The Global Historical Climatology Network (GHCN-Monthly) data base contains historical temperature, precipitation, and pressure data for thousands of land stations worldwide. The period of record varies from station to station, with several thousand extending back to 1950 and several hundred being updated monthly via CLIMAT reports. The data are available without charge through NCDC's anonymous FTP service.
Both historical and near-real-time GHCN data undergo rigorous quality assurance reviews. These reviews include preprocessing checks on source data, time series checks that identify spurious changes in the mean and variance, spatial comparisons that verify the accuracy of the climatological mean and the seasonal cycle, and neighbor checks that identify outliers from both a serial and a spatial perspective.
GHCN-Monthly is used operationally by NCDC to monitor long-term trends in temperature and precipitation. It has also been employed in several international climate assessments, including the Intergovernmental Panel on Climate Change 4th Assessment Report, the Arctic Climate Impact Assessment, and the "State of the Climate" report published annually by the Bulletin of the American Meteorological Society.
One of the primary goals of GHCN-Monthly was to acquire additional data in order to enhance spatial and temporal coverage. There were three reasons for this goal: data for recent months allows one to assess current climatic conditions and place them in historical perspective, denser coverage facilitates the analysis of regional climate change, and certain areas (or certain times in certain areas) are under-sampled even from the perspective of a global analysis. Because numerous institutions operate weather stations and because no single repository archives all of the data for all stations, five acquisition strategies were employed to maximize the available pool of data: contacting data centers, exploiting personal contacts, tapping related projects, conducting literature searches, and distributing miscellaneous requests. As a result, GHCN-Monthly contains data from dozens of diverse sources.
|Data Source||Number of Mean Temperature Stations||Number of Max/Min Temperature Stations|
|NCAR's World Monthly Surface Station Climatology||3,563||0|
|NCDC's Maximum/Minimum Temperature Data Set||3,179||3,179|
|Deutscher Wetterdienst's Global Monthly Surface Summaries Data Set||2,559||0|
|Monthly Climatic Data for the World||2,176||0|
|World Weather Records (1971-80)||1,912||0|
|World Weather Records (1961-70)||1,858||0|
|U.S. Summary of the Day Data Set||1,463||1,463|
|U.S. Historical Climatology Network||1,221||1,221|
|A Climatological Database for Northern Hemisphere Land Areas||920||0|
|Australian National Climate Center's Data Set for Australia||785||785|
|North American Climate Data, NCDC||764||764|
|Bo-Min's Data Set for the People's Republic of China||378||0|
|USSR Network of CLIMAT stations||243||0|
|Daily Temperature and Precipitation Data for 223 USSR Stations (NDP-040)||223||223|
|Two Long-Term Databases for the People's Republic of China (NDP-039)||205||60|
|ASEAN Climatic Atlas||162||162|
|Pakistan's Meteorological and Climatological Data Set||132||132|
|Diaz's Data Set for High-Elevation Areas||100||0|
|Douglas' Data Set for Mexico||92||0|
|Ku-nil's Data Set for Korea||71||71|
|Jacka's Data Set for Antarctic Locales||70||0|
|Monthly Data for the Pacific Ocean / Western Americas||60||0|
|U.S. Historical Climatology Network (Alaska)||47||47|
|Muthurajah's Data Set for Malaysia||18||18|
|Hardjawinata's Data Set for Indonesia||13||13|
|Fitzgerald's Data Set for Ireland||11||11|
|Sala's Data Set for Spain||3||0|
|Al-kubaisi's Data Set for Qatar||1||1|
|Al-sane's Data Set for Kuwait||1||1|
|Stekl's Data Set for Ireland||1||1|
|Data Source||Number of Precipitation Stations|
|African Historical Precipitation Data||1,239|
|ASEAN Climatic Atlas||868|
|Bo-min's Dataset for the People's Republic of China||378|
|Brewster's data set for Australia/Oceana||2,369|
|Canadian Climatological Data||848|
|Comprehensive Pacific Rainfall Data Set||303|
|Davidson's data set for Mexico||30|
|Earnest's data set for the Amazon Basin||925|
|Garcia's data set for Argentina||157|
|Griffith's Colonial Archives data set||251|
|Groisman's data set for the former USSR||610|
|Hulme's data set for the world||11,785|
|Ku-nil's data set for Korea||71|
|Monthly Climatic Data for the World||2,000|
|NCAR's data sets for India||4,602|
|NCAR's data set for South America||678|
|Nichol's data set for Australia||191|
|Non-African Historical Precipitation Data||1,164|
|Oladipo's data set for Nigeria||13|
|Roucou's data set for Africa||69|
|Sinica's data set for China||336|
|Two Long-Term Data Bases for the People's Republic of China||265|
|Waylen's data set for Panama||61|
|Waylen's data set for Costa Rica||329|
|Wernstedt's data set for the world||4,659|
|U.S. Historical Climatology Network||1,221|
GHCN-Monthly contains mean temperature data for 7,280 stations and maximum/minimum temperature data for 4,966 stations. All have at least 10 years of data. The archive also contains homogeneity-adjusted data for a subset of this network (5,206 mean temperature stations and 3,647 maximum/minimum temperature stations). The homogeneity-adjusted network is somewhat smaller because at least 20 years of data were required to compute reliable discontinuity adjustments and the homogeneity of some isolated stations could not be adequately assessed. Precipitation data are available for 20,590 stations and sea level pressure data for 2,668 stations. In general, the best spatial coverage is evident in North America, Europe, Australia, and parts of Asia. Likewise, coverage in the Northern Hemisphere is better than the Southern Hemisphere.
The following journal articles describe the methods used in developing the GHCN-Monthly Temperature dataset:
A precipitation time series for a given station can frequently be obtained from more than one source. For example, rainfall data for Beijing were available in three different source datasets. In brief, duplicate stations were identified by comparing each station with all other stations in all source datasets. Several statistics were used to describe the similarity between stations, including the number of identical months of data, the length of the longest run of identical months, and the number of identical values that were zero. These diagnostic statistics were used in conjunction with station metadata to subjectively determine whether stations were duplicates. In most cases, the decision was relatively straightforward, although a few degenerate time series posed proved more challenging.
A variety of tests were employed to assess data quality. The first step involved comparing stations with a gridded climatology and plotting the stations for visual inspection. Both of these processes uncovered mislocated stations and the former uncovered stations that were digitized 6 months out of phase. Additionally, each time series was tested for significant discontinuities using the Cumulative Sum test (which looks for changes in the mean) and an analogous test that looks for changes in the variance or scale. Each time series was also evaluated for runs of three or more months of the same nonzero value. Finally, each individual precipitation total was evaluated to determine if it was an outlier in space and/or time using a variety of nonparametric statistics.
The automated bias correction software (Peterson and Easterling, 1994; Easterling and Peterson, 1995) used to detect and adjust for documented and undocumented inhomogeneities in the GHCN-Monthly version 2 monthly temperature dataset is available via ftp at:
Please refer to the README text file in this directory for information on this software.
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Please provide acknowledgement to NOAA's National Climatic Data Center and the version 2 publication: