library(climaemet)
library(mapSpain) # Base maps of Spain
library(sf) # spatial shape handling
library(terra) # Spatial raster handling
library(gstat) # for spatial interpolation
library(tidyverse) # data handling
library(ggplot2) # for plots
library(tidyterra) # Plotting SpatRasters with tidyterra
library(gifski) # we create an animationclimaemet can retrieve data from the stations included on AEMET Open Data. However, for spatial However, for spatial analysis and visualization it can be useful to extend the point data to cover the whole extent of Spain. In this article we explain a method to interpolate climatic data using Spatial Interpolation, which is the process of using points with known values to estimate values at other unknown locations.
Initial config
For this analysis, we need the following libraries:
Retrieving data
We use daily climatic data for winter 2020–2021 in Spain. Note that in the first half of January, Storm Filomena brought unusually heavy snowfall to parts of Spain, with Madrid recording its heaviest snowfall since 1971. We should be able to spot that.
clim_data <- aemet_daily_clim(
start = "2020-12-21",
end = "2021-03-20",
return_sf = TRUE
)Let’s keep only the stations on mainland Spain:
clim_data_clean <- clim_data |>
# Exclude Canary Islands from analysis
filter(str_detect(provincia, "PALMAS|TENERIFE", negate = TRUE)) |>
dplyr::select(fecha, tmed) |>
# Exclude NAs
filter(!is.na(tmed))
summary(clim_data_clean$tmed)
#> Min. 1st Qu. Median Mean 3rd Qu. Max.
#> -16.200 5.500 9.000 8.369 11.800 23.200
ccaa_esp <- esp_get_ccaa(epsg = 4326) |>
# Exclude Canary Islands from analysis
filter(ine.ccaa.name != "Canarias")
# We load also a basic shape file of Spain using mapSpain
ggplot(ccaa_esp) +
geom_sf() +
geom_sf(data = clim_data_clean)
As it can be seen, the climatic data we have available so far is restricted to the stations (points), but we want to extend these values to the whole territory.
Filling the gaps: Interpolation
As we need to predict values at locations where no measurements have been made, we need to interpolate the data. In this example we use the terra package and apply the Inverse Distance Weighted method, one of several approaches to perform spatial interpolation. We recommend consulting Hijmans and Ghosh (2023) on how to perform this analysis in R.
The process is as follows:
- Create a spatial object (
SpatRaster) where the predicted values are applied. - Perform a spatial interpolation.
- Visualize the results.
Creating a grid
For this analysis, we need a destination object with the locations to be predicted. A common technique is to create a spatial grid (a “raster”) covering the targeted locations.
In this example we use terra to create a regular grid that we use for interpolation.
An important thing to consider in any spatial analysis or visualization is the coordinate reference system (CRS). We won’t cover this in detail in this article, but we should mention a few key considerations:
- When using multiple spatial objects, ensure that all of them use the same CRS. This can be achieved by projecting the objects (i.e. transforming their coordinates) to the same CRS.
- Longitude/latitude coordinates are unprojected. When we project an object (for example, to a Mercator projection) we change the x/y values of every point — a projection transforms coordinates.
- To measure distance in meters, choose a projection appropriate for the region. Distances in longitude/latitude are not uniform: one degree of longitude is about 111 km at the equator but much smaller near the poles. Degrees divide a circle into equal angular segments, but the Earth’s meridians converge toward the poles, so ground distances vary with latitude.
In this exercise, we choose to project our objects to ETRS89 / UTM zone 30N EPSG:25830, which provides x and y values in meters and maximizes the accuracy for Spain.
clim_data_utm <- st_transform(clim_data_clean, 25830)
ccaa_utm <- st_transform(ccaa_esp, 25830)
# Note the original projection
st_crs(ccaa_esp)$proj4string
#> [1] "+proj=longlat +datum=WGS84 +no_defs"
# vs the utm projection
st_crs(ccaa_utm)$proj4string
#> [1] "+proj=utm +zone=30 +ellps=GRS80 +towgs84=0,0,0,0,0,0,0 +units=m +no_defs"Now, we create a regular grid using terra. This grid is composed of equally spaced points over the whole extent (bounding box) of Spain.
We use here a density of 5,000 (m), so the grid density is 5 x 5 kms (25 km2):
Interpolating the data
Now we just need to populate the (empty) grid with the predicted values based on the observations:
# Test with a single day
test_day <- clim_data_utm |> filter(fecha == "2021-01-08")
# Interpolate temp
init_p <- test_day |>
vect() |>
as_tibble(geom = "XY")
gs <- gstat(
formula = tmed ~ 1,
locations = ~ x + y,
data = init_p,
set = list(idp = 2)
)
interp_temp <- interpolate(grd, gs)
#> [inverse distance weighted interpolation]
#> [inverse distance weighted interpolation]
# Plot with tidyterra
ggplot() +
geom_spatraster(data = interp_temp |> select(var1.pred)) +
scale_fill_whitebox_c(
palette = "bl_yl_rd",
labels = scales::label_number(suffix = "ºC")
) +
labs(
title = "(interpolated) temperature",
subtitle = "2021-01-08"
)
Let’s create a nice ggplot2 plot! See also Royé (2020) for more on this.
# Making a nice plot on ggplot2
temp_values <- interp_temp |>
pull(var1.pred) |>
range(na.rm = TRUE)
# Get min and max from interpolated values
min_temp <- floor(min(temp_values))
max_temp <- ceiling(max(temp_values))
ggplot() +
geom_sf(data = ccaa_utm, fill = "grey95") +
geom_spatraster(data = interp_temp |> select(var1.pred)) +
scale_fill_gradientn(
colours = hcl.colors(11, "Spectral", rev = TRUE, alpha = 0.7),
limits = c(min_temp, max_temp)
) +
theme_minimal() +
labs(
title = "Avg. Temperature in Spain",
subtitle = "2021-01-08",
caption = "Data: AEMET, IGN",
fill = "C"
)
Animation
In this section, we loop over the dates to create a single SpatRaster with several layers, each one holding the interpolation for a specific date. After that, we create an animation to observe the evolution of temperature through the winter of 2020/21.
# Create a SpatRaster with a layer for each date
dates <- sort(unique(clim_data_clean$fecha))
# Loop through days and create interpolation
interp_list <- lapply(dates, function(x) {
thisdate <- x
tmp_day <- clim_data_utm |>
filter(fecha == thisdate) |>
vect() |>
as_tibble(geom = "XY")
gs_day <- gstat(formula = tmed ~ 1, locations = ~ x + y, data = tmp_day)
interp_day <- interpolate(grd, gs_day, idp = 2.0) |>
select(interpolated = var1.pred)
names(interp_day) <- format(thisdate)
interp_day
})
# Concatenate to a single SpatRaster
interp_rast <- do.call(c, interp_list) |> mask(vect(ccaa_utm))
time(interp_rast) <- datesNow we can check the results:
interp_rast
#> class : SpatRaster
#> size : 193, 228, 90 (nrow, ncol, nlyr)
#> resolution : 5000.706, 5006.959 (x, y)
#> extent : -13882.95, 1126278, 3892802, 4859145 (xmin, xmax, ymin, ymax)
#> coord. ref. : ETRS89 / UTM zone 30N (EPSG:25830)
#> source(s) : memory
#> names : 2020-12-21, 2020-12-22, 2020-12-23, 2020-12-24, 2020-12-25, 2020-12-26, ...
#> min values : 0.8441982, 4.199887, 2.470233, -1.814927, -7.80700, -9.228323, ...
#> max values : 18.9977714, 18.864570, 16.683431, 16.854208, 16.01072, 14.617574, ...
#> time (days) : 2020-12-21 to 2021-03-20 (90 steps)
nlyr(interp_rast)
#> [1] 90
head(names(interp_rast))
#> [1] "2020-12-21" "2020-12-22" "2020-12-23" "2020-12-24" "2020-12-25"
#> [6] "2020-12-26"
# Facet map with some data
ggplot() +
geom_spatraster(data = interp_rast |> select(1:16)) +
facet_wrap(~lyr) +
scale_fill_whitebox_c(palette = "pi_y_g", alpha = 1) +
theme_minimal() +
theme(axis.text = element_blank()) +
labs(title = "Temperatures (selected)")
And finally we loop through each layer to produce a plot (png file) for each date. The last step is to concatenate each png file into a gif file with gifski.
# Extending and animating
# Create gif
# We need a common scale using all the observed values on each layer
allvalues <- values(interp_rast, mat = FALSE, na.rm = TRUE)
min_temp2 <- floor(min(allvalues))
max_temp2 <- ceiling(max(allvalues))
# Loop through all the layers
all_layers <- names(interp_rast)
for (i in seq_along(all_layers)) {
# Create a gif for each date
this <- all_layers[i]
interp_rast_day <- interp_rast |> select(all_of(this))
this_date <- as.Date(gsub("interp_", "", this))
g <- ggplot() +
geom_spatraster(data = interp_rast_day) +
geom_sf(data = ccaa_utm, fill = NA) +
coord_sf(expand = FALSE) +
scale_fill_gradientn(
colours = hcl.colors(20, "Spectral", rev = TRUE, alpha = 0.8),
limits = c(min_temp2, max_temp2),
na.value = NA,
labels = scales::label_number(suffix = "º")
) +
theme_minimal() +
labs(
title = "Avg. Temperature in Spain",
subtitle = this_date,
caption = "Data: AEMET, IGN",
fill = ""
)
tmp <- file.path(tempdir(), paste0(this, ".png"))
ggsave(tmp, g, width = 1600, height = 1200, units = "px", bg = "white")
}Finally, we use gifski to create the animation:
References
Hijmans, Robert J., and Aniruddha Ghosh. 2023. “Interpolation.” Chap. 4 in Spatial Data Analysis with R. Spatial Data Science with R and "terra". Online. https://rspatial.org/analysis/analysis.pdf.
Royé, Dominic. 2020. Climate Animation of Maximum Temperatures. https://dominicroye.github.io/blog/climate-animation-maximum-temperature/.