaged sites: A comparison of methods

USGS Publications Warehouse

Murphy, Jennifer C.; Knight, Rodney R.; Wolfe, William J.; Gain, W. Scott

2012-01-01

Nineteen ecologically relevant streamflow characteristics were estimated using published rainfall–runoff and regional regression models for six sites with observed daily streamflow records in Kentucky. The regional regression model produced median estimates closer to the observed median for all but two characteristics. The variability of predictions from both models was generally less than the observed variability. The variability of the predictions from the rainfall–runoff model was greater than that from the regional regression model for all but three characteristics. Eight characteristics predicted by the rainfall–runoff model display positive or negative bias across all six sites; biases are not as pronounced for the regional regression model. Results suggest that a rainfall–runoff model calibrated on a single characteristic is less likely to perform well as a predictor of a range of other characteristics (flow regime) when compared with a regional regression model calibrated individually on multiple characteristics used to represent the flow regime. Poor model performance may misrepresent hydrologic conditions, potentially distorting the perceived risk of ecological degradation. Without prior selection of streamflow characteristics, targeted calibration, and error quantification, the widespread application of general hydrologic models to ecological flow studies is problematic. Published 2012. This article is a U.S. Government work and is in the public domain in the USA.

Improved calibration-based non-uniformity correction method for uncooled infrared camera

NASA Astrophysics Data System (ADS)

Liu, Chengwei; Sui, Xiubao

2017-08-01

With the latest improvements of microbolometer focal plane arrays (FPA), uncooled infrared (IR) cameras are becoming the most widely used devices in thermography, especially in handheld devices. However the influences derived from changing ambient condition and the non-uniform response of the sensors make it more difficult to correct the nonuniformity of uncooled infrared camera. In this paper, based on the infrared radiation characteristic in the TEC-less uncooled infrared camera, a novel model was proposed for calibration-based non-uniformity correction (NUC). In this model, we introduce the FPA temperature, together with the responses of microbolometer under different ambient temperature to calculate the correction parameters. Based on the proposed model, we can work out the correction parameters with the calibration measurements under controlled ambient condition and uniform blackbody. All correction parameters can be determined after the calibration process and then be used to correct the non-uniformity of the infrared camera in real time. This paper presents the detail of the compensation procedure and the performance of the proposed calibration-based non-uniformity correction method. And our method was evaluated on realistic IR images obtained by a 384x288 pixels uncooled long wave infrared (LWIR) camera operated under changed ambient condition. The results show that our method can exclude the influence caused by the changed ambient condition, and ensure that the infrared camera has a stable performance.

Climate, soil or both? Which variables are better predictors of the distributions of Australian shrub species?

PubMed Central

Esperón-Rodríguez, Manuel; Baumgartner, John B.; Beaumont, Linda J.

2017-01-01

Background Shrubs play a key role in biogeochemical cycles, prevent soil and water erosion, provide forage for livestock, and are a source of food, wood and non-wood products. However, despite their ecological and societal importance, the influence of different environmental variables on shrub distributions remains unclear. We evaluated the influence of climate and soil characteristics, and whether including soil variables improved the performance of a species distribution model (SDM), Maxent. Methods This study assessed variation in predictions of environmental suitability for 29 Australian shrub species (representing dominant members of six shrubland classes) due to the use of alternative sets of predictor variables. Models were calibrated with (1) climate variables only, (2) climate and soil variables, and (3) soil variables only. Results The predictive power of SDMs differed substantially across species, but generally models calibrated with both climate and soil data performed better than those calibrated only with climate variables. Models calibrated solely with soil variables were the least accurate. We found regional differences in potential shrub species richness across Australia due to the use of different sets of variables. Conclusions Our study provides evidence that predicted patterns of species richness may be sensitive to the choice of predictor set when multiple, plausible alternatives exist, and demonstrates the importance of considering soil properties when modeling availability of habitat for plants. PMID:28652933

Bayesian calibration of terrestrial ecosystem models: a study of advanced Markov chain Monte Carlo methods

NASA Astrophysics Data System (ADS)

Lu, Dan; Ricciuto, Daniel; Walker, Anthony; Safta, Cosmin; Munger, William

2017-09-01

Calibration of terrestrial ecosystem models is important but challenging. Bayesian inference implemented by Markov chain Monte Carlo (MCMC) sampling provides a comprehensive framework to estimate model parameters and associated uncertainties using their posterior distributions. The effectiveness and efficiency of the method strongly depend on the MCMC algorithm used. In this work, a differential evolution adaptive Metropolis (DREAM) algorithm is used to estimate posterior distributions of 21 parameters for the data assimilation linked ecosystem carbon (DALEC) model using 14 years of daily net ecosystem exchange data collected at the Harvard Forest Environmental Measurement Site eddy-flux tower. The calibration of DREAM results in a better model fit and predictive performance compared to the popular adaptive Metropolis (AM) scheme. Moreover, DREAM indicates that two parameters controlling autumn phenology have multiple modes in their posterior distributions while AM only identifies one mode. The application suggests that DREAM is very suitable to calibrate complex terrestrial ecosystem models, where the uncertain parameter size is usually large and existence of local optima is always a concern. In addition, this effort justifies the assumptions of the error model used in Bayesian calibration according to the residual analysis. The result indicates that a heteroscedastic, correlated, Gaussian error model is appropriate for the problem, and the consequent constructed likelihood function can alleviate the underestimation of parameter uncertainty that is usually caused by using uncorrelated error models.

Mixture EMOS model for calibrating ensemble forecasts of wind speed.

PubMed

Baran, S; Lerch, S

2016-03-01

Ensemble model output statistics (EMOS) is a statistical tool for post-processing forecast ensembles of weather variables obtained from multiple runs of numerical weather prediction models in order to produce calibrated predictive probability density functions. The EMOS predictive probability density function is given by a parametric distribution with parameters depending on the ensemble forecasts. We propose an EMOS model for calibrating wind speed forecasts based on weighted mixtures of truncated normal (TN) and log-normal (LN) distributions where model parameters and component weights are estimated by optimizing the values of proper scoring rules over a rolling training period. The new model is tested on wind speed forecasts of the 50 member European Centre for Medium-range Weather Forecasts ensemble, the 11 member Aire Limitée Adaptation dynamique Développement International-Hungary Ensemble Prediction System ensemble of the Hungarian Meteorological Service, and the eight-member University of Washington mesoscale ensemble, and its predictive performance is compared with that of various benchmark EMOS models based on single parametric families and combinations thereof. The results indicate improved calibration of probabilistic and accuracy of point forecasts in comparison with the raw ensemble and climatological forecasts. The mixture EMOS model significantly outperforms the TN and LN EMOS methods; moreover, it provides better calibrated forecasts than the TN-LN combination model and offers an increased flexibility while avoiding covariate selection problems. © 2016 The Authors Environmetrics Published by JohnWiley & Sons Ltd.

Analyzing the effect of transmissivity uncertainty on the reliability of a model of the northwestern Sahara aquifer system

NASA Astrophysics Data System (ADS)

Zammouri, Mounira; Ribeiro, Luis

2017-05-01

Groundwater flow model of the transboundary Saharan aquifer system is developed in 2003 and used for management and decision-making by Algeria, Tunisia and Libya. In decision-making processes, reliability plays a decisive role. This paper looks into the reliability assessment of the Saharan aquifers model. It aims to detect the shortcomings of the model considered properly calibrated. After presenting the calibration results of the effort modelling in 2003, the uncertainty in the model which arising from the lack of the groundwater level and the transmissivity data is analyzed using kriging technique and stochastic approach. The structural analysis of piezometry in steady state and logarithms of transmissivity were carried out for the Continental Intercalaire (CI) and the Complexe Terminal (CT) aquifers. The available data (piezometry and transmissivity) were compared to the calculated values, using geostatistics approach. Using a stochastic approach, 2500 realizations of a log-normal random transmissivity field of the CI aquifer has been performed to assess the errors of the model output, due to the uncertainty in transmissivity. Two types of bad calibration are shown. In some regions, calibration should be improved using the available data. In others areas, undertaking the model refinement requires gathering new data to enhance the aquifer system knowledge. Stochastic simulations' results showed that the calculated drawdowns in 2050 could be higher than the values predicted by the calibrated model.

An information theoretic approach to use high-fidelity codes to calibrate low-fidelity codes

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

Lewis, Allison, E-mail: lewis.allison10@gmail.com; Smith, Ralph; Williams, Brian

For many simulation models, it can be prohibitively expensive or physically infeasible to obtain a complete set of experimental data to calibrate model parameters. In such cases, one can alternatively employ validated higher-fidelity codes to generate simulated data, which can be used to calibrate the lower-fidelity code. In this paper, we employ an information-theoretic framework to determine the reduction in parameter uncertainty that is obtained by evaluating the high-fidelity code at a specific set of design conditions. These conditions are chosen sequentially, based on the amount of information that they contribute to the low-fidelity model parameters. The goal is tomore » employ Bayesian experimental design techniques to minimize the number of high-fidelity code evaluations required to accurately calibrate the low-fidelity model. We illustrate the performance of this framework using heat and diffusion examples, a 1-D kinetic neutron diffusion equation, and a particle transport model, and include initial results from the integration of the high-fidelity thermal-hydraulics code Hydra-TH with a low-fidelity exponential model for the friction correlation factor.« less

Application of iterative robust model-based optimal experimental design for the calibration of biocatalytic models.

PubMed

Van Daele, Timothy; Gernaey, Krist V; Ringborg, Rolf H; Börner, Tim; Heintz, Søren; Van Hauwermeiren, Daan; Grey, Carl; Krühne, Ulrich; Adlercreutz, Patrick; Nopens, Ingmar

2017-09-01

The aim of model calibration is to estimate unique parameter values from available experimental data, here applied to a biocatalytic process. The traditional approach of first gathering data followed by performing a model calibration is inefficient, since the information gathered during experimentation is not actively used to optimize the experimental design. By applying an iterative robust model-based optimal experimental design, the limited amount of data collected is used to design additional informative experiments. The algorithm is used here to calibrate the initial reaction rate of an ω-transaminase catalyzed reaction in a more accurate way. The parameter confidence region estimated from the Fisher Information Matrix is compared with the likelihood confidence region, which is not only more accurate but also a computationally more expensive method. As a result, an important deviation between both approaches is found, confirming that linearization methods should be applied with care for nonlinear models. © 2017 American Institute of Chemical Engineers Biotechnol. Prog., 33:1278-1293, 2017. © 2017 American Institute of Chemical Engineers.

Calibration of the maximum carboxylation velocity (Vcmax) using data mining techniques and ecophysiological data from the Brazilian semiarid region, for use in Dynamic Global Vegetation Models.

PubMed

Rezende, L F C; Arenque-Musa, B C; Moura, M S B; Aidar, S T; Von Randow, C; Menezes, R S C; Ometto, J P B H

2016-06-01

The semiarid region of northeastern Brazil, the Caatinga, is extremely important due to its biodiversity and endemism. Measurements of plant physiology are crucial to the calibration of Dynamic Global Vegetation Models (DGVMs) that are currently used to simulate the responses of vegetation in face of global changes. In a field work realized in an area of preserved Caatinga forest located in Petrolina, Pernambuco, measurements of carbon assimilation (in response to light and CO2) were performed on 11 individuals of Poincianella microphylla, a native species that is abundant in this region. These data were used to calibrate the maximum carboxylation velocity (Vcmax) used in the INLAND model. The calibration techniques used were Multiple Linear Regression (MLR), and data mining techniques as the Classification And Regression Tree (CART) and K-MEANS. The results were compared to the UNCALIBRATED model. It was found that simulated Gross Primary Productivity (GPP) reached 72% of observed GPP when using the calibrated Vcmax values, whereas the UNCALIBRATED approach accounted for 42% of observed GPP. Thus, this work shows the benefits of calibrating DGVMs using field ecophysiological measurements, especially in areas where field data is scarce or non-existent, such as in the Caatinga.

Firefly algorithm versus genetic algorithm as powerful variable selection tools and their effect on different multivariate calibration models in spectroscopy: A comparative study.

PubMed

Attia, Khalid A M; Nassar, Mohammed W I; El-Zeiny, Mohamed B; Serag, Ahmed

2017-01-05

For the first time, a new variable selection method based on swarm intelligence namely firefly algorithm is coupled with three different multivariate calibration models namely, concentration residual augmented classical least squares, artificial neural network and support vector regression in UV spectral data. A comparative study between the firefly algorithm and the well-known genetic algorithm was developed. The discussion revealed the superiority of using this new powerful algorithm over the well-known genetic algorithm. Moreover, different statistical tests were performed and no significant differences were found between all the models regarding their predictabilities. This ensures that simpler and faster models were obtained without any deterioration of the quality of the calibration. Copyright © 2016 Elsevier B.V. All rights reserved.

The SPAtial EFficiency metric (SPAEF): multiple-component evaluation of spatial patterns for optimization of hydrological models

NASA Astrophysics Data System (ADS)

Koch, Julian; Cüneyd Demirel, Mehmet; Stisen, Simon

2018-05-01

The process of model evaluation is not only an integral part of model development and calibration but also of paramount importance when communicating modelling results to the scientific community and stakeholders. The modelling community has a large and well-tested toolbox of metrics to evaluate temporal model performance. In contrast, spatial performance evaluation does not correspond to the grand availability of spatial observations readily available and to the sophisticate model codes simulating the spatial variability of complex hydrological processes. This study makes a contribution towards advancing spatial-pattern-oriented model calibration by rigorously testing a multiple-component performance metric. The promoted SPAtial EFficiency (SPAEF) metric reflects three equally weighted components: correlation, coefficient of variation and histogram overlap. This multiple-component approach is found to be advantageous in order to achieve the complex task of comparing spatial patterns. SPAEF, its three components individually and two alternative spatial performance metrics, i.e. connectivity analysis and fractions skill score, are applied in a spatial-pattern-oriented model calibration of a catchment model in Denmark. Results suggest the importance of multiple-component metrics because stand-alone metrics tend to fail to provide holistic pattern information. The three SPAEF components are found to be independent, which allows them to complement each other in a meaningful way. In order to optimally exploit spatial observations made available by remote sensing platforms, this study suggests applying bias insensitive metrics which further allow for a comparison of variables which are related but may differ in unit. This study applies SPAEF in the hydrological context using the mesoscale Hydrologic Model (mHM; version 5.8), but we see great potential across disciplines related to spatially distributed earth system modelling.

Groundwater flow simulation of the Savannah River Site general separations area

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

Flach, G.; Bagwell, L.; Bennett, P.

The most recent groundwater flow model of the General Separations Area, Savannah River Site, is referred to as the “GSA/PORFLOW” model. GSA/PORFLOW was developed in 2004 by porting an existing General Separations Area groundwater flow model from the FACT code to the PORFLOW code. The preceding “GSA/FACT” model was developed in 1997 using characterization and monitoring data through the mid-1990’s. Both models were manually calibrated to field data. Significantly more field data have been acquired since the 1990’s and model calibration using mathematical optimization software has become routine and recommended practice. The current task involved updating the GSA/PORFLOW model usingmore » selected field data current through at least 2015, and use of the PEST code to calibrate the model and quantify parameter uncertainty. This new GSA groundwater flow model is named “GSA2016” in reference to the year in which most development occurred. The GSA2016 model update is intended to address issues raised by the DOE Low-Level Waste (LLW) Disposal Facility Federal Review Group (LFRG) in a 2008 review of the E-Area Performance Assessment, and by the Nuclear Regulatory Commission in reviews of tank closure and Saltstone Disposal Facility Performance Assessments.« less

A Semi-Automatic Image-Based Close Range 3D Modeling Pipeline Using a Multi-Camera Configuration

PubMed Central

Rau, Jiann-Yeou; Yeh, Po-Chia

2012-01-01

The generation of photo-realistic 3D models is an important task for digital recording of cultural heritage objects. This study proposes an image-based 3D modeling pipeline which takes advantage of a multi-camera configuration and multi-image matching technique that does not require any markers on or around the object. Multiple digital single lens reflex (DSLR) cameras are adopted and fixed with invariant relative orientations. Instead of photo-triangulation after image acquisition, calibration is performed to estimate the exterior orientation parameters of the multi-camera configuration which can be processed fully automatically using coded targets. The calibrated orientation parameters of all cameras are applied to images taken using the same camera configuration. This means that when performing multi-image matching for surface point cloud generation, the orientation parameters will remain the same as the calibrated results, even when the target has changed. Base on this invariant character, the whole 3D modeling pipeline can be performed completely automatically, once the whole system has been calibrated and the software was seamlessly integrated. Several experiments were conducted to prove the feasibility of the proposed system. Images observed include that of a human being, eight Buddhist statues, and a stone sculpture. The results for the stone sculpture, obtained with several multi-camera configurations were compared with a reference model acquired by an ATOS-I 2M active scanner. The best result has an absolute accuracy of 0.26 mm and a relative accuracy of 1:17,333. It demonstrates the feasibility of the proposed low-cost image-based 3D modeling pipeline and its applicability to a large quantity of antiques stored in a museum. PMID:23112656

A semi-automatic image-based close range 3D modeling pipeline using a multi-camera configuration.

PubMed

Rau, Jiann-Yeou; Yeh, Po-Chia

2012-01-01

The generation of photo-realistic 3D models is an important task for digital recording of cultural heritage objects. This study proposes an image-based 3D modeling pipeline which takes advantage of a multi-camera configuration and multi-image matching technique that does not require any markers on or around the object. Multiple digital single lens reflex (DSLR) cameras are adopted and fixed with invariant relative orientations. Instead of photo-triangulation after image acquisition, calibration is performed to estimate the exterior orientation parameters of the multi-camera configuration which can be processed fully automatically using coded targets. The calibrated orientation parameters of all cameras are applied to images taken using the same camera configuration. This means that when performing multi-image matching for surface point cloud generation, the orientation parameters will remain the same as the calibrated results, even when the target has changed. Base on this invariant character, the whole 3D modeling pipeline can be performed completely automatically, once the whole system has been calibrated and the software was seamlessly integrated. Several experiments were conducted to prove the feasibility of the proposed system. Images observed include that of a human being, eight Buddhist statues, and a stone sculpture. The results for the stone sculpture, obtained with several multi-camera configurations were compared with a reference model acquired by an ATOS-I 2M active scanner. The best result has an absolute accuracy of 0.26 mm and a relative accuracy of 1:17,333. It demonstrates the feasibility of the proposed low-cost image-based 3D modeling pipeline and its applicability to a large quantity of antiques stored in a museum.

Using Lunar Observations to Validate In-Flight Calibrations of Clouds and Earth Radiant Energy System Instruments

NASA Technical Reports Server (NTRS)

Daniels, Janet L.; Smith, G. Louis; Priestley, Kory J.; Thomas, Susan

2014-01-01

The validation of in-orbit instrument performance requires stability in both instrument and calibration source. This paper describes a method of validation using lunar observations scanning near full moon by the Clouds and Earth Radiant Energy System (CERES) instruments. Unlike internal calibrations, the Moon offers an external source whose signal variance is predictable and non-degrading. From 2006 to present, in-orbit observations have become standardized and compiled for the Flight Models-1 and -2 aboard the Terra satellite, for Flight Models-3 and -4 aboard the Aqua satellite, and beginning 2012, for Flight Model-5 aboard Suomi-NPP. Instrument performance parameters which can be gleaned are detector gain, pointing accuracy and static detector point response function validation. Lunar observations are used to examine the stability of all three detectors on each of these instruments from 2006 to present. This validation method has yielded results showing trends per CERES data channel of 1.2% per decade or less.

Assessment of bitterness intensity and suppression effects using an Electronic Tongue

NASA Astrophysics Data System (ADS)

Legin, A.; Rudnitskaya, A.; Kirsanov, D.; Frolova, Yu.; Clapham, D.; Caricofe, R.

2009-05-01

Quantification of bitterness intensity and effectivness of bitterness suppression of a novel active pharmacological ingredient (API) being developed by GSK was performed using an Electronic Tongue (ET) based on potentiometric chemical sensors. Calibration of the ET was performed with solutions of quinine hydrochloride in the concentration range 0.4-360 mgL-1. An MLR calibration model was developed for predicting bitterness intensity expressed as "equivalent quinine concentration" of a series of solutions of quinine, bittrex and the API. Additionally the effectiveness of sucralose, mixture of aspartame and acesulfame K, and grape juice in masking the bitter taste of the API was assessed using two approaches. PCA models were produced and distances between compound containing solutions and corresponding placebos were calculated. The other approach consisted in calculating "equivalent quinine concentration" using a calibration model with respect to quinine concentration. According to both methods, the most effective taste masking was produced by grape juice, followed by the mixture of aspartame and acesulfame K.

QUANTIFYING AN UNCERTAIN FUTURE: HYDROLOGIC MODEL PERFORMANCE FOR A SERIES OF REALIZED "/FUTURE" CONDITIONS

EPA Science Inventory

A systematic analysis of model performance during simulations based on observed landcover/use change is used to quantify errors associated with simulations of known "future" conditions. Calibrated and uncalibrated assessments of relative change over different lengths of...

Space-based infrared scanning sensor LOS determination and calibration using star observation

NASA Astrophysics Data System (ADS)

Chen, Jun; Xu, Zhan; An, Wei; Deng, Xin-Pu; Yang, Jun-Gang

2015-10-01

This paper provides a novel methodology for removing sensor bias from a space based infrared (IR) system (SBIRS) through the use of stars detected in the background field of the sensor. Space based IR system uses the LOS (line of sight) of target for target location. LOS determination and calibration is the key precondition of accurate location and tracking of targets in Space based IR system and the LOS calibration of scanning sensor is one of the difficulties. The subsequent changes of sensor bias are not been taking into account in the conventional LOS determination and calibration process. Based on the analysis of the imaging process of scanning sensor, a theoretical model based on the estimation of bias angles using star observation is proposed. By establishing the process model of the bias angles and the observation model of stars, using an extended Kalman filter (EKF) to estimate the bias angles, and then calibrating the sensor LOS. Time domain simulations results indicate that the proposed method has a high precision and smooth performance for sensor LOS determination and calibration. The timeliness and precision of target tracking process in the space based infrared (IR) tracking system could be met with the proposed algorithm.

Diagnostic utility of appetite loss in addition to existing prediction models for community-acquired pneumonia in the elderly: a prospective diagnostic study in acute care hospitals in Japan

PubMed Central

Yamamoto, Yosuke; Terada, Kazuhiko; Ohta, Mitsuyasu; Mikami, Wakako; Yokota, Hajime; Hayashi, Michio; Miyashita, Jun; Azuma, Teruhisa; Fukuma, Shingo; Fukuhara, Shunichi

2017-01-01

Objective Diagnosis of community-acquired pneumonia (CAP) in the elderly is often delayed because of atypical presentation and non-specific symptoms, such as appetite loss, falls and disturbance in consciousness. The aim of this study was to investigate the external validity of existing prediction models and the added value of the non-specific symptoms for the diagnosis of CAP in elderly patients. Design Prospective cohort study. Setting General medicine departments of three teaching hospitals in Japan. Participants A total of 109 elderly patients who consulted for upper respiratory symptoms between 1 October 2014 and 30 September 2016. Main outcome measures The reference standard for CAP was chest radiograph evaluated by two certified radiologists. The existing models were externally validated for diagnostic performance by calibration plot and discrimination. To evaluate the additional value of the non-specific symptoms to the existing prediction models, we developed an extended logistic regression model. Calibration, discrimination, category-free net reclassification improvement (NRI) and decision curve analysis (DCA) were investigated in the extended model. Results Among the existing models, the model by van Vugt demonstrated the best performance, with an area under the curve of 0.75(95% CI 0.63 to 0.88); calibration plot showed good fit despite a significant Hosmer-Lemeshow test (p=0.017). Among the non-specific symptoms, appetite loss had positive likelihood ratio of 3.2 (2.0–5.3), negative likelihood ratio of 0.4 (0.2–0.7) and OR of 7.7 (3.0–19.7). Addition of appetite loss to the model by van Vugt led to improved calibration at p=0.48, NRI of 0.53 (p=0.019) and higher net benefit by DCA. Conclusions Information on appetite loss improved the performance of an existing model for the diagnosis of CAP in the elderly. PMID:29122806

Effect of year-to-year variability of leaf area index on variable infiltration capacity model performance and simulation of streamflow during drought

NASA Astrophysics Data System (ADS)

Tesemma, Z. K.; Wei, Y.; Peel, M. C.; Western, A. W.

2014-09-01

This study assessed the effect of using observed monthly leaf area index (LAI) on hydrologic model performance and the simulation of streamflow during drought using the variable infiltration capacity (VIC) hydrological model in the Goulburn-Broken catchment of Australia, which has heterogeneous vegetation, soil and climate zones. VIC was calibrated with both observed monthly LAI and long-term mean monthly LAI, which were derived from the Global Land Surface Satellite (GLASS) observed monthly LAI dataset covering the period from 1982 to 2012. The model performance under wet and dry climates for the two different LAI inputs was assessed using three criteria, the classical Nash-Sutcliffe efficiency, the logarithm transformed flow Nash-Sutcliffe efficiency and the percentage bias. Finally, the percentage deviation of the simulated monthly streamflow using the observed monthly LAI from simulated streamflow using long-term mean monthly LAI was computed. The VIC model predicted monthly streamflow in the selected sub-catchments with model efficiencies ranging from 61.5 to 95.9% during calibration (1982-1997) and 59 to 92.4% during validation (1998-2012). Our results suggest systematic improvements from 4 to 25% in the Nash-Sutcliffe efficiency in pasture dominated catchments when the VIC model was calibrated with the observed monthly LAI instead of the long-term mean monthly LAI. There was limited systematic improvement in tree dominated catchments. The results also suggest that the model overestimation or underestimation of streamflow during wet and dry periods can be reduced to some extent by including the year-to-year variability of LAI in the model, thus reflecting the responses of vegetation to fluctuations in climate and other factors. Hence, the year-to-year variability in LAI should not be neglected; rather it should be included in model calibration as well as simulation of monthly water balance.

The effect of year-to-year variability of leaf area index on Variable Infiltration Capacity model performance and simulation of runoff

NASA Astrophysics Data System (ADS)

Tesemma, Z. K.; Wei, Y.; Peel, M. C.; Western, A. W.

2015-09-01

This study assessed the effect of using observed monthly leaf area index (LAI) on hydrological model performance and the simulation of runoff using the Variable Infiltration Capacity (VIC) hydrological model in the Goulburn-Broken catchment of Australia, which has heterogeneous vegetation, soil and climate zones. VIC was calibrated with both observed monthly LAI and long-term mean monthly LAI, which were derived from the Global Land Surface Satellite (GLASS) leaf area index dataset covering the period from 1982 to 2012. The model performance under wet and dry climates for the two different LAI inputs was assessed using three criteria, the classical Nash-Sutcliffe efficiency, the logarithm transformed flow Nash-Sutcliffe efficiency and the percentage bias. Finally, the deviation of the simulated monthly runoff using the observed monthly LAI from simulated runoff using long-term mean monthly LAI was computed. The VIC model predicted monthly runoff in the selected sub-catchments with model efficiencies ranging from 61.5% to 95.9% during calibration (1982-1997) and 59% to 92.4% during validation (1998-2012). Our results suggest systematic improvements, from 4% to 25% in Nash-Sutcliffe efficiency, in sparsely forested sub-catchments when the VIC model was calibrated with observed monthly LAI instead of long-term mean monthly LAI. There was limited systematic improvement in tree dominated sub-catchments. The results also suggest that the model overestimation or underestimation of runoff during wet and dry periods can be reduced to 25 mm and 35 mm respectively by including the year-to-year variability of LAI in the model, thus reflecting the responses of vegetation to fluctuations in climate and other factors. Hence, the year-to-year variability in LAI should not be neglected; rather it should be included in model calibration as well as simulation of monthly water balance.

The effects of numerical-model complexity and observation type on estimated porosity values

USGS Publications Warehouse

Starn, Jeffrey; Bagtzoglou, Amvrossios C.; Green, Christopher T.

2015-01-01

The relative merits of model complexity and types of observations employed in model calibration are compared. An existing groundwater flow model coupled with an advective transport simulation of the Salt Lake Valley, Utah (USA), is adapted for advective transport, and effective porosity is adjusted until simulated tritium concentrations match concentrations in samples from wells. Two calibration approaches are used: a “complex” highly parameterized porosity field and a “simple” parsimonious model of porosity distribution. The use of an atmospheric tracer (tritium in this case) and apparent ages (from tritium/helium) in model calibration also are discussed. Of the models tested, the complex model (with tritium concentrations and tritium/helium apparent ages) performs best. Although tritium breakthrough curves simulated by complex and simple models are very generally similar, and there is value in the simple model, the complex model is supported by a more realistic porosity distribution and a greater number of estimable parameters. Culling the best quality data did not lead to better calibration, possibly because of processes and aquifer characteristics that are not simulated. Despite many factors that contribute to shortcomings of both the models and the data, useful information is obtained from all the models evaluated. Although any particular prediction of tritium breakthrough may have large errors, overall, the models mimic observed trends.

Design and development of an ultrasound calibration phantom and system

NASA Astrophysics Data System (ADS)

Cheng, Alexis; Ackerman, Martin K.; Chirikjian, Gregory S.; Boctor, Emad M.

2014-03-01

Image-guided surgery systems are often used to provide surgeons with informational support. Due to several unique advantages such as ease of use, real-time image acquisition, and no ionizing radiation, ultrasound is a common medical imaging modality used in image-guided surgery systems. To perform advanced forms of guidance with ultrasound, such as virtual image overlays or automated robotic actuation, an ultrasound calibration process must be performed. This process recovers the rigid body transformation between a tracked marker attached to the ultrasound transducer and the ultrasound image. A phantom or model with known geometry is also required. In this work, we design and test an ultrasound calibration phantom and software. The two main considerations in this work are utilizing our knowledge of ultrasound physics to design the phantom and delivering an easy to use calibration process to the user. We explore the use of a three-dimensional printer to create the phantom in its entirety without need for user assembly. We have also developed software to automatically segment the three-dimensional printed rods from the ultrasound image by leveraging knowledge about the shape and scale of the phantom. In this work, we present preliminary results from using this phantom to perform ultrasound calibration. To test the efficacy of our method, we match the projection of the points segmented from the image to the known model and calculate a sum squared difference between each point for several combinations of motion generation and filtering methods. The best performing combination of motion and filtering techniques had an error of 1.56 mm and a standard deviation of 1.02 mm.

A frequentist approach to computer model calibration

DOE PAGES

Wong, Raymond K. W.; Storlie, Curtis Byron; Lee, Thomas C. M.

2016-05-05

The paper considers the computer model calibration problem and provides a general frequentist solution. Under the framework proposed, the data model is semiparametric with a non-parametric discrepancy function which accounts for any discrepancy between physical reality and the computer model. In an attempt to solve a fundamentally important (but often ignored) identifiability issue between the computer model parameters and the discrepancy function, the paper proposes a new and identifiable parameterization of the calibration problem. It also develops a two-step procedure for estimating all the relevant quantities under the new parameterization. This estimation procedure is shown to enjoy excellent rates ofmore » convergence and can be straightforwardly implemented with existing software. For uncertainty quantification, bootstrapping is adopted to construct confidence regions for the quantities of interest. As a result, the practical performance of the methodology is illustrated through simulation examples and an application to a computational fluid dynamics model.« less

Statistical analysis of target acquisition sensor modeling experiments

NASA Astrophysics Data System (ADS)

Deaver, Dawne M.; Moyer, Steve

2015-05-01

The U.S. Army RDECOM CERDEC NVESD Modeling and Simulation Division is charged with the development and advancement of military target acquisition models to estimate expected soldier performance when using all types of imaging sensors. Two elements of sensor modeling are (1) laboratory-based psychophysical experiments used to measure task performance and calibrate the various models and (2) field-based experiments used to verify the model estimates for specific sensors. In both types of experiments, it is common practice to control or measure environmental, sensor, and target physical parameters in order to minimize uncertainty of the physics based modeling. Predicting the minimum number of test subjects required to calibrate or validate the model should be, but is not always, done during test planning. The objective of this analysis is to develop guidelines for test planners which recommend the number and types of test samples required to yield a statistically significant result.

Dynamic trends in cardiac surgery: why the logistic EuroSCORE is no longer suitable for contemporary cardiac surgery and implications for future risk models

PubMed Central

Hickey, Graeme L.; Grant, Stuart W.; Murphy, Gavin J.; Bhabra, Moninder; Pagano, Domenico; McAllister, Katherine; Buchan, Iain; Bridgewater, Ben

2013-01-01

OBJECTIVES Progressive loss of calibration of the original EuroSCORE models has necessitated the introduction of the EuroSCORE II model. Poor model calibration has important implications for clinical decision-making and risk adjustment of governance analyses. The objective of this study was to explore the reasons for the calibration drift of the logistic EuroSCORE. METHODS Data from the Society for Cardiothoracic Surgery in Great Britain and Ireland database were analysed for procedures performed at all National Health Service and some private hospitals in England and Wales between April 2001 and March 2011. The primary outcome was in-hospital mortality. EuroSCORE risk factors, overall model calibration and discrimination were assessed over time. RESULTS A total of 317 292 procedures were included. Over the study period, mean age at surgery increased from 64.6 to 67.2 years. The proportion of procedures that were isolated coronary artery bypass grafts decreased from 67.5 to 51.2%. In-hospital mortality fell from 4.1 to 2.8%, but the mean logistic EuroSCORE increased from 5.6 to 7.6%. The logistic EuroSCORE remained a good discriminant throughout the study period (area under the receiver-operating characteristic curve between 0.79 and 0.85), but calibration (observed-to-expected mortality ratio) fell from 0.76 to 0.37. Inadequate adjustment for decreasing baseline risk affected calibration considerably. DISCUSSIONS Patient risk factors and case-mix in adult cardiac surgery change dynamically over time. Models like the EuroSCORE that are developed using a ‘snapshot’ of data in time do not account for this and can subsequently lose calibration. It is therefore important to regularly revalidate clinical prediction models. PMID:23152436

A Hierarchical Bayesian Model for Calibrating Estimates of Species Divergence Times

PubMed Central

Heath, Tracy A.

2012-01-01

In Bayesian divergence time estimation methods, incorporating calibrating information from the fossil record is commonly done by assigning prior densities to ancestral nodes in the tree. Calibration prior densities are typically parametric distributions offset by minimum age estimates provided by the fossil record. Specification of the parameters of calibration densities requires the user to quantify his or her prior knowledge of the age of the ancestral node relative to the age of its calibrating fossil. The values of these parameters can, potentially, result in biased estimates of node ages if they lead to overly informative prior distributions. Accordingly, determining parameter values that lead to adequate prior densities is not straightforward. In this study, I present a hierarchical Bayesian model for calibrating divergence time analyses with multiple fossil age constraints. This approach applies a Dirichlet process prior as a hyperprior on the parameters of calibration prior densities. Specifically, this model assumes that the rate parameters of exponential prior distributions on calibrated nodes are distributed according to a Dirichlet process, whereby the rate parameters are clustered into distinct parameter categories. Both simulated and biological data are analyzed to evaluate the performance of the Dirichlet process hyperprior. Compared with fixed exponential prior densities, the hierarchical Bayesian approach results in more accurate and precise estimates of internal node ages. When this hyperprior is applied using Markov chain Monte Carlo methods, the ages of calibrated nodes are sampled from mixtures of exponential distributions and uncertainty in the values of calibration density parameters is taken into account. PMID:22334343

Improving regression-model-based streamwater constituent load estimates derived from serially correlated data

USGS Publications Warehouse

Aulenbach, Brent T.

2013-01-01

A regression-model based approach is a commonly used, efficient method for estimating streamwater constituent load when there is a relationship between streamwater constituent concentration and continuous variables such as streamwater discharge, season and time. A subsetting experiment using a 30-year dataset of daily suspended sediment observations from the Mississippi River at Thebes, Illinois, was performed to determine optimal sampling frequency, model calibration period length, and regression model methodology, as well as to determine the effect of serial correlation of model residuals on load estimate precision. Two regression-based methods were used to estimate streamwater loads, the Adjusted Maximum Likelihood Estimator (AMLE), and the composite method, a hybrid load estimation approach. While both methods accurately and precisely estimated loads at the model’s calibration period time scale, precisions were progressively worse at shorter reporting periods, from annually to monthly. Serial correlation in model residuals resulted in observed AMLE precision to be significantly worse than the model calculated standard errors of prediction. The composite method effectively improved upon AMLE loads for shorter reporting periods, but required a sampling interval of at least 15-days or shorter, when the serial correlations in the observed load residuals were greater than 0.15. AMLE precision was better at shorter sampling intervals and when using the shortest model calibration periods, such that the regression models better fit the temporal changes in the concentration–discharge relationship. The models with the largest errors typically had poor high flow sampling coverage resulting in unrepresentative models. Increasing sampling frequency and/or targeted high flow sampling are more efficient approaches to ensure sufficient sampling and to avoid poorly performing models, than increasing calibration period length.

Experiences in Automated Calibration of a Nickel Equation of State

NASA Astrophysics Data System (ADS)

Carpenter, John H.

2017-06-01

Wide availability of large computers has led to increasing incorporation of computational data, such as from density functional theory molecular dynamics, in the development of equation of state (EOS) models. Once a grid of computational data is available, it is usually left to an expert modeler to model the EOS using traditional techniques. One can envision the possibility of using the increasing computing resources to perform black-box calibration of EOS models, with the goal of reducing the workload on the modeler or enabling non-experts to generate good EOSs with such a tool. Progress towards building such a black-box calibration tool will be explored in the context of developing a new, wide-range EOS for nickel. While some details of the model and data will be shared, the focus will be on what was learned by automatically calibrating the model in a black-box method. Model choices and ensuring physicality will also be discussed. Sandia National Laboratories is a multi-mission laboratory managed and operated by Sandia Corporation, a wholly owned subsidiary of Lockheed Martin Corporation, for the U.S. Department of Energy's National Nuclear Security Administration under contract DE-AC04-94AL85000.

Calibration of an agricultural-hydrological model (RZWQM2) using surrogate global optimization

DOE PAGES

Xi, Maolong; Lu, Dan; Gui, Dongwei; ...

2016-11-27

Robust calibration of an agricultural-hydrological model is critical for simulating crop yield and water quality and making reasonable agricultural management. However, calibration of the agricultural-hydrological system models is challenging because of model complexity, the existence of strong parameter correlation, and significant computational requirements. Therefore, only a limited number of simulations can be allowed in any attempt to find a near-optimal solution within an affordable time, which greatly restricts the successful application of the model. The goal of this study is to locate the optimal solution of the Root Zone Water Quality Model (RZWQM2) given a limited simulation time, so asmore » to improve the model simulation and help make rational and effective agricultural-hydrological decisions. To this end, we propose a computationally efficient global optimization procedure using sparse-grid based surrogates. We first used advanced sparse grid (SG) interpolation to construct a surrogate system of the actual RZWQM2, and then we calibrate the surrogate model using the global optimization algorithm, Quantum-behaved Particle Swarm Optimization (QPSO). As the surrogate model is a polynomial with fast evaluation, it can be efficiently evaluated with a sufficiently large number of times during the optimization, which facilitates the global search. We calibrate seven model parameters against five years of yield, drain flow, and NO 3-N loss data from a subsurface-drained corn-soybean field in Iowa. Results indicate that an accurate surrogate model can be created for the RZWQM2 with a relatively small number of SG points (i.e., RZWQM2 runs). Compared to the conventional QPSO algorithm, our surrogate-based optimization method can achieve a smaller objective function value and better calibration performance using a fewer number of expensive RZWQM2 executions, which greatly improves computational efficiency.« less

Calibration of an agricultural-hydrological model (RZWQM2) using surrogate global optimization

NASA Astrophysics Data System (ADS)

Xi, Maolong; Lu, Dan; Gui, Dongwei; Qi, Zhiming; Zhang, Guannan

2017-01-01

Robust calibration of an agricultural-hydrological model is critical for simulating crop yield and water quality and making reasonable agricultural management. However, calibration of the agricultural-hydrological system models is challenging because of model complexity, the existence of strong parameter correlation, and significant computational requirements. Therefore, only a limited number of simulations can be allowed in any attempt to find a near-optimal solution within an affordable time, which greatly restricts the successful application of the model. The goal of this study is to locate the optimal solution of the Root Zone Water Quality Model (RZWQM2) given a limited simulation time, so as to improve the model simulation and help make rational and effective agricultural-hydrological decisions. To this end, we propose a computationally efficient global optimization procedure using sparse-grid based surrogates. We first used advanced sparse grid (SG) interpolation to construct a surrogate system of the actual RZWQM2, and then we calibrate the surrogate model using the global optimization algorithm, Quantum-behaved Particle Swarm Optimization (QPSO). As the surrogate model is a polynomial with fast evaluation, it can be efficiently evaluated with a sufficiently large number of times during the optimization, which facilitates the global search. We calibrate seven model parameters against five years of yield, drain flow, and NO3-N loss data from a subsurface-drained corn-soybean field in Iowa. Results indicate that an accurate surrogate model can be created for the RZWQM2 with a relatively small number of SG points (i.e., RZWQM2 runs). Compared to the conventional QPSO algorithm, our surrogate-based optimization method can achieve a smaller objective function value and better calibration performance using a fewer number of expensive RZWQM2 executions, which greatly improves computational efficiency.

Calibration of an agricultural-hydrological model (RZWQM2) using surrogate global optimization

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

Xi, Maolong; Lu, Dan; Gui, Dongwei

Robust calibration of an agricultural-hydrological model is critical for simulating crop yield and water quality and making reasonable agricultural management. However, calibration of the agricultural-hydrological system models is challenging because of model complexity, the existence of strong parameter correlation, and significant computational requirements. Therefore, only a limited number of simulations can be allowed in any attempt to find a near-optimal solution within an affordable time, which greatly restricts the successful application of the model. The goal of this study is to locate the optimal solution of the Root Zone Water Quality Model (RZWQM2) given a limited simulation time, so asmore » to improve the model simulation and help make rational and effective agricultural-hydrological decisions. To this end, we propose a computationally efficient global optimization procedure using sparse-grid based surrogates. We first used advanced sparse grid (SG) interpolation to construct a surrogate system of the actual RZWQM2, and then we calibrate the surrogate model using the global optimization algorithm, Quantum-behaved Particle Swarm Optimization (QPSO). As the surrogate model is a polynomial with fast evaluation, it can be efficiently evaluated with a sufficiently large number of times during the optimization, which facilitates the global search. We calibrate seven model parameters against five years of yield, drain flow, and NO 3-N loss data from a subsurface-drained corn-soybean field in Iowa. Results indicate that an accurate surrogate model can be created for the RZWQM2 with a relatively small number of SG points (i.e., RZWQM2 runs). Compared to the conventional QPSO algorithm, our surrogate-based optimization method can achieve a smaller objective function value and better calibration performance using a fewer number of expensive RZWQM2 executions, which greatly improves computational efficiency.« less

Evaluation of Hydrologic Simulations Developed Using Multi-Model Synthesis and Remotely-Sensed Data within a Portfolio of Calibration Strategies

NASA Astrophysics Data System (ADS)

Lafontaine, J.; Hay, L.; Markstrom, S. L.

2016-12-01

The United States Geological Survey (USGS) has developed a National Hydrologic Model (NHM) to support coordinated, comprehensive and consistent hydrologic model development, and facilitate the application of hydrologic simulations within the conterminous United States (CONUS). As many stream reaches in the CONUS are either not gaged, or are substantially impacted by water use or flow regulation, ancillary information must be used to determine reasonable parameter estimations for streamflow simulations. Hydrologic models for 1,576 gaged watersheds across the CONUS were developed to test the feasibility of improving streamflow simulations linking physically-based hydrologic models with remotely-sensed data products (i.e. snow water equivalent). Initially, the physically-based models were calibrated to measured streamflow data to provide a baseline for comparison across multiple calibration strategy tests. In addition, not all ancillary datasets are appropriate for application to all parts of the CONUS (e.g. snow water equivalent in the southeastern U.S., where snow is a rarity). As it is not expected that any one data product or model simulation will be sufficient for representing hydrologic behavior across the entire CONUS, a systematic evaluation of which data products improve hydrologic simulations for various regions across the CONUS was performed. The resulting portfolio of calibration strategies can be used to guide selection of an appropriate combination of modeled and measured information for hydrologic model development and calibration. In addition, these calibration strategies have been developed to be flexible so that new data products can be assimilated. This analysis provides a foundation to understand how well models work when sufficient streamflow data are not available and could be used to further inform hydrologic model parameter development for ungaged areas.

Modelling exploration of non-stationary hydrological system

NASA Astrophysics Data System (ADS)

Kim, Kue Bum; Kwon, Hyun-Han; Han, Dawei

2015-04-01

Traditional hydrological modelling assumes that the catchment does not change with time (i.e., stationary conditions) which means the model calibrated for the historical period is valid for the future period. However, in reality, due to change of climate and catchment conditions this stationarity assumption may not be valid in the future. It is a challenge to make the hydrological model adaptive to the future climate and catchment conditions that are not observable at the present time. In this study a lumped conceptual rainfall-runoff model called IHACRES was applied to a catchment in southwest England. Long observation data from 1961 to 2008 were used and seasonal calibration (in this study only summer period is further explored because it is more sensitive to climate and land cover change than the other three seasons) has been done since there are significant seasonal rainfall patterns. We expect that the model performance can be improved by calibrating the model based on individual seasons. The data is split into calibration and validation periods with the intention of using the validation period to represent the future unobserved situations. The success of the non-stationary model will depend not only on good performance during the calibration period but also the validation period. Initially, the calibration is based on changing the model parameters with time. Methodology is proposed to adapt the parameters using the step forward and backward selection schemes. However, in the validation both the forward and backward multiple parameter changing models failed. One problem is that the regression with time is not reliable since the trend may not be in a monotonic linear relationship with time. The second issue is that changing multiple parameters makes the selection process very complex which is time consuming and not effective in the validation period. As a result, two new concepts are explored. First, only one parameter is selected for adjustment while the other parameters are set as constant. Secondly, regression is made against climate condition instead of against time. It has been found that such a new approach is very effective and this non-stationary model worked very well both in the calibration and validation period. Although the catchment is specific in southwest England and the data are for only the summer period, the methodology proposed in this study is general and applicable to other catchments. We hope this study will stimulate the hydrological community to explore a variety of sites so that valuable experiences and knowledge could be gained to improve our understanding of such a complex modelling issue in climate change impact assessment.

Parameter Set Cloning Based on Catchment Similarity for Large-scale Hydrologic Modeling

NASA Astrophysics Data System (ADS)

Liu, Z.; Kaheil, Y.; McCollum, J.

2016-12-01

Parameter calibration is a crucial step to ensure the accuracy of hydrological models. However, streamflow gauges are not available everywhere for calibrating a large-scale hydrologic model globally. Thus, assigning parameters appropriately for regions where the calibration cannot be performed directly has been a challenge for large-scale hydrologic modeling. Here we propose a method to estimate the model parameters in ungauged regions based on the values obtained through calibration in areas where gauge observations are available. This parameter set cloning is performed according to a catchment similarity index, a weighted sum index based on four catchment characteristic attributes. These attributes are IPCC Climate Zone, Soil Texture, Land Cover, and Topographic Index. The catchments with calibrated parameter values are donors, while the uncalibrated catchments are candidates. Catchment characteristic analyses are first conducted for both donors and candidates. For each attribute, we compute a characteristic distance between donors and candidates. Next, for each candidate, weights are assigned to the four attributes such that higher weights are given to properties that are more directly linked to the hydrologic dominant processes. This will ensure that the parameter set cloning emphasizes the dominant hydrologic process in the region where the candidate is located. The catchment similarity index for each donor - candidate couple is then created as the sum of the weighted distance of the four properties. Finally, parameters are assigned to each candidate from the donor that is "most similar" (i.e. with the shortest weighted distance sum). For validation, we applied the proposed method to catchments where gauge observations are available, and compared simulated streamflows using the parameters cloned by other catchments to the results obtained by calibrating the hydrologic model directly using gauge data. The comparison shows good agreement between the two models for different river basins as we show here. This method has been applied globally to the Hillslope River Routing (HRR) model using gauge observations obtained from the Global Runoff Data Center (GRDC). As next step, more catchment properties can be taken into account to further improve the representation of catchment similarity.

Calibration of material models for estimating impact of construction quality on life cycle performance of pavements.

DOT National Transportation Integrated Search

2006-02-01

Constructing a pavement that will perform well throughout its expected design life is the main goal of any highway agency. The relationship between construction parameters and pavement life, defined by structural models, can be described using materi...

In-flight calibration/validation of the ENVISAT/MWR

NASA Astrophysics Data System (ADS)

Tran, N.; Obligis, E.; Eymard, L.

2003-04-01

Retrieval algorithms for wet tropospheric correction, integrated vapor and liquid water contents, atmospheric attenuations of backscattering coefficients in Ku and S band, have been developed using a database of geophysical parameters from global analyses from a meteorological model and corresponding simulated brightness temperatures and backscattering cross-sections by a radiative transfer model. Meteorological data correspond to 12 hours predictions from the European Center for Medium range Weather Forecasts (ECMWF) model. Relationships between satellite measurements and geophysical parameters are determined using a statistical method. The quality of the retrieval algorithms depends therefore on the representativity of the database, the accuracy of the radiative transfer model used for the simulations and finally on the quality of the inversion model. The database has been built using the latest version of the ECMWF forecast model, which has been operationally run since November 2000. The 60 levels in the model allow a complete description of the troposphere/stratosphere profiles and the horizontal resolution is now half of a degree. The radiative transfer model is the emissivity model developed at the Université Catholique de Louvain [Lemaire, 1998], coupled to an atmospheric model [Liebe et al, 1993] for gaseous absorption. For the inversion, we have replaced the classical log-linear regression with a neural networks inversion. For Envisat, the backscattering coefficient in Ku band is used in the different algorithms to take into account the surface roughness as it is done with the 18 GHz channel for the TOPEX algorithms or an additional term in wind speed for ERS2 algorithms. The in-flight calibration/validation of the Envisat radiometer has been performed with the tuning of 3 internal parameters (the transmission coefficient of the reflector, the sky horn feed transmission coefficient and the main antenna transmission coefficient). First an adjustment of the ERS2 brightness temperatures to the simulations for the 2000/2001 version of the ECMWF model has been applied. Then, Envisat brightness temperatures have been calibrated on these adjusted ERS2 values. The advantages of this calibration approach are that : i) such a method provides the relative discrepancy with respect to the simulation chain. The results, obtained simultaneously for several radiometers (we repeat the same analyze with TOPEX and JASON radiometers), can be used to detect significant calibration problems, more than 2 3 K). ii) the retrieval algorithms have been developed using the same meteorological model (2000/2001 version of the ECMWF model), and the same radiative transfer model than the calibration process, insuring the consistency between calibration and retrieval processing. Retrieval parameters are then optimized. iii) the calibration of the Envisat brightness temperatures over the 2000/2001 version of the ECMWF model, as well as the recommendation to use the same model as a reference to correct ERS2 brightness temperatures, allow the use of the same retrieval algorithms for the two missions, providing the continuity between the two. iv) by comparison with other calibration methods (such as systematic calibration of an instrument or products by using respectively the ones from previous mission), this method is more satisfactory since improvements in terms of technology, modelisation, retrieval processing are taken into account. For the validation of the brightness temperatures, we use either a direct comparison with measurements provided by other instruments in similar channel, or the monitoring over stable areas (coldest ocean points, stable continental areas). The validation of the wet tropospheric correction can be also provided by comparison with other radiometer products, but the only real validation rely on the comparison between in-situ measurements (performed by radiosonding) and retrieved products in coincidence.

Calibration and Data Analysis of the MC-130 Air Balance

NASA Technical Reports Server (NTRS)

Booth, Dennis; Ulbrich, N.

2012-01-01

Design, calibration, calibration analysis, and intended use of the MC-130 air balance are discussed. The MC-130 balance is an 8.0 inch diameter force balance that has two separate internal air flow systems and one external bellows system. The manual calibration of the balance consisted of a total of 1854 data points with both unpressurized and pressurized air flowing through the balance. A subset of 1160 data points was chosen for the calibration data analysis. The regression analysis of the subset was performed using two fundamentally different analysis approaches. First, the data analysis was performed using a recently developed extension of the Iterative Method. This approach fits gage outputs as a function of both applied balance loads and bellows pressures while still allowing the application of the iteration scheme that is used with the Iterative Method. Then, for comparison, the axial force was also analyzed using the Non-Iterative Method. This alternate approach directly fits loads as a function of measured gage outputs and bellows pressures and does not require a load iteration. The regression models used by both the extended Iterative and Non-Iterative Method were constructed such that they met a set of widely accepted statistical quality requirements. These requirements lead to reliable regression models and prevent overfitting of data because they ensure that no hidden near-linear dependencies between regression model terms exist and that only statistically significant terms are included. Finally, a comparison of the axial force residuals was performed. Overall, axial force estimates obtained from both methods show excellent agreement as the differences of the standard deviation of the axial force residuals are on the order of 0.001 % of the axial force capacity.

Linking Statistically- and Physically-Based Models for Improved Streamflow Simulation in Gaged and Ungaged Areas

NASA Astrophysics Data System (ADS)

Lafontaine, J.; Hay, L.; Archfield, S. A.; Farmer, W. H.; Kiang, J. E.

2014-12-01

The U.S. Geological Survey (USGS) has developed a National Hydrologic Model (NHM) to support coordinated, comprehensive and consistent hydrologic model development, and facilitate the application of hydrologic simulations within the continental US. The portion of the NHM located within the Gulf Coastal Plains and Ozarks Landscape Conservation Cooperative (GCPO LCC) is being used to test the feasibility of improving streamflow simulations in gaged and ungaged watersheds by linking statistically- and physically-based hydrologic models. The GCPO LCC covers part or all of 12 states and 5 sub-geographies, totaling approximately 726,000 km2, and is centered on the lower Mississippi Alluvial Valley. A total of 346 USGS streamgages in the GCPO LCC region were selected to evaluate the performance of this new calibration methodology for the period 1980 to 2013. Initially, the physically-based models are calibrated to measured streamflow data to provide a baseline for comparison. An enhanced calibration procedure then is used to calibrate the physically-based models in the gaged and ungaged areas of the GCPO LCC using statistically-based estimates of streamflow. For this application, the calibration procedure is adjusted to address the limitations of the statistically generated time series to reproduce measured streamflow in gaged basins, primarily by incorporating error and bias estimates. As part of this effort, estimates of uncertainty in the model simulations are also computed for the gaged and ungaged watersheds.

Complete Tri-Axis Magnetometer Calibration with a Gyro Auxiliary

PubMed Central

Yang, Deng; You, Zheng; Li, Bin; Duan, Wenrui; Yuan, Binwen

2017-01-01

Magnetometers combined with inertial sensors are widely used for orientation estimation, and calibrations are necessary to achieve high accuracy. This paper presents a complete tri-axis magnetometer calibration algorithm with a gyro auxiliary. The magnetic distortions and sensor errors, including the misalignment error between the magnetometer and assembled platform, are compensated after calibration. With the gyro auxiliary, the magnetometer linear interpolation outputs are calculated, and the error parameters are evaluated under linear operations of magnetometer interpolation outputs. The simulation and experiment are performed to illustrate the efficiency of the algorithm. After calibration, the heading errors calculated by magnetometers are reduced to 0.5° (1σ). This calibration algorithm can also be applied to tri-axis accelerometers whose error model is similar to tri-axis magnetometers. PMID:28587115

Data filtering with support vector machines in geometric camera calibration.

PubMed

Ergun, B; Kavzoglu, T; Colkesen, I; Sahin, C

2010-02-01

The use of non-metric digital cameras in close-range photogrammetric applications and machine vision has become a popular research agenda. Being an essential component of photogrammetric evaluation, camera calibration is a crucial stage for non-metric cameras. Therefore, accurate camera calibration and orientation procedures have become prerequisites for the extraction of precise and reliable 3D metric information from images. The lack of accurate inner orientation parameters can lead to unreliable results in the photogrammetric process. A camera can be well defined with its principal distance, principal point offset and lens distortion parameters. Different camera models have been formulated and used in close-range photogrammetry, but generally sensor orientation and calibration is performed with a perspective geometrical model by means of the bundle adjustment. In this study, support vector machines (SVMs) using radial basis function kernel is employed to model the distortions measured for Olympus Aspherical Zoom lens Olympus E10 camera system that are later used in the geometric calibration process. It is intended to introduce an alternative approach for the on-the-job photogrammetric calibration stage. Experimental results for DSLR camera with three focal length settings (9, 18 and 36 mm) were estimated using bundle adjustment with additional parameters, and analyses were conducted based on object point discrepancies and standard errors. Results show the robustness of the SVMs approach on the correction of image coordinates by modelling total distortions on-the-job calibration process using limited number of images.

Precision process calibration and CD predictions for low-k1 lithography

NASA Astrophysics Data System (ADS)

Chen, Ting; Park, Sangbong; Berger, Gabriel; Coskun, Tamer H.; de Vocht, Joep; Chen, Fung; Yu, Linda; Hsu, Stephen; van den Broeke, Doug; Socha, Robert; Park, Jungchul; Gronlund, Keith; Davis, Todd; Plachecki, Vince; Harris, Tom; Hansen, Steve; Lambson, Chuck

2005-06-01

Leading resist calibration for sub-0.3 k1 lithography demands accuracy <2nm for CD through pitch. An accurately calibrated resist process is the prerequisite for establishing production-worthy manufacturing under extreme low k1. From an integrated imaging point of view, the following key components must be simultaneously considered during the calibration - high numerical aperture (NA>0.8) imaging characteristics, customized illuminations (measured vs. modeled pupil profiles), resolution enhancement technology (RET) mask with OPC, reticle metrology, and resist thin film substrate. For imaging at NA approaching unity, polarized illumination can impact significantly the contrast formation in the resist film stack, and therefore it is an important factor to consider in the CD-based resist calibration. For aggressive DRAM memory core designs at k1<0.3, pattern-specific illumination optimization has proven to be critical for achieving the required imaging performance. Various optimization techniques from source profile optimization with fixed mask design to the combined source and mask optimization have been considered for customer designs and available imaging capabilities. For successful low-k1 process development, verification of the optimization results can only be made with a sufficiently tunable resist model that can predicate the wafer printing accurately under various optimized process settings. We have developed, for resist patterning under aggressive low-k1 conditions, a novel 3D diffusion model equipped with double-Gaussian convolution in each dimension. Resist calibration with the new diffusion model has demonstrated a fitness and CD predication accuracy that rival or outperform the traditional 3D physical resist models. In this work, we describe our empirical approach to achieving the nm-scale precision for advanced lithography process calibrations, using either measured 1D CD through-pitch or 2D memory core patterns. We show that for ArF imaging, the current resist development and diffusion modeling can readily achieve ~1-2nm max CD errors for common 1D through-pitch and aggressive 2D memory core resist patterns. Sensitivities of the calibrated models to various process parameters are analyzed, including the comparison between the measured and modeled (Gaussian or GRAIL) pupil profiles. We also report our preliminary calibration results under selected polarized illumination conditions.

Single baseline GLONASS observations with VLBI: data processing and first results

NASA Astrophysics Data System (ADS)

Tornatore, V.; Haas, R.; Duev, D.; Pogrebenko, S.; Casey, S.; Molera Calvés, G.; Keimpema, A.

2011-07-01

Several tests to observe signals transmitted by GLONASS (GLObal NAvigation Satellite System) satellites have been performed using the geodetic VLBI (Very Long Baseline Interferometry) technique. The radio telescopes involved in these experiments were Medicina (Italy) and Onsala (Sweden), both equipped with L-band receivers. Observations at the stations were performed using the standard Mark4 VLBI data acquisition rack and Mark5A disk-based recorders. The goals of the observations were to develop and test the scheduling, signal acquisition and processing routines to verify the full tracking pipeline, foreseeing the cross-correlation of the recorded data on the baseline Onsala-Medicina. The natural radio source 3c286 was used as a calibrator before the starting of the satellite observation sessions. Delay models, including the tropospheric and ionospheric corrections, which are consistent for both far- and near-field sources are under development. Correlation of the calibrator signal has been performed using the DiFX software, while the satellite signals have been processed using the narrow band approach with the Metsaehovi software and analysed with a near-field delay model. Delay models both for the calibrator signals and the satellites signals, using the same geometrical, tropospheric and ionospheric models, are under investigation to make a correlation of the satellite signals possible.

A methodology for reduced order modeling and calibration of the upper atmosphere

NASA Astrophysics Data System (ADS)

Mehta, Piyush M.; Linares, Richard

2017-10-01

Atmospheric drag is the largest source of uncertainty in accurately predicting the orbit of satellites in low Earth orbit (LEO). Accurately predicting drag for objects that traverse LEO is critical to space situational awareness. Atmospheric models used for orbital drag calculations can be characterized either as empirical or physics-based (first principles based). Empirical models are fast to evaluate but offer limited real-time predictive/forecasting ability, while physics based models offer greater predictive/forecasting ability but require dedicated parallel computational resources. Also, calibration with accurate data is required for either type of models. This paper presents a new methodology based on proper orthogonal decomposition toward development of a quasi-physical, predictive, reduced order model that combines the speed of empirical and the predictive/forecasting capabilities of physics-based models. The methodology is developed to reduce the high dimensionality of physics-based models while maintaining its capabilities. We develop the methodology using the Naval Research Lab's Mass Spectrometer Incoherent Scatter model and show that the diurnal and seasonal variations can be captured using a small number of modes and parameters. We also present calibration of the reduced order model using the CHAMP and GRACE accelerometer-derived densities. Results show that the method performs well for modeling and calibration of the upper atmosphere.

Using CREST to Model Floods for the Upper Missouri Basin

NASA Astrophysics Data System (ADS)

Rodriguez, L.; Spelman, D.; Skym, P.; Oguamanam, M.

2012-12-01

The Upper Missouri River basin in Montana is prone to frequent floods during the months of May through June. During the summer of 2011, the duration of heavy rain and high snow melt caused floods in the Missouri Watershed to last for several weeks. Although organizations and citizens are aware of the flooding, the severity of any given flood is difficult to predict. Thus, a flood forecasting system would benefit the community by providing advance notice of areas prone to floods. The team addressed these issues by calibrating a fully distributed hydrological model on this region using the University of Oklahoma's Coupled Routing Excess STorage (CREST) 2.0 model. The CREST model contains ten internal parameters that must be optimized through calibration to in-situ data which was done using only remotely sensed data as inputs; these include: rainfall from TRMM, Digital Elevation Model (DEM) from SRTM, and potential evapotranspiration (PET). At a one kilometer spatial and three hour temporal resolutions, CREST was calibrated over a three year period. This calibration was unsuccessful, resulting in a maximum NSCE of just 0.24, due to the heavy impact of snow-pack melt on the hydrology of the Upper Missouri River watershed and the lack of snow melt inputs to the model. Because of this, a modeled snow melt product, which was selected to be the Snow Data Assimilation System (SNODAS), was implemented into the model for the first time. This implementation required substantial data processing prior to input as well as substantial troubleshooting within the CREST model. Additionally, the San Bernard watershed, which is in the absence of snow, was used and calibrated in the model for comparison. The arrangement of these calibrations was performed, and the necessary steps required were documented. However, the actual calibrations were not carried out due to lack of access needed in computational power and time. Significant progress was made in understanding the fundamental steps used in the implementation of various types of input data into the CREST model. This progress was documented in the aid of future calibrations and widespread use of the model. With the information and groundwork established by the team, final calibration and validation can be done on the Upper Missouri River which will allow insight for our partners to incorporate a global application of CREST.

Comparison of Three Contemporary Risk Scores for Mortality Following Elective Abdominal Aortic Aneurysm Repair

PubMed Central

Grant, S.W.; Hickey, G.L.; Carlson, E.D.; McCollum, C.N.

2014-01-01

Objective/background A number of contemporary risk prediction models for mortality following elective abdominal aortic aneurysm (AAA) repair have been developed. Before a model is used either in clinical practice or to risk-adjust surgical outcome data it is important that its performance is assessed in external validation studies. Methods The British Aneurysm Repair (BAR) score, Medicare, and Vascular Governance North West (VGNW) models were validated using an independent prospectively collected sample of multicentre clinical audit data. Consecutive, data on 1,124 patients undergoing elective AAA repair at 17 hospitals in the north-west of England and Wales between April 2011 and March 2013 were analysed. The outcome measure was in-hospital mortality. Model calibration (observed to expected ratio with chi-square test, calibration plots, calibration intercept and slope) and discrimination (area under receiver operating characteristic curve [AUC]) were assessed in the overall cohort and procedural subgroups. Results The mean age of the population was 74.4 years (SD 7.7); 193 (17.2%) patients were women and the majority of patients (759, 67.5%) underwent endovascular aneurysm repair. All three models demonstrated good calibration in the overall cohort and procedural subgroups. Overall discrimination was excellent for the BAR score (AUC 0.83, 95% confidence interval [CI] 0.76–0.89), and acceptable for the Medicare and VGNW models, with AUCs of 0.78 (95% CI 0.70–0.86) and 0.75 (95% CI 0.65–0.84) respectively. Only the BAR score demonstrated good discrimination in procedural subgroups. Conclusion All three models demonstrated good calibration and discrimination for the prediction of in-hospital mortality following elective AAA repair and are potentially useful. The BAR score has a number of advantages, which include being developed on the most contemporaneous data, excellent overall discrimination, and good performance in procedural subgroups. Regular model validations and recalibration will be essential. PMID:24837173

Comparison of three contemporary risk scores for mortality following elective abdominal aortic aneurysm repair.

PubMed

Grant, S W; Hickey, G L; Carlson, E D; McCollum, C N

2014-07-01

A number of contemporary risk prediction models for mortality following elective abdominal aortic aneurysm (AAA) repair have been developed. Before a model is used either in clinical practice or to risk-adjust surgical outcome data it is important that its performance is assessed in external validation studies. The British Aneurysm Repair (BAR) score, Medicare, and Vascular Governance North West (VGNW) models were validated using an independent prospectively collected sample of multicentre clinical audit data. Consecutive, data on 1,124 patients undergoing elective AAA repair at 17 hospitals in the north-west of England and Wales between April 2011 and March 2013 were analysed. The outcome measure was in-hospital mortality. Model calibration (observed to expected ratio with chi-square test, calibration plots, calibration intercept and slope) and discrimination (area under receiver operating characteristic curve [AUC]) were assessed in the overall cohort and procedural subgroups. The mean age of the population was 74.4 years (SD 7.7); 193 (17.2%) patients were women and the majority of patients (759, 67.5%) underwent endovascular aneurysm repair. All three models demonstrated good calibration in the overall cohort and procedural subgroups. Overall discrimination was excellent for the BAR score (AUC 0.83, 95% confidence interval [CI] 0.76-0.89), and acceptable for the Medicare and VGNW models, with AUCs of 0.78 (95% CI 0.70-0.86) and 0.75 (95% CI 0.65-0.84) respectively. Only the BAR score demonstrated good discrimination in procedural subgroups. All three models demonstrated good calibration and discrimination for the prediction of in-hospital mortality following elective AAA repair and are potentially useful. The BAR score has a number of advantages, which include being developed on the most contemporaneous data, excellent overall discrimination, and good performance in procedural subgroups. Regular model validations and recalibration will be essential. Copyright © 2014 European Society for Vascular Surgery. Published by Elsevier Ltd. All rights reserved.

Machine Learning Principles Can Improve Hip Fracture Prediction.

PubMed

Kruse, Christian; Eiken, Pia; Vestergaard, Peter

2017-04-01

Apply machine learning principles to predict hip fractures and estimate predictor importance in Dual-energy X-ray absorptiometry (DXA)-scanned men and women. Dual-energy X-ray absorptiometry data from two Danish regions between 1996 and 2006 were combined with national Danish patient data to comprise 4722 women and 717 men with 5 years of follow-up time (original cohort n = 6606 men and women). Twenty-four statistical models were built on 75% of data points through k-5, 5-repeat cross-validation, and then validated on the remaining 25% of data points to calculate area under the curve (AUC) and calibrate probability estimates. The best models were retrained with restricted predictor subsets to estimate the best subsets. For women, bootstrap aggregated flexible discriminant analysis ("bagFDA") performed best with a test AUC of 0.92 [0.89; 0.94] and well-calibrated probabilities following Naïve Bayes adjustments. A "bagFDA" model limited to 11 predictors (among them bone mineral densities (BMD), biochemical glucose measurements, general practitioner and dentist use) achieved a test AUC of 0.91 [0.88; 0.93]. For men, eXtreme Gradient Boosting ("xgbTree") performed best with a test AUC of 0.89 [0.82; 0.95], but with poor calibration in higher probabilities. A ten predictor subset (BMD, biochemical cholesterol and liver function tests, penicillin use and osteoarthritis diagnoses) achieved a test AUC of 0.86 [0.78; 0.94] using an "xgbTree" model. Machine learning can improve hip fracture prediction beyond logistic regression using ensemble models. Compiling data from international cohorts of longer follow-up and performing similar machine learning procedures has the potential to further improve discrimination and calibration.

The site-scale saturated zone flow model for Yucca Mountain: Calibration of different conceptual models and their impact on flow paths

USGS Publications Warehouse

Zyvoloski, G.; Kwicklis, E.; Eddebbarh, A.-A.; Arnold, B.; Faunt, C.; Robinson, B.A.

2003-01-01

This paper presents several different conceptual models of the Large Hydraulic Gradient (LHG) region north of Yucca Mountain and describes the impact of those models on groundwater flow near the potential high-level repository site. The results are based on a numerical model of site-scale saturated zone beneath Yucca Mountain. This model is used for performance assessment predictions of radionuclide transport and to guide future data collection and modeling activities. The numerical model is calibrated by matching available water level measurements using parameter estimation techniques, along with more informal comparisons of the model to hydrologic and geochemical information. The model software (hydrologic simulation code FEHM and parameter estimation software PEST) and model setup allows for efficient calibration of multiple conceptual models. Until now, the Large Hydraulic Gradient has been simulated using a low-permeability, east-west oriented feature, even though direct evidence for this feature is lacking. In addition to this model, we investigate and calibrate three additional conceptual models of the Large Hydraulic Gradient, all of which are based on a presumed zone of hydrothermal chemical alteration north of Yucca Mountain. After examining the heads and permeabilities obtained from the calibrated models, we present particle pathways from the potential repository that record differences in the predicted groundwater flow regime. The results show that Large Hydraulic Gradient can be represented with the alternate conceptual models that include the hydrothermally altered zone. The predicted pathways are mildly sensitive to the choice of the conceptual model and more sensitive to the quality of calibration in the vicinity on the repository. These differences are most likely due to different degrees of fit of model to data, and do not represent important differences in hydrologic conditions for the different conceptual models. ?? 2002 Elsevier Science B.V. All rights reserved.

[Application of near infrared spectroscopy combined with particle swarm optimization based least square support vactor machine to rapid quantitative analysis of Corni Fructus].

PubMed

Liu, Xue-song; Sun, Fen-fang; Jin, Ye; Wu, Yong-jiang; Gu, Zhi-xin; Zhu, Li; Yan, Dong-lan

2015-12-01

A novel method was developed for the rapid determination of multi-indicators in corni fructus by means of near infrared (NIR) spectroscopy. Particle swarm optimization (PSO) based least squares support vector machine was investigated to increase the levels of quality control. The calibration models of moisture, extractum, morroniside and loganin were established using the PSO-LS-SVM algorithm. The performance of PSO-LS-SVM models was compared with partial least squares regression (PLSR) and back propagation artificial neural network (BP-ANN). The calibration and validation results of PSO-LS-SVM were superior to both PLS and BP-ANN. For PSO-LS-SVM models, the correlation coefficients (r) of calibrations were all above 0.942. The optimal prediction results were also achieved by PSO-LS-SVM models with the RMSEP (root mean square error of prediction) and RSEP (relative standard errors of prediction) less than 1.176 and 15.5% respectively. The results suggest that PSO-LS-SVM algorithm has a good model performance and high prediction accuracy. NIR has a potential value for rapid determination of multi-indicators in Corni Fructus.

Dynamic Black-Level Correction and Artifact Flagging in the Kepler Data Pipeline

NASA Technical Reports Server (NTRS)

Clarke, B. D.; Kolodziejczak, J. J.; Caldwell, D. A.

2013-01-01

Instrument-induced artifacts in the raw Kepler pixel data include time-varying crosstalk from the fine guidance sensor (FGS) clock signals, manifestations of drifting moiré pattern as locally correlated nonstationary noise and rolling bands in the images which find their way into the calibrated pixel time series and ultimately into the calibrated target flux time series. Using a combination of raw science pixel data, full frame images, reverse-clocked pixel data and ancillary temperature data the Keplerpipeline models and removes the FGS crosstalk artifacts by dynamically adjusting the black level correction. By examining the residuals to the model fits, the pipeline detects and flags spatial regions and time intervals of strong time-varying blacklevel (rolling bands ) on a per row per cadence basis. These flags are made available to downstream users of the data since the uncorrected rolling band artifacts could complicate processing or lead to misinterpretation of instrument behavior as stellar. This model fitting and artifact flagging is performed within the new stand-alone pipeline model called Dynablack. We discuss the implementation of Dynablack in the Kepler data pipeline and present results regarding the improvement in calibrated pixels and the expected improvement in cotrending performances as a result of including FGS corrections in the calibration. We also discuss the effectiveness of the rolling band flagging for downstream users and illustrate with some affected light curves.

Hindcasting of decadal‐timescale estuarine bathymetric change with a tidal‐timescale model

USGS Publications Warehouse

Ganju, Neil K.; Schoellhamer, David H.; Jaffe, Bruce E.

2009-01-01

Hindcasting decadal-timescale bathymetric change in estuaries is prone to error due to limited data for initial conditions, boundary forcing, and calibration; computational limitations further hinder efforts. We developed and calibrated a tidal-timescale model to bathymetric change in Suisun Bay, California, over the 1867–1887 period. A general, multiple-timescale calibration ensured robustness over all timescales; two input reduction methods, the morphological hydrograph and the morphological acceleration factor, were applied at the decadal timescale. The model was calibrated to net bathymetric change in the entire basin; average error for bathymetric change over individual depth ranges was 37%. On a model cell-by-cell basis, performance for spatial amplitude correlation was poor over the majority of the domain, though spatial phase correlation was better, with 61% of the domain correctly indicated as erosional or depositional. Poor agreement was likely caused by the specification of initial bed composition, which was unknown during the 1867–1887 period. Cross-sectional bathymetric change between channels and flats, driven primarily by wind wave resuspension, was modeled with higher skill than longitudinal change, which is driven in part by gravitational circulation. The accelerated response of depth may have prevented gravitational circulation from being represented properly. As performance criteria became more stringent in a spatial sense, the error of the model increased. While these methods are useful for estimating basin-scale sedimentation changes, they may not be suitable for predicting specific locations of erosion or deposition. They do, however, provide a foundation for realistic estuarine geomorphic modeling applications.

Investigating temporal field sampling strategies for site-specific calibration of three soil moisture-neutron intensity parameterisation methods

NASA Astrophysics Data System (ADS)

Iwema, J.; Rosolem, R.; Baatz, R.; Wagener, T.; Bogena, H. R.

2015-07-01

The Cosmic-Ray Neutron Sensor (CRNS) can provide soil moisture information at scales relevant to hydrometeorological modelling applications. Site-specific calibration is needed to translate CRNS neutron intensities into sensor footprint average soil moisture contents. We investigated temporal sampling strategies for calibration of three CRNS parameterisations (modified N0, HMF, and COSMIC) by assessing the effects of the number of sampling days and soil wetness conditions on the performance of the calibration results while investigating actual neutron intensity measurements, for three sites with distinct climate and land use: a semi-arid site, a temperate grassland, and a temperate forest. When calibrated with 1 year of data, both COSMIC and the modified N0 method performed better than HMF. The performance of COSMIC was remarkably good at the semi-arid site in the USA, while the N0mod performed best at the two temperate sites in Germany. The successful performance of COSMIC at all three sites can be attributed to the benefits of explicitly resolving individual soil layers (which is not accounted for in the other two parameterisations). To better calibrate these parameterisations, we recommend in situ soil sampled to be collected on more than a single day. However, little improvement is observed for sampling on more than 6 days. At the semi-arid site, the N0mod method was calibrated better under site-specific average wetness conditions, whereas HMF and COSMIC were calibrated better under drier conditions. Average soil wetness condition gave better calibration results at the two humid sites. The calibration results for the HMF method were better when calibrated with combinations of days with similar soil wetness conditions, opposed to N0mod and COSMIC, which profited from using days with distinct wetness conditions. Errors in actual neutron intensities were translated to average errors specifically to each site. At the semi-arid site, these errors were below the typical measurement uncertainties from in situ point-scale sensors and satellite remote sensing products. Nevertheless, at the two humid sites, reduction in uncertainty with increasing sampling days only reached typical errors associated with satellite remote sensing products. The outcomes of this study can be used by researchers as a CRNS calibration strategy guideline.

Experimental Investigation of Nozzle/Plume Aerodynamics at Hypersonic Speeds

NASA Technical Reports Server (NTRS)

Heinemann, K.; Bogdanoff, David W.; Cambier, Jean-Luc

1992-01-01

The work performed by D. W. Bogdanoff and J.-L. Cambier during the period of 1 Feb. - 31 Oct. 1992 is presented. The following topics are discussed: (1) improvement in the operation of the facility; (2) the wedge model; (3) calibration of the new test section; (4) combustor model; (5) hydrogen fuel system for combustor model; (6) three inch calibration/development tunnel; (7) shock tunnel unsteady flow; (8) pulse detonation wave engine; (9) DCAF flow simulation; (10) high temperature shock layer simulation; and (11) the one dimensional Godunov CFD code.

Multi-objective vs. single-objective calibration of a hydrologic model using single- and multi-objective screening

NASA Astrophysics Data System (ADS)

Mai, Juliane; Cuntz, Matthias; Shafii, Mahyar; Zink, Matthias; Schäfer, David; Thober, Stephan; Samaniego, Luis; Tolson, Bryan

2016-04-01

Hydrologic models are traditionally calibrated against observed streamflow. Recent studies have shown however, that only a few global model parameters are constrained using this kind of integral signal. They can be identified using prior screening techniques. Since different objectives might constrain different parameters, it is advisable to use multiple information to calibrate those models. One common approach is to combine these multiple objectives (MO) into one single objective (SO) function and allow the use of a SO optimization algorithm. Another strategy is to consider the different objectives separately and apply a MO Pareto optimization algorithm. In this study, two major research questions will be addressed: 1) How do multi-objective calibrations compare with corresponding single-objective calibrations? 2) How much do calibration results deteriorate when the number of calibrated parameters is reduced by a prior screening technique? The hydrologic model employed in this study is a distributed hydrologic model (mHM) with 52 model parameters, i.e. transfer coefficients. The model uses grid cells as a primary hydrologic unit, and accounts for processes like snow accumulation and melting, soil moisture dynamics, infiltration, surface runoff, evapotranspiration, subsurface storage and discharge generation. The model is applied in three distinct catchments over Europe. The SO calibrations are performed using the Dynamically Dimensioned Search (DDS) algorithm with a fixed budget while the MO calibrations are achieved using the Pareto Dynamically Dimensioned Search (PA-DDS) algorithm allowing for the same budget. The two objectives used here are the Nash Sutcliffe Efficiency (NSE) of the simulated streamflow and the NSE of the logarithmic transformation. It is shown that the SO DDS results are located close to the edges of the Pareto fronts of the PA-DDS. The MO calibrations are hence preferable due to their supply of multiple equivalent solutions from which the user can choose at the end due to the specific needs. The sequential single-objective parameter screening was employed prior to the calibrations reducing the number of parameters by at least 50% in the different catchments and for the different single objectives. The single-objective calibrations led to a faster convergence of the objectives and are hence beneficial when using a DDS on single-objectives. The above mentioned parameter screening technique is generalized for multi-objectives and applied before calibration using the PA-DDS algorithm. Two different alternatives of this MO-screening are tested. The comparison of the calibration results using all parameters and using only screened parameters shows for both alternatives that the PA-DDS algorithm does not profit in terms of trade-off size and function evaluations required to achieve converged pareto fronts. This is because the PA-DDS algorithm automatically reduces search space with progress of the calibration run. This automatic reduction should be different for other search algorithms. It is therefore hypothesized that prior screening can but must not be beneficial for parameter estimation dependent on the chosen optimization algorithm.

Bayesian calibration of terrestrial ecosystem models: a study of advanced Markov chain Monte Carlo methods

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

Lu, Dan; Ricciuto, Daniel M.; Walker, Anthony P.

Calibration of terrestrial ecosystem models is important but challenging. Bayesian inference implemented by Markov chain Monte Carlo (MCMC) sampling provides a comprehensive framework to estimate model parameters and associated uncertainties using their posterior distributions. The effectiveness and efficiency of the method strongly depend on the MCMC algorithm used. In this work, a differential evolution adaptive Metropolis (DREAM) algorithm is used to estimate posterior distributions of 21 parameters for the data assimilation linked ecosystem carbon (DALEC) model using 14 years of daily net ecosystem exchange data collected at the Harvard Forest Environmental Measurement Site eddy-flux tower. The calibration of DREAM results inmore » a better model fit and predictive performance compared to the popular adaptive Metropolis (AM) scheme. Moreover, DREAM indicates that two parameters controlling autumn phenology have multiple modes in their posterior distributions while AM only identifies one mode. The application suggests that DREAM is very suitable to calibrate complex terrestrial ecosystem models, where the uncertain parameter size is usually large and existence of local optima is always a concern. In addition, this effort justifies the assumptions of the error model used in Bayesian calibration according to the residual analysis. Here, the result indicates that a heteroscedastic, correlated, Gaussian error model is appropriate for the problem, and the consequent constructed likelihood function can alleviate the underestimation of parameter uncertainty that is usually caused by using uncorrelated error models.« less

Bayesian calibration of terrestrial ecosystem models: a study of advanced Markov chain Monte Carlo methods

DOE PAGES

Lu, Dan; Ricciuto, Daniel M.; Walker, Anthony P.; ...

2017-09-27

Calibration of terrestrial ecosystem models is important but challenging. Bayesian inference implemented by Markov chain Monte Carlo (MCMC) sampling provides a comprehensive framework to estimate model parameters and associated uncertainties using their posterior distributions. The effectiveness and efficiency of the method strongly depend on the MCMC algorithm used. In this work, a differential evolution adaptive Metropolis (DREAM) algorithm is used to estimate posterior distributions of 21 parameters for the data assimilation linked ecosystem carbon (DALEC) model using 14 years of daily net ecosystem exchange data collected at the Harvard Forest Environmental Measurement Site eddy-flux tower. The calibration of DREAM results inmore » a better model fit and predictive performance compared to the popular adaptive Metropolis (AM) scheme. Moreover, DREAM indicates that two parameters controlling autumn phenology have multiple modes in their posterior distributions while AM only identifies one mode. The application suggests that DREAM is very suitable to calibrate complex terrestrial ecosystem models, where the uncertain parameter size is usually large and existence of local optima is always a concern. In addition, this effort justifies the assumptions of the error model used in Bayesian calibration according to the residual analysis. Here, the result indicates that a heteroscedastic, correlated, Gaussian error model is appropriate for the problem, and the consequent constructed likelihood function can alleviate the underestimation of parameter uncertainty that is usually caused by using uncorrelated error models.« less

Improving Photometric Calibration of Meteor Video Camera Systems

NASA Technical Reports Server (NTRS)

Ehlert, Steven; Kingery, Aaron; Cooke, William

2016-01-01

Current optical observations of meteors are commonly limited by systematic uncertainties in photometric calibration at the level of approximately 0.5 mag or higher. Future improvements to meteor ablation models, luminous efficiency models, or emission spectra will hinge on new camera systems and techniques that significantly reduce calibration uncertainties and can reliably perform absolute photometric measurements of meteors. In this talk we discuss the algorithms and tests that NASA's Meteoroid Environment Office (MEO) has developed to better calibrate photometric measurements for the existing All-Sky and Wide-Field video camera networks as well as for a newly deployed four-camera system for measuring meteor colors in Johnson-Cousins BV RI filters. In particular we will emphasize how the MEO has been able to address two long-standing concerns with the traditional procedure, discussed in more detail below.

Integrating dynamic stopping, transfer learning and language models in an adaptive zero-training ERP speller.

PubMed

Kindermans, Pieter-Jan; Tangermann, Michael; Müller, Klaus-Robert; Schrauwen, Benjamin

2014-06-01

Most BCIs have to undergo a calibration session in which data is recorded to train decoders with machine learning. Only recently zero-training methods have become a subject of study. This work proposes a probabilistic framework for BCI applications which exploit event-related potentials (ERPs). For the example of a visual P300 speller we show how the framework harvests the structure suitable to solve the decoding task by (a) transfer learning, (b) unsupervised adaptation, (c) language model and (d) dynamic stopping. A simulation study compares the proposed probabilistic zero framework (using transfer learning and task structure) to a state-of-the-art supervised model on n = 22 subjects. The individual influence of the involved components (a)-(d) are investigated. Without any need for a calibration session, the probabilistic zero-training framework with inter-subject transfer learning shows excellent performance--competitive to a state-of-the-art supervised method using calibration. Its decoding quality is carried mainly by the effect of transfer learning in combination with continuous unsupervised adaptation. A high-performing zero-training BCI is within reach for one of the most popular BCI paradigms: ERP spelling. Recording calibration data for a supervised BCI would require valuable time which is lost for spelling. The time spent on calibration would allow a novel user to spell 29 symbols with our unsupervised approach. It could be of use for various clinical and non-clinical ERP-applications of BCI.

Comparison of APACHE III, APACHE IV, SAPS 3, and MPM0III and Influence of Resuscitation Status on Model Performance

PubMed Central

Gajic, Ognjen; Afessa, Bekele

2012-01-01

Background: There are few comparisons among the most recent versions of the major adult ICU prognostic systems (APACHE [Acute Physiology and Chronic Health Evaluation] IV, Simplified Acute Physiology Score [SAPS] 3, Mortality Probability Model [MPM]0III). Only MPM0III includes resuscitation status as a predictor. Methods: We assessed the discrimination, calibration, and overall performance of the models in 2,596 patients in three ICUs at our tertiary referral center in 2006. For APACHE and SAPS, the analyses were repeated with and without inclusion of resuscitation status as a predictor variable. Results: Of the 2,596 patients studied, 283 (10.9%) died before hospital discharge. The areas under the curve (95% CI) of the models for prediction of hospital mortality were 0.868 (0.854-0.880), 0.861 (0.847-0.874), 0.801 (0.785-0.816), and 0.721 (0.704-0.738) for APACHE III, APACHE IV, SAPS 3, and MPM0III, respectively. The Hosmer-Lemeshow statistics for the models were 33.7, 31.0, 36.6, and 21.8 for APACHE III, APACHE IV, SAPS 3, and MPM0III, respectively. Each of the Hosmer-Lemeshow statistics generated P values < .05, indicating poor calibration. Brier scores for the models were 0.0771, 0.0749, 0.0890, and 0.0932, respectively. There were no significant differences between the discriminative ability or the calibration of APACHE or SAPS with and without “do not resuscitate” status. Conclusions: APACHE III and IV had similar discriminatory capability and both were better than SAPS 3, which was better than MPM0III. The calibrations of the models studied were poor. Overall, models with more predictor variables performed better than those with fewer. The addition of resuscitation status did not improve APACHE III or IV or SAPS 3 prediction. PMID:22499827

Transferability of species distribution models for the detection of an invasive alien bryophyte using imaging spectroscopy data

NASA Astrophysics Data System (ADS)

Skowronek, Sandra; Van De Kerchove, Ruben; Rombouts, Bjorn; Aerts, Raf; Ewald, Michael; Warrie, Jens; Schiefer, Felix; Garzon-Lopez, Carol; Hattab, Tarek; Honnay, Olivier; Lenoir, Jonathan; Rocchini, Duccio; Schmidtlein, Sebastian; Somers, Ben; Feilhauer, Hannes

2018-06-01

Remote sensing is a promising tool for detecting invasive alien plant species. Mapping and monitoring those species requires accurate detection. So far, most studies relied on models that are locally calibrated and validated against available field data. Consequently, detecting invasive alien species at new study areas requires the acquisition of additional field data which can be expensive and time-consuming. Model transfer might thus provide a viable alternative. Here, we mapped the distribution of the invasive alien bryophyte Campylopus introflexus to i) assess the feasibility of spatially transferring locally calibrated models for species detection between four different heathland areas in Germany and Belgium and ii) test the potential of combining calibration data from different sites in one species distribution model (SDM). In a first step, four different SDMs were locally calibrated and validated by combining field data and airborne imaging spectroscopy data with a spatial resolution ranging from 1.8 m to 4 m and a spectral resolution of about 10 nm (244 bands). A one-class classifier, Maxent, which is based on the comparison of probability densities, was used to generate all SDMs. In a second step, each model was transferred to the three other study areas and the performance of the models for predicting C. introflexus occurrences was assessed. Finally, models combining calibration data from three study areas were built and tested on the remaining fourth site. In this step, different combinations of Maxent modelling parameters were tested. For the local models, the area under the curve for a test dataset (test AUC) was between 0.57-0.78, while the test AUC for the single transfer models ranged between 0.45-0.89. For the combined models the test AUC was between 0.54-0.9. The success of transferring models calibrated in one site to another site highly depended on the respective study site; the combined models provided higher test AUC values than the locally calibrated models for three out of four study sites. Furthermore, we also demonstrated the importance of optimizing the Maxent modelling parameters. Overall, our results indicate the potential of a combined model to map C. introflexus without the need for new calibration data.

Comparison between a model-based and a conventional pyramid sensor reconstructor.

PubMed

Korkiakoski, Visa; Vérinaud, Christophe; Le Louarn, Miska; Conan, Rodolphe

2007-08-20

A model of a non-modulated pyramid wavefront sensor (P-WFS) based on Fourier optics has been presented. Linearizations of the model represented as Jacobian matrices are used to improve the P-WFS phase estimates. It has been shown in simulations that a linear approximation of the P-WFS is sufficient in closed-loop adaptive optics. Also a method to compute model-based synthetic P-WFS command matrices is shown, and its performance is compared to the conventional calibration. It was observed that in poor visibility the new calibration is better than the conventional.

Basis material decomposition in spectral CT using a semi-empirical, polychromatic adaption of the Beer-Lambert model.

PubMed

Ehn, S; Sellerer, T; Mechlem, K; Fehringer, A; Epple, M; Herzen, J; Pfeiffer, F; Noël, P B

2017-01-07

Following the development of energy-sensitive photon-counting detectors using high-Z sensor materials, application of spectral x-ray imaging methods to clinical practice comes into reach. However, these detectors require extensive calibration efforts in order to perform spectral imaging tasks like basis material decomposition. In this paper, we report a novel approach to basis material decomposition that utilizes a semi-empirical estimator for the number of photons registered in distinct energy bins in the presence of beam-hardening effects which can be termed as a polychromatic Beer-Lambert model. A maximum-likelihood estimator is applied to the model in order to obtain estimates of the underlying sample composition. Using a Monte-Carlo simulation of a typical clinical CT acquisition, the performance of the proposed estimator was evaluated. The estimator is shown to be unbiased and efficient according to the Cramér-Rao lower bound. In particular, the estimator is capable of operating with a minimum number of calibration measurements. Good results were obtained after calibration using less than 10 samples of known composition in a two-material attenuation basis. This opens up the possibility for fast re-calibration in the clinical routine which is considered an advantage of the proposed method over other implementations reported in the literature.

Basis material decomposition in spectral CT using a semi-empirical, polychromatic adaption of the Beer-Lambert model

NASA Astrophysics Data System (ADS)

Ehn, S.; Sellerer, T.; Mechlem, K.; Fehringer, A.; Epple, M.; Herzen, J.; Pfeiffer, F.; Noël, P. B.

2017-01-01

Following the development of energy-sensitive photon-counting detectors using high-Z sensor materials, application of spectral x-ray imaging methods to clinical practice comes into reach. However, these detectors require extensive calibration efforts in order to perform spectral imaging tasks like basis material decomposition. In this paper, we report a novel approach to basis material decomposition that utilizes a semi-empirical estimator for the number of photons registered in distinct energy bins in the presence of beam-hardening effects which can be termed as a polychromatic Beer-Lambert model. A maximum-likelihood estimator is applied to the model in order to obtain estimates of the underlying sample composition. Using a Monte-Carlo simulation of a typical clinical CT acquisition, the performance of the proposed estimator was evaluated. The estimator is shown to be unbiased and efficient according to the Cramér-Rao lower bound. In particular, the estimator is capable of operating with a minimum number of calibration measurements. Good results were obtained after calibration using less than 10 samples of known composition in a two-material attenuation basis. This opens up the possibility for fast re-calibration in the clinical routine which is considered an advantage of the proposed method over other implementations reported in the literature.

Simple transfer calibration method for a Cimel Sun-Moon photometer: calculating lunar calibration coefficients from Sun calibration constants.

PubMed

Li, Zhengqiang; Li, Kaitao; Li, Donghui; Yang, Jiuchun; Xu, Hua; Goloub, Philippe; Victori, Stephane

2016-09-20

The Cimel new technologies allow both daytime and nighttime aerosol optical depth (AOD) measurements. Although the daytime AOD calibration protocols are well established, accurate and simple nighttime calibration is still a challenging task. Standard lunar-Langley and intercomparison calibration methods both require specific conditions in terms of atmospheric stability and site condition. Additionally, the lunar irradiance model also has some known limits on its uncertainty. This paper presents a simple calibration method that transfers the direct-Sun calibration constant, V_0,Sun, to the lunar irradiance calibration coefficient, C_Moon. Our approach is a pure calculation method, independent of site limits, e.g., Moon phase. The method is also not affected by the lunar irradiance model limitations, which is the largest error source of traditional calibration methods. Besides, this new transfer calibration approach is easy to use in the field since C_Moon can be obtained directly once V_0,Sun is known. Error analysis suggests that the average uncertainty of C_Moon over the 440-1640 nm bands obtained with the transfer method is 2.4%-2.8%, depending on the V_0,Sun approach (Langley or intercomparison), which is comparable with that of lunar-Langley approach, theoretically. In this paper, the Sun-Moon transfer and the Langley methods are compared based on site measurements in Beijing, and the day-night measurement continuity and performance are analyzed.

Predicting trauma patient mortality: ICD [or ICD-10-AM] versus AIS based approaches.

PubMed

Willis, Cameron D; Gabbe, Belinda J; Jolley, Damien; Harrison, James E; Cameron, Peter A

2010-11-01

The International Classification of Diseases Injury Severity Score (ICISS) has been proposed as an International Classification of Diseases (ICD)-10-based alternative to mortality prediction tools that use Abbreviated Injury Scale (AIS) data, including the Trauma and Injury Severity Score (TRISS). To date, studies have not examined the performance of ICISS using Australian trauma registry data. This study aimed to compare the performance of ICISS with other mortality prediction tools in an Australian trauma registry. This was a retrospective review of prospectively collected data from the Victorian State Trauma Registry. A training dataset was created for model development and a validation dataset for evaluation. The multiplicative ICISS model was compared with a worst injury ICISS approach, Victorian TRISS (V-TRISS, using local coefficients), maximum AIS severity and a multivariable model including ICD-10-AM codes as predictors. Models were investigated for discrimination (C-statistic) and calibration (Hosmer-Lemeshow statistic). The multivariable approach had the highest level of discrimination (C-statistic 0.90) and calibration (H-L 7.65, P= 0.468). Worst injury ICISS, V-TRISS and maximum AIS had similar performance. The multiplicative ICISS produced the lowest level of discrimination (C-statistic 0.80) and poorest calibration (H-L 50.23, P < 0.001). The performance of ICISS may be affected by the data used to develop estimates, the ICD version employed, the methods for deriving estimates and the inclusion of covariates. In this analysis, a multivariable approach using ICD-10-AM codes was the best-performing method. A multivariable ICISS approach may therefore be a useful alternative to AIS-based methods and may have comparable predictive performance to locally derived TRISS models. © 2010 The Authors. ANZ Journal of Surgery © 2010 Royal Australasian College of Surgeons.

Calibration of the advanced microwave sounding unit-A for NOAA-K

NASA Technical Reports Server (NTRS)

Mo, Tsan

1995-01-01

The thermal-vacuum chamber calibration data from the Advanced Microwave Sounding Unit-A (AMSU-A) for NOAA-K, which will be launched in 1996, were analyzed to evaluate the instrument performance, including calibration accuracy, nonlinearity, and temperature sensitivity. The AMSU-A on NOAA-K consists of AMSU-A2 Protoflight Model and AMSU-A1 Flight Model 1. The results show that both models meet the instrument specifications, except the AMSU-A1 antenna beamwidths, which exceed the requirement of 3.3 +/- 10%. We also studied the instrument's radiometric characterizations which will be incorporated into the operational calibration algorithm for processing the in-orbit AMSU-A data from space. Particularly, the nonlinearity parameters which will be used for correcting the nonlinear contributions from an imperfect square-law detector were determined from this data analysis. It was found that the calibration accuracies (differences between the measured scene radiances and those calculated from a linear two-point calibration formula) are polarization-dependent. Channels with vertical polarizations show little cold biases at the lowest scene target temperature 84K, while those with horizontal polarizations all have appreciable cold biases, which can be up to 0.6K. It is unknown where these polarization-dependent cold biases originate, but it is suspected that some chamber contamination of hot radiances leaked into the cold scene target area. Further investigation in this matter is required. The existence and magnitude of nonlinearity in each channel were established and a quadratic formula for modeling these nonlinear contributions was developed. The model was characterized by a single parameter u, values of which were obtained for each channel via least-squares fit to the data. Using the best-fit u values, we performed a series of simulations of the quadratic corrections which would be expected from the space data after the launch of AMSU-A on NOAA-K. In these simulations, the cosmic background radiance corresponding to a cold space temperature 2.73K was adopted as one of the two reference points of calibration. The largest simulated nonlinear correction is about 0.3K, which occurs at channel 15 when the instrument temperature is at 38.09 deg C. Others are less than 0.2K in the remaining channels. Possible improvement for future instrument calibration is also discussed.

Item Calibration Samples and the Stability of Achievement Estimates and System Rankings: Another Look at the PISA Model

ERIC Educational Resources Information Center

Rutkowski, Leslie; Rutkowski, David; Zhou, Yan

2016-01-01

Using an empirically-based simulation study, we show that typically used methods of choosing an item calibration sample have significant impacts on achievement bias and system rankings. We examine whether recent PISA accommodations, especially for lower performing participants, can mitigate some of this bias. Our findings indicate that standard…

A Kinematic Calibration Process for Flight Robotic Arms

NASA Technical Reports Server (NTRS)

Collins, Curtis L.; Robinson, Matthew L.

2013-01-01

The Mars Science Laboratory (MSL) robotic arm is ten times more massive than any Mars robotic arm before it, yet with similar accuracy and repeatability positioning requirements. In order to assess and validate these requirements, a higher-fidelity model and calibration processes were needed. Kinematic calibration of robotic arms is a common and necessary process to ensure good positioning performance. Most methodologies assume a rigid arm, high-accuracy data collection, and some kind of optimization of kinematic parameters. A new detailed kinematic and deflection model of the MSL robotic arm was formulated in the design phase and used to update the initial positioning and orientation accuracy and repeatability requirements. This model included a higher-fidelity link stiffness matrix representation, as well as a link level thermal expansion model. In addition, it included an actuator backlash model. Analytical results highlighted the sensitivity of the arm accuracy to its joint initialization methodology. Because of this, a new technique for initializing the arm joint encoders through hardstop calibration was developed. This involved selecting arm configurations to use in Earth-based hardstop calibration that had corresponding configurations on Mars with the same joint torque to ensure repeatability in the different gravity environment. The process used to collect calibration data for the arm included the use of multiple weight stand-in turrets with enough metrology targets to reconstruct the full six-degree-of-freedom location of the rover and tool frames. The follow-on data processing of the metrology data utilized a standard differential formulation and linear parameter optimization technique.

More accurate, calibrated bootstrap confidence intervals for correlating two autocorrelated climate time series

NASA Astrophysics Data System (ADS)

Olafsdottir, Kristin B.; Mudelsee, Manfred

2013-04-01

Estimation of the Pearson's correlation coefficient between two time series to evaluate the influences of one time depended variable on another is one of the most often used statistical method in climate sciences. Various methods are used to estimate confidence interval to support the correlation point estimate. Many of them make strong mathematical assumptions regarding distributional shape and serial correlation, which are rarely met. More robust statistical methods are needed to increase the accuracy of the confidence intervals. Bootstrap confidence intervals are estimated in the Fortran 90 program PearsonT (Mudelsee, 2003), where the main intention was to get an accurate confidence interval for correlation coefficient between two time series by taking the serial dependence of the process that generated the data into account. However, Monte Carlo experiments show that the coverage accuracy for smaller data sizes can be improved. Here we adapt the PearsonT program into a new version called PearsonT3, by calibrating the confidence interval to increase the coverage accuracy. Calibration is a bootstrap resampling technique, which basically performs a second bootstrap loop or resamples from the bootstrap resamples. It offers, like the non-calibrated bootstrap confidence intervals, robustness against the data distribution. Pairwise moving block bootstrap is used to preserve the serial correlation of both time series. The calibration is applied to standard error based bootstrap Student's t confidence intervals. The performances of the calibrated confidence intervals are examined with Monte Carlo simulations, and compared with the performances of confidence intervals without calibration, that is, PearsonT. The coverage accuracy is evidently better for the calibrated confidence intervals where the coverage error is acceptably small (i.e., within a few percentage points) already for data sizes as small as 20. One form of climate time series is output from numerical models which simulate the climate system. The method is applied to model data from the high resolution ocean model, INALT01 where the relationship between the Agulhas Leakage and the North Brazil Current is evaluated. Preliminary results show significant correlation between the two variables when there is 10 year lag between them, which is more or less the time that takes the Agulhas Leakage water to reach the North Brazil Current. Mudelsee, M., 2003. Estimating Pearson's correlation coefficient with bootstrap confidence interval from serially dependent time series. Mathematical Geology 35, 651-665.

In-flight radiometric calibration of the Airborne Visible/Infrared Imaging Spectrometer (AVIRIS)

NASA Technical Reports Server (NTRS)

Conel, James E.; Green, Robert O.; Alley, Ronald E.; Bruegge, Carol J.; Carrere, Veronique; Margolis, Jack S.; Vane, Gregg; Chrien, Thomas G.; Slater, Philip N.; Biggard, Stuart F.

1988-01-01

A reflectance-based method was used to provide an analysis of the in-flight radiometric performance of AVIRIS. Field spectral reflectance measurements of the surface and extinction measurements of the atmosphere using solar radiation were used as input to atmospheric radiative transfer calculations. Five separate codes were used in the analysis. Four include multiple scattering, and the computed radiances from these for flight conditions were in good agreement. Code-generated radiances were compared with AVIRIS-predicted radiances based on two laboratory calibrations (pre- and post-season of flight) for a uniform highly reflecting natural dry lake target. For one spectrometer (C), the pre- and post-season calibration factors were found to give identical results, and to be in agreement with the atmospheric models that include multiple scattering. This positive result validates the field and laboratory calibration technique. Results for the other spectrometers (A, B and D) were widely at variance with the models no matter which calibration factors were used. Potential causes of these discrepancies are discussed.

Effects of Serum Creatinine Calibration on Estimated Renal Function in African Americans: the Jackson Heart Study

PubMed Central

Wang, Wei; Young, Bessie A.; Fülöp, Tibor; de Boer, Ian H.; Boulware, L. Ebony; Katz, Ronit; Correa, Adolfo; Griswold, Michael E.

2015-01-01

Background The calibration to Isotope Dilution Mass Spectroscopy (IDMS) traceable creatinine is essential for valid use of the new Chronic Kidney Disease Epidemiology Collaboration (CKD-EPI) equation to estimate the glomerular filtration rate (GFR). Methods For 5,210 participants in the Jackson Heart Study (JHS), serum creatinine was measured with a multipoint enzymatic spectrophotometric assay at the baseline visit (2000–2004) and re-measured using the Roche enzymatic method, traceable to IDMS in a subset of 206 subjects. The 200 eligible samples (6 were excluded, 1 for failure of the re-measurement and 5 for outliers) were divided into three disjoint sets - training, validation, and test - to select a calibration model, estimate true errors, and assess performance of the final calibration equation. The calibration equation was applied to serum creatinine measurements of 5,210 participants to estimate GFR and the prevalence of CKD. Results The selected Deming regression model provided a slope of 0.968 (95% Confidence Interval (CI), 0.904 to 1.053) and intercept of −0.0248 (95% CI, −0.0862 to 0.0366) with R squared 0.9527. Calibrated serum creatinine showed high agreement with actual measurements when applying to the unused test set (concordance correlation coefficient 0.934, 95% CI, 0.894 to 0.960). The baseline prevalence of CKD in the JHS (2000–2004) was 6.30% using calibrated values, compared with 8.29% using non-calibrated serum creatinine with the CKD-EPI equation (P < 0.001). Conclusions A Deming regression model was chosen to optimally calibrate baseline serum creatinine measurements in the JHS and the calibrated values provide a lower CKD prevalence estimate. PMID:25806862

An Innovative Software Tool Suite for Power Plant Model Validation and Parameter Calibration using PMU Measurements

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

Li, Yuanyuan; Diao, Ruisheng; Huang, Renke

Maintaining good quality of power plant stability models is of critical importance to ensure the secure and economic operation and planning of today’s power grid with its increasing stochastic and dynamic behavior. According to North American Electric Reliability (NERC) standards, all generators in North America with capacities larger than 10 MVA are required to validate their models every five years. Validation is quite costly and can significantly affect the revenue of generator owners, because the traditional staged testing requires generators to be taken offline. Over the past few years, validating and calibrating parameters using online measurements including phasor measurement unitsmore » (PMUs) and digital fault recorders (DFRs) has been proven to be a cost-effective approach. In this paper, an innovative open-source tool suite is presented for validating power plant models using PPMV tool, identifying bad parameters with trajectory sensitivity analysis, and finally calibrating parameters using an ensemble Kalman filter (EnKF) based algorithm. The architectural design and the detailed procedures to run the tool suite are presented, with results of test on a realistic hydro power plant using PMU measurements for 12 different events. The calibrated parameters of machine, exciter, governor and PSS models demonstrate much better performance than the original models for all the events and show the robustness of the proposed calibration algorithm.« less

Basin-scale geothermal model calibration: experience from the Perth Basin, Australia

NASA Astrophysics Data System (ADS)

Wellmann, Florian; Reid, Lynn

2014-05-01

The calibration of large-scale geothermal models for entire sedimentary basins is challenging as direct measurements of rock properties and subsurface temperatures are commonly scarce and the basal boundary conditions poorly constrained. Instead of the often applied "trial-and-error" manual model calibration, we examine here if we can gain additional insight into parameter sensitivities and model uncertainty with a model analysis and calibration study. Our geothermal model is based on a high-resolution full 3-D geological model, covering an area of more than 100,000 square kilometers and extending to a depth of 55 kilometers. The model contains all major faults (>80 ) and geological units (13) for the entire basin. This geological model is discretised into a rectilinear mesh with a lateral resolution of 500 x 500 m, and a variable resolution at depth. The highest resolution of 25 m is applied to a depth range of 1000-3000 m where most temperature measurements are available. The entire discretised model consists of approximately 50 million cells. The top thermal boundary condition is derived from surface temperature measurements on land and ocean floor. The base of the model extents below the Moho, and we apply the heat flux over the Moho as a basal heat flux boundary condition. Rock properties (thermal conductivity, porosity, and heat production) have been compiled from several existing data sets. The conductive geothermal forward simulation is performed with SHEMAT, and we then use the stand-alone capabilities of iTOUGH2 for sensitivity analysis and model calibration. Simulated temperatures are compared to 130 quality weighted bottom hole temperature measurements. The sensitivity analysis provided a clear insight into the most sensitive parameters and parameter correlations. This proved to be of value as strong correlations, for example between basal heat flux and heat production in deep geological units, can significantly influence the model calibration procedure. The calibration resulted in a better determination of subsurface temperatures, and, in addition, provided an insight into model quality. Furthermore, a detailed analysis of the measurements used for calibration highlighted potential outliers, and limitations with the model assumptions. Extending the previously existing large-scale geothermal simulation with iTOUGH2 provided us with a valuable insight into the sensitive parameters and data in the model, which would clearly not be possible with a simple trial-and-error calibration method. Using the gained knowledge, future work will include more detailed studies on the influence of advection and convection.

Development of PBPK Models for Gasoline in Adult and ...

EPA Pesticide Factsheets

Concern for potential developmental effects of exposure to gasoline-ethanol blends has grown along with their increased use in the US fuel supply. Physiologically-based pharmacokinetic (PBPK) models for these complex mixtures were developed to address dosimetric issues related to selection of exposure concentrations for in vivo toxicity studies. Sub-models for individual hydrocarbon (HC) constituents were first developed and calibrated with published literature or QSAR-derived data where available. Successfully calibrated sub-models for individual HCs were combined, assuming competitive metabolic inhibition in the liver, and a priori simulations of mixture interactions were performed. Blood HC concentration data were collected from exposed adult non-pregnant (NP) rats (9K ppm total HC vapor, 6h/day) to evaluate performance of the NP mixture model. This model was then converted to a pregnant (PG) rat mixture model using gestational growth equations that enabled a priori estimation of life-stage specific kinetic differences. To address the impact of changing relevant physiological parameters from NP to PG, the PG mixture model was first calibrated against the NP data. The PG mixture model was then evaluated against data from PG rats that were subsequently exposed (9K ppm/6.33h gestation days (GD) 9-20). Overall, the mixture models adequately simulated concentrations of HCs in blood from single (NP) or repeated (PG) exposures (within ~2-3 fold of measured values of

Comparative study of transient hydraulic tomography with varying parameterizations and zonations: Laboratory sandbox investigation

NASA Astrophysics Data System (ADS)

Luo, Ning; Zhao, Zhanfeng; Illman, Walter A.; Berg, Steven J.

2017-11-01

Transient hydraulic tomography (THT) is a robust method of aquifer characterization to estimate the spatial distributions (or tomograms) of both hydraulic conductivity (K) and specific storage (Ss). However, the highly-parameterized nature of the geostatistical inversion approach renders it computationally intensive for large-scale investigations. In addition, geostatistics-based THT may produce overly smooth tomograms when head data used to constrain the inversion is limited. Therefore, alternative model conceptualizations for THT need to be examined. To investigate this, we simultaneously calibrated different groundwater models with varying parameterizations and zonations using two cases of different pumping and monitoring data densities from a laboratory sandbox. Specifically, one effective parameter model, four geology-based zonation models with varying accuracy and resolution, and five geostatistical models with different prior information are calibrated. Model performance is quantitatively assessed by examining the calibration and validation results. Our study reveals that highly parameterized geostatistical models perform the best among the models compared, while the zonation model with excellent knowledge of stratigraphy also yields comparable results. When few pumping tests with sparse monitoring intervals are available, the incorporation of accurate or simplified geological information into geostatistical models reveals more details in heterogeneity and yields more robust validation results. However, results deteriorate when inaccurate geological information are incorporated. Finally, our study reveals that transient inversions are necessary to obtain reliable K and Ss estimates for making accurate predictions of transient drawdown events.

Gradient nonlinearity calibration and correction for a compact, asymmetric magnetic resonance imaging gradient system.

PubMed

Tao, S; Trzasko, J D; Gunter, J L; Weavers, P T; Shu, Y; Huston, J; Lee, S K; Tan, E T; Bernstein, M A

2017-01-21

Due to engineering limitations, the spatial encoding gradient fields in conventional magnetic resonance imaging cannot be perfectly linear and always contain higher-order, nonlinear components. If ignored during image reconstruction, gradient nonlinearity (GNL) manifests as image geometric distortion. Given an estimate of the GNL field, this distortion can be corrected to a degree proportional to the accuracy of the field estimate. The GNL of a gradient system is typically characterized using a spherical harmonic polynomial model with model coefficients obtained from electromagnetic simulation. Conventional whole-body gradient systems are symmetric in design; typically, only odd-order terms up to the 5th-order are required for GNL modeling. Recently, a high-performance, asymmetric gradient system was developed, which exhibits more complex GNL that requires higher-order terms including both odd- and even-orders for accurate modeling. This work characterizes the GNL of this system using an iterative calibration method and a fiducial phantom used in ADNI (Alzheimer's Disease Neuroimaging Initiative). The phantom was scanned at different locations inside the 26 cm diameter-spherical-volume of this gradient, and the positions of fiducials in the phantom were estimated. An iterative calibration procedure was utilized to identify the model coefficients that minimize the mean-squared-error between the true fiducial positions and the positions estimated from images corrected using these coefficients. To examine the effect of higher-order and even-order terms, this calibration was performed using spherical harmonic polynomial of different orders up to the 10th-order including even- and odd-order terms, or odd-order only. The results showed that the model coefficients of this gradient can be successfully estimated. The residual root-mean-squared-error after correction using up to the 10th-order coefficients was reduced to 0.36 mm, yielding spatial accuracy comparable to conventional whole-body gradients. The even-order terms were necessary for accurate GNL modeling. In addition, the calibrated coefficients improved image geometric accuracy compared with the simulation-based coefficients.

Multi-response calibration of a conceptual hydrological model in the semiarid catchment of Wadi al Arab, Jordan

NASA Astrophysics Data System (ADS)

Rödiger, T.; Geyer, S.; Mallast, U.; Merz, R.; Krause, P.; Fischer, C.; Siebert, C.

2014-02-01

A key factor for sustainable management of groundwater systems is the accurate estimation of groundwater recharge. Hydrological models are common tools for such estimations and widely used. As such models need to be calibrated against measured values, the absence of adequate data can be problematic. We present a nested multi-response calibration approach for a semi-distributed hydrological model in the semi-arid catchment of Wadi al Arab in Jordan, with sparsely available runoff data. The basic idea of the calibration approach is to use diverse observations in a nested strategy, in which sub-parts of the model are calibrated to various observation data types in a consecutive manner. First, the available different data sources have to be screened for information content of processes, e.g. if data sources contain information on mean values, spatial or temporal variability etc. for the entire catchment or only sub-catchments. In a second step, the information content has to be mapped to relevant model components, which represent these processes. Then the data source is used to calibrate the respective subset of model parameters, while the remaining model parameters remain unchanged. This mapping is repeated for other available data sources. In that study the gauged spring discharge (GSD) method, flash flood observations and data from the chloride mass balance (CMB) are used to derive plausible parameter ranges for the conceptual hydrological model J2000g. The water table fluctuation (WTF) method is used to validate the model. Results from modelling using a priori parameter values from literature as a benchmark are compared. The estimated recharge rates of the calibrated model deviate less than ±10% from the estimates derived from WTF method. Larger differences are visible in the years with high uncertainties in rainfall input data. The performance of the calibrated model during validation produces better results than applying the model with only a priori parameter values. The model with a priori parameter values from literature tends to overestimate recharge rates with up to 30%, particular in the wet winter of 1991/1992. An overestimation of groundwater recharge and hence available water resources clearly endangers reliable water resource managing in water scarce region. The proposed nested multi-response approach may help to better predict water resources despite data scarcity.

Calibration of the head direction network: a role for symmetric angular head velocity cells.

PubMed

Stratton, Peter; Wyeth, Gordon; Wiles, Janet

2010-06-01

Continuous attractor networks require calibration. Computational models of the head direction (HD) system of the rat usually assume that the connections that maintain HD neuron activity are pre-wired and static. Ongoing activity in these models relies on precise continuous attractor dynamics. It is currently unknown how such connections could be so precisely wired, and how accurate calibration is maintained in the face of ongoing noise and perturbation. Our adaptive attractor model of the HD system that uses symmetric angular head velocity (AHV) cells as a training signal shows that the HD system can learn to support stable firing patterns from poorly-performing, unstable starting conditions. The proposed calibration mechanism suggests a requirement for symmetric AHV cells, the existence of which has previously been unexplained, and predicts that symmetric and asymmetric AHV cells should be distinctly different (in morphology, synaptic targets and/or methods of action on postsynaptic HD cells) due to their distinctly different functions.

A calibrated, high-resolution goes satellite solar insolation product for a climatology of Florida evapotranspiration

USGS Publications Warehouse

Paech, S.J.; Mecikalski, J.R.; Sumner, D.M.; Pathak, C.S.; Wu, Q.; Islam, S.; Sangoyomi, T.

2009-01-01

Estimates of incoming solar radiation (insolation) from Geostationary Operational Environmental Satellite observations have been produced for the state of Florida over a 10-year period (1995-2004). These insolation estimates were developed into well-calibrated half-hourly and daily integrated solar insolation fields over the state at 2 km resolution, in addition to a 2-week running minimum surface albedo product. Model results of the daily integrated insolation were compared with ground-based pyranometers, and as a result, the entire dataset was calibrated. This calibration was accomplished through a three-step process: (1) comparison with ground-based pyranometer measurements on clear (noncloudy) reference days, (2) correcting for a bias related to cloudiness, and (3) deriving a monthly bias correction factor. Precalibration results indicated good model performance, with a station-averaged model error of 2.2 MJ m-2/day (13%). Calibration reduced errors to 1.7 MJ m -2/day (10%), and also removed temporal-related, seasonal-related, and satellite sensor-related biases. The calibrated insolation dataset will subsequently be used by state of Florida Water Management Districts to produce statewide, 2-km resolution maps of estimated daily reference and potential evapotranspiration for water management-related activities. ?? 2009 American Water Resources Association.

The effect of rainfall measurement uncertainties on rainfall-runoff processes modelling.

PubMed

Stransky, D; Bares, V; Fatka, P

2007-01-01

Rainfall data are a crucial input for various tasks concerning the wet weather period. Nevertheless, their measurement is affected by random and systematic errors that cause an underestimation of the rainfall volume. Therefore, the general objective of the presented work was to assess the credibility of measured rainfall data and to evaluate the effect of measurement errors on urban drainage modelling tasks. Within the project, the methodology of the tipping bucket rain gauge (TBR) was defined and assessed in terms of uncertainty analysis. A set of 18 TBRs was calibrated and the results were compared to the previous calibration. This enables us to evaluate the ageing of TBRs. A propagation of calibration and other systematic errors through the rainfall-runoff model was performed on experimental catchment. It was found that the TBR calibration is important mainly for tasks connected with the assessment of peak values and high flow durations. The omission of calibration leads to up to 30% underestimation and the effect of other systematic errors can add a further 15%. The TBR calibration should be done every two years in order to catch up the ageing of TBR mechanics. Further, the authors recommend to adjust the dynamic test duration proportionally to generated rainfall intensity.

Development and Application of a Process-based River System Model at a Continental Scale

NASA Astrophysics Data System (ADS)

Kim, S. S. H.; Dutta, D.; Vaze, J.; Hughes, J. D.; Yang, A.; Teng, J.

2014-12-01

Existing global and continental scale river models, mainly designed for integrating with global climate model, are of very course spatial resolutions and they lack many important hydrological processes, such as overbank flow, irrigation diversion, groundwater seepage/recharge, which operate at a much finer resolution. Thus, these models are not suitable for producing streamflow forecast at fine spatial resolution and water accounts at sub-catchment levels, which are important for water resources planning and management at regional and national scale. A large-scale river system model has been developed and implemented for water accounting in Australia as part of the Water Information Research and Development Alliance between Australia's Bureau of Meteorology (BoM) and CSIRO. The model, developed using node-link architecture, includes all major hydrological processes, anthropogenic water utilisation and storage routing that influence the streamflow in both regulated and unregulated river systems. It includes an irrigation model to compute water diversion for irrigation use and associated fluxes and stores and a storage-based floodplain inundation model to compute overbank flow from river to floodplain and associated floodplain fluxes and stores. An auto-calibration tool has been built within the modelling system to automatically calibrate the model in large river systems using Shuffled Complex Evolution optimiser and user-defined objective functions. The auto-calibration tool makes the model computationally efficient and practical for large basin applications. The model has been implemented in several large basins in Australia including the Murray-Darling Basin, covering more than 2 million km2. The results of calibration and validation of the model shows highly satisfactory performance. The model has been operalisationalised in BoM for producing various fluxes and stores for national water accounting. This paper introduces this newly developed river system model describing the conceptual hydrological framework, methods used for representing different hydrological processes in the model and the results and evaluation of the model performance. The operational implementation of the model for water accounting is discussed.

A Data-driven Approach for Forecasting Next-day River Discharge

NASA Astrophysics Data System (ADS)

Sharif, H. O.; Billah, K. S.

2017-12-01

This study focuses on evaluating the performance of the Soil and Water Assessment Tool (SWAT) eco-hydrological model, a simple Auto-Regressive with eXogenous input (ARX) model, and a Gene expression programming (GEP)-based model in one-day-ahead forecasting of discharge of a subtropical basin (the upper Kentucky River Basin). The three models were calibrated with daily flow at the US Geological Survey (USGS) stream gauging station not affected by flow regulation for the period of 2002-2005. The calibrated models were then validated at the same gauging station as well as another USGS gauge 88 km downstream for the period of 2008-2010. The results suggest that simple models outperform a sophisticated hydrological model with GEP having the advantage of being able to generate functional relationships that allow scientific investigation of the complex nonlinear interrelationships among input variables. Unlike SWAT, GEP, and to some extent, ARX are less sensitive to the length of the calibration time series and do not require a spin-up period.

Validation of pavement performance curves for the mechanistic-empirical pavement design guide.

DOT National Transportation Integrated Search

2009-02-01

The objective of this research is to determine whether the nationally calibrated performance models used in the Mechanistic-Empirical : Pavement Design Guide (MEPDG) provide a reasonable prediction of actual field performance, and if the desired accu...

Energy Performance Assessment of Radiant Cooling System through Modeling and Calibration at Component Level

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

Khan, Yasin; Mathur, Jyotirmay; Bhandari, Mahabir S

2016-01-01

The paper describes a case study of an information technology office building with a radiant cooling system and a conventional variable air volume (VAV) system installed side by side so that performancecan be compared. First, a 3D model of the building involving architecture, occupancy, and HVAC operation was developed in EnergyPlus, a simulation tool. Second, a different calibration methodology was applied to develop the base case for assessing the energy saving potential. This paper details the calibration of the whole building energy model to the component level, including lighting, equipment, and HVAC components such as chillers, pumps, cooling towers, fans,more » etc. Also a new methodology for the systematic selection of influence parameter has been developed for the calibration of a simulated model which requires large time for the execution. The error at the whole building level [measured in mean bias error (MBE)] is 0.2%, and the coefficient of variation of root mean square error (CvRMSE) is 3.2%. The total errors in HVAC at the hourly are MBE = 8.7% and CvRMSE = 23.9%, which meet the criteria of ASHRAE 14 (2002) for hourly calibration. Different suggestions have been pointed out to generalize the energy saving of radiant cooling system through the existing building system. So a base case model was developed by using the calibrated model for quantifying the energy saving potential of the radiant cooling system. It was found that a base case radiant cooling system integrated with DOAS can save 28% energy compared with the conventional VAV system.« less

Using Bayesian Model Averaging (BMA) to calibrate probabilistic surface temperature forecasts over Iran

NASA Astrophysics Data System (ADS)

Soltanzadeh, I.; Azadi, M.; Vakili, G. A.

2011-07-01

Using Bayesian Model Averaging (BMA), an attempt was made to obtain calibrated probabilistic numerical forecasts of 2-m temperature over Iran. The ensemble employs three limited area models (WRF, MM5 and HRM), with WRF used with five different configurations. Initial and boundary conditions for MM5 and WRF are obtained from the National Centers for Environmental Prediction (NCEP) Global Forecast System (GFS) and for HRM the initial and boundary conditions come from analysis of Global Model Europe (GME) of the German Weather Service. The resulting ensemble of seven members was run for a period of 6 months (from December 2008 to May 2009) over Iran. The 48-h raw ensemble outputs were calibrated using BMA technique for 120 days using a 40 days training sample of forecasts and relative verification data. The calibrated probabilistic forecasts were assessed using rank histogram and attribute diagrams. Results showed that application of BMA improved the reliability of the raw ensemble. Using the weighted ensemble mean forecast as a deterministic forecast it was found that the deterministic-style BMA forecasts performed usually better than the best member's deterministic forecast.

A comparison of the Injury Severity Score and the Trauma Mortality Prediction Model.

PubMed

Cook, Alan; Weddle, Jo; Baker, Susan; Hosmer, David; Glance, Laurent; Friedman, Lee; Osler, Turner

2014-01-01

Performance benchmarking requires accurate measurement of injury severity. Despite its shortcomings, the Injury Severity Score (ISS) remains the industry standard 40 years after its creation. A new severity measure, the Trauma Mortality Prediction Model (TMPM), uses either the Abbreviated Injury Scale (AIS) or DRG International Classification of Diseases-9th Rev. (ICD-9) lexicons and may better quantify injury severity compared with ISS. We compared the performance of TMPM with ISS and other measures of injury severity in a single cohort of patients. We included 337,359 patient records with injuries reliably described in both the AIS and the ICD-9 lexicons from the National Trauma Data Bank. Five injury severity measures (ISS, maximum AIS score, New Injury Severity Score [NISS], ICD-9-Based Injury Severity Score [ICISS], TMPM) were computed using either the AIS or ICD-9 codes. These measures were compared for discrimination (area under the receiver operating characteristic curve), an estimate of proximity to a model that perfectly predicts the outcome (Akaike information criterion), and model calibration curves. TMPM demonstrated superior receiver operating characteristic curve, Akaike information criterion, and calibration using either the AIS or ICD-9 lexicons. Calibration plots demonstrate the monotonic characteristics of the TMPM models contrasted by the nonmonotonic features of the other prediction models. Severity measures were more accurate with the AIS lexicon rather than ICD-9. NISS proved superior to ISS in either lexicon. Since NISS is simpler to compute, it should replace ISS when a quick estimate of injury severity is required for AIS-coded injuries. Calibration curves suggest that the nonmonotonic nature of ISS may undermine its performance. TMPM demonstrated superior overall mortality prediction compared with all other models including ISS whether the AIS or ICD-9 lexicons were used. Because TMPM provides an absolute probability of death, it may allow clinicians to communicate more precisely with one another and with patients and families. Disagnostic study, level I; prognostic study, level II.

Analysis of ecstasy tablets: comparison of reflectance and transmittance near infrared spectroscopy.

PubMed

Schneider, Ralph Carsten; Kovar, Karl-Artur

2003-07-08

Calibration models for the quantitation of commonly used ecstasy substances have been developed using near infrared spectroscopy (NIR) in diffuse reflectance and in transmission mode by applying seized ecstasy tablets for model building and validation. The samples contained amphetamine, N-methyl-3,4-methylenedioxy-amphetamine (MDMA) and N-ethyl-3,4-methylenedioxy-amphetamine (MDE) in different concentrations. All tablets were analyzed using high performance liquid chromatography (HPLC) with diode array detection as reference method. We evaluated the performance of each NIR measurement method with regard to its ability to predict the content of each tablet with a low root mean square error of prediction (RMSEP). Best calibration models could be generated by using NIR measurement in transmittance mode with wavelength selection and 1/x-transformation of the raw data. The models build in reflectance mode showed higher RMSEPs using as data pretreatment, wavelength selection, 1/x-transformation and a second order Savitzky-Golay derivative with five point smoothing was applied to obtain the best models. To estimate the influence of inhomogeneities in the illegal tablets, a calibration of the destroyed, i.e. triturated samples was build and compared to the corresponding data of the whole tablets. The calibrations using these homogenized tablets showed lower RMSEPs. We can conclude that NIR analysis of ecstasy tablets in transmission mode is more suitable than measurement in diffuse reflectance to obtain quantification models for their active ingredients with regard to low errors of prediction. Inhomogeneities in the samples are equalized when measuring the tablets as powdered samples.

Estimation of water table level and nitrate pollution based on geostatistical and multiple mass transport models

NASA Astrophysics Data System (ADS)

Matiatos, Ioannis; Varouhakis, Emmanouil A.; Papadopoulou, Maria P.

2015-04-01

As the sustainable use of groundwater resources is a great challenge for many countries in the world, groundwater modeling has become a very useful and well established tool for studying groundwater management problems. Based on various methods used to numerically solve algebraic equations representing groundwater flow and contaminant mass transport, numerical models are mainly divided into Finite Difference-based and Finite Element-based models. The present study aims at evaluating the performance of a finite difference-based (MODFLOW-MT3DMS), a finite element-based (FEFLOW) and a hybrid finite element and finite difference (Princeton Transport Code-PTC) groundwater numerical models simulating groundwater flow and nitrate mass transport in the alluvial aquifer of Trizina region in NE Peloponnese, Greece. The calibration of groundwater flow in all models was performed using groundwater hydraulic head data from seven stress periods and the validation was based on a series of hydraulic head data for two stress periods in sufficient numbers of observation locations. The same periods were used for the calibration of nitrate mass transport. The calibration and validation of the three models revealed that the simulated values of hydraulic heads and nitrate mass concentrations coincide well with the observed ones. The models' performance was assessed by performing a statistical analysis of these different types of numerical algorithms. A number of metrics, such as Mean Absolute Error (MAE), Root Mean Square Error (RMSE), Bias, Nash Sutcliffe Model Efficiency (NSE) and Reliability Index (RI) were used allowing the direct comparison of models' performance. Spatiotemporal Kriging (STRK) was also applied using separable and non-separable spatiotemporal variograms to predict water table level and nitrate concentration at each sampling station for two selected hydrological stress periods. The predictions were validated using the respective measured values. Maps of water table level and nitrate concentrations were produced and compared with those obtained from groundwater and mass transport numerical models. Preliminary results showed similar efficiency of the spatiotemporal geostatistical method with the numerical models. However data requirements of the former model were significantly less. Advantages and disadvantages of the methods performance were analysed and discussed indicating the characteristics of the different approaches.

Development of speed models for improving travel forecasting and highway performance evaluation : [technical summary].

DOT National Transportation Integrated Search

2013-12-01

Travel forecasting models predict travel demand based on the present transportation system and its use. Transportation modelers must develop, validate, and calibrate models to ensure that predicted travel demand is as close to reality as possible. Mo...

High Accuracy Transistor Compact Model Calibrations

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

Hembree, Charles E.; Mar, Alan; Robertson, Perry J.

2015-09-01

Typically, transistors are modeled by the application of calibrated nominal and range models. These models consists of differing parameter values that describe the location and the upper and lower limits of a distribution of some transistor characteristic such as current capacity. Correspond- ingly, when using this approach, high degrees of accuracy of the transistor models are not expected since the set of models is a surrogate for a statistical description of the devices. The use of these types of models describes expected performances considering the extremes of process or transistor deviations. In contrast, circuits that have very stringent accuracy requirementsmore » require modeling techniques with higher accuracy. Since these accurate models have low error in transistor descriptions, these models can be used to describe part to part variations as well as an accurate description of a single circuit instance. Thus, models that meet these stipulations also enable the calculation of quantifi- cation of margins with respect to a functional threshold and uncertainties in these margins. Given this need, new model high accuracy calibration techniques for bipolar junction transis- tors have been developed and are described in this report.« less

Estimating groundwater levels using system identification models in Nzhelele and Luvuvhu areas, Limpopo Province, South Africa

NASA Astrophysics Data System (ADS)

Makungo, Rachel; Odiyo, John O.

2017-08-01

This study was focused on testing the ability of a coupled linear and non-linear system identification model in estimating groundwater levels. System identification provides an alternative approach for estimating groundwater levels in areas that lack data required by physically-based models. It also overcomes the limitations of physically-based models due to approximations, assumptions and simplifications. Daily groundwater levels for 4 boreholes, rainfall and evaporation data covering the period 2005-2014 were used in the study. Seventy and thirty percent of the data were used to calibrate and validate the model, respectively. Correlation coefficient (R), coefficient of determination (R2), root mean square error (RMSE), percent bias (PBIAS), Nash Sutcliffe coefficient of efficiency (NSE) and graphical fits were used to evaluate the model performance. Values for R, R2, RMSE, PBIAS and NSE ranged from 0.8 to 0.99, 0.63 to 0.99, 0.01-2.06 m, -7.18 to 1.16 and 0.68 to 0.99, respectively. Comparisons of observed and simulated groundwater levels for calibration and validation runs showed close agreements. The model performance mostly varied from satisfactory, good, very good and excellent. Thus, the model is able to estimate groundwater levels. The calibrated models can reasonably capture description between input and output variables and can, thus be used to estimate long term groundwater levels.

Numerical simulation of damage evolution for ductile materials and mechanical properties study

NASA Astrophysics Data System (ADS)

El Amri, A.; Hanafi, I.; Haddou, M. E. Y.; Khamlichi, A.

2015-12-01

This paper presents results of a numerical modelling of ductile fracture and failure of elements made of 5182H111 aluminium alloys subjected to dynamic traction. The analysis was performed using Johnson-Cook model based on ABAQUS software. The modelling difficulty related to prediction of ductile fracture mainly arises because there is a tremendous span of length scales from the structural problem to the micro-mechanics problem governing the material separation process. This study has been used the experimental results to calibrate a simple crack propagation criteria for shell elements of which one has often been used in practical analyses. The performance of the proposed model is in general good and it is believed that the presented results and experimental-numerical calibration procedure can be of use in practical finite-element simulations.

Climate downscaling over South America for 1971-2000: application in SMAP rainfall-runoff model for Grande River Basin

NASA Astrophysics Data System (ADS)

da Silva, Felipe das Neves Roque; Alves, José Luis Drummond; Cataldi, Marcio

2018-03-01

This paper aims to validate inflow simulations concerning the present-day climate at Água Vermelha Hydroelectric Plant (AVHP—located on the Grande River Basin) based on the Soil Moisture Accounting Procedure (SMAP) hydrological model. In order to provide rainfall data to the SMAP model, the RegCM regional climate model was also used working with boundary conditions from the MIROC model. Initially, present-day climate simulation performed by RegCM model was analyzed. It was found that, in terms of rainfall, the model was able to simulate the main patterns observed over South America. A bias correction technique was also used and it was essential to reduce mistakes related to rainfall simulation. Comparison between rainfall simulations from RegCM and MIROC showed improvements when the dynamical downscaling was performed. Then, SMAP, a rainfall-runoff hydrological model, was used to simulate inflows at Água Vermelha Hydroelectric Plant. After calibration with observed rainfall, SMAP simulations were evaluated in two different periods from the one used in calibration. During calibration, SMAP captures the inflow variability observed at AVHP. During validation periods, the hydrological model obtained better results and statistics with observed rainfall. However, in spite of some discrepancies, the use of simulated rainfall without bias correction captured the interannual flow variability. However, the use of bias removal in the simulated rainfall performed by RegCM brought significant improvements to the simulation of natural inflows performed by SMAP. Not only the curve of simulated inflow became more similar to the observed inflow, but also the statistics improved their values. Improvements were also noticed in the inflow simulation when the rainfall was provided by the regional climate model compared to the global model. In general, results obtained so far prove that there was an added value in rainfall when regional climate model was compared to global climate model and that data from regional models must be bias-corrected so as to improve their results.

Calibration of the ``Simplified Simple Biosphere Model—SSiB'' for the Brazilian Northeast Caatinga

NASA Astrophysics Data System (ADS)

do Amaral Cunha, Ana Paula Martins; dos Santos Alvalá, Regina Célia; Correia, Francis Wagner Silva; Kubota, Paulo Yoshio

2009-03-01

The Brazilian Northeast region is covered largely by vegetation adapted to the arid conditions and with varied physiognomy, called caatinga. It occupies an extension of about 800.000 km2 that corresponds to 70% of the region. In recent decades, considerable progress in understanding the micrometeorological processes has been reached, with results that were incorporated into soil-vegetation-atmosphere transfer schemes (SVATS) to study the momentum, energy, water vapor, carbon cycle and vegetation dynamics changes of different ecosystems. Notwithstanding, the knowledge of the parameters and physical or physiological characteristics of the vegetation and soil of the caatinga region is very scarce. So, the objective of this work was performing a calibration of the parameters of the SSiB model for the Brazilian Northeast Caatinga. Micrometeorological and hydrological data collected from July 2004 to June 2005, obtained in the Agricultural Research Center of the Semi-Arid Tropic (CPATSA), were used. Preceding the calibration process, a sensibility study of the SSiB model was performed in order to find the parameters that are sensible to the exchange processes between the surface and atmosphere. The results showed that the B parameter, soil moisture potential at saturation (ψs), hydraulic conductivity of saturated soil (ks) and the volumetric moisture at saturation (θs) present high variations on turbulent fluxes. With the initial parameters, the SSiB model showed best results for net radiation, and the latent heat (sensible heat) flux was over-estimated (under-estimated) for all simulation periods. Considering the calibrated parameters, better values of latent flux and sensible flux were obtained. The calibrated parameters were also used for a validation of the surface fluxes considering data from July 2005 to September 2005. The results showed that the model generated better estimations of latent heat and sensible heat fluxes, with low root mean square error. With better estimations of the turbulent fluxes, it was possible to obtain a more representative energy partitioning for the caatinga. Therefore, it is expected that from this calibrated SSiB model, coupled to the meteorological models, it will be possible to obtain more realistic climate and weather forecasts for the Brazilian Northeast region.

3D aquifer characterization using stochastic streamline calibration

NASA Astrophysics Data System (ADS)

Jang, Minchul

2007-03-01

In this study, a new inverse approach, stochastic streamline calibration is proposed. Using both a streamline concept and a stochastic technique, stochastic streamline calibration optimizes an identified field to fit in given observation data in a exceptionally fast and stable fashion. In the stochastic streamline calibration, streamlines are adopted as basic elements not only for describing fluid flow but also for identifying the permeability distribution. Based on the streamline-based inversion by Agarwal et al. [Agarwal B, Blunt MJ. Streamline-based method with full-physics forward simulation for history matching performance data of a North sea field. SPE J 2003;8(2):171-80], Wang and Kovscek [Wang Y, Kovscek AR. Streamline approach for history matching production data. SPE J 2000;5(4):353-62], permeability is modified rather along streamlines than at the individual gridblocks. Permeabilities in the gridblocks which a streamline passes are adjusted by being multiplied by some factor such that we can match flow and transport properties of the streamline. This enables the inverse process to achieve fast convergence. In addition, equipped with a stochastic module, the proposed technique supportively calibrates the identified field in a stochastic manner, while incorporating spatial information into the field. This prevents the inverse process from being stuck in local minima and helps search for a globally optimized solution. Simulation results indicate that stochastic streamline calibration identifies an unknown permeability exceptionally quickly. More notably, the identified permeability distribution reflected realistic geological features, which had not been achieved in the original work by Agarwal et al. with the limitations of the large modifications along streamlines for matching production data only. The constructed model by stochastic streamline calibration forecasted transport of plume which was similar to that of a reference model. By this, we can expect the proposed approach to be applied to the construction of an aquifer model and forecasting of the aquifer performances of interest.

The influence of the in situ camera calibration for direct georeferencing of aerial imagery

NASA Astrophysics Data System (ADS)

Mitishita, E.; Barrios, R.; Centeno, J.

2014-11-01

The direct determination of exterior orientation parameters (EOPs) of aerial images via GNSS/INS technologies is an essential prerequisite in photogrammetric mapping nowadays. Although direct sensor orientation technologies provide a high degree of automation in the process due to the GNSS/INS technologies, the accuracies of the obtained results depend on the quality of a group of parameters that models accurately the conditions of the system at the moment the job is performed. One sub-group of parameters (lever arm offsets and boresight misalignments) models the position and orientation of the sensors with respect to the IMU body frame due to the impossibility of having all sensors on the same position and orientation in the airborne platform. Another sub-group of parameters models the internal characteristics of the sensor (IOP). A system calibration procedure has been recommended by worldwide studies to obtain accurate parameters (mounting and sensor characteristics) for applications of the direct sensor orientation. Commonly, mounting and sensor characteristics are not stable; they can vary in different flight conditions. The system calibration requires a geometric arrangement of the flight and/or control points to decouple correlated parameters, which are not available in the conventional photogrammetric flight. Considering this difficulty, this study investigates the feasibility of the in situ camera calibration to improve the accuracy of the direct georeferencing of aerial images. The camera calibration uses a minimum image block, extracted from the conventional photogrammetric flight, and control point arrangement. A digital Vexcel UltraCam XP camera connected to POS AV TM system was used to get two photogrammetric image blocks. The blocks have different flight directions and opposite flight line. In situ calibration procedures to compute different sets of IOPs are performed and their results are analyzed and used in photogrammetric experiments. The IOPs from the in situ camera calibration improve significantly the accuracies of the direct georeferencing. The obtained results from the experiments are shown and discussed.

Measuring nursing competencies in the operating theatre: instrument development and psychometric analysis using Item Response Theory.

PubMed

Nicholson, Patricia; Griffin, Patrick; Gillis, Shelley; Wu, Margaret; Dunning, Trisha

2013-09-01

Concern about the process of identifying underlying competencies that contribute to effective nursing performance has been debated with a lack of consensus surrounding an approved measurement instrument for assessing clinical performance. Although a number of methodologies are noted in the development of competency-based assessment measures, these studies are not without criticism. The primary aim of the study was to develop and validate a Performance Based Scoring Rubric, which included both analytical and holistic scales. The aim included examining the validity and reliability of the rubric, which was designed to measure clinical competencies in the operating theatre. The fieldwork observations of 32 nurse educators and preceptors assessing the performance of 95 instrument nurses in the operating theatre were used in the calibration of the rubric. The Rasch model, a particular model among Item Response Models, was used in the calibration of each item in the rubric in an attempt at improving the measurement properties of the scale. This is done by establishing the 'fit' of the data to the conditions demanded by the Rasch model. Acceptable reliability estimates, specifically a high Cronbach's alpha reliability coefficient (0.940), as well as empirical support for construct and criterion validity for the rubric were achieved. Calibration of the Performance Based Scoring Rubric using Rasch model revealed that the fit statistics for most items were acceptable. The use of the Rasch model offers a number of features in developing and refining healthcare competency-based assessments, improving confidence in measuring clinical performance. The Rasch model was shown to be useful in developing and validating a competency-based assessment for measuring the competence of the instrument nurse in the operating theatre with implications for use in other areas of nursing practice. Crown Copyright © 2012. Published by Elsevier Ltd. All rights reserved.

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

Kao, Kuo-Hsing; Meyer, Kristin De; Department of Electrical Engineering, KU Leuven, Leuven

Band-to-band tunneling parameters of strained indirect bandgap materials are not well-known, hampering the reliability of performance predictions of tunneling devices based on these materials. The nonlocal band-to-band tunneling model for compressively strained SiGe is calibrated based on a comparison of strained SiGe p-i-n tunneling diode measurements and doping-profile-based diode simulations. Dopant and Ge profiles of the diodes are determined by secondary ion mass spectrometry and capacitance-voltage measurements. Theoretical parameters of the band-to-band tunneling model are calculated based on strain-dependent properties such as bandgap, phonon energy, deformation-potential-based electron-phonon coupling, and hole effective masses of strained SiGe. The latter is determined withmore » a 6-band k·p model. The calibration indicates an underestimation of the theoretical electron-phonon coupling with nearly an order of magnitude. Prospects of compressively strained SiGe tunneling transistors are made by simulations with the calibrated model.« less

Crop physiology calibration in the CLM

DOE PAGES

Bilionis, I.; Drewniak, B. A.; Constantinescu, E. M.

2015-04-15

Farming is using more of the land surface, as population increases and agriculture is increasingly applied for non-nutritional purposes such as biofuel production. This agricultural expansion exerts an increasing impact on the terrestrial carbon cycle. In order to understand the impact of such processes, the Community Land Model (CLM) has been augmented with a CLM-Crop extension that simulates the development of three crop types: maize, soybean, and spring wheat. The CLM-Crop model is a complex system that relies on a suite of parametric inputs that govern plant growth under a given atmospheric forcing and available resources. CLM-Crop development used measurementsmore » of gross primary productivity (GPP) and net ecosystem exchange (NEE) from AmeriFlux sites to choose parameter values that optimize crop productivity in the model. In this paper, we calibrate these parameters for one crop type, soybean, in order to provide a faithful projection in terms of both plant development and net carbon exchange. Calibration is performed in a Bayesian framework by developing a scalable and adaptive scheme based on sequential Monte Carlo (SMC). The model showed significant improvement of crop productivity with the new calibrated parameters. We demonstrate that the calibrated parameters are applicable across alternative years and different sites.« less

Crop physiology calibration in the CLM

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

Bilionis, I.; Drewniak, B. A.; Constantinescu, E. M.

Farming is using more of the land surface, as population increases and agriculture is increasingly applied for non-nutritional purposes such as biofuel production. This agricultural expansion exerts an increasing impact on the terrestrial carbon cycle. In order to understand the impact of such processes, the Community Land Model (CLM) has been augmented with a CLM-Crop extension that simulates the development of three crop types: maize, soybean, and spring wheat. The CLM-Crop model is a complex system that relies on a suite of parametric inputs that govern plant growth under a given atmospheric forcing and available resources. CLM-Crop development used measurementsmore » of gross primary productivity (GPP) and net ecosystem exchange (NEE) from AmeriFlux sites to choose parameter values that optimize crop productivity in the model. In this paper, we calibrate these parameters for one crop type, soybean, in order to provide a faithful projection in terms of both plant development and net carbon exchange. Calibration is performed in a Bayesian framework by developing a scalable and adaptive scheme based on sequential Monte Carlo (SMC). The model showed significant improvement of crop productivity with the new calibrated parameters. We demonstrate that the calibrated parameters are applicable across alternative years and different sites.« less

Gamma model and its analysis for phase measuring profilometry.

PubMed

Liu, Kai; Wang, Yongchang; Lau, Daniel L; Hao, Qi; Hassebrook, Laurence G

2010-03-01

Phase measuring profilometry is a method of structured light illumination whose three-dimensional reconstructions are susceptible to error from nonunitary gamma in the associated optical devices. While the effects of this distortion diminish with an increasing number of employed phase-shifted patterns, gamma distortion may be unavoidable in real-time systems where the number of projected patterns is limited by the presence of target motion. A mathematical model is developed for predicting the effects of nonunitary gamma on phase measuring profilometry, while also introducing an accurate gamma calibration method and two strategies for minimizing gamma's effect on phase determination. These phase correction strategies include phase corrections with and without gamma calibration. With the reduction in noise, for three-step phase measuring profilometry, analysis of the root mean squared error of the corrected phase will show a 60x reduction in phase error when the proposed gamma calibration is performed versus 33x reduction without calibration.

Large scale modelling of catastrophic floods in Italy

NASA Astrophysics Data System (ADS)

Azemar, Frédéric; Nicótina, Ludovico; Sassi, Maximiliano; Savina, Maurizio; Hilberts, Arno

2017-04-01

The RMS European Flood HD model® is a suite of country scale flood catastrophe models covering 13 countries throughout continental Europe and the UK. The models are developed with the goal of supporting risk assessment analyses for the insurance industry. Within this framework RMS is developing a hydrologic and inundation model for Italy. The model aims at reproducing the hydrologic and hydraulic properties across the domain through a modeling chain. A semi-distributed hydrologic model that allows capturing the spatial variability of the runoff formation processes is coupled with a one-dimensional river routing algorithm and a two-dimensional (depth averaged) inundation model. This model setup allows capturing the flood risk from both pluvial (overland flow) and fluvial flooding. Here we describe the calibration and validation methodologies for this modelling suite applied to the Italian river basins. The variability that characterizes the domain (in terms of meteorology, topography and hydrologic regimes) requires a modeling approach able to represent a broad range of meteo-hydrologic regimes. The calibration of the rainfall-runoff and river routing models is performed by means of a genetic algorithm that identifies the set of best performing parameters within the search space over the last 50 years. We first establish the quality of the calibration parameters on the full hydrologic balance and on individual discharge peaks by comparing extreme statistics to observations over the calibration period on several stations. The model is then used to analyze the major floods in the country; we discuss the different meteorological setup leading to the historical events and the physical mechanisms that induced these floods. We can thus assess the performance of RMS' hydrological model in view of the physical mechanisms leading to flood and highlight the main controls on flood risk modelling throughout the country. The model's ability to accurately simulate antecedent conditions and discharge hydrographs over the affected area is also assessed, showing that spatio-temporal correlation is retained through the modelling chain. Results show that our modelling approach can capture a wide range of conditions leading to major floods in the Italian peninsula. Under the umbrella of the RMS European Flood HD models this constitutes, to our knowledge, the only operational flood risk model to be applied at continental scale with a coherent model methodology and a domain wide MonteCarlo stochastic set.

Parameterisation of rainfall-runoff models for forecasting low and average flows, I: Conceptual modelling

NASA Astrophysics Data System (ADS)

Castiglioni, S.; Toth, E.

2009-04-01

In the calibration procedure of continuously-simulating models, the hydrologist has to choose which part of the observed hydrograph is most important to fit, either implicitly, through the visual agreement in manual calibration, or explicitly, through the choice of the objective function(s). Changing the objective functions it is in fact possible to emphasise different kind of errors, giving them more weight in the calibration phase. The objective functions used for calibrating hydrological models are generally of the quadratic type (mean squared error, correlation coefficient, coefficient of determination, etc) and are therefore oversensitive to high and extreme error values, that typically correspond to high and extreme streamflow values. This is appropriate when, like in the majority of streamflow forecasting applications, the focus is on the ability to reproduce potentially dangerous flood events; on the contrary, if the aim of the modelling is the reproduction of low and average flows, as it is the case in water resource management problems, this may result in a deterioration of the forecasting performance. This contribution presents the results of a series of automatic calibration experiments of a continuously-simulating rainfall-runoff model applied over several real-world case-studies, where the objective function is chosen so to highlight the fit of average and low flows. In this work a simple conceptual model will be used, of the lumped type, with a relatively low number of parameters to be calibrated. The experiments will be carried out for a set of case-study watersheds in Central Italy, covering an extremely wide range of geo-morphologic conditions and for whom at least five years of contemporary daily series of streamflow, precipitation and evapotranspiration estimates are available. Different objective functions will be tested in calibration and the results will be compared, over validation data, against those obtained with traditional squared functions. A companion work presents the results, over the same case-study watersheds and observation periods, of a system-theoretic model, again calibrated for reproducing average and low streamflows.

Simulation of daily streamflow for 12 river basins in western Iowa using the Precipitation-Runoff Modeling System

USGS Publications Warehouse

Christiansen, Daniel E.; Haj, Adel E.; Risley, John C.

2017-10-24

The U.S. Geological Survey, in cooperation with the Iowa Department of Natural Resources, constructed Precipitation-Runoff Modeling System models to estimate daily streamflow for 12 river basins in western Iowa that drain into the Missouri River. The Precipitation-Runoff Modeling System is a deterministic, distributed-parameter, physical-process-based modeling system developed to evaluate the response of streamflow and general drainage basin hydrology to various combinations of climate and land use. Calibration periods for each basin varied depending on the period of record available for daily mean streamflow measurements at U.S. Geological Survey streamflow-gaging stations.A geographic information system tool was used to delineate each basin and estimate initial values for model parameters based on basin physical and geographical features. A U.S. Geological Survey automatic calibration tool that uses a shuffled complex evolution algorithm was used for initial calibration, and then manual modifications were made to parameter values to complete the calibration of each basin model. The main objective of the calibration was to match daily discharge values of simulated streamflow to measured daily discharge values. The Precipitation-Runoff Modeling System model was calibrated at 42 sites located in the 12 river basins in western Iowa.The accuracy of the simulated daily streamflow values at the 42 calibration sites varied by river and by site. The models were satisfactory at 36 of the sites based on statistical results. Unsatisfactory performance at the six other sites can be attributed to several factors: (1) low flow, no flow, and flashy flow conditions in headwater subbasins having a small drainage area; (2) poor representation of the groundwater and storage components of flow within a basin; (3) lack of accounting for basin withdrawals and water use; and (4) limited availability and accuracy of meteorological input data. The Precipitation-Runoff Modeling System models of 12 river basins in western Iowa will provide water-resource managers with a consistent and documented method for estimating streamflow at ungaged sites and aid in environmental studies, hydraulic design, water management, and water-quality projects.

A Vision-Based Self-Calibration Method for Robotic Visual Inspection Systems

PubMed Central

Yin, Shibin; Ren, Yongjie; Zhu, Jigui; Yang, Shourui; Ye, Shenghua

2013-01-01

A vision-based robot self-calibration method is proposed in this paper to evaluate the kinematic parameter errors of a robot using a visual sensor mounted on its end-effector. This approach could be performed in the industrial field without external, expensive apparatus or an elaborate setup. A robot Tool Center Point (TCP) is defined in the structural model of a line-structured laser sensor, and aligned to a reference point fixed in the robot workspace. A mathematical model is established to formulate the misalignment errors with kinematic parameter errors and TCP position errors. Based on the fixed point constraints, the kinematic parameter errors and TCP position errors are identified with an iterative algorithm. Compared to the conventional methods, this proposed method eliminates the need for a robot-based-frame and hand-to-eye calibrations, shortens the error propagation chain, and makes the calibration process more accurate and convenient. A validation experiment is performed on an ABB IRB2400 robot. An optimal configuration on the number and distribution of fixed points in the robot workspace is obtained based on the experimental results. Comparative experiments reveal that there is a significant improvement of the measuring accuracy of the robotic visual inspection system. PMID:24300597

Assessing the Performance of a Computer-Based Policy Model of HIV and AIDS

PubMed Central

Rydzak, Chara E.; Cotich, Kara L.; Sax, Paul E.; Hsu, Heather E.; Wang, Bingxia; Losina, Elena; Freedberg, Kenneth A.; Weinstein, Milton C.; Goldie, Sue J.

2010-01-01

Background Model-based analyses, conducted within a decision analytic framework, provide a systematic way to combine information about the natural history of disease and effectiveness of clinical management strategies with demographic and epidemiological characteristics of the population. Among the challenges with disease-specific modeling include the need to identify influential assumptions and to assess the face validity and internal consistency of the model. Methods and Findings We describe a series of exercises involved in adapting a computer-based simulation model of HIV disease to the Women's Interagency HIV Study (WIHS) cohort and assess model performance as we re-parameterized the model to address policy questions in the U.S. relevant to HIV-infected women using data from the WIHS. Empiric calibration targets included 24-month survival curves stratified by treatment status and CD4 cell count. The most influential assumptions in untreated women included chronic HIV-associated mortality following an opportunistic infection, and in treated women, the ‘clinical effectiveness’ of HAART and the ability of HAART to prevent HIV complications independent of virologic suppression. Good-fitting parameter sets required reductions in the clinical effectiveness of 1st and 2nd line HAART and improvements in 3rd and 4th line regimens. Projected rates of treatment regimen switching using the calibrated cohort-specific model closely approximated independent analyses published using data from the WIHS. Conclusions The model demonstrated good internal consistency and face validity, and supported cohort heterogeneities that have been reported in the literature. Iterative assessment of model performance can provide information about the relative influence of uncertain assumptions and provide insight into heterogeneities within and between cohorts. Description of calibration exercises can enhance the transparency of disease-specific models. PMID:20844741

Assessing the performance of a computer-based policy model of HIV and AIDS.

PubMed

Rydzak, Chara E; Cotich, Kara L; Sax, Paul E; Hsu, Heather E; Wang, Bingxia; Losina, Elena; Freedberg, Kenneth A; Weinstein, Milton C; Goldie, Sue J

2010-09-09

Model-based analyses, conducted within a decision analytic framework, provide a systematic way to combine information about the natural history of disease and effectiveness of clinical management strategies with demographic and epidemiological characteristics of the population. Among the challenges with disease-specific modeling include the need to identify influential assumptions and to assess the face validity and internal consistency of the model. We describe a series of exercises involved in adapting a computer-based simulation model of HIV disease to the Women's Interagency HIV Study (WIHS) cohort and assess model performance as we re-parameterized the model to address policy questions in the U.S. relevant to HIV-infected women using data from the WIHS. Empiric calibration targets included 24-month survival curves stratified by treatment status and CD4 cell count. The most influential assumptions in untreated women included chronic HIV-associated mortality following an opportunistic infection, and in treated women, the 'clinical effectiveness' of HAART and the ability of HAART to prevent HIV complications independent of virologic suppression. Good-fitting parameter sets required reductions in the clinical effectiveness of 1st and 2nd line HAART and improvements in 3rd and 4th line regimens. Projected rates of treatment regimen switching using the calibrated cohort-specific model closely approximated independent analyses published using data from the WIHS. The model demonstrated good internal consistency and face validity, and supported cohort heterogeneities that have been reported in the literature. Iterative assessment of model performance can provide information about the relative influence of uncertain assumptions and provide insight into heterogeneities within and between cohorts. Description of calibration exercises can enhance the transparency of disease-specific models.

Review and evaluation of performance measures for survival prediction models in external validation settings.

PubMed

Rahman, M Shafiqur; Ambler, Gareth; Choodari-Oskooei, Babak; Omar, Rumana Z

2017-04-18

When developing a prediction model for survival data it is essential to validate its performance in external validation settings using appropriate performance measures. Although a number of such measures have been proposed, there is only limited guidance regarding their use in the context of model validation. This paper reviewed and evaluated a wide range of performance measures to provide some guidelines for their use in practice. An extensive simulation study based on two clinical datasets was conducted to investigate the performance of the measures in external validation settings. Measures were selected from categories that assess the overall performance, discrimination and calibration of a survival prediction model. Some of these have been modified to allow their use with validation data, and a case study is provided to describe how these measures can be estimated in practice. The measures were evaluated with respect to their robustness to censoring and ease of interpretation. All measures are implemented, or are straightforward to implement, in statistical software. Most of the performance measures were reasonably robust to moderate levels of censoring. One exception was Harrell's concordance measure which tended to increase as censoring increased. We recommend that Uno's concordance measure is used to quantify concordance when there are moderate levels of censoring. Alternatively, Gönen and Heller's measure could be considered, especially if censoring is very high, but we suggest that the prediction model is re-calibrated first. We also recommend that Royston's D is routinely reported to assess discrimination since it has an appealing interpretation. The calibration slope is useful for both internal and external validation settings and recommended to report routinely. Our recommendation would be to use any of the predictive accuracy measures and provide the corresponding predictive accuracy curves. In addition, we recommend to investigate the characteristics of the validation data such as the level of censoring and the distribution of the prognostic index derived in the validation setting before choosing the performance measures.

Modeling hydrodynamics, temperature and water quality in Henry Hagg Lake, Oregon, 2000-2003

USGS Publications Warehouse

Sullivan, Annette B.; Rounds, Stewart A.

2004-01-01

The two-dimensional model CE-QUAL-W2 was used to simulate hydrodynamics, temperature, and water quality in Henry Hagg Lake, Oregon, for the years 2000 through 2003. Input data included lake bathymetry, meteorologic conditions, tributary inflows, tributary temperature and water quality, and lake outflows. Calibrated constituents included lake hydrodynamics, water temperature, orthophosphate, total phosphorus, ammonia, algae, chlorophyll a, zooplankton, and dissolved oxygen. Other simulated constituents included nitrate, dissolved and particulate organic matter, dissolved solids, and suspended sediment. Two algal groups (blue-green algae, and all other algae) were included in the model to simulate the lakes algal communities. Measured lake stage data were used to calibrate the lakes water balance; calibration of water temperature and water quality relied upon vertical profile data taken in the deepest part of the lake near the dam. The model initially was calibrated with data from 200001 and tested with data from 200203. Sensitivity tests were performed to examine the response of the model to specific parameters and coefficients, including the light-extinction coefficient, wind speed, tributary inflows of phosphorus, nitrogen and organic matter, sediment oxygen demand, algal growth rates, and zooplankton feeding preference factors.

Calibrating Parameters of Power System Stability Models using Advanced Ensemble Kalman Filter

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

Huang, Renke; Diao, Ruisheng; Li, Yuanyuan

With the ever increasing penetration of renewable energy, smart loads, energy storage, and new market behavior, today’s power grid becomes more dynamic and stochastic, which may invalidate traditional study assumptions and pose great operational challenges. Thus, it is of critical importance to maintain good-quality models for secure and economic planning and real-time operation. Following the 1996 Western Systems Coordinating Council (WSCC) system blackout, North American Electric Reliability Corporation (NERC) and Western Electricity Coordinating Council (WECC) in North America enforced a number of policies and standards to guide the power industry to periodically validate power grid models and calibrate poor parametersmore » with the goal of building sufficient confidence in model quality. The PMU-based approach using online measurements without interfering with the operation of generators provides a low-cost alternative to meet NERC standards. This paper presents an innovative procedure and tool suites to validate and calibrate models based on a trajectory sensitivity analysis method and an advanced ensemble Kalman filter algorithm. The developed prototype demonstrates excellent performance in identifying and calibrating bad parameters of a realistic hydro power plant against multiple system events.« less

Uncertainty Evaluation of Computational Model Used to Support the Integrated Powerhead Demonstration Project

NASA Technical Reports Server (NTRS)

Steele, W. G.; Molder, K. J.; Hudson, S. T.; Vadasy, K. V.; Rieder, P. T.; Giel, T.

2005-01-01

NASA and the U.S. Air Force are working on a joint project to develop a new hydrogen-fueled, full-flow, staged combustion rocket engine. The initial testing and modeling work for the Integrated Powerhead Demonstrator (IPD) project is being performed by NASA Marshall and Stennis Space Centers. A key factor in the testing of this engine is the ability to predict and measure the transient fluid flow during engine start and shutdown phases of operation. A model built by NASA Marshall in the ROCket Engine Transient Simulation (ROCETS) program is used to predict transient engine fluid flows. The model is initially calibrated to data from previous tests on the Stennis E1 test stand. The model is then used to predict the next run. Data from this run can then be used to recalibrate the model providing a tool to guide the test program in incremental steps to reduce the risk to the prototype engine. In this paper, they define this type of model as a calibrated model. This paper proposes a method to estimate the uncertainty of a model calibrated to a set of experimental test data. The method is similar to that used in the calibration of experiment instrumentation. For the IPD example used in this paper, the model uncertainty is determined for both LOX and LH flow rates using previous data. The successful use of this model is then demonstrated to predict another similar test run within the uncertainty bounds. The paper summarizes the uncertainty methodology when a model is continually recalibrated with new test data. The methodology is general and can be applied to other calibrated models.

Theoretical relationship between vibration transmissibility and driving-point response functions of the human body.

PubMed

Dong, Ren G; Welcome, Daniel E; McDowell, Thomas W; Wu, John Z

2013-11-25

The relationship between the vibration transmissibility and driving-point response functions (DPRFs) of the human body is important for understanding vibration exposures of the system and for developing valid models. This study identified their theoretical relationship and demonstrated that the sum of the DPRFs can be expressed as a linear combination of the transmissibility functions of the individual mass elements distributed throughout the system. The relationship is verified using several human vibration models. This study also clarified the requirements for reliably quantifying transmissibility values used as references for calibrating the system models. As an example application, this study used the developed theory to perform a preliminary analysis of the method for calibrating models using both vibration transmissibility and DPRFs. The results of the analysis show that the combined method can theoretically result in a unique and valid solution of the model parameters, at least for linear systems. However, the validation of the method itself does not guarantee the validation of the calibrated model, because the validation of the calibration also depends on the model structure and the reliability and appropriate representation of the reference functions. The basic theory developed in this study is also applicable to the vibration analyses of other structures.

Prediction of Outcome after Moderate and Severe Traumatic Brain Injury: External Validation of the IMPACT and CRASH Prognostic Models

PubMed Central

Roozenbeek, Bob; Lingsma, Hester F.; Lecky, Fiona E.; Lu, Juan; Weir, James; Butcher, Isabella; McHugh, Gillian S.; Murray, Gordon D.; Perel, Pablo; Maas, Andrew I.R.; Steyerberg, Ewout W.

2012-01-01

Objective The International Mission on Prognosis and Analysis of Clinical Trials (IMPACT) and Corticoid Randomisation After Significant Head injury (CRASH) prognostic models predict outcome after traumatic brain injury (TBI) but have not been compared in large datasets. The objective of this is study is to validate externally and compare the IMPACT and CRASH prognostic models for prediction of outcome after moderate or severe TBI. Design External validation study. Patients We considered 5 new datasets with a total of 9036 patients, comprising three randomized trials and two observational series, containing prospectively collected individual TBI patient data. Measurements Outcomes were mortality and unfavourable outcome, based on the Glasgow Outcome Score (GOS) at six months after injury. To assess performance, we studied the discrimination of the models (by AUCs), and calibration (by comparison of the mean observed to predicted outcomes and calibration slopes). Main Results The highest discrimination was found in the TARN trauma registry (AUCs between 0.83 and 0.87), and the lowest discrimination in the Pharmos trial (AUCs between 0.65 and 0.71). Although differences in predictor effects between development and validation populations were found (calibration slopes varying between 0.58 and 1.53), the differences in discrimination were largely explained by differences in case-mix in the validation studies. Calibration was good, the fraction of observed outcomes generally agreed well with the mean predicted outcome. No meaningful differences were noted in performance between the IMPACT and CRASH models. More complex models discriminated slightly better than simpler variants. Conclusions Since both the IMPACT and the CRASH prognostic models show good generalizability to more recent data, they are valid instruments to quantify prognosis in TBI. PMID:22511138

Biotrickling filter modeling for styrene abatement. Part 2: Simulating a two-phase partitioning bioreactor.

PubMed

San-Valero, Pau; Dorado, Antonio D; Quijano, Guillermo; Álvarez-Hornos, F Javier; Gabaldón, Carmen

2018-01-01

A dynamic model describing styrene abatement was developed for a two-phase partitioning bioreactor operated as a biotrickling filter (TPPB-BTF). The model was built as a coupled set of two different systems of partial differential equations depending on whether an irrigation or a non-irrigation period was simulated. The maximum growth rate was previously calibrated from a conventional BTF treating styrene (Part 1). The model was extended to simulate the TPPB-BTF based on the hypothesis that the main change associated with the non-aqueous phase is the modification of the pollutant properties in the liquid phase. The three phases considered were gas, a water-silicone liquid mixture, and biofilm. The selected calibration parameters were related to the physical properties of styrene: Henry's law constant, diffusivity, and the gas-liquid mass transfer coefficient. A sensitivity analysis revealed that Henry's law constant was the most sensitive parameter. The model was successfully calibrated with a goodness of fit of 0.94. It satisfactorily simulated the performance of the TPPB-BTF at styrene loads ranging from 13 to 77 g C m -3 h -1 and empty bed residence times of 30-15 s with the mass transfer enhanced by a factor of 1.6. The model was validated with data obtained in a TPPB-BTF removing styrene continuously. The experimental outlet emissions associated to oscillating inlet concentrations were satisfactorily predicted by using the calibrated parameters. Model simulations demonstrated the potential improvement of the mass-transfer performance of a conventional BTF degrading styrene by adding silicone oil. Copyright © 2017 Elsevier Ltd. All rights reserved.

Effects of serum creatinine calibration on estimated renal function in african americans: the Jackson heart study.

PubMed

Wang, Wei; Young, Bessie A; Fülöp, Tibor; de Boer, Ian H; Boulware, L Ebony; Katz, Ronit; Correa, Adolfo; Griswold, Michael E

2015-05-01

The calibration to isotope dilution mass spectrometry-traceable creatinine is essential for valid use of the new Chronic Kidney Disease Epidemiology Collaboration equation to estimate the glomerular filtration rate. For 5,210 participants in the Jackson Heart Study (JHS), serum creatinine was measured with a multipoint enzymatic spectrophotometric assay at the baseline visit (2000-2004) and remeasured using the Roche enzymatic method, traceable to isotope dilution mass spectrometry in a subset of 206 subjects. The 200 eligible samples (6 were excluded, 1 for failure of the remeasurement and 5 for outliers) were divided into 3 disjoint sets-training, validation and test-to select a calibration model, estimate true errors and assess performance of the final calibration equation. The calibration equation was applied to serum creatinine measurements of 5,210 participants to estimate glomerular filtration rate and the prevalence of chronic kidney disease (CKD). The selected Deming regression model provided a slope of 0.968 (95% confidence interval [CI], 0.904-1.053) and intercept of -0.0248 (95% CI, -0.0862 to 0.0366) with R value of 0.9527. Calibrated serum creatinine showed high agreement with actual measurements when applying to the unused test set (concordance correlation coefficient 0.934, 95% CI, 0.894-0.960). The baseline prevalence of CKD in the JHS (2000-2004) was 6.30% using calibrated values compared with 8.29% using noncalibrated serum creatinine with the Chronic Kidney Disease Epidemiology Collaboration equation (P < 0.001). A Deming regression model was chosen to optimally calibrate baseline serum creatinine measurements in the JHS, and the calibrated values provide a lower CKD prevalence estimate.

The In-flight Spectroscopic Performance of the Swift XRT CCD Camera During 2006-2007

NASA Technical Reports Server (NTRS)

Godet, O.; Beardmore, A.P.; Abbey, A.F.; Osborne, J.P.; Page, K.L.; Evans, P.; Starling, R.; Wells, A.A.; Angelini, L.; Burrows, D.N.;

L5 TM radiometric recalibration procedure using the internal calibration trends from the NLAPS trending database

USGS Publications Warehouse

Chander, G.; Haque, Md. O.; Micijevic, E.; Barsi, J.A.

2008-01-01

From the Landsat program's inception in 1972 to the present, the earth science user community has benefited from a historical record of remotely sensed data. The multispectral data from the Landsat 5 (L5) Thematic Mapper (TM) sensor provide the backbone for this extensive archive. Historically, the radiometric calibration procedure for this imagery used the instrument's response to the Internal Calibrator (IC) on a scene-by-scene basis to determine the gain and offset for each detector. The IC system degraded with time causing radiometric calibration errors up to 20 percent. In May 2003 the National Landsat Archive Production System (NLAPS) was updated to use a gain model rather than the scene acquisition specific IC gains to calibrate TM data processed in the United States. Further modification of the gain model was performed in 2007. L5 TM data that were processed using IC prior to the calibration update do not benefit from the recent calibration revisions. A procedure has been developed to give users the ability to recalibrate their existing Level-1 products. The best recalibration results are obtained if the work order report that was originally included in the standard data product delivery is available. However, many users may not have the original work order report. In such cases, the IC gain look-up table that was generated using the radiometric gain trends recorded in the NLAPS database can be used for recalibration. This paper discusses the procedure to recalibrate L5 TM data when the work order report originally used in processing is not available. A companion paper discusses the generation of the NLAPS IC gain and bias look-up tables required to perform the recalibration.

Statistical emulation of landslide-induced tsunamis at the Rockall Bank, NE Atlantic

PubMed Central

Guillas, S.; Georgiopoulou, A.; Dias, F.

2017-01-01

Statistical methods constitute a useful approach to understand and quantify the uncertainty that governs complex tsunami mechanisms. Numerical experiments may often have a high computational cost. This forms a limiting factor for performing uncertainty and sensitivity analyses, where numerous simulations are required. Statistical emulators, as surrogates of these simulators, can provide predictions of the physical process in a much faster and computationally inexpensive way. They can form a prominent solution to explore thousands of scenarios that would be otherwise numerically expensive and difficult to achieve. In this work, we build a statistical emulator of the deterministic codes used to simulate submarine sliding and tsunami generation at the Rockall Bank, NE Atlantic Ocean, in two stages. First we calibrate, against observations of the landslide deposits, the parameters used in the landslide simulations. This calibration is performed under a Bayesian framework using Gaussian Process (GP) emulators to approximate the landslide model, and the discrepancy function between model and observations. Distributions of the calibrated input parameters are obtained as a result of the calibration. In a second step, a GP emulator is built to mimic the coupled landslide-tsunami numerical process. The emulator propagates the uncertainties in the distributions of the calibrated input parameters inferred from the first step to the outputs. As a result, a quantification of the uncertainty of the maximum free surface elevation at specified locations is obtained. PMID:28484339

Statistical emulation of landslide-induced tsunamis at the Rockall Bank, NE Atlantic.

PubMed

Salmanidou, D M; Guillas, S; Georgiopoulou, A; Dias, F

2017-04-01

Statistical methods constitute a useful approach to understand and quantify the uncertainty that governs complex tsunami mechanisms. Numerical experiments may often have a high computational cost. This forms a limiting factor for performing uncertainty and sensitivity analyses, where numerous simulations are required. Statistical emulators, as surrogates of these simulators, can provide predictions of the physical process in a much faster and computationally inexpensive way. They can form a prominent solution to explore thousands of scenarios that would be otherwise numerically expensive and difficult to achieve. In this work, we build a statistical emulator of the deterministic codes used to simulate submarine sliding and tsunami generation at the Rockall Bank, NE Atlantic Ocean, in two stages. First we calibrate, against observations of the landslide deposits, the parameters used in the landslide simulations. This calibration is performed under a Bayesian framework using Gaussian Process (GP) emulators to approximate the landslide model, and the discrepancy function between model and observations. Distributions of the calibrated input parameters are obtained as a result of the calibration. In a second step, a GP emulator is built to mimic the coupled landslide-tsunami numerical process. The emulator propagates the uncertainties in the distributions of the calibrated input parameters inferred from the first step to the outputs. As a result, a quantification of the uncertainty of the maximum free surface elevation at specified locations is obtained.

Calibration of EMG to force for knee muscles is applicable with submaximal voluntary contractions.

PubMed

Doorenbosch, Caroline A M; Joosten, Annemiek; Harlaar, Jaap

2005-08-01

In this study, the influence of using submaximal isokinetic contractions about the knee compared to maximal voluntary contractions as input to obtain the calibration of an EMG-force model for knee muscles is investigated. Isokinetic knee flexion and extension contractions were performed by healthy subjects at five different velocities and at three contraction levels (100%, 75% and 50% of MVC). Joint angle, angular velocity, joint moment and surface EMG of five knee muscles were recorded. Individual calibration values were calculated according to [C.A.M. Doorenbosch, J. Harlaar, A clinically applicable EMG-force model to quantify active stabilization of the knee after a lesion of the anterior cruciate ligament, Clinical Biomechanics 18 (2003) 142-149] for each contraction level. First, the output of the model, calibrated with the 100% MVC was compared to the actually exerted net knee moment at the dynamometer. Normalized root mean square errors were calculated [A.L. Hof, C.A.N. Pronk, J.A. van Best, Comparison between EMG to force processing and kinetic analysis for the calf muscle moment in walking and stepping, Journal of Biomechanics 20 (1987) 167-187] to compare the estimated moments with the actually exerted moments. Mean RMSD errors ranged from 0.06 to 0.21 for extension and from 0.12 to 0.29 for flexion at the 100% trials. Subsequently, the calibration results of the 50% and 75% MVC calibration procedures were used. A standard signal, representing a random EMG level was used as input in the EMG force model, to compare the three models. Paired samples t-tests between the 100% MVC and the 75% MVC and 50% MVC, respectively, showed no significant differences (p>0.05). The application of submaximal contractions of larger than 50% MVC is suitable to calibrate a simple EMG to force model for knee extension and flexion. This means that in clinical practice, the EMG to force model can be applied by patients who cannot exert maximal force.

Hydrologic modeling strategy for the Islamic Republic of Mauritania, Africa

USGS Publications Warehouse

Friedel, Michael J.

2008-01-01

The government of Mauritania is interested in how to maintain hydrologic balance to ensure a long-term stable water supply for minerals-related, domestic, and other purposes. Because of the many complicating and competing natural and anthropogenic factors, hydrologists will perform quantitative analysis with specific objectives and relevant computer models in mind. Whereas various computer models are available for studying water-resource priorities, the success of these models to provide reliable predictions largely depends on adequacy of the model-calibration process. Predictive analysis helps us evaluate the accuracy and uncertainty associated with simulated dependent variables of our calibrated model. In this report, the hydrologic modeling process is reviewed and a strategy summarized for future Mauritanian hydrologic modeling studies.

Sharing hydrological knowledge: an international comparison of hydrological models in the Meuse River Basin

NASA Astrophysics Data System (ADS)

Bouaziz, Laurène; Sperna Weiland, Frederiek; Drogue, Gilles; Brauer, Claudia; Weerts, Albrecht

2015-04-01

International collaboration between institutes and universities working and studying the same transboundary basin is needed for consensus building around possible effects of climate change and climate adaptation measures. Education, experience and expert knowledge of the hydrological community have resulted in the development of a great variety of model concepts, calibration and analysis techniques. Intercomparison could be a first step into consensus modeling or an ensemble based modeling strategy. Besides these practical objectives, such an intercomparison offers the opportunity to explore different ranges of models and learn from each other, hopefully increasing the insight into the hydrological processes that play a role in the transboundary basin. In this experiment, different international research groups applied their rainfall-runoff model in the Ourthe, a Belgium sub-catchment of the Meuse. Data preparation involved the interpolation of hourly precipitation station data collected and owned by the Service Public de Wallonie1 and the freely available E-OBS dataset for daily temperature (Haylock et al., 2008). Daily temperature was disaggregated to hourly values and potential evaporation was derived with the Hargreaves formula. The data was made available to the researchers through an FTP server. The protocol for the modeling involved a split-sample calibration and validation for pre-defined periods. Objective functions for calibration were fixed but the calibration algorithm was a free choice of the research groups. The selection of calibration algorithm was considered model dependent because lumped as well as computationally less efficient distributed models were used. For each model, an ensemble of best performing parameter sets was selected and several performance metrics enabled to assess the models' abilities to simulate discharge. The aim of this experiment is to identify those model components and structures that increase model performance and may best represent the catchment's hydrological behavior. Further steps in the collaboration may include (1) repeating the experiment for other sub-catchments of the Meuse River Basin where different hydrological processes may be relevant and where other models may perform better; and (2) the comparison of hydrological model results obtained by forcing the model with daily local measured precipitation and lower resolution gridded precipitation from the E-OBS (Haylock et at., 2008) dataset to estimate the value of high-resolution data versus lower resolution gridded products. 1 Service Publique de Wallonie, Direction générale opérationnelle de la Mobilité et des Voies hydrauliques, Département des Etudes et de l'Appui à la Gestion, Direction de la Gestion hydrologique intégrée, Boulevard du Nord 8 - 5000 Namur "Haylock, M.R., N. Hofstra, A.M.G. Klein Tank, E.J. Klok, P.D. Jones and M. New. 2008: A European daily high-resolution gridded dataset of surface temperature and precipitation. J. Geophys. Res (Atmospheres), 113, D20119, doi:10.1029/2008JD10201"

Diagnostic utility of appetite loss in addition to existing prediction models for community-acquired pneumonia in the elderly: a prospective diagnostic study in acute care hospitals in Japan.

PubMed

Takada, Toshihiko; Yamamoto, Yosuke; Terada, Kazuhiko; Ohta, Mitsuyasu; Mikami, Wakako; Yokota, Hajime; Hayashi, Michio; Miyashita, Jun; Azuma, Teruhisa; Fukuma, Shingo; Fukuhara, Shunichi

2017-11-08

Diagnosis of community-acquired pneumonia (CAP) in the elderly is often delayed because of atypical presentation and non-specific symptoms, such as appetite loss, falls and disturbance in consciousness. The aim of this study was to investigate the external validity of existing prediction models and the added value of the non-specific symptoms for the diagnosis of CAP in elderly patients. Prospective cohort study. General medicine departments of three teaching hospitals in Japan. A total of 109 elderly patients who consulted for upper respiratory symptoms between 1 October 2014 and 30 September 2016. The reference standard for CAP was chest radiograph evaluated by two certified radiologists. The existing models were externally validated for diagnostic performance by calibration plot and discrimination. To evaluate the additional value of the non-specific symptoms to the existing prediction models, we developed an extended logistic regression model. Calibration, discrimination, category-free net reclassification improvement (NRI) and decision curve analysis (DCA) were investigated in the extended model. Among the existing models, the model by van Vugt demonstrated the best performance, with an area under the curve of 0.75(95% CI 0.63 to 0.88); calibration plot showed good fit despite a significant Hosmer-Lemeshow test (p=0.017). Among the non-specific symptoms, appetite loss had positive likelihood ratio of 3.2 (2.0-5.3), negative likelihood ratio of 0.4 (0.2-0.7) and OR of 7.7 (3.0-19.7). Addition of appetite loss to the model by van Vugt led to improved calibration at p=0.48, NRI of 0.53 (p=0.019) and higher net benefit by DCA. Information on appetite loss improved the performance of an existing model for the diagnosis of CAP in the elderly. © Article author(s) (or their employer(s) unless otherwise stated in the text of the article) 2017. All rights reserved. No commercial use is permitted unless otherwise expressly granted.

Automating calibration, sensitivity and uncertainty analysis of complex models using the R package Flexible Modeling Environment (FME): SWAT as an example

USGS Publications Warehouse

Wu, Y.; Liu, S.

2012-01-01

Parameter optimization and uncertainty issues are a great challenge for the application of large environmental models like the Soil and Water Assessment Tool (SWAT), which is a physically-based hydrological model for simulating water and nutrient cycles at the watershed scale. In this study, we present a comprehensive modeling environment for SWAT, including automated calibration, and sensitivity and uncertainty analysis capabilities through integration with the R package Flexible Modeling Environment (FME). To address challenges (e.g., calling the model in R and transferring variables between Fortran and R) in developing such a two-language coupling framework, 1) we converted the Fortran-based SWAT model to an R function (R-SWAT) using the RFortran platform, and alternatively 2) we compiled SWAT as a Dynamic Link Library (DLL). We then wrapped SWAT (via R-SWAT) with FME to perform complex applications including parameter identifiability, inverse modeling, and sensitivity and uncertainty analysis in the R environment. The final R-SWAT-FME framework has the following key functionalities: automatic initialization of R, running Fortran-based SWAT and R commands in parallel, transferring parameters and model output between SWAT and R, and inverse modeling with visualization. To examine this framework and demonstrate how it works, a case study simulating streamflow in the Cedar River Basin in Iowa in the United Sates was used, and we compared it with the built-in auto-calibration tool of SWAT in parameter optimization. Results indicate that both methods performed well and similarly in searching a set of optimal parameters. Nonetheless, the R-SWAT-FME is more attractive due to its instant visualization, and potential to take advantage of other R packages (e.g., inverse modeling and statistical graphics). The methods presented in the paper are readily adaptable to other model applications that require capability for automated calibration, and sensitivity and uncertainty analysis.

Early Prediction of Intensive Care Unit-Acquired Weakness: A Multicenter External Validation Study.

PubMed

Witteveen, Esther; Wieske, Luuk; Sommers, Juultje; Spijkstra, Jan-Jaap; de Waard, Monique C; Endeman, Henrik; Rijkenberg, Saskia; de Ruijter, Wouter; Sleeswijk, Mengalvio; Verhamme, Camiel; Schultz, Marcus J; van Schaik, Ivo N; Horn, Janneke

2018-01-01

An early diagnosis of intensive care unit-acquired weakness (ICU-AW) is often not possible due to impaired consciousness. To avoid a diagnostic delay, we previously developed a prediction model, based on single-center data from 212 patients (development cohort), to predict ICU-AW at 2 days after ICU admission. The objective of this study was to investigate the external validity of the original prediction model in a new, multicenter cohort and, if necessary, to update the model. Newly admitted ICU patients who were mechanically ventilated at 48 hours after ICU admission were included. Predictors were prospectively recorded, and the outcome ICU-AW was defined by an average Medical Research Council score <4. In the validation cohort, consisting of 349 patients, we analyzed performance of the original prediction model by assessment of calibration and discrimination. Additionally, we updated the model in this validation cohort. Finally, we evaluated a new prediction model based on all patients of the development and validation cohort. Of 349 analyzed patients in the validation cohort, 190 (54%) developed ICU-AW. Both model calibration and discrimination of the original model were poor in the validation cohort. The area under the receiver operating characteristics curve (AUC-ROC) was 0.60 (95% confidence interval [CI]: 0.54-0.66). Model updating methods improved calibration but not discrimination. The new prediction model, based on all patients of the development and validation cohort (total of 536 patients) had a fair discrimination, AUC-ROC: 0.70 (95% CI: 0.66-0.75). The previously developed prediction model for ICU-AW showed poor performance in a new independent multicenter validation cohort. Model updating methods improved calibration but not discrimination. The newly derived prediction model showed fair discrimination. This indicates that early prediction of ICU-AW is still challenging and needs further attention.

Evaluation of a numerical model's ability to predict bed load transport observed in braided river experiments

NASA Astrophysics Data System (ADS)

Javernick, Luke; Redolfi, Marco; Bertoldi, Walter

2018-05-01

New data collection techniques offer numerical modelers the ability to gather and utilize high quality data sets with high spatial and temporal resolution. Such data sets are currently needed for calibration, verification, and to fuel future model development, particularly morphological simulations. This study explores the use of high quality spatial and temporal data sets of observed bed load transport in braided river flume experiments to evaluate the ability of a two-dimensional model, Delft3D, to predict bed load transport. This study uses a fixed bed model configuration and examines the model's shear stress calculations, which are the foundation to predict the sediment fluxes necessary for morphological simulations. The evaluation is conducted for three flow rates, and model setup used highly accurate Structure-from-Motion (SfM) topography and discharge boundary conditions. The model was hydraulically calibrated using bed roughness, and performance was evaluated based on depth and inundation agreement. Model bed load performance was evaluated in terms of critical shear stress exceedance area compared to maps of observed bed mobility in a flume. Following the standard hydraulic calibration, bed load performance was tested for sensitivity to horizontal eddy viscosity parameterization and bed morphology updating. Simulations produced depth errors equal to the SfM inherent errors, inundation agreement of 77-85%, and critical shear stress exceedance in agreement with 49-68% of the observed active area. This study provides insight into the ability of physically based, two-dimensional simulations to accurately predict bed load as well as the effects of horizontal eddy viscosity and bed updating. Further, this study highlights how using high spatial and temporal data to capture the physical processes at work during flume experiments can help to improve morphological modeling.

Modelling High-temperature EBPR by Incorporating Glycogen and GAOs: Challenges from a Preliminary Study.

PubMed

Liau, Kee Fui; Yeoh, Hak Koon; Shoji, Tadashi; Chua, Adeline Seak May; Ho, Pei Yee

2017-01-01

  Recently reported kinetic and stoichiometric parameters of the Activated Sludge Model no. 2d (ASM2d) for high-temperature EBPR processes suggested that the absence of glycogen in the model contributed to underestimation of PHA accumulation at 32 °C. Here, two modified ASM2d models were used to further explore the contribution of glycogen in the process. The ASM2d-1G model incorporated glycogen metabolism by PAOs (polyphosphate-accumulating organisms), while the ASM2d-2G model further included processes by GAOs (glycogen-accumulating organisms). These models were calibrated and validated using experimental data at 32 °C. The ASM2d-1G model supported the hypothesis that the excess PHA was attributed to glycogen, but remained inadequate to capture the dynamics of glycogen without considering GAOs activities. The ASM2d-2G model performed better, but it was challenging to calibrate as it often led to wash-out of either PAOs or GAOs. Associated hurdles are highlighted and additional efforts in calibrating ASM2d-2G more effectively are proposed.

Calibration and validation of an activated sludge model for greenhouse gases no. 1 (ASMG1): prediction of temperature-dependent N₂O emission dynamics.

PubMed

Guo, Lisha; Vanrolleghem, Peter A

2014-02-01

An activated sludge model for greenhouse gases no. 1 was calibrated with data from a wastewater treatment plant (WWTP) without control systems and validated with data from three similar plants equipped with control systems. Special about the calibration/validation approach adopted in this paper is that the data are obtained from simulations with a mathematical model that is widely accepted to describe effluent quality and operating costs of actual WWTPs, the Benchmark Simulation Model No. 2 (BSM2). The calibration also aimed at fitting the model to typical observed nitrous oxide (N₂O) emission data, i.e., a yearly average of 0.5% of the influent total nitrogen load emitted as N₂O-N. Model validation was performed by challenging the model in configurations with different control strategies. The kinetic term describing the dissolved oxygen effect on the denitrification by ammonia-oxidizing bacteria (AOB) was modified into a Haldane term. Both original and Haldane-modified models passed calibration and validation. Even though their yearly averaged values were similar, the two models presented different dynamic N₂O emissions under cold temperature conditions and control. Therefore, data collected in such situations can potentially permit model discrimination. Observed seasonal trends in N₂O emissions are simulated well with both original and Haldane-modified models. A mechanistic explanation based on the temperature-dependent interaction between heterotrophic and autotrophic N₂O pathways was provided. Finally, while adding the AOB denitrification pathway to a model with only heterotrophic N₂O production showed little impact on effluent quality and operating cost criteria, it clearly affected N2O emission productions.

Calibrating emergent phenomena in stock markets with agent based models

PubMed Central

Sornette, Didier

2018-01-01

Since the 2008 financial crisis, agent-based models (ABMs), which account for out-of-equilibrium dynamics, heterogeneous preferences, time horizons and strategies, have often been envisioned as the new frontier that could revolutionise and displace the more standard models and tools in economics. However, their adoption and generalisation is drastically hindered by the absence of general reliable operational calibration methods. Here, we start with a different calibration angle that qualifies an ABM for its ability to achieve abnormal trading performance with respect to the buy-and-hold strategy when fed with real financial data. Starting from the common definition of standard minority and majority agents with binary strategies, we prove their equivalence to optimal decision trees. This efficient representation allows us to exhaustively test all meaningful single agent models for their potential anomalous investment performance, which we apply to the NASDAQ Composite index over the last 20 years. We uncover large significant predictive power, with anomalous Sharpe ratio and directional accuracy, in particular during the dotcom bubble and crash and the 2008 financial crisis. A principal component analysis reveals transient convergence between the anomalous minority and majority models. A novel combination of the optimal single-agent models of both classes into a two-agents model leads to remarkable superior investment performance, especially during the periods of bubbles and crashes. Our design opens the field of ABMs to construct novel types of advanced warning systems of market crises, based on the emergent collective intelligence of ABMs built on carefully designed optimal decision trees that can be reversed engineered from real financial data. PMID:29499049

Calibrating emergent phenomena in stock markets with agent based models.

PubMed

Fievet, Lucas; Sornette, Didier

2018-01-01

Since the 2008 financial crisis, agent-based models (ABMs), which account for out-of-equilibrium dynamics, heterogeneous preferences, time horizons and strategies, have often been envisioned as the new frontier that could revolutionise and displace the more standard models and tools in economics. However, their adoption and generalisation is drastically hindered by the absence of general reliable operational calibration methods. Here, we start with a different calibration angle that qualifies an ABM for its ability to achieve abnormal trading performance with respect to the buy-and-hold strategy when fed with real financial data. Starting from the common definition of standard minority and majority agents with binary strategies, we prove their equivalence to optimal decision trees. This efficient representation allows us to exhaustively test all meaningful single agent models for their potential anomalous investment performance, which we apply to the NASDAQ Composite index over the last 20 years. We uncover large significant predictive power, with anomalous Sharpe ratio and directional accuracy, in particular during the dotcom bubble and crash and the 2008 financial crisis. A principal component analysis reveals transient convergence between the anomalous minority and majority models. A novel combination of the optimal single-agent models of both classes into a two-agents model leads to remarkable superior investment performance, especially during the periods of bubbles and crashes. Our design opens the field of ABMs to construct novel types of advanced warning systems of market crises, based on the emergent collective intelligence of ABMs built on carefully designed optimal decision trees that can be reversed engineered from real financial data.

External validation of a Cox prognostic model: principles and methods

PubMed Central

2013-01-01

Background A prognostic model should not enter clinical practice unless it has been demonstrated that it performs a useful role. External validation denotes evaluation of model performance in a sample independent of that used to develop the model. Unlike for logistic regression models, external validation of Cox models is sparsely treated in the literature. Successful validation of a model means achieving satisfactory discrimination and calibration (prediction accuracy) in the validation sample. Validating Cox models is not straightforward because event probabilities are estimated relative to an unspecified baseline function. Methods We describe statistical approaches to external validation of a published Cox model according to the level of published information, specifically (1) the prognostic index only, (2) the prognostic index together with Kaplan-Meier curves for risk groups, and (3) the first two plus the baseline survival curve (the estimated survival function at the mean prognostic index across the sample). The most challenging task, requiring level 3 information, is assessing calibration, for which we suggest a method of approximating the baseline survival function. Results We apply the methods to two comparable datasets in primary breast cancer, treating one as derivation and the other as validation sample. Results are presented for discrimination and calibration. We demonstrate plots of survival probabilities that can assist model evaluation. Conclusions Our validation methods are applicable to a wide range of prognostic studies and provide researchers with a toolkit for external validation of a published Cox model. PMID:23496923

Optimal test selection for prediction uncertainty reduction

DOE PAGES

Mullins, Joshua; Mahadevan, Sankaran; Urbina, Angel

2016-12-02

Economic factors and experimental limitations often lead to sparse and/or imprecise data used for the calibration and validation of computational models. This paper addresses resource allocation for calibration and validation experiments, in order to maximize their effectiveness within given resource constraints. When observation data are used for model calibration, the quality of the inferred parameter descriptions is directly affected by the quality and quantity of the data. This paper characterizes parameter uncertainty within a probabilistic framework, which enables the uncertainty to be systematically reduced with additional data. The validation assessment is also uncertain in the presence of sparse and imprecisemore » data; therefore, this paper proposes an approach for quantifying the resulting validation uncertainty. Since calibration and validation uncertainty affect the prediction of interest, the proposed framework explores the decision of cost versus importance of data in terms of the impact on the prediction uncertainty. Often, calibration and validation tests may be performed for different input scenarios, and this paper shows how the calibration and validation results from different conditions may be integrated into the prediction. Then, a constrained discrete optimization formulation that selects the number of tests of each type (calibration or validation at given input conditions) is proposed. Furthermore, the proposed test selection methodology is demonstrated on a microelectromechanical system (MEMS) example.« less

Calibration and comparison of the NASA Lewis free-piston Stirling engine model predictions with RE-1000 test data

NASA Technical Reports Server (NTRS)

Geng, Steven M.

1987-01-01

A free-piston Stirling engine performance code is being upgraded and validated at the NASA Lewis Research Center under an interagency agreement between the Department of Energy's Oak Ridge National Laboratory and NASA Lewis. Many modifications were made to the free-piston code in an attempt to decrease the calibration effort. A procedure was developed that made the code calibration process more systematic. Engine-specific calibration parameters are often used to bring predictions and experimental data into better agreement. The code was calibrated to a matrix of six experimental data points. Predictions of the calibrated free-piston code are compared with RE-1000 free-piston Stirling engine sensitivity test data taken at NASA Lewis. Reasonable agreement was obtained between the code prediction and the experimental data over a wide range of engine operating conditions.

Calibration and comparison of the NASA Lewis free-piston Stirling engine model predictions with RE-1000 test data

NASA Technical Reports Server (NTRS)

Geng, Steven M.

1987-01-01

A free-piston Stirling engine performance code is being upgraded and validated at the NASA Lewis Research Center under an interagency agreement between the Department of Energy's Oak Ridge National Laboratory and NASA Lewis. Many modifications were made to the free-piston code in an attempt to decrease the calibration effort. A procedure was developed that made the code calibration process more systematic. Engine-specific calibration parameters are often used to bring predictions and experimental data into better agreement. The code was calibrated to a matrix of six experimental data points. Predictions of the calibrated free-piston code are compared with RE-1000 free-piston Stirling engine sensitivity test data taken at NASA Lewis. Resonable agreement was obtained between the code predictions and the experimental data over a wide range of engine operating conditions.

External validation of the diffuse intrinsic pontine glioma survival prediction model: a collaborative report from the International DIPG Registry and the SIOPE DIPG Registry.

PubMed

Veldhuijzen van Zanten, Sophie E M; Lane, Adam; Heymans, Martijn W; Baugh, Joshua; Chaney, Brooklyn; Hoffman, Lindsey M; Doughman, Renee; Jansen, Marc H A; Sanchez, Esther; Vandertop, William P; Kaspers, Gertjan J L; van Vuurden, Dannis G; Fouladi, Maryam; Jones, Blaise V; Leach, James

2017-08-01

We aimed to perform external validation of the recently developed survival prediction model for diffuse intrinsic pontine glioma (DIPG), and discuss its utility. The DIPG survival prediction model was developed in a cohort of patients from the Netherlands, United Kingdom and Germany, registered in the SIOPE DIPG Registry, and includes age <3 years, longer symptom duration and receipt of chemotherapy as favorable predictors, and presence of ring-enhancement on MRI as unfavorable predictor. Model performance was evaluated by analyzing the discrimination and calibration abilities. External validation was performed using an unselected cohort from the International DIPG Registry, including patients from United States, Canada, Australia and New Zealand. Basic comparison with the results of the original study was performed using descriptive statistics, and univariate- and multivariable regression analyses in the validation cohort. External validation was assessed following a variety of analyses described previously. Baseline patient characteristics and results from the regression analyses were largely comparable. Kaplan-Meier curves of the validation cohort reproduced separated groups of standard (n = 39), intermediate (n = 125), and high-risk (n = 78) patients. This discriminative ability was confirmed by similar values for the hazard ratios across these risk groups. The calibration curve in the validation cohort showed a symmetric underestimation of the predicted survival probabilities. In this external validation study, we demonstrate that the DIPG survival prediction model has acceptable cross-cohort calibration and is able to discriminate patients with short, average, and increased survival. We discuss how this clinico-radiological model may serve a useful role in current clinical practice.

Orbit-determination performance of Doppler data for interplanetary cruise trajectories. Part 1: Error analysis methodology

NASA Technical Reports Server (NTRS)

Ulvestad, J. S.; Thurman, S. W.

1992-01-01

An error covariance analysis methodology is used to investigate different weighting schemes for two-way (coherent) Doppler data in the presence of transmission-media and observing-platform calibration errors. The analysis focuses on orbit-determination performance in the interplanetary cruise phase of deep-space missions. Analytical models for the Doppler observable and for transmission-media and observing-platform calibration errors are presented, drawn primarily from previous work. Previously published analytical models were improved upon by the following: (1) considering the effects of errors in the calibration of radio signal propagation through the troposphere and ionosphere as well as station-location errors; (2) modelling the spacecraft state transition matrix using a more accurate piecewise-linear approximation to represent the evolution of the spacecraft trajectory; and (3) incorporating Doppler data weighting functions that are functions of elevation angle, which reduce the sensitivity of the estimated spacecraft trajectory to troposphere and ionosphere calibration errors. The analysis is motivated by the need to develop suitable weighting functions for two-way Doppler data acquired at 8.4 GHz (X-band) and 32 GHz (Ka-band). This weighting is likely to be different from that in the weighting functions currently in use; the current functions were constructed originally for use with 2.3 GHz (S-band) Doppler data, which are affected much more strongly by the ionosphere than are the higher frequency data.

Development and Calibration of a System-Integrated Rotorcraft Finite Element Model for Impact Scenarios

NASA Technical Reports Server (NTRS)

Annett, Martin S.; Horta, Lucas G.; Jackson, Karen E.; Polanco, Michael A.; Littell, Justin D.

2012-01-01

Two full-scale crash tests of an MD-500 helicopter were conducted in 2009 and 2010 at NASA Langley's Landing and Impact Research Facility in support of NASA s Subsonic Rotary Wing Crashworthiness Project. The first crash test was conducted to evaluate the performance of an externally mounted composite deployable energy absorber (DEA) under combined impact conditions. In the second crash test, the energy absorber was removed to establish baseline loads that are regarded as severe but survivable. The presence of this energy absorbing device reduced the peak impact acceleration levels by a factor of three. Accelerations and kinematic data collected from the crash tests were compared to a system-integrated finite element model of the test article developed in parallel with the test program. In preparation for the full-scale crash test, a series of sub-scale and MD-500 mass simulator tests were conducted to evaluate the impact performances of various components and subsystems, including new crush tubes and the DEA blocks. Parameters defined for the system-integrated finite element model were determined from these tests. Results from 19 accelerometers placed throughout the airframe were compared to finite element model responses. The model developed for the purposes of predicting acceleration responses from the first crash test was inadequate when evaluating more severe conditions seen in the second crash test. A newly developed model calibration approach that includes uncertainty estimation, parameter sensitivity, impact shape orthogonality, and numerical optimization was used to calibrate model results for the full-scale crash test without the DEA. This combination of heuristic and quantitative methods identified modeling deficiencies, evaluated parameter importance, and proposed required model changes. The multidimensional calibration techniques presented here are particularly effective in identifying model adequacy. Acceleration results for the calibrated model were compared to test results and the original model results. There was a noticeable improvement in the pilot and copilot region, a slight improvement in the occupant model response, and an over-stiffening effect in the passenger region. One lesson learned was that this approach should be adopted early on, in combination with the building-block approaches that are customarily used, for model development and pretest predictions. Complete crash simulations with validated finite element models can be used to satisfy crash certification requirements, potentially reducing overall development costs.

Simulation of runoff and nutrient export from a typical small watershed in China using the Hydrological Simulation Program-Fortran.

PubMed

Li, Zhaofu; Liu, Hongyu; Luo, Chuan; Li, Yan; Li, Hengpeng; Pan, Jianjun; Jiang, Xiaosan; Zhou, Quansuo; Xiong, Zhengqin

2015-05-01

The Hydrological Simulation Program-Fortran (HSPF), which is a hydrological and water-quality computer model that was developed by the United States Environmental Protection Agency, was employed to simulate runoff and nutrient export from a typical small watershed in a hilly eastern monsoon region of China. First, a parameter sensitivity analysis was performed to assess how changes in the model parameters affect runoff and nutrient export. Next, the model was calibrated and validated using measured runoff and nutrient concentration data. The Nash-Sutcliffe efficiency (E NS ) values of the yearly runoff were 0.87 and 0.69 for the calibration and validation periods, respectively. For storms runoff events, the E NS values were 0.93 for the calibration period and 0.47 for the validation period. Antecedent precipitation and soil moisture conditions can affect the simulation accuracy of storm event flow. The E NS values for the total nitrogen (TN) export were 0.58 for the calibration period and 0.51 for the validation period. In addition, the correlation coefficients between the observed and simulated TN concentrations were 0.84 for the calibration period and 0.74 for the validation period. For phosphorus export, the E NS values were 0.89 for the calibration period and 0.88 for the validation period. In addition, the correlation coefficients between the observed and simulated orthophosphate concentrations were 0.96 and 0.94 for the calibration and validation periods, respectively. The nutrient simulation results are generally satisfactory even though the parameter-lumped HSPF model cannot represent the effects of the spatial pattern of land cover on nutrient export. The model parameters obtained in this study could serve as reference values for applying the model to similar regions. In addition, HSPF can properly describe the characteristics of water quantity and quality processes in this area. After adjustment, calibration, and validation of the parameters, the HSPF model is suitable for hydrological and water-quality simulations in watershed planning and management and for designing best management practices.

Risk assessment model for development of advanced age-related macular degeneration.

PubMed

Klein, Michael L; Francis, Peter J; Ferris, Frederick L; Hamon, Sara C; Clemons, Traci E

2011-12-01

To design a risk assessment model for development of advanced age-related macular degeneration (AMD) incorporating phenotypic, demographic, environmental, and genetic risk factors. We evaluated longitudinal data from 2846 participants in the Age-Related Eye Disease Study. At baseline, these individuals had all levels of AMD, ranging from none to unilateral advanced AMD (neovascular or geographic atrophy). Follow-up averaged 9.3 years. We performed a Cox proportional hazards analysis with demographic, environmental, phenotypic, and genetic covariates and constructed a risk assessment model for development of advanced AMD. Performance of the model was evaluated using the C statistic and the Brier score and externally validated in participants in the Complications of Age-Related Macular Degeneration Prevention Trial. The final model included the following independent variables: age, smoking history, family history of AMD (first-degree member), phenotype based on a modified Age-Related Eye Disease Study simple scale score, and genetic variants CFH Y402H and ARMS2 A69S. The model did well on performance measures, with very good discrimination (C statistic = 0.872) and excellent calibration and overall performance (Brier score at 5 years = 0.08). Successful external validation was performed, and a risk assessment tool was designed for use with or without the genetic component. We constructed a risk assessment model for development of advanced AMD. The model performed well on measures of discrimination, calibration, and overall performance and was successfully externally validated. This risk assessment tool is available for online use.

Geographic and temporal validity of prediction models: Different approaches were useful to examine model performance

PubMed Central

Austin, Peter C.; van Klaveren, David; Vergouwe, Yvonne; Nieboer, Daan; Lee, Douglas S.; Steyerberg, Ewout W.

2017-01-01

Objective Validation of clinical prediction models traditionally refers to the assessment of model performance in new patients. We studied different approaches to geographic and temporal validation in the setting of multicenter data from two time periods. Study Design and Setting We illustrated different analytic methods for validation using a sample of 14,857 patients hospitalized with heart failure at 90 hospitals in two distinct time periods. Bootstrap resampling was used to assess internal validity. Meta-analytic methods were used to assess geographic transportability. Each hospital was used once as a validation sample, with the remaining hospitals used for model derivation. Hospital-specific estimates of discrimination (c-statistic) and calibration (calibration intercepts and slopes) were pooled using random effects meta-analysis methods. I2 statistics and prediction interval width quantified geographic transportability. Temporal transportability was assessed using patients from the earlier period for model derivation and patients from the later period for model validation. Results Estimates of reproducibility, pooled hospital-specific performance, and temporal transportability were on average very similar, with c-statistics of 0.75. Between-hospital variation was moderate according to I2 statistics and prediction intervals for c-statistics. Conclusion This study illustrates how performance of prediction models can be assessed in settings with multicenter data at different time periods. PMID:27262237

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

NASA Astrophysics Data System (ADS)

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

2015-09-01

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

Deriving global parameter estimates for the Noah land surface model using FLUXNET and machine learning

NASA Astrophysics Data System (ADS)

Chaney, Nathaniel W.; Herman, Jonathan D.; Ek, Michael B.; Wood, Eric F.

2016-11-01

With their origins in numerical weather prediction and climate modeling, land surface models aim to accurately partition the surface energy balance. An overlooked challenge in these schemes is the role of model parameter uncertainty, particularly at unmonitored sites. This study provides global parameter estimates for the Noah land surface model using 85 eddy covariance sites in the global FLUXNET network. The at-site parameters are first calibrated using a Latin Hypercube-based ensemble of the most sensitive parameters, determined by the Sobol method, to be the minimum stomatal resistance (rs,min), the Zilitinkevich empirical constant (Czil), and the bare soil evaporation exponent (fxexp). Calibration leads to an increase in the mean Kling-Gupta Efficiency performance metric from 0.54 to 0.71. These calibrated parameter sets are then related to local environmental characteristics using the Extra-Trees machine learning algorithm. The fitted Extra-Trees model is used to map the optimal parameter sets over the globe at a 5 km spatial resolution. The leave-one-out cross validation of the mapped parameters using the Noah land surface model suggests that there is the potential to skillfully relate calibrated model parameter sets to local environmental characteristics. The results demonstrate the potential to use FLUXNET to tune the parameterizations of surface fluxes in land surface models and to provide improved parameter estimates over the globe.

A Fast Surrogate-facilitated Data-driven Bayesian Approach to Uncertainty Quantification of a Regional Groundwater Flow Model with Structural Error

NASA Astrophysics Data System (ADS)

Xu, T.; Valocchi, A. J.; Ye, M.; Liang, F.

2016-12-01

Due to simplification and/or misrepresentation of the real aquifer system, numerical groundwater flow and solute transport models are usually subject to model structural error. During model calibration, the hydrogeological parameters may be overly adjusted to compensate for unknown structural error. This may result in biased predictions when models are used to forecast aquifer response to new forcing. In this study, we extend a fully Bayesian method [Xu and Valocchi, 2015] to calibrate a real-world, regional groundwater flow model. The method uses a data-driven error model to describe model structural error and jointly infers model parameters and structural error. In this study, Bayesian inference is facilitated using high performance computing and fast surrogate models. The surrogate models are constructed using machine learning techniques to emulate the response simulated by the computationally expensive groundwater model. We demonstrate in the real-world case study that explicitly accounting for model structural error yields parameter posterior distributions that are substantially different from those derived by the classical Bayesian calibration that does not account for model structural error. In addition, the Bayesian with error model method gives significantly more accurate prediction along with reasonable credible intervals.

A coupled hydrological-hydraulic flood inundation model calibrated using post-event measurements and integrated uncertainty analysis in a poorly gauged Mediterranean basin

NASA Astrophysics Data System (ADS)

Hdeib, Rouya; Abdallah, Chadi; Moussa, Roger; Colin, Francois

2017-04-01

Developing flood inundation maps of defined exceedance probabilities is required to provide information on the flood hazard and the associated risk. A methodology has been developed to model flood inundation in poorly gauged basins, where reliable information on the hydrological characteristics of floods are uncertain and partially captured by the traditional rain-gauge networks. Flood inundation is performed through coupling a hydrological rainfall-runoff (RR) model (HEC-HMS) with a hydraulic model (HEC-RAS). The RR model is calibrated against the January 2013 flood event in the Awali River basin, Lebanon (300 km2), whose flood peak discharge was estimated by post-event measurements. The resulting flows of the RR model are defined as boundary conditions of the hydraulic model, which is run to generate the corresponding water surface profiles and calibrated against 20 post-event surveyed cross sections after the January-2013 flood event. An uncertainty analysis is performed to assess the results of the models. Consequently, the coupled flood inundation model is simulated with design storms and flood inundation maps are generated of defined exceedance probabilities. The peak discharges estimated by the simulated RR model were in close agreement with the results from different empirical and statistical methods. This methodology can be extended to other poorly gauged basins facing common stage-gauge failure or characterized by floods with a stage exceeding the gauge measurement level, or higher than that defined by the rating curve.

Determination of rice syrup adulterant concentration in honey using three-dimensional fluorescence spectra and multivariate calibrations

NASA Astrophysics Data System (ADS)

Chen, Quansheng; Qi, Shuai; Li, Huanhuan; Han, Xiaoyan; Ouyang, Qin; Zhao, Jiewen

2014-10-01

To rapidly and efficiently detect the presence of adulterants in honey, three-dimensional fluorescence spectroscopy (3DFS) technique was employed with the help of multivariate calibration. The data of 3D fluorescence spectra were compressed using characteristic extraction and the principal component analysis (PCA). Then, partial least squares (PLS) and back propagation neural network (BP-ANN) algorithms were used for modeling. The model was optimized by cross validation, and its performance was evaluated according to root mean square error of prediction (RMSEP) and correlation coefficient (R) in prediction set. The results showed that BP-ANN model was superior to PLS models, and the optimum prediction results of the mixed group (sunflower ± longan ± buckwheat ± rape) model were achieved as follow: RMSEP = 0.0235 and R = 0.9787 in the prediction set. The study demonstrated that the 3D fluorescence spectroscopy technique combined with multivariate calibration has high potential in rapid, nondestructive, and accurate quantitative analysis of honey adulteration.

Relevance of the c-statistic when evaluating risk-adjustment models in surgery.

PubMed

Merkow, Ryan P; Hall, Bruce L; Cohen, Mark E; Dimick, Justin B; Wang, Edward; Chow, Warren B; Ko, Clifford Y; Bilimoria, Karl Y

2012-05-01

The measurement of hospital quality based on outcomes requires risk adjustment. The c-statistic is a popular tool used to judge model performance, but can be limited, particularly when evaluating specific operations in focused populations. Our objectives were to examine the interpretation and relevance of the c-statistic when used in models with increasingly similar case mix and to consider an alternative perspective on model calibration based on a graphical depiction of model fit. From the American College of Surgeons National Surgical Quality Improvement Program (2008-2009), patients were identified who underwent a general surgery procedure, and procedure groups were increasingly restricted: colorectal-all, colorectal-elective cases only, and colorectal-elective cancer cases only. Mortality and serious morbidity outcomes were evaluated using logistic regression-based risk adjustment, and model c-statistics and calibration curves were used to compare model performance. During the study period, 323,427 general, 47,605 colorectal-all, 39,860 colorectal-elective, and 21,680 colorectal cancer patients were studied. Mortality ranged from 1.0% in general surgery to 4.1% in the colorectal-all group, and serious morbidity ranged from 3.9% in general surgery to 12.4% in the colorectal-all procedural group. As case mix was restricted, c-statistics progressively declined from the general to the colorectal cancer surgery cohorts for both mortality and serious morbidity (mortality: 0.949 to 0.866; serious morbidity: 0.861 to 0.668). Calibration was evaluated graphically by examining predicted vs observed number of events over risk deciles. For both mortality and serious morbidity, there was no qualitative difference in calibration identified between the procedure groups. In the present study, we demonstrate how the c-statistic can become less informative and, in certain circumstances, can lead to incorrect model-based conclusions, as case mix is restricted and patients become more homogenous. Although it remains an important tool, caution is advised when the c-statistic is advanced as the sole measure of a model performance. Copyright © 2012 American College of Surgeons. All rights reserved.

Parameter estimation and sensitivity analysis in an agent-based model of Leishmania major infection

PubMed Central

Jones, Douglas E.; Dorman, Karin S.

2009-01-01

Computer models of disease take a systems biology approach toward understanding host-pathogen interactions. In particular, data driven computer model calibration is the basis for inference of immunological and pathogen parameters, assessment of model validity, and comparison between alternative models of immune or pathogen behavior. In this paper we describe the calibration and analysis of an agent-based model of Leishmania major infection. A model of macrophage loss following uptake of necrotic tissue is proposed to explain macrophage depletion following peak infection. Using Gaussian processes to approximate the computer code, we perform a sensitivity analysis to identify important parameters and to characterize their influence on the simulated infection. The analysis indicates that increasing growth rate can favor or suppress pathogen loads, depending on the infection stage and the pathogen’s ability to avoid detection. Subsequent calibration of the model against previously published biological observations suggests that L. major has a relatively slow growth rate and can replicate for an extended period of time before damaging the host cell. PMID:19837088

Exploring uncertainty and model predictive performance concepts via a modular snowmelt-runoff modeling framework

Treesearch

Tyler Jon Smith; Lucy Amanda Marshall

2010-01-01

Model selection is an extremely important aspect of many hydrologic modeling studies because of the complexity, variability, and uncertainty that surrounds the current understanding of watershed-scale systems. However, development and implementation of a complete precipitation-runoff modeling framework, from model selection to calibration and uncertainty analysis, are...

Calibration of GOES-derived solar radiation data using a distributed network of surface measurements in Florida, USA

USGS Publications Warehouse

Sumner, David M.; Pathak, Chandra S.; Mecikalski, John R.; Paech, Simon J.; Wu, Qinglong; Sangoyomi, Taiye; Babcock, Roger W.; Walton, Raymond

2008-01-01

Solar radiation data are critically important for the estimation of evapotranspiration. Analysis of visible-channel data derived from Geostationary Operational Environmental Satellites (GOES) using radiative transfer modeling has been used to produce spatially- and temporally-distributed datasets of solar radiation. An extensive network of (pyranometer) surface measurements of solar radiation in the State of Florida has allowed refined calibration of a GOES-derived daily integrated radiation data product. This refinement of radiation data allowed for corrections of satellite sensor drift, satellite generational change, and consideration of the highly-variable cloudy conditions that are typical of Florida. To aid in calibration of a GOES-derived radiation product, solar radiation data for the period 1995–2004 from 58 field stations that are located throughout the State were compiled. The GOES radiation product was calibrated by way of a three-step process: 1) comparison with ground-based pyranometer measurements on clear reference days, 2) correcting for a bias related to cloud cover, and 3) deriving month-by-month bias correction factors. Pre-calibration results indicated good model performance, with a station-averaged model error of 2.2 MJ m–2 day–1 (13 percent). Calibration reduced errors to 1.7 MJ m–2 day–1 (10 percent) and also removed time- and cloudiness-related biases. The final dataset has been used to produce Statewide evapotranspiration estimates.

Hierarchical calibration and validation of computational fluid dynamics models for solid sorbent-based carbon capture

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

Lai, Canhai; Xu, Zhijie; Pan, Wenxiao

2016-01-01

To quantify the predictive confidence of a solid sorbent-based carbon capture design, a hierarchical validation methodology—consisting of basic unit problems with increasing physical complexity coupled with filtered model-based geometric upscaling has been developed and implemented. This paper describes the computational fluid dynamics (CFD) multi-phase reactive flow simulations and the associated data flows among different unit problems performed within the said hierarchical validation approach. The bench-top experiments used in this calibration and validation effort were carefully designed to follow the desired simple-to-complex unit problem hierarchy, with corresponding data acquisition to support model parameters calibrations at each unit problem level. A Bayesianmore » calibration procedure is employed and the posterior model parameter distributions obtained at one unit-problem level are used as prior distributions for the same parameters in the next-tier simulations. Overall, the results have demonstrated that the multiphase reactive flow models within MFIX can be used to capture the bed pressure, temperature, CO2 capture capacity, and kinetics with quantitative accuracy. The CFD modeling methodology and associated uncertainty quantification techniques presented herein offer a solid framework for estimating the predictive confidence in the virtual scale up of a larger carbon capture device.« less

Predicting non return to work after orthopaedic trauma: the Wallis Occupational Rehabilitation RisK (WORRK) model.

PubMed

Luthi, François; Deriaz, Olivier; Vuistiner, Philippe; Burrus, Cyrille; Hilfiker, Roger

2014-01-01

Workers with persistent disabilities after orthopaedic trauma may need occupational rehabilitation. Despite various risk profiles for non-return-to-work (non-RTW), there is no available predictive model. Moreover, injured workers may have various origins (immigrant workers), which may either affect their return to work or their eligibility for research purposes. The aim of this study was to develop and validate a predictive model that estimates the likelihood of non-RTW after occupational rehabilitation using predictors which do not rely on the worker's background. Prospective cohort study (3177 participants, native (51%) and immigrant workers (49%)) with two samples: a) Development sample with patients from 2004 to 2007 with Full and Reduced Models, b) External validation of the Reduced Model with patients from 2008 to March 2010. We collected patients' data and biopsychosocial complexity with an observer rated interview (INTERMED). Non-RTW was assessed two years after discharge from the rehabilitation. Discrimination was assessed by the area under the receiver operating curve (AUC) and calibration was evaluated with a calibration plot. The model was reduced with random forests. At 2 years, the non-RTW status was known for 2462 patients (77.5% of the total sample). The prevalence of non-RTW was 50%. The full model (36 items) and the reduced model (19 items) had acceptable discrimination performance (AUC 0.75, 95% CI 0.72 to 0.78 and 0.74, 95% CI 0.71 to 0.76, respectively) and good calibration. For the validation model, the discrimination performance was acceptable (AUC 0.73; 95% CI 0.70 to 0.77) and calibration was also adequate. Non-RTW may be predicted with a simple model constructed with variables independent of the patient's education and language fluency. This model is useful for all kinds of trauma in order to adjust for case mix and it is applicable to vulnerable populations like immigrant workers.

Predicting Non Return to Work after Orthopaedic Trauma: The Wallis Occupational Rehabilitation RisK (WORRK) Model

PubMed Central

Luthi, François; Deriaz, Olivier; Vuistiner, Philippe; Burrus, Cyrille; Hilfiker, Roger

2014-01-01

Background Workers with persistent disabilities after orthopaedic trauma may need occupational rehabilitation. Despite various risk profiles for non-return-to-work (non-RTW), there is no available predictive model. Moreover, injured workers may have various origins (immigrant workers), which may either affect their return to work or their eligibility for research purposes. The aim of this study was to develop and validate a predictive model that estimates the likelihood of non-RTW after occupational rehabilitation using predictors which do not rely on the worker’s background. Methods Prospective cohort study (3177 participants, native (51%) and immigrant workers (49%)) with two samples: a) Development sample with patients from 2004 to 2007 with Full and Reduced Models, b) External validation of the Reduced Model with patients from 2008 to March 2010. We collected patients’ data and biopsychosocial complexity with an observer rated interview (INTERMED). Non-RTW was assessed two years after discharge from the rehabilitation. Discrimination was assessed by the area under the receiver operating curve (AUC) and calibration was evaluated with a calibration plot. The model was reduced with random forests. Results At 2 years, the non-RTW status was known for 2462 patients (77.5% of the total sample). The prevalence of non-RTW was 50%. The full model (36 items) and the reduced model (19 items) had acceptable discrimination performance (AUC 0.75, 95% CI 0.72 to 0.78 and 0.74, 95% CI 0.71 to 0.76, respectively) and good calibration. For the validation model, the discrimination performance was acceptable (AUC 0.73; 95% CI 0.70 to 0.77) and calibration was also adequate. Conclusions Non-RTW may be predicted with a simple model constructed with variables independent of the patient’s education and language fluency. This model is useful for all kinds of trauma in order to adjust for case mix and it is applicable to vulnerable populations like immigrant workers. PMID:24718689

Hydrologic Modeling and Parameter Estimation under Data Scarcity for Java Island, Indonesia

NASA Astrophysics Data System (ADS)

Yanto, M.; Livneh, B.; Rajagopalan, B.; Kasprzyk, J. R.

2015-12-01

The Indonesian island of Java is routinely subjected to intense flooding, drought and related natural hazards, resulting in severe social and economic impacts. Although an improved understanding of the island's hydrology would help mitigate these risks, data scarcity issues make the modeling challenging. To this end, we developed a hydrological representation of Java using the Variable Infiltration Capacity (VIC) model, to simulate the hydrologic processes of several watersheds across the island. We measured the model performance using Nash-Sutcliffe Efficiency (NSE) at monthly time step. Data scarcity and quality issues for precipitation and streamflow warranted the application of a quality control procedure to data ensure consistency among watersheds resulting in 7 watersheds. To optimize the model performance, the calibration parameters were estimated using Borg Multi Objective Evolutionary Algorithm (Borg MOEA), which offers efficient searching of the parameter space, adaptive population sizing and local optima escape facility. The result shows that calibration performance is best (NSE ~ 0.6 - 0.9) in the eastern part of the domain and moderate (NSE ~ 0.3 - 0.5) in the western part of the island. The validation results are lower (NSE ~ 0.1 - 0.5) and (NSE ~ 0.1 - 0.4) in the east and west, respectively. We surmise that the presence of outliers and stark differences in the climate between calibration and validation periods in the western watersheds are responsible for low NSE in this region. In addition, we found that approximately 70% of total errors were contributed by less than 20% of total data. The spatial variability of model performance suggests the influence of both topographical and hydroclimatic controls on the hydrological processes. Most watersheds in eastern part perform better in wet season and vice versa for the western part. This modeling framework is one of the first attempts at comprehensively simulating the hydrology in this maritime, tropical continent and, offers insights for skillful hydrologic projections crucial for natural hazard mitigation.

Challenges in modeling the X-29 flight test performance

NASA Technical Reports Server (NTRS)

Hicks, John W.; Kania, Jan; Pearce, Robert; Mills, Glen

1987-01-01

Presented are methods, instrumentation, and difficulties associated with drag measurement of the X-29A aircraft. The initial performance objective of the X-29A program emphasized drag polar shapes rather than absolute drag levels. Priorities during the flight envelope expansion restricted the evaluation of aircraft performance. Changes in aircraft configuration, uncertainties in angle-of-attack calibration, and limitations in instrumentation complicated the analysis. Limited engine instrumentation with uncertainties in overall in-flight thrust accuracy made it difficult to obtain reliable values of coefficient of parasite drag. The aircraft was incapable of tracking the automatic camber control trim schedule for optimum wing flaperon deflection during typical dynamic performance maneuvers; this has also complicated the drag polar shape modeling. The X-29A was far enough off the schedule that the developed trim drag correction procedure has proven inadequate. However, good drag polar shapes have been developed throughout the flight envelope. Preliminary flight results have compared well with wind tunnel predictions. A more comprehensive analysis must be done to complete performance models. The detailed flight performance program with a calibrated engine will benefit from the experience gained during this preliminary performance phase.

Challenges in modeling the X-29A flight test performance

NASA Technical Reports Server (NTRS)

Hicks, John W.; Kania, Jan; Pearce, Robert; Mills, Glen

1987-01-01

The paper presents the methods, instrumentation, and difficulties associated with drag measurement of the X-29A aircraft. The initial performance objective of the X-29A program emphasized drag polar shapes rather than absolute drag levels. Priorities during the flight envelope expansion restricted the evaluation of aircraft performance. Changes in aircraft configuration, uncertainties in angle-of-attack calibration, and limitations in instrumentation complicated the analysis. Limited engine instrumentation with uncertainties in overall in-flight thrust accuracy made it difficult to obtain reliable values of coefficient of parasite drag. The aircraft was incapable of tracking the automatic camber control trim schedule for optimum wing flaperon deflection during typical dynamic performance maneuvers; this has also complicated the drag polar shape modeling. The X-29A was far enough off the schedule that the developed trim drag correction procedure has proven inadequate. Despite these obstacles, good drag polar shapes have been developed throughout the flight envelope. Preliminary flight results have compared well with wind tunnel predictions. A more comprehensive analysis must be done to complete the performance models. The detailed flight performance program with a calibrated engine will benefit from the experience gained during this preliminary performance phase.

Approaches to highly parameterized inversion-A guide to using PEST for groundwater-model calibration

USGS Publications Warehouse

Doherty, John E.; Hunt, Randall J.

2010-01-01

Highly parameterized groundwater models can create calibration difficulties. Regularized inversion-the combined use of large numbers of parameters with mathematical approaches for stable parameter estimation-is becoming a common approach to address these difficulties and enhance the transfer of information contained in field measurements to parameters used to model that system. Though commonly used in other industries, regularized inversion is somewhat imperfectly understood in the groundwater field. There is concern that this unfamiliarity can lead to underuse, and misuse, of the methodology. This document is constructed to facilitate the appropriate use of regularized inversion for calibrating highly parameterized groundwater models. The presentation is directed at an intermediate- to advanced-level modeler, and it focuses on the PEST software suite-a frequently used tool for highly parameterized model calibration and one that is widely supported by commercial graphical user interfaces. A brief overview of the regularized inversion approach is provided, and techniques for mathematical regularization offered by PEST are outlined, including Tikhonov, subspace, and hybrid schemes. Guidelines for applying regularized inversion techniques are presented after a logical progression of steps for building suitable PEST input. The discussion starts with use of pilot points as a parameterization device and processing/grouping observations to form multicomponent objective functions. A description of potential parameter solution methodologies and resources available through the PEST software and its supporting utility programs follows. Directing the parameter-estimation process through PEST control variables is then discussed, including guidance for monitoring and optimizing the performance of PEST. Comprehensive listings of PEST control variables, and of the roles performed by PEST utility support programs, are presented in the appendixes.

Empirical Corrections for MISR Calibration Temporal Trends, Point-Spread Function, Flat-Fielding, and Ghosting

NASA Astrophysics Data System (ADS)

Limbacher, J.; Kahn, R. A.

2015-12-01

MISR aerosol optical depth retrievals are fairly robust to small radiometric calibration artifacts, due to the multi-angle observations. However, even small errors in the MISR calibration, especially structured artifacts in the imagery, have a disproportionate effect on the retrieval of aerosol properties from these data. Using MODIS, POLDER-3, AERONET, MAN, and MISR lunar images, we diagnose and correct various calibration and radiometric artifacts found in the MISR radiance (Level 1) data, using empirical image analysis. The calibration artifacts include temporal trends in MISR top-of-atmosphere reflectance at relatively stable desert sites and flat-fielding artifacts detected by comparison to MODIS over bright, low-contrast scenes. The radiometric artifacts include ghosting (as compared to MODIS, POLDER-3, and forward model results) and point-spread function mischaracterization (using the MISR lunar data and MODIS). We minimize the artifacts to the extent possible by parametrically modeling the artifacts and then removing them from the radiance (reflectance) data. Validation is performed using non-training scenes (reflectance comparison), and also by using the MISR Research Aerosol retrieval algorithm results compared to MAN and AERONET.

A calibration rig for multi-component internal strain gauge balance using the new design-of-experiment (DOE) approach

NASA Astrophysics Data System (ADS)

Nouri, N. M.; Mostafapour, K.; Kamran, M.

2018-02-01

In a closed water-tunnel circuit, the multi-component strain gauge force and moment sensor (also known as balance) are generally used to measure hydrodynamic forces and moments acting on scaled models. These balances are periodically calibrated by static loading. Their performance and accuracy depend significantly on the rig and the method of calibration. In this research, a new calibration rig was designed and constructed to calibrate multi-component internal strain gauge balances. The calibration rig has six degrees of freedom and six different component-loading structures that can be applied separately and synchronously. The system was designed based on the applicability of formal experimental design techniques, using gravity for balance loading and balance positioning and alignment relative to gravity. To evaluate the calibration rig, a six-component internal balance developed by Iran University of Science and Technology was calibrated using response surface methodology. According to the results, calibration rig met all design criteria. This rig provides the means by which various methods of formal experimental design techniques can be implemented. The simplicity of the rig saves time and money in the design of experiments and in balance calibration while simultaneously increasing the accuracy of these activities.

Urban tree growth modeling

Treesearch

E. Gregory McPherson; Paula J. Peper

2012-01-01

This paper describes three long-term tree growth studies conducted to evaluate tree performance because repeated measurements of the same trees produce critical data for growth model calibration and validation. Several empirical and process-based approaches to modeling tree growth are reviewed. Modeling is more advanced in the fields of forestry and...

Exploring the calibration of a wind forecast ensemble for energy applications

NASA Astrophysics Data System (ADS)

Heppelmann, Tobias; Ben Bouallegue, Zied; Theis, Susanne

2015-04-01

In the German research project EWeLiNE, Deutscher Wetterdienst (DWD) and Fraunhofer Institute for Wind Energy and Energy System Technology (IWES) are collaborating with three German Transmission System Operators (TSO) in order to provide the TSOs with improved probabilistic power forecasts. Probabilistic power forecasts are derived from probabilistic weather forecasts, themselves derived from ensemble prediction systems (EPS). Since the considered raw ensemble wind forecasts suffer from underdispersiveness and bias, calibration methods are developed for the correction of the model bias and the ensemble spread bias. The overall aim is to improve the ensemble forecasts such that the uncertainty of the possible weather deployment is depicted by the ensemble spread from the first forecast hours. Additionally, the ensemble members after calibration should remain physically consistent scenarios. We focus on probabilistic hourly wind forecasts with horizon of 21 h delivered by the convection permitting high-resolution ensemble system COSMO-DE-EPS which has become operational in 2012 at DWD. The ensemble consists of 20 ensemble members driven by four different global models. The model area includes whole Germany and parts of Central Europe with a horizontal resolution of 2.8 km and a vertical resolution of 50 model levels. For verification we use wind mast measurements around 100 m height that corresponds to the hub height of wind energy plants that belong to wind farms within the model area. Calibration of the ensemble forecasts can be performed by different statistical methods applied to the raw ensemble output. Here, we explore local bivariate Ensemble Model Output Statistics at individual sites and quantile regression with different predictors. Applying different methods, we already show an improvement of ensemble wind forecasts from COSMO-DE-EPS for energy applications. In addition, an ensemble copula coupling approach transfers the time-dependencies of the raw ensemble to the calibrated ensemble. The calibrated wind forecasts are evaluated first with univariate probabilistic scores and additionally with diagnostics of wind ramps in order to assess the time-consistency of the calibrated ensemble members.

Simulation of daily streamflows at gaged and ungaged locations within the Cedar River Basin, Iowa, using a Precipitation-Runoff Modeling System model

USGS Publications Warehouse

Christiansen, Daniel E.

2012-01-01

The U.S. Geological Survey, in cooperation with the Iowa Department of Natural Resources, conducted a study to examine techniques for estimation of daily streamflows using hydrological models and statistical methods. This report focuses on the use of a hydrologic model, the U.S. Geological Survey's Precipitation-Runoff Modeling System, to estimate daily streamflows at gaged and ungaged locations. The Precipitation-Runoff Modeling System is a modular, physically based, distributed-parameter modeling system developed to evaluate the impacts of various combinations of precipitation, climate, and land use on surface-water runoff and general basin hydrology. The Cedar River Basin was selected to construct a Precipitation-Runoff Modeling System model that simulates the period from January 1, 2000, to December 31, 2010. The calibration period was from January 1, 2000, to December 31, 2004, and the validation periods were from January 1, 2005, to December 31, 2010 and January 1, 2000 to December 31, 2010. A Geographic Information System tool was used to delineate the Cedar River Basin and subbasins for the Precipitation-Runoff Modeling System model and to derive parameters based on the physical geographical features. Calibration of the Precipitation-Runoff Modeling System model was completed using a U.S. Geological Survey calibration software tool. The main objective of the calibration was to match the daily streamflow simulated by the Precipitation-Runoff Modeling System model with streamflow measured at U.S. Geological Survey streamflow gages. The Cedar River Basin daily streamflow model performed with a Nash-Sutcliffe efficiency ranged from 0.82 to 0.33 during the calibration period, and a Nash-Sutcliffe efficiency ranged from 0.77 to -0.04 during the validation period. The Cedar River Basin model is meeting the criteria of greater than 0.50 Nash-Sutcliffe and is a good fit for streamflow conditions for the calibration period at all but one location, Austin, Minnesota. The Precipitation-Runoff Modeling System model accurately simulated streamflow at four of six uncalibrated sites within the basin. Overall, there was good agreement between simulated and measured seasonal and annual volumes throughout the basin for calibration and validation sites. The calibration period ranged from 0.2 to 20.8 percent difference, and the validation period ranged from 0.0 to 19.5 percent difference across all seasons and total annual runoff. The Precipitation-Runoff Modeling System model tended to underestimate lower streamflows compared to the observed streamflow values. This is an indication that the Precipitation-Runoff Modeling model needs more detailed groundwater and storage information to properly model the low-flow conditions in the Cedar River Basin.

Development of an innovative validation strategy of gas-surface interaction modelling for re-entry applications

NASA Astrophysics Data System (ADS)

Joiner, N.; Esser, B.; Fertig, M.; Gülhan, A.; Herdrich, G.; Massuti-Ballester, B.

2016-12-01

This paper summarises the final synthesis of an ESA technology research programme entitled "Development of an Innovative Validation Strategy of Gas Surface Interaction Modelling for Re-entry Applications". The focus of the project was to demonstrate the correct pressure dependency of catalytic surface recombination, with an emphasis on Low Earth Orbit (LEO) re-entry conditions and thermal protection system materials. A physics-based model describing the prevalent recombination mechanisms was proposed for implementation into two CFD codes, TINA and TAU. A dedicated experimental campaign was performed to calibrate and validate the CFD model on TPS materials pertinent to the EXPERT space vehicle at a wide range of temperatures and pressures relevant to LEO. A new set of catalytic recombination data was produced that was able to improve the chosen model calibration for CVD-SiC and provide the first model calibration for the Nickel-Chromium super-alloy PM1000. The experimentally observed pressure dependency of catalytic recombination can only be reproduced by the Langmuir-Hinshelwood recombination mechanism. Due to decreasing degrees of (enthalpy and hence) dissociation with facility stagnation pressure, it was not possible to obtain catalytic recombination coefficients from the measurements at high experimental stagnation pressures. Therefore, the CFD model calibration has been improved by this activity based on the low pressure results. The results of the model calibration were applied to the existing EXPERT mission profile to examine the impact of the experimentally calibrated model at flight relevant conditions. The heat flux overshoot at the CVD-SiC/PM1000 junction on EXPERT is confirmed to produce radiative equilibrium temperatures in close proximity to the PM1000 melt temperature.This was anticipated within the margins of the vehicle design; however, due to the measurements made here for the first time at relevant temperatures for the junction, an increased confidence in this finding is placed on the computations.

Analysis of Sting Balance Calibration Data Using Optimized Regression Models

NASA Technical Reports Server (NTRS)

Ulbrich, Norbert; Bader, Jon B.

2009-01-01

Calibration data of a wind tunnel sting balance was processed using a search algorithm that identifies an optimized regression model for the data analysis. The selected sting balance had two moment gages that were mounted forward and aft of the balance moment center. The difference and the sum of the two gage outputs were fitted in the least squares sense using the normal force and the pitching moment at the balance moment center as independent variables. The regression model search algorithm predicted that the difference of the gage outputs should be modeled using the intercept and the normal force. The sum of the two gage outputs, on the other hand, should be modeled using the intercept, the pitching moment, and the square of the pitching moment. Equations of the deflection of a cantilever beam are used to show that the search algorithm s two recommended math models can also be obtained after performing a rigorous theoretical analysis of the deflection of the sting balance under load. The analysis of the sting balance calibration data set is a rare example of a situation when regression models of balance calibration data can directly be derived from first principles of physics and engineering. In addition, it is interesting to see that the search algorithm recommended the same regression models for the data analysis using only a set of statistical quality metrics.

An eye model for uncalibrated eye gaze estimation under variable head pose

NASA Astrophysics Data System (ADS)

Hnatow, Justin; Savakis, Andreas

2007-04-01

Gaze estimation is an important component of computer vision systems that monitor human activity for surveillance, human-computer interaction, and various other applications including iris recognition. Gaze estimation methods are particularly valuable when they are non-intrusive, do not require calibration, and generalize well across users. This paper presents a novel eye model that is employed for efficiently performing uncalibrated eye gaze estimation. The proposed eye model was constructed from a geometric simplification of the eye and anthropometric data about eye feature sizes in order to circumvent the requirement of calibration procedures for each individual user. The positions of the two eye corners and the midpupil, the distance between the two eye corners, and the radius of the eye sphere are required for gaze angle calculation. The locations of the eye corners and midpupil are estimated via processing following eye detection, and the remaining parameters are obtained from anthropometric data. This eye model is easily extended to estimating eye gaze under variable head pose. The eye model was tested on still images of subjects at frontal pose (0 °) and side pose (34 °). An upper bound of the model's performance was obtained by manually selecting the eye feature locations. The resulting average absolute error was 2.98 ° for frontal pose and 2.87 ° for side pose. The error was consistent across subjects, which indicates that good generalization was obtained. This level of performance compares well with other gaze estimation systems that utilize a calibration procedure to measure eye features.

Acidity measurement of iron ore powders using laser-induced breakdown spectroscopy with partial least squares regression.

PubMed

Hao, Z Q; Li, C M; Shen, M; Yang, X Y; Li, K H; Guo, L B; Li, X Y; Lu, Y F; Zeng, X Y

2015-03-23

Laser-induced breakdown spectroscopy (LIBS) with partial least squares regression (PLSR) has been applied to measuring the acidity of iron ore, which can be defined by the concentrations of oxides: CaO, MgO, Al₂O₃, and SiO₂. With the conventional internal standard calibration, it is difficult to establish the calibration curves of CaO, MgO, Al₂O₃, and SiO₂ in iron ore due to the serious matrix effects. PLSR is effective to address this problem due to its excellent performance in compensating the matrix effects. In this work, fifty samples were used to construct the PLSR calibration models for the above-mentioned oxides. These calibration models were validated by the 10-fold cross-validation method with the minimum root-mean-square errors (RMSE). Another ten samples were used as a test set. The acidities were calculated according to the estimated concentrations of CaO, MgO, Al₂O₃, and SiO₂ using the PLSR models. The average relative error (ARE) and RMSE of the acidity achieved 3.65% and 0.0048, respectively, for the test samples.

Hydrologic Model Development and Calibration: Contrasting a Single- and Multi-Objective Approach for Comparing Model Performance

NASA Astrophysics Data System (ADS)

Asadzadeh, M.; Maclean, A.; Tolson, B. A.; Burn, D. H.

2009-05-01

Hydrologic model calibration aims to find a set of parameters that adequately simulates observations of watershed behavior, such as streamflow, or a state variable, such as snow water equivalent (SWE). There are different metrics for evaluating calibration effectiveness that involve quantifying prediction errors, such as the Nash-Sutcliffe (NS) coefficient and bias evaluated for the entire calibration period, on a seasonal basis, for low flows, or for high flows. Many of these metrics are conflicting such that the set of parameters that maximizes the high flow NS differs from the set of parameters that maximizes the low flow NS. Conflicting objectives are very likely when different calibration objectives are based on different fluxes and/or state variables (e.g., NS based on streamflow versus SWE). One of the most popular ways to balance different metrics is to aggregate them based on their importance and find the set of parameters that optimizes a weighted sum of the efficiency metrics. Comparing alternative hydrologic models (e.g., assessing model improvement when a process or more detail is added to the model) based on the aggregated objective might be misleading since it represents one point on the tradeoff of desired error metrics. To derive a more comprehensive model comparison, we solved a bi-objective calibration problem to estimate the tradeoff between two error metrics for each model. Although this approach is computationally more expensive than the aggregation approach, it results in a better understanding of the effectiveness of selected models at each level of every error metric and therefore provides a better rationale for judging relative model quality. The two alternative models used in this study are two MESH hydrologic models (version 1.2) of the Wolf Creek Research basin that differ in their watershed spatial discretization (a single Grouped Response Unit, GRU, versus multiple GRUs). The MESH model, currently under development by Environment Canada, is a coupled land-surface and hydrologic model. Results will demonstrate the conclusions a modeller might make regarding the value of additional watershed spatial discretization under both an aggregated (single-objective) and multi-objective model comparison framework.

Evaluation, Calibration and Comparison of the Precipitation-Runoff Modeling System (PRMS) National Hydrologic Model (NHM) Using Moderate Resolution Imaging Spectroradiometer (MODIS) and Snow Data Assimilation System (SNODAS) Gridded Datasets

NASA Astrophysics Data System (ADS)

Norton, P. A., II; Haj, A. E., Jr.

2014-12-01

The United States Geological Survey is currently developing a National Hydrologic Model (NHM) to support and facilitate coordinated and consistent hydrologic modeling efforts at the scale of the continental United States. As part of this effort, the Geospatial Fabric (GF) for the NHM was created. The GF is a database that contains parameters derived from datasets that characterize the physical features of watersheds. The GF was used to aggregate catchments and flowlines defined in the National Hydrography Dataset Plus dataset for more than 100,000 hydrologic response units (HRUs), and to establish initial parameter values for input to the Precipitation-Runoff Modeling System (PRMS). Many parameter values are adjusted in PRMS using an automated calibration process. Using these adjusted parameter values, the PRMS model estimated variables such as evapotranspiration (ET), potential evapotranspiration (PET), snow-covered area (SCA), and snow water equivalent (SWE). In order to evaluate the effectiveness of parameter calibration, and model performance in general, several satellite-based Moderate Resolution Imaging Spectroradiometer (MODIS) and Snow Data Assimilation System (SNODAS) gridded datasets including ET, PET, SCA, and SWE were compared to PRMS-simulated values. The MODIS and SNODAS data were spatially averaged for each HRU, and compared to PRMS-simulated ET, PET, SCA, and SWE values for each HRU in the Upper Missouri River watershed. Default initial GF parameter values and PRMS calibration ranges were evaluated. Evaluation results, and the use of MODIS and SNODAS datasets to update GF parameter values and PRMS calibration ranges, are presented and discussed.

Influence of three common calibration metrics on the diagnosis of climate change impacts on water resources

NASA Astrophysics Data System (ADS)

Seiller, G.; Roy, R.; Anctil, F.

2017-04-01

Uncertainties associated to the evaluation of the impacts of climate change on water resources are broad, from multiple sources, and lead to diagnoses sometimes difficult to interpret. Quantification of these uncertainties is a key element to yield confidence in the analyses and to provide water managers with valuable information. This work specifically evaluates the influence of hydrological modeling calibration metrics on future water resources projections, on thirty-seven watersheds in the Province of Québec, Canada. Twelve lumped hydrologic models, representing a wide range of operational options, are calibrated with three common objective functions derived from the Nash-Sutcliffe efficiency. The hydrologic models are forced with climate simulations corresponding to two RCP, twenty-nine GCM from CMIP5 (Coupled Model Intercomparison Project phase 5) and two post-treatment techniques, leading to future projections in the 2041-2070 period. Results show that the diagnosis of the impacts of climate change on water resources are quite affected by the hydrologic models selection and calibration metrics. Indeed, for the four selected hydrological indicators, dedicated to water management, parameters from the three objective functions can provide different interpretations in terms of absolute and relative changes, as well as projected changes direction and climatic ensemble consensus. The GR4J model and a multimodel approach offer the best modeling options, based on calibration performance and robustness. Overall, these results illustrate the need to provide water managers with detailed information on relative changes analysis, but also absolute change values, especially for hydrological indicators acting as security policy thresholds.

The impact of standard and hard-coded parameters on the hydrologic fluxes in the Noah-MP land surface model

NASA Astrophysics Data System (ADS)

Thober, S.; Cuntz, M.; Mai, J.; Samaniego, L. E.; Clark, M. P.; Branch, O.; Wulfmeyer, V.; Attinger, S.

2016-12-01

Land surface models incorporate a large number of processes, described by physical, chemical and empirical equations. The agility of the models to react to different meteorological conditions is artificially constrained by having hard-coded parameters in their equations. Here we searched for hard-coded parameters in the computer code of the land surface model Noah with multiple process options (Noah-MP) to assess the model's agility during parameter estimation. We found 139 hard-coded values in all Noah-MP process options in addition to the 71 standard parameters. We performed a Sobol' global sensitivity analysis to variations of the standard and hard-coded parameters. The sensitivities of the hydrologic output fluxes latent heat and total runoff, their component fluxes, as well as photosynthesis and sensible heat were evaluated at twelve catchments of the Eastern United States with very different hydro-meteorological regimes. Noah-MP's output fluxes are sensitive to two thirds of its standard parameters. The most sensitive parameter is, however, a hard-coded value in the formulation of soil surface resistance for evaporation, which proved to be oversensitive in other land surface models as well. Latent heat and total runoff show very similar sensitivities towards standard and hard-coded parameters. They are sensitive to both soil and plant parameters, which means that model calibrations of hydrologic or land surface models should take both soil and plant parameters into account. Sensible and latent heat exhibit almost the same sensitivities so that calibration or sensitivity analysis can be performed with either of the two. Photosynthesis has almost the same sensitivities as transpiration, which are different from the sensitivities of latent heat. Including photosynthesis and latent heat in model calibration might therefore be beneficial. Surface runoff is sensitive to almost all hard-coded snow parameters. These sensitivities get, however, diminished in total runoff. It is thus recommended to include the most sensitive hard-coded model parameters that were exposed in this study when calibrating Noah-MP.

Improving near-infrared prediction model robustness with support vector machine regression: a pharmaceutical tablet assay example.

PubMed

Igne, Benoît; Drennen, James K; Anderson, Carl A

2014-01-01

Changes in raw materials and process wear and tear can have significant effects on the prediction error of near-infrared calibration models. When the variability that is present during routine manufacturing is not included in the calibration, test, and validation sets, the long-term performance and robustness of the model will be limited. Nonlinearity is a major source of interference. In near-infrared spectroscopy, nonlinearity can arise from light path-length differences that can come from differences in particle size or density. The usefulness of support vector machine (SVM) regression to handle nonlinearity and improve the robustness of calibration models in scenarios where the calibration set did not include all the variability present in test was evaluated. Compared to partial least squares (PLS) regression, SVM regression was less affected by physical (particle size) and chemical (moisture) differences. The linearity of the SVM predicted values was also improved. Nevertheless, although visualization and interpretation tools have been developed to enhance the usability of SVM-based methods, work is yet to be done to provide chemometricians in the pharmaceutical industry with a regression method that can supplement PLS-based methods.

[Determination of soluble solids content in Nanfeng Mandarin by Vis/NIR spectroscopy and UVE-ICA-LS-SVM].

PubMed

Sun, Tong; Xu, Wen-Li; Hu, Tian; Liu, Mu-Hua

2013-12-01

The objective of the present research was to assess soluble solids content (SSC) of Nanfeng mandarin by visible/near infrared (Vis/NIR) spectroscopy combined with new variable selection method, simplify prediction model and improve the performance of prediction model for SSC of Nanfeng mandarin. A total of 300 Nanfeng mandarin samples were used, the numbers of Nanfeng mandarin samples in calibration, validation and prediction sets were 150, 75 and 75, respectively. Vis/NIR spectra of Nanfeng mandarin samples were acquired by a QualitySpec spectrometer in the wavelength range of 350-1000 nm. Uninformative variables elimination (UVE) was used to eliminate wavelength variables that had few information of SSC, then independent component analysis (ICA) was used to extract independent components (ICs) from spectra that eliminated uninformative wavelength variables. At last, least squares support vector machine (LS-SVM) was used to develop calibration models for SSC of Nanfeng mandarin using extracted ICs, and 75 prediction samples that had not been used for model development were used to evaluate the performance of SSC model of Nanfeng mandarin. The results indicate t hat Vis/NIR spectroscopy combinedwith UVE-ICA-LS-SVM is suitable for assessing SSC o f Nanfeng mandarin, and t he precision o f prediction ishigh. UVE--ICA is an effective method to eliminate uninformative wavelength variables, extract important spectral information, simplify prediction model and improve the performance of prediction model. The SSC model developed by UVE-ICA-LS-SVM is superior to that developed by PLS, PCA-LS-SVM or ICA-LS-SVM, and the coefficient of determination and root mean square error in calibration, validation and prediction sets were 0.978, 0.230%, 0.965, 0.301% and 0.967, 0.292%, respectively.

Improving Risk Adjustment for Mortality After Pediatric Cardiac Surgery: The UK PRAiS2 Model.

PubMed

Rogers, Libby; Brown, Katherine L; Franklin, Rodney C; Ambler, Gareth; Anderson, David; Barron, David J; Crowe, Sonya; English, Kate; Stickley, John; Tibby, Shane; Tsang, Victor; Utley, Martin; Witter, Thomas; Pagel, Christina

2017-07-01

Partial Risk Adjustment in Surgery (PRAiS), a risk model for 30-day mortality after children's heart surgery, has been used by the UK National Congenital Heart Disease Audit to report expected risk-adjusted survival since 2013. This study aimed to improve the model by incorporating additional comorbidity and diagnostic information. The model development dataset was all procedures performed between 2009 and 2014 in all UK and Ireland congenital cardiac centers. The outcome measure was death within each 30-day surgical episode. Model development followed an iterative process of clinical discussion and development and assessment of models using logistic regression under 25 × 5 cross-validation. Performance was measured using Akaike information criterion, the area under the receiver-operating characteristic curve (AUC), and calibration. The final model was assessed in an external 2014 to 2015 validation dataset. The development dataset comprised 21,838 30-day surgical episodes, with 539 deaths (mortality, 2.5%). The validation dataset comprised 4,207 episodes, with 97 deaths (mortality, 2.3%). The updated risk model included 15 procedural, 11 diagnostic, and 4 comorbidity groupings, and nonlinear functions of age and weight. Performance under cross-validation was: median AUC of 0.83 (range, 0.82 to 0.83), median calibration slope and intercept of 0.92 (range, 0.64 to 1.25) and -0.23 (range, -1.08 to 0.85) respectively. In the validation dataset, the AUC was 0.86 (95% confidence interval [CI], 0.82 to 0.89), and the calibration slope and intercept were 1.01 (95% CI, 0.83 to 1.18) and 0.11 (95% CI, -0.45 to 0.67), respectively, showing excellent performance. A more sophisticated PRAiS2 risk model for UK use was developed with additional comorbidity and diagnostic information, alongside age and weight as nonlinear variables. Copyright © 2017 The Authors. Published by Elsevier Inc. All rights reserved.

Precipitation Estimate Using NEXRAD Ground-Based Radar Images: Validation, Calibration and Spatial Analysis

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

Zhang, Xuesong

2012-12-17

Precipitation is an important input variable for hydrologic and ecological modeling and analysis. Next Generation Radar (NEXRAD) can provide precipitation products that cover most of the continental United States with a high resolution display of approximately 4 × 4 km2. Two major issues concerning the applications of NEXRAD data are (1) lack of a NEXRAD geo-processing and geo-referencing program and (2) bias correction of NEXRAD estimates. In this chapter, a geographic information system (GIS) based software that can automatically support processing of NEXRAD data for hydrologic and ecological models is presented. Some geostatistical approaches to calibrating NEXRAD data using rainmore » gauge data are introduced, and two case studies on evaluating accuracy of NEXRAD Multisensor Precipitation Estimator (MPE) and calibrating MPE with rain-gauge data are presented. The first case study examines the performance of MPE in mountainous region versus south plains and cold season versus warm season, as well as the effect of sub-grid variability and temporal scale on NEXRAD performance. From the results of the first case study, performance of MPE was found to be influenced by complex terrain, frozen precipitation, sub-grid variability, and temporal scale. Overall, the assessment of MPE indicates the importance of removing bias of the MPE precipitation product before its application, especially in the complex mountainous region. The second case study examines the performance of three MPE calibration methods using rain gauge observations in the Little River Experimental Watershed in Georgia. The comparison results show that no one method can perform better than the others in terms of all evaluation coefficients and for all time steps. For practical estimation of precipitation distribution, implementation of multiple methods to predict spatial precipitation is suggested.« less

Review of technological advancements in calibration systems for laser vision correction

NASA Astrophysics Data System (ADS)

Arba-Mosquera, Samuel; Vinciguerra, Paolo; Verma, Shwetabh

2018-02-01

Using PubMed and our internal database, we extensively reviewed the literature on the technological advancements in calibration systems, with a motive to present an account of the development history, and latest developments in calibration systems used in refractive surgery laser systems. As a second motive, we explored the clinical impact of the error introduced due to the roughness in ablation and its corresponding effect on system calibration. The inclusion criterion for this review was strict relevance to the clinical questions under research. The existing calibration methods, including various plastic models, are highly affected by various factors involved in refractive surgery, such as temperature, airflow, and hydration. Surface roughness plays an important role in accurate measurement of ablation performance on calibration materials. The ratio of ablation efficiency between the human cornea and calibration material is very critical and highly dependent on the laser beam characteristics and test conditions. Objective evaluation of the calibration data and corresponding adjustment of the laser systems at regular intervals are essential for the continuing success and further improvements in outcomes of laser vision correction procedures.

Measurement of non-sugar solids content in Chinese rice wine using near infrared spectroscopy combined with an efficient characteristic variables selection algorithm.

PubMed

Ouyang, Qin; Zhao, Jiewen; Chen, Quansheng

2015-01-01

The non-sugar solids (NSS) content is one of the most important nutrition indicators of Chinese rice wine. This study proposed a rapid method for the measurement of NSS content in Chinese rice wine using near infrared (NIR) spectroscopy. We also systemically studied the efficient spectral variables selection algorithms that have to go through modeling. A new algorithm of synergy interval partial least square with competitive adaptive reweighted sampling (Si-CARS-PLS) was proposed for modeling. The performance of the final model was back-evaluated using root mean square error of calibration (RMSEC) and correlation coefficient (Rc) in calibration set and similarly tested by mean square error of prediction (RMSEP) and correlation coefficient (Rp) in prediction set. The optimum model by Si-CARS-PLS algorithm was achieved when 7 PLS factors and 18 variables were included, and the results were as follows: Rc=0.95 and RMSEC=1.12 in the calibration set, Rp=0.95 and RMSEP=1.22 in the prediction set. In addition, Si-CARS-PLS algorithm showed its superiority when compared with the commonly used algorithms in multivariate calibration. This work demonstrated that NIR spectroscopy technique combined with a suitable multivariate calibration algorithm has a high potential in rapid measurement of NSS content in Chinese rice wine. Copyright © 2015 Elsevier B.V. All rights reserved.

Strategy for design NIR calibration sets based on process spectrum and model space: An innovative approach for process analytical technology.

PubMed

Cárdenas, V; Cordobés, M; Blanco, M; Alcalà, M

2015-10-10

The pharmaceutical industry is under stringent regulations on quality control of their products because is critical for both, productive process and consumer safety. According to the framework of "process analytical technology" (PAT), a complete understanding of the process and a stepwise monitoring of manufacturing are required. Near infrared spectroscopy (NIRS) combined with chemometrics have lately performed efficient, useful and robust for pharmaceutical analysis. One crucial step in developing effective NIRS-based methodologies is selecting an appropriate calibration set to construct models affording accurate predictions. In this work, we developed calibration models for a pharmaceutical formulation during its three manufacturing stages: blending, compaction and coating. A novel methodology is proposed for selecting the calibration set -"process spectrum"-, into which physical changes in the samples at each stage are algebraically incorporated. Also, we established a "model space" defined by Hotelling's T(2) and Q-residuals statistics for outlier identification - inside/outside the defined space - in order to select objectively the factors to be used in calibration set construction. The results obtained confirm the efficacy of the proposed methodology for stepwise pharmaceutical quality control, and the relevance of the study as a guideline for the implementation of this easy and fast methodology in the pharma industry. Copyright © 2015 Elsevier B.V. All rights reserved.

Aquarius Whole Range Calibration: Celestial Sky, Ocean, and Land Targets

NASA Technical Reports Server (NTRS)

Dinnat, Emmanuel P.; Le Vine, David M.; Bindlish, Rajat; Piepmeier, Jeffrey R.; Brown, Shannon T.

2014-01-01

Aquarius is a spaceborne instrument that uses L-band radiometers to monitor sea surface salinity globally. Other applications of its data over land and the cryosphere are being developed. Combining its measurements with existing and upcoming L-band sensors will allow for long term studies. For that purpose, the radiometers calibration is critical. Aquarius measurements are currently calibrated over the oceans. They have been found too cold at the low end (celestial sky) of the brightness temperature scale, and too warm at the warm end (land and ice). We assess the impact of the antenna pattern model on the biases and propose a correction. We re-calibrate Aquarius measurements using the corrected antenna pattern and measurements over the Sky and oceans. The performances of the new calibration are evaluated using measurements over well instrument land sites.

Using Lunar Observations for Calibration Stability and Data Continuity for SNPP VIIRS and MODIS Reflected Solar Bands

NASA Astrophysics Data System (ADS)

Xiong, X.; Stone, T. C.

2017-12-01

To meet objectives for assembling continuous Earth environmental data records from multiple satellite instruments, a key consideration is to assure consistent and stable sensor calibration across platforms and spanning mission lifetimes. Maintaining and verifying calibration stability in orbit is particularly challenging for reflected solar band (RSB) radiometer instruments, as options for stable references are limited. The Moon is used regularly as a calibration target, which has capabilities for long-term sensor performance monitoring and for use as a common reference for RSB sensor inter-calibration. Suomi NPP VIIRS has viewed the Moon nearly every month since launch, utilizing spacecraft roll maneuvers to acquire lunar observations within a small range of phase angles. The VIIRS Characterization Support Team (VCST) at NASA GSFC has processed the Moon images acquired by SNPP VIIRS into irradiance measurements for calibration purposes; however, the variations in the Moon's brightness still require normalizing the VIIRS lunar measurements using radiometric reference values generated by the USGS lunar calibration system, i.e. the ROLO model. Comparison of the lunar irradiance time series to the calibration f-factors derived from the VIIRS on-board solar diffuser system shows similar overall trends in sensor response, but also reveals residual geometric anomalies in the lunar model results. The excellent lunar radiometry achieved by SNPP VIIRS is actively being used to advance lunar model development at USGS. Both MODIS instruments also have viewed the Moon regularly since launch, providing a practical application of sensor inter-calibration using the Moon as a common reference. This paper discusses ongoing efforts aimed toward demonstrating and utilizing the full potential of lunar observations to support long-term calibration stability and consistency for SNPP VIIRS and MODIS, thus contributing to level-1B data quality assurance for continuity and monitoring global environmental changes.

Automatic Calibration of a Distributed Rainfall-Runoff Model, Using the Degree-Day Formulation for Snow Melting, Within DMIP2 Project

NASA Astrophysics Data System (ADS)

Frances, F.; Orozco, I.

2010-12-01

This work presents the assessment of the TETIS distributed hydrological model in mountain basins of the American and Carson rivers in Sierra Nevada (USA) at hourly time discretization, as part of the DMIP2 Project. In TETIS each cell of the spatial grid conceptualizes the water cycle using six tanks connected among them. The relationship between tanks depends on the case, although at the end in most situations, simple linear reservoirs and flow thresholds schemes are used with exceptional results (Vélez et al., 1999; Francés et al., 2002). In particular, within the snow tank, snow melting is based in this work on the simple degree-day method with spatial constant parameters. The TETIS model includes an automatic calibration module, based on the SCE-UA algorithm (Duan et al., 1992; Duan et al., 1994) and the model effective parameters are organized following a split structure, as presented by Francés and Benito (1995) and Francés et al. (2007). In this way, the calibration involves in TETIS up to 9 correction factors (CFs), which correct globally the different parameter maps instead of each parameter cell value, thus reducing drastically the number of variables to be calibrated. This strategy allows for a fast and agile modification in different hydrological processes preserving the spatial structure of each parameter map. With the snowmelt submodel, automatic model calibration was carried out in three steps, separating the calibration of rainfall-runoff and snowmelt parameters. In the first step, the automatic calibration of the CFs during the period 05/20/1990 to 07/31/1990 in the American River (without snow influence), gave a Nash-Sutcliffe Efficiency (NSE) index of 0.92. The calibration of the three degree-day parameters was done using all the SNOTEL stations in the American and Carson rivers. Finally, using previous calibrations as initial values, the complete calibration done in the Carson River for the period 10/01/1992 to 07/31/1993 gave a NSE index of 0.86. The temporal and spatial validation using five periods must be considered in both rivers excellent for discharges (NSEs higher than 0.76) and good for snow distribution (daily spatial coverage errors ranging from -10 to 27%). In conclusion, this work demonstrates: 1.- The viability of automatic calibration of distributed models, with the corresponding personal time saving and maximum exploitation of the available information. 2.- The good performance of the degree-day snowmelt formulation even at hourly time discretization, in spite of its simplicity.

Geometrical calibration of an AOTF hyper-spectral imaging system

NASA Astrophysics Data System (ADS)

Špiclin, Žiga; Katrašnik, Jaka; Bürmen, Miran; Pernuš, Franjo; Likar, Boštjan

2010-02-01

Optical aberrations present an important problem in optical measurements. Geometrical calibration of an imaging system is therefore of the utmost importance for achieving accurate optical measurements. In hyper-spectral imaging systems, the problem of optical aberrations is even more pronounced because optical aberrations are wavelength dependent. Geometrical calibration must therefore be performed over the entire spectral range of the hyper-spectral imaging system, which is usually far greater than that of the visible light spectrum. This problem is especially adverse in AOTF (Acousto- Optic Tunable Filter) hyper-spectral imaging systems, as the diffraction of light in AOTF filters is dependent on both wavelength and angle of incidence. Geometrical calibration of hyper-spectral imaging system was performed by stable caliber of known dimensions, which was imaged at different wavelengths over the entire spectral range. The acquired images were then automatically registered to the caliber model by both parametric and nonparametric transformation based on B-splines and by minimizing normalized correlation coefficient. The calibration method was tested on an AOTF hyper-spectral imaging system in the near infrared spectral range. The results indicated substantial wavelength dependent optical aberration that is especially pronounced in the spectral range closer to the infrared part of the spectrum. The calibration method was able to accurately characterize the aberrations and produce transformations for efficient sub-pixel geometrical calibration over the entire spectral range, finally yielding better spatial resolution of hyperspectral imaging system.

Wrong, but useful: regional species distribution models may not be improved by range-wide data under biased sampling.

PubMed

El-Gabbas, Ahmed; Dormann, Carsten F

2018-02-01

Species distribution modeling (SDM) is an essential method in ecology and conservation. SDMs are often calibrated within one country's borders, typically along a limited environmental gradient with biased and incomplete data, making the quality of these models questionable. In this study, we evaluated how adequate are national presence-only data for calibrating regional SDMs. We trained SDMs for Egyptian bat species at two different scales: only within Egypt and at a species-specific global extent. We used two modeling algorithms: Maxent and elastic net, both under the point-process modeling framework. For each modeling algorithm, we measured the congruence of the predictions of global and regional models for Egypt, assuming that the lower the congruence, the lower the appropriateness of the Egyptian dataset to describe the species' niche. We inspected the effect of incorporating predictions from global models as additional predictor ("prior") to regional models, and quantified the improvement in terms of AUC and the congruence between regional models run with and without priors. Moreover, we analyzed predictive performance improvements after correction for sampling bias at both scales. On average, predictions from global and regional models in Egypt only weakly concur. Collectively, the use of priors did not lead to much improvement: similar AUC and high congruence between regional models calibrated with and without priors. Correction for sampling bias led to higher model performance, whatever prior used, making the use of priors less pronounced. Under biased and incomplete sampling, the use of global bats data did not improve regional model performance. Without enough bias-free regional data, we cannot objectively identify the actual improvement of regional models after incorporating information from the global niche. However, we still believe in great potential for global model predictions to guide future surveys and improve regional sampling in data-poor regions.

Status of the national transonic facility

NASA Technical Reports Server (NTRS)

Mckinney, L. W.; Gloss, B. B.

1982-01-01

The National Transonic Facility at NASA Langley Research Center, scheduled for completion in July, 1982, is described with emphasis on model and instrumentation activities, calibration plans and some initial research plans. Performance capabilities include a Mach number range of 0.2-1.2, a pressure range of 1-9 atmospheres, and a temperature range of 77-350 K, which will produce a maximum Reynolds number of 120 million at a Mach number of 1.0, based on a 0.25 m chord. A comprehensive tunnel calibration program is planned, which will cover basic tunnel calibration, data qualities, and data comparisons with other facilites and flights.

Transport modeling of L- and H-mode discharges with LHCD on EAST

NASA Astrophysics Data System (ADS)

Li, M. H.; Ding, B. J.; Imbeaux, F.; Decker, J.; Zhang, X. J.; Kong, E. H.; Zhang, L.; Wei, W.; Shan, J. F.; Liu, F. K.; Wang, M.; Xu, H. D.; Yang, Y.; Peysson, Y.; Basiuk, V.; Artaud, J.-F.; Yuynh, P.; Wan, B. N.

2013-04-01

High-confinement (H-mode) discharges with lower hybrid current drive (LHCD) as the only heating source are obtained on EAST. In this paper, an empirical transport model of mixed Bohm/gyro-Bohm for electron and ion heat transport was first calibrated against a database of 3 L-mode shots on EAST. The electron and ion temperature profiles are well reproduced in the predictive modeling with the calibrated model coupled to the suite of codes CRONOS. CRONOS calculations with experimental profiles are also performed for electron power balance analysis. In addition, the time evolutions of LHCD are calculated by the C3PO/LUKE code involving current diffusion, and the results are compared with experimental observations.

Gradient nonlinearity calibration and correction for a compact, asymmetric magnetic resonance imaging gradient system

PubMed Central

Tao, S; Trzasko, J D; Gunter, J L; Weavers, P T; Shu, Y; Huston, J; Lee, S K; Tan, E T; Bernstein, M A

2017-01-01

Due to engineering limitations, the spatial encoding gradient fields in conventional magnetic resonance imaging cannot be perfectly linear and always contain higher-order, nonlinear components. If ignored during image reconstruction, gradient nonlinearity (GNL) manifests as image geometric distortion. Given an estimate of the GNL field, this distortion can be corrected to a degree proportional to the accuracy of the field estimate. The GNL of a gradient system is typically characterized using a spherical harmonic polynomial model with model coefficients obtained from electromagnetic simulation. Conventional whole-body gradient systems are symmetric in design; typically, only odd-order terms up to the 5th-order are required for GNL modeling. Recently, a high-performance, asymmetric gradient system was developed, which exhibits more complex GNL that requires higher-order terms including both odd- and even-orders for accurate modeling. This work characterizes the GNL of this system using an iterative calibration method and a fiducial phantom used in ADNI (Alzheimer’s Disease Neuroimaging Initiative). The phantom was scanned at different locations inside the 26-cm diameter-spherical-volume of this gradient, and the positions of fiducials in the phantom were estimated. An iterative calibration procedure was utilized to identify the model coefficients that minimize the mean-squared-error between the true fiducial positions and the positions estimated from images corrected using these coefficients. To examine the effect of higher-order and even-order terms, this calibration was performed using spherical harmonic polynomial of different orders up to the 10th-order including even- and odd-order terms, or odd-order only. The results showed that the model coefficients of this gradient can be successfully estimated. The residual root-mean-squared-error after correction using up to the 10th-order coefficients was reduced to 0.36 mm, yielding spatial accuracy comparable to conventional whole-body gradients. The even-order terms were necessary for accurate GNL modeling. In addition, the calibrated coefficients improved image geometric accuracy compared with the simulation-based coefficients. PMID:28033119

Calibration and Validation of a Finite ELement Model of THor-K Anthropomorphic Test Device for Aerospace Safety Applications

NASA Technical Reports Server (NTRS)

Putnam, J. B.; Unataroiu, C. D.; Somers, J. T.

2014-01-01

The THOR anthropomorphic test device (ATD) has been developed and continuously improved by the National Highway Traffic Safety Administration to provide automotive manufacturers an advanced tool that can be used to assess the injury risk of vehicle occupants in crash tests. Recently, a series of modifications were completed to improve the biofidelity of THOR ATD [1]. The updated THOR Modification Kit (THOR-K) ATD was employed at Wright-Patterson Air Base in 22 impact tests in three configurations: vertical, lateral, and spinal [2]. Although a computational finite element (FE) model of the THOR had been previously developed [3], updates to the model were needed to incorporate the recent changes in the modification kit. The main goal of this study was to develop and validate a FE model of the THOR-K ATD. The CAD drawings of the THOR-K ATD were reviewed and FE models were developed for the updated parts. For example, the head-skin geometry was found to change significantly, so its model was re-meshed (Fig. 1a). A protocol was developed to calibrate each component identified as key to the kinematic and kinetic response of the THOR-K head/neck ATD FE model (Fig. 1b). The available ATD tests were divided in two groups: a) calibration tests where the unknown material parameters of deformable parts (e.g., head skin, pelvis foam) were optimized to match the data and b) validation tests where the model response was only compared with test data by calculating their score using CORrelation and Analysis (CORA) rating system. Finally, the whole ATD model was validated under horizontal-, vertical-, and lateral-loading conditions against data recorded in the Wright Patterson tests [2]. Overall, the final THOR-K ATD model developed in this study is shown to respond similarly to the ATD in all validation tests. This good performance indicates that the optimization performed during calibration by using the CORA score as objective function is not test specific. Therefore confidence is provided in the ATD model for uses in predicting response in test conditions not performed in this study such those observed in the spacecraft landing. Comparison studies with ATD and human models may also be performed to contribute to future changes in THOR ATD design in an effort to improve its biofidelity, which has been traditionally based on post-mortem human subject testing and designer experience.

Spatial and Temporal Self-Calibration of a Hydroeconomic Model

NASA Astrophysics Data System (ADS)

Howitt, R. E.; Hansen, K. M.

2008-12-01

Hydroeconomic modeling of water systems where risk and reliability of water supply are of critical importance must address explicitly how to model water supply uncertainty. When large fluctuations in annual precipitation and significant variation in flows by location are present, a model which solves with perfect foresight of future water conditions may be inappropriate for some policy and research questions. We construct a simulation-optimization model with limited foresight of future water conditions using positive mathematical programming and self-calibration techniques. This limited foresight netflow (LFN) model signals the value of storing water for future use and reflects a more accurate economic value of water at key locations, given that future water conditions are unknown. Failure to explicitly model this uncertainty could lead to undervaluation of storage infrastructure and contractual mechanisms for managing water supply risk. A model based on sequentially updated information is more realistic, since water managers make annual storage decisions without knowledge of yet to be realized future water conditions. The LFN model runs historical hydrological conditions through the current configuration of the California water system to determine the economically efficient allocation of water under current economic conditions and infrastructure. The model utilizes current urban and agricultural demands, storage and conveyance infrastructure, and the state's hydrological history to indicate the scarcity value of water at key locations within the state. Further, the temporal calibration penalty functions vary by year type, reflecting agricultural water users' ability to alter cropping patterns in response to water conditions. The model employs techniques from positive mathematical programming (Howitt, 1995; Howitt, 1998; Cai and Wang, 2006) to generate penalty functions that are applied to deviations from observed data. The functions are applied to monthly flows across key nodes on the network and to annual carryover storage at ground and surface water storage facilities. To our knowledge, this is the first hydroeconomic model to perform spatial and temporal calibration simultaneously. The base for the LFN model is CALVIN, a hydroeconomic optimization model of the California water system developed at the University of California, Davis (Draper, et al. 2003). The LFN model, programmed in GAMS, is nonlinear, which permits incorporation of dynamic groundwater pumping costs that reflect head elevation. Hydropower production, also nonlinear in storage levels, could be added in the future. In this paper, we describe model implementation and performance over a sequence of water years drawn from the historical hydrologic record in California. Preliminary findings indicate that calibration occurs within acceptable limits and simulations replicate base case results well. Cai, X., and Wang, D. 2006. "Calibrating Holistic Water Resources-Economic Models." Journal of Water Resources Planning and Management November-December. Draper, A.J., M.W. Jenkins, K.W. Kirby, J.R. Lund, and R.E. Howitt. 2003. "Economic-Engineering Optimization for California Water Management." Journal of Water Resources Planning and Management 129(3):155-164. Howitt, R.E. 1995. "Positive Mathematical Programming." American Journal of Agricultural Economics 77:329-342. Howitt, R.E. 1998. "Self-Calibrating Network Flow Models." Working Paper, Department of Agricultural and Resource Economics, University of California, Davis. October 1998. class="ab'>

New Opportunities of Low-Cost Photogrammetry for Culture Heritage Preservation

NASA Astrophysics Data System (ADS)

Shults, R.

2017-05-01

In the paper, the questions of using the technologies of low-cost photogrammetry in combination with the additional capabilities of modern smartphones are considered. The research was carried out on the example of documenting the historical construction of the II World War - the Kiev Fortified Region. Brief historical information about the object of research is given. The possibilities of using modern smartphones as measuring instruments are considered. To get high-quality results, the camera of the smartphone was calibrated. The calibration results were used in the future to perform 3D modeling of defense facilities. Photographing of three defense structures in a different state: destroyed, partially destroyed and operating was carried out. Based on the results of photography using code targets, 3D object models were constructed. To verify the accuracy of the 3D modelling, control measurements of the lines between the code targets at the objects were performed. The obtained results are satisfying, and the technology considered in the paper can be recommended for use in performing archaeological and historical studies.

Comparison of Test and Finite Element Analysis for Two Full-Scale Helicopter Crash Tests

NASA Technical Reports Server (NTRS)

Annett, Martin S.; Horta,Lucas G.

2011-01-01

Finite element analyses have been performed for two full-scale crash tests of an MD-500 helicopter. The first crash test was conducted to evaluate the performance of a composite deployable energy absorber under combined flight loads. In the second crash test, the energy absorber was removed to establish the baseline loads. The use of an energy absorbing device reduced the impact acceleration levels by a factor of three. Accelerations and kinematic data collected from the crash tests were compared to analytical results. Details of the full-scale crash tests and development of the system-integrated finite element model are briefly described along with direct comparisons of acceleration magnitudes and durations for the first full-scale crash test. Because load levels were significantly different between tests, models developed for the purposes of predicting the overall system response with external energy absorbers were not adequate under more severe conditions seen in the second crash test. Relative error comparisons were inadequate to guide model calibration. A newly developed model calibration approach that includes uncertainty estimation, parameter sensitivity, impact shape orthogonality, and numerical optimization was used for the second full-scale crash test. The calibrated parameter set reduced 2-norm prediction error by 51% but did not improve impact shape orthogonality.

A monolithic 3D-0D coupled closed-loop model of the heart and the vascular system: Experiment-based parameter estimation for patient-specific cardiac mechanics.

PubMed

Hirschvogel, Marc; Bassilious, Marina; Jagschies, Lasse; Wildhirt, Stephen M; Gee, Michael W

2016-10-15

A model for patient-specific cardiac mechanics simulation is introduced, incorporating a 3-dimensional finite element model of the ventricular part of the heart, which is coupled to a reduced-order 0-dimensional closed-loop vascular system, heart valve, and atrial chamber model. The ventricles are modeled by a nonlinear orthotropic passive material law. The electrical activation is mimicked by a prescribed parameterized active stress acting along a generic muscle fiber orientation. Our activation function is constructed such that the start of ventricular contraction and relaxation as well as the active stress curve's slope are parameterized. The imaging-based patient-specific ventricular model is prestressed to low end-diastolic pressure to account for the imaged, stressed configuration. Visco-elastic Robin boundary conditions are applied to the heart base and the epicardium to account for the embedding surrounding. We treat the 3D solid-0D fluid interaction as a strongly coupled monolithic problem, which is consistently linearized with respect to 3D solid and 0D fluid model variables to allow for a Newton-type solution procedure. The resulting coupled linear system of equations is solved iteratively in every Newton step using 2  ×  2 physics-based block preconditioning. Furthermore, we present novel efficient strategies for calibrating active contractile and vascular resistance parameters to experimental left ventricular pressure and stroke volume data gained in porcine experiments. Two exemplary states of cardiovascular condition are considered, namely, after application of vasodilatory beta blockers (BETA) and after injection of vasoconstrictive phenylephrine (PHEN). The parameter calibration to the specific individual and cardiovascular state at hand is performed using a 2-stage nonlinear multilevel method that uses a low-fidelity heart model to compute a parameter correction for the high-fidelity model optimization problem. We discuss 2 different low-fidelity model choices with respect to their ability to augment the parameter optimization. Because the periodic state conditions on the model (active stress, vascular pressures, and fluxes) are a priori unknown and also dependent on the parameters to be calibrated (and vice versa), we perform parameter calibration and periodic state condition estimation simultaneously. After a couple of heart beats, the calibration algorithm converges to a settled, periodic state because of conservation of blood volume within the closed-loop circulatory system. The proposed model and multilevel calibration method are cost-efficient and allow for an efficient determination of a patient-specific in silico heart model that reproduces physiological observations very well. Such an individual and state accurate model is an important predictive tool in intervention planning, assist device engineering and other medical applications. Copyright © 2016 John Wiley & Sons, Ltd.

National Transonic Facility Wall Pressure Calibration Using Modern Design of Experiments (Invited)

NASA Technical Reports Server (NTRS)

Underwood, Pamela J.; Everhart, Joel L.; DeLoach, Richard

2001-01-01

The Modern Design of Experiments (MDOE) has been applied to wind tunnel testing at NASA Langley Research Center for several years. At Langley, MDOE has proven to be a useful and robust approach to aerodynamic testing that yields significant reductions in the cost and duration of experiments while still providing for the highest quality research results. This paper extends its application to include empty tunnel wall pressure calibrations. These calibrations are performed in support of wall interference corrections. This paper will present the experimental objectives, and the theoretical design process. To validate the tunnel-empty-calibration experiment design, preliminary response surface models calculated from previously acquired data are also presented. Finally, lessons learned and future wall interference applications of MDOE are discussed.

GEO-LEO reflectance band inter-comparison with BRDF and atmospheric scattering corrections

NASA Astrophysics Data System (ADS)

Chang, Tiejun; Xiong, Xiaoxiong Jack; Keller, Graziela; Wu, Xiangqian

2017-09-01

The inter-comparison of the reflective solar bands between the instruments onboard a geostationary orbit satellite and onboard a low Earth orbit satellite is very helpful to assess their calibration consistency. GOES-R was launched on November 19, 2016 and Himawari 8 was launched October 7, 2014. Unlike the previous GOES instruments, the Advanced Baseline Imager on GOES-16 (GOES-R became GOES-16 after November 29 when it reached orbit) and the Advanced Himawari Imager (AHI) on Himawari 8 have onboard calibrators for the reflective solar bands. The assessment of calibration is important for their product quality enhancement. MODIS and VIIRS, with their stringent calibration requirements and excellent on-orbit calibration performance, provide good references. The simultaneous nadir overpass (SNO) and ray-matching are widely used inter-comparison methods for reflective solar bands. In this work, the inter-comparisons are performed over a pseudo-invariant target. The use of stable and uniform calibration sites provides comparison with appropriate reflectance level, accurate adjustment for band spectral coverage difference, reduction of impact from pixel mismatching, and consistency of BRDF and atmospheric correction. The site in this work is a desert site in Australia (latitude -29.0 South; longitude 139.8 East). Due to the difference in solar and view angles, two corrections are applied to have comparable measurements. The first is the atmospheric scattering correction. The satellite sensor measurements are top of atmosphere reflectance. The scattering, especially Rayleigh scattering, should be removed allowing the ground reflectance to be derived. Secondly, the angle differences magnify the BRDF effect. The ground reflectance should be corrected to have comparable measurements. The atmospheric correction is performed using a vector version of the Second Simulation of a Satellite Signal in the Solar Spectrum modeling and BRDF correction is performed using a semi-empirical model. AHI band 1 (0.47μm) shows good matching with VIIRS band M3 with difference of 0.15%. AHI band 5 (1.69μm) shows largest difference in comparison with VIIRS M10.

Bayesian Monte Carlo and Maximum Likelihood Approach for ...

EPA Pesticide Factsheets

Model uncertainty estimation and risk assessment is essential to environmental management and informed decision making on pollution mitigation strategies. In this study, we apply a probabilistic methodology, which combines Bayesian Monte Carlo simulation and Maximum Likelihood estimation (BMCML) to calibrate a lake oxygen recovery model. We first derive an analytical solution of the differential equation governing lake-averaged oxygen dynamics as a function of time-variable wind speed. Statistical inferences on model parameters and predictive uncertainty are then drawn by Bayesian conditioning of the analytical solution on observed daily wind speed and oxygen concentration data obtained from an earlier study during two recovery periods on a eutrophic lake in upper state New York. The model is calibrated using oxygen recovery data for one year and statistical inferences were validated using recovery data for another year. Compared with essentially two-step, regression and optimization approach, the BMCML results are more comprehensive and performed relatively better in predicting the observed temporal dissolved oxygen levels (DO) in the lake. BMCML also produced comparable calibration and validation results with those obtained using popular Markov Chain Monte Carlo technique (MCMC) and is computationally simpler and easier to implement than the MCMC. Next, using the calibrated model, we derive an optimal relationship between liquid film-transfer coefficien

TOPEX/POSEIDON microwave radiometer performance and in-flight calibration

NASA Technical Reports Server (NTRS)

Ruf, C. S.; Keihm, Stephen J.; Subramanya, B.; Janssen, Michael A.

1994-01-01

Results of the in-flight calibration and performance evaluation campaign for the TOPEX/POSEIDON microwave radiometer (TMR) are presented. Intercomparisons are made between TMR and various sources of ground truth, including ground-based microwave water vapor radiometers, radiosondes, global climatological models, special sensor microwave imager data over the Amazon rain forest, and models of clear, calm, subpolar ocean regions. After correction for preflight errors in the processing of thermal/vacuum data, relative channel offsets in the open ocean TMR brightness temperatures were noted at the approximately = 1 K level for the three TMR frequencies. Larger absolute offsets of 6-9 K over the rain forest indicated a approximately = 5% gain error in the three channel calibrations. This was corrected by adjusting the antenna pattern correction (APC) algorithm. AS 10% scale error in the TMR path delay estimates, relative to coincident radiosondes, was corrected in part by the APC adjustment and in part by a 5% modification to the value assumed for the 22.235 FGHz water vapor line strength in the path delay retrieval algorithm. After all in-flight corrections to the calibration, TMR global retrieval accuracy for the wet tropospheric range correction is estimated at 1.1 cm root mean square (RMS) with consistent peformance under clear, cloudy, and windy conditions.

Atmospheric drag model calibrations for spacecraft lifetime prediction

NASA Technical Reports Server (NTRS)

Binebrink, A. L.; Radomski, M. S.; Samii, M. V.

1989-01-01

Although solar activity prediction uncertainty normally dominates decay prediction error budget for near-Earth spacecraft, the effect of drag force modeling errors for given levels of solar activity needs to be considered. Two atmospheric density models, the modified Harris-Priester model and the Jacchia-Roberts model, to reproduce the decay histories of the Solar Mesosphere Explorer (SME) and Solar Maximum Mission (SMM) spacecraft in the 490- to 540-kilometer altitude range were analyzed. Historical solar activity data were used in the input to the density computations. For each spacecraft and atmospheric model, a drag scaling adjustment factor was determined for a high-solar-activity year, such that the observed annual decay in the mean semimajor axis was reproduced by an averaged variation-of-parameters (VOP) orbit propagation. The SME (SMM) calibration was performed using calendar year 1983 (1982). The resulting calibration factors differ by 20 to 40 percent from the predictions of the prelaunch ballistic coefficients. The orbit propagations for each spacecraft were extended to the middle of 1988 using the calibrated drag models. For the Jaccia-Roberts density model, the observed decay in the mean semimajor axis of SME (SMM) over the 4.5-year (5.5-year) predictive period was reproduced to within 1.5 (4.4) percent. The corresponding figure for the Harris-Priester model was 8.6 (20.6) percent. Detailed results and conclusions regarding the importance of accurate drag force modeling for lifetime predictions are presented.

A Comparative Investigation of the Combined Effects of Pre-Processing, Wavelength Selection, and Regression Methods on Near-Infrared Calibration Model Performance.

PubMed

Wan, Jian; Chen, Yi-Chieh; Morris, A Julian; Thennadil, Suresh N

2017-07-01

Near-infrared (NIR) spectroscopy is being widely used in various fields ranging from pharmaceutics to the food industry for analyzing chemical and physical properties of the substances concerned. Its advantages over other analytical techniques include available physical interpretation of spectral data, nondestructive nature and high speed of measurements, and little or no need for sample preparation. The successful application of NIR spectroscopy relies on three main aspects: pre-processing of spectral data to eliminate nonlinear variations due to temperature, light scattering effects and many others, selection of those wavelengths that contribute useful information, and identification of suitable calibration models using linear/nonlinear regression . Several methods have been developed for each of these three aspects and many comparative studies of different methods exist for an individual aspect or some combinations. However, there is still a lack of comparative studies for the interactions among these three aspects, which can shed light on what role each aspect plays in the calibration and how to combine various methods of each aspect together to obtain the best calibration model. This paper aims to provide such a comparative study based on four benchmark data sets using three typical pre-processing methods, namely, orthogonal signal correction (OSC), extended multiplicative signal correction (EMSC) and optical path-length estimation and correction (OPLEC); two existing wavelength selection methods, namely, stepwise forward selection (SFS) and genetic algorithm optimization combined with partial least squares regression for spectral data (GAPLSSP); four popular regression methods, namely, partial least squares (PLS), least absolute shrinkage and selection operator (LASSO), least squares support vector machine (LS-SVM), and Gaussian process regression (GPR). The comparative study indicates that, in general, pre-processing of spectral data can play a significant role in the calibration while wavelength selection plays a marginal role and the combination of certain pre-processing, wavelength selection, and nonlinear regression methods can achieve superior performance over traditional linear regression-based calibration.

Performance optimization of CO 2 heat pump water heater

DOE PAGES

Nawaz, Kashif; Shen, Bo; Elatar, Ahmed; ...

2017-10-14

A preliminary analysis was conducted to analyze the performance of a heat pump water heater (HPWH) that uses CO 2 as the refrigerant. A model to predict the performance was developed and calibrated based on the experimental data for an existing HPWH using a CO 2 refrigerant. The calibrated model was then used to run a parametric analysis in which factors such as water supply temperature, water circulation rate, tank stratification, and condenser configuration were considered. The performance of a commercial CO 2 system was compared with that of a similar system using R-134a as the refrigerant. It was foundmore » that CO 2 HPWH performance was comparable to that of an R-134a HPWH, more so for a separated gas cooler configuration. For comparable performance, the compressor size and the tube-in-tube heat exchanger (condenser/gas cooler) size were compared for CO 2- and R-134a-based systems. Finally, the impact of the water circulation rate on the water temperature stratification in the tank, an essential requirement for higher performance for CO 2 HPWH systems was also investigated.« less

Performance optimization of CO 2 heat pump water heater

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

Nawaz, Kashif; Shen, Bo; Elatar, Ahmed

A preliminary analysis was conducted to analyze the performance of a heat pump water heater (HPWH) that uses CO 2 as the refrigerant. A model to predict the performance was developed and calibrated based on the experimental data for an existing HPWH using a CO 2 refrigerant. The calibrated model was then used to run a parametric analysis in which factors such as water supply temperature, water circulation rate, tank stratification, and condenser configuration were considered. The performance of a commercial CO 2 system was compared with that of a similar system using R-134a as the refrigerant. It was foundmore » that CO 2 HPWH performance was comparable to that of an R-134a HPWH, more so for a separated gas cooler configuration. For comparable performance, the compressor size and the tube-in-tube heat exchanger (condenser/gas cooler) size were compared for CO 2- and R-134a-based systems. Finally, the impact of the water circulation rate on the water temperature stratification in the tank, an essential requirement for higher performance for CO 2 HPWH systems was also investigated.« less

Calibrating Building Energy Models Using Supercomputer Trained Machine Learning Agents

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

Sanyal, Jibonananda; New, Joshua Ryan; Edwards, Richard

2014-01-01

Building Energy Modeling (BEM) is an approach to model the energy usage in buildings for design and retrofit purposes. EnergyPlus is the flagship Department of Energy software that performs BEM for different types of buildings. The input to EnergyPlus can often extend in the order of a few thousand parameters which have to be calibrated manually by an expert for realistic energy modeling. This makes it challenging and expensive thereby making building energy modeling unfeasible for smaller projects. In this paper, we describe the Autotune research which employs machine learning algorithms to generate agents for the different kinds of standardmore » reference buildings in the U.S. building stock. The parametric space and the variety of building locations and types make this a challenging computational problem necessitating the use of supercomputers. Millions of EnergyPlus simulations are run on supercomputers which are subsequently used to train machine learning algorithms to generate agents. These agents, once created, can then run in a fraction of the time thereby allowing cost-effective calibration of building models.« less

Calibration of a distributed routing rainfall-runoff model at four urban sites near Miami, Florida

USGS Publications Warehouse

Doyle, W. Harry; Miller, Jeffrey E.

1980-01-01

Urban stormwater data from four Miami, Fla. catchments were collected and compiled by the U.S. Geological Survey and were used for testing the applicability of deterministic modeling for characterizing stormwater flows from small land-use areas. A description of model calibration and verification is presented for: (1) A 40.8 acre single-family residential area, (2) a 58.3-acre highway area, (3) a 20.4-acre commercial area, and (4) a 14.7-acre multifamily residential area. Rainfall-runoff data for 80, 108, 114, and 52 storms at sites, 1, 2, 3, and 4, respectively, were collected, analyzed, and stored on direct-access files. Rainfall and runoff data for these storms (at 1-minute time intervals) were used in flow-modeling simulation analyses. A distributed routing Geological Survey rainfall-runoff model was used to determine rainfall excess and route overland and channel flows at each site. Optimization of soil-moisture- accounting and infiltration parameters was performed during the calibration phases. The results of this study showed that, with qualifications, an acceptable verification of the Geological Survey model can be achieved. (Kosco-USGS)

Evaluation of the CMODIS-measured radiance

NASA Astrophysics Data System (ADS)

Mao, Zhihua; Pan, Delu; Huang, Haiqing

2006-12-01

A Chinese Moderate Resolution Imaging Spectrometer (CMODIS) on "Shenzhou-3" spaceship was launched on March 25, 2002. CMODIS has 34 channels, with 30 visible and near-infrared channels and 4 infrared channels. The 30 channels are 20nm width with wavelength ranging from 403nm to 1023nm. The radiance calibration of CMODIS was finished in the laboratory measurements before it was launched and the laboratory calibration coefficients were used to calibrate the CMODIS raw data. Since none of on-board radiance absolute calibration devices including internal lamps system and calibration system which is based on solar reflectance and lunar irradiance were installed with the sensor, how about the accuracy of CMODIS-measured radiance is a key question for the remote sensing data processing and ocean applications. A new model was developed as a program to evaluate the accuracy of calibrated radiance measured by CMODIS at the top of the atmosphere (TOA). The program can compute the Rayleigh scattering radiance and aerosol scattering radiance together with the radiance component from the water-leaving radiance to deduce the total radiance at TOA under some similar observation conditions of CMODIS. Both the multiple-scattering effects and atmosphere absorbing effects are taken into account on the radiative transfer model to improve the accuracy of atmospheric scattering radiances. The model was used to deduce the spectral radiances at TOA and compared with the radiances measured by Sea-viewing Wide Field-of-view Sensor (SeaWiFS) to check the performance of the model, showing that the spectral radiances from the model with small differences from those of SeaWiFS. The spectral radiances of the model can be taken as reference values to evaluate the accuracy of CMODIS calibrated radiance. The relative differences of the two radiances are large from 16% to 300%, especially for CMODIS at the near-infrared channels with more than one time larger than those of the model. It is shown that the calibration coefficients from the laboratory measurements are not reliable and the radiance of CMODIS needs to be recalibrated before the data are used for oceanography applications. The results show that the model is effective in evaluating the CMODIS sensor and easily to be modified to evaluate other kinds of ocean color satellite sensors.

Comparative Assessment of a New Hydrological Modelling Approach for Prediction of Runoff in Gauged and Ungauged Basins, and Climate Change Impacts Assessment: A Case Study from Benin.

NASA Astrophysics Data System (ADS)

GABA, C. O. U.; Alamou, E.; Afouda, A.; Diekkrüger, B.

2016-12-01

Assessing water resources is still an important challenge especially in the context of climatic changes. Although numerous hydrological models exist, new approaches are still under investigation. In this context, we investigate a new modelling approach based on the Physics Principle of Least Action which was first applied to the Bétérou catchment in Benin and gave very good results. The study presents new hypotheses to go further in the model development with a view of widening its application. The improved version of the model MODHYPMA was applied to sixteen (16) subcatchments in Bénin, West Africa. Its performance was compared to two well-known lumped conceptual models, the GR4J and HBV models. The model was successfully calibrated and validated and showed a good performance in most catchments. The analysis revealed that the three models have similar performance and timing errors. But in contrary to other models, MODHYMA is subject to a less loss of performance from calibration to validation. In order to evaluate the usefulness of our model for the prediction of runoff in ungauged basins, model parameters were estimated from the physical catchments characteristics. We relied on statistical methods applied on calibrated model parameters to deduce relationships between parameters and physical catchments characteristics. These relationships were further tested and validated on gauged basins that were considered ungauged. This regionalization was also performed for GR4J model.We obtained NSE values greater than 0.7 for MODHYPMA while the NSE values for GR4J were inferior to 0.5. In the presented study, the effects of climate change on water resources in the Ouémé catchment at the outlet of Savè (about 23 500 km2) are quantified. The output of a regional climate model was used as input to the hydrological models.Computed within the GLOWA-IMPETUS project, the future climate projections (describing a rainfall reduction of up to 15%) are derived from the regional climate model REMO driven by the global ECHAM model.The results reveal a significant decrease in future water resources (of -66% to -53% for MODHYPMA and of -59% to -46% for GR4J) for the IPCC climate scenarios A1B and B1.

A calibration hierarchy for risk models was defined: from utopia to empirical data.

PubMed

Van Calster, Ben; Nieboer, Daan; Vergouwe, Yvonne; De Cock, Bavo; Pencina, Michael J; Steyerberg, Ewout W

2016-06-01

Calibrated risk models are vital for valid decision support. We define four levels of calibration and describe implications for model development and external validation of predictions. We present results based on simulated data sets. A common definition of calibration is "having an event rate of R% among patients with a predicted risk of R%," which we refer to as "moderate calibration." Weaker forms of calibration only require the average predicted risk (mean calibration) or the average prediction effects (weak calibration) to be correct. "Strong calibration" requires that the event rate equals the predicted risk for every covariate pattern. This implies that the model is fully correct for the validation setting. We argue that this is unrealistic: the model type may be incorrect, the linear predictor is only asymptotically unbiased, and all nonlinear and interaction effects should be correctly modeled. In addition, we prove that moderate calibration guarantees nonharmful decision making. Finally, results indicate that a flexible assessment of calibration in small validation data sets is problematic. Strong calibration is desirable for individualized decision support but unrealistic and counter productive by stimulating the development of overly complex models. Model development and external validation should focus on moderate calibration. Copyright © 2016 Elsevier Inc. All rights reserved.

Identifying dominant controls on hydrologic parameter transfer from gauged to ungauged catchments: a comparative hydrology approach

USGS Publications Warehouse

Singh, R.; Archfield, S.A.; Wagener, T.

2014-01-01

Daily streamflow information is critical for solving various hydrologic problems, though observations of continuous streamflow for model calibration are available at only a small fraction of the world’s rivers. One approach to estimate daily streamflow at an ungauged location is to transfer rainfall–runoff model parameters calibrated at a gauged (donor) catchment to an ungauged (receiver) catchment of interest. Central to this approach is the selection of a hydrologically similar donor. No single metric or set of metrics of hydrologic similarity have been demonstrated to consistently select a suitable donor catchment. We design an experiment to diagnose the dominant controls on successful hydrologic model parameter transfer. We calibrate a lumped rainfall–runoff model to 83 stream gauges across the United States. All locations are USGS reference gauges with minimal human influence. Parameter sets from the calibrated models are then transferred to each of the other catchments and the performance of the transferred parameters is assessed. This transfer experiment is carried out both at the scale of the entire US and then for six geographic regions. We use classification and regression tree (CART) analysis to determine the relationship between catchment similarity and performance of transferred parameters. Similarity is defined using physical/climatic catchment characteristics, as well as streamflow response characteristics (signatures such as baseflow index and runoff ratio). Across the entire US, successful parameter transfer is governed by similarity in elevation and climate, and high similarity in streamflow signatures. Controls vary for different geographic regions though. Geology followed by drainage, topography and climate constitute the dominant similarity metrics in forested eastern mountains and plateaus, whereas agricultural land use relates most strongly with successful parameter transfer in the humid plains.

Multi-Scale Soil Moisture Monitoring and Modeling at ARS Watersheds for NASA's Soil Moisture Active Passive (SMAP) Calibration/Validation Mission

NASA Astrophysics Data System (ADS)

Coopersmith, E. J.; Cosh, M. H.

2014-12-01

NASA's SMAP satellite, launched in November of 2014, produces estimates of average volumetric soil moisture at 3, 9, and 36-kilometer scales. The calibration and validation process of these estimates requires the generation of an identically-scaled soil moisture product from existing in-situ networks. This can be achieved via the integration of NLDAS precipitation data to perform calibration of models at each ­in-situ gauge. In turn, these models and the gauges' volumetric estimations are used to generate soil moisture estimates at a 500m scale throughout a given test watershed by leveraging, at each location, the gauge-calibrated models deemed most appropriate in terms of proximity, calibration efficacy, soil-textural similarity, and topography. Four ARS watersheds, located in Iowa, Oklahoma, Georgia, and Arizona are employed to demonstrate the utility of this approach. The South Fork watershed in Iowa represents the simplest case - the soil textures and topography are relative constants and the variability of soil moisture is simply tied to the spatial variability of precipitation. The Little Washita watershed in Oklahoma adds soil textural variability (but remains topographically simple), while the Little River watershed in Georgia incorporates topographic classification. Finally, the Walnut Gulch watershed in Arizona adds a dense precipitation network to be employed for even finer-scale modeling estimates. Results suggest RMSE values at or below the 4% volumetric standard adopted for the SMAP mission are attainable over the desired spatial scales via this integration of modeling efforts and existing in-situ networks.

True Zero-Training Brain-Computer Interfacing – An Online Study

PubMed Central

Kindermans, Pieter-Jan; Schreuder, Martijn; Schrauwen, Benjamin; Müller, Klaus-Robert; Tangermann, Michael

2014-01-01

Despite several approaches to realize subject-to-subject transfer of pre-trained classifiers, the full performance of a Brain-Computer Interface (BCI) for a novel user can only be reached by presenting the BCI system with data from the novel user. In typical state-of-the-art BCI systems with a supervised classifier, the labeled data is collected during a calibration recording, in which the user is asked to perform a specific task. Based on the known labels of this recording, the BCI's classifier can learn to decode the individual's brain signals. Unfortunately, this calibration recording consumes valuable time. Furthermore, it is unproductive with respect to the final BCI application, e.g. text entry. Therefore, the calibration period must be reduced to a minimum, which is especially important for patients with a limited concentration ability. The main contribution of this manuscript is an online study on unsupervised learning in an auditory event-related potential (ERP) paradigm. Our results demonstrate that the calibration recording can be bypassed by utilizing an unsupervised trained classifier, that is initialized randomly and updated during usage. Initially, the unsupervised classifier tends to make decoding mistakes, as the classifier might not have seen enough data to build a reliable model. Using a constant re-analysis of the previously spelled symbols, these initially misspelled symbols can be rectified posthoc when the classifier has learned to decode the signals. We compare the spelling performance of our unsupervised approach and of the unsupervised posthoc approach to the standard supervised calibration-based dogma for n = 10 healthy users. To assess the learning behavior of our approach, it is unsupervised trained from scratch three times per user. Even with the relatively low SNR of an auditory ERP paradigm, the results show that after a limited number of trials (30 trials), the unsupervised approach performs comparably to a classic supervised model. PMID:25068464

Impacts of the spatial extent of pollen-climate calibration-set on the absolute values, range and trends of reconstructed Holocene precipitation

NASA Astrophysics Data System (ADS)

Cao, X.; Tian, F.; Telford, R.; Ni, J.; Xu, Q.; Chen, F.; Liu, X.; Stebich, M.; Zhao, Y.; Herzschuh, U.

2017-12-01

Pollen-based quantitative reconstructions of past climate variables is a standard palaeoclimatic approach. Despite knowing that the spatial extent of the calibration-set affects the reconstruction result, guidance is lacking as to how to determine a suitable spatial extent of the pollen-climate calibration-set. In this study, past mean annual precipitation (Pann) during the Holocene (since 11.5 cal ka BP) is reconstructed repeatedly for pollen records from Qinghai Lake (36.7°N, 100.5°E; north-east Tibetan Plateau), Gonghai Lake (38.9°N, 112.2°E; north China) and Sihailongwan Lake (42.3°N, 126.6°E; north-east China) using calibration-sets of varying spatial extents extracted from the modern pollen dataset of China and Mongolia (2559 sampling sites and 168 pollen taxa in total). Results indicate that the spatial extent of the calibration-set has a strong impact on model performance, analogue quality and reconstruction diagnostics (absolute value, range, trend, optimum). Generally, these effects are stronger with the modern analogue technique (MAT) than with weighted averaging partial least squares (WA-PLS). With respect to fossil spectra from northern China, the spatial extent of calibration-sets should be restricted to ca. 1000 km in radius because small-scale calibration-sets (<800 km radius) will likely fail to include enough spatial variation in the modern pollen assemblages to reflect the temporal range shifts during the Holocene, while too broad a scale calibration-set (>1500 km radius) will include taxa with very different pollen-climate relationships. Based on our results we conclude that the optimal calibration-set should 1) cover a reasonably large spatial extent with an even distribution of modern pollen samples; 2) possess good model performance as indicated by cross-validation, high analogue quality, and excellent fit with the target fossil pollen spectra; 3) possess high taxonomic resolution, and 4) obey the modern and past distribution ranges of taxa inferred from palaeo-genetic and macrofossil studies.

Four years of Landsat-7 on-orbit geometric calibration and performance

USGS Publications Warehouse

Lee, D.S.; Storey, James C.; Choate, M.J.; Hayes, R.W.

2004-01-01

Unlike its predecessors, Landsat-7 has undergone regular geometric and radiometric performance monitoring and calibration since launch in April 1999. This ongoing activity, which includes issuing quarterly updates to calibration parameters, has generated a wealth of geometric performance data over the four-year on-orbit period of operations. A suite of geometric characterization (measurement and evaluation procedures) and calibration (procedures to derive improved estimates of instrument parameters) methods are employed by the Landsat-7 Image Assessment System to maintain the geometric calibration and to track specific aspects of geometric performance. These include geodetic accuracy, band-to-band registration accuracy, and image-to-image registration accuracy. These characterization and calibration activities maintain image product geometric accuracy at a high level - by monitoring performance to determine when calibration is necessary, generating new calibration parameters, and verifying that new parameters achieve desired improvements in accuracy. Landsat-7 continues to meet and exceed all geometric accuracy requirements, although aging components have begun to affect performance.

Analysis of calibration data for the uranium active neutron coincidence counting collar with attention to errors in the measured neutron coincidence rate

DOE PAGES

Croft, Stephen; Burr, Thomas Lee; Favalli, Andrea; ...

2015-12-10

We report that the declared linear density of 238U and 235U in fresh low enriched uranium light water reactor fuel assemblies can be verified for nuclear safeguards purposes using a neutron coincidence counter collar in passive and active mode, respectively. The active mode calibration of the Uranium Neutron Collar – Light water reactor fuel (UNCL) instrument is normally performed using a non-linear fitting technique. The fitting technique relates the measured neutron coincidence rate (the predictor) to the linear density of 235U (the response) in order to estimate model parameters of the nonlinear Padé equation, which traditionally is used to modelmore » the calibration data. Alternatively, following a simple data transformation, the fitting can also be performed using standard linear fitting methods. This paper compares performance of the nonlinear technique to the linear technique, using a range of possible error variance magnitudes in the measured neutron coincidence rate. We develop the required formalism and then apply the traditional (nonlinear) and alternative approaches (linear) to the same experimental and corresponding simulated representative datasets. Lastly, we find that, in this context, because of the magnitude of the errors in the predictor, it is preferable not to transform to a linear model, and it is preferable not to adjust for the errors in the predictor when inferring the model parameters« less

Dynamic Stability Instrumentation System (DSIS). Volume 3; User Manual

NASA Technical Reports Server (NTRS)

Daniels, Taumi S.; Boyden, Richmond P.; Dress, David A.; Jordan, Thomas L.

1996-01-01

The paper is an operating manual for the Dynamic Stability Instrumentation System in specific NASA Langley wind tunnels. The instrumentation system performs either a synchronous demodulation or a Fast Fourier Transform on dynamic balance strain gage signals, and ultimately computes aerodynamic coefficients. The dynamic balance converts sting motor rotation into pitch or yaw plane or roll axis oscillation, with timing information provided by a shaft encoder. Additional instruments control model attitude and balance temperature and monitor sting vibrations. Other instruments perform self-calibration and diagnostics. Procedures for conducting calibrations and wind-off and wind-on tests are listed.

Wavelet analysis techniques applied to removing varying spectroscopic background in calibration model for pear sugar content

NASA Astrophysics Data System (ADS)

Liu, Yande; Ying, Yibin; Lu, Huishan; Fu, Xiaping

2005-11-01

A new method is proposed to eliminate the varying background and noise simultaneously for multivariate calibration of Fourier transform near infrared (FT-NIR) spectral signals. An ideal spectrum signal prototype was constructed based on the FT-NIR spectrum of fruit sugar content measurement. The performances of wavelet based threshold de-noising approaches via different combinations of wavelet base functions were compared. Three families of wavelet base function (Daubechies, Symlets and Coiflets) were applied to estimate the performance of those wavelet bases and threshold selection rules by a series of experiments. The experimental results show that the best de-noising performance is reached via the combinations of Daubechies 4 or Symlet 4 wavelet base function. Based on the optimization parameter, wavelet regression models for sugar content of pear were also developed and result in a smaller prediction error than a traditional Partial Least Squares Regression (PLSR) mode.

Calibration for the SAGE III/EOS instruments

NASA Technical Reports Server (NTRS)

Chu, W. P.; Mccormick, M. P.; Zawodny, J. M.; Mcmaster, L. R.

1991-01-01

The calibration plan for the SAGE III instruments for maintaining instrument performance during the Earth Observing System (EOS) mission lifetime is described. The SAGE III calibration plan consists of detailed preflight and inflight calibration on the instrument performance together with the correlative measurement program to validate the data products from the inverted satellite measurements. Since the measurement technique is primarily solar/lunar occultation, the instrument will be self-calibrating by using the sun as the calibration source during the routine operation of the instrument in flight. The instrument is designed to perform radiometric calibration of throughput, spectral, and spatial response in flight during routine operation. Spectral calibration can be performed in-flight from observation of the solar Fraunhofer lines within the spectral region from 290 to 1030 nm wavelength.

Performance of a distributed semi-conceptual hydrological model under tropical watershed conditions

USDA-ARS?s Scientific Manuscript database

Many hydrologic models have been developed to help manage natural resources all over the world. Nevertheless, most models have presented a high complexity in terms of data base requirements, as well as, many calibration parameters. This has resulted in serious difficulties to application in catchmen...

Dynamic Bayesian Network Modeling of Game Based Diagnostic Assessments. CRESST Report 837

ERIC Educational Resources Information Center

Levy, Roy

2014-01-01

Digital games offer an appealing environment for assessing student proficiencies, including skills and misconceptions in a diagnostic setting. This paper proposes a dynamic Bayesian network modeling approach for observations of student performance from an educational video game. A Bayesian approach to model construction, calibration, and use in…

Modeling the Psychometric Properties of Complex Performance Assessment Tasks Using Confirmatory Factor Analysis: A Multistage Model for Calibrating Tasks

ERIC Educational Resources Information Center

Kahraman, Nilufer; De Champlain, Andre; Raymond, Mark

2012-01-01

Item-level information, such as difficulty and discrimination are invaluable to the test assembly, equating, and scoring practices. Estimating these parameters within the context of large-scale performance assessments is often hindered by the use of unbalanced designs for assigning examinees to tasks and raters because such designs result in very…

Prediction of hydrographs and flow-duration curves in almost ungauged catchments: Which runoff measurements are most informative for model calibration?

NASA Astrophysics Data System (ADS)

Pool, Sandra; Viviroli, Daniel; Seibert, Jan

2017-11-01

Applications of runoff models usually rely on long and continuous runoff time series for model calibration. However, many catchments around the world are ungauged and estimating runoff for these catchments is challenging. One approach is to perform a few runoff measurements in a previously fully ungauged catchment and to constrain a runoff model by these measurements. In this study we investigated the value of such individual runoff measurements when taken at strategic points in time for applying a bucket-type runoff model (HBV) in ungauged catchments. Based on the assumption that a limited number of runoff measurements can be taken, we sought the optimal sampling strategy (i.e. when to measure the streamflow) to obtain the most informative data for constraining the runoff model. We used twenty gauged catchments across the eastern US, made the assumption that these catchments were ungauged, and applied different runoff sampling strategies. All tested strategies consisted of twelve runoff measurements within one year and ranged from simply using monthly flow maxima to a more complex selection of observation times. In each case the twelve runoff measurements were used to select 100 best parameter sets using a Monte Carlo calibration approach. Runoff simulations using these 'informed' parameter sets were then evaluated for an independent validation period in terms of the Nash-Sutcliffe efficiency of the hydrograph and the mean absolute relative error of the flow-duration curve. Model performance measures were normalized by relating them to an upper and a lower benchmark representing a well-informed and an uninformed model calibration. The hydrographs were best simulated with strategies including high runoff magnitudes as opposed to the flow-duration curves that were generally better estimated with strategies that captured low and mean flows. The choice of a sampling strategy covering the full range of runoff magnitudes enabled hydrograph and flow-duration curve simulations close to a well-informed model calibration. The differences among such strategies covering the full range of runoff magnitudes were small indicating that the exact choice of a strategy might be less crucial. Our study corroborates the information value of a small number of strategically selected runoff measurements for simulating runoff with a bucket-type runoff model in almost ungauged catchments.

Ecosystem biogeochemistry model parameterization: Do more flux data result in a better model in predicting carbon flux?

DOE PAGES

Zhu, Qing; Zhuang, Qianlai

2015-12-21

Reliability of terrestrial ecosystem models highly depends on the quantity and quality of thedata that have been used to calibrate the models. Nowadays, in situ observations of carbon fluxes areabundant. However, the knowledge of how much data (data length) and which subset of the time seriesdata (data period) should be used to effectively calibrate the model is still lacking. This study uses theAmeriFlux carbon flux data to parameterize the Terrestrial Ecosystem Model (TEM) with an adjoint-baseddata assimilation technique for various ecosystem types. Parameterization experiments are thus conductedto explore the impact of both data length and data period on the uncertaintymore » reduction of the posteriormodel parameters and the quantification of site and regional carbon dynamics. We find that: the modelis better constrained when it uses two-year data comparing to using one-year data. Further, two-year datais sufficient in calibrating TEM’s carbon dynamics, since using three-year data could only marginallyimprove the model performance at our study sites; the model is better constrained with the data thathave a higher‘‘climate variability’’than that having a lower one. The climate variability is used to measurethe overall possibility of the ecosystem to experience all climatic conditions including drought and extremeair temperatures and radiation; the U.S. regional simulations indicate that the effect of calibration datalength on carbon dynamics is amplified at regional and temporal scales, leading to large discrepanciesamong different parameterization experiments, especially in July and August. Our findings areconditioned on the specific model we used and the calibration sites we selected. The optimal calibrationdata length may not be suitable for other models. However, this study demonstrates that there may exist athreshold for calibration data length and simply using more data would not guarantee a better modelparameterization and prediction. More importantly, climate variability might be an effective indicator ofinformation within the data, which could help data selection for model parameterization. As a result, we believe ourfindings will benefit the ecosystem modeling community in using multiple-year data to improve modelpredictability.« less

Ecosystem biogeochemistry model parameterization: Do more flux data result in a better model in predicting carbon flux?

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

Zhu, Qing; Zhuang, Qianlai

Reliability of terrestrial ecosystem models highly depends on the quantity and quality of thedata that have been used to calibrate the models. Nowadays, in situ observations of carbon fluxes areabundant. However, the knowledge of how much data (data length) and which subset of the time seriesdata (data period) should be used to effectively calibrate the model is still lacking. This study uses theAmeriFlux carbon flux data to parameterize the Terrestrial Ecosystem Model (TEM) with an adjoint-baseddata assimilation technique for various ecosystem types. Parameterization experiments are thus conductedto explore the impact of both data length and data period on the uncertaintymore » reduction of the posteriormodel parameters and the quantification of site and regional carbon dynamics. We find that: the modelis better constrained when it uses two-year data comparing to using one-year data. Further, two-year datais sufficient in calibrating TEM’s carbon dynamics, since using three-year data could only marginallyimprove the model performance at our study sites; the model is better constrained with the data thathave a higher‘‘climate variability’’than that having a lower one. The climate variability is used to measurethe overall possibility of the ecosystem to experience all climatic conditions including drought and extremeair temperatures and radiation; the U.S. regional simulations indicate that the effect of calibration datalength on carbon dynamics is amplified at regional and temporal scales, leading to large discrepanciesamong different parameterization experiments, especially in July and August. Our findings areconditioned on the specific model we used and the calibration sites we selected. The optimal calibrationdata length may not be suitable for other models. However, this study demonstrates that there may exist athreshold for calibration data length and simply using more data would not guarantee a better modelparameterization and prediction. More importantly, climate variability might be an effective indicator ofinformation within the data, which could help data selection for model parameterization. As a result, we believe ourfindings will benefit the ecosystem modeling community in using multiple-year data to improve modelpredictability.« less

Parallel computing for automated model calibration

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

Burke, John S.; Danielson, Gary R.; Schulz, Douglas A.

2002-07-29

Natural resources model calibration is a significant burden on computing and staff resources in modeling efforts. Most assessments must consider multiple calibration objectives (for example magnitude and timing of stream flow peak). An automated calibration process that allows real time updating of data/models, allowing scientists to focus effort on improving models is needed. We are in the process of building a fully featured multi objective calibration tool capable of processing multiple models cheaply and efficiently using null cycle computing. Our parallel processing and calibration software routines have been generically, but our focus has been on natural resources model calibration. Somore » far, the natural resources models have been friendly to parallel calibration efforts in that they require no inter-process communication, only need a small amount of input data and only output a small amount of statistical information for each calibration run. A typical auto calibration run might involve running a model 10,000 times with a variety of input parameters and summary statistical output. In the past model calibration has been done against individual models for each data set. The individual model runs are relatively fast, ranging from seconds to minutes. The process was run on a single computer using a simple iterative process. We have completed two Auto Calibration prototypes and are currently designing a more feature rich tool. Our prototypes have focused on running the calibration in a distributed computing cross platform environment. They allow incorporation of?smart? calibration parameter generation (using artificial intelligence processing techniques). Null cycle computing similar to SETI@Home has also been a focus of our efforts. This paper details the design of the latest prototype and discusses our plans for the next revision of the software.« less

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.

Calibration and error analysis of metal-oxide-semiconductor field-effect transistor dosimeters for computed tomography radiation dosimetry.

PubMed

Trattner, Sigal; Prinsen, Peter; Wiegert, Jens; Gerland, Elazar-Lars; Shefer, Efrat; Morton, Tom; Thompson, Carla M; Yagil, Yoad; Cheng, Bin; Jambawalikar, Sachin; Al-Senan, Rani; Amurao, Maxwell; Halliburton, Sandra S; Einstein, Andrew J

2017-12-01

Metal-oxide-semiconductor field-effect transistors (MOSFETs) serve as a helpful tool for organ radiation dosimetry and their use has grown in computed tomography (CT). While different approaches have been used for MOSFET calibration, those using the commonly available 100 mm pencil ionization chamber have not incorporated measurements performed throughout its length, and moreover, no previous work has rigorously evaluated the multiple sources of error involved in MOSFET calibration. In this paper, we propose a new MOSFET calibration approach to translate MOSFET voltage measurements into absorbed dose from CT, based on serial measurements performed throughout the length of a 100-mm ionization chamber, and perform an analysis of the errors of MOSFET voltage measurements and four sources of error in calibration. MOSFET calibration was performed at two sites, to determine single calibration factors for tube potentials of 80, 100, and 120 kVp, using a 100-mm-long pencil ion chamber and a cylindrical computed tomography dose index (CTDI) phantom of 32 cm diameter. The dose profile along the 100-mm ion chamber axis was sampled in 5 mm intervals by nine MOSFETs in the nine holes of the CTDI phantom. Variance of the absorbed dose was modeled as a sum of the MOSFET voltage measurement variance and the calibration factor variance, the latter being comprised of three main subcomponents: ionization chamber reading variance, MOSFET-to-MOSFET variation and a contribution related to the fact that the average calibration factor of a few MOSFETs was used as an estimate for the average value of all MOSFETs. MOSFET voltage measurement error was estimated based on sets of repeated measurements. The calibration factor overall voltage measurement error was calculated from the above analysis. Calibration factors determined were close to those reported in the literature and by the manufacturer (~3 mV/mGy), ranging from 2.87 to 3.13 mV/mGy. The error σ V of a MOSFET voltage measurement was shown to be proportional to the square root of the voltage V: σV=cV where c = 0.11 mV. A main contributor to the error in the calibration factor was the ionization chamber reading error with 5% error. The usage of a single calibration factor for all MOSFETs introduced an additional error of about 5-7%, depending on the number of MOSFETs that were used to determine the single calibration factor. The expected overall error in a high-dose region (~30 mGy) was estimated to be about 8%, compared to 6% when an individual MOSFET calibration was performed. For a low-dose region (~3 mGy), these values were 13% and 12%. A MOSFET calibration method was developed using a 100-mm pencil ion chamber and a CTDI phantom, accompanied by an absorbed dose error analysis reflecting multiple sources of measurement error. When using a single calibration factor, per tube potential, for different MOSFETs, only a small error was introduced into absorbed dose determinations, thus supporting the use of a single calibration factor for experiments involving many MOSFETs, such as those required to accurately estimate radiation effective dose. © 2017 American Association of Physicists in Medicine.

Are our dynamic water quality models too complex? A comparison of a new parsimonious phosphorus model, SimplyP, and INCA-P

NASA Astrophysics Data System (ADS)

Jackson-Blake, L. A.; Sample, J. E.; Wade, A. J.; Helliwell, R. C.; Skeffington, R. A.

2017-07-01

Catchment-scale water quality models are increasingly popular tools for exploring the potential effects of land management, land use change and climate change on water quality. However, the dynamic, catchment-scale nutrient models in common usage are complex, with many uncertain parameters requiring calibration, limiting their usability and robustness. A key question is whether this complexity is justified. To explore this, we developed a parsimonious phosphorus model, SimplyP, incorporating a rainfall-runoff model and a biogeochemical model able to simulate daily streamflow, suspended sediment, and particulate and dissolved phosphorus dynamics. The model's complexity was compared to one popular nutrient model, INCA-P, and the performance of the two models was compared in a small rural catchment in northeast Scotland. For three land use classes, less than six SimplyP parameters must be determined through calibration, the rest may be based on measurements, while INCA-P has around 40 unmeasurable parameters. Despite substantially simpler process-representation, SimplyP performed comparably to INCA-P in both calibration and validation and produced similar long-term projections in response to changes in land management. Results support the hypothesis that INCA-P is overly complex for the study catchment. We hope our findings will help prompt wider model comparison exercises, as well as debate among the water quality modeling community as to whether today's models are fit for purpose. Simpler models such as SimplyP have the potential to be useful management and research tools, building blocks for future model development (prototype code is freely available), or benchmarks against which more complex models could be evaluated.

Regionalisation of a distributed method for flood quantiles estimation: Revaluation of local calibration hypothesis to enhance the spatial structure of the optimised parameter

NASA Astrophysics Data System (ADS)

Odry, Jean; Arnaud, Patrick

2016-04-01

The SHYREG method (Aubert et al., 2014) associates a stochastic rainfall generator and a rainfall-runoff model to produce rainfall and flood quantiles on a 1 km2 mesh covering the whole French territory. The rainfall generator is based on the description of rainy events by descriptive variables following probability distributions and is characterised by a high stability. This stochastic generator is fully regionalised, and the rainfall-runoff transformation is calibrated with a single parameter. Thanks to the stability of the approach, calibration can be performed against only flood quantiles associated with observated frequencies which can be extracted from relatively short time series. The aggregation of SHYREG flood quantiles to the catchment scale is performed using an areal reduction factor technique unique on the whole territory. Past studies demonstrated the accuracy of SHYREG flood quantiles estimation for catchments where flow data are available (Arnaud et al., 2015). Nevertheless, the parameter of the rainfall-runoff model is independently calibrated for each target catchment. As a consequence, this parameter plays a corrective role and compensates approximations and modelling errors which makes difficult to identify its proper spatial pattern. It is an inherent objective of the SHYREG approach to be completely regionalised in order to provide a complete and accurate flood quantiles database throughout France. Consequently, it appears necessary to identify the model configuration in which the calibrated parameter could be regionalised with acceptable performances. The revaluation of some of the method hypothesis is a necessary step before the regionalisation. Especially the inclusion or the modification of the spatial variability of imposed parameters (like production and transfer reservoir size, base flow addition and quantiles aggregation function) should lead to more realistic values of the only calibrated parameter. The objective of the work presented here is to develop a SHYREG evaluation scheme focusing on both local and regional performances. Indeed, it is necessary to maintain the accuracy of at site flood quantiles estimation while identifying a configuration leading to a satisfactory spatial pattern of the calibrated parameter. This ability to be regionalised can be appraised by the association of common regionalisation techniques and split sample validation tests on a set of around 1,500 catchments representing the whole diversity of France physiography. Also, the presence of many nested catchments and a size-based split sample validation make possible to assess the relevance of the calibrated parameter spatial structure inside the largest catchments. The application of this multi-objective evaluation leads to the selection of a version of SHYREG more suitable for regionalisation. References: Arnaud, P., Cantet, P., Aubert, Y., 2015. Relevance of an at-site flood frequency analysis method for extreme events based on stochastic simulation of hourly rainfall. Hydrological Sciences Journal: on press. DOI:10.1080/02626667.2014.965174 Aubert, Y., Arnaud, P., Ribstein, P., Fine, J.A., 2014. The SHYREG flow method-application to 1605 basins in metropolitan France. Hydrological Sciences Journal, 59(5): 993-1005. DOI:10.1080/02626667.2014.902061

Monte Carlo simulation of gamma-ray interactions in an over-square high-purity germanium detector for in-vivo measurements

NASA Astrophysics Data System (ADS)

Saizu, Mirela Angela

2016-09-01

The developments of high-purity germanium detectors match very well the requirements of the in-vivo human body measurements regarding the gamma energy ranges of the radionuclides intended to be measured, the shape of the extended radioactive sources, and the measurement geometries. The Whole Body Counter (WBC) from IFIN-HH is based on an “over-square” high-purity germanium detector (HPGe) to perform accurate measurements of the incorporated radionuclides emitting X and gamma rays in the energy range of 10 keV-1500 keV, under conditions of good shielding, suitable collimation, and calibration. As an alternative to the experimental efficiency calibration method consisting of using reference calibration sources with gamma energy lines that cover all the considered energy range, it is proposed to use the Monte Carlo method for the efficiency calibration of the WBC using the radiation transport code MCNP5. The HPGe detector was modelled and the gamma energy lines of 241Am, 57Co, 133Ba, 137Cs, 60Co, and 152Eu were simulated in order to obtain the virtual efficiency calibration curve of the WBC. The Monte Carlo method was validated by comparing the simulated results with the experimental measurements using point-like sources. For their optimum matching, the impact of the variation of the front dead layer thickness and of the detector photon absorbing layers materials on the HPGe detector efficiency was studied, and the detector’s model was refined. In order to perform the WBC efficiency calibration for realistic people monitoring, more numerical calculations were generated simulating extended sources of specific shape according to the standard man characteristics.

Improving gross count gamma-ray logging in uranium mining with the NGRS probe

NASA Astrophysics Data System (ADS)

Carasco, C.; Pérot, B.; Ma, J.-L.; Toubon, H.; Dubille-Auchère, A.

2018-01-01

AREVA Mines and the Nuclear Measurement Laboratory of CEA Cadarache are collaborating to improve the sensitivity and precision of uranium concentration measurement by means of gamma ray logging. The determination of uranium concentration in boreholes is performed with the Natural Gamma Ray Sonde (NGRS) based on a NaI(Tl) scintillation detector. The total gamma count rate is converted into uranium concentration using a calibration coefficient measured in concrete blocks with known uranium concentration in the AREVA Mines calibration facility located in Bessines, France. Until now, to take into account gamma attenuation in a variety of boreholes diameters, tubing materials, diameters and thicknesses, filling fluid densities and compositions, a semi-empirical formula was used to correct the calibration coefficient measured in Bessines facility. In this work, we propose to use Monte Carlo simulations to improve gamma attenuation corrections. To this purpose, the NGRS probe and the calibration measurements in the standard concrete blocks have been modeled with MCNP computer code. The calibration coefficient determined by simulation, 5.3 s-1.ppmU-1 ± 10%, is in good agreement with the one measured in Bessines, 5.2 s-1.ppmU-1. Based on the validated MCNP model, several parametric studies have been performed. For instance, the rock density and chemical composition proved to have a limited impact on the calibration coefficient. However, gamma self-absorption in uranium leads to a nonlinear relationship between count rate and uranium concentration beyond approximately 1% of uranium weight fraction, the underestimation of the uranium content reaching more than a factor 2.5 for a 50 % uranium weight fraction. Next steps will concern parametric studies with different tubing materials, diameters and thicknesses, as well as different borehole filling fluids representative of real measurement conditions.

The MeqTrees software system and its use for third-generation calibration of radio interferometers

NASA Astrophysics Data System (ADS)

Noordam, J. E.; Smirnov, O. M.

2010-12-01

Context. The formulation of the radio interferometer measurement equation (RIME) for a generic radio telescope by Hamaker et al. has provided us with an elegant mathematical apparatus for better understanding, simulation and calibration of existing and future instruments. The calibration of the new radio telescopes (LOFAR, SKA) would be unthinkable without the RIME formalism, and new software to exploit it. Aims: The MeqTrees software system is designed to implement numerical models, and to solve for arbitrary subsets of their parameters. It may be applied to many problems, but was originally geared towards implementing Measurement Equations in radio astronomy for the purposes of simulation and calibration. The technical goal of MeqTrees is to provide a tool for rapid implementation of such models, while offering performance comparable to hand-written code. We are also pursuing the wider goal of increasing the rate of evolution of radio astronomical software, by offering a tool that facilitates rapid experimentation, and exchange of ideas (and scripts). Methods: MeqTrees is implemented as a Python-based front-end called the meqbrowser, and an efficient (C++-based) computational back-end called the meqserver. Numerical models are defined on the front-end via a Python-based Tree Definition Language (TDL), then rapidly executed on the back-end. The use of TDL facilitates an extremely short turn-around time (hours rather than weeks or months) for experimentation with new ideas. This is also helped by unprecedented visualization capabilities for all final and intermediate results. A flexible data model and a number of important optimizations in the back-end ensures that the numerical performance is comparable to that of hand-written code. Results: MeqTrees is already widely used as the simulation tool for new instruments (LOFAR, SKA) and technologies (focal plane arrays). It has demonstrated that it can achieve a noise-limited dynamic range in excess of a million, on WSRT data. It is the only package that is specifically designed to handle what we propose to call third-generation calibration (3GC), which is needed for the new generation of giant radio telescopes, but can also improve the calibration of existing instruments.

Calibration of Airframe and Occupant Models for Two Full-Scale Rotorcraft Crash Tests

NASA Technical Reports Server (NTRS)

Annett, Martin S.; Horta, Lucas G.; Polanco, Michael A.

2012-01-01

Two full-scale crash tests of an MD-500 helicopter were conducted in 2009 and 2010 at NASA Langley's Landing and Impact Research Facility in support of NASA s Subsonic Rotary Wing Crashworthiness Project. The first crash test was conducted to evaluate the performance of an externally mounted composite deployable energy absorber under combined impact conditions. In the second crash test, the energy absorber was removed to establish baseline loads that are regarded as severe but survivable. Accelerations and kinematic data collected from the crash tests were compared to a system integrated finite element model of the test article. Results from 19 accelerometers placed throughout the airframe were compared to finite element model responses. The model developed for the purposes of predicting acceleration responses from the first crash test was inadequate when evaluating more severe conditions seen in the second crash test. A newly developed model calibration approach that includes uncertainty estimation, parameter sensitivity, impact shape orthogonality, and numerical optimization was used to calibrate model results for the second full-scale crash test. This combination of heuristic and quantitative methods was used to identify modeling deficiencies, evaluate parameter importance, and propose required model changes. It is shown that the multi-dimensional calibration techniques presented here are particularly effective in identifying model adequacy. Acceleration results for the calibrated model were compared to test results and the original model results. There was a noticeable improvement in the pilot and co-pilot region, a slight improvement in the occupant model response, and an over-stiffening effect in the passenger region. This approach should be adopted early on, in combination with the building-block approaches that are customarily used, for model development and test planning guidance. Complete crash simulations with validated finite element models can be used to satisfy crash certification requirements, thereby reducing overall development costs.

Stability analysis for a multi-camera photogrammetric system.

PubMed

Habib, Ayman; Detchev, Ivan; Kwak, Eunju

2014-08-18

Consumer-grade digital cameras suffer from geometrical instability that may cause problems when used in photogrammetric applications. This paper provides a comprehensive review of this issue of interior orientation parameter variation over time, it explains the common ways used for coping with the issue, and describes the existing methods for performing stability analysis for a single camera. The paper then points out the lack of coverage of stability analysis for multi-camera systems, suggests a modification of the collinearity model to be used for the calibration of an entire photogrammetric system, and proposes three methods for system stability analysis. The proposed methods explore the impact of the changes in interior orientation and relative orientation/mounting parameters on the reconstruction process. Rather than relying on ground truth in real datasets to check the system calibration stability, the proposed methods are simulation-based. Experiment results are shown, where a multi-camera photogrammetric system was calibrated three times, and stability analysis was performed on the system calibration parameters from the three sessions. The proposed simulation-based methods provided results that were compatible with a real-data based approach for evaluating the impact of changes in the system calibration parameters on the three-dimensional reconstruction.

Landsat 8 thermal infrared sensor geometric characterization and calibration

USGS Publications Warehouse

Storey, James C.; Choate, Michael J.; Moe, Donald

2014-01-01

The Landsat 8 spacecraft was launched on 11 February 2013 carrying two imaging payloads: the Operational Land Imager (OLI) and the Thermal Infrared Sensor (TIRS). The TIRS instrument employs a refractive telescope design that is opaque to visible wavelengths making prelaunch geometric characterization challenging. TIRS geometric calibration thus relied heavily on on-orbit measurements. Since the two Landsat 8 payloads are complementary and generate combined Level 1 data products, the TIRS geometric performance requirements emphasize the co-alignment of the OLI and TIRS instrument fields of view and the registration of the OLI reflective bands to the TIRS long-wave infrared emissive bands. The TIRS on-orbit calibration procedures include measuring the TIRS-to-OLI alignment, refining the alignment of the three TIRS sensor chips, and ensuring the alignment of the two TIRS spectral bands. The two key TIRS performance metrics are the OLI reflective to TIRS emissive band registration accuracy, and the registration accuracy between the TIRS thermal bands. The on-orbit calibration campaign conducted during the commissioning period provided an accurate TIRS geometric model that enabled TIRS Level 1 data to meet all geometric accuracy requirements. Seasonal variations in TIRS-to-OLI alignment have led to several small calibration parameter adjustments since commissioning.

Stability Analysis for a Multi-Camera Photogrammetric System

PubMed Central

Habib, Ayman; Detchev, Ivan; Kwak, Eunju

2014-01-01

Consumer-grade digital cameras suffer from geometrical instability that may cause problems when used in photogrammetric applications. This paper provides a comprehensive review of this issue of interior orientation parameter variation over time, it explains the common ways used for coping with the issue, and describes the existing methods for performing stability analysis for a single camera. The paper then points out the lack of coverage of stability analysis for multi-camera systems, suggests a modification of the collinearity model to be used for the calibration of an entire photogrammetric system, and proposes three methods for system stability analysis. The proposed methods explore the impact of the changes in interior orientation and relative orientation/mounting parameters on the reconstruction process. Rather than relying on ground truth in real datasets to check the system calibration stability, the proposed methods are simulation-based. Experiment results are shown, where a multi-camera photogrammetric system was calibrated three times, and stability analysis was performed on the system calibration parameters from the three sessions. The proposed simulation-based methods provided results that were compatible with a real-data based approach for evaluating the impact of changes in the system calibration parameters on the three-dimensional reconstruction. PMID:25196012

A pollution fate and transport model application in a semi-arid region: Is some number better than no number?

PubMed

Özcan, Zeynep; Başkan, Oğuz; Düzgün, H Şebnem; Kentel, Elçin; Alp, Emre

2017-10-01

Fate and transport models are powerful tools that aid authorities in making unbiased decisions for developing sustainable management strategies. Application of pollution fate and transport models in semi-arid regions has been challenging because of unique hydrological characteristics and limited data availability. Significant temporal and spatial variability in rainfall events, complex interactions between soil, vegetation and topography, and limited water quality and hydrological data due to insufficient monitoring network make it a difficult task to develop reliable models in semi-arid regions. The performances of these models govern the final use of the outcomes such as policy implementation, screening, economical analysis, etc. In this study, a deterministic distributed fate and transport model, SWAT, is applied in Lake Mogan Watershed, a semi-arid region dominated by dry agricultural practices, to estimate nutrient loads and to develop the water budget of the watershed. To minimize the discrepancy due to limited availability of historical water quality data extensive efforts were placed in collecting site-specific data for model inputs such as soil properties, agricultural practice information and land use. Moreover, calibration parameter ranges suggested in the literature are utilized during calibration in order to obtain more realistic representation of Lake Mogan Watershed in the model. Model performance is evaluated using comparisons of the measured data with 95%CI for the simulated data and comparison of unit pollution load estimations with those provided in the literature for similar catchments, in addition to commonly used evaluation criteria such as Nash-Sutcliffe simulation efficiency, coefficient of determination and percent bias. These evaluations demonstrated that even though the model prediction power is not high according to the commonly used model performance criteria, the calibrated model may provide useful information in the comparison of the effects of different management practices on diffuse pollution and water quality in Lake Mogan Watershed. Copyright © 2017 Elsevier B.V. All rights reserved.

Four pi calibration and modeling of a bare germanium detector in a cylindrical field source

NASA Astrophysics Data System (ADS)

Dewberry, R. A.; Young, J. E.

2012-05-01

In this paper we describe a 4π cylindrical field acquisition configuration surrounding a bare (unshielded, uncollimated) high purity germanium detector. We perform an efficiency calibration with a flexible planar source and model the configuration in the 4π cylindrical field. We then use exact calculus to model the flux on the cylindrical sides and end faces of the detector. We demonstrate that the model accurately represents the experimental detection efficiency compared to that of a point source and to Monte Carlo N-particle (MCNP) calculations of the flux. The model sums over the entire source surface area and the entire detector surface area including both faces and the detector's cylindrical sides. Agreement between the model and both experiment and the MCNP calculation is within 8%.

Automatic Camera Calibration Using Multiple Sets of Pairwise Correspondences.

PubMed

Vasconcelos, Francisco; Barreto, Joao P; Boyer, Edmond

2018-04-01

We propose a new method to add an uncalibrated node into a network of calibrated cameras using only pairwise point correspondences. While previous methods perform this task using triple correspondences, these are often difficult to establish when there is limited overlap between different views. In such challenging cases we must rely on pairwise correspondences and our solution becomes more advantageous. Our method includes an 11-point minimal solution for the intrinsic and extrinsic calibration of a camera from pairwise correspondences with other two calibrated cameras, and a new inlier selection framework that extends the traditional RANSAC family of algorithms to sampling across multiple datasets. Our method is validated on different application scenarios where a lack of triple correspondences might occur: addition of a new node to a camera network; calibration and motion estimation of a moving camera inside a camera network; and addition of views with limited overlap to a Structure-from-Motion model.

Microscope in orbit calibration procedure for a test of the equivalence principle at 10(-15).

PubMed

Pradels, G; Touboul, P

2003-01-01

The scientific objectives of the MICROSCOPE space mission impose a very fine calibration of the on-board accelerometers. However the required performance cannot be achieved on ground because of the presence of high disturbing sources. On-board the CHAMP satellite, accelerometers similar in the concept to the MICROSCOPE instrument, have already flown and analysis of the provided data then allowed to characterise the vibration environment at low altitude as well as the fluctuation of the drag. The requirements of the in-orbit calibration procedure for the MICROSCOPE instrument are demonstrated by modelling the expected applied acceleration signals with the developed analytic model of the mission. The proposed approach exploits the drag-free system of the satellite and the sensitivity of the accelerometers. A specific simulator of the attitude control system of the satellite has been developed and tests of the proposed solution are performed using nominal conditions or disturbing conditions as observed during the CHAMP mission. c2003 International Astronautical Federation. Published by Elsevier Science Ldt. All rights reserved.

On cup anemometer rotor aerodynamics.

PubMed

Pindado, Santiago; Pérez, Javier; Avila-Sanchez, Sergio

2012-01-01

The influence of anemometer rotor shape parameters, such as the cups' front area or their center rotation radius on the anemometer's performance was analyzed. This analysis was based on calibrations performed on two different anemometers (one based on magnet system output signal, and the other one based on an opto-electronic system output signal), tested with 21 different rotors. The results were compared to the ones resulting from classical analytical models. The results clearly showed a linear dependency of both calibration constants, the slope and the offset, on the cups' center rotation radius, the influence of the front area of the cups also being observed. The analytical model of Kondo et al. was proved to be accurate if it is based on precise data related to the aerodynamic behavior of a rotor's cup.