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Global historical climatology network (GHCN) quality control of monthly temperature data

Global historical climatology network (GHCN) quality control of monthly temperature data All geophysical data bases need some form of quality assurance. Otherwise, erroneous data points may produce faulty analyses. However, simplistic quality control procedures have been known to contribute to erroneous conclusions by removing valid data points that were more extreme than the data set compilers expected. In producing version 2 of the global historical climatology network's (GHCN's) temperature data sets, a variety of quality control tests were evaluated and a specialized suite of procedures was developed. Quality control traditionally relies primarily on checks for outliers from both a time series and spatial perspective, the latter accomplished by comparisons with neighbouring stations. This traditional approach was used, and it was determined that there are many data problems that require additional tests to detect. In this paper a suite of quality control tests are justified and documented and applied to this global temperature data base, emphasizing the logic and limitations of each test. © 1998 Royal Meteorological Society http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png International Journal of Climatology Wiley

Global historical climatology network (GHCN) quality control of monthly temperature data

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References (17)

Publisher
Wiley
Copyright
Copyright © 1998 Royal Meteorological Society
ISSN
0899-8418
eISSN
1097-0088
DOI
10.1002/(SICI)1097-0088(199809)18:11<1169::AID-JOC309>3.0.CO;2-U
Publisher site
See Article on Publisher Site

Abstract

All geophysical data bases need some form of quality assurance. Otherwise, erroneous data points may produce faulty analyses. However, simplistic quality control procedures have been known to contribute to erroneous conclusions by removing valid data points that were more extreme than the data set compilers expected. In producing version 2 of the global historical climatology network's (GHCN's) temperature data sets, a variety of quality control tests were evaluated and a specialized suite of procedures was developed. Quality control traditionally relies primarily on checks for outliers from both a time series and spatial perspective, the latter accomplished by comparisons with neighbouring stations. This traditional approach was used, and it was determined that there are many data problems that require additional tests to detect. In this paper a suite of quality control tests are justified and documented and applied to this global temperature data base, emphasizing the logic and limitations of each test. © 1998 Royal Meteorological Society

Journal

International Journal of ClimatologyWiley

Published: Sep 1, 1998

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