Title:	Climate AEMET Tools
Version:	1.4.2
Description:	Tools to download the climatic data of the Spanish Meteorological Agency (AEMET) directly from R using their API and create scientific graphs (climate charts, trend analysis of climate time series, temperature and precipitation anomalies maps, warming stripes graphics, climatograms, etc.).
License:	GPL-3
URL:	https://ropenspain.github.io/climaemet/, https://github.com/rOpenSpain/climaemet
BugReports:	https://github.com/rOpenSpain/climaemet/issues
Depends:	R (≥ 3.6.0)
Imports:	cli (≥ 3.0.0), dplyr (≥ 1.0.0), ggplot2 (≥ 3.5.0), httr2 (≥ 1.0.0), jsonlite (≥ 1.7.0), rappdirs (≥ 0.3.3), readr (≥ 1.4.0), rlang (≥ 0.4.6), tibble (≥ 3.0.3), tidyr (≥ 1.1.0), xml2
Suggests:	climatol (≥ 3.1.2), gganimate (≥ 1.0.5), jpeg (≥ 0.1.8), knitr, lifecycle, lubridate, mapSpain, rmarkdown, scales, sf (≥ 0.9.0), terra (≥ 1.8-10), testthat (≥ 3.0.0)
VignetteBuilder:	knitr
Config/Needs/website:	cpp11, crosstalk, devtools, geofacet, geoR, gifski, gstat, leaflet, reactable, scales, tidyterra, tidyverse, usethis
Config/testthat/edition:	3
Config/testthat/parallel:	false
Copyright:	© AEMET. See file COPYRIGHTS
Encoding:	UTF-8
LazyData:	true
RoxygenNote:	7.3.2
X-schema.org-applicationCategory:	Meteorology
X-schema.org-isPartOf:	https://ropenspain.es/
X-schema.org-keywords:	aemet, climate, cran, data, forecast-api, r, r-package, ropenspain, rstats, science, spain, weather-api
NeedsCompilation:	no
Packaged:	2025-06-24 19:23:27 UTC; diego
Author:	Manuel Pizarro [aut, cph], Diego Hernangómez [aut, cre], Gema Fernández-Avilés [aut]
Maintainer:	Diego Hernangómez <diego.hernangomezherrero@gmail.com>
Repository:	CRAN
Date/Publication:	2025-06-25 12:20:29 UTC

climaemet: Climate AEMET Tools

Description

Tools to download the climatic data of the Spanish Meteorological Agency (AEMET) directly from R using their API and create scientific graphs (climate charts, trend analysis of climate time series, temperature and precipitation anomalies maps, warming stripes graphics, climatograms, etc.).

Author(s)

Maintainer: Diego Hernangómez diego.hernangomezherrero@gmail.com (ORCID)

Authors:

• Manuel Pizarro (ORCID) [copyright holder]

• Gema Fernández-Avilés (ORCID)

See Also

Useful links:

• https://ropenspain.github.io/climaemet/

• https://github.com/rOpenSpain/climaemet

• Report bugs at https://github.com/rOpenSpain/climaemet/issues

AEMET alert zones

Description

Get AEMET alert zones.

Usage

aemet_alert_zones(verbose = FALSE, return_sf = FALSE)

Arguments

Details

The first result of the call on each session is (temporarily) cached in the assigned tempdir() for avoiding unneeded API calls.

Value

A tibble or a sf object.

Source

https://www.aemet.es/es/eltiempo/prediccion/avisos/ayuda. See also Annex 2 and Annex 3 docs, linked in this page.

See Also

aemet_alerts()

Other aemet_api_data: aemet_alerts(), aemet_beaches(), aemet_daily_clim(), aemet_extremes_clim(), aemet_forecast_beaches(), aemet_forecast_daily(), aemet_forecast_fires(), aemet_last_obs(), aemet_monthly, aemet_normal, aemet_stations()

Examples

library(tibble)
alert_zones <- aemet_alert_zones()
alert_zones

# Cached during this R session
alert_zones2 <- aemet_alert_zones(verbose = TRUE)

identical(alert_zones, alert_zones2)

# Select an map beaches
library(dplyr)
library(ggplot2)


# Galicia
alert_zones_sf <- aemet_alert_zones(return_sf = TRUE) %>%
  filter(COD_CCAA == "71")

# Coast zones are identified by a "C" in COD_Z
alert_zones_sf$type <- ifelse(grepl("C$", alert_zones_sf$COD_Z),
  "Coast", "Mainland"
)


ggplot(alert_zones_sf) +
  geom_sf(aes(fill = NOM_PROV)) +
  facet_wrap(~type) +
  scale_fill_brewer(palette = "Blues")

AEMET Meteorological warnings

Description

Get a database of current meteorological alerts.

Usage

aemet_alerts(
  ccaa = NULL,
  lang = c("es", "en"),
  verbose = FALSE,
  return_sf = FALSE,
  extract_metadata = FALSE,
  progress = TRUE
)

Arguments

Value

A tibble or a sf object.

Source

https://www.aemet.es/en/eltiempo/prediccion/avisos.

https://www.aemet.es/es/eltiempo/prediccion/avisos/ayuda. See also Annex 2 and Annex 3 docs, linked in this page.

See Also

aemet_alert_zones(). See also mapSpain::esp_codelist, mapSpain::esp_dict_region_code() to get the names of the autonomous communities.

Other aemet_api_data: aemet_alert_zones(), aemet_beaches(), aemet_daily_clim(), aemet_extremes_clim(), aemet_forecast_beaches(), aemet_forecast_daily(), aemet_forecast_fires(), aemet_last_obs(), aemet_monthly, aemet_normal, aemet_stations()

Examples

# Display names of CCAAs
library(dplyr)
aemet_alert_zones() %>%
  select(NOM_CCAA) %>%
  distinct()

# Base map
cbasemap <- mapSpain::esp_get_ccaa(ccaa = c(
  "Galicia", "Asturias", "Cantabria",
  "Euskadi"
))

# Alerts
alerts_north <- aemet_alerts(
  ccaa = c("Galicia", "Asturias", "Cantabria", "Euskadi"),
  return_sf = TRUE
)

# If any alert
if (inherits(alerts_north, "sf")) {
  library(ggplot2)
  library(lubridate)


  alerts_north$day <- date(alerts_north$effective)

  ggplot(alerts_north) +
    geom_sf(data = cbasemap, fill = "grey60") +
    geom_sf(aes(fill = `AEMET-Meteoalerta nivel`)) +
    geom_sf(
      data = cbasemap, fill = "transparent", color = "black",
      linewidth = 0.5
    ) +
    facet_grid(vars(`AEMET-Meteoalerta fenomeno`), vars(day)) +
    scale_fill_manual(values = c(
      "amarillo" = "yellow", naranja = "orange",
      "rojo" = "red"
    ))
}

Install an AEMET API Key

Description

This function will store your AEMET API key on your local machine so it can be called securely without being stored in your code.

Alternatively, you can install the API Key manually:

• Run Sys.setenv(AEMET_API_KEY = "Your_Key"). You would need to run this command on each session (Similar to install = FALSE).

• Write this line on your .Renviron file: AEMET_API_KEY = "Your_Key" (same behavior than install = TRUE). This would store your API key permanently.

Usage

aemet_api_key(apikey, overwrite = FALSE, install = FALSE)

Arguments

Details

You can pass several apikey values as a vector c(api1, api2), in this case several AEMET_API_KEY values would be generated. In each subsequent api call climaemet would choose the API Key with the highest remaining quota.

This is useful when performing batch queries to avoid API throttling.

Value

None

Note

To locate your API Key on your local machine, run rappdirs::user_cache_dir("climaemet", "R").

See Also

Other aemet_auth: aemet_detect_api_key()

Examples

# Don't run these examples!

if (FALSE) {
  aemet_api_key("111111abc", install = TRUE)

  # You can check it with:
  Sys.getenv("AEMET_API_KEY")
}

if (FALSE) {
  # If you need to overwrite an existing key:
  aemet_api_key("222222abc", overwrite = TRUE, install = TRUE)

  # You can check it with:
  Sys.getenv("AEMET_API_KEY")
}

AEMET beaches

Description

Get AEMET beaches.

Usage

aemet_beaches(verbose = FALSE, return_sf = FALSE)

Arguments

Details

The first result of the API call on each session is (temporarily) cached in the assigned tempdir() for avoiding unneeded API calls.

Value

A tibble or a sf object.

API Key

You need to set your API Key globally using aemet_api_key().

See Also

aemet_forecast_beaches()

Other aemet_api_data: aemet_alert_zones(), aemet_alerts(), aemet_daily_clim(), aemet_extremes_clim(), aemet_forecast_beaches(), aemet_forecast_daily(), aemet_forecast_fires(), aemet_last_obs(), aemet_monthly, aemet_normal, aemet_stations()

Examples

library(tibble)
beaches <- aemet_beaches()
beaches

# Cached during this R session
beaches2 <- aemet_beaches(verbose = TRUE)

identical(beaches, beaches2)

# Select an map beaches
library(dplyr)
library(ggplot2)
library(mapSpain)

# Alicante / Alacant
beaches_sf <- aemet_beaches(return_sf = TRUE) %>%
  filter(ID_PROVINCIA == "03")

prov <- mapSpain::esp_get_prov("Alicante")

ggplot(prov) +
  geom_sf() +
  geom_sf(
    data = beaches_sf, shape = 4, size = 2.5,
    color = "blue"
  )

Daily/annual climatology values

Description

Get climatology values for a station or for all the available stations. Note that aemet_daily_period() and aemet_daily_period_all() are shortcuts of aemet_daily_clim().

Usage

aemet_daily_clim(
  station = "all",
  start = Sys.Date() - 7,
  end = Sys.Date(),
  verbose = FALSE,
  return_sf = FALSE,
  extract_metadata = FALSE,
  progress = TRUE
)

aemet_daily_period(
  station,
  start = as.integer(format(Sys.Date(), "%Y")),
  end = start,
  verbose = FALSE,
  return_sf = FALSE,
  extract_metadata = FALSE,
  progress = TRUE
)

aemet_daily_period_all(
  start = as.integer(format(Sys.Date(), "%Y")),
  end = start,
  verbose = FALSE,
  return_sf = FALSE,
  extract_metadata = FALSE,
  progress = TRUE
)

Arguments

Details

start and end parameters should be:

• For aemet_daily_clim(): A Date object or a string with format: YYYY-MM-DD ("2020-12-31") coercible with as.Date().

• For aemet_daily_period() and aemet_daily_period_all(): A string representing the year(s) to be extracted: "2020", "2018".

Value

A tibble or a sf object.

API Key

You need to set your API Key globally using aemet_api_key().

See Also

aemet_api_key(), as.Date()

Other aemet_api_data: aemet_alert_zones(), aemet_alerts(), aemet_beaches(), aemet_extremes_clim(), aemet_forecast_beaches(), aemet_forecast_daily(), aemet_forecast_fires(), aemet_last_obs(), aemet_monthly, aemet_normal, aemet_stations()

Examples

library(tibble)
obs <- aemet_daily_clim(c("9434", "3195"))
glimpse(obs)

# Metadata
meta <- aemet_daily_clim(c("9434", "3195"), extract_metadata = TRUE)

glimpse(meta$campos)

Check if an AEMET API Key is present for the current session

Description

The function would detect if an API Key is available on this session:

• If an API Key is already set as an environment variable it would be preserved

• If no environment variable has been set and you have stored permanently an API Key using aemet_api_key(), the latter would be loaded.

Usage

aemet_detect_api_key(...)

aemet_show_api_key(...)

Arguments

Value

TRUE or FALSE. aemet_show_api_key() would display your stored API keys.

See Also

Other aemet_auth: aemet_api_key()

Examples

aemet_detect_api_key()

# CAUTION: This may reveal API Keys
if (FALSE) {
  aemet_show_api_key()
}

Extreme values for a station

Description

Get recorded extreme values for a station.

Usage

aemet_extremes_clim(
  station = NULL,
  parameter = "T",
  verbose = FALSE,
  return_sf = FALSE,
  extract_metadata = FALSE,
  progress = TRUE
)

Arguments

Value

A tibble or a sf object. If the function finds an error when parsing it would return the result as a list() object.

API Key

You need to set your API Key globally using aemet_api_key().

See Also

aemet_api_key()

Other aemet_api_data: aemet_alert_zones(), aemet_alerts(), aemet_beaches(), aemet_daily_clim(), aemet_forecast_beaches(), aemet_forecast_daily(), aemet_forecast_fires(), aemet_last_obs(), aemet_monthly, aemet_normal, aemet_stations()

Examples

library(tibble)
obs <- aemet_extremes_clim(c("9434", "3195"))
glimpse(obs)

Forecast database for beaches

Description

Get a database of daily weather forecasts for a beach. Beach database can be accessed with aemet_beaches().

Usage

aemet_forecast_beaches(
  x,
  verbose = FALSE,
  return_sf = FALSE,
  extract_metadata = FALSE,
  progress = TRUE
)

Arguments

Value

A tibble or a sf object.

API Key

You need to set your API Key globally using aemet_api_key().

See Also

aemet_beaches() for beaches codes.

Other aemet_api_data: aemet_alert_zones(), aemet_alerts(), aemet_beaches(), aemet_daily_clim(), aemet_extremes_clim(), aemet_forecast_daily(), aemet_forecast_fires(), aemet_last_obs(), aemet_monthly, aemet_normal, aemet_stations()

Other forecasts: aemet_forecast_daily(), aemet_forecast_fires(), aemet_forecast_tidy()

Examples

# Forecast for beaches in Palma, Mallorca
library(dplyr)
library(ggplot2)

palma_b <- aemet_beaches() %>%
  filter(ID_MUNICIPIO == "07040")

forecast_b <- aemet_forecast_beaches(palma_b$ID_PLAYA)
glimpse(forecast_b)

ggplot(forecast_b) +
  geom_line(aes(fecha, tagua_valor1, color = nombre)) +
  facet_wrap(~nombre, ncol = 1) +
  labs(
    title = "Water temperature in beaches of Palma (ES)",
    subtitle = "Forecast 3-days",
    x = "Date",
    y = "Temperature (Celsius)",
    color = "Beach"
  )

Forecast database by municipality

Description

Get a database of daily or hourly weather forecasts for a given municipality.

Usage

aemet_forecast_daily(
  x,
  verbose = FALSE,
  extract_metadata = FALSE,
  progress = TRUE
)

aemet_forecast_hourly(
  x,
  verbose = FALSE,
  extract_metadata = FALSE,
  progress = TRUE
)

Arguments

Details

Forecasts format provided by the AEMET API have a complex structure. Although climaemet returns a tibble, each forecasted value is provided as a nested tibble. aemet_forecast_tidy() helper function can unnest these values an provide a single unnested tibble for the requested variable.

If extract_metadata = TRUE a simple tibble describing the value of each field of the forecast is returned.

Value

A nested tibble. Forecasted values can be extracted with aemet_forecast_tidy(). See also Details.

API Key

You need to set your API Key globally using aemet_api_key().

See Also

aemet_munic for municipality codes and mapSpain package for working with sf objects of municipalities (see mapSpain::esp_get_munic() and Examples).

Other aemet_api_data: aemet_alert_zones(), aemet_alerts(), aemet_beaches(), aemet_daily_clim(), aemet_extremes_clim(), aemet_forecast_beaches(), aemet_forecast_fires(), aemet_last_obs(), aemet_monthly, aemet_normal, aemet_stations()

Other forecasts: aemet_forecast_beaches(), aemet_forecast_fires(), aemet_forecast_tidy()

Examples

# Select a city
data("aemet_munic")
library(dplyr)
munis <- aemet_munic %>%
  filter(municipio_nombre %in% c("Santiago de Compostela", "Lugo")) %>%
  pull(municipio)

daily <- aemet_forecast_daily(munis)

# Metadata
meta <- aemet_forecast_daily(munis, extract_metadata = TRUE)
glimpse(meta$campos)

# Vars available
aemet_forecast_vars_available(daily)


# This is nested
daily %>%
  select(municipio, fecha, nombre, temperatura)

# Select and unnest
daily_temp <- aemet_forecast_tidy(daily, "temperatura")

# This is not
daily_temp

# Wrangle and plot
daily_temp_end <- daily_temp %>%
  select(
    elaborado, fecha, municipio, nombre, temperatura_minima,
    temperatura_maxima
  ) %>%
  tidyr::pivot_longer(cols = contains("temperatura"))

# Plot
library(ggplot2)
ggplot(daily_temp_end) +
  geom_line(aes(fecha, value, color = name)) +
  facet_wrap(~nombre, ncol = 1) +
  scale_color_manual(
    values = c("red", "blue"),
    labels = c("max", "min")
  ) +
  scale_x_date(
    labels = scales::label_date_short(),
    breaks = "day"
  ) +
  scale_y_continuous(
    labels = scales::label_comma(suffix = "º")
  ) +
  theme_minimal() +
  labs(
    x = "", y = "",
    color = "",
    title = "Forecast: 7-day temperature",
    subtitle = paste(
      "Forecast produced on",
      format(daily_temp_end$elaborado[1], usetz = TRUE)
    )
  )

# Spatial with mapSpain
library(mapSpain)
library(sf)

lugo_sf <- esp_get_munic(munic = "Lugo") %>%
  select(LAU_CODE)

daily_temp_end_lugo_sf <- daily_temp_end %>%
  filter(nombre == "Lugo" & name == "temperatura_maxima") %>%
  # Join by LAU_CODE
  left_join(lugo_sf, by = c("municipio" = "LAU_CODE")) %>%
  st_as_sf()

ggplot(daily_temp_end_lugo_sf) +
  geom_sf(aes(fill = value)) +
  facet_wrap(~fecha) +
  scale_fill_gradientn(
    colors = c("blue", "red"),
    guide = guide_legend()
  ) +
  labs(
    main = "Forecast: 7-day max temperature",
    subtitle = "Lugo, ES"
  )

AEMET fires forecast

Description

Get a SpatRaster as provided by terra with the daily meteorological risk level for wildfires.

Usage

aemet_forecast_fires(
  area = c("p", "c"),
  verbose = FALSE,
  extract_metadata = FALSE
)

Arguments

Details

The SpatRaster provides 5 (factor())levels with the following meaning:

• "1": Low risk.

• "2": Moderate risk.

• "3": High risk.

• "4": Very high risk.

• "5": Extreme risk.

The resulting object has several layers, each one representing the forecast for the upcoming 7 days. It also has additional attributes provided by the terra package, such as terra::time() and terra::coltab().

Value

A tibble or a SpatRaster object.

Source

https://www.aemet.es/en/eltiempo/prediccion/incendios.

See Also

Other aemet_api_data: aemet_alert_zones(), aemet_alerts(), aemet_beaches(), aemet_daily_clim(), aemet_extremes_clim(), aemet_forecast_beaches(), aemet_forecast_daily(), aemet_last_obs(), aemet_monthly, aemet_normal, aemet_stations()

Other forecasts: aemet_forecast_beaches(), aemet_forecast_daily(), aemet_forecast_tidy()

Examples

aemet_forecast_fires(extract_metadata = TRUE)

# Extract alerts
alerts <- aemet_forecast_fires()

alerts

# Nice plotting with terra
library(terra)
plot(alerts, all_levels = TRUE)

# Zoom in an area
cyl <- mapSpain::esp_get_ccaa("Castilla y Leon", epsg = 4326)

# SpatVector
cyl <- vect(cyl)

fires_cyl <- crop(alerts, cyl)
fires_cyl <- crop(alerts, cyl)
title <- names(fires_cyl)[1]

plot(fires_cyl[[1]], main = title, all_levels = TRUE)
plot(cyl, add = TRUE)

Helper functions for extracting forecasts

Description

Helpers for aemet_forecast_daily() and aemet_forecast_hourly():

• aemet_forecast_vars_available() extracts the values available on the dataset.

• aemet_forecast_tidy() produces a tibble with the forecast for var.

Usage

aemet_forecast_tidy(x, var)

aemet_forecast_vars_available(x)

Arguments

Value

A vector of characters (aemet_forecast_vars_available()) or a tibble (aemet_forecast_tidy()).

See Also

Other forecasts: aemet_forecast_beaches(), aemet_forecast_daily(), aemet_forecast_fires()

Examples

# Hourly values
hourly <- aemet_forecast_hourly(c("15030", "28079"))

# Vars available
aemet_forecast_vars_available(hourly)

# Get temperature
temp <- aemet_forecast_tidy(hourly, "temperatura")

library(dplyr)
# Make hour - Need lubridate to adjust timezones
temp_end <- temp %>%
  mutate(
    forecast_time = lubridate::force_tz(
      as.POSIXct(fecha) + hora,
      tz = "Europe/Madrid"
    )
  )

# Add also sunset and sunrise
suns <- temp_end %>%
  select(nombre, fecha, orto, ocaso) %>%
  distinct_all() %>%
  group_by(nombre) %>%
  mutate(
    ocaso_end = lubridate::force_tz(
      as.POSIXct(fecha) + ocaso,
      tz = "Europe/Madrid"
    ),
    orto_end = lubridate::force_tz(
      as.POSIXct(fecha) + orto,
      tz = "Europe/Madrid"
    ),
    orto_lead = lead(orto_end)
  ) %>%
  tidyr::drop_na()



# Plot

library(ggplot2)

ggplot(temp_end) +
  geom_rect(data = suns, aes(
    xmin = ocaso_end, xmax = orto_lead,
    ymin = min(temp_end$temperatura),
    ymax = max(temp_end$temperatura)
  ), alpha = .4) +
  geom_line(aes(forecast_time, temperatura), color = "blue4") +
  facet_wrap(~nombre, nrow = 2) +
  scale_x_datetime(labels = scales::label_date_short()) +
  scale_y_continuous(labels = scales::label_number(suffix = "º")) +
  labs(
    x = "", y = "",
    title = "Forecast: Temperature",
    subtitle = paste("Forecast produced on", format(temp_end$elaborado[1],
      usetz = TRUE
    ))
  )

Last observation values for a station

Description

Get last observation values for a station.

Usage

aemet_last_obs(
  station = "all",
  verbose = FALSE,
  return_sf = FALSE,
  extract_metadata = FALSE,
  progress = TRUE
)

Arguments

Value

A tibble or a sf object

API Key

You need to set your API Key globally using aemet_api_key().

See Also

Other aemet_api_data: aemet_alert_zones(), aemet_alerts(), aemet_beaches(), aemet_daily_clim(), aemet_extremes_clim(), aemet_forecast_beaches(), aemet_forecast_daily(), aemet_forecast_fires(), aemet_monthly, aemet_normal, aemet_stations()

Examples

library(tibble)
obs <- aemet_last_obs(c("9434", "3195"))
glimpse(obs)

Monthly/annual climatology

Description

Get monthly/annual climatology values for a station or all the stations. aemet_monthly_period() and aemet_monthly_period_all() allows requests that span several years.

Usage

aemet_monthly_clim(
  station = NULL,
  year = as.integer(format(Sys.Date(), "%Y")),
  verbose = FALSE,
  return_sf = FALSE,
  extract_metadata = FALSE,
  progress = TRUE
)

aemet_monthly_period(
  station = NULL,
  start = as.integer(format(Sys.Date(), "%Y")),
  end = start,
  verbose = FALSE,
  return_sf = FALSE,
  extract_metadata = FALSE,
  progress = TRUE
)

aemet_monthly_period_all(
  start = as.integer(format(Sys.Date(), "%Y")),
  end = start,
  verbose = FALSE,
  return_sf = FALSE,
  extract_metadata = FALSE,
  progress = TRUE
)

Arguments

Value

A tibble or a sf object.

API Key

You need to set your API Key globally using aemet_api_key().

See Also

Other aemet_api_data: aemet_alert_zones(), aemet_alerts(), aemet_beaches(), aemet_daily_clim(), aemet_extremes_clim(), aemet_forecast_beaches(), aemet_forecast_daily(), aemet_forecast_fires(), aemet_last_obs(), aemet_normal, aemet_stations()

Examples

library(tibble)
obs <- aemet_monthly_clim(station = c("9434", "3195"), year = 2000)
glimpse(obs)

Data set with all the municipalities of Spain

Description

A tibble with all the municipalities of Spain as defined by the INE (Instituto Nacional de Estadistica) as of January 2025.

Format

A tibble with 8,132 rows and fields:

municipio

INE code of the municipality.

municipio_nombre

INE name of the municipality.

cpro

INE code of the province.

cpro_nombre

INE name of the province.

codauto

INE code of the autonomous community.

codauto_nombre

INE code of the autonomous community.

Source

INE,Municipality codes by province:

https://www.ine.es/daco/daco42/codmun/diccionario25.xlsx

See Also

aemet_forecast_daily(), aemet_forecast_hourly()

Other dataset: climaemet_9434_climatogram, climaemet_9434_temp, climaemet_9434_wind

Examples

data(aemet_munic)

aemet_munic

Normal climatology values

Description

Get normal climatology values for a station (or all the stations with aemet_normal_clim_all(). Standard climatology from 1981 to 2010.

Usage

aemet_normal_clim(
  station = NULL,
  verbose = FALSE,
  return_sf = FALSE,
  extract_metadata = FALSE,
  progress = TRUE
)

aemet_normal_clim_all(
  verbose = FALSE,
  return_sf = FALSE,
  extract_metadata = FALSE,
  progress = TRUE
)

Arguments

Value

A tibble or a sf object.

API Key

You need to set your API Key globally using aemet_api_key().

Note

Code modified from project https://github.com/SevillaR/aemet.

See Also

Other aemet_api_data: aemet_alert_zones(), aemet_alerts(), aemet_beaches(), aemet_daily_clim(), aemet_extremes_clim(), aemet_forecast_beaches(), aemet_forecast_daily(), aemet_forecast_fires(), aemet_last_obs(), aemet_monthly, aemet_stations()

Examples

library(tibble)
obs <- aemet_normal_clim(c("9434", "3195"))
glimpse(obs)

AEMET stations

Description

Get AEMET stations.

Usage

aemet_stations(verbose = FALSE, return_sf = FALSE)

Arguments

Details

The first result of the API call on each session is (temporarily) cached in the assigned tempdir() for avoiding unneeded API calls.

Value

A tibble or a sf object.

API Key

You need to set your API Key globally using aemet_api_key().

Note

Code modified from project https://github.com/SevillaR/aemet.

See Also

Other aemet_api_data: aemet_alert_zones(), aemet_alerts(), aemet_beaches(), aemet_daily_clim(), aemet_extremes_clim(), aemet_forecast_beaches(), aemet_forecast_daily(), aemet_forecast_fires(), aemet_last_obs(), aemet_monthly, aemet_normal

Examples

library(tibble)
stations <- aemet_stations()
stations

# Cached during this R session
stations2 <- aemet_stations(verbose = TRUE)

identical(stations, stations2)

Climatogram data for Zaragoza Airport ("9434") period 1981-2010

Description

Normal data for Zaragoza Airport (1981-2010). This is an example dataset used to plot climatograms.

Format

A data.frame with columns 1 to 12 (months) and rows:

p_mes_md

Precipitation (mm).

tm_max_md

Maximum temperature (Celsius).

tm_min_md

Minimum temperature (Celsius).

ta_min_md

Absolute monthly minimum temperature (Celsius).

Source

AEMET.

See Also

ggclimat_walter_lieth(), climatogram_period(), climatogram_normal()

Other dataset: aemet_munic, climaemet_9434_temp, climaemet_9434_wind

Other climatogram: climatogram_normal(), climatogram_period(), ggclimat_walter_lieth()

Examples

data(climaemet_9434_climatogram)

Average annual temperatures for Zaragoza Airport ("9434") period 1950-2020

Description

Yearly observations of average temperature for Zaragoza Airport (1950-2020). This is an example dataset.

Format

A tibble with columns:

year

Year of reference.

indicativo

Identifier of the station.

temp

Average temperature (Celsius).

Source

AEMET.

See Also

Other dataset: aemet_munic, climaemet_9434_climatogram, climaemet_9434_wind

Other stripes: climatestripes_station(), ggstripes()

Examples

data(climaemet_9434_temp)

Wind conditions for Zaragoza Airport ("9434") period 2000-2020

Description

Daily observations of wind speed and directions for Zaragoza Airport (2000-2020). This is an example dataset.

Format

A tibble with columns:

fecha

Date of observation.

dir

Wind directions (0-360).

velmedia

Average wind speed (km/h)

Source

AEMET.

See Also

Other dataset: aemet_munic, climaemet_9434_climatogram, climaemet_9434_temp

Other wind: ggwindrose(), windrose_days(), windrose_period()

Examples

data(climaemet_9434_wind)

Show the latest news of climaemet

Description

Open the NEWS file of the climaemet package.

Usage

climaemet_news()

Value

Nothing, this function is called by its side effects.

See Also

Other helpers: dms2decdegrees(), first_day_of_year()

Examples

## Not run: 
climaemet_news()

## End(Not run)

Station climate stripes graph

Description

Plot climate stripes graph for a station.

Usage

climatestripes_station(
  station,
  start = 1950,
  end = 2020,
  with_labels = "yes",
  verbose = FALSE,
  ...
)

Arguments

Value

A ggplot2 object

API Key

You need to set your API Key globally using aemet_api_key().

See Also

ggstripes()

Other aemet_plots: climatogram_normal(), climatogram_period(), ggclimat_walter_lieth(), ggstripes(), ggwindrose(), windrose_days(), windrose_period()

Other stripes: climaemet_9434_temp, ggstripes()

Examples

# Don't run example
if (FALSE) {
  # Data download may take a few minutes...
  climatestripes_station(
    "9434",
    start = 2020,
    end = 2024,
    with_labels = "yes",
    col_pal = "Inferno"
  )
}

Walter & Lieth climatic diagram from normal climatology values

Description

Plot of a Walter & Lieth climatic diagram from normal climatology data for a station. This climatogram are great for showing a summary of climate conditions for a place over a time period (1981-2010).

Usage

climatogram_normal(
  station,
  labels = "en",
  verbose = FALSE,
  ggplot2 = TRUE,
  ...
)

Arguments

Value

A plot.

API Key

You need to set your API Key globally using aemet_api_key().

Note

The code is based on code from the CRAN package climatol.

References

• Walter, H. K., Harnickell, E., Lieth, F. H. H., & Rehder, H. (1967). Klimadiagramm-weltatlas. Jena: Fischer, 1967.

• Guijarro J. A. (2023). climatol: Climate Tools (Series Homogenization and Derived Products). R package version 4.0.0, https://climatol.eu.

See Also

Other aemet_plots: climatestripes_station(), climatogram_period(), ggclimat_walter_lieth(), ggstripes(), ggwindrose(), windrose_days(), windrose_period()

Other climatogram: climaemet_9434_climatogram, climatogram_period(), ggclimat_walter_lieth()

Examples

climatogram_normal("9434")

Walter & Lieth climatic diagram for a time period

Description

Plot of a Walter & Lieth climatic diagram from monthly climatology data for a station. This climatogram are great for showing a summary of climate conditions for a place over a specific time period.

Usage

climatogram_period(
  station = NULL,
  start = 1990,
  end = 2020,
  labels = "en",
  verbose = FALSE,
  ggplot2 = TRUE,
  ...
)

Arguments

Value

A plot.

API Key

You need to set your API Key globally using aemet_api_key().

Note

The code is based on code from the CRAN package climatol.

References

• Walter, H. K., Harnickell, E., Lieth, F. H. H., & Rehder, H. (1967). Klimadiagramm-weltatlas. Jena: Fischer, 1967.

• Guijarro J. A. (2023). climatol: Climate Tools (Series Homogenization and Derived Products). R package version 4.0.0, https://climatol.eu.

See Also

Other aemet_plots: climatestripes_station(), climatogram_normal(), ggclimat_walter_lieth(), ggstripes(), ggwindrose(), windrose_days(), windrose_period()

Other climatogram: climaemet_9434_climatogram, climatogram_normal(), ggclimat_walter_lieth()

Examples

climatogram_period("9434", start = 2015, end = 2020, labels = "en")

Converts dms format to decimal degrees

Description

Converts degrees, minutes and seconds to decimal degrees.

Usage

dms2decdegrees(input = NULL)

dms2decdegrees_2(input = NULL)

Arguments

Value

A numeric value.

Note

Code for dms2decdegrees() modified from project https://github.com/SevillaR/aemet.

See Also

Other helpers: climaemet_news(), first_day_of_year()

Examples

dms2decdegrees("055245W")
dms2decdegrees_2("-3º 40' 37\"")

First and last day of year

Description

Get first and last day of year.

Usage

first_day_of_year(year = NULL)

last_day_of_year(year = NULL)

Arguments

Value

Character string as date (format: YYYY-MM-DD).

See Also

Other helpers: climaemet_news(), dms2decdegrees()

Examples

first_day_of_year(2000)
last_day_of_year(2020)

Client tool for AEMET API

Description

Client tool to get data and metadata from AEMET and convert json to tibble.

Usage

get_data_aemet(apidest, verbose = FALSE)

get_metadata_aemet(apidest, verbose = FALSE)

Arguments

Value

A tibble (if possible) or the results of the query as provided by httr2::resp_body_raw() or httr2::resp_body_string().

Source

https://opendata.aemet.es/dist/index.html.

See Also

Some examples on how to use these functions on vignette("extending-climaemet").

Examples

# Run this example only if AEMET_API_KEY is detected

url <- "/api/valores/climatologicos/inventarioestaciones/todasestaciones"

get_data_aemet(url)


# Metadata

get_metadata_aemet(url)

# We can get data from any API endpoint

# Plain text

plain <- get_data_aemet("/api/prediccion/nacional/hoy")

cat(plain)

# An image

image <- get_data_aemet("/api/mapasygraficos/analisis")

# Write and read
tmp <- tempfile(fileext = ".gif")

writeBin(image, tmp)

gganimate::gif_file(tmp)

Walter and Lieth climatic diagram on ggplot2

Description

Plot of a Walter and Lieth climatic diagram of a station. This function is an updated version of climatol::diagwl(), by Jose A. Guijarro.

Usage

ggclimat_walter_lieth(
  dat,
  est = "",
  alt = NA,
  per = NA,
  mlab = "es",
  pcol = "#002F70",
  tcol = "#ff0000",
  pfcol = "#9BAEE2",
  sfcol = "#3C6FC4",
  shem = FALSE,
  p3line = FALSE,
  ...
)

Arguments

Details

See Details on climatol::diagwl().

Climatic data must be passed as a 4x12 matrix or data.frame of monthly (January to December) data, in the following order:

• Row 1: Mean precipitation.

• Row 2: Mean maximum daily temperature.

• Row 3: Mean minimum daily temperature.

• Row 4: Absolute monthly minimum temperature.

See climaemet_9434_climatogram for a sample dataset.

Value

A ggplot2 object. See help("ggplot2").

API Key

You need to set your API Key globally using aemet_api_key().

References

• Walter, H. K., Harnickell, E., Lieth, F. H. H., & Rehder, H. (1967). Klimadiagramm-weltatlas. Jena: Fischer, 1967.

See Also

climatol::diagwl(), readr::locale()

Other aemet_plots: climatestripes_station(), climatogram_normal(), climatogram_period(), ggstripes(), ggwindrose(), windrose_days(), windrose_period()

Other climatogram: climaemet_9434_climatogram, climatogram_normal(), climatogram_period()

Examples

library(ggplot2)

wl <- ggclimat_walter_lieth(
  climaemet::climaemet_9434_climatogram,
  alt = "249",
  per = "1981-2010",
  est = "Zaragoza Airport"
)

wl

# As it is a ggplot object we can modify it

wl + theme(
  plot.background = element_rect(fill = "grey80"),
  panel.background = element_rect(fill = "grey70"),
  axis.text.y.left = element_text(
    colour = "black",
    face = "italic"
  ),
  axis.text.y.right = element_text(
    colour = "black",
    face = "bold"
  )
)

Warming stripes graph

Description

Plot different "climate stripes" or "warming stripes" using ggplot2. This graphics are visual representations of the change in temperature as measured in each location over the past 70-100+ years. Each stripe represents the temperature in that station averaged over a year.

Usage

ggstripes(
  data,
  plot_type = "stripes",
  plot_title = "",
  n_temp = 11,
  col_pal = "RdBu",
  ...
)

Arguments

Value

A ggplot2 object

API Key

You need to set your API Key globally using aemet_api_key().

Note

"Warming stripes" charts are a conceptual idea of Professor Ed Hawkins (University of Reading) and are specifically designed to be as simple as possible and alert about risks of climate change. For more details see ShowYourStripes.

See Also

climatestripes_station(), ggplot2::theme() for more possible arguments to pass to ggstripes.

Other aemet_plots: climatestripes_station(), climatogram_normal(), climatogram_period(), ggclimat_walter_lieth(), ggwindrose(), windrose_days(), windrose_period()

Other stripes: climaemet_9434_temp, climatestripes_station()

Examples

library(ggplot2)

data <- climaemet::climaemet_9434_temp

ggstripes(data, plot_title = "Zaragoza Airport") +
  labs(subtitle = "(1950-2020)")

ggstripes(data, plot_title = "Zaragoza Airport", plot_type = "trend") +
  labs(subtitle = "(1950-2020)")

Windrose (speed/direction) diagram

Description

Plot a windrose showing the wind speed and direction using ggplot2.

Usage

ggwindrose(
  speed,
  direction,
  n_directions = 8,
  n_speeds = 5,
  speed_cuts = NA,
  col_pal = "GnBu",
  legend_title = "Wind speed (m/s)",
  calm_wind = 0,
  n_col = 1,
  facet = NULL,
  plot_title = "",
  stack_reverse = FALSE,
  ...
)

Arguments

Value

A ggplot2 object.

API Key

You need to set your API Key globally using aemet_api_key().

See Also

ggplot2::theme() for more possible arguments to pass to ggwindrose.

Other aemet_plots: climatestripes_station(), climatogram_normal(), climatogram_period(), ggclimat_walter_lieth(), ggstripes(), windrose_days(), windrose_period()

Other wind: climaemet_9434_wind, windrose_days(), windrose_period()

Examples

library(ggplot2)

speed <- climaemet::climaemet_9434_wind$velmedia
direction <- climaemet::climaemet_9434_wind$dir

rose <- ggwindrose(
  speed = speed,
  direction = direction,
  speed_cuts = seq(0, 16, 4),
  legend_title = "Wind speed (m/s)",
  calm_wind = 0,
  n_col = 1,
  plot_title = "Zaragoza Airport"
)
rose + labs(
  subtitle = "2000-2020",
  caption = "Source: AEMET"
)

# Reverse stack

ggwindrose(
  speed = speed,
  direction = direction,
  speed_cuts = seq(0, 16, 4),
  legend_title = "Wind speed (m/s)",
  calm_wind = 0,
  n_col = 1,
  plot_title = "Zaragoza Airport",
  stack_reverse = TRUE
) +
  labs(
    subtitle = "2000-2020",
    caption = "Source: AEMET"
  )

Windrose (speed/direction) diagram of a station over a days period

Description

Plot a windrose showing the wind speed and direction for a station over a days period.

Usage

windrose_days(
  station,
  start = "2000-12-01",
  end = "2000-12-31",
  n_directions = 8,
  n_speeds = 5,
  speed_cuts = NA,
  col_pal = "GnBu",
  calm_wind = 0,
  legend_title = "Wind Speed (m/s)",
  verbose = FALSE
)

Arguments

Value

A ggplot2 object.

API Key

You need to set your API Key globally using aemet_api_key().

See Also

aemet_daily_clim()

Other aemet_plots: climatestripes_station(), climatogram_normal(), climatogram_period(), ggclimat_walter_lieth(), ggstripes(), ggwindrose(), windrose_period()

Other wind: climaemet_9434_wind, ggwindrose(), windrose_period()

Examples

windrose_days("9434",
  start = "2000-12-01",
  end = "2000-12-31",
  speed_cuts = 4
)

Windrose (speed/direction) diagram of a station over a time period

Description

Plot a windrose showing the wind speed and direction for a station over a time period.

Usage

windrose_period(
  station,
  start = 2000,
  end = 2010,
  n_directions = 8,
  n_speeds = 5,
  speed_cuts = NA,
  col_pal = "GnBu",
  calm_wind = 0,
  legend_title = "Wind Speed (m/s)",
  verbose = FALSE
)

Arguments

Value

A ggplot2 object

API Key

You need to set your API Key globally using aemet_api_key().

See Also

aemet_daily_period()

Other aemet_plots: climatestripes_station(), climatogram_normal(), climatogram_period(), ggclimat_walter_lieth(), ggstripes(), ggwindrose(), windrose_days()

Other wind: climaemet_9434_wind, ggwindrose(), windrose_days()

Examples

# Don't run example
if (FALSE) {
  # Data download may take a few minutes...
  windrose_period("9434",
    start = 2000, end = 2010,
    speed_cuts = 4
  )
}