Correction Of Systematic Errors Research Articles

How Well Can We Correct Systematic Errors in Historical XBT Data?

AbstractBiases have been identified in historical expendable bathythermograph (XBT) datasets, which are one of the major sources of uncertainty in the ocean subsurface database. More than 10 correction schemes were proposed; however, their performance has not been collectively evaluated and compared. This study quantifies how well 10 different available schemes can correct the historical XBT data by comparing the corrected XBT data with collocated reference data in both the World Ocean Database (WOD) 2013 and the EN4 dataset. Four different metrics are proposed to quantify their performances. The results indicate CH14 is the best among the currently available methods, and L09/G12/GR10 can be used with some caveats. To test the robustness of the schemes, we further train the CH14 and L09 by using 50% of the XBT–reference data and the schemes are tested by using the remaining data. The results indicate that the two schemes are robust. Moreover, the EN4 and WOD comparison datasets show a systematic difference of XBT error (~0.01°C on a global scale and 0–700 m on average). influences of quality control and data processing have been investigated. Additionally, the side-by-side XBT–CTD comparison experiment is used to examine the correction schemes and provides independent high-quality data for the assessment. The schemes that best correct the global datasets do not always perform as well at correcting the side-by-side dataset, and further examination of the discrepancy in performance is still required. Finally, CH14 and L09 result in very similar ocean heat content (OHC) change estimates in the upper 700 m since 1966, suggesting the potential of reducing XBT-induced error in OHC estimates.

Validity of the total body centre of gravity during gait using a markerless motion capture system

Purpose: The purpose of this study was to examine the validity of total body centre of gravity (COG) measurement during gait with markerless motion capture system (MLS) on the basis of values acquired with a marker-based motion capture system (MBS).Materials and methods: Thirty young healthy subjects walked on a flat surface as coordinate data from their bodies were acquired using the Kinect v2 (as a MLS) and Vicon systems (as a MBS). COG was calculated using coordinate data of the total body. Comparisons of COG ensemble curves in the mediolateral and vertical directions were performed between MLS and MBS throughout the gait cycle. The relative consistency between these systems was assessed using Pearson correlation coefficients.Results: The COG trajectory made by using MLS data followed the trend of the COG trajectory with MBS in the mediolateral direction. In the vertical direction, however, the COG trajectories did not match between two systems. High correlation coefficients (r > 0.79) were observed from 30% to 80% of the gait cycle. The greatest difference of COG between MLS and MBS in the mediolateral direction was 1.1 mm. Differences in the vertical direction appeared to be proportional to the distance between the participant and the Kinect v2 sensor.Conclusion: In the mediolateral direction, COG calculated with MLS data during gait was validated with COG calculated on the basis of a MBS. Further correction of systematic error is necessary to improve the validity of COG calculations in the vertical direction.

Set-up errors in head and neck cancer treated with IMRT technique assessed by cone-beam computed tomography: a feasible protocol.

PurposeTo investigate set-up errors, suggest the adequate planning target volume (PTV) margin and image-guided radiotherapy frequency in head and neck (H&N) cancer treated with intensity-modulated radiotherapy (IMRT) assessed by kV cone-beam computed tomography (CBCT).MethodsWe analyzed 360 CBCTs in 60 patients with H&N cancer treated with IMRT. The target delineation was contoured according to ICRU62. PTVs were generated by adding a 3–5 mm margin in all directions to the respective clinical target volumes. The kV CBCT images were obtained at first three days of irradiation and weekly thereafter. The overall mean displacement, range, systematic (∑) and random (σ) errors were calculated. Adequate PTV margins were calculated according to the van Herk formula (2.5∑ + 0.7r).ResultsThe mean of set-up errors was less than 2 mm in any direction. The overall frequency of set-up displacements greater than 3 mm was 3.9% in medial-lateral (ML) direction, 8% in superior-inferior (SI) direction, and 15.5% in anterior-posterior (AP) direction. The range of translations shifts was 0–9 mm in ML direction, 0–5 mm in SI direction and 0–10 mm in AP direction, respectively. After systematic set-up errors correction, the adequate margin to overcome the problem of set-up errors was found to be less than 3 mm.ConclusionImage-guided kV CBCT was effective for the evaluation of set-up accuracy in H&N cancer. The kV CBCT at first three fractions and followed-by weekly appears adequate for reducing significantly set-up errors in H&N cancer treated with IMRT technique. Finally, 3–5 mm PTV margins appear adequate and safe to overcome the problem of set-up errors.

Quantifying and reducing errors in equilibrium moisture content measurements with dynamic vapor sorption (DVS) experiments

Dynamic vapor sorption (DVS) measurements are widely used to collect water vapor sorption isotherms for wood and other cellulosic materials. Equilibrium moisture content (EMC) is typically assumed to have been reached when the rate of change in moisture content with time ( $${\text{d}}M/{\text{d}}t$$ ) drops below a certain value. However, the errors associated with determining EMC in this manner have never been characterized. Here, an operational definition of equilibrium for DVS measurements is provided, and twenty test cases over four cellulosic materials are presented where the relative humidity was stepped up or down and then held constant until equilibrium was reached. Then, both the time to reach various $${\text{d}}M/{\text{d}}t$$ “stop criteria” and the errors in EMC associated with those stop criteria are quantified. The errors in the EMC from the widely used 0.002% min−1 stop criterion are found to be as large as 1.2% MC, and the average error for 20 test cases is 0.5% MC, which are much larger than the 0.1% MC error claimed in the literature. Longer data collection times are recommended, and a more stringent $${\text{d}}M/{\text{d}}t$$ criterion (0.0003% min−1, using a 2-h window) for cellulosic materials is proposed. The errors with this criterion are less than 0.75% MC, and the average error is 0.3% MC. Furthermore, it is shown that the errors for a given stop criterion are systematic and can be fairly well characterized with a simple linear regression. Finally, a correction for systematic error is proposed that results in more accurate EMC values with shorter hold times.

An innovative LDV data processing method for statistical error corrections. Application to homogeneous and non-homogeneous seeding

In this experimental study, statistical errors in the processing of raw Laser Doppler Velocimetry (LDV) data are discussed. Acquisitions have been made in an open-circuit wind tunnel in the wake flow of a simplified car model, for validation rates up to hundreds of Hertz. Both homogeneous and non-homogenous seeding conditions are considered. For homogeneous spatial concentration seeding, the velocity systematic error is presented regarding previous works. Correction methods are then discussed according to their assumptions. Then, this paper aims at characterizing the burst random error related to non-homogeneous seeding. The experimental assessment of the available methods (interarrival time weighting and transit time weighting) according to the seeding conditions allows a deeper understanding of their ability to correct both velocity systematic error and burst random error. Finally, a new method is proposed mixing the previous one, leading to reliable (velocity systematic error correction) and repeatable (burst random error correction) results whatever the seeding conditions in our range of validation rates.

Land Subsidence in Chiayi, Taiwan, from Compaction Well, Leveling and ALOS/PALSAR: Aquaculture-Induced Relative Sea Level Rise

Chiayi County is located in the largest alluvial plain of Taiwan with extensive aquaculture and rice farming sustained by water extracted from groundwater wells. Chiayi is a typical aquaculture area affected by land subsidence, yet such lands worldwide combine to provide nearly 90% of global aquaculture products, greatly reducing oceanic overfishing problems. This study uses precision leveling, multi-layer compaction monitoring well (MLCW) and spaceborne SAR interferometry (InSAR) to examine the cause and effect of land subsidence in Chiayi associated with groundwater extractions and changes. Heights at benchmarks in a leveling network are measured annually and soil compactions at 24–26 layers up to 300-m depths at 7 MLCWs are collected at one-month intervals. Over 2007–2011, 15 ALOS/PALSAR images are processed by the method of TCPInSAR to produce subsidence rates. All sensors show that land subsidence occur in most parts of Chiayi, with rates reaching 4.5 cm/year around its coast, a result of groundwater pumping from shallow to deep aquifers. MLCWs detect mm-accuracy seasonal soil compactions coinciding with groundwater level fluctuations and causing dynamic compactions. Compactions near Taiwan High Speed Rail may reduce the strength of the rail’s supporting columns to degrade its safety. The SAR images yield subsidence rates consistent with those from leveling and compaction wells after corrections for systematic errors by the leveling result. Subsidence in Chiayi’s coastal area leads to relative sea level rises at rates up to 15 times larger than the global eustatic sea level rising rate, a risk typical for world’s aquaculture-rich regions. At the fish pond-covered Budai Township, InSAR identifies subsidence spots not detected by leveling, providing crucial geo-information for a sustainable land management for aquaculture industry.

In-Situ Systematic Error Correction for Digital Volume Correlation Using a Reference Sample

The self-heating effect of a laboratory X-ray computed tomography (CT) scanner causes slight change in its imaging geometry, which induces translation and dilatation (i.e., artificial displacement and strain) in reconstructed volume images recorded at different times. To realize high-accuracy internal full-field deformation measurements using digital volume correlation (DVC), these artificial displacements and strains associated with unstable CT imaging must be eliminated. In this work, an effective and easily implemented reference sample compensation (RSC) method is proposed for in-situ systematic error correction in DVC. The proposed method utilizes a stationary reference sample, which is placed beside the test sample to record the artificial displacement fields caused by the self-heating effect of CT scanners. The detected displacement fields are then fitted by a parametric polynomial model, which is used to remove the unwanted artificial deformations in the test sample. Rescan tests of a stationary sample and real uniaxial compression tests performed on copper foam specimens demonstrate the accuracy, efficacy, and practicality of the presented RSC method.

Cluster mass calibration at high redshift: HST weak lensing analysis of 13 distant galaxy clusters from the South Pole Telescope Sunyaev–Zel'dovich Survey

We present an HST/ACS weak gravitational lensing analysis of 13 massive high-redshift (z_median=0.88) galaxy clusters discovered in the South Pole Telescope (SPT) Sunyaev-Zel'dovich Survey. This study is part of a larger campaign that aims to robustly calibrate mass-observable scaling relations over a wide range in redshift to enable improved cosmological constraints from the SPT cluster sample. We introduce new strategies to ensure that systematics in the lensing analysis do not degrade constraints on cluster scaling relations significantly. First, we efficiently remove cluster members from the source sample by selecting very blue galaxies in V-I colour. Our estimate of the source redshift distribution is based on CANDELS data, where we carefully mimic the source selection criteria of the cluster fields. We apply a statistical correction for systematic photometric redshift errors as derived from Hubble Ultra Deep Field data and verified through spatial cross-correlations. We account for the impact of lensing magnification on the source redshift distribution, finding that this is particularly relevant for shallower surveys. Finally, we account for biases in the mass modelling caused by miscentring and uncertainties in the concentration-mass relation using simulations. In combination with temperature estimates from Chandra we constrain the normalisation of the mass-temperature scaling relation ln(E(z) M_500c/10^14 M_sun)=A+1.5 ln(kT/7.2keV) to A=1.81^{+0.24}_{-0.14}(stat.) +/- 0.09(sys.), consistent with self-similar redshift evolution when compared to lower redshift samples. Additionally, the lensing data constrain the average concentration of the clusters to c_200c=5.6^{+3.7}_{-1.8}.

Software correction of geometric errors for multi-axis systems

Accuracy control for software-controlled multi-axis systems (i.e. machining centers and CMMs) is an important problem for contemporary industry. The paper concerns a method of systematic geometric errors correction for such systems. Practically this method increases machining and measuring accuracy without mechanical adjustments of the machine. The goal of accuracy improvement was achieved by means of completing the following tasks: measuring components of volumetric error by means of multi-function laser interferometer; visualizing volumetric error distribution within the machine’s workspace (error mapping); developing of error correction strategy based on the mapping; developing of postprocessor for CNC programs in order to get minimal errors within the machine’s workspace. The paper includes results of volumetric errors correction for three-axis CNC-controlled precision machine-tools. The results prove adequacy of models and efficiency of the method.

Automated Fractional Limb Volume Measurements Improve the Precision of Birth Weight Predictions in Late Third-Trimester Fetuses.

Fetal soft tissue can be assessed by using fractional limb volume as a proxy for in utero nutritional status. We investigated automated fractional limb volume for rapid estimate fetal weight assessment. Pregnant women were prospectively scanned for 2- and 3-dimensional fetal biometric measurements within 4 days of delivery. Performance of birth weight prediction models was compared: (1) Hadlock (Am J Obstet Gynecol 1985; 151:333-337; biparietal diameter, abdominal circumference, and femur diaphysis length); and (2) Lee (Ultrasound Obstet Gynecol 2009; 34:556-565; biparietal diameter, abdominal circumference, and automated fractional limb volume). Percent differences were calculated: [(estimated birth weight - actual birth weight) ÷ (actual birth weight] × 100. Systematic errors (accuracy) were summarized as signed mean percent differences. Random errors (precision) were calculated as ± 1 SD of percent differences. Fifty neonates were delivered at 39.4 weeks' gestation. The Hadlock model generated the most accurate birth weight (0.31%) with a mean random error of ±7.9%. Despite systematic underestimations, the most precise results occurred with fractional arm volume (-9.1% ± 5.1%) and fractional thigh (-5.2% ± 5.2%) models. The size and distribution of these prediction errors were improved after correction for systematic errors. Automated fractional limb volume measurements can improve the precision of weight predictions in third-trimester fetuses. Correction factors may be necessary to adjust underestimated systematic errors when using automated fractional limb volume with prediction models that are based on manual tracing of fetal limb soft tissue borders.

Phase retrieval via propagation-based interferometry

We present an extension of the Gerchberg-Saxton algorithm to allow for phase retrieval from interferometric data recorded at multiple sample-detector distances. A system of coupled waves is introduced where the information exchange can be controlled by use of relaxation parameters. Optimal parameters are investigated by numerical simulation and demonstrated to work with experimental data. We demonstrate that systematic errors such as pointing instabilities of the interfering waves involved and position uncertainties of the detector can be overcome by dimensional extension of the search space. Further it is shown that the proposed approach offers superior reconstruction quality as compared to conventional Gerchberg-Saxton type algorithms. We expect that the method described here will open up numerous possibilities for the correction of systematic errors in optical phase retrieval and lensless microscopy.

Mathematical Problems in Creating Large Astronomical Catalogs

Abstract The next stage after performing observations and their primary reduction is to transform the set of observations into a catalog. To this end, objects that are irrelevant to the catalog should be excluded from observations and gross errors should be discarded. To transform such a prepared data set into a high-precision catalog, we need to identify and correct systematic errors. Therefore, each object of the survey should be observed several, preferably many, times. The problem formally reduces to solving an overdetermined set of equations. However, in the case of catalogs this system of equations has a very specific form: it is extremely sparse, and its sparseness increases rapidly with the number of objects in the catalog. Such equation systems require special methods for storing data on disks and in RAM, and for the choice of the techniques for their solving. Another specific feature of such systems is their high “stiffiness”, which also increases with the volume of a catalog. Special stable mathematical methods should be used in order not to lose precision when solving such systems of equations. We illustrate the problem by the example of photometric star catalogs, although similar problems arise in the case of positional, radial-velocity, and parallax catalogs.

Regionalized PM2.5 Community Multiscale Air Quality model performance evaluation across a continuous spatiotemporal domain

The regulatory Community Multiscale Air Quality (CMAQ) model is a means to understanding the sources, concentrations and regulatory attainment of air pollutants within a model's domain. Substantial resources are allocated to the evaluation of model performance. The Regionalized Air quality Model Performance (RAMP) method introduced here explores novel ways of visualizing and evaluating CMAQ model performance and errors for daily Particulate Matter ≤ 2.5 μm (PM2.5) concentrations across the continental United States. The RAMP method performs a non-homogenous, non-linear, non-homoscedastic model performance evaluation at each CMAQ grid. This work demonstrates that CMAQ model performance, for a well-documented 2001 regulatory episode, is non-homogeneous across space/time. The RAMP correction of systematic errors outperforms other model evaluation methods as demonstrated by a 22.1% reduction in Mean Square Error compared to a constant domain wide correction. The RAMP method is able to accurately reproduce simulated performance with a correlation of r = 76.1%. Most of the error coming from CMAQ is random error with only a minority of error being systematic. Areas of high systematic error are collocated with areas of high random error, implying both error types originate from similar sources. Therefore, addressing underlying causes of systematic error will have the added benefit of also addressing underlying causes of random error.

A hybrid variational-ensemble data assimilation scheme with systematic error correction for limited-area ocean models

Abstract. A hybrid variational-ensemble data assimilation scheme to estimate the vertical and horizontal parts of the background error covariance matrix for an ocean variational data assimilation system is presented and tested in a limited-area ocean model implemented in the western Mediterranean Sea. An extensive data set collected during the Recognized Environmental Picture Experiments conducted in June 2014 by the Centre for Maritime Research and Experimentation has been used for assimilation and validation. The hybrid scheme is used to both correct the systematic error introduced in the system from the external forcing (initialisation, lateral and surface open boundary conditions) and model parameterisation, and improve the representation of small-scale errors in the background error covariance matrix. An ensemble system is run offline for further use in the hybrid scheme, generated through perturbation of assimilated observations. Results of four different experiments have been compared. The reference experiment uses the classical stationary formulation of the background error covariance matrix and has no systematic error correction. The other three experiments account for, or not, systematic error correction and hybrid background error covariance matrix combining the static and the ensemble-derived errors of the day. Results show that the hybrid scheme when used in conjunction with the systematic error correction reduces the mean absolute error of temperature and salinity misfit by 55 and 42 % respectively, versus statistics arising from standard climatological covariances without systematic error correction.

Total station survey monitoring through an observation window: a systematic error correction to remove the effects of glass properties

Total stations are used extensively for taking geodetic and engineering survey measurements. These measurements are made possible by accurate observation of targeted points. One example is deformation surveys, slope stability monitoring, in mines. Continuous monitoring necessitates sheltering or housing the instrument to protect it against harsh weather conditions that are characteristic of mining environments. Previous studies carried out by Afeni and Cawood (2012) revealed that the properties of the glass material matters when using a total station to take observations through a glass window of a structure that houses the total station at a mine. This study briefly discusses total station survey monitoring and developed systematic error correction formula to reduce the effect of glass properties, such as thickness and colour, on distance measurements through a shelter window glass in a surface mine environment. Each developed formula is combined with the atmospheric corrections formula given by Leica Geosystem, who is the manufacturer of the total station used in this research, to form a combined atmospheric and glass correction formula. The formula performed well when tested on two sets of observations.

An Automatic Quality Control Pipeline for High-Throughput Screening Hit Identification

The correction or removal of signal errors in high-throughput screening (HTS) data is critical to the identification of high-quality lead candidates. Although a number of strategies have been previously developed to correct systematic errors and to remove screening artifacts, they are not universally effective and still require fair amount of human intervention. We introduce a fully automated quality control (QC) pipeline that can correct generic interplate systematic errors and remove intraplate random artifacts. The new pipeline was first applied to ~100 large-scale historical HTS assays; in silico analysis showed auto-QC led to a noticeably stronger structure-activity relationship. The method was further tested in several independent HTS runs, where QC results were sampled for experimental validation. Significantly increased hit confirmation rates were obtained after the QC steps, confirming that the proposed method was effective in enriching true-positive hits. An implementation of the algorithm is available to the screening community.

Methods for Improving the Prediction Skill of Summer Precipitation over East Asia–West Pacific

AbstractThe performance of summer precipitation prediction skill of the Beijing Climate Center Climate System Model (BCC_CSM1.1; hereafter CSM) over the East Asia–west Pacific (EA–WP) region indicates the need for further development in order to improve model prediction skill. Two methods, namely the statistical alone prediction and the statistical and model combined prediction, are proposed to improve prediction skill. For the former, more than 2000 combinations of precursory predictors are considered in the predictive model; both most similar and dissimilar information are considered in the prediction process. For the latter, both the statistical analog information and the CSM’s forecast are considered in the predictive model; this new prediction method is based on merging information from both a dynamical model and historical analogs. Cross validation of summer precipitation over EA–WP for the years 1991–2010 shows that the average spatial anomaly correlation coefficient (ACC) of the statistical alone prediction is 0.16 and the average spatial root-mean-standard error (RMSE) is 22.7, which are much improved compared to the systematic error correction with ACC and RMSE being 0.09 and 25.0 respectively. The ACC and RMSE results from the statistical and CSM combined prediction are 0.22 and 21.8, respectively, which also indicates an improvement compare to the statistical alone prediction. Independent sample validation for the years 2011–13 indicates that the average ACCs of the statistical alone prediction and the statistical and CSM combined prediction (0.25 and 0.24) are enhanced compared to the systematic error correction (−0.02). The average RMSE is also improved from 25.0 to 23.6 and 22.7. Therefore, the two new prediction methods proposed in this paper both demonstrate the possibilities for the operational prediction of summer precipitation over EA–WP.

Value‐added quantitative medium‐range rainfall forecasts for the BIMSTEC region

ABSTRACT:Daily operational forecasts from the global models run at the National Centre for Medium Range Weather Forecasting (NCMRWF), India were assessed for their skill during the southwest monsoon for member countries of the Bay of Bengal Initiative for Multi‐Sectoral Technical and Economic Cooperation (BIMSTEC). The rainfall forecasts from two models, NCMRWF Global Forecast System (NGFS) and NCMRWF Unified Model (NCUM), were used to carry out the verification study for July and August 2013 and 2014. The skills were analysed in terms of equitable threat score (ETS), Hansen–Kuiper skill score (HKS) and the economic value for various rainfall thresholds. The ETS had a positive value for all of the thresholds up to the Day 10 forecast for both years. The HKS and economic value for binary forecast of Yes rain (prediction that rainfall event will occur) suggest that the model has significant positive skills. Deterministic forecast for four rainfall thresholds show negative skills, suggesting that false alarms increase as forecasts are made for smaller rainfall thresholds. To improve the skill scores, forecasted data were processed using simple bias correction, relative co‐efficient of variance (Rvar) method and the best easy systematic error (BESE) correction method. Correlation of the processed forecasts with model forecasts suggests an improvement using the bias correction and Rvar method but not using the BESE method. Significant improvement in the HKS and economic value of processed forecasts using the bias correction method has been noticed over most parts of the BIMSTEC region, except for the Western Ghats.

Precipitation totals at two Czech meteorological stations after correction of systematic errors in measurement

This contribution presents the results of correcting the systematic errors that accompanied precipitation measurements at the Svratouch and Hradec Kralove meteorological stations from 1961 to 1990. The selected stations are located in eastern Bohemia: the Svratouch station is situated 735 metres above sea level, the Hradec station at 270 metres above sea level. A slightly modified correction procedure constructed by R. Tihlarik was used in this study. After correction, the yearly precipitation totals recorded at the Svratouch and Hradec stations were greater by 32% and 17% of the original values, respectively. Undervaluation in Central European conditions is more significant in winter – between November and March, the average monthly undervaluation at Svratouch and Hradec reached 58–69% and 31–33%, respectively. From May to September, the average monthly undervaluation reached 7–17% at both stations. April and October were transitional months. Corrections in winter are generally indicative; corrections in the summer months are more reliable. According to the results and to literature, we can conclude that there is an undervaluation of at least 5% from May to September and an undervaluation of at least 25% from November to March at most stations in the Czech Republic. The year-to-year variability in annual and monthly corrected precipitation totals was also studied. The purpose of correcting such data is to provide more accurate precipitation totals, which subsequently provides a more accurate picture of trends in precipitation and of rainfall-runoff relationships.

A NEW OPTIMIZED RFM OF HIGH-RESOLUTION SATELLITE IMAGERY

Abstract. Over-parameterization and over-correction are two of the major problems in the rational function model (RFM). A new approach of optimized RFM (ORFM) is proposed in this paper. By synthesizing stepwise selection, orthogonal distance regression, and residual systematic error correction model, the proposed ORFM can solve the ill-posed problem and over-correction problem caused by constant term. The least square, orthogonal distance, and the ORFM are evaluated with control and check grids generated from satellite observation Terre (SPOT-5) high-resolution satellite data. Experimental results show that the accuracy of the proposed ORFM, with 37 essential RFM parameters, is more accurate than the other two methods, which contain 78 parameters, in cross-track and along-track plane. Moreover, the over-parameterization and over-correction problems have been efficiently alleviated by the proposed ORFM, so the stability of the estimated RFM parameters and its accuracy have been significantly improved.