Type:	Package
Title:	Import Surface Meteorological Data from NOAA
Version:	1.0.0
Description:	Functions to import data from more than 30,000 surface meteorological sites around the world managed by the National Oceanic and Atmospheric Administration (NOAA) Global Historical Climate Network (GHCN) and Integrated Surface Database (ISD).
License:	MIT + file LICENSE
URL:	https://openair-project.github.io/worldmet/, https://github.com/openair-project/worldmet
BugReports:	https://github.com/openair-project/worldmet/issues
Depends:	R (≥ 4.1.0)
Imports:	carrier, cli, dplyr, lifecycle, mirai, purrr (≥ 1.1.0), readr, rlang, sf, tibble, tidyr
Suggests:	arrow, knitr, leaflet, rmarkdown
ByteCompile:	true
Config/Needs/website:	openair-project/openairpkgdown,openair
Encoding:	UTF-8
Language:	en-GB
LazyData:	true
LazyLoad:	true
RoxygenNote:	7.3.3
NeedsCompilation:	no
Packaged:	2026-02-02 11:41:20 UTC; JD38
Author:	David Carslaw [aut, cre], Jack Davison [aut]
Maintainer:	David Carslaw <david.carslaw@york.ac.uk>
Repository:	CRAN
Date/Publication:	2026-02-02 14:40:02 UTC

worldmet: Import Surface Meteorological Data from NOAA

Description

Functions to import data from more than 30,000 surface meteorological sites around the world managed by the National Oceanic and Atmospheric Administration (NOAA) Global Historical Climate Network (GHCN) and Integrated Surface Database (ISD).

Details

This package contains functions to import surface meteorological data from over 30,000 sites around the world. These data are curated by NOAA as part of the Global Historical Climate Network (GHCN), which replaced the Integrated Surface Database (ISD) in 2025.

If you access these data using the worldmet package please give full acknowledgement to NOAA. Users should also take a note of the usage restrictions.

These data work well with the openair package that has been developed to analyse air pollution data.

Author(s)

Maintainer: David Carslaw david.carslaw@york.ac.uk (ORCID)

Authors:

• Jack Davison jack.davison@ricardo.com (ORCID)

References

For general information about the GHCNh, see https://www.ncei.noaa.gov/products/global-historical-climatology-network-hourly.

For general information about the legacy ISD service, see https://www.ncei.noaa.gov/products/land-based-station/integrated-surface-database.

See Also

See https://github.com/openair-project/openair for information on the related openair package.

Deprecated data functions

Description

This function is part of an old worldmet API. Please use the following alternatives:

• exportADMS() -> write_adms()

• the path argument -> write_met()

Usage

exportADMS(dat, out = "./ADMS_met.MET", interp = FALSE, maxgap = 2)

Arguments

Deprecated ISD access functions

Description

This function is part of an old worldmet API. Please use the following alternatives:

• getMeta() -> import_isd_stations()

• getMetaLive() -> import_isd_stations_live()

• importNOAA() -> import_isd_hourly()

• importNOAAlite() -> import_isd_lite()

• the path argument -> write_met()

Usage

getMeta(
  site = NULL,
  lat = NULL,
  lon = NULL,
  crs = 4326,
  country = NULL,
  state = NULL,
  n = 10,
  end.year = "current",
  provider = c("OpenStreetMap", "Esri.WorldImagery"),
  plot = TRUE,
  returnMap = FALSE
)

getMetaLive(...)

importNOAA(
  code = "037720-99999",
  year = 2014,
  hourly = TRUE,
  source = c("delim", "fwf"),
  quiet = FALSE,
  path = NA,
  n.cores = NULL
)

importNOAAlite(code = "037720-99999", year = 2025, quiet = FALSE, path = NA)

Arguments

Value

A data frame is returned with all available meta data, mostly importantly including a code that can be supplied to importNOAA(). If latitude and longitude searches are made an approximate distance, dist in km is also returned.

Import FIPS country codes and State/Province/Territory codes used by the Global Historical Climatology Network

Description

This function returns a two-column dataframe either of "Federal Information Processing Standards" (FIPS) codes and the countries to which they are associated, or state codes and their associated states. These may be a useful reference when examining GHCN site metadata.

Usage

import_ghcn_countries(
  table = c("countries", "states"),
  database = c("hourly", "daily", "monthly")
)

Arguments

Value

a tibble

Author(s)

Jack Davison

See Also

Other GHCN functions: import_ghcn_daily(), import_ghcn_hourly(), import_ghcn_inventory(), import_ghcn_monthly_temp(), import_ghcn_stations()

Import data from the Global Historical Climatology daily (GHCNd) database

Description

This function flexibly accesses meteorological data from the GHCNd database. Users can provide any of stations, and control whether attribute codes are returned with the data.

Usage

import_ghcn_daily(
  station,
  year = NULL,
  source = c("csv"),
  extra = FALSE,
  append_codes = FALSE,
  codes = c("measurement_flag", "quality_flag", "source_flag"),
  progress = rlang::is_interactive()
)

Arguments

Value

a tibble

Data Definition

The core elements in the GHCNd are:

• PRCP: Precipitation (mm)

• SNOW: Snowfall (mm)

• SNWD: Snow depth (mm)

• TMAX: Maximum temperature (degrees C)

• TMIN: Minimum temperature (degrees C)

Other elements which may appear are:

• ACMC: Average cloudiness midnight to midnight from 30-second ceilometer data (percent)

• ACMH: Average cloudiness midnight to midnight from manual observations (percent)

• ACSC: Average cloudiness sunrise to sunset from 30-second ceilometer data (percent)

• ACSH: Average cloudiness sunrise to sunset from manual observations (percent)

• ADPT: Average Dew Point Temperature for the day (degrees C)

• ASLP: Average Sea Level Pressure for the day (hPa)

• ASTP: Average Station Level Pressure for the day (hPa)

• AWBT: Average Wet Bulb Temperature for the day (degrees C)

• AWDR: Average daily wind direction (degrees)

• AWND: Average daily wind speed (meters per second)

• DAEV: Number of days included in the multiday evaporation total (MDEV)

• DAPR: Number of days included in the multiday precipitation total (MDPR)

• DASF: Number of days included in the multiday snowfall total (MDSF)

• DATN: Number of days included in the multiday minimum temperature (MDTN)

• DATX: Number of days included in the multiday maximum temperature (MDTX)

• DAWM: Number of days included in the multiday wind movement (MDWM)

• DWPR: Number of days with non-zero precipitation included in multiday precipitation total (MDPR)

• EVAP: Evaporation of water from evaporation pan (mm)

• FMTM: Time of fastest mile or fastest 1-minute wind (hours and minutes, i.e., HHMM)

• FRGB: Base of frozen ground layer (cm)

• FRGT: Top of frozen ground layer (cm)

• FRTH: Thickness of frozen ground layer (cm)

• GAHT: Difference between river and gauge height (cm)

• MDEV: Multiday evaporation total (mm; use with DAEV)

• MDPR: Multiday precipitation total (mm; use with DAPR and DWPR, if available)

• MDSF: Multiday snowfall total

• MDTN: Multiday minimum temperature (degrees C; use with DATN)

• MDTX: Multiday maximum temperature (degrees C; use with DATX)

• MDWM: Multiday wind movement (km)

• MNPN: Daily minimum temperature of water in an evaporation pan (degrees C)

• MXPN: Daily maximum temperature of water in an evaporation pan (degrees C)

• PGTM: Peak gust time (hours and minutes, i.e., HHMM)

• PSUN: Daily percent of possible sunshine (percent)

• RHAV: Average relative humidity for the day (percent)

• RHMN: Minimum relative humidity for the day (percent)

• RHMX: Maximum relative humidity for the day (percent)

• SN$#: Minimum soil temperature (degrees C) where $ corresponds to a code for ground cover and # corresponds to a code for soil depth. Ground cover codes: 0=unknown, 1=grass, 2=fallow, 3=bare ground, 4=brome grass, 5=sod, 6=straw mulch, 7=grass muck, 8=bare muck. Depth codes: 1=5cm, 2=10cm, 3=20cm, 4=50cm, 5=100cm, 6=150cm, 7=180cm

• SX$#: Maximum soil temperature (degrees C) where $ corresponds to a code for ground cover and # corresponds to a code for soil depth. See SN$# for ground cover and depth codes

• TAXN: Average daily temperature computed as (TMAX+TMIN)/2.0 (degrees C)

• TAVG: Average daily temperature (degrees C). Note that TAVG from source 'S' corresponds to an average of hourly readings for the period ending at 2400 UTC rather than local midnight or other Local Standard Time according to a specific Met Service's protocol. For sources other than 'S' TAVG is computed in a variety of ways including traditional fixed hours of the day whereas TAXN is solely computed as (TMAX+TMIN)/2.0

• THIC: Thickness of ice on water (mm)

• TOBS: Temperature at the time of observation (degrees C)

• TSUN: Daily total sunshine (minutes)

• WDF1: Direction of fastest 1-minute wind (degrees)

• WDF2: Direction of fastest 2-minute wind (degrees)

• WDF5: Direction of fastest 5-second wind (degrees)

• WDFG: Direction of peak wind gust (degrees)

• WDFI: Direction of highest instantaneous wind (degrees)

• WDFM: Fastest mile wind direction (degrees)

• WDMV: 24-hour wind movement (km)

• WESD: Water equivalent of snow on the ground (mm)

• WESF: Water equivalent of snowfall (mm)

• WSF1: Fastest 1-minute wind speed (meters per second)

• WSF2: Fastest 2-minute wind speed (meters per second)

• WSF5: Fastest 5-second wind speed (meters per second)

• WSFG: Peak gust wind speed (meters per second)

• WSFI: Highest instantaneous wind speed (meters per second)

• WSFM: Fastest mile wind speed (meters per second)

There can be any number of weather types (WT$$)

• WT01: Fog, ice fog, or freezing fog (may include heavy fog)

• WT02: Heavy fog or heaving freezing fog (not always distinguished from fog)

• WT03: Thunder

• WT04: Ice pellets, sleet, snow pellets, or small hail

• WT05: Hail (may include small hail)

• WT06: Glaze or rime

• WT07: Dust, volcanic ash, blowing dust, blowing sand, or blowing obstruction

• WT08: Smoke or haze

• WT09: Blowing or drifting snow

• WT10: Tornado, waterspout, or funnel cloud

• WT11: High or damaging winds

• WT12: Blowing spray

• WT13: Mist

• WT14: Drizzle

• WT15: Freezing drizzle

• WT16: Rain (may include freezing rain, drizzle, and freezing drizzle)

• WT17: Freezing rain

• WT18: Snow, snow pellets, snow grains, or ice crystals

• WT19: Unknown source of precipitation

• WT21: Ground fog

• WT22: Ice fog or freezing fog

There can also be any number of 'weather in the vicinity' columns (WV$$)

• WV01: Fog, ice fog, or freezing fog (may include heavy fog)

• WV03: Thunder

• WV07: Ash, dust, sand, or other blowing obstruction

• WV18: Snow or ice crystals

• WV20: Rain or snow shower

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. Many data import functions in {worldmet} can use parallel processing to speed downloading up, powered by the capable {mirai} package. If users have any {mirai} "daemons" set, these functions 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 the import function!
big_met <- import_ghcn_hourly(code = "UKI0000EGLL", year = 2010:2025)

Author(s)

Jack Davison

See Also

Other GHCN functions: import_ghcn_countries(), import_ghcn_hourly(), import_ghcn_inventory(), import_ghcn_monthly_temp(), import_ghcn_stations()

Import data from the Global Historical Climatology hourly (GHCNh) database

Description

This function flexibly accesses meteorological data from the GHCNh database. Users can provide any number of years and stations, and fully control the sorts of data flag codes that are returned with the data. By default, column names are shortened for easier use in R, but longer, more descriptive names can be requested.

Usage

import_ghcn_hourly(
  station = "UKI0000EGLL",
  year = NULL,
  source = c("psv", "parquet"),
  hourly = TRUE,
  extra = FALSE,
  abbr_names = TRUE,
  append_codes = FALSE,
  codes = c("measurement_code", "quality_code", "report_type", "source_code",
    "source_id"),
  progress = rlang::is_interactive()
)

Arguments

Value

a tibble

Data Definition

The following core elements are in the GHCNh:

• wind_direction: (wd) Wind direction from true north (degrees). 360 = north, 180 = south, 270 = west; 000 indicates calm winds.

• wind_speed: (ws) Average wind speed (m/s).

• temperature: (air_temp) Air (dry bulb) temperature at approximately 2 meters above ground level, in degrees Celsius (to tenths).

• station_level_pressure: (atmos_pres) Pressure observed at station elevation; true barometric pressure at the location (hPa).

• visibility: (visibility) Horizontal visibility distance (km).

• dew_point_temperature: (dew_point) Dew point temperature in degrees Celsius (to tenths).

• relative_humidity: (rh) Relative humidity in whole percent, measured or calculated from temperature and dew point.

• sky_cover: (cl) Maximum of all sky_cover_X elements (oktas).

• sky_cover_baseht: (cl_baseht) The height above ground level (AGL) of the lowest cloud or obscuring phenomena layer aloft with 5/8 or more summation total sky cover, which may be predominantly opaque, or the vertical visibility into a surface-based obstruction.

• sky_cover_X: (cl_X) Fraction of sky covered by clouds (oktas). Up to 3 layers reported.

• sky_cover_baseht_X: (cl_baseht_X) Cloud base height for lowest layer (m). Up to 3 layers reported.

• precipitation: (precip) Total liquid precipitation (rain or melted snow) for the hour (mm). "T" indicates trace amounts.

• precipitation_XX_hour: (precip_XX) 3-hour total liquid precipitation (mm). "T" indicates trace amounts.

When extra = TRUE, the following additional columns are included:

• sea_level_pressure: (sea_pres) Estimated pressure at sea level directly below the station using actual temperature profile (hPa).

• wind_gust: (wg) Peak short-duration wind speed (m/s) exceeding the average wind speed.

• wet_bulb_temperature: (wet_bulb) Wet bulb temperature in degrees Celsius (to tenths), measured or calculated from temperature, dew point, and station pressure.

• snow_depth: (snow_depth) Depth of snowpack on the ground (mm).

• altimeter: (altimeter) Pressure reduced to mean sea level using standard atmosphere profile (hPa).

• pressure_3hr_change: (pres_03) Change in atmospheric pressure over a 3-hour period (hPa), with tendency code.

If hourly = FALSE, the following character columns may also be present.

• pres_wx_MWX: (wx_mwX) Present weather observation from manual reports (code). Up to 3 observations per report.

• pres_wx_AUX: (wx_auX) Present weather observation from automated ASOS/AWOS sensors (code). Up to 3 observations per report.

• pres_wx_AWX: (wx_aqX) Present weather observation from automated sensors (code). Up to 3 observations per report.

• remarks: (remarks) Raw observation remarks encoded in ICAO-standard METAR/SYNOP format.

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. Many data import functions in {worldmet} can use parallel processing to speed downloading up, powered by the capable {mirai} package. If users have any {mirai} "daemons" set, these functions 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 the import function!
big_met <- import_ghcn_hourly(code = "UKI0000EGLL", year = 2010:2025)

Author(s)

Jack Davison

See Also

Other GHCN functions: import_ghcn_countries(), import_ghcn_daily(), import_ghcn_inventory(), import_ghcn_monthly_temp(), import_ghcn_stations()

Import station inventory for the Global Historical Climatology Network

Description

This function accesses a data inventory of GHCN stations available through either the GHCNh or GHCNd. The returned data.frame contains data which reveals the earliest and latest years of data available for each station from the NOAA database.

Usage

import_ghcn_inventory(
  database = c("hourly", "daily"),
  pivot = c("wide", "long"),
  progress = rlang::is_interactive()
)

Arguments

Value

a tibble

Author(s)

Jack Davison

See Also

Other GHCN functions: import_ghcn_countries(), import_ghcn_daily(), import_ghcn_hourly(), import_ghcn_monthly_temp(), import_ghcn_stations()

Import data from the Global Historical Climatology monthly (GHCNm) database

Description

This function is a convenient way to access the monthly summaries of the GHCN. Monthly average temperature is available via import_ghcn_monthly_temp() and monthly precipitation via import_ghcn_monthly_prcp(). Note that these functions can take a few minutes to run, and parallelism is only enabled for precipitation data.

Usage

import_ghcn_monthly_temp(
  table = c("inventory", "data"),
  dataset = c("qcu", "qcf", "qfe")
)

import_ghcn_monthly_prcp(
  station = NULL,
  year = NULL,
  table = c("inventory", "data"),
  progress = rlang::is_interactive()
)

Arguments

Value

a list of tibbles

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. Many data import functions in {worldmet} can use parallel processing to speed downloading up, powered by the capable {mirai} package. If users have any {mirai} "daemons" set, these functions 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 the import function!
big_met <- import_ghcn_hourly(code = "UKI0000EGLL", year = 2010:2025)

Author(s)

Jack Davison

See Also

Other GHCN functions: import_ghcn_countries(), import_ghcn_daily(), import_ghcn_hourly(), import_ghcn_inventory(), import_ghcn_stations()

Import station metadata for the Global Historical Climatology Network

Description

This function accesses a full list of GHCN stations available through either the GHCNh or GHCNd. As well as the station id, needed for importing measurement data, useful geographic and network metadata is also returned.

Usage

import_ghcn_stations(
  name = NULL,
  country = NULL,
  state = NULL,
  lat = NULL,
  lng = NULL,
  crs = 4326,
  n_max = 10L,
  database = c("hourly", "daily"),
  return = c("table", "sf", "map")
)

Arguments

Value

One of:

• a tibble

• an sf object

• an interactive leaflet map

Author(s)

Jack Davison

See Also

Other GHCN functions: import_ghcn_countries(), import_ghcn_daily(), import_ghcn_hourly(), import_ghcn_inventory(), import_ghcn_monthly_temp()

Import Meteorological data from the NOAA Integrated Surface Database (ISD)

Description

This is the main function to import data from the NOAA Integrated Surface Database (ISD). The ISD contains detailed surface meteorological data from around the world for over 30,000 locations.

Usage

import_isd_hourly(
  code = "037720-99999",
  year = 2014,
  hourly = TRUE,
  source = c("delim", "fwf"),
  progress = rlang::is_interactive(),
  quiet = FALSE
)

Arguments

Details

Note the following units for the main variables:

date

Date/time in POSIXct format. Note the time zone is GMT (UTC) and may need to be adjusted to merge with other local data. See details below.

latitude

Latitude in decimal degrees (-90 to 90).

longitude

Longitude in decimal degrees (-180 to 180). Negative numbers are west of the Greenwich Meridian.

elevation

Elevation of site in metres.

wd

Wind direction in degrees. 90 is from the east.

ws

Wind speed in m/s.

ceil_hgt

The height above ground level (AGL) of the lowest cloud or obscuring phenomena layer aloft with 5/8 or more summation total sky cover, which may be predominantly opaque, or the vertical visibility into a surface-based obstruction.

visibility

The visibility in metres.

air_temp

Air temperature in degrees Celcius.

dew_point

The dew point temperature in degrees Celcius.

atmos_pres

The sea level pressure in millibars.

RH

The relative humidity (%).

cl_1, ..., cl_3

Cloud cover for different layers in Oktas (1-8).

cl

Maximum of cl_1 to cl_3 cloud cover in Oktas (1-8).

cl_1_height, ..., cl_3_height

Height of the cloud base for each later in metres.

precip_12

12-hour precipitation in mm. The sum of this column should give the annual precipitation.

precip_6

6-hour precipitation in mm.

precip

This value of precipitation spreads the 12-hour total across the previous 12 hours.

pwc

The description of the present weather description (if available).

The data are returned in GMT (UTC). It may be necessary to adjust the time zone when combining with other data. For example, if air quality data were available for Beijing with time zone set to "Etc/GMT-8" (note the negative offset even though Beijing is ahead of GMT. See the openair package and manual for more details), then the time zone of the met data can be changed to be the same. One way of doing this would be attr(met$date, "tzone") <- "Etc/GMT-8" for a meteorological data frame called met. The two data sets could then be merged based on date.

Value

Returns a data frame of surface observations. The data frame is consistent for use with the openair package. Note that the data are returned in GMT (UTC) time zone format. Users may wish to express the data in other time zones, e.g., to merge with air pollution data.

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. Many data import functions in {worldmet} can use parallel processing to speed downloading up, powered by the capable {mirai} package. If users have any {mirai} "daemons" set, these functions 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 the import function!
big_met <- import_ghcn_hourly(code = "UKI0000EGLL", year = 2010:2025)

Author(s)

David Carslaw

See Also

Other NOAA ISD functions: import_isd_lite(), import_isd_stations(), import_isd_stations_live()

Examples

## Not run: 
# import some data
beijing_met <- import_isd_hourly(code = "545110-99999", year = 2010:2011)

# importing lots of data? use mirai for parallel processing
mirai::daemons(4)
beijing_met2 <- import_isd_hourly(code = "545110-99999", year = 2010:2025)

## End(Not run)

Import "Lite" Meteorological data from the NOAA Integrated Surface Database (ISD)

Description

This function is an alternative to importNOAA(), and provides access to the "Lite" format of the data. This a subset of the larger importNOAA() dataset featuring eight common climatological variables. As it assigns the nearest measurement to the "top of the hour" to the data, specific values are likely similar but different to those returned by importNOAA(). Read the technical document for more information.

Usage

import_isd_lite(
  code = "037720-99999",
  year = 2025,
  progress = rlang::is_interactive(),
  quiet = FALSE
)

Arguments

Details

Note the following units for the main variables:

date

Date/time in POSIXct format. **Note the time zone is UTC and may need to be adjusted to merge with other local data.

latitude

Latitude in decimal degrees (-90 to 90).

longitude

Longitude in decimal degrees (-180 to 180). Negative numbers are west of the Greenwich Meridian.

elev

Elevation of site in metres.

ws

Wind speed in m/s.

wd

Wind direction in degrees. 90 is from the east.

air_temp

Air temperature in degrees Celcius.

atmos_pres

The sea level pressure in millibars.

dew_point

The dew point temperature in degrees Celcius.

precip_6

6-hour precipitation in mm.

precip_1

1-hour precipitation in mm.

sky

Sky Condition Total Coverage Code.

The data are returned in GMT (UTC). It may be necessary to adjust the time zone when combining with other data. For example, if air quality data were available for Beijing with time zone set to "Etc/GMT-8" (note the negative offset even though Beijing is ahead of GMT. See the openair package and manual for more details), then the time zone of the met data can be changed to be the same. One way of doing this would be attr(met$date, "tzone") <- "Etc/GMT-8" for a meteorological data frame called met. The two data sets could then be merged based on date.

Value

Returns a data frame of surface observations. The data frame is consistent for use with the openair package. Note that the data are returned in GMT (UTC) time zone format. Users may wish to express the data in other time zones, e.g., to merge with air pollution data.

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. Many data import functions in {worldmet} can use parallel processing to speed downloading up, powered by the capable {mirai} package. If users have any {mirai} "daemons" set, these functions 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 the import function!
big_met <- import_ghcn_hourly(code = "UKI0000EGLL", year = 2010:2025)

Author(s)

Jack Davison

See Also

getMeta() to obtain the codes based on various site search approaches.

Other NOAA ISD functions: import_isd_hourly(), import_isd_stations(), import_isd_stations_live()

Examples

## Not run: 
heathrow_lite <- import_isd_lite(code = "037720-99999", year = 2025)

## End(Not run)

Import station metadata for the Integrated Surface Databse

Description

This function is primarily used to find a site code that can be used to access data using import_isd_hourly(). Sites searches of approximately 30,000 sites can be carried out based on the site name and based on the nearest locations based on user-supplied latitude and longitude.

Usage

import_isd_stations(
  site = NULL,
  lat = NULL,
  lon = NULL,
  crs = 4326,
  country = NULL,
  state = NULL,
  n_max = 10,
  end_year = "current",
  provider = c("OpenStreetMap", "Esri.WorldImagery"),
  return = c("table", "sf", "map")
)

Arguments

Value

A data frame is returned with all available meta data, mostly importantly including a code that can be supplied to importNOAA(). If latitude and longitude searches are made an approximate distance, dist in km is also returned.

Author(s)

David Carslaw

See Also

Other NOAA ISD functions: import_isd_hourly(), import_isd_lite(), import_isd_stations_live()

Examples

## Not run: 
## search for sites with name beijing
getMeta(site = "beijing")

## End(Not run)

## Not run: 
## search for near a specified lat/lon - near Beijing airport
## returns 'n_max' nearest by default
getMeta(lat = 40, lon = 116.9)

## End(Not run)

Obtain site meta data from NOAA server

Description

Download all NOAA meta data, allowing for re-use and direct querying.

Usage

import_isd_stations_live(...)

Arguments

Value

a tibble

See Also

Other NOAA ISD functions: import_isd_hourly(), import_isd_lite(), import_isd_stations()

Examples

## Not run: 
meta <- import_isd_stations_live()
head(meta)

## End(Not run)

Codes for weather types

Description

This data frame consists of the weather description codes used in the ISD. It is not of general use to most users.

Usage

weatherCodes

Format

An object of class tbl_df (inherits from tbl, data.frame) with 100 rows and 2 columns.

Details

pwc

Weather code, which can be merged with the pwc column in importNOAA() datasets.

description

Description associated with the weather codes.

Examples

weatherCodes

Export a meteorological data frame in ADMS format

Description

Writes a text file in the ADMS format to a location of the user's choosing, with optional interpolation of missing values. This function works with data from both import_ghcn_hourly() and import_isd_hourly().

Usage

write_adms(x, file = "./ADMS_met.MET", interp = FALSE, max_gap = 2)

Arguments

Value

write_adms() returns the input dat invisibly.

See Also

Other Met writing functions: write_met()

Examples

## Not run: 
# import some data then export it
dat <- import_isd_hourly(year = 2012)
write_adms(dat, file = "~/adms_met.MET")

## End(Not run)

Export a meteorological data frame in files, chunked by site and year

Description

Writes a text file in the ADMS format to a location of the user's choosing, with optional interpolation of missing values. At present this function only works with data from import_isd_hourly(); it will later be expanded to work with import_ghcn_hourly() also.

Usage

write_met(
  x,
  path = ".",
  ext = c("rds", "delim", "parquet"),
  delim = ",",
  suffix = "",
  progress = rlang::is_interactive()
)

Arguments

Value

write_met() returns path invisibly.

See Also

Other Met writing functions: write_adms()

Examples

## Not run: 
# import some data then export it
dat <- import_isd_hourly(year = 2012)
write_met(dat)

## End(Not run)