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Error Variance (error + variance)
Selected AbstractsA new recursive neural network algorithm to forecast electricity price for PJM day-ahead marketINTERNATIONAL JOURNAL OF ENERGY RESEARCH, Issue 6 2010Paras Mandal Abstract This paper evaluates the usefulness of publicly available electricity market information in predicting the hourly prices in the PJM day-ahead electricity market using recursive neural network (RNN) technique, which is based on similar days (SD) approach. RNN is a multi-step approach based on one output node, which uses the previous prediction as input for the subsequent forecasts. Comparison of forecasting performance of the proposed RNN model is done with respect to SD method and other literatures. To evaluate the accuracy of the proposed RNN approach in forecasting short-term electricity prices, different criteria are used. Mean absolute percentage error, mean absolute error and forecast mean square error (FMSE) of reasonably small values were obtained for the PJM data, which has correlation coefficient of determination (R2) of 0.7758 between load and electricity price. Error variance, one of the important performance criteria, is also calculated in order to measure robustness of the proposed RNN model. The numerical results obtained through the simulation to forecast next 24 and 72,h electricity prices show that the forecasts generated by the proposed RNN model are significantly accurate and efficient, which confirm that the proposed algorithm performs well for short-term price forecasting. Copyright © 2009 John Wiley & Sons, Ltd. [source] Flow dependence of background errors and their vertical correlations for radiance-data assimilationTHE QUARTERLY JOURNAL OF THE ROYAL METEOROLOGICAL SOCIETY, Issue 647 2010Reinhold Hess Abstract This article examines the dependence of background-error statistics on synoptic conditions and flow patterns. Error variances and vertical correlations of background temperatures as used for variational radiance-data assimilation are estimated for two different weather regimes over Europe using the NMC method. The results are validated with real observations, i.e. radiosonde data and microwave satellite radiances and generalised with half a year of global data from the ECMWF forecasting system, where weather conditions are distinguished using model fields of wind speed, mean sea level pressure, and relative vorticity. Strong winds, low pressure, and cyclonic flow generally induce larger background errors of 500 hPa temperature than calm winds, high pressure, and anticyclonic flow, and also broader temperature correlations in the vertical with other tropospheric levels. Copyright © 2010 Royal Meteorological Society [source] How to find what's in a name: Scrutinizing the optimality of five scoring algorithms for the name-letter taskEUROPEAN JOURNAL OF PERSONALITY, Issue 2 2009Etienne P. LeBel Abstract Although the name-letter task (NLT) has become an increasingly popular technique to measure implicit self-esteem (ISE), researchers have relied on different algorithms to compute NLT scores and the psychometric properties of these differently computed scores have never been thoroughly investigated. Based on 18 independent samples, including 2690 participants, the current research examined the optimality of five scoring algorithms based on the following criteria: reliability; variability in reliability estimates across samples; types of systematic error variance controlled for; systematic production of outliers and shape of the distribution of scores. Overall, an ipsatized version of the original algorithm exhibited the most optimal psychometric properties, which is recommended for future research using the NLT. Copyright © 2009 John Wiley & Sons, Ltd. [source] Pedometric mapping of soil organic matter using a soil map with quantified uncertaintyEUROPEAN JOURNAL OF SOIL SCIENCE, Issue 3 2010B. Kempen This paper compares three models that use soil type information from point observations and a soil map to map the topsoil organic matter content for the province of Drenthe in the Netherlands. The models differ in how the information on soil type is obtained: model 1 uses soil type as depicted on the soil map for calibration and prediction; model 2 uses soil type as observed in the field for calibration and soil type as depicted on the map for prediction; and model 3 uses observed soil type for calibration and a pedometric soil map with quantified uncertainty for prediction. Calibration of the trend on observed soil type resulted in a much stronger predictive relationship between soil organic matter content and soil type than calibration on mapped soil type. Validation with an independent probability sample showed that model 3 out-performed models 1 and 2 in terms of the mean squared error. However, model 3 over-estimated the prediction error variance and so was too pessimistic about prediction accuracy. Model 2 performed the worst: it had the largest mean squared error and the prediction error variance was strongly under-estimated. Thus validation confirmed that calibration on observed soil type is only valid when the uncertainty about soil type at prediction sites is explicitly accounted for by the model. We conclude that whenever information about the uncertainty of the soil map is available and both soil property and soil type are observed at sampling sites, model 3 can be an improvement over the conventional model 1. [source] Optimal filtering for incompletely measured polynomial states over linear observationsINTERNATIONAL JOURNAL OF ADAPTIVE CONTROL AND SIGNAL PROCESSING, Issue 5 2008Michael Basin Abstract In this paper, the optimal filtering problem for polynomial system states over linear observations with an arbitrary, not necessarily invertible, observation matrix is treated proceeding from the general expression for the stochastic Ito differential of the optimal estimate and the error variance. As a result, the Ito differentials for the optimal estimate and error variance corresponding to the stated filtering problem are first derived. A transformation of the observation equation is introduced to reduce the original problem to the previously solved one with an invertible observation matrix. The procedure for obtaining a closed system of the filtering equations for any polynomial state over linear observations is then established, which yields the explicit closed form of the filtering equations in the particular case of a third-order state equation. In the example, performance of the designed optimal filter is verified against a conventional extended Kalman,Bucy filter. Copyright © 2007 John Wiley & Sons, Ltd. [source] Optimal filtering for polynomial system states with polynomial multiplicative noiseINTERNATIONAL JOURNAL OF ROBUST AND NONLINEAR CONTROL, Issue 6 2006Michael Basin Abstract In this paper, the optimal filtering problem for polynomial system states with polynomial multiplicative noise over linear observations is treated proceeding from the general expression for the stochastic Ito differential of the optimal estimate and the error variance. As a result, the Ito differentials for the optimal estimate and error variance corresponding to the stated filtering problem are first derived. The procedure for obtaining a closed system of the filtering equations for any polynomial state with polynomial multiplicative noise over linear observations is then established, which yields the explicit closed form of the filtering equations in the particular cases of a linear state equation with linear multiplicative noise and a bilinear state equation with bilinear multiplicative noise. In the example, performance of the designed optimal filter is verified for a quadratic state with a quadratic multiplicative noise over linear observations against the optimal filter for a quadratic state with a state-independent noise and a conventional extended Kalman,Bucy filter. Copyright © 2006 John Wiley & Sons, Ltd. [source] PANEL PERFORMANCE AND NUMBER OF EVALUATIONS IN A DESCRIPTIVE SENSORY STUDYJOURNAL OF SENSORY STUDIES, Issue 4 2004JÉRÔME PAGÈS ABSTRACT The assessor performance is a key point in a sensory evaluation. In particular, at the end of a session, a decrease of the performance can be feared. We propose to analyze this performance with various criteria: usual ones as the main product effect or the error variance; a new one measuring the perceived products variability. The performance can then be studied all along the session from two points of view: in taking into account the only products tested at a given instant (named instantaneous); in taking into account all the products tested up to a given instant (named cumulative). In the presented example, in spite of the large number of products successively tested by each assessor, the instantaneous performance of the panel shows no significant deterioration. Furthermore, when the number of products tested by each assessor increases, more significant product effects can be obtained thanks to the accumulation of the amount of data. This shows that the number of products that can be reasonably studied by one assessor during one session is generally underestimated. [source] Influence of Missing Values on the Prediction of a Stationary Time SeriesJOURNAL OF TIME SERIES ANALYSIS, Issue 4 2005Pascal Bondon Primary 62M10; secondary 60G25 Abstract., The influence of missing observations on the linear prediction of a stationary time series is investigated. Simple bounds for the prediction error variance and asymptotic behaviours for short and long-memory processes respectively are presented. [source] Prediction Variance and Information Worth of Observations in Time SeriesJOURNAL OF TIME SERIES ANALYSIS, Issue 4 2000Mohsen Pourahmadi The problem of developing measures of worth of observations in time series has not received much attention in the literature. Any meaningful measure of worth should naturally depend on the position of the observation as well as the objectives of the analysis, namely parameter estimation or prediction of future values. We introduce a measure that quantifies worth of a set of observations for the purpose of prediction of outcomes of stationary processes. The worth is measured as the change in the information content of the entire past due to exclusion or inclusion of a set of observations. The information content is quantified by the mutual information, which is the information theoretic measure of dependency. For Gaussian processes, the measure of worth turns out to be the relative change in the prediction error variance due to exclusion or inclusion of a set of observations. We provide formulae for computing predictive worth of a set of observations for Gaussian autoregressive moving-average processs. For non-Gaussian processes, however, a simple function of its entropy provides a lower bound for the variance of prediction error in the same manner that Fisher information provides a lower bound for the variance of an unbiased estimator via the Cramer-Rao inequality. Statistical estimation of this lower bound requires estimation of the entropy of a stationary time series. [source] Use and misuse of the reduced major axis for line-fittingAMERICAN JOURNAL OF PHYSICAL ANTHROPOLOGY, Issue 3 2009Richard J. Smith Abstract Many investigators use the reduced major axis (RMA) instead of ordinary least squares (OLS) to define a line of best fit for a bivariate relationship when the variable represented on the X -axis is measured with error. OLS frequently is described as requiring the assumption that X is measured without error while RMA incorporates an assumption that there is error in X. Although an RMA fit actually involves a very specific pattern of error variance, investigators have prioritized the presence versus the absence of error rather than the pattern of error in selecting between the two methods. Another difference between RMA and OLS is that RMA is symmetric, meaning that a single line defines the bivariate relationship, regardless of which variable is X and which is Y, while OLS is asymmetric, so that the slope and resulting interpretation of the data are changed when the variables assigned to X and Y are reversed. The concept of error is reviewed and expanded from previous discussions, and it is argued that the symmetry-asymmetry issue should be the criterion by which investigators choose between RMA and OLS. This is a biological question about the relationship between variables. It is determined by the investigator, not dictated by the pattern of error in the data. If X is measured with error but OLS should be used because the biological question is asymmetric, there are several methods available for adjusting the OLS slope to reflect the bias due to error. RMA is being used in many analyses for which OLS would be more appropriate. Am J Phys Anthropol, 2009. © 2009 Wiley-Liss, Inc. [source] Change over Tenure: Voting, Variance, and Decision Making on the U.S. Courts of AppealsAMERICAN JOURNAL OF POLITICAL SCIENCE, Issue 3 2008Erin B. Kaheny Existing scholarship on the voting behavior of U.S. Courts of Appeals judges finds that their decisions are best understood as a function of law, policy preferences, and factors relating to the institutional context of the circuit court. What previous studies have failed to consider, however, is that the ability to predict circuit judge decisions can vary in substantively important ways and that judges, in different stages of their careers, may behave distinctively. This article develops a theoretical framework which conceptualizes career stage to account for variability in voting by circuit judges and tests hypotheses by modeling the error variance in a vote choice model. The findings indicate that judges are more predictable in their voting during their early and late career stages. Case characteristics and institutional features of the circuit also affect voting consistency. [source] Corrected local polynomial estimation in varying-coefficient models with measurement errorsTHE CANADIAN JOURNAL OF STATISTICS, Issue 3 2006Jinhong You Abstract The authors study a varying-coefficient regression model in which some of the covariates are measured with additive errors. They find that the usual local linear estimator (LLE) of the coefficient functions is biased and that the usual correction for attenuation fails to work. They propose a corrected LLE and show that it is consistent and asymptotically normal, and they also construct a consistent estimator for the model error variance. They then extend the generalized likelihood technique to develop a goodness of fit test for the model. They evaluate these various procedures through simulation studies and use them to analyze data from the Framingham Heart Study. Estimation polynomiale locale corrigée dans les modèles à coefficients variables comportant des erreurs de mesure Les auteurs s'intéressent à un modèle de régression à coefficients variables dont certaines cova-riables sont entachées d'erreurs additives. Ils montrent que l'estimateur localement linéaire (ELL) usuel des coefficients fonctionnels est biaisé et que le facteur de correction habituel du phénomène d'atténuation est inefficace. Ils proposent une version corrigée de l'ELL qui s'avère convergente et asymptotiquement normale; ils suggèrent aussi une estimation convergente de la variance du terme d'erreur du modèle. Une adaptation de la technique de vraisemblance généralisée leur permet en outre d'élaborer un test d'adéquation du modèle. Ils évaluent ces diverses procédures par voie de simulation et s'en servent pour analyser des données issues de l'étude Framingham sur les risques cardiométaboliques. [source] Influence of the Quasi-Biennial Oscillation on the ECMWF model short-range-forecast errors in the tropical stratosphereTHE QUARTERLY JOURNAL OF THE ROYAL METEOROLOGICAL SOCIETY, Issue 628 2007Nedjeljka, agar Abstract This paper addresses the impact of the Quasi-Biennial Oscillation (QBO) on the background-error covariances in the tropical atmosphere of the ECMWF model. The tropical short-range-forecast-error covariances are represented in terms of equatorial waves coupled to convection. By comparing the forecast-error proxy data from two different phases of the QBO, it is shown that the phase of the QBO has an effect on the distribution of tropical forecast-error variances between various equatorial waves. The influence of the QBO is limited to the stratospheric levels between 50 hPa and 5 hPa. In the easterly QBO phase, the percentage of error variance in Kelvin waves is significantly greater than in the westerly phase. In the westerly phase, westward-propagating inertio-gravity waves become more important, at the expense of Kelvin modes, eastward-propagating mixed Rossby-gravity waves and inertio-gravity modes. Comparison of datasets from two easterly phases shows that the maxima of stratospheric error variance in various equatorial modes follow the theory of the interaction of waves with descending shear zones of the horizontal wind. Single-observation experiments illustrate an impact of the phase of the QBO on stratospheric analysis increments, which is mostly seen in the balanced geopotential field. Idealized 3D-Var assimilation experiments suggest that background-error statistics from the easterly QBO period are on average more useful for the multivariate variational assimilation, as a consequence of a stronger mass-wind coupling due to increased impact of Kelvin waves in the easterly phase. By comparing the tropical forecast errors in two operational versions of the model a few years apart, it is shown here that recent model improvements, primarily in the model physics, have substantially reduced the errors in both wind and geopotential throughout the tropical atmosphere. In particular, increased wind-field errors associated with the intertropical convergence zone have been removed. Consequently, the ability of the applied background-error model to represent the error fields has improved. Copyright © 2007 Royal Meteorological Society [source] A study on the optimization of the deployment of targeted observations using adjoint-based methodsTHE QUARTERLY JOURNAL OF THE ROYAL METEOROLOGICAL SOCIETY, Issue 583 2002Thierry Bergot Abstract A new adjoint-based method to find the optimal deployment of targeted observations, called Kalman Filter Sensitivity (KFS), is introduced. The major advantage of this adjoint-based method is that it allows direct computation of the reduction of the forecast-score error variance that would result from future deployment of targeted observations. This method is applied in a very simple one-dimensional context, and is then compared to other adjoint-based products, such as classical gradients and gradients with respect to observations. The major conclusion is that the deployment of targeted observation is strongly constrained by the aspect ratio between the length-scale of the sensitivity area and the length-scale of the analysis-error covariance matrix. This very simple example also clearly illustrates that the reduction of forecast-error variance is stronger for assimilation schemes which have a smaller characteristic length-scale. Finally, the KFS technique is applied in a diagnostic way (i.e. once the observations are done) to four FASTEX cases. For these cases, the reduction of the forecasterror variance is in agreement with the efficiency of targeted observations as previously studied. A preliminary step towards an operational use has been performed on FASTEX IOP18, and results seem to validate the KFS approach of targeting. Copyright © 2002 Royal Meteorological Society. [source] Impact and Quantification of the Sources of Error in DNA Pooling DesignsANNALS OF HUMAN GENETICS, Issue 1 2009A. Jawaid Summary The analysis of genome wide variation offers the possibility of unravelling the genes involved in the pathogenesis of disease. Genome wide association studies are also particularly useful for identifying and validating targets for therapeutic intervention as well as for detecting markers for drug efficacy and side effects. The cost of such large-scale genetic association studies may be reduced substantially by the analysis of pooled DNA from multiple individuals. However, experimental errors inherent in pooling studies lead to a potential increase in the false positive rate and a loss in power compared to individual genotyping. Here we quantify various sources of experimental error using empirical data from typical pooling experiments and corresponding individual genotyping counts using two statistical methods. We provide analytical formulas for calculating these different errors in the absence of complete information, such as replicate pool formation, and for adjusting for the errors in the statistical analysis. We demonstrate that DNA pooling has the potential of estimating allele frequencies accurately, and adjusting the pooled allele frequency estimates for differential allelic amplification considerably improves accuracy. Estimates of the components of error show that differential allelic amplification is the most important contributor to the error variance in absolute allele frequency estimation, followed by allele frequency measurement and pool formation errors. Our results emphasise the importance of minimising experimental errors and obtaining correct error estimates in genetic association studies. [source] ERROR VARIANCE ESTIMATION FOR THE SINGLE-INDEX MODELAUSTRALIAN & NEW ZEALAND JOURNAL OF STATISTICS, Issue 2 2010K. B. Kulasekera Summary Single-index models provide one way of reducing the dimension in regression analysis. The statistical literature has focused mainly on estimating the index coefficients, the mean function, and their asymptotic properties. For accurate statistical inference it is equally important to estimate the error variance of these models. We examine two estimators of the error variance in a single-index model and compare them with a few competing estimators with respect to their corresponding asymptotic properties. Using a simulation study, we evaluate the finite-sample performance of our estimators against their competitors. [source] Area-to-Point Prediction Under Boundary ConditionsGEOGRAPHICAL ANALYSIS, Issue 4 2008E. -H. This article proposes a geostatistical solution for area-to-point spatial prediction (downscaling) taking into account boundary effects. Such effects are often poorly considered in downscaling, even though they often have significant impact on the results. The geostatistical approach proposed in this article considers two types of boundary conditions (BC), that is, a Dirichlet-type condition and a Neumann-type condition, while satisfying several critical issues in downscaling: the coherence of predictions, the explicit consideration of support differences, and the assessment of uncertainty regarding the point predictions. An updating algorithm is used to reduce the computational cost of area-to-point prediction under a given BC. In a case study, area-to-point prediction under a Dirichlet-type BC and a Neumann-type BC is illustrated using simulated data, and the resulting predictions and error variances are compared with those obtained without considering such conditions. [source] The distribution of QTL additive and dominance effects in porcine F2 crossesJOURNAL OF ANIMAL BREEDING AND GENETICS, Issue 3 2010J. Bennewitz Summary The present study used published quantitative trait loci (QTL) mapping data from three F2 crosses in pigs for 34 meat quality and carcass traits to derive the distribution of additive QTL effects as well as dominance coefficients. Dominance coefficients were calculated as the observed QTL dominance deviation divided by the absolute value of the observed QTL additive effect. The error variance of this ratio was approximated using the delta method. Mixtures of normal distributions (mixtures of normals) were fitted to the dominance coefficient using a modified EM-algorithm that considered the heterogeneous error variances of the data points. The results suggested clearly to fit one component which means that the dominance coefficients are normally distributed with an estimated mean (standard deviation) of 0.193 (0.312). For the additive effects mixtures of normals and a truncated exponential distribution were fitted. Two components were fitted by the mixtures of normals. The mixtures of normals did not predict enough QTL with small effects compared to the exponential distribution and to literature reports. The estimated rate parameter of the exponential distribution was 5.81 resulting in a mean effect of 0.172. [source] Random error reduction in analytic hierarchies: a comparison of holistic and decompositional decision strategiesJOURNAL OF BEHAVIORAL DECISION MAKING, Issue 3 2001Osvaldo F. Morera Abstract The principle of ,divide and conquer' (DAC) suggests that complex decision problems should be decomposed into smaller, more manageable parts, and that these parts should be logically aggregated to derive an overall value for each alternative. Decompositional procedures have been contrasted with holistic evaluations that require decision makers to simultaneously consider all the relevant attributes of the alternatives under consideration (Fischer, 1977). One area where decompositional procedures have a clear advantage over holistic procedures is in the reduction of random error (Ravinder, 1992; Ravinder and Kleinmuntz, 1991; Kleinmuntz, 1990). Adopting the framework originally developed by Ravinder and colleagues, this paper details the results of a study of the random error variances associated with another popular multi-criteria decision-making technique, the Analytic Hierarchy Process (AHP); (Saaty, 1977, 1980), as well as the random error variances of a holistic version of the Analytic Hierarchy Process (Jensen, 1983). In addition, data concerning various psychometric properties (e.g. the convergent validity and temporal stability) and values of AHP inconsistency are reported for both the decompositional and holistic evaluations. The results of the study show that the Ravinder and Kleinmuntz (1991) error-propagation framework extends to the AHP and decompositional AHP judgments are more consistent than their holistic counterparts. Copyright © 2001 John Wiley & Sons, Ltd. [source] Integrated estimation of measurement error with empirical process modeling,A hierarchical Bayes approachAICHE JOURNAL, Issue 11 2009Hongshu Chen Abstract Advanced empirical process modeling methods such as those used for process monitoring and data reconciliation rely on information about the nature of noise in the measured variables. Because this likelihood information is often unavailable for many practical problems, approaches based on repeated measurements or process constraints have been developed for their estimation. Such approaches are limited by data availability and often lack theoretical rigor. In this article, a novel Bayesian approach is proposed to tackle this problem. Uncertainty about the error variances is incorporated in the Bayesian framework by setting noninformative priors for the noise variances. This general strategy is used to modify the Sampling-based Bayesian Latent Variable Regression (Chen et al., J Chemom., 2007) approach, to make it more robust to inaccurate information about the likelihood functions. Different noninformative priors for the noise variables are discussed and unified in this work. The benefits of this new approach are illustrated via several case studies. © 2009 American Institute of Chemical Engineers AIChE J, 2009 [source] Quantitative-genetic analysis of leaf-rust resistance in seedling and adult-plant stages of inbred lines and their testcrosses in winter ryePLANT BREEDING, Issue 6 2002T. Miedaner Abstract Leaf rust is the most frequent leaf disease of winter rye in Germany. All widely grown population and hybrid varieties are susceptible. This study was undertaken to estimate quantitative-genetic parameters of leaf-rust resistance in self-fertile breeding materials with introgressed foreign leaf-rust resistances and to analyze the relative importance of seedling and adult-plant resistance. Forty-four inbred lines and their corresponding testcrosses with a highly susceptible tester line were grown in a field in four different environments (location-year combinations) with artificial inoculation. Plots were separated by a nonhost to promote autoinfections and minimize interplot interference. Leaf-rust severity was rated on three leaf insertions at three dates. The testcrosses showed a considerably higher disease severity than the lines. High correlations (r , 0.9, P = 0.01) existed among the leaf insertions and the rating dates. Large genotypic variation for resistance was found in both the inbred and testcross populations. Genotype-environment interaction and error variances were of minor importance, thus high entry-mean heritabilities were achieved. A tight correlation between the inbreds and their corresponding testcrosses was found (r = 0.88, P = 0.01). Heterosis for resistance was significant (P = 0.05), but not very important. In a seedling test with 20,30 single-pustule isolates, 34 out of 44 inbreds reacted race-specifically. From the remaining inbred lines, three were medium and seven highly susceptible. In a further greenhouse test with 16 inbreds, seven were susceptible and five were resistant in both seedling and adult-plant stages. The remaining four lines had adult-plant resistance. In conclusion, race-specific leaf-rust resistance can be selected among inbred lines per se. Lines should also be tested in the adult-plant stage. [source] Diagnosis and formulation of heterogeneous background-error covariances at the mesoscaleTHE QUARTERLY JOURNAL OF THE ROYAL METEOROLOGICAL SOCIETY, Issue 651 2010Thibaut Montmerle Abstract This study focuses on diagnosing variations of background-error covariances between precipitating and non-precipitating areas, and on presenting a heterogeneous covariance formulation to represent these variations in a variational framework. The context of this work is the assimilation of observations linked to precipitation (radar data especially) in the AROME model, which has been running operationally at Météo-France since December 2008 over French territory with a 2.5 km horizontal resolution. This system uses multivariate background-error covariances deduced from an ensemble-based method. At first, such statistics have been computed for 17 precipitating cases using an ensemble of AROME forecasts coupled with an ALADIN ensemble assimilation. Results, obtained from 3 h forecast differences performed separately for non-precipitating and precipitating columns, display large discrepancies in error variances, correlation lengths and the correlations between humidity, temperature and divergence errors. These results argue in favour of including heterogeneous background-error covariances in AROME incremental 3D-Var, allowing different covariances to be used in regions with different meteorological patterns. Such a method enables us to get increments more adequately structured in those regions, and thus potentially to make better use of observations in a data assimilation system. The implementation consists of expressing the analysis increment as the sum of two terms, one for precipitating areas and the other for non-precipitating areas, making use of a mask that defines rainy regions. This implies a doubling in the size of the control variable and of the gradient of the cost function. The feasibility of this method is shown through experiments with four isolated observations. Copyright © 2010 Royal Meteorological Society [source] Application of SSM/I satellite data to a hurricane simulationTHE QUARTERLY JOURNAL OF THE ROYAL METEOROLOGICAL SOCIETY, Issue 598 2004Shu-hua Chen Abstract The impact of Special Sensor Microwave/Imager (SSM/I) data on simulations of hurricane Danny is assessed. The assimilation of SSM/I data is found to increase the atmospheric moisture content over the Gulf of Mexico, strengthen the low-level cyclonic circulation, shorten the model spin-up time, and significantly improve the simulation of the storm's intensity. Two different approaches for assimilating SSM/I data, namely assimilating retrieved products and assimilating raw measurements, are further compared. The data-assimilation analyses from these two approaches give different moisture distributions in both the horizontal and vertical directions in the storm's vicinity, which may potentially affect the simulated storm's development; however, the simulated storm intensities are considered comparable for the Danny case. From sensitivity tests performed in this study, it is also found that the choice of the observational error variances could be potentially important to the model simulations. Copyright © 2004 Royal Meteorological Society. [source] On-line adaptive metabolic flux analysis: Application to PHB production by mixed microbial culturesBIOTECHNOLOGY PROGRESS, Issue 2 2009João Dias Abstract In this work, an algorithm for on-line adaptive metabolic flux analysis (MFA) is proposed and applied to polyhydroxybutyrate (PHB) production by mixed microbial cultures (MMC). In this process, population dynamics constitutes an important source of perturbation to MFA calculations because some stoichiometric and energetic parameters of the underlying metabolic network are continuously changing over time. The proposed algorithm is based on the application of the observer-based estimator (OBE) to the central MFA equation, whereby the role of the OBE is to force the accumulation of intracellular metabolites to converge to zero by adjusting the values of unknown network parameters. The algorithm was implemented in a reactor equipped with on-line analyses of dissolved oxygen and carbon dioxide through respirometric and titrimetric measurements. The oxygen and carbon dioxide fluxes were measured directly, whereas acetate, PHB, and sludge production fluxes were estimated indirectly using a projection of latent structures model calibrated a priori with off-line measurements. The algorithm was implemented in a way that the network parameters associated with biosynthesis were adjusted on-line. The algorithm proofed to converge exponentially with the steady state error always below 1 mmol/L. The estimated fluxes passed the consistency index test for experimental error variances as low as 1%. The comparison of measured and estimated respiratory coefficient and of the theoretical and estimated yield of sludge on acetate further confirmed the metabolic consistency of the parameters that were estimated on-line. © 2009 American Institute of Chemical Engineers Biotechnol. Prog., 2009 [source] | ||||||||||||||||||||||||||||