The GSOD or Global Surface Summary of the Day (GSOD) data provided by the US National Centers for Environmental Information (NCEI) are a valuable source of weather data with global coverage. GSODR aims to make it easy to find, transfer and format the data you need for use in analysis and provides five main functions for facilitating this:
get_GSOD() - this function queries and transfers files from the NCEI’s web server, reformats them and returns a data frame.
reformat_GSOD() - this function takes individual station files from the local disk and re-formats them returning a data frame.
nearest_stations() - this function returns a vector of station IDs that fall within the given radius (kilometres) of a point given as latitude and longitude.
update_station_list() - this function downloads the latest station list from the NCEI’s server updates the package’s internal database of stations and their metadata.
get_inventory() - this function downloads the latest station inventory information from the NCEI’s server and returns the header information about the latest version as a message in the console and a tidy data frame of the stations’ inventory for each month that data are reported.
When reformatting data either with
reformat_GSOD(), all units are converted to International System of Units (SI), e.g., inches to millimetres and Fahrenheit to Celsius. File output is returned as a
tibble(), summarising each year by station, which also includes vapour pressure and relative humidity elements calculated from existing data in GSOD. Additional data are calculated by this R package using the original data and included in the final data. These include vapour pressure (ea and es) and relative humidity.
For more information see the description of the data provided by NCEI, http://www7.ncdc.noaa.gov/CDO/GSOD_DESC.txt.
A development version is available from from GitHub. If you wish to install the development version that may have new features or bug fixes before the CRAN version does (but also may not work properly), please install the remotes package, available from CRAN. We strive to keep the master branch on GitHub functional and working properly.
There are several other sources of weather data and ways of retrieving them through R. Several are also rOpenSci projects.
The gsod package from DataBrew aims to streamline the way that researchers and data scientists interact with and utilize weather data and relies on [GSODR], but provides data in the package rather than downloading so it is faster (though available data may be out of date).
bomrang, from rOpenSci provides functions to interface with Australia Government Bureau of Meteorology (BoM) data, fetching current and historical data including précis and marine forecasts, current weather data from stations, agriculture bulletin data, BoM 0900 or 1500 weather bulletins and satellite and radar imagery.
CliFlo from rOpenSci is a web portal to the New Zealand National Climate Database and provides public access (via subscription) to around 6,500 various climate stations (see https://cliflo.niwa.co.nz/ for more information). Collating and manipulating data from CliFlo (hence clifro) and importing into R for further analysis, exploration and visualisation is now straightforward and coherent. The user is required to have an Internet connection, and a current CliFlo subscription (free) if data from stations, other than the public Reefton electronic weather station, is sought.
weatherData provides a selection of functions to fetch weather data from Weather Underground and return it as a clean data frame.
Users of these data should take into account the following (from the NCEI website):
“The following data and products may have conditions placed on their international commercial use. They can be used within the U.S. or for non-commercial international activities without restriction. The non-U.S. data cannot be redistributed for commercial purposes. Re-distribution of these data by others must provide this same notification.” WMO Resolution 40. NOAA Policy
Please report any issues or bugs.
To cite GSODR, please use: Adam H Sparks, Tomislav Hengl and Andrew Nelson (2017). GSODR: Global Summary Daily Weather Data in R. The Journal of Open Source Software, 2(10). DOI: 10.21105/joss.00177.
Please note that the GSODR project is released with a Contributor Code of Conduct By participating in the GSODR project you agree to abide by its terms.