Drift Correction Method Research Articles

An Assessment of Drift Correction Alternatives for CMIP5 Decadal Predictions

AbstractDrift correction is an important step before using the outputs of decadal prediction experiments and has seen considerable research. However, most drift correction studies consider a relatively small sample of variables and models. Here, we present a systematic application of the existing drift correction strategies for decadal predictions of various sea surface temperature‐based metrics from a suite of five state‐of‐the‐art climate models (CanCM4i1, GFDL‐CM2.1, HadCM3i2&i3, MIROC5, and MPI‐ESM‐LR). The best method of drift correction for each metric and model is reported. Preliminary analysis suggests that there is no single method of drift correction that consistently performs best. Initial condition‐based drift correction provides the lowest errors in most regions for MIROC5 and the two HadCM3 models, whereas the trend‐based drift correction produces lowest errors for CanCM4i1, GFDL‐CM2.1, and MPI‐ESM‐LR over the largest share of the area. There is no merit in using a k‐nearest neighbor approach for these drift correction methods. Further, in almost all cases, the multimodel ensemble outperforms the individual models, and thus, the study conclusively suggests using forecasts based on multimodel averages. We also show some additional benefit to be gained by drift correcting each model/metric using their best correction method prior to model averaging and suggest that the results presented here would help potential users expend time and resources judiciously while dealing with outputs from these experiments.

Detecting Turns and Correcting Headings Using Low-Cost INS

Unlike industrial-grade Inertial Navigation Sensors (INSs) that can provide credible tracking performance, more affordable consumer-grade low-cost INSs produce drifts in heading angles and positions that result in a poor tracking accuracy. Researchers have proposed drift correction methods that attempt to attenuate the drifts when walking straight along the dominant directions is detected. While determining the type of a pedestrian's walk is essential before the heading corrections are made, the current detection techniques heavily rely on thresholding. This paper proposes a novel threshold-less method to detect turns in walking by using pelvic rotation and correct the heading angle based on consumer-grade INSs. The experiments indicate the proposed turn detector and heading correction methods produce very good results which can be applied for future pedestrian tracking, activity recognition or rehabilitation.

Floating reference position-based correction method for near-infrared spectroscopy in long-term glucose concentration monitoring.

We present a floating reference position (FRP)-based drift correction method for near-infrared (NIR) spectroscopy-based long-term blood glucose concentration (BGC) monitoring. Previously, we reported that it is difficult to quantify the systematic drift caused by the fluctuation of incident light intensity at different source–detector (SD) separations based on the absolute FRP change. We use the relative FRP change as a baseline reference to quantitatively characterize the signal drift at different SD separations. For the wavelengths that were used, a uniform equation was developed to describe the relationship between the drift and the relative FRP change. With the help of this equation, the correction can easily be performed by subtracting the systematic drift estimated by the equation. A theoretical analysis and an experimental phantom study demonstrated that our method could be used for systematic drift correction in NIR spectroscopy for long-term BGC monitoring. Moreover, the analysis method can also be referenced to reduce drifts from multiple sources.

A Simple Marker-Assisted 3D Nanometer Drift Correction Method for Superresolution Microscopy

High-precision fluorescence microscopy such as superresolution imaging or single-particle tracking often requires an online drift correction method to maintain the stability of the three-dimensional (3D) position of the sample at a nanometer precision throughout the entire data acquisition process. Current online drift correction methods require modification of the existing two-dimensional (2D) fluorescence microscope with additional optics and detectors, which can be cumbersome and limit its use in many biological laboratories. Here we report a simple marker-assisted online drift correction method in which all 3D positions can be derived from fiducial markers on the coverslip of the sample on a standard 2D fluorescence microscope without additional optical components. We validate this method by tracking the long-term 3D stability of single-molecule localization microscopy at a precision of <2 and 5 nm in the lateral and axial dimension, respectively. We then provide three examples to evaluate the performance of the marker-assisted drift correction method. Finally, we give an example of a biological application of superresolution imaging of spatiotemporal alteration for a DNA replication structure with both low-abundance newly synthesized DNAs at the early onset of DNA synthesis and gradually condensed DNA structures during DNA replication. Using an isogenic breast cancer progression cell line model that recapitulates normal-like, precancerous, and tumorigenic stages, we characterize a distinction in the DNA replication process in normal, precancerous, and tumorigenic cells.

Exploring the Validity of the Long-Term Data Record V4 Database for Land Surface Monitoring

A new version of the long-term data record (LTDR)—Version 4—has been released recently by NASA. This database includes daily information for all advanced very high resolution radiometer channels, as well as ancillary data, from July 1981 up to present. This dataset is the longest available record of remotely sensed data useful for land surface monitoring, since it allows the daily estimation of vegetation indices, as well as the estimation of land surface temperature (LST). Here, we analyze the fitness of this database for land surface monitoring, especially as regards long-term trends and their validity. To that end, we estimated normalized difference vegetation index (NDVI), LST, as well as extracted solar zenith angle (SZA) from the ancillary data. Then, we reconstructed the yearly temporal profiles of NDVI and LST using the iterative interpolation for data reconstruction approach, from which we extracted parameters such as minimum and maximum values and corresponding dates, as well as the dates of mid-amplitude crossing. We also retrieved SZA values at all mentioned dates. In the following step, we checked for the presence and estimated values for trends using the Mann–Kendall statistical framework for retrieved dates as well as for minimum and maximum values. We then compared the retrieved trends and values to previous results as well as to independent data. As a conclusion, the LTDR-V4 dataset seems adequate for regional-to-global land surface monitoring, provided that time-series reconstruction techniques and orbital drift correction methods are applied.

Optical imaging of individual biomolecules in densely packed clusters.

Recent advances in fluorescence super-resolution microscopy have allowed sub-cellular features and synthetic nanostructures down to ~15 nm in size to be imaged. However, direct optical observation of individual molecular targets (~5 nm) in a densely packed biomolecular cluster remains a challenge. Here, we show that such discrete molecular imaging is possible using DNA-PAINT (points accumulation for imaging in nanoscale topography) - a super-resolution fluorescence microscopy technique that exploits programmable transient oligonucleotide hybridisation - on synthetic DNA nanostructures. We examined the effects of high photon count, high blinking statistics, and appropriate blinking duty cycle on imaging quality, and developed a software-based drift correction method that achieves <1 nm residual drift (r.m.s.) over hours. This allowed us to image a densely packed triangular lattice pattern with ~5 nm point-to-point distance, and analyse DNA origami structural offset with angstrom-level precision (2 Å) from single-molecule studies. By combining the approach with multiplexed Exchange-PAINT imaging, we further demonstrated an optical nano-display with 5×5 nm pixel size and three distinct colours, and with <1 nm cross-channel registration accuracy. This method opens up possibilities for direct and quantitative optical observation of individual biomolecular features in crowded environments.

Explicit integration of a porosity‐dependent hydro‐mechanical model for unsaturated soils

SummaryAn adaptive substepping explicit integration scheme is developed for a porosity‐dependent hydro‐mechanical model for unsaturated soils. The model is referred to as the modified σ–Θ model in this paper, which features the employment of the subloading surface plasticity and the stress–saturation approach. On numerical aspects, convex/nonconvex subloading surfaces in the σ–Θ space may result in incorrect loading–unloading decisions during the integration. A new loading–unloading decision method is developed here to solve the problem and then embedded into the explicit integration scheme for the modified σ–Θ model. In addition, to enhance the accuracy of the explicit integration, local errors from both hydraulic and mechanical components are included in the error control for each substep. A drift correction method is also developed to ensure the state point lies on the subloading surface in the σ–Θ space within a set error level. The performance of the loading–unloading decision method for the modified σ–Θ model is discussed through comparing it with the conventional loading–unloading decision method. The importance of involving the hydraulic component in the error control is also demonstrated. The accuracy and efficiency of the proposed adaptive substepping explicit integration scheme for the modified p–Θ model are also studied via several numerical examples. Copyright © 2016 John Wiley &amp; Sons, Ltd.

In Situ Ammonium Profiling Using Solid-Contact Ion-Selective Electrodes in Eutrophic Lakes.

A promising profiling setup for in situ measurements in lakes with potentiometric solid-contact ion-selective electrodes (SC-ISEs) and a data processing method for sensor calibration and drift correction are presented. The profiling setup consists of a logging system, which is equipped with a syringe sampler and sensors for the measurement of standard parameters including temperature, conductivity, oxygen and photosynthetically active radiation (PAR). The setup was expanded with SC-ISEs in galvanically separated amplifiers. The potential for high-resolution profiling is investigated by deploying the setup in the eutrophic Lake Rotsee (Lucerne, Switzerland), using two different designs of ammonium sensing SC-ISEs. Ammonium was chosen as a target analyte, since it is the most common reduced inorganic nitrogen species involved in various pathways of the nitrogen cycle and is therefore indicative of numerous biogeochemical processes that occur in lakes such as denitrification and primary production. One of the designs, which uses a composite carbon-nanotube-PVC-based membrane, suffered from sulfide poisoning in the deeper, sulfidic regions of the lake. In contrast, electrodes containing a plasticizer-free methacrylate copolymer-based sensing layer on top of a conducting polymer layer as a transducer did not show this poisoning effect. The syringe samples drawn during continuous profiling were utilized to calibrate the electrode response. Reaction hotspots and steep gradients of ammonium concentrations were identified on-site by monitoring the electrode potential online. Upon conversion to high-resolution concentration profiles, fine scale features between the calibration points were displayed, which would have been missed by conventional limnological sampling and subsequent laboratory analyses. Thus, the presented setup with SC-ISEs tuned to analytes of interest can facilitate the study of biogeochemical processes that occur at the centimeter scale.

High Temperature Nanoindentation Response of RTM6 Epoxy Resin at Different Strain Rates

This paper explores the feasibility of characterizing the mechanical response of the commercial aerospace grade epoxy resin RTM6 by nanoindentation tests at varying temperatures and strain rates. Since glassy polymers exhibit time-dependent mechanical properties, a dynamic nanoindentation technique was used. This method consists on superimposing a small sinusoidal force oscillation on the applied force. Viscoelastic properties are then characterized by their storage and loss moduli, whereas the visco-plastic response of the material can be associated to its hardness. In such experiments, thermal stability of the measuring technique is critical to achieve a low thermal drift and it becomes increasingly important as the measuring temperature increases. Our results show that conventional methods applied for drift correction in nanoindentation of inorganic materials are not applicable to glassy polymers leading to physically inconsistent results. We propose a method for drift correction based on the hypothesis that viscoelastic modulus should be a function of the applied load and frequency but independent of the global strain rate. Using this method, it was possible to determine the viscoplastic properties of RTM6 between RT and 200 °C.

Sub-nanometer drift correction for super-resolution imaging.

Spatial resolution of conventional far-field fluorescence microscopy is limited by diffraction of light. Single-molecule localization microscopy (SMLM), such as (direct) stochastic optical reconstruction microscopy (dSTORM/STORM), and (fluorescence) photoactivation localization microscopy (fPALM/PALM), can break this barrier by localizing single emitters and reconstructing super-resolution image with much higher precision. Nevertheless, a SMLM measurement needs to record a large number of image frames and takes considerable recording time. In this process, sample drift becomes a critical problem and cannot be neglected. In this Letter, we present a sub-nanometer precision, low-cost sample drift correction method based on minimizing normalized root-mean-square error (NRMSE) between bright field images. Two optical configurations are suggested for recording bright field and fluorescence images simultaneously or alternately. The method was demonstrated on simulated data, and better than 0.3 nm drift correction precision was achieved. It was also applied on dSTORM imaging of F-actins of 3T3 cell, and the quality of reconstructed super-resolution image was improved observably. This method does not require special hardware, extra labelling or markers, and no precision decline due to photobleaching. It can be applied as an add-on for SMLM setups and achieves sub-nanometer precision drift correction for post-measurement or real time drift compensation.

Localization events-based sample drift correction for localization microscopy with redundant cross-correlation algorithm

Highly accurate sample drift correction is essential in super-resolution localization microscopy to guarantee a high spatial resolution, especially when the technique is used to visualize small cell organelle. Here we present a localization events-based drift correction method using a redundant cross-correlation algorithm originally developed to correct beam-induced motion in cryo-electron microscopy. With simulated, synthesized as well as experimental data, we have demonstrated its superior precision compared to previously published localization events-based drift correction methods. The major advantage of this method is the robustness when the number of localization events is low, either because a short correction time step is required or because the imaged structure is small and sparse. This method has allowed us to improve the effective resolution when imaging Golgi apparatus in mammalian cells.

Full-field and anomaly initialization using a low-order climate model: a comparison and proposals for advanced formulations

Abstract. Initialization techniques for seasonal-to-decadal climate predictions fall into two main categories; namely full-field initialization (FFI) and anomaly initialization (AI). In the FFI case the initial model state is replaced by the best possible available estimate of the real state. By doing so the initial error is efficiently reduced but, due to the unavoidable presence of model deficiencies, once the model is let free to run a prediction, its trajectory drifts away from the observations no matter how small the initial error is. This problem is partly overcome with AI where the aim is to forecast future anomalies by assimilating observed anomalies on an estimate of the model climate. The large variety of experimental setups, models and observational networks adopted worldwide make it difficult to draw firm conclusions on the respective advantages and drawbacks of FFI and AI, or to identify distinctive lines for improvement. The lack of a unified mathematical framework adds an additional difficulty toward the design of adequate initialization strategies that fit the desired forecast horizon, observational network and model at hand. Here we compare FFI and AI using a low-order climate model of nine ordinary differential equations and use the notation and concepts of data assimilation theory to highlight their error scaling properties. This analysis suggests better performances using FFI when a good observational network is available and reveals the direct relation of its skill with the observational accuracy. The skill of AI appears, however, mostly related to the model quality and clear increases of skill can only be expected in coincidence with model upgrades. We have compared FFI and AI in experiments in which either the full system or the atmosphere and ocean were independently initialized. In the former case FFI shows better and longer-lasting improvements, with skillful predictions until month 30. In the initialization of single compartments, the best performance is obtained when the stabler component of the model (the ocean) is initialized, but with FFI it is possible to have some predictive skill even when the most unstable compartment (the extratropical atmosphere) is observed. Two advanced formulations, least-square initialization (LSI) and exploring parameter uncertainty (EPU), are introduced. Using LSI the initialization makes use of model statistics to propagate information from observation locations to the entire model domain. Numerical results show that LSI improves the performance of FFI in all the situations when only a portion of the system's state is observed. EPU is an online drift correction method in which the drift caused by the parametric error is estimated using a short-time evolution law and is then removed during the forecast run. Its implementation in conjunction with FFI allows us to improve the prediction skill within the first forecast year. Finally, the application of these results in the context of realistic climate models is discussed.

Baseline Drift Filtering for an Arterial Pulse Signal

Different baseline drift filtering methods are examined for an arterial pulse signal. A baseline drift correction method is proposed that is based on generating an adaptive filter reference signal using multiresolution wavelet transforms of the original biosignal, making it possible to achieve the least distortions in processing the signal compared with model signals free of distorting effects. The effectiveness of other methods for filtering the signal is studied when noise of different intensities is present.

Frequency and phase drift correction of magnetic resonance spectroscopy data by spectral registration in the time domain

Frequency and phase drifts are a common problem in the acquisition of in vivo magnetic resonance spectroscopy (MRS) data. If not accounted for, frequency and phase drifts will result in artifactual broadening of spectral peaks, distortion of spectral lineshapes, and a reduction in signal-to-noise ratio (SNR). We present herein a new method for estimating and correcting frequency and phase drifts in in vivo MRS data. We used a simple method of fitting each spectral average to a reference scan (often the first average in the series) in the time domain through adjustment of frequency and phase terms. Due to the similarity with image registration, this method is referred to as "spectral registration." Using simulated data with known frequency and phase drifts, the performance of spectral registration was compared with two existing methods at various SNR levels. Spectral registration performed well in comparison with the other methods tested in terms of both frequency and phase drift estimation. Spectral registration provides an effective method for frequency and phase drift correction. It does not involve the collection of navigator echoes, and does not rely on any specific resonances, such as residual water or creatine, making it highly versatile.

Method for isotope ratio drift correction by internal amplifier signal synchronization in MC-ICPMS transient signals

This work provides a simple method for isotope ratio drift correction in MC-ICPMS transient signals.

A Polynomial Curve Fitting Method for Baseline Drift Correction in the Chromatographic Analysis of Hydrocarbons in Environmental Samples

In this paper we describe an algorithm for removing the baseline drift often observed in GC and HPLC plots of complex environmental samples which causes reduction of the analytical accuracy of peak area measurements. The proposed method determines the real baseline (i.e. baseline modeling) directly on the chromatographic plot of each analyzed sample and then substrate it to the analytical time series signals of the chromatogram, so reducing instrumental noise and then improving analytical accuracy. The proposed algorithm, based on a polynomial fitting estimation of the baseline, has been applied to the GC analysis of the hydrocarbon content in marine sediments.

根据环境温度进行红外成像系统漂移补偿

For radiation measurement using infrared imaging systems, gray value drift caused by ambient temperature variation is a significant source of error. By studying the effects of ambient temperature on the output gray value of cooled infrared systems, a drift compensation method is proposed. The sources of gray value drift are analyzed, and the relationship between drift and ambient temperature is derived to establish a theoretical model. A simple drift correction method is proposed. The radiometric calibration experiment is carried out for verification. The results indicate that proposed method can not only improve the measurement accuracy of the infrared imaging system, but also achieve drift compensation under arbitrary integration time, which improves the efficiency of radiometric calibration and measurement.

A wavelet-based baseline drift correction method for grounded electrical source airborne transient electromagnetic signals

A grounded electrical source airborne transient electromagnetic (GREATEM) system on an airship enjoys high depth of prospecting and spatial resolution, as well as outstanding detection efficiency and easy flight control. However, the movement and swing of the front-fixed receiving coil can cause severe baseline drift, leading to inferior resistivity image formation. Consequently, the reduction of baseline drift of GREATEM is of vital importance to inversion explanation. To correct the baseline drift, a traditional interpolation method estimates the baseline ‘envelope’ using the linear interpolation between the calculated start and end points of all cycles, and obtains the corrected signal by subtracting the envelope from the original signal. However, the effectiveness and efficiency of the removal is found to be low. Considering the characteristics of the baseline drift in GREATEM data, this study proposes a wavelet-based method based on multi-resolution analysis. The optimal wavelet basis and decomposition levels are determined through the iterative comparison of trial and error. This application uses the sym8 wavelet with 10 decomposition levels, and obtains the approximation at level-10 as the baseline drift, then gets the corrected signal by removing the estimated baseline drift from the original signal. To examine the performance of our proposed method, we establish a dipping sheet model and calculate the theoretical response. Through simulations, we compare the signal-to-noise ratio, signal distortion, and processing speed of the wavelet-based method and those of the interpolation method. Simulation results show that the wavelet-based method outperforms the interpolation method. We also use field data to evaluate the methods, compare the depth section images of apparent resistivity using the original signal, the interpolation-corrected signal and the wavelet-corrected signal, respectively. The results confirm that our proposed wavelet-based method is an effective, practical method to remove the baseline drift of GREATEM signals and its performance is significantly superior to the interpolation method.

Climate Drift in the CMIP5 Models*

AbstractClimate models often exhibit spurious long-term changes independent of either internal variability or changes to external forcing. Such changes, referred to as model “drift,” may distort the estimate of forced change in transient climate simulations. The importance of drift is examined in comparison to historical trends over recent decades in the Coupled Model Intercomparison Project (CMIP). Comparison based on a selection of metrics suggests a significant overall reduction in the magnitude of drift from phase 3 of CMIP (CMIP3) to phase 5 of CMIP (CMIP5). The direction of both ocean and atmospheric drift is systematically biased in some models introducing statistically significant drift in globally averaged metrics. Nevertheless, for most models globally averaged drift remains weak compared to the associated forced trends and is often smaller than the difference between trends derived from different ensemble members or the error introduced by the aliasing of natural variability. An exception to this is metrics that include the deep ocean (e.g., steric sea level) where drift can dominate in forced simulations. In such circumstances drift must be corrected for using information from concurrent control experiments. Many CMIP5 models now include ocean biogeochemistry. Like physical models, biogeochemical models generally undergo long spinup integrations to minimize drift. Nevertheless, based on a limited subset of models, it is found that drift is an important consideration and must be accounted for. For properties or regions where drift is important, the drift correction method must be carefully considered. The use of a drift estimate based on the full control time series is recommended to minimize the contamination of the drift estimate by internal variability.

Real-time calibration of laser absorption spectrometer using spectral correlation performed with an in-line gas cell

A real-time drift correction and calibration method using spectral correlation based on a revolving in-line gas cell for laser-based spectroscopic trace-gas measurements has been developed and evaluated experimentally. This technique is relatively simple to implement in laser spectroscopy systems and assures long-term stability of trace-gas measurements by minimizing the effects of external sources of drift in real-time. Spectroscopic sensitivity sufficient for environmental monitoring and effective drift suppression has been achieved for long-term measurements of CO₂ with a quantum cascade laser based spectrometer.