Package {climate}

Title:	Interface to Download Meteorological (and Hydrological) Datasets
Version:	1.4.0
Description:	Automatize downloading of meteorological and hydrological data from publicly available repositories: OGIMET (http://ogimet.com/index.phtml.en), University of Wyoming - atmospheric vertical profiling data (http://weather.uwyo.edu/upperair/), Polish Institute of Meteorology and Water Management - National Research Institute (https://danepubliczne.imgw.pl), and National Oceanic & Atmospheric Administration (NOAA). This package also allows for searching geographical coordinates for each observation and calculate distances to the nearest stations.
License:	MIT + file LICENSE
Encoding:	UTF-8
LazyData:	true
Depends:	R (≥ 4.1.0)
Imports:	archive, curl, data.table, httr, R6, stringi
Suggests:	dplyr, knitr, maps, testthat, tidyr, rmarkdown
URL:	https://github.com/bczernecki/climate, https://bczernecki.github.io/climate/
BugReports:	https://github.com/bczernecki/climate/issues
VignetteBuilder:	knitr
Config/roxygen2/version:	8.0.0
RoxygenNote:	7.3.3
NeedsCompilation:	no
Packaged:	2026-07-10 20:57:17 UTC; bartosz
Author:	Bartosz Czernecki [aut, cre], Arkadiusz Głogowski [aut], Jakub Nowosad [aut], IMGW-PIB [ctb] (source of the data)
Maintainer:	Bartosz Czernecki <nwp@amu.edu.pl>
Repository:	CRAN
Date/Publication:	2026-07-10 23:10:15 UTC

climate: Interface to Download Meteorological (and Hydrological) Datasets

Description

Automatize downloading of meteorological and hydrological data from publicly available repositories: OGIMET (http://ogimet.com/index.phtml.en), University of Wyoming - atmospheric vertical profiling data (http://weather.uwyo.edu/upperair/), Polish Institute of Meteorology and Water Management - National Research Institute (https://danepubliczne.imgw.pl), and National Oceanic & Atmospheric Administration (NOAA). This package also allows for searching geographical coordinates for each observation and calculate distances to the nearest stations.

Author(s)

Maintainer: Bartosz Czernecki nwp@amu.edu.pl (ORCID)

Authors:

• Arkadiusz Głogowski arkadiusz.glogowski@upwr.edu.pl (ORCID)

• Jakub Nowosad nowosad.jakub@gmail.com (ORCID)

Other contributors:

• IMGW-PIB (source of the data) [contributor]

See Also

Useful links:

• https://github.com/bczernecki/climate

• https://bczernecki.github.io/climate/

• Report bugs at https://github.com/bczernecki/climate/issues

Examplary CO2 dataset from Mauna Loa Observatory (NOAA dataset)

Description

The object contains pre-downloaded CO2 dataset from Mauna Loa observatory The snapshot was taken 2020/05/05.

Usage

co2_demo

Format

An object of class data.frame with 745 rows and 7 columns.

Value

data.frame with historical CO2 concentrations data(co2_demo) head(co2_demo)

Compute relative humidity from air temperature and dew-point temperature

Description

Uses the August-Roche-Magnus approximation to derive relative humidity from the 2-metre air temperature and dew-point temperature.

Usage

compute_relative_humidity(t2m, dpt2m)

Arguments

Details

The August-Roche-Magnus approximation is:

RH = 100 \times \frac{\exp\!\bigl(\tfrac{17.625\,T_d}{243.04 + T_d}\bigr)} {\exp\!\bigl(\tfrac{17.625\,T}{243.04 + T}\bigr)}

where T is the air temperature and T_d is the dew-point temperature, both in degrees Celsius. The coefficients (17.625 and 243.04) follow Alduchov & Eskridge (1996).

Value

Numeric vector of relative humidity values in percent (0-100). Returns NA where either input is NA. Values are not clamped, so rounding errors may produce results marginally outside 0-100.

References

Alduchov, O. A., & Eskridge, R. E. (1996). Improved Magnus form approximation of saturation vapor pressure. Journal of Applied Meteorology, 35(4), 601–609.

Examples

compute_relative_humidity(t2m = 20, dpt2m = 10)   # ~52 %
compute_relative_humidity(t2m = 0,  dpt2m = 0)    # 100 %
compute_relative_humidity(t2m = c(20, 15, NA), dpt2m = c(10, 12, 8))

Find all variants of station' names

Description

For IMGW-PIB stations different naming were used historically. For example, POZNAŃ and “POZNAŃ-ŁAWICA, thus both names should be used when searching for the station. This function finds all variants of station' names status information and expand the created object

Usage

find_all_station_names(station_name)

Arguments

Value

character vector of station names with all variants of station's names

Examples

{
find_all_station_names(c("WARSZAWA", "POZNAŃ"))
}

Hydrological data from IMGW

Description

Downloading daily, and monthly hydrological data from the measurement stations available in the danepubliczne.imgw.pl collection

Usage

hydro_imgw(interval, year, value = "H", station = NULL, ...)

Arguments

Value

A data.frame with columns describing the hydrological parameters (e.g. flow, water level) where each row represent a measurement, depending on the interval, at a given hour, month or year. If coords = TRUE additional two columns with geographic coordinates are added.

Examples

  x = hydro_imgw("monthly", year = 1999)
  head(x)

Daily hydrological data

Description

Downloading daily hydrological data from the danepubliczne.imgw.pl collection

Usage

hydro_imgw_daily(year, station = NULL, allow_failure = TRUE, ...)

Arguments

Value

data.frame with historical hydrological data for the daily time interval

Examples

  daily = hydro_imgw_daily(year = 2000)

IMGW hydrological data from the IMGW datastore repository

Description

Downloading hourly and sub-hourly (hydrological) data from the telemetric stations available in the danepubliczne.imgw.pl/datastore collection since 2008. Most parameters are collected with 10 minutes interval and thus it is recommended to download only the mandatory years, parameters or stations. For example, 1 year of data with all available parameters requires processing around 2-4GB of uncompressed data.

Usage

hydro_imgw_datastore(
  year,
  parameters = NULL,
  stations = NULL,
  coords = TRUE,
  allow_failure = TRUE
)

Arguments

Details

Data from the IMGW automated (telemetry) systems are non validated by experts and may contain invalid values.

Value

data.table with a raw hydrorological measurements in 10-min or 60-min intervals. Please note that this dataset is not validated by experts and may contain invalid values.

Examples

imgw_hydro_telemetry = hydro_imgw_datastore(year = 2022,
                                            parameters = "flow",
                                            stations = "FORDON",
                                            coords = TRUE)

Monthly hydrological data

Description

Downloading monthly hydrological data from the danepubliczne.imgw.pl collection

Usage

hydro_imgw_monthly(
  year,
  coords = FALSE,
  station = NULL,
  allow_failure = TRUE,
  ...
)

Arguments

Value

data.frame with historical hydrological data for the monthly summaries

Examples

  monthly = hydro_imgw_monthly(year = 2000)

Shortening column names for hydrological variables

Description

Shortening column names of hydrological parameters to improve the readability of downloaded dataset from the danepubliczne.imgw.pl collection and removing duplicated column names

Usage

hydro_shortening_imgw(data, col_names = "short", remove_duplicates = TRUE)

Arguments

Value

data.frame with shorten names of hydrological parameters

Examples

  monthly = data = hydro_imgw("monthly", year = 1969, col_names = "polish")
  
  if (is.data.frame(monthly)) {
  abbr = hydro_shortening_imgw(data = monthly,
      col_names = "full",
      remove_duplicates = TRUE)
  head(abbr)
  }

Definitions of hydrological parameters used for shortening column names from the danepubliczne.imgw.pl collection

Description

The object contains 3 columns that are currently used for improving readability of the downloaded dataset: fullname, abbr_eng, and fullname_eng

Usage

imgw_hydro_abbrev

Format

The data contains a data.frame with ca. 20 elements described in three ways:

fullname

original column names as downloaded from the repository

abbr_eng

shorten column names with abbreviations derived from the most popular scheme used for meteorological parameters

fullname_eng

detailed description of downloaded meteorological variables

The object is created mostly to be used altogether with the hydro_shortening_imgw() function

Examples

data(imgw_hydro_abbrev)
head(imgw_hydro_abbrev)

Location of the hydrological stations from the danepubliczne.imgw.pl collection

Description

The object contains weather stations coordinates, ID numbers, and elevations

Usage

imgw_hydro_stations

Format

The data contains a data.frame with 1304 obs. of 3 variables:

id

Station ID

X

Longitude

Y

Latitude

The object is in the geographic coordinates using WGS84 (EPSG:4326).

Examples

data(imgw_hydro_stations)
head(imgw_hydro_stations)

Definitions of meteorological parameters used for shortening column names for the meteorological data from the danepubliczne.imgw.pl collection

Description

The object contains 3 columns that are currently used for improving readability of the downloaded dataset: fullname, abbr_eng, and fullname_eng

Usage

imgw_meteo_abbrev

Format

The data contains a data.frame with ca. 250 elements described in three ways:

fullname

original column names as downloaded from the repository

abbr_eng

shorten column names with abbreviations derived from the most popular scheme used for meteorological parameters

fullname_eng

detailed description of downloaded meteorological variables

The object is created mostly to be used altogether with the meteo_shortening_imgw function

Examples

data(imgw_meteo_abbrev)
head(imgw_meteo_abbrev)

Location of the meteorological stations from the danepubliczne.imgw.pl collection

Description

The object contains weather stations coordinates, ID numbers, and elevations

Usage

imgw_meteo_stations

Format

The data contains a data.frame with 1998 obs. of 3 variables:

id

Station ID

X

Longitude

Y

Latitude

station

Station name

id2

IMGW-PIB ID for station rank

The object is in the geographic coordinates using WGS84 (EPSG:4326).

Examples

data(imgw_meteo_stations)
head(imgw_meteo_stations)

Meteorological data from the IMGW-PIB official repository

Description

Downloading hourly, daily, and monthly meteorological data from the SYNOP / CLIMATE / PRECIP stations, or sub-hourly (10-minute) telemetry data from the automated network, all available in the danepubliczne.imgw.pl collection.

Usage

meteo_imgw(
  interval = NULL,
  rank = "synop",
  year,
  status = FALSE,
  coords = FALSE,
  station = NULL,
  col_names = "short",
  parameters = NULL,
  ...
)

Arguments

Value

A data.frame with meteorological parameters where each row is a measurement. For ranks "synop", "climate", "precip": measurements at a given hour, day, or month, depending on interval. If coords = TRUE two additional coordinate columns are appended. For rank = "telemetry": a data.table with 10-minute interval observations (not expert-validated). If coords = TRUE columns name, lon, lat, and alt are appended.

Examples

  x = meteo_imgw("monthly", year = 2018, coords = TRUE)
  head(x)

  # Telemetry (10-minute) data from automated stations (available since 2008):
  tel = meteo_imgw(rank = "telemetry", year = 2022,
                   parameters = "t2m",
                   station = "HALA GĄSIENICOWA")
  head(tel)

Daily IMGW meteorological data

Description

Downloading daily (meteorological) data from the SYNOP / CLIMATE / PRECIP stations available in the danepubliczne.imgw.pl collection

Usage

meteo_imgw_daily(
  rank = "synop",
  year,
  status = FALSE,
  coords = FALSE,
  station = NULL,
  col_names = "short",
  allow_failure = TRUE,
  ...
)

Arguments

Value

data.frame with a daily meteorological measurements

Examples

daily = meteo_imgw_daily(rank = "climate", year = 2000)

IMGW meteorological data from the IMGW datastore repository

Description

Downloading hourly (meteorological) data from the telemetric stations available in the danepubliczne.imgw.pl/datastore collection since 2008. Most parameters are collected with 10 minutes interval and thus it is recommended to download only the mandatory years, parameters or stations. For example, 1 year of data with all available parameters requires processing around 4GB of uncompressed data.

Usage

meteo_imgw_datastore(
  year,
  parameters = NULL,
  stations = NULL,
  coords = TRUE,
  allow_failure = TRUE
)

Arguments

Details

Data from the IMGW automated (telemetry) systems are non validated by experts and may contain invalid values.

Value

data.table with a raw meteorological measurements in 10-min intervals. Please note that this dataset is not validated by experts and may contain invalid values.

Examples

imgw_telemetry = meteo_imgw_datastore(year = 2022:2023,
                                      parameters = "t2m",
                                      stations = c("HALA GĄSIENICOWA",
                                                   "DOLINA 5 STAWÓW"),
                                      coords = TRUE)

Hourly IMGW meteorological data

Description

Downloading hourly (meteorological) data from the SYNOP / CLIMATE / PRECIP stations available in the danepubliczne.imgw.pl collection

Usage

meteo_imgw_hourly(
  rank = "synop",
  year,
  status = FALSE,
  coords = FALSE,
  station = NULL,
  col_names = "short",
  allow_failure = TRUE,
  ...
)

Arguments

Value

meteorological data for the hourly time interval

Examples

hourly = meteo_imgw_hourly(rank = "climate", year = 1984)
head(hourly)

Monthly IMGW meteorological data

Description

Downloading monthly (meteorological) data from the SYNOP / CLIMATE / PRECIP stations available in the danepubliczne.imgw.pl collection

Usage

meteo_imgw_monthly(
  rank = "synop",
  year,
  status = FALSE,
  coords = FALSE,
  station = NULL,
  col_names = "short",
  allow_failure = TRUE,
  ...
)

Arguments

Value

meteorological data with monthly summaries

Examples

monthly = meteo_imgw_monthly(rank = "climate", year = 1969)
head(monthly)

# a descriptive (long) column names:
monthly2 = meteo_imgw_monthly(
  rank = "synop", year = 2018,
  col_names = "full"
)
head(monthly2)

CO2 Mauna Loa (NOAA) dataset

Description

Carbon Dioxide (CO2) monthly measurements from Mauna Loa observatory. The source file is available at: ftp://aftp.cmdl.noaa.gov/products/trends/co2/co2_mm_mlo.txt with all further details.

Usage

meteo_noaa_co2()

Details

Data from March 1958 through April 1974 have been obtained by C. David Keeling of the Scripps Institution of Oceanography (SIO) and were obtained from the Scripps website (scrippsco2.ucsd.edu).

The "average" column contains the monthly mean CO2 mole fraction determined from daily averages. The mole fraction of CO2, expressed as parts per million (ppm) is the number of molecules of CO2 in every one million molecules of dried air (water vapor removed). If there are missing days concentrated either early or late in the month, the monthly mean is corrected to the middle of the month using the average seasonal cycle. Missing months are denoted by -99.99. The "interpolated" column includes average values from the preceding column and interpolated values where data are missing. Interpolated values are computed in two steps. First, we compute for each month the average seasonal cycle in a 7-year window around each monthly value. In this way the seasonal cycle is allowed to change slowly over time. We then determine the "trend" value for each month by removing the seasonal cycle; this result is shown in the "trend" column. Trend values are linearly interpolated for missing months. The interpolated monthly mean is then the sum of the average seasonal cycle value and the trend value for the missing month. NOTE: In general, the data presented for the last year are subject to change, depending on recalibration of the reference gas mixtures used, and other quality control procedures. Occasionally, earlier years may also be changed for the same reasons. Usually these changes are minor. CO2 expressed as a mole fraction in dry air, micromol/mol, abbreviated as ppm

Value

Data frame with historical CO2 concentrations

Examples

  co2 = meteo_noaa_co2()
  head(co2)

Hourly NOAA Integrated Surface Hourly (ISH) meteorological data

Description

Downloading hourly (meteorological) data from the SYNOP stations available in the NOAA ISD collection. Some stations in the dataset are dated back even up to 1900. By default only records that follow FM-12 (SYNOP) convention are processed. Further details available at: https://www.ncei.noaa.gov/pub/data/noaa/readme.txt

Usage

meteo_noaa_hourly(
  station = NULL,
  year = 2019,
  fm12 = TRUE,
  allow_failure = TRUE
)

Arguments

Value

data.frame with historical meteorological data in hourly intervals

Examples

# London-Heathrow, United Kingdom
  noaa = meteo_noaa_hourly(station = "037720-99999", year = 1949)

Download meteorological (Synop) data from the Ogimet service

Description

Unified entry point for downloading hourly or daily meteorological data from Ogimet. Two backends are supported:

Usage

meteo_ogimet(
  interval = "hourly",
  date = c(Sys.Date() - 30, Sys.Date()),
  station = NULL,
  country_name = NULL,
  source = NULL,
  ...
)

Arguments

Details

• "synop" (default for hourly): Downloads raw SYNOP messages from the Ogimet getsynop endpoint and decodes them with synop_parser(). Supports station mode (one or more WMO IDs) and/or country mode (country_name). A default output columns are described in the synop output section below, but can be enhanced optionally with simplified = FALSE or return_list = TRUE to include more of decoded SYNOP fields.

• "html" (default for daily): Scrapes pre-formatted summary tables from the Ogimet using HTML parsing. Supports station mode only (one or more WMO IDs). Output columns are described in the html output section below.

Value

synop output (source = "synop", simplified = TRUE or return_list = TRUE ⁠$data⁠): A data.frame with one row per decoded SYNOP observation and approx. 20 columns: date (POSIXct UTC), station, t2m, dpt2m, rel_hum, tmax, tmin, wd, ws, gust, press, slp, press_tend, precip, Nt, Nh, N_base, insol, visibility, snow.

synop output (source = "synop", simplified = FALSE): A data.frame with 30+ columns from synop_parser(), prefixed by station_id and Date.

html output (source = "html", interval = "hourly"): A data.frame with columns: station_ID, optionally Lon/Lat, Date, TC, TdC, TmaxC, TminC, ddd, ffkmh, Gustkmh, P0hPa, PseahPa, PTnd, Nt, Nh, HKm, InsoD1, Viskm, Snowcm, and (when precip_split = TRUE) pr6, pr12, pr24.

html output (source = "html", interval = "daily"): A data.frame with columns: station_ID, optionally Lon/Lat, Date, TemperatureCAvg, TemperatureCMax, TemperatureCMin, TdAvgC, HrAvg, WindkmhDir, WindkmhInt, WindkmhGust, PresslevHp, PreselevHp, Precmm, SunD1h, SnowDepcm, TotClOct, lowClOct, VisKm.

Returns NULL invisibly on failure when allow_failure = TRUE.

Examples

  # Hourly SYNOP data for Poznan-Lawica (default source = "synop")
  poznan_h = meteo_ogimet(interval = "hourly",
                          station  = 12330,
                          date     = c("2009-12-01", "2009-12-04"))

  # Daily HTML summaries for New York - La Guardia (default source = "html")
  new_york = meteo_ogimet(interval = "daily", station  = 72503)

  # Hourly with full parser output as a list
  poznan_list = meteo_ogimet(interval     = "hourly",
                             station      = 12330,
                             date         = c("2009-12-01", "2009-12-04"),
                             return_list  = TRUE)
  head(poznan_list$data)  # simplified
  head(poznan_list$full)  # all parser columns

  # Country mode: all Polish stations for one day
  germany = meteo_ogimet(interval      = "hourly",
                         country_name  = "Germany",
                         date          = c("2009-12-15", "2009-12-15"))

  # Force SYNOP backend for daily data
  poznan_d = meteo_ogimet(interval = "daily",
                          station  = 12330,
                          date     = c("2009-12-01", "2009-12-04"),
                          source   = "synop")

  # Force HTML backend for hourly data
  poznan_h2 = meteo_ogimet(interval = "hourly",
                           station  = 12330,
                           date     = c("2019-06-01", "2019-06-08"),
                           source   = "html")

Download and decode raw SYNOP messages from the Ogimet getsynop service

Description

Downloads raw SYNOP messages from the Ogimet getsynop endpoint and decodes them into a tidy data.frame using the synop_parser() function. Two retrieval modes are supported:

Usage

meteo_ogimet_synop(
  station = NULL,
  date = c(Sys.Date() - 30, Sys.Date()),
  country = NULL,
  country_name = NULL,
  simplified = TRUE,
  allow_failure = TRUE
)

Arguments

Details

• Station mode (station provided): fetches messages for one or more WMO station IDs. URL form: ⁠http://www.ogimet.com/cgi-bin/getsynop?block=<id>&begin=<YYYYMMDDhhmm>&end=<YYYYMMDDhhmm>⁠

• Country mode (country_name provided): fetches messages for all Ogimet stations in a country in a single request. URL form: ⁠http://www.ogimet.com/cgi-bin/getsynop?begin=<YYYYMMDDhhmm>&end=<YYYYMMDDhhmm>&state=<country_name>⁠

When both station and country_name are supplied, country_name takes precedence and a warning is issued.

Each line of the response is a comma-separated record: ⁠station_id,year,month,day,hour,minute,<SYNOP message>⁠. The SYNOP message is decoded via synop_parser() with as_data_frame = TRUE.

Value

By default (simplified = TRUE), a compact data.frame with one row per decoded SYNOP observation. Columns:

• date — Observation date-time (POSIXct, UTC).

• station — WMO station identifier (character).

• t2m — Air temperature at 2 m (°C).

• dpt2m — Dew-point temperature at 2 m (°C).

• rel_hum — Relative humidity (%), derived via compute_relative_humidity().

• tmax — Daily maximum temperature from Section 3 (°C).

• tmin — Daily minimum temperature from Section 3 (°C).

• wd — Wind direction (degrees).

• ws — Wind speed (m/s or knots, per wind_unit).

• gust — Highest gust speed from Section 3, same unit as ws.

• press — Station-level pressure (hPa).

• slp — Sea-level pressure (hPa).

• press_tend — 3-hour pressure change (hPa).

• precip — Precipitation amount (mm).

• Nt — Total cloud cover (oktas, 0–8) from the Nddff group.

• Nh — Cover of low clouds (genera Sc, St, Cu, Cb) in oktas (0–8), from Section 1 group ⁠8NhCLCMCH⁠; NA when not reported.

• N_base — Height of base of lowest observed cloud (m).

• insol — Daily sunshine duration (hours).

• visibility — Horizontal visibility (m).

• snow — Total snow depth (cm); 0 for trace amounts.

When simplified = FALSE, a data.frame with the first two columns station_id (WMO identifier, character) and Date (POSIXct, UTC), followed by all columns produced by synop_parser() with as_data_frame = TRUE: station_type, region, obs_day, obs_hour, wind_unit, wind_estimated, visibility, cloud_cover, wind_direction, wind_speed, air_temperature, dewpoint_temperature, station_pressure, sea_level_pressure, pressure_tendency, pressure_change, precipitation_amount, precipitation_time, cloud_base_min, cloud_base_max, low_cloud_type, middle_cloud_type, high_cloud_type, low_cloud_amount, maximum_temperature, minimum_temperature, gust, sunshine_duration, snow_depth, snow_depth_state, source.

Returns NULL invisibly when the download fails and allow_failure = TRUE.

Examples

  # Station mode: Poznan-Lawica (Poland)
  poznan = meteo_ogimet_synop(station = 12330,
                              date = c("2009-12-01", "2009-12-04"))
  head(poznan)

  # Station mode: multiple stations
  two_stations = meteo_ogimet_synop(station = c(12330, 12375),
                                    date = c("2019-06-01", "2019-06-03"))
  head(two_stations)
  
  # Country mode: all Polish stations for one day
  poland = meteo_ogimet_synop(country_name = "Poland",
                              date = c("2009-12-15", "2009-12-15"))
  head(poland)

  # Simplified view
  poznan_simple = meteo_ogimet_synop(station = 12330,
                                     date = c("2009-12-01", "2009-12-04"),
                                     simplified = TRUE)
  head(poznan_simple)

Shortening column names for meteorological variables

Description

Shortening column names of meteorological parameters to improve the readability of downloaded dataset from the danepubliczne.imgw.pl collection and removing duplicated column names

Usage

meteo_shortening_imgw(data, col_names = "short", remove_duplicates = TRUE)

Arguments

Value

data.frame with modified names of meteorological parameters

Examples

  monthly = meteo_imgw("monthly", rank = "climate", year = 1969)
  
  abbr = meteo_shortening_imgw(data = monthly,
      col_names = "full", 
      remove_duplicates = TRUE)
  head(abbr)

List of nearby meteorological or hydrological IMGW-PIB stations in Poland

Description

Returns a data frame of meteorological or hydrological stations with their coordinates in particular year. The returned object is valid only for a given year and type of stations (e.g. "synop", "climate" or "precip"). If add_map = TRUE additional map of downloaded data is added.

Usage

nearest_stations_imgw(
  type = "meteo",
  rank = "synop",
  year = 2018,
  add_map = TRUE,
  point = NULL,
  no_of_stations = 50,
  allow_failure = TRUE,
  ...
)

Arguments

Value

A data.frame with a list of nearest stations. Each row represents metadata for station which collected measurements in a given year. Particular columns contain stations metadata (e.g. station ID, geographical coordinates, official name, distance in kilometers from a given coordinates).

Examples

  df =  nearest_stations_imgw(type = "meteo", 
  rank = "synop",
  year = 2018,
  point = c(17, 52),
  add_map = TRUE, 
  no_of_stations = 4)

List of nearby SYNOP stations for a defined geographical location

Description

Returns a data frame of meteorological stations with their coordinates and distance from a given location based on the noaa website. The returned list is valid only for a given day.

Usage

nearest_stations_noaa(
  country,
  date = Sys.Date(),
  add_map = TRUE,
  point = NULL,
  no_of_stations = 10,
  allow_failure = TRUE
)

Arguments

Value

A data.frame with number of nearest station according to given point columns describing stations parameters (e.g. ID station, distance from point, geographic coordinates, etc.) where each row represent a measurement, each station which has a measurements on selected date. If add_map = TRUE additional map of downloaded data is added.

Examples

  nearest_stations_noaa(country = "SRI LANKA", 
  point = c(80, 6),
  add_map = TRUE, 
  no_of_stations = 10)
  
  uk_stations = nearest_stations_noaa(country = "UNITED KINGDOM", no_of_stations = 100)

List of nearby synop stations for a defined geographical location

Description

Returns a data frame of meteorological stations with their coordinates and distance from a given location based on the ogimet webpage. The returned list is valid only for a given day.

Usage

nearest_stations_ogimet(
  country = "United Kingdom",
  date = Sys.Date() - 1,
  add_map = FALSE,
  point = c(2, 50),
  no_of_stations = 10,
  allow_failure = TRUE,
  ...
)

Arguments

Value

A data.frame with number of nearest station according to given point columns describing stations parameters (e.g. ID station, distance from point in km, geographic coordinates, etc.). Each row represent a measurement, each station which has a measurements on selected date. If add_map = TRUE additional map of downloaded data is added.

Examples

  nearest_stations_ogimet(country = "United Kingdom", 
                          point = c(-2, 50),
                          add_map = TRUE, 
                          no_of_stations = 50, 
                          allow_failure = TRUE,
                          main = "Meteo stations in UK")

Scrapping daily meteorological (Synop) data from the Ogimet webpage

Description

Downloading daily (meteorological) data from the Synop stations available in the https://www.ogimet.com/ repository. The data are processed only if temperature or precipitation fields are present.

Usage

ogimet_daily(
  date = c(Sys.Date() - 30, Sys.Date()),
  station = NA,
  hour = 6,
  allow_failure = TRUE,
  ...
)

Arguments

Value

data.frame with historical meteorological data for the daily summaries

Examples

  # downloading daily summaries for last 30 days. station: New York - La Guardia
  new_york = ogimet_daily(station = 72503)

Scrapping hourly meteorological (Synop) data from the Ogimet webpage

Description

Downloading hourly (meteorological) data from the Synop stations available in the https://www.ogimet.com/ repository

Usage

ogimet_hourly(
  date = c(Sys.Date() - 30, Sys.Date()),
  station = 12330,
  precip_split = TRUE,
  allow_failure = TRUE
)

Arguments

Value

data.frame with historical meteorological data for hourly time interval

Examples

  # downloading hourly data for Poznan-Lawica, Poland for (default) last 30 days:
  poznan = ogimet_hourly(station = 12330)

A simplistic HTML parser for Synop data from the Ogimet webpage

Description

Parses an HTML table into a data.frame using base R only with no external dependencies The code relies purely on regular expressions and basic string and regex functions.

Usage

parse_html_table(html, table_start_pattern = "<TABLE[^>]*>")

Arguments

Details

By default the function grabs the first TABLE element it finds. If the HTML code has several tables and you need a specific one, pass a regex that matches its opening tag via table_start_pattern, e.g.:

parse_html_table(html, table_start_pattern = '<TABLE align="center" border=0[^>]*>')

Value

description

data.frame with automatically detected column names (built from

Exemplary sounding profile from University of Wyoming dataset

Description

The object contains pre-downloaded atmospheric (sounding) profile for Leba, PL rawinsonde station. The measurement was taken 2000/03/23 at 00 UTC.

Usage

profile_demo

Format

The data contains list of two data.frames as derived using sounding_wyoming() function

Examples

data(profile_demo)
head(profile_demo)

Sounding data

Description

Downloading the measurements of the vertical profile of atmosphere (also known as sounding data). Data can be retrieved using TEMP and BUFR sounding formatting. By default automatic detection of input format is used.

Usage

sounding_wyoming(
  wmo_id,
  yy,
  mm,
  dd,
  hh,
  min = 0,
  source = "AUTODETECT",
  allow_failure = TRUE
)

Arguments

Value

Returns two lists with values described at: weather.uwyo.edu ; The first list contains:

1. PRES - Pressure (hPa)

2. HGHT - Height (metres)

3. TEMP - Temperature (C)

4. DWPT - Dew point (C)

5. RELH - Relative humidity (%)

6. MIXR - Mixing ratio (g/kg)

7. DRCT - Wind direction (deg)

8. SPED - Wind speed (m/s)

9. THTA - (K)

10. THTE - (K)

11. THTV - (K)

The second list contains metadata for time of observation and station location

A list of 2 data.frames where first data frame represents parameters of upper parts o with columns describing the meteorogical parameters (e.g. temperature, air pressure) where each row represent a measurement, depending on the height. Second data.frame presents a description of the conditions under which the sounding was carried out.

Source

http://weather.uwyo.edu/upperair/sounding.html

Examples

# download data for Station 45004 starting 1120Z 11 Jul 2021; Kowloon, HONG KONG, CHINA
# using AUTODETECT, TEMP and BUFR sounding formats

# autodect input format:
sounding_auto = sounding_wyoming(wmo_id = 45004, 
                                 yy = 2021, mm = 07, dd = 17, hh = 12)

# temp (fm35) input format:
sounding_temp = sounding_wyoming(wmo_id = 45004, 
                                 yy = 2021, mm = 07, dd = 17, hh = 12, 
                                 source = "TEMP")
# bufr input format:  
sounding_bufr = sounding_wyoming(wmo_id = 45004, 
                                 yy = 2021, mm = 07, dd = 17, hh = 12, min = 00, 
                                 source = "BUFR")

Distance between two points on a spheroid

Description

Calculate the distance between two points on the surface of a spheroid using Vincenty's formula. This function can be used when GIS libraries for calculating distance are not available.

Usage

spheroid_dist(p1, p2)

Arguments

Value

numerical vector with distance between two locations (in kilometers)

Examples

 p1 = c(18.633333, 54.366667) # longitude and latitude for Gdansk, PL
 p2 = c(17.016667, 54.466667) # longitude and latitude for Slupsk, PL
 spheroid_dist(p1, p2)
 

IMGW hydrological telemetry stations

Description

Retrieving current metadata for hydrological stations used in the telemetric systems of the IMGW-PIB datastore (danepubliczne.imgw.pl/datastore)

Usage

stations_hydro_imgw_telemetry()

Value

data table with metadata for over 850 stations. Metadata contains: station ID, station name, river, latitude, longitude, altitude, km_of_river

Examples

  hydro_telemetry_stations = stations_hydro_imgw_telemetry()

IMGW meteorological telemetry stations

Description

Retrieving current metadata for meteorological stations used in the telemetric systems of the IMGW-PIB datastore (danepubliczne.imgw.pl/datastore)

Usage

stations_meteo_imgw_telemetry()

Value

data table with metadata for over 500 stations. Metadata contains: station ID, station name, river, year_est, latitude, longitude, altitude

Examples

  meteo_telemetry_stations = stations_meteo_imgw_telemetry()

Scrapping a list of meteorological (Synop) stations for a defined country from the Ogimet webpage

Description

Returns a list of meteorological stations with their coordinates from the Ogimet webpage. The returned list is valid only for a given day

Usage

stations_ogimet(
  country = "United Kingdom",
  date = Sys.Date(),
  add_map = FALSE,
  allow_failure = TRUE
)

Arguments

Value

A data.frame with columns describing the synoptic stations in selected countries where each row represent a statation. If add_map = TRUE additional map of downloaded data is visualized.

Examples

  stations_ogimet(country = "Australia", add_map = TRUE)

Parse SYNOP messages into structured lists or a data frame

Description

This function decodes SYNOP FM-12 meteorological messages which are commonly used for reporting weather observations under the regulations of the World Meteorological Organization (WMO). It parses one or more SYNOP messages and returns their structured representation as generated by the SYNOP R6 decoder.

Usage

synop_parser(message, country = NULL, simplify = TRUE, as_data_frame = FALSE)

Arguments

Details

Currently, the decoder contains most of the core logic for parsing the main sections of SYNOP messages that are commonly used in atmospheric sciences. However, it does not yet cover all possible SYNOP groups and fields, and some fields may be missing or incomplete.

Value

When as_data_frame = FALSE (default): a list of decoded SYNOP messages, or the decoded list directly when simplify = TRUE and a single message is supplied. When as_data_frame = TRUE: a data.frame with one row per message and the following columns (all numeric/character as appropriate, NA when not present in the message): station_type, station_id, region, obs_day, obs_hour, wind_unit, wind_estimated, visibility, cloud_cover, wind_direction, wind_speed, air_temperature, dewpoint_temperature, station_pressure, sea_level_pressure, pressure_tendency, pressure_change, precipitation_amount, precipitation_time, cloud_base_min, cloud_base_max, low_cloud_type, middle_cloud_type, high_cloud_type, low_cloud_amount, maximum_temperature (Section 3 daily maximum, °C), minimum_temperature (Section 3 daily minimum, °C), gust (highest gust speed from Section 3 group 910ff/911ff, in the wind unit of the message), cloudiness_height (cloud cover in oktas of the highest cloud layer reported in Section 3, i.e. cirrus/cirrocumulus/cirrostratus; NA when absent), sunshine_duration (daily sunshine in hours, from Section 3 group 55SSS), snow_depth (total snow depth in cm; 0 for trace amounts, NA for non-continuous cover or unmeasurable depth), snow_depth_state (descriptive state of ground with snow/ice per WMO code table 0975, e.g. "Even layer of loose dry snow covering ground completely"), source (the original SYNOP message string). Row names are sequential integers.

Examples

synop_code = "AAXX 01004 88889 12782 61506 10094 20047 30111 40197 53007 60001 81541"
synop_parser(synop_code)
synop_parser(rep(synop_code, 2), simplify = FALSE)
synop_parser(synop_code, as_data_frame = TRUE)
synop_parser(rep(synop_code, 2), as_data_frame = TRUE)

Download file in a graceful way

Description

Function for downloading & testing url/internet connection according to CRAN policy Example solution strongly based on https://community.rstudio.com/t/internet-resources-should-fail-gracefully/49199/12 as suggested by kvasilopoulos

Usage

test_url(link, output, quiet = TRUE)

Arguments

Value

No return value, called for side effects

Examples

link = "https://www.ncei.noaa.gov/pub/data/noaa/2019/123300-99999-2019.gz"
output = tempfile()
test_url(link = link, output = output)