Tools for AEMET Climate Data • climaemet

The goal of climaemet is to provide an interface for downloading climate data from the Spanish Meteorological Agency (AEMET) directly in R and creating scientific visualizations (climate charts, trend analysis of climate time series, temperature and precipitation anomaly maps, “warming stripes”, climatograms, etc.).

Browse the manual and vignettes at https://ropenspain.github.io/climaemet/.

AEMET Open Data

AEMET Open Data is a REST API developed by AEMET for disseminating and reusing the agency’s meteorological and climatological information. For more details, visit https://opendata.aemet.es/centrodedescargas/inicio.

License for the original data

Information prepared by the Spanish Meteorological Agency (© AEMET). You can read about it here.

A summary of data usage is:

People can use these data freely. You should mention AEMET as the collector of the original data in every situation except when you are using these data privately and individually. AEMET makes no warranty as to the accuracy or completeness of the data. All data are provided on an “as is” basis. AEMET is not responsible for any damage or loss derived from the interpretation or use of these data.

Installation

You can install the released version of climaemet from CRAN with:

install.packages("climaemet")

You can install the development version of climaemet using the r-universe:

# Install climaemet in R:
install.packages(
  "climaemet",
  repos = c(
    "https://ropenspain.r-universe.dev",
    "https://cloud.r-project.org"
  )
)

Alternatively, you can install the development version of climaemet with:

# install.packages("pak")
pak::pak("ropenspain/climaemet")

API key

To download data from AEMET, you need a free API key, which you can get here.

library(climaemet)

## Get API key from AEMET.
browseURL("https://opendata.aemet.es/centrodedescargas/altaUsuario")

## Use this function to register your API key temporarily or permanently.
aemet_api_key("MY API KEY")

Changes in v1.0.0

The apikey argument in the functions is now deprecated. You may need to set your API key globally using aemet_api_key(). Note that you also need to remove the apikey argument from old code.

Tidy outputs

From v1.0.0 onward, climaemet provides its results in tibble format. The functions also try to infer the correct format of fields. For example, date and hour fields are parsed as date-time objects and numeric fields are parsed as doubles.

library(climaemet)

# See a tibble in action

aemet_last_obs("9434")
#> # A tibble: 12 × 25
#>    idema   lon fint                 prec   alt  vmax    vv    dv   lat  dmax
#>    <chr> <dbl> <dttm>              <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
#>  1 9434  -1.00 2026-05-18 10:00:00     0   249   3.9   0.9   174  41.7   313
#>  2 9434  -1.00 2026-05-18 11:00:00     0   249   2.8   1.2    32  41.7   285
#>  3 9434  -1.00 2026-05-18 12:00:00     0   249   3.7   1.9    97  41.7    83
#>  4 9434  -1.00 2026-05-18 13:00:00     0   249   3.8   1.3    31  41.7    65
#>  5 9434  -1.00 2026-05-18 14:00:00     0   249   4.8   2.1    80  41.7    63
#>  6 9434  -1.00 2026-05-18 15:00:00     0   249   4.3   1.7   337  41.7    70
#>  7 9434  -1.00 2026-05-18 16:00:00     0   249   5.4   2.1   328  41.7   298
#>  8 9434  -1.00 2026-05-18 17:00:00     0   249   6.5   4.6   324  41.7   330
#>  9 9434  -1.00 2026-05-18 18:00:00     0   249   6.8   3.3   326  41.7   310
#> 10 9434  -1.00 2026-05-18 19:00:00     0   249   5.4   1.4   350  41.7   345
#> 11 9434  -1.00 2026-05-18 20:00:00     0   249   3.6   2.7   316  41.7   310
#> 12 9434  -1.00 2026-05-18 21:00:00     0   249   4.2   3     273  41.7   313
#> # ℹ 15 more variables: ubi <chr>, pres <dbl>, hr <dbl>, stdvv <dbl>, ts <dbl>,
#> #   pres_nmar <dbl>, tamin <dbl>, ta <dbl>, tamax <dbl>, tpr <dbl>,
#> #   stddv <dbl>, inso <dbl>, tss5cm <dbl>, pacutp <dbl>, tss20cm <dbl>

Spatial outputs

Another major change in v1.0.0 is the ability to return information as spatial sf objects using return_sf = TRUE. The coordinate reference system (CRS) is EPSG 4326, which corresponds to the World Geodetic System (WGS) and returns coordinates in latitude/longitude (unprojected coordinates):

# You need to install sf if it is not already installed.
# Run install.packages("sf") to install it.
library(ggplot2)
library(dplyr)

all_stations <- aemet_daily_clim(
  start = "2021-01-08",
  end = "2021-01-08",
  return_sf = TRUE
)

ggplot(all_stations) +
  geom_sf(aes(colour = tmed), shape = 19, size = 2, alpha = 0.95) +
  labs(
    title = "Average temperature in Spain",
    subtitle = "8 Jan 2021",
    color = "Max temp.\n(celsius)",
    caption = "Source: AEMET"
  ) +
  scale_colour_gradientn(
    colours = hcl.colors(10, "RdBu", rev = TRUE),
    breaks = c(-10, -5, 0, 5, 10, 15, 20),
    guide = "legend"
  ) +
  theme_bw() +
  theme(
    panel.border = element_blank(),
    plot.title = element_text(face = "bold"),
    plot.subtitle = element_text(face = "italic")
  )

Example of map created with climaemet and sf.

Plots

You can also draw a “warming stripes” graph with the downloaded data from a weather station. These functions return ggplot2 plots:

# Plot a climate stripes graph for a period of years for a station.

library(ggplot2)

# Example data
temp_data <- climaemet::climaemet_9434_temp

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

Example of climate stripes plot created with climaemet.

You can also draw the well-known Walter & Lieth climatic diagram for a weather station and over a specified period of time:

# Plot a Walter & Lieth climatic diagram for a station.

# Example data
wl_data <- climaemet::climaemet_9434_climatogram

ggclimat_walter_lieth(
  wl_data,
  alt = "249",
  per = "1981-2010",
  est = "Zaragoza Airport"
)

Plot of a Walter & Lieth climatic diagram for a station.

Additionally, you can plot wind speed and direction over time for weather station data.

# Plot a windrose showing wind speed and direction for a station.

# Example data
wind_data <- climaemet::climaemet_9434_wind

speed <- wind_data$velmedia
direction <- wind_data$dir

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"
) +
  labs(subtitle = "2000-2020", caption = "Source: AEMET")

Plot of a windrose showing wind speed and direction.

Code of Conduct

Please note that this project is released with a Contributor Code of Conduct. By participating in this project you agree to abide by its terms.

Citation

Using climaemet for a paper you are writing? Consider citing it:

Pizarro M, Hernangómez D, Fernández-Avilés G (2021). climaemet: Climate AEMET Tools. doi:10.32614/CRAN.package.climaemet.

A BibTeX entry for LaTeX users is:

@Manual{R-climaemet,
  title = {{climaemet}: Climate {AEMET} Tools},
  author = {Manuel Pizarro and Diego Hernangómez and Gema Fernández-Avilés},
  abstract = {The goal of climaemet is to serve as an interface to download the climatic data of the Spanish Meteorological Agency (AEMET) directly from R using their API (https://opendata.aemet.es/) and create scientific graphs (climate charts, trend analysis of climate time series, temperature and precipitation anomalies maps, “warming stripes” graphics, climatograms, etc.).},
  year = {2021},
  month = {8},
  doi = {10.32614/CRAN.package.climaemet},
  keywords = {Climate, Rcran,  Tools, Graphics, Interpolation, Maps},
}

climaemet