The Global Historical Climatology Network-Monthly (GHCN-M) temperature dataset was first developed in the early 1990s (Vose et al. 1992). A second version was released in 1997 following extensive efforts to increase the number of stations and length of the data record (Peterson and Vose, 1997). Methods for removing inhomogeneities from the data record associated with non-climatic influences such as changes in instrumentation, station environment, and observing practices that occur over time were also included in the version 2 release (Peterson and Easterling, 1994; Easterling and Peterson 1995). Since that time efforts have focused on continued improvements in dataset development methods including new quality control processes and advanced techniques for removing data inhomogeneities (Menne and Williams, 2009). Effective May 2, 2011, the Global Historical Climatology Network-Monthly (GHCN-M) version 3 dataset of monthly mean temperature has replaced GHCN-M version 2 as the dataset for operational climate monitoring activities. The dataset is available at ftp://ftp.ncdc.noaa.gov/pub/data/ghcn/v3/. The formal designation is ghcnm.x.y.z[optionally -betan].yyyymmdd
Each evening as part of the GHCN-M update process, the output files will be labeled with the current version number and date and time stamped to indicate when the update occurred. For example, the filename for the dataset produced on April 28, 2011 will include the string "v220.127.116.1110428" to indicate the data were developed using version 3.0.0 station data and quality control and homogeneity adjustment methods. This notation also will indicate the most recent update occurred in the year 2011, in the 4th month (April) and the 28th day of the month. The data will be placed on line at the conclusion of each update process and available via the link noted above. This versioning format facilitates documentation and communication of updates and modifications that occur as a normal part of the life of this climate dataset.
Unadjusted and adjusted GHCN-Monthly data and inventory files:
Directions on Uncompressing and Extracting Files (Includes description of inventory file and format of data files [measurement, quality, and source flags])
Detailed station-level information on monthly mean temperatures and trends for unadjusted and adjusted station data is provided in graphical form:
Graphs of station versus neighbor comparisons (with additional information on station-level bias adjustments):
With the development of GHCN-M v3, new quality control (QC) procedures were instituted using methods established as part of other dataset development efforts during the past five years. The QC algorithms are based on methods used for the GHCN-Daily temperature dataset and subsequently applied to USHCN-Monthly Version 2 data (Menne et al. 2009). The QC process used in GHCN-M v3 consists of the seven checks listed in Table 1. They are performed in the order listed. The thresholds shown in column two were selected and their performance evaluated based on the method outlined in Durre et al. (2008).
|Data Problem||Description of Check|
|Consecutive month duplication||Used to identify duplicate retransmission and mislabeling of previous month's temperature for current month. Occurs in GTS transmitted CLIMAT bulletins|
|Series Duplication||Identifies duplication of data between years|
|Streak||Identifies runs of the same value in four or more consecutive months|
|Isolated value||Identifies months that are isolated in time. One to three consecutive months that are separated from other non-missing months by 36 or more consecutive months of missing values|
|Climatological Outlier||Identifies temperatures that exceed their respective climatological means for the corresponding station and calendar month by at least five standard deviations|
|Spatial inconsistency 1||Compares z scores (relative to their respective climatological means) to concurrent z scores at the nearest 20 neighbors located within 500 km of the target; a temperature fails if (i) its z score differs from the regional (target and neighbor) mean z score by at least 3.5 standard deviations and (ii) the target's temperature anomaly differs by at least 2.5°C from all concurrent temperature anomalies at the neighbors|
|Spatial inconsistency 2||Identifies temperatures whose anomalies differ by more than 4°C from concurrent anomalies at the five nearest neighboring stations whose temperature anomalies are well correlated with the target (correlation >0.7 for the corresponding calendar month)|
Many surface weather stations undergo minor relocations through their history of observation. Stations may also be subject to changes in instrumentation as measurement technology evolves. Further, the land use/land cover in the vicinity of an observing site may also change with time. Such modifications to an observing site have the potential to alter a thermometer's microclimate exposure characteristics and/or change the bias of measurements, the impact of which can be a systematic shift in the mean level of temperature readings that is unrelated to true climate variations. The process of removing such "non-climatic" artifacts in a climate time series is called homogenization.
In version 3 of the GHCN-Monthly temperature data, the apparent impacts of documented and undocumented inhomogeneities are detected and removed through automated pairwise comparisons of mean monthly temperature series as detailed in Menne and Williams . In this approach, comparisons are made between numerous combinations of temperature series in a region to identify cases in which there is an abrupt shift in one station series relative to many others. The algorithm starts by forming a large number of pairwise difference series between serial monthly temperature values from a region. Each difference series is then statistically evaluated for abrupt shifts, and the station series responsible for a particular break is identified in an automated and reproducible way. After all of the shifts that are detectable by the algorithm are attributed to the appropriate station within the network, an adjustment is made for each target shift. Adjustments are determined by estimating the magnitude of change in pairwise difference series form between the target series and highly correlated neighboring series that have no apparent shifts at the same time as the target.
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Please provide acknowledgement to NOAA's National Climatic Data Center and the version 3 publication: