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 Climatic Data Center (NCDC) are a valuable source of weather data with global coverage. However, the data files are cumbersome and difficult to work with. The GSODR package aims to make it easy to find, transfer and format the data you need for use in analysis. The GSODR package 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,get_station_list()- downloads the latest station list from the NCDC FTP server and returns adata.table()object in R session.
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 variables 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 NCDC, http://www7.ncdc.noaa.gov/CDO/GSOD_DESC.txt.
Other Sources of Weather Data
There are several other sources of weather data and ways of retrieving them through R. In particular, the excellent rnoaa package 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.
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.
If you find bugs, please file a report as an issue.
#install.packages("devtools")
devtools::install_github("ropensci/GSODR", build_vignettes = TRUE)Final data format and contents
The GSODR package returns data with the following fields/units.
STNID - Station number (WMO/DATSAV3 number) for the location;
WBAN - number where applicable--this is the historical "Weather Bureau Air Force Navy" number - with WBAN being the acronym;
STN_NAME - Unique text identifier;
CTRY - Country in which the station is located;
LAT - Latitude. Station dropped in cases where values are < -90 or > 90 degrees or Lat = 0 and Lon = 0;
LON - Longitude. Station dropped in cases where values are < -180 or > 180 degrees or Lat = 0 and Lon = 0;
ELEV_M - Elevation in metres;
ELEV_M_SRTM_90m - Elevation in metres corrected for possible errors, derived from the CGIAR-CSI SRTM 90m database (Jarvis et al. 2008);
YEARMODA - Date in YYYY-mm-dd format;
YEAR - The year (YYYY);
MONTH - The month (mm);
DAY - The day (dd);
YDAY - Sequential day of year (not in original GSOD);
TEMP - Mean daily temperature converted to degrees C to tenths. Missing = NA;
TEMP_CNT - Number of observations used in calculating mean daily temperature;
DEWP - Mean daily dew point converted to degrees C to tenths. Missing = NA;
DEWP_CNT - Number of observations used in calculating mean daily dew point;
SLP - Mean sea level pressure in millibars to tenths. Missing = NA;
SLP_CNT - Number of observations used in calculating mean sea level pressure;
STP - Mean station pressure for the day in millibars to tenths. Missing = NA;
STP_CNT - Number of observations used in calculating mean station pressure;
VISIB - Mean visibility for the day converted to kilometres to tenths Missing = NA;
VISIB_CNT - Number of observations used in calculating mean daily visibility;
WDSP - Mean daily wind speed value converted to metres/second to tenths Missing = NA;
WDSP_CNT - Number of observations used in calculating mean daily wind speed;
MXSPD - Maximum sustained wind speed reported for the day converted to metres/second to tenths. Missing = NA;
GUST - Maximum wind gust reported for the day converted to metres/second to tenths. Missing = NA;
MAX - Maximum temperature reported during the day converted to Celsius to tenths--time of max temp report varies by country and region, so this will sometimes not be the max for the calendar day. Missing = NA;
MAX_FLAG - Blank indicates max temp was taken from the explicit max temp report and not from the 'hourly' data. An "*" indicates max temp was derived from the hourly data (i.e., highest hourly or synoptic-reported temperature);
MIN - Minimum temperature reported during the day converted to Celsius to tenths--time of min temp report varies by country and region, so this will sometimes not be the max for the calendar day. Missing = NA;
MIN_FLAG - Blank indicates max temp was taken from the explicit min temp report and not from the 'hourly' data. An "*" indicates min temp was derived from the hourly data (i.e., highest hourly or synoptic-reported temperature);
PRCP - Total precipitation (rain and/or melted snow) reported during the day converted to millimetres to hundredths; will usually not end with the midnight observation, i.e., may include latter part of previous day. A value of ".00" indicates no measurable precipitation (includes a trace). Missing = NA; Note: Many stations do not report '0' on days with no precipitation-- therefore, 'NA' will often appear on these days. For example, a station may only report a 6-hour amount for the period during which rain fell. See FLAGS_PRCP column for source of data;
PRCP_FLAG -
A = 1 report of 6-hour precipitation amount;
B = Summation of 2 reports of 6-hour precipitation amount;
C = Summation of 3 reports of 6-hour precipitation amount;
D = Summation of 4 reports of 6-hour precipitation amount;
E = 1 report of 12-hour precipitation amount;
F = Summation of 2 reports of 12-hour precipitation amount;
G = 1 report of 24-hour precipitation amount;
H = Station reported '0' as the amount for the day (e.g., from 6-hour reports), but also reported at least one occurrence of precipitation in hourly observations--this could indicate a rrace occurred, but should be considered as incomplete data for the day;
I = Station did not report any precipitation data for the day and did not report any occurrences of precipitation in its hourly observations--it's still possible that precipitation occurred but was not reported;
SNDP - Snow depth in millimetres to tenths. Missing = NA;
I_FOG - Indicator for fog, (1 = yes, 0 = no/not reported) for the occurrence during the day;
I_RAIN_DRIZZLE - Indicator for rain or drizzle, (1 = yes, 0 = no/not reported) for the occurrence during the day;
I_SNOW_ICE - Indicator for snow or ice pellets, (1 = yes, 0 = no/not reported) for the occurrence during the day;
I_HAIL - Indicator for hail, (1 = yes, 0 = no/not reported) for the occurrence during the day;
I_THUNDER - Indicator for thunder, (1 = yes, 0 = no/not reported) for the occurrence during the day;
I_TORNADO_FUNNEL - Indicator for tornado or funnel cloud, (1 = yes, 0 = no/not reported) for the occurrence during the day;
ea - Mean daily actual vapour pressure;
es - Mean daily saturation vapour pressure;
RH - Mean daily relative humidity.
Using GSODR
Query the NCDC FTP server for GSOD data
GSODR's main function, get_GSOD(), downloads and cleans GSOD data from
the NCDC 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
#>
|
| | 0%
|
|=================================================================| 100%
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 (5.5Mb
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/GSODdata")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 the GSODRdata package.
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 the GSODR package in the CHELSA data set.
library(GSODRdata)
head(CHELSA)## STNID LON LAT CHELSA_bio10_1979-2013_V1_1 CHELSA_bio11_1979-2013_V1_1 CHELSA_bio1_1979-2013_V1_1
## 1 008268-99999 65.567 32.950 30.0 5.8 18.3
## 2 010014-99999 5.341 59.792 14.3 1.0 7.1
## 3 010015-99999 5.867 61.383 12.5 -3.2 4.2
## 4 010882-99999 11.342 62.578 12.6 -7.0 2.3
## 5 010883-99999 6.117 61.833 15.2 -0.2 7.0
## 6 010884-99999 9.567 59.185 16.3 -2.2 6.6
## CHELSA_bio12_1979-2013_V1_1 CHELSA_bio13_1979-2013_V1_1 CHELSA_bio14_1979-2013_V1_1 CHELSA_bio15_1979-2013_V1_1
## 1 214.0 54.6 0.1 104.9
## 2 1889.3 223.7 87.7 30.3
## 3 2209.1 254.7 108.9 30.1
## 4 563.0 73.0 26.7 30.7
## 5 1710.3 200.6 78.5 32.1
## 6 987.0 120.9 52.3 24.4
## CHELSA_bio16_1979-2013_V1_1 CHELSA_bio17_1979-2013_V1_1 CHELSA_bio18_1979-2013_V1_1 CHELSA_bio19_1979-2013_V1_1
## 1 155.4 0.6 1.7 96.9
## 2 650.7 273.5 433.3 490.4
## 3 752.8 332.6 448.3 621.1
## 4 216.2 85.5 198.7 122.0
## 5 590.0 244.7 330.9 494.4
## 6 338.0 175.3 242.3 182.7
## CHELSA_bio2_1979-2013_V1_1 CHELSA_bio3_1979-2013_V1_1 CHELSA_bio4_1979-2013_V1_1 CHELSA_bio5_1979-2013_V1_1
## 1 23.0 48.3 884.1 40.5
## 2 12.5 44.8 486.3 21.5
## 3 16.7 45.3 591.5 22.2
## 4 20.1 45.5 734.1 23.6
## 5 15.6 45.0 573.4 23.9
## 6 17.1 44.3 693.1 25.4
## CHELSA_bio6_1979-2013_V1_1 CHELSA_bio7_1979-2013_V1_1 CHELSA_bio8_1979-2013_V1_1 CHELSA_bio9_1979-2013_V1_1 CHELSA_prec_10_1979-2013
## 1 -7.2 47.7 10.5 26.7 3.6
## 2 -6.5 28.0 5.7 7.8 230.6
## 3 -14.5 36.8 -1.7 8.4 237.5
## 4 -20.7 44.3 12.5 -0.1 48.9
## 5 -10.7 34.7 1.3 12.2 192.8
## 6 -13.3 38.7 8.5 3.8 122.1
## CHELSA_prec_11_1979-2013 CHELSA_prec_1_1979-2013 CHELSA_prec_12_1979-2013 CHELSA_prec_1979-2013_land CHELSA_prec_2_1979-2013
## 1 10.5 35.8 25.4 214.5 46.5
## 2 219.9 198.4 215.5 1912.0 153.3
## 3 237.8 227.7 248.3 2170.2 188.4
## 4 41.3 40.3 40.5 559.3 32.5
## 5 189.6 189.5 201.5 1719.6 153.7
## 6 106.2 79.0 82.4 998.1 53.0
## CHELSA_prec_3_1979-2013 CHELSA_prec_4_1979-2013 CHELSA_prec_5_1979-2013 CHELSA_prec_6_1979-2013 CHELSA_prec_7_1979-2013
## 1 54.8 28.3 7.0 1.2 1.0
## 2 151.9 101.1 90.6 100.5 116.7
## 3 176.4 114.4 107.0 110.1 119.2
## 4 31.8 26.6 38.9 58.0 69.7
## 5 145.0 91.6 79.0 84.0 90.1
## 6 63.7 57.0 72.1 76.1 84.3
## CHELSA_prec_8_1979-2013 CHELSA_prec_9_1979-2013 CHELSA_temp_10_1979-2013 CHELSA_temp_11_1979-2013 CHELSA_temp_1_1979-2013
## 1 0.3 0.1 19.2 13.2 4.9
## 2 165.6 204.0 8.0 4.5 1.2
## 3 159.6 229.6 4.3 0.2 -3.4
## 4 72.7 57.9 2.3 -3.0 -7.2
## 5 121.6 181.4 7.2 3.0 -0.4
## 6 105.0 97.2 6.7 2.2 -2.4
## CHELSA_temp_12_1979-2013 CHELSA_temp_1979-2013_land CHELSA_temp_2_1979-2013 CHELSA_temp_3_1979-2013 CHELSA_temp_4_1979-2013
## 1 7.7 18.3 7.0 12.2 18.3
## 2 2.1 7.1 0.8 2.3 5.1
## 3 -2.7 4.1 -3.2 -0.8 3.0
## 4 -6.5 2.3 -6.6 -3.3 1.5
## 5 0.3 6.9 -0.2 2.2 5.8
## 6 -1.4 6.5 -2.2 0.8 5.1
## CHELSA_temp_5_1979-2013 CHELSA_temp_6_1979-2013 CHELSA_temp_7_1979-2013 CHELSA_temp_8_1979-2013 ## CHELSA_temp_9_1979-2013
## 1 23.5 28.2 28.2 29.7 25.1
## 2 9.0 12.0 12.0 14.2 11.5
## 3 7.6 10.9 10.9 12.2 8.6
## 4 6.4 10.9 10.9 12.1 7.7
## 5 10.1 13.2 13.2 14.9 11.5
## 6 10.7 14.7 14.7 15.8 11.6Using 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 ...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/
EarthEnv MODIS cloud fraction
ESA's CCI-LC cloud probability
http://maps.elie.ucl.ac.be/CCI/viewer/index.php
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/devel/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 NCDC 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
Code of Conduct
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/)