Bias Correction

Physically-based groundwater models (PBMs), such as MODFLOW, contain numerous parameters which are usually estimated using statistically-based methods, which assume that the underlying error is white noise. However, because of the practical difficulties of representing all the natural subsurface complexity, numerical simulations are often prone to large uncertainties that can result in both random and systematic model error. The systematic errors can be attributed to conceptual, parameter, and measurement uncertainty, and most often it can be difficult to determine their physical cause. In this paper, we have developed a framework to handle systematic error in physically-based groundwater flow model applications that uses error-correcting data-driven models (DDMs) in a complementary fashion. The data-driven models are separately developed to predict the MODFLOW head prediction errors, which were subsequently used to update the head predictions at existing and proposed observation wells. The framework is evaluated using a hypothetical case study developed based on a phytoremediation site at the Argonne National Laboratory. This case study includes structural, parameter, and measurement uncertainties. In terms of bias and prediction uncertainty range, the complementary modeling framework has shown substantial improvements (up to 64% reduction in RMSE and prediction error ranges) over the original MOD-FLOW model, in both the calibration and the verification periods. Moreover, the spatial and temporal correlations of the prediction errors are significantly reduced, thus resulting in reduced local biases and structures in the model prediction errors.

Two different data assimilation techniques have been applied to assess exceedances of the daily and annual mean limit values for PM 10 on the regional scale in Europe. The two methods include a statistical interpolation method (SI), based on residual kriging after linear regression of the model, and Ensemble Kalman filtering (EnKF). Both methods are applied using the LOTOS-EUROS model. Observations for the assimilation and validation of the methods have been retrieved from the Airbase database using rural background stations only. For the period studied, 2003, 127 suitable stations were available. The LOTOS-EUROS model is found to underestimate PM 10 concentrations by a factor of 2. This large model bias is found to be prohibitive for the effective use of the EnKF methodology and a bias correction was required for the filter to function effectively. The results of the study show that both methods provide significant improvement on the model calculations when compared to an independent set of validation stations. The total root mean square error of the daily mean concentrations of PM 10 at the validation stations was reduced from 16.7 mg m À3 for the model to 9.2 mg m À3 using SI and to 13.5 mg m À3 using EnKF. Similarly, correlation (R 2 ) is also significantly improved from 0.21 for the model to 0.66 using SI and 0.41 using EnKF. Significant improvement in the annual mean and number of exceedance days of PM 10 is also seen. In addition to the validation of the methods, maps of exceedances and their associated uncertainty are presented. The most effective methodology is found to be the statistical interpolation method. The application of EnKF is novel and yields promising results, although its application to PM 10 still needs to be improved.

This paper considers the estimation of a dynamic ordered probit of self-assessed health status with two fixed effects: one in the linear index equation and one in the cut points. The two fixed effects allow us to robustly control for heterogeneity in unobserved health status and in reporting behaviour, even though we can not separate both sources of heterogeneity. The contributions of this paper are twofold. First it contributes to the literature that studies the determinants and dynamics of Self-Assessed Health measures. Second, this paper contributes to the recent literature on bias correction in nonlinear panel data models with fixed effects by applying and studying the finite sample properties of two of the existing proposals to our model. The most direct and easily applicable correction to our model is not the best one, and has important biases in our sample sizes.

This study evaluates the application of (i) skull-stripping methods (hybrid watershed algorithm (HWA), brain surface extractor (BSE) and brain-extraction tool (BET2)) and (ii) bias correction algorithms (nonparametric nonuniform intensity normalisation (N3), bias field corrector (BFC) and FMRIB's automated segmentation tool (FAST)) as pre-processing pipelines for the technique of voxel-based morphometry (VBM) using statistical parametric mapping v.5 (SPM5). The pipelines were evaluated using a BrainWeb phantom, and those that performed consistently were further assessed using artificial-lesion masks applied to 10 healthy controls compared to the original unlesioned scans, and finally, 20 Alzheimer's disease (AD) patients versus 23 controls. In each case, pipelines were compared to each other and to those from default SPM5 methodology. The BET2+N3 pipeline was found to produce the least miswarping to template induced by real abnormalities, and performed consistently better th...

Malaysia's yearly steady growth in electricity consumption as a result of fast development in various sectors of the Malaysian economy have increased the need to have a more robust, reliable and accurate load forecasting for short -, medium-, or long-term. A reliable method for short term load forecasting is crucial to any decision maker in a power utility company. Many studies have been made to improve the forecasting accuracy using various methods. The forecasting errors for the holiday seasons are known to be higher than those for weekends. This paper aims to determine which model would be a better model to estimate the holiday effects and therefore give a better forecasting accuracy for the peak daily load in Malaysia. Some of the holiday effects in Malaysia are from Eid ul-Fitr, Christmas, Independence Day and Chinese New Year. The seasonal ARIMA (SARIMA) and Dynamic Regression (DR) or Transfer function modelling are considered. Furthermore, the final selection of the models depends on the Mean Absolute Percentage Error (MAPE) and others such as the sample autocorrelation function (ACF), the sample partial autocorrelation function (PACF) and a bias-corrected version of the Akaike's information criterion (AICC) statistic. The Dynamic Regression (DR) model recorded 2.22% as the lowest MAPE value for the 2004 New Year's Eve and 2.39% for the seven days ahead forecasting. And therefore, DR model is the most appropriate model to be considered for forecasting any public holidays in Malaysia.

This paper presents a method to adjust the mean field bias of radar rainfall estimates in real time. The underlying philosophy for estimation of radar rainfall in real-time used in this study is that the estimated radar rainfall must first be corrected for the reflectivity measurement errors and the Z-R conversion errors based on the physical methods, and thereafter a statistical method is used to remove the average difference (mean field bias) between radar estimates at the rain gauge locations and the corresponding gauge rainfall amounts. Kalman filtering techniques are used as the basis for correcting the mean filed bias in real time. The logarithmic mean field radar rainfall bias is modelled as an Autoregressive order 1 process having a stationary variance. The observation error variance is estimated from a difference between a variance of the observed mean field bias and the stationary variance of the logarithmic bias process. The variance of the observed mean field bias is estimated using a radar rainfall error variance model which depends on (a) the number of rain gauges that measure non-zero rainfall at that hour; (b) the location of these rain gauges; (c) the conditional mean of rain gauge rainfall; and (d) the number of pulses used for reflectivity measurements. A 7-month radar and rain gauge data record from the Kurnell radar in Sydney, Australia are used to test the efficiency of the proposed method. The results shown that the Kalman filtering approach outperforms the use of sample bias correction method when rain gauge density is less than about 1 gauge per 90 km 2 for both climatological and stratiform rainfall, and has a higher accuracy compared to the case where no bias correction is performed, irrespective of the number of rain gauges used in the calibration. q

Social problems are a prevalent feature of ADHD and reflect a major source of functional impairment for these children. The current study examined the impact of working memory deficits on parent-and teacher-reported social problems in a sample of children with ADHD and typically developing boys (N=39). Bootstrapped, biascorrected mediation analyses revealed that the impact of working memory deficits on social problems is primarily indirect. That is, impaired social interactions in children with ADHD reflect, to a significant extent, the behavioral outcome of being unable to maintain a focus of attention on information within working memory while simultaneously dividing attention among multiple, on-going events and social cues occurring within the environment. Central executive deficits impacted social problems through both inattentive and impulsive-hyperactive symptoms, whereas the subsidiary phonological and visuospatial storage/ rehearsal systems demonstrated a more limited yet distinct relationship with children's social problems.

Comparing the responsiveness over time of the Harris Hip Score (HHS) and the SF-36 in patients who underwent total hip arthroplasty (THA) and assessing variation in the responsiveness of these measures by the number of co-morbidities. This prospective study analyzed 335 THA patients treated at two southern Taiwan hospitals from 1997 to 2000. Magnitude of change in HRQoL was compared by generalized estimating equation. Bias-corrected and accelerated bootstrapping was used to measure magnitude of change in HHS and SF-36 subscale scores for five different time intervals spanning a 5-year period. The analytical results indicated that the pain and physical function subscales of the HHS are more responsive than those of the SF-36 for short-term (within 1 year post-surgery) measurements but are less responsive for long-term measurements. At various follow-up intervals, the HHS and the SF-36 significantly differed in ES of changes in pain and physical function subscale scores for patients with one co-morbidity and for patients with two or more co-morbidities. For long-term evaluation of THA patients, clinicians and health researchers should weight both measures equally and should also consider co-morbidities.

A variant of the popular nonparametric nonuniform intensity normalization (N3) algorithm is proposed for bias field correction. Given the superb performance of N3 and its public availability, it has been the subject of several evaluation studies. These studies have demonstrated the importance of certain parameters associated with the B-spline least-squares fitting. We propose the substitution of a recently developed fast and robust B-spline approximation routine and a modified hierarchical optimization scheme for improved bias field correction over the original N3 algorithm. Similar to the N3 algorithm, we also make the source code, testing, and technical documentation of our contribution, which we denote as "N4ITK," available to the public through the Insight Toolkit of the National Institutes of Health. Performance assessment is demonstrated using simulated data from the publicly available Brainweb database, hyperpolarized 3 He lung image data, and 9.4T postmortem hippocampus data.

a b s t r a c t Self-start capability is an important feature of wind turbines. It allows to obtain simpler and cheaper turbines not actively controlled. Different approaches to describe the self-start of an H-blade Darrieus rotor are presented and compared in the present work. The Blade Element Momentum (BEM) approach was compared with two and three-dimensional CFD simulations. The tipespeed ratio versus power coefficient curves and the evolution of the trust forces over a blade revolution highlighted the limits and the strengths of each approach.

The CALIOPE air quality modelling system is developed and applied to Europe with high spatial resolution (12 km × 12 km). The modelled daily-to-seasonal aerosol variability over Europe in 2004 is evaluated and analysed. Aerosols are estimated from two models, CMAQv4.5 (AERO4) and BSC-DREAM8b. CMAQv4.5 calculates biogenic, anthropogenic and sea salt aerosol and BSC-DREAM8b provides the natural mineral dust contribution from North African deserts. For the evaluation, we use daily PM 10 , PM 2.5 and aerosol components data from 55 stations of the EMEP/CREATE network and total, coarse and fine aerosol optical depth (AOD) data from 35 stations of the AERONET sun photometer network. Annual correlations between modelled and observed values for PM 10 and PM 2.5 are 0.55 and 0.47, respectively. Correlations for total, coarse and fine AOD are 0.51, 0.63, and 0.53, respectively. The higher correlations of the PM 10 and the coarse mode AOD are largely due to the accurate representation of the African dust influence in the forecasting system. Overall PM and AOD levels are underestimated. The evaluation of the aerosol components highlights underestimations in the fine fraction of carbonaceous matter (EC and OC) and secondary inorganic aerosols (SIA; i.e. nitrate, sulphate and ammonium). The scores of the bulk parameters are significantly improved after applying a simple model bias correction based on the observed aerosol composition. The simulated PM 10 and AOD present maximum values over the in-dustrialized and populated Po Valley and Benelux regions. SIA are dominant in the fine fraction representing up to 80 % of the aerosol budget in latitudes north of 40 • N. In southern Europe, high PM 10 and AOD are linked to the desert dust transport from the Sahara which contributes up to 40 % of the aerosol budget. Maximum seasonal ground-level concentrations (PM 10 > 30 µg m −3 ) are found between spring and early autumn. We estimate that desert dust causes daily exceedances of the PM 10 European air quality limit value (50 µg m −3 ) in large areas south of 45 • N with more than 75 exceedances per year in the southernmost regions.

The signal-to-noise ratio (SNR) is a commonly used measure of system fidelity estimated as the ratio of the variance of a signal to the variance of the noise. Although widely used in analyses of physical systems, this estimator is not appropriate for point process models of neural systems or other non-Gaussian and/or non-additive signal and noise systems. We show that the extension of the standard estimator to the class of generalized linear models (GLM) yields a new SNR estimator that is ratio of two estimated prediction errors. Each prediction error estimate is an approximate chi-squared random variable whose expected value is given by its number of degrees of freedom. This allows us to compute a new bias-corrected SNR estimator. We illustrate its application in a study of simulated neural spike trains from a point process model in which the signal is task-specific modulation across multiple trials of a neurophysiological experiment. The new estimator characterizes the SNR of a neural system in terms commonly used for physical systems. It can be further extended to analyze any system in which modulation of the system's response by distinct signal components can be expressed as separate components of a likelihood function.

This article investigates the production efficiency of rice farming in Sri Lanka using cross section survey data of 90 farms. Past studies on rice farming have mostly focused on technical efficiency (TE). Here, we examine technical efficiency, allocative efficiency (AE) and cost efficiency (CE) using the data envelopment analysis (DEA) approach. On average, the farms were 87% technically efficient; irrigated farms were more efficient (88%) than rain-fed farms (82%). Average cost, allocative and scale efficiencies were 73%, 84% and 87%. Bias corrected TE estimate suggests an expected output expansion of 25% with a given input combination in order to become fully efficient as opposed to 16% based on the original estimates. In addition, a second stage Tobit regression shows that efficiency is influenced by farm size, water security, ownership, seed quality, family labour endowment and female labour participation.

In hydrological climate-change impact studies, regional
climate models (RCMs) are commonly used to transfer
large-scale global climate model (GCM) data to smaller
scales and to provide more detailed regional information.
Due to systematic and random model errors, however, RCM
simulations often show considerable deviations from observations.
This has led to the development of a number of correction
approaches that rely on the assumption that RCM errors
do not change over time. It is in principle not possible
to test whether this underlying assumption of error stationarity
is actually fulfilled for future climate conditions. In this
study, however, we demonstrate that it is possible to evaluate
how well correction methods perform for conditions different
from those used for calibration with the relatively simple
differential split-sample test.
For five Swedish catchments, precipitation and temperature
simulations from 15 different RCMs driven by ERA40
(the 40 yr reanalysis product of the European Centre for
Medium-Range Weather Forecasts (ECMWF)) were corrected
with different commonly used bias correction methods.
We then performed differential split-sample tests by dividing
the data series into cold and warm respective dry and
wet years. This enabled us to cross-evaluate the performance
of different correction procedures under systematically varying
climate conditions. The differential split-sample test
identified major differences in the ability of the applied correction
methods to reduce model errors and to cope with nonstationary
biases. More advanced correction methods performed
better, whereas large deviations remained for climate
model simulations corrected with simpler approaches. Therefore,
we question the use of simple correction methods such
as the widely used delta-change approach and linear transformation
for RCM-based climate-change impact studies. Instead,
we recommend using higher-skill correction methods
such as distribution mapping.

General circulation model projections are inadequate for impact studies due to their coarse resolution and inherent biases. Therefore, there is a need to accurately correct these biases and examine the abilities of these correction techniques in replicating the observed climate change signals. Over the Komadugu-Yobe Basin, this study investigates the performance of univariate empirical and parametric quantile mapping, as well as multivariate bias-correction (BC) techniques , using; N-dimensional probability density function transform (MBCN), Spearman rank correlation dependency (MBCR) and Pearson correlation dependency (MBCP) in correcting the biases in some selected CMIP5-Coordinated Regional Climate Downscaling Experiment model temperature projections based on the historical period (1975-2005) and the future (2020-2050) for annual, dry and wet periods under two emission scenarios (RCP 4.5 and 8.5). The temporal temperature variability of the BC outputs is further assessed. In correcting the temperature distribution, both univari-ate BC perform well for all seasons, while MBCN performs best during the dry season. The latitudinal-time cross-section result shows that high temperatures are mismatched either by the years of occurrence or by the latitudes at which they occurred. The univariate BC techniques and MBCN performed best in replicating the observed monthly variability. There is a positive temperature trend in most parts of the basin, however, with increased magnitudes in the future. Overall, the bias-corrected model ensemble mean performs better in replicating the trend in temperature, while the multivariate BC methods correct the joint dependence structure between them modelled variables, thereby providing a general-purpose methodology to the climate community.

Closed chambers are the most common method to determine methane (CH4) fluxes in peatlands. The concentration change over time is monitored, and the flux is usually calculated by the slope of a linear regression function. However, chambers tend to slow down the gas diffusion by changing the concentration gradient between soil and atmosphere. Theoretically, this would result in a near-exponential concentration change in the chamber headspace. Here, we present data from a laboratory experiment and from two field campaigns on the basis of which we evaluate flux calculation approaches based either on linear or exponential regression models. To compare the fit performances of the two models, we used the Akaike Information Criterion with small sample second order bias correction (AICc). For checking the quality of flux data, we used the standard deviation of residuals. The calibration system in the laboratory experiment used during the chamber calibration campaign at Hyytiälä Forestry Field Station in August 2008 has been described by Pumpanen et al. (2004). Five different flux levels on two different soil porosities where tested. Preliminary results show that most concentration-over-time datasets were best described by the exponential model as evaluated by the AICc. It appeared that the flux calculation using the exponential model was better suited to determine the preset fluxes than that using the linear model. In the dataset of the first field campaign (April to October 2007) from Salmisuo (Finland, 62.46˚ N, 30.58˚ E), however, the majority of fluxes was best fitted with a linear regression on all microsite types. Those fluxes which are best fitted exponentially are most probable due to chamber artefacts. They occurred mostly during a drought period in August 2007, which seemed to increase the artificial impact of the chamber. However, these results might be site-specific: In Ust-Pojeg (Russia, 61.56˚ N, 50.13˚ E), where CH4 emissions are supposed to be higher than in Salmisuo, more fluxes (ca. 30% during April to June 2008) were selected to be best fitted with an exponential regression. References: Pumpanen, J., Kolari, P., Ilvesniemi, H., Minkkinen, K., Vesala, T., Niinistö, S., Lohila, A., Larmola, T., Morero, M., Pihlatie, M., Janssens, I., Curiel Yuste, J., Grünzweig, J. M., Reth, S., Subke, J.-A., Savage, K., Kutsch, W., Østreng, G., Ziegler, W., Anthoni, P., Lindroth, A. & Hari, P. 2004. Comparison of different chamber techniques for measuring soil CO2 efflux. Agricultural and Forest Meteorology 123, 159-176.

Currently three air quality modelling systems operate routinely with high-resolution over mainland Portugal for forecasting purposes, namely MM5-CHIMERE, MM5-EURAD, and CALIOPE. They each operate daily using different horizontal resolutions (10 km  10 km, 5 km  5 km, and 4 km  4 km, respectively), specific physical and chemical parameterizations, and their own emission pre-processors (with a common EMEP emission database source but different spatial disaggregation methodologies). The operational BSC-DREAM8b model is coupled offline within the aforementioned air quality systems to provide the Saharan dust contribution to particulate matter. Bias-correction studies have demonstrated the benefit of using past observational data to reduce systematic model forecast errors. The present contribution aims to evaluate the application of two bias-correction techniques, the multiplicative ratio and the Kalman filter, in order to improve air quality forecasts for Portugal. Both techniques are applied to the three modelling systems over the full year of 2010. Raw and unbiased model results for the main atmospheric pollutants (O 3 , NO 2 , SO 2 , PM10, and PM2.5) are analysed and compared with data from 18 monitoring stations distributed within inland Portugal on an hourly basis. Statistical analysis shows that both bias-correction techniques improve the raw forecast skills (for all the modelling systems and pollutants). In the case of O 3 max-8 h, correlation coefficients improve by 19e45%, from 0.56e0.81 (raw models) to 0.78e0.86 (corrected models). PM2.5 also presents significant improvements, for example correlation coefficients increase by more than 50% (with both techniques), reaching values between 0.50 and 0.64. The corrected primary pollutants NO 2 and SO 2 demonstrate significant relative improvements compared to O 3 , mostly because the original modelling system skills are lower for those species. Although the applied techniques have different mathematical formulations and complexity levels, there are comparable answers for all of the forecasting systems. Analysis performed over specific situations such as air quality episodes and cases of unvalidated or missing data reveals different behaviours of the bias-correction techniques under study. The results confirm the advantage of the application of biascorrection techniques for air quality forecasts. Both techniques can be applied routinely in operational forecast systems and they will be useful to provide accurate alerts about exceedances to the population.

Many polarimetric classification algorithms have been proposed in the literature. It has been shown that the use of spatial information provides better sensitivity for class separation such as forests. In this paper, we wish to address this issue, testing and comparing two polarimetric SAR (Synthetic Aperture Radar) segmentation approaches incorporating contextual information. The first approach is contextual fuzzy clustering based on the use of bias correction defined by a texture feature, while the second one is a Markovian segmentation based on non parametric textural modeling. These approaches have been tested on Oberpfaffenhofen area in Munich and the PolSAR images are acquired in the P band. In both cases, texture considering allowed to improve greatly the rates of good identification.

Background Information theory is an increasingly popular framework for studying how the brain encodes sensory information. Despite its widespread use for the analysis of spike trains of single neurons and of small neural populations, its application to the analysis of other types of neurophysiological signals (EEGs, LFPs, BOLD) has remained relatively limited so far. This is due to the limited-sampling bias which affects calculation of information, to the complexity of the techniques to eliminate the bias, and to the lack of publicly available fast routines for the information analysis of multi-dimensional responses. Results Here we introduce a new C- and Matlab-based information theoretic toolbox, specifically developed for neuroscience data. This toolbox implements a novel computationally-optimized algorithm for estimating many of the main information theoretic quantities and bias correction techniques used in neuroscience applications. We illustrate and test the toolbox in several ways. First, we verify that these algorithms provide accurate and unbiased estimates of the information carried by analog brain signals (i.e. LFPs, EEGs, or BOLD) even when using limited amounts of experimental data. This test is important since existing algorithms were so far tested primarily on spike trains. Second, we apply the toolbox to the analysis of EEGs recorded from a subject watching natural movies, and we characterize the electrodes locations, frequencies and signal features carrying the most visual information. Third, we explain how the toolbox can be used to break down the information carried by different features of the neural signal into distinct components reflecting different ways in which correlations between parts of the neural signal contribute to coding. We illustrate this breakdown by analyzing LFPs recorded from primary visual cortex during presentation of naturalistic movies. Conclusion The new toolbox presented here implements fast and data-robust computations of the most relevant quantities used in information theoretic analysis of neural data. The toolbox can be easily used within Matlab, the environment used by most neuroscience laboratories for the acquisition, preprocessing and plotting of neural data. It can therefore significantly enlarge the domain of application of information theory to neuroscience, and lead to new discoveries about the neural code.

a b s t r a c t Self-start capability is an important feature of wind turbines. It allows to obtain simpler and cheaper turbines not actively controlled. Different approaches to describe the self-start of an H-blade Darrieus rotor are presented and compared in the present work. The Blade Element Momentum (BEM) approach was compared with two and three-dimensional CFD simulations. The tipespeed ratio versus power coefficient curves and the evolution of the trust forces over a blade revolution highlighted the limits and the strengths of each approach.

The oceans are a major source for particles that play an important role in many atmospheric processes. In Europe sea salt may contribute significantly to particulate matter concentrations. We have compiled sodium concentration data as a tracer for sea salt for 89 sites in Europe to provide more insight in the distribution of sea salt across Europe. The annual average sea salt concentrations above land were estimated to range between 0.3 and almost 13 mg m À3 . Maximum concentrations are found at the Irish coast. At coastal sites along the Atlantic and North Sea coast concentrations tend to be around 5 mg m À3 . More inland locations up to about 300 km away from the coast tend to show concentrations between 2 and 5 mg m À3 , whereas sites further away from the coast are characterized by lower concentrations. An analysis of the representativity of the data with respect to a long term average showed that the long average is associated with a standard deviation of around 15%. The compilation of observations provides an improved overview of sea salt concentrations in Europe as well as an improved basis for model validation. Verification of the results of the LOTOS-EUROS model learned that the model represents well the spatial variability of the observed sea salt concentrations very well. However, the absolute concentrations are significantly overestimated due to large uncertainties in the emission and dry deposition parameterizations. Using the high explained variability in the gradients across Europe, the bias-corrected modelled distribution serves as a best estimate of the sea salt distribution across Europe for 2005.

The purpose of this paper is to investigate wage structures of professional workers in the Israeli labor market using data from the 1983 Israeli census and correcting for selectivity at the state of entrance into the occupation. The sample of professionals is decomposed into several subsamples: Jewish men and Jewish women; within the Jewish sample a distinction is made between Westerners and Easterners. The core of this study is the investigation of wage differentials between the various groups, taking into account differences in entrance probabilities. The standard Oaxaca decomposition does not take into account different probabilities of entering the professional occupations (i.e., occupational segregation). In order to incorporate this type of segregation into the wage differential decompositions, two statistical methodologies are merged: the Oaxaca methodology and the Heckman selectivity bias correction procedure. The decomposition procedure is then modified in order to take int...

Although estimation of average blood pressure is commonly done with oscillometric measurements, confidence intervals (CIs) for systolic blood pressure (SBP) and diastolic blood pressure (DBP) are not usually estimated. This paper adopts bootstrap methodologies to build CI from a small sample set of measurements, which is a situation commonly encountered in practice. Three bootstrap methodologies, namely, nonparametric percentile bootstrap, standard bootstrap, and bias-corrected and accelerated bootstrap are investigated. A two-step methodology is proposed based on pseudomeasurements using bootstrap principles to first derive the pseudomaximum amplitudes and then the pseudoenvelopes (PEs). The SBP and DBP are estimated using the new relationships between mean cuff pressure and PE and then the CIs for such estimates are obtained. In order to reduce the amount of processing, a single-step methodology that directly derives PE using bootstrap principles is also presented. Application of the proposed methodology on an experimental data set of 85 patients with five sets of measurements for each patient has yielded a narrower CI than the currently available conventional methods such as Student's t-distribution method.

1] In this study, we evaluate the accuracy of four regional climate models (NHRCM, NRAMS, TRAMS, and TWRF) and one bias correction-type statistical model (CDFDM) for daily precipitation indices under the present-day climate (1985)(1986)(1987)(1988)(1989)(1990)(1991)(1992)(1993)(1994)(1995)(1996)(1997)(1998)(1999)(2000)(2001)(2002)(2003)(2004) over Japan on a 20 km grid interval. The evaluated indices are (1) mean precipitation, (2) number of days with precipitation ≥1 mm/d (corresponds to number of wet days), (3) mean amount per wet day, (4) 90th percentile of daily precipitation, and (5) number of days with precipitation ≥90th percentile of daily precipitation. The boundary conditions of the dynamical models and the predictors of the statistical model are given from the single reanalysis data, i.e., JRA25. Both types of models successfully improved the accuracy of the indices relative to the reanalysis data in terms of bias, seasonal cycle, geographical pattern, cumulative distribution function of wet-day amount, and interannual variation pattern. In most aspects, NHRCM is the best model of all indices. Through the intercomparison between the dynamical and statistical models, respective strengths and weaknesses emerged. Briefly, (1) many dynamical models simulate too many wet days with a small amount of precipitation in humid climate zones, such as summer in Japan, relative to the statistical model, unless the cumulus convection scheme improved for such a condition is incorporated; (2) a few dynamical models can derive a better high-order percentile of daily precipitation (e.g., 90th percentile) than the statistical model;

It is becoming widely accepted that radar polarimetry provides accurate and informative weather measurements, while phased-array technology can shorten data updating time. In this paper, a theory of phased array radar (PAR) polarimetry is developed to establish the relation between electric fields at the antenna of the PAR and the fields in a resolution volume filled with hydrometeors. It is shown that polarimetric measurements with an electronically steered beam can cause measurement biases that are comparable to or even larger than the intrinsic polarimetric characteristics of hydrometeors. However, these biases are correctable if the transmitted electric fields are known. A correction to the measured scattering matrix that removes biases in meteorological variables is derived. The challenges and opportunities for weather sensing with a polarimetric PAR are discussed.

Although Global Climate Models (GCMs) are regarded as the best tools available for future climate projections, there are biases in simulating precipitation and temperature due to their coarse spatial resolution and cannot be used directly to assess the impact of projected climate change. The study objective was to investigate how bias correction methods impact the modelled future climate

We discuss analytical bias corrections for maximum likelihood estimators in a regression model where the errors are Student-t distributed with unknown degrees of freedom. We propose a reparameterization of the number of degrees of freedom that produces a bias corrected estimator with very good small sample properties. This unknown number of degrees of freedom is assumed greater than 1, to guarantee a bounded likelihood function. We discuss some special cases of the general model and present some simulations which show that the corrected estimates perform better than their corresponding uncorrected versions in finite samples.

The non-traditional stable strontium (Sr) isotopes have received increasing attention recently as new geochemical tracers for studying Sr isotopic fractionation and source identification. This has been attributed to the advancement in multiple-collector inductively coupled plasma mass spectrometry (MC-ICP-MS), allows to determine precisely and simultaneously of the triple Sr isotopes. In this study, we applied a modified empirical external normalization (EEN) MC-ICPMS procedure for mass bias correction in Sr isotopic measurement using 92 Zr/ 90 Zr. High-purity Zr Standard was spiked into sample solutions and the degree of fractionation was calculated off-line using an exponential law. The long-term external reproducibility for NIST SRM 987 ı 87 Sr and ı 88 Sr was better than 0.040‰ and 0.018‰ (2SD), respectively. The IAPSO standard seawater was used as a secondary standard to validate the analytical protocol and the absolute ratios measured were 0.709161 ± 0.000018 for 87 Sr/ 86 Sr, 0.177 ± 0.021‰ for ı 87 Sr, and 0.370 ± 0.026‰ for ı 88 Sr (2SD, n = 7). These values are in good agreement with the literature data analyzed by thermal ionization mass spectrometry (TIMS) double spike technique. Rock standards, BHVO-2, BCR-2 and AGV-2 were also analyzed to validate the robustness of the methodology and showed identical results with literature data. Compared to previous 91 Zr/ 90 Zr correction, we obtained improved results based on 92 Zr/ 90 Zr, probably due to similar mass difference between 92 Zr/ 90 Zr and measured Sr isotopes. The new analytical protocol presented in this study not only improves the analytical precision but also increases sample efficiency by omitting the use of the standard-sample bracketing (SSB) procedure.

Global circulation models (GCMs) depict different pictures for the future of Nile basin flows in general and for the Blue Nile sub-basin in particular. This study analyses the output of 17 GCMs included in the 4th IPCC assessment report. Downscaled precipitation and potential evapotranspiration (PET) scenarios for the 2081-2098 period 5 were constructed for the upper Blue Nile basin. These were used to drive a fine-scale hydrological model of the Nile Basin to assess their impacts on the flows of the upper Blue Nile at Diem, which accounts for about 60% of the total annual Nile yield. All models showed increases in temperature with annual values ranging from 2 • C to 5 • C. All GCMs also showed increases in total annual PET varying from +2 to +14%.

This paper presents a new Copula-based method for further downscaling regional climate simulations. It is developed, applied and evaluated for selected stations in the alpine region of Germany. Apart from the common way to use Copulas to model the extreme values, a strategy is proposed which allows to model continous time series. In this paper, we focus on the positive pairs of observed and modelled (RCM) precipitation.
As the concept of Copulas requires independent and identically distributed (iid) random variables, meteorological fields are transformed using an ARMA-GARCH time series model. The dependence structures between modelled and observed precipitation are conditioned on the prevailing large-scale weather situation. The impact of the altitude of the stations and their distance to the surrounding modelled grid cells is analyzed.
Based on the derived theoretical Copula models, stochastic rainfall simulations are performed, finally allowing for bias corrected and locally refined RCM simulations.

We introduce, for the first time, a new class of Birnbaum-Saunders nonlinear regression models potentially useful in lifetime data analysis. The class generalizes the regression model described by Rieck and Nedelman [1991, A log-linear model for the Birnbaum-Saunders distribution, Technometrics, 33, 51-60]. We discuss maximum likelihood estimation for the parameters of the model, and derive closedform expressions for the second-order biases of these estimates. Our formulae are easily computed as ordinary linear regressions and are then used to define bias corrected maximum likelihood estimates. Some simulation results show that the bias correction scheme yields nearly unbiased estimates without increasing the mean squared errors. We also give an application to a real fatigue data set.

A method has been developed for the in situ determination of Re-Os isotopes in single grains of sulfides in mantle-derived peridotites using a laser ablation microprobe attached to a multicollector-induced coupled plasma mass spectrometer (MC-ICPMS). High-precision Os isotope analysis by MC-ICPMS is demonstrated by the measurement of interlaboratory Os standards. Evaluation of mass bias correction procedures shows that the exponential law provides the best fit to the Os isotope data and that the ratio of the mass fractionation coefficients for Re and Os remains constant for the range of typical instrument operating conditions. This relationship enables the accurate and precise correction of the isobaric interference of 187Re on 187Os for 187Re/188Os values up to 1.6.Results are presented for single sulfide inclusions in olivine macrocryts from kimberlites in the Siberian and Slave Cratons, and sulfides enclosed in silicates and interstitial to silicates in peridotite xenoliths from the Slave Craton and Massif Central, France. Enclosed sulfides larger than 50 μm in diameter and with Os contents ≥40 ppm give 187Os/188Os ratios with a precision of 0.1% (2 SE), which is equivalent to N-TIMS whole-rock data. Interstitial sulfides typically have lower Os (10 to 30 ppm) and give analyses with lower precision (∼1 to 2%) but still provide valuable information on the movement of Os within the lithosphere. The sulfide inclusions in silicates preserve significantly less radiogenic Os isotopic compositions than interstitial sulfides and accordingly produce significantly older and more realistic Re-Os age information. Whole-rock Os isotope compositions reflect the proportions of different generations of enclosed and interstitial sulfides; this calls into question the significance of many published “depletion ages.”

Bhattacharyya coefficient is a popular method that uses color histograms to correlate images. Bhattacharyya Coefficient is believed to be the absolute similarity measure for frequency coded data and it needs no bias correction. In this paper, we show that when this method is applied to gray scale images, it produces biased results. Correlation based on this measure is not adequate for common gray scale images, as the color in grayscale is not a sufficient feature. The b iased ness is explored and demonstrated through numerous experiments with different kinds of non-rigid maneuvering objects in cluttered and less cluttered environments, in context to the object tracking. The spectral performance of the Bhattacharyya curve is compared with the spatial matching criterion i.e. Mean Square Difference.

Abstract This work presents hydrological simulations on the Ebro River basin (Spain), using both control (1961–1990) and future (2071–2100) climate scenarios, to investigate the effect of climate change on water resources. Using the Soil and Water Assessment Tool hydrological model, simulations were carried out in four subcatchments representative of typical situations within the basin. Model parameters were identified using sensitivity analysis and long-term calibration procedures, which enabled the historical behaviour of the catchments to be reproduced. Following validation, the parameters were used to simulate the effects of climate change on future streamflows. Bias-corrected daily time series of precipitation and mean temperature from two regional climate models (RCMs), using the same medium-high SRES A2 emissions scenario, were used as drivers of the hydrological simulations during the future scenarios. Important annual and seasonal differences in the projected future precipitation and air temperature fields were observed among the RCMs. However, the two models project an overall increase in the mean annual temperature accompanied by a reduction in the annual precipitation, with the strongest differences with respect to the control period observed during the summer season. When these changes were used to project future streamflows, a general decrease was observed in the streamflows at the outlet of the selected catchments. Changes in streamflows were in general agreement with the projections of daily precipitation and temperature fields, with the largest drop in predicted monthly streamflows for the subcatchments with the lowest aridity index, and seasonal differences that appears to be related to the elevation range of the subcatchments. Graphical Abstract

Machine learning approaches (Neural Networks and Support Vector Machines) are used to explore the reasons for a persistent bias between aerosol optical depth (AOD) retrieved from the MODerate resolution Imaging Spectroradiometer (MODIS) and the accurate ground-based Aerosol Robotics Network (AERONET). While this bias falls within the expected uncertainty of the MODIS algorithms, there is room for algorithm improvement. The results of the machine learning approaches suggest a link between the MODIS AOD biases and surface type. MODIS-derived AOD may showing dependency on surface type either because of the link between surface type and surface reflectance, or because of the covariance between aerosol properties and surface type.

a v a i l a b l e a t w w w . s c i e n c e d i r e c t . c o m j o u r n a l h o m e p a g e : w w w . e l s e v i e r . c o m / l o c a t e / j h y d r o l understanding of sediment transport mechanisms and predictions in reforested watersheds, which should ultimately lead to better management of forest ecosystems. ª

This paper investigates the determinants of bilateral immigrant flows to 19 OECD countries between 1998 and 2007 from both advanced and developing origin countries. We pay particular attention to dynamics by including both the lagged migrant flow and the migrant stock to capture partial adjustment and network effects. To correct for the dynamic panel data bias of the fixed effects estimator we use a bootstrap algorithm. Our results indicate that immigrants are primarily attracted by better income opportunities and higher growth rates abroad. Also short-run increases in the host country’s employment rate positively affect migration from both advanced and developing countries. High public services, on the other hand, discourage migration from advanced countries but exert a pull on migration from developing sources, in line with the welfare state hypothesis. Finally, we find evidence for both partial adjustment and the presence of strong network effects. This confirms that both should ...

In this paper, we propose a novel approach to econometric forecasting of stationary and ergodic time series within a panel-data framework. Our key element is to employ the (feasible) bias-corrected average forecast. Using panel-data sequential asymptotics we show that it is potentially superior to other techniques in several contexts. In particular, it is asymptotically equivalent to the conditional expectation, i.e., has an optimal limiting mean-squared error. We also develop a zeromean test for the average bias and discuss the forecast-combination puzzle in small and large samples. Monte-Carlo simulations are conducted to evaluate the performance of the feasible bias-corrected average forecast in …nite samples. An empirical exercise based upon data We are especially grateful for the comments and suggestions given by two anonymous referees,

Lutz-Kelker bias corrected absolute magnitude calibrations for the detached binary systems with main-sequence components are presented. The absolute magnitudes of the calibrator stars were derived at intrinsic colours of Johnson-Cousins and 2MASS (Two Micron All Sky Survey) photometric systems. As for the calibrator stars, 44 detached binaries were selected from the Hipparcos catalogue, which have relative observed parallax errors smaller than 15% ($\sigma_{\pi}/\pi\leq0.15$). The calibration equations which provide the corrected absolute magnitude for optical and near-infrared pass bands are valid for wide ranges of colours and absolute magnitudes: $-0.18<(B-V)_{0}<0.91$, $-1.6<M_{V}<5.5$ and $-0.15<(J-H)_{0}<0.50$, $-0.02<(H-K_{s})_{0}<0.13$, $0<M_{J}<4$, respectively. The distances computed using the luminosity-colours (LCs) relation with optical (BV) and near-infrared ($JHK_{s}$) observations were compared to the distances found from various other methods. The results show that new absolute magnitude calibrations of this study can be used as a convenient statistical tool to estimate the true distances of detached binaries out of Hipparcos' distance limit.

We infer CO 2 surface fluxes using satellite observations of mid-tropospheric CO 2 from the Tropospheric Emission Spectrometer (TES) and measurements of CO 2 from surface flasks in a time-independent inversion analysis based on the GEOS-Chem model. Using TES CO 2 observations over oceans, spanning 40 • S-40 • N, we find that the hor-5 izontal and vertical coverage of the TES and flask data are complementary. This complementarity is demonstrated by combining the datasets in a joint inversion, which provides better constraints than from either dataset alone, when a posteriori CO 2 distributions are evaluated against independent ship and aircraft CO 2 data. In particular, the joint inversion offers improved constraints in the tropics where surface measure-10 ments are sparse, such as the tropical forests of South America, which the joint inversion suggests was a weak sink of −0.17 ± 0.20 Pg C in 2006. Aggregating the annual surface-to-atmosphere fluxes from the joint inversion yields −1.13 ± 0.21 Pg C for the global ocean, −2.77 ± 0.20 Pg C for the global land biosphere and −3.90 ± 0.29 Pg C for the total global natural flux (defined as the sum of all biospheric, oceanic, and 15 biomass burning contributions but excluding CO 2 emissions from fossil fuel combustion). These global ocean, global land and total global fluxes are shown to be in the range of other inversion results for 2006. To achieve these results, a latitude dependent bias in TES CO 2 in the Southern Hemisphere was assessed and corrected using aircraft flask data, and we demonstrate that our results have low sensitivity to variations 20 in the bias correction approach. Overall, this analysis suggests that future carbon data assimilation systems can benefit by integrating in situ and satellite observations of CO 2 and that the vertical information provided by satellite observations of mid-tropospheric CO 2 combined with measurements of surface CO 2 , provides an important additional constraint for flux inversions. Chevallier et al., 2010a) using in situ observations from instruments at surface stations, towers, ships and aircraft and/or flask samples collected from these platforms, then later analyzed in a laboratory (Conway et al., 1994). Measurement 25 coverage has increased over the years, and forward and inverse modeling techniques have also improved, but a major limitation in achieving further reductions in CO 2 flux uncertainties is the sparse data coverage that remains throughout the tropics, extra-4266