aemet_forecast: Municipality forecast dataset
In climaemet: Tools for AEMET Climate Data

aemet_forecast_daily

R Documentation

Municipality forecast dataset

Description

Get daily or hourly weather forecasts for one or more municipalities.

Usage

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

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

Arguments

`x`	Character vector with municipality codes to extract. For convenience, climaemet provides these data in the aemet_munic dataset (see `municipio` field) as of January 2024.
`verbose`	Logical. If `TRUE`, provides information about the flow of information between the client and server.
`extract_metadata`	Logical. If `TRUE`, the output is a tibble with the description of the fields. See also `get_metadata_aemet()`.
`progress`	Logical. Displays a `cli::cli_progress_bar()` object. If `verbose = TRUE`, it will not be displayed.

Details

Forecasts provided by the AEMET API have a complex structure. Although climaemet returns a tibble, each forecast value is provided as a nested tibble. The aemet_forecast_tidy() helper can unnest these values and 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. Forecast 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(). Query timeout can be controlled with options(climaemet_timeout = 60) (default value). See httr2::req_timeout() for details.

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)

# Variables 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 nested.
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"
  )

climaemet documentation built on June 3, 2026, 5:07 p.m.