scholarly journals Optimization and validation of patient‐based real‐time quality control procedure using moving average and average of normals with multi‐rules for TT3, TT4, FT3, FT3, and TSH on three analyzers

2020 ◽  
Vol 34 (8) ◽  
Author(s):  
Chao Song ◽  
Jun Zhou ◽  
Jun Xia ◽  
Deli Ye ◽  
Qian Chen ◽  
...  
Keyword(s):  
Quality Control ◽  
Real Time ◽  
Moving Average ◽  
Control Procedure ◽  
Quality Control Procedure

Inefficacy of Moving Average Algorithm as Principal Quality Control Procedure on Technicon System H6000®

1991 ◽  
Vol 95 (2) ◽  
pp. 218-221
Author(s):  
Pieriluigi Tramacere ◽  
Alessandro Marocchi ◽  
Piermario Gerthoux ◽  
Claudio Beretta ◽  
Rinaldo Brivio ◽  
...  
Keyword(s):  
Quality Control ◽  
Moving Average ◽  
Control Procedure ◽  
Quality Control Procedure

scholarly journals A Novel Near-Real-Time Quality-Control Procedure for Radiometric Profiles Measured by Bio-Argo Floats: Protocols and Performances

2016 ◽  
Vol 33 (5) ◽  
pp. 937-951 ◽  
Author(s):  
Emanuele Organelli ◽  
Hervé Claustre ◽  
Annick Bricaud ◽  
Catherine Schmechtig ◽  
Antoine Poteau ◽  
...  
Keyword(s):  
Quality Control ◽  
Real Time ◽  
Global Scale ◽  
Control Procedure ◽  
Data Quality Control ◽  
Time Data ◽  
Automatic Data ◽  
Argo Floats ◽  
Quality Control Procedure ◽  
Management Procedure

AbstractAn array of Bio-Argo floats equipped with radiometric sensors has been recently deployed in various open ocean areas representative of the diversity of trophic and bio-optical conditions prevailing in the so-called case 1 waters. Around solar noon and almost every day, each float acquires 0–250-m vertical profiles of photosynthetically available radiation and downward irradiance at three wavelengths (380, 412, and 490 nm). Up until now, more than 6500 profiles for each radiometric channel have been acquired. As these radiometric data are collected out of an operator’s control and regardless of meteorological conditions, specific and automatic data processing protocols have to be developed. This paper presents a data quality-control procedure aimed at verifying profile shapes and providing near-real-time data distribution. This procedure is specifically developed to 1) identify main issues of measurements (i.e., dark signal, atmospheric clouds, spikes, and wave-focusing occurrences) and 2) validate the final data with a hierarchy of tests to ensure a scientific utilization. The procedure, adapted to each of the four radiometric channels, is designed to flag each profile in a way compliant with the data management procedure used by the Argo program. Main perturbations in the light field are identified by the new protocols with good performances over the whole dataset. This highlights its potential applicability at the global scale. Finally, the comparison with modeled surface irradiances allows for assessing the accuracy of quality-controlled measured irradiance values and identifying any possible evolution over the float lifetime due to biofouling and instrumental drift.

scholarly journals Expanding HadISD: quality-controlled, sub-daily station data from 1931

2016 ◽  
Author(s):  
Robert J. H. Dunn ◽  
Kate M. Willett ◽  
David E. Parker ◽  
Lorna Mitchell
Keyword(s):  
Quality Control ◽  
Wind Speed ◽  
Time Range ◽  
Underlying Structure ◽  
Control Procedure ◽  
Future Projections ◽  
Quality Control Procedure ◽  
Temporal Coverage ◽  
Extremes Of Temperature ◽  
Selection Of

Abstract. HadISD is a sub-daily, station-based, quality-controlled dataset designed to study past extremes of temperature, pressure and humidity and allow comparisons to future projections. Herein we describe the first major update to the HadISD dataset. The temporal coverage of the dataset has been extended to 1931 to present, doubling the time range over which data are provided. Improvements made to the station selection and merging procedures result in 7677 stations being provided in version 2.0.0.2015p of this dataset. The selection of stations to merge together making composites has also been improved and made more robust. The underlying structure of the quality control procedure is the same as for HadISD.1.0.x, but a number of improvements have been implemented in individual tests. Also, more detailed quality control tests for wind speed and direction have been added. The data will be made available as netCDF files at www.metoffice.gov.uk/hadobs/hadisd and updated annually.

scholarly journals Migration from NetWISP to Nutritics – a quality control procedure

2018 ◽  
Vol 77 (OCE3) ◽  
Author(s):  
S. Cassidy ◽  
B. Phillips ◽  
J. Caldeira Fernandes da Silva ◽  
A. Parle
Keyword(s):  
Quality Control ◽  
Control Procedure ◽  
Quality Control Procedure

scholarly journals Automated Quality Control of In Situ Soil Moisture from the North American Soil Moisture Database Using NLDAS-2 Products

2015 ◽  
Vol 54 (6) ◽  
pp. 1267-1282 ◽  
Author(s):  
Youlong Xia ◽  
Trent W. Ford ◽  
Yihua Wu ◽  
Steven M. Quiring ◽  
Michael B. Ek
Keyword(s):  
Quality Control ◽  
Soil Moisture ◽  
North American ◽  
Soil Layer ◽  
Control Procedure ◽  
Multiple Model ◽  
Quality Control Procedure ◽  
The North ◽  
Automated Quality Control

AbstractThe North American Soil Moisture Database (NASMD) was initiated in 2011 to provide support for developing climate forecasting tools, calibrating land surface models, and validating satellite-derived soil moisture algorithms. The NASMD has collected data from over 30 soil moisture observation networks providing millions of in situ soil moisture observations in all 50 states, as well as Canada and Mexico. It is recognized that the quality of measured soil moisture in NASMD is highly variable because of the diversity of climatological conditions, land cover, soil texture, and topographies of the stations, and differences in measurement devices (e.g., sensors) and installation. It is also recognized that error, inaccuracy, and imprecision in the data can have significant impacts on practical operations and scientific studies. Therefore, developing an appropriate quality control procedure is essential to ensure that the data are of the best quality. In this study, an automated quality control approach is developed using the North American Land Data Assimilation System, phase 2 (NLDAS-2), Noah soil porosity, soil temperature, and fraction of liquid and total soil moisture to flag erroneous and/or spurious measurements. Overall results show that this approach is able to flag unreasonable values when the soil is partially frozen. A validation example using NLDAS-2 multiple model soil moisture products at the 20-cm soil layer showed that the quality control procedure had a significant positive impact in Alabama, North Carolina, and west Texas. It had a greater impact in colder regions, particularly during spring and autumn. Over 433 NASMD stations have been quality controlled using the methodology proposed in this study, and the algorithm will be implemented to control data quality from the other ~1200 NASMD stations in the near future.

Sign in / Sign up

Export Citation Format

Share Document