Sampling Time Error Research Articles

High Resolution Ranging with Small Sample Number under Low SNR Utilizing RIP-OMCS Strategy and AHRC l1 Minimization for Laser Radar

This manuscript presents a novel scheme to achieve high-resolution laser-radar ranging with a small sample number under low signal-to-noise ratio (SNR) conditions. To reduce the sample number, the Restricted Isometry Property-based optimal multi-channel coprime-sampling (RIP-OMCS) strategy is established. In the RIP-OMCS strategy, the data collected across multiple channels with very low coprime-sampling rates can record accurate range information on each target. Further, the asynchronous problem caused by channel sampling-time errors is considered. The sampling-time errors are estimated using the cross-correlation function. After canceling the asynchronous problem, the data collected by multiple channels are then merged into non-uniform sampled signals. Using data combination, target-range estimation is converted into an optimization problem of sparse representation consisting of a non-uniform Fourier dictionary. This optimization problem is solved using adaptive hybrid re-weighted constraint (AHRC) l1 minimization. Two constraints are formed from statistical attributes of the targets and clutter. Moreover, as the detailed characteristics of the target, clutter, and noise are unknown before the solution, the two constraints can be adaptively modified, which guarantees that l1 minimization obtains the high-resolution range profile and accurate distance of all targets under a low SNR. Our experiments confirmed the effectiveness of the proposed method.

Synchronized measurement method for urban distribution networks based on proportional-integral control

In the context of the rising prevalence of renewable energy, the need for precise and synchronized measurements in urban distribution networks has become increasingly critical. Conventional synchronization techniques, which predominantly depend on Global Navigation Satellite System (GNSS) timing signals, are often plagued by significant sampling time errors. Addressing this challenge, this study introduces an innovative automatic synchronization measurement method employing proportional-integral (PI) control. This method is composed of three integral components: the monitoring of local clock phases via pulses per second (PPS) signals, modulation of the clock phase through a PI controller, and the estimation of grid phasors from the synchronized sampled data. The test results of this method on a hardware platform demonstrate its capability to significantly reduce the sampling error from 2 × 10−3 V to an impressive 1 × 10−5 V, while also meeting the requirements of the synchronized phasor measurement standards outlined in C37.118.1. These advancements underscore the significant potential of PI-controlled simultaneous sampling in enhancing the operational efficiency and reliability of urban distribution networks, particularly in environments with a high integration of renewable energy sources.

Design and theoretical error analysis of wireless electrochemical reader to be integrated in smart mask for breath monitoring

Herein, we describe the development of a mask for electrochemical breath gas measurement. The readout board, with dimensions of 16 × 43 × 12 mm, enables Bluetooth connectivity. The device exhibits a current measurement range of up to 582 µA. Sources of measurement error were identified and quantified using the generalized Randles model with 10% accuracy. Device accuracy was determined to be 5% of current signals. Other sources of error included hardware noise (0.021 µA), sampling time error (19.6 µA), and voltage resolution error (4.6 µA at 1 mV). Enhancing the voltage resolution of the LMP91000 chip from 66 mV to 1 mV was achieved by utilizing the microcontroller DAC output voltage as the chip reference voltage, leading to a reduction in the measurement error from 293 to 4.6 µA. The mask was successfully tested for ammonia measurement within the 0–1000 ppm range, indicating its potential for breath monitoring applications.

Evaluating the Simple View of Reading for Children With Attention-Deficit/Hyperactivity Disorder.

The 'simple view of reading' is an influential model of reading comprehension which asserts that children's reading comprehension performance can be explained entirely by their decoding and language comprehension skills. Children with ADHD often exhibit difficulty across all three of these reading domains on standardized achievement tests, yet it is unclear whether the simple view of reading is sufficient to explain reading comprehension performance for these children. The current study was the first to use multiple indicators and latent estimates to examine the veracity of key predictions from the simple view of reading in a clinically-evaluated sample of 250 children with and without ADHD (ages 8-13, Mage=10.29, SD=1.47; 93 girls; 70% White/Non-Hispanic). Results of the full-sample structural equation model revealed that decoding and language comprehension explained all (R2=.99) of the variance in reading comprehension for children with and without ADHD. Further, multigroup modeling (ADHD, Non-ADHD) indicated that there was no difference in the quantity of variance explained for children with ADHD versus clinically-evaluated children without ADHD, and that the quantity of explained variance did not differ from 100% for either group. Sensitivity analyses indicated that these effects were generally robust to control for monomethod bias, time sampling error, and IQ. These findings are consistent with 'simple view' predictions that decoding and language comprehension are both necessary and together sufficient for explaining children's reading comprehension skills. The findings extend prior work by indicating that the 'simple view' holds for both children with ADHD and clinically-evaluated children without ADHD.

Correction model of the sampling time error on the blood trough concentration of tacrolimus in non-sustained-release dosage form for renal transplant recipients

Objective: To establish correction model of the sampling time error on the blood trough concentration of tacrolimus in non-sustained-release dosage form for renal transplant recipient and improve the accuracy of drug dose assessment and clinical adjustment in renal transplant recipients. Methods: Visit records of 206 outpatients in the Department of Transplantation, Nanfang Hospital, Southern Medical University were retrospectively collected from October 15, 2022 to October 30, 2022. The distribution of sampling time of tacrolimus blood drug concentration was described and the time range of correction was determined. Twenty inpatients after renal transplantation in the Department of Transplantation, Nanfang Hospital, Southern Medical University from October 1, 2022 to November 30, 2022 were prospectively included, and their demography data, laboratory test results during follow-ups, and CYP3A5 genotype were collected. The patients took tacrolimus in non-sustained-release dosage form every 12 h starting from 19∶30 on the day of admission. Peripheral blood samples were collected from the patients on the second day of admission at 7∶30 and on the third day at 6∶00-10∶00 every 30 minutes to test the blood concentration of tacrolimus. Using the collection time as the independent variable and the blood tacrolimus concentration as the dependent variable, a simple linear regression was performed to fitting a linear model of tacrolimus blood concentration-sampling time. Multiple linear regression was performed to analyze the influencing factors of the tacrolimus metabolic rate within a specific period and generate the regression equation. Results: The 206 outpatients aged (46±13) years, including 131 males (63.6%). The time gap [M (Q1, Q3)] between the sampling time of the follow-up outpatients and standard C12 was 24 (13.0, 46.5) min, and the maximum time gap was 135 min. The 20 enrolled inpatients aged (45±12) years, including 15 males (75.0%). There was no significant difference in the blood concentration of tacrolimus collected at 7∶30 on the second (7.87±2.21)ng/ml and third days (7.84±2.33)ng/ml after admission of the enrolled inpatients (P=0.917), and the blood tacrolimus concentration rhythm was stable in the trial. The plasma concentration of C10.5-C14.5 was linearly related to the time, with R2 [M (Q1, Q3)] 0.88 (0.85, 0.92) and all P<0.05. The metabolic rate of tacrolimus during C10.5-C14.5=0.984+0.090×basic concentration of tacrolimus (ng/ml)-0.036×body mass index+0.489×CYP3A5 genotype-0.007×hemolobin(g/L)-0.035×alanine aminotransferase (U/L)+0.143×total cholesterol (mmol/L)+0.027×total bilirubin (μmol/L), with R2=0.85. Conclusion: This study propose a correction model for tacrolimus (non-sustained-release dosage form) trough concentration around C12, which is helpful for clinicians to easily and accurately assess renal transplant recipients' tacrolimus exposure.

Applying Time-Expended Sampling to Ensemble Assimilation of Remote-Sensing Data for Short-Term Predictions of Thunderstorms

By sampling perturbed state vectors from each ensemble forecast at additional time levels shifted by ±τ (where τ is a selected time interval) from the analysis time, time-expanded sampling (TES) can not only sample timing errors (or phase errors) but also triple the analysis ensemble size for covariance construction without increasing the forecast ensemble size. In this study, TES was applied to the convection-allowing ensemble-based warn-on-forecast system (WoFS), for four severe storm events, to reduce the computational costs that constrain real-time applications in the assimilation of remote-sensing data from radars and the geostationary satellite GOES-16. For each event, TES was implemented against a 36-member control experiment (E36) by reducing the forecast ensemble size to 12 but tripling the analysis ensemble size to 12 × 3 = 36 with τ = 2.5 min, 5 min and 7.5 min in three TES experiments, named E12×3τ2.5, E12×3τ5 and E12×3τ7.5, respectively. A 0–6-h forecast was created hourly after the second hour during the assimilation in each experiment. The assimilation statistics were evaluated for each experiment applied to each event and were found to be little affected by the TES, while reducing the computational cost. The forecasts produced in each experiment were verified against multi-sensor observed/estimated rainfall, reported tornadoes and damaging winds for each event. The verifications indicated that the forecasts produced in the three TES experiments had about the same capability and quality as that in the E36 for predicting hourly rainfall and the probabilities of tornadoes and damaging winds; in addition, the predictive capability and quality were not sensitive to τ, although they were slightly enhanced by selecting τ = 7.5 min. These results suggest that TES is attractive and useful for cost-saving real-time applications of WoFS in the assimilation of remote-sensing data and the generation of short-term severe-weather forecasts.

Evaluation and Validation of the Limited Sampling Strategy of Polymyxin B in Patients with Multidrug-Resistant Gram-Negative Infection.

Polymyxin B (PMB) is the final option for treating multidrug-resistant Gram-negative bacterial infections. The acceptable pharmacokinetic/pharmacodynamic target is an area under the concentration-time curve across 24 h at a steady state (AUCss,24h) of 50-100 mg·h/L. The limited sampling strategy (LSS) is useful for predicting AUC values. However, establishing an LSS is a time-consuming process requiring a relatively dense sampling of patients. Further, given the variability among different centers, the predictability of LSSs is frequently questioned when it is extrapolated to other clinical centers. Currently, limited data are available on a reliable PMB LSS for estimating AUCss,24h. This study assessed and validated the practicability of LSSs established in the literature based on data from our center to provide reliable and ready-made PMB LSSs for laboratories performing therapeutic drug monitoring (TDM) of PMB. The influence of infusion and sampling time errors on predictability was also explored to obtain the optimal time points for routine PMB TDM. Using multiple regression analysis, PMB LSSs were generated from a model group of 20 patients. A validation group (10 patients) was used to validate the established LSSs. PMB LSSs from two published studies were validated using a dataset of 30 patients from our center. A population pharmacokinetic model was established to simulate the individual plasma concentration profiles for each infusion and sampling time error regimen. Pharmacokinetic data obtained from the 30 patients were fitted to a two-compartment model. Infusion and sampling time errors observed in real-world clinical practice could considerably affect the predictability of PMB LSSs. Moreover, we identified specific LSSs to be superior in predicting PMB AUCss,24h based on different infusion times. We also discovered that sampling time error should be controlled within -10 to 15 min to obtain better predictability. The present study provides validated PMB LSSs that can more accurately predict PMB AUCss,24h in routine clinical practice, facilitating PMB TDM in other laboratories and pharmacokinetics/pharmacodynamics-based clinical studies in the future.

2D scanning SERS probe for early biofilm boundary determination

Surface-enhanced Raman scattering (SERS) enables the biofilm detection by the characteristic Raman spectrum of extracellular polymeric substance (EPS). However, its clinical application in wound biofilm delineation is hindered by long sampling time and unpredictable sampling errors. To overcome these limitations, we propose a two dimensional (2D) scanning SERS probe that combines the advantages of galvo mirrors and a custom designed SERS active substrate. In this design, the SERS active substrate with dendritic nano-silver structure is fixed on the focal plane of the scan lens to maintain not only the required distance of the sampling plane but also the stable sampling condition as the excitation laser beam driven by the galvo mirrors scans quickly over the region of interest (ROI). Our benchtop study shows that the ± 3.75 mm offset between the sampling plane and the focal plane results in less than 10% change in both laser spot size and fluctuation in Raman peak intensity for the 2D scanning SERS probe, superior to the commercial Raman probe where 1700% change in laser spot size and 95% fluctuation in Raman peak intensity. Our in vitro study in two typical biofilms shows that the 2D SERS scanning probe can detect the biofilm as early as 4 h of cultivation and delineate the biofilm boundary with accuracy at 8 h of cultivation. Furthermore, this substrate is disposable, antibacterial, and capable of enhancing Raman intensity with consistency. Our studies imply the clinical potential of using the 2D SERS scanning probe for biofilm delineation in chronic wound management.

A Lightweight Sampling Time Error Correction Technique for Micro Phasor Measurement Units

In this paper, we deal with enhancing the accuracy of the sampling instant of Micro Phasor Measurement Units (&#x03BC;PMUs) used in distribution grids. The pulse per second (PPS) signal of the GPS synchronizes the start of each second of all &#x03BC;PMUs in the grid. The internal clock of each &#x03BC;PMUs then takes over and determines the sampling instants till the next PPS. Due to the inaccuracy and drift of the clock and the unavoidably needed fractional count of the clock pulses, accumulation of error leads to excessive phase error. A lightweight algorithm is proposed for the correction of the sampling period. The algorithm fine tunes the timer count used to determine the sampling instant. As the proposed algorithm aims at correcting the sampling instant, it can be applied by any other platform, but it needs an accurate time reference like that of the 1PPS (1 pulse per second) of the GPS. The performance evaluation shows that, the maximum phase error is lower than the accepted phase error by about two orders of magnitude. The theoretical results are confirmed by implementing the algorithm on a prototype &#x03BC;PMU.

A 4GS/s 8‐bit time‐interleaved SAR ADC with an energy‐efficient architecture in 130 nm CMOS

AbstractThis paper presents the design, implementation, and measurements of a 4 GS/s, 8‐bit resolution, time‐interleaved (TI) analog‐to‐digital converter (ADC) comprised of 32 asynchronous successive approximation register (SAR) ADCs. The chip is fabricated in a 130 nm CMOS process. This prototype achieves the highest sampling rate and the best efficiency for a SAR TI‐ADC in the process used. An energy‐efficient hierarchical T&amp;H architecture, ranked in a 4 × 8 structure, has been used to interleave the aforementioned high number of SAR ADCs avoiding the power hungry buffers typically used in the input signal path and/or T&amp;H outputs. The sampling architecture includes programmable delay cells with up to 104 fs resolution to calibrate sampling time errors. Additionally, the input matching network uses an on‐chip inductance to mitigate the impact of the packaging on the analog bandwidth. An efficient SAR ADC implementation is achieved by an optimized comparator design, which allows for both, noise and asynchronous clock control, and includes background DC offset calibration. The test chip is the core of a measurement platform dedicated to the evaluation of mismatch calibration techniques for ADCs used in high speed digital communication systems. To enable this application, a 32Gb/s low‐voltage differential signaling interface is included to transmit the samples off‐chip without any decimation. The TI‐ADC achieves a peak 7.09 effective number of bits (ENOB) (5.47ENOB at Nyquist) and 1.3 GHz input bandwidth with a power consumption of 93 mW at 1.2 V. Each SAR ADC channel achieves a Walden figure of merit (FOM) of 123fJ/conv‐step and owing to the efficient interleaved architecture the full TI‐ADC achieves a peak FOM of 171fJ/conv‐step (526fJ/conv‐step at Nyquist).

OFDM System Performance, Variability and Optimality With Design Imperfections and Channel Impediments

An important indicator commonly used to evaluate the quality of a communication link is signal-to-interference ratio (SIR). However, studies in the literature dealing with SIR for orthogonal frequency division multiplexing (OFDM) systems are relatively few. This paper analytically investigates OFDM system performance by including major channel impairments such as excess delay spread, synchronization error, sample timing error, frequency offset, and time-varying fading. We derive a closed-form expression for SIR, accounting for both variations of inter-carrier interference (ICI) and inter-symbol interference (ISI) with the number of subcarriers, and the susceptibility to channel impairments. A condition relating peak ISI to peak ICI is derived. An expression quantifying the impact of synchronization error in ISI is obtained. SIR behavior with respect to the tradeoff among numbers of subcarriers and other system parameters are investigated. A numerical method, as well as a polynomial equation for an analytical solution, is formulated to find the optimal number of subcarriers for a given set of constraints. We demonstrate the variations of SIR with various parameters and analyze the leakage of power (unwanted power distribution across subcarriers).

The cortisol awakening response predicts a same-day index of executive function in healthy young adults

The cortisol awakening response (CAR) is associated with various aspects of cognition, including executive function, in older adult and clinical samples. However, the association between these variables in the healthy functioning population is not well understood due to the limited number of appropriately controlled studies. This study explored the association between the CAR and a set shifting index of executive function in 55 (44 females) healthy participants aged 20.2 ± 3.0 years. Notoriously, assessment of the CAR from self-collected saliva samples within the domestic setting is subject to sample timing error, so electronic monitoring of both awakening and sampling times were employed. Participants attended the laboratory in the afternoon of CAR assessment for testing on the Attention Switching Task of the CANTAB neuropsychological testing battery. A positive association was found between CAR magnitude and attention-switching performance in the afternoon of the same day. This was independent of known relevant CAR covariates, but only evident in CAR data collected without delay exceeding 8 min post-awakening. These findings offer insight into a potential role for the CAR in modulating cognitive functions associated with the pre-frontal cortex.

Operational Bayesian GLS Regression for Regional Hydrologic Analyses

AbstractThis paper develops the quasi‐analytic Bayesian analysis of the generalized least squares (GLS) (B‐GLS) model introduced by Reis et al. (2005, https://doi.org/10.1029/2004WR003445) into an operational and statistically comprehensive GLS regional hydrologic regression methodology to estimate flood quantiles, regional shape parameters, low flows, and other statistics with spatially correlated flow records. New GLS regression diagnostic statistics include a Bayesian plausibility value, pseudo adjusted R2, pseudo analysis of variance table, and two diagnostic error variance ratios. Traditional leverage and influence are extended to identify rogue observations, address lack of fit, and support gauge network design and region‐of‐influence regression. Formulas are derived for the Bayesian computation of estimators, standard errors, and diagnostic statistics. The use of B‐GLS and the new diagnostic statistics are illustrated with a regional log‐space skew regression analysis for the Piedmont region in the Southeastern U.S. A comparison of ordinary, weighted, and GLS analyses documents the advantages of the Bayesian estimator over the method‐of‐moment estimator of model error variance introduced by Stedinger and Tasker (1985, https://doi.org/10.1029/WR021i009p01421). Of the three types of analyses, only GLS considers the covariance among concurrent flows. The example demonstrates that GLS regional skewness models can be highly accurate when correctly analyzed: The B‐GLS average variance of prediction is 0.090 for South Carolina (92 stations), whereas a traditional ordinary least squares analysis published by the U.S. Geological Survey yielded 0.193 (Feaster &amp; Tasker, 2002, https://doi.org/10.3133/wri024140). B‐GLS provides a statistical valid framework for the rigorous analysis of spatially correlated hydrologic data, allowing for the estimation of parameters and their actual precision and computation of several diagnostic statistics, as well as correctly attributing variability to the three key sources: time sampling error, model error, and signal.

Phase calibration and uncertainty evaluation for a RF comb generator

In this paper, we present calibration and uncertainty evaluation methods for the phase of a radio-frequency comb generator using a calibrated sampling oscilloscope. In addition, we present many solutions that are indispensable for the precise calibration of phase up to 50 GHz. First, we correct the sampling time error, which is the systematic error of the equivalent time sampling system. Second, the frequency response of the oscilloscope is de-convoluted from the measured pulse, and the impedance mismatch between the radio-frequency comb generator and the oscilloscope is calibrated. Finally, we present the calibrated phase of the radio-frequency comb generator and the measurement uncertainty, which demonstrated 95% confidence intervals within ±4.4° up to 50 GHz. The calibrated radio-frequency comb generator, which is traceable to SI units, is used to calibrate a nonlinear vector network analyzer, vector signal analyzer, and oscilloscope. Uncertainties are easily propagated to the measurement uncertainty of other apparatus since it is obtained in the form of covariance.

The cortisol awakening response predicts a same-day index of executive function in healthy young adults

The cortisol awakening response (CAR) is associated with various aspects of cognition, including executive function, in older adult and clinical samples. However, the association between these variables in the healthy functioning population is not well understood due to the limited number of appropriately controlled studies. This study explored the association between the CAR and a set shifting index of executive function in 55 (44 females) healthy participants aged 20.2 ± 3.0 years. Notoriously, assessment of the CAR from self-collected saliva samples within the domestic setting is subject to sample timing error, so electronic monitoring of both awakening and sampling times were employed. Participants attended the laboratory in the afternoon of CAR assessment for testing on the Attention Switching Task of the CANTAB neuropsychological testing battery. A positive association was found between CAR magnitude and attention-switching performance in the afternoon of the same day. This was independent of known relevant CAR covariates, but only evident in CAR data collected without delay exceeding 8 min post-awakening. These findings offer insight into a potential role for the CAR in modulating cognitive functions associated with the pre-frontal cortex.

Digital background calibration of ultra-high-speed time-interleaved of PAM4 2-bit DACs

Sample-time error (STE) between time-interleaved channels is one of the main limitations on the performance of time-interleaved digital to analog converters (DACs). This paper presents techniques for detecting and correcting the STE in ultra-high speed 53 GBaud 4-level pulse amplitude modulation (PAM4) with a 2-bit DAC. Simulation results and lab measurements of a TSMC 16 nm CMOS process are presented. Test results show that the peak-to-peak STE of the 53 GBaud DAC is improved by more than one order of magnitude.

Digital background calibration algorithm and its FPGA implementation for timing mismatch correction of time-interleaved ADC

Sample time error can degrade the performance of time-interleaved analog to digital converters (TIADCs). A fully digital background algorithm is presented in this paper to estimate and correct the timing mismatch errors between four interleaved channels, together with its hardware implementation. The proposed algorithm provides low computation burden and high performance. It is based on the simplified representation of the coefficients of the Lagrange interpolator. Simulation results show that it can suppress error tones in all of the Nyquist band. Results show that, for a four-channel TIADC with 10-bit resolution, the proposed algorithm improves the signal to noise and distortion ratio (SNDR) and spurious-free dynamic range (SFDR) by 19.27 dB and 35.2 dB, respectively. This analysis was done for an input signal frequency of $$0.09f_s$$ . In the case of an input signal frequency of $$0.45f_s$$ , an improvement by 33.06 dB and 43.14 dB is respectively achieved in SNDR and SFDR. In addition to the simulation, the algorithm was implemented in hardware for real-time evaluation. The low computational burden of the algorithm allowed an FPGA implementation with a low logic resource usage and a high system clock speed (926.95 MHz for four channel algorithm implementation). Thus, the proposed architecture can be used as a post-processing algorithm in host processors for data acquisition systems to improve the performance of TIADC.

Deep Learning-based Frame and Timing Synchronization for End-to-End Communications

End-to-end learning-based communications systems are more likely to achieve the global optimal performance. In this work, we implement a complete communications system as an end-to-end deep neural network, including transmitter, channel model, synchronization and receiver. There are a lot of issues which can result to the out of synchronization between the transmitter and the receiver, including sampling timing offset, sampling frequency offset and so on. Therefore, we focus on frame and timing synchronization in the end-to-end learning-based communications systems. Our results show that such an end-to-end learning-based communications system is robust to the impairments that arise from sampling frequency offset. An extra synchronization model based on the convolutional neural networks is introduced to achieve frame synchronization and compensate for the impairments which arise from sampling time offset and sampling time error. The performances of the synchronization model are evaluated from the symbol error rate and the frame header detection results. It can correctly detect most of the actual position of frame header, and it is about 2dB better than that of the direct correlation detection in the end-to-end communications systems.

On the Use of Cost-Effective Valid-Time-Shifting (VTS) Method to Increase Ensemble Size in the GFS Hybrid 4DEnVar System

Abstract Valid-time-shifting (VTS) ensembles, either in the form of full ensemble members (VTSM) or ensemble perturbations (VTSP), were investigated as inexpensive means to increase ensemble size in the NCEP Global Forecast System (GFS) hybrid four-dimensional ensemble–variational (4DEnVar) data assimilation system. VTSM is designed to sample timing and/or phase errors, while VTSP can eliminate spurious covariances through temporal smoothing. When applying a shifting time interval (τ = 1, 2, or 3 h), VTSM and VTSP triple the baseline background ensemble size from 80 (ENS80) to 240 (ENS240) in the EnVar variational update, where the overall cost is only increased by 23%–27%, depending on the selected τ. Experiments during a 10-week summer period show the best-performing VTSP with τ = 2 h improves global temperature and wind forecasts out to 5 days over ENS80. This could be attributed to the improved background ensemble distribution, ensemble correlation accuracy, and increased effective rank in the populated background ensemble. VTSM generally degrades global forecasts in the troposphere. Improved global forecasts above 100 hPa by VTSM may benefit from the increased spread that alleviates the underdispersiveness of the original background ensemble at such levels. Both VTSM and VTSP improve tropical cyclone track forecasts over ENS80. Although VTSM and VTSP are much less expensive than directly running a 240-member background ensemble, owing to the improved ensemble covariances, the best-performing VTSP with τ = 1 h performs comparably or only slightly worse than ENS240. The best-performing VTSM with τ = 3 h even shows more accurate track forecasts than ENS240, likely contributed to by its better sampling of timing and/or phase errors for cases with small ensemble track spread.

Calibration and Uncertainty Analysis of Sample-Time Error on High Jitter of Samplers

Calibration and Uncertainty Analysis of Sample-Time Error on High Jitter of Samplers