The U.S. Historical Climatology Network Monthly Data, Version 2.5 consists of precipitation and temperature data "corrected" for changes in station location, instrumentation, and observing practices. The vast majority of stations are from the NOAA Cooperative Observer Program (COOP) Network. Stations have been selected according to coverage, length of data record and completeness, and historical stability. Data includes sets of Maximum, Minimum and Average Temperature and Precipitation data that are either (1) raw (non-adjusted though flagged for possible quality issues), (2) adjusted due to time of observation bias (TOB) or (3) put through the Pairwise Homogenization Algorithm (PHA). The data also is archived with station information and source code for reading the data.

This dataset contains daily observations of maximum and minimum temperature, precipitation, snowfall, and snow depth for U.S. Historical Climatology Network observing stations in the contiguous 48 United States. These stations are part of the U.S. Cooperative network, but also have long periods of record.

The United States Historical Climatology Network (USHCN) is a high-quality data set of daily and monthly records of basic meteorological variables from 1218 observing stations across the 48 contiguous United States. Daily data include observations of maximum and minimum temperature, precipitation amount, snowfall amount, and snow depth; monthly data consist of monthly-averaged maximum, minimum, and mean temperature and total monthly precipitation. Most of these stations are U.S. Cooperative Observing Network stations located generally in rural locations, while some are National Weather Service First-Order stations that are often located in more urbanized environments. The USHCN has been developed over the years at the National Oceanic and Atmospheric Administration's (NOAA) National Climatic Data Center (NCDC) to assist in the detection of regional climate change. Furthermore, it has been widely used in analyzing U.S. climte. The period of record varies for each station. USHCN stations were chosen using a number of criteria including length of record, percent of missing data, number of station moves and other station changes that may affect data homogeneity, and resulting network spatial coverage. Collaboration between NCDC and CDIAC on the USHCN project dates to the 1980s (Quinlan et al. 1987). At that time, in response to the need for an accurate, unbiased, modern historical climate record for the United States, the Global Change Research Program of the U.S. Department of Energy and NCDC chose a network of 1219 stations in the contiguous United States that would become a key baseline data set for monitoring U.S. climate. This initial USHCN data set contained monthly data and was made available free of charge from CDIAC. Since then it has been comprehensively updated several times [e.g., Karl et al. (1990) and Easterling et al. (1996)]. The initial USHCN daily data set was made available through CDIAC via Hughes et al. (1992) and contained a 138-station subset of the USHCN. This product was updated by Easterling et al. (1999) and expanded to include 1062 stations. In 2009 the daily USHCN dataset was expanded to include all 1218 stations in the USHCN.

Please note, this dataset has been superseded by a newer version (see below). Users should not use this version except in rare cases (e.g., when reproducing previous studies that used this version). The Global Historical Climatology Network - Daily (GHCN-Daily) dataset addresses the need for historical daily records over global land areas. Like its monthly counterpart (GHCN-Monthly), GHCN-Daily is a composite of climate records from numerous sources that were merged and then subjected to a suite of quality assurance reviews. The meteorological elements measured for the data set include, but are not limited to, daily maximum and minimum temperature, temperature at the time of observation, precipitation (i.e., rainfall and snow water equivalent), snowfall and snow depth. GHCN-Daily serves as the official archive for daily data from the Global Climate Observing System (GCOS) Surface Network (GSN) and is particularly well suited for monitoring and assessment activities related to the frequency and magnitude of extremes. Sources for the GHCN-Daily data set include, but are not limited, to U.S. Cooperative Summary of the Day, U.S. Fort data, U.S. Climate Reference Network, Community Collaborative Rain, Hail and Snow Network, and numerous international sources. The dataset contains measurements from over 75,000 stations worldwide,about two thirds of which are for precipitation measurement only. Approximately 8500 are regularly updated with observations from within the last month. While most of these sites report precipitation, daily maximum and minimum temperatures are available at more than 25,000 of them, and over 24,000 contain records of snowfall and/or snow depth. The process of integrating data from multiple sources into the GHCN-Daily dataset takes place in three steps: screening the source data for stations whose identity is unknown or questionable; classifying each station in a source dataset either as one that is already represented in GHCN-Daily or as a new site; and mingling the data from the different sources. The first two of these steps are performed whenever a new source dataset or additional stations become available, while the actual mingling of data is part of the automated processing that creates GHCN-Daily on a regular basis. GHCN-Daily data are subject to a suite of quality assurance checks. The checks consist of several types of carefully evaluated tests that detect duplicated data, climatological outliers, and various inconsistencies (internal, temporal, and spatial). Manual review of random samples of flagged values was used to set the threshold for each procedure such that the tests false-positive rate is minimized. In addition, the tests are performed in a deliberate sequence in an effort to enhance the performance of the later checks by detecting errors with the checks applied earlier in the sequence.

The Global Historical Climatology Network - Daily (GHCN-Daily/GHCNd) dataset integrates daily climate observations from approximately 30 different data sources. Version 3 was released in September 2012 with the addition of data from two additional station networks. Changes to the processing system associated with the version 3 release also allowed for updates to occur 7 days a week rather than only on most weekdays. Version 3 contains station-based measurements from well over 90,000 land-based stations worldwide, about two thirds of which are for precipitation measurement only. Other meteorological elements include, but are not limited to, daily maximum and minimum temperature, temperature at the time of observation, snowfall and snow depth. Over 25,000 stations are regularly updated with observations from within roughly the last month. The dataset is also routinely reconstructed (usually every week) from its roughly 30 data sources to ensure that GHCNd is generally in sync with its growing list of constituent sources. During this process, quality assurance checks are applied to the full dataset. Where possible, GHCNd station data are also updated daily from a variety of data streams. Station values for each daily update also undergo a suite of quality checks.

Global Historical Climatology Network-hourly (GHCNh) is a multisource collection of weather station (meteorological) observations from the late 18th Century to the present from fixed weather stations over land across the globe. It is replacing the Integrated Surface Dataset (ISD) and will be used to generate the Local Climatological Data and Global Summary of the Day datasets. It is constructed to align with GHCN daily. Version 1 contains approximately 110 separate data sources and will be updated daily using the United States Air Force and NOAA Surface Weather Observations data streams. GHCNh v1 contains the following variables: altimeter; dew_point_temperature; precipitation; pressure_3hr_change; pres_wx_AU1; pres_wx_AU2; pres_wx_AU3; pres_wx_AW1; pres_wx_AW2; pres_wx_AW3; pres_wx_MW1; pres_wx_MW2; pres_wx_MW3; relative_humidity; Remarks; sea_level_pressure; sky_cov_baseht_1; sky_cov_baseht_2; sky_cov_baseht_3; sky_cover_1; sky_cover_2; sky_cover_3; station_level_pressure; dry bulb temperature; visibility; wet_bulb_temperature; wind_direction; wind_gust; wind_speed.

Please note, this dataset has been superseded by a newer version (see below). Users should not use this version except in rare cases (e.g., when reproducing previous studies that used this version). The Global Historical Climatology Network - Daily (GHCN-Daily) dataset addresses the need for historical daily records over global land areas. Like its monthly counterpart (GHCN-Monthly), GHCN-Daily is a composite of climate records from numerous sources that were merged and then subjected to a suite of quality assurance reviews. The meteorological elements measured for the data set include, but are not limited to, daily maximum and minimum temperature, temperature at the time of observation, precipitation (i.e., rainfall and snow water equivalent), snowfall and snow depth. GHCN-Daily serves as the official archive for daily data from the Global Climate Observing System (GCOS) Surface Network (GSN) and is particularly well suited for monitoring and assessment activities related to the frequency and magnitude of extremes. Sources for the GHCN-Daily data set include, but are not limited, to U.S. Cooperative Summary of the Day, U.S. Fort data, U.S. Climate Reference Network, Community Collaborative Rain, Hail and Snow Network, and numerous international sources. The dataset contains measurements from over 75,000 stations worldwide,about two thirds of which are for precipitation measurement only. Approximately 8500 are regularly updated with observations from within the last month. While most of these sites report precipitation, daily maximum and minimum temperatures are available at more than 25,000 of them, and over 24,000 contain records of snowfall and/or snow depth. The process of integrating data from multiple sources into the GHCN-Daily dataset takes place in three steps: screening the source data for stations whose identity is unknown or questionable; classifying each station in a source dataset either as one that is already represented in GHCN-Daily or as a new site; and mingling the data from the different sources. The first two of these steps are performed whenever a new source dataset or additional stations become available, while the actual mingling of data is part of the automated processing that creates GHCN-Daily on a regular basis. GHCN-Daily data are subject to a suite of quality assurance checks. The checks consist of several types of carefully evaluated tests that detect duplicated data, climatological outliers, and various inconsistencies (internal, temporal, and spatial). Manual review of random samples of flagged values was used to set the threshold for each procedure such that the tests false-positive rate is minimized. In addition, the tests are performed in a deliberate sequence in an effort to enhance the performance of the later checks by detecting errors with the checks applied earlier in the sequence.

NOTE: Version 3 of GHCN has been discontinued at NCEI, and so this dataset is no longer being updated. Version 4 of GHCN can be accessed here [https://www.ncdc.noaa.gov/data-access/land-based-station-data/land-based-datasets/global-historical-climatology-network-monthly-version-4]. The Global Historical Climatology Network (GHCN) is a baseline network of long-running surface global stations for the purpose of monitoring and detecting climate change. Monthly-summarized data from various global sources are processed on a daily basis assembled into version 3 of NCDC's GHCNM dataset. The CISL Research Data Archive is hosting a copy of the monthly-average surface temperatures in support of UCAR's Global Learning and Observation to Benefit the Environment (GLOBE) program.

The U.S. Historical Climatology Network Monthly Data, Version 2.5 consists of precipitation and temperature data "corrected" for changes in station location, instrumentation, and observing practices. The vast majority of stations are from the NOAA Cooperative Observer Program (COOP) Network. Stations have been selected according to coverage, length of data record and completeness, and historical stability. Data includes sets of Maximum, Minimum and Average Temperature and Precipitation data that are either (1) raw (non-adjusted though flagged for possible quality issues), (2) adjusted due to time of observation bias (TOB) or (3) put through the Pairwise Homogenization Algorithm (PHA). The data also is archived with station information and source code for reading the data.

About

Background page states:

The United States Historical Climatology Network (USHCN) is a high-quality data set of daily and monthly records of basic meteorological variables from 1218 observing stations across the 48 contiguous United States. Daily data include observations of maximum and minimum temperature, precipitation amount, snowfall amount, and snow depth; monthly data consist of monthly-averaged maximum, minimum, and mean temperature and total monthly precipitation. Most of these stations are U.S. Cooperative Observing Network stations located generally in rural locations, while some are National Weather Service First-Order stations that are often located in more urbanized environments. The USHCN has been developed over the years at the National Oceanic and Atmospheric Administration's (NOAA) National Climatic Data Center (NCDC) to assist in the detection of regional climate change. Furthermore, it has been widely used in analyzing U.S. climte. The period of record varies for each station. USHCN stations were chosen using a number of criteria including length of record, percent of missing data, number of station moves and other station changes that may affect data homogeneity, and resulting network spatial coverage.

Openness

Assumed open as from US government, but Disclaimer page states:

Documents provided from the web server were sponsored by a contractor of the U.S. Government under contract DE-AC05-00OR22725. Accordingly, the U.S. Government retains a nonexclusive, royalty-free license to publish or reproduce these documents, or to allow others to do so, for U.S. Government purposes. These documents may be freely distributed and used for non-commercial, scientific and educational purposes.

Not clear whether this applies just to documents - or also to data.

Abstract

The Global Historical Climatology Network daily (GHCNd) is an integrated database of daily climate summaries from land surface stations across the globe. GHCNd is made up of daily climate records from numerous sources that have been integrated and subjected to a common suite of quality assurance reviews.

GHCNd contains records from more than 100,000 stations in 180 countries and territories. NCEI provides numerous daily variables, including maximum and minimum temperature, total daily precipitation, snowfall, and snow depth. About half the stations only report precipitation. Both record length and period of record vary by station and cover intervals ranging from less than a year to more than 175 years.

Methodology

The process of integrating data from multiple sources into GHCNd takes place in three steps:

1. Screening the source data for stations whose identity is unknown or questionable
2. Classifying each station in a source dataset either as one that is already represented in GHCN-D or as a new site
3. Mingling the data from the different sources

%3C!-- --%3E

The process performs the first two of these steps whenever a new source dataset or additional stations become available, while the mingling of data is part of the automated processing that creates GHCNd on a regular basis.

Screening the Source Data

A station within a source dataset is considered for inclusion in GHCNd if it meets all of the following conditions:

• It can be identified with a name, latitude, and longitude contained in metadata provided as part of the source dataset or in standard station history information
• Its record contains 100 or more values for at least one of the GHCNd elements
• It does not fail the interstation duplicate check that compares records from all stations within a source dataset in order to identify cases in which more than 50% of a station's record is identical to the data from another station

%3C!-- --%3E

Classifying Stations

The next step is to determine for each station in the source dataset if data for the same location are already contained in GHCNd, or if the station represents a new site. Whenever possible, stations are matched on the basis of network affiliation and station identification number. If no match exists, then there is consultation from different networks for existing cross-referenced lists that identify the correspondence of station identification numbers.

For example, data for Alabaster Shelby County Airport, Alabama, USA, is stored under Cooperative station ID 010116 in NCEI's datasets 3200 and 3206 as well as in the data stream from the High Plains Regional Climate Center; they are combined into one GHCNd record based on the ID. In data set 3210 and the various sources for ASOS stations, however, the data for this location are stored under WBAN ID 53864 and must be matched with the corresponding Cooperative station ID using NCEI's Master Station History Record.

A third approach is to match stations on the basis of their names and location. This strategy is more difficult to automate than the other two approaches because identification of multiple stations within the same city or town, with the same name and small differences in coordinates, can be the result of either differences in accuracy or the existence of multiple stations in close proximity to each other. As a result, the employment of the third approach is used only when stations cannot be matched on the basis of station identification numbers or cross-reference information. This is the case, for example, when there is a need for matching stations outside the U.S. whose data originate from the Global Summary of the Day dataset and from the International Collection.

Mingling

The implementation of the above classification strategies yields a list of GHCNd stations and an inventory of the source datasets for integration of each station. This list forms the basis for integrating, or mingling, the data from the various sources to create GHCNd. Mingling takes place according to a hierarchy of data sources and in a manner that attempts to maximize the amount of data included while also minimizing the degree to which data from sources with different characteristics are mixed. While the mingling of precipitation, snowfall, and snow depth are separate, consideration of maximum and minimum temperatures is performed together in order to ensure the temperatures for a particular station and day always originate from the same source. Data from the Global Summary of the Day dataset are used only if no observations are available from any other source for that station, month, and element. Among the other sources, consideration of each day is made individually; if an observation for a particular station and day is available from more than one source, GHCNd uses the observation from the most preferred source available.

Several criteria are used for the hierarchy of data sources used in cases of overlap. In gener

This is a dataset of daily precipitation totals in the western United States (US) from January 1 1950 – February 29 2024. The dataset is based on daily precipitation measurements from 642 gauges across the western US, with periodic data gap-filling perfomed using estimates based on nearby gauges. From the daily records at the 642 gauge sites we produced a gridded record of daily precipitation totals at 0.25-degree resolution across the western US. The 642 gauges used as the basis for this dataset were selected for their uniquely long records and continuous coverage from the early 1950s through 2023. The purpose of producing this dataset was for evaluation of trends in sub-monthly cool-season (November–March) precipitation characteristics., Considering all daily gauge records available from the Global Historical Climatology Network-Daily (Menne et al., 2012) we first compile a master database of daily precipitation totals for the 3,528 gauges within a 1° buffer of our western US study area with valid data for ≥50% of days in each of ≥20 years during calendar years 1950–2023. We then perform an initial zero-filling procedure in which missing values are replaced with zero on days when all (and ≥2) other gauges with valid data within 50-km report no precipitation. When there are <2 gauges within 50 km with valid data, but at least two gauges within 100 km with valid data, the zero-filling is repeated if all available (and ≥2) gauges within 100 km report zero precipitation. From this master database we identify 645 “primary gauges†within the boundaries of our western US study area with particularly thorough coverage over the study period of cool seasons (November–March) 1951–2023. Long data records are critical to our asse..., , # GHCN daily western United States precipitation

https://doi.org/10.5061/dryad.c866t1gfp

This dataset contains daily precipitation totals for the western United States (US) as represented by gauge data from Global Historical Climatology Network (GHCN) daily dataset.

Description of the data and file structure

This dataset contains daily precipitation totals for the western United States (US) as represented by the Global Historical Climatology Network (GHCN) daily dataset. Components of the dataset are:

prec_daily.nc: This is a netcdf file file called prec_daily.nc with gridded 1/4-degree estimates of daily precipitation total from Jan 1 1950 – Feb 29 2024. There are 100 latitudes, from 28.125N to 52.875N and 108 longitudes, from 126.875W to 100.125W. There are 27,088 daily time steps, corresponding with a time vector with units of days since Jan 1 1800. Grid cells lying outside of the western US do not have values. Here, western US is de...

Supplementary information dataset for the following article: R. Connolly and M. Connolly (2014). Has poor station quality biased U.S. temperature trends? Open Peer Rev. J., 11 (Clim. Sci.), ver 0.1 (non peer-reviewed draft)

Abstract of article Two independent surveys have found that about 70% of the thermometer stations in the U.S. Historical Climatology Network (USHCN) dataset are currently poorly or badly sited. Previous investigations into how this poor siting has affected estimates of U.S. temperature trends have led to apparently contradictory conclusions. However, in this study, these contradictions are resolved, and it is shown that poor station quality has introduced a noticeable warming bias into temperature trend estimates for the U.S. For the unadjusted station records, this poor siting increased the mean temperature trends by about 32%. When time-of-observation adjustments were applied to the records, this increased temperature trends by about 39%, and so the relative fraction of the trends due to the siting bias decreased. However, the siting biases were still substantial, and increased trends by about 18%. The step-change homogenization algorithm which had been developed to remove non-climatic biases such as siting biases was shown to be seriously problematic. Instead of correcting the poorly- and badly-sited station records to match the trends of the well-sited stations, it appears to have blended the temperature records of all stations to match the trends of the poorly-sited stations. It seems likely that similar poor siting biases also exist in global thermometer datasets, and this has probably led to an overestimation of the amount of “global warming” since the 19th century.

Extending through 1994, this data base contains monthly total precipitation and temperature data from 1219 stations in the contiguous U.S. To be included in the Historical Climatology Network (HCN), a station had to be currently active (1994), have at least 80 years of monthly temperature and precipitation data, and have experienced few station changes. These data were derived from a variety of sources including the National Climatic Data Center archives, state climatologists, and published literature. The data base contains several hundred variables, including state number; station number; monthly temperatures (minimum, maximum, and mean); total monthly precipitation; and time of observation. This is probably the best monthly temperature and precipitation data set available for the contiguous U.S. because station moves, instrument changes, urbanization effects, and time-of-observation differences have been considered and, where necessary, the data have been corrected.

Please note, this dataset has been superseded by a newer version (see below). Users should not use this version except in rare cases (e.g., when reproducing previous studies that used this version). The Global Historical Climatology Network (GHCN) Version 1 is a comprehensive, global, surface, baseline, climate data set designed for monitoring and detecting climate change. Comprised of surface station observations of temperature, precipitation, and pressure, all GHCN data are monthly. GHCN was produced jointly by the NCDC, Arizona State University, and the Carbon Dioxide Information Analysis Center (CDIAC) at Oak Ridge National Laboratory (ORNL). GHCN version 1 was released in August of 1992 and has data from ~6,000 Temperature stations ~7,500 Precipitation stations ~2,000 Pressure stations The earliest station data was from 1697. The most recent is from 1990. Version 1 was created from 15 source data sets. Quality Control includes visual inspection of graphs of all station time series, tests for precipitation digitized 6 months out of phase, tests for different stations having identical data, and other tests.

Please note, the temperature portion of this dataset has been superseded by a newer version. Users should not use this version except in rare cases (e.g., when reproducing previous studies that used this version). The precipitation subset of this dataset is still the latest version available. The Global Historical Climatology Network Monthly (GHCN-M) Version 2 dataset created by the NCDC 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. Both historical and near-real-time GHCN-M data undergo rigorous quality assurance reviews. These reviews include pre-processing 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 temporal and a spatial perspective. Only one file for the temperature data is available from the archive (file is for period of record ending in 2011), but precipitation data has been archived daily since 2012.

The GHCN-Daily was developed to meet the needs of climate analysis and monitoring studies that require data at a sub-monthly time resolution (e.g., assessments of the frequency of heavy rainfall, heat wave duration, etc.). It also serves as NCDC's sole source of U.S. Summary of the Day data, providing a diverse array of users in the public and private sector with weather and climate observations that meet needs from the local to national level. By bringing together contributions from dozens of national and international sources and combining historical with near real-time observations, this dataset helps users understand todays climate and how it impacts society while helping users prepare for weather and climate conditions in the future. The Global Historical Climatology Network - Daily (GHCN-Daily) dataset addresses the need for historical daily records over global land areas. Like its monthly counterpart (GHCN-Monthly), GHCN-Daily is a composite of climate records from numerous sources that were merged and then subjected to a suite of quality assurance reviews. The meteorological elements measured for the data set include, but are not limited to, daily maximum and minimum temperature, temperature at the time of observation, precipitation (i.e., rainfall and snow water equivalent), snowfall and snow depth. GHCN-Daily serves as the official archive for daily data from the Global Climate Observing System (GCOS) Surface Network (GSN) and is particularly well suited for monitoring and assessment activities related to the frequency and magnitude of extremes. Sources for the GHCN-Daily data set include, but are not limited, to U.S. Cooperative Summary of the Day, U.S. Fort data, U.S. Climate Reference Network, Community Collaborative Rain, Hail and Snow Network, and numerous international sources. The dataset contains measurements from over 75,000 stations worldwide, about two thirds of which are for precipitation measurement only. Approximately 8500 are regularly updated with observations from within the last month. While most of these sites report precipitation, daily maximum and minimum temperatures are available at more than 25,000 of them, and over 24,000 contain records of snowfall and/or snow depth.MapData

Supplementary information dataset for the following article: R. Connolly and M. Connolly (2014). Urbanization bias III. Estimating the extent of bias in the Historical Climatology Network datasets. Open Peer Rev. J., 34 (Clim. Sci.), ver 0.1 (non peer-reviewed draft)

Abstract of article The extent to which two widely-used monthly temperature datasets are affected by urbanization bias was considered. These were the Global Historical Climatology Network (GHCN) and the United States Historical Climatology Network (USHCN). These datasets are currently the main data sources used to construct the various weather station-based global temperature trend estimates. Although the global network nominally contains temperature records for a large number of rural stations, most of these records are quite short, or are missing large periods of data. Only eight of the records with data for at least 95 of the last 100 years are for completely rural stations. In contrast, the U.S. network is a relatively rural dataset, and less than 10% of the stations are highly urbanized. However, urbanization bias is still a significant problem, which seems to have introduced an artificial warming trend into current estimates of U.S. temperature trends. The homogenization adjustments developed by the National Climatic Data Center to reduce the extent of non-climatic biases in the networks were found to be inadequate, inappropriate and problematic for urbanization bias. As a result, the current estimates of the amount of “global warming” since the Industrial Revolution have probably been overestimated.

Contains global weather station locations with data for monthly means from 1981 through 2010 for: Daily Mean Temperature °C Daily Maximum Temperature °C Daily Minimum Temperature °C Precipitation in mm Highest Daily Temperature °C Lowest Daily Temperature °C Additional monthly fields containing the equivalent values in °F and inches are available at the far right of the attribute table. GHCND stations were included if there were at least fifteen average daily values available in each month for all twelve months of the year, and for at least ten years between 1981 and 2010. 3,197 of the 7,480 stations did not collect or lacked sufficient precipitation data. These data are compiled from archived station values which have not undergone rigorous curation, and thus, there may be unexpected values, particularly in the daily extreme high and low fields. Esri is working to further curate this layer and will make updates as improvements are found. If your area of study is within the United States, we recommend using the U.S. Historical Climate - Monthly Averages for GHCN-D Stations 1981 - 2010 layer because the data in that service were compiled from web services produced by the Applied Climate Information System ( ACIS). ACIS staff curate the values for the U.S., including correcting erroneous values, reconciling data from stations that have been moved over their history, etc., thus the data in the U.S. service is of higher quality. Revision History: Initially Published: 6 Feb 2019 Updated: 12 Feb 2019 - Improved initial extraction algorithm to remove stations with extreme values. This included values higher than the highest temperature ever recorded on Earth, or those with mean values that were considerably different than adjacent neighboring stations.Updated: 18 Feb 2019 - Updated after finding an error in initial processing that excluded a 2,870 stations. Updated 16 Apr 2019 - We learned more precise coordinates for station locations were available from the Enhanced Master Station History Report (EMSHR) published by NOAA NCDC. With the publication of this layer the geometry and attributes for 635 of 7,452 stations now have more precise coordinates. The schema was updated to include the NCDC station identifier and elevation fields for feet and meters are also included. A large subset of the EMSHR metadata is available via EMSHR Stations Locations and Metadata 1738 to Present. Cite as:

Esri, 2019: World Historical Climate - Monthly Averages for GHCN-D Stations for 1981 - 2010. ArcGIS Online, Accessed April 2019. https://www.arcgis.com/home/item.html?id=ed59d3b4a8c44100914458dd722f054f Source Data: Station locations compiled from: Initially compiled using station locations from ftp://ftp.ncdc.noaa.gov/pub/data/ghcn/daily/ghcnd-stations.txt Menne, M.J., I. Durre, B. Korzeniewski, S. McNeal, K. Thomas, X. Yin, S. Anthony, R. Ray, R.S. Vose, B.E.Gleason, and T.G. Houston, 2012: Global Historical Climatology Network - Daily (GHCN-Daily), Version 3.24 Amended to use the most recent station locations from Russell S. Vose, Shelley McNeill, Kristy Thomas, Ethan Shepherd (2011): Enhanced Master Station History Report of March 2019. NOAA National Climatic Data Center. Access Date: April 10, 2019 doi:10.7289/V5NV9G8D. Station Monthly Means compiled from Daily Data: ftp://ftp.ncdc.noaa.gov/pub/data/ghcn/daily/ghcnd_all.tar.gz Menne, M.J., I. Durre, B. Korzeniewski, S. McNeal, K. Thomas, X. Yin, S. Anthony, R. Ray, R.S. Vose, B.E.Gleason, and T.G. Houston, 2012: Global Historical Climatology Network - Daily (GHCN-Daily), Version 3.24