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climaemet

rOS-badge CRAN status CRAN_time_from_release CRAN_latest_release_date CRAN results r-universe R-CMD-check R-hub codecov DOI metacran downloads license Project Status: Active – The project has reached a stable, usable state and is being actively developed.

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 and create scientific graphs (climate charts, trend analysis of climate time series, temperature and precipitation anomalies maps, “warming stripes” graphics, climatograms, etc.).

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

AEMET Open Data

AEMET OpenData is a REST API developed by AEMET that allows the dissemination and reuse of the Agency’s meteorological and climatological information. To see 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 for the usage of the data could be interpreted as:

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.

Installation

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:

library(remotes)
install_github("ropenspain/climaemet")

API key

To be able to download data from AEMET you will 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 temporarly or permanently
aemet_api_key("MY API KEY")

Changes on v1.0.0!

Now the apikey parameter on the functions have been deprecated. You may need to set your API Key globally using aemet_api_key(). Note that you would need also to remove the apikey parameter on your old codes.

Now climaemet is tidy…

From v1.0.0 onward, climaemet provides its results in tibble format. Also, the functions try to guess the correct format of the fields (i.e. something as a Date/Hour now is an hour, numbers are parsed as double, etc.).

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 2024-08-27 23:00:00     0   249   3.6   1      44  41.7    93
#>  2 9434  -1.00 2024-08-28 00:00:00     0   249   4.4   3     101  41.7   105
#>  3 9434  -1.00 2024-08-28 01:00:00     0   249   4.9   2.4    83  41.7    88
#>  4 9434  -1.00 2024-08-28 02:00:00     0   249   6.3   3.6   113  41.7   120
#>  5 9434  -1.00 2024-08-28 03:00:00     0   249   6.2   3.1   117  41.7   130
#>  6 9434  -1.00 2024-08-28 04:00:00     0   249   5.9   4     121  41.7   130
#>  7 9434  -1.00 2024-08-28 05:00:00     0   249   5.4   2.3   132  41.7   140
#>  8 9434  -1.00 2024-08-28 06:00:00     0   249   5     2.9   121  41.7   128
#>  9 9434  -1.00 2024-08-28 07:00:00     0   249   5.7   3.3   117  41.7    95
#> 10 9434  -1.00 2024-08-28 08:00:00     0   249   7.5   4.4   112  41.7   110
#> 11 9434  -1.00 2024-08-28 09:00:00     0   249   6.5   3.8   110  41.7   110
#> 12 9434  -1.00 2024-08-28 10:00:00     0   249   6.5   3.8   128  41.7   155
#> 13 9434  -1.00 2024-08-28 11:00:00     0   249   6.5   3.4   121  41.7   115
#> # 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>

Examples

The package provides several functions to access the data of the API. Here you can find some examples:

## Get AEMET stations
stations <- aemet_stations() # Need to have the API Key registered

knitr::kable(head(stations))
indicativo indsinop nombre provincia altitud longitud latitud
B013X 08304 ESCORCA, LLUC ILLES BALEARS 490 2.885833 39.82333
B051A 08316 SÓLLER, PUERTO ILLES BALEARS 5 2.691389 39.79556
B087X BANYALBUFAR ILLES BALEARS 60 2.512778 39.68917
B103B 99103 ANDRATX - SANT ELM ILLES BALEARS 52 2.368889 39.57917
B158X CALVIÀ, ES CAPDELLÀ ILLES BALEARS 50 2.466389 39.55139
B228 08301 PALMA, PUERTO ILLES BALEARS 3 2.625278 39.55528 

station <- "9434" # Zaragoza Aeropuerto

## Get last observation values for a station
data_observation <- aemet_last_obs(station)

knitr::kable(head(data_observation))
idema lon fint prec alt vmax vv dv lat dmax ubi pres hr stdvv ts pres_nmar tamin ta tamax tpr stddv inso tss5cm pacutp tss20cm
9434 -1.004167 2024-08-27 23:00:00 0 249 3.6 1.0 44 41.66056 93 ZARAGOZA AEROPUERTO 984.1 56 0.3 24.4 1012.6 25.7 25.7 27.5 16.2 60 0 30.9 0 31.9
9434 -1.004167 2024-08-28 00:00:00 0 249 4.4 3.0 101 41.66056 105 ZARAGOZA AEROPUERTO 984.3 69 0.4 24.9 1012.9 25.0 25.2 25.7 19.1 8 0 30.3 0 31.8
9434 -1.004167 2024-08-28 01:00:00 0 249 4.9 2.4 83 41.66056 88 ZARAGOZA AEROPUERTO 984.5 67 0.6 24.3 1013.1 24.7 24.7 25.2 18.2 16 0 29.9 0 31.5
9434 -1.004167 2024-08-28 02:00:00 0 249 6.3 3.6 113 41.66056 120 ZARAGOZA AEROPUERTO 984.7 76 0.9 23.8 1013.4 24.1 24.1 24.7 19.6 9 0 29.5 0 31.3
9434 -1.004167 2024-08-28 03:00:00 0 249 6.2 3.1 117 41.66056 130 ZARAGOZA AEROPUERTO 984.8 79 0.6 23.5 1013.5 23.7 23.7 24.1 19.8 9 0 29.1 0 31.1
9434 -1.004167 2024-08-28 04:00:00 0 249 5.9 4.0 121 41.66056 130 ZARAGOZA AEROPUERTO 984.8 81 0.6 23.2 1013.5 23.4 23.4 23.7 20.0 7 0 28.7 0 30.9 

## Get daily/annual climatology values for a station
data_daily <- aemet_daily_clim(station,
  start = "2022-01-01",
  end = "2022-06-30"
)

knitr::kable(head(data_daily))
fecha indicativo nombre provincia altitud tmed prec tmin horatmin tmax horatmax dir velmedia racha horaracha sol presMax horaPresMax presMin horaPresMin hrMedia hrMax horaHrMax hrMin horaHrMin
2022-01-01 9434 ZARAGOZA, AEROPUERTO ZARAGOZA 249 4.5 0,0 3.2 07:50 5.8 15:00 24 1.7 5.6 17:10 0.0 1000.6 10 997.5 15 98 100 11:00 98 Varias
2022-01-02 9434 ZARAGOZA, AEROPUERTO ZARAGOZA 249 5.6 0,0 2.8 08:00 8.3 17:50 24 2.2 6.7 19:20 1.7 1000.2 10 997.1 16 96 100 Varias 89 15:40
2022-01-03 9434 ZARAGOZA, AEROPUERTO ZARAGOZA 249 7.8 0,0 2.5 06:50 13.0 15:10 10 1.1 5.6 21:40 5.8 997.6 00 988.4 24 88 100 10:50 67 15:10
2022-01-04 9434 ZARAGOZA, AEROPUERTO ZARAGOZA 249 11.2 7,0 5.3 07:30 17.2 14:20 32 2.8 16.4 19:00 3.5 988.4 00 976.6 17 87 95 01:40 47 13:50
2022-01-05 9434 ZARAGOZA, AEROPUERTO ZARAGOZA 249 7.0 0,0 4.2 23:59 9.9 14:20 31 9.2 18.6 05:10 4.9 987.9 10 982.1 00 67 83 00:00 53 14:00
2022-01-06 9434 ZARAGOZA, AEROPUERTO ZARAGOZA 249 5.6 0,0 2.9 06:20 8.2 15:20 30 7.5 16.4 03:20 8.9 991.4 24 986.4 00 49 73 22:30 38 14:10 


## Get monthly/annual climatology values for a station
data_monthly <- aemet_monthly_clim(station, year = 2022)
knitr::kable(head(data_monthly))
fecha indicativo p_max n_cub hr n_gra n_fog inso q_max nw_55 q_mar q_med tm_min ta_max ts_min nt_30 nv_0050 n_des w_racha np_100 n_nub p_sol nw_91 np_001 ta_min w_rec e np_300 nv_1000 p_mes n_llu n_tor w_med nt_00 ti_max n_nie tm_mes tm_max nv_0100 q_min np_010 evap
2022-01 9434 7.0(04) 4 68 0 3 7.6 1005.7(29) 7 1028.5 996.7 1.0 17.2(04) 11.2 0 0 21 31/24.2(31) 0 6 79 0 2 -3.2(23) 353 65 0 3 7.4 4 0 15 16 5.8 0 6.3 11.6 0 976.6(04) 1 1021
2022-02 9434 0.4(13) 2 57 0 2 7.8 1002.2(08) 9 1025.4 994.3 5.0 20.5(02) 9.3 0 0 7 30/23.9(01) 0 19 74 0 2 0.3(10) 408 73 0 0 0.8 4 0 17 0 12.4 0 10.4 15.8 0 982.3(14) 0 1632
2022-03 9434 6.2(11) 19 69 0 0 3.8 997.2(22) 7 1019.3 988.4 7.4 19.5(01) 11.8 0 0 0 31/18.6(17) 0 12 32 0 13 3.0(09) 388 92 0 0 33.6 13 1 16 0 10.5 0 11.1 14.8 0 971.2(30) 8 1367
2022-04 9434 11.8(27) 9 56 0 0 8.5 996.0(29) 9 1014.1 983.9 8.8 25.8(16) 14.2 0 0 5 28/22.2(08) 1 16 64 0 9 0.5(04) 469 95 0 0 31.0 11 0 20 0 10.2 0 14.1 19.3 0 965.4(23) 7 2357
2022-05 9434 5.6(03) 6 45 0 0 10.7 994.8(26) 5 1016.2 986.6 14.8 35.3(21) 19.9 14 0 11 31/19.2(26) 0 14 74 0 5 11.3(03) 399 118 0 0 13.8 5 2 18 0 18.3 0 21.6 28.2 0 972.7(29) 4 3486
2022-06 9434 6.4(16) 0 38 0 0 11.5 992.3(12) 12 1013.1 984.1 19.5 41.2(15) 24.0 26 0 6 28/22.5(11) 0 24 76 0 4 14.4(28) 383 132 0 0 9.4 5 5 16 0 24.8 0 26.7 33.9 0 975.2(19) 3 3560 


## Get recorded extreme values of temperature for a station
data_extremes <- aemet_extremes_clim(station, parameter = "T")
knitr::kable(head(data_extremes))
indicativo nombre ubicacion codigo temMin diaMin anioMin mesMin temMax diaMax anioMax mesMax temMedBaja anioMedBaja mesMedBaja temMedAlta anioMedAlta mesMedAlta temMedMin anioMedMin mesMedMin temMedMax anioMedMax mesMedMax
9434 ZARAGOZA, AEROPUERTO ZARAGOZA 023000 -104 4 1971 2 206 8 2016 7 29 1953 2 97 2016 8 -12 1957 2 135 2016 7
9434 ZARAGOZA, AEROPUERTO ZARAGOZA 023000 -114 5 1963 2 255 27 2019 7 15 1956 2 121 1990 8 -30 1956 2 180 1990 7
9434 ZARAGOZA, AEROPUERTO ZARAGOZA 023000 -63 9 1964 2 287 13 2023 7 71 1971 2 147 2023 8 19 1973 2 211 2023 7
9434 ZARAGOZA, AEROPUERTO ZARAGOZA 023000 -24 3 1967 2 324 9 2011 7 104 1986 2 174 2014 8 54 1970 2 240 2023 7
9434 ZARAGOZA, AEROPUERTO ZARAGOZA 023000 5 4 1967 2 365 29 2001 7 132 1984 2 216 2022 8 85 1984 2 282 2022 7
9434 ZARAGOZA, AEROPUERTO ZARAGOZA 023000 52 11 1971 2 432 29 2019 7 182 1953 2 267 2022 8 126 1969 2 339 2022 7

We can also draw a “warming stripes” graph with the downloaded data from a weather station. These functions returns 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 may be interested in drawing the wind speed and direction over a period of time for the data downloaded from a weather station.:

# 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")

… and spatial!

Another major change in v1.0.0 is the ability of return information on spatial sf format, using return_sf = TRUE. The coordinate reference system (CRS) used is EPSG 4326, that correspond to the World Geodetic System (WGS) and return coordinates in latitude/longitude (unprojected coordinates):

# You would need to install `sf` if not installed yet
# 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")
  )

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:10.5281/zenodo.5205573, https://hdl.handle.net/10261/250390.

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},
  url = {https://hdl.handle.net/10261/250390},
  doi = {10.5281/zenodo.5205573},
  keywords = {Climate, Rcran,  Tools, Graphics, Interpolation, Maps},
}

These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.
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