Introduction
The Iowa Environmental Mesonet (IEM) provides public access to many meteorological and hydrological observing networks. IEM groups stations into network identifiers such as IA_ASOS, IA_COOP, and many state or special-purpose networks.
preMetabolizer provides helpers for selected IEM API v1 endpoints:
-
iem_networks()lists available IEM network identifiers. -
iem_stations()retrieves station metadata for one network. -
iem_station()looks up one station identifier across networks. -
iem_current()retrieves current observations. -
iem_obhistory()retrieves one local day of station observations. -
iem_daily()retrieves daily summary observations.
Note: These functions contact IEM web services and require an internet connection. Code chunks that call the API will not run during package installation if the service is unreachable. Run them interactively in your own session.
Caching downloaded data
Repeated calls to the IEM API download the same data on every run. This vignette saves each result to a local cache directory the first time it is downloaded and reloads from disk on subsequent runs. The cache lives in tools::R_user_dir("preMetabolizer", which = "cache"), a platform-appropriate, user-specific directory that persists across sessions. Each data-fetching chunk below checks for a cached .rds file, downloads and saves on the first run, and reloads from disk on all subsequent runs.
Discover networks and stations
Start with iem_networks() when you need to find the exact network identifier for a station source. The network column is the value to pass to other IEM helpers.
cache_file <- file.path(cache_dir, "iem_networks.rds")
if (!file.exists(cache_file)) {
networks <- iem_networks()
saveRDS(networks, cache_file)
} else {
networks <- readRDS(cache_file)
}
networks |>
filter(grepl("Iowa", network_name)) |>
select(network, network_name, tzname)
#> # A tibble: 9 × 3
#> network network_name tzname
#> <chr> <chr> <chr>
#> 1 IA_ASOS Iowa ASOS America/Chicago
#> 2 IACLIMATE Iowa Long Term Climate Sites America/Chicago
#> 3 IA_COCORAHS Iowa CoCoRaHS America/Chicago
#> 4 IA_COOP Iowa COOP America/Chicago
#> 5 IA_DCP Iowa DCP America/Chicago
#> 6 IA_HPD Iowa HPD Sites America/Chicago
#> 7 IA_RWIS Iowa RWIS America/Chicago
#> 8 ISUAG Iowa State Univ Ag Climate America/Chicago
#> 9 ISUSM Iowa State Univ Soil Moisture America/ChicagoStation metadata for a network includes station identifiers, names, coordinates, archive dates, and the station time zone when IEM provides it.
cache_file <- file.path(cache_dir, "iem_stations_ia_asos.rds")
if (!file.exists(cache_file)) {
ia_asos <- iem_stations("IA_ASOS")
saveRDS(ia_asos, cache_file)
} else {
ia_asos <- readRDS(cache_file)
}
ia_asos |>
select(station_id, station_name, network, tzname, archive_begin, longitude, latitude) |>
arrange(station_id)
#> # A tibble: 62 × 7
#> station_id station_name network tzname archive_begin longitude latitude
#> <chr> <chr> <chr> <chr> <dttm> <dbl> <dbl>
#> 1 ADU AUDUBON IA_ASOS Ameri… 1994-12-25 00:00:00 -94.9 41.7
#> 2 AIO ATLANTIC IA_ASOS Ameri… 1995-04-23 00:00:00 -95.0 41.4
#> 3 ALO Waterloo IA_ASOS Ameri… 1949-01-01 00:00:00 -92.4 42.6
#> 4 AMW Ames IA_ASOS Ameri… 1996-09-30 00:00:00 -93.6 42.0
#> 5 AWG WASHINGTON IA_ASOS Ameri… 1994-12-25 00:00:00 -91.7 41.3
#> 6 AXA Algona IA_ASOS Ameri… 1994-12-25 00:00:00 -94.3 43.1
#> 7 BNW BOONE MUNI IA_ASOS Ameri… 1995-03-01 00:00:00 -93.8 42.0
#> 8 BRL BURLINGTON IA_ASOS Ameri… 1931-09-01 00:00:00 -91.1 40.8
#> 9 CAV CLARION IA_ASOS Ameri… 1994-12-25 00:00:00 -93.8 42.7
#> 10 CBF COUNCIL BLU… IA_ASOS Ameri… 1994-12-25 00:00:00 -95.8 41.3
#> # ℹ 52 more rowsSome station identifiers appear in more than one network. Use iem_station() when you want to check how IEM resolves a station ID before requesting data.
cache_file <- file.path(cache_dir, "iem_station_amw.rds")
if (!file.exists(cache_file)) {
amw <- iem_station("AMW")
saveRDS(amw, cache_file)
} else {
amw <- readRDS(cache_file)
}
amw |>
select(station_id, station_name, network, tzname, archive_begin)
#> # A tibble: 2 × 5
#> station_id station_name network tzname archive_begin
#> <chr> <chr> <chr> <chr> <dttm>
#> 1 AMW Ames IA_ASOS America/Chicago 1996-09-30 00:00:00
#> 2 AMW AMES MUNICIPAL AP IA_DCP America/Chicago 2010-12-28 00:00:00Retrieve current observations
iem_current() requires at least one filter. You can request all current observations in a network, stations within a network, stations by forecast office, or one or more station identifiers.
cache_file <- file.path(cache_dir, "iem_current_ia_asos.rds")
if (!file.exists(cache_file)) {
current <- iem_current(network = "IA_ASOS")
saveRDS(current, cache_file)
} else {
current <- readRDS(cache_file)
}
current |>
select(station_id, station_name, utc_valid, tmpf, dwpf, sknt, drct, mslp) |>
arrange(station_id)
#> # A tibble: 60 × 8
#> station_id station_name utc_valid tmpf dwpf sknt drct mslp
#> <chr> <chr> <dttm> <dbl> <dbl> <dbl> <dbl> <dbl>
#> 1 ADU AUDUBON 2026-05-11 00:55:00 64.4 28.4 4 330 NA
#> 2 AIO ATLANTIC 2026-05-11 00:55:00 66.2 35.6 7 330 NA
#> 3 ALO Waterloo 2026-05-11 00:54:00 62 27 10 360 1019.
#> 4 AMW Ames 2026-05-11 00:53:00 67 28 10 310 1018.
#> 5 AWG WASHINGTON 2026-05-11 00:55:00 64.4 30.2 7 230 NA
#> 6 AXA Algona 2026-05-11 00:55:00 60.8 26.6 11 360 NA
#> 7 BNW BOONE MUNI 2026-05-11 00:55:00 66.2 30.2 7 300 NA
#> 8 BRL BURLINGTON 2026-05-11 00:53:00 61 35 5 240 1020.
#> 9 CAV CLARION 2026-05-11 00:55:00 64.4 23 10 340 NA
#> 10 CBF COUNCIL BLUFFS 2026-05-11 00:55:00 68 33.8 0 0 NA
#> # ℹ 50 more rowsFor station-specific work, include both network and stations so shared station identifiers do not surprise you.
cache_file <- file.path(cache_dir, "iem_current_dsm_amw.rds")
if (!file.exists(cache_file)) {
dsm_current <- iem_current(
network = "IA_ASOS",
stations = c("DSM", "AMW"),
minutes = 120
)
saveRDS(dsm_current, cache_file)
} else {
dsm_current <- readRDS(cache_file)
}
dsm_current |>
select(station_id, station_name, utc_valid, tmpf, relh, sknt, pres)
#> # A tibble: 2 × 7
#> station_id station_name utc_valid tmpf relh sknt pres
#> <chr> <chr> <dttm> <dbl> <dbl> <dbl> <lgl>
#> 1 AMW Ames 2026-05-11 00:53:00 67 23.0 10 NA
#> 2 DSM Des Moines 2026-05-11 00:54:00 67 26.0 11 NARetrieve one day of observations
iem_obhistory() returns observations for a single station and local station date. UTC timestamp columns are parsed as UTC POSIXct values. Local timestamp columns are returned as character values because station time zones vary across networks.
cache_file <- file.path(cache_dir, "iem_obhistory_dsm_2024-06-01.rds")
if (!file.exists(cache_file)) {
obs <- iem_obhistory(
station = "DSM",
network = "IA_ASOS",
date = "2024-06-01"
)
saveRDS(obs, cache_file)
} else {
obs <- readRDS(cache_file)
}
obs |>
select(utc_valid, local_valid, tmpf, dwpf, sknt, drct, p01_i)
#> # A tibble: 297 × 7
#> utc_valid local_valid tmpf dwpf sknt drct p01_i
#> <dttm> <chr> <dbl> <dbl> <dbl> <dbl> <dbl>
#> 1 2024-06-01 05:00:00 2024-06-01T00:00 NA NA 5 140 NA
#> 2 2024-06-01 05:05:00 2024-06-01T00:05 NA NA 5 140 NA
#> 3 2024-06-01 05:10:00 2024-06-01T00:10 NA NA 5 140 NA
#> 4 2024-06-01 05:15:00 2024-06-01T00:15 NA NA 5 140 NA
#> 5 2024-06-01 05:20:00 2024-06-01T00:20 NA NA 5 130 NA
#> 6 2024-06-01 05:25:00 2024-06-01T00:25 NA NA 5 130 NA
#> 7 2024-06-01 05:30:00 2024-06-01T00:30 NA NA 5 120 NA
#> 8 2024-06-01 05:35:00 2024-06-01T00:35 NA NA 5 110 NA
#> 9 2024-06-01 05:40:00 2024-06-01T00:40 NA NA 4 100 NA
#> 10 2024-06-01 05:45:00 2024-06-01T00:45 NA NA 5 130 NA
#> # ℹ 287 more rowsHourly or sub-hourly records can be plotted directly after selecting the variables you need.
Retrieve daily summaries
Use iem_daily() for daily summary observations. Provide either one date, or provide year and optional month for a longer request.
cache_file <- file.path(cache_dir, "iem_daily_dsm_2024-06.rds")
if (!file.exists(cache_file)) {
dsm_daily <- iem_daily(
network = "IA_ASOS",
station = "DSM",
year = 2024,
month = 6
)
saveRDS(dsm_daily, cache_file)
} else {
dsm_daily <- readRDS(cache_file)
}
dsm_daily |>
select(station_id, date, max_tmpf, min_tmpf, precip) |>
arrange(date)
#> # A tibble: 30 × 5
#> station_id date max_tmpf min_tmpf precip
#> <chr> <date> <dbl> <dbl> <dbl>
#> 1 DSM 2024-06-01 82 63 0
#> 2 DSM 2024-06-02 83 63 0
#> 3 DSM 2024-06-03 88 71 0
#> 4 DSM 2024-06-04 84 63 0.63
#> 5 DSM 2024-06-05 85 56 0.0001
#> 6 DSM 2024-06-06 83 62 0
#> 7 DSM 2024-06-07 83 58 0.02
#> 8 DSM 2024-06-08 80 59 0
#> 9 DSM 2024-06-09 84 56 0
#> 10 DSM 2024-06-10 76 52 0
#> # ℹ 20 more rowsFor a one-day network request, omit station and supply date.
cache_file <- file.path(cache_dir, "iem_daily_ia_asos_2024-06-01.rds")
if (!file.exists(cache_file)) {
ia_day <- iem_daily("IA_ASOS", date = "2024-06-01")
saveRDS(ia_day, cache_file)
} else {
ia_day <- readRDS(cache_file)
}
ia_day |>
select(station_id, date, max_tmpf, min_tmpf, precip) |>
arrange(desc(date))
#> # A tibble: 61 × 5
#> station_id date max_tmpf min_tmpf precip
#> <chr> <date> <dbl> <dbl> <dbl>
#> 1 ADU 2024-06-01 77 59 0.05
#> 2 AIO 2024-06-01 77 62.6 0
#> 3 ALO 2024-06-01 78 61 0
#> 4 AMW 2024-06-01 81 60 0.13
#> 5 AWG 2024-06-01 69.8 59 NA
#> 6 AXA 2024-06-01 75.2 60.8 0.04
#> 7 BNW 2024-06-01 77 62.6 0.14
#> 8 BRL 2024-06-01 71 57 0.47
#> 9 CAV 2024-06-01 77 60.8 NA
#> 10 CBF 2024-06-01 77 59 NA
#> # ℹ 51 more rowsPractical notes
IEM combines many observing networks, and each network can have different available variables, units, reporting intervals, and station identifiers. A good workflow is to first inspect iem_networks() and iem_stations(), then request observations from a specific network and station pair.