climaemet

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 2024-09-02 00:00:00     0   249   4.6   3     309  41.7   295
#>  2 9434  -1.00 2024-09-02 01:00:00     0   249   4.9   2.3   310  41.7   323
#>  3 9434  -1.00 2024-09-02 02:00:00     0   249   4     2.9   316  41.7   313
#>  4 9434  -1.00 2024-09-02 03:00:00     0   249   4.7   3     287  41.7   305
#>  5 9434  -1.00 2024-09-02 04:00:00     0   249   5.5   2.6   302  41.7   300
#>  6 9434  -1.00 2024-09-02 05:00:00     0   249   5.2   3.1   281  41.7   288
#>  7 9434  -1.00 2024-09-02 06:00:00     0   249   5.9   2.5   292  41.7   238
#>  8 9434  -1.00 2024-09-02 07:00:00     0   249   4     2.6   291  41.7   283
#>  9 9434  -1.00 2024-09-02 08:00:00     0   249   3.5   1     323  41.7   308
#> 10 9434  -1.00 2024-09-02 09:00:00     0   249   2.1   0.7   190  41.7    40
#> 11 9434  -1.00 2024-09-02 10:00:00     0   249   2.8   1.4   147  41.7   100
#> 12 9434  -1.00 2024-09-02 11:00:00     0   249   3.2   1.9   115  41.7    78
#> 13 9434  -1.00 2024-09-02 12:00:00     0   249   4.4   1.8    72  41.7    93
#> # 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	alt	vmax	vv	dv	lat	dmax	ubi	pres	hr	stdvv	ts	pres_nmar	tamin	ta	tamax	tpr	stddv	tss5cm	tss20cm
9434	-1.004167	2024-09-02 00:00:00	249	4.6	3.0	309	41.66056	295	ZARAGOZA AEROPUERTO	984.9	72	0.6	24.0	1013.6	24.3	24.3	24.9	18.9	11	26.8	29.0
9434	-1.004167	2024-09-02 01:00:00	249	4.9	2.3	310	41.66056	323	ZARAGOZA AEROPUERTO	984.7	78	0.4	22.8	1013.5	23.2	23.2	24.3	19.1	11	26.3	28.8
9434	-1.004167	2024-09-02 02:00:00	249	4.0	2.9	316	41.66056	313	ZARAGOZA AEROPUERTO	984.7	79	0.4	22.4	1013.5	22.5	22.5	23.2	18.7	11	25.9	28.6
9434	-1.004167	2024-09-02 03:00:00	249	4.7	3.0	287	41.66056	305	ZARAGOZA AEROPUERTO	984.5	78	0.4	22.2	1013.4	22.2	22.3	22.5	18.3	8	25.5	28.4
9434	-1.004167	2024-09-02 04:00:00	249	5.5	2.6	302	41.66056	300	ZARAGOZA AEROPUERTO	984.3	79	0.6	21.6	1013.2	21.8	21.8	22.3	18.0	11	25.0	28.1
9434	-1.004167	2024-09-02 05:00:00	249	5.2	3.1	281	41.66056	288	ZARAGOZA AEROPUERTO	984.3	82	0.4	20.9	1013.3	21.1	21.1	21.9	17.9	7	24.6	27.9

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

Links

Download from CRAN at https://cran.r-project.org/package=climaemet
Browse source code at https://github.com/ropenspain/climaemet

Name		Name	Last commit message	Last commit date
Latest commit History 336 Commits
.github		.github
R		R
data-raw		data-raw
data		data
inst		inst
man		man
pkgdown		pkgdown
revdep		revdep
tests		tests
vignettes		vignettes
.Rbuildignore		.Rbuildignore
.gitignore		.gitignore
.lintr		.lintr
CITATION.cff		CITATION.cff
CODE_OF_CONDUCT.md		CODE_OF_CONDUCT.md
CONTRIBUTING.md		CONTRIBUTING.md
DESCRIPTION		DESCRIPTION
LICENSE		LICENSE
LICENSE.md		LICENSE.md
NAMESPACE		NAMESPACE
NEWS.md		NEWS.md
README.Rmd		README.Rmd
README.md		README.md
climaemet.Rproj		climaemet.Rproj
codecov.yml		codecov.yml
codemeta.json		codemeta.json

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climaemet

AEMET Open Data

License for the original data

Installation

API key

Changes on v1.0.0!

Now climaemet is tidy…

Examples

… and spatial!

Code of Conduct

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climaemet

AEMET Open Data

License for the original data

Installation

API key

Changes on v1.0.0!

Now climaemet is tidy…

Examples

… and spatial!

Code of Conduct

Citation

Links

About

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