NEW RESULTS IN REDUCED ESTIMATION WITH SYSTEMATIC ERRORS IN MEASUREMENTS AND THEIR APPLICATION FOR THE FORMATION OF A GROUP TIME SCALE

2018 ◽  
pp. 30-37
Author(s):  
A. P. Aleshkin ◽  
A. A. Makarov ◽  
Yu. F. Matasov
Keyword(s):  
Time Scale ◽  
Statistical Estimation ◽  
Systematic Errors ◽  
Random Component ◽  
Customer Support ◽  
Group Time ◽  
Reduction Coefficient ◽  
Systematic Component ◽  
Compression Parameter ◽  
Positive Effect

The article deals with the behavior of reduced scalar estimates in the presence of systematic errors in the observational data. The proposed procedure with a different method of forming the reduction coefficient. A quasi-optimal variant of the compression parameter formation is considered. Simulation results for different conditions of application of the proposed algorithms are presented. Currently, one of the ways to improve the accuracy of the formation of the time scale in solving the problems of frequency-time customer support is the averaging of the readings of several generators. At the same time, this approach, as shown in the theory of statistical estimation, is effective for parrying the random component of the error of the estimated process. However, for frequency generators random error can be effectively compensated for a long range of observations, but the systematic component - frequency drift - is a serious problem, which can be eliminated by averaging only under certain conditions. Therefore, the article proposes a version of the reduced estimate, effective, as shown, to parry the departure of the time scale by introducing a shift in the implementation of compression, defined by the reduction procedure. The conditions in which the degree of the achieved positive effect has a practical sense are considered.

On Improving the Accuracy of Lattice Models in Crashworthiness Analysis

2000 ◽  
Author(s):  
Cori L. Ignatovich ◽  
Alejandro R. Diaz ◽  
Ciro A. Soto
Keyword(s):  
Wavelet Transforms ◽  
Optimization Problem ◽  
Complex Structure ◽  
Lattice Models ◽  
Structural Behavior ◽  
Random Component ◽  
Impact Event ◽  
Systematic Component ◽  
Crashworthiness Analysis

Abstract Lattice models are used to represent the behavior of a complex structure in an impact event. An optimization problem is proposed to “tune” the model to capture the relevant behavior of the structure. Wavelet transforms are used to identify the systematic component of a signal characterizing the structural behavior from its random component.

Pulsar group time scale

1990 ◽  
Vol 33 (11) ◽  
pp. 1154-1155
Author(s):  
B. I. Loboiko ◽  
O. B. Borodkina
Keyword(s):  
Time Scale ◽  
Group Time

scholarly journals Calibration of radiocarbon dates: tables based on the consensus data of the Workshop on Calibrating the Radiocarbon Time Scale

Radiocarbon ◽  
1982 ◽  
Vol 24 (2) ◽  
pp. 103-150 ◽  
Author(s):  
Jeffrey Klein ◽  
J C Lerman ◽  
P E Damon ◽  
E K Ralph
Keyword(s):  
Confidence Intervals ◽  
Time Scale ◽  
Systematic Errors ◽  
Data Set ◽  
Radiocarbon Dates ◽  
Distinctive Features ◽  
Extended Range

A calibration is presented for conventional radiocarbon ages ranging from 10 to 7240 years BP and thus covering a calendric range of 8000 years from 6050 BC to AD 1950. Distinctive features of this calibration include 1) an improved data set consisting of 1154 radiocarbon measurements on samples of known age, 2) an extended range over which radiocarbon ages may be calibrated (an additional 530 years), 3) separate 95% confidence intervals (in tabular from) for six different radiocarbon uncertainties (20, 50, 100, 150, 200, 300 years), and 4) an estimate of the non-Poisson errors related to radiocarbon determinations, including an estimate of the systematic errors between laboratories.

Statistical estimation of random and systematic errors in determination of laser wavelengths

1977 ◽  
Vol 20 (2) ◽  
pp. 187-190
Author(s):  
T. P. Mikhailova ◽  
K. A. Bikmukhametov ◽  
V. I. Bobrik
Keyword(s):  
Statistical Estimation ◽  
Systematic Errors

scholarly journals On the Correspondence between Short- and Long-Time-Scale Systematic Errors in CAM4/CAM5 for the Year of Tropical Convection

2012 ◽  
Vol 25 (22) ◽  
pp. 7937-7955 ◽  
Author(s):  
Shaocheng Xie ◽  
Hsi-Yen Ma ◽  
James S. Boyle ◽  
Stephen A. Klein ◽  
Yuying Zhang
Keyword(s):  
Time Scale ◽  
Large Scale ◽  
Lower Stratosphere ◽  
Atmospheric Model ◽  
Systematic Errors ◽  
Tropical Convection ◽  
Simulated Precipitation ◽  
Long Time ◽  
Large Scale Flow ◽  
Zone Pattern

Abstract The correspondence between short- and long-time-scale systematic errors in the Community Atmospheric Model, version 4 (CAM4) and version 5 (CAM5), is systematically examined. The analysis is based on the annual-mean data constructed from long-term “free running” simulations and short-range hindcasts. The hindcasts are initialized every day with the ECMWF analysis for the Year(s) of Tropical Convection. It has been found that most systematic errors, particularly those associated with moist processes, are apparent in day 2 hindcasts. These errors steadily grow with the hindcast lead time and typically saturate after five days with amplitudes comparable to the climate errors. Examples include the excessive precipitation in much of the tropics and the overestimate of net shortwave absorbed radiation in the stratocumulus cloud decks over the eastern subtropical oceans and the Southern Ocean at about 60°S. This suggests that these errors are likely the result of model parameterization errors as the large-scale flow remains close to observed in the first few days of the hindcasts. In contrast, other climate errors are present in the hindcasts, but with amplitudes that are significantly smaller than and do not approach their climate errors during the 6-day hindcasts. These include the cold biases in the lower stratosphere, the unrealistic double–intertropical convergence zone pattern in the simulated precipitation, and an annular mode bias in extratropical sea level pressure. This indicates that these biases could be related to slower processes such as radiative and chemical processes, which are important in the lower stratosphere, or the result of poor interactions of the parameterized physics with the large-scale flow.

Elements of an Approach to the Assessment of Systematic Uncertainty in Transient Measurements

1998 ◽  
Vol 120 (4) ◽  
pp. 755-759
Author(s):  
Paul K. Maciejewski
Keyword(s):  
Measurement Uncertainty ◽  
Experimental Research ◽  
Systematic Uncertainty ◽  
Time Dependent ◽  
Random Component ◽  
Systematic Component ◽  
Transient Measurements

Although there has been an increasing interest in experimental research investigating time-dependent fluid phenomena, accepted methods for assessing and reporting measurement uncertainty, i.e., those contained in ANSI/ASME PTC 1991-1998. do not consider issues pertaining specifically to the assessment of uncertainty in transient measurements. Complementing the author’s previous work which presented a method for assessing the random component of uncertainty in transient measurements, this paper presents a method for assessing the systematic component of uncertainty in transient measurements.

Scatterometer-Based Assessment of 10-m Wind Analyses from the Operational ECMWF and NCEP Numerical Weather Prediction Models

2005 ◽  
Vol 133 (2) ◽  
pp. 409-429 ◽  
Author(s):  
Dudley B. Chelton ◽  
Michael H. Freilich
Keyword(s):  
Wind Stress ◽  
Numerical Weather Prediction ◽  
Measurement Errors ◽  
Prediction Models ◽  
Weather Prediction ◽  
Systematic Errors ◽  
Random Component ◽  
Numerical Weather ◽  
Wind Speeds ◽  
Time Periods

Abstract Wind measurements by the National Aeronautics and Space Administration (NASA) scatterometer (NSCAT) and the SeaWinds scatterometer on the NASA QuikSCAT satellite are compared with buoy observations to establish that the accuracies of both scatterometers are essentially the same. The scatterometer measurement errors are best characterized in terms of random component errors, which are about 0.75 and 1.5 m s−1 for the along-wind and crosswind components, respectively. The NSCAT and QuikSCAT datasets provide a consistent baseline from which recent changes in the accuracies of 10-m wind analyses from the European Centre for Medium-Range Weather Forecasts (ECMWF) and the U.S. National Centers for Environmental Prediction (NCEP) operational numerical weather prediction (NWP) models are assessed from consideration of three time periods: September 1996–June 1997, August 1999–July 2000, and February 2002–January 2003. These correspond, respectively, to the 9.5-month duration of the NSCAT mission, the first 12 months of the QuikSCAT mission, and the first year after both ECMWF and NCEP began assimilating QuikSCAT observations. There were large improvements in the accuracies of both NWP models between the 1997 and 2000 time periods. Though modest in comparison, there were further improvements in 2002, at least partly attributable to the assimilation of QuikSCAT observations in both models. There is no evidence of bias in the 10-m wind speeds in the NCEP model. The 10-m wind speeds in the ECMWF model, however, are shown to be biased low by about 0.4 m s−1. While it is difficult to eliminate systematic errors this small, a bias of 0.4 m s−1 corresponds to a typical wind stress bias of more than 10%. This wind stress bias increases to nearly 20% if atmospheric stability effects are not taken into account. Biases of these magnitudes will result in significant systematic errors in ocean general circulation models that are forced by ECMWF winds.

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