Detailed Meteorological Data from Stations All Over the World.
stationaRy 
Overview
Get meteorological data from met stations located all over the world. That’s what you can do with this R package. There are LOTS of stations too (29,729 available in this dataset) and many have data that go pretty far back in time. The data comes from the Integrated Surface Dataset (ISD), which is maintained by the National Oceanic and Atmospheric Administration (NOAA).
Retrieving Met Data with a station_id
Let’s get some met data from La Guardia Airport in New York City (the station ID value is "725030-14732"). This station has a pretty long history (starting operations in 1973) but we’ll just obtain data from the years of 2017 and 2018.
lga_met_data <-
get_met_data(
station_id = "725030-14732",
years = 2017:2018
)
lga_met_data
#> # A tibble: 17,520 x 10
#> id time temp wd ws atmos_pres dew_point rh
#> <chr> <dttm> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
#> 1 7250… 2017-01-01 00:00:00 7.2 230 5.7 1012. -4.4 43.5
#> 2 7250… 2017-01-01 01:00:00 7.8 230 4.6 1012. -3.9 43.4
#> 3 7250… 2017-01-01 02:00:00 7.2 230 3.6 1012. -2.2 51.3
#> 4 7250… 2017-01-01 03:00:00 7.8 240 5.7 1013. -3.3 45.4
#> 5 7250… 2017-01-01 04:00:00 7.8 240 4.6 1013. -3.9 43.4
#> 6 7250… 2017-01-01 05:00:00 8.3 240 4.6 1014. -4.4 40.4
#> 7 7250… 2017-01-01 06:00:00 8.3 250 5.1 1015. -3.9 41.9
#> 8 7250… 2017-01-01 07:00:00 8.3 260 5.7 1016. -3.3 43.8
#> 9 7250… 2017-01-01 08:00:00 8.3 240 5.1 1017. -2.8 45.5
#> 10 7250… 2017-01-01 09:00:00 8.3 260 6.2 1019. -2.8 45.5
#> # … with 17,510 more rows, and 2 more variables: ceil_hgt <dbl>,
#> # visibility <dbl>
Discovering Met Stations
At a minimum we need a station’s identifier to obtain its met data. We can start the process of getting an identifier by accessing the entire catalog of station metadata with the get_station_metadata() function. The output tibble has station id values in the first column. Let’s get a subset of stations from that: those stations that are located in Norway.
stations_norway <-
get_station_metadata() %>%
dplyr::filter(country == "NO")
stations_norway
#> # A tibble: 405 x 16
#> id usaf wban name country state icao lat lon elev begin_date
#> <chr> <chr> <chr> <chr> <chr> <chr> <chr> <dbl> <dbl> <dbl> <date>
#> 1 0100… 0100… 99999 BOGU… NO <NA> ENRS NA NA NA 2001-09-27
#> 2 0100… 0100… 99999 JAN … NO <NA> ENJA 70.9 -8.67 9 1931-01-01
#> 3 0100… 0100… 99999 ROST NO <NA> <NA> NA NA NA 1986-11-20
#> 4 0100… 0100… 99999 SORS… NO <NA> ENSO 59.8 5.34 48.8 1986-11-20
#> 5 0100… 0100… 99999 BRIN… NO <NA> <NA> 61.4 5.87 327 1987-01-17
#> 6 0100… 0100… 99999 RORV… NO <NA> <NA> 64.8 11.2 14 1987-01-16
#> 7 0100… 0100… 99999 FRIGG NO <NA> ENFR 60.0 2.25 48 1988-03-20
#> 8 0100… 0100… 99999 VERL… NO <NA> <NA> 80.0 16.2 8 1986-11-09
#> 9 0100… 0100… 99999 HORN… NO <NA> <NA> 77 15.5 12 1985-06-01
#> 10 0100… 0100… 99999 NY-A… NO <NA> ENAS 78.9 11.9 8 1973-01-01
#> # … with 395 more rows, and 5 more variables: end_date <date>,
#> # begin_year <int>, end_year <int>, tz_name <chr>, years <list>
This table can be even more greatly reduced to isolate the stations of interest. For example, we could elect to get only high-altitude stations (above 1000 meters) in Norway.
norway_high_elev <-
stations_norway %>%
dplyr::filter(elev > 1000)
norway_high_elev
#> # A tibble: 12 x 16
#> id usaf wban name country state icao lat lon elev begin_date
#> <chr> <chr> <chr> <chr> <chr> <chr> <chr> <dbl> <dbl> <dbl> <date>
#> 1 0122… 0122… 99999 MANN… NO <NA> <NA> 62.4 7.77 1294 2010-03-15
#> 2 0123… 0123… 99999 HJER… NO <NA> <NA> 62.2 9.55 1012 2010-09-07
#> 3 0134… 0134… 99999 MIDT… NO <NA> <NA> 60.6 7.27 1162 2011-11-25
#> 4 0135… 0135… 99999 FINS… NO <NA> <NA> 60.6 7.53 1208 2003-03-30
#> 5 0135… 0135… 99999 FINS… NO <NA> <NA> 60.6 7.5 1224 1973-01-02
#> 6 0135… 0135… 99999 SAND… NO <NA> <NA> 60.2 7.48 1250 2004-01-07
#> 7 0136… 0136… 99999 JUVV… NO <NA> <NA> 61.7 8.37 1894 2009-06-26
#> 8 0136… 0136… 99999 SOGN… NO <NA> <NA> 61.6 8 1413 1979-03-01
#> 9 0137… 0137… 99999 KVIT… NO <NA> <NA> 61.5 10.1 1028 1973-01-01
#> 10 0143… 0143… 99999 MIDT… NO <NA> <NA> 59.8 6.98 1081 1973-01-01
#> 11 0144… 0144… 99999 BLAS… NO <NA> <NA> 59.3 6.87 1105. 1973-01-01
#> 12 0146… 0146… 99999 GAUS… NO <NA> <NA> 59.8 8.65 1804. 2014-06-05
#> # … with 5 more variables: end_date <date>, begin_year <int>,
#> # end_year <int>, tz_name <chr>, years <list>
The station IDs from the tibble can be transformed into a vector of station IDs with dplyr::pull().
norway_high_elev %>% dplyr::pull(id)
#> [1] "012200-99999" "012390-99999" "013460-99999" "013500-99999"
#> [5] "013510-99999" "013520-99999" "013620-99999" "013660-99999"
#> [9] "013750-99999" "014330-99999" "014400-99999" "014611-99999"
Suppose you’d like to collect several years of met data from a particular station and fetch only the observations that meet some set of conditions. Here’s an example of obtaining temperatures above 15 degrees Celsius from the high-altitude "JUVVASSHOE" station in Norway and adding a column with temperatures in degrees Fahrenheit.
station_data <-
get_station_metadata() %>%
dplyr::filter(name == "JUVVASSHOE") %>%
dplyr::pull(id) %>%
get_met_data(years = 2011:2019)
high_temp_data <-
station_data %>%
dplyr::select(id, time, wd, ws, temp) %>%
dplyr::filter(temp > 16) %>%
dplyr::mutate(temp_f = ((temp * (9/5)) + 32) %>% round(1)) %>%
dplyr::arrange(dplyr::desc(temp_f))
high_temp_data
#> # A tibble: 50 x 6
#> id time wd ws temp temp_f
#> <chr> <dttm> <dbl> <dbl> <dbl> <dbl>
#> 1 013620-99999 2019-07-26 15:00:00 160 5 18.5 65.3
#> 2 013620-99999 2019-07-26 17:00:00 210 3 18.4 65.1
#> 3 013620-99999 2019-07-26 18:00:00 180 2 18.3 64.9
#> 4 013620-99999 2019-07-26 16:00:00 180 4 18.2 64.8
#> 5 013620-99999 2014-07-23 16:00:00 270 2 17.6 63.7
#> 6 013620-99999 2019-07-26 14:00:00 150 4 17.5 63.5
#> 7 013620-99999 2014-07-23 17:00:00 300 4 17.3 63.1
#> 8 013620-99999 2019-07-28 16:00:00 130 6 17.3 63.1
#> 9 013620-99999 2014-07-23 18:00:00 280 3 17.2 63
#> 10 013620-99999 2018-07-04 15:00:00 340 2 17.2 63
#> # … with 40 more rows
Additional Data Fields
There can be a substantial amount of additional met data beyond wind speed, ambient temperature, etc. However, these additional fields can vary greatly across stations. The nomenclature for the additional categories of data uses ‘two-letter + digit’ identifiers (e.g., AA1, GA1, etc.). Within each category are numerous fields, where the variables are coded as [identifer]_[index]). More information about these additional data fields can be found in this PDF document.
To find out which categories of additional data fields are available for a station, we can use the station_coverage() function. You’ll get a tibble with the available additional categories and their counts over the specified period.
additional_data_fields <-
get_station_metadata() %>%
dplyr::filter(name == "JUVVASSHOE") %>%
dplyr::pull(id) %>%
station_coverage(years = 2015)
additional_data_fields
#> # A tibble: 87 x 3
#> id category count
#> <chr> <chr> <int>
#> 1 013620-99999 AA1 0
#> 2 013620-99999 AB1 0
#> 3 013620-99999 AC1 0
#> 4 013620-99999 AD1 0
#> 5 013620-99999 AE1 0
#> 6 013620-99999 AG1 0
#> 7 013620-99999 AH1 0
#> 8 013620-99999 AI1 0
#> 9 013620-99999 AJ1 194
#> 10 013620-99999 AK1 0
#> # … with 77 more rows
We can use purrr’s map_df() function to get additional data field coverage for a subset of stations (those that are near sea level and have data in 2019). With the station_coverage() function set to output tibbles in wide mode (one row per station, field categories as columns, and counts of observations as values), we can ascertain which stations have the particular fields we need.
stns <-
get_station_metadata() %>%
dplyr::filter(country == "NO", elev <= 5 & end_year == 2019)
coverage_tbl <-
purrr::map_df(
seq(nrow(stns)),
function(x) {
stns %>%
dplyr::pull(id) %>%
.[[x]] %>%
station_coverage(
years = 2019,
wide_tbl = TRUE
)
}
)
coverage_tbl
#> # A tibble: 16 x 88
#> id AA1 AB1 AC1 AD1 AE1 AG1 AH1 AI1 AJ1 AK1 AL1
#> <chr> <int> <int> <int> <int> <int> <int> <int> <int> <int> <int> <int>
#> 1 01023… 0 0 0 0 0 0 0 0 0 0 0
#> 2 01046… 0 0 0 0 0 0 0 0 0 0 0
#> 3 01049… 5810 0 0 0 0 0 0 0 0 0 0
#> 4 01107… 1048 0 0 0 0 0 0 0 0 0 0
#> 5 01139… 0 0 0 0 0 0 0 0 0 0 0
#> 6 01146… 6014 0 0 0 0 0 0 0 0 0 0
#> 7 01162… 1 0 0 0 0 0 0 0 0 0 0
#> 8 01167… 376 0 0 0 0 0 0 0 122 0 0
#> 9 01217… 0 0 0 0 0 0 0 0 0 0 0
#> 10 01225… 0 0 0 0 0 0 0 0 0 0 0
#> 11 01234… 1047 0 0 0 0 0 0 0 0 0 0
#> 12 01290… 0 0 0 0 0 0 0 0 0 0 0
#> 13 01332… 6288 0 0 0 0 0 0 0 0 0 0
#> 14 01355… 6125 0 0 0 0 0 0 0 0 0 0
#> 15 01467… 0 0 0 0 0 0 0 0 0 0 0
#> 16 01476… 0 0 0 0 0 0 0 0 0 0 0
#> # … with 76 more variables: AM1 <int>, AN1 <int>, AO1 <int>, AP1 <int>,
#> # AU1 <int>, AW1 <int>, AX1 <int>, AY1 <int>, AZ1 <int>, CB1 <int>,
#> # CF1 <int>, CG1 <int>, CH1 <int>, CI1 <int>, CN1 <int>, CN2 <int>,
#> # CN3 <int>, CN4 <int>, CR1 <int>, CT1 <int>, CU1 <int>, CV1 <int>,
#> # CW1 <int>, CX1 <int>, CO1 <int>, CO2 <int>, ED1 <int>, GA1 <int>,
#> # GD1 <int>, GF1 <int>, GG1 <int>, GH1 <int>, GJ1 <int>, GK1 <int>,
#> # GL1 <int>, GM1 <int>, GN1 <int>, GO1 <int>, GP1 <int>, GQ1 <int>,
#> # GR1 <int>, HL1 <int>, IA1 <int>, IA2 <int>, IB1 <int>, IB2 <int>,
#> # IC1 <int>, KA1 <int>, KB1 <int>, KC1 <int>, KD1 <int>, KE1 <int>,
#> # KF1 <int>, KG1 <int>, MA1 <int>, MD1 <int>, ME1 <int>, MF1 <int>,
#> # MG1 <int>, MH1 <int>, MK1 <int>, MV1 <int>, MW1 <int>, OA1 <int>,
#> # OB1 <int>, OC1 <int>, OE1 <int>, RH1 <int>, SA1 <int>, ST1 <int>,
#> # UA1 <int>, UG1 <int>, UG2 <int>, WA1 <int>, WD1 <int>, WG1 <int>
For the "KAWAIHAE" station in Hawaii, some interesting data fields are available. In particular, its SA1 category provides sea surface temperature data, where the sa1_1 and sa1_2 variables represent the sea surface temperature and its quality code.
Combining the use of get_met_data() with functions from dplyr, we can create a table of the mean ambient and sea-surface temperatures by month. The additional data is included in the met data table by using the add_fields argument and specifying the "SA1" category (multiple categories can be included).
kawaihae_sst <-
get_met_data(
station_id = "997173-99999",
years = 2017:2018,
add_fields = "SA1"
) %>%
dplyr::mutate(
year = lubridate::year(time),
month = lubridate::month(time)
) %>%
dplyr::filter(sa1_2 == 1) %>%
dplyr::group_by(year, month) %>%
dplyr::summarize(
avg_temp = mean(temp, na.rm = TRUE),
avg_sst = mean(sa1_1, na.rm = TRUE)
)
kawaihae_sst
#> # A tibble: 6 x 4
#> # Groups: year [2]
#> year month avg_temp avg_sst
#> <dbl> <dbl> <dbl> <dbl>
#> 1 2017 12 24.0 25.7
#> 2 2018 1 23.8 25.2
#> 3 2018 2 23.7 25.1
#> 4 2018 3 23.8 25.0
#> 5 2018 4 25.6 26.3
#> 6 2018 12 26.5 25.9
Installation
The stationaRy package can be easily installed from CRAN.
install.packages("stationaRy")
To install the development version of stationaRy, use the following:
install.packages("devtools")
remotes::install_github("rich-iannone/stationaRy")
If you encounter a bug, have usage questions, or want to share ideas to make this package better, feel free to file an issue.
License
MIT © Richard Iannone.