DOI: 10.57761/t56d-ax51
title: GHCN Daily Weather Data

description: NOAA’s Global Historical Climatology Network (GHCN) is an integrated database of climate summaries from land surface stations across the globe that have been subjected to a common suite of quality assurance reviews.
description: #Documentation  
The GHCN-Daily is an integrated database of daily climate summaries from land surface stations across the globe, and is comprised of daily climate records from over 100,000 stations in 180 countries and territories, and includes some data from every year since 1763. The data are obtained from more than 20 sources.



For a complete description of data variables available in this dataset, see NOAA’s [GHCN-D documentation.](<https://www.ncei.noaa.gov/products/land-based-station/global-historical-climatology-network-daily>)



**Methodology**



![ndp041_precip.gif](<https://redivis.com/fileUploads/8285d904-a4a7-4e66-90ff-8db756b9755f>)

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

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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.



<u>Screening the Source Data</u>

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

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<u>Classifying Stations</u>

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.



**Citation**

Menne, M.J., I. Durre, R.S. Vose, B.E. Gleason, and T.G. Houston, 2012: An overview of the Global Historical Climatology Network-Daily Database. Journal of Atmospheric and Oceanic Technology, 29, 897-910, doi:10.1175/JTECH-D-11-00103.1. [http://doi.org/10.7289/V5D21VHZ](<http://doi.org/10.7289/V5D21VHZ>)