GSODR

Adam H Sparks

Introduction

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:

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. Data in the R session summarise each year by station, which also includes vapour pressure and relative humidity elements calculated from existing data in GSOD.

For more information see the description of the data provided by NCEI, http://www7.ncdc.noaa.gov/CDO/GSOD_DESC.txt.

Using get_GSOD()

Find Stations in Australia

GSODR provides lists of weather station locations and elevation values. Using dplyr, we can find all the stations in Australia.

## 
## GSOD is distributed free by the U.S. NCEI with the
## following conditions.
## 'The following data and products may have conditions placed
## 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'
## 
## GSODR does not redistribute any weather data itself. It 
## only provides an interface for R users to download these
## data, however it does redistribute station metadata in the
## package.
## 
## 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
## # A tibble: 1,390 x 16
##    USAF  WBAN  STN_NAME CTRY  STATE CALL    LAT   LON ELEV_M  BEGIN    END
##    <chr> <chr> <chr>    <chr> <chr> <chr> <dbl> <dbl>  <dbl>  <dbl>  <dbl>
##  1 6950… 99999 HORN IS… AS    <NA>  KQXC  -10.6  142.     NA 2.00e7 2.00e7
##  2 7494… 99999 AIDELAI… AS    <NA>  <NA>  -13.3  131.    131 1.94e7 1.94e7
##  3 7494… 99999 REID EA… AS    <NA>  <NA>  -19.8  147.    122 1.94e7 1.94e7
##  4 7494… 99999 WOODSTO… AS    <NA>  <NA>  -19.6  147.     64 1.94e7 1.94e7
##  5 7494… 99999 LAKE BU… AS    <NA>  <NA>  -21.4  145.    396 1.94e7 1.94e7
##  6 7494… 99999 DALBY S… AS    <NA>  <NA>  -27.2  151.    413 1.94e7 1.94e7
##  7 7494… 99999 NORTH P… AS    <NA>  <NA>  -33.8  151      19 1.94e7 1.94e7
##  8 9142… 99999 MIDDLE … AS    <NA>  <NA>  -12.6  131.     10 2.00e7 2.00e7
##  9 9210… 99999 BROOSE … AS    <NA>  <NA>  -14.1  124.      5 2.00e7 2.00e7
## 10 9212… 99999 DARWIN … AS    <NA>  <NA>  -12.4  131.     29 2.00e7 2.00e7
## # ... with 1,380 more rows, and 5 more variables: STNID <chr>,
## #   ELEV_M_SRTM_90m <dbl>, COUNTRY_NAME <chr>, iso2c <chr>, iso3c <chr>
## # A tibble: 2 x 16
##   USAF  WBAN  STN_NAME CTRY  STATE CALL    LAT   LON ELEV_M  BEGIN    END
##   <chr> <chr> <chr>    <chr> <chr> <chr> <dbl> <dbl>  <dbl>  <dbl>  <dbl>
## 1 9455… 99999 TOOWOOM… AS    <NA>  <NA>  -27.6  152.    676 1.96e7 2.01e7
## 2 9555… 99999 TOOWOOM… AS    <NA>  <NA>  -27.6  152.    642 2.00e7 2.02e7
## # ... with 5 more variables: STNID <chr>, ELEV_M_SRTM_90m <dbl>,
## #   COUNTRY_NAME <chr>, iso2c <chr>, iso3c <chr>

Download a Single Station and Year

Now that we’ve seen where the reporting stations are located, we can download weather data from the station Toowoomba, Queensland, Australia for 2010 by using the STNID in the station parameter of get_GSOD().

## # A tibble: 365 x 48
##    USAF  WBAN  STNID STN_NAME CTRY  STATE CALL    LAT   LON ELEV_M
##    <chr> <chr> <chr> <chr>    <chr> <chr> <chr> <dbl> <dbl>  <dbl>
##  1 9555… 99999 9555… TOOWOOM… AS    <NA>  <NA>  -27.6  152.    642
##  2 9555… 99999 9555… TOOWOOM… AS    <NA>  <NA>  -27.6  152.    642
##  3 9555… 99999 9555… TOOWOOM… AS    <NA>  <NA>  -27.6  152.    642
##  4 9555… 99999 9555… TOOWOOM… AS    <NA>  <NA>  -27.6  152.    642
##  5 9555… 99999 9555… TOOWOOM… AS    <NA>  <NA>  -27.6  152.    642
##  6 9555… 99999 9555… TOOWOOM… AS    <NA>  <NA>  -27.6  152.    642
##  7 9555… 99999 9555… TOOWOOM… AS    <NA>  <NA>  -27.6  152.    642
##  8 9555… 99999 9555… TOOWOOM… AS    <NA>  <NA>  -27.6  152.    642
##  9 9555… 99999 9555… TOOWOOM… AS    <NA>  <NA>  -27.6  152.    642
## 10 9555… 99999 9555… TOOWOOM… AS    <NA>  <NA>  -27.6  152.    642
## # ... with 355 more rows, and 38 more variables: ELEV_M_SRTM_90m <dbl>,
## #   BEGIN <dbl>, END <dbl>, YEARMODA <date>, YEAR <chr>, MONTH <chr>,
## #   DAY <chr>, YDAY <dbl>, TEMP <dbl>, TEMP_CNT <int>, DEWP <dbl>,
## #   DEWP_CNT <int>, SLP <dbl>, SLP_CNT <int>, STP <dbl>, STP_CNT <int>,
## #   VISIB <dbl>, VISIB_CNT <int>, WDSP <dbl>, WDSP_CNT <int>, MXSPD <dbl>,
## #   GUST <dbl>, MAX <dbl>, MAX_FLAG <chr>, MIN <dbl>, MIN_FLAG <chr>,
## #   PRCP <dbl>, PRCP_FLAG <chr>, SNDP <dbl>, I_FOG <int>,
## #   I_RAIN_DRIZZLE <int>, I_SNOW_ICE <int>, I_HAIL <int>, I_THUNDER <int>,
## #   I_TORNADO_FUNNEL <int>, EA <dbl>, ES <dbl>, RH <dbl>

Using nearest_stations()

Using the nearest_stations() function, you can find stations closest to a given point specified by latitude and longitude in decimal degrees. This can be used to generate a vector to pass along to get_GSOD() and download the stations of interest.

There are missing stations in this query. Not all that are listed and queried actually have files on the server.

If you wished to drop the stations, 949999-00170 and 949999-00183 from the query, you could do this.

Plot Maximum and Minimum Temperature Values

Using the first data downloaded for a single station, 955510-99999, plot the temperature for 2010.

## 
## Attaching package: 'lubridate'
## The following object is masked from 'package:base':
## 
##     date

Creating Spatial Files

Because the stations provide geospatial location information, it is possible to create a spatial file. GeoPackage files are a open, standards-based, platform-independent, portable, self-describing compact format for transferring geospatial information, which handle vector files much like shapefiles do, but eliminate many of the issues that shapefiles have with field names and the number of files.

Converting GSOD Data to a Spatial Objects and Saving

Converting the GSOD data to a spatial object is rather simple. In R there are two options, sp and sf. The sp format is older and more well established, but sf offers some new exciting possibilities.

Convert to an sp SpatialPointsDataFrame

For this example we convert the data to a sp object. You might use this one to save GeoPackage or ESRI shapefiles for sharing or analysis in another GIS program.

#> Formal class 'SpatialPointsDataFrame' [package "sp"] with 5 slots
#>   ..@ data       :Classes ‘tbl_df’, ‘tbl’ and 'data.frame':  186977 obs. of  46 variables:
#>   .. ..$ USAF            : chr [1:186977] "941000" "941000" "941000" "941000" ...
#>   .. ..$ WBAN            : chr [1:186977] "99999" "99999" "99999" "99999" ...
#>   .. ..$ STNID           : chr [1:186977] "941000-99999" "941000-99999" "941000-99999" "941000-99999" ...
#>   .. ..$ STN_NAME        : chr [1:186977] "KALUMBURU" "KALUMBURU" "KALUMBURU" "KALUMBURU" ...
#>   .. ..$ CTRY            : chr [1:186977] "AS" "AS" "AS" "AS" ...
#>   .. ..$ STATE           : chr [1:186977] NA NA NA NA ...
#>   .. ..$ CALL            : chr [1:186977] NA NA NA NA ...
#>   .. ..$ ELEV_M          : num [1:186977] 24 24 24 24 24 24 24 24 24 24 ...
#>   .. ..$ ELEV_M_SRTM_90m : num [1:186977] 17 17 17 17 17 17 17 17 17 17 ...
#>   .. ..$ BEGIN           : num [1:186977] 2e+07 2e+07 2e+07 2e+07 2e+07 ...
#>   .. ..$ END             : num [1:186977] 20171212 20171212 20171212 20171212 20171212 ...
#>   .. ..$ YEARMODA        : Date[1:186977], format: "2015-01-01" "2015-01-02" "2015-01-03" ...
#>   .. ..$ YEAR            : chr [1:186977] "2015" "2015" "2015" "2015" ...
#>   .. ..$ MONTH           : chr [1:186977] "01" "01" "01" "01" ...
#>   .. ..$ DAY             : chr [1:186977] "01" "02" "03" "04" ...
#>   .. ..$ YDAY            : num [1:186977] 1 2 3 4 5 6 7 8 9 10 ...
#>   .. ..$ TEMP            : num [1:186977] 26.9 27.2 26.3 27.3 27.3 26.7 28.1 28.7 27.6 27.8 ...
#>   .. ..$ TEMP_CNT        : int [1:186977] 16 16 16 16 16 16 16 16 16 16 ...
#>   .. ..$ DEWP            : num [1:186977] 23.2 23.3 23.8 24.1 24.3 24 23.6 23.3 21.9 22.3 ...
#>   .. ..$ DEWP_CNT        : int [1:186977] 16 16 16 16 16 16 16 16 16 16 ...
#>   .. ..$ SLP             : num [1:186977] 1004 1003 1003 1002 1001 ...
#>   .. ..$ SLP_CNT         : int [1:186977] 16 16 16 16 16 16 16 16 16 16 ...
#>   .. ..$ STP             : num [1:186977] 1002 1000 NA 999 998 ...
#>   .. ..$ STP_CNT         : int [1:186977] 16 16 16 16 16 16 16 16 16 16 ...
#>   .. ..$ VISIB           : num [1:186977] NA NA NA NA NA NA NA NA NA NA ...
#>   .. ..$ VISIB_CNT       : int [1:186977] 0 0 0 0 0 0 0 0 0 0 ...
#>   .. ..$ WDSP            : num [1:186977] 1.7 2.2 1.7 2 2.6 3.4 4.5 5.9 4.4 2.4 ...
#>   .. ..$ WDSP_CNT        : int [1:186977] 16 16 16 16 16 16 16 16 16 16 ...
#>   .. ..$ MXSPD           : num [1:186977] 4.1 4.1 4.1 4.1 5.7 5.7 6.7 9.3 7.7 4.6 ...
#>   .. ..$ GUST            : num [1:186977] NA NA NA NA NA NA NA NA NA NA ...
#>   .. ..$ MAX             : num [1:186977] 31.5 31.3 29.2 32.1 31.4 28.6 30.2 31 32.7 32.9 ...
#>   .. ..$ MAX_FLAG        : chr [1:186977] NA NA NA NA ...
#>   .. ..$ MIN             : num [1:186977] 25 24.9 24.4 24.3 24.6 25.1 24.5 26.3 22.8 22.6 ...
#>   .. ..$ MIN_FLAG        : chr [1:186977] NA "*" "*" NA ...
#>   .. ..$ PRCP            : num [1:186977] 16.5 12.4 5.1 20.3 41.1 20.1 82.3 2.3 6.1 27.4 ...
#>   .. ..$ PRCP_FLAG       : chr [1:186977] "G" "G" "G" "G" ...
#>   .. ..$ SNDP            : num [1:186977] NA NA NA NA NA NA NA NA NA NA ...
#>   .. ..$ I_FOG           : int [1:186977] 0 0 0 0 0 0 0 0 0 0 ...
#>   .. ..$ I_RAIN_DRIZZLE  : int [1:186977] 1 1 1 1 1 1 1 1 1 0 ...
#>   .. ..$ I_SNOW_ICE      : int [1:186977] 0 0 0 0 0 0 0 0 0 0 ...
#>   .. ..$ I_HAIL          : int [1:186977] 0 0 0 0 0 0 0 0 0 0 ...
#>   .. ..$ I_THUNDER       : int [1:186977] 0 0 0 0 0 0 0 0 0 0 ...
#>   .. ..$ I_TORNADO_FUNNEL: int [1:186977] 0 0 0 0 0 0 0 0 0 0 ...
#>   .. ..$ EA              : num [1:186977] 2.8 2.9 2.9 3 3 3 2.9 2.9 2.6 2.7 ...
#>   .. ..$ ES              : num [1:186977] 3.5 3.6 3.4 3.6 3.6 3.5 3.8 3.9 3.7 3.7 ...
#>   .. ..$ RH              : num [1:186977] 80 80.6 85.3 83.3 83.3 85.7 76.3 74.4 70.3 73 ...
#>   ..@ coords.nrs : int [1:2] 9 8
#>   ..@ coords     : num [1:186977, 1:2] 127 127 127 127 127 ...
#>   .. ..- attr(*, "dimnames")=List of 2
#>   .. .. ..$ : chr [1:186977] "1" "2" "3" "4" ...
#>   .. .. ..$ : chr [1:2] "LON" "LAT"
#>   ..@ bbox       : num [1:2, 1:2] 73.7 -54.5 159.1 -10.1
#>   .. ..- attr(*, "dimnames")=List of 2
#>   .. .. ..$ : chr [1:2] "LON" "LAT"
#>   .. .. ..$ : chr [1:2] "min" "max"
#>   ..@ proj4string:Formal class 'CRS' [package "sp"] with 1 slot
#>   .. .. ..@ projargs: chr "+proj=longlat +datum=WGS84 +ellps=WGS84 +towgs84=0,0,0"

Saving sp objects

Using writeOGR() it is simple to save as a GeoPackage file.

Using writeOGR() it is simple to save as an ESRI shapefile.

Converting to an sf object

Simple features are an ISO defined standard that now have support in R. From the sf vignette:

Simple features or simple feature access refers to a formal standard (ISO 19125-1:2004) that describes how objects in the real world can be represented in computers, with emphasis on the spatial geometry of these objects. It also describes how such objects can be stored in and retrieved from databases, and which geometrical operations should be defined for them. The standard is widely implemented in spatial databases (such as PostGIS), commercial GIS (e.g., ESRI ArcGIS) and forms the vector data basis for libraries such as GDAL. A subset of simple features forms the GeoJSON standard.

For this example, we convert the GSOD data to an sf object.

#> Classes ‘sf’ and 'data.frame':   186977 obs. of  47 variables:
#>  $ USAF            : chr  "941000" "941000" "941000" "941000" ...
#>  $ WBAN            : chr  "99999" "99999" "99999" "99999" ...
#>  $ STNID           : chr  "941000-99999" "941000-99999" "941000-99999" "941000-99999" ...
#>  $ STN_NAME        : chr  "KALUMBURU" "KALUMBURU" "KALUMBURU" "KALUMBURU" ...
#>  $ CTRY            : chr  "AS" "AS" "AS" "AS" ...
#>  $ STATE           : chr  NA NA NA NA ...
#>  $ CALL            : chr  NA NA NA NA ...
#>  $ ELEV_M          : num  24 24 24 24 24 24 24 24 24 24 ...
#>  $ ELEV_M_SRTM_90m : num  17 17 17 17 17 17 17 17 17 17 ...
#>  $ BEGIN           : num  2e+07 2e+07 2e+07 2e+07 2e+07 ...
#>  $ END             : num  20171212 20171212 20171212 20171212 20171212 ...
#>  $ YEARMODA        : Date, format: "2015-01-01" "2015-01-02" ...
#>  $ 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  26.9 27.2 26.3 27.3 27.3 26.7 28.1 28.7 27.6 27.8 ...
#>  $ TEMP_CNT        : int  16 16 16 16 16 16 16 16 16 16 ...
#>  $ DEWP            : num  23.2 23.3 23.8 24.1 24.3 24 23.6 23.3 21.9 22.3 ...
#>  $ DEWP_CNT        : int  16 16 16 16 16 16 16 16 16 16 ...
#>  $ SLP             : num  1004 1003 1003 1002 1001 ...
#>  $ SLP_CNT         : int  16 16 16 16 16 16 16 16 16 16 ...
#>  $ STP             : num  1002 1000 NA 999 998 ...
#>  $ STP_CNT         : int  16 16 16 16 16 16 16 16 16 16 ...
#>  $ VISIB           : num  NA NA NA NA NA NA NA NA NA NA ...
#>  $ VISIB_CNT       : int  0 0 0 0 0 0 0 0 0 0 ...
#>  $ WDSP            : num  1.7 2.2 1.7 2 2.6 3.4 4.5 5.9 4.4 2.4 ...
#>  $ WDSP_CNT        : int  16 16 16 16 16 16 16 16 16 16 ...
#>  $ MXSPD           : num  4.1 4.1 4.1 4.1 5.7 5.7 6.7 9.3 7.7 4.6 ...
#>  $ GUST            : num  NA NA NA NA NA NA NA NA NA NA ...
#>  $ MAX             : num  31.5 31.3 29.2 32.1 31.4 28.6 30.2 31 32.7 32.9 ...
#>  $ MAX_FLAG        : chr  NA NA NA NA ...
#>  $ MIN             : num  25 24.9 24.4 24.3 24.6 25.1 24.5 26.3 22.8 22.6 ...
#>  $ MIN_FLAG        : chr  NA "*" "*" NA ...
#>  $ PRCP            : num  16.5 12.4 5.1 20.3 41.1 20.1 82.3 2.3 6.1 27.4 ...
#>  $ 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  1 1 1 1 1 1 1 1 1 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  2.8 2.9 2.9 3 3 3 2.9 2.9 2.6 2.7 ...
#>  $ ES              : num  3.5 3.6 3.4 3.6 3.6 3.5 3.8 3.9 3.7 3.7 ...
#>  $ RH              : num  80 80.6 85.3 83.3 83.3 85.7 76.3 74.4 70.3 73 ...
#>  $ geometry        :sfc_POINT of length 186977; first list element: Classes 'XY', 'POINT', 'sfg'  num [1:2] 126.6 -14.3
#>  - attr(*, "sf_column")= chr "geometry"
#>  - attr(*, "agr")= Factor w/ 3 levels "constant","aggregate",..: NA NA NA NA NA NA NA NA NA NA ...
#>   ..- attr(*, "names")= chr  "USAF" "WBAN" "STNID" "STN_NAME" ...

Saving sf objects

Writing a shapefile is a simple matter.

As is writing a GeoPackage from the sf object.

After getting weather stations for Australia and creating a GeoPackage file, rgdal can import the data into R again in a spatial format.

Importing a GeoPackage file can be a bit tricky. The dsn will be the full path along with the file name. The layer to be specified is “GSOD”, this is specified in the get_GSOD() function and will not change. The file name, specified in the dsn will, but the layer name will not.

Since GeoPackage files are formatted as SQLite databases you can use the existing R tools for SQLite files (J. Stachelek 2016). One easy way is using dplyr. This option is much faster to load since it does not load the geometry.

Using reformat_GSOD()

You may have already downloaded GSOD data or may just wish to use an FTP client to download the files from the server to you local disk and not use the capabilities of get_GSOD(). In that case the reformat_GSOD() function is useful.

There are two ways, you can either provide reformat_GSOD() with a list of specified station files or you can supply it with a directory containing all of the “WBAN-WMO-YYYY.op.gz” station files that you wish to reformat.

Reformat a list of local files

Reformat all local files found in directory

Using update_station_list()

GSODR uses internal databases of station data from the NCEI to provide location and other metadata, e.g. elevation, station names, WMO codes, etc. to make the process of querying for weather data faster. This database is created and packaged with GSODR for distribution and is updated with new releases. Users have the option of updating these databases after installing GSODR. While this option gives the users the ability to keep the database up-to-date and gives GSODR’s authors flexibility in maintaining it, this also means that reproducibility may be affected since the same version of GSODR may have different databases on different machines. If reproducibility is necessary, care should be taken to ensure that the version of the databases is the same across different machines.

The database file isd_history.rda can be located on your local system by using the following command, paste0(.libPaths(), "/GSODR/extdata")[1], unless you have specified another location for library installations and installed GSODR there, in which case it would still be in GSODR/extdata.

To update GSODR’s internal database of station locations simply use update_station_list(), which will update the internal station database according to the latest data available from the NCEI.

Using get_inventory()

GSODR provides a function, get_inventory() to retrieve an inventory of the number of weather observations by station-year-month for the beginning of record through to current.

Following is an example of how to retrieve the inventory and check a station in Toowoomba, Queensland, Australia, which was used in an earlier example.

inventory <- get_inventory()

inventory

subset(inventory, STNID == "955510-99999")

Additional Climate Data Availability

Additional climate data, GSODRdata, formatted for use with GSOD data provided by GSODR are available as an R package, which can only be installed through GitHub due to the package size, 5.1Mb, being too large for CRAN.

#install.packages("devtools")
devtools::install_github("adamhsparks/GSODRdata")
library("GSODRdata")

Notes

Elevation Values

90 metre (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

References

Stachelek, J. (2016) Using the Geopackage Format with R. URL: https://jsta.github.io/2016/07/14/geopackage-r.html

Appendices

Appendix 1: GSODR Final Data Format, Contents and Units

GSODR formatted data include the following fields and units:

Appendix 2: Map of Current GSOD Station Locations