scholarly journals Quality assurance/quality control procedure for New Jersey’s water-use data for the New Jersey Water Transfer Data System (NJWaTr)

2020 ◽  
Author(s):  
Jennifer L. Shourds
Keyword(s):  
Quality Control ◽  
Quality Assurance ◽  
New Jersey ◽  
Water Use ◽  
Water Transfer ◽  
Data System ◽  
Control Procedure ◽  
Transfer Data ◽  
Quality Control Procedure

scholarly journals Sample Confirmation Testing: A Short Tandem Repeat-Based Quality Assurance and Quality Control Procedure for the eyeGENE Biorepository

2016 ◽  
Vol 14 (2) ◽  
pp. 149-155 ◽  
Author(s):  
Rebecca S. Parrish ◽  
Alexandra V. Garafalo ◽  
Vida Ndifor ◽  
Kerry E. Goetz ◽  
Melissa J. Reeves ◽  
...  
Keyword(s):  
Quality Control ◽  
Quality Assurance ◽  
Tandem Repeat ◽  
Short Tandem Repeat ◽  
Control Procedure ◽  
Quality Control Procedure ◽  
Short Tandem

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.

SU-FF-J-25: Quality Control Procedure for the PET-Guided Interventions

2007 ◽  
Vol 34 (6Part5) ◽  
pp. 2373-2373
Author(s):  
D Beylin ◽  
S Sherry ◽  
E Anashkin ◽  
D Narayanan ◽  
P Stepanov ◽  
...  
Keyword(s):  
Quality Control ◽  
Control Procedure ◽  
Quality Control Procedure
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