interpolate_data {meteoland} | R Documentation |
Interpolation process for spatial data
Description
Interpolate spatial data to obtain downscaled meteorologic variables
Usage
interpolate_data(
spatial_data,
interpolator,
dates = NULL,
variables = NULL,
ignore_convex_hull_check = FALSE,
verbose = getOption("meteoland_verbosity", TRUE)
)
Arguments
spatial_data |
An sf or stars raster object to interpolate |
interpolator |
A meteoland interpolator object, as created by
|
dates |
vector with dates to interpolate (must be within the interpolator date range). Default to NULL (all dates present in the interpolator object) |
variables |
vector with variable names to be interpolated. NULL (default), will interpolate all variables. Accepted names are "Temperature", "Precipitation", "RelativeHumidity", "Radiation" and "Wind" |
ignore_convex_hull_check |
Logical indicating whether errors in convex hull checks should be ignored.
Checking for points to be inside the convex hull will normally raise an error
if >10% of points are outside. Setting |
verbose |
Logical indicating if the function must show messages and info.
Default value checks |
Details
This function takes a spatial data object (sf or stars raster), an
interpolator object (create_meteo_interpolator
) and a
vector of dates to perform the interpolation of the meteorologic variables
for the spatial locations present in the spatial_data
object.
Value
an object with the same class and structure as the provided spatial
data with the results of the interpolation joined. In the case of spatial
data being an sf, the results are added as a list-type column that can be
unnested with unnest
. In the case of a stars raster
object, interpolation results are added as attributes (variables)
Spatial data
The spatial data provided must be of two types. (I) A sf object containing
POINT for each location to interpolate or (II) a stars raster object for
which the interpolation should be done. Independently of the class of
spatial_data
it has to have some mandatory variables, namely
elevation
. It should also contain aspect
and slope
for
a better interpolation process, though this two variables are not mandatory.
Author(s)
Victor Granda García, EMF-CREAF
Miquel De Cáceres Ainsa, EMF-CREAF
Examples
# example of data to interpolate and example interpolator
data("points_to_interpolate_example")
data("meteoland_interpolator_example")
# interpolate data
res <- interpolate_data(points_to_interpolate_example, meteoland_interpolator_example)
# check result
# same class as input data
class(res)
# data
res
# results for the first location
res[["interpolated_data"]][1]
# unnest results
tidyr::unnest(res, cols = "interpolated_data")