The goal of climaemet is to provide an interface for downloading climate data from the Spanish Meteorological Agency (AEMET) directly in R and for creating scientific visualizations, including climate charts, trend analysis of climate time series, temperature and precipitation anomaly maps, warming stripes and climatograms.
Browse the manual and vignettes at https://ropenspain.github.io/climaemet/.
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.
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.
You can install the released version of climaemet from CRAN with:
install.packages("climaemet")You can install the development version of climaemet from 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")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")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 older code.
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: 13 × 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 2026-05-20 05:00:00 0 249 5.9 4 283 41.7 285
#> 2 9434 -1.00 2026-05-20 06:00:00 0 249 7.6 6 300 41.7 295
#> 3 9434 -1.00 2026-05-20 07:00:00 0 249 9.3 7.2 308 41.7 320
#> 4 9434 -1.00 2026-05-20 08:00:00 0 249 9.8 6.1 302 41.7 308
#> 5 9434 -1.00 2026-05-20 09:00:00 0 249 8.4 4.8 325 41.7 315
#> 6 9434 -1.00 2026-05-20 10:00:00 0 249 7 4.5 301 41.7 308
#> 7 9434 -1.00 2026-05-20 11:00:00 0 249 6.3 3 277 41.7 310
#> 8 9434 -1.00 2026-05-20 12:00:00 0 249 5 2.2 329 41.7 298
#> 9 9434 -1.00 2026-05-20 13:00:00 0 249 3.3 1.4 74 41.7 95
#> 10 9434 -1.00 2026-05-20 14:00:00 0 249 3.8 1.4 23 41.7 60
#> 11 9434 -1.00 2026-05-20 15:00:00 0 249 4 1.1 312 41.7 88
#> 12 9434 -1.00 2026-05-20 16:00:00 0 249 4 1.8 82 41.7 80
#> 13 9434 -1.00 2026-05-20 17:00:00 0 249 4.1 2 81 41.7 105
#> # 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>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 1984 (WGS 84) 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")
)
You can also draw a warming stripes graph from downloaded weather-station data. 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)")
You can also draw the well-known Walter & Lieth climatic diagram for a weather station over a specified period:
# 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"
)
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")
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.
If you use climaemet in a paper, please 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},
}
- Download from CRAN at https://cran.r-project.org/package=climaemet
- Browse source code at https://github.com/ropenspain/climaemet