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

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

GISS analysis of surface temperature change

“…The GHCN data have undergone extensive quality control, as described by Peterson et al [1998c]. In their data cleaning procedure they nominally exclude individual station months (i.e., monthly mean temperatures at a given station) that differ by more than five standard deviations (50) from the long-term mean for that station month.…”

“…This procedure may exclude valid data points, but the number is so small in a physically plausible distribution that such deletions have little effect on the average long-term global change. They also examine those station months that differ from the longterm mean by between 2.50 and 50, retaining those that are consistent with nearest neighbor stations, and they perform several other quality checks that are described by Peterson et al [ 1998c].…”

Homogenization of long‐term monthly Spanish temperature data

“…Only a few seasonally distinct adjustments are applied, when the seasonal discrepancies are particularly large. In the literature, different types of adjustments can be found (see for example Peterson et al, 1998a). Inhomogeneities in climate data often depend on the month or season, because of the seasonally diverging impacts of instrumental or environmental changes.…”

A description of the global land-surface precipitation data products of the Global Precipitation Climatology Centre with sample applications including centennial (trend) analysis from 1901–present

“…There have been similar data archives and products compiled and published by the Climate Research Unit (CRU) of the University of East Anglia (New et al, 1999(New et al, , 2000(New et al, , 2001(New et al, , 2002; by Peterson and Vose (1997) and Peterson et al (1998) based on the Global Historical Climatology Network (GHCN) data set, by Hijmans et al (2005), and by Mitchell and Jones (2005), all for a number of atmospheric ECVs including precipitation. For precipitation only, there are also the data sets published by Dai et al (1997) and Matsuura and Willmott (2012).…”