worldmet - R package for accessing NOAA Integrated Surface Database (ISD) meteorological observations

worldmet provides an easy way to access data from the NOAA Integrated Surface Database (ISD). The ISD contains detailed surface meteorological data from around the world for over 35,000 locations. See also the map.

The package outputs (typically hourly meteorological data) work very well with the openair package.

Installation

Installation can be done in the normal way:

install.packages("worldmet")

The development version can be installed from GitHub. Installation of worldmet from GitHub is easy using the devtools package.

# install.packages("devtools")
devtools::install_github("davidcarslaw/worldmet")

Example

To search for meteorological sites the user can search by the name or partial name of the site in upper or lower case. The getMeta() function will return all site names that match the search string. The most important information returned is the code, which can then be supplied to the importNOAA() function that downloads the data.

For example, to search for site “heathrow”:

library(worldmet)
## user getMeta function to search for sites
## note code to be used in importNOAA

getMeta(site = "heathrow")
#> # A tibble: 1 × 13
#>   usaf   wban  station  ctry  st    call  latitude longitude elev(m…¹ begin     
#>   <chr>  <chr> <chr>    <chr> <chr> <chr>    <dbl>     <dbl>    <dbl> <date>    
#> 1 037720 99999 HEATHROW UK    <NA>  EGLL      51.5    -0.461     25.3 1948-12-01
#> # … with 3 more variables: end <date>, code <chr>, dist <lgl>, and abbreviated
#> #   variable name ¹​`elev(m)`

Often we have a latitude / longitude of interest. A search can be made based on supplied decimal coordinates and the top n nearest sites are returned. The map shows the location searched by the user (red dot) and markers showing the nearest meteorological stations. Click on a station marker to obtain the code and other basic information.

## search for near a specified lat/lon - near Beijing airport
## returns 'n' nearest by default
info <- getMeta(lat = 40, lon = 116.9)
info
#> # A tibble: 10 × 15
#>    usaf   wban  station     ctry  st    call  latit…¹ longi…² elev(…³ begin     
#>    <chr>  <chr> <chr>       <chr> <chr> <chr>   <dbl>   <dbl>   <dbl> <date>    
#>  1 545110 99999 BEIJING - … CH    <NA>  ZBAA     40.1    117.    35.4 1945-10-31
#>  2 545270 99999 TIANJIN     CH    <NA>  <NA>     39.1    117.     5   1956-08-20
#>  3 545273 99999 BINHAI      CH    <NA>  ZBTJ     39.1    117.     3   1981-11-25
#>  4 545340 99999 TANGSHAN    CH    <NA>  <NA>     39.6    118.    29   1956-08-20
#>  5 544050 99999 HUAILAI     CH    <NA>  <NA>     40.4    116.   538   1956-08-20
#>  6 543080 99999 FENGNING    CH    <NA>  <NA>     41.2    117.   661   1957-06-01
#>  7 544230 99999 CHENGDE     CH    <NA>  <NA>     41.0    118.   423   1956-08-20
#>  8 544360 99999 QINGLONG    CH    <NA>  <NA>     40.4    119.   228   1957-06-02
#>  9 545390 99999 LETING      CH    <NA>  <NA>     39.4    119.    12   1957-06-01
#> 10 546020 99999 BAODING     CH    <NA>  <NA>     38.7    115.    17   1956-08-20
#> # … with 5 more variables: end <date>, code <chr>, longr <dbl>, latr <dbl>,
#> #   dist <dbl>, and abbreviated variable names ¹​latitude, ²​longitude,
#> #   ³​`elev(m)`

To obtain the data the user must supply a code (see above) and year or years 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                latit…¹ longi…²  elev    ws      wd air_t…³
#>   <fct>  <fct>   <dttm>                <dbl>   <dbl> <dbl> <dbl>   <dbl>   <dbl>
#> 1 03772… HEATHR… 2010-01-01 00:00:00    51.5  -0.461  25.3  3.27  17.4    1.03  
#> 2 03772… HEATHR… 2010-01-01 01:00:00    51.5  -0.461  25.3  3.1    6.13   0.967 
#> 3 03772… HEATHR… 2010-01-01 02:00:00    51.5  -0.461  25.3  3.1   15.6    1.03  
#> 4 03772… HEATHR… 2010-01-01 03:00:00    51.5  -0.461  25.3  2.93  17.0    1     
#> 5 03772… HEATHR… 2010-01-01 04:00:00    51.5  -0.461  25.3  2.77   0.606  0.267 
#> 6 03772… HEATHR… 2010-01-01 05:00:00    51.5  -0.461  25.3  2.43 356.     0.0667
#> # … with 16 more variables: 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>, and abbreviated variable names ¹​latitude, ²​longitude,
#> #   ³​air_temp

A wind rose (for example) can easily be plotted using openair:

# load openair
library(openair)
windRose(met_london)

For the main worldmet website, see https://davidcarslaw.github.io/worldmet/.