Calibration Bias Research Articles

Intercomparison of the SNPP and NOAA-20 VIIRS DNB High-Gain Stage Using Observations of Bright Stars

The Visible Infrared Imaging Radiometer Suite (VIIRS) on board the Suomi-NPP (SNPP) and NOAA-20 (N20) spacecrafts is a multispectral Earth-observing instrument with bands covering wavelengths from visible to long-wave infrared. Among these bands is a panchromatic day/night band (DNB) with a broad spectral response ranging from 500 to 900 nm, and a high dynamic range spanning over seven orders of magnitude, allowing for observations to take place during both daytime and nighttime. The DNB operates at three gain levels, with low- and mid-gain stages and two high-gain stages (HGSs). The HGS is capable of detecting dim city lights during Earth-view observations at night as well as bright stars through the instrument space-view port. Since SNPP and N20 are at opposite points of the same orbit, each VIIRS instrument is able to observe the same stars with the DNB in successive orbits. This will allow us to make a direct comparison of the relative calibration of each instrument using stars over a range of spectral classes. In this article, we develop methodology for accurately identifying target stars in order to make proper comparisons between the DNB HGS of each instrument. We then take observations from multiple stars in order to compute the ratio in the measured irradiance for each instrument as a function of spectral class. For K-type stars, which have the least spectral change over the DNB wavelength range, we measure a calibration bias between the SNPP and N20 DNB HGS of approximately 4%, which is stable over the duration of the N20 mission.

UncorK – A Monte Carlo simulation tool for calculating combined uncertainties associated with mass bias calibration factors for isotope ratio measurements

A complete uncertainty budget including all relevant uncertainty contributions is one of the requirements for an unbroken traceability chain to the International System of Units (SI). Measured ion intensity ratios need to be corrected for any mass bias effects. Correcting the measured ratios is usually done with mass bias correction factors (K-factors). These can be derived from gravimetric isotope mixtures. The resulting absolute isotope ratios are traceable to the SI, provided the uncertainty contribution stemming from the calibration is also considered. Since the computation of K-factors for even small systems containing only a small number of isotopes is challenging, the uncertainty evaluation is also not a straightforward task. This paper presents a way of calculating the uncertainty associated with calibration factors by applying the Monte Carlo method. An EXCEL-based tool is presented that is capable of performing such uncertainty calculations for systems with up to 12 isotopes. Additionally, two examples are given to demonstrate the application of this tool and to validate it.

A New Approach to 3D ICP Covariance Estimation

In mobile robotics, scan matching of point clouds using Iterative Closest Point (ICP) allows estimating sensor displacements. It may prove important to assess the associated uncertainty about the obtained rigid transformation, especially for sensor fusion purposes. In this paper we propose a novel approach to 3D uncertainty of ICP that accounts for all the sources of error as listed in Censi's pioneering work [1], namely wrong convergence, underconstrained situations, and sensor noise. Our approach builds on two facts. First, the uncertainty about the ICP's output fully depends on the initialization accuracy. Thus speaking of the covariance of ICP makes sense only in relation to the initialization uncertainty, which generally stems from odometry errors. We capture this using the unscented transform, which also reflects correlations between initial and final uncertainties. Then, assuming white sensor noise leads to overoptimism as ICP is biased owing to e.g. calibration biases, which we account for. Our solution is tested on publicly available real data ranging from structured to unstructured environments, where our algorithm predicts consistent results with actual uncertainty, and compares favorably to previous methods.

Kernel-phase analysis: Aperture modeling prescriptions that minimize calibration errors

Context. Kernel phase is a data analysis method based on a generalization of the notion of closure phase, which was invented in the context of interferometry, but it applies to well corrected diffraction dominated images produced by an arbitrary aperture. The linear model upon which it relies theoretically leads to the formation of observable quantities robust against residual aberrations. Aims. In practice, the detection limits that have been reported thus far seem to be dominated by systematic errors induced by calibration biases that were not sufficiently filtered out by the kernel projection operator. This paper focuses on the impact the initial modeling of the aperture has on these errors and introduces a strategy to mitigate them, using a more accurate aperture transmission model. Methods. The paper first uses idealized monochromatic simulations of a nontrivial aperture to illustrate the impact modeling choices have on calibration errors. It then applies the outlined prescription to two distinct data sets of images whose analysis has previously been published. Results. The use of a transmission model to describe the aperture results is a significant improvement over the previous type of analysis. The thus reprocessed data sets generally lead to more accurate results, which are less affected by systematic errors. Conclusions. As kernel-phase observing programs are becoming more ambitious, accuracy in the aperture description is becoming paramount to avoid situations where contrast detection limits are dominated by systematic errors. The prescriptions outlined in this paper will benefit from any attempt at exploiting kernel phase for high-contrast detection.

Phasor measuring unit calibration considering topology expansion of electric power utilities

Phasor Measuring Units (PMU) is considered as a feasible alternative for power system data communication and for the preparation of smart grid. To operate multiple PMUs in stable mode, synchronization is a much essential procedure. After examining the concise literature survey on topical PMU applications, this paper step ahead by considering prevailing objectives such as: extension of network topology and PMU calibration considering bias errors. The appropriate deployment of PMUs and intimidating situation influence the reliance on physical configuration. Hence, it is obligatory to extend the topology of the networks. Also, calibrating the PMUs in a definite trail is crucial as the phasor measurements will be corrupted by bias error. This paper proposes a novel framework for bias error detection and calibration of PMU while expanding the topology network. With a whale optimization algorithm based method to improve the accuracy of PMU measurements for enhancing power system state estimation, monitoring and control operations. Case studies on the standard test systems have been conducted to test the efficacy of the intended tool.

Testing the WEAP Hydrologic Model for Awash Basin, Ethiopia “Soil Moisture Module with Watershed Demand Approach”

The research is financed by the Egyptian Government through the Nile Sector bilateral cooperation project Abstract Currently, there are several hydrological simulation models in use worldwide. Prior to adoption of a model to a specific basin, its feasibility and practicality should be tested. This study highlights on the application of Water Evaluation and Planning system (WEAP) model for hydrologic simulation of Awash river basin in Ethiopia. A monthly time step hydrologic model was developed using the soil moisture rainfall-runoff method incorporated in WEAP with an aim to check the suitability of the model for Awash river basin. For this purpose five selected flow gauge stations located at the upper, middle and lower Awash basin were used as control stations. The model is configured taking into account the effects of development and hence the water abstractions, storage, loss rate, etc. are estimated using the data provided through various kinds of research and survey in Ethiopia. Standard methods are also used to prepare the hydro-metrological and landuse input data for each sub-catchment. Based on data availability, the time period 1986-2005 was selected for the hydrologic simulation. The observed data were split for calibration (1986–1995) and validation (1996–2005) purposes. Initially, the model was set up using the default model parameters. Then, manual calibration is performed to reproduce the observed streamflow. The model-simulated values are compared with those obtained from observations using standard statistical tests on monthly and monthly average basis. From the performance test results, it is observed that the coefficient of determination (R 2 ) and the Index of Agreement (IA) show a good fit. Furthermore, the Nash-Sutcliffe Efficiency (NSE) and the Percent Bias (PBIAS) calibration and validation results show good performance for Upper Awash stations and satisfactory results for the middle and lower Awash control stations. As a conclusion, the main modelling constraints were discussed and possible solutions were also suggested in order to improve the performance of water resources simulation models for the Awash basin. Keywords : Awash Basin, Hydrologic Simulation, Calibration, Validation, Model Performance, WEAP DOI: 10.7176/CER/12-3-07 Publication date: March 31 st 2020

An analysis of the calibration method of accelerometer bias and scale factor

Accelerometer bias and scale factor are important parameters of the accelerometer. The accuracy of calibration of bias and scale factor will directly affect navigation accuracy. This paper studies the calibration of accelerometers under out-door conditions. In some applications, the inertial measurement unit (IMU) cannot rotate with any angle due to the restriction of equipment and site. The pitch and roll rotation range is -45° to 45°, and the yaw is -180° to 180°. In this paper, the bias and scale factor identification equations under different positions are derived based on the inertial navigation error equation. The results show that when the IMU rotates around the pitch axis, as long as there are three pitch positions, the bias and scale factor of the y-direction and z-direction accelerometers can be calibrated. When the IMU rotates 45° around the roll axis and then rotates 90° around the yaw axis, the x-direction accelerometer scale factor can be calibrated. If the bias and scale factor of the y-direction and z-direction accelerometer are known, the bias of the x-direction accelerometer can also be calibrated. Then, several simulations are conducted which are consistent with the theoretical results. At last, optimized calibration path is designed. Within 20 min, the calibration accuracy of bias is better than 5 mGal, and the calibration accuracy of scale factor is better than 5 ppm.

Development and temporal validation of a prognostic model for 1-year clinical outcome after decompression surgery for lumbar disc herniation.

Surgeons need tools to provide individualised estimates of surgical outcomes and the uncertainty surrounding these, to convey realistic expectations to the patient. This study developed and validated prognostic models for patients undergoing surgical treatment of lumbar disc herniation, to predict outcomes 1year after surgery, and implemented these models in an online prediction tool. Using the data of 1244 patients from a large spine unit, LASSO and linear regression models were fitted with 90% upper prediction limits, to predict scores on the Core Outcome Measures Index, and back and leg pain. Candidate predictors included sociodemographic factors, baseline symptoms, medical history, and surgeon characteristics. Temporal validation was conducted on 364more recent patients at the same unit, by examining the proportion of observed outcomes exceeding the threshold of the 90% upper prediction limit (UPL), and by calculating mean bias and other calibration measures. Poorer outcome was predicted by obesity, previous spine surgery, and having basic obligatory (rather than private) insurance. In the validation data, fewer than 12% of outcomes were above the 90% UPL. Calibration plots for the model validation showed values for mean bias < 0.5 score points and regression slopes close to 1. While the model accuracy was good overall, the prediction intervals indicated considerable predictive uncertainty on the individual level. Implementation studies will assess the clinical usefulness of the online tool. Updating the models with additional predictors may improve the accuracy and precision of outcome predictions. These slides can be retrieved under Electronic Supplementary Material.

Using ground radar overlaps to verify the retrieval of calibration bias estimates from spaceborne platforms

Abstract. Many institutions struggle to tap into the potential of their large archives of radar reflectivity: these data are often affected by miscalibration, yet the bias is typically unknown and temporally volatile. Still, relative calibration techniques can be used to correct the measurements a posteriori. For that purpose, the usage of spaceborne reflectivity observations from the Tropical Rainfall Measuring Mission (TRMM) and Global Precipitation Measurement (GPM) platforms has become increasingly popular: the calibration bias of a ground radar (GR) is estimated from its average reflectivity difference to the spaceborne radar (SR). Recently, Crisologo et al. (2018) introduced a formal procedure to enhance the reliability of such estimates: each match between SR and GR observations is assigned a quality index, and the calibration bias is inferred as a quality-weighted average of the differences between SR and GR. The relevance of quality was exemplified for the Subic S-band radar in the Philippines, which is greatly affected by partial beam blockage. The present study extends the concept of quality-weighted averaging by accounting for path-integrated attenuation (PIA) in addition to beam blockage. This extension becomes vital for radars that operate at the C or X band. Correspondingly, the study setup includes a C-band radar that substantially overlaps with the S-band radar. Based on the extended quality-weighting approach, we retrieve, for each of the two ground radars, a time series of calibration bias estimates from suitable SR overpasses. As a result of applying these estimates to correct the ground radar observations, the consistency between the ground radars in the region of overlap increased substantially. Furthermore, we investigated if the bias estimates can be interpolated in time, so that ground radar observations can be corrected even in the absence of prompt SR overpasses. We found that a moving average approach was most suitable for that purpose, although limited by the absence of explicit records of radar maintenance operations.

A Stable Adaptive Observer for Hard-Iron and Soft-Iron Bias Calibration and Compensation for Two-Axis Magnetometers: Theory and Experimental Evaluation

This letter addresses the problem of on-line estimation and compensation of the hard-iron and soft-iron biases of a 2-axis magnetometer under dynamic motion, utilizing only biased measurements from a 2-axis magnetometer. The proposed adaptive observer formulates the relation between the true magnetic field vector and the magnetometer measurements as an algebraic system where the unknown biases enter linearly. The observer is shown to be globally stable. When the magnetometer measurements are persistently exciting (PE), the system is shown to be globally asymptotically stable, and the biases are shown to converge to their true values. The estimated biases are used to provide a calibrated magnetic field direction vector which is utilized to estimate magnetic geodetic heading. The adaptive observer is evaluated in a numerical simulation and a full-scale vehicle trial. For the proposed observer: $(i)$ knowledge of the instrument attitude is not required for sensor bias estimation, $(ii)$ zero a priori knowledge of the local magnetic field vector magnitude or vector direction is needed, $(iii)$ the system is shown to be globally stable, $(iv)$ the error system is shown to be globally asymptotically stable when the measured magnetometer signal is PE. $(v)$ magnetometer hard-iron and soft-iron bias compensation is shown to dramatically improve dynamic heading estimation accuracy.

Calibration and Impact of BeiDou Satellite-Dependent Timing Group Delay Bias

The accuracy of the timing group delay (TGD) transmitted in the broadcast ephemeris is an important factor that affects the service performance of a GNSS system. In this contribution, an apparent bias is found by comparing the orbit and clock difference using half-year data of the BeiDou navigation satellite system (BDS) broadcast ephemeris and precise post-processed products. The bias differs at each satellite on each frequency and shows a general systematic difference between BDS-2 and BDS-3. We attribute this to the satellite-dependent TGD bias of the BDS broadcast ephemeris, which is subsequently calibrated. Moreover, to calibrate the bias independently, a network solution strategy is proposed based on 87 globally distributed multi-GNSS experiment (MGEX) stations spanning 25 weeks. The estimated bias shows good agreement with the values observed from the orbit and clock comparison. For the validation of the bias, we compared the signal-in-space range error (SISRE) performance with and without the TGD bias correction. The results show that the SISRE of the BDS improved from 0.71, 0.81, and 1.40 m to 0.64, 0.66, and 0.64 m in the B1I, B3I, and B1I/B3I frequencies. For BDS-3, the SISRE is well within 0.50 m after the bias correction. To further validate the bias, a week’s data were collected at 97 globally distributed MGEX stations. When the TGD bias is corrected, the root mean square (RMS) of single point positioning (SPP) can be improved by 5.6, 8.4, and 21.6% in the B1I, B3I, and B1I/B3I frequencies. Meanwhile, the SISRE and SPP assessment results also indicate that the TGD bias should be corrected by each satellite rather than only corrected between BDS-2 and BDS-3.

Intercalibration of FY-3C MWRI Against GMI Using the Ocean Microwave Radiative Transfer Model

This work addresses the intercalibration of the Microwave Radiation Imager (MWRI) on the Chinese second-generation polar-orbiting meteorological satellite Fengyun 3C (FY-3C) against the Microwave Imager (GMI) on the Global Precipitation Measurement (GPM) Core Observatory , in which a modified Double Difference (DD) method is developed and the Brightness Temperatures (TBs) in FY-3C MWRI and GMI channels are simulated using the ocean microwave Radiative Transfer Model (RTM) fed with the fifth generation of European Centre for Medium-range Weather Forecast (ECMWF) atmospheric reanalysis (ERA5) data. With the modified DD method, the intercalibration of FY-3C MWRI in 2017 are obtained. The results show that the MWRI observations are underestimated, especially for the low frequency channels. The calibration biases (mean of DDs) in FY-3C MWRI channels are temperature dependent, and decrease with the frequency increment. The in-orbit calibration of the MWRI descending (MWRID) data is 1~2 K worse than that of the MWRI ascending (MWRIA) data. At the TBs of standard scene defined by the Global Space-based Inter-Calibration System (GSICS), the calibration errors (mean of DDs ± standard deviation at the mean) of MWRIA data in January 2017 are −6.7±0.4 K, −8.3±0.8 K, −3.0±0.7 K, −1.9±1.0 K, −2.5±1.1 K, −3.9±0.8 K, −2.1±1.5 K, −1.5±1.0 K and −0.4±2.3 K in the 10V/H, 18V/H, 23V, 36V/H and 89V/H channels, respectively, while the calibration errors of MWRID data in January 2017 are −7.6±0.8 K, −9.1±1.2 K, −4.4±0.8 K, −2.9±1.3 K, −3.6±1.2 K, −5.1±0.8 K, −2.6±1.4 K, −2.5±1.1 K and −1.2±2.5 K in the nine channels, respectively. Although calibration biases exist, the in-orbit calibration of FY-3C MWRI is generally stable in 2017. The results of the modified DD method are consistent with that of the DD method. The modified DD method is promising to be applied to the intercalibration with both target and reference radiometers on polar-orbiting satellites.

Beyond three frequencies: an extendable model for single-epoch decimeter-level point positioning by exploiting Galileo and BeiDou-3 signals

GNSS is indispensable to self-driving vehicles by delivering decimeter-level or better absolute positioning solutions. Such a high precision normally requires a convergence time spanning seconds to minutes, which is, however, unrealistic in extremely difficult driving conditions where GNSS signals are obstructed frequently. Such convergences, no matter how short, will greatly risk and discredit autonomous driving in satisfying stringent life safety standards. In this study, we therefore developed an extendable GNSS precise point positioning (PPP) model to exploit the advanced Galileo/BeiDou-3 more-than-three-frequency signals with the goal of achieving instant or single-epoch 10–30 cm positioning accuracy and over 99% availability for the horizontal components over wide areas. In particular, uncombined Galileo/BeiDou-3 signals on all available frequencies were injected simultaneously into PPP to perform single-epoch wide-lane ambiguity resolution (PPP-WAR) after phase bias calibrations on raw observations. Experimenting on the Galileo five-frequency data from 36 stations in Australia, we found that instant PPP-WAR was accomplished at more than 99.5% of all epochs; we achieved an instant positioning accuracy of 0.10 and 0.11 m (1σ) for the east and north components, respectively, using Galileo E1/E5a/E5/E5b/E6 signals from less than 10 satellites, while 0.16 and 0.23 m using BeiDou-3 B1C/B1I/B2a/B2b/B3I signals from only 5–6 satellites per epoch observed by 10 stations within China. Moreover, we carried out vehicle-borne experiments collecting multi-frequency Galileo/BeiDou-3 signals in the case of overpass and tunnel adversities. With 7 Galileo/BeiDou-3 satellites per epoch on average, instant PPP-WAR reached a mean positioning accuracy of 0.23 and 0.24 m for the horizontal components, which can be further improved to 0.14 and 0.12 m when multi-epoch filtering is preferably enabled. More encouragingly, though this positioning accuracy can also be ensured with triple-frequency data, the data redundancy favored by even more frequencies can reduce the high-precision recovery time from up to 4 s to 2 s in the case of total signal blockages. With the rapidly ongoing deployment of Galileo, BeiDou-3 and other GNSS constellations, we can envision an instant global positioning service characterized by around 20 cm horizontal accuracy and over 99% availability for self-driving vehicles.

Real-Time Estimation and Calibration of GLONASS Inter-Frequency Phase and Code Bias

The frequency division multiple access (FDMA) strategy used in GLONASS causes inter-frequency phase bias (IFPB) and inter-frequency code bias (IFCB) between receivers from different manufacturers. The existence of IFPB and IFCB significantly increases the difficulties of fixing GLONASS ambiguity and limits the accuracy and reliability of GLONASS positioning. Moreover, the initial value of IFPB and IFCB may be unavailable or unreliable with the increasing number of receivers from different manufacturers in recent years. In this study, a real-time and reliable calibration algorithm of IFPB and IFCB based on multi-GNSS assistance is proposed by providing a fixed solution. Real-time IFPB rate and IFCB can be obtained using this algorithm without the initial IFPB and IFCB. The IFPB rate for all GLONASS satellites and IFCB for each GLONASS satellite are estimated due to different characteristics of IFPB and IFCB. IFPB calibration can be divided into constant and real-time IFPB calibrations to meet the different positioning requirements. Results show that constant IFPB rate has only 2 mm difference from the mean value of real-time IFPB rate. The IFPB rate and IFCB estimated by this algorithm have excellent stability, and the change in reference satellite cannot affect the results of IFPB rate and the stability of IFCB. The centimetre-level positioning results can be obtained using two calibration methods, and the positioning results with real-time calibration method are 10%–20% better than those with the constant calibration method. Under satellite-deprived environments, the improvements of multi-GNSS positioning accuracy with constant inter-frequency bias calibration gradually increase as the satellite cut-off elevation angle increases compared with GPS/BDS, which can reach up to 0·9 cm in the vertical direction.

Mapping Water Surface Elevation and Slope in the Mississippi River Delta Using the AirSWOT Ka-Band Interferometric Synthetic Aperture Radar

AirSWOT is an airborne Ka-band synthetic aperture radar, capable of mapping water surface elevation (WSE) and water surface slope (WSS) using single-pass interferometry. AirSWOT was designed as a calibration and validation instrument for the forthcoming Surface Water and Ocean Topography (SWOT) mission, an international spaceborne synthetic aperture radar mission planned for launch in 2022 which will enable global mapping of WSE and WSS. As an airborne instrument, capable of quickly repeating overflights, AirSWOT enables measurement of high frequency and fine scale hydrological processes encountered in coastal regions. In this paper, we use data collected by AirSWOT in the Mississippi River Delta and surrounding wetlands of coastal Louisiana, USA, to investigate the capabilities of Ka-band interferometry for mapping WSE and WSS in coastal marsh environments. We introduce a data-driven method to estimate the time-varying interferometric phase drift resulting from radar hardware response to environmental conditions. A system of linear equations based on AirSWOT measurements is solved for elevation bias and time-varying phase calibration parameters using weighted least squares. We observed AirSWOT WSE uncertainty of 12 cm RMS compared to in situ water level measurements when averaged over an area of 0.5 km 2 at incidence angles below 15 ∘ . At higher incidence angles, the observed AirSWOT elevation bias is possibly due to residual phase calibration errors or radar backscatter from vegetation. Elevation profiles along the Wax Lake Outlet river channel indicate AirSWOT can measure WSS over a 24 km distance with uncertainty below 0.3 cm/km, 8% of the true water surface slope as measured by in situ data. The data analysis and results presented in this paper demonstrate the potential of AirSWOT to measure water surface elevation and slope within highly dynamic and spatially complex coastal environments.

Uncertainty in the R2 Calibration Curve for High Iron Concentration

Introduction: MRI assessment of liver iron concentration (LIC) has become the standard of care for monitoring iron chelation strategies. The Ferriscan R2 quantification of LIC has been validated against 338 biopsies in two large cohorts, demonstrates good interstudy reproducibility, and has strong quality control practices. However, its cost remains a challenge for many institutions, making iron measurements by R2* acquisitions more attractive. Several studies comparing LIC by R2* and by Ferriscan R2 have identified substantial bias between R2* and R2 LIC estimates. We postulated that the original Ferriscan R2 calibration overestimates LIC at high iron concentrations, causing the disagreement between the two techniques. Methods: We identified three studies comparing single echo R2 to liver biopsy (362 biopsies). We used a publicly available digital capture program(www.arizona-software.ch/graphclick) to capture the values of R2 for each liver iron concentration level. We fit the data to the existing FDA-approved calibration and compared Bland Altman agreement to liver biopsy against two other calibration curves. The first was data-derived from the aggregate liver biopsy results using a linear fit in log transformed LIC and R2 coordinates (resulting in a Power Law fit). The second was derived from a spline fit to data generated from previously published Monte Carlo simulation model from our laboratory. This model generates "synthetic" R2-iron pairs over the entire physiological range of iron overload, using ideal mathematical approximations to the MRI imaging physics. Results: Panel A demonstrates R2-LIC pairs for the aggregate liver biopsy data and the three evaluated R2-iron calibration curves. Using Bland-Altman analysis, both the Power Law (0.5 ± 4.9 mg/g) and Simulation (1.7 ± 5.1 mg/g) calibrations had 17-22% lower variance than the Ferriscan calibration (-1.1 ± 6.2 mg/g), p&amp;lt;0.0001 by two sample variance test. More importantly, the predicted LIC values diverge dramatically for R2 values greater than 155 Hz (roughly 14 mg/g) (Panel B). For example, a R2 value of 300 Hz will yield an estimated LIC of 48 mg/g by Ferriscan but only 34 mg/g and 32.5 mg/g by the Power Law and Simulation calibrations respectively. Discussion: These data suggest that Ferriscan overestimates true liver iron concentration for R2 values exceeding 155 Hz, with the differences growing geometrically. The calibration error is sufficient to completely explain the differences between LIC by R2* and Ferriscan R2 described in previously studies. Importantly, any future attempt to "calibrate" R2* or other MRI methods against Ferriscan need to account for this bias. Fortunately, the clinical impact of the observed calibration bias is manageable. Patients with LIC below 14 mg/g are being accurately risk stratified. Patients with LIC greater than 14 mg/g in their liver are universally at high risk and should be treated aggressively, regardless. Serial trends in LIC-R2 values also lessen the impact of calibration bias. Both Ferriscan and R2* LIC estimates and provide accurate estimates of chelator efficiency on an annual basis. Further, both Ferriscan and liver R2* LIC estimates are more accurate than liver biopsy in tracking changes in total body iron concentration. However, hematologists should treat Ferriscan predicted LIC values of more than 14 mg/g with appropriate caution and integrate the predicted LIC values with the patient's entire clinical picture to avoid over-reacting to large changes in predicted LIC. ACKNOWLEDGEMENTS This work supported by the National Institutes of Health, Diabetes, Digestive and Kidney Diseases (1R01DK097115-01A1). DISCLOSURES Dr. Wood serves as a consultant to Apopharma, Biomedinformatics, Bluebirdbio, Celgene, Ionis Pharmaceuticals, Imago Biosciences, Silence Therapeutics, and World Care Clinical. Figure Disclosures Wood: National Institutes of Health: Research Funding; Celgene: Consultancy; Apopharma: Consultancy; WorldcareClinical: Consultancy; BluebirdBio: Consultancy; Imago Biosciences: Consultancy; BiomedInformatics: Consultancy; Philips Healthcare: Research Funding. Coates:celgene: Consultancy, Honoraria, Other: steering committee of clinical study; vifor: Consultancy, Honoraria; apo pharma: Consultancy, Honoraria, Speakers Bureau; agios pharma: Consultancy, Honoraria.

Long‐Term Precipitation Estimates Generated by a Downscaling‐Calibration Procedure Over the Tibetan Plateau From 1983 to 2015

AbstractThe World Meteorological Organization stipulates a minimum of 30 years of historical data is needed to obtain meaningful results in climatological research. However, large numbers of studies have explored downscaling approaches based on the TRMM Multi‐Satellite Precipitation Analysis (TMPA) data, which span only from 1998 to the present, to obtain the precipitation estimates (~1‐km resolution). The main aim of the present study was to develop a new method for obtaining long‐term (&gt;30 years) precipitation estimates at ~1‐km resolution and to apply that method to a region with complex topography, the Tibetan Plateau. First, PERSIANN‐CDR (Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks—Climate Data Record) data were used for downscaling. Considering the characteristics of the PERSIANN‐CDR data, a new downscaling‐calibration procedure utilizing a combination of a spatial data mining downscaling algorithm (Cubist) and a geographical ratio analysis calibration method was proposed. We found that (1) both the original PERSIANN‐CDR data (Bias ~40.79%) and the downscaled results before calibration (Bias ~26.78%) overestimated the precipitation compared with ground observations; (2) the final downscaled results based on the PERSIANN‐CDR data after calibration were close to the ground observations (Bias ~5%); (3) compared to the results interpolated based on the PERSIANN‐CDR data (E &lt;−1.0), both the downscaling procedure and calibration procedure contributed significantly to the accuracy of the final downscaled results (E ~0.83). These findings suggest that the proposed downscaling‐calibration procedure has great potential as an approach for retrieving long‐term precipitation estimates (~1‐km resolution) over the Tibetan Plateau.

Measurements of Sea Surface Currents in the Baltic Sea Region Using Spaceborne Along-Track InSAR

The main challenging problems in ocean current retrieval from along-track interferometric (ATI)-synthetic aperture radar (SAR) are phase calibration and wave bias removal. In this paper, a method based on differential InSAR (DInSAR) technique for correcting the phase offset and its variation is proposed. The wave bias removal is assessed using two different Doppler models and two different wind sources. In addition to the wind provided by an atmospheric model, the wind speed used for wave correction in this work is extracted from the calibrated SAR backscatter. This demonstrates that current retrieval from ATI-SAR can be completed independently of atmospheric models. The retrieved currents, from four TanDEM-X (TDX) acquisitions over the Oresund channel in the Baltic Sea, are compared to a regional ocean circulation model. It is shown that by applying the proposed phase correction and wave bias removal, a good agreement in spatial variation and current direction is achieved. The residual bias , between the ocean model and the current retrievals, varies between 0.013 and 0.3 m/s depending on the Doppler model and wind source used for wave correction. This paper shows that using SAR as a source of wind speed reduces the bias and root-mean-squared-error ( RMSE ) of the retrieved currents by 20% and 15%, respectively. Finally, the sensitivity of the sea current retrieval to Doppler model and wind errors are discussed.

Проектирование многокристальных конвейерных АЦП высокого разрешения (12-14 бит) с частотой преобразования 0,5-1 ГГц

Low-voltage amplifiers of telescopic type based on CMOS technology with design norms of 180 nm and 60 nm are investigated. It is established that the best characteristics of nonlinearity in the frequency range up to 250 MHz have amplifiers manufactured by technology with design standards of 180 nm. It is shown that amplifiers of the folded CAS-code type with a supply voltage of 2.5-3 V and cascade transistors with maximum permissible interelectrode voltages of 1.2-1.8 V can degrade and even catastrophically fail when exposed to TKCH. Technical solutions for calibration of bias and gain equalization of channels in the creation of 4-channel 12-14-bit ADC with conversion frequency up to 1 GHz and 0.5 GHz are considered. It is shown that the multi-chip use of this type of ADC in the form of microassemblies allows to achieve typical values of integral nonlinearity, signal-to-noise ratio and dynamic range.

Does Performance Calibration Generalize Across Sport Tasks? A Multiexperiment Study in Physical Education.

Three experiments examined students' calibration in physical education in relation to task characteristics. Participants in the 3 experiments were 388 students. Calibration accuracy and bias were calculated based on students' predicted and actual performance in tests including variations of a sport task (basketball shooting) and tasks from different sports (basketball and soccer). An overconfidence effect was found in all experiments, and evidence regarding the hard-easy effect emerged. High compared with low performers were more accurate, and some variations with respect to gender also emerged. The magnitude of calibration error was similar across tasks, whereas approximately half of the students were consistent in the direction of calibration (most of them were overestimators). Results are discussed with reference to theoretical and empirical evidence associated with performance calibration and self-regulated learning in physical education. Methodological issues, practical implications, and future directions are also discussed.