GSODR: Global Summary Daily Weather Data in R
Introduction to GSODR
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. However, the data files are cumbersome and difficult to work with. GSODR aims to make it easy to find, transfer and format the data you need for use in analysis and provides four main functions for facilitating this:
get_GSOD()- queries and transfers files from the FTP server, reformats them and returns adata.frame()object in R session or saves a file to disk with options for a GeoPackage spatially enabled file or comma separated values (CSV) file,reformat_GSOD()- the workhorse, takes individual station files on the local disk and reformats them, returns adata.frame()object in R session or saves a file to disk with options for a GeoPackage spatially enabled file or comma separated values (CSV) file,nearest_stations()- returns avector()object containing a list of stations and their metadata that fall within the given radius of a point specified by the user,update_station_list()- downloads the latest station list from the NCEI FTP server updates the package's internal database of stations and their metadata.
When reformatting data either with get_GSOD() or reformat_GSOD(),
all units are converted to International System of Units (SI), e.g.,
inches to millimetres and Fahrenheit to Celsius. File output can be
saved as a Comma Separated Value (CSV) file or in a spatial GeoPackage
(GPKG) file, implemented by most major GIS software, 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.
It is recommended that you have a good Internet connection to download the data files as they can be quite large and slow to download.
For more information see the description of the data provided by NCEI, http://www7.ncdc.noaa.gov/CDO/GSOD_DESC.txt.
Quick Start
Install
Stable version
A stable version of GSODR is available from CRAN.
install.packages("GSODR")Development version
A development version is available from from GitHub. If you wish to install the development version that may have new features (but also may not work properly), install the devtools package, available from CRAN. We strive to keep the master branch on GitHub functional and working properly, although this may not always happen.
#install.packages("devtools")
devtools::install_github("ropensci/GSODR", build_vignettes = TRUE)Using GSODR
Query the NCEI FTP server for GSOD data
GSODR's main function, get_GSOD(), downloads and cleans GSOD data
from the NCEI server. Following are a few examples of its capabilities.
Example 1 - Download weather station data for Toowoomba, Queensland for 2010
library(GSODR)
Tbar <- get_GSOD(years = 2010, station = "955510-99999")
#>
#> Checking requested station file for availability on server.
#> Starting data file processing
head(Tbar)
#> WBAN STNID STN_NAME CTRY STATE CALL LAT LON
#> 1 99999 955510-99999 TOOWOOMBA AIRPORT AS <NA> <NA> -27.55 151.917
#> 2 99999 955510-99999 TOOWOOMBA AIRPORT AS <NA> <NA> -27.55 151.917
#> 3 99999 955510-99999 TOOWOOMBA AIRPORT AS <NA> <NA> -27.55 151.917
#> 4 99999 955510-99999 TOOWOOMBA AIRPORT AS <NA> <NA> -27.55 151.917
#> 5 99999 955510-99999 TOOWOOMBA AIRPORT AS <NA> <NA> -27.55 151.917
#> 6 99999 955510-99999 TOOWOOMBA AIRPORT AS <NA> <NA> -27.55 151.917
#> ELEV_M ELEV_M_SRTM_90m BEGIN END YEARMODA YEAR MONTH DAY YDAY
#> 1 642 635 19980301 20161122 20100101 2010 01 01 1
#> 2 642 635 19980301 20161122 20100102 2010 01 02 2
#> 3 642 635 19980301 20161122 20100103 2010 01 03 3
#> 4 642 635 19980301 20161122 20100104 2010 01 04 4
#> 5 642 635 19980301 20161122 20100105 2010 01 05 5
#> 6 642 635 19980301 20161122 20100106 2010 01 06 6
#> TEMP TEMP_CNT DEWP DEWP_CNT SLP SLP_CNT STP STP_CNT VISIB VISIB_CNT
#> 1 21.2 8 17.9 8 1013.4 8 942.0 8 NA 0
#> 2 23.2 8 19.4 8 1010.5 8 939.3 8 NA 0
#> 3 21.4 8 18.9 8 1012.3 8 940.9 8 14.3 6
#> 4 18.9 8 16.4 8 1015.7 8 944.1 8 23.3 4
#> 5 20.5 8 16.4 8 1015.5 8 944.0 8 NA 0
#> 6 21.9 8 18.7 8 1013.7 8 942.3 8 NA 0
#> WDSP WDSP_CNT MXSPD GUST MAX MAX_FLAG MIN MIN_FLAG PRCP PRCP_FLAG
#> 1 2.2 8 6.7 NA 25.78 17.78 1.5 G
#> 2 1.9 8 5.1 NA 26.50 19.11 0.3 G
#> 3 3.9 8 10.3 NA 28.72 19.28 * 19.8 G
#> 4 4.5 8 10.3 NA 24.11 16.89 * 1.0 G
#> 5 3.9 8 10.8 NA 24.61 16.72 0.3 G
#> 6 3.2 8 7.7 NA 26.78 17.50 0.0 G
#> SNDP I_FOG I_RAIN_DRIZZLE I_SNOW_ICE I_HAIL I_THUNDER I_TORNADO_FUNNEL
#> 1 NA 0 0 0 0 0 0
#> 2 NA 0 0 0 0 0 0
#> 3 NA 1 1 0 0 0 0
#> 4 NA 0 0 0 0 0 0
#> 5 NA 0 0 0 0 0 0
#> 6 NA 1 0 0 0 0 0
#> EA ES RH
#> 1 2.1 2.5 84.0
#> 2 2.3 2.8 82.1
#> 3 2.2 2.5 88.0
#> 4 1.9 2.2 86.4
#> 5 1.9 2.4 79.2
#> 6 2.2 2.6 84.6Example 2 - Download GSOD data and generate agroclimatology files
For years 2010 and 2011, download data and create the files, GSOD-agroclimatology-2010.csv and GSOD-agroclimatology-2011.csv, in the user's home directory with a maximum of five missing days per weather station allowed.
get_GSOD(years = 2010:2011, dsn = "~/", filename = "GSOD-agroclimatology",
agroclimatology = TRUE, CSV = TRUE, max_missing = 5)Example 3 - Download and plot data for a single country
Download data for Philippines for year 2010 and generate a spatial, year summary file, PHL-2010.gpkg, in the user's home directory.
get_GSOD(years = 2010, country = "Philippines", dsn = "~/",
filename = "PHL-2010", GPKG = TRUE, max_missing = 5)#install.packages("spacetime")
#install.packages("plotKML")
library(rgdal)
#> Loading required package: sp
#> rgdal: version: 1.2-4, (SVN revision 643)
#> Geospatial Data Abstraction Library extensions to R successfully loaded
#> Loaded GDAL runtime: GDAL 1.11.5, released 2016/07/01
#> Path to GDAL shared files: /usr/local/Cellar/gdal/1.11.5_1/share/gdal
#> Loaded PROJ.4 runtime: Rel. 4.9.3, 15 August 2016, [PJ_VERSION: 493]
#> Path to PROJ.4 shared files: (autodetected)
#> Linking to sp version: 1.2-3
library(spacetime)
library(plotKML)
#> plotKML version 0.5-6 (2016-05-02)
#> URL: http://plotkml.r-forge.r-project.org/
layers <- ogrListLayers(dsn = path.expand("~/PHL-2010.gpkg"))
pnts <- readOGR(dsn = path.expand("~/PHL-2010.gpkg"), layers[1])
#> OGR data source with driver: GPKG
#> Source: "/Users/U8004755/PHL-2010.gpkg", layer: "GSOD"
#> with 4703 features
#> It has 46 fields
# Plot results in Google Earth as a spacetime object:
pnts$DATE = as.Date(paste(pnts$YEAR, pnts$MONTH, pnts$DAY, sep = "-"))
row.names(pnts) <- paste("point", 1:nrow(pnts), sep = "")
tmp_ST <- STIDF(sp = as(pnts, "SpatialPoints"),
time = pnts$DATE - 0.5,
data = pnts@data[, c("TEMP", "STNID")],
endTime = pnts$DATE + 0.5)
shape = "http://maps.google.com/mapfiles/kml/pal2/icon18.png"
kml(tmp_ST, dtime = 24 * 3600, colour = TEMP, shape = shape, labels = TEMP,
file.name = "Temperatures_PHL_2010-2010.kml", folder.name = "TEMP")
#> KML file opened for writing...
#> Writing to KML...
#> Closing Temperatures_PHL_2010-2010.kml
system("zip -m Temperatures_PHL_2010-2010.kmz Temperatures_PHL_2010-2010.kml")Example 4 - Working with climate data from GSODRdata
This example will demonstrate how to download data for Philippines for year 2010 and generate a spatial, year summary file, PHL-2010.gpkg, in the user's home directory and link it with climate data from the GSODRdata package.
Install GSODRdata package
This package is only available from GitHub; due to its large size (<9Mb installed) it is not allowed on CRAN. It provides optional data for use with the GSODR package. See https://github.com/adamhsparks/GSODRdata for more.
# install.packages("devtools")
devtools::install_github("adamhsparks/GSODRdata")Working with climate data from GSODRdata
Now that the extra data have been installed, take a look at the CHELSA data that are one of the data sets included in GSODRdata.
CHELSA (Climatologies at high resolution for the earth’s land surface areas) are climate data at (30 arc sec) for the earth land surface areas.
Description of CHELSA data from CHELSA website
CHELSA is a high resolution (30 arc sec) climate data set for the earth land surface areas currently under development in coorporation with the Department of Geography of the University of Hamburg (Prof. Dr. Jürgen Böhner, Dr. Olaf Conrad, Tobias Kawohl), the Swiss Federal Institute for Forest, Snow and Landscape Research WSL (Prof. Dr. Niklaus Zimmermann), the University of Zurich (Dr. Dirk N. Karger, Dr. Michael Kessler), and the University of Göttingen (Prof. Dr. Holger Kreft). It includes monthly mean temperature and precipitation patterns for the time period 1979-2013. CHELSA is based on a quasi-mechanistical statistical downscaling of the ERA interim global circulation model (http://www.ecmwf.int/en/research/climate-reanalysis/era-interim) with a GPCC bias correction (https://www.dwd.de/EN/ourservices/gpcc/gpcc.html) and is freely available in the download section.
See http://chelsa-climate.org for more information on these data.
Compare the GSOD weather data from the Philippines with climatic data
provided by GSODRdata in the CHELSA data set.
library(GSODRdata)
str(CHELSA)
#> 'data.frame': 23927 obs. of 46 variables:
#> $ STNID : chr "008268-99999" "010014-99999" "010015-99999" "010882-99999" ...
#> $ CHELSA_bio10_1979-2013_V1_1: num 30 14.3 12.5 12.6 15.2 16.3 13.9 12.7 13.4 12.9 ...
#> $ CHELSA_bio11_1979-2013_V1_1: num 5.8 1 -3.2 -7 -0.2 -2.2 -3 -6.9 -2.1 -1.2 ...
#> $ CHELSA_bio1_1979-2013_V1_1 : num 18.3 7.1 4.2 2.3 7 6.6 4.8 2.4 5 5.2 ...
#> $ CHELSA_bio12_1979-2013_V1_1: num 214 1889 2209 563 1710 ...
#> $ CHELSA_bio13_1979-2013_V1_1: num 54.6 223.7 254.7 73 200.6 ...
#> $ CHELSA_bio14_1979-2013_V1_1: num 0.1 87.7 108.9 26.7 78.5 ...
#> $ CHELSA_bio15_1979-2013_V1_1: num 104.9 30.3 30.1 30.7 32.1 ...
#> $ CHELSA_bio16_1979-2013_V1_1: num 155 651 753 216 590 ...
#> $ CHELSA_bio17_1979-2013_V1_1: num 0.6 273.5 332.6 85.5 244.7 ...
#> $ CHELSA_bio18_1979-2013_V1_1: num 1.7 433.3 448.3 198.7 330.9 ...
#> $ CHELSA_bio19_1979-2013_V1_1: num 96.9 490.4 621.1 122 494.4 ...
#> $ CHELSA_bio2_1979-2013_V1_1 : num 23 12.5 16.7 20.1 15.6 17.1 16.4 18.6 14.8 13.3 ...
#> $ CHELSA_bio3_1979-2013_V1_1 : num 48.3 44.8 45.3 45.5 45 44.3 43.8 44.6 43.8 43.8 ...
#> $ CHELSA_bio4_1979-2013_V1_1 : num 884 486 592 734 573 ...
#> $ CHELSA_bio5_1979-2013_V1_1 : num 40.5 21.5 22.2 23.6 23.9 25.4 23.1 23 21.8 20.4 ...
#> $ CHELSA_bio6_1979-2013_V1_1 : num -7.2 -6.5 -14.5 -20.7 -10.7 -13.3 -14.3 -18.7 -11.9 -9.9 ...
#> $ CHELSA_bio7_1979-2013_V1_1 : num 47.7 28 36.8 44.3 34.7 38.7 37.4 41.7 33.8 30.3 ...
#> $ CHELSA_bio8_1979-2013_V1_1 : num 10.5 5.7 -1.7 12.5 1.3 8.5 6.8 12.5 7 7.2 ...
#> $ CHELSA_bio9_1979-2013_V1_1 : num 26.7 7.8 8.4 -0.1 12.2 3.8 9.6 0.1 9.8 9.3 ...
#> $ CHELSA_prec_10_1979-2013 : num 3.6 230.6 237.5 48.9 192.8 ...
#> $ CHELSA_prec_11_1979-2013 : num 10.5 219.9 237.8 41.3 189.6 ...
#> $ CHELSA_prec_1_1979-2013 : num 35.8 198.4 227.7 40.3 189.5 ...
#> $ CHELSA_prec_12_1979-2013 : num 25.4 215.5 248.3 40.5 201.5 ...
#> $ CHELSA_prec_1979-2013_land : num 214 1912 2170 559 1720 ...
#> $ CHELSA_prec_2_1979-2013 : num 46.5 153.3 188.4 32.5 153.7 ...
#> $ CHELSA_prec_3_1979-2013 : num 54.8 151.9 176.4 31.8 145 ...
#> $ CHELSA_prec_4_1979-2013 : num 28.3 101.1 114.4 26.6 91.6 ...
#> $ CHELSA_prec_5_1979-2013 : num 7 90.6 107 38.9 79 72.1 75.6 46.7 91.3 85.1 ...
#> $ CHELSA_prec_6_1979-2013 : num 1.2 100.5 110.1 58 84 ...
#> $ CHELSA_prec_7_1979-2013 : num 1 116.7 119.2 69.7 90.1 ...
#> $ CHELSA_prec_8_1979-2013 : num 0.3 165.6 159.6 72.7 121.6 ...
#> $ CHELSA_prec_9_1979-2013 : num 0.1 204 229.6 57.9 181.4 ...
#> $ CHELSA_temp_10_1979-2013 : num 19.2 8 4.3 2.3 7.2 6.7 5.7 2.4 5.6 5.9 ...
#> $ CHELSA_temp_11_1979-2013 : num 13.2 4.5 0.2 -3 3 2.2 1.2 -2.7 1.6 2.2 ...
#> $ CHELSA_temp_1_1979-2013 : num 4.9 1.2 -3.4 -7.2 -0.4 -2.4 -2.5 -7 -1.9 -1 ...
#> $ CHELSA_temp_12_1979-2013 : num 7.7 2.1 -2.7 -6.5 0.3 -1.4 -1.3 -6.3 -0.7 -0.1 ...
#> $ CHELSA_temp_1979-2013_land : num 18.3 7.1 4.1 2.3 6.9 6.5 5.1 2.5 5 5.2 ...
#> $ CHELSA_temp_2_1979-2013 : num 7 0.8 -3.2 -6.6 -0.2 -2.2 -2.8 -6.4 -2.1 -1.2 ...
#> $ CHELSA_temp_3_1979-2013 : num 12.2 2.3 -0.8 -3.3 2.2 0.8 -0.9 -2.7 -0.6 0.1 ...
#> $ CHELSA_temp_4_1979-2013 : num 18.3 5.1 3 1.5 5.8 5.1 3 1.7 2.9 3.1 ...
#> $ CHELSA_temp_5_1979-2013 : num 23.5 9 7.6 6.4 10.1 10.7 7.8 6.6 7.3 7.1 ...
#> $ CHELSA_temp_6_1979-2013 : num 28.2 12 10.9 10.9 13.2 14.7 11.9 11.3 11.1 10.5 ...
#> $ CHELSA_temp_7_1979-2013 : num 28.2 12 10.9 10.9 13.2 14.7 11.9 11.3 11.1 10.5 ...
#> $ CHELSA_temp_8_1979-2013 : num 29.7 14.2 12.2 12.1 14.9 15.8 14 12.2 13.3 12.8 ...
#> $ CHELSA_temp_9_1979-2013 : num 25.1 11.5 8.6 7.7 11.5 11.6 10.2 7.6 9.9 9.8 ...Using dplyr functions, join the CHELSA and GSODR data for plotting.
library(dplyr)
#>
#> Attaching package: 'dplyr'
#> The following objects are masked from 'package:stats':
#>
#> filter, lag
#> The following objects are masked from 'package:base':
#>
#> intersect, setdiff, setequal, union
library(ggplot2)
library(reshape2)
cnames <- paste0("CHELSA_temp_", 1:12, "_1979-2013")
clim_temp <- CHELSA[CHELSA$STNID %in% pnts$STNID,
paste(c("STNID", cnames))]
clim_temp_df <- data.frame(STNID = rep(clim_temp$STNID, 12),
MONTHC = as.vector(sapply(1:12, rep,
times = nrow(clim_temp))),
CHELSA_TEMP = as.vector(unlist(clim_temp[, cnames])))
pnts$MONTHC <- as.numeric(paste(pnts$MONTH))
temp <- left_join(pnts@data, clim_temp_df, by = c("STNID", "MONTHC"))
#> Warning in left_join_impl(x, y, by$x, by$y, suffix$x, suffix$y): joining
#> factors with different levels, coercing to character vector
temp <- temp %>%
group_by(MONTH) %>%
mutate(AVG_DAILY_TEMP = round(mean(TEMP), 1))
df_melt <- na.omit(melt(temp[, c("STNID", "DATE", "CHELSA_TEMP", "TEMP", "AVG_DAILY_TEMP")],
id = c("DATE", "STNID")))
ggplot(df_melt, aes(x = DATE, y = value)) +
geom_point(aes(color = variable), alpha = 0.2) +
scale_x_date(date_labels = "%b") +
ylab("Temperature (C)") +
xlab("Month") +
labs(colour = "") +
scale_color_brewer(palette = "Dark2") +
facet_wrap( ~ STNID)Example 5 - Finding stations within a given radius of a point
Using the nearest_station() function will return a list of stations in
the GSOD data set that are within a specified radius (kilometres) of a
given point expressed as latitude and longitude in decimal degrees
[WGS84].
# Find stations within 50km of Toowoomba, QLD.
n <- nearest_stations(LAT = -27.5598, LON = 151.9507, distance = 50)
n
[1] "945510-99999" "945520-99999" "945620-99999" "949999-00170" "949999-00183" "955510-99999"
toowoomba <- get_GSOD(years = 2015, station = n)
#> This station, 945510-99999, only provides data for years 1956 to 2012.
#> This station, 949999-00170, only provides data for years 1971 to 1984.
#> This station, 949999-00183, only provides data for years 1983 to 1984.
#> Checking requested station file for availability on server.
#> Downloading individual station files.
|=============================================================================================================================|100% ~0 s remaining
Starting data file processing
|=============================================================================================================================================| 100%
str(toowoomba)
#> 'data.frame': 1094 obs. of 47 variables:
#> $ WBAN : chr "99999" "99999" "99999" "99999" ...
#> $ STNID : chr "945520-99999" "945520-99999" "945520-99999" "945520-99999" ...
#> $ STN_NAME : chr "OAKEY" "OAKEY" "OAKEY" "OAKEY" ...
#> $ CTRY : chr "AS" "AS" "AS" "AS" ...
#> $ STATE : chr NA NA NA NA ...
#> $ CALL : chr "YBOK" "YBOK" "YBOK" "YBOK" ...
#> $ LAT : num -27.4 -27.4 -27.4 -27.4 -27.4 ...
#> $ LON : num 152 152 152 152 152 ...
#> $ ELEV_M : num 407 407 407 407 407 ...
#> $ ELEV_M_SRTM_90m : num 404 404 404 404 404 404 404 404 404 404 ...
#> $ BEGIN : num 19730430 19730430 19730430 19730430 19730430 ...
#> $ END : num 20170111 20170111 20170111 20170111 20170111 ...
#> $ YEARMODA : chr "20150101" "20150102" "20150103" "20150104" ...
#> $ YEAR : chr "2015" "2015" "2015" "2015" ...
#> $ MONTH : chr "01" "01" "01" "01" ...
#> $ DAY : chr "01" "02" "03" "04" ...
#> $ YDAY : num 1 2 3 4 5 6 7 8 9 10 ...
#> $ TEMP : num 24.9 24.3 22.3 23 22.5 22.6 21.8 22.6 24.6 24.9 ...
#> $ TEMP_CNT : int 24 24 24 24 24 24 24 24 24 24 ...
#> $ DEWP : num 18.4 18.2 17.4 16.3 17.7 14.3 15.7 16.4 16.4 16.4 ...
#> $ DEWP_CNT : int 24 24 24 24 24 24 24 24 24 24 ...
#> $ SLP : num 1014 1017 1017 1014 1015 ...
#> $ SLP_CNT : int 16 16 16 16 16 16 16 16 16 16 ...
#> $ STP : num 968 971 971 969 969 ...
#> $ STP_CNT : int 16 16 16 16 16 16 16 16 16 16 ...
#> $ VISIB : num 10 10 10 10 10 10 NA NA 10 NA ...
#> $ VISIB_CNT : int 8 8 4 5 7 4 0 0 4 0 ...
#> $ WDSP : num 2.3 2.8 2.8 2.3 3 3.5 3.1 2.5 2.9 2.7 ...
#> $ WDSP_CNT : int 24 24 24 24 24 24 24 24 24 24 ...
#> $ MXSPD : num 8.2 9.8 10.3 7.2 9.8 9.8 9.8 8.8 8.2 8.2 ...
#> $ GUST : num NA NA NA NA NA NA NA NA NA 12.4 ...
#> $ MAX : num 31.8 31 27.3 29.3 27.6 ...
#> $ MAX_FLAG : chr "" "" "" "" ...
#> $ MIN : num 18.9 18.2 17.7 16.8 16.4 ...
#> $ MIN_FLAG : chr "*" "" "*" "*" ...
#> $ PRCP : num 0 0 0 0 0 0 0 0 0 0 ...
#> $ PRCP_FLAG : chr "G" "G" "G" "G" ...
#> $ SNDP : num NA NA NA NA NA NA NA NA NA NA ...
#> $ I_FOG : int 0 0 0 0 0 0 0 0 0 0 ...
#> $ I_RAIN_DRIZZLE : int 0 0 0 0 0 0 0 0 0 0 ...
#> $ I_SNOW_ICE : int 0 0 0 0 0 0 0 0 0 0 ...
#> $ I_HAIL : int 0 0 0 0 0 0 0 0 0 0 ...
#> $ I_THUNDER : int 0 0 0 0 0 0 0 0 0 0 ...
#> $ I_TORNADO_FUNNEL: int 0 0 0 0 0 0 0 0 0 0 ...
#> $ EA : num 2.1 2.1 2 1.9 2 1.6 1.8 1.9 1.9 1.9 ...
#> $ ES : num 3.1 3 2.7 2.8 2.7 2.7 2.6 2.7 3.1 3.1 ...
#> $ RH : num 67.7 70 74.1 67.9 74.1 59.3 69.2 70.4 61.3 61.3 ...Other Sources of Weather Data in R
There are several other sources of weather data and ways of retrieving
them through R. In particular, the excellent
rnoaa package also from
rOpenSci offers tools for interacting with and
downloading weather data from the United States National Oceanic and
Atmospheric Administration but lacks support GSOD data.
Other Sources for Fetching GSOD Weather Data
The GSODTools by Florian Detsch is an R package that offers similar functionality as GSODR but also has the ability to graph the data and working with data for time series analysis.
The ULMO library offers an interface to retrieve GSOD data using Python.
Notes
Data Sources
CHELSA climate layers
CHELSA (climatic surfaces at 1 km resolution) is based on a quasi-mechanistical statistical downscaling of the ERA interim global circulation model (Karger et al. 2016). ESA's CCI-LC cloud probability monthly averages are based on the MODIS snow products (MOD10A2). http://chelsa-climate.org/
Elevation Values
90m hole-filled SRTM digital elevation (Jarvis et al. 2008) was used to identify and correct/remove elevation errors in data for station locations between -60˚ and 60˚ latitude. This applies to cases here where elevation was missing in the reported values as well. In case the station reported an elevation and the DEM does not, the station reported is taken. For stations beyond -60˚ and 60˚ latitude, the values are station reported values in every instance. See https://github.com/ropensci/GSODR/blob/master/data-raw/fetch_isd-history.md for more detail on the correction methods.
WMO Resolution 40. NOAA Policy
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
Meta
Please report any issues or bugs.
License: MIT
Get citation information for getCRUCLdata in R typing
citation(package = "GSODR")Please note that this project is released with a Contributor Code of Conduct. By participating in this project you agree to abide by its terms.
References
Jarvis, A., Reuter, H. I., Nelson, A., Guevara, E. (2008) Hole-filled SRTM for the globe Version 4, available from the CGIAR-CSI SRTM 90m Database (http://srtm.csi.cgiar.org)
Karger, D. N., Conrad, O., Bohner, J., Kawohl, T., Kreft, H., Soria-Auza, R. W., et al. (2016) Climatologies at high resolution for the Earth land surface areas. arXiv preprint arXiv:1607.00217. (http://chelsa-climate.org/)