Analysing the climate at spatial points for a given period • easyclimate

With easyclimate you can easily download daily climate data for a given set of points or polygons within Europe. To download and install the latest version of easyclimate from github follow the instructions in https://github.com/VeruGHub/easyclimate

In this tutorial we will work through the basics of using easyclimate with coordinate points. You can enter coordinates as a data.frame, matrix, sf or SpatVector object. At the end we will have a data frame with climate variables for each point.

Example 1: Introducing coordinates as a data frame

First, specify longitude and latitude coordinates in a data frame with the column names lon and lat. Here we are simulating coordinates for three random sites in southern Spain

library(easyclimate)
library(ggplot2)
library(dplyr)

coords <- data.frame(
  lon = rnorm(3, mean = -5.36, sd = 0.3), 
  lat = rnorm(3, mean = 37.40, sd = 0.3)
  )

ggplot() +
  borders(regions = c("Spain", "Portugal", "France")) +
    geom_point(data = coords, aes(x = lon, y = lat)) +
  coord_fixed(xlim = c(-10, 2), ylim = c(36, 44), ratio = 1.3) + 
    xlab("Longitude") +
    ylab("Latitude") +
  theme_bw()

Now, download the climatic data for the selected locations. All you have to do is use the function get_daily_climate, specifying the period (e.g. 2008-05-25 for a single day or 2008:2010 for several years), and the variables to be downloaded (precipitation Prcp, minimum temperature Tmin or maximum temperature Tmax).


Sys.time() # to know how much time it takes to download
## [1] "2024-11-21 19:08:00 CET"

daily <- get_daily_climate(
  coords = coords, 
  period = 2008:2010, 
  climatic_var = c("Prcp","Tmin","Tmax"))

Sys.time()
## [1] "2024-11-21 19:10:58 CET"

kable(head(daily))

ID_coords	lon	lat	date	Prcp	Tmin	Tmax
1	-5.780013	37.39833	2008-01-01	6.58	3.20	14.12
1	-5.780013	37.39833	2008-01-02	25.05	8.59	15.38
1	-5.780013	37.39833	2008-01-03	5.56	8.27	14.31
1	-5.780013	37.39833	2008-01-04	0.00	5.23	14.00
1	-5.780013	37.39833	2008-01-05	0.00	5.58	14.80
1	-5.780013	37.39833	2008-01-06	0.00	11.54	15.49

Here we extract different components of the date.


daily <- daily |> 
  mutate(
    date = as.Date(date),
    month = months(date),
    year = format(date, format = "%y")
    )

Finally, you can visualize the daily climate results. For example, let’s plot the precipitation for one of the sites:

clim_site1 <- daily |> 
  filter(ID_coords == 1)

ggplot(clim_site1) +
  geom_line(aes(x = date, y = Prcp), colour = "steelblue") +
  labs(x = "Date", y = "Daily precipitation (mm)") +
  theme_bw()

Or calculate the daily mean temperature and plot it against tmin and tmax:

library(tidyr)

temp_long <- daily |> 
  mutate(Tmean = (Tmin + Tmax) / 2) |> 
  pivot_longer(
    cols = c("Tmin", "Tmax", "Tmean"),
    names_to = "temp_vars",
    values_to = "temp_values")

ggplot(temp_long, aes(x = factor(ID_coords), y = temp_values, 
                      fill = temp_vars, color = temp_vars)) +
  geom_violin(size = 1, alpha = .7) +
    scale_fill_manual(values = c("#C54A52", "#FAFBC5", "#4B8AB8")) +
  scale_color_manual(values = c("#C54A52", "#FAFBC5", "#4B8AB8")) +
  ylab("Temperature (ºC)") + xlab("") +
  theme_bw()


ggplot(temp_long, aes(x = date, y = temp_values, color = temp_vars)) + 
  geom_point(alpha = .3) +
  scale_color_manual(name = "Variables",
                     values = c("#C54A52", "#FAFBC5", "#4B8AB8"),
                     guide = guide_legend(override.aes = list(alpha = 1))) +
  ylab("Temperature (ºC)") + xlab("Date") +
  theme_bw()

Example 2: Introducing coordinates as a matrix

easyclimate handles different input data, try now with matrices!

Here we are retrieving daily precipitation data for a single year (2008).


coords_mat <- as.matrix(coords)

Sys.time()
## [1] "2024-11-21 19:11:00 CET"

mat_prcp <- get_daily_climate( 
  coords = coords_mat, 
  period = 2008, # single year
  climatic_var = "Prcp"
)

Sys.time()
## [1] "2024-11-21 19:11:13 CET"

kable(head(mat_prcp))

ID_coords	lon	lat	date	Prcp
1	-5.780013	37.39833	2008-01-01	6.58
1	-5.780013	37.39833	2008-01-02	25.05
1	-5.780013	37.39833	2008-01-03	5.56
1	-5.780013	37.39833	2008-01-04	0.00
1	-5.780013	37.39833	2008-01-05	0.00
1	-5.780013	37.39833	2008-01-06	0.00


mat_prcp <- mat_prcp |> 
  mutate(
    date = as.Date(date),
    month = months(date),
    year = format(date, format = "%y")
  ) |> 
  relocate(lon, lat, date, year, month, Prcp)

ggplot(mat_prcp, aes(x = date, y = Prcp, color = Prcp)) +
  geom_point() +
  scale_color_continuous(name = "Precipitation (mm)") +
  scale_y_continuous(expand = c(0, 0)) +
  coord_cartesian(ylim = c(0, 60)) +
  ylab("Daily precipitation (mm)") + xlab("Date") +
  theme_bw()


month_name <- format(ISOdate(2021, 1:12, 1), "%B")

mat_prcp |> 
  mutate(month = factor(month, rev(month_name))) |> 
  ggplot(aes(x = month, y = Prcp, color = month)) +
  geom_jitter(size = 2, alpha = .3, width = .3, show.legend = FALSE) +
  scale_y_continuous(expand = c(0,0)) +
  coord_flip(ylim = c(0, 60)) +
  ylab("Daily precipitation (mm)") + xlab("Month") +
  theme_bw()

Example 3: Introducing coordinates as simple feature objects

Here we introduce coordinates as a sf object, and retrieve minimum temperature for a single day (1 January 2001).

library(sf)

coords_sf <- st_as_sf(
  coords, 
  coords = c("lon", "lat")
  )

sf_tmin <- get_daily_climate(
  coords = coords_sf, 
  period = "2001-01-01", # single day
  climatic_var = "Tmin"
  ) 

ggplot() +
  borders(regions = c("Spain", "Portugal", "France")) +
    geom_point(data = sf_tmin, aes(x = lon, y = lat, color = Tmin), size = 2) + 
  coord_fixed(xlim = c(-10, 2), ylim = c(36, 44), ratio = 1.3) +  
  scale_color_gradient2(name = "Minimum\ntemperature (ºC)",
                        low = "#4B8AB8", mid = "#FAFBC5", high = "#C54A52",
                        midpoint = mean(sf_tmin$Tmin)) +
    ylab("Latitude") + xlab("Longitude") +
  theme_bw()

Learn more

Now you know how to obtain a data frame with different climatic variables with easyclimate, using point coordinates in different formats and downloading data for multiple locations and periods. Check out this other vignette if you need to extract the data of a complete area.