Analysing the climate of an area 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 a spatial polygon.

If you wish to download the climatic data of a specific region, you need to specify at least four corners of the polygon including the area and specify the type of output you want to obtain (i.e. a data frame - df or a raster - raster). You can also provide the polygons of interest in a sf object.

library(easyclimate)
library(terra)

coords_t <- vect("POLYGON ((-4.5 41, -4.5 40.5, -5 40.5, -5 41))")

Sys.time() # to know how much it takes to download
## [1] "2023-06-06 11:06:17 CEST"

df_tmax <- get_daily_climate(
  coords_t,
  period = c("2012-01-01", "2012-08-01"),
  climatic_var = "Tmax",
  output = "df" # return dataframe
  )

Sys.time()
## [1] "2023-06-06 11:06:37 CEST"

head(df_tmax)
##   ID_coords       lon      lat       date  Tmax
## 1         1 -4.995833 40.99583 2012-01-01 10.71
## 2         1 -4.987500 40.99583 2012-01-01 10.65
## 3         1 -4.979167 40.99583 2012-01-01 10.79
## 4         1 -4.970833 40.99583 2012-01-01 10.83
## 5         1 -4.962500 40.99583 2012-01-01 10.87
## 6         1 -4.954167 40.99583 2012-01-01 10.90

library(dplyr)

clim_df <- df_tmax |> 
  mutate(
    date = as.Date(date)
  )

Then, you can visualize the results and compare both dates


library(ggplot2)

tapply(clim_df$Tmax, clim_df$date, summary)
## $`2012-01-01`
##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
##   10.35   12.49   13.28   13.23   13.99   16.13 
## 
## $`2012-08-01`
##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
##   26.39   28.83   29.49   29.34   29.98   31.94

ggplot() +
    geom_raster(data = clim_df,
        aes(x = lon, y = lat, fill = Tmax)) +
  scale_fill_gradient2(name = "Maximum\ntemperature",
                       low = "#4B8AB8", mid = "#FAFBC5", high = "#C54A52",
                       midpoint = 21, ) +
  facet_wrap(~date) +
    ylab("Latitude") + xlab("Longitude") +
  theme_bw()

You can get a (multi-layer) raster directly as output, if you specify output = raster:


library(tidyterra)

Sys.time()
## [1] "2023-06-06 11:06:39 CEST"

ras_tmax <- get_daily_climate(
  coords_t,
  period = c("2012-01-01", "2012-08-01"),
  climatic_var = "Tmax",
  output = "raster" # return raster
  )

Sys.time()
## [1] "2023-06-06 11:06:58 CEST"

ras_tmax
## class       : SpatRaster 
## dimensions  : 60, 60, 2  (nrow, ncol, nlyr)
## resolution  : 0.008333333, 0.008333333  (x, y)
## extent      : -5, -4.5, 40.5, 41  (xmin, xmax, ymin, ymax)
## coord. ref. : lon/lat WGS 84 (EPSG:4326) 
## source(s)   : memory
## names       : 2012-01-01, 2012-08-01 
## min values  :      10.35,      26.39 
## max values  :      16.13,      31.94

ggplot() +
  geom_spatraster(data = ras_tmax, alpha = 0.9) +
  facet_wrap(~lyr, ncol = 2) +
  scale_fill_whitebox_c(name = "Minimum\ntemperature (ºC)", palette = "muted") +
  theme_bw()

Learn more

Now you know how to extract climatic variables with easyclimate, for a specific area. Check out this other vignette if you need to extract the data for specific points.