The goal of climaemet is to provide an interface for downloading climatic 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 manual and vignettes at https://ropenspain.github.io/climaemet/.
AEMET OpenData is a REST API developed by AEMET that allows dissemination and reuse of 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 freely this data. You should mention AEMET as the collector of the original data in every situation except if you are using this 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 this data.
You can install the released version of climaemet from CRAN with:
install.packages("climaemet")You can install the developing 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 developing 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/obtencionAPIKey")
## Use this function to register your API Key temporarily or permanently
aemet_api_key("MY API KEY")Now the apikey argument in the functions has been 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.
From v1.0.0 onward, climaemet provides its results in tibble
format. Also, the functions try to infer
the correct format of fields (for example, date/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 2026-04-10 19:00:00 0 249 4.3 2.8 143 41.7 165
#> 2 9434 -1.00 2026-04-10 20:00:00 0 249 4.2 2.2 116 41.7 150
#> 3 9434 -1.00 2026-04-10 21:00:00 0 249 3.9 2.5 99 41.7 105
#> 4 9434 -1.00 2026-04-10 22:00:00 0 249 4.5 2.7 85 41.7 115
#> 5 9434 -1.00 2026-04-10 23:00:00 0 249 4.4 2.1 91 41.7 90
#> 6 9434 -1.00 2026-04-11 00:00:00 0 249 4 2.7 98 41.7 103
#> 7 9434 -1.00 2026-04-11 01:00:00 0 249 3.8 2.6 94 41.7 110
#> 8 9434 -1.00 2026-04-11 02:00:00 0 249 3.1 1.9 101 41.7 83
#> 9 9434 -1.00 2026-04-11 03:00:00 0 249 2.2 1 59 41.7 100
#> 10 9434 -1.00 2026-04-11 04:00:00 0 249 2.8 1.7 80 41.7 108
#> 11 9434 -1.00 2026-04-11 05:00:00 0 249 2.5 1.5 101 41.7 70
#> 12 9434 -1.00 2026-04-11 06:00:00 0 249 4.6 1.5 76 41.7 73
#> 13 9434 -1.00 2026-04-11 07:00:00 0 249 4.5 1.9 110 41.7 88
#> # 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 in
spatial sf format using return_sf = TRUE. The coordinate reference
system (CRS) used 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") for installation
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")
)
We 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)")
Furthermore, we can draw the well-known Walter & Lieth climatic diagram for a weather station and over a specified period of time:
# Plot of 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, we can plot wind speed and direction over time for weather station data.
# Plot a windrose showing the 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.
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},
}
- Download from CRAN at https://cran.r-project.org/package=climaemet
- Browse source code at https://github.com/ropenspain/climaemet