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.
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## Attaching package: 'dplyr'## The following objects are masked from 'package:stats':
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## filter, lag## The following objects are masked from 'package:base':
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## intersect, setdiff, setequal, unionload(system.file("extdata", "country_list.rda", package = "GSODR"))
load(system.file("extdata", "isd_history.rda", package = "GSODR"))
station_locations <- left_join(isd_history, country_list,
by = c("CTRY" = "FIPS"))
# create data.frame for Australia only
Oz <- subset(station_locations, COUNTRY_NAME == "AUSTRALIA")
Oz## # A tibble: 1,412 x 16
## USAF WBAN STN_NAME CTRY STATE CALL LAT LON ELEV_M BEGIN END
## <chr> <chr> <chr> <chr> <chr> <chr> <dbl> <dbl> <dbl> <int> <int>
## 1 6950… 99999 HORN IS… AS <NA> KQXC -10.6 142. NA 1.94e7 2.00e7
## 2 7494… 99999 AIDELAI… AS <NA> <NA> -13.3 131. 131 1.94e7 1.94e7
## 3 7494… 99999 BATCHEL… AS <NA> <NA> -13.0 131. 107 1.94e7 1.94e7
## 4 7494… 99999 IRON RA… AS <NA> <NA> -12.7 143. 18 1.94e7 1.94e7
## 5 7494… 99999 MAREEBA… AS <NA> <NA> -17.0 145. 443 1.94e7 1.94e7
## 6 7494… 99999 REID EA… AS <NA> <NA> -19.8 147. 122 1.94e7 1.94e7
## 7 7494… 99999 TOWNSVI… AS <NA> ABTL -19.2 147. 6 1.94e7 1.95e7
## 8 7494… 99999 WOODSTO… AS <NA> <NA> -19.6 147. 64 1.94e7 1.94e7
## 9 7494… 99999 JACKY J… AS <NA> <NA> -10.9 142. 14 1.94e7 1.94e7
## 10 7494… 99999 LAKE BU… AS <NA> <NA> -21.4 145. 396 1.94e7 1.94e7
## # … with 1,402 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> <int> <int>
## 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 <int>, END <int>, 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.
tbar_stations <- nearest_stations(LAT = -27.5598,
LON = 151.9507,
distance = 50)
tbar <- get_GSOD(years = 2010, station = tbar_stations)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.
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## Attaching package: 'lubridate'## The following object is masked from 'package:base':
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## datelibrary(tidyr)
# Create a dataframe of just the date and temperature values that we want to
# plot
tbar_temps <- tbar[, c("YEARMODA", "TEMP", "MAX", "MIN")]
# Gather the data from wide to long
tbar_temps <- gather(tbar_temps, Measurement, gather_cols = TEMP:MIN)
ggplot(data = tbar_temps, aes(x = ymd(YEARMODA), y = value,
colour = Measurement)) +
geom_line() +
scale_color_brewer(type = "qual", na.value = "black") +
scale_y_continuous(name = "Temperature") +
scale_x_date(name = "Date") +
theme_bw()
Download and Process Files in Parallel
GSODR supports the future package for parallel processing on varied platforms with the user determining the parallel backend to be used. The most simple way is to use multisession as an argument for future::plan(), which will default to available local cores as workers for the run. This can greatly reduce the time necessary to process GSOD files.
