Using {worldmet} to Locate, Import & Visualise Meteorological Data
Source:vignettes/articles/worldmet.Rmd
worldmet.RmdShow all sites
The function to use to find which sites are available is
getMeta(). While this function can be run using different
options, it can be easiest to run it without any options. This produces
a map of all the available sites, which can be quickly accessed.
Click to expand the markers until you find the site of interest (shown by a blue marker). This reveals some site information including the site name and the start and end date of available data. The most important information revealed in the marker is the code, which is used to access the data.
getMeta()
#> # A tibble: 12,753 × 12
#> usaf wban station ctry st call latitude longitude `elev(m)` begin
#> <chr> <chr> <chr> <chr> <chr> <chr> <dbl> <dbl> <dbl> <date>
#> 1 0100… 99999 EDGEOYA NO NA NA 78.2 22.8 14 1973-01-01
#> 2 0100… 99999 NY-ALE… SV NA NA 78.9 11.9 7.7 1973-01-06
#> 3 0100… 99999 LONGYE… SV NA ENSB 78.2 15.5 26.8 1975-09-29
#> 4 0100… 99999 KARL X… SV NA NA 80.6 25 5 1955-01-01
#> 5 0101… 99999 ANDOYA NO NA ENAN 69.3 16.1 13.1 1931-01-03
#> 6 0101… 99999 KVITOYA SV NA NA 80.1 31.5 10 1986-11-18
#> 7 0101… 99999 HEKKIN… NO NA NA 69.6 17.8 14 1980-03-14
#> 8 0101… 99999 KONGSO… NO NA NA 78.9 28.9 20 1993-05-01
#> 9 0101… 99999 AKSELO… SV NA NA 77.7 14.8 6 1973-01-01
#> 10 0102… 99999 SORKAP… SV NA NA 76.5 16.6 10 2010-10-08
#> # ℹ 12,743 more rows
#> # ℹ 2 more variables: end <date>, code <chr>When using getMeta() it will probably be useful to read
the information into a data frame. Note that plot = FALSE
stops the function from providing the map.
met_info <- getMeta(plot = FALSE)Search based on latitude and longitude
Often, one has an idea of the region in which a site is of interest. For example, if the interest was in sites close to London, the latitude and longitude can be supplied and a search is carried out of the 10 nearest sites to that location. There is also an option n that can be used in change the number of nearest sites shown. If a lat/lon is provided, clicking on the blue marker will show the approximate distance between the site and the search coordinates.
getMeta(lat = 51.5, lon = 0)
#> # A tibble: 10 × 13
#> usaf wban station ctry st call latitude longitude `elev(m)` begin
#> <chr> <chr> <chr> <chr> <chr> <chr> <dbl> <dbl> <dbl> <date>
#> 1 0376… 99999 LONDON… GB NA EGLC 51.5 0.055 5.8 1988-01-29
#> 2 0377… 99999 ST JAM… UK NA NA 51.5 -0.117 5 2009-12-18
#> 3 0376… 99999 BIGGIN… UK NA EGKB 51.3 0.033 182. 1988-01-05
#> 4 0378… 99999 KENLEY… UK NA NA 51.3 -0.083 170 1988-02-01
#> 5 0367… 99999 NORTHO… UK NA EGWU 51.6 -0.418 37.8 1973-01-01
#> 6 0377… 99999 LONDON… GB NA EGLL 51.5 -0.461 25.3 1948-12-01
#> 7 0377… 99999 LONDON… GB NA EGKK 51.1 -0.19 61.6 1973-01-01
#> 8 0368… 99999 ROTHAM… UK NA NA 51.8 -0.358 128 2013-08-20
#> 9 0376… 99999 CHARLW… UK NA NA 51.1 -0.229 68 1992-03-01
#> 10 0368… 99999 LONDON… GB NA EGSS 51.9 0.235 106. 1973-01-01
#> # ℹ 3 more variables: end <date>, code <chr>, dist <dbl>Importing Data
To obtain the data the user must supply a code (see
above) and year(s) of interest. For example, to download data for
Heathrow Airport in 2010 (code 037720-99999):
met_london <- importNOAA(code = "037720-99999", year = 2010)
head(met_london)#> # A tibble: 6 × 25
#> code station date latitude longitude elev ws wd
#> <fct> <fct> <dttm> <dbl> <dbl> <dbl> <dbl> <dbl>
#> 1 037720-999… HEATHR… 2010-01-01 00:00:00 51.5 -0.461 25.3 3.27 17.4
#> 2 037720-999… HEATHR… 2010-01-01 01:00:00 51.5 -0.461 25.3 3.1 6.13
#> 3 037720-999… HEATHR… 2010-01-01 02:00:00 51.5 -0.461 25.3 3.1 15.6
#> 4 037720-999… HEATHR… 2010-01-01 03:00:00 51.5 -0.461 25.3 2.93 17.0
#> 5 037720-999… HEATHR… 2010-01-01 04:00:00 51.5 -0.461 25.3 2.77 0.606
#> 6 037720-999… HEATHR… 2010-01-01 05:00:00 51.5 -0.461 25.3 2.43 356.
#> # ℹ 17 more variables: air_temp <dbl>, atmos_pres <dbl>, visibility <dbl>,
#> # dew_point <dbl>, RH <dbl>, ceil_hgt <dbl>, cl_1 <dbl>, cl_2 <dbl>,
#> # cl_3 <dbl>, cl <dbl>, cl_1_height <dbl>, cl_2_height <dbl>,
#> # cl_3_height <dbl>, precip_12 <dbl>, precip_6 <dbl>, pwc <chr>, precip <dbl>A wind rose (for example) can easily be plotted using
openair:
# load openair
openair::windRose(met_london)
Parallel Processing
If you are importing a lot of meteorological data, this can take a
long while. This is because each combination of year and station
requires downloading a separate data file from NOAA’s online data
directory, and the time each download takes can quickly add up.
importNOAA() can use parallel processing to speed
downloading up, powered by the capable mirai package. If
users have any mirai “daemons” set,
importNOAA() will download files in parallel. The greatest
benefits will be seen if you spawn as many daemons as you have cores on
your machine, although one fewer than the available cores is often a
good rule of thumb. Your mileage may vary, however, and naturally
spawning more daemons than station-year combinations will lead to
diminishing returns.
# set workers - once per session
mirai::daemons(4)
# import lots of data - NB: no change in importNOAA()!
big_met <- importNOAA(code = "037720-99999", year = 2010:2020)Historically, parallelism in worldmet was achieved
with doParallel, foreach and
{parallel} and was activated using the n.cores
argument. While this will still work, it is recommended users set their
own daemons before using importNOAA(). This should avoid
any issues with mistakenly spawning too many daemons, or accidentally
spawning daemons within other daemons.